Search

Incorporating conductive fillers into the fabric structure as the strain sensor for wearable smart devices has attracted considerable attention recently. However, the cooperative work between textiles and conductive materials is still a huge challenge, with particular reference to the development of stable strain sensors based on rGO-decorated polyester fabrics with the superb balance between tunable sensitivity and subtle- and large-scale workable strain ranges. In this study, a proposed strain sensor with the laminated structure containing a co-conductive network comprising reduced graphene oxide (rGO)@ polydopamine (PDA) and silver nanoparticles (AgNPs)@carboxylated carbon nanotubes (CNTs-COOH) composite layers was fabricated via a facile layered generating technique. Interestingly, rGO and AgNPs@CNTs-COOH were successfully grown onto the PDA-templated poly (ethylene terephthalate) (PET)/polyurethane (PU) fabric (PPPF) respectively. Furthermore, it demonstrated impressive performances in terms of tunable sensitivity (0–549.78), suitable stability under various strain ranges, superb electrical conductivity, wonderful durability over 2000 cycles, broad workable strain range (0–240%), and remarkable electrical heating property. Importantly, the proposed strain sensor was investigated to be used for human motion detection including tiny human movements and robust body motions, as well as temperature detection, implying its further favorable application in flexible wearable devices.

Privacy protection in vehicular ad hoc networks (VANETs) has always been a research hotspot, especially the issue of vehicle authentication, which is critical to ensure the safe communication of vehicles. However, using the real identity in the process of authentication can easily result in a leak of the privacy information of the vehicles. Therefore, most existing privacy-protection schemes use anonymous authentication and require one-to-one communication between vehicles and the trusted authority (TA). However, when the number of vehicles is too large, network congestion can take place. In addition, the process of updating the anonymous by the TA or the vehicle itself, can result in both poor real-time performance and leakage of the system master key. To solve these problems, this study proposes a fog-computing-based anonymous-authentication scheme for VANETs; the scheme reduces the communication burden of the TA by enabling self-authentication between vehicles and road-side units (RSUs), thus improving the vehicle-authentication efficiency. For updating the anonymous, we design a fog-computing-based pseudonym-updating and -tracking strategy, which guarantees real-time communication and reduces the instances of re-authentication interactions for legitimate vehicles. The experimental results show that the scheme not only meets the privacy-protection requirements of VANETs but also offers better performance than that of the existing anonymous-authentication schemes.

The medical polypropylene monofilament with a diameter of 0.10 mm was used as the material. Four different pull densities and two different warp run-ins were set up on the electronic traverse high-speed Tricot warp knitting machine, with the gauge of E28. The raw material was used to knit four variations of single bar plain knitted fabrics with 1 in-1 miss setting. Each variation required eight samples. The mechanical properties of the above 32 warp-knitted fabric samples are tested, including their tensile stress (in both vertical and horizontal directions), tearing stress (in both vertical and horizontal directions) and bursting stress. The results obtained shows that the relationship between the vertical, the horizontal stress, and the pull density are not monotonic. The tensile stress in the vertical direction firstly decreases and then increases with an increase of the pull density; however, the tensile stress in the horizontal direction firstly increases and then slightly decreases with an increase of the pull density; again the vertical tensile stress of all fabrics was always higher than the horizontal tensile stress. The bursting stress has a positive linear relation to the pull density. The vertical tearing stresses of four samples were greater than the horizontal tearing stress.

