Enhancing intersection safety in autonomous traffic: A grid-based approach with risk quantification

Existing studies on autonomous intersection management (AIM) primarily focus on traffic efficiency, often overlooking the overall intersection safety, where conflict separation is simplified and traffic conflicts are inadequately assessed. In this paper, we introduce a calculation method for the grid-based Post Encroachment Time (PET) and the total kinetic energy change before and after collisions. The improved grid-based PET metric provides a more accurate estimation of collision probability, and the total kinetic energy change serves as a precise measure of collision severity. Consequently, we establish the Grid-Based Conflict Index (GBCI) to systematically quantify collision risks between vehicles at an autonomous intersection. Then, we propose a traffic-safety-based AIM model aimed at minimizing the weighted sum of total delay and conflict risk at the intersection. This entails the optimization of entry time and trajectory for each vehicle within the intersection, achieving traffic control that prioritizes overall intersection safety. Our results demonstrate that GBCI effectively assesses conflict risks within the intersection, and the proposed AIM model significantly reduces conflict risks between vehicles and enhances traffic safety while ensuring intersection efficiency.

The research of river basin ecological compensation based on water emissions trading mechanism

By integrating the successful case of the European Union emissions trading system, this study proposes a water emissions trading system, a novel method of reducing water pollution. Assuming that upstream governments allocate initial quotas to upstream businesses as the compensation standard, this approach defines the foundational principles of market trading mechanisms and establishes a robust watershed ecological compensation model to address challenges in water pollution prevention. To be specific, the government establishes a reasonable initial quota for upstream enterprises, which can be used to limit the emissions of upstream pollution. When enterprises exceed their allocated emissions quota, they face financial penalties. Conversely, these emissions rights can be transformed into profitable assets by participating in the trading market as a form of ecological compensation. Numerical simulations demonstrate that various pollutant emissions from upstream businesses will have various effects on the profits of other businesses. Businesses in the upstream region received reimbursement from the assigned emission rights through the market mechanism, demonstrating that ecological compensation for the watershed can be achieved through the market mechanism. This novel market trading system aims at controlling emissions management from the perspectives of individual enterprises and ultimately optimizing the aquatic environment.

MRI radiomics predicts the efficacy of EGFR-TKI in EGFR-mutant non-small-cell lung cancer with brain metastasis

AIM

To develop and validate models based on magnetic resonance imaging (MRI) radiomics for predicting the efficacy of epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in EGFR-mutant non-small-cell lung cancer (NSCLC) patients with brain metastases.

MATERIALS AND METHODS

117 EGFR-mutant NSCLC patients with brain metastases who received EGFR-TKI treatment were included in this study from January 1, 2014 to December 31, 2021. Patients were randomly divided into training and validation cohorts in a ratio of 2:1. Radiomics features extracted from brain MRI were screened by least absolute shrinkage and selection operator (LASSO) algorithm. Logistic regression analysis and Cox proportional hazard regression analysis were used to screen clinical risk factors. Clinical (C), radiomics (R), and combined (C + R) nomograms were constructed in models predicting short-term efficacy and intracranial progression-free survival (iPFS), respectively. Calibration curves, Harrell's concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of models.

RESULTS

Overall response rate (ORR) was 57.3% and median iPFS was 12.67 months. The C + R nomograms were more effective. In the short-term efficacy model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.860 (0.820-0.901, 95%CI) and 0.843 (0.783-0.904, 95%CI). In iPFS model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.837 (0.751-0.923, 95%CI) and 0.850 (0.763-0.937, 95%CI).

CONCLUSION

The C + R nomograms were more effective in predicting EGFR-TKI efficacy of EGFR-mutant NSCLC patients with brain metastases than single clinical or radiomics nomograms.

Effects of intermittent cold stimulation on growth performance, meat quality, antioxidant capacity and liver lipid metabolism in broiler chickens

Intermittent cold stimulation (ICS) enhances broilers' resistance to cold stress. Nonetheless, further research is needed to investigate the underlying mechanisms that enhance cold stress resistance. A total of 160 one-day-old male Ross 308 broilers were randomly divided into 2 groups (CC and CS5), with the CC group managing temperature according to the standard for broiler growth stages, while the CS5 group were subjected to cold stimulation at a temperature 3℃ lower than the CC group for 5 h, every 2 d from 15 to 35 d. Sampling was conducted at 36 d (36D), 50 d (50D) and after acute cold stress for 24 h (Y24). First, we examined the effects of ICS on broiler growth performance, meat quality, antioxidant capacity, and lipid metabolism. The results demonstrated that ICS enhanced the performance of broilers to a certain degree. Specifically, the average weight gain in the CS5 group was significantly higher than that of the CC group, and the feed conversion ratio significantly decreased compared to CC at 4 W and 6 W (P ≤ 0.05). Compared with the CC group, cold stimulation significantly reduced drip loss, shearing force, and yellowness (a* value) of chicken meat, while significantly increased redness (b* value) (P ≤ 0.05). At Y24, the levels of T-AOC and GSH-PX in the serum of the CS5 group were significantly higher than those of the CC group, while the level of MDA was significantly lower (P ≤ 0.05). The content of TG, FFA, and VLDL in the serum of the CS5 group was significantly elevated, whereas the level of TC and HDL was significantly lower (P ≤ 0.05). In addition, we further explored whether AMPK-mTOR pathway is involved in the regulation of changes in lipid metabolism and the possible regulatory mechanisms downstream of the signaling pathway. The results showed that ICS significantly upregulated the expression levels of AMPK mRNA and protein in the liver of the CS5 group at 36D and Y24, while significantly down-regulating mTOR (P ≤ 0.05). Compared with the CC group, ICS significantly down-regulated the mRNA expression levels of lipid synthesis and endoplasmic reticulum stress-related genes (SREBP1c, FAS, SCD, ACC, GRP78 and PERK) at 36D and Y24, while significantly up-regulating the mRNA expression levels of lipid decomposition and autophagy-related genes (PPAR and LC3) (P ≤ 0.05). In addition, at Y24, the protein expression levels of endoplasmic reticulum stress-related genes (GRP78) in the CS5 group were significantly lower, while autophagy-related genes (LC3 and ATG7) were significantly higher (P ≤ 0.05). ICS can affect meat quality and lipid metabolism in broilers, and when broilers are subjected to acute cold stress, broilers trained with cold stimulation have stronger lipid metabolism capacity.

Gold(I) ion and the phosphine ligand are necessary for the anti-Toxoplasma gondii activity of auranofin

Auranofin, an FDA-approved drug for rheumatoid arthritis, has emerged as a promising antiparasitic medication in recent years. The gold(I) ion in auranofin is postulated to be responsible for its antiparasitic activity. Notably, aurothiomalate and aurothioglucose also contain gold(I), and, like auranofin, they were previously used to treat rheumatoid arthritis. Whether they have antiparasitic activity remains to be elucidated. Herein, we demonstrated that auranofin and similar derivatives, but not aurothiomalate and aurothioglucose, inhibited the growth of Toxoplasma gondii in vitro. We found that auranofin affected the T. gondii biological cycle (lytic cycle) by inhibiting T. gondii's invasion and triggering its egress from the host cell. However, auranofin could not prevent parasite replication once T. gondii resided within the host. Auranofin treatment induced apoptosis in T. gondii parasites, as demonstrated by its reduced size and elevated phosphatidylserine externalization (PS). Notably, the gold from auranofin enters the cytoplasm of T. gondii, as demonstrated by scanning transmission electron microscopy-energy dispersive X-ray spectroscopy (STEM-EDS) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).IMPORTANCEToxoplasmosis, caused by Toxoplasma gondii, is a devastating disease affecting the brain and the eyes, frequently affecting immunocompromised individuals. Approximately 60 million people in the United States are already infected with T. gondii, representing a population at-risk of developing toxoplasmosis. Recent advances in treating cancer, autoimmune diseases, and organ transplants have contributed to this at-risk population's exponential growth. Paradoxically, treatments for toxoplasmosis have remained the same for more than 60 years, relying on medications well-known for their bone marrow toxicity and allergic reactions. Discovering new therapies is a priority, and repurposing FDA-approved drugs is an alternative approach to speed up drug discovery. Herein, we report the effect of auranofin, an FDA-approved drug, on the biological cycle of T. gondii and how both the phosphine ligand and the gold molecule determine the anti-parasitic activity of auranofin and other gold compounds. Our studies would contribute to the pipeline of candidate anti-T. gondii agents.

A chitosan-based conductive double network hydrogel doped by tannic acid-reduced graphene oxide with excellent stretchability and high sensitivity for wearable strain sensors

Conductive hydrogels usually suffer from weak mechanical properties and are easily destroyed, resulting in limited applications in flexible electronics. Concurrently, adding conductive additives to the hydrogel solution increases the probability of agglomeration and uneven dispersion issues. In this study, the biocompatible natural polymer chitosan was used as the network substrate. The rigid network employed was the Cit3-ion crosslinked chitosan (CS) network, and the MBA chemically crosslinked polyacrylamide (PAM) network was used as the flexible network. Tannic acid-reduced graphene oxide (TA-rGO), which has excellent conductivity and dispersibility, is used as a conductive filler. Thus, a CS/TA-rGO/PAM double network conductive hydrogel with excellent performance, high toughness, high conductivity, and superior sensing sensitivity was prepared. The prepared CS/TA-rGO/PAM double network conductive hydrogels have strong tensile properties (strain and toughness as high as 2009 % and 1045 kJ/cm3), excellent sensing sensitivity (GF value was 4.01), a wider strain detection range, high cycling stability and durability, good biocompatibility, and antimicrobial properties. The hydrogel can be assembled into flexible wearable devices that can not only dynamically detect human movements, such as joint bending, facial expression changes, swallowing, and saying, but also recognize handwriting and enable human-computer interaction.

Spatio-temporal effects of digital inclusive finance on the synergy between CO2 and air pollution emissions in 251 Chinese cities

Achieving the synergistic reduction of CO2 and air pollution emissions (SRCAPEs) holds great significance in promoting the green transformation. However, limited research has been conducted on the spatio-temporal impact of digital inclusive finance (DIF) on the synergy between CO2 and air pollution emissions (SCAPEs). To address this gap, we comprehensively employ the linear regression model, geographically and the temporally weighted regression (GTWR) model, and the ordered probit model to empirically analyze the influence of DIF on SCAPE. Our research reveals the following: (1) The linear regression model demonstrates that, on average, DIF can achieve a weak synergistic emission reduction effect. This result remains robust after a battery of robustness tests. (2) The GTWR model reveals that the impact of DIF on both emissions exhibits evident spatio-temporal characteristics. Its emission reduction effect gradually increases, especially after 2014. (3) On the basis of the estimates from the GTWR model, we can identify four distinct synergy types driven by DIF. The number of cities with the preferred type (i.e., achieving SRCAPE) increases the most, from 59 in 2011 to 233 in 2019. (4) On the basis of the built ordered probit models, green technology innovation is an important path for DIF to achieve synergistic emission reduction. The synergistic emission reduction effect is also significantly moderated by the regional economic level and environmental regulation intensity. Our findings have policy implications for central and local governments in achieving SRCAPE and support efforts to achieve sustainable development.

