432 research outputs found
Contrasting patterns and drivers of soil bacterial and fungal diversity across a mountain gradient
No full textMicrobial elevational diversity patterns have been extensively studied, but their shaping mechanisms remain to be explored. Here, we examined soil bacterial and fungal diversity and community compositions across a 3.4 km elevational gradient (consists of five elevations) on Mt. Kilimanjaro located in East Africa. Bacteria and fungi had different diversity patterns across this extensive mountain gradient-bacterial diversity had a U shaped pattern while fungal diversity monotonically decreased. Random forest analysis revealed that pH (12.61% importance) was the most important factor affecting bacterial diversity, whereas mean annual temperature (9.84% importance) had the largest impact on fungal diversity, which was consistent with results obtained from mixed-effects model. Meanwhile, the diversity patterns and drivers of those diversity patterns differ among taxonomic groups (phyla/classes) within bacterial or fungal communities. Taken together, our study demonstrated that bacterial and fungal diversity and community composition responded differently to climate and edaphic properties along an extensive mountain gradient, and suggests that the elevational diversity patterns across microbial groups are determined by distinct environmental variables. These findings enhanced our understanding of the formation and maintenance of microbial diversity along elevation, as well as microbial responses to climate change in montane ecosystems
Mono-crystalline perovskite photovoltaics toward ultrahigh efficiency?
No full textDr. Kai Wang joined CEHMS, Virginia Tech as a Postdoctoral Associate in 2017 after his graduation from The University of Akron. In the fall of 2018, Kai joined Pennsylvania State University as a Research Assistant Professor in the College of Earth and Mineral Sciences, Department of Materials Science and Engineering. His research interests include halide perovskite photovoltaics, two-dimensional multiple quantum well physics, and bioelectronics. Dr. Shashank Priya currently serves as the Associate Vice President for Research and Director, Strategic Initiatives at Pennsylvania State University. He is a professor in the Department of Materials Science and Engineering at Pennsylvania State University and Adjunct Professor in the Department of Mechanical Engineering at Virginia Tech. Priya's research focuses on the intersection of multifunctional materials, bio-inspired systems and technologies, and energy harvesting and storage. As the principal investigator, he leads multiple programs targeting the development of thermoelectrics, photovoltaics, piezoelectrics, and other emerging energy-conversion and storage devices. Dr. Dong Yang worked with Professor Shengzhong (Frank) Liu in Shaanxi Normal University, China since 2014 and became a full professor in 2017. Dong joined Virginia Tech in 2017 and moved to Pennsylvania State University in the fall of 2018 as Research Assistant Professor. His research interests include solar cells, semiconductor materials, materials science, and engineering of graphene carbon materials. Dr. Congcong Wu has led the solar cell team in CEHMS, Virginia Tech since 2014. In the fall of 2018, Congcong joined Pennsylvania State University as Research Associate Professor. His research mainly focuses on developing next-generation photovoltaic systems for clean and efficient energy conversion. Dr. Joe Shapter received his PhD in Reaction Dynamics from the University of Toronto in 1990. He subsequently held an NSERC Fellowship at The University of Western Ontario before moving to Australia in 1996 to take up a position at Flinders University. Joe served as Dean of the School of Chemical and Physical Sciences for 6.5 years and headed the Flinders involvement in both the Australian Microscopy and Microanalysis Research Facility (AMMRF) and the Australian National Fabrication Facility (ANFF), and was SA Director for AMMRF. His major interests are in the area of novel nanomaterial production, nanometer-scale characterization of these materials, and their applications in, for example, sensors or solar cells. The realization of ultrahigh solar conversion efficiency is the defining problem of photovoltaics. Single-/mono-crystalline halide perovskite photovoltaic technology provides the avenue toward this target by virtue of their intrinsic distinguished electronic properties. Current advances in epitaxial synthesis of single-crystalline halide perovskite films/wafers, and innovations in strategic device designs, point toward an opportunity for a true breakthrough in solar cell performance. We posit that mono-crystalline perovskite photovoltaics technology has the first-mover advantage in future energy deployment
Deep learning and computer chess
No full textThis report presents two supervised learning approach for training neural networks to evaluate chess positions. The architecture used to build the neural network model is based on the Giraffe’s architecture [2] and Stockfish NNUE -HalfKP [3]. Implemented a method to train a neural network architecture to understand chess movement and techniques that a grandmaster would play. Both approaches implemented as a 7-class classification problem on a dataset of over 10,000 samples games.
We collected different chess game played by grandmaster, then used the evaluation function of stockfish [5], one of the strongest existing chess engines, to get the score of the positions and label it accordingly. We extracted the positions from the games using Forsyth-Edwards notation and stored them in csv files which are later used for training the model.Bachelor of Engineering (Computer Engineering
Analysis of the Molecular Mechanism of Autophagosome Formation in Yeast and Zebrafish Models.
