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Abstract The study of mechanical properties and failure mechanism of propellants under impact loads is crucial for analyzing structural integrity of propellant charges. An experimental investigation was conducted on NEPE propellant using a separated Hopkinson pressure bar to conduct high strain rate uniaxial impact tests. Deformation and failure processes of the propellant under impact conditions were recorded with a high-speed camera. The microscopic failure forms of the propellants were observed using a scanning electron microscope and an optical microscope. Stress-strain curves and high-speed images revealed that the damage behavior and failure mechanism of NEPE propellants are significantly influenced by strain rate. As the strain rate increases, there is a notable increase in the deformation degree of the propellant specimens, with a more pronounced shear effect. This leads to an earlier occurrence of failure and a more severe degree of failure. The predominant failure forms observed in NEPE propellants include transgranular failure, matrix tearing, and cavity merging. A nonlinear visco-hyperelastic constitutive model with damage at high strain rates was established to provide a precise account of the mechanical response of NEPE propellant under high strain rates.

Abstract This work revisits the notion of complex step derivative approximation (CSDA) and presents its use in constitutive model of a class of nonlinear viscoelastic materials. The effectiveness of a CSDA is evaluated by putting it through a series of straightforward examples. After that, the idea of the CSDA is put to use in order to carry out a numerical evaluation of the algorithmic tangent moduli of a viscoelastic constitutive model. The performance of the constitutive models is evaluated through the use of three different numerical tests, and the results are compared to those that were achieved by the application of an analytical method. In comparison to other numerical differentiation techniques, It has been found that the CSDA scheme is the most computationally efficient and robust method of numerical differentiation, regardless of the size of the finite difference interval.

The association between blood flow rate (BFR) and clinical outcomes in patients undergoing maintenance hemodialysis (MHD) is inconclusive. This retrospective study included 175 patients undergoing MHD treatment between July 2015 and March 2022, divided into two groups based on time-averaged effective blood flow rate (eBFR) median value. We investigated arteriovenous fistula (AVF) outcomes and the association of eBFR with all-cause mortality and new major adverse cardiovascular events (MACE). Mean ± SD and median time-averaged eBFR values were 276 ± 24 and 275 mL/min, respectively. After adjusting for relevant factors including age, sex, vintage, diabetes, CVD, receiving hemodiafiltration (HDF) treatment and spKt/V, Cox models indicated a low time-averaged eBFR (≤ 275 ml/min) was associated with increased risks of all-cause mortality (hazard ratio [HR] 14.18; 95% confidence interval [CI], 3.14–64.1) and new MACE (HR 3.76; 95% CI, 1.91–7.40) in MHD patients. Continuous Cox models demonstrated each 20 ml/min increase in eBFR linked to a 63% decrease in the risk of all-cause mortality (HR: 0.37, 95% CI: 0.23–0.59) and a 38% decrease in the occurrence of new MACE (HR: 0.62, 95% CI: 0.46–0.84). There was no significant difference in AVF outcomes between the two groups. Our study noted higher eBFR (>275 mL/min) is associated with lower risks of both all-cause mortality and new MACE compared with low eBFR (≤275 mL/min). Increased eBFR is not associated with a higher risk of AVF failure.

Aims This study aimed to investigate the efficacy and safety of sacubitril/valsartan in abnormal renal function (eGFR < 60 ml/min/1.73m2) patients combined with heart failure based on randomized controlled trials (RCTs) and observational studies.Methods The Embase, PubMed and the Cochrane Library were searched for relevant studies from inception to December 2023. Dichotomous variables were described as event counts with the odds ratio (OR) and 95% confidence interval (CI) values. Continuous variables were expressed as mean standard deviation (SD) with 95% CIs.Results A total of 6 RCTs and 8 observational studies were included, involving 17335 eGFR below 60 ml/min/1.73m2 patients combined with heart failure. In terms of efficacy, we analyzed the incidence of cardiovascular events and found that sacubitril/valsartan significantly reduced the risk of cardiovascular death or heart failure hospitalization in chronic kidney disease (CKD) stages 3–5 patients with heart failure (OR: 0.65, 95%CI: 0.54–0.78). Moreover, sacubitril/valsartan prevented the serum creatinine elevation (OR: 0.81, 95%CI: 0.68–0.95), the eGFR decline (OR: 0.83, 95% CI: 0.73–0.95) and the development of end-stage renal disease in this population (OR:0.73, 95%CI:0.60–0.89). As for safety outcomes, we did not find that the rate of hyperkalemia (OR:1.31, 95%CI:0.79–2.17) and hypotension (OR:1.57, 95%CI:0.94–2.62) were increased in sacubitril/valsartan group among CKD stages 3–5 patients with heart failure.Conclusions Our meta-analysis proves that sacubitril/valsartan has a favorable effect on cardiac function without obvious risk of adverse events in abnormal renal function patients combined with heart failure, indicating that sacubitril/valsartan has the potential to become perspective treatment for these patients.

