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Magnesium (Mg)-based amorphous alloys hold significant potential for applications in the automotive, aerospace, and biomedical industries. However, they are limited by their smaller size compared to other amorphous alloys. A higher reduced glass transition temperature (Trg) is associated with larger sizes in Mg-based amorphous alloys. Yet, due to the vast chemical space involved, designing Mg-based amorphous alloys with higher Trg using traditional ‘trial and error’ method is a challenging endeavor. In this work, we developed a property-driven active learning strategy to customize Mg-based amorphous alloys with enhanced Trg. After just two iterations, we successfully tailored four amorphous alloys with high Trg values. Under identical experimental conditions, two of these alloys exhibited Trg values surpassing that of Mg0·65Ag0·19Cu0.06Gd0.1, the alloy with best Trg value in the reported references. SHAP analysis revealed that Trg tends to be higher when the Ag atomic ratio exceeds 0.045, the Cu atomic ratio is below 0.18, the Ni atomic ratio is below 0.025, and the Mg atomic ratio is below 0.665. Our work offers a reliable strategy for designing Mg-based amorphous alloys with higher Trg and provides valuable insights for the rational design of these alloys.

Abstract Background Bacteriophages are prokaryotic viruses that rank among the most abundant microbes in the gut but remain among the least understood, especially in quails. In this study, we surveyed the gut bacteriophage communities in 22 quails at different ages (days 20 and 70) using shotgun metagenomic sequencing. We then systematically evaluated the relationships with gut bacteria and host serum metabolites. Results We discovered that Myoviridae and Siphoviridae were the dominant bacteriophage families in quails. Through a random forest and LEfSe analysis, we identified 23 differential bacteriophages with overlapping presence. Of these, 21 bacteriophages (e.g., Enterococcus phage IME-EFm5 and Enterococcus phage IME-EFm1) showed higher abundances in the day 20 group, while two bacteriophages (Bacillus phage Silence and Bacillus virus WPh) were enriched in the day 70 group. These key bacteriophages can serve as biomarkers for quail sexual maturity. Additionally, the differential bacteriophages significantly correlated with specific bacterial species and shifts in the functional capacities of the gut microbiome. For example, Enterococcus phages (e.g., Enterococcus phage EFP01, Enterococcus phage IME-EFm5, and Enterococcus phage IME-EFm1) were significantly (P 0.05, FDR) correlated with nicotinamide riboside and triacetate lactone. In contrast, some differential bacterial species (e.g., Christensenella massiliensis and Bacteroides neonati) significantly (P

Based on the Michaelis-Menten reaction model with catalytic effects, a more comprehensive one-dimensional stochastic Langevin equation with immune surveillance for a tumor cell growth system is obtained by considering the fluctuations in growth rate and mortality rate. To explore the impact of environmental fluctuations on the growth of tumor cells, the analytical solution of the steady-state probability distribution function of the system is derived using the Liouville equation and Novikov theory, and the influence of noise intensity and correlation intensity on the steady-state probability distributional function are discussed. The results show that the three extreme values of the steady-state probability distribution function exhibit a structure of two peaks and one valley. Variations of the noise intensity, cross-correlation intensity and correlation time can modulate the probability distribution of the number of tumor cells, which provides theoretical guidance for determining treatment plans in clinical treatment. Furthermore, the increase of noise intensity will inhibit the growth of tumor cells when the number of tumor cells is relatively small, while the increase in noise intensity will further promote the growth of tumor cells when the number of tumor cells is relatively large. The color cross-correlated strength and cross-correlated time between noise also have a certain impact on tumor cell proliferation. The results help people understand the growth kinetics of tumor cells, which can a provide theoretical basis for clinical research on tumor cell growth.