Abstract Rheum pumilum stands as both a quintessential alpine plant and a significant traditional Chinese and Tibetan medicinal herb. Unraveling the molecular intricacies of seed germination in Rh. pumilum not only unveils the genetic foundations of plant seed germination strategies in high-altitude environments but also offers insights for cultivating Rh. pumilum medicinal materials. Employing transcriptome sequencing and the Weighted Gene Co-expression Network Analysis, this study delved into the shifts in gene expression levels across various stages of seed germination in Rh. pumilum. The process of seed germination in Rh. pumilum entails a cascade of complex physiological events. Six hormones (ABA, IAA, ETH, GA, BR, CK) emerged as pivotal players in seeds breaking in shells and the facilitation of rapid seed germination in Rh. pumilum. Fourteen transcription factor families (LOB, GRAS, B3, bHLH, bZIP, EIL, MYB, MYB related, NAC, TCP, WRKY, HSF, PLATZ, and SBP) along with four key genes (E2.4.1.13, EIN3, BZR, and BIN2) were identified that may be associated with both biotic and abiotic environmental stress. The ETR, ACACA and ATPeV0C genes were linked with energy accumulation during the initial stages of seed germination, CYP707A may play an important role in breaking seed dormancy, while the BRI1 gene may be correlated with swift seed germination. Additionally, several unidentified genes were recognized to play key roles in seed germination of Rh. pumilum, warranting further investigation. Moreover, Rh. pumilum demonstrates full activation of crucial physiological functions such as energy metabolism, signal transduction, and responses to biological and abiotic stresses during the seed breaking in shells. This study provides molecular evidence elucidating the swift seed germination strategies adopted by alpine plants to thrive in high-altitude environments. Furthermore, it serves as a foundational reference for enhancing seed germination rates and breeding practices to promote the sustainable development of Rh. pumilum medicinal materials.

Summary: Hepatocellular carcinoma (HCC) is a prevalent and aggressive cancer that presents significant challenges for early detection. This study introduces the GlyExo-Capture method for isolating fucosylated extracellular vesicles (Fu-EVs) from serum. We analyze microRNA (miRNA) profiles from Fu-EVs in 88 HCC patients and 179 non-HCC controls using next-generation sequencing (NGS) and identify five miRNAs (hsa-let-7a, hsa-miR-21, hsa-miR-125a, hsa-miR-200a, and hsa-miR-150) as biomarkers for HCC diagnosis. The five-miRNA panel demonstrates exceptional HCC diagnostic performance, with a sensitivity of 0.90 and specificity of 0.92 in a combined cohort of 194 HCC and 412 non-HCC controls, significantly surpassing the performance of alpha-fetoprotein (AFP) and des-gamma-carboxy prothrombin (DCP). Notably, the miRNA model achieves recall rates of 85.7% and 90.8% for stage 0 and stage A early-stage HCC, respectively, identifies 88.1% of AFP-negative HCC cases, and effectively differentiates HCC from other cancers. This study provides a high-throughput, rapid, and non-invasive approach for early HCC detection.

Amidst the global antimicrobial resistance (AMR) crisis, antibiotic resistance has permeated even the most remote environments. To understand the dissemination and evolution of AMR in minimally impacted ecosystems, the resistome and mobilome of wetlands across the Qinghai-Tibetan Plateau and its marginal regions were scrutinized using metagenomic sequencing techniques. The composition of wetland microbiomes exhibits significant variability, with dominant phyla including Proteobacteria, Actinobacteria, Bacteroidetes, and Verrucomicrobia. Notably, a substantial abundance of Antibiotic Resistance Genes (ARGs) and Mobile Genetic Elements (MGEs) was detected, encompassing 17 ARG types, 132 ARG subtypes, and 5 types of MGEs (Insertion Sequences, Insertions Sequences, Genomic Islands, Transposons, and Integrative Conjugative Elements). No significant variance was observed in the prevalence of resistome and mobilome across different wetland types (i.e., the Yellow River, other rivers, lakes, and marshes) (R=-0.5882, P=0.607). The co-occurrence of 74 ARG subtypes and 22 MGEs was identified, underscoring the pivotal role of MGEs in shaping ARG pools within the Qinghai-Tibetan Plateau wetlands. Metagenomic binning and analysis of assembled genomes (MAGs) revealed that 93 out of 206 MAGs harbored ARGs (45.15 %). Predominantly, Burkholderiales, Pseudomonadales, and Enterobacterales were identified as the primary hosts of these ARGs, many of which represent novel species. Notably, a substantial proportion of ARG-carrying MAGs also contained MGEs, reaffirming the significance of MGEs in AMR dissemination. Furthermore, utilizing the arg_ranker framework for risk assessment unveiled severe contamination of high-risk ARGs across most plateau wetlands. Moreover, some prevalent human pathogens were identified as potential hosts for these high-risk ARGs, posing substantial transmission risks. This study aims to investigate the prevalence of resistome and mobilome in wetlands, along with evaluating the risk posed by high-risk ARGs. Such insights are crucial for informing environmental protection strategies and facilitating the management of water resources on the Qinghai-Tibetan Plateau.

