Argonaute CSR-1A promotes H3K9me3 maintenance to protect somatic development in offspring.

Parental stress can be encoded into altered epigenetic information to influence their offspring. Concurrently, it is vital for the preservation of a parent's epigenetic information, despite environmental challenges, to ensure accurate inheritance by the next generation. Nevertheless, the complexities of this process and the specific molecular mechanisms involved are not yet fully understood.

PFB-Diff: Progressive Feature Blending diffusion for text-driven image editing.

Diffusion models have demonstrated their ability to generate diverse and high-quality images, sparking considerable interest in their potential for real image editing applications. However, existing diffusion-based approaches for local image editing often suffer from undesired artifacts due to the latent-level blending of the noised target images and diffusion latent variables, which lack the necessary semantics for maintaining image consistency. To address these issues, we propose PFB-Diff, a Progressive Feature Blending method for Diffusion-based image editing.

De Novo Drug Design by Multi-Objective Path Consistency Learning With Beam A Search.

Generating high-quality and drug-like molecules from scratch within the expansive chemical space presents a significant challenge in the field of drug discovery. In prior research, value-based reinforcement learning algorithms have been employed to generate molecules with multiple desired properties iteratively. The immediate reward was defined as the evaluation of intermediate-state molecules at each step, and the learning objective would be maximizing the expected cumulative evaluation scores for all molecules along the generative path.

Mammalian PIWI-piRNA-target complexes reveal features for broad and efficient target silencing.

The PIWI-interacting RNA (piRNA) pathway is an adaptive defense system wherein piRNAs guide PIWI family Argonaute proteins to recognize and silence ever-evolving selfish genetic elements and ensure genome integrity. Driven by this intensive host-pathogen arms race, the piRNA pathway and its targeted transposons have coevolved rapidly in a species-specific manner, but how the piRNA pathway adapts specifically to target silencing in mammals remains elusive. Here, we show that mouse MILI and human HILI piRNA-induced silencing complexes (piRISCs) bind and cleave targets more efficiently than their invertebrate counterparts from the sponge Ephydatia fluviatilis.

Efficient retrosynthetic planning with MCTS exploration enhanced A search.

Retrosynthetic planning, which aims to identify synthetic pathways for target molecules from starting materials, is a fundamental problem in synthetic chemistry. Computer-aided retrosynthesis has made significant progress, in which heuristic search algorithms, including Monte Carlo Tree Search (MCTS) and A search, have played a crucial role. However, unreliable guiding heuristics often cause search failure due to insufficient exploration.

DrugGen: a database of de novo-generated molecular binders for specified target proteins.

De novo molecular generation is a promising approach to drug discovery, building novel molecules from the scratch that can bind the target proteins specifically. With the increasing availability of machine learning algorithms and computational power, artificial intelligence (AI) has emerged as a valuable tool for this purpose. Here, we have developed a database of 3D ligands that collects six AI models for de novo molecular generation based on target proteins, including 20 disease-associated targets.

KGDiff: towards explainable target-aware molecule generation with knowledge guidance.

Designing 3D molecules with high binding affinity for specific protein targets is crucial in drug design. One challenge is that the atomic interaction between molecules and proteins in 3D space has to be taken into account. However, the existing target-aware methods solely model the joint distribution between the molecules and proteins, disregarding the binding affinities between them, which leads to limited performance.

Self-Organizing a Latent Hierarchy of Sketch Patterns for Controllable Sketch Synthesis.

Encoding sketches as Gaussian mixture model (GMM)-distributed latent codes is an effective way to control sketch synthesis. Each Gaussian component represents a specific sketch pattern, and a code randomly sampled from the Gaussian can be decoded to synthesize a sketch with the target pattern. However, existing methods treat the Gaussians as individual clusters, which neglects the relationships between them.

A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk.

It is critical to accurately predict the rupture risk of an intracranial aneurysm (IA) for timely and appropriate treatment because the fatality rate after rupture is . Existing methods relying on morphological features (e.g.

RefinePocket: An Attention-Enhanced and Mask-Guided Deep Learning Approach for Protein Binding Site Prediction.

Protein binding site prediction is an important prerequisite task of drug discovery and design. While binding sites are very small, irregular and varied in shape, making the prediction very challenging. Standard 3D U-Net has been adopted to predict binding sites but got stuck with unsatisfactory prediction results, incomplete, out-of-bounds, or even failed.

MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders.

Accurate prediction of synergistic effects of drug combinations can reduce the experimental costs for drug development and facilitate the discovery of novel efficacious combination therapies for clinical studies. The drug combinations with high synergy scores are regarded as synergistic ones, while those with moderate or low synergy scores are additive or antagonistic ones. The existing methods usually exploit the synergy data from the aspect of synergistic drug combinations, paying little attention to the additive or antagonistic ones.

AlphaDrug: protein target specific de novo molecular generation.

