On the Stress Transfer of Nanoscale Interlayer with Surface Effects

An improved shear-lag model is proposed to investigate the mechanism through which the surface effect influences the stress transfer of multilayered structures. The surface effect of the interlayer is characterized in terms of interfacial stress and surface elasticity by using Gurtin–Murdoch elasticity theory. Our calculation result shows that the surface effect influences the efficiency of stress transfer. The surface effect is enhanced with decreasing interlayer thickness and elastic modulus. Nonuniform and large residual surface stress distribution amplifies the influence of the surface effect on stress concentration

StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides

Many tools exist for extracting structural and physiochemical descriptors from linear peptides to predict their properties, but similar tools for hydrocarbon-stapled peptides are lacking.Here, we present StaPep, a Python-based toolkit designed for generating 2D/3D structures and calculating 21 distinct features for hydrocarbon-stapled peptides.The current version supports hydrocarbon-stapled peptides containing 2 non-standard amino acids (norleucine and 2-aminoisobutyric acid) and 6 nonnatural anchoring residues (S3, S5, S8, R3, R5 and R8).Then we established a hand-curated dataset of 201 hydrocarbon-stapled peptides and 384 linear peptides with sequence information and experimental membrane permeability, to showcase StaPep's application in artificial intelligence projects.A machine learning-based predictor utilizing above calculated features was developed with AUC of 0.85, for identifying cell-penetrating hydrocarbon-stapled peptides.StaPep's pipeline spans data retrieval, cleaning, structure generation, molecular feature calculation, and machine learning model construction for hydrocarbon-stapled peptides.The source codes and dataset are freely available on Github: https://github.com/dahuilangda/stapep_package.Comment: 26 pages, 6 figure

Screening candidate genes for fruit size based on QTL-seq in Chinese jujube

IntroductionFruit size is an important economic trait affecting jujube fruit quality, which has always been the focus of marker-assisted breeding of jujube traits. However, despite a large number of studies have been carried out, the mechanism and key genes regulating jujube fruit size are mostly unknown.MethodsIn this study, we used a new analysis method Quantitative Trait Loci sequencing (QTL-seq) (bulked segregant analysis) to screen the parents ‘Yuhong’ and ‘Jiaocheng 5’ with significant phenotypic differences and mixed offspring group with extreme traits of large fruit and small fruit, respectively, and, then, DNA mixed pool sequencing was carried out to further shortening the QTL candidate interval for fruit size trait and excavated candidate genes for controlling fruit size.ResultsThe candidate intervals related to jujube fruit size were mainly located on chromosomes 1, 5, and 10, and the frequency of chromosome 1 was the highest. Based on the QTL-seq results, the annotation results of ANNOVAR were extracted from 424 SNPs (single-nucleotide polymorphisms) and 164 InDels (insertion-deletion), from which 40 candidate genes were selected, and 37 annotated candidate genes were found in the jujube genome. Four genes (LOC107428904, LOC107415626, LOC125420708, and LOC107418290) that are associated with fruit size growth and development were identified by functional annotation of the genes in NCBI (National Center for Biotechnology Information). The genes can provide a basis for further exploration and identification on genes regulating jujube fruit size.DiscussionIn summary, the data obtained in this study revealed that QTL intervals and candidate genes for fruit size at the genomic level provide valuable resources for future functional studies and jujube breeding

Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector’s recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model’s ability to capture local regions of interest. CIoU loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1–8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios

Activity-Based Household Travel Survey Through Smartphone Apps in Tennessee

RES 2020-19Activity-based household travel surveys (HTS) are one of primary data sources for many research fields at Tennessee Department of Transportation (TDOT). Traditional HTS methods are often costly, time-consuming, less scalable, and difficult to achieve high quality and accuracy. Recent years have witnessed a fast-growing interest in conducting HTS through smartphone apps to address survey issues and improve quality of collected survey data. A research project on activity based HTS through smartphone apps for both Android and iOS has been performed. The overarching goal of this research project is to develop an effective, economical, scalable HTS solution for TDOT. To achieve this goal, with the guidance and support from TDOT, the research team has 1) developed a smartphone-based effective, scalable, and secure application for household travel surveys that can span from days to months, 2) integrated fine-grained location information in submitted travel data by leveraging smartphone built-in sensor technologies, and 3) validated the developed HTS application by running a pilot HTS with the application. The pilot survey lasted three months. During the survey study, over 800 people downloaded the mobile apps and registered an account. Over 200 participants have been given a reward for completing the survey. Over 1,800 trips were submitted by those rewarded participants. This research project brings the following benefits to TDOT: 1) A tested, comprehensive smartphone app based HTS solution, 2) Important findings about smartphone app based HTS gained from running the pilot survey study, and 3) An anonymized survey dataset for research exploration obtained from the pilot survey study. A number of key findings as well as recommendations are also generated from this research project and they will help TDOT conduct HTS more effectively and generate more research results in the future

Effect of supplementation with yeast polysaccharides on intestinal function in piglets infected with porcine epidemic diarrhea virus

Porcine epidemic diarrhea virus (PEDV) has caused huge economic losses to the pig industry. Yeast polysaccharides (YP) has been used as a feed additive in recent years and poses good anti-inflammatory and antiviral effects. The present study aimed to explore the protective effect of YP on intestinal damage in PEDV-infected piglets. Eighteen 7-day-old piglets with similar body weights were randomly divided into three groups: Control group (basal diet), PEDV group (basal diet), and PEDV+YP group (basal diet +20 mg/kg BW YP), six replicates per group and one pig per replicate. Piglets in PEDV group and PEDV+YP group were orally given PEDV (dose: 1 × 106 TCID50) at 19:30 PM on the 8th day of the experiment. The control group received the same volume of PBS solution. Weight was taken on an empty stomach in the morning of the 11th day, blood was collected and then anesthetic was administered with pentobarbital sodium (50 mg/kg·BW) by intramuscular injection, and samples were slaughtered after the anesthetic was complete. The results showed that YP could alleviate the destruction of intestinal villus morphology of piglets caused by PEDV. Meanwhile, PEDV infection can reduce the activity of glutathione peroxidase, superoxide dismutase and catalase, and increase the content of malondialdehyde. YP can improve the antioxidative capacity in the serum and small intestine of PEDV-infected piglets. In addition, YP inhibited the replication of PEDV in the jejunum ileum and colon. Moreover, YP can regulate the mRNA levels of inflammatory genes (IL-1β and iNOS) and lipid metabolic genes (APOA4 and APOC3) in the small intestine. In summary, YP could inhibit virus replicates, improve intestinal morphology, enhance antioxidant capacity, relieve inflammation and regulate the metabolism of the intestine in PEDV-infected piglets