Ten SSR markers based on transcriptome sequencing were employed to genotype 231 samples of G. littoralis subsp. littoralis (Apiaceae) from nine cultivated populations and seven wild populations, aiming to assess the genetic diversity and genetic structure, and elucidate the origin of the cultivated populations. Cultivated populations exhibited relatively high genetic diversity (h = 0.441, I = 0.877), slightly lower than that of their wild counterparts (h = 0.491, I = 0.930), likely due to recent domestication and ongoing gene flow between wild and cultivated germplasm. The primary cultivated population in Shandong have the crucial genetic status. A single origin of domestication was inferred through multiple analysis, and wild populations from Liaoning and Shandong are inferred to be potentially the ancestor source for the present cultivated populations. Phenotypic analysis revealed a relatively high heritability of root length across three growth periods (0.683, 0.284, 0.402), with significant correlations observed between root length and petiole length (Pearson correlation coefficient = 0.30, P

Although MALDI-TOF mass spectrometry (MS) is widely known as a rapid and cost-effective reference method for identifying microorganisms, its commercial databases face limitations in accurately distinguishing specific subspecies of Bifidobacterium. This study aimed to explore the potential of MALDI-TOF MS protein profiles, coupled with prediction methods, to differentiate between Bifidobacterium longum subsp. infantis (B. infantis) and Bifidobacterium longum subsp. longum (B. longum). The investigation involved the analysis of mass spectra of 59 B. longum strains and 41 B. infantis strains, leading to the identification of five distinct biomarker peaks, specifically at m/z 2,929, 4,408, 5,381, 5,394, and 8,817, using Recurrent Feature Elimination (RFE). To facilate classification between B. longum and B. infantis based on the mass spectra, machine learning models were developed, employing algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM). The evaluation of the mass spectrometry data showed that the RF model exhibited the highest performace, boasting an impressive AUC of 0.984. This model outperformed other algorithms in terms of accuracy and sensitivity. Furthermore, when employing a voting mechanism on multi-mass spectrometry data for strain identificaton, the RF model achieved the highest accuracy of 96.67%. The outcomes of this research hold the significant potential for commercial applications, enabling the rapid and precise discrimination of B. longum and B. infantis using MALDI-TOF MS in conjunction with machine learning. Additionally, the approach proposed in this study carries substantial implications across various industries, such as probiotics and pharmaceuticals, where the precise differentiation of specific subspecies is essential for product development and quality control.

AbstractAlthough collaborative decision-making with multiple stakeholders in supply chain has become an important research issue in recent years, current research on the mechanism/process, methods, and performance assessment of collaborative decision-making still lags behind the practical needs in supply chain management. This paper reviews the existing literature in the past decade on collaborative supply chain from the perspective of decision-making in four stages with a causal framework. This paper reveals a general retrospect of the main subjects developed and investigated with related applications of cases, which includes both theoretical assumptions and practical data. The findings indicate that most articles in this field are based on a direct relationship between collaboration and performance improvement but lack of a mechanism between them. Additionally, dynamic collaboration with members in the whole supply chain both horizontally and vertically has not been conducted by previous studies. This paper provides a new conceptual framework and future research directions for further study in supply chain collaboration.

