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This review offers an overview of the design principles, progress, and perspectives of applying multi-site electrocatalysts for the HER in neutral media.

Rational design and fabrication of efficient and low-cost catalysts for both the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are crucial for hydrogen production from water electrolysis. Herein, we report heteroatom Fe-incorporated Ni

In this paper, we investigate a novel multi-sensor odor detection system (electronic nose) for low concentration volatile organic compounds (VOCs). In order to break through the limitation for detecting low concentration volatile organic compounds, we design a conformal symmetric pre-concentration unit structure based on the finite element method. A typical application for exhaled breath is developed to investigate the proposed scheme. The designed pre-concentration system can increase the concentration of the concentrated substance, so that the sensor can detect it. Besides, we choose the alveolar gas at the bottom of the human lung, which can better represent the health status of the human body, to carry out the effectiveness test of the system. In order to combine the gas acquisition system with the pre-concentration system, this paper further designs a cost-effective alveolar gas collection device by a doubled adaptive Pulse Width Modulation (PWM) operation. Finally, the effectiveness of the pre-concentration system is judged by the detection results of gas chromatography-mass spectrometry (GC-MS). We also use the classical algorithm to classify lung cancer and non-lung cancer patients, which proves the effectiveness of our proposed integrated design system and shows the potential of practical application of the system.

PurposeThe Alberta Stroke Program Early Computed Tomography Score (ASPECTS) was designed for semi-quantitative assessment of early ischemic changes on non-contrast computed tomography (NCCT) for acute ischemic stroke (AIS). We evaluated two automated ASPECTS software in comparison with reference standard.MethodsNCCT of 276 AIS patients were retrospectively reviewed (March 2018–June 2020). A three-radiologist consensus for ASPECTS was used as reference standard. Imaging data from both baseline and follow-up were evaluated for reference standard. Automated ASPECTS were calculated from baseline NCCT with 1-mm and 5-mm slice thickness, respectively. Agreement between automated ASPECTS and reference standard was assessed using intra-class correlation coefficient (ICC). Correlation of automated ASPECTS with baseline stroke severity (NIHSS) and follow-up ASPECTS were evaluated using Spearman correlation analysis.ResultsIn score-based analysis, automated ASPECTS calculated from 5-mm slice thickness images agreed well with reference standard (software A: ICC = 0.77; software B: ICC = 0.65). Bland–Altman analysis revealed that the mean differences between automated ASPECTS and reference standard were ≤ 0.6. In region-based analysis, automated ASPECTS derived from 5-mm slice thickness images by software A showed higher sensitivity (0.60 vs. 0.54), lower specificity (0.91 vs. 0.94), and higher AUC (0.76 vs. 0.74) than those using 1-mm slice thickness images (p p ConclusionAutomated ASPECTS showed good reliability and 5 mm was the optimal slice thickness.

Electrochemical water oxidation enables the conversion of H2O to H2O2. It holds distinct advantages to the O2 reduction reaction, which is restricted by the inefficient mass transfer and limited solubility of O2 in aqueous media. Nonetheless, most reported anodes suffer from high overpotentials (usually >1000 mV) and low selectivity. Electrolysis at high overpotentials often causes serious decomposition of peroxides and leads to declined selectivity. Herein, we report a ZnGa2O4 anode with dual active sites to improve the selectivity and resist the decomposition of peroxides. Its faradaic efficiency reaches 82% at 2.3 V versus RHE for H2O2 generation through both direct (via OH−) and indirect (via HCO3−) pathways. The percarbonate is the critical species generated through the conversion of bicarbonate at Ga-Ga dual sites. The peroxy bond is stable on the surface of the ZnGa2O4 anode, significantly improving faradaic efficiency.

