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Lithium-ion batteries (LIBs) are widely used as important energy storage and energy supply devices. The porous design and heteroatomization modification of carbon-based anode materials are crucial for achieving high-capacity and reversible energy storage in LIBs. Sol-gel method and pyrolysis treatment were used to obtain silica/silver composite particles used as templates. Polyphosphazene-coated silica/silver composite composite carbon materials (SiO2/Ag@PZS-C) were synthesized through in-situ self-assembly and carbonization of polyphosphazene. The electrochemical behavior and lithium storage mechanism of SiO2/Ag@PZS-C was also studied. The results reveal that the composite exhibited high specific capacity, stable cycling and superior rate performance. The double modification of silver nanoparticles and polyphosphazene carbon significantly improves the conductivity of silica and reduces the volume change. Moreover, the carbon shell of polyphosphazene facilitated the formation of a stable solid electrolyte interface film (SEI), preventing direct contact between the active material and the electrolyte, thereby substantially enhancing lithium storage performance.

Abstract With the popularity of smart terminals, wearable electronic devices have shown great market prospects, especially high-sensitivity pressure sensors, which can monitor micro-stimuli and high-precision dynamic external stimuli, and will have an important impact on future functional development. Compressible flexible sensors have attracted wide attention due to their simple sensing mechanism and the advantages of light weight and convenience. Sensors with high sensitivity are very sensitive to pressure and can detect resistance/current changes under pressure, which has been widely studied. On this basis, this review focuses on analyzing the performance impact of device structure design strategies on high sensitivity pressure sensors. The design of structures can be divided into interface microstructures and three-dimensional framework structures. The preparation methods of various structures are introduced in detail, and the current research status and future development challenges are summarized.

Abstract Though the recovery rate of coal resources can be effectively improved by adopting the gob‐side entry longwall face, which is also easy to cause tension between mining and excavation. Therefore, a new type of roadway layout, narrow coal pillar entry retained (NCPER), was invented in the study. The NCPER is excavated together with the tailgate of next longwall face before the mining of prior longwall face, which is retained and reused during the mining of the next longwall face. The layout is based on the advantages and successful experience of the double roadways excavation, the gob‐side entry retained, and the roadway driven along gob‐side and heading for adjacent advancing coal face; moreover, the NCPER significantly shortens the preparation time of the longwall face while improving the recovery rate. Taking the production geological conditions of Yushuling Mine as the engineering background, the influence of factors such as roof‐cutting pressure relief and coal pillar width on the stability of the narrow coal pillar from the perspective of improving the stress environment of the roadway and strengthening the weak structure support of the surrounding rock was studied, and reasonable suggestions for selecting parameters of roof cutting and coal pillar width were given. Furthermore, the bidirectional grouting cable and its joist were used to strengthen the narrow coal pillar and the roadway roof, respectively, and a good effect of roadway control has been obtained. The invention and application of NCPER provided a new approach for the longwall face roadway layout and effectively eased the strained relationship between mining and excavating, which has a strong popularization and application value.

Abstract Ovarian cancer is a leading cause of death among gynecologic tumors, often detected at advanced stages. Metabolic reprogramming and increased lipid biosynthesis are key factors driving cancer cell growth. Stearoyl-CoA desaturase 1 (SCD1) is a crucial enzyme involved in de novo lipid synthesis, producing mono-unsaturated fatty acids (MUFAs). Here, we aimed to investigate the expression and significance of SCD1 in epithelial ovarian cancer (EOC). Comparative analysis of normal ovarian surface epithelial (NOSE) tissues and cell lines revealed elevated SCD1 expression in EOC tissues and cells. Inhibition of SCD1 significantly reduced the proliferation of EOC cells and patient-derived organoids and induced apoptotic cell death. Interestingly, SCD1 inhibition did not affect the viability of non-cancer cells, indicating selective cytotoxicity against EOC cells. SCD1 inhibition on EOC cells induced endoplasmic reticulum (ER) stress by activating the unfolded protein response (UPR) sensors and resulted in apoptosis. The addition of exogenous oleic acid, a product of SCD1, rescued EOC cells from ER stress-mediated apoptosis induced by SCD1 inhibition, underscoring the importance of lipid desaturation for cancer cell survival. Taken together, our findings suggest that the inhibition of SCD1 is a promising biomarker as well as a novel therapeutic target for ovarian cancer by regulating ER stress and inducing cancer cell apoptosis.