Because of softness and lightness, various flexible sensors have attracted extensive attention and been widely studied. Sensing mechanism of most wearable sensors is derived from an elastic substrate, such as fabric or polymer materials. Although the mechanical-electrical performance of several flexible sensors has been reported, including sensitivity, linearity hysteresis and repeatability, research on the effects of substrate elasticity on sensor capacity is scarce. In this paper, the impact of spandex content, washing and ironing processing on the elasticity of weft knitted sensors was investigated by the constant-extension test method. Afterwards, differences in sensing properties between diverse elastic sensors under single as well as repeated stretch were reported. The experimental results showed that spandex content does influence the elasticity of knitted fabric, which has a further great effect on sensing properties. A highly elastic sensor is capable of detecting large-scale human motions, while sensors with lower elasticity are opposite, which demonstrates that elastic sensors can be designed and chosen to meet the requirements of detecting and monitoring distinct human motions. [ABSTRACT FROM AUTHOR]

Advanced triboelectric nanogenerator techniques provide a massive opportunity for the development of new generation wearable electronics, which toward multi-function and self-powering. Textiles have been refreshed with the requirement of flexible electronics in recent decades. In particular, knitted-textiles have exhibited enormous and prominent potential possibilities for smart wearable devices, which are based on the merits of high stretchability, excellent elasticity, comfortability as well as compatibility. Combined knitted textiles with nanogenerator techniques will promote the knitted textile triboelectric nanogenerators (KNGs) emerging, endowing conventional textiles with biomechanical energy harvesting and sensing energy supplied abilities. However, the design of KNGs and the construction of KNGs are based on features of human motions symbolizing considerable challenges in both high efficiency and excellent comfort. Currently, this review is concerned with KNGs construction account of triboelectric effects referring to knittedtextile classifications, structural features, human motion energy traits, working mechanisms, and practical applications. Moreover, the remaining challenges of industrial production and the future prospects of knitted-textile triboelectric nanogenerators of harvesting biomechanical energy are presented. [ABSTRACT FROM AUTHOR]

Rhizosphere microorganisms play a key role in affecting plant quality and productivity through its interaction with plant root system. To figure out the bottleneck of the decline of yield and quality in the traditional Chinese medicinal herbs Glehnia littoralis they now encounter, it is important to study the dynamics of rhizosphere microbiota during the cultivation of G. littoralis. In the present study, the composition, diversity and function of rhizosphere microbes at different development stages of G. littoralis, as well as the correlation between rhizosphere microbes and environmental factors were systematically studied by high-throughput sequencing. There were significant differences between the rhizosphere microbes at early and middle-late development stages. More beneficial bacteria, such as Proteobacteria, and more symbiotic and saprophytic fungi were observed at the middle-late development stage of G. littoralis, while beneficial bacteria such as Actinobacteria and polytrophic transitional fungi were abundant at all development stages. The results of redundancy analysis show that eight environmental factors drive the changes of microflora at different development stages. pH, soil organic matter (SOM) and available phosphorus (AP) had important positive effects on the bacterial and fungal communities at the early development stage; saccharase (SC) and nitrate nitrogen (NN) showed significant positive effects on the bacterial and fungal communities at the middle and late stages; while urease (UE), available potassium (AK), and alkaline phosphatase (AKP) have different effects on bacterial and fungal communities at different development stages. Random forest analysis identified 47 bacterial markers and 22 fungal markers that could be used to distinguish G. littoralis at different development stages. Network analysis showed that the rhizosphere microbes formed a complex mutualistic symbiosis network, which is beneficial to the growth and development of G. littoralis. These results suggest that host development stage and environmental factors have profound influence on the composition, diversity, community structure and function of plant rhizosphere microorganisms. This study provides a reference for optimizing the cultivation of G. littoralis.

