Parametric Linear Blend Skinning Model for Multiple-Shape 3D Garments.

We present a novel data-driven Parametric Linear Blend Skinning (PLBS) model meticulously crafted for generalized 3D garment dressing and animation. Previous data-driven methods are impeded by certain challenges including overreliance on human body modeling and limited adaptability across different garment shapes. Our method resolves these challenges via two goals: 1) Develop a model based on garment modeling rather than human body modeling. 2) Separately construct low-dimensional sub-spaces for modeling in-plane deformation (such as variation in garment shape and size) and out-of-plane deformation (such as deformation due to varied body size and motion). Therefore, we formulate garment deformation as a PLBS model controlled by canonical 3D garment mesh, vertex-based skinning weights and associated local patch transformation. Unlike traditional LBS models specialized for individual objects, PLBS model is capable of uniformly expressing varied garments and bodies, the in-plane deformation is encoded on the canonical 3D garment and the out-of-plane deformation is controlled by the local patch transformation. Besides, we propose novel 3D garment registration and skinning weight decomposition strategies to obtain adequate data to build PLBS model under different garment categories. Furthermore, we employ dynamic fine-tuning to complement high-frequency signals missing from LBS for unseen testing data. Experiments illustrate that our method is capable of modeling dynamics for loose-fitting garments, outperforming previous data-driven modeling methods using different sub-space modeling strategies. We showcase that our method can factorize and be generalized for varied body sizes, garment shapes, garment sizes and human motions under different garment categories.

PNNGS, a multi-convolutional parallel neural network for genomic selection.

Genomic selection (GS) can accomplish breeding faster than phenotypic selection. Improving prediction accuracy is the key to promoting GS. To improve the GS prediction accuracy and stability, we introduce parallel convolution to deep learning for GS and call it a parallel neural network for genomic selection (PNNGS). In PNNGS, information passes through convolutions of different kernel sizes in parallel. The convolutions in each branch are connected with residuals. Four different Lp loss functions train PNNGS. Through experiments, the optimal number of parallel paths for rice, sunflower, wheat, and maize is found to be 4, 6, 4, and 3, respectively. Phenotype prediction is performed on 24 cases through ridge-regression best linear unbiased prediction (RRBLUP), random forests (RF), support vector regression (SVR), deep neural network genomic prediction (DNNGP), and PNNGS. Serial DNNGP and parallel PNNGS outperform the other three algorithms. On average, PNNGS prediction accuracy is 0.031 larger than DNNGP prediction accuracy, indicating that parallelism can improve the GS model. Plants are divided into clusters through principal component analysis (PCA) and K-means clustering algorithms. The sample sizes of different clusters vary greatly, indicating that this is unbalanced data. Through stratified sampling, the prediction stability and accuracy of PNNGS are improved. When the training samples are reduced in small clusters, the prediction accuracy of PNNGS decreases significantly. Increasing the sample size of small clusters is critical to improving the prediction accuracy of GS.

Mendelian randomization analysis identified potential genes pleiotropically associated with gout.

Background: This study aims to prioritize genes potentially involved in multifactorial or causal relationships with gout. Methods: Using the Summary Data-based Mendelian Randomization (SMR) approach, this research analyzed expression quantitative trait loci (eQTL) data from blood and renal tissues and genome-wide association study (GWAS) data related to gout. It sought to identify genetic loci potentially involved in gout. Heterogeneity testing was conducted with the HEIDI test, and results were adjusted for the False Discovery Rate (FDR). Blood cis-eQTL data were sourced from the eQTLGen Consortium's summary-level data, and renal tissue data came from the V8 release of the GTEx eQTL summary data. Gout GWAS data was sourced from the FinnGen Documentation of the R10 release. Result: SMR analysis identified 14 gene probes in the eQTLGen blood summary-level data significantly associated with gout. The top five ranked genes are: ENSG00000169231 (labeled THBS3, PSMR = 4.16 × 10-13), ENSG00000231064 (labeled THBS3-AS1, PSMR = 1.88 × 10-8), ENSG00000163463 (labeled KRTCAP2, PSMR = 3.88 × 10-6), ENSG00000172977 (labeled KAT5, PSMR = 1.70 × 10-5), and ENSG00000161395 (labeled PGAP3, PSMR = 3.24 × 10-5). Notably, increased expression of KRTCAP2 and PGAP3 is associated with an increased risk of gout, whereas increased expression of THBS3, THBS3-AS1, and KAT5 is associated with a reduced gout risk. No significant gene associations with gout were observed in renal tissue, likely due to the limited sample size of kidney tissue. Conclusion: Our findings have highlighted several genes potentially involved in the pathogenesis of gout. These results offer valuable insights into the mechanisms of gout and identify potential therapeutic targets for its treatment.

