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Person re-identification (ReID) technology has many applications in intelligent surveillance and public safety. However, the domain difference between the source and target domains makes the generalization ability of the model extremely challenging. To reduce the dependence on labeled data, Unsupervised Domain Adaptation (UDA) methods have become an effective way to solve this problem. However, the influence of pseudo-label generated noise on model training in existing UDA methods is still significant, resulting in limited model performance on the target domain. For this reason, this paper proposes a contrast learning-based pseudo-label refinement with probabilistic uncertainty in the unsupervised domain, adapted to Person re-identification, aiming to improve the effectiveness of the unsupervised domain adapted to Person re-identification. We first enhance the feature representation of the target domain samples based on the contrast learning technique to improve their discrimination in the feature space, thereby enhancing the cross-domain migration performance of the model. Subsequently, an innovative loss function is proposed to effectively reduce the interference of label noise on the training process by refining the generation process of pseudo-labels, which solves the negative impact of inaccurate pseudo-labels on model training. Through a series of experimental validation, the method experiments on two large-scale public datasets, Market1501 and DukeMTMC, and the Rank-1 accuracy of the proposed method reaches 91.4% and 81.4%, with the mean average precision (mAP) of 79.0% and 67.9%, respectively, which proves that the research in this paper provides a good solution for the Person re-identification task with effective technical support for label noise processing and model generalization capability improvement.

Mitigation solutions are often evaluated in terms of costs and greenhouse gas reduction potentials, missing out on the consideration of direct effects on human well-being. Here, we systematically assess the mitigation potential of demand-side options categorized into avoid, shift and improve, and their human well-being links. We show that these options, bridging socio-behavioural, infrastructural and technological domains, can reduce counterfactual sectoral emissions by 40–80% in end-use sectors. Based on expert judgement and an extensive literature database, we evaluate 306 combinations of well-being outcomes and demand-side options, finding largely beneficial effects in improvement in well-being (79% positive, 18% neutral and 3% negative), even though we find low confidence on the social dimensions of well-being. Implementing such nuanced solutions is based axiomatically on an understanding of malleable rather than fixed preferences, and procedurally on changing infrastructures and choice architectures. Results demonstrate the high mitigation potential of demand-side mitigation options that are synergistic with well-being.Evaluation of mitigation actions often focuses on cost and overlooks the direct effects on well-being. This work shows demand-side measures have large mitigation potential and beneficial effects on well-being outcomes.

Xuemei Bai and colleagues call for long-term, cross-disciplinary studies to reduce carbon emissions and urban risks from global warming. Xuemei Bai and colleagues call for long-term, cross-disciplinary studies to reduce carbon emissions and urban risks from global warming.

Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1 , PPM1A and DLG2 . We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity. Gene modules are widespread and important for studying cancer. Here, authors propose an explainable deep learning-based framework, CGMega, which incorporates multi-omics information from the three-dimensional genome, epigenome, and protein-protein interactions to dissect cancer gene modules.

In recent years, the increasing adoption of High-Performance Computing (HPC) clusters in scientific research and engineering has exposed challenges such as resource imbalance, node idleness, and overload, which hinder scheduling efficiency. Accurate multidimensional task prediction remains a key bottleneck. To address this, we propose a hybrid prediction model that integrates Informer, Long Short-Term Memory (LSTM), and Graph Neural Networks (GNN), enhanced by a hierarchical attention mechanism combining multi-head self-attention and cross-attention. The model captures both long- and short-term temporal dependencies and deep semantic relationships across features. Built on a multitask learning framework, it predicts task execution time, CPU usage, memory, and storage demands with high accuracy. Experiments show prediction accuracies of 89.9%, 87.9%, 86.3%, and 84.3% on these metrics, surpassing baselines like Transformer-XL. The results demonstrate that our approach effectively models complex HPC workload dynamics, offering robust support for intelligent cluster scheduling and holding strong theoretical and practical significance.

