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.

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.

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.

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.

Probiotic Bacillus as fermentation agents: Status, potential insights, and future perspectives

Probiotic Bacillus strains can solve the problems of single flavor and long fermentation time of fermented products caused by the lack of certain functional genes and insufficient metabolism ability of fermenter strains (Lactobacillus and Bifidobacterium) at the present stage. There is a lack of systematic evaluation and review of probiotic Bacillus as food fermentation agents. In this paper, it is observed that probiotic Bacillus strains are involved to varying degrees in liquid-state, semi-solid state, and solid-state fermentation and are widely present in solid-state fermented foods. Probiotic Bacillus strains not only produce abundant proteases and lipases, but also effective antifungal lipopeptides and extracellular polymers, thus enhancing the flavor, nutritional value and safety of fermented foods. Bacillus with probiotic qualities is an underutilized group of probiotic food fermentation agents, which give a potential for the development of fermentation technology in the food business and the integration of ancient traditional fermentation techniques.

Characteristic substance analysis and rapid detection of bacteria spores in cooked meat products by surface enhanced Raman scattering based on Ag@AuNP array substrate

BACKGROUND

Bacterial spores are the main potential hazard in medium- and high-temperature sterilized meat products, and their germination and subsequent reproduction and metabolism can lead to food spoilage. Moreover, the spores of some species pose a health and safety threat to consumers. The rapid detection, prevention, and control of bacterial spores has always been a scientific problem and a major challenge for the medium and high-temperature meat industry. Early and sensitive identification of spores in meat products is a decisive factor in contributing to consumer health and safety.

RESULTS

In this study, we developed a novel and stable Ag@AuNP array substrate by using a two-step synthesis approach and a liquid-interface self-assembly method that can directly detect bacterial spores in actual meat product samples without the need for additional in vitro bacterial culture. The results indicate that the Ag@AuNP array substrate exhibits high reproducibility and Raman enhancement effects (1.35 × 105). The differentiation in the Surface enhanced Raman scattering (SERS) spectra of five bacterial spores primarily arises from proteins in the spore coat and inner membrane, peptidoglycan of cortex, and Ca2⁺-DPA within the spore core. The correct recognition rate of linear discriminant analysis for spores in the meat product matrix can reach 100 %. The average recovery accuracy of the SERS quantitative model was at around 101.77 %, and the limit of detection can reach below 10 CFU/mL.

SIGNIFICANCE

It provides a promising technological strategy for the characteristic substance analysis and timely monitoring of spores in meat products.

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.

Light field image super-resolution based on dual learning and deep Fourier channel attention

Light field (LF) imaging has gained significant attention in the field of computational imaging due to its unique capability to capture both spatial and angular information of a scene. In recent years, super-resolution (SR) techniques based on deep learning have shown considerable advantages in enhancing LF image resolution. However, the inherent challenges of obtaining rich structural information and reconstructing complex texture details persist, particularly in scenarios where spatial and angular information are intricately interwoven. This Letter introduces a novel, to the best of our knowledge, approach for Disentangling LF Image SR Network (DLISN) by leveraging the synergy of dual learning and Fourier channel attention (FCA) mechanisms. Dual learning strategies are employed to enhance reconstruction results, addressing limitations in model generalization caused by the difficulty in acquiring paired datasets in real-world LF scenarios. The integration of FCA facilitates the extraction of high-frequency information associated with different structures, contributing to improved spatial resolution. Experimental results consistently demonstrate superior performance in enhancing the resolution of LF images.

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.

Accurate Whole-Brain Image Enhancement for Low-Dose Integrated PET/MR Imaging Through Spatial Brain Transformation

Positron emission tomography/magnetic resonance imaging (PET/MRI) systems can provide precise anatomical and functional information with exceptional sensitivity and accuracy for neurological disorder detection. Nevertheless, the radiation exposure risks and economic costs of radiopharmaceuticals may pose significant burdens on patients. To mitigate image quality degradation during low-dose PET imaging, we proposed a novel 3D network equipped with a spatial brain transform (SBF) module for low-dose whole-brain PET and MR images to synthesize high-quality PET images. The FreeSurfer toolkit was applied to derive the spatial brain anatomical alignment information, which was then fused with low-dose PET and MR features through the SBF module. Moreover, several deep learning methods were employed as comparison measures to evaluate the model performance, with the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and Pearson correlation coefficient (PCC) serving as quantitative metrics. Both the visual results and quantitative results illustrated the effectiveness of our approach. The obtained PSNR and SSIM were 41.96 ±4.91 dB (p<0.01) and 0.9654 ±0.0215 (p<0.01), which achieved a 19% and 20% improvement, respectively, compared to the original low-dose brain PET images. The volume of interest (VOI) analysis of brain regions such as the left thalamus (PCC = 0.959) also showed that the proposed method could achieve a more accurate standardized uptake value (SUV) distribution while preserving the details of brain structures. In future works, we hope to apply our method to other multimodal systems, such as PET/CT, to assist clinical brain disease diagnosis and treatment.

Local Mechanical Modulation-Driven Evagination in Invaginated Epithelia

Local cells can actively create reverse bending (evagination) in invaginated epithelia, which plays a crucial role in the formation of elaborate organisms. However, the precise physical mechanism driving the evagination remains elusive. Here, we present a three-dimensional vertex model, incorporating the intrinsic cell polarity, to explore the complex morphogenesis induced by local mechanical modulations. We find that invaginated tissues can spontaneously generate local reverse bending due to the shift of the apicobasal polarity. Their exact shapes can be analytically determined by the local apicobasal differential tension and the internal stress. Our continuum theory exhibits three regions in a phase diagram controlled by these two parameters, showing curvature transitions from ordered to disordered states. Additionally, we delve into epithelial curvature transition induced by the nucleus repositioning, revealing its active contribution to the apicobasal force generation. The uncovered mechanical principles could potentially guide more studies on epithelial folding in diverse systems.