Research progress of the detection and analysis methods of heavy metals in plants

Heavy metal (HM)-induced stress can lead to the enrichment of HMs in plants thereby threatening people's lives and health via the food chain. For this reason, there is an urgent need for some reliable and practical techniques to detect and analyze the absorption, distribution, accumulation, chemical form, and transport of HMs in plants for reducing or regulating HM content. Not only does it help to explore the mechanism of plant HM response, but it also holds significant importance for cultivating plants with low levels of HMs. Even though this field has garnered significant attention recently, only minority researchers have systematically summarized the different methods of analysis. This paper outlines the detection and analysis techniques applied in recent years for determining HM concentration in plants, such as inductively coupled plasma mass spectrometry (ICP-MS), atomic absorption spectrometry (AAS), atomic fluorescence spectrometry (AFS), X-ray absorption spectroscopy (XAS), X-ray fluorescence spectrometry (XRF), laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), non-invasive micro-test technology (NMT) and omics and molecular biology approaches. They can detect the chemical forms, spatial distribution, uptake and transport of HMs in plants. For this paper, the principles behind these techniques are clarified, their advantages and disadvantages are highlighted, their applications are explored, and guidance for selecting the appropriate methods to study HMs in plants is provided for later research. It is also expected to promote the innovation and development of HM-detection technologies and offer ideas for future research concerning HM accumulation in plants.

Remnant cholesterol is associated with hip BMD and low bone mass in young and middle-aged men: a cross-sectional study

PURPOSE

Remnant cholesterol (RC) is a contributor to cardiovascular diseases, obesity, diabetes, and metabolic syndrome. However, the specific relationship between RC and bone metabolism remains unexplored. Therefore, we aimed to investigate the relationships of RC with hip bone mineral density (BMD) and the risk of low bone mass.

METHODS

Physical examination data was collected from men aged < 60 years as part of the Kailuan Study between 2014 and 2018. The characteristics of the participants were compared between RC quartile groups. A generalized linear regression model was used to evaluate the relationship between RC and hip BMD and a logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for low bone mass. Additional analyses were performed after stratification by body mass index (BMI) (≥ or < 24 kg/m2). Sensitivity analyses were performed by excluding individuals who were taking lipid-lowering therapy or had cancer, cardiovascular diseases, or diabetes.

RESULTS

Data from a total of 7,053 participants were included in the analysis. After adjustment for confounding factors, RC negatively correlated with hip BMD (β =  - 0.0079, 95% CI: - 0.0133, - 0.0025). The risk of low bone mass increased from the lowest to the highest RC quartile, with ORs of 1 (reference), 1.09 (95% CI: (0.82, 1.44), 1.35 (95%CI: 1.02, 1.77), and 1.43 (95% CI: 1.09, 1.89) for Q1, Q2, Q3, and Q4, respectively (P for trend = 0.004) in the fully adjusted model. Compared to RC < 0.80 mmol/l group, the risk of low bone mass increased 39% in RC ≥ 0.80 mmol/l group (P < 0.001). The correlation between RC and hip BMD was stronger in participants with BMI ≥ 24 kg/m2 group (β =  - 0.0159, 95% CI: - 0.0289, - 0.0029). The results of sensitivity analyses were consistent with the main results.

CONCLUSION

We have identified a negative correlation between serum RC and hip BMD, and a higher RC concentration was found to be associated with a greater risk of low bone mass in young and middle-aged men.

Comparison of different dosages of propofol combined with its equivalent alfentanil in outpatient abortion: a prospective, double-blinded, randomized trial

OBJECTIVE

This study aimed at determining the optimal dose combination of alfentanil and propofol for outpatient abortion anesthesia.

PATIENTS AND METHODS

The study was separated into two parts. In the first part, patients were to determine the median effective dose (ED50) and the 95% effective dose (ED95) of alfentanil in combination with 2.5 mg·kg-1 propofol to inhibit body movements during the abortion using the Dixon up-and-down sequential allocation method. In the second part, 170 patients were randomly divided into group C (2.0 mg·kg-1 propofol with alfentanil 12.16 μg·kg-1) and group E (2.5 mg·kg-1 propofol with its ED95) to compare the anesthetic effect. The primary outcome was the sedation level during general anesthesia. The secondary outcomes were circulation, respiratory complications, and postoperative recovery quality.

RESULTS

The ED50 and the ED95 values of alfentanil were 3.37 μg·kg-1 (95% CI: 2.58-3.97 μg·kg-1) and 4.68 μg·kg-1 (95% CI: 4.04-9.32 μg·kg-1). The frequency of deep sedation in group E was significantly higher than in group C (76.5% vs. 60%). Patients in group C showed more wakefulness even during the surgery (14.3% vs. 4.4%). The results of our exploratory analyses did not reveal differences in respiratory depression, circulatory depression, postoperative side effects, or recovery outcomes.

CONCLUSIONS

The combination of 2.5 mg·kg-1 propofol and 4.68 μg·kg-1 alfentanil produces a better sedative effect than the combination of 2.0 mg·kg-1 propofol and 12.16 μg·kg-1 alfentanil without increasing additional risks associated with anesthesia.

Intermittent mild cold stimulation alleviates cold stress-induced pulmonary fibrosis by inhibiting the TGF-β1/Smad signaling pathway in broilers

To investigate the potential protective effect of intermittent cold stimulation on lung tissues of broilers exposed to acute cold stress (ACS). A total of 384 one-day-old broilers were assigned to 4 experimental groups with 6 replicates of 16 birds each: control (CON) and ACS groups were reared at normal feeding temperature from d 1 to 42; cold treatment groups (CS3+ACS and CS9+ACS) were reared, respectively, at 3°C or 9°C for 5 h on alternate days below the CON group from d 15 to 35. Animals in CS3+ACS, CS9+ACS, and ACS groups were exposed at 10°C for 24 h on d 43. Subsequently, lung tissues were collected to perform histopathological examination and measurement of relevant indexes. The results showed that lung tissues in CS9+ACS and ACS groups exhibited increased inflammatory cell infiltrates and collagen deposition compared to the CON group, while this pathological phenomenon was less pronounced in the CS3+ACS group. Compared to CON group, H2O2 and MDA contents were increased, and the activities of antioxidant enzymes (CAT, SOD, GPx, T-AOC) were reduced in CS9+ACS and ACS group (P < 0.05); mRNA and protein levels of inhibitor of NF-κB, Smad7, matrix metallopeptidase (MMP)-2, MMP9, and antioxidant-related genes were downregulated, whereas mRNA and protein levels of genes related to NF-κB/NLRP3 pathway-regulated inflammation and TGF-β1/Smad pathway-regulated fibrosis were upregulated in cold-stressed broilers (P < 0.05). mRNA levels of heme oxygenase-1, NAD(P)H quinone oxidoreductase-1, and MMP9 were increased in CS3+ACS group (P < 0.05). Moreover, the expression of most antioxidant-related genes was increased, and that of inflammation- and fibrosis-related genes was reduced in CS3+ACS group (P < 0.05). Therefore, cold stress caused oxidative stress and inflammation, leading to pulmonary fibrosis in broilers, whereas intermittent mild cold stimulation at 3°C below normal rearing temperature alleviated fibrosis by inhibiting the TGF-β1/Smad pathway modulated by the Nrf2/HO-1 and NF-κB/NLRP3 signaling pathway. This study suggests that intermittent mild cold stimulation can be a potential strategy to reduce ACS-induced lung damage in broilers.

Intermittent cold stimulation affects energy metabolism and improves stress resistance in broiler heart

To investigate the effect of intermittent cold stimulation on cardiac energy metabolism and cold resistance of broilers, 288 broilers were divided into 3 groups: control group (CC) and 2 cold stimulation groups (CS3 and CS9). The CS3 and CS9 groups received cold stimulation at temperatures of 3°C and 9°C lower than CC group for 5 h from d 15 to 35. Three groups were subjected to acute cold stress (ACS) of 10°C for 12 and 24 h at 44 d. Performance, cardiac histopathological changes, heat shock proteins (HSPs), and lipid metabolism levels were measured. Results showed that the performance was not different among groups at 22 and 29 d (P > 0.05), but the mRNA levels of Acyl CoA synthase long-chain family member 1 (ACSL1) and acyl-coenzyme oxidase (ACO) in CS group were upregulated compared to CC group (P < 0.05). At 36 d, the performance of the CS3 group was better than the other 2 groups, myocardial structure was normal and other lipid metabolism indexes, except for peroxisome proliferator-activated receptor coactivator 1α (PGC-1α) levels, were similar to those of CC group (P > 0.05). The myocardial fiber disorder, Triglyceride (TG), and leptin (LEP) contents were significantly lower in CS9 group than in CC and CS3 groups at 36 d (P < 0.05). The HSP protein levels were significantly higher in CS group than in CC group before ACS (P < 0.05). After 24 h of ACS, the mRNA of lipid metabolism genes, the protein levels of HSP40 and HSP60, and the contents of TG and LEP in the CS3 group were upregulated compared to other groups. The CC and CS9 groups showed myocardial structure was destroyed, with lower TG and LEP levels compared to before ACS (P < 0.05). Therefore, cold stimulation at 3°C lower than the normal feeding temperature for 5 h did not impair performance but can increase the resistance of broilers to ACS by promoting lipid metabolism.