Abstract Aging results from the accumulation of molecular damage that impairs normal biochemical processes. We previously reported that age‐linked damage to amino acid sequence NGR (Asn‐Gly‐Arg) results in “gain‐of‐function” conformational switching to isoDGR (isoAsp‐Gly‐Arg). This integrin‐binding motif activates leukocytes and promotes chronic inflammation, which are characteristic features of age‐linked cardiovascular disorders. We now report that anti‐isoDGR immunotherapy mitigates lifespan reduction of Pcmt1−/− mouse. We observed extensive accumulation of isoDGR and inflammatory cytokine expression in multiple tissues from Pcmt1−/− and naturally aged WT animals, which could also be induced via injection of isoDGR‐modified plasma proteins or synthetic peptides into young WT animals. However, weekly injection of anti‐isoDGR mAb (1 mg/kg) was sufficient to significantly reduce isoDGR‐protein levels in body tissues, decreased pro‐inflammatory cytokine concentrations in blood plasma, improved cognition/coordination metrics, and extended the average lifespan of Pcmt1−/− mice. Mechanistically, isoDGR‐mAb mediated immune clearance of damaged isoDGR‐proteins via antibody‐dependent cellular phagocytosis (ADCP). These results indicate that immunotherapy targeting age‐linked protein damage may represent an effective intervention strategy in a range of human degenerative disorders.

Abstract Human cerebellar development is orchestrated by molecular regulatory networks to achieve cytoarchitecture and coordinate motor and cognitive functions. Here, we combined single-cell transcriptomics, spatial transcriptomics and single cell chromatin accessibility states to systematically depict an integrative spatiotemporal landscape of human fetal cerebellar development. We revealed that combinations of transcription factors and cis-regulatory elements (CREs) play roles in governing progenitor differentiation and cell fate determination along trajectories in a hierarchical manner, providing a gene expression regulatory map of cell fate and spatial information for these cells. We also illustrated that granule cells located in different regions of the cerebellar cortex showed distinct molecular signatures regulated by different signals during development. Finally, we mapped single-nucleotide polymorphisms (SNPs) of disorders related to cerebellar dysfunction and discovered that several disorder-associated genes showed spatiotemporal and cell type-specific expression patterns only in humans, indicating the cellular basis and possible mechanisms of the pathogenesis of neuropsychiatric disorders.

This subject took yak meat balls added with different kinds of edible fungus powder (shiitake mushrooms, hericium, porcini, tea tree mushroom) as the research objects, aiming to study the effects of edible fungi on the edible quality of yak meat balls during storage. Changes in quality indexes such as color, pH value, water retention, TBARS, texture properties, rheological properties, EAI and ESI of yak meat ball products of different treatment group during the storage were determined and analyzed. The results showed that in the storage period of 0 to 15 d, the addition of shiitake mushrooms could significantly improve the b* value of yak meat balls under refrigeration compared with the blank control group (b* value was 4.52 lower than blank group on 15 d), the pH value of yak meat balls was increased by adding porcini mushrooms and tea tree mushrooms (both were 6.64 on the 15 d), water retention was increased by adding shiitake mushrooms. Moreover, the elasticity and chewiness of yak meat balls were significantly improved by the addition of mushrooms and hericium, porcini (P

【Objective】 Roots and leaves are highly influenced by a multitude of biotic and abiotic factors. Taking oat under drip irrigation as an example, this study delves into the response of its root and leave traits to planting density and soil amendment with water absorbent polymer. 【Method】 The field experiment was conducted in 2019—2020 with intra-row planting spacing being 20 cm, and the inter-row planting spacing being 20 cm (A), 15 cm (B) and 10 cm (C), respectively. In each treatment there were two soil amendments by applying 0 and 22.5 kg/hm2 of a water absorbent polymer. Senescence traits of the roots and flag leaves in each treatment were measured. 【Result】 ①Both row spacing and soil amendment affected root activity, SOD and MDA content, and soluble protein content in the roots and leaves significantly with PA>C, while the impact on MDA content in the roots and leaves was in the order of C>A>B. Combined with soil amendment, reducing row spacing from 20 cm to 15 cm increased root activity, SOD activity in the roots and leaves, soluble protein content in the roots and leaves by 5.56%~15.27%, 4.78%~5.89%, 3.98%~10.62%, 5.20%~23.74% and 5.89%~15.04% respectively, while reducing MDA content in the roots and leaves by 11.79%~13.86% and 6.01%~14.08%, respectively. Reducing the row spacing further to 10 cm significantly reduced the above traits, except the soluble protein content, which did not show significant changes, and MDA content in the roots and leaves, which showed a significant increase. Without soil amendment, the impact of row spacing on root and leaf traits varied with growth stage and year. ③When row spacing was 20 cm, compared with no amendment, amending the soil increased SOD activity and soluble protein content in the roots and leaves by 7.52%~15.75%, 3.21%~6.47%, 7.25%~10.47%, 18.01%~22.47% and 4.87%~9.10%, respectively, while reducing MDA content in the roots and leaves by 8.79%~13.96% and 7.35%~11.67%, respectively. In contrast, the effect of soil amendment on the above traits varied when the row spacing was 10 cm. ④The senescence traits of roots and leaves were correlated with biomass and grain yield of the oat, both at significant levels. 【Conclusion】 Reducing the row spacing from 20 cm to 15 cm, combined with soil amendment by applying 22.5 kg/hm2 of the water-absorbent polymer, is effective in reducing senescence of roots and leaves of the drip-irrigated oat and consequently promoting its biological yield and grain yield.