T2-prepared (T2prep) blood oxygenation level dependent (BOLD) functional MRI (fMRI) is an alternative fMRI approach developed to mitigate the susceptibility artifacts that are typically observed in brain regions near air-filled cavities, bleeding and calcification, and metallic objects in echo-planar-imaging (EPI) based fMRI images. Here, T2prep BOLD fMRI was evaluated in an event-related paradigm for the first time. Functional experiments were performed using gradient-echo (GRE) EPI, spin-echo (SE) EPI, and T2prep BOLD fMRI during an event-related visual task in 10 healthy human subjects. Each fMRI method was performed with a low (3.4 × 3.4 × 4 mm3) and a high (1.5 mm isotropic) spatial resolution on 3T and a high resolution (1.5 mm isotropic) on 7T. Robust activation were detected in the visual cortex with all three fMRI methods. In each group of fMRI scans (3T low resolution, 3T high resolution, and 7T high resolution), GRE EPI showed the highest signal change (ΔS/S), largest full-width-at-half-maximum (FWHM) and longest time-to-peak (TTP) extracted from the hemodynamic response functions (HRF), indicating substantial signal contribution from large draining veins which have longer response times than microvessels. In contrast, T2prep BOLD showed the lowest ΔS/S, smallest FWHM, and shortest TTP, suggesting that T2prep BOLD may have a purer T2-weighted BOLD contrast that is more sensitive to microvessels compared to GRE/SE EPI BOLD. This trend was more obvious in fMRI scans performed with a lower spatial resolution on a lower field (3T with a 3.4 × 3.4 × 4 mm3 voxel). Scan-rescan reproducibility in the same subjects was comparable among the three fMRI methods. The results from the current study are expected to be useful to establish T2prep BOLD as a useful alternative fMRI approach for event-related fMRI in brain regions with large susceptibility artifacts.

Smooth filtering, a common blurring and denoising operator, has often been utilized postoperatively to diminish the traces left by malicious manipulations. Most of the existing forensic methods only focus on one specific filtering artifact such as median filtering, which is insufficient to reveal the manipulation history of digital images. Unlike traditional convolutional neural network (CNN)-based networks, which normally introduce handcrafted features, including frequency domain features and median filtering residuals, into the preprocessing layer, this paper proposes an end-to-end deep learning model for robust smooth filtering identification. First, a distinctive network structure named the Squeeze-and-Excitation (SE) block is introduced to select discriminative features adaptively and suppress the irrelevant features to the smooth filtering effect. Then, as the network depth increases, multiple inception-residual blocks are stacked to extract discriminative features and reduce the information loss. Finally, different smooth filtering operations can be classified through learning hierarchical features. The experimental results on a composite database show that the proposed model outperforms the state-of-the-art methods, especially in small size and JPEG compression scenarios.

In agriculture, Trehalase is considered the main target of the biological fungicide validamycin A, and the toxicology mechanism of validamycin A is unknown. 14-3-3 proteins, highly conserved proteins, participate in diverse cellular processes, including enzyme activation, protein localization, and acting as a molecular chaperone. In Saccharomyces cerevisiae, the 14-3-3 protein Bmh1could interact with Nth1 to respond to specific external stimuli. Here, we characterized FgNth, FgBmh1, and FgBmh2 in Fusarium graminearum. ΔFgNth, ΔFgBmh1, and ΔFgBmh2 displayed great growth defects and their peripheral tips hyphae generated more branches when compared with wild-type (WT) PH-1. When exposed to validamycin A as well as high osmotic and high temperature stresses, ΔFgNth, ΔFgBmh1, and ΔFgBmh2 showed more tolerance than WT. Both ΔFgNth and ΔFgBmh1 displayed reduced deoxynivalenol production but opposite for ΔFgBmh2, and all three deletion mutants showed reduced virulence on wheat coleoptiles. In addition, coimmunoprecipitation (Co-IP) experiments suggested that FgBmh1 and FgBmh2 both interact with FgNth, but no interaction was detected between FgBmh1 and FgBmh2 in our experiments. Further, validamycin A enhances the interaction between FgBmh1 and FgNth in a positive correlation under concentrations of 1 to 100 μg/ml. In addition, both high osmotic and high temperature stresses promote the interaction between FgBmh1 and FgNth. Co-IP assay also showed that neither FgBmh1 nor FgBmh2 could interact with FgPbs2, a MAPKK kinase in the high-osmolarity glycerol pathway. However, FgBmh2 but not FgBmh1 binds to the heat shock protein FgHsp70 in F. graminearum. Taken together, our results demonstrate that FgNth and FgBmh proteins are involved in growth and responses to external stresses and virulence; and validamycin enhanced the interaction between FgNth and FgBmh1in F. graminearum.

An ultrafast room-temperature method for converting metal–organic frameworks into high-performance OER catalysts with good universality and scalability is demonstrated.

S leaching induces rapid self-reconstruction and the ready formation of Ni4+ in an Fe–NiSOH electrode, supporting high-efficiency and stable alkaline large-current-density water/seawater oxidation.

A novel electrochemical reconstruction strategy enables the best seawater electrolysis performance.

