Clonidine Patch for Tourette Syndrome With Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

To explore the efficacy and safety of clonidine adhesive patch in Tourette syndrome (TS) patients with comorbid attentiondeficit/hyperactivity disorder (ADHD).

METHODS

This study was conducted on a sample of children and adolescents with TS who had comorbid ADHD between May 2012 and March 2015. The patients were diagnosed according to Diagnostic and Statistical Manual of Mental Disorders Fourth Edition, and were randomly assigned to four different dose groups: 1.0 mg/week, 1.5 mg/week, 2.0 mg/week and placebo group, and the symptom was evaluated by Swanson, Nolan, and Pelham Rating Scale, Version IV (SNAP-IV) and Yale Global Tic Severity Scale scales every 2 weeks. The primary outcome was tic disorders (TD) effective rate at week 8.

RESULTS

One hundred and twenty-seven TS patients with comorbid ADHD in 2.0 mg/week (n=35), 1.5 mg/week (n=27), 1.0 mg/week (n=36) and placebo groups (n=29) were included in this subgroup analysis. The TD effective rate of the 2.0 mg, 1.5 mg, and 1.0 mg groups at week 8 were significantly better than that in placebo group (85.7%, 81.5%, and 86.1% vs. 20.7%, all p<0.0001). All groups demonstrated significant improvements in SNAP-IV total scale scores compared to baseline (p=0.0004), with treatment groups showing only a trend for better performance compared to placebo group at week 8, without statistical differences (22.1±15.41, 21.3±11.96, and 21.2±12.48 vs. 26.0±13.37, p=0.3385). A total of 9 adverse reactions occurred, all recovered spontaneously without additional medication.

CONCLUSION

Clonidine adhesive patch could safely and effectively reduce the tic symptoms of TS patients with comorbid ADHD, and might be potentially helpful in the ADHD symptoms control.

Few-shot segmentation framework for lung nodules via an optimized active contour model

BACKGROUND

Accurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longstanding challenge.

PURPOSE

Existing deep learning and active contour models each have their limitations. This paper aims to integrate the strengths of both approaches while mitigating their respective shortcomings.

METHODS

In this paper, we propose a few-shot segmentation framework that combines a deep neural network with an active contour model. We introduce heat kernel convolutions and high-order total variation into the active contour model and solve the challenging nonsmooth optimization problem using the alternating direction method of multipliers. Additionally, we use the presegmentation results obtained from training a deep neural network on a small sample set as the initial contours for our optimized active contour model, addressing the difficulty of manually setting the initial contours.

RESULTS

We compared our proposed method with state-of-the-art methods for segmentation effectiveness using clinical computed tomography (CT) images acquired from two different hospitals and the publicly available LIDC dataset. The results demonstrate that our proposed method achieved outstanding segmentation performance according to both visual and quantitative indicators.

CONCLUSION

Our approach utilizes the output of few-shot network training as prior information, avoiding the need to select the initial contour in the active contour model. Additionally, it provides mathematical interpretability to the deep learning, reducing its dependency on the quantity of training samples.

High efficiency free-breathing 3D thoracic aorta vessel wall imaging using self-gating image reconstruction

PURPOSE

To improve the scan efficiency of thoracic aorta vessel wall imaging using a self-gating (SG)-based motion correction scheme.

MATERIALS AND METHODS

A slab-selective variable-flip-angle 3D turbo spin-echo (SPACE) sequence was modified to acquire SG signals and imaging data. Cartesian sampling with a tiny golden-step spiral profile ordering was used to obtain the imaging data during the systolic period, and then the image data were subsequently corrected based on the SG signals and binned to different respiratory cycles. Finally, respiratory artifacts were estimated from image-based registration of 3D undersampled respiratory bins that were reconstructed with L1 iterative self-consistent parallel imaging reconstruction (SPIRiT). This method was evaluated in 11 healthy volunteers and compared against conventional diaphragmatic navigator-gated acquisition to assess the feasibility of the proposed framework.

