Germline-Encoded TCR-MHC Contacts Promote TCR V Gene Bias in Umbilical Cord Blood T Cell Repertoire.

T cells recognize antigens as peptides bound to major histocompatibility complex (MHC) proteins through T cell receptors (TCRs) on their surface. To recognize a wide range of pathogens, each individual possesses a substantial number of TCRs with an extremely high degree of variability. It remains controversial whether germline-encoded TCR repertoire is shaped by MHC polymorphism and, if so, what is the preference between MHC genetic variants and TCR V gene compatibility. To investigate the "net" genetic association between MHC variations and TRBV genes, we applied quantitative trait locus (QTL) mapping to test the associations between MHC polymorphism and TCR β chain V (TRBV) genes usage using umbilical cord blood (UCB) samples of 201 Chinese newborns. We found TRBV gene and MHC loci that are predisposed to interact with one another differ from previous conclusions. The majority of MHC amino acid residues associated with the TRBV gene usage show spatial proximities in known structures of TCR-pMHC complexes. These results show for the first time that MHC variants bias TRBV gene usage in UCB of Chinese ancestry and indicate that germline-encoded contacts influence TCR-MHC interactions in intact T cell repertoires

Performance evaluation of deep feature learning for RGB-D image/video classification

Deep Neural Networks for image/video classification have obtained much success in various computer vision applications. Existing deep learning algorithms are widely used on RGB images or video data. Meanwhile, with the development of low-cost RGB-D sensors (such as Microsoft Kinect and Xtion Pro-Live), high-quality RGB-D data can be easily acquired and used to enhance computer vision algorithms [14]. It would be interesting to investigate how deep learning can be employed for extracting and fusing features from RGB-D data. In this paper, after briefly reviewing the basic concepts of RGB-D information and four prevalent deep learning models (i.e., Deep Belief Networks (DBNs), Stacked Denoising Auto-Encoders (SDAE), Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) Neural Networks), we conduct extensive experiments on five popular RGB-D datasets including three image datasets and two video datasets. We then present a detailed analysis about the comparison between the learned feature representations from the four deep learning models. In addition, a few suggestions on how to adjust hyper-parameters for learning deep neural networks are made in this paper. According to the extensive experimental results, we believe that this evaluation will provide insights and a deeper understanding of different deep learning algorithms for RGB-D feature extraction and fusion

A Virtual Reality and Online Learning Immersion Experience Evaluation Model Based on SVM and Wearable Recordings

The increasing development in the field of biosensing technologies makes it feasible to monitor students’ physiological signals in natural learning scenarios. With the rise of mobile learning, educators are attaching greater importance to the learning immersion experience of students, especially with the global background of COVID-19. However, traditional methods, such as questionnaires and scales, to evaluate the learning immersion experience are greatly influenced by individuals’ subjective factors. Herein, our research aims to explore the relationship and mechanism between human physiological recordings and learning immersion experiences to eliminate subjectivity as much as possible. We collected electroencephalogram and photoplethysmographic signals, as well as self-reports on the immersive experience of thirty-seven college students during virtual reality and online learning to form the fundamental feature set. Then, we proposed an evaluation model based on a support vector machine and got a precision accuracy of 89.72%. Our research results provide evidence supporting the possibility of predicting students’ learning immersion experience by their EEGs and PPGs

Asia's Growing Contribution to Obesity Surgery Research: A 40-year Bibliometric Analysis

Bariatric metabolic surgery’s global research interest is growing, particularly in Asia due to its high obesity rates. This study focuses on Asia, especially China, analyzing 3904 publications (1221 from China) from 1980 to 2022. Research output accelerated until the COVID-19 pandemic, driven by economic growth and rising obesity rates. China led contributions from 2010, but Western Asia led when adjusted for population. An intra-regional research collaboration network emerged, driven by geographic proximity and similar economic environments. Keyword analysis highlighted emerging topics like “laparoscopic sleeve gastrectomy” and “non-alcoholic fatty liver disease,” indicating a shift in focus. The study recommends disseminating research in top-tier journals to enhance visibility and impact.</p

Comprehensive Genomic Survey, Structural Classification, and Expression Analysis of WRKY Transcription Factor&nbsp; Family in Rhododendron simsii

