Nerfstudio: A Modular Framework for Neural Radiance Field Development

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more. In order to streamline the development and deployment of NeRF research, we propose a modular PyTorch framework, Nerfstudio. Our framework includes plug-and-play components for implementing NeRF-based methods, which make it easy for researchers and practitioners to incorporate NeRF into their projects. Additionally, the modular design enables support for extensive real-time visualization tools, streamlined pipelines for importing captured in-the-wild data, and tools for exporting to video, point cloud and mesh representations. The modularity of Nerfstudio enables the development of Nerfacto, our method that combines components from recent papers to achieve a balance between speed and quality, while also remaining flexible to future modifications. To promote community-driven development, all associated code and data are made publicly available with open-source licensing at https://nerf.studio.Comment: Project page at https://nerf.studi

Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data

Systematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single-cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single-cell expression data. The zero-inflated version of Poisson/negative binomial and log-normal distributions have emerged as the most popular alternatives owing to their ability to accommodate high dropout rates, as commonly observed in single-cell data. Although the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression ranks, as robust surrogates for transcript abundance. Here we examined the performance of the discrete generalized beta distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method to understand its advantages compared with some of the existing best-practice approaches. We concluded that besides striking a reasonable balance between Type I and Type II errors, ROSeq, the proposed differential expression test, is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq and made it available on the Bioconductor platform.</p

Phage delivered CRISPR-Cas system to combat multidrug-resistant pathogens in gut microbiome

The Host-microbiome interactions that exist inside the gut microbiota operate in a synergistic and abnormal manner. Additionally, the normal homeostasis and functioning of gut microbiota are frequently disrupted by the intervention of Multi-Drug Resistant (MDR) pathogens. CRISPR-Cas (CRISPR-associated protein with clustered regularly interspersed short palindromic repeats) recognized as a prokaryotic immune system has emerged as an effective genome-editing tool to edit and delete specific microbial genes for the expulsion of bacteria through bactericidal action. In this review, we demonstrate many functioning CRISPR-Cas systems against the antimicrobial resistance of multiple pathogens, which infiltrate the gastrointestinal tract. Moreover, we discuss the advancement in the development of a phage-delivered CRISPR-Cas system for killing a gut MDR pathogen. We also discuss a combinatorial approach to use bacteriophage as a delivery system for the CRISPR-Cas gene for targeting a pathogenic community in the gut microbiome to resensitize the drug sensitivity. Finally, we discuss engineered phage as a plausible potential option for the CRISPR-Cas system for pathogenic killing and improvement of the efficacy of the system