Integrating data distribution prior via Langevin dynamics for end-to-end MR reconstruction

PURPOSE

To develop a novel deep learning-based method inheriting the advantages of data distribution prior and end-to-end training for accelerating MRI.

METHODS

Langevin dynamics is used to formulate image reconstruction with data distribution before facilitate image reconstruction. The data distribution prior is learned implicitly through the end-to-end adversarial training to mitigate the hyper-parameter selection and shorten the testing time compared to traditional probabilistic reconstruction. By seamlessly integrating the deep equilibrium model, the iteration of Langevin dynamics culminates in convergence to a fix-point, ensuring the stability of the learned distribution.

RESULTS

The feasibility of the proposed method is evaluated on the brain and knee datasets. Retrospective results with uniform and random masks show that the proposed method demonstrates superior performance both quantitatively and qualitatively than the state-of-the-art.

CONCLUSION

The proposed method incorporating Langevin dynamics with end-to-end adversarial training facilitates efficient and robust reconstruction for MRI. Empirical evaluations conducted on brain and knee datasets compellingly demonstrate the superior performance of the proposed method in terms of artifact removing and detail preserving.

Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

Variable augmentation network for invertible MR coil compression

To improve the efficiency of multi-coil data compression and recover the compressed image reversibly, increasing the possibility of applying the proposed method to medical scenarios. A deep learning algorithm is employed for MR coil compression in the presented work. The approach introduces a variable augmentation network for invertible coil compression (VAN-ICC). This network utilizes the inherent reversibility of normalizing flow-based models. The aim is to enhance the readability of the sentence and clearly convey the key components of the algorithm. By applying the variable augmentation technology to image/k-space variables from multi-coils, VAN-ICC trains the invertible network by finding an invertible and bijective function, which can map the original data to the compressed counterpart and vice versa. Experiments conducted on both fully-sampled and under-sampled data verified the effectiveness and flexibility of VAN-ICC. Quantitative and qualitative comparisons with traditional non-deep learning-based approaches demonstrated that VAN-ICC carries much higher compression effects. The proposed method trains the invertible network by finding an invertible and bijective function, which improves the defects of traditional coil compression method by utilizing inherent reversibility of normalizing flow-based models. In addition, the application of variable augmentation technology ensures the implementation of reversible networks. In short, VAN-ICC offered a competitive advantage over other traditional coil compression algorithms.

Amendment of straw with decomposing inoculants benefits the ecosystem carbon budget and carbon footprint in a subtropical wheat cropping field

The incorporation of straw with decomposing inoculants into soils has been widely recommended to sustain agricultural productivity. However, comprehensive analyses assessing the effects of straw combined with decomposing inoculants on greenhouse gas (GHG) emissions, net primary production (NPP), the net ecosystem carbon budget (NECB), and the carbon footprint (CF) in farmland ecosystems are scant. Here, we carried out a 2-year field study in a wheat cropping system with six treatments: rice straw (S), a straw-decomposing Bacillus subtilis inoculant (K), a straw-decomposing Aspergillus oryzae inoculant (Q), a combination of straw and Bacillus subtilis inoculant (SK), a combination of straw and Aspergillus oryzae inoculant (SQ), and a control with no rice straw or decomposing inoculant (Control). We found that all the treatments resulted in a positive NECB ranging between 838 and 5065 kg C ha-1. Relative to the Control, the S treatment increased CO2 emissions by 16%, while considerably enhancing the NECB by 349%. This difference might be attributed to the straw C input and an increase in plant productivity (NPP, 30%). More importantly, in comparison to that in S, the NECB in SK and SQ significantly increased by 27-35% due to the positive response of NPP to the decomposing inoculants. Although the combination of straw and decomposing inoculants yielded a 3% increase in indirect GHG emissions, it also exhibited the lowest CF (0.18 kg CO2-eq kg-1 of grain). This result was attributed to the synergistic effects of straw and decomposing inoculants, which reduced direct N2O emissions and increased wheat productivity. Overall, the findings of the present study suggested that the combined amendment of straw and decomposing inoculants is an environmentally sustainable management practice in wheat cropping systems that can generate win-win scenarios through improvements in soil C stock, crop productivity, and GHG mitigation.

A PCR-independent approach for mtDNA enrichment and next-generation sequencing: comprehensive evaluation and clinical application

BACKGROUND

Sequencing the mitochondrial genome has been increasingly important for the investigation of primary mitochondrial diseases (PMD) and mitochondrial genetics. To overcome the limitations originating from PCR-based mtDNA enrichment, we set out to develop and evaluate a PCR-independent approach in this study, named Pime-Seq (PCR-independent mtDNA enrichment and next generation Sequencing).

