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The redox reactions occurring in the Li-S battery positive electrode conceal various and critical electrocatalytic processes, which strongly influence the performances of this electrochemical energy storage system. Here, we report the development of a single-dispersed molecular cluster catalyst composite comprising of a polyoxometalate framework ([Co 4 (PW 9 O 34 ) 2 ] 10− ) and multilayer reduced graphene oxide. Due to the interfacial charge transfer and exposure of unsaturated cobalt sites, the composite demonstrates efficient polysulfides adsorption and reduced activation energy for polysulfides conversion, thus serving as a bifunctional electrocatalyst. When tested in full Li-S coin cell configuration, the composite allows for a long-term Li-S battery cycling with a capacity fading of 0.015% per cycle after 1000 cycles at 2 C (i.e., 3.36 A g −1 ). An areal capacity of 4.55 mAh cm −2 is also achieved with a sulfur loading of 5.6 mg cm − 2 and E/S ratio of 4.5 μL mg −1 . Moreover, Li-S single-electrode pouch cells tested with the bifunctional electrocatalyst demonstrate a specific capacity of about 800 mAh g −1 at a sulfur loading of 3.6 mg cm −2 for 100 cycles at 0.2 C (i.e., 336 mA g −1 ) with E/S ratio of 5 μL mg −1 . Efficient electrochemical energy storage in Li-S batteries is hindered by sluggish sulfur redox reactions. Here, the authors propose a polyoxometalate/multilayer graphene composite as a bifunctional electrocatalyst for battery performance improvement.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients. Methods to stratify patients according to mortality risk are essential to allocate limited heath resources during the COVID-19 crisis. Here, using machine learning methods, the authors present a mortality risk prediction model for COVID-19 that uses patients’ clinical data on admission to stratify patients by mortality risk.

Fluid‐rock interactions play an important role in element mobilization, mass transfer, and formation of critical metals in subduction zones. However, tracking the multistage fluid‐rock interactions within subduction channels remains elusive. Here we conducted bulk‐rock major and trace element and magnesium (Mg) isotopic analyses on a suite of subducted ophiolitic mélange rocks from Wadi Al Barramiyah in the Arabian‐Nubian Shield of the Eastern Desert (ED) of Egypt. The rock suite includes serpentinites, talc rocks, talc‐dolomite rocks, tremolite‐dominated schists, and marbles. Talc rocks are characterized by low MgO contents and high δ26MgDSM‐3 values (0.03–0.13‰) relative to serpentinites (−0.18‰), indicating the release of isotopically light fluid during the metasomatic replacement of antigorite by talc. Tremolite‐dominated schists and talc‐dolomite rocks display higher CaO contents and lower δ26Mg (−0.25‰ to −0.03‰ and −1.04‰ to −0.18‰, respectively) than those of talc rocks and serpentinites. These signatures, along with high CaO/Al2O3 and low Rb/Sr ratios, indicate infiltration of low‐δ26Mg carbonate‐rich fluids, supported by extremely low δ26Mg (down to −2.38‰) observed in nearby marbles. Our findings demonstrate that antigorite dehydration liberates substantial numbers of H2O‐rich fluids, facilitating the dissolution of carbonate minerals in marbles. Subsequent carbonate metasomatism effectively sequesters carbon from aqueous carbon‐bearing fluids, transforming silicate minerals into carbonates. These new results highlight the significant role of mélange rocks in the multistage fluid‐rock interactions and carbon recycling in subduction zones, offering valuable insights into mantle Mg isotopic heterogeneity and crust‐mantle interactions. Plain Language Summary Subduction zones are geodynamic regions characterized by extensive fluid‐rock interactions that significantly influence the physics and chemistry of Earth's crust and mantle. We investigated Mg mobility and Mg isotope fractionation in subducted ophiolitic mélanges from Wadi Al Barramiyah, located in the Arabian‐Nubian Shield of the ED of Egypt, in order to constrain multistage fluid‐rock interactions in subduction zones. We found distinct Mg isotopic signatures across different rock types, reflecting diverse types and extents of fluid metasomatism. Such components, transported into the mantle via subduction, would cause Mg isotopic heterogeneity in the mantle and mantle‐derived magmas. These findings shed light on crust‐mantle interactions and carbon recycling in subduction zones, emphasizing the utility of Mg isotopes for tracing such processes. Key Points Talc rocks have δ26Mg higher than serpentinites, reflecting the release of 24Mg‐rich fluids Low δ26Mg of tremolite‐dominated schists and talc‐dolomite rocks document carbonate fluid infiltration Mg isotopes reveal multistage fluid‐rock interactions and carbon recycling in subduction zones

The spatial resolution of functional magnetic resonance imaging (fMRI) is fundamentally limited by effects from large draining veins. Here we describe an analysis method that provides data-driven estimates of these effects in task-based fMRI. The method involves fitting a one-dimensional manifold that characterizes variation in response timecourses observed in a given dataset, and then using identified early and late timecourses as basis functions for decomposing responses into components related to the microvasculature (capillaries and small venules) and the macrovasculature (large veins), respectively. We show the removal of late components substantially reduces the superficial cortical depth bias of fMRI responses and helps eliminate artifacts in cortical activity maps. This method provides insight into the origins of the fMRI signal and can be used to improve the spatial accuracy of fMRI. Temporal decomposition through manifold fitting (TDM) is an analysis technique that decomposes blood oxygenation level dependent (BOLD) responses in task-based fMRI into different components that likely correspond to microvasculature- and macrovasculature-driven signals.