Abstract Background Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases. Results In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback–Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD. Conclusion Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.

Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utilize expression data to identify the most physiologically relevant targets. Although these methods are effective, they typically require a large sample size and high-depth sequencing to detect potential miRNA-target pairs, thereby limiting their applicability in improving plant breeding. In this study, we propose a novel miRNA-target prediction framework named kmerPMTF (k-mer-based prediction framework for plant miRNA-target). Our framework effectively extracts the latent semantic embeddings of sequences by utilizing k-mer splitting and a deep self-supervised neural network. We construct multiple similarity networks based on k-mer embeddings and employ graph convolutional networks to derive deep representations of miRNAs and targets and calculate the probabilities of potential associations. We evaluated the performance of kmerPMTF on four typical plant datasets: Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, and Prunus persica. The results demonstrate its ability to achieve AUPRC values of 84.9%, 91.0%, 80.1%, and 82.1% in 5-fold cross-validation, respectively. Compared with several state-of-the-art existing methods, our framework achieves better performance on threshold-independent evaluation metrics. Overall, our study provides an efficient and simplified methodology for identifying plant miRNA-target associations, which will contribute to a deeper comprehension of miRNA regulatory mechanisms in plants.

Abstract Background Up to now, there is no unequivocal intervention to mitigate vascular calcification (VC) in patients with hemodialysis. This network meta-analysis aimed to systematically evaluate the clinical efficacy of sodium thiosulfate, bisphosphonates, and cinacalcet in treating vascular calcification. Methods A comprehensive study search was performed using PubMed, Web of Science, the Cochrane Library, EMBASE and China National Knowledge Internet (CNKI) to collect randomized controlled trials (RCTs) of sodium thiosulfate, bisphosphonates, and cinacalcet for vascular calcification among hemodialysis patients. Then, network meta-analysis was conducted using Stata 17.0 software. Results In total, eleven RCTs including 1083 patients were qualified for this meta-analysis. We found that cinacalcet (SMD − 0.59; 95% CI [–0.95, -0.24]) had significant benefit on vascular calcification compared with conventional therapy, while sodium thiosulfate or bisphosphonates did not show such efficiency. Furthermore, as for ranking the efficacy assessment, cinacalcet possessed the highest surface under the cumulative ranking curve (SUCRA) value (88.5%) of lessening vascular calcification and was superior to sodium thiosulfate (50.4%) and bisphosphonates (55.4%). Thus, above results suggested that cinacalcet might be the most promising drug for vascular calcification treatment in hemodialysis patients. Mechanistically, our findings illustrated that cinacalcet reduced serum calcium (SMD − 1.20; 95% CI [–2.08, − 0.33]) and showed the tendency in maintaining the balance of intact Parathyroid Hormone (iPTH) level. Conclusions This network meta-analysis indicated that cinacalcet appear to be more effective than sodium thiosulfate and bisphosphonates in mitigating vascular calcification through decreasing serum calcium and iPTH. And cinacalcet might be a reasonable option for hemodialysis patients with VC in clinical practice. Systematic Review Registration [ http://www.crd.york.ac.uk/PROSPERO ], identifier [CRD42022379965].

Abstract Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution (SCRaMbLE) is a promising tool to study genomic rearrangements. However, the potential of SCRaMbLE to study genomic rearrangements is currently hindered, because a strain containing all 16 synthetic chromosomes is not yet available. Here, we construct SparLox83R, a yeast strain containing 83 loxPsym sites distributed across all 16 chromosomes. SCRaMbLE of SparLox83R produces versatile genome-wide genomic rearrangements, including inter-chromosomal events. Moreover, when combined with synthetic chromosomes, SCRaMbLE of hetero-diploids with SparLox83R leads to increased diversity of genomic rearrangements and relatively faster evolution of traits compared to hetero-diploids only with wild-type chromosomes. Analysis of the SCRaMbLEd strain with increased tolerance to nocodazole demonstrates that genomic rearrangements can perturb the transcriptome and 3D genome structure and consequently impact phenotypes. In summary, a genome with sparsely distributed loxPsym sites can serve as a powerful tool for studying the consequence of genomic rearrangements and accelerating strain engineering in Saccharomyces cerevisiae.