Additional file 11: Upregulated GO functions of differentially expressed circRNAs in DPSCs regulated by PDLSCs.

Additional file 23: Pathways of differentially expressed miRNA target mRNAs in DPSCs regulated by PDLSCs.

Machine learning force fields (MLFFs) are an increasingly popular choice for atomistic simulations due to their high fidelity and improvable nature. Here, we propose a hybrid small-cell approach that combines attributes of both offline and active learning to systematically expand a quantum mechanical (QM) database while constructing MLFFs with increasing model complexity. Our MLFFs employ the moment tensor potential formalism. During this process, we quantitatively assessed structural properties, elastic properties, dimer potential energies, melting temperatures, phase stability, point defect formation energies, point defect migration energies, free surface energies, and generalized stacking fault (GSF) energies of Zr as predicted by our MLFFs. Unsurprisingly, model complexity has a positive correlation with prediction accuracy. We also find that the MLFFs wee able to predict the properties of out-of-sample configurations without directly including these specific configurations in the training dataset. Additionally, we generated 100 MLFFs of high complexity (1513 parameters each) that reached different local optima during training. Their predictions cluster around the benchmark DFT values, but subtle physical features such as the location of local minima on the GSFE surface are washed out by statistical noise.

While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, allow for the construction of reliable training sets, but these processes can be very resource-intensive when training a potential for use in large-scale simulations. Current training approaches often use density functional theory (DFT) calculations that have the same cell size as the simulations that use the potential. Here, we demonstrate an easy-to-implement small-cell structures training protocol and use it to train a potential for zirconium and hydrides. This training leads to a convex hull in good agreement with DFT when applied to known stable phases. Compared to traditional active learning, small-cell training decreased the training time of a potential able to capture the {\alpha}-\b{eta} zirconium phase transition by approximately 20 times. The potential describes the phase transition with a degree of accuracy similar to that of the large-cell training method., Comment: 10 pages

Additional file 14: GO functions of differentially expressed miRNA target mRNAs in DPSCs regulated by PDLSCs.

Additional file 9: Downregulated GO functions of differentially expressed lncRNAs in DPSCs regulated by PDLSCs.

Additional file 12: Downregulated GO functions of differentially expressed circRNAs in DPSCs regulated by PDLSCs.

Meta-devices have gained significant attention and have been widely utilized in optical systems for focusing and imaging, owing to their lightweight, high-integration, and exceptional-flexibility capabilities. However, based on the assumption of local phase approximation, traditional design method neglect the local lattice coupling effect between adjacent meta-atoms, thus harming the practical performance of meta-devices. Using physics-driven or data-driven optimization algorithms can effectively solve the aforementioned problems. Nevertheless, both of the methods either involve considerable time costs or require a substantial amount of data sets. Here, we propose a physics-data-driven approach based "intelligent optimizer" that enables us to adaptively modify the sizes of the studied meta-atom according to the sizes of its surrounding ones. Such a scheme allows to mitigate the undesired local lattice coupling effect, and the proposed network model works well on thousands of datasets with a validation loss of 3*10-3. Experimental results show that the 1-mm-diameter metalens designed with the "intelligent optimizer" possesses a relative focusing efficiency of 93.4% (as compared to ideal focusing) and a Strehl ratio of 0.94. In contrast to the previous inverse design method, our method significantly boosts designing efficiency with five orders of magnitude reduction in time. Our design approach may sets a new paradigm for devising large-scale meta-devices., Comment: manuscripts:19 pages, 4 figures; Supplementary Information: 11 pages, 12 figures

Additional file 17: Upregulated pathways of differentially expressed lncRNAs in DPSCs regulated by PDLSCs.

Additional file 21: Downregulated pathways of differentially expressed circRNAs in DPSCs regulated by PDLSCs.

This thesis provides an in-depth exploration of the Decentralized Co-governance Crowdfunding (DCC) Ecosystem, a novel solution addressing prevailing challenges in conventional crowdfunding methods faced by MSMEs and innovative projects. Among the problems it seeks to mitigate are high transaction costs, lack of transparency, fraud, and inefficient resource allocation. Leveraging a comprehensive review of the existing literature on crowdfunding economic activities and blockchain's impact on organizational governance, we propose a transformative socio-economic model based on digital tokens and decentralized co-governance. This ecosystem is marked by a tripartite community structure - the Labor, Capital, and Governance communities - each contributing uniquely to the ecosystem's operation. Our research unfolds the evolution of the DCC ecosystem through distinct phases, offering a novel understanding of socioeconomic dynamics in a decentralized digital world. It also delves into the intricate governance mechanism of the ecosystem, ensuring integrity, fairness, and a balanced distribution of value and wealth.