Mild Intermittent Cold Stimulation Affects Cardiac Substance Metabolism via the Neuroendocrine Pathway in Broilers

This study aimed to investigate the impact of cold adaptation on the neuroendocrine and cardiac substance metabolism pathways in broilers. The broilers were divided into the control group (CC), cold adaptation group (C3), and cold-stressed group (C9), and experimental period was divided into the training period (d 1-35), recovery period (d 36-43), and cold stress period (d 43-44). During the training period, the CC group was reared at ambient temperature, while C3 and C9 groups were reared at 3 °C and 9 °C lower than the ambient temperature, respectively, for 5 h/d at 1 d intervals. During the recovery period, all the groups were maintained at 20 °C. Lastly, during the cold stress period, the groups were divided into two sub-groups, and each sub-group was placed at 10 °C for 12 h (Y12) or 24 h (Y24) for acute cold stimulation. The blood, hypothalamic, and cardiac tissues samples were obtained from all the groups during the training, recovery, and acute stress periods. The results revealed that the transcription of calcium voltage-gated channel subunit alpha 1 C (CACNAIC) was increased in the hypothalamic tissues of the C3 group (p < 0.05). Moreover, compared to the CC group, the serum norepinephrine (NE) was increased in the C9 group (p < 0.05), but insulin (INS) was decreased in the C9 group (p < 0.05). In addition, the transcription of the phosphoinositide-3 kinase (PI3K), protein kinase B (Akt), mammalian target of rapamycin (mTOR), SREBP1c, FASN, ACC1, and SCD genes was down-regulated in the C3 and C9 groups (p < 0.05); however, their expression increased in the C3 and C9 groups after acute cold stimulation (p < 0.05). Compared to the CC group, the transcription of forkhead box O1 (FoxO1), PEPCK, G6Pase, GLUT1, HK1, PFK, and LDHB genes was up-regulated in the C3 and C9 groups (p < 0.05. Furthermore, compared to the CC and C9 groups, the protein and mRNA expressions of heat shock protein (HSP) 70 and HSP90 were significantly increased in the C3 group (p < 0.05). These results indicate that intermittent cold training can enhance cold stress tolerance in broilers by regulating their neuroendocrine and cardiac substance metabolism pathways.

Government digital policy breaks the mystery of "limited participation" in China's home finance market

This study uses a two-step approach to construct a multi-period double-difference model and introduces a quasi-natural experiment of the Broadband China pilot policy to investigate whether household financial market participation at the urban level is affected by the digital economy, which is significant for promoting Chinese households' shift from savings to investment and alleviating the long-standing problem of insufficient household financial market participation in China. In terms of direct impact, the digital economy increases the household financial market participation rate of urban residents by 3.26%, and increases the financial market participation rate of highly financially literate households by 2.14%; in terms of indirect impact, the development of the digital economy increases the total number of household smart Internet devices by 8.27%, and similarly increases the attention to household financial information by a significant 4.22%, which further positively influences the household financial market participation rate. This paper also evaluates the individual and regional differences of the digital economy on household financial market participation, and the estimated causal effect of the digital economy on household financial market participation is purer, which expands the scope of research on the digital economy and household financial market participation, and provides a certain reference basis and policy inspiration for the government to promote the construction of the digital economy.

Exosomes Derived from M2 Microglial Cells Modulated by 1070-nm Light Improve Cognition in an Alzheimer's Disease Mouse Model

Near-infrared photobiomodulation has been identified as a potential strategy for Alzheimer's disease (AD). However, the mechanisms underlying this therapeutic effect remain poorly characterize. Herein, it is illustrate that 1070-nm light induces the morphological alteration of microglia from an M1 to M2 phenotype that secretes exosomes, which alleviates the β-amyloid burden to improve cognitive function by ameliorating neuroinflammation and promoting neuronal dendritic spine plasticity. The results show that 4 J cm-2 1070-nm light at a 10-Hz frequency prompts microglia with an M1 inflammatory type to switch to an M2 anti-inflammatory type. This induces secretion of M2 microglial-derived exosomes containing miR-7670-3p, which targets activating transcription factor 6 (ATF6) during endoplasmic reticulum (ER) stress. Moreover, it is found that miR-7670-3p reduces ATF6 expression to further ameliorate ER stress, thus attenuating the inflammatory response and protecting dendritic spine integrity of neurons in the cortex and hippocampus of 5xFAD mice, ultimately leading to improvements in cognitive function. This study highlights the critical role of exosomes derive from 1070-nm light-modulated microglia in treating AD mice, which may provide a theoretical basis for the treatment of AD with the use of near-infrared photobiomodulation.

Crash injury severity prediction considering data imbalance: A Wasserstein generative adversarial network with gradient penalty approach

For each road crash event, it is necessary to predict its injury severity. However, predicting crash injury severity with the imbalanced data frequently results in ineffective classifier. Due to the rarity of severe injuries in road traffic crashes, the crash data is extremely imbalanced among injury severity classes, making it challenging to the training of prediction models. To achieve interclass balance, it is possible to generate certain minority class samples using data augmentation techniques. Aiming to address the imbalance issue of crash injury severity data, this study applies a novel deep learning method, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP), to investigate a massive amount of crash data, which can generate synthetic injury severity data linked to traffic crashes to rebalance the dataset. To evaluate the effectiveness of the WGAN-GP model, we systematically compare performances of various commonly-used sampling techniques (random under-sampling, random over-sampling, synthetic minority over-sampling technique and adaptive synthetic sampling) with respect to dataset balance and crash injury severity prediction. After rebalancing the dataset, this study categorizes the crash injury severity using logistic regression, multilayer perceptron, random forest, AdaBoost and XGBoost. The AUC, specificity and sensitivity are employed as evaluation indicators to compare the prediction performances. Results demonstrate that sampling techniques can considerably improve the prediction performance of minority classes in an imbalanced dataset, and the combination of XGBoost and WGAN-GP performs best with an AUC of 0.794 and a sensitivity of 0.698. Finally, the interpretability of the model is improved by the explainable machine learning technique SHAP (SHapley Additive exPlanation), allowing for a deeper understanding of the effects of each variable on crash injury severity. Findings of this study shed light on the prediction of crash injury severity with data imbalance using data-driven approaches.

A Self-Attention-Based Neural Network for Predicting Immune Checkpoint Inhibitors Response

PURPOSE/OBJECTIVE(S)

Cancer cells evade immune system by negatively regulating T cells via immune checkpoints (e.g., PD-1). By blocking these checkpoints, the ability of immune system to recognize and kill cancer cells restores. Individual response rate of checkpoint blockade varies among patients, with 50%-80% in specific types of cancer such as melanoma, while only 15%-30% in most other tumors. Yet it is still an open question what is the set of biomarkers that are crucial to the response to immune checkpoint inhibitors (ICI). The overall goal of this study is to develop and validate a biologically-aware interpretable deep learning model to identify the biomarkers that can predict the survival outcome to ICI treatment.

MATERIALS/METHODS

The self-attention mechanism could yield interpretable results where important biomarkers may have more "attention". However, in classical self-attention mechanism, the prior biological knowledge of protein interactions (PPI) and gene pathways are not incorporated. In this study, we propose a weighted biologically-aware attention score, where it is weighted against the gene centrality and pathway length. The genes that are closely connected to mutated genes receive 'high attention', while the genes that are far away from mutated genes along the pathway receive "lower attention". We then train, validate and test our model using 1,660 patients of nine types of cancer. To validate the prediction, 1. We evaluate the accuracy via concordance index. 2. We identified the genes that receive high attention and verify their functions in existed literature. 3. We perform sanity check by removing these genes from the data, re-training and predicting again, and comparing the prediction accuracy.

RESULTS

Our framework has achieved an average accuracy (measured via c-index) of 0.60 ± 0.06 for NSCLC and 0.58 ± 0.07 for melanoma, which is superior to both the gold standard COX-PH model (0.57 ± 0.06 for NSCLC and 0.53 ± 0.03 for melanoma) and DeepSurv (0.54 ± 0.05 for NSCLC and 0.51 ± 0.10 for melanoma). Genes that receive high attention have been validated by supporting literature, which provides an additional means of verifying the prediction in comparison to "black box" deep learning models, where there is no way to comprehend the reason behind predictions. Removing the top 8% high-attention genes (∼25 genes) from the data while using the remaining 92% for making predictions resulted in a drop in accuracy to 0.55 ± 0.073 for NSCLC and 0.56 ± 0.03 for melanoma, underscoring the significance of these genes. Patient stratification is also performed by dividing patients into responders and non-responders based on prediction score.

CONCLUSION

In this study, we propose and validate a biologically-aware self-attention based deep learning model which outperforms commonly-used survival models. Additionally, this tool has the potential to identify key biomarkers while assist in clinical decision-making, which demonstrates a promising step for immunotherapy response prediction.

Accelerating Volumetric CT and MRI Imaging by Reference-Free Deep Learning Transformation from Low-Resolution to High-Resolution

PURPOSE/OBJECTIVE(S)

High-resolution (HR) images are important in precision radiation oncology. However, acquiring HR volumetric CT and MRI images is often time consuming; also, the resolution in some direction(s) (e.g., z-direction in the case of CT) is often limited by imaging hardware or fundamental imaging principle. Super-resolution (SR) imaging, i.e., the low-resolution (LR) to HR image transformation, is widely used to improve image resolution. Data-driven deep learning (DL) methods have achieved great success in SR imaging, yet they can hardly be applied to medical imaging as they require large amount of LR-HR image pairs to train the model. We therefore propose a reference-free DL method to increase resolutions of volumetric medical images in an efficient way.

MATERIALS/METHODS

We propose a maximum likelihood estimation (MLE)-based implicit neural representation (INR) network for SR imaging. The INR network aims to represent an image as a continuous function parameterized by a coordinate-based multi-layer perceptron. The INR network takes image coordinates as input and outputs corresponding pixel intensities. To train the network without using any HR images, we use a MLE framework to model LR observations' statistics and their relation to the latent HR image. The predicted HR image from the INR's output is transformed to LR images based on the MLE, and the network parameters are then optimized by minimizing the distance between the transformed LR images and actual LR observations. We demonstrate the efficacy of the proposed method on CT and MRI images for 2x, 4x, and 8x SR using only one or two LR image(s). The performance is compared with a conventional SR method named plain MLE, in terms of visual quality and numerical qualities of PSNR and SSIM.

RESULTS

Our method outperformed the plain MLE method in the experiment. Table 1 reports the numerical improvements of our method over the compared plain MLE method. For 2x SR with a single LR image, our method achieved significant improvements in both PSNR and SSIM. When using two LR images, the better structural restoration capability of our method became more obvious with higher SR magnifications, as indicated by the increased SSIM differences. Better noise suppression capability of our method is observed in all our studies, as indicated by the PSNR values. In visual quality evaluation, we observed sharper image details with less noise in SR images generated by the proposed method, compared with the plain MLE method.

CONCLUSION

The proposed novel reference-free DL method can efficiently provide high-quality HR images with only one or two LR images for CT and MRI imaging. This method can be easily generalized to many other radiation therapy related applications without the requirement for HR reference images.

Reinforcement Learning Powered Station Parameter Optimized Radiation Therapy (SPORT): A Novel Treatment Planning and Beam Delivery Technique

PURPOSE/OBJECTIVE(S)

Conventional intensity modulated radiation therapy (IMRT) with a typical 5-20 fixed beams often does not provide sufficient angular sampling required for conformal dose shaping, whereas current volumetric modulated arc therapy (VMAT) discretizes the angular space into equally spaced control points without considering the differential need for intensity modulation of different angles, leading to undersampling at some angles while oversampling at some other angles. Our goal is to develop a node or station parameter optimized radiation therapy (SPORT) strategy with simultaneously optimized angular sampling and beam modulation by leveraging state-of-the-art reinforcement learning and the unique capability of modern digital LINACs in dose delivery through programmable nodal points.