Intraspecific Differentiation of Styrax japonicus (Styracaceae) as Revealed by Comparative Chloroplast and Evolutionary Analyses.

Styrax japonicus is a medicinal and ornamental shrub belonging to the Styracaceae family. To explore the diversity and characteristics of the chloroplast genome of S. japonicus, we conducted sequencing and comparison of the chloroplast genomes of four naturally distributed S. japonicus. The results demonstrated that the four chloroplast genomes (157,914-157,962 bp) exhibited a typical quadripartite structure consisting of a large single copy (LSC) region, a small single copy (SSC) region, and a pair of reverse repeats (IRa and IRb), and the structure was highly conserved. DNA polymorphism analysis revealed that three coding genes (infA, psbK, and rpl33) and five intergene regions (petA-psbJ, trnC-petN, trnD-trnY, trnE-trnT, and trnY-trnE) were identified as mutation hotspots. These genetic fragments have the potential to be utilized as DNA barcodes for future identification purposes. When comparing the boundary genes, a small contraction was observed in the IR region of four S. japonicus. Selection pressure analysis indicated positive selection for ycf1 and ndhD. These findings collectively suggest the adaptive evolution of S. japonicus. The phylogenetic structure revealed conflicting relationships among several S. japonicus, indicating divergent evolutionary paths within this species. Our study concludes by uncovering the genetic traits of the chloroplast genome in the differentiation of S. japonicus variety, offering fresh perspectives on the evolutionary lineage of this species.

Synchronously in vivo real-time monitoring bacterial load and temperature with evaluating immune response to decipher bacterial infection.

Determining the precise course of bacterial infection requires abundant in vivo real-time data. Synchronous monitoring of the bacterial load, temperature, and immune response can satisfy the shortage of real-time in vivo data. Here, we conducted a study in the joint-infected mouse model to synchronously monitor the bacterial load, temperature, and immune response using the second near-infrared (NIR-II) fluorescence imaging, infrared thermography, and immune response analysis for 2 weeks. Staphylococcus aureus (S. aureus) was proved successfully labeled with glucose-conjugated quantum dots in vitro and in subcutaneous-infected model. The bacterial load indicated by NIR-II fluorescence imaging underwent a sharp drop at 1 day postinfection. At the same time, the temperature gap detected through infrared thermography synchronously brought by infection reached lowest value. Meanwhile, the flow cytometry analysis demonstrated that immune response including macrophage, neutrophil, B lymphocyte, and T lymphocyte increased to the peak at 1 day postinfection. Moreover, both M1 macrophage and M2 macrophage in the blood have an obvious change at ~ 1 day postinfection, and the change was opposite. In summary, this study not only obtained real-time and long-time in vivo data on the bacterial load, temperature gap, and immune response in the mice model of S. aureus infection, but also found that 1 day postinfection was the key time point during immune response against S. aureus infection. Our study will contribute to synchronously and precisely studying the complicated complex dynamic relationship after bacterial infection at the animal level.

[Analysis of current situation of outcome indicators in randomized controlled trials on traditional Chinese medicine in treatment of Alzheimer's disease].

