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Recent evidence shows that the North Pacific subtropical gyre and the Kuroshio Extension (KE) and Oyashio Extension (OE) fronts have moved poleward in the past few decades. However, changes of the North Pacific Subtropical Fronts (STFs), anchored by the North Pacific subtropical countercurrent in the southern subtropical gyre, remain to be quantified. By synthesizing observations, reanalysis, and eddy-resolving ocean hindcasts, we show that the STFs, especially their eastern part, weakened (20% ± 5%) and moved poleward (1.6° ± 0.4°) from 1980 to 2018. Changes of the STFs are modified by mode waters to the north. We find that the central mode water (CMW) (180°–160°W) shows most significant weakening (18% ± 7%) and poleward shifting (2.4° ± 0.9°) trends, while the eastern part of the subtropical mode water (STMW) (160°E–180°) has similar but moderate changes (10% ± 8%; 0.9° ± 0.4°). Trends of the western part of the STMW (140°–160°E) are not evident. The weakening and poleward shifting of mode waters and STFs are enhanced to the east and are mainly associated with changes of the northern deep mixed layers and outcrop lines—which have a growing northward shift as they elongate to the east. The eastern deep mixed layer shows the largest shallowing trend, where the subduction rate also decreases the most. The mixed layer and outcrop line changes are strongly coupled with the northward migration of the North Pacific subtropical gyre and the KE/OE jets as a result of the poleward expanded Hadley cell, indicating that the KE/OE fronts, mode waters, and STFs change as a whole system.

This study investigates the interannual variability of the North Pacific Central Mode Water (CMW) under the phase relationship of the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), based on multiple observational data sets. Peaks and troughs of the CMW variability are primarily observed when ENSO and PDO are in phase, but only moderate variation when ENSO and PDO are out of phase. In El Niño spring during positive PDO, extreme CMW ventilation takes place in the central North Pacific (180°–155°W, 30°–40°N), where no local ventilation occurs for other cases. Such extreme CMW ventilation induces stronger temperature anomalies, which persist longer and penetrate deeper. Our results suggest that CMW, representing a long‐term ocean memory, may play a more significant role in tropical‐extratropical interactions than ever expected. Plain Language Summary The North Pacific Central Mode Water (CMW) is a vertically homogeneous thermocline water mass in the central North Pacific, affecting the subtropical gyre's large‐scale circulation and the storage of heat and carbon. Previous studies mainly focus on the CMW variability forced by Pacific Decadal Oscillation (PDO), without considering the relative phase of the El Niño–Southern Oscillation (ENSO). The present study finds that the interannual variability of CMW is significantly related to the ENSO‐PDO phase relationship. For in‐phase conditions, the CMW is strongest in the decay year of El Niño during positive PDO, and the CMW is weakest in the decay year of La Niña during negative PDO. For out‐of‐phase conditions, by contrast, the CMW variation is moderate. After El Niño winter during positive PDO, the strongest surface buoyancy loss sharply deepens the mixed layer and injects well‐mixed surface water into the thermocline in the central North Pacific (180°–155°W, 30°–40°N), where no such “ventilation” occurs for other cases. The extreme CMW ventilation favors the transport of anthropogenic heat, carbon, oxygen & nutrient rich waters into the deep ocean, important for the climate system. Key Points Abnormal ventilation of the North Pacific Central Mode Water (CMW) occurs east of the Dateline after El Niño winter during positive Pacific Decadal Oscillation (PDO) The CMW is strongest (weakest) in the decay year of El Niño (La Niña) when accompanied by a positive (negative) PDO When ENSO and PDO are in phase, the CMW temperature anomalies are stronger, persist longer and penetrate deeper