RESULTS

Results showed that the proposed method achieved image quality comparable to that of conventional diaphragmatic navigator-gated acquisition with an average scan time of 4 min. The sharpness of the vessel wall and the definition of the liver boundary were in good agreement with the navigator-gated acquisition, which took approximately above 8.5 min depend on the respiratory rate. Further valuation of this technique in patients will be conducted to determine its clinical use.

Development and application of LC-MS/MS method for the quantification of hydrogen sulfide in the eye

There are limited studies that report the physiological levels of H2S in the eye. The currently available UV/Vis methods lack the required sensitivity and precision. Hence, the purpose of this study was to develop and validate a sensitive and robust pre-column derivatization LC-MS/MS method to measure changes in H2S levels in tissues from isolated porcine eyes. H2S was derivatized and an LC-MS/MS method was developed to monitor the derivatized product, Sulfide-dibimane (Sdb) using a reverse phase Waters Acquity BEH C18 column (1.7 μm, 2.1 × 100 mm). H2S quantification was performed using multiple-ion reaction monitoring (MRM) in positive mode, with the transitions of m/z 415.0 → m/z 223.0 for Sdb and m/z 353.0 → m/z 285.0 for internal standard (griseofulvin). This method provided a suitable way to quantify H2S and was then successfully adapted to measure H2S levels in isolated porcine iris-ciliary body tissues previously treated in the presence or absence of varying concentrations of lipopolysaccharide (LPS, 5-100 ng/ml), a pro-inflammatory agent. Isolated iris-ciliary bodies (ICB) from porcine eyes were cut into quadrants of approximately 50 mg and homogenized using a 1:3 volume of homogenizing buffer. H2S in the supernatant was then derivatized with monobromobimane and quantified.

AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging

Typical tomographic image reconstruction methods require that the imaged object is static and stationary during the time window to acquire a minimally complete data set. The violation of this requirement leads to temporal-averaging errors in the reconstructed images. For a fixed gantry rotation speed, to reduce the errors, it is desired to reconstruct images using data acquired over a narrower angular range, i.e., with a higher temporal resolution. However, image reconstruction with a narrower angular range violates the data sufficiency condition, resulting in severe data-insufficiency-induced errors. The purpose of this work is to decouple the trade-off between these two types of errors in contrast-enhanced computed tomography (CT) imaging. We demonstrated that using the developed data consistency constrained deep temporal extrapolation method (AIRPORT), the entire time-varying imaged object can be accurately reconstructed with 40 frames-per-second temporal resolution, the time window needed to acquire a single projection view data using a typical C-arm cone-beam CT system. AIRPORT is applicable to general non-sparse imaging tasks using a single short-scan data acquisition.

Eudesmane sesquiterpenoids with inhibitory effects on NO production from Artemisia princeps

Five undescribed eudesmane methyl esters (1-5), three undescribed eudesmane-12,6-olides (6-8), and 21 known analogues (9-29) were isolated from the aerial part of Artemisia princeps Pamp. Their structures were established by detailed analysis of the NMR and HRESIMS data. The absolute configurations of 1-8 were determined based on single-crystal X-ray diffraction analysis and ECD calculations. Moreover, the inhibitory effects on LPS-induced NO production in BV-2 microglial cells of all the isolated compounds were assessed. Except for compounds 2, 4, 10, and 11, the others showed significant inhibitory activities, with IC50 values of 0.73-18.66 μM, wherein the potential structure-activity relationship was also discussed.