RESULTS

By using the optimized mtDNA enrichment procedure, the mtDNA reads ratio reached 88.0 ± 7.9% in the sequencing library when applied on human PBMC samples. We found the variants called by Pime-Seq were highly consistent among technical repeats. To evaluate the accuracy and reliability of this method, we compared Pime-Seq with lrPCR based NGS by performing both methods simultaneously on 45 samples, yielding 1677 concordant variants, as well as 146 discordant variants with low-level heteroplasmic fraction, in which Pime-Seq showed higher reliability. Furthermore, we applied Pime-Seq on 4 samples of PMD patients retrospectively, and successfully detected all the pathogenic mtDNA variants. In addition, we performed a prospective study on 192 apparently healthy pregnant women during prenatal screening, in which Pime-Seq identified pathogenic mtDNA variants in 4 samples, providing extra information for better health monitoring in these cases.

CONCLUSIONS

Pime-Seq can obtain highly enriched mtDNA in a PCR-independent manner for high quality and reliable mtDNA deep-sequencing, which provides us an effective and promising tool for detecting mtDNA variants for both clinical and research purposes.

Adalimumab Dose Reduction and Withdrawal in Stable Non-Infectious Pediatric Uveitis: An Open-Label, Prospective, Pilot Study

PURPOSES

This study investigated the feasibility of adalimumab (ADA) dose reduction and withdrawal strategy in children with stable pediatric non-infectious uveitis (PNIU).

METHODS

This open-label prospective pilot trial recruited 18 stable PNIU patients (33 eyes) between two and eighteen years old who were treated with standard doses of ADA (20/40 mg every 2 weeks) plus oral methotrexate. The interval of ADA injection was extended to 4 weeks and followed up for 24 weeks. If the uveitis remained stable, ADA was discontinued and followed up for another 24 weeks. ADA was considered successfully stopped if no relapse occurred during this period. The relapse-free survival rate, best corrected visual acuity (BVCA), anterior chamber cell (ACC), vitritis, macular thickness (MT), and serum ADA levels were evaluated. Approval Number: 2021KYPJ201. ClinicalTrials.gov identifier: NCT05155592.

RESULTS

The relapse-free survival rate was 22.2% (4/18) at 48 weeks. 33.3% (6/18) of patients relapsed when ADA was given every 4 weeks, while 44.5% of patients (8/18) relapsed after ADA was stopped. The four patients successfully withdrawn from ADA were all diagnosed with BD. No statistically significant differences (p > 0.05) were observed in BCVA and MT between baseline and final follow-up. The proportion of ACC and vitritis exhibited an upward trend (p < 0.05) during follow-up. Serum ADA gradually decreased to zero during follow-up in both non-recurrence and recurrence groups.

CONCLUSIONS

In PNIU children who reached remission for 6 months, ADA dose reduction and withdrawal were associated with a high risk of inflammation recurrence. Timely adjustment of ADA to the last effective dosage frequency can regain control of the inflammation. Detection of ADA serum levels in patients with recurrence may help find the appropriate interval of ADA use.

B1 inhomogeneity corrected CEST MRI based on direct saturation removed omega plot model at 5T

PURPOSE

CEST can image macromolecules/compounds via detecting chemical exchange between labile protons and bulk water. B1 field inhomogeneity impairs CEST quantification. Conventional B1 inhomogeneity correction methods depend on interpolation algorithms, B1 choices, acquisition number or calibration curves, making reliable correction challenging. This study proposed a novel B1 inhomogeneity correction method based on a direct saturation (DS) removed omega plot model.

METHODS

Four healthy volunteers underwent B1 field mapping and CEST imaging under four nominal B1 levels of 0.75, 1.0, 1.5, and 2.0 μT at 5T. DS was resolved using a multi-pool Lorentzian model and removed from respective Z spectrum. Residual spectral signals were used to construct the omega plot as a linear function of 1/B 1 2 $$ {B}_1^2 $$ , from which corrected signals at nominal B1 levels were calculated. Routine asymmetry analysis was conducted to quantify amide proton transfer (APT) effect. Its distribution across white matter was compared before and after B1 inhomogeneity correction and also with the conventional interpolation approach.

RESULTS

B1 inhomogeneity yielded conspicuous artifact on APT images. Such artifact was mitigated by the proposed method. Homogeneous APT maps were shown with SD consistently smaller than that before B1 inhomogeneity correction and the interpolation method. Moreover, B1 inhomogeneity correction from two and four CEST acquisitions yielded similar results, superior over the interpolation method that derived inconsistent APT contrasts among different B1 choices.

CONCLUSION

The proposed method enables reliable B1 inhomogeneity correction from at least two CEST acquisitions, providing an effective way to improve quantitative CEST MRI.