No full textAutophagy is a conserved intracellular degradative pathway induced by various stress or developmental signals in eukaryotes, and its malfunction contributes to a variety of diseases. During autophagy, cargos such as cytosolic proteins, damaged organelles and invasive pathogens are engulfed into double-membrane autophagosomes, transported to, and degraded in the lysosome/vacuole.
Over 30 ATG (autophagy-related) genes have been identified in the budding yeast S. cerevisiae. To understand the molecular mechanism controlling membrane delivery during autophagy, I studied protein interactions involving Atg9, the only known transmembrane protein required for autophagosome formation. In yeast, Atg9 cycles between peripheral sites and the phagophore assembly site (PAS), suggesting its role in supplying membrane for autophagosome nucleation and expansion. Through a yeast two-hybrid screen aimed to find interaction partners of Atg9, I identified Atg11, a component involved in autophagic cargo recognition. Subsequently, I demonstrated that Atg11 mediates Atg9 movement to the PAS along the actin cytoskeleton. Thus, my model suggests that the anterograde transport of Atg9 to the PAS mediated by Atg11 may serve as a membrane shuttle for autophagosome biogenesis. Furthermore, I characterized the self-interaction of Atg9 and generated an Atg9 mutant defective in this interaction. This mutation results in abnormal autophagy, due to altered phagophore formation as well as inefficient membrane delivery to the PAS. Based on my analyses, I propose a model suggesting dual functions for the Atg9 complex: by reversibly binding to another Atg9 molecule, Atg9 can both promote lipid transport from the membrane origins, and help assemble an intact phagophore membrane.
I also extended my analysis on autophagy in the zebrafish model, which represents a unique system to study autophagy due to its rapid embryonic development and technical advantages in high-throughput drug screens. I identified two zebrafish Atg8 (an autophagosome marker protein) homologs, lc3 and gabarap, and generated two transgenic zebrafish lines expressing GFP-tagged versions of the corresponding proteins. I observed a high level of autophagy activity in zebrafish embryos, which can be further upregulated by the TOR inhibitor rapamycin or the calpain inhibitor calpeptin. Thus, I established a convenient zebrafish tool to assay autophagic activity during embryogenesis in vivo.PhDMolecular, Cellular, and Developmental BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/63756/1/cconghe_1.pd
Genetic Evolution and Molecular Selection of the <em>HE</em> Gene of Influenza C Virus
No full textInfluenza C virus (ICV) was first identified in humans and swine, but recently also in cattle, indicating a wider host range and potential threat to both the livestock industry and public health than was originally anticipated. The ICV hemagglutinin-esterase (HE) glycoprotein has multiple functions in the viral replication cycle and is the major determinant of antigenicity. Here, we developed a comparative approach integrating genetics, molecular selection analysis, and structural biology to identify the codon usage and adaptive evolution of ICV. We show that ICV can be classified into six lineages, consistent with previous studies. The HE gene has a low codon usage bias, which may facilitate ICV replication by reducing competition during evolution. Natural selection, dinucleotide composition, and mutation pressure shape the codon usage patterns of the ICV HE gene, with natural selection being the most important factor. Codon adaptation index (CAI) and relative codon deoptimization index (RCDI) analysis revealed that the greatest adaption of ICV was to humans, followed by cattle and swine. Additionally, similarity index (SiD) analysis revealed that swine exerted a stronger evolutionary pressure on ICV than humans, which is considered the primary reservoir. Furthermore, a similar tendency was also observed in the M gene. Of note, we found HE residues 176, 194, and 198 to be under positive selection, which may be the result of escape from antibody responses. Our study provides useful information on the genetic evolution of ICV from a new perspective that can help devise prevention and control strategies
Genetic Evolution and Molecular Selection of the HE Gene of Influenza C Virus
No full textInfluenza C virus (ICV) was first identified in humans and swine, but recently also in cattle, indicating a wider host range and potential threat to both the livestock industry and public health than was originally anticipated. The ICV hemagglutinin-esterase (HE) glycoprotein has multiple functions in the viral replication cycle and is the major determinant of antigenicity. Here, we developed a comparative approach integrating genetics, molecular selection analysis, and structural biology to identify the codon usage and adaptive evolution of ICV. We show that ICV can be classified into six lineages, consistent with previous studies. The HE gene has a low codon usage bias, which may facilitate ICV replication by reducing competition during evolution. Natural selection, dinucleotide composition, and mutation pressure shape the codon usage patterns of the ICV HE gene, with natural selection being the most important factor. Codon adaptation index (CAI) and relative codon deoptimization index (RCDI) analysis revealed that the greatest adaption of ICV was to humans, followed by cattle and swine. Additionally, similarity index (SiD) analysis revealed that swine exerted a stronger evolutionary pressure on ICV than humans, which is considered the primary reservoir. Furthermore, a similar tendency was also observed in the M gene. Of note, we found HE residues 176, 194, and 198 to be under positive selection, which may be the result of escape from antibody responses. Our study provides useful information on the genetic evolution of ICV from a new perspective that can help devise prevention and control strategies
Spatial transcriptome of one sample of psoraisis.
No full textSpatial transcriptome of one sample of psoraisis.</p
- …