OIF-Net: An Optical Flow Registration-Based PET/MR Cross-Modal Interactive Fusion Network for Low-Count Brain PET Image Denoising

The short frames of low-count positron emission tomography (PET) images generally cause high levels of statistical noise. Thus, improving the quality of low-count images by using image postprocessing algorithms to achieve better clinical diagnoses has attracted widespread attention in the medical imaging community. Most existing deep learning-based low-count PET image enhancement methods have achieved satisfying results, however, few of them focus on denoising low-count PET images with the magnetic resonance (MR) image modality as guidance. The prior context features contained in MR images can provide abundant and complementary information for single low-count PET image denoising, especially in ultralow-count (2.5%) cases. To this end, we propose a novel two-stream dual PET/MR cross-modal interactive fusion network with an optical flow pre-alignment module, namely, OIF-Net. Specifically, the learnable optical flow registration module enables the spatial manipulation of MR imaging inputs within the network without any extra training supervision. Registered MR images fundamentally solve the problem of feature misalignment in the multimodal fusion stage, which greatly benefits the subsequent denoising process. In addition, we design a spatial-channel feature enhancement module (SC-FEM) that considers the interactive impacts of multiple modalities and provides additional information flexibility in both the spatial and channel dimensions. Furthermore, instead of simply concatenating two extracted features from these two modalities as an intermediate fusion method, the proposed cross-modal feature fusion module (CM-FFM) adopts cross-attention at multiple feature levels and greatly improves the two modalities' feature fusion procedure. Extensive experimental assessments conducted on real clinical datasets, as well as an independent clinical testing dataset, demonstrate that the proposed OIF-Net outperforms the state-of-the-art methods.

Biochar and Manure Co-Application Increases Rice Yield in Low Productive Acid Soil by Increasing Soil pH, Organic Carbon, and Nutrient Retention and Availability

In recent years, overuse of chemical fertilization has led to soil acidification and decreased rice yield productivity in southern China. Biochar and manure co-application remediation may have positive effects on rice yield and improve acid paddy soil fertility. This study was conducted to understand the effects of co-application of wood biochar and pig manure on rice yield and acid paddy soil quality (0-40 cm soil layers) in a 5-year field experiment. The experiment consisted of six treatments: no biochar and no fertilizer (CK); biochar only (BC); mineral fertilizer (N); mineral fertilizer combined with biochar (N + BC); manure (25% manure N replacing fertilizer N) combined with mineral fertilizer (MN); and manure combined with mineral fertilizer and biochar (MN + BC). Total nitrogen application for each treatment was the same at 270 kg nitrogen ha-1y-1, and 30 t ha-1 biochar was added to the soil only in the first year. After five years, compared with N treatments, N + BC, MN, and MN + BC treatments increased the rice yield rate to 2.8%, 4.3%, and 6.3%, respectively, by improving soil organic matter, total nitrogen, and available phosphate under a 0-40 cm soil layer. MN + BC had the strongest resistance to soil acidification among all the treatments. The interaction between fertilizers and biochar application was significant (p < 0.05) in rice yield, soil electrical conductivity (10-20 cm), and soil available phosphate (20-40 cm). Principal component analysis indicated that the effect of manure on soil property was stronger than that of biochar in the 0-40 cm soil layer. The overall rice yield and soil fertility decreased in the order of biochar + mineral fertilizer + manure > mineral fertilizer + manure > biochar + mineral fertilizer > mineral fertilizer > biochar > control. These results suggest that biochar and manure co-application is a long-term viable strategy for improving acid soil productivity due to its improvements in soil pH, organic carbon, nutrient retention, and availability.

Emergence, Pattern, and Frequency of Spontaneous Waves in Spreading Epithelial Monolayers

A striking phenomenon of collective cell motion is that they can exhibit a spontaneously emerging wave during epithelia expansions. However, the fundamental mechanism, governing the emergence and its crucial characteristics (e.g., the eigenfrequency and the pattern), remains an enigma. By introducing a mechanochemical feedback loop, we develop a highly efficient discrete vertex model to investigate the spatiotemporal evolution of spreading epithelia. We find both numerically and analytically that expanding cell monolayers display a power-law dependence of wave frequency on the local heterogeneities (i.e., cell density) with a scaling exponent of -1/2. Moreover, our study demonstrates the quantitative capability of the proposed model in capturing distinct X-, W-, and V-mode wave patterns. We unveil that the phase transition between these modes is governed by the distribution of active self-propulsion forces. Our work provides an avenue for rigorous quantitative investigations into the collective motion and pattern formation of cell groups.