Revealing the mechanisms of semantic satiation with deep learning models

The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process. Unlike existing macroscopic psychological studies that suggest that semantic satiation is a top-down process, our simulations use a similar experimental paradigm as classical psychology experiments and observe similar results. Satiation of semantic objectives, similar to the learning process of our network model used for object recognition, relies on continuous learning and switching between objects. The underlying neural coupling strengthens or weakens satiation. Taken together, both neural and network mechanisms play a role in controlling semantic satiation.

The Comparative Genomics of Botryosphaeriaceae Suggests Gene Families of Botryosphaeria dothidea Related to Pathogenicity on Chinese Hickory Tree

Trunk canker poses a major threat to the production of Chinese hickory tree (Carya cathayensis Sarg.), which is primarily determined by Botryosphaeriaceae. In our previous work, we identified Botryosphaeria dothidea as the predominant pathogen of this disease. However, it is still unclear about corresponding gene families and mechanisms associated with B. dothidea's pathogenicity on Chinese hickory tree. Here, we present a comparative analysis of high-quality genome assemblies of Botryosphaeria dothidea and other isolated pathogens, showing highly syntenic relationships between B. dothidea and its closely related species and the conservative evolution of the Botryosphaeriaceae family. Higher GC contents were found in the genomes of B. dothidea and three other isolated pathogens (Botryshaeria cortices, Botryshaeria fabicerciana, and Botryshaeria qingyuanensis) compared to Macrophomina phaseolina, Neofusicoccum parvum, Diplodia corticola, and Lasiodiplodia theobromae. An investigation of genes specific to or expanded in B. dothidea revealed that one secreted glucanase, one orsellinic acid biosynthesis enzyme, and two MFS transporters positively regulated B. dothidea's pathogenicity. We also observed an overrepresentation of viral integrase like gene and heterokaryon incompatibility proteins in the B. dothidea's genome. In addition, we observed one LRR-domain-containing protein and two Sec-domain-containing proteins (Sec_1 and Sec_7) that underwent positive selection. This study will help to understand B. dothidea's pathogenicity and potential influence on the infection of Chinese hickory, which will help in the development of disease control and ensure the security of Chinese hickory production.

Multi-omics and chemical profiling approaches to understand the material foundation and pharmacological mechanism of Sophorae Tonkinensis Radix et Rhizome-induced liver injury in mice

ETHNOPHARMACOLOGICAL RELEVANCE

Sophorae tonkinensis Radix et Rhizoma (STR) is an extensively applied traditional Chinese medicine (TCM) in southwest China. However, its clinical application is relatively limited due to its hepatotoxicity effects.

AIM OF THE STUDY

To understand the material foundation and liver injury mechanism of STR.

MATERIALS AND METHODS

Chemical compositions in STR and its prototypes in mice were profiled by ultra-performance liquid chromatography coupled quadrupole-time of flight mass spectrometry (UPLC-Q/TOF MS). STR-induced liver injury (SILI) was comprehensively evaluated by STR-treated mice mode. The histopathologic and biochemical analyses were performed to evaluate liver injury levels. Subsequently, network pharmacology and multi-omics were used to analyze the potential mechanism of SILI in vivo. And the target genes were further verified by western blot.

RESULTS

A total of 152 compounds were identified or tentatively characterized in STR, including 29 alkaloids, 21 organic acids, 75 flavonoids, 1 quinone, and 26 other types. Among them, 19 components were presented in STR-medicated serum. The histopathologic and biochemical analysis revealed that hepatic injury occurred after 4 weeks of intragastric administration of STR. Network pharmacology analysis revealed that IL6, TNF, STAT3, etc. were the main core targets, and the bile secretion might play a key role in SILI. The metabolic pathways such as taurine and hypotaurine metabolism, purine metabolism, and vitamin B6 metabolism were identified in the STR exposed groups. Among them, taurine, hypotaurine, hypoxanthine, pyridoxal, and 4-pyridoxate were selected based on their high impact value and potential biological function in the process of liver injury post STR treatment.

CONCLUSIONS

The mechanism and material foundation of SILI were revealed and profiled by a multi-omics strategy combined with network pharmacology and chemical profiling. Meanwhile, new insights were taken into understand the pathological mechanism of SILI.

