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Germ-free (GF) mice, which are depleted of their resident microbiota, are the gold standard for exploring the role of the microbiome in health and disease; however, they are of limited value in the study of human-specific pathogens because they do not support their replication. Here, we develop GF mice systemically reconstituted with human immune cells and use them to evaluate the role of the resident microbiome in the acquisition, replication and pathogenesis of two human-specific pathogens, Epstein–Barr virus (EBV) and human immunodeficiency virus (HIV). Comparison with conventional (CV) humanized mice showed that resident microbiota enhance the establishment of EBV infection and EBV-induced tumorigenesis and increase mucosal HIV acquisition and replication. HIV RNA levels were higher in plasma and tissues of CV humanized mice compared with GF humanized mice. The frequency of CCR5 + CD4 + T cells throughout the intestine was also higher in CV humanized mice, indicating that resident microbiota govern levels of HIV target cells. Thus, resident microbiota promote the acquisition and pathogenesis of two clinically relevant human-specific pathogens. Resident microbiota contribute to HIV and EBV infection in a germ-free humanized mouse model.

Polyhydroxyalkanoates (PHAs) are storage granules found in bacteria that are essentially hydroxy fatty acid polyesters. PHA molecules appear in variety of structures, and amongst all types of PHAs, polyhydroxybutyrate (PHB) is used in versatile fields as it is a biodegradable, biocompatible, and ecologically safe thermoplastic. The unique physicochemical characteristics of these PHAs have made them applicable in nanotechnology, tissue engineering, and other biomedical applications. In this review, the optimization, extraction, and characterization of PHAs are described. Their production and application in nanotechnology are also portrayed in this review, and the precise and various production methods of PHA-based nanoparticles, such as emulsion solvent diffusion, nanoprecipitation, and dialysis are discussed. The characterization techniques such as UV-Vis, FTIR, SEM, Zeta Potential, and XRD are also elaborated.

The system is novel approach to combine wireless power transmission system (WPT) and automated guided vehicle (AGV). The wireless power transfer for charging the mobile robot is implemented using inductive coupling method. The system setup consists of transmitter coils whose switching action is controlled through transistors, receiver coil connected to full ridge rectifier and mobile robot. The track consists of number of transmitter coil which transmits power in form of electromagnetic waves. The receivers in the robot, which receives these waves and converts it back to electric power to charge the battery. The robot tracks its target destination based on the user command from the smart phone through Bluetooth. Very few theoretical researches are available on this field. A prototype was developed and tested based on the researches. The system achieves good range but falls short in efficiency to charge a battery, charging of battery takes longer time than regular charging time. Further research and extensive exploration can bring this technology from theory to practice.

We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. Building on recent advancements, such as OpenAI's Sora, we explore the latent diffusion model (LDM) architecture and introduce a video variational autoencoder (VidVAE). VidVAE compresses video data both spatially and temporally, significantly reducing the length of visual tokens and the computational demands associated with generating long-sequence videos. To further address the computational costs, we propose a divide-and-merge strategy that maintains temporal consistency across video segments. Our Diffusion Transformer (DiT) model incorporates spatial and temporal self-attention layers, enabling robust generalization across different timeframes and aspect ratios. We have devised a data processing pipeline from the very beginning and collected over 13M high-quality video-text pairs. The pipeline includes multiple steps such as clipping, text detection, motion estimation, aesthetics scoring, and dense captioning based on our in-house video-LLM model. Training the VidVAE and DiT models required approximately 40 and 642 H100 days, respectively. Our model supports over 14-second 720p video generation in an end-to-end way and demonstrates competitive performance against state-of-the-art T2V models.