Abstract Background F-box proteins represent a diverse class of adaptor proteins of the ubiquitin-proteasome system (UPS) that play critical roles in the cell cycle, signal transduction, and immune response by removing or modifying cellular regulators. Among closely related organisms of the Caenorhabditis genus, remarkable divergence in F-box gene copy numbers was caused by sizeable species-specific expansion and contraction. Although F-box gene number expansion plays a vital role in shaping genomic diversity, little is known about molecular evolutionary mechanisms responsible for substantial differences in gene number of F-box genes and their functional diversification in Caenorhabditis. Here, we performed a comprehensive evolution and underlying mechanism analysis of F-box genes in five species of Caenorhabditis genus, including C. brenneri, C. briggsae, C. elegans, C. japonica, and C. remanei. Results Herein, we identified and characterized 594, 192, 377, 39, 1426 F-box homologs encoding putative F-box proteins in the genome of C. brenneri, C. briggsae, C. elegans, C. japonica, and C. remanei, respectively. Our work suggested that extensive species-specific tandem duplication followed by a small amount of gene loss was the primary mechanism responsible for F-box gene number divergence in Caenorhabditis genus. After F-box gene duplication events occurred, multiple mechanisms have contributed to gene structure divergence, including exon/intron gain/loss, exonization/pseudoexonization, exon/intron boundaries alteration, exon splits, and intron elongation by tandem repeats. Based on high-throughput RNA sequencing data analysis, we proposed that F-box gene functions have diversified by sub-functionalization through highly divergent stage-specific expression patterns in Caenorhabditis species. Conclusions Massive species-specific tandem duplications and occasional gene loss drove the rapid evolution of the F-box gene family in Caenorhabditis, leading to complex gene structural variation and diversified functions affecting growth and development within and among Caenorhabditis species. In summary, our findings outline the evolution of F-box genes in the Caenorhabditis genome and lay the foundation for future functional studies.

Abstract Background Genomic islands (GIs) play an important role in the chromosome diversity of Enterococcus. In the current study, we aimed to investigate the spread of GIs between Enterococcus strains and their correlation with antibiotic resistance genes (ARGs). Bitsliced Genomic Signature Indexes (BIGSI) were used to screen the NCBI Sequence Read Archive (SRA) for multiple resistant Enterococcus. A total of 37 pairs of raw reads were screened from 457,000 whole-genome sequences (WGS) in the SRA database, which come from 37 Enterococci distributed in eight countries. These raw reads were assembled for the prediction and analysis of GIs, ARGs, plasmids and prophages. Results The results showed that GIs were universal in Enterococcus, with an average of 3.2 GIs in each strain. Network analysis showed that frequent genetic information exchanges mediated by GIs occurred between Enterococcus strains. Seven antibiotic-resistant genomic islands (ARGIs) were found to carry one to three ARGs, mdtG, tetM, dfrG, lnuG, and fexA, in six strains. These ARGIs were involved in the spread of antibiotic resistance in 45.9% of the 37 strains, although there was no significant positive correlation between the frequency of GI exchanges and the number of ARGs each strain harboured (r = 0. 287, p = 0.085). After comprehensively analysing the genome data, we found that partial GIs were associated with multiple mobile genetic elements (transposons, integrons, prophages and plasmids) and had potential natural transformation characteristics. Conclusions All of these results based on genomic sequencing suggest that GIs might mediate the acquisition of some ARGs and might be involved in the high genome plasticity of Enterococcus through transformation, transduction and conjugation, thus providing a fitness advantage for Enterococcus hosts under complex environmental factors.

Network security has become a growing concern within the popularity and development of the Internet. Malicious code is one of the main threats to network security. Different types of malicious code have different functions and cause different harms. Therefore, improving the detection efficiency and recognition accuracy of malicious code is becoming an urgent problem to be solved. While traditional machine learning methods for malicious code detection largely depend on hand-designed features with experts’ knowledge of the domain or focus on the images which come from malicious code binary files. These methods spend too much time on feature extraction. With the emergence of a large amount of malicious code data, the efficiency of traditional machine learning algorithms is getting worse and worse. In this paper, a workflow based on deep learning is proposed to detect and classify malicious codes. This workflow adopts a convolutional neural network (CNN) and the regularization algorithms to classify malicious code with N_gram semantic feature as input of the model. The convolutional neural network can automatically extract the features of malicious code while avoiding the need for manual feature selection. Regularization algorithms not only speed up the training process of the deep model but also improve the generalization ability in the case of effective prevention of over-fitting of the model. The proposed method is compared with the state-of-the-art methods and other deep learning models. Experimental results show that our workflow can improve the accuracy and efficiency of malicious code classification.