MMCA-NET: A Multimodal Cross Attention Transformer Network for Nasopharyngeal Carcinoma Tumor Segmentation Based on a Total-Body PET/CT System

Nasopharyngeal carcinoma (NPC) is a malignant tumor primarily treated by radiotherapy. Accurate delineation of the target tumor is essential for improving the effectiveness of radiotherapy. However, the segmentation performance of current models is unsatisfactory due to poor boundaries, large-scale tumor volume variation, and the labor-intensive nature of manual delineation for radiotherapy. In this paper, MMCA-Net, a novel segmentation network for NPC using PET/CT images that incorporates an innovative multimodal cross attention transformer (MCA-Transformer) and a modified U-Net architecture, is introduced to enhance modal fusion by leveraging cross-attention mechanisms between CT and PET data. Our method, tested against ten algorithms via fivefold cross-validation on samples from Sun Yat-sen University Cancer Center and the public HECKTOR dataset, consistently topped all four evaluation metrics with average Dice similarity coefficients of 0.815 and 0.7944, respectively. Furthermore, ablation experiments were conducted to demonstrate the superiority of our method over multiple baseline and variant techniques. The proposed method has promising potential for application in other tasks.

Exogenous indole-3-acetic acid promotes the plant growth and accumulation of selenium in grapevine under selenium stress

To alleviate the selenium (Se) stress in fruit trees and improve its accumulation, the effects of exogenous indole-3-acetic acid (IAA) on the growth and Se accumulation of grapevine under Se stress were studied. The application of exogenous IAA increased the biomass of grapevine, and the concentration of exogenous IAA had a regression relationship with the biomass. The root and shoot biomass were the maximum at 60 mg L- 1 IAA, increasing by 15.61% and 23.95%, respectively, compared with the control. Exogenous IAA also increased the photosynthetic pigments and the activities of superoxide dismutase and peroxidase in grapevine. Moreover, exogenous IAA increased the contents of total Se, organic Se, and inorganic Se, and the concentration of exogenous IAA had a regression relationship with the total Se content. The highest contents of root total Se and shoot total Se were accumulated at 90 mg L- 1 IAA, increasing by 29.94% and 55.77% respectively,. In addition, the correlation and path analyses revealed that the carotenoid content and root total Se content were closely associated with the shoot total Se content. Therefore, the application of exogenous IAA can alleviate the stress of Se to grape and promote its uptake and the most effective amount for the uptake of Se is 90 mg L- 1 IAA.

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.

DPP: deep phase prior for parallel imaging with wave encoding

Objective.In Magnetic Resonance (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 Auto-Calibration Signals (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 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.

High-Frequency Space Diffusion Model for Accelerated MRI

Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation. It can serve as a deep generative prior to solving the inverse problem in magnetic resonance (MR) reconstruction. However, low-frequency regions of k -space data are typically fully sampled in fast MR imaging, while existing diffusion models are performed throughout the entire image or k -space, inevitably introducing uncertainty in the reconstruction of low-frequency regions. Additionally, existing diffusion models often demand substantial iterations to converge, resulting in time-consuming reconstructions. To address these challenges, we propose a novel SDE tailored specifically for MR reconstruction with the diffusion process in high-frequency space (referred to as HFS-SDE). This approach ensures determinism in the fully sampled low-frequency regions and accelerates the sampling procedure of reverse diffusion. Experiments conducted on the publicly available fastMRI dataset demonstrate that the proposed HFS-SDE method outperforms traditional parallel imaging methods, supervised deep learning, and existing diffusion models in terms of reconstruction accuracy and stability. The fast convergence properties are also confirmed through theoretical and experimental validation. Our code and weights are available at https://github.com/Aboriginer/HFS-SDE.

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 New Species of Diploderma (Squamata, Agamidae) from the Valley of Dadu River in Sichuan Province, with a Redescription of Topotypes of D. splendidum from Hubei Province, China

This study describes a novel species of Diploderma (Squamata, Agamidae) from the lower valley of the Dadu River of the Sichuan Province of Western China based on its distinct morphological features and molecular evidence. D. daduense sp. nov. can be distinguished from its congeners by its tympanum concealed; head mainly green-yellow, supplemented by black; skin folds under the nuchal and dorsal crest obviously present in adult males only, its vertebral crest discontinuous between nuchal and dorsal sections with a distinct gap; transverse gular fold present but not obvious in some individuals; gular spot absent in both sexes; dorsolateral stripes green-yellow anteriorly, cyan in the center and blurry off-white posteriorly in adult males, the upper edge of dorsolateral stripes strongly jagged in adult males; no radial stripes around the eyes; inner-lip coloration smoky-white, and the coloration of the tongue and oral cavity as a light-flesh color in life; bright green-yellow transverse stripes on dorsal body in males; black patches are evenly distributed along the vertebral line between the dorsolateral stripes from the neck to the base of the tail in males; beech-brown or gray-brown line along the vertebral line with heart-shaped or diamond-shaped black patches on the dorsal body in females; and supratemporals fewer than four on at least one side. The phylogenetic tree based on mitochondrial ND2 sequences indicates that D. daduense sp. nov. forms an independent clade with strong support 1/100 in ML bootstrap/Bayesian posterior probability and is the sister group to D. splendidum. At the inter-species level, the p-distance is at least 6.95%, further confirming that an independent species had been identified. Our work raises the number of species within the genus Diploderma to 47.

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.

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.

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.