MATERIALS/METHODS

We developed a SPORT optimization framework, in which, the process of programming control points (or station parameters) was formulated as a stochastic dynamic programming problem, which was solved by a reinforcement learning-based algorithm. On-policy reinforcement learning method, namely, state-action-reward-state-action (SARSA) was integrated with deep convolutional neural network to predict station parameters by utilizing the patient's anatomical structures meanwhile considering the delivery capability of a typical digital LINAC machine. Here, the deep convolutional neural network estimated the state-action value by using the quality of the plan with current station parameters when a next potential station parameter was selected. The state-action value was then updated by SARSA learning. The quality of the plan was quantified by dosimetry constraints. The model was assessed by a retrospective study on a cohort of patients underwent head-and-neck radiation therapy. Dosimetric analysis and delivery efficiency comparisons were used to evaluate the performance of the proposed framework.

RESULTS

Our model was used to generate 16 plans unseen in the original training set. All the plans predicted by our model achieved better dose distributions without violating clinical planning constraints. Moreover, instead of using 4 full standard arcs in the original clinically used plans obtained via manual optimization, the predicted plans only used one full standard arc (about 178 control points) plus boost from a few sub-arcs (less than 30 degrees of gantry angles), which significantly improved the efficiency of the beam delivery. We are in the process of integrating the sub-arcs into the full arc by considering the programmable capability of modern LINACs.

CONCLUSION

We demonstrated that a machine learning-based SPORT framework capable of optimizing the spatial sampling and beam modulation simultaneously for modern radiation therapy. The framework not only significantly improves the quality and efficiency of beam delivery, but also has the potential to be incorporated into current clinical workflow to improve the efficiency of dose planning and delivery.

Automated Small Tumor Segmentation by a Template-Based Global Hierarchical Attention Method

PURPOSE/OBJECTIVE(S)

Accurate segmentation of tumors is significant for radiation therapy treatment planning and clinical decision-making. While deep convolutional neural network-based methods have found valuable applications in automatic medical segmentation, tumor segmentation, especially small tumor segmentation, remains challenging due to deficiencies of current deep learning in convolutional and pooling operations, which often results in the loss of small object information. This research proposes a global hierarchical attention-based method for accurate and automated segmentation of small tumors by exploiting the associations between small tumors and the feature maps of large tumors.

MATERIALS/METHODS

This study included 131 patients with liver cancer. The in-plane resolution of the patients' CTs is from 0.55 mm to 1.0 mm and slice spacing from 0.45 mm to 6.0 mm. We randomly selected 100 CT scans as the training set and others as the testing set. Each CT slice of the testing set was separated into groups according to tumor size as follows: 0.1-2.0, 2.1-5.0, 5.1-10.0, and 10.1-20.0 cm. The CT slice without tumor or tumor size > 20 cm were excluded. This work presents a tumor template-based hierarchical attention method to quantify the relation between small and large tumors by computing their feature maps. The relation of small-large tumors can compensate for the information loss of small tumors during the convolutional and pooling operations and improve the performance of small tumor segmentation.

RESULTS

Among 20,693 CT slices of the 31 testing patients, 3.0% CT slices with tumors ≤2 cm, 6.7% ≤5 cm, 10.6% ≤10 cm, and 13.4%≤20 cm. We compared our method with six widely used segmentation models. The results show our model outperforms other methods on all sizes of liver tumors, especially for small size tumors: For the 0.1-2.0 cm liver tumor, it achieved 8.4%, 10.0%, 11.3%, 9.1%, 10.9%, and 9.6% improvement compared to Unet, PAN, DeepLabV3, FPN, LinkNet, and PSPNet, respectively.

CONCLUSION

We found that the small-large tumors relation can significantly improve small tumor segmentation, which is valuable for treatment planning, and clinical decision-making. Our experimental results show that our method can significantly improve the accuracy of segmenting small liver tumors compared to existing deep-learning-based models. The method is quite general and can be extended to other types of tumor detection and segmentation. We discovered that the relationship between small and large tumors can significantly enhance the segmentation of small tumors, which has significant value for treatment planning and clinical decision-making. Our experiments demonstrate that our approach significantly improves the accuracy of small liver tumor segmentation compared to existing deep learning-based models. Our method is quite versatile and can be extended to other types of tumor detection and segmentation.

An Efficient Transformer Model for Synthesizing Dual Energy CT from Single Energy Scanner

PURPOSE/OBJECTIVE(S)

Dual-energy CT can be used to optimize radiation treatment. Recently, deep learning has been demonstrated to synthesize high-energy CT images from low-energy ones for dose reduction and lower CT system burden. As the state-of-the-art deep learning architecture, the computation burden of Transformer increases quadratically with the feature size, making the model training resource-demanding or even infeasible. Here, we introduce an efficient transformer for the balance between CT image synthesis quality and computational burden.

MATERIALS/METHODS

The model is a U-shape deep neural network with encoders and decoders built by Transformer blocks. The model input is low-energy 100kVp CT image and the output is high-energy 140kVp one. Each block has a Self Channel Correlation Unit (SCCU) and a Self Spatial Attention Unit (SSAU). Local shortcuts are applied for both units. Under-sampling operation achieved by pixel shuffling is used to obtain multi-scale feature maps, and the transformer block is applied on each feature scale. Symmetric skip connection sending features from shallow layers to deep layers, thus an additional 1 × 1 convolution is used for feature fusion in each decoder. In a SCCU, the feature is first mapped to one Query, one Key, and one Value. Then the Query and the Key tensors perform matrix multiplication to compute cross covariance of feature channels. The channel correlation score can be obtained by normalization of the covariance, and it is used to weight the Value tensor. As a result, the model complexity only increases linearly with the feature size. Besides the channel weighting, we enhance spatial information using SSAU, where the feature is mapped to two tensors. One tensor after activation is used to point-wisely calibrate another tensor. Additional Transformer blocks are cascaded to the last decoder for feature refinement. Because of the structure similarity of low- and high-energy CT images, a global shortcut is used to ease model training. Clinical iodine contrast-enhanced dual energy CT image datasets of 19 patients are used in this study. The dual-energy scanning is performed by a SOMATOM Definition Flash DECT scanner. We split the datasets into training dataset of 15 patients, validation dataset of 1 patient, and testing dataset of 3 patients. The image size is 512 × 512 with pixel size 0.5 × 0.5 mm2.

RESULTS

The U-Net model with 1.95M parameters and 44.87G FLOPS achieved the averaged PSNR value of 44.55 dB (s.t.d. 1.34) and averaged RMSE value of 0.0060 (s.t.d. 0.001). In comparison, our efficient Transformer with 1.408M parameters and 31.375G FLOPS achieved the averaged PSNR value of 44.78 dB (s.t.d. 1.37) and RMSE value of 0.0059 (s.t.d. 0.001), demonstrating our model has better performance with small model size and less computation.

CONCLUSION

The efficient Transformer model allows high-resolution CT image synthesis with small model scale and computation burden from low-energy CT image.

The Role of CT-Based Radiomics Nomogram in Differential Diagnosis of Immune Checkpoint Inhibitor-Related Pneumonitis from Radiation Pneumonitis for Patients with ESCC

PURPOSE/OBJECTIVE(S)

The combination of immunotherapy and chemoradiotherapy has widely used for patients with esophageal squamous cell carcinoma (ESCC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by the CT radiomics and clinical or radiological parameters.

MATERIALS/METHODS

A total of 76 ESCC patients with pneumonitis were enrolled in this retrospective study and divided into training dataset (n = 53) and validation dataset (n = 23). A total of 837 radiomics features were extracted from regions of interest (ROIs) based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator (LASSO). A logistic regression was applied to develop radiomics nomogram. Receiver operating characteristics (ROC) curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification.

RESULTS

No significant difference was detected between training dataset and validation dataset. The radiomics signature which was made up of four radiomics features shown a favorable performance on differentiating between CIP and RP with the α-binormal-based and empirical AUC = 0.831 and 0.843. Patients with RP had a close relationship with location (p = 0.003) and shape of lesions (p = 0.002). The nomogram that combined with radiomics signature and clinical factors improved the classifying performance on discrimination in the training dataset (AUCαbin = 0.963 and AUCemp = 0.964). The results were verified in the validation dataset with AUC = 0.967 and 0.964.

CONCLUSION

CT-based radiomics features have potential values for differentiating between patients with CIP and RP. Addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making.

Deep Learning-Based Single-View Fluorescence Dose Reconstruction for 3D Dosimetry

PURPOSE/OBJECTIVE(S)

3D dose distribution measurement is crucial for precise radiotherapy. Radiation-excited fluorescence imaging has potential for the 3D dosimetry with high spatial resolution, but multiple fluorescence images from different view-angles are required for analytical reconstruction techniques. Furthermore, the imaging data are contaminated by anisotropic Cherenkov light emission and statistical noise. This project aims to establish a novel deep learning-based model to predict 3D dose distributions from a single-view 2D fluorescence image while simultaneously removing the adverse effects of Cherenkov signals and other noises.

MATERIALS/METHODS

A total of 124 single-aperture static photon beams were delivered to an acrylic tank containing 1 g/L quinine hemisulfate water solution with varying aperture shapes and collimator angle. The emitted optical signals were detected by a low-cost CMOS camera for 20 seconds, and image pre-processing was performed to obtain input 2D fluorescence images with 0.3 × 0.3 mm spatial resolution. 3D back-projected dose distribution images were also calculated from the input fluorescence images. Ground-truth of 3D dose distributions and 2D field map images were obtained from a clinical treatment planning system with 1.4 × 1.4 × 1.4 mm spatial resolution. The proposed deep learning-based dose reconstruction method involved 3 steps. First, 2D fluence map images at the bottom plane of the tank were predicted from the fluorescence images by using a customized convolutional neural network (CNN). Second, the predicted fluence map images were transformed into the 2D field map images on the isocenter plane by applying perspective transformation. Finally, 2D dose distributions at a given radiological depth were calculated by using the predicted field map images, the back-projected dose distribution images, and the radiological depth value as inputs of a shallow CNN. Both CNN models were trained separately, and the 3D dose distributions were predicted by concatenating the output 2D dose distributions at various radiological depths.

RESULTS

The proposed CNN model yielded accurate 2D field map images. Averaged Dice similarity coefficient and mean absolute error of the field maps in the test data was 92.0% ± 4.6% and 0.0132 ± 0.0113, respectively. Moreover, our deep learning-based approach was able to predict accurate 3D dose distributions from the 2D fluorescence images. Mean squared error and averaged 3D gamma passing ratio (3%/3mm) were 9.55 mGy ± 6.8 mGy and 86.3% ± 9.86%, respectively.