Objective Vascular calcification is a common chronic kidney disease complication. This study aimed to investigate the function of long non-coding RNA (LncRNA) H19 in vascular calcification to explore new therapeutic strategies. Methods We induced osteogenic differentiation and calcification of vascular smooth muscle cells (VSMCs) using β-glycerophosphate. Then, we detected the LncRNA H19 promoter methylation status and Erk1/2 pathways using methylation-specific polymerase chain reaction and western blotting, respectively. Results Compared with the control group, high phosphorus levels induced VSMC calcification, accompanied by increases in LncRNA H19 and the osteogenic marker Runx2 and reduction of the contractile phenotype marker SM22a. LncRNA H19 knockdown inhibited osteogenic differentiation and calcification of VSMCs. However, the suppressed role of VSMC calcification caused by shRNA H19 was partially reversed by simultaneous activation of the Erk1/2 pathways. Mechanically, we found that the methylation rate of CpG islands in the LncRNA H19 promoter region was significantly lower in the high-phosphorus group, and the hypomethylation state elevated LncRNA H19 levels, which in turn regulated phosphorylated Erk1/2 expression. Conclusions LncRNA H19 promoted osteogenic differentiation and calcification of VSMCs by regulating the Erk1/2 pathways. Additionally, hypomethylation of LncRNA H19 promoter CpG islands upregulated LncRNA H19 levels and subsequently activated Erk1/2 phosphorylation.

Abstract Background As an important source of genetic variation, copy number variation (CNV) can alter the dosage of DNA segments, which in turn may affect gene expression level and phenotype. However, our knowledge of CNV in apple is still limited. Here, we obtained high-confidence CNVs and investigated their functional impact based on genome resequencing data of two apple populations, cultivars and wild relatives. Results In this study, we identified 914,610 CNVs comprising 14,839 CNV regions (CNVRs) from 346 apple accessions, including 289 cultivars and 57 wild relatives. CNVRs summed to 71.19 Mb, accounting for 10.03% of the apple genome. Under the low linkage disequilibrium (LD) with nearby SNPs, they could also accurately reflect the population structure of apple independent of SNPs. Furthermore, A total of 3,621 genes were covered by CNVRs and functionally involved in biological processes such as defense response, reproduction and metabolic processes. In addition, the population differentiation index ( $${V}_{st}$$ V st ) analysis between cultivars and wild relatives revealed 127 CN-differentiated genes, which may contribute to trait differences in these two populations. Conclusions This study was based on identification of CNVs from 346 diverse apple accessions, which to our knowledge was the largest dataset for CNV analysis in apple. Our work presented the first comprehensive CNV map and provided valuable resources for understanding genomic variations in apple.

This review presents a comprehensive analysis of plant-based proteins from soybeans, pulses, cereals, and pseudo-cereals by examining their structural properties, modification techniques, bioactivities, and applicability in food systems. It addresses the critical need for a proper utilization strategy of proteins from various plant sources amidst the rising environmental footprint of animal protein production. The inherent composition diversity among plant proteins, their nutritional profiles, digestibility, environmental impacts, and consumer acceptance are compared. The innovative modification techniques to enhance the functional properties of plant proteins are also discussed. The review also investigates the bioactive properties of plant proteins, including their antioxidant, antimicrobial, and antitumoral activities, and their role in developing meat analogs, dairy alternatives, baked goods, and 3D-printed foods. It underscores the consideration parameters of using plant proteins as sustainable, nutritious, and functional ingredients and advocates for research to overcome sensory and functional challenges for improved consumer acceptance and marketability.

This paper presents a computational fluid dynamics study of morphology and structure dynamics of the flow over a supersonic secant-ogive cylinder boat tail projectile under heavy rain. The discrete phase model is employed to approximate the process of droplet–particle collision with the projectile wall and the formation of a liquid film. The simulation results indicate that at certain angles of attack, rain impact decreases lift coefficient of projectile by as much as 14.9%, as the wall pressure distribution is distinctly reformed. Moreover, the rain condition induces the formation of a liquid film on the front end of the projectile, as the angle of attack increases, the stability of this liquid film gradually improves, while its thickness and coverage rise to the peak values and then decrease. On the projectile’s trailing half, however, the liquid film developed a watery pattern. The collision of liquid droplets causes momentum loss to the projectile, while an unstable liquid film tends to exacerbate aerodynamic performance loss, a stable liquid film would mitigate the performance loss.