Since the outbreak of SARS-CoV-2 in 2019, the Chinese horseshoe bats were considered as a potential original host of SARS-CoV-2. In addition, cats, tigers, lions, mints, and ferrets were naturally or experimentally infected with SARS-CoV-2. For the surveillance and control of this highly infectious disease, it is critical to trace susceptible animals and predict the consequence of potential mutations at the binding region of viral spike protein and host ACE2 protein. This study proposed a novel bioinformatics framework to systematically trace susceptible animals to SARS-CoV-2 and predict the binding affinity between susceptible animals’ mutated/un-mutated ACE2 receptors. As a result, we identified a few animals posing a potential risk of infection with SARS-CoV-2 using the docking analysis of ACE2 protein and viral spike protein. The binding affinity of some of these species is weaker than that of humans but more potent than that of Chinese horseshoe bats. We also found that a few point mutations in human ACE2 protein or viral spike protein could significantly enhance their binding affinity, posing an enormous potential threat to public health. The ancestors of the Omicron may evolve rapidly through the accumulation of mutations in infecting the host and jumped into human beings. These findings indicate that if the epidemic expands, there may be a human-animal-human transmission route, which will increase the difficulty of disease prevention and control.

COVID-19 is spreading widely, and the pandemic is seriously threatening public health throughout the world. A comprehensive study on the optimal sampling types and timing for an efficient SARS-CoV-2 test has not been reported. We collected clinical information and the values of 55 biochemical indices for 237 COVID-19 patients, with 37 matched non-COVID-19 pneumonia patients and 131 healthy people in Inner Mongolia as control. In addition, the results of dynamic detection of SARS-CoV-2 using oropharynx swab, pharynx swab, and feces were collected from 197 COVID-19 patients. SARS-CoV-2 RNA positive in feces specimen was present in approximately one-third of COVID-19 patients. The positive detection rate of SARS-CoV-2 RNA in feces was significantly higher than both in the oropharynx and nasopharynx swab (P < 0.05) in the late period of the disease, which is not the case in the early period of the disease. There were statistically significant differences in the levels of blood LDH, CRP, platelet count, neutrophilic granulocyte count, white blood cell number, and lymphocyte count between COVID-19 and non-COVID-19 pneumonia patients. Finally, we developed and compared five machine-learning models to predict the prognosis of COVID-19 patients based on biochemical indices at disease onset and demographic characteristics. The best model achieved an area under the curve of 0.853 in the 10-fold cross-validation.

Certain biological mechanisms of Glehnia littoralis, an endangered medicinal plant in China, may be underlying its endangered status and this has attracted extensive attention from researchers. In this study, nuclear ribosomal DNA internal transcribed spacer (nrDNA ITS) sequences and random amplified polymorphic DNA (RAPD) were investigated to determine the genetic diversity of G. littoralis. A total of 291 samples from 11 G. littoralis populations were collected from the east coast of China. The high genetic diversity and large genetic differentiation within populations were reconfirmed by both ITS (Ht = 0.576, Hs = 0.512, Gst = 0.1526) and RAPD analyses (Ht = 0.51, Hs = 0.35, Gst = 0.31). The phylogenetic trees showed consistent topologies based on ITS and RAPD; all populations were divided into two clades. Mantel tests (ITS: r = 0.3296, p = 0.067; RAPD: r = 0.036, p = 0.418) showed no significant positive correlation between the geographical distance and genetic distance for G. littoralis populations. These results showed that G. littoralis has great evolutionary potential and its status as endangered might be related to special dispersal and germination mechanisms of its seeds, as well as habitat destruction. In conclusion, we suggest that the conservation strategy for G. littoralis should be based on the protection of all the wild populations to the extent possible.