CONCLUSION

Theproposed deep learning-based method calculated accurate 3D dose distributions from a single-view 2D fluorescence image. Since this technique require only a single CMOS camera image and fluorescent material, it can be readily used for any external radiotherapy modalities, including SRS/SBRT with small fields. This method is useful for acquiring 3D dose distribution data for precise dose verification within a few seconds.

Clinical Implementation of an Automated IMRT/VMAT Treatment Planning Tool

PURPOSE/OBJECTIVE(S)

To create an in-house automated treatment planning tool for IMRT/VMAT treatments and evaluate the dosimetric plan quality against manually generated plans.

MATERIALS/METHODS

A scripting application programming interface is employed to interact with a commercial treatment planning system (TPS) to implement automatic plan evaluation and update optimization parameters by mimicking the human planning process. The automated planning performs in an iterative fashion until reaching an acceptable tradeoff among target coverage/dose homogeneity and sparing of critical organs at risk. In each iteration, the dose constraints, priorities, and optimization structures for are automatically updated based on the results of the current iteration. Twenty previously treated plans (10 prostate and 10 head and neck), were preliminarily used to evaluate the performance of the automated planning tool. The differences in target and organ-at-risk metrics from the manually generated clinical plans were analyzed using paired t-test to evaluate clinical acceptability of tour automated planning tool. The current in-house-developed automated planning solution is able to create plans for different disease sites, including head & neck, prostate, pelvis, and lung. So far, the VMAT plans for more than 150 different cases have been generated with the tool. The results for these were also evaluated.

RESULTS

Compared to the manually generated clinical head and neck plans, all auto plans achieved PTV D95% coverage and critical organs at risk sparing without statistically significant change in average global Dmax (107.4% for manual vs 107.3% for automated plans). The auto-planning solution provided reduced maximum doses to brainstem and spinal cord (average reductions with standard deviations of 5.1 ± 2.6 Gy and 2.9 ± 1.4 Gy, respectively, all p <0.03), reduced average mean doses to contralateral parotid, ipsilateral parotid, contralateral submandibular gland, pharynx, esophagus, cochleae (reductions of 2.2 ± 2.9 Gy, 4.8 ± 4.7 Gy, 3.6 ± 5.2 Gy, 2.0 ± 7.1 Gy, 3.9 ± 2.6 Gy, 3.8 ± 5.0 Gy, respectively, all p < 0.045). Similar results were observed for the prostate plans. With the same PTV coverage and without statistically significant change in average global Dmax (106.5% for manual vs 106.8% for automated plans), the automated solution provided superior sparing for both bladder and rectum. Bladder V75, V70, V65 were reduced by 0.6% ± 2.1%, 0.8% ± 2.5%, and 0.9% ± 2.9% (all p <0.04), respectively. Rectum V75, V70, V65, V60 were reduced by 1.0% ± 2.3%, 1.2% ± 2.8%, 1.3% ± 3.2%, 1.6% ± 3.6% (all p < 0.01), respectively.

CONCLUSION

Our automated treatment planning solution is capable of efficiently generating VMAT plans for different disease sites with superior dosimetric indices compared to manually generated plans. Our tool is integrated within a commercial TPS platform, so it has the advantage of seamless adoption into the standard workflow to improve plan quality and treatment planning efficiency in our clinic.

Patient-Adaptive Automated Segmentation in Daily kVCT Images for Radiotherapy of Head and Neck and Prostate Cancer

PURPOSE/OBJECTIVE(S)

The purpose of this study was to examine the use of transfer learning in deep learning-based auto-segmentation of daily kilovoltage computed tomography (kVCT) images for patient-specific adaptive radiotherapy. Using data from the first cohort of patients treated with the innovative BgRT system, the objective of this study was to evaluate the potential benefits of this approach in facilitating efficient and effective adaptive radiotherapy.

MATERIALS/METHODS

For the head and neck (HaN) site and pelvic site, we first trained a deep convolutional segmentation network using a population dataset, consisting of 67 and 56 patient cases, respectively. This population network was then fine-tuned for a specific patient using a transfer learning approach, adapting the network weights. The auto-segmentation network utilized in this study was a 23-layer U-Net with batch normalization, a dropout rate of 0.5, and four skip connections between the encoder and decoder at different levels. We used initial planning CT and 5-26 sets of daily kVCT scans with a total of 8,039 images for patient-specific learning in the 6 HaN cases and 4 pelvic cases, particularly analyzing the relationship between the number of sequential patient-specific training data and the performance of the auto-segmentation. We compared the performance of the patient-specific network with the population network and the clinical rigid registration method, using the Dice similarity coefficient (DSC) as the evaluation metric. Additionally, we investigated the corresponding dosimetric impacts of the different auto-segmentation and registration methods.

RESULTS

The patient-specific network showed improved mean DSC scores of 0.88 and 0.90 for three HaN organs at risk (OARs) and eight pelvic targets and OARs, respectively, compared to the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network steadily improved as the number of longitudinal training cases increased, reaching near saturation after 6 training cases. The use of the patient-specific auto-segmentation resulted in a reduction of the mean discrepancy in target and OAR doses between delivery and planning from 5.5% with the clinical rigid registration to 1.1%.

CONCLUSION

The use of patient-specific transfer learning in auto-segmenting kVCT images showed higher accuracy compared to a conventional population network and clinical registration-based method. This approach holds promise for enhancing dose evaluation accuracy in adaptive radiotherapy.

Real Time Volumetric MRI for MR-Guided 3D Motion Tracking via Sparse Prior-Augmented Neural Representation Learning

PURPOSE/OBJECTIVE(S)

To reconstruct volumetric MRI from orthogonal cine acquisition aided by sparse priors of 2 static 3D MRI through implicit neural representation (NeRP) learning, with the goal of eliminating large-scale training datasets for data-driven sparse MRI reconstruction and supporting clinical workflow of real time 3D motion tracking during MR-guided radiotherapy.

MATERIALS/METHODS

A multi-layer perceptron network was trained to learn the NeRP of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first learning the NeRP of 2 static 3D MRI with different breathing motion states, prior knowledge of patient breathing motion was embedded into network weights through optimization. The prior knowledge was then augmented from 2 to 31 motion states by querying the optimized network at interpolated/extrapolated motion state coordinates. Starting from the prior-augmented network as an initialization point, the network was further trained using sparse samples of 2 orthogonal cine slices. The final volumetric reconstruction was obtained by querying the trained network at desired 3D spatial locations. We evaluated the proposed method using 5-minute volumetric MRI time series with 340 ms temporal resolution collected from 7 liver carcinoma patients. The time series was acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two MRI with inhale and exhale states respectively were selected from the first 30 sec of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to 2 orthogonal slices and compared network-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking.

RESULTS

Across the 7 patients evaluated, the peak signal to noise ratio between model reconstruction and ground truth was 54.66 ± 6.16 dB and the structural similarity index measure was 0.99 ± 0.01. Gross tumor volume (GTV) contours estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 1.89 ± 1.13 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all 3 orthogonal directions.

CONCLUSION

Volumetric MRI from orthogonal cine acquisition with sparse priors is feasible by modeling prior knowledge through implicit neural representation learning. The model-reconstructed images showed sufficient accuracy in supporting 3D motion tracking of abdominal targets. By eliminating the need for large scale training datasets, the method promises to enable clinical implementation of 3D motion tracking for precision radiation therapy.

Analyzing the Predictive Effects of PD-L1 Expression, Early Changes of bTMB and Circulated CD8+T Cells during Treatment for Responses of RT Combined with ICI in NSCLC

PURPOSE/OBJECTIVE(S)

The beneficial role of immunotherapy and the clinical relevance of current biomarkers remain inconclusive; thus, appropriate strategies and reliable predictors need further definition. A rational combination of biomarkers is needed. Here, we estimated potential predictive factors for responses of radiotherapy (RT) combined with immune checkpoint inhibitor (ICI) in a phase II trial to determine the efficacy and safety of combination of moderate hypofractionated RT with ICI in patients with oligometastatic NSCLC (NCT03557411).

MATERIALS/METHODS

Pretreatment tumor tissue samples and longitudinal blood were collected for immune and tumor biomarker analysis. We examined pre-treatment (pre-ICI) PD-L1 expression in tumor cells. Circulating tumor cell (CTC), PD-L1+CTC, blood tumor mutation burden (bTMB), CD8+T cells, CD4+T cells, NK cells, B cells in circulation were acquired pre-ICI and 1 month after ICI starting (1-mth). In addition, early changes of CTC (CTC), PD-L1+CTC (PD-L1+CTC), bTMB (bTMB), CD8+T cells (CD8+T cells), CD4+T cells (CD4+T cells), NK cells (NK cells), B cells (B cells) were also analyzed to estimate the predictive effects for treatment.

RESULTS

High pre-ICI bTMB and increased CD8+T cells at 1 month was associated with better PFS (p = 0.016; p = 0.006). Interaction analyses revealed that each combination of two markers in the 5 markers including PD-L1, pre-ICI bTMB, 1-mth bTMB, 1-mth CD8+T cells and CD8+T cells was significantly associated with PFS, except for CTC, PD-L1+CTC, CD4+T cells, NK cells and B cells in circulation due to low power. Unsupervised cluster analysis based on these markers revealed three sub-cohorts. Cohort-1 was overrepresented by patients with progressive disease (81%) of whom were negative for 3-4 of the 5 biomarkers. Cohort-3 was overrepresented by patients with partial response (70%) of whom were positive for 3-4 of the 5 biomarkers. Survival analyses of the 3 cohorts indicated a significant association with PFS (p = 0.017).

CONCLUSION

This study suggests that a combination of PD-L1 expression, early changes of bTMB and circulated CD8+T cells as a better predictive biomarker for response to RT combined with ICI. Consequently, refinement of this set of biomarkers and validation in a larger set of patients is warranted.

Does Epigallocatechin Gallate as a Radiation Protective Agent Reduce the Anti-Tumor Effect of Radiotherapy in Postoperative Breast Cancer Radiotherapy?

PURPOSE/OBJECTIVE(S)

Based on the previous encouraging results, we further explored whether EGCG would have a protective effect on potential tumor lesions, that is, reduce the efficacy of radiotherapy. We selected patients with stage III breast cancer with or without EGCG. The local control rate, distant metastasis rate, DFS and OS were compared between the two groups.