Abstract Background Emerging evidences show that Piwi-interacting RNAs (piRNAs) play a pivotal role in numerous complex human diseases. Identifying potential piRNA-disease associations (PDAs) is crucial for understanding disease pathogenesis at molecular level. Compared to the biological wet experiments, the computational methods provide a cost-effective strategy. However, few computational methods have been developed so far. Results Here, we proposed an end-to-end model, referred to as PDA-PRGCN (PDA prediction using subgraph Projection and Residual scaling-based feature augmentation through Graph Convolutional Network). Specifically, starting with the known piRNA-disease associations represented as a graph, we applied subgraph projection to construct piRNA-piRNA and disease-disease subgraphs for the first time, followed by a residual scaling-based feature augmentation algorithm for node initial representation. Then, we adopted graph convolutional network (GCN) to learn and identify potential PDAs as a link prediction task on the constructed heterogeneous graph. Comprehensive experiments, including the performance comparison of individual components in PDA-PRGCN, indicated the significant improvement of integrating subgraph projection, node feature augmentation and dual-loss mechanism into GCN for PDA prediction. Compared with state-of-the-art approaches, PDA-PRGCN gave more accurate and robust predictions. Finally, the case studies further corroborated that PDA-PRGCN can reliably detect PDAs. Conclusion PDA-PRGCN provides a powerful method for PDA prediction, which can also serve as a screening tool for studies of complex diseases.

Adversarial attacks have threatened the credibility of machine learning models and cast doubts over the integrity of data. The attacks have created much harm in the fields of computer vision, and natural language processing. In this paper, we focus on the adversarial attack, in particular the poisoning attack, against the network intrusion detection system (NIDS), which is often viewed as the first line of defense against cyber threats. We develop a generative adversarial network (GAN) in AIGAN, which uses deep learning techniques to generate adversarial data and to conduct an anomaly attack on IoT networks. To evaluate the effectiveness of our generator, we measure the similarities between real and fake data using the Jaccard similarity index, in addition comparing the F1-scores from four generic algorithms: multilayer perception, logistic regression, decision tree, random forest. We contrast the performance of ten machine learning classifiers experimented on two real IoT datasets and their fake adversarial samples. Our work highlights a vulnerable side of NIDS created by machine learning when attacked with adversarial perturbation.

Identification of long non-coding RNAs (lncRNAs) associated with common diseases is crucial for patient self-diagnosis and monitoring of health conditions using artificial intelligence (AI) technology at home. LncRNAs have gained significant attention due to their crucial roles in the pathogenesis of complex human diseases and identifying their associations with diseases can aid in developing diagnostic biomarkers at the molecular level. Computational methods for predicting lncRNA-disease associations (LDAs) have become necessary due to the time-consuming and labor-intensive nature of wet biological experiments in hospitals, enabling patients to access LDAs through their AI terminal devices at any time. Here, we have developed a predictive tool, LDAGRL, for identifying potential LDAs using a bridge heterogeneous information network (BHnet) constructed via Structural Deep Network Embedding (SDNE). The BHnet consists of three types of molecules as bridge nodes to implicitly link the lncRNA with disease nodes and the SDNE is used to learn high-quality node representations and make LDA predictions in a unified graph space. To assess the feasibility and performance of LDAGRL, extensive experiments, including 5-fold cross-validation, comparison with state-of-the-art methods, comparison on different classifiers and comparison of different node feature combinations, were conducted, and the results showed that LDAGRL achieved satisfactory prediction performance, indicating its potential as an effective LDAs prediction tool for family medicine and primary care.

Abstract Background Estimates of cervical lymph node (LN) metastasis in patients with middle and lower thoracic esophageal squamous cell carcinoma (ESCC) are important. A nomogram is a useful tool for individualized prediction. Methods A total of 235 patients were enrolled in this study. Univariate and multivariate analyses were performed to screen for independent risk factors and construct a nomogram to predict the risk of cervical LN metastasis. The nomogram performance was assessed by discrimination, calibration, and clinical use. Results Totally, four independent predictors, including the maximum diameter of tumor, paraesophageal lymph node status, recurrent laryngeal nerve lymph node status, and the CT-reported cervical LN status, were enrolled in the nomogram. The AUC of the nomogram model in the training and validation dataset were 0.833 (95% CI 0.762–0.905), 0.808 (95% CI 0.696–0.920), respectively. The calibration curve demonstrated a strong consistency between nomogram and clinical findings in predicting cervical LN metastasis. Decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion We developed a nomogram that could be conveniently used to predict the individualized risk of cervical LN metastasis in patients with middle and lower thoracic ESCC.