Objectives: COVID-19 is highly infectious and has been widely spread worldwide, with more than 159 million confirmed cases and more than 3 million deaths as of May 11, 2021. It has become a serious public health event threatening people’s lives and safety. Due to the rapid transmission and long incubation period, shortage of medical resources would easily occur in the short term of discovering disease cases. Therefore, we aimed to construct an artificial intelligent framework to rapidly distinguish patients with COVID-19 from common pneumonia and non-pneumonia populations based on computed tomography (CT) images. Furthermore, we explored artificial intelligence (AI) algorithms to integrate CT features and laboratory findings on admission to predict the clinical classification of COVID-19. This will ease the burden of doctors in this emergency period and aid them to perform timely and appropriate treatment on patients.Methods: We collected all CT images and clinical data of novel coronavirus pneumonia cases in Inner Mongolia, including domestic cases and those imported from abroad; then, three models based on transfer learning to distinguish COVID-19 from other pneumonia and non-pneumonia population were developed. In addition, CT features and laboratory findings on admission were combined to predict clinical types of COVID-19 using AI algorithms. Lastly, Spearman’s correlation test was applied to study correlations of CT characteristics and laboratory findings.Results: Among three models to distinguish COVID-19 based on CT, vgg19 showed excellent diagnostic performance, with area under the curve (AUC) of the receiver operating characteristic (ROC) curve at 95%. Together with laboratory findings, we were able to predict clinical types of COVID-19 with AUC of the ROC curve at 90%. Furthermore, biochemical markers, such as C-reactive protein (CRP), LYM, and lactic dehydrogenase (LDH) were identified and correlated with CT features.Conclusion: We developed an AI model to identify patients who were positive for COVID-19 according to the results of the first CT examination after admission and predict the progression combined with laboratory findings. In addition, we obtained important clinical characteristics that correlated with the CT image features. Together, our AI system could rapidly diagnose COVID-19 and predict clinical types to assist clinicians perform appropriate clinical management.

Summary: COVID-19 has spread globally to over 200 countries with more than 40 million confirmed cases and one million deaths as of November 1, 2020. The SARS-CoV-2 virus, leading to COVID-19, shows extremely high rates of infectivity and replication, and can result in pneumonia, acute respiratory distress, or even mortality. SARS-CoV-2 has been found to continue to rapidly evolve, with several genomic variants emerging in different regions throughout the world. In addition, despite intensive study of the spike protein, its origin, and molecular mechanisms in mediating host invasion are still only partially resolved. Finally, the repertoire of drugs for COVID-19 treatment is still limited, with several candidates still under clinical trial and no effective therapeutic yet reported. Although vaccines based on either DNA/mRNA or protein have been deployed, their efficacy against emerging variants requires ongoing study, with multivalent vaccines supplanting the first-generation vaccines due to their low efficacy against new strains. Here, we provide a systematic review of studies on the epidemiology, immunological pathogenesis, molecular mechanisms, and structural biology, as well as approaches for drug or vaccine development for SARS-CoV-2. Public summary: • Clinical manifestations and epidemiology of COVID-19 • The efficacy of the developed vaccine against SARS-CoV-2 • Phylogenetic tree of evolutionary relationships in current SARS-CoV-2 strains • Structural analysis and origin tracking of SARS-CoV-2 • Mechanism involved in infection and immunological pathogenesis of SARS-CoV-2