MATERIALS/METHODS

Patients with stage III breast cancer who were treated with EGCG and radiotherapy was selected from a phase II clinical study (ClinicalTrials.gov, No. NCT02580279). Each patient was matched with one control patient without EGCG From the medical database of our hospital matching for age and stage. The control group of stage-and age-matched patients was selected at random from the medical database of our hospital RESULTS: We identified 43 EGCG patients and 43 matched controls. The median age was 45 years (range: 26-67). Between the two groups, there were no obvious differences in the baseline demographic or clinical features. When compared to the placebo group, the mean radiation-induced dermatitis index (RIDI) in the EGCG group was substantially lower (2.56±1.14 vs 3.36±1.16 T = -3.232, P = 0.002). Repeated measures ANOVA indicated the significant differences in the RTOG score during the course of radiotherapy between the two groups (F = 9.611 P = 0.003). The patients mostly experienced RID two or three weeks after starting radiotherapy, although in the EGCG group, it appeared later (3.19±0.91 weeks) than it did in the placebo group (2.67±0.84 weeks), P = 0.008. The median follow-up for patients in the EGCG group at the time of data collection was 50.6 months with 95% confidence interval (CI) from 43.9 to 57.3. While it was 48.6 months (95% CI: 43.4-53.9) for patients in the control group. There was no significant difference in overall survival (OS), disease free-survival (DFS) and freedom from locoregional (LRF) and distant failure (DMF) (P > 0.05). At the data cut-off (December 2021), the 4-year DFS with EGCG was 71.4% compared to 65.4% with conventional therapy, and the 4-year OS was 77.0% compared to 80.3%.

CONCLUSION

The prophylactic use of EGCG solution reduced the RID score of stage III breast cancer patients without negatively impacting the therapeutic effect of radiotherapy on the tumor. EGCG is safe and feasible choice for RID for breast cancer during radiotherapy.

Prospective Clinical Adoption of Artificial Intelligence for Organ Contouring in Head and Neck Radiation Treatment Planning

PURPOSE/OBJECTIVE(S)

Patients that undergo head and neck (H&N) radiation therapy (RT) require laborious delineation of organs-at-risk (OARs) on computed tomography (CT) scans in a treatment planning system (TPS) to minimize radiation to normal tissue. This task can be completed rapidly and accurately with recently developed artificial intelligence-based semantic segmentation models. The current study aims to deploy and evaluate a strategy for improving clinical practice with this technology.

MATERIALS/METHODS

Deep learning models were trained and tested with CT scans and OAR contours from previous H&N RT cases at our clinic. Two medical physicists vetted the models and selected a 2.5D U-Net for further implementation. The model was embedded in a dedicated server at the hospital, programmed to read H&N CT scans staged for import into the TPS, generate auto-contours, and write them into a TPS-compatible format made available alongside the scan. In the pilot implementation, the auto-contouring service was utilized for more than 60 cases, prospectively. The auto-contours were quantitatively evaluated against the treatment-approved contours to determine how much modification was performed by the clinical team.

RESULTS

The 2.5D U-Net selected for clinical integration segments 21 OARs in less than 3 minutes per scan. Across all the prospective cases, the mean Dice score and mean 95th percentile Hausdorff distance (mm) between the auto-contour and treatment-approved contour for each of the 21 OARs were as follows, respectively: brainstem (0.93, 1.94), optic chiasm (0.70, 2.96), left cochlea (0.69, 2.37), right cochlea (0.68, 2.44), esophagus (0.88, 2.46), left globe (0.93, 1.50), right globe (0.93, 1.63), glottis (0.91, 2.13), larynx (0.93, 2.76), mandible (0.90, 4.86), left optic nerve (0.78, 1.64), right optic nerve (0.82, 1.65), oral cavity (0.86, 8.46), left parotid gland (0.91, 2.78), right parotid gland (0.91, 2.39), pharynx (0.85, 2.39), spinal cord (0.87, 2.27), left submandibular gland (0.85, 3.46), right submandibular gland (0.83, 3.69), left temporal lobe (0.94, 2.20), and right temporal lobe (0.95, 2.09). The auto-contours for the optic chiasm, optic nerves, cochleas, and submandibular glands differed substantially from the final contours, a finding corroborated by the clinical team; the rest were clinically acceptable with minor or no edits necessary.

CONCLUSION

The proposed strategy provides a sophisticated starting point for treatment planning that has garnered overall favorable feedback from the participating radiation oncologists and dosimetrists. Consequently, the technique is being extended to other treatment sites.

XGBoost-aided prediction of lip prominence based on hard-tissue measurements and demographic characteristics in an Asian population

INTRODUCTION

Prediction of lip prominence based on hard-tissue measurements could be helpful in orthodontic treatment planning and has been challenging and formidable thus far.

METHODS

A machine learning-based cross-sectional study was conducted on 1549 patients. Hard-tissue measurements and demographic information were used as the input features. Seven popular machine learning algorithms were applied to the datasets to predict upper and lower lip prominence. The algorithm that performed the best was selected for the construction of the prediction model. Evaluation of feature importance was conducted using 3 classical methods.

RESULTS

Among the 7 algorithms, the XGBoost model performed the best in the prediction of the distances between labrale superius or labrale inferius to the esthetics plane (UL-EP and LL-EP distances), with root mean square error values of 1.25, 1.49 and r2 values of 0.755 and 0.683, respectively. Among the 14 input features, the L1-NB distance contributed the most to the prominences of the upper and lower lips. A lip prominence predictor was developed to facilitate clinical application by deploying the prediction model into a downloadable tool kit.

CONCLUSIONS

The XGBoost model performed well with high accuracy and practicability in predicting upper and lower lip prominence. The artificial intelligence-aided predictor could serve as a reference for orthodontic treatment planning.

Clinical correlation research of 3D reconstruction of retrolabyrinthine space based on HRCT of temporal bone

Background: The retrolabyrinthine approach helps clinicians perform complex surgeries such as vestibular neurectomy, resection of petrous apex cholesteatoma, or use this space to complete endoscopic combined with microscope surgical operations in a relatively safe buffer space. Some of our current studies using 3D reconstruction in the clinic have also helped us perform some complex surgical procedures.Objective: This study aims to reveal the relationship between important structures in retrolabyrinthine space through objective parameters. These measurement data help clinicians locate intraoperatively and provide a reference for clinical surgery. Also, we are intended to help improve surgical techniques and expand the operating space to increase reachable anatomic structure.Material and Methods: The inner structures of the temporal bone from HRCT (High-resolution computed tomography) images which were taken at the Eye & ENT Hospital of Fudan University were reconstructed. Precise measurement of the structures was accomplished by using the software 3D-Slicer (3D Slicer, https://www.slicer.org/; version 4.8.0, Massachusetts, USA).Results: 3D model of temporal bone structures, including the cochlea, semicircular canals (SCCs), the internal auditory canal (IAC), facial nerve (FN), jugular bulb(JB), and carotid artery was reconstructed. The combination of HRCT and 3D models is utilized to analyze the Quantitative data of the retrolabyrinthine space and its adjacent structures.Conclusions and Significance: 3D reconstruction of CT images clearly displayed the detailed structures of the temporal bone. Surgical adaptability of the retrolabyrinthine approach can be assessed preoperatively by image and other methods, and anatomical parameters play an important role in the retrolabyrinthine space. Therefore, this study helps to skeleton the bone as much as possible to expand the surgical space, so that the surgeon can contact the anatomical structure more diversified to expand the surgical indications.

Mapping of major QTL and candidate gene analysis for hull colour in foxtail millet (Setaria italica (L.) P. Beauv.)

BACKGROUND

Hull colour is an important morphological marker for selection in seed production of foxtail millet. However, the molecular mechanisms underlying hull colour variation remain unknown.

RESULTS

An F7 recombinant inbred line (RIL) population containing 215 lines derived from Hongjiugu × Yugu18 was used to analyze inheritance and detect the quantitative trait loci (QTL) for four hull colour traits using major gene plus polygene mixed inheritance analysis and composite interval mapping (CIM) in four environments. Genetic analysis revealed that the hull colour L* value (HCL*) was controlled by two major genes plus additive polygenes, the hull colour a* value (HCa*) was controlled by three major genes, the hull colour b* value (HCb*) was controlled by two major genes plus polygenes, and the hull colour C* value (HCC*) was controlled by four major genes. A high-density genetic linkage map covering 1227.383 cM of the foxtail millet genome, with an average interval of 0.879 cM between adjacent bin markers, was constructed using 1420 bin markers. Based on the genetic linkage map and the phenotypic data, a total of 39 QTL were detected for these four hull colour traits across four environments, each explaining 1.50%-49.20% of the phenotypic variation. Of these, six environmentally stable major QTL were co-localized to regions on chromosomes 1 and 9, playing a major role in hull colour. There were 556 annotated genes within the two QTL regions. Based on the functions of homologous genes in Arabidopsis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) gene annotations, five genes were predicted as candidate genes for further studies.

CONCLUSIONS

This is the first study to use an inheritance model and QTL mapping to determine the genetic mechanisms of hull colour trait in foxtail millet. We identified six major environmentally stable QTL and predicted five potential candidate genes to be associated with hull colour. These results advance the current understanding of the genetic mechanisms underlying hull colour traits in foxtail millet and provide additional resources for application in genomics-assisted breeding and potential isolation and functional characterization of the candidate genes.

Roles of hydrogen bonding interactions and hydrophobic effects on enhanced water structure in aqueous solutions of amphiphilic organic molecules

Insights into the solute-induced water structural transformations are essential to understand the role of water in biological and chemical reaction processes. Herein, the structural changes in water induced by amphiphilic organic molecules were investigated using concentration-dependent derivative Raman spectroscopy (DRS) combined with two-dimensional Raman correlation spectroscopy (2D Raman-COS). We shall restrict our attention in this work to binary mixtures of water with dimethyl sulfoxide (DMSO), acetone, and isopropanol (IPA), all of which have similar chemical structures. The spectral changes in O:H and OH stretching modes illustrate that the solute molecules induce an enhancement of the water structure in dilute solutions, where the enhanced degree of water structure is closely related to the size of the dipole moment of organic molecules. In addition, the transformations of solute-induced water-specific structures were evaluated by 2D Raman-COS, which shows that the strong hydrogen bond (H-bond) structure of water is more sensitive to organic molecules and induces a transition to the weak H-bond structure of water.

A Nitroreductase-MOF Biocatalyst for the Degradation of Nitroaromatic Contaminants and Fluorescent Labelling of Biomolecules

NfsB (nitroreductase from Escherichia coli) can catalyze nitroaromatic compounds to aromatic amines under mild conditions. Compared with the purified enzyme NfsB, we found that the crude enzyme demonstrated better thermal stability and tolerance against a wide pH range, rendering it convenient to use and cost-effective as it did not require any downstream processing. In addition, we introduced metal-organic frameworks (MOFs) to immobilize the crude-NfsB. The resulting composite, crude-NfsB@ZIF-90, showed excellent catalytic performance and reusability, and it also demonstrated good catalytic activity in organic solvents, rendering it more efficient for the removal of nitroaromatic contaminants in complex environments. The nitroreductase-ZIF-90 biocatalyst can be used for fluorescent labeling of carbohydrates, which is favorable for the study of the function of carbohydrates.