Summary: Transposable elements (TEs) are abundant in metazoan genomes and have multifaceted effects on host fitness. However, the mechanisms underlying the functions of TEs are still not fully understood. Here, we combine Hi-C, ATAC-seq, and ChIP-seq assays to report the existence of multimegabase supersized loop (SSL) clusters in the Xenopus tropicalis sperm. We show that SSL anchors are inaccessible and devoid of the architectural protein CTCF, RNA polymerase II, and modified histones. Nearly all SSL anchors are marked by Helitrons, a class II DNA transposon. Molecular dynamics simulations indicate that SSL clusters are likely formed via a molecular agent-mediated chromatin condensation process. However, only slightly more SSL anchor-associated genes are expressed at late embryo development stages, suggesting that SSL anchors might only function in sperm. Our work shows an evolutionarily distinct and sperm-specific genome structure marked by a subset of Helitrons, whose establishment and function remain to be explored.

The recently emerging high-throughput Pore-C (HiPore-C) can identify whole-genome high-order chromatin multi-way interactions with an ultra-high output, contributing to deciphering three-dimensional (3D) genome organization. However, it also brings new challenges to relevant data analysis. To alleviate this problem, we proposed the EpiMCI, a model for multi-way chromatin interaction prediction based on a hypergraph neural network with epigenomic signals as the input. The EpiMCI integrated separate hyperedge representations with coupling hyperedge information and obtained AUCs of 0.981 and 0.984 in the GM12878 and K562 datasets, respectively, which outperformed the current available method. Moreover, the EpiMCI can be applied to denoise the HiPore-C data and improve the data quality efficiently. Furthermore, the vertex embeddings extracted from the EpiMCI reflected the global chromatin architecture accurately. The principal component analysis suggested that it was well aligned with the activities of genomic regions at the chromatin compartment level. Taken together, the EpiMCI can accurately predict multi-way chromatin interactions and can be applied to studies relying on chromatin architecture.

Abstract Background As an important group of the multidrug efflux transporter family, the multidrug and toxic compound extrusion (MATE) family has a wide range of functions and is distributed in all kingdoms of living organisms. However, only two MATE genes in apple have been analyzed and genome-wide comprehensive analysis of MATE family is needed. Results In this study, a total of 66 MATE (MdMATE) candidates encoding putative MATE transporters were identified in the apple genome. These MdMATE genes were classified into four groups by phylogenetic analysis with MATE genes in Arabidopsis. Synteny analysis reveals that whole genome duplication (WGD) and segmental duplication events played a major role in the expansion of MATE gene family in apple. MdMATE genes show diverse expression patterns in different tissues/organs and developmental stages. Analysis of cis-regulatory elements in MdMATE promoter regions indicates that the function of MdMATE genes is mainly related to stress response. Besides, the changes of gene expression levels upon different pathogen infections reveal that MdMATE genes are involved in biotic stress response. Conclusions In this work, we systematically identified MdMATE genes in apple genome using a set of bioinformatics approaches. Our comprehensive analysis provided valuable resources for improving disease resistance in apple and further functional characterization of MATE genes in other species.

The immunotoxicity induced by heavy metals on herbivorous insects reflect the alterations of the susceptibility to entomopathogenic agents in herbivorous insects exposed to heavy metal. In the present study, the susceptibility of gypsy moth larvae to Bacillus thuringiensis under Cd treatment at low and high dosages was investigated, and the gut microbiome-hemolymph metabolome responses that affected larval disease susceptibility caused by Cd exposure were examined. Our results showed that mortality of gypsy moth larvae caused by B. thuringiensis was significantly higher in larvae pre-exposed to Cd stress, and there was a synergistic effect between Cd pre-exposure and bacterial infection. Exposure to Cd significantly decreased the abundance of several probiotics (e.g., Serratia for the low Cd dosage and Weissella, Aeroonas, and Serratia for the high Cd dosage) and increased the abundances of several pathogenic bacteria (e.g., Stenotrophomonas, Gardnerella, and Cutibacterium for the low Cd dosage and Pluralibacter and Tsukamurella for the high Cd dosage) compared to the controls. Moreover, metabolomics analysis indicated that amino acid biosynthesis and metabolism were significantly perturbed in larval hemolymph under Cd exposure at both the low and high dosages. Correlation analysis demonstrated that several altered metabolites in larval hemolymph were significantly correlated with changes in the gut microbial community. The results demonstrate that prior exposure to Cd increases the susceptibility of gypsy moth larvae to B. thuringiensis in a synergistic fashion due to gut microbiota dysbiosis and hemolymph metabolic disorder, and thus microbial-based biological control may be the best pest control strategy in heavy metal-polluted areas.