DPP: deep phase prior for parallel imaging with wave encoding

OBJECTIVE

In MR parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency ACS data for calibration. Approach: To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors (DPP) is proposed with integrated virtual conjugate coil (VCC) extension. Concretely, within the proposed framework, the background phase is implicitly characterized by employing a carefully designed decoder convolutional neural network, leveraging the inherent characteristics of phase smoothness and compact support in the transformed domain. Furthermore, the proposed model with wave encoding benefits from additional priors, which incorporate transmission sparsity of the latent image and coil sensitivity smoothness. Main results: Ablation experiments were conducted to ascertain the proposed method's capability to implicitly represent CSM and the background phase. Subsequently, the superiority of the proposed method is demonstrated through confidence comparisons with competing methods, employing 4-fold and 5-fold acceleration experiments. In achieving 4-fold and 5-fold acceleration, the optimal quantitative metrics (PSNR/SSIM/NMSE) are 44.1359 dB/0.9863/0.0008 (4-fold) and 41.2074/0.9846/0.0017 (5-fold), respectively. Furthermore, the generalizability of the proposed method is further validated by conducting acceleration experiments with T1, T2, T2*, and various undersampling patterns. In addition, the DPP delivered much better performance than the conventional methods by exploring accelerated phase-sensitive SWI imaging. In SWI accelerated imaging, it also surpasses the optimal competing method in terms of (PSNR/SSIM/NMSE) with 0.096%/0.009%/0.0017%. Significance: The proposed method enables precise characterization of the background phase in the integrated VCC and wave encoding framework, supported via theoretical analysis and empirical findings. Our code is available at: https://github.com/sober235/DPP.

Progressive thalamic nuclear atrophy in blepharospasm and blepharospasm-oromandibular dystonia

The thalamus is considered a key region in the neuromechanisms of blepharospasm. However, previous studies considered it as a single, homogeneous structure, disregarding potentially useful information about distinct thalamic nuclei. Herein, we aimed to examine (i) whether grey matter volume differs across thalamic subregions/nuclei in patients with blepharospasm and blepharospasm-oromandibular dystonia; (ii) causal relationships among abnormal thalamic nuclei; and (iii) whether these abnormal features can be used as neuroimaging biomarkers to distinguish patients with blepharospasm from blepharospasm-oromandibular dystonia and those with dystonia from healthy controls. Structural MRI data were collected from 56 patients with blepharospasm, 20 with blepharospasm-oromandibular dystonia and 58 healthy controls. Differences in thalamic nuclei volumes between groups and their relationships to clinical information were analysed in patients with dystonia. Granger causality analysis was employed to explore the causal effects among abnormal thalamic nuclei. Support vector machines were used to test whether these abnormal features could distinguish patients with different forms of dystonia and those with dystonia from healthy controls. Compared with healthy controls, patients with blepharospasm exhibited reduced grey matter volume in the lateral geniculate and pulvinar inferior nuclei, whereas those with blepharospasm-oromandibular dystonia showed decreased grey matter volume in the ventral anterior and ventral lateral anterior nuclei. Atrophy in the pulvinar inferior nucleus in blepharospasm patients and in the ventral lateral anterior nucleus in blepharospasm-oromandibular dystonia patients was negatively correlated with clinical severity and disease duration, respectively. The proposed machine learning scheme yielded a high accuracy in distinguishing blepharospasm patients from healthy controls (accuracy: 0.89), blepharospasm-oromandibular dystonia patients from healthy controls (accuracy: 0.82) and blepharospasm from blepharospasm-oromandibular dystonia patients (accuracy: 0.94). Most importantly, Granger causality analysis revealed that a progressive driving pathway from pulvinar inferior nuclear atrophy extends to lateral geniculate nuclear atrophy and then to ventral lateral anterior nuclear atrophy with increasing clinical severity in patients with blepharospasm. These findings suggest that the pulvinar inferior nucleus in the thalamus is the focal origin of blepharospasm, extending to pulvinar inferior nuclear atrophy and subsequently extending to the ventral lateral anterior nucleus causing involuntary lower facial and masticatory movements known as blepharospasm-oromandibular dystonia. Moreover, our results also provide potential targets for neuromodulation especially deep brain stimulation in patients with blepharospasm and blepharospasm-oromandibular dystonia.

Connectional-style-guided contextual representation learning for brain disease diagnosis

Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.

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.

Triterpenoids and triterpenoid saponins from Vitex negundo and their anti-inflammatory activities

Seven undescribed polyoxygenated ursane-type triterpenoids (vitnegundins A-G), three undescribed triterpenoid saponins (vitnegundins H-J), and 17 known ones were isolated from an EtOH extract of the aerial parts of Vitex negundo L. The structures of the undescribed compounds were established by extensive spectroscopic analysis. The absolute configurations of vitnegundins A, B, and E were determined by single-crystal X-ray diffraction data. Vitnegundins B-D are pentacyclic triterpenoids possessing rare cis-fused C/D rings and vitnegundins C-H represent undescribed ursane-type triterpenoids with 12,19-epoxy moiety. In the biological activity assay, vitnegundin A, vitnegundin E, and swinhoeic acid displayed inhibitory effects against LPS-induced NO release in BV-2 microglial cells, with IC50 values of 11.8, 44.2, and 19.6 μM, respectively.