Additional file 20: Upregulated pathways of differentially expressed circRNAs in DPSCs regulated by PDLSCs.

Additional file 18: Downregulated pathways of differentially expressed lncRNAs in DPSCs regulated by PDLSCs.

The proposal for the critical experiment is registered in AsPredicted format.

The current study aims to provide an attentional account for why lower-income individuals are more affected by nudges and sludges than higher-income individuals. We propose a study to investigate the impact of nudge and sludge interventions on financial decision-making for lower-income and higher-income participants. Specifically, participants are asked to choose the better credit card out of two options. We measure their attention with the bubbleview technique, choice with a binary choice question, and memory with a surprising memory test. The survey process will be 1-time distribution.

Climate change is driven in part by the lifestyle choices individuals make every day and yet the emissions associated with these decisions are seldom considered and are challenging for people to conceptualize. Personal carbon calculators offer a unique opportunity to provide people with information about the climate-cost of different actions alongside tailored guidance for lowering one’s own impact. This project will use a randomized control trial to evaluate how effective this tool may be for promoting climate action. Behavioural intentions for 2023 and reported behaviours in 2019 will be used to quantify a change in carbon footprint—including domains of food, transportation, housing, and material purchases—and we will compare how this intended shift in emissions differs between groups.

A remarkable ability of the cognitive system is to make novel inferences based on prior experiences. What mechanism supports such inference? We propose that statistical learning is a process where transitive inferences of new associations are made between objects that have never been directly associated. After viewing a continuous sequence containing two base pairs (e.g., A-B, B-C), participants automatically inferred a transitive pair (e.g., A-C) where the two objects had never co-occurred before (Experiment 1). This transitive inference occurred in the absence of explicit awareness of the base pairs. However, participants failed to infer the transitive pair from three base pairs (Experiment 2), showing the limits of the transitive inference (Experiment 3). We further demonstrated that this transitive inference can operate across the categorical hierarchy (Experiments 4-7). The findings revealed a novel consequence of statistical learning where new transitive associations between objects are implicitly inferred.

Global carbon emissions are driven in part by individual actions and yet the emissions associated with these lifestyle choices are very challenging for people to conceptualize. Personal carbon calculators and carbon labels are promising options to aid climate-friendly decision-making and motivate behavioural change. This project will use a randomized control trial to evaluate how effective these two interventions (carbon calculators and labels) are for promoting climate action. Behavioural intentions for 2023 will be used to quantify a carbon footprint—including domains of food, transportation, housing, and material purchases—and we will compare how intended emissions differ between groups.

The daily decisions individuals make are important for mitigating climate change. However, most people are unaware of the climate impacts associated with their choices. Carbon labels are one method to boost decision making capacity by providing both information and visual cues. The majority of existing carbon label research focuses solely on food or household products. We extend this scope to evaluate the effectiveness of carbon labels within other consumption domains (e.g., transportation and home energy). This project will use a randomized control trial to compare decision making and attention in three groups: one viewing carbon labels and a climate framing message, another viewing only the climate framing message, and a control with no message or labels. We will quantify the carbon footprint associated with discrete choices made in six hypothetical scenarios and determine whether this footprint is lower when provided with labelling aids.

Carbon labels are gaining popularity in real-world settings and in academic studies including field experiments (e.g., Brunner et al., 2018) and controlled online experiments (e.g., Edenbrandt et al., 2021) with mostly positive results. There is some evidence that the effect of carbon labels on purchasing decisions is mediated by knowledge of the greenhouse gas (GHG) emissions associated with the foods (e.g., Camilleri et al., 2019). This project aims to replicate these findings (regarding both food selections and GHG knowledge) using an online randomized between subjects experiment. We will compare the menu selections of participants who view labelled and unlabelled menus and then test their knowledge of the emissions associated with numerous menu options. We will also add a third condition in which participants view no labels but are primed to consider climate change with a brief message about the importance of food choices for climate mitigation. This will allow us to better understand whether labels are simply reminding people of climate change (and in the case of research, creating demand characteristics) or whether labels provide further information that is useful for decision making.