The gut microbiome has been linked to host obesity; however, sex-specific associations between microbiome and fat distribution are not well understood. Here we show sex-specific microbiome signatures contributing to obesity despite both sexes having similar gut microbiome characteristics, including overall abundance and diversity. Our comparisons of the taxa associated with the android fat ratio in men and women found that there is no widespread species-level overlap. We did observe overlap between the sexes at the genus and family levels in the gut microbiome, such as Holdemanella and Gemmiger ; however, they had opposite correlations with fat distribution in men and women. Our findings support a role for fat distribution in sex-specific relationships with the composition of the microbiome. Our results suggest that studies of the gut microbiome and abdominal obesity-related disease outcomes should account for sex-specific differences. The gut microbiome has been reported to be associated with obesity; here, the authors show that there are sex-specific differences in the relationship between gut microbes and abdominal obesity.

Rapid economic growth and urbanization have brought notable changes in China. This trend had dramatic impacts to food industry and diet-related behaviors, which leads to increasing prevalence of overweight and obesity as well as diet-related non-communicable diseases (NCDs). With a rapid development of internet and e-commerce market, another emerging trend that should not be overlooked is the change in food shopping habits induced by the popularity of online-to-offline (O2O) food delivery service in China. The O2O food market is a booming industry in China, the market scale of O2O food industry is increasing with remarkable speed. More than 1/5 of total population in China has already became the users of O2O food delivery market. Development of this new trend in food environment is inevitable. This game-changing trend brings great opportunities to improve food accessibility and availability but meanwhile poses inevitable challenges to the whole public health system and social environment in China. This paper offers a unique perspective of the opportunities and challenges that the new industry brings to food environment, health outcomes caused by related behavior change, and its broader influence on social environment.

Visual neuroscientists have long characterized attention as inducing a scaling or additive effect on fixed parametric functions describing neural responses (e.g., contrast response functions). Here, we instead propose that top-down effects are more complex and manifest in ways that depend not only on attention but also other cognitive processes involved in executing a task. To substantiate this theory, we analyze fMRI responses in human ventral temporal cortex (VTC) in a study where stimulus eccentricity and cognitive task are varied. We find that as stimuli are presented farther into the periphery, bottom-up stimulus-driven responses decline but top-down attentional enhancement increases substantially. This disproportionate enhancement of weak responses cannot be easily explained by conventional models of attention. Furthermore, we find that attentional effects depend on the specific cognitive task performed by the subject, indicating the influence of additional cognitive processes other than attention (e.g., decision-making). The effects we observe replicate in an independent experiment from the same study, and also generalize to a separate study involving different stimulus manipulations (contrast and phase coherence). Our results suggest that a quantitative understanding of top-down modulation requires more nuanced characterization of the multiple cognitive factors involved in completing a perceptual task.

Object detection, a crucial aspect of computer vision, has seen significant advancements in accuracy and robustness. Despite these advancements, practical applications still face notable challenges, primarily the inaccurate detection or missed detection of small objects. Moreover, the extensive parameter count and computational demands of the detection models impede their deployment on equipment with limited resources. In this paper, we propose YOLO-TLA, an advanced object detection model building on YOLOv5. We first introduce an additional detection layer for small objects in the neck network pyramid architecture, thereby producing a feature map of a larger scale to discern finer features of small objects. Further, we integrate the C3CrossCovn module into the backbone network. This module uses sliding window feature extraction, which effectively minimizes both computational demand and the number of parameters, rendering the model more compact. Additionally, we have incorporated a global attention mechanism into the backbone network. This mechanism combines the channel information with global information to create a weighted feature map. This feature map is tailored to highlight the attributes of the object of interest, while effectively ignoring irrelevant details. In comparison to the baseline YOLOv5s model, our newly developed YOLO-TLA model has shown considerable improvements on the MS COCO validation dataset, with increases of 4.6% in mAP@0.5 and 4% in mAP@0.5:0.95, all while keeping the model size compact at 9.49M parameters. Further extending these improvements to the YOLOv5m model, the enhanced version exhibited a 1.7% and 1.9% increase in mAP@0.5 and mAP@0.5:0.95, respectively, with a total of 27.53M parameters. These results validate the YOLO-TLA model’s efficient and effective performance in small object detection, achieving high accuracy with fewer parameters and computational demands.

Sulfur, widely present in the soil and atmosphere, is one of the essential elements for plants. Sulfate is a dominant form of sulfur in soils taken up by plant roots. In addition to the assimilation into sulfur compounds essential for plant growth and development, it has been reported recently that sulfate as well as other sulfur containing compounds can also induce stomatal movement. Here, we first summarized the uptake and transport of sulfate and atmospheric sulfur, including H 2 O and SO 2 , and then, focused on the effects of inorganic and organic sulfur on stomatal movement. We concluded all the transporters for different sulfur compounds, and compared the expression level of those transporters in guard cells and mesophyll cells. The relationship between abscisic acid and sulfur compounds in regulation of stomatal movement were also discussed.