Interference of Apoptosis by Hepatitis B Virus

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Hepatitis B virus (HBV) causes liver diseases that have been a consistent problem for human health, leading to more than one million deaths every year worldwide. A large proportion of hepatocellular carcinoma (HCC) cases across the world are closely associated with chronic HBV infection. Apoptosis is a programmed cell death and is frequently altered in cancer development. HBV infection interferes with the apoptosis signaling to promote HCC progression and viral proliferation. The HBV-mediated alteration of apoptosis is achieved via interference with cellular signaling pathways and regulation of epigenetics. HBV X protein (HBX) plays a major role in the interference of apoptosis. There are conflicting reports on the HBV interference of apoptosis with the majority showing inhibition of and the rest reporting induction of apoptosis. In this review, we described recent studies on the mechanisms of the HBV interference with the apoptosis signaling during the virus infection and provided perspective

T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations

In this work, we investigate a simple and must-known conditional generative framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transformer (GPT) for human motion generation from textural descriptions. We show that a simple CNN-based VQ-VAE with commonly used training recipes (EMA and Code Reset) allows us to obtain high-quality discrete representations. For GPT, we incorporate a simple corruption strategy during the training to alleviate training-testing discrepancy. Despite its simplicity, our T2M-GPT shows better performance than competitive approaches, including recent diffusion-based approaches. For example, on HumanML3D, which is currently the largest dataset, we achieve comparable performance on the consistency between text and generated motion (R-Precision), but with FID 0.116 largely outperforming MotionDiffuse of 0.630. Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach. Our work suggests that VQ-VAE still remains a competitive approach for human motion generation.Comment: Accepted to CVPR 2023. Project page: https://mael-zys.github.io/T2M-GPT

Navigating Uncertainty: A Framework for Optimising Public Transport Networks’ Performance

Public transport (PT) networks face significant challenges in achieving optimal outcomes due to the presence of risk and uncertainty. Despite the importance of optimising PT networks’ performance, limited research has applied risk management tools to tackle this issue. In response, this study presents a three-stage framework to optimise PT networks’ performance in uncertain conditions. First, we establish a PT criteria matrix using an analytic hierarchy process to develop a criteria model and calculate the criteria weightings. Second, we propose a multi-aspiration-level goal programming approach to optimise a PT network’s performance based on the weighted results. To manage uncertainty, we use Monte Carlo simulation to analyse the probability of the optimal solution. Finally, to validate our approach, we apply the three-stage framework to three case study areas in Australia. The results of this research offer significant insights into identifying the likelihood of criteria optimisation scenarios, thereby assisting decision makers in allocating resources for optimising the delivery of PT network performance solutions in accordance with government requirements

One-Shot Fine-Grained Instance Retrieval

Fine-Grained Visual Categorization (FGVC) has achieved significant progress recently. However, the number of fine-grained species could be huge and dynamically increasing in real scenarios, making it difficult to recognize unseen objects under the current FGVC framework. This raises an open issue to perform large-scale fine-grained identification without a complete training set. Aiming to conquer this issue, we propose a retrieval task named One-Shot Fine-Grained Instance Retrieval (OSFGIR). "One-Shot" denotes the ability of identifying unseen objects through a fine-grained retrieval task assisted with an incomplete auxiliary training set. This paper first presents the detailed description to OSFGIR task and our collected OSFGIR-378K dataset. Next, we propose the Convolutional and Normalization Networks (CN-Nets) learned on the auxiliary dataset to generate a concise and discriminative representation. Finally, we present a coarse-to-fine retrieval framework consisting of three components, i.e., coarse retrieval, fine-grained retrieval, and query expansion, respectively. The framework progressively retrieves images with similar semantics, and performs fine-grained identification. Experiments show our OSFGIR framework achieves significantly better accuracy and efficiency than existing FGVC and image retrieval methods, thus could be a better solution for large-scale fine-grained object identification.Comment: Accepted by MM2017, 9 pages, 7 figure

Transcriptome Analysis of Host-Associated Differentiation in \u3cem\u3eBemisia tabaci\u3c/em\u3e (Hemiptera: Aleyrodidae)

