Photoconductivity of Single-crystalline Selenium Nanotubes

Photoconductivity of single-crystalline selenium nanotubes (SCSNT) under a range of illumination intensities of a 633nm laser is carried out with a novel two terminal device arrangement at room temperature. It's found that SCSNT forms Schottky barriers with the W and Au contacts, and the barrier height is a function of the light intensities. In low illumination regime below 1.46x10E-4 muWmum-2, the Au-Se-W hybrid structure exhibits sharp switch on/off behavior, and the turn-on voltages decrease with increasing illuminating intensities. In the high illumination regime above 7x10E-4 muWmum-2, the device exhibits ohmic conductance with a photoconductivity as high as 0.59Ohmcm-1, significantly higher that reported values for carbon and GaN nanotubes. This finding suggests that SCSNT is potentially a good photo-sensor material as well we a very effective solar cell material.Comment: 12pages including 5 figures, submitted to Nanotechnolog

Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition

This paper presents Ske2Grid, a new representation learning framework for improved skeleton-based action recognition. In Ske2Grid, we define a regular convolution operation upon a novel grid representation of human skeleton, which is a compact image-like grid patch constructed and learned through three novel designs. Specifically, we propose a graph-node index transform (GIT) to construct a regular grid patch through assigning the nodes in the skeleton graph one by one to the desired grid cells. To ensure that GIT is a bijection and enrich the expressiveness of the grid representation, an up-sampling transform (UPT) is learned to interpolate the skeleton graph nodes for filling the grid patch to the full. To resolve the problem when the one-step UPT is aggressive and further exploit the representation capability of the grid patch with increasing spatial size, a progressive learning strategy (PLS) is proposed which decouples the UPT into multiple steps and aligns them to multiple paired GITs through a compact cascaded design learned progressively. We construct networks upon prevailing graph convolution networks and conduct experiments on six mainstream skeleton-based action recognition datasets. Experiments show that our Ske2Grid significantly outperforms existing GCN-based solutions under different benchmark settings, without bells and whistles. Code and models are available at https://github.com/OSVAI/Ske2GridComment: The paper of Ske2Grid is published at ICML 2023. Code and models are available at https://github.com/OSVAI/Ske2Gri

Synchronization of stochastic genetic oscillator networks with time delays and Markovian jumping parameters

The official published version of the article can be found at the link below.Genetic oscillator networks (GONs) are inherently coupled complex systems where the nodes indicate the biochemicals and the couplings represent the biochemical interactions. This paper is concerned with the synchronization problem of a general class of stochastic GONs with time delays and Markovian jumping parameters, where the GONs are subject to both the stochastic disturbances and the Markovian parameter switching. The regulatory functions of the addressed GONs are described by the sector-like nonlinear functions. By applying up-to-date ‘delay-fractioning’ approach for achieving delay-dependent conditions, we construct novel matrix functional to derive the synchronization criteria for the GONs that are formulated in terms of linear matrix inequalities (LMIs). Note that LMIs are easily solvable by the Matlab toolbox. A simulation example is used to demonstrate the synchronization phenomena within biological organisms of a given GON and therefore shows the applicability of the obtained results.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Royal Society of the UK, the National Natural Science Foundation of China under Grant 60804028, the Teaching and Research Fund for Excellent Young Teachers at Southeast University of China, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany

Transcriptomic diversification of granulosa cells during follicular development between White Leghorn and Silky Fowl hens

Egg production rate in chicken is related to the continuity of follicle development. In this study, we found that the numbers of white prehierarchical, dominant, and yellow preovulatory follicles in the high-yielding layer breed, White Leghorn (WL), were significantly higher than those in the low egg-yielding variety, Silky Fowl (SF). The proliferation and differentiation of granulosa cells (GCs) play an important role in follicle maturation. Histological observation revealed a large number of melanocytes in the outer granulosa layer of follicles in SF but not in WL. Finally, RNA-sequencing was used to analyze the gene expression profiles and pathways of the GC layer in the follicles in both WL and SF hens. Transcriptome analysis of prehierarchical GCs (phGCs) and preovulatory GCs (poGCs) between WL and SF showed that steroid hormone-, oxytocin synthesis-, tight junction-, and endocytosis-related genes were expressed at higher levels in WL phGCs than in SF phGCs, whereas the insulin signaling pathway- and vascular smooth muscle contraction-related genes were upregulated in SF phGCs. Fatty acid synthesis, calcium signaling, and Wnt signaling pathway-related genes were expressed at higher levels in WL poGCs than in SF poGCs; however, adrenergic signaling, cGMP-PKG, and melanogenesis-related genes were upregulated in SF poGCs. These results indicate that genes that promote GC proliferation and secretion of various sex hormones are more active in WL than in SF hens. The upregulated signaling pathways in SF help in providing energy to GCs and for angiogenesis and melanogenesis. In vitro experiments confirmed that both the proliferation of poGCs and synthesis of reproductive hormones were higher in WL than in SF hens