(1) Rhododendron is one of the top ten traditional flowers in China, with both high ornamental and economic values. However, with the change of the environment, Rhododendron suffers from various biological stresses. The WRKY transcription factor is a member of the most crucial transcription factor families, which plays an essential regulatory role in a variety of physiological processes and developmental stresses. (2) In this study, 57 RsWRKYs were identified using genome data and found to be randomly distributed on 13 chromosomes. Based on gene structure and phylogenetic relationships, 57 proteins were divided into three groups: I, II, and III. Multiple alignments of RsWRKYs with Arabidopsis thaliana homologous genes revealed that WRKY domains in different groups had different conserved sites. RsWRKYs have a highly conserved domain, WRKYGQK, with three variants, WRKYGKK, WRKYGEK, and WRKYGRK. Furthermore, cis-acting elements analysis revealed that all of the RsWRKYs had stress and plant hormone cis-elements, with figures varying by group. Finally, the expression patterns of nine WRKY genes treated with gibberellin acid (GA), methyl jasmonate (MeJA), heat, and drought in Rhododendron were also measured using quantitative real-time PCR (qRT-PCR). The results showed that the expression levels of the majority of RsWRKY genes changed in response to multiple phytohormones and abiotic stressors. (3) This current study establishes a theoretical basis for future studies on the response of RsWRKY transcription factors to various hormone and abiotic stresses as well as a significant foundation for the breeding of new stress-tolerant Rhododendron varieties

Genome-Wide Identification of Petunia <i>HSF</i> Genes and Potential Function of <i>PhHSF19</i> in Benzenoid/Phenylpropanoid Biosynthesis

Volatile benzenoids/phenylpropanoids are the main flower scent compounds in petunia (Petunia hybrida). Heat shock factors (HSFs), well known as the main regulator of heat stress response, have been found to be involved in the biosynthesis of benzenoid/phenylpropanoid and other secondary metabolites. In order to figure out the potential function of HSFs in the regulation of floral scent in petunia, we systematically identified the genome-wide petunia HSF genes and analyzed their expression and then the interaction between the key petunia HSF gene with target gene involved in benzenoid/phenylpropanoid biosynthesis. The results revealed that 34 HSF gene family members were obtained in petunia, and most petunia HSFs contained one intron. The phylogenetic analysis showed that 23 petunia HSFs were grouped into the largest subfamily HSFA, while only two petunia HSFs were in HSFC subfamily. The DBD domain and NLS motif were well conserved in most petunia HSFs. Most petunia HSF genes’ promoters contained STRE motifs, the highest number of cis-acting element. PhHSF19 is highly expressed in petal tubes, followed by peduncles and petal limbs. During flower development, the expression level of PhHSF19 was dramatically higher at earlier flower opening stages than that at the bud stage, suggesting that PhHSF19 may have potential roles in regulating benzenoid/phenylpropanoid biosynthesis. The expression pattern of PhHSF19 is positively related with PhPAL2, which catalyzes the first committed step in the phenylpropanoid pathway. In addition, there are three STRE elements in the promoter of PhPAL2. PhHSF19 was proven to positively regulate the expression of PhPAL2 according to the yeast one hybrid and dual luciferase assays. These results lay a theoretical foundation for further studies of the regulation of HSFs on plant flower scent biosynthesis

Lipid Droplet Is an Ancient and Inheritable Organelle in Bacteria

Abstract Lipid droplet (LD) is a monolayer phospholipid membrane-bound organelle found in all eukaryotes and several prokaryotes which plays key roles in cellular lipid homeostasis and human health. The origin and evolution of the organelle remains unknown. Here, we report that through screening over 660 bacteria using biophysical and biochemical methods, plus LD isolation and proteomic tool, LDs were identified in most of these microbes, affiliated with five main bacterial phyla. Moreover, LDs were also identified in E. coli overexpressing lipid synthesis enzymes, indicating that bacteria without detectable LDs possessed the ability of LD biogenesis. The similarity of isolated LDs from representative strains and evolutionary analysis of LD major protein PspA demonstrate that LDs were conserved in bacteria. Furthermore, time-lapse imaging revealed that LDs were inheritable accompanying with bacterial growth and division. Finally, a common ancestor of LD-containing bacteria was predicted to originate 3.19 billion years ago by a phylogenetic analysis. Our findings suggest that LD is a widespread and inheritable organelle from an ancient common ancestor

Silica-copper catalyst interfaces enable carbon-carbon coupling towards ethylene electrosynthesis

