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The paternal genome undergoes a massive exchange of histone with protamine for compaction into sperm during spermiogenesis. Upon fertilization, this process is potently reversed, which is essential for parental genome reprogramming and subsequent activation; however, it remains poorly understood how this fundamental process is initiated and regulated. Here, we report that the previously characterized splicing kinase SRPK1 initiates this life-beginning event by catalyzing site-specific phosphorylation of protamine, thereby triggering protamine-to-histone exchange in the fertilized oocyte. Interestingly, protamine undergoes a DNA-dependent phase transition to gel-like condensates and SRPK1-mediated phosphorylation likely helps open up such structures to enhance protamine dismissal by nucleoplasmin (NPM2) and enable the recruitment of HIRA for H3.3 deposition. Remarkably, genome-wide assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis reveals that selective chromatin accessibility in both sperm and MII oocytes is largely erased in early pronuclei in a protamine phosphorylation-dependent manner, suggesting that SRPK1-catalyzed phosphorylation initiates a highly synchronized reorganization program in both parental genomes.

Federated learning technology solves the problems of data islands and privacy leakage to a certain extent.However it has shortcomings such as high communication cost,unstable communication,and uneven distribution of participant performance.In order to overcome these shortcomings and achieve a balance between model effectiveness,fairness,and communication costs,an improved NSGA-III algorithm for multi-objective optimization of federated learning is proposed.First,a federated learning multi-objective optimization model is constructed to maximize the accuracy of the global model,minimize the variance of the global mo-del accuracy distribution and minimize the communication cost of participant,and an improved NSGA-III algorithm based on fast greedy initialization is proposed,which improves the efficiency of NSGA-III for multi-objective optimization of federated learning.Experimental results show that the proposed optimization method can obtain a better Pareto solution than the classical multi-objective evolutionary algorithm.Compared with the standard model of federated learning,the optimized model can effectively lower the communication cost and the variance of the global model accuracy distribution while ensuring the accuracy of the global model.

Based on the idea of crowdsourcing,mobile crowdsensing(MCS) collects mobile sensing devices to sense the surroun-ding environment,which can make environment sensing and information collection more flexible,convenient and efficient.Whe-ther the task allocation strategy is reasonable or not directly affects the success of the sensing task.Therefore,formulating a reasonable task allocation strategy is a hotspot and focus in the research of MCS.At present,most of the task allocation methods in MCS systems are offline and targeted at single type tasks.However,in practice,online multi-type task allocation is more common.Therefore,this paper studies the task allocation method in MCS for multiple types of tasks,and proposes an online task allocation strategy oriented to system benefits combined with the characteristics of MCS technology in the military field.In this paper,a long-term,dynamic online task allocation system model is established,and the problem is solved based on Lyapunov optimization theory with the system benefit as the optimization goal,so that the online dynamic control of task admission strategy and task allocation scheme is realized.Experiment shows that the online task allocation algorithm proposed in this paper is effective and feasible.It can reasonably allocate the tasks arriving at the MCS system online,ensure the stability of the task queue,and increase the system utility by adjusting the parameter value.

The cardiac transcription factor (TF) gene NKX2-5 has been associated with electrocardiographic (EKG) traits through genome-wide association studies (GWASs), but the extent to which differential binding of NKX2-5 at common regulatory variants contributes to these traits has not yet been studied. We analyzed transcriptomic and epigenomic data from induced pluripotent stem cell-derived cardiomyocytes from seven related individuals, and identified ~2,000 single-nucleotide variants associated with allele-specific effects (ASE-SNVs) on NKX2-5 binding. NKX2-5 ASE-SNVs were enriched for altered TF motifs, for heart-specific expression quantitative trait loci and for EKG GWAS signals. Using fine-mapping combined with epigenomic data from induced pluripotent stem cell-derived cardiomyocytes, we prioritized candidate causal variants for EKG traits, many of which were NKX2-5 ASE-SNVs. Experimentally characterizing two NKX2-5 ASE-SNVs (rs3807989 and rs590041) showed that they modulate the expression of target genes via differential protein binding in cardiac cells, indicating that they are functional variants underlying EKG GWAS signals. Our results show that differential NKX2-5 binding at numerous regulatory variants across the genome contributes to EKG phenotypes.

Nuclear receptors induce both transcriptional activation and repression programs responsible for development, homeostasis, and disease. Here, we report a previously overlooked enhancer decommissioning strategy underlying a large estrogen receptor alpha (ERα)-dependent transcriptional repression program. The unexpected signature for this E2-induced program resides in indirect recruitment of ERα to a large cohort of pioneer factor basally active FOXA1-bound enhancers that lack cognate ERα DNA-binding elements. Surprisingly, these basally active estrogen-repressed (BAER) enhancers are decommissioned by ERα-dependent recruitment of the histone demethylase KDM2A, functioning independently of its demethylase activity. Rather, KDM2A tethers the E3 ubiquitin-protein ligase NEDD4 to ubiquitylate/dismiss Pol II to abrogate eRNA transcription, with consequent target gene downregulation. Thus, our data reveal that Pol II ubiquitylation/dismissal may serve as a potentially broad strategy utilized by indirectly bound nuclear receptors to abrogate large programs of pioneer factor-mediated, eRNA-producing enhancers.

