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Rad54 is a dsDNA-dependent ATPase that translocates on duplex DNA. Its ATPase function is essential for homologous recombination, a pathway critical for meiotic chromosome segregation, repair of complex DNA damage, and recovery of stalled or broken replication forks. In recombination, Rad54 cooperates with Rad51 protein and is required to dissociate Rad51 from heteroduplex DNA to allow access by DNA polymerases for recombination-associated DNA synthesis. Sequence analysis revealed that Rad54 contains a perfect match to the consensus PIP box sequence, a widely spread PCNA interaction motif. Indeed, Rad54 interacts directly with PCNA, but this interaction is not mediated by the Rad54 PIP box-like sequence. This sequence is located as an extension of motif III of the Rad54 motor domain and is essential for full Rad54 ATPase activity. Mutations in this motif render Rad54 non-functional in vivo and severely compromise its activities in vitro. Further analysis demonstrated that such mutations affect dsDNA binding, consistent with the location of this sequence motif on the surface of the cleft formed by two RecA-like domains, which likely forms the dsDNA binding site of Rad54. Our study identified a novel sequence motif critical for Rad54 function and showed that even perfect matches to the PIP box consensus may not necessarily identify PCNA interaction sites.

Rad54 is a dsDNA-dependent ATPase that translocates on duplex DNA. Its ATPase function is essential for homologous recombination, a pathway critical for meiotic chromosome segregation, repair of complex DNA damage, and recovery of stalled or broken replication forks. In recombination, Rad54 cooperates with Rad51 protein and is required to dissociate Rad51 from heteroduplex DNA to allow access by DNA polymerases for recombination-associated DNA synthesis. Sequence analysis revealed that Rad54 contains a perfect match to the consensus PIP box sequence, a widely spread PCNA interaction motif. Indeed, Rad54 interacts directly with PCNA, but this interaction is not mediated by the Rad54 PIP box-like sequence. This sequence is located as an extension of motif III of the Rad54 motor domain and is essential for full Rad54 ATPase activity. Mutations in this motif render Rad54 non-functional in vivo and severely compromise its activities in vitro. Further analysis demonstrated that such mutations affect dsDNA binding, consistent with the location of this sequence motif on the surface of the cleft formed by two RecA-like domains, which likely forms the dsDNA binding site of Rad54. Our study identified a novel sequence motif critical for Rad54 function and showed that even perfect matches to the PIP box consensus may not necessarily identify PCNA interaction sites.

Reinforcement learning (RL) techniques have been developed to optimize industrial cooling systems, offering substantial energy savings compared to traditional heuristic policies. A major challenge in industrial control involves learning behaviors that are feasible in the real world due to machinery constraints. For example, certain actions can only be executed every few hours while other actions can be taken more frequently. Without extensive reward engineering and experimentation, an RL agent may not learn realistic operation of machinery. To address this, we use hierarchical reinforcement learning with multiple agents that control subsets of actions according to their operation time scales. Our hierarchical approach achieves energy savings over existing baselines while maintaining constraints such as operating chillers within safe bounds in a simulated HVAC control environment.

Enteroviral infection can lead to dilated cardiomyopathy （DCM）, which is a major cause of cardiovascular mortal- ity worldwide. However, the pathogenetic mechanisms have not been fully elucidated. Serum response factor （SRF） is a cardiac-enriched transcription regulator controlling the expression of a variety of target genes, including those involved in the contractile apparatus and immediate early response, as well as microRNAs that silence the expres- sion of cardiac regulatory factors. Knockout of SRF in the heart results in downregulation of cardiac contractile gene expression and development of DCM. The goal of this study is to understand the role of SRF in enterovirus-induced cardiac dysfunction and progression to DCM. Here we report that SRF is cleaved following enteroviral infection of mouse heart and cultured cardiomyocytes. This cleavage is accompanied by impaired cardiac function and downreg- ulation of cardiac-specific contractile and regulatory genes. Further investigation by antibody epitope mapping and site-directed mutagenesis demonstrates that SRF cleavage occurs at the region of its transactivation domain through the action of virus-encoded protease 2A. Moreover, we demonstrate that cleavage of SRF dissociates its transactiva- tion domain from DNA-binding domain, resulting in the disruption of SRF-mediated gene transactivation. In addi- tion to loss of functional SRF, finally we report that the N-terminal fragment of SRF cleavage products can also act as a dominant-negative transcription factor, which likely competes with the native SRF for DNA binding. Our results suggest a mechanism by which virus infection impairs heart function and may offer a new therapeutic strategy to ameliorate myocardial damage and progression to DCM.

We present a hybrid industrial cooling system model that embeds analytical solutions within a multi-physics simulation. This model is designed for reinforcement learning (RL) applications and balances simplicity with simulation fidelity and interpretability. The model's fidelity is evaluated against real world data from a large scale cooling system. This is followed by a case study illustrating how the model can be used for RL research. For this, we develop an industrial task suite that allows specifying different problem settings and levels of complexity, and use it to evaluate the performance of different RL algorithms.