Exploring the knowledge, attitudes, behaviors and training needs of obstetric and gynecological nurses regarding COVID-19 during the peak period of the pandemic in middle-risk areas of China: A cross-sectional study

Background

COVID-19 has become a global pandemic, which necessitates that health workers be capable of protecting themselves and their patients. This article aimed to describe knowledge levels, attitudes, behaviors and training needs regarding COVID-19 among obstetric and gynecological nurses in medium-risk areas during the pandemic.

Method

A cross-sectional survey of obstetric and gynecological nurses in medium-risk areas was performed during the peak period of the pandemic in China. The main survey tool was the self-designed COVID-19 Knowledge, Attitude, Behavior and Training Needs Questionnaire. Pearson correlation analysis was conducted to analyze the relationships among knowledge, attitudes, behaviors and training needs.

Results

A total of 599 nurses were recruited, of whom 27.7% failed the knowledge part of the questionnaire. Positive correlations were found between knowledge and attitudes (r = 0.100, P = 0.015) and between attitudes and behaviors (r = 0.352, P = 0.000) regarding occupational protection against COVID-19. A total of 88.5% of nurses preferred online training to traditional training, and more than 70% believed that demonstration of and training in operations by their own department were effective ways to learn about how to protect themselves against COVID-19.

Conclusion

The higher the level of knowledge about the disease was, the more positive the attitude toward occupational protection, and consequently, the more actively protective behaviors were adopted. Training affected nurses' knowledge of COVID-19 occupational protection and promoted positive attitudes, which further facilitated the effective prevention and control of the disease. Online training with demonstrations are recommended for COVID-19 training of nurses.

Intermittent and Mild Cold Stimulation Maintains Immune Function Stability through Increasing the Levels of Intestinal Barrier Genes of Broilers

In order to improve the adaptability of broilers to low-temperature environments and their ability to resist acute cold stress (ACS), 240 one-day-old broilers were selected and randomly divided into three groups. The control treatment (CC) group was raised at the conventional feeding temperature from 1-43 days (d), the cold stimulation treatment (CS) group was kept at 3 °C below the temperature of CC at 1 d intervals for 3 and 6 h from 15 to 35 d, namely, CS3 and CS6, respectively. Then, all broilers were kept at 20 °C from 36 to 43 d. ACS was then carried out at 44 d, and the ambient temperature was dropped to 10 °C for 6 h. The study investigated the production performance, as well as levels of intestinal barrier genes (including Claudin-1, E-cadherin, Occludin, ZO-1, ZO-2 and Mucin2), secretory IgA in duodenum and jejunum, and immunoglobulins (IgA and IgG) in serum. The results showed that IMCS could increase the daily weight gain and decrease the feed conversion ratio. During IMCS, the expression levels of intestinal barrier genes were up-regulated and the content of secretory IgA was increased. When IMCS ceased for one week, the level of immunoglobulins in serum stabilized, and the expression levels of Occludin, ZO-2 and Mucin2 still maintained high levels. After ACS, broilers that received IMCS training maintained high levels of intestinal barrier genes and secretory IgA.

Data generation for connected and automated vehicle tests using deep learning models

For the simulation-based test and evaluation of connected and automated vehicles (CAVs), the trajectory of the background vehicle has a direct effect on the performance of CAVs and experiment outcomes. The collected real trajectory data are limited by the sample size and diversity, and may exclude critical attribute combinations that are of vital importance for CAVs' tests. Consequently, it is indispensable to increase the richness of accessible trajectory data. In this study, we developed the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and a hybrid model of variational autoencoder and generative adversarial network (VAE-GAN) for trajectory data generation. These models are capable of learning a compressed representation of the observed data space, and generating data by sampling in the latent space and then mapping back to the original space. The real data and the generated data are applied in the car-following model of CAVs with cooperative adaptive cruise control (CACC) to evaluate safety performance using the time-to-collision (TTC) index. The results indicate that the generated data of the two generative models have reasonable differences while maintaining a certain similarity with the real samples. When real and generated trajectory data are applied to the car-following model of CAVs, the generated trajectory data increases the number of new critical fragments whose TTC is smaller than the threshold. The WGAN-GP model performs better than the VAE-GAN model according to the ratio of critical fragments. Findings of this study provide useful insights for CAVs' tests and safety performance improvement.

A graph-based genome and pan-genome variation of the model plant Setaria

Setaria italica (foxtail millet), a founder crop of East Asian agriculture, is a model plant for C4 photosynthesis and developing approaches to adaptive breeding across multiple climates. Here we established the Setaria pan-genome by assembling 110 representative genomes from a worldwide collection. The pan-genome is composed of 73,528 gene families, of which 23.8%, 42.9%, 29.4% and 3.9% are core, soft core, dispensable and private genes, respectively; 202,884 nonredundant structural variants were also detected. The characterization of pan-genomic variants suggests their importance during foxtail millet domestication and improvement, as exemplified by the identification of the yield gene SiGW3, where a 366-bp presence/absence promoter variant accompanies gene expression variation. We developed a graph-based genome and performed large-scale genetic studies for 68 traits across 13 environments, identifying potential genes for millet improvement at different geographic sites. These can be used in marker-assisted breeding, genomic selection and genome editing to accelerate crop improvement under different climatic conditions.

Appropriate cold stimulation changes energy distribution to improve stress resistance in broilers

Appropriate cold stimulation can improve stress resistance in broilers and alleviate the adverse impacts of a cold environment. To investigate the effects of intermittent mild cold stimulation (IMCS) on energy distribution in the livers of broilers, 96 healthy 1-day-old Ross-308 male broilers were randomly divided into the control group (CC) and the cold stimulation group (H5). The CC group was raised at a normal thermal temperature, i.e. 35°C until 3 d, after which the temperature was dropped gradually by 0.5°C/day until 20°C at 33 d. This temperature was maintained until 49 d. The H5 group was raised at the same temperature as the CC group until 14 d (35-29.5°C) and at 3°C ​​below the temperature of the CC group starting at 09:30 for 5 h every other day from 15 to 35 d (26-17℃). The temperature was returned to 20°C at 36 d and maintained until 49 d. At 50 d, all broilers were subjected to acute cold stress (ACS) at 10°C for 6 h and 12 h. We found that IMCS had positive effects on production performance. Using transcriptome sequencing of the broiler livers, 327 differentially expressed genes (DEG) were identified, and highly enriched in fatty acid biosynthesis, fatty acid degradation, and the pyruvate metabolism pathway. When compared to the CC group, the mRNA levels of ACAA1, ACAT2, ACSL1, CPT1A, LDHB, and PCK1 in the H5 group were increased at 22 d (P < 0.05). The LDHB mRNA level was upregulated in the H5 group at 29 d compared to the CC group (P < 0.05). After 21 days of IMCS (at 36 d), the mRNA expression levels of ACAT2 and PCK1 were found to be significantly increased in the H5 group compared to the CC group (P < 0.05). Seven days after the IMCS had ended (at 43 d), the mRNA levels of ACAA1, ACAT2, and LDHB in the H5 group were higher than in the CC group (P < 0.05). The mRNA levels of heat shock protein (HSP) 70, HSP90 and HSP110 in the H5 group were higher than in the CC group after 6 h of ACS (P < 0.05). The protein levels of HSP70 and HSP90 in the H5 group were downregulated after 12 h of ACS, compared to the CC group (P < 0.05). These results indicated that IMCS at 3°C lower than the normal temperature could improve energy metabolism and stress resistance in the livers of broilers, alleviate the damage of short-term ACS on broilers, help broilers adapt to the low temperature, and maintain stable of energy metabolism in the body.

Explore traffic conflict risks considering motion constraint degree in the diverging area of toll plazas

In the diverging area of toll plazas, the absence of lane markings, the gradual widening of lanes, and the crossing of vehicles with different tolling methods increase the likelihood of collisions. This study proposed a concept of motion constraint degree to investigate traffic conflict risks in the toll plaza diverging area. On the basis of the motion constraint degree, a two-step method was developed, in which all potentially influencing factors were separated into two parts. The first part was used to analyze the association between the motion constraint degree and some factors, while the remaining factors were utilized for risk regression/prediction together with the motion constraint degree. The random parameters logit model was applied for regression analysis and four prevalent machine learning models were employed for risk prediction. Results indicate that (1) the proposed approach considering motion constraint degree outperforms the conventional direct method, no matter for conflict risk regression or prediction; (2) the motion constraint degree is not monotonically correlated with the risk level of vehicles; (3) due to the layout of the toll plaza, ETC vehicles are less likely to be at risk in the diverging area; and (4) lane-changing behaviors in the restricted space increase the conflict risk.

Efficacy and Safety of Pulse Magnetic Therapy System in Insomnia Disorder: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial

OBJECTIVE

This study's objective is to assess the efficacy and safety of Pulsed Magnetic Therapy System (PMTS) in improving insomnia disorder.

METHODS

Participants with insomnia disorder were randomly assigned to receive either PMTS or sham treatment for four weeks (n= 153; PMTS: 76, sham: 77). Primary outcomes are the Insomnia Severity Index (ISI) scores at week 0 (baseline), 1, 2, 3, 4 (treatment), and 5 (follow-up). Secondary outcomes are the Pittsburgh Sleep Quality Index at baseline and week 4, and weekly sleep diary-derived values for sleep latency, sleep efficiency, real sleep time, waking after sleep onset, and sleep duration.

RESULTS

The ISI scores of the PMTS group and the sham group were 7.13±0.50, 11.07±0.51 at week 4, respectively. There was a significant group×time interaction for ISI (F3.214, 485.271=24.25, p<0.001, ηp 2=0.138). Only the PMTS group experienced continuous improvement throughout the study; in contrast, the sham group only experienced a modest improvement after the first week of therapy. At the end of the treatment and one week after it, the response of the PMTS group were 69.7% (95% confidence interval [CI]: 58.6%-79.0%), 75.0% (95% CI: 64.1%-83.4%), respectively, which were higher than the response of the sham group (p<0.001). For each of the secondary outcomes, similar group×time interactions were discovered. The effects of the treatment persisted for at least a week.

CONCLUSION

PMTS is safe and effective in improving insomnia disorders.