This study aims to analyze the current situation of outcome indicators in randomized controlled trial(RCT) of traditional Chinese medicine(TCM) treatment for Alzheimer's disease(AD), so as to provide a reference for establishing a core indicator set in this field. The researchers systematically searched CNKI, Wanfang, VIP, Sino Med, EMbase, PubMed, Medline, and Cochrane Library. Independent screening of literature and extraction of information was conducted according to the inclusion and exclusion criteria. In addition, the Ro B 2. 0 tool was used for bias risk assessment. A total of 78 RCTs were included, involving 6 379 patients,with 122 kinds of outcome indicators. According to functional attributes, the outcome indicators could be categorized into seven groups:TCM diseases(3 kinds, 13 times), symptoms and signs(26 kinds, 196 times), physical and chemical tests(68 kinds, 149 times),qua-lity of life(1 kind, 2 times), long-term prognosis(2 kinds, 2 times), economic evaluation(0 kind), safety events(21 kinds,194 times), and other indicators(1 kind, 1 time). The results show that the literature evaluation of RCTs of TCM treatment for AD is generally risky, and there are some problems in the selection of outcome indicators, such as lack of TCM characteristics, insignificant distinction between primary and secondary outcome indicators, lack of long-term prognosis and economic evaluation indicators, and non-standard safety event reports. It is suggested that future researchers should establish a core indicator set for AD that highlights the characteristics of TCM and then work to improve the quality of clinical trials.

Q48K mutation in the type IB nitroreductase NrmA is responsible for nitrofurantoin resistance in Enterococcus faecium.

OBJECTIVES: Nitrofurantoin is recommended as first-line therapy for the optimal treatment of uncomplicated urinary tract infections (UTIs) caused by enterococci and Escherichia coli. However, the mechanisms of nitrofurantoin resistance in enterococci have not been elucidated. This study aimed to investigate the mechanisms of nitrofurantoin resistance in E. faecium, focusing on the role of the nitroreductase NrmA. METHODS: Enterococcus strains isolated from the urinary tract samples were collected and were tested for nitrofurantoin susceptibility. Potential genes associated with nitrofurantoin resistance were screened in the NCBI nucleotide database and by polymerase chain reaction (PCR). Complementation assays and enzyme kinetic tests were performed to assess the impact of the Q48K mutation in NrmA on nitrofurantoin resistance. RESULTS: Of the 128 E. faecium isolates tested, 59 (46.1%) were resistant to nitrofurantoin. Analysis revealed the presence of a type IB nitroreductase, designated NrmA, in all E. faecium strains studied, shared 18.7% sequence identity with nitroreductase NfsB in E. coli. Different from NrmA in nitrofurantoin-susceptible E. faecium, nitrofurantoin-resistant strains had a single amino acid substitution, i.e., a lysine instead of a glutamine at position 48 (Q48K mutation). Complementation assays of nitrofurantoin-resistant E. faecium HS17-112 showed that the nitrofurantoin minimal inhibitory concentration of the complemented strain HS17-112: pIB166-nrmA (wild type [WT]) decreased from 128 mg/L to 4 mg/L. Compared with NrmA (WT), NrmA (Q48K) showed significantly reduced catalytic efficiency, with a kcat/Km value decreasing from 0.122 µM-1 s-1 to 0.000042 µM-1 s-1. CONCLUSION: The Q48K mutation in nitroreductase NrmA is responsible for nitrofurantoin resistance in E. faecium.

DEKR-SPrior: An Efficient Bottom-Up Keypoint Detection Model for Accurate Pod Phenotyping in Soybean.