To investigate the flow mechanism of feather-like rough airfoils based on swift wings, computational simulations were employed to explore their overall aerodynamic characteristics in comparison to equivalent smooth airfoils. The study focused on angles of attack ranging from 0° to 20° at low Reynolds numbers. The results reveal that the rough airfoil exhibits higher lift and lower drag compared to the smooth airfoil at moderate angles of attack ranging from 6° to 10°, resulting in significantly improved aerodynamic efficiency. Notably, at an angle of attack of 8°, the aerodynamic efficiency is increased by 19%. However, at angles of attack smaller than 6°, the increase in drag outweighs the increase in lift, leading to lower aerodynamic efficiency for the rough airfoil. Conversely, when the angle of attack exceeds 16°, both airfoils experience separated flow-dominated flow fields, resulting in comparable effective aerodynamic shapes and similar aerodynamic efficiencies. Furthermore, the study found that increasing the Reynolds number results in greater pressure differences in the flow field, leading to higher aerodynamic efficiency. These preliminary conclusions are valuable for elucidating the flight mechanisms of bird-feather-like wings and can inform the design or morphing design of bio-inspired micro aerial vehicles in the near future.

Abstract Background Lack of accurate and effective assessment tools of fluid status is one of the major challenges to reach proper dry weight (DW) in chronic hemodialysis (HD) population. The aim of this randomized study was to evaluate the effect of bioimpedance guided DW assessment on long-term outcomes in Chinese HD patients. Eligible patients were randomly assigned (1:1) to two groups in each center, the control group and body composition monitor (BCM) group. In the BCM group, DW has been evaluated by bioimpedance technic every 2 months during follow-up. The primary composite endpoint consisted of death, acute myocardial infarction, cerebral infarction, cerebral hemorrhage, and peripheral vascular disease. Methods A total of 445 patients were recruited from 11 hemodialysis centers from Beijing, Tianjin and Shijiazhuang cities from Jan 1, 2013 to Dec 31, 2014. They were randomized into either BCM group or control group. All patients have been followed up for 1 year or until Dec 31, 2014 or censoring. Results At baseline, there were no significant differences between two groups in terms of demographic parameters, dialysis vintage, percentage of vascular access, and comorbid conditions. At the end of the study, 18 (4.04%) patients had died (11 in control group and 7 in BCM group). Kaplan-Meier survival analysis showed no significant difference in survival rates between two groups (log-rank test P = 0.07). However, there was an increasing trend of survival rates in BCM group compared to the control group. In the multivariable Cox analysis, there was a nonsignificant trend toward less primary composite end points in the BCM group in the adjusted analysis, the hazard ratio was impressive (0.487, 95% CI 0.217–1.091, P = 0.08). Conclusion Bioimpedance technic has been applied to assess fluid status for decades and has been proved to be a promising tool for clinical practice. Although short-term outcomes were not improved in the randomized, controlled trial, the ascending trend in survival has been observed. Further studies are needed to investigate the survival benefit of bioimpedance method in DW assessment in a larger sample with longer follow-up period. Trial registration ClinicalTrials.org, NCT01509937 . Registered 13 January 2012,

Heavy metal exposure-triggered growth retardation and physiology disorder in phytophagous insects have been widely understood, but only a few studies have investigated its immunomodulatory effects on herbivorous insects. Here, the innate immunity of gypsy moth (Lymantria dispar) larvae under Cd stress was evaluated by integrating cellular and humoral immunity, and the immunomodulation mechanism of Cd stress was further understood by the proteomics analysis of larval hemolymph. Our results showed that the total hemocyte count, as well as phagocytic, encapsulation and bacteriostatic activity, of hemolymph in gypsy moth larvae exposed to Cd stress was significantly lower than that in un-treated larvae. Further proteomic analysis revealed that Cd exposure may reduce the total hemocyte count in larval hemolymph by inducing endoplasmic reticulum pathway-mediated hemocyte apoptosis, thereby causing the collapse of cellular immunity in gypsy moth larvae. In addition, the transcriptional level of signal transduction genes (IMD, Toll, Relish, JAK and STAT) and antimicrobial peptide genes (cecropin and lebocin), as well as the protein abundance of pattern recognition receptors (PGRP and GNBP3) in the Toll, IMD and JAK/STAT signaling pathways was significantly decreased in Cd-treated larvae, clearly implying an immunosuppresive effect of Cd stress on pathogen recognition, signal transduction and effector synthesis of humoral immunity in gypsy moth larvae. Taken together, these results suggest that Cd exposure decreases both cellular immunity and humoral immunity of gypsy moth larvae, and provides a new entry point for systematically and comprehensively unraveling the heavy metal pollutants-caused immunotoxicity.