Duolang sheep, a meat-fat dual-purpose breed indigenous to Xinjiang, China, has been cultivated traditionally by the local Uyghur people for its prolificacy and precocious sexual maturity, while little research on the population structure and trait inheritance characteristics of Duolang sheep is available. This study employed whole-genome resequencing data from a cohort of 60 Duolang sheep to dissect their genetic population structure and genes related to reproductive traits. A total of 1565 Gb of high-quality data with an average depth of 14.06× was generated. After SNP calling and quality control, 31,300,060 SNPs were identified. Following linkage disequilibrium (LD)-based pruning, a total of 4,479,177 high-quality SNPs were retained for subsequent analyses. Based on these SNPs, the internal genetic structure of the Duolang sheep population was elucidated, with 14 kinship outliers detected through principal component analysis (PCA). Furthermore, LD decay analysis revealed that the r declined below 0.1 at approximately 10 kb, indicating a relatively low level of selection pressure in the population. Within the population, Tajima's D and iHS methods detected 517,218 and 82,534 candidate SNPs under selection, respectively, with 24,453 SNPs overlapping between the two methods. By splitting Duolang sheep into single-lamb ( = 29) and multiple-lamb ( = 12) subgroups according to litter size, 267,654 SNPs were identified by XP-CLR, while 184,179 SNPs suffering from selection were detected by and 62,150 by XP-EHH. Functional enrichment analysis of selected genes reveals the selection directions (domestication, growth, and reproduction) and related candidate genes in the Duolang sheep population, including , , , , , , and . This study provides the first comprehensive genomic landscape of Duolang sheep, elucidating genetic signatures of its adaptive traits.

This study delves into the therapeutic potential of Qi Ling Gui Fu Prescription (QLGFP) in broiler ascites syndrome (AS) by investigating its impact on the phenotypic transformation of vascular smooth muscle. Utilizing network pharmacology, we identified 267 active ingredients and 120 core targets of QLGFP, revealing its multifaceted mechanism of action. Gene enrichment analysis highlighted the pivotal roles of Toll-like receptor, FoxO, and MAPK signaling pathways in QLGFP’s therapeutic effects. Experimental validation in a broiler AS model demonstrated that QLGFP regulated the expression of key markers (SM-22α, OPN, and KLF4) associated with the phenotypic transformation of pulmonary artery vascular smooth muscle (PASMC). Clinical improvements were evident, with a significant reduction in ascites cardiac index (AHI). Furthermore, QLGFP suppressed the protein expression of MAPK1 (ERK1), p-MAPK1, MAPK9 (JNK2), p-MAPK9, MA3.PK14 (P38α), and p-MAPK14, along with downstream factors AP1 and ATF4. These findings suggest that QLGFP effectively prevents and treats AS in broilers by modulating the MAPKs-AP1/ATF4 pathway, thereby inhibiting the phenotypic transformation and proliferation of PASMCs. This study contributes a theoretical foundation for understanding the role of QLGFP in the prevention and treatment of AS in broilers.

Although broiler ascites syndrome (AS) has been extensively studied, its pathogenesis remains unclear. The lack of cardiopulmonary function in broilers causes relative hypoxia in the body; hence, the lung is the main target organ of AS. However, the transcriptome of AS lung tissue in broilers has not been studied. In this study, an AS model was successfully constructed, and lung tissues of three AS broilers and three healthy broilers were obtained for RNA sequencing (RNA-seq) and pathological observation. The results showed that 614 genes were up-regulated and 828 genes were down-regulated in the AS group compared with the normal group. Gene Ontology (GO) functional annotation revealed the following up-regulated genes: FABP4, APLN, EIF2AK4, HMOX1, MMP9, THBS1, TLR4, BCL2; and down-regulated genes: APELA, FGF7, WNT5A, CDK6, IL7, IL7R, APLNR. These genes have attracted much attention in cardiovascular diseases such as pulmonary hypertension. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that multiple metabolic processes were enriched, indicating abnormal lung metabolism of AS in broilers. These findings elucidate the potential genes and signal pathways in the lungs of broilers with AS and provide a potential target for studying the pathogenesis and preventing AS.

Domain generalization based Face Anti-spoofing (FAS) aims to enhance its ability to work in unseen domains. Existing methods endeavor to extract a discriminative common space through the alignment of distribution in each domain. However, he inherent diversity within spoof faces significantly challenges the establishment of such a unified space. In this work, we reframe domain generalization-based FAS as an anomaly detection problem, positing that real faces tend to aggregate within a compact, closed space, whereas spoof faces exhibit a preference for dispersion within an open space. Specifically, we introduce a novel Closed Space Domain Generalization (CSDG) framework, consisting of a novel designed Dynamic Feature Queue and a Domain Alignment Module. The former is dedicated to maintaining a distinct class center for real faces, achieved by continuously widening its separation from the dynamically evolving spoof face queue; The latter aims to further align the distribution of real faces across diverse domains. Moreover, we propose a Progressive Training Strategy to effectively mine challenging samples across multiple domains during the training phase. Furthermore, we highlight the success of our proposed methods by achieving the first prize in the Surveillance Face Anti-Spoofing track at Challenge@CVPR 2023. Subsequently, we demonstrate the efficacy of the CSDG framework on two intra-domain datasets, as well as in two challenging cross-domain FAS experiments.