Enhancers for embryonic stem (ES) cell-expressed genes and lineage-determining factors are characterized by conventional marks of enhancer activation in ES cells1-3, but it remains unclear whether enhancers destined to regulate cell-type-restricted transcription units might also have distinct signatures in ES cells. Here we show that cell-type-restricted enhancers are 'premarked' and activated as transcription units by the binding of one or two ES cell transcription factors, although they do not exhibit traditional enhancer epigenetic marks in ES cells, thus uncovering the initial temporal origins of cell-type-restricted enhancers. This premarking is required for future cell-type-restricted enhancer activity in the differentiated cells, with the strength of the ES cell signature being functionally important for the subsequent robustness of cell-type-restricted enhancer activation. We have experimentally validated this model in macrophage-restricted enhancers and neural precursor cell (NPC)-restricted enhancers using ES cell-derived macrophages or NPCs, edited to contain specific ES cell transcription factor motif deletions. DNA hydroxyl-methylation of enhancers in ES cells, determined by ES cell transcription factors, may serve as a potential molecular memory for subsequent enhancer activation in mature macrophages. These findings suggest that the massive repertoire of cell-type-restricted enhancers are essentially hierarchically and obligatorily premarked by binding of a defining ES cell transcription factor in ES cells, dictating the robustness of enhancer activation in mature cells.

The molecular mechanisms underlying the opposing functions of glucocorticoid receptors (GRs) and estrogen receptor α (ERα) in breast cancer development remain poorly understood. Here we report that, in breast cancer cells, liganded GR represses a large ERα-activated transcriptional program by binding, in trans, to ERα-occupied enhancers. This abolishes effective activation of these enhancers and their cognate target genes, and it leads to the inhibition of ERα-dependent binding of components of the MegaTrans complex. Consistent with the effects of SUMOylation on other classes of nuclear receptors, dexamethasone (Dex)-induced trans-repression of the estrogen E2 program appears to depend on GR SUMOylation, which leads to stable trans-recruitment of the GR-N-CoR/SMRT-HDAC3 corepressor complex on these enhancers. Together, these results uncover a mechanism by which competitive recruitment of DNA-binding nuclear receptors/transcription factors in trans to hot spot enhancers serves as an effective biological strategy for trans-repression, with clear implications for breast cancer and other diseases.

Knowledge graph embedding represents entities and relations as low-dimensional continuous vectors. Recently, researchers have attempted to leverage the potential semantic connections between entities with hierarchical relationships in the knowledge graph. However, existing knowledge embedding methods that model entity hierarchical structures face the following challenges: (1) Setting the same embedding dimension for entities at different hierarchical levels can lead to overfitting issues and the waste of storage and computational resources; (2) Manually searching the embedding dimension in discrete space makes it easy to miss the optimal parameters and increases training costs. Therefore, we propose a knowledge embedding method, HEAES, that automatically learns the embedding dimensions for entities based on their hierarchical structure. Specifically, we first propose a new modeling method to capture the hierarchical relationships of entities, and we then train the model on a triple classification task. Then, we adaptively learn the pruning threshold to trim the embedding vectors, automatically learning different embedding dimensions for entities at different hierarchical levels. Experiments on the YAGO26K and DB111K datasets verify that introducing entity hierarchical information helps boost the model performance. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) is a promising distributed machine learning framework that emphasizes privacy protection. However, inconsistencies between local optimization objectives and the global objective, commonly referred to as client drift, primarily arise due to non-independently and identically distributed (Non-IID) data, multiple local training steps, and partial client participation in training. The majority of current research tackling this challenge is mainly based on the empirical risk minimization (ERM) principle, while giving little consideration to the connection between the global loss landscape and generalization capability. This study proposes FedGAM, an innovative FL algorithm that incorporates Gradient Norm-Aware Minimization (GAM) to efficiently search for a local flat landscape. FedGAM specifically modifies the client model training objective to simultaneously minimize the loss value and first-order flatness, thereby seeking flat minima. To directly smooth the global flatness, we propose the more significant FedGAM-CV, which employs control variables to correct local updates, guiding each client to train models in a globally flat direction. Experiments on three datasets (CIFAR-10, MNIST, and FashionMNIST) demonstrate that our proposed algorithms outperform existing FL baselines, effectively finding flat minima and addressing the client drift problem. [ABSTRACT FROM AUTHOR]

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks. [ABSTRACT FROM AUTHOR]

Motivation Small non-coding RNAs (ncRNAs), especially microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs), play key roles in many biological processes. However, only a few tools can be used to develop the optimal primer or probe design for the expression profile of small ncRNAs. Here, we developed sRNAPrimerDB, the first automated primer designing and query web service for small ncRNAs. Results The primer online designing module of sRNAPrimerDB is composed of primer design algorithms and quality evaluation of the polymerase chain reaction (PCR) primer. Five types of primers, namely, generic or specific reverse transcription primers, specific PCR primers pairs, TaqMan probe, double-hairpin probe and hybridization probe for different small ncRNA detection methods, can be designed and searched using this service. The quality of PCR primers is further evaluated using melting temperature, primer dimer, hairpin structure and specificity. Moreover, the sequence and size of each amplicon are also provided for the subsequent experiment verification. At present, 531 306 and 2 941 669 primer pairs exist across 223 species for miRNAs and piRNAs, respectively, according to sRNAPrimerDB. Several primers designed by sRNAPrimerDB are further successfully validated by subsequent experiments. Availability and implementation sRNAPrimerDB is a valuable platform that can be used to detect small ncRNAs. This module can be publicly accessible at http://www.srnaprimerdb.com or http://123.57.239.141. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]