The pod and seed counts are important yield-related traits in soybean. High-precision soybean breeders face the major challenge of accurately phenotyping the number of pods and seeds in a high-throughput manner. Recent advances in artificial intelligence, especially deep learning (DL) models, have provided new avenues for high-throughput phenotyping of crop traits with increased precision. However, the available DL models are less effective for phenotyping pods that are densely packed and overlap in in situ soybean plants; thus, accurate phenotyping of the number of pods and seeds in soybean plant is an important challenge. To address this challenge, the present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping, which considers soybean pods and seeds analogous to human people and joints, respectively. In particular, we designed a novel structural prior (SPrior) module that utilizes cosine similarity to improve feature discrimination, which is important for differentiating closely located seeds from highly similar seeds. To further enhance the accuracy of pod location, we cropped full-sized images into smaller and high-resolution subimages for analysis. The results on our image datasets revealed that DEKR-SPrior outperformed multiple bottom-up models, viz., Lightweight-OpenPose, OpenPose, HigherHRNet, and DEKR, reducing the mean absolute error from 25.81 (in the original DEKR) to 21.11 (in the DEKR-SPrior) in pod phenotyping. This paper demonstrated the great potential of DEKR-SPrior for plant phenotyping, and we hope that DEKR-SPrior will help future plant phenotyping.

A crucial role of adenosine deaminase in regulating gluconeogenesis in mice.

Adenosine deaminase (ADA) catalyzes the irreversible deamination of adenosine (ADO) to inosine and regulates ADO concentration. ADA ubiquitously expresses in various tissues to mediate ADO-receptor signaling. A significant increase in plasma ADA activity has been shown to be associated with the pathogenesis of type 2 diabetes mellitus. Here, we show that elevated plasma ADA activity is a compensated response to high level of ADO in type 2 diabetes mellitus and plays an essential role in the regulation of glucose homeostasis. Supplementing with more ADA, instead of inhibiting ADA, can reduce ADO levels and decrease hepatic gluconeogenesis. ADA restores a euglycemic state and recovers functional islets in db/db and high-fat streptozotocin diabetic mice. Mechanistically, ADA catabolizes ADO and increases Akt and FoxO1 phosphorylation independent of insulin action. ADA lowers blood glucose at a slower rate and longer duration compared to insulin, delaying or blocking the incidence of insulinogenic hypoglycemia shock. Finally, ADA suppresses gluconeogenesis in fasted mice and insulin-deficient diabetic mice, indicating the ADA regulating gluconeogenesis is a universal biological mechanism. Overall, these results suggest that ADA is expected to be a new therapeutic target for diabetes.

Epigenetic Insights Into Necrotizing Enterocolitis: Unraveling Methylation-Regulated Biomarkers.

Necrotizing enterocolitis (NEC) is a multifactorial gastrointestinal disease with high morbidity and mortality among premature infants. This study aimed to identify novel methylation-regulated biomarkers in NEC intestinal tissue through multiomics analysis. We analyzed DNA methylation and transcriptome datasets from ileum and colon tissues of patients with NEC. We identify methylation-related differential genes (MrDEGs) based on the rule that the degree of methylation in the promoter region is inversely proportional to RNA transcription. These MrDEGs included ADAP1, GUCA2A, BCL2L14, FUT3, MISP, USH1C, ITGA3, UNC93A and IL22RA1. Single-cell data revealed that MrDEGs were mainly located in the intestinal epithelial part of intestinal tissue. These MrDEGs were verified through Target gene bisulfite sequencing and RT-qPCR. We successfully identified and verified the ADAP1, GUCA2A, IL22RA1 and MISP, primarily expressed in intestinal epithelial villus cells through single-cell data. Through single-gene gene set enrichment analysis, we found that these genes participate mainly in the pathological process of T-cell differentiation and the suppression of intestinal inflammation in NEC. This study enhances our understanding of the pathogenesis of NEC and may promote the development of new precision medicine methods for NEC prediction and diagnosis.

Residual networks without pooling layers improve the accuracy of genomic predictions.