The global wildlife trade is increasingly a threat to biodiversity and public health. Reducing demand for wildlife products and engaging individuals in civic actions for wildlife conservation is key to combating the unsustainable trade and transforming the current practices. Past research in diffusion of innovations suggests that messaging should use audience segmentation to target members of the intended groups and spread pro-environmental behaviors through their social networks across populations. However, there is a lack of understanding about how conservation messages may appeal to certain segments of the population and promote different types of actions in each. The current study uses a randomized control trial to investigate the effectiveness of messaging for promoting three types of actions (i.e., avoiding wildlife entertainment visitation, signing a petition, and volunteering for a conservation campaign) across four self-identified segments. We will use comic-style visual narratives to communicate (1) the consequences of the exotic pet trade, (2) the need for system change to tackle the exotic pet trade, and (3) neutral information about wild animals as a control condition. Our measures include intentions to avoid wildlife entertainment and click-through behaviors on call to action. We will analyze how participants’ intentions and behaviors in each segment differ in response to three types of messaging.

The volatile organic compounds (VOCs) enrichment technology is of great significance to an electronic nose (e-nose) system. In this article, a novel preconcentration system and conformal sensor chamber are designed by combining conventional e-nose technology with VOCs enrichment technology. The aim of this article is to find the optimum sweeping speed of the preconcentration system based on the conformal sensor chamber. The connectivity and diffusion of the chamber are analyzed by FLUENT simulation so that it is meaningful to measure the optimum sweeping speed of the preconcentration system based on the sensor array in the chamber. The performance of the adsorption tube at different sweeping speeds is tested by gas chromatography mass spectrometry; then, the optimal sweeping speed for the conformal sensor chamber and the conventional sensor chamber is obtained based on the sensor comprehensive response change using Newton method. Finally, the sensor comprehensive responses toward the exhaled gases of healthy people and patients under different sensor chamber conditions demonstrate that the sweeping speed obtained by the Newton method is analytically proved optimal.

Water electrolysis provides a promising route to produce high energy density hydrogen. Compared with the limited amount of fresh water, seawater is an abundant resource that has attracted increasing attention for electrolysis. However, seawater electrolysis has thus far suffered from degraded activity and stability, and from low oxygen evolution reaction (OER) selectivity, due to the existence of chloride ions and insoluble solids in seawater. This short review summarizes trends in the rational design of OER catalysts, providing some effective strategies, including constructing 3D hierarchical porous structures, employing protective layers, and engineering surface wettability, to synthesize efficient and stable OER catalysts for seawater electrolysis. Finally, a perspective regarding designing high-performance catalysts for seawater electrolysis is also provided.

Objectives: Autism spectrum disorder (ASD) is a juvenile onset neurodevelopmental disorder with social impairment and stereotyped behavior as the main symptoms. Unaffected relatives may also exhibit similar ASD features due to genetic factors. Although previous studies have demonstrated atypical brain morphological features as well as task-state brain function abnormalities in unaffected parents with ASD children, it remains unclear the pattern of brain function in the resting state.Methods: A total of 42 unaffected parents of ASD children (pASD) and 39 age-, sex-, and handedness-matched controls were enrolled. Multiple resting-state fMRI (rsfMRI) analyzing methods were applied, including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC), to reveal the functional abnormalities of unaffected parents in ASD-related brain regions. Spearman Rho correlation analysis between imaging metric values and the severity of ASD traits were evaluated as well.Results: ALFF, ReHo, and DC methods all revealed abnormal brain regions in the pASD group, such as the left medial orbitofrontal cortex (mOFC) and rectal gyrus (ROI-1), bilateral supplementary motor area (ROI-2), right caudate nucleus head and right amygdala/para-hippocampal gyrus (ROI-3). FC decreasing was observed between ROI-1 and right anterior cingulate cortex (ACC), ROI-2, and bilateral precuneus. FC enhancing was observed between ROI-3 and right anterior cerebellar lobe, left medial temporal gyrus, left superior temporal gyrus, left medial frontal gyrus, left precentral gyrus, right postcentral gyrus in pASD. In addition, ALFF values in ROI-1, DC values in ROI-3 were positively correlated with AQ scores in pASD (ρ1 = 0.298, P1 = 0.007; ρ2 = 0.220, P2 = 0.040), while FC values between ROI-1 and right ACC were negatively correlated with AQ scores (ρ3 = −0.334, P3 = 0.002).Conclusion: rsfMRI metrics could be used as biomarkers to reveal the underlying neurobiological feature of ASD for unaffected parents.