Variable selection involves selecting truly important predictors from p-dimensional multivariate functional predictors in functional predictive models. In this paper, a variable selection method is designed for scalar-on-function predictions entangled with nonlinear joint associations among scalar response and multiple functional predictors. First, a nonparametric functional nonlinear conditional correlation coefficient, namely, the FunNCC coefficient, is proposed to measure complex dependencies, including the nonmonotonic marginal dependence, along with the conditional associations of redundancy, complement, and interaction. Then, a model-free feature ordering and selection method is designed, where the FunNCC is utilized to rank relevance, enabling the selection of a subset of predictors with the strongest joint dependence. Since this method allows for quantitatively evaluating the contributions of predictors in explaining responses, it achieves moderate model interpretability. Finally, extensive simulation studies and two real-data cases involving air pollution regression and hand gesture recognition are conducted to evaluate the finite sample performance of the proposed method, and the results show that the proposed FunNCC and variable selection methods outperform state-of-the-art baselines.

This paper attempts to derive a mathematical formulation for real-practice clinical laboratory schedul-ing, and to present a syntactic adaptive problem solver by leveraging landscape structures. After formulating scheduling of medical tests as a distributed scheduling problem in heterogeneous, flexible job shop environment, we establish a mixed integer programming model to minimize mean test turn-around time. Preliminary landscape analysis sustains that these clinics-orientated scheduling instances are difficult to solve. The search difficulty motivates the search for an adaptive problem solver to reduce repetitive algorithm-tuning work, but with a guaranteed convergence. Yet, under a search strategy, relatedness from exploitation competence to landscape topology is not transparent. Under strategies that impose different-magnitude perturbations, we investigate changes in landscape struc-ture and find that disturbance amplitude, local-global optima connectivity, landscape's ruggedness and plateau size fairly predict strategies' efficacy. Medium-size instances of 100 tasks are easier un-der smaller-perturbation strategies that lead to smoother landscapes with smaller plateaus. For large-size instances of 200-500 tasks, existing strategies at hand, having either larger or smaller perturba-tions, face more rugged landscapes with larger plateaus that impede search. Our hypothesis that me-dium perturbations may generate smoother landscapes with smaller plateaus drives our design of this new strategy and its verification by experiments. Composite neighborhoods managed by meta-Lamarckian learning show beyond average performance, implying reliability when prior knowledge of landscape is unknown.

Recent advancements have shown that reinforcement learning (RL) can substantially improve the reasoning abilities of large language models (LLMs). The effectiveness of such RL training, however, depends critically on the exploration space defined by the pre-trained model's token-output distribution. In this paper, we revisit the standard cross-entropy loss, interpreting it as a specific instance of policy gradient optimization applied within a single-step episode. To systematically study how the pre-trained distribution shapes the exploration potential for subsequent RL, we propose a generalized pre-training objective that adapts on-policy RL principles to supervised learning. By framing next-token prediction as a stochastic decision process, we introduce a reward-shaping strategy that explicitly balances diversity and precision. Our method employs a positive reward scaling factor to control probability concentration on ground-truth tokens and a rank-aware mechanism that treats high-ranking and low-ranking negative tokens asymmetrically. This allows us to reshape the pre-trained token-output distribution and investigate how to provide a more favorable exploration space for RL, ultimately enhancing end-to-end reasoning performance. Contrary to the intuition that higher distribution entropy facilitates effective exploration, we find that imposing a precision-oriented prior yields a superior exploration space for RL.

Variable selection is a procedure to attain the truly important predictors from inputs. Complex nonlinear dependencies and strong coupling pose great challenges for variable selection in high-dimensional data. In addition, real-world applications have increased demands for interpretability of the selection process. A pragmatic approach should not only attain the most predictive covariates, but also provide ample and easy-to-understand grounds for removing certain covariates. In view of these requirements, this paper puts forward an approach for transparent and nonlinear variable selection. In order to transparently decouple information within the input predictors, a three-step heuristic search is designed, via which the input predictors are grouped into four subsets: the relevant to be selected, and the uninformative, redundant, and conditionally independent to be removed. A nonlinear partial correlation coefficient is introduced to better identify the predictors which have nonlinear functional dependence with the response. The proposed method is model-free and the selected subset can be competent input for commonly used predictive models. Experiments demonstrate the superior performance of the proposed method against the state-of-the-art baselines in terms of prediction accuracy and model interpretability.