This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted live experiments on two real-world facilities in partnership with Trane Technologies, a building management system provider. These live experiments had a variety of challenges in areas such as evaluation, learning from offline data, and constraint satisfaction. Our paper describes these challenges in the hope that awareness of them will benefit future applied RL work. We also describe the way we adapted our RL system to deal with these challenges, resulting in energy savings of approximately 9% and 13% respectively at the two live experiment sites.

We propose two practical non-convex approaches for learning near-isometric, linear embeddings of finite sets of data points. Given a set of training points \\(\\mathcal{X}\\), we consider the secant set \\(S(\\mathcal{X})\\) that consists of all pairwise difference vectors of \\(\\mathcal{X}\\), normalized to lie on the unit sphere. The problem can be formulated as finding a symmetric and positive semi-definite matrix \\(\\boldsymbol{\\Psi}\\) that preserves the norms of all the vectors in \\(S(\\mathcal{X})\\) up to a distortion parameter \\(\\delta\\). Motivated by non-negative matrix factorization, we reformulate our problem into a Frobenius norm minimization problem, which is solved by the Alternating Direction Method of Multipliers (ADMM) and develop an algorithm, FroMax. Another method solves for a projection matrix \\(\\boldsymbol{\\Psi}\\) by minimizing the restricted isometry property (RIP) directly over the set of symmetric, postive semi-definite matrices. Applying ADMM and a Moreau decomposition on a proximal mapping, we develop another algorithm, NILE-Pro, for dimensionality reduction. FroMax is shown to converge faster for smaller \\(\\delta\\) while NILE-Pro converges faster for larger \\(\\delta\\). Both non-convex approaches are then empirically demonstrated to be more computationally efficient than prior convex approaches for a number of applications in machine learning and signal processing.

Acquired heterotopic ossification (HO) is the extraskeletal bone formation after trauma. Various mesenchymal progenitors are reported to participate in ectopic bone formation. Here we induce acquired HO in mice by Achilles tenotomy and observe that conditional knockout (cKO) of fibroblast growth factor receptor 3 ( FGFR3 ) in Col2 + cells promote acquired HO development. Lineage tracing studies reveal that Col2 + cells adopt fate of lymphatic endothelial cells (LECs) instead of chondrocytes or osteoblasts during HO development. FGFR3 cKO in Prox1 + LECs causes even more aggravated HO formation. We further demonstrate that FGFR3 deficiency in LECs leads to decreased local lymphatic formation in a BMPR1a-pSmad1/5-dependent manner, which exacerbates inflammatory levels in the repaired tendon. Local administration of FGF9 in Matrigel inhibits heterotopic bone formation, which is dependent on FGFR3 expression in LECs. Here we uncover Col2 + lineage cells as an origin of lymphatic endothelium, which regulates local inflammatory microenvironment after trauma and thus influences HO development via FGFR3-BMPR1a pathway. Activation of FGFR3 in LECs may be a therapeutic strategy to inhibit acquired HO formation via increasing local lymphangiogenesis. Different types of mesenchymal progenitors participate in ectopic bone formation. Here, the authors show Col2 + lineage cells adopt a lymphatic endothelium cell fate, which regulates local inflammatory microenvironment after trauma, thus influencing heterotopic ossification (HO) development via a FGFR3-BMPR1a pathway.

Anopheles sinensis is the main vector of malaria with a wide distribution in China and its adjacent countries. The smoke from burning dried mugwort leaves has been commonly used to repel and kill mosquito adults especially in southern Chinese provinces. In this study, the essential oils of mugwort leaves collected from seven provinces in China were extracted by steam distillation and their chemical compositions were analyzed. Among a total of 56–87 chemical constituents confirmed by GC–MS analyses, four compounds, eucalyptol, β-caryophyllene, phytol and caryophyllene oxide, were identified with appearances from all seven distilled essential oils. The effectiveness varied in larvicidal, fumigant and repellent activities against An . sinensis from these seven essential oils with different geographic origins. The essential oil from Hubei province showed the highest larvicidal activity against the 4th instar larvae of An. sinensis , with a median lethal concentration at 40.23 µg/mL. For fumigation toxicity, essential oils from 4 provinces (Gansu, Shandong, Sichuan and Henan) were observed with less than 10 min in knockdown time. The essential oil distilled from Gansu province displayed the highest repellent activity against Anopheles mosquitoes and provided similar level of protection as observed from DEET. Eucalyptol was the most toxic fumigant compound and phytol showed the strongest larvicidal activity among all tested mugwort essential oil constituents.

Purpose of Review The changes or updates in ocean biogeochemistry component have been mapped between CMIP5 and CMIP6 model versions, and an assessment made of how far these have led to improvements in the simulated mean state of marine biogeochemical models within the current generation of Earth system models (ESMs). Recent Findings The representation of marine biogeochemistry has progressed within the current generation of Earth system models. However, it remains difficult to identify which model updates are responsible for a given improvement. In addition, the full potential of marine biogeochemistry in terms of Earth system interactions and climate feedback remains poorly examined in the current generation of Earth system models. Summary Increasing availability of ocean biogeochemical data, as well as an improved understanding of the underlying processes, allows advances in the marine biogeochemical components of the current generation of ESMs. The present study scrutinizes the extent to which marine biogeochemistry components of ESMs have progressed between the 5th and the 6th phases of the Coupled Model Intercomparison Project (CMIP).