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.

The correlation between serum asprosin and left ventricular diastolic dysfunction in elderly patients with type 2 diabetes mellitus in the community

AIMS/INTRODUCTION

Serum asprosin is expected to become a screening indicator in early-stage diabetic heart disease. The relationship between serum asprosin and left ventricular diastolic dysfunction (LVDD) was studied in elderly patients with type 2 diabetes mellitus in the community.

MATERIALS AND METHODS

A total of 252 elderly patients with type 2 diabetes mellitus were recruited from Zhuoma Community Care Station and Chengbei West Street Community Care Service Center in Changzhi City of Shanxi Province from November 2019 to July 2021. Patients were divided into the LVDD group (n = 195) and the non-LVDD group (n = 57). The t-test, Mann-Whitney U test, and χ2 test were used to compare indicators between the LVDD group and the non-LVDD group. Pearson or Spearman correlation analysis was adopted to evaluate the correlation between serum asprosin and other clinical data. Multivariate logistic regression analysis was applied to analyze the influencing factors on LVDD.

RESULTS

Compared with patients without LVDD, patients with LVDD had a higher level of low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FPG), and asprosin, but a lower level of early diastolic movement speed (A) to diastolic movement velocity (E) (E/A). Asprosin was positively associated with waist circumference (WC), body mass index (BMI), creatinine, triglycerides (P < 0.05), and negatively associated with E/A and high density lipoprotein cholesterol HDL-C (P < 0.05). The risk of LVDD increased with elevated asprosin levels after adjustment for age, systolic blood pressure (SBP), BMI, FPG, and LDL-C. Compared with patients in the lowest tertile of serum asprosin (<275.25 pg/mL), a serum level of asprosin between 275.25-355.08 pg/mL [OR (95% CI) is 2.368 (1.169-4.796), P < 0.05] and asprosin >355.08 pg/mL [OR (95% CI) is 2.549 (1.275-5.095), P < 0.05] patients have a higher risk of left ventricular diastolic dysfunction.

CONCLUSIONS

Serum asprosin was positively associated with left ventricular diastolic dysfunction, and the risk of LVDD increased significantly with increased serum levels of asprosin.

Learning CT-free attenuation-corrected total-body PET images through deep learning

OBJECTIVES

Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Therefore, we attempted to generate CT-free attenuation-corrected (CTF-AC) total-body PET images through deep learning.

METHODS

Based on total-body PET data from 122 subjects (29 females and 93 males), a well-established cycle-consistent generative adversarial network (Cycle-GAN) was employed to generate CTF-AC total-body PET images directly while introducing site structures as prior information. Statistical analyses, including Pearson correlation coefficient (PCC) and t-tests, were utilized for the correlation measurements.

RESULTS

The generated CTF-AC total-body PET images closely resembled real AC PET images, showing reduced noise and good contrast in different tissue structures. The obtained peak signal-to-noise ratio and structural similarity index measure values were 36.92 ± 5.49 dB (p < 0.01) and 0.980 ± 0.041 (p < 0.01), respectively. Furthermore, the standardized uptake value (SUV) distribution was consistent with that of real AC PET images.

CONCLUSION

Our approach could directly generate CTF-AC total-body PET images, greatly reducing the radiation risk to patients from redundant anatomical examinations. Moreover, the model was validated based on a multidose-level NAC-AC PET dataset, demonstrating the potential of our method for low-dose PET attenuation correction. In future work, we will attempt to validate the proposed method with total-body PET/CT systems in more clinical practices.

CLINICAL RELEVANCE STATEMENT

The ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Our CT-free PET attenuation correction method would be beneficial for a wide range of patient populations, especially for pediatric examinations and patients who need multiple scans or who require long-term follow-up.

KEY POINTS

• CT is the main source of radiation in PET/CT imaging, especially for total-body PET/CT devices, and reduced radiopharmaceutical doses make the radiation burden from CT more obvious. • The CT-free PET attenuation correction method would be beneficial for patients who need multiple scans or long-term follow-up by reducing additional radiation from redundant anatomical examinations. • The proposed method could directly generate CT-free attenuation-corrected (CTF-AC) total-body PET images, which is beneficial for PET/MRI or PET-only devices lacking CT image poses.