Genetic Background of Blood β-Hydroxybutyrate Acid Concentrations in Early-Lactating Holstein Dairy Cows Based on Genome-Wide Association Analyses

Ketosis is a common metabolic disorder in the early lactation of dairy cows. It is typically diagnosed by measuring the concentration of β-hydroxybutyrate (BHB) in the blood. This study aimed to estimate the genetic parameters of blood BHB and conducted a genome-wide association study (GWAS) based on the estimated breeding value. Phenotypic data were collected from December 2019 to August 2023, comprising blood BHB concentrations in 45,617 Holstein cows during the three weeks post-calving across seven dairy farms. Genotypic data were obtained using the Neogen Geneseek Genomic Profiler (GGP) Bovine 100 K SNP Chip and GGP Bovine SNP50 v3 (Illumina Inc., San Diego, CA, USA) for genotyping. The estimated heritability and repeatability values for blood BHB levels were 0.167 and 0.175, respectively. The GWAS result detected a total of ten genome-wide significant associations with blood BHB. Significant SNPs were distributed in Bos taurus autosomes (BTA) 2, 6, 9, 11, 13, and 23, with 48 annotated candidate genes. These potential genes included those associated with insulin regulation, such as INSIG2, and those linked to fatty acid metabolism, such as HADHB, HADHA, and PANK2. Enrichment analysis of the candidate genes for blood BHB revealed the molecular functions and biological processes involved in fatty acid and lipid metabolism in dairy cattle. The identification of novel genomic regions in this study contributes to the characterization of key genes and pathways that elucidate susceptibility to ketosis in dairy cattle.

TiO2-modified two-dimensional composite of nitrogen-doped molybdenum trioxide nanosheets as a high-performance anode for lithium-ion batteries

Nitrogen-doped molybdenum trioxide (MoO3/NC) has drawbacks such as volume expansion during long-term charging and discharging cycles, which severely limit its further application. This work proposes the addition of titanium dioxide nanoparticles (TiO2 NPs) for performance improvement of MoO3/NC. TiO2 NPs embedded on the surface of a MoO3/NC nanosheet can alleviate its volume expansion and the accumulation of lithiated products and improve the conductivity of the electrode material. The results show that the MoO3/NC nanosheet decorated with TiO2 NPs (TiO2@MoO3/NC), when used as an electrode material, exhibited a discharge specific capacity of 419 mA h g-1 at a current density of 0.05 A g-1 and retained a discharge specific capacity of 517 mA h g-1 after 600 cycles at a current density of 1 A g-1.

The dyadic effects of family resilience and social support on quality of life among older adults with chronic illness and their primary caregivers in multigenerational families in China: A cross-sectional study

Older adults with chronic illness, as well as their primary caregivers in multigenerational families, may experience a complex interplay of factors that affect their quality of life (QOL). However, this interplay is not yet well-characterized for Chinese multigenerational families in particular. In this study, we analyzed how family resilience and social support affect the QOL of both older adults and caregivers in multigenerational Chinese families specifically. We enrolled 258 pairs of older adults with chronic illness and their primary caregivers in a multicenter cross-sectional study conducted in southern China in December 2021. Using the Actor-Partner Interdependence Model (APIM), we then examined the correlation between family resilience, social support, and QOL in dyadic analysis and found that QOL, family resilience, and social support for primary caregivers were better than those of older adults with chronic illness (t = 3.66-16.3, p＜0.01). These factors were found to be positively correlated (r = 0.22-0.60, p＜0.05), except for the family resilience of primary caregivers and the QOL of older adults with chronic illness (r = -0.14, p = 0.04). Additionally, actor effect results showed that when a dyadic member has high family resilience and objective social support, they tend to have a better QOL (β = 0.5-1.48, P < 0.01). However, partner effect results showed that when the primary caregiver has high family resilience, this is associated with a worse QOL for the older adult (β = -1.06, P < 0.01). Furthermore, we found that objective social support of dyads does not significantly influence their partner's QOL (β = 0.88/0.31, P＞0.05) for any pair. This suggests that medical staff should pay attention to the impact of family resilience on the QOL of older adult and caregiver dyads and explore health management plans that focus on binary coping in multigenerational families.