Aiming at the shortcomings of sparrow search algorithm (SSA), such as decreased population diversity in the late iteration and easily falling into local optimal solution, a hybrid algorithm of sparrow search algorithm and arithmetic optimization algorithm based on Hamiltonian graph (HSSAAOAH) is proposed. Firstly, the multiplica-tion and division operator of arithmetic optimization algorithm (AOA) is introduced into the discoverer-follower model and reconnaissance mechanism of SSA. The high distribution of multiplication and division operator is used to improve the randomness of later optimization. Secondly, the population composed by all the individuals is transferred into an undirected weight graph. After each iteration, a Hamiltonian cycle and its length composed of individuals will be computed according to a modified cycle algorithm, and the population optimization trend is measured by the ratio of the Hamiltonian cycle length. Then, for the progeny that fails to perform effective convergence, a certain number of individuals are randomly generated to replace the last ones according to the greedy algorithm, which improves the quality of solution and enhances the ability to jump out of local extrema. Finally, HSSAAOAH and different optimization algorithms are simulated on the benchmark function and two engineering design problems. The results show that HSSAAOAH algorithm has faster convergence speed, higher optimization accuracy, and good robustness and optimization performance.

Two-dimensional (2D) organic–inorganic hybrid perovskites (OIHPs) have drawn increased attention due to rich physical properties such as ferroelectricity and photovoltaic properties. Nevertheless, it is challenging to discover novel 2D OIHPs within the vast chemical composition space. Herein, a diagrammatic machine learning model was employed to improve this issue. We collected 179 OIHPs with a variety of organic cations and screened out 6 features from 10,622 descriptors. Subsequently, a decision tree model was created to predict the dimensionality of OIHPs, achieving a LOOCV accuracy of 0.94 and a test accuracy of 0.89, respectively. Then, one candidate from a virtual space with 8256 samples was successfully synthesized, which was consistent with the prediction of the model. Finally, three rules were produced by visualization of the tree structure to generally discriminate 2D from non-2D OIHPs. It is believed that the diagrammatic model has reliability in identifying 2D OIHPs and will serve further property studies of OIHPs in the future.

The application of artificial intelligence in the field of painting has been greatly developed, which is a new way of thinking in the digital era, bringing innovation and infinite possibilities to the art of painting. The article first researches the art and expression of AI painting, mainly taking various network models as the main form of expression. Then, color theory and color space are introduced to provide a theoretical basis for the proposal of a color optimization model. Considering that the ACE algorithm has problems of high complexity and image enlargement, the acceleration algorithm LLLUT is used to improve data processing. The AI imitation painting works “Monet’s Garden” and “Neon” are taken as case studies. In terms of color preference, AI painting prefers to use three primary colors: black, white, and red. For example, AI’s imitation painting work Han Xizai Night Banquet has three peak points higher than the average peak, respectively 0.04, 0.09, 0.15, and 0.99. The corresponding colors used are red, black, and yellow. The main colors used in the composition are red and yellow. In the application evaluation of the color model, the comparative analysis concludes that the color mixing efficiency is higher using the research method in this paper, and in the practical application, both painting professionals and amateurs have a stable effectiveness in generating color preference schemes after using the AI painting assistant based on the ACE color model optimized by LLLUT.