KEY MESSAGE: Residual neural network genomic selection is the first GS algorithm to reach 35 layers, and its prediction accuracy surpasses previous algorithms. With the decrease in DNA sequencing costs and the development of deep learning, phenotype prediction accuracy by genomic selection (GS) continues to improve. Residual networks, a widely validated deep learning technique, are introduced to deep learning for GS. Since each locus has a different weighted impact on the phenotype, strided convolutions are more suitable for GS problems than pooling layers. Through the above technological innovations, we propose a GS deep learning algorithm, residual neural network for genomic selection (ResGS). ResGS is the first neural network to reach 35 layers in GS. In 15 cases from four public data, the prediction accuracy of ResGS is higher than that of ridge-regression best linear unbiased prediction, support vector regression, random forest, gradient boosting regressor, and deep neural network genomic prediction in most cases. ResGS performs well in dealing with gene-environment interaction. Phenotypes from other environments are imported into ResGS along with genetic data. The prediction results are much better than just providing genetic data as input, which demonstrates the effectiveness of GS multi-modal learning. Standard deviation is recommended as an auxiliary GS evaluation metric, which could improve the distribution of predicted results. Deep learning for GS, such as ResGS, is becoming more accurate in phenotype prediction.

Clinical characteristics and antimicrobial therapy of healthcare-associated carbapenem-non-susceptible gram-negative bacterial meningitis: a 16-year retrospective cohort study.

OBJECTIVE: Healthcare-associated Gram-negative bacterial meningitis is a substantial clinical issue with poor outcomes, especially for neurosurgical patients. Here, we aimed to study the characteristics and treatment options of patients with healthcare-associated carbapenem-non-susceptible (Carba-NS) Gram-negative bacterial meningitis. METHODS: This observational cohort study was conducted at a teaching hospital from 2004 to 2019. The clinical characteristics of patients with meningitis with Carba-NS and carbapenem-susceptible (Carba-S) bacilli were compared, and the antimicrobial chemotherapy regimens and outcomes for Carba-NS Gram-negative bacterial meningitis were analyzed. RESULTS: A total of 505 patients were included, of whom 83.8% were post-neurosurgical patients. The most common isolates were Acinetobacter spp. and Klebsiella spp., which had meropenem-resistance rates of 50.6% and 42.5%, respectively, and showed a markedly growing carbapenem-resistance trend. Kaplan-Meier curve analysis revealed that Carba-NS Gram-negative bacilli were associated with a significantly higher in-hospital mortality rate (18.8%, 35/186) compared to the Carba-S group (7.4%, 9/122; P = 0.001). For Carba-NS Enterobacterales meningitis, aminoglycoside-based and trimethoprim-sulfamethoxazole-based regimens yielded significantly higher clinical efficacy rates than non-aminoglycoside-based and non-trimethoprim-sulfamethoxazole-based regimens (69.0% vs. 38.7%, P = 0.019 and 81.8% vs. 46.9%, P = 0.036, respectively). For Carba-NS A. baumannii complex meningitis, tetracycline-based (including doxycycline, minocycline, or tigecycline) therapy achieved a significantly higher clinical efficacy rate (62.9%, 22/35) than the non-tetracycline-based therapy group (40.4%, 19/47; P = 0.044). CONCLUSIONS: Our findings revealed that Carba-NS Gram-negative bacilli are associated with higher in-hospital mortality in patients with healthcare-associated meningitis. The combination therapies involving particular old antibiotics may improve patients' outcome. TRIAL REGISTRATION: This study was registered on the Chinese Clinical Trial Register under ChiCTR2000036572 (08/2020).

Characterization of erm(B)-Carrying Integrative and Conjugative Elements Transferred from Streptococcus anginosus to Other Streptococci and Enterococci.