Genetic diversity studies of endangered plants help to understand the endangering causes and evolutionary history of species. To provide reliable data on the protection of the endangered species Glehnia littoralis, we assessed its genetic diversity and genetic structure based on amplified fragment length polymorphism (AFLP). A total of 127 individuals from 9 wild populations were detected by 10 pairs of AFLP primers. The results showed high levels of genetic diversity in G. littoralis. Ten primer combinations produced a total of 1929 DNA bands with a polymorphic frequency of 100%. The overall gene diversity (Ht) was 0.182, average gene diversity within populations (Hw) was 0.168, Nei’s genetic diversity (Hj) of different populations ranged from 0.142 (Zh15) to 0.1908 (Gld15), and Shannon’s information index (I) was 0.238. The analysis of molecular variance (AMOVA) revealed that the variation within populations accounted for 84.2% of the total variation, indicating the differentiation mainly existed within populations. The gene flow (Nm) among populations was 2.665, revealing more gene communication between populations. The UPGMA cluster analysis, the principal coordinate analysis (PCoA), and the result of mantel test (r = −0.0898, p = 0.259) all showed that there was no obvious correlation between genetic distance and geographical distribution. In conclusion, we speculate that the endangered status of this species could be attributed to the destruction of wildlife habitats rather than to the loss of genetic diversity. Therefore, we suggest strengthening the germplasm resources conservation and doing ecological conservation and restoration work to protect the wild populations of G. littoralis.

Single-cell sequencing technology can not only view the heterogeneity of cells from a molecular perspective, but also discover new cell types. Although there are many effective methods on dropout imputation, cell clustering, and lineage reconstruction based on single cell RNA sequencing (RNA-seq) data, there is no systemic pipeline on how to compare two single cell clusters at the molecular level. In the study, we present a novel pipeline on comparing two single cell clusters, including calling differential gene expression, coexpression network modules, and so on. The pipeline could reveal mechanisms behind the biological difference between cell clusters and cell types, and identify cell type specific molecular mechanisms. We applied the pipeline to two famous single-cell databases, Usoskin from mouse brain and Xin from human pancreas, which contained 622 and 1,600 cells, respectively, both of which were composed of four types of cells. As a result, we identified many significant differential genes, differential gene coexpression and network modules among the cell clusters, which confirmed that different cell clusters might perform different functions.

Diabetes-related diseases (DRDs), especially cancers pose a big threat to public health. Although people have explored pathological pathways of a few common DRDs, there is a lack of systematic studies on important biological processes (BPs) connecting diabetes and its related diseases/cancers. We have proposed and compared 10 protein–protein interaction (PPI)-based computational methods to study the connections between diabetes and 254 diseases, among which a method called DIconnectivity_eDMN performs the best in the sense that it infers a disease rank (according to its relation with diabetes) most consistent with that by literature mining. DIconnectivity_eDMN takes diabetes-related genes, other disease-related genes, a PPI network, and genes in BPs as input. It first maps genes in a BP into the PPI network to construct a BP-related subnetwork, which is expanded (in the whole PPI network) by a random walk with restart (RWR) process to generate a so-called expanded modularized network (eMN). Since the numbers of known disease genes are not high, an RWR process is also performed to generate an expanded disease-related gene list. For each eMN and disease, the expanded diabetes-related genes and disease-related genes are mapped onto the eMN. The association between diabetes and the disease is measured by the reachability of their genes on all eMNs, in which the reachability is estimated by a method similar to the Kolmogorov–Smirnov (KS) test. DIconnectivity_eDMN achieves an area under receiver operating characteristic curve (AUC) of 0.71 for predicting both Type 1 DRDs and Type 2 DRDs. In addition, DIconnectivity_eDMN reveals important BPs connecting diabetes and DRDs. For example, “respiratory system development” and “regulation of mRNA metabolic process” are critical in associating Type 1 diabetes (T1D) and many Type 1 DRDs. It is also found that the average proportion of diabetes-related genes interacting with DRDs is higher than that of non-DRDs.

The fibre fatigue fibrillation and even fracture of knitted wool fabric under the rubbing force have been studied using scanning electron microscopy. The fatigue fracture sections, involved in the process of abrasion, show that loop hairs predominantly participate in fuzzing and the free ends exist in either pills formation or wearing-off. The major form of failure consists of the bend fatigue which is about 70 - 80% of the total failure and cracks. Fibrils occur in torsional fatigue (10 - 18%) and fibrillation head-ends comprise both bending and twisting. [ABSTRACT FROM AUTHOR]