Comparative proteome analysis revealed the differences in response to both Mycobacterium tuberculosis and Mycobacterium bovis infection of bovine alveolar macrophages

Tuberculosis (TB), attributed to the Mycobacterium tuberculosis complex, is one of the most serious zoonotic diseases worldwide. Nevertheless, the host mechanisms preferentially leveraged by Mycobacterium remain unclear. After infection, both Mycobacterium tuberculosis (MTB) and Mycobacterium bovis (MB) bacteria exhibit intimate interactions with host alveolar macrophages; however, the specific mechanisms underlying these macrophage responses remain ambiguous. In our study, we performed a comparative proteomic analysis of bovine alveolar macrophages (BAMs) infected with MTB or MB to elucidate the differential responses of BAMs to each pathogen at the protein level. Our findings revealed heightened TB infection susceptibility of BAMs that had been previously infected with MTB or MB. Moreover, we observed that both types of mycobacteria triggered significant changes in BAM energy metabolism. A variety of proteins and signalling pathways associated with autophagy and inflammation-related progression were highly activated in BAMs following MB infection. Additionally, proteins linked to energy metabolism were highly expressed in BAMs following MTB infection. In summary, we propose that BAMs may resist MTB and MB infections via different mechanisms. Our findings provide critical insights into TB pathogenesis, unveiling potential biomarkers to facilitate more effective TB treatment strategies. Additionally, our data lend support to the hypothesis that MTB may be transmitted via cross-species infection

Classification of Inhibitors of Hepatic Organic Anion Transporting Polypeptides (OATPs): Influence of Protein Expression on Drug–Drug Interactions

ABSTRACT: The hepatic organic anion transporting poly-peptides (OATPs) influence the pharmacokinetics of several drug classes and are involved in many clinical drug−drug interactions. Predicting potential interactions with OATPs is, therefore, of value. Here, we developed in vitro and in silico models for identification and prediction of specific and general inhibitors of OATP1B1, OATP1B3, and OATP2B1. The maximal transport activity (MTA) of each OATP in human liver was predicted from transport kinetics and protein quantification. We then used MTA to predict the effects of a subset of inhibitors on atorvastatin uptake in vivo. Using a data set of 225 drug-like compounds, 91 OATP inhibitors were identified. In silico models indicated that lipophilicity and polar surface area are key molecular features of OATP inhibition. MTA predictions identified OATP1B1 and OATP1B3 as major determinants of atorvastatin uptake in vivo. The relative contributions to overall hepatic uptake varied with isoform specificities of the inhibitors

Crack the Cross-border Network Governance Path of Disruptive Technology Innovation

 Disruptive technology innovation is a process in which technologies, products or services are initially inferior than those provided by incumbents in the attributes that mainstream consumers value, but they can attract and satisfy the consumers in low-end or new markets with advantages in performance attributes (such as being cheap, simple, or convenient) that these consumers value but which at the same time are neglected by mainstream markets. At the same time, this paradigm can be applied on higher levels (i.e. by international business corporations or/and governments for political and economic decision-making processes). In order to make the research on disruptive innovation more comprehensive, we built up a multilevel theoretical framework of influence factors for disruptive innovation, and we proposed the level of analysis of disruptive innovation into five levels. In this paper, we propose a case study for evaluating governance models based on the cross-border region (CBR) among three countries. The results show that CBI and CBC have shaped opportunities for environmental governance and for governance models in other sectors. The CBGS framework can be used for measuring and evaluating the CBG according to the evaluation criteria and elaborate some recommendations. This framework could contribute to further systematic analyses and research of disruptive innovations in the future and can provide reference for policy-makers to successfully predict disruptive innovation