Integrative and conjugative elements (ICEs) are important vectors of lateral gene transfer and contribute to the evolution of bacterial pathogens. However, studies on the transfer among species and the physiological consequences of ICEs are rare. The objective of this study was to investigate the cross-species transferability of newly identified erm(B)-carried ICE in Streptococcus anginosus San95 and its physiological consequences after transfer. The erm(B)-carried ICE, characterized by a triple serine integrase module, integrated into hsdM genes, thus designated ICESan95_hsdM. Analysis of ICESan95_hsdM revealed 32 additional ICESan95-like ICEs in the available NCBI genome (n = 24) and sequence of clinical isolates (n = 8). Polymerase chain reaction (PCR) was used to evaluate the 467 clinical isolates, of which 84 were positive for core genes (integrase, relaxase, and T4SS genes) of ICESan95_hsdM. Cross-species transfer experiments demonstrated that ICESan95_hsdM could transfer from S. anginosus to different streptococcal and enterococcal recipients. Growth and competitive culture assays showed acquisition of ICESan95_hsdM incurred no fitness cost. Our work discovered a group of ICEs in Streptococci and Enterococci. For the first time, we demonstrated the broad cross-species transferability to different species or genera of ICEs with no fitness cost that enables commensal S. anginosus to deliver antimicrobial resistance genes to other streptococci and enterococci.

Targeted drug delivery of engineered mesenchymal stem/stromal-cell-derived exosomes in cardiovascular disease: recent trends and future perspectives.

In recent years, stem cells and their secretomes, notably exosomes, have received considerable attention in biomedical applications. Exosomes are cellular secretomes used for intercellular communication. They perform the function of intercellular messengers by facilitating the transport of proteins, lipids, nucleic acids, and therapeutic substances. Their biocompatibility, minimal immunogenicity, targetability, stability, and engineerable characteristics have additionally led to their application as drug delivery vehicles. The therapeutic efficacy of exosomes can be improved through surface modification employing functional molecules, including aptamers, antibodies, and peptides. Given their potential as targeted delivery vehicles to enhance the efficiency of treatment while minimizing adverse effects, exosomes exhibit considerable promise. Stem cells are considered advantageous sources of exosomes due to their distinctive characteristics, including regenerative and self-renewal capabilities, which make them well-suited for transplantation into injured tissues, hence promoting tissue regeneration. However, there are notable obstacles that need to be addressed, including immune rejection and ethical problems. Exosomes produced from stem cells have been thoroughly studied as a cell-free strategy that avoids many of the difficulties involved with cell-based therapy for tissue regeneration and cancer treatment. This review provides an in-depth summary and analysis of the existing knowledge regarding exosomes, including their engineering and cardiovascular disease (CVD) treatment applications.

Microwave hyperthermia enhances radiosensitization by decreasing DNA repair efficiency and inducing oxidative stress in PC3 prostatic adenocarcinoma cells.

PURPOSE: Radiotherapy (RT) is the primary treatment for prostate cancer (PCa); however, the emergence of castration-resistant prostate cancer (CRPC) often leads to treatment failure and cancer-related deaths. In this study, we aimed to explore the use of microwave hyperthermia (MW-HT) to sensitize PCa to RT and investigate the underlying molecular mechanisms. METHODS: We developed a dedicated MW-HT heating setup, created an in vitro and in vivo MW-HT + RT treatment model for CRPC. We evaluated PC3 cell proliferation using CCK-8, colony experiments, DAPI staining, comet assay and ROS detection method. We also monitored nude mouse models of PCa during treatment, measured tumor weight, and calculated the tumor inhibition rate. Western blotting was used to detect DNA damage repair protein expression in PC3 cells and transplanted tumors. RESULTS: Compared to control, PC3 cell survival and clone formation rates decreased in RT + MW-HT group, demonstrating significant increase in apoptosis, ROS levels, and DNA damage. Lower tumor volumes and weights were observed in treatment groups. Ki-67 expression level was reduced in all treatment groups, with significant decrease in RT + MW-HT groups. The most significant apoptosis induction was confirmed in RT + MW-HT group by TUNEL staining. Protein expression levels of DNA-PKcs, ATM, ATR, and P53/P21 signaling pathways significantly decreased in RT + MW-HT groups. CONCLUSION: MW-HT + RT treatment significantly inhibited DNA damage repair by downregulating DNA-PKcs, ATM, ATR, and P53/P21 signaling pathways, leading to increased ROS levels, aggravate DNA damage, apoptosis, and necrosis in PC3 cells, a well-established model of CRPC.