A temporal analysis of crash injury severities in multivehicle crashes involving distracted and non-distracted driving on tollways

Distracted driving is a prominent cause of traffic crashes and may increase the severity of collisions. Due to the larger speeds on toll ways, distracted driving crashes are more severe than on other types of roads, making it worthwhile to investigate. This study examined the variation in the influence of factors affecting injury severity in crashes involving distracted and non-distracted driving, as well as the change over time, using crash data from Florida toll ways from the 2017 to 2019. A series of random parameters logit models with heterogeneity in the means and variances were developed to analyze different driver-injury severities (no injury, minor injury, and severe injury) in crashes involving distracted and non-distracted driving. In addition, likelihood ratio tests were conducted to determine whether model parameters differed between different driver behaviors (distracted/non-distracted driving) and among years. Several factors potentially impacting injury severities were studied, including driver, crash, vehicle, roadway, environment, temporal, and others. Significant disparities were observed between the contributing factors of the severity of crashes involving distracted and non-distracted driving. Results showed that considerable differences were also observed in the severity of injuries caused by two types of crashes and distracted driving resulted in more serious crashes than non-distracted driving. Despite model results indicated that factors influencing injury severity altered over time, several factors, such as motorcycle involvement and commercial car involvement, still exhibited relative temporal stability in non-distracted driving crashes over the three years. Temporal instability and non-transferability were also captured by out-of-sample predictions to verify the temporal shifts of contributing variables from year to year. This study is useful for distinguishing the influence mechanisms between the two types of crashes involving distracted and non-distracted driving, and the results can be applied for safety countermeasures development.

Single-cell sequencing reveals the immune microenvironment landscape related to anti-PD-1 resistance in metastatic colorectal cancer with high microsatellite instability

BACKGROUND

The objective response rate of microsatellite instability-high (MSI-H) metastatic colorectal cancer (mCRC) patients with first-line anti-programmed cell death protein-1 (PD-1) monotherapy is only 40-45%. Single-cell RNA sequencing (scRNA-seq) enables unbiased analysis of the full variety of cells comprising the tumor microenvironment. Thus, we used scRNA-seq to assess differences among microenvironment components between therapy-resistant and therapy-sensitive groups in MSI-H/mismatch repair-deficient (dMMR) mCRC. Resistance-related cell types and genes identified by this analysis were subsequently verified in clinical samples and mouse models to further reveal the molecular mechanism of anti-PD-1 resistance in MSI-H or dMMR mCRC.

METHODS

The response of primary and metastatic lesions to first-line anti-PD-1 monotherapy was evaluated by radiology. Cells from primary lesions of patients with MSI-H/dMMR mCRC were analyzed using scRNA-seq. To identify the marker genes in each cluster, distinct cell clusters were identified and subjected to subcluster analysis. Then, a protein‒protein interaction network was constructed to identify key genes. Immunohistochemistry and immunofluorescence were applied to verify key genes and cell marker molecules in clinical samples. Immunohistochemistry, quantitative real-time PCR, and western blotting were performed to examine the expression of IL-1β and MMP9. Moreover, quantitative analysis and sorting of myeloid-derived suppressor cells (MDSCs) and CD8⁺ T cells were performed using flow cytometry.

RESULTS

Tumor responses in 23 patients with MSI-H/dMMR mCRC were evaluated by radiology. The objective response rate was 43.48%, and the disease control rate was 69.57%. ScRNA-seq analysis showed that, compared with the treatment-resistant group, the treatment-sensitive group accumulated more CD8⁺ T cells. Experiments with both clinical samples and mice indicated that infiltration of IL-1β-driven MDSCs and inactivation of CD8⁺ T cells contribute to anti-PD-1 resistance in MSI-H/dMMR CRC.

CONCLUSIONS

CD8⁺ T cells and IL-1β were identified as the cell type and gene, respectively, with the highest correlation with anti-PD-1 resistance. Infiltration of IL-1β-driven MDSCs was a significant factor in anti-PD-1 resistance in CRC. IL-1β antagonists are expected to be developed as a new treatment for anti-PD-1 inhibitor resistance.

[Preparation, properties and antibacterial applications of medical nano-metals and their oxides: a review]

Antibiotics are playing an increasingly important role in clinical antibacterial applications. However, their abuse has also brought toxic and side effects, drug-resistant pathogens, decreased immunity and other problems. New antibacterial schemes in clinic are urgently needed. In recent years, nano-metals and their oxides have attracted wide attention due to their broad-spectrum antibacterial activity. Nano-silver, nano-copper, nano-zinc and their oxides are gradually applied in biomedical field. In this study, the classification and basic properties of nano-metallic materials such as conductivity, superplasticity, catalysis, and antibacterial activities were firstly introduced. Secondly, the common preparation techniques, including physical, chemical and biological methods, were summarized. Subsequently, four main antibacterial mechanisms, such as cell membrane, oxidative stress, DNA destruction and cell respiration reduction, were summarized. Finally, the effect of size, shape, concentration and surface chemical characteristics of nano-metals and their oxides on antibacterial effectiveness and the research status of biological safety such as cytotoxicity, genotoxicity and reproductive toxicity were reviewed. At present, although nano-metals and their oxides have been applied in medical antibacterial, cancer treatment and other clinical fields, some issues such as the development of green preparation technology, the understanding of antibacterial mechanism, the improvement of biosafety, and the expansion of application fields, require further exploration.

Iterative learning control for lane-changing trajectories upstream off-ramp bottlenecks and safety evaluation

This paper proposes an iterative learning control framework for lane changing to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). This framework controls CAVs in the off-ramp bottlenecks by imitating the trajectories optimized by machine learning algorithms. Next Generation Simulation (NGSIM) dataset is utilized as the raw data and filtered by cost function. The traffic models, including lane-changing decision (LCD) models and lane-changing execution (LCE) models, are completed by Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms. Based on simulation results, simulation data satisfying the predetermined criterion will be added to dataset in the next iteration. Various metrics are considered to evaluate the proposed framework systematically from both lateral and longitudinal aspects, including time exposed time-to collision (TET), time integrated time-to-collision (TIT), rear-end collision risk indexes (RCRI) and lane-changing risk index (LCRI). The results present that the iterative framework can decrease the longitudinal risk of the system by a factor of two times, and can reduce the lateral risk by 28.7%. When the CAVs market penetration rate (MPR) reaches 100%, the longitudinal and lateral risk values of the off-ramp system can be reduced by 90% and 35%, respectively. However, it is worth noting that only when the CAVs MPR reaches 50% does the system's value at risk change significantly.

Exploring electronic energy level structure and excited electronic states of β-carotene using DFT

Carotenoids are a class of natural pigments that play a fundamental role in photosynthesis and optoelectronics. However, the complexity of their energy level structure and electronic states has prevented a clear interpretation of their photophysics and photochemistry. The mediating nonradiative decay of the bright S₂ state to the dark S₁ state of carotenoids involves a population of bridging intermediate state. Herein, time-dependent DFT was used to study the energy level and electronic excitation process of β-carotene. A π-π* transition and π electron delocalization of electron excitation could be inferred based on the difference in the electron cloud distribution of the HOMO and LUMO orbitals. Through the electronic transition contribution in the UV-vis spectra and the electron density difference between the ground state and the excited state, the electronic energy level structure and possible dark state were analyzed. On this basis, the electronic excitation process of β-carotene was theoretically studied by combining electron-hole analysis and transition density matrix (TDM). There was a charge transfer from the β-ionone ring to the long-chain in the (S₀) → (S₂), (S₀) → (S₄) and (S₀) → (S₅).

High-resolution mapping of the rainfall runoff pollution: case study of Shiwuli River watershed, China

Rainfall runoff is the key factor of water quality deterioration in highly urbanized area, which is characterized by intensive human activities and frequent extreme weather events. Urban landscape system is composed of highly diverse and heterogeneous land patches, which makes the effective management of urban runoff pollution difficult. Therefore, high-resolution land-use data is imperative for the identification and analysis of spatial-temporal characteristics of urban runoff pollution. In this study, Shiwuli River watershed, a rapidly urbanizing area in China, is selected as the study area. We first interpret nine kinds of land-use types with a high-resolution remote sensing data of 2 m [Formula: see text] 2 m. Then, a localized Soil Conservation Service model based on field observation and rainfall experiments is applied to map the spatial-temporal pattern of runoff pollution. The results indicate that the COD, NH₃-N, TP, and TN load generated by the runoff in the watershed accounted for 23.4%, 3.7%, 8.2%, and 9.0% of the total pollution load in 2016, respectively. Furthermore, the spatial-temporal pattern of the assessed runoff pollution was mainly subject to the distribution of rainfall and land-use patterns. We suggest that the sponge city construction combined with surface pollution control is an effective way to reduce the runoff pollution. This study highlights the necessity to identify spatial-temporal hotspots in developing precise pollution control measures, which provides valuable information for pollution control policy-making in Shiwuli River watershed and could serve as a reference for other river watersheds undergoing rapid urbanization.

Hydrogen-Bond Dynamics and Water Structure in Aqueous Ethylene Glycol Solution via Two-Dimensional Raman Correlation Spectroscopy

The hydrogen-bond (H-bond) dynamics and water structural transitions in aqueous ethylene glycol (EG) solution were investigated on the basis of concentration- and temperature-dependent two-dimensional Raman correlation spectroscopy (2D Raman-COS). At room temperature, EG-induced enhancement of the water structure when the EG/water molar ratio is less than 1:28 resulted from the hydrophobic effect around the methylene groups of EG. The decrease in the temperature caused an enhancement of the Raman peak at about 3200 cm^-1, representing an increase in the orderliness of water molecules. Further analysis of the water-specific structures by 2D Raman-COS reveals that the strong H-bond structure preferentially responds to external perturbations and induces a weak H-bond structural transition in water. Finally, EG-induced water structural transitions were calculated by the density functional theory (DFT). Hopefully, 2D Raman-COS combined with DFT calculations would advance the study of solute-induced water structural transitions in water-organic chemistry.

Intermolecular energy transfer-enhanced super-broadband stimulated Raman scattering in cyclohexane-benzene mixtures

Supercontinuum radiation has found numerous applications in diverse fields encompassing spectroscopy, pulse compression, and tunable laser sources. Anomalous enhanced stimulated Raman scattering (SRS) of cyclohexane-benzene mixtures was obtained in this study. SRS of the pure solvent, the multi-order Stokes of the strongest fundamental vibration modes, and energy transfer in intra-molecular modes were observed. SRS of the mixture revealed that the cross-pumping effect was generated between the C-H stretching (v₂) mode of cyclohexane and the C=C ring skeleton (v₁) mode of benzene, thereby producing the intermolecular secondary stimulated Raman emission and the appearance of two super-broadband radiations at 664.36-673.9 nm and 704.62-729.22 nm. The results suggest that the energy transfer of intermolecular vibrational modes, where the strongest vibrational mode excites other vibrational modes, is a simple approach for generating supercontinuum coherent radiation.