A/B compartments are observed in Hi-C data and coincide with eu/hetero-chromatin. However, many genomic regions are ambiguous under A/B compartment scheme. We develop MOSAIC (MOdularity and Singular vAlue decomposition-based Identification of Compartments), an accurate compartmental state detection scheme. MOSAIC reveals that those ambiguous regions segregate into two additional compartmental states, which typically correspond to short genomic regions flanked by long canonical A/B compartments with opposite activities. They are denoted as micro-compartments accordingly. In contrast to the canonical A/B compartments, micro-compartments cover ∼30% of the genome and are highly dynamic across cell types. More importantly, distinguishing the micro-compartments underpins accurate characterization of chromatin structure-function relationship. By applying MOSAIC to GM12878 and K562 cells, we identify CD86, ILDR1 and GATA2 which show concordance between gene expression and compartmental states beyond the scheme of A/B compartments. Taken together, MOSAIC uncovers fine-scale and dynamic compartmental states underlying transcriptional regulation and disease.

The modeling of the deformation and failure process of a cyclotetramethylene tetranitramine modified double-base (HMX-MDB) propellant based on the microstructure is of great importance for its manufacturing process and storage lifetime. In the present work, the debonding, nucleation, and crack propagation in an HMX-MDB propellant were investigated experimentally and numerically. Stress-strain responses and viscoelastic parameters were obtained through uniaxial tensile tests on the HMX-MDB propellant and stress relaxation tests on a nitrocellulose-nitroglycerine (NC-NG) propellant, respectively. A two-dimensional representative volume element (RVE) model was developed based on the molecular dynamics method by embedding HMX particles in the NC-NG matrix. A zero-thickness cohesive element was used to simulate the damage behavior of the matrix and the interface. A piecewise cohesive zone model (PCZM) was established to capture stress-strain responses resulted from the matrix rupture and the debonding process at the particle-matrix interface. The effects of matrix strength and interface strength on the mechanical properties of the HMX-MDB propellant were investigated by PCZM. The different ratio of matrix strength to interface strength revealed three kinds of HMX-MDB propellant’s damage patterns: matrix cracking, matrix cracking followed by particle-matrix interfacial debonding, and particle-matrix interfacial debonding followed by matrix cracking.

We present a novel design of few-mode fiber (FMF) with a uniform differential mode group delay (U-DMGD) among propagating modes and apply the FMF to a single-fiber delay line module for implementing microwave photonic finite impulse response (FIR) filters. By optimizing key parameters such as the core grading exponent and the dimension of the trench of FMF, a U-DMGD between adjacent modes among four modes (LP01, LP11, LP02 and LP31) over the entire C band is achieved. Wavelength dependence is entirely removed. An FIR microwave photonic filter (MPF) implemented using the designed 1-km FMF is investigated through numerical simulations. The free spectral range (FSR) of the MPF is 5.7 GHz, the 3-dB bandwidth is 1.26 GHz, and the main lobe-to-side lobe ratio (MSR) is 10.42 dB. Discussions on fabrication aspects have also been presented. The proposed single-fiber delay line structure based on FMF can significantly reduce the system complexity of microwave photonic signal processing.

Arbuscular mycorrhizal fungi (AMF) are considered as important biological factors that can affect insect resistance of plants. Herein, we used AMF-poplar seedlings that could either increase or decrease the resistance to gypsy moth larvae, to elucidate the mechanism of mycorrhizal-induced insect resistance/susceptibility at the larval microbial and metabolic levels. Our results found that larval plant consumption and growth were significantly inhibited in the Glomus mossae (GM)-colonized seedlings, whereas they were enhanced in the Glomus intraradices (GI)-colonized seedlings. GM inoculation reduced the beneficial bacteria abundance in the larval gut and inhibited the detoxification and metabolic functions of gut microbiota. However, GI inoculation improved the larval gut environment by decreasing the pathogenic bacteria and activating specific metabolic pathways. Furthermore, GM inoculation triggers a metabolic disorder in the larval fat body, accompanied by the suppression of detoxification and energy production pathways. The levels of differentially accumulated metabolites related to amino acid synthesis and metabolism and exogenous toxin metabolism pathways were significantly increased in the GI group. Taken together, the disadaptation of gypsy moth larvae to leaves of GM-colonized seedlings led to the GM-induced insect resistance in poplar, and to the GI-induced insect susceptibility involved in the improvement of larval gut environment and fat body energy metabolism.