Multiple-cycle photochemical cascade reactions

Cascade reactions represent an efficient and economical synthetic approach, enabling the rapid synthesis of a wide array of structurally complex organic compounds. These compounds, previously inaccessible, can now be synthesized in a remarkably limited number of steps. Concurrently, the photochemical reactions of organic molecules have gained prominence as a potent strategy for accessing a diverse range of radical species and intermediates. This is achieved in a controlled manner under mild conditions. Owing to the relentless endeavors of chemists, significant strides have been made in the realm of photochemical cascade reactions. These advancements have facilitated the synthesis of novel molecular structures with high complexity, structures that are typically challenging to generate under thermal conditions. In this review, we comprehensively summarize and underscore the recent pivotal advancements in visible-light-induced cascade reactions. Our focus is on the elucidation of multiple photochemical catalytic cycles, emphasizing the catalytic activation modes and the types of reactions involved.

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection

Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution. In this paper, we propose a novel framework Two-Stage Generative Model (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and Variance Exploding stochastic differential equation using joint probability (VE-JP) to improve brain tumor detection and segmentation. The CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. Then VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide, which alters only pathological regions but not regions of healthy. Notably, our method directly learned the joint probability distribution for conditional generation. The residual between input and reconstructed images suggests the abnormalities and a thresholding method is subsequently applied to obtain segmentation results. Furthermore, the multimodal results are weighted with different weights to improve the segmentation accuracy further. We validated our method on three datasets, and compared with other unsupervised methods for anomaly detection and segmentation. The DSC score of 0.8590 in BraTs2020 dataset, 0.6226 in ITCS dataset and 0.7403 in In-house dataset show that our method achieves better segmentation performance and has better generalization.

High-quality genome assembly and genetic transformation system of Lasiodiplodia theobromae strain LTTK16-3, a fungal pathogen of Chinese hickory

Lasiodiplodia theobromae, as one of the causative agents associated with Chinese hickory trunk cankers, has caused huge economic losses to the Chinese hickory industry. Although the biological characteristics of this pathogen and the occurrence pattern of this disease have been well studied, few studies have addressed the related mechanisms due to the poor molecular and genetic study basis of this fungus. In this study, we sequenced and assembled L. theobromae strain LTTK16-3, isolated from a Chinese hickory tree (cultivar of Linan) in Linan, Zhejiang province, China. Phylogenetic analysis and comparative genomics analysis presented crucial cues in the prediction of LTTK16-3, which shared similar regulatory mechanisms of transcription, DNA replication, and DNA damage response with the other four Chinese hickory trunk canker-associated Botryosphaeria strains including, Botryosphaeria dothidea, Botryosphaeria fabicerciana, Botryosphaeria qingyuanensis, and Botryosphaeria corticis. Moreover, it contained 18 strain-specific protein clusters (not conserved in the other L. theobromae strains, AM2As and CITRA15), with potential roles in specific host-pathogen interactions during the Chinese hickory infection. Additionally, an efficient system for L. theobromae protoplast preparation and polyethylene glycol (PEG) -mediated genetic transformation was firstly established as the foundation for its future mechanisms study. Collectively, the high-quality genome data and the efficient transformation system of L. theobromae here set up the possibility of targeted molecular improvements for Chinese hickory canker control.IMPORTANCEFungi with disparate genomic features are physiologically diverse, possessing species-specific survival strategies and environmental adaptation mechanisms. The high-quality genome data and related molecular genetic studies are the basis for revealing the mechanisms behind the physiological traits that are responsible for their environmental fitness. In this study, we sequenced and assembled the LTTK16-3 strain, the genome of Lasiodiplodia theobromae first obtained from a diseased Chinese hickory tree (cultivar of Linan) in Linan, Zhejiang province, China. Further phylogenetic analysis and comparative genomics analysis provide crucial cues in the prediction of the proteins with potential roles in specific host-pathogen interactions during the Chinese hickory infection. An efficient PEG-mediated genetic transformation system of L. theobromae was established as the foundation for the future mechanisms exploration. The above genetic information and tools set up valuable clues to study L. theobromae pathogenesis and assist in Chinese hickory canker control.