Abstract Two‐dimensional material‐based field‐effect transistors (2DM‐FETs) are playing a revolutionary role in electronic devices. However, before electronic design automation (EDA) for 2DM‐FETs can be achieved, it remains necessary to determine how to incorporate contact transports into model. Reported methods compromise between physical intelligibility and model compactness due to the heterojunction nature. To address this, quasi‐Fermi‐level phase space theory (QFLPS) is generalized to incorporate contact transports using the Landauer formula. It turns out that the Landauer‐QFLPS model effectively overcomes the issue of concern. The proposed new formula can describe 2DM‐FETs with Schottky or Ohmic contacts with superior accuracy and efficiency over previous methods, especially when describing non‐monotonic drain conductance characteristics. A three‐bit threshold inverter quantizer (TIQ) circuit is fabricated using ambipolar black phosphorus and it is demonstrated that the model accurately predicts circuit performance. The model could be very effective and valuable in the development of 2DM‐FET‐based integrated circuits.

Abstract The vulnerability of the current network has become an urgent problem to be solved. The focus of network protection should be shifted from traditional network protection to the direction of effective recovery even after being attacked and damaged, and then, the concept of resilience came into being. This paper selects physical explosion attacks to establish damaged network. An improved system resilience recovery strategy is established which considers task importance and time efficiency. Aiming at the initial population is too random, easy to mature and with poor solution, this paper improves genetic algorithm by new greedy model in population initialization and head-to-head mutation operator. Simulation shows that the improved genetic algorithm is better and more stable, the improved quotient model is more effective in system resilience recovery measured by index-E proposed in this paper.

Abstract Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we found that patients with long viral RNA course (LC) exhibited prolonged high-level IgG antibodies and higher regulatory T (Treg) cell counts compared to those with short viral RNA course (SC) in terms of viral load. Longitudinal proteomics and metabolomics analyses of the patient sera uncovered that prolonged viral RNA shedding was associated with inhibition of the liver X receptor/retinoid X receptor (LXR/RXR) pathway, substantial suppression of diverse metabolites, activation of the complement system, suppressed cell migration, and enhanced viral replication. Furthermore, a ten-molecule learning model was established which could potentially predict viral RNA shedding period. In summary, this study uncovered enhanced inflammation and suppressed adaptive immunity in COVID-19 patients with prolonged viral RNA shedding, and proposed a multi-omic classifier for viral RNA shedding prediction.

BackgroundPrevious studies suggest that physiological feedback can be an effective method for emotion regulation (ER). However, studies on the specific effects of physiological feedback have shown conflicting results due to inconsistencies in study designs. Therefore, we present this systematic review to further validate the effectiveness of physiological feedback for ER, clarify its specific effects, as well as summarize the factors that influence its effectiveness.MethodThis systematic review following PRISMA guidelines covers all studies using physiological feedback in emotions. A literature search was conducted in Web of Science, PubMed, PsychINFO, China National Knowledge Infrastructure, and WANFANG DATA. And a standardized quality assessment was performed.ResultsWe identified 27 relevant articles (25 studies), and the majority of these studies showed a significant regulatory effect of physiological feedback on different emotions. The feedback's content, explanation, authenticity, real-time capability, and modality were the key factors that influenced its effects, and this technology will achieve its optimal ER effect when these factors are considered comprehensively.ConclusionsThese findings further confirmed the effectiveness of physiological feedback as an ER method, as well as providing key factors that should be addressed in its application. Meanwhile, due to the limitations of these studies, more well-designed studies are still needed.

The experimental and computational studies for MILD combustion of diesel were conducted in this paper. The experiment was carried out in a constant pressure bomb and the computation was completed with the SAGE combustion model. The initial condition in the experiment covers two temperatures (723 K and 773 K), two pressures (18 bar and 25 bar), with a constant fuel flow of 2 kg/h, an oxygen flow of 1 kg/h, and a wide range of carbon dioxide flow containing 35–160 kg/h. Normal combustion has bright yellow flame and MILD combustion has blue flame according to observation in the experiment. The effects of ejection ratio, initial temperature and pressure are investigated to illustrate the mechanism of MILD combustion. The results show an increase in ejection ratio and an increase in initial temperature are propitious to achieve MILD combustion, while increased pressure needs more strict requirements to realize MILD combustion. Generally, experiment results and computations have similar trends for the effects of initial conditions on MILD combustion.