Objective To identify serum microRNAs (miRNAs) as potential non-invasive biomarkers for patients with chronic kidney disease (CKD). Methods We collected serum samples from healthy controls, CKD stage 1 (CKD1), and stage 5 (CKD5) patients with primary glomerulonephritis (GN), screened differentially expressed miRNAs (DEMs) using next-generation sequencing (NGS), and confirmed the sequencing data using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Results We identified 20 and 42 DEMs in the CKD1 and CKD5 patients compared with the controls, respectively, and 70 DEMs in the CKD5 compared with the CKD1 patients. The qRT-PCR results showed that miR-483-5p was up-regulated in the CKD1 and CKD5 patients compared with controls (fold change = 2.56 and 18.77, respectively). miR-363-3p was down-regulated in the CKD5 patients compared with the controls and CKD1 patients (fold change = 0.27 and 0.48, respectively). Conclusion We identified a genome-wide serum miRNA expression profile in CKD patients, and serum miR-483-5p and miR-363-3p may act as potential diagnostic biomarkers for CKD.

Vascular calcification such as arteriosclerosis, which is characterized by a calcification of the tunica media, is a severe complication of chronic kidney disease (CKD), contributing to the high prevalence of cardiovascular morbidity and mortality in patients with CKD. An essential step during the development of arteriosclerosis is the transdifferentiation/calcification of vascular smooth muscle cells (VSMCs), resembling osteogenesis. Metabolic acidosis, a common clinical manifestation in CKD, is known to decrease vascular calcification. To understand the underlying regulatory mechanisms of acidosis, we investigated whether the acidosis-decreased VSMC calcification involves altered signaling of the LTCC/Ca2+/Runx2 pathway. Vascular calcifications, calcium content, runt-related transcription factor 2 (Runx2), alkaline phosphatase (ALP), L-type calcium channel (LTCC) β3 subunits, and calcium influx were measured in vivo or in vitro. Calcified nodules and calcium content increased either in aorta sections of vascular calcified rats or in VSMCs induced by β-GP. The expression of Runx2 and ALP activity markedly rose, accompanied by the increasing expression of LTCC β3 subunits and calcium influx. However, acidosis supplementation successfully attenuated VC and VSMC calcification and inhibited Runx2, ALP, LTCC β3 subunits, and calcium influx. In conclusion, acidosis significantly attenuated vascular calcification in association with downregulation of the LTCC/Ca2+/Runx2 pathway.

This study was aimed at determining the effect of microstructure on the macro-mechanical behavior of a composite solid propellant. The microstructure model of a composite solid propellant was generated using molecular dynamics algorithm. The correlation of how microstructural mechanical properties and the effect of initial interface defects in propellant act on the macro-mechanics were studied. Results of this study showed that the mechanical properties of propellant rely heavily on its mesoscopic structure. The grain filling volume fraction mainly influences the propellant initial modulus, the higher the volume fraction, the higher initial modulus. Additionally, it was found that the ratio of particles influences the tensile strength and breaking elongation rate of the propellant. The big particles could also improve the initial modulus of a propellant, but decrease its tensile strength and breaking elongation rate. Furthermore, the initial defects lowered the uniaxial tensile modulus, tensile strength, and the relaxation modulus of propellant, but did not affect the relaxation behavior of the propellant.

This report describes the design and development of an integrated electrochemical cell culture monitoring system, based on enzyme-biosensors and chemical sensors, for monitoring indicators of mammalian cell metabolic status. MEMS technology was used to fabricate a microwell-format silicon platform including a thermometer, onto which chemical sensors (pH, O2) and screen-printed biosensors (glucose, lactate), were grafted/deposited. Microwells were formed over the fabricated sensors to give 5-well sensor strips which were interfaced with a multipotentiostat via a bespoke connector box interface. The operation of each sensor/biosensor type was examined individually, and examples of operating devices in five microwells in parallel, in either potentiometric (pH sensing) or amperometric (glucose biosensing) mode are shown. The performance characteristics of the sensors/biosensors indicate that the system could readily be applied to cell culture/toxicity studies.