AST to ALT ratio as a prospective risk predictor for liver cirrhosis in patients with chronic HBV infection

BACKGROUND

Serum aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio (AAR) is one of the most frequent indicators to discriminate fibrosis and cirrhosis. However, the results remained controversial. The aim of this study was to evaluate the predictive effect of AAR on hepatitis B virus (HBV)-related cirrhosis development.

METHOD

A retrospective cohort study was conducted based on 1754 chronic HBV-infected patients. Clinical variables at their initial visit and follow-up data were collected. Cox proportional hazards model was constructed to evaluate the predictive value of AAR on cirrhosis risk, and its discrimination accuracy was determined by receiver operating characteristic (ROC). The time-dependent effect was assessed by a Fine and Gray competing risk model.

RESULTS

Compared to patients with lower AAR, those with elevated AAR level had higher risk of cirrhosis development by adjusting for host characteristics (dichotomized analyses: hazard ratio = 2.77, P = 8.25 × 10 -4 ; tertile analyses: hazard ratio = 2.95, P = 1.61 × 10 -3 ), with an increasing risk trend ( Ptrend = 4.56 × 10 -4 ). The effect remained prominent when ALT or AST was abnormal, while no significant risk was observed when AST and ALT were simultaneously normal. Time-dependent effect analysis demonstrated a persistently higher risk, with the average hazard ratio equivalent to 1.92. AAR level could improve the discrimination efficacy of host variables with area under the curve increased from 0.684 to 0.711 ( P  =  0.039 ).

CONCLUSION

Higher AAR was significantly associated with increased risk of HBV-related cirrhosis, and might be a potential predictor of cirrhosis development.

Hysterolides A-I, dimeric or monomeric sesquiterpene lactones from Parthenium hysterophorus L

Nine undescribed sesquiterpene lactones, including two pseudoguaianolide dimers (1 and 2), a pseudoguaiac dilactone (3), and six pseudoguaianolides (4-9), along with 13 known analogues (10-22) were isolated from Parthenium hysterophorus. Among them, hysterolide A (1) possesses an unusual carbon skeleton with a unique cyclobutane ring connecting two pseudoguaianolides. Hysterolide C (3) is a sesquiterpene dilactone incorporating a bicyclo[5.1.0]octane core. Spectroscopic analyses, 13C NMR and ECD calculations, and X-ray diffraction elucidated their structures and absolute configurations. Moreover, all the isolates were assayed for their anti-inflammatory activities by inhibiting LPS-induced nitric oxide production in BV-2 microglia cells, wherein, nine compounds displayed significant inhibitory activities with IC50 of 0.52-6.32 μM. Furthermore, the preliminary structure-activity relationship was also established.