Abstract Background Zinc (Zn) is an important nutrient for human beings, which is also an essential micronutrient for crop growth. This study investigated the role of Zn in coordinating the mineral elements absorption in modern wheat (Triticum aestivum L.) cultivars with a new developed method. Results A method was developed, and showed a robust capability to simultaneously investigate seven mineral elements uptake in wheat seedling. With this method, we found low Zn supply (< 1 μM) promoted the absorption of potassium (K), magnesium (Mg) and manganese (Mn) in wheat seedling, while high Zn supply (> 1 μM) significantly inhibited the absorption of these elements. Cultivars with the green genes (Rht-B1b and Rht-D1b) showed a higher uptake capability on ammonium (NH4 +), and cultivars with Rht-B1b allele can uptake more phosphors (P), K, calcium (Ca), Mn and Zn compared to cultivars with Rht-D1b. Further analysis indicated higher uptake capability of NH4 + in cultivars contained Rhts was independent of Zn. Conclusion The key role of Zn in coordinating for mineral elements absorption was identified in modern wheat cultivars, providing the reference for Zn application in wheat. Meanwhile, this study provides a robust method for quantifying the absorption of mineral elements, which may be adopted into the broadly investigations on the coordinated nutrients absorption of plant.

The TEA domain (TEAD) family proteins (TEAD1‒4) are essential transcription factors that control cell differentiation and organ size in the Hippo pathway. Although the sequences and structures of TEAD family proteins are highly conserved, each TEAD isoform has unique physiological and pathological functions. Therefore, the development and discovery of subtype selective inhibitors for TEAD protein will provide important chemical probes for the TEAD-related function studies in development and diseases. Here, we identified a novel TEAD1/3 covalent inhibitor (DC-TEADin1072) with biochemical IC50 values of 0.61 ± 0.02 and 0.58 ± 0.12 μmol/L against TEAD1 and TEAD3, respectively. Further chemical optimization based on DC-TEAD in 1072 yielded a selective TEAD3 inhibitor DC-TEAD3in03 with the IC50 value of 0.16 ± 0.03 μmol/L, which shows 100-fold selectivity over other TEAD isoforms in activity-based protein profiling (ABPP) assays. In cells, DC-TEAD3in03 showed selective inhibitory effect on TEAD3 in GAL4-TEAD (1–4) reporter assays with the IC50 value of 1.15 μmol/L. When administered to zebrafish juveniles, experiments showed that DC-TEAD3in03 reduced the growth rate of zebrafish caudal fins, indicating the importance of TEAD3 activity in controlling proportional growth of vertebrate appendages.

Boron-dipyrromethene (BODIPY) is one promising class of sensitizers for dye-sensitized solar cells (DSSCs) due to unique merits of high absorption coefficient and versatile structural modification capability. However, such derivatives usually suffer from limited power conversion efficiencies (PCEs) because of narrow light absorption band and low electron injection. To aid the discovery of BODIPY sensitizers, we employ an inverse design method to design efficient sensitizers by integrating data mining and first-principle techniques. We establish robust data-mining models using genetic algorithm and multiple linear regression, where the features are filtered from 5515 descriptors and their meanings are explicitly explored for next inverse designs. Based on the features’ understanding, we design candidates NH1-6 and predict their PCEs, demonstrating remarkable enhancements (58% maximum) compared to previous works. Furthermore, their optoelectronic properties including maximum absorption wavelengths, oscillator strengths, bandgaps, transferred charges, charge transferred distances, TiO2 conduction band shifts, short-circuit currents and electron injection efficiencies simulated via first-principle calculations indicate significant increasements (93 nm, 122.41%, 23.70%, 36.36%, 471.17%, 63.64%, 28.55%, 107.86% maximum), which testifies the corresponding highly predicted PCEs and may overcome BODIPY dyes’ shortcomings. The as-designed BODIPY sensitizers can be promising candidates for DSSCs, and such method could help accelerate the discovery of other energy materials.

The complete mitochondrial genome of Limnophyes minimus (Meigen 1818) was sequenced and annotated, and its general features and base composition were analyzed. The phylogenetic relationships of the families Chironomidae, Simuliidae, Sciaridae and Culicidae based on 25 metagenomes were reconstructed using maximum likelihood (ML) methods based on the concatenated nucleotide sequences, the phylogenetic analysis showed that L. minimus belongs family Chironomidae, which is consistent with the traditional morphological classification.