Case Report: Robotic pylorus-preserving pancreatoduodenectomy for periampullary rhabdomyosarcoma in a 3-year-old patient.

Periampullary neoplasm is rare in pediatric patients and has constituted a strict indication for pancreatoduodenectomy (PD), which is a procedure sporadically reported in the literature among children. Robotic PD has been routinely performed for periampullary neoplasm in periampullary neoplasm, but only a few cases in pediatric patients have been reported. Here, we report the case of a 3-year-old patient with periampullary rhabdomyosarcoma treated with robotic pylorus-preserving PD and share our experience with this procedure in pediatric patients. A 3-year-old patient presented with obstructive jaundice and a mass in the pancreatic head revealed by imaging. A laparoscopic biopsy was performed. Jaundice progressed with abdominal pain and elevated alpha-amylase leading to urgent robotic exploration in which a periampullary neoplasm was revealed and pathologically diagnosed as rhabdomyosarcoma by frozen section examination. After pylorus-preserving PD, we performed a conventional jejunal loop following a child reconstruction, including an end-to-end pancreaticojejunostomy, followed by end-to-side hepaticojejunostomy and duodenojejunostomy. Delayed gastric emptying (DGE) presented with increasing drain from the nasogastric tube (NGT) a week after the surgery and improved spontaneously within 10 days. In a 13-month follow-up until the present, our case patient recovered well without potentially fatal complications, such as pancreatic fistula. Robotic PD in pediatric patients was safe and effective without intra- or postoperative complications.

Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling.

The rate of soybean canopy establishment largely determines photoperiodic sensitivity, subsequently influencing yield potential. However, assessing the rate of soybean canopy development in large-scale field breeding trials is both laborious and time-consuming. High-throughput phenotyping methods based on unmanned aerial vehicle (UAV) systems can be used to monitor and quantitatively describe the development of soybean canopies for different genotypes. In this study, high-resolution and time-series raw data from field soybean populations were collected using UAVs. The RGB (red, green, and blue) and infrared images are used as inputs to construct the multimodal image segmentation model-the RGB & Infrared Feature Fusion Segmentation Network (RIFSeg-Net). Subsequently, the segment anything model was employed to extract complete individual leaves from the segmentation results obtained from RIFSeg-Net. These leaf aspect ratios facilitated the accurate categorization of soybean populations into 2 distinct varieties: oval leaf type variety and lanceolate leaf type variety. Finally, dynamic modeling was conducted to identify 5 phenotypic traits associated with the canopy development rate that differed substantially among the classified soybean varieties. The results showed that the developed multimodal image segmentation model RIFSeg-Net for extracting soybean canopy cover from UAV images outperformed traditional deep learning image segmentation networks (precision = 0.94, recall = 0.93, F1-score = 0.93). The proposed method has high practical value in the field of germplasm resource identification. This approach could lead to the use of a practical tool for further genotypic differentiation analysis and the selection of target genes.

Effects of different traditional Chinese exercise in the treatment of essential hypertension: a systematic review and network meta-analysis.