Effect of lignin on the formation of polycyclic aromatic hydrocarbons in smoked and grilled meat products

In order to explore the influence of wood types on formation of polycyclic aromatic hydrocarbons (PAHs) in traditional smoked and grilled meat products, the effect of lignin in woods on formation of PAHs was investigated in meat model systems. The results showed that PAHs formation was much dependent on the heating conditions. The addition of lignin led to significantly increased PAHs, which being connected with lignin structure. In comparison, the formation of PAHs was more facilitated by lignin with G structure than that with G/S structure. However, further study of adding lignin precursors demonstrated that lignin precursors with S structure were more favorable to the formation of PAHs than those with G structure. It was proposed that the relative content and activity of G/S structure of lignin in wood played a significant role in the formation of PAHs, which might provide theoretical reference for inhibition of PAHs fundamentally.

Quasi-Consensus Control for Stochastic Multiagent Systems: When Energy Harvesting Constraints Meet Multimodal FDI Attacks

In this article, the quasi-consensus control problem is investigated for a class of stochastic nonlinear time-varying multiagent systems (MASs). The innovation points of this research can be highlighted as follows: first of all, the dynamics of the plant are stochastic, nonlinear, and time varying, which resembles the natural systems in practice closely. Meanwhile, an energy harvesting protocol is put forward to collect adequate energy from the external environment. Second, as a generalization of the existing result, the ultimate control objective is quasi-consensus in a probabilistic sense, that is, designing a distributed control protocol in order that the probability of centering the allowable region for the states of each agent is larger than some predetermined values. Third, the MASs are subject to false data-injection (FDI) attacks, and a more general multimodal FDI model is proposed. On the basis of the probabilistic-constrained analysis technique and the recursive linear matrix inequalities (RLMIs), sufficient conditions are provided to guarantee the probabilistic quasi-consensus property. To derive the controller gains, an optimal probabilistic-constrained algorithm is designed by solving a convex optimization problem. Finally, two examples are provided to substantiate the validity of the proposed framework.

Glycosides with galloyl groups from Balakata baccata and their antineuroinflammatory activities

Seven new glycosides (1 - 7) with galloyl groups and two known kaempferol glycosides (8 and 9) were obtained from the overground parts of Balakata baccata. The structures of the new compounds were determined by comprehensive spectroscopic analyses. The rarely seen allene moiety in compounds 6 and 7 were described by detailed analysis of 1D and 2D NMR data. The antineuroinflammatory effect of all the isolates was assessed through inhibiting nitric oxide (NO) production in lipopolysaccharide (LPS)-induced BV-2 microglial cells. Compounds 1, 2, 6, and 7 showed potent inhibitory activities with IC50 values of 25.7, 17.2, 15.5 and 24.4 μM, respectively, compared with the positive control minocycline (IC50 = 16.1 μM).

Annihilation-Net: Learned annihilation relation for dynamic MR imaging

BACKGROUND

Deep learning methods driven by the low-rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. The effectiveness of existing methods lies mainly in their ability to capture interframe relationships using network modules, which are lack interpretability.

PURPOSE

This study aims to design an interpretable methodology for modeling interframe relationships using convolutiona networks, namely Annihilation-Net and use it for accelerating dynamic MRI.

METHODS

Based on the equivalence between Hankel matrix product and convolution, we utilize convolutional networks to learn the null space transform for characterizing low-rankness. We employ low-rankness to represent interframe correlations in dynamic MR imaging, while combining with sparse constraints in the compressed sensing framework. The corresponding optimization problem is solved in an iterative form with the semi-quadratic splitting method (HQS). The iterative steps are unrolled into a network, dubbed Annihilation-Net. All the regularization parameters and null space transforms are set as learnable in the Annihilation-Net.

RESULTS

Experiments on the cardiac cine dataset show that the proposed model outperforms other competing methods both quantitatively and qualitatively. The training set and test set have 800 and 118 images, respectively.

CONCLUSIONS

The proposed Annihilation-Net improves the reconstruction quality of accelerated dynamic MRI with better interpretability.