Sensors enable the detection of physiological indicators and pathological markers to assist in the diagnosis, treatment, and long-term monitoring of diseases, in addition to playing an essential role in the observation and evaluation of physiological activities. The development of modern medical activities cannot be separated from the precise detection, reliable acquisition, and intelligent analysis of human body information. Therefore, sensors have become the core of new-generation health technologies along with the Internet of Things (IoTs) and artificial intelligence (AI). Previous research on the sensing of human information has conferred many superior properties on sensors, of which biocompatibility is one of the most important. Recently, biocompatible biosensors have developed rapidly to provide the possibility for the long-term and in-situ monitoring of physiological information. In this review, we summarize the ideal features and engineering realization strategies of three different types of biocompatible biosensors, including wearable, ingestible, and implantable sensors from the level of sensor designing and application. Additionally, the detection targets of the biosensors are further divided into vital life parameters (e.g., body temperature, heart rate, blood pressure, and respiratory rate), biochemical indicators, as well as physical and physiological parameters based on the clinical needs. In this review, starting from the emerging concept of next-generation diagnostics and healthcare technologies, we discuss how biocompatible sensors revolutionize the state-of-art healthcare system unprecedentedly, as well as the challenges and opportunities faced in the future development of biocompatible health sensors.

The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.

A novel series of triazoloquinazolinone derivatives were designed, synthesised, and evaluated for their in vitro biological activities against the SHP2 protein. Moreover, some compounds were evaluated against A375 cells. The results revealed that target compounds possessed moderate to excellent inhibitory activity against SHP2 protein, whereas compounds 12f, 12l, 12j, 17e, and 17f have strong antiproliferative activity on A375 cells. The compound 12l showed remarkable cytotoxicity against A375 cells and a strong inhibitory effect against SHP2 protein when compared with SHP244. The structure-activity relationships (SARs) indicated that electron-donating groups (EDGs) on phenyl rings are beneficial for improving the antitumor activity; compounds with a hydroxyl substituent at the 2-position of phenyl ring exhibited superior activities than compounds with a substituent at the 4-position. In addition, compound 12l displayed improved physicochemical properties as well as metabolic stability compared to SHP244. Our efforts identified 12l as a promising SHP2 protein inhibitor, warranting its further investigation.

The gigantically unexplored chemical space for the combinations of donor and acceptor poses a mammoth challenge in enhancing power conversion efficiency. Herein, a strategy integrating machine learning and density functional theory was proposed to assist the rational screening of prominent donor/acceptor pairs. Notably, 10 promising donor/acceptor pairs were ultimately sieved out with power conversion efficiency lager than 18.22%. Furthermore, the density functional theory computation expressed that the new donor/acceptor pair of D18/BTP-eC9 possessed stronger absorption intensity, while the KCS/KCR (ratio of charge separation rate and charge recombination rate) and μe (electron mobility) were larger than those of the best known D18/Y6 with the increments of 201.31% and 125.98% respectively. The SHapley Additive exPlanations analysis revealed that the two most important features of A_nR08 and D_D/Dtr12 were positively related to the improvement of power conversion efficiency. It was found that A_nR08 was positively associated with π-conjugation strength, and high D_D/Dtr12 suggested the highly fused chemical structure, indicating the practical clues in discovering top-performing donor/acceptor pairs.

Abstract Conventional antiplatelet agents indiscriminately inhibit both thrombosis and hemostasis, and the increased bleeding risk thus hampers their use at more aggressive dosages to achieve adequate effect. Blocking integrin αIIbβ3 outside‐in signaling by separating the β3/Src interaction, yet to be proven in vivo, may nonetheless resolve this dilemma. Identification of a specific druggable target for this strategy remains a fundamental challenge as Src SH3 is known to be responsible for binding to not only integrin β3 but also the proteins containing the PXXP motif. In vitro and in vivo mutational analyses show that the residues, especially E97, in the RT loop of Src SH3 are critical for interacting with β3. DCDBS84, a small molecule resulting from structure‐based virtual screening, is structurally validated to be directed toward the projected target. It specifically disrupts β3/Src interaction without affecting canonical PXXP binding and thus inhibits the outside‐in signaling‐regulated platelet functions. Treatment of mice with DCDBS84 causes a profound inhibition of thrombosis, equivalent to that induced by extremely high doses of αIIbβ3 antagonist, but does not compromise primary hemostasis. Specific targets are revealed for a preferential inhibition of thrombosis that may lead to new classes of potent antithrombotics without hemorrhagic side effects.