Traditional drug discovery is very laborious, expensive, and time-consuming, due to the huge combinatorial complexity of the discrete molecular search space. Researchers have turned to machine learning methods for help to tackle this difficult problem. However, most existing methods are either virtual screening on the available database of compounds by protein-ligand affinity prediction, or unconditional molecular generation, which does not take into account the information of the protein target.

IA-FaceS: A bidirectional method for semantic face editing.

Semantic face editing has achieved substantial progress in recent years. However, existing face editing methods, which often encode the entire image into a single code, still have difficulty in enabling flexible editing while keeping high-fidelity reconstruction. The one-code scheme also brings entangled face manipulations and limited flexibility in editing face components.

GLH/VASA helicases promote germ granule formation to ensure the fidelity of piRNA-mediated transcriptome surveillance.

piRNAs function as guardians of the genome by silencing non-self nucleic acids and transposable elements in animals. Many piRNA factors are enriched in perinuclear germ granules, but whether their localization is required for piRNA biogenesis or function is not known. Here we show that GLH/VASA helicase mutants exhibit defects in forming perinuclear condensates containing PIWI and other small RNA cofactors.

Deep Rival Penalized Competitive Learning for low-resolution face recognition.

Current face recognition tasks are usually carried out on high-quality face images, but in reality, most face images are captured under unconstrained or poor conditions, e.g., by video surveillance.

Controllable stroke-based sketch synthesis from a self-organized latent space.

Learning to synthesize free-hand sketches controllably according to specified categories and sketching styles is a challenging task, due to the lack of training data with category labels and style labels. One choice to control the synthesis is by self-organizing a latent coding space to preserve the similarity of structural patterns of the observed data. A practical way is introducing a Gaussian mixture prior over the latent codes, where each Gaussian component represents a specific categorical or stylistic pattern.

Detection of Phenotype-Related Mutations of COVID-19 via the Whole Genomic Data.

The coronavirus disease 2019 (COVID-19) epidemic continues to spread rapidly around the world and nearly 20 millions people are infected. This paper utilises both single-locus analysis and joint-SNPs analysis for detection of significant single nucleotide polymorphisms (SNPs) in the phenotypes of symptomatic versus asymptomatic, the early collection time versus the late collection time, the old versus the young, and the male versus the female. Also, this paper analyses the relationship between any two SNPs via linkage disequilibrium analysis, and visualises the patterns of cumulative mutations of SNPs over collection time.

Integrated analysis of transcriptome-wide mA methylome of osteosarcoma stem cells enriched by chemotherapy.

To analyze the mA methylome of osteosarcoma stem cells (OSCs). Chemoresistant OSCs were enriched by doxorubicin treatment. Expression of mA-related enzymes was detected by quantitative real-time-PCR and western blot.

The TRIM-NHL protein NHL-2 is a co-factor in the nuclear and somatic RNAi pathways in . e.

Proper regulation of germline gene expression is essential for fertility and maintaining species integrity. In the germline, a diverse repertoire of regulatory pathways promote the expression of endogenous germline genes and limit the expression of deleterious transcripts to maintain genome homeostasis. Here we show that the conserved TRIM-NHL protein, NHL-2, plays an essential role in the germline, modulating germline chromatin and meiotic chromosome organization.

piRTarBase: a database of piRNA targeting sites and their roles in gene regulation.

PIWI-interacting RNAs (piRNAs) are a class of small noncoding RNAs that guard animal genomes against mutation by silencing transposons. In addition, recent studies have reported that piRNAs silence various endogenous genes. Tens of thousands of distinct piRNAs made in animals do not pair well to transposons and currently the functions and targets of piRNAs are largely unexplored.

Co-dependent Assembly of Drosophila piRNA Precursor Complexes and piRNA Cluster Heterochromatin.

In Drosophila, the piRNAs that guide germline transposon silencing are produced from heterochromatic clusters marked by the HP1 homolog Rhino. We show that Rhino promotes cluster transcript association with UAP56 and the THO complex, forming RNA-protein assemblies that are unique to piRNA precursors. UAP56 and THO are ubiquitous RNA-processing factors, and null alleles of uap56 and the THO subunit gene tho2 are lethal.

pirScan: a webserver to predict piRNA targeting sites and to avoid transgene silencing in C. elegans.

pirScan is a web-based tool for identifying C. elegans piRNA-targeting sites within a given mRNA or spliced DNA sequence. The purpose of our tool is to allow C.

A Sex Chromosome piRNA Promotes Robust Dosage Compensation and Sex Determination in C. elegans.

In metazoans, Piwi-related Argonaute proteins engage piRNAs (Piwi-interacting small RNAs) to defend the genome against invasive nucleic acids, such as transposable elements. Yet many organisms-including worms and humans-express thousands of piRNAs that do not target transposons, suggesting that piRNA function extends beyond genome defense. Here, we show that the X chromosome-derived piRNA 21ux-1 downregulates XOL-1 (XO Lethal), a master regulator of X chromosome dosage compensation and sex determination in Caenorhabditis elegans.