Effects of Potassium-Containing Fertilizers on Sugar and Organic Acid Metabolism in Grape Fruits

To identify suitable potassium fertilizers for grape (Vitis vinifera L.) production and study their mechanism of action, the effects of four potassium-containing fertilizers (complex fertilizer, potassium nitrate, potassium sulfate, and potassium dihydrogen phosphate) on sugar and organic acid metabolism in grape fruits were investigated. Potassium-containing fertilizers increased the activity of sugar and organic acid metabolism-related enzymes at all stages of grape fruit development. During the later stages of fruit development, potassium-containing fertilizers increased the total soluble solid content and the sugar content of the different sugar fractions and decreased the titratable acid content and organic acid content of the different organic acid fractions. At the ripening stage of grape fruit, compared with the control, complex fertilizer, potassium nitrate, potassium sulfate, and potassium dihydrogen phosphate increased the total soluble solid content by 1.5, 1.2, 3.5, and 3.4 percentage points, decreased the titratable acid content by 0.09, 0.06, 0.18, and 0.17 percentage points, respectively, and also increased the total potassium content in grape fruits to a certain degree. Transcriptome analysis of the differentially expressed genes (DEGs) in the berries showed that applying potassium-containing fertilizers enriched the genes in pathways involved in fruit quality, namely, carbon metabolism, carbon fixation in photosynthetic organisms, glycolysis and gluconeogenesis, and fructose and mannose metabolism. Potassium-containing fertilizers affected the expression levels of genes regulating sugar metabolism and potassium ion uptake and transport. Overall, potassium-containing fertilizers can promote sugar accumulation and reduce acid accumulation in grape fruits, and potassium sulfate and potassium dihydrogen phosphate had the best effects among the fertilizers tested.

Excellent Room Temperature Thermoelectric Performance in Mg3 Sb2 -Based Alloys via Multi-Functional Doping of Nb

Mg3 Sb2 -based alloys are attracting increasing attention due to the excellent room temperature thermoelectric properties. However, due to the presence and easy segregation of charged Mg vacancies, the carrier mobility in Mg3 Sb2 -based alloys is always severely compromised that significantly restricts the room temperature performance. General vacancy compensation strategies cannot synergistically optimize the complicated Mg3 Sb2 structures involving both interior and boundary scattering. Herein, due to the multi-functional doping effect of Nb, the electron scattering inside and across grains is significantly suppressed by inhibiting the accumulation of Mg vacancies, and leading to a smooth transmission channel of electrons. The increased Mg vacancies migration barrier and optimized interface potential are also confirmed theoretically and experimentally, respectively. As a result, a leading room temperature zT of 1.02 is achieved. This work reveals the multi-functional doping effect as an efficient approach in improving room temperature thermoelectric performance in complicated defect/interface associated Mg3 Sb2 -based alloys.

Non-invasive quantification of the brain [18F]FDG-PET using inferred blood input function learned from total-body data with physical constraint

Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for Ki (K1). In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling.

Dynamic X-ray imaging with screen-printed perovskite CMOS array

High performance X-ray detector with ultra-high spatial and temporal resolution are crucial for biomedical imaging. This study reports a dynamic direct-conversion CMOS X-ray detector assembled with screen-printed CsPbBr3, whose mobility-lifetime product is 5.2 × 10-4 cm2 V-1 and X-ray sensitivity is 1.6 × 104 µC Gyair-1 cm-2. Samples larger than 5 cm[Formula: see text]10 cm can be rapidly imaged by scanning this detector at a speed of 300 frames per second along the vertical and horizontal directions. In comparison to traditional indirect-conversion CMOS X-ray detector, this perovskite CMOS detector offers high spatial resolution (5.0 lp mm-1) X-ray radiographic imaging capability at low radiation dose (260 nGy). Moreover, 3D tomographic images of a biological specimen are also successfully reconstructed. These results highlight the perovskite CMOS detector's potential in high-resolution, large-area, low-dose dynamic biomedical X-ray and CT imaging, as well as in non-destructive X-ray testing and security scanning.