Thorough validation of siRNA-induced cell death phenotypes defines new anti-apoptotic protein

Loss-of-function by means of RNA interference in cultured human cells enables rapid pathway dissection on a genome-scale. Improved siRNA design and key validation protocols are required to eliminate falsely identified phenotypes resulting from potential off-target consequences. Here, we demonstrate a validation strategy involving several steps for verifying cell death phenotypes revealed during loss-of-function screening. First, from a set of 45 novel human genes we identified gene candidates that, when silenced, induce apoptosis in cultured HeLa cells. For those candidates, we performed more extensive validation with multiple effective siRNAs. In addition, we designed rescue experiments involving candidate genes delivered exogenously and containing silent mutations in the siRNA target regions. Rescue of the observed knockdown phenotype demonstrated an original and more stringent validation of the siRNA's selectivity and the phenotype specificity for the target gene. As a result, our data reveals an anti-apoptotic function for novel human breast adenocarcinoma marker BC-2, adding new depth to BC-2′s description as a putative tumor marker involved in cancer related pathways

Modification of wool fiber using steam explosion

Wool fiber was modified by steam explosion in this study. SEM results show that some scales on the fiber surface were cleaved and tiny grooves generated during the explosion. FTIR results suggest no evident changes in the chemical composition of the fiber after the explosion treatment. However, the crystallinity of the fiber decreased slightly as the steam pressure increased based on the X-ray results. In the thermal analysis, DSC results show that the temperature corresponding to vaporization of absorbed water and cleavage of disulfide bonds respectively decreased as the steam pressure increased. The reduction in thermal decomposition energy of the treated fiber indicates that steam explosion might have destroyed some crystals and crosslinks of macromolecular chains in the fiber. The treatment also led to some alterations of the fiber properties, including reduction in strength, moisture regain and solubility in caustic solution.<br /

TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer

In this paper, we introduce a set of effective TOken REduction (TORE) strategies for Transformer-based Human Mesh Recovery from monocular images. Current SOTA performance is achieved by Transformer-based structures. However, they suffer from high model complexity and computation cost caused by redundant tokens. We propose token reduction strategies based on two important aspects, i.e., the 3D geometry structure and 2D image feature, where we hierarchically recover the mesh geometry with priors from body structure and conduct token clustering to pass fewer but more discriminative image feature tokens to the Transformer. As a result, our method vastly reduces the number of tokens involved in high-complexity interactions in the Transformer, achieving competitive accuracy of shape recovery at a significantly reduced computational cost. We conduct extensive experiments across a wide range of benchmarks to validate the proposed method and further demonstrate the generalizability of our method on hand mesh recovery. Our code will be publicly available once the paper is published

Sno/scaRNAbase: a curated database for small nucleolar RNAs and cajal body-specific RNAs

Small nucleolar RNAs (snoRNAs) and Cajal body-specific RNAs (scaRNAs) are named for their subcellular localization within nucleoli and Cajal bodies (conserved subnuclear organelles present in the nucleoplasm), respectively. They have been found to play important roles in rRNA, tRNA, snRNAs, and even mRNA modification and processing. All snoRNAs fall in two categories, box C/D snoRNAs and box H/ACA snoRNAs, according to their distinct sequence and secondary structure features. Box C/D snoRNAs and box H/ACA snoRNAs mainly function in guiding 2′-O-ribose methylation and pseudouridilation, respectively. ScaRNAs possess both box C/D snoRNA and box H/ACA snoRNA sequence motif features, but guide snRNA modifications that are transcribed by RNA polymerase II. Here we present a Web-based sno/scaRNA database, called sno/scaRNAbase, to facilitate the sno/scaRNA research in terms of providing a more comprehensive knowledge base. Covering 1979 records derived from 85 organisms for the first time, sno/scaRNAbase is not only dedicated to filling gaps between existing organism-specific sno/scaRNA databases that are focused on different sno/scaRNA aspects, but also provides sno/scaRNA scientists with an opportunity to adopt a unified nomenclature for sno/scaRNAs. Derived from a systematic literature curation and annotation effort, the sno/scaRNAbase provides an easy-to-use gateway to important sno/scaRNA features such as sequence motifs, possible functions, homologues, secondary structures, genomics organization, sno/scaRNA gene's chromosome location, and more. Approximate searches, in addition to accurate and straightforward searches, make the database search more flexible. A BLAST search engine is implemented to enable blast of query sequences against all sno/scaRNAbase sequences. Thus our sno/scaRNAbase serves as a more uniform and friendly platform for sno/scaRNA research. The database is free available at

Genetic engineering of vaccine manufacturing cell lines enhances poliovirus and enterovirus 71 production

Vaccine manufacturing costs and production limitations represent two fundamental challenges facing researchers, public health officials and vaccine manufacturers committed to global health solutions. To address these issues, we have investigated whether the cell lines employed by vaccine manufacturers can be engineered to enhance vaccine virus production. As a first step in a proof-of-principle study, a genome-wide RNA Interference (RNAi) screen was conducted to identify host gene modulation events that increased Sabin 2 poliovirus (PV) replication. Primary screen hits were validated in a Vero vaccine manufacturing cell line using both attenuated and wild type poliovirus strains. This approach identified multiple single and dual gene knockdown events that increased PV titers \u3e20-fold and \u3e50-fold, respectively. Top candidate genes did not affect virus antigenicity, cell viability, or cell doubling times. Moreover, CRISPR/Cas9-mediated knockout (KO) of the top three targets created stable cell substrates with improved viral vaccine strain production. Please click Additional Files below to see the full abstract

DiffusionPhase: Motion Diffusion in Frequency Domain

In this study, we introduce a learning-based method for generating high-quality human motion sequences from text descriptions (e.g., ``A person walks forward"). Existing techniques struggle with motion diversity and smooth transitions in generating arbitrary-length motion sequences, due to limited text-to-motion datasets and the pose representations used that often lack expressiveness or compactness. To address these issues, we propose the first method for text-conditioned human motion generation in the frequency domain of motions. We develop a network encoder that converts the motion space into a compact yet expressive parameterized phase space with high-frequency details encoded, capturing the local periodicity of motions in time and space with high accuracy. We also introduce a conditional diffusion model for predicting periodic motion parameters based on text descriptions and a start pose, efficiently achieving smooth transitions between motion sequences associated with different text descriptions. Experiments demonstrate that our approach outperforms current methods in generating a broader variety of high-quality motions, and synthesizing long sequences with natural transitions

Dynamic Graph Representation Learning via Graph Transformer Networks

Dynamic graph representation learning is an important task with widespread applications. Previous methods on dynamic graph learning are usually sensitive to noisy graph information such as missing or spurious connections, which can yield degenerated performance and generalization. To overcome this challenge, we propose a Transformer-based dynamic graph learning method named Dynamic Graph Transformer (DGT) with spatial-temporal encoding to effectively learn graph topology and capture implicit links. To improve the generalization ability, we introduce two complementary self-supervised pre-training tasks and show that jointly optimizing the two pre-training tasks results in a smaller Bayesian error rate via an information-theoretic analysis. We also propose a temporal-union graph structure and a target-context node sampling strategy for efficient and scalable training. Extensive experiments on real-world datasets illustrate that DGT presents superior performance compared with several state-of-the-art baselines