Host-associated differentiation is one of the driving forces behind the diversification of phytophagous insects. In this study, host induced transcriptomic differences were investigated in the sweetpotato whitefly Bemisia tabaci, an invasive agricultural pest worldwide. Comparative transcriptomic analyses using coding sequence (CDS), 5′ and 3′ untranslated regions (UTR) showed that sequence divergences between the original host plant, cabbage, and the derived hosts, including cotton, cucumber and tomato, were 0.11–0.14%, 0.19–0.26%, and 0.15–0.21%, respectively. In comparison to the derived hosts, 418 female and 303 male transcripts, respectively, were up-regulated in the original cabbage strain. Among them, 17 transcripts were consistently up-regulated in both female and male whiteflies originated from the cabbage host. Specifically, two ESTs annotated as Cathepsin B or Cathepsin B-like genes were significantly up-regulated in the original cabbage strain, representing a transcriptomic response to the dietary challenges imposed by the host shifting. Results from our transcriptome analysis, in conjunction with previous reports documenting the minor changes in their reproductive capacity, insecticide susceptibility, symbiotic composition and feeding behavior, suggest that the impact of host-associated differentiation in whiteflies is limited. Furthermore, it is unlikely the major factor contributing to their rapid range expansion/invasiveness

TapMo: Shape-aware Motion Generation of Skeleton-free Characters

Previous motion generation methods are limited to the pre-rigged 3D human model, hindering their applications in the animation of various non-rigged characters. In this work, we present TapMo, a Text-driven Animation Pipeline for synthesizing Motion in a broad spectrum of skeleton-free 3D characters. The pivotal innovation in TapMo is its use of shape deformation-aware features as a condition to guide the diffusion model, thereby enabling the generation of mesh-specific motions for various characters. Specifically, TapMo comprises two main components - Mesh Handle Predictor and Shape-aware Diffusion Module. Mesh Handle Predictor predicts the skinning weights and clusters mesh vertices into adaptive handles for deformation control, which eliminates the need for traditional skeletal rigging. Shape-aware Motion Diffusion synthesizes motion with mesh-specific adaptations. This module employs text-guided motions and mesh features extracted during the first stage, preserving the geometric integrity of the animations by accounting for the character's shape and deformation. Trained in a weakly-supervised manner, TapMo can accommodate a multitude of non-human meshes, both with and without associated text motions. We demonstrate the effectiveness and generalizability of TapMo through rigorous qualitative and quantitative experiments. Our results reveal that TapMo consistently outperforms existing auto-animation methods, delivering superior-quality animations for both seen or unseen heterogeneous 3D characters

Exploring Valid Reference Genes for Quantitative Real-time PCR Analysis in \u3cem\u3ePlutella xylostella\u3c/em\u3e (Lepidoptera: Plutellidae)

Quantitative real-time PCR (qRT-PCR), a primary tool in gene expression analysis, requires an appropriate normalization strategy to control for variation among samples. The best option is to compare the mRNA level of a target gene with that of reference gene(s) whose expression level is stable across various experimental conditions. In this study, expression profiles of eight candidate reference genes from the diamondback moth, Plutella xylostella, were evaluated under diverse experimental conditions. RefFinder, a web-based analysis tool, integrates four major computational programs including geNorm, Normfinder, BestKeeper, and the comparative ΔCt method to comprehensively rank the tested candidate genes. Elongation factor 1 (EF1) was the most suited reference gene for the biotic factors (development stage, tissue, and strain). In contrast, although appropriate reference gene(s) do exist for several abiotic factors (temperature, photoperiod, insecticide, and mechanical injury), we were not able to identify a single universal reference gene. Nevertheless, a suite of candidate reference genes were specifically recommended for selected experimental conditions. Our finding is the first step toward establishing a standardized qRT-PCR analysis of this agriculturally important insect pest

Carbon nanotube foils for electron stripping in tandem accelerators

Author Posting. © Elsevier B.V., 2007. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 261 (2007): 44-48, doi:10.1016/j.nimb.2007.03.023.Carbon nanotube technology has rapidly advanced in recent years, making it possible to create meter-long, ~4 cm wide films of multi-walled tubes of less than 3 μg/cm2 areal density in a bench top open-air procedure [1]. The physical properties of individual carbon nanotubes have been well established, equaling or surpassing electrical and thermal conductivity and mechanical strength of most other materials, graphite in particular. The handling and transport of such nanotube films, dry-mounted self-supporting on metal frames with several cm2 of open area, is problem-free: the aerogel films having a volumetric density of about 1.5 mg/cm3 survived the trip by car and air from Dallas to Oak Ridge without blemish. In this paper we will present the results of first tests of these nanotube films as electron stripper media in a tandem accelerator. The tests were performed in the Model 25 URC tandem [2] of the Holifield Radioactive Ion Beam Facility (HRIBF) at Oak Ridge National Laboratory. We will discuss the performance of nanotube films in comparison with chemical vapor deposition and laser-ablated carbon foils.This work was supported by a grant from the “Cecil H. and Ida M. Green Technology Innovation Awards” program of the Woods Hole Oceanographic Institution and in part by the U.S. National Science Foundation through Cooperative Agreement 82899613 and the Robert A. Welch Foundation grant AT-0029