The signaling adaptor MAVS forms prion-like aggregates to activate the innate antiviral immune response after viral infection. However, spontaneous aggregation of MAVS can lead to autoimmune diseases. The molecular mechanism that prevents MAVS from spontaneous aggregation in resting cells has been enigmatic. Here we report that protein arginine methyltransferase 9 targets MAVS directly and catalyzes the arginine methylation of MAVS at the Arg41 and Arg43. In the resting state, this modification inhibits MAVS aggregation and autoactivation of MAVS. Upon virus infection, PRMT9 dissociates from the mitochondria, leading to the aggregation and activation of MAVS. Our study implicates a form of post-translational modification on MAVS, which can keep MAVS inactive in physiological conditions to maintain innate immune homeostasis. The anti-viral protein MAVS forms aggregates as part of the antiviral response and promoting type I IFN responses. Here the authors show that protein arginine methyltransferase 9 (PRMT9) methylates MAVS to keep the protein in a non-aggregated state and propose a regulatory mechanism for MAVS.

With the rapid growth of computation-intensive applications, high-performance computing (HPC) clusters have become essential for scientific computing, AI training, and industrial simulation. However, job scheduling in HPC clusters remains challenging due to heterogeneous resources, diverse task demands, and complex constraints. Traditional scheduling methods such as FCFS, SJF, and Backfilling show limited adaptability and struggle to achieve global optimization in large-scale environments. To address these issues, this paper proposes an intelligent scheduling method based on graph neural networks (GNNs) and deep reinforcement learning. A resource-constrained job–node bipartite graph is constructed to model task–node matching relationships, with node and task features capturing resource states and task demands. A GNN is employed to encode the scheduling state, and an Actor–Critic reinforcement learning framework is used to guide scheduling decisions. Simulation results show that, compared with other schedulers, the proposed GNN–Actor–Critic approach significantly improves average waiting time, average turnaround time, average slowdown, and overall resource utilization, demonstrating its effectiveness and practicality for HPC cluster scheduling.

Integrating urban green infrastructure (UGI) into cities is receiving increasing attention owing to its potential to provide various urban ecosystem services (UES). This review assesses the multifaceted services of UGI as benefits and co-benefits. By combining systematic and narrative review processes, we aim to synthesise existing knowledge along six current themes and identify research gaps. A total of 690 peer-reviewed articles published during 2000–2020 from Web of Science were selected, followed by bibliometric and full-text analysis. Based on the frequency of appearance in the network visualisation of keywords, six themes of current trends were identified, namely: (1) benefits of UGI as UES; (2) mitigating climate and urban climate impacts by UGI; (3) UGI contribution to sustainable development goals; (4) reconceptualising greenspaces as ‘safe havens’; (5) public acknowledgement and supportive governance for UGI; and (6) rethinking the operationalisability of UGI. The first two themes represent existing focus on categories of ecosystem services, the next two encompass broader emerging co-benefits and the last two focus on how to operationalise UGI and support widespread adoption and implementation. Within Theme 2, the most frequently discussed service with the largest number of research, we conducted a detailed analysis of the methods and content focus in the existing literature. Through a narrative review, we identified 15 research gaps throughout these 6 themes. This review provides a comprehensive overview for urban researchers and practitioners to inform the integration of urban green infrastructure into urban planning and management.

Transformative governance is key to addressing the global environmental crisis. We explore how transformative governance of complex biodiversity–climate–society interactions can be achieved, drawing on the first joint report between the Intergovernmental Panel on Climate Change and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services to reflect on the current opportunities, barriers, and challenges for transformative governance. We identify principles for transformative governance under a biodiversity–climate–society nexus frame using four case studies: forest ecosystems, marine ecosystems, urban environments, and the Arctic. The principles are focused on creating conditions to build multifunctional interventions, integration, and innovation across scales; coalitions of support; equitable approaches; and positive social tipping dynamics. We posit that building on such transformative governance principles is not only possible but essential to effectively keep climate change within the desired 1.5 degrees Celsius global mean temperature increase, halt the ongoing accelerated decline of global biodiversity, and promote human well-being.

Well-intended climate actions are confounding each other. Cities must take a strategic and integrated approach to lock into a climate-resilient and low-emission future.