Membrane electrode assembly (MEA) electrolyzers offer a means to scale up CO2-to-ethylene electroconversion using renewable electricity and close the anthropogenic carbon cycle. To date, excessive CO2 coverage at the catalyst surface with limited active sites in MEA systems interferes with the carbon-carbon coupling reaction, diminishing ethylene production. With the aid of density functional theory calculations and spectroscopic analysis, here we report an oxide modulation strategy in which we introduce silica on Cu to create active Cu-SiOx interface sites, decreasing the formation energies of OCOH* and OCCOH*-key intermediates along the pathway to ethylene formation. We then synthesize the Cu-SiOx catalysts using one-pot coprecipitation and integrate the catalyst in a MEA electrolyzer. By tuning the CO2 concentration, the Cu-SiOx catalyst based MEA electrolyzer shows high ethylene Faradaic efficiencies of up to 65% at high ethylene current densities of up to 215 mA cm-2; and features sustained operation over 50 h.This work has received financial support from the Ontario Research Fund Research-Excellence Program, the Natural Sciences and Engineering Research Council (NSERC) of Canada, the CIFAR Bio-Inspired Solar Energy Program, and the University of Toronto Connaught grant. This research used synchrotron resources of the Advanced Photon Source (APS), an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Argonne National Laboratory, and was supported by the U.S. DOE under Contract No. DE-AC02-06CH11357, and the Canadian Light Source and its funding partners. This research also used infrastructure provided by the Canada Foundation for Innovation and the Ontario Research Fund. We thank Dr. T.P. Wu, Dr. Y.Z. Finfrock and Dr. L. Ma for technical support at 9BM beamline of APS. D.S. acknowledges the NSERC E.W.R Steacie Memorial Fellowship. J.L. acknowledges the Banting Postdoctoral Fellowships program. DFT calculations were performed on the Massachusetts Green High Performance Computing Center (MGHPCC). The authors also acknowledge the Texas Advanced Computing Center (TACC) at the University of Texas at Austin for partially providing HPC resources that have contributed to the research results reported within this paper. Our thanks also goes to institutional faculty start-up funds from University of Massachusetts Lowell

Hydroxide promotes carbon dioxide electroreduction to ethanol on copper via tuning of adsorbed hydrogen

Producing liquid fuels such as ethanol from CO2, H2O, and renewable electricity offers a route to store sustainable energy. The search for efficient electrocatalysts for the CO2 reduction reaction relies on tuning the adsorption strength of carbonaceous intermediates. Here, we report a complementary approach in which we utilize hydroxide and oxide doping of a catalyst surface to tune the adsorbed hydrogen on Cu. Density functional theory studies indicate that this doping accelerates water dissociation and changes the hydrogen adsorption energy on Cu. We synthesize and investigate a suite of metal-hydroxide-interface-doped-Cu catalysts, and find that the most efficient, Ce(OH)x-doped-Cu, exhibits an ethanol Faradaic efficiency of 43% and a partial current density of 128 mA cm-2. Mechanistic studies, wherein we combine investigation of hydrogen evolution performance with the results of operando Raman spectroscopy, show that adsorbed hydrogen hydrogenates surface *HCCOH, a key intermediate whose fate determines branching to ethanol versus ethylene.The authors acknowledge funding supporting from Suncor Energy, the Ontario Research Fund and the Natural Sciences and Engineering Research Council (NSERC). All DFT calculations were performed on the IBM BlueGene/Q supercomputer with support from the Southern Ontario Smart Computing Innovation Platform (SOSCIP) and Niagara supercomputer at the SciNet HPC Consortium. SOSCIP is funded by the Federal Economic Development Agency of Southern Ontario, the Province of Ontario, IBM Canada Ltd., Ontario Centres of Excellence, Mitacs, and 15 Ontario academic member institutions. SciNet is funded by the Canada Foundation for Innovation, the Government of Ontario, Ontario Research Fund – Research Excellence, and the University of Toronto.This research used synchrotron resources of the Advanced Photon Source (APS), an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Argonne National Laboratory, and was supported by the U.S. DOE under Contract No. DE-AC02-06CH11357, and the Canadian Light Source and its funding partners

Dopant-tuned stabilization of intermediates promotes electrosynthesis of valuable C3 products

The upgrading of CO2/CO feedstocks to higher-value chemicals via energy-efficient electrochemical processes enables carbon utilization and renewable energy storage. Substantial progress has been made to improve performance at the cathodic side; whereas less progress has been made on improving anodic electro-oxidation reactions to generate value. Here we report the efficient electroproduction of value-added multi-carbon dimethyl carbonate (DMC) from CO and methanol via oxidative carbonylation. We find that, compared to pure palladium controls, boron-doped palladium (Pd-B) tunes the binding strength of intermediates along this reaction pathway and favors DMC formation. We implement this doping strategy and report the selective electrosynthesis of DMC experimentally. We achieve a DMC Faradaic efficiency of 83 ± 5%, fully a 3x increase in performance compared to the corresponding pure Pd electrocatalyst.This work was supported by the Ontario Research Fund Research-Excellence Program, the Natural Sciences and Engineering Research Council (NSERC) of Canada, and the CIFAR Bio-Inspired Solar Energy program