N-Cinnamoylpyrrole-derived alkaloids from the genus Piper as promising agents for ischemic stroke by targeting eEF1A1

BACKGROUND

Ischemic stroke (IS) is a serious cerebrovascular disease characterized by significantly elevated mortality and disability rates, and the treatments available for this disease are limited. Neuroinflammation and oxidative stress are deemed the major causes of cerebral ischemic injury. N-Cinnamoylpyrrole alkaloids form a small group of natural products from the genus Piper and have not been extensively analyzed pharmacologically. Thus, identifying the effect and mechanism of N-cinnamoylpyrrole-derived alkaloids on IS is worthwhile.

PURPOSE

The present research aimed to explore the antineuroinflammatory and antioxidative stress effects of N-cinnamoylpyrrole-derived alkaloids isolated from the genus Piper and to explain the effects and mechanism on IS.

METHODS

N-cinnamoylpyrrole-derived alkaloids were isolated from Piper boehmeriaefolium var. tonkinense and Piper sarmentosum and identified by various chromatographic methods. Lipopolysaccharide (LPS)-induced BV-2 microglia and a mouse model intracerebroventricularly injected with LPS were used to evaluate the antineuroinflammatory and antioxidative stress effects. Oxygen‒glucose deprivation/reperfusion (OGD/R) and transient middle cerebral artery occlusion (tMCAO) models were used to evaluate the effect of PB-1 on IS. To elucidate the fundamental mechanism, the functional target of PB-1 was identified by affinity-based protein profiling (ABPP) strategy and verified by cellular thermal shift assay (CETSA), drug affinity responsive target stability (DARTS), and circular dichroism (CD) analyses. The effect of PB-1 on the NF-κB and NRF2 signaling pathways was subsequently evaluated via western blotting and immunofluorescence staining.

RESULTS

The results showed that N-cinnamoylpyrrole-derived alkaloids significantly affected neuroinflammation and oxidative stress. The representative compound, PB-1 not only inhibited neuroinflammation and oxidative stress induced by LPS or OGD/R insult, but also alleviated cerebral ischemic injury induced by tMCAO. Further molecular mechanism research found that PB-1 promoted antineuroinflammatory and antioxidative stress activities via the NF-κB and NRF2 signaling pathways by targeting eEF1A1.

CONCLUSION

Our research initially unveiled that the therapeutic impact of PB-1 on cerebral ischemic injury might rely on its ability to target eEF1A1, leading to antineuroinflammatory and antioxidative stress effects. The novel discovery highlights eEF1A1 as a potential target for IS treatment and shows that PB-1, as a lead compound that targets eEF1A1, may be a promising therapeutic agent for IS.

Melatonin and 14-hydroxyed brassinosteroid combined promote kiwifruit seedling growth by improving soil microbial distribution, enzyme activity and nutrients uptake

Kiwifruit, a nutrient-dense fruit, has become increasingly popular with consumers in recent decades. However, kiwifruit trees are prone to stunted growth after a few years of planting, called early tree decline. In this study, melatonin (MT), pollen polysaccharide (SF), 14-hydroxyed brassinosteroid (14-HBR) were applied alone or in combination to investigate their influence on plant growth, nutrition absorption and rhizosphere bacterial abundance in kiwifruit seedlings. The results revealed that MT, SF and 14-HBR alone treatments significantly increased leaf chlorophyll content, photosynthetic capacity and activities of dismutase and catalase compared with the control. Among them, MT treatment significantly increased the dry root biomass by 35.7%, while MT+14-HBR treatment significant enhanced the dry shoot biomass by 36.9%. Furthermore, both MT and MT+14-HBR treatments markedly improved the activities of invertase, urease, protease and phosphatase in soil, as well as the abundance of Proteobacteria and Acidobacteria in rhizosphere microorganisms based on 16S rDNA sequencing. In addition, MT treatment improved the content of available K and organic matter in soil, and increased the uptake of P, K and Fe by seedlings. In summary, 14-HBR and MT combined had the best effect on promoting rhizosphere bacterial distribution, nutrient absorption and plant growth. These findings may provide valuable guidance for solving growth weakness problem in kiwifruit cultivation.

Antireflective vertical-cavity surface-emitting laser for LiDAR

Multijunction vertical-cavity surface-emitting lasers (VCSELs) have gained popularity in automotive LiDARs, yet achieving a divergence of less than 16° (D86) is difficult for conventional extended cavity designs due to multiple-longitudinal-mode lasing. Our innovation, the antireflective vertical-cavity surface-emitting laser (AR-VCSEL), addresses this challenge by introducing an antireflective light reservoir, where the electric field intensity is substantially higher than the gain region. This reduces the required cavity length for minimal divergence, preserving the single-longitudinal-mode lasing. A 6-junction AR-VCSEL array showcases a halved divergence and tripled brightness compared to its conventional counterpart. Various multijunction AR-VCSEL array designs achieve a divergence range of 8° to 16° (D86). Notably, a 7 μm AR-VCSEL emitter achieves 28.4 mW in single transverse mode lasing. AR-VCSEL stands out among semiconductor lasers, offering a well-balanced power density and brightness, making it a cost-effective solution for long-distance LiDARs. The antireflective cavity concept may inspire diverse applications in photonic devices beyond LiDARs.