Background: As a therapy to prevent and treat essential hypertension (EH), traditional Chinese exercises (TCEs) were widely used in clinical practice. However, there is a lack of strictly comparison of the antihypertensive efficacy of different TCEs, which not conducive to the selection of the best and most optimal treatment. This study aimed to perform a network meta-analysis to objectively evaluate which TCE has the best effects in assisting with lowering blood pressure. Methods: PubMed, Embase, the Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), VIP, SinoMed and Wanfang Data were searched for all randomized controlled trials (RCTs) on TCEs for the treatment of EH published up to July 10, 2023. RoB2.0 tool was utilized to evaluate the quality of the RCTs. The network meta-analysis was performed by R 4.1.2 and Stata 17.0. Weighted mean difference (WMD) was calculated for continuous outcomes. Results: A total of 29 studies, including 2,268 patients were included to analyze 6 different interventions. The network meta-analysis results presented that in comparison with control group, Tai Chi + antihypertensive medication [WMD = -10.18, 95% CI, (-14.94, -5.44)] is the most effective intervention for lowering systolic blood pressure (SBP), and Wuqinxi + antihypertensive medication [WMD = -10.36, 95% CI (-18.98, -1.66)] is the most effective intervention for lowering diastolic blood pressure (DBP). Conclusion: TCEs combined with antihypertensive medication may be able to achieve more prominent antihypertensive effects with Tai Chi and Wuqinxi potentially being the higher-priority options. However, well-designed randomized studies are warranted to further verify currently conclusion.

Alterations of gut microbiota in infants with biliary atresia identified by 16S rRNA-sequencing.

BACKGROUND: Biliary atresia (BA) is a severe neonatal disease with progressive intra- and extra-hepatic bile ducts inflammation and hepatic fibrosis. Characterization of gut microbiome profiles in infants with biliary atresia can provide valuable information and potential disease biomarkers. Our study aims to explore the relationship between gut microbiota and biliary atresia. METHODS: 16 S ribosomal RNA (rRNA) gene sequencing was carried out to identify the differences in composition and diversity of gut microbiota between infants with BA and healthy subjects. A total of 31 infants with biliary atresia and 20 healthy subjects were recruited. RESULTS: The composition of gut microbiota in BA group was significantly different with the normal control group (P < 0.05) and the abundance ratio of Klebsiella/Bifidobacterium showed great potential for identification of BA (P < 0.01). In addition, the differential bacterial taxa were involved in lipid and vitamins metabolism. CONCLUSION: Our results could provide potential non-invasive biomarker for identification of biliary atresia and contribute to the treatment in terms of ameliorating microbiota dysbiosis.

Type I BREX system defends against antibiotic-resistant plasmids in Escherichia coli.

The Bacteriophage Exclusion (BREX) system is a novel antiphage defense system identified in Bacillus cereus in 2015. The purpose of this study was to investigate the presence of the BREX system defenses against antibiotic-resistant plasmids such as blaKPC and blaNDM invasion in Escherichia coli. The BREX system was present in 5.4% (23/424) of E. coli clinical isolates and 6.5% (84/1283) of E. coli strains with completely sequenced genomes in the GenBank database. All 23 BREX-positive E. coli clinical isolates were susceptible to carbapenems, while all five isolates carrying blaKPC and 11 carrying blaNDM were BREX-negative. For E. coli strains in the GenBank database, 37 of 38 strains carrying blaKPC and 109 of 111 strains carrying blaNDM were BREX negative. The recognition site sequence of methyltransferase PglX in a clinical E. coli 3756 was 5'-CANCATC-3' using PacBio single-molecular real-time sequencing. The transformation efficiency of plasmid psgRNA-ColAori-target with the PglX recognition site was reduced by 100% compared with the plasmid without the recognition site in E. coli DH5α-pHSG398-BREX. The BREX showed lower defense efficacy against plasmid psgRNA-15Aori-target which had the same plasmid backbone but different surrounding sequences of recognition sites with psgRNA-ColAori-target. The conjugation frequency of the KPC-2 plasmid and NDM-5 plasmid in E. coli 3756-ΔBREX was higher than that in E. coli 3756 clinical isolate (1.0 × 10-6 vs 1.3 × 10-7 and 5.5 × 10-7 vs 1.7 × 10-8, respectively). This study demonstrated that the type I BREX system defends against antibiotic-resistant plasmids in E. coli.