Accurate target recognition and location is one of the key technologies in the field of smart city application. In order to solve the problem of large pedestriain flow impact in crowded metro stations, a method of in‐depth learning detection based on SSD (single shot multibox detector) is proposed. The algorithm extracts the feature information of the input image, then returns the boundary box of the location on the feature map and classifies the object categories. Using the method of local feature extraction, the features of different positions, different aspect ratios and sizes are obtained, and VGG16 is used as the base network to optimise and improve the network structure. The results of simulation experiments on VOC2007 and data_sub show that the maximum value of mAP is 77% and the highest accuracy is 96.31%. Compared with other mainstream deep learning target detection methods, SSD has higher accuracy, better real‐time and robustness. It can solve the problem of different pedestrian target sizes and better realise pedestrians in subway station environment. Detection provides decision‐making basis for flow statistics.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

The simulation model of 2 mm thick 2 A12 aluminum alloy thin plate impacted by blade projectile was established by using the ABAQUS finite element software, including impact of yaw and oblique, and the yaw angle is 0-90° and the incidence angle is 0-60°. The effect of impact angle on the impact process, the ballistic limit of the target and the energy absorption of the target were investigated. The simulation results show that the target is mainly shear failure when the yaw angle is 0°, and a long plug and rectangular reaming are remained. When the yaw angle is 15°-60°, the failure mode of the target is ductile expansion and the hole area increases with the increase of the yaw angle. For the yaw angle ranges from 75° to 90°, the target is tore by tensile and the damage is serious. The failure mechanisms of target is both plug and petal damage when oblique impact. The ballistic limit of the target increases with the increase of the yaw angle, and the energy absorption of the target is related with the yaw angle and impact velocity of the projectile. The energy consumption of the target is not independent of the incidence angle.

Micro-milling can fabricate 3 D free-form surface and complex components and has a promising prospect. However, due to sharp decreases of the tool scale and machining parameters, micro-milling process shows remarkable differences from traditional milling, thus researchers conduct broad studies on micro-milling technology recent years. The process of micro-milling is complicated and involves many aspects, the domestic and overseas researches in micro-milling are summed up in this paper, the development of minimum chip thickness, cutting force, surface roughness, tool wear, and other aspects of researches are summarized. The development trend of micro-milling and the application prospect are discussed.

Micro-machining can produce size effect under mesoscopic scale,by using the strain gradient plasticity theory of dislocation mechanism,considering the microstructure characteristics of the material,the material constitutive model under mesoscopic scale is established,which can be used to explain the size effect of micro-machining. The material constitutive model under the mesoscopic scale derived from the theory is analyzed and studied,and the stress-strain curves and simulation results under different conditions are obtained in this article. The research shows that with the increase of cutting thickness,the stressstrain curve of the material will tend to the stress-strain curve of macro cutting. As the rake angle of the tool increases,the internal stress of the material increases accordingly,and the scale effect of the material becomes more obvious. The rake angle of the tool also has a great influence on the formation of the chip,and the smaller tool rake angle is more conducive to accumulating material and thus forming chips. The cutting edge radius of the tool is the root cause of the phenomenon of minimum cutting thickness. With the decrease of the cutting edge radius,the scale effect of the material becomes more prominent.