A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma

Background

The use of segmentation architectures in medical imaging, particularly for glioma diagnosis, marks a significant advancement in the field. Traditional methods often rely on post-processed images; however, key details can be lost during the fast Fourier transformation (FFT) process. Given the limitations of these techniques, there is a growing interest in exploring more direct approaches. The adaption of segmentation architectures originally designed for road extraction for medical imaging represents an innovative step in this direction. By employing K-space data as the modal input, this method completely eliminates the information loss inherent in FFT, thereby potentially enhancing the precision and effectiveness of glioma diagnosis.

Methods

In the study, a novel architecture based on a deep-residual U-net was developed to accomplish the challenging task of automatically segmenting brain tumors from K-space data. Brain tumors from K-space data with different under-sampling rates were also segmented to verify the clinical application of our method.

Results

Compared to the benchmarks set in the 2018 Brain Tumor Segmentation (BraTS) Challenge, our proposed architecture had superior performance, achieving Dice scores of 0.8573, 0.8789, and 0.7765 for the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions, respectively. The corresponding Hausdorff distances were 2.5649, 1.6146, and 2.7187 for the WT, TC, and ET regions, respectively. Notably, compared to traditional image-based approaches, the architecture also exhibited an improvement of approximately 10% in segmentation accuracy on the K-space data at different under-sampling rates.

Conclusions

These results show the superiority of our method compared to previous methods. The direct performance of lesion segmentation based on K-space data eliminates the time-consuming and tedious image reconstruction process, thus enabling the segmentation task to be accomplished more efficiently.

Super Resolution Dual-Energy Cone-Beam CT Imaging With Dual-Layer Flat-Panel Detector

In flat-panel detector (FPD) based cone-beam computed tomography (CBCT) imaging, the native receptor array is usually binned into a smaller matrix size. By doing so, the signal readout speed could be increased by 4-9 times at the expense of a spatial resolution loss of 50%-67%. Clearly, such manipulation poses a key bottleneck in generating high spatial and high temporal resolution CBCT images at the same time. In addition, the conventional FPD is also difficult in generating dual-energy CBCT images. In this paper, we propose an innovative super resolution dual-energy CBCT imaging method, named as suRi, based on dual-layer FPD (DL-FPD) to overcome these aforementioned difficulties at once. With suRi, specifically, a 1D or 2D sub-pixel (half pixel in this study) shifted binning is applied instead of the conventionally aligned binning to double the spatial sampling rate during the dual-energy data acquisition. As a result, the suRi approach provides a new strategy to enable high spatial resolution CBCT imaging while at high readout speed. Moreover, a penalized likelihood material decomposition algorithm is developed to directly reconstruct the high resolution bases from these dual-energy CBCT projections containing sub-pixel shifts. Numerical and physical experiments are performed to validate this newly developed suRi method with phantoms and biological specimen. Results demonstrate that suRi can significantly improve the spatial resolution of the CBCT image. We believe this developed suRi method would greatly enhance the imaging performance of the DL-FPD based dual-energy CBCT systems in future.

Three new phenylpropanoid glucosides and a new tyramine-type alkamide from Piper puberulum

Three new phenylpropanoid glycosides, piperpubelide (1), 1-propionyl-3-hydroxy-phenyl-4-O-β-D-glucopyranoside (2), and 1-propionyl-4-hydroxy-phenyl-3-O-β-D-glucopyranoside (3), a new tyramine-type alkamide, puberulumine L (4), together with thirteen known compounds (5-17) were isolated from Piper puberulum (Benth.) Maxim. Their structures were elucidated by analysis of spectroscopic data involving NMR, IR, UV, and HRESIMS data. Calculated and experimental ECD was used to confirm the configuration of compound 1. Compounds 14, 16, and 17 exhibited relatively positive DPPH radical scavenging activities, with corresponding EC50 of 10.23, 24.12, and 21.83 μM, respectively. In addition, compound 5 inhibited LPS-induced NO production in BV-2 microglia with an IC50 value of 18.05 μM.