ImportanceSignificant insurance status disparities have been demonstrated in head and neck cancer (HNC) outcomes. The effects of insurance status on HNC outcomes may be explained by differential access to high-quality care.ObjectiveTo evaluate the association of insurance status with the quality of the treating hospital and receipt of guideline-compliant care among patients with HNC.Design, setting, and participantsThis retrospective cohort study of data from the California Cancer Registry dataset linked with discharge records and hospital characteristics from the California Department of Health Care Access and Information included adult patients with HNC diagnosed between January 1, 2010, and December 31, 2019. Data were analyzed from May 10, 2023, to March 25, 2024.ExposuresInsurance status: commercial, Medicare, Medicaid, uninsured, other, or unknown.Main outcomes and measuresQuality of the treating hospital (tertiles), receipt of National Comprehensive Cancer Network guideline-compliant care, and overall survival.ResultsA total of 23 933 patients (mean [SD] age, 64.8 [12.3] years; 75.3% male) met the inclusion criteria. Treatment in top-tertile hospitals (hazard ratio, 0.87; 95% CI, 0.79-0.95) was associated with improved overall survival compared with treatment in bottom-tertile hospitals. Medicare (odds ratio [OR], 0.78; 95% CI, 0.73-0.84), Medicaid (OR, 0.60; 95% CI, 0.54-0.66), and uninsured (OR, 0.38; 95% CI, 0.29-0.49) status were associated with lower likelihood of treatment in high-quality hospitals compared with commercial insurance. Among patients with advanced disease, Medicaid (OR, 0.72; 95% CI, 0.62-0.83) and uninsured (OR, 0.64; 95% CI, 0.44-0.93) patients were less likely to receive dual-modality therapy. Among patients with surgically resected advanced disease, Medicaid coverage (OR, 0.73; 95% CI, 0.58-0.93) was associated with lower likelihood of receiving adjuvant radiotherapy.Conclusions and relevanceThis study found significant insurance disparities in quality of care among patients with HNC. These findings highlight the need for continued health insurance reform in the US to improve the quality of insurance coverage, in addition to expanding access to health insurance.

Multiscale design of catalyst layers (CLs) is important to advancing hydrogen electrochemical conversion devices toward commercialized deployment, which has nevertheless been greatly hampered by the complex interplay among multiscale CL components, high synthesis cost and vast design space. We lack rational design and optimization techniques that can accurately reflect the nanostructure-performance relationship and cost-effectively search the design space. Here, we fill this gap with a deep generative artificial intelligence (AI) framework, GLIDER, that integrates recent generative AI, data-driven surrogate techniques and collective intelligence to efficiently search the optimal CL nanostructures driven by their electrochemical performance. GLIDER achieves realistic multiscale CL digital generation by leveraging the dimensionality-reduction ability of quantized vector-variational autoencoder. The powerful generative capability of GLIDER allows the efficient search of the optimal design parameters for the Pt-carbon-ionomer nanostructures of CLs. We also demonstrate that GLIDER is transferable to other fuel cell electrode microstructure generation, e.g., fibrous gas diffusion layers and solid oxide fuel cell anode. GLIDER is of potential as a digital tool for the design and optimization of broad electrochemical energy devices.

Hyperglycemia induces vascular endothelial dysfunction, which contributes to the development of vascular complication of diabetes. A classic prescription of traditional medicine, HuangqiGuizhiWuwu Decoction (HGWWD) has been used for the treatment of various cardiovascular and cerebrovascular diseases, which all are related with vascular pathology. The present study investigated the effect of HGWWD treatment in streptozocin (STZ)-induced vascular dysfunction in mouse models. In vivo studies were performed using wild type mice as well as arginase 1 knockout specific in endothelial cells (EC-A1–/–) of control mice, diabetes mice and diabetes mice treated with HGWWD (60 g crude drugs/kg/d) for 2 weeks. For in vitro studies, aortic tissues were treated with mice serum containing HGWWD with or without adenoviral arginase 1 (Ad-A1) transduction in high glucose (HG) medium. We found that HGWWD treatment restored STZ-induced impaired mean velocity and pulsatility index of mouse left femoral arteries, aortic pulse wave velocity and vascular endothelial relaxation accompanied by elevated NO production in the aorta and plasma, as well as reduced endothelial arginase activity and aortic arginase 1 expression. The protective effect of HGWWD is reversed by an inhibitor of nitric oxide synthesis. Meanwhile, the preventive effect of serum containing HGWWD in endothelial vascular dysfunction is completely blocked by Ad-A1 transduction in HG incubated aortas. HGWWD treatment further improved endothelial vascular dysfunction in STZ induced EC-A1–/– mice. This study demonstrates that HGWWD improved STZ-induced vascular dysfunction through arginase 1 – NO signaling, specifically targeting endothelial arginase 1.