Structurally diverse stilbenes from Gnetum parvifolium and their anti-neuroinflammatory activities

Phytochemical investigation on the aerial parts of Gnetum parvifolium led to the isolation of 15 new and eight known structurally diverse stilbenes. The isolated compounds comprised (E)- or (Z)-stilbene (1-6, 15-20), dihydrostilbene (21), phenylbenzofuran (7, 8, 22), benzylated stilbene (9-11), benzylated stilbene dimer (12), and nitrogen-containing stilbene (13a, 13b, 14) types. The structures of the new compounds (1-12, 13a, 13b, 14) were established through spectroscopic analyses and experimental and calculated ECD data. Compound 12 is the first stilbene dimer connected through a benzyl group. In the anti-neuroinflammatory activity assay, compounds 4, 5, 9-11, 13b, and 16-21 displayed significant inhibitory effects against LPS-induced NO release in BV-2 microglial cells, with IC50 values of 0.35-16.1 μM. Compound 10 had the most potent activity (IC50 = 0.35 μM), and the further research indicated that it could decrease the mRNA levels of iNOS, IL-1β, IL-6, and TNF-α in a dose-dependent manner.

Ability of dynamic gadoxetic acid-enhanced magnetic resonance imaging combined with water-specific T1 mapping to reflect inflammation in a rat model of early-stage nonalcoholic steatohepatitis

Background

Gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) has shown potential in reflecting the hepatic function alterations in nonalcoholic steatohepatitis (NASH). The purpose of this study was to evaluate whether Gd-EOB-DTPA combined with water-specific T1 (wT1) mapping can be used to detect liver inflammation in the early-stage of NASH in rats.

Methods

In this study, 54 rats with methionine- and choline-deficient (MCD) diet-induced NASH and 10 normal control rats were examined. A multiecho variable flip angle gradient echo (VFA-GRE) sequence was performed and repeated 40 times after the injection of Gd-EOB-DTPA. The wT1 of the liver and the reduction rate of wT1 (rrT1) were calculated. All rats were histologically evaluated and grouped according to the NASH Clinical Research Network scoring system. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the expression of Gd-EOB-DTPA transport genes. Analysis of variance and least significant difference tests were used for multiple comparisons of quantitative results between all groups. Multiple regression analysis was applied to identify variables associated with precontrast wT1 (wT1pre), and receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance.

Results

The rats were grouped according to inflammatory stage (G0 =4, G1 =15, G2 =12, G3 =23) and fibrosis stage (F0 =26, F1 =19, F2 =9). After the infusion of Gd-EOB-DTPA, the rrT1 showed significant differences between the control and NASH groups (P<0.05) but no difference between the different inflammation and fibrosis groups at any time points. The areas under curve (AUCs) of rrT1 at 10, 20, and 30 minutes were only 0.53, 0.58, and 0.61, respectively, for differentiating between low inflammation grade (G0 + G1) and high inflammation grade (G2 + G3). The MRI findings were verified by qRT-PCR examination, in which the Gd-EOB-DTPA transporter expressions showed no significant differences between any inflammation groups.

Conclusions

The wT1 mapping quantitative method combined with Gd-EOB-DTPA was not capable of discerning the inflammation grade in a rat model of early-stage NASH.

JAK/STAT in leukemia: a clinical update

Over the past three decades, considerable efforts have been expended on understanding the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway in leukemia, following the identification of the JAK2V617F mutation in myeloproliferative neoplasms (MPNs). The aim of this review is to summarize the latest progress in our understanding of the involvement of the JAK/STAT signaling pathway in the development of leukemia. We also attempt to provide insights into the current use of JAK/STAT inhibitors in leukemia therapy and explore pertinent clinical trials in this field.

The effect of FOXP3 genetic polymorphisms on correlations with hepatitis B virus-hepatocellular carcinoma: A case-control study

Background

Previous studies have reported that transcription factor forkhead box protein 3 (FOXP3) polymorphisms are correlated with the progress of some cancers, but the relationships between the FOXP3 polymorphisms and hepatocellular carcinoma (HCC) risk remain unclear.

Method

Genotypes were detected in156 hepatitis B virus (HBV)-HCC patients, 109 HBV-liver cirrhosis (LC) patients, 125 chronic hepatitis B (CHB) patients, and 188 healthy controls. The FOXP3 rs3761547 and rs3761548 polymorphisms were genotyped by polymerase chain reaction (PCR) combined with restriction fragment length polymorphism, and the rs2232365 polymorphism was genotyped using PCR with sequence-specific primers.

Results

We did not obtain any significant results with the FOXP3 rs3761547, rs3761548, and rs2232365 polymorphisms in groups of patients compared to healthy controls (all p > 0.05), no matter the overall group or subgroup.

Conclusions

Our findings suggest that the FOXP3 polymorphisms at rs3761547, rs3761548, and rs2232365 were not related to HBV-HCC risk in the Chinese population.