Essential mineral elements such as zinc and iron play a crucial role in maintaining crop growth and development, as well as ensuring human health. Foxtail millet is an ancient food crop rich in mineral elements and constitutes an important dietary supplement for nutrient-deficient populations. The ZIP (ZRT, IRT-like protein) transporters are primarily responsible for the absorption, transportation and accumulation of Zn, Fe and other metal ions in plants. Here, we identified 14 ZIP transporters in foxtail millet (SiZIP) and systematically characterized their phylogenetic relationships, expression characteristics, sequence variations, and responses to various abiotic stresses. As a result, SiZIPs display rich spatiotemporal expression characteristics in foxtail millet. Multiple SiZIPs demonstrated significant responses to Fe, Cd, Na, and K metal ions, as well as drought and cold stresses. Based on homologous comparisons, expression characteristics and previous studies, the functions of SiZIPs were predicted as being classified into several categories: absorption/efflux, transport/distribution and accumulation of metal ions. Simultaneously, a schematic diagram of SiZIP was drawn. In general, SiZIPs have diverse functions and extensively involve in the transport of metal ions and osmotic regulation under abiotic stresses. This work provides a fundamental framework for the transport and accumulation of mineral elements and will facilitate the quality improvement of foxtail millet.

Abstract Tropical forest diversity governs forest structures, compositions, and influences the ecosystem response to environmental changes. Better representation of forest diversity in ecosystem demography (ED) models within Earth system models is thus necessary to accurately capture and predict how tropical forests affect Earth system dynamics subject to climate changes. However, achieving forest coexistence in ED models is challenging due to their computational expense and limited understanding of the mechanisms governing forest functional diversity. This study applies the advanced Multi‐Objective Population‐based Parallel Local Surrogate‐assisted search (MOPLS) optimization algorithm to simultaneously calibrate ecosystem fluxes and coexistence of two physiologically distinct tropical forest species in a size‐ and age‐structured ED model with realistic representation of wood harvest. MOPLS exhibits satisfactory model performance, capturing hydrological and biogeochemical dynamics observed in Barro Colorado Island, Panama, and robustly achieving coexistence for the two representative forest species. This demonstrates its effectiveness in calibrating tropical forest coexistence. The optimal solution is applied to investigate the recovery trajectories of forest biomass after various intensities of clear‐cut deforestation. We find that a 20% selective logging can take approximately 40 years for aboveground biomass to return to the initial level. This is due to the slow recovery rate of late successional trees, which only increases by 4% over the 40‐year period. This study lays the foundation to calibrate coexistence in ED models. MOPLS can be an effective tool to help better represent tropical forest diversity in Earth system models and inform forest management practices.

Abstract Introduction Inaccurate, untimely diagnoses of fundus diseases leads to vision-threatening complications and even blindness. We built a deep learning platform (DLP) for automatic detection of 30 fundus diseases using ultra-widefield fluorescein angiography (UWFFA) with deep experts aggregation. Methods This retrospective and cross-sectional database study included a total of 61,609 UWFFA images dating from 2016 to 2021, involving more than 3364 subjects in multiple centers across China. All subjects were divided into 30 different groups. The state-of-the-art convolutional neural network architecture, ConvNeXt, was chosen as the backbone to train and test the receiver operating characteristic curve (ROC) of the proposed system on test data and external test date. We compared the classification performance of the proposed system with that of ophthalmologists, including two retinal specialists. Results We built a DLP to analyze UWFFA, which can detect up to 30 fundus diseases, with a frequency-weighted average area under the receiver operating characteristic curve (AUC) of 0.940 in the primary test dataset and 0.954 in the external multi-hospital test dataset. The tool shows comparable accuracy with retina specialists in diagnosis and evaluation. Conclusions This is the first study on a large-scale UWFFA dataset for multi-retina disease classification. We believe that our UWFFA DLP advances the diagnosis by artificial intelligence (AI) in various retinal diseases and would contribute to labor-saving and precision medicine especially in remote areas.

Abstract Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. Here, we present ReSisTrace that uses shared transcriptomic features of sister cells to predict the states priming treatment resistance. Applying ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells reveals pre-resistant phenotypes defined by proteostatic and mRNA surveillance features, reflecting traits enriched in the upcoming subclonal selection. Furthermore, we show that DNA repair deficiency renders cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leverage the obtained pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

Abstract Background Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti–PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. Methods Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. Results A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression–based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. Conclusions This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. Trial registration: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Abstract We examine the Sommerfeld enhancement effect for the puffy self-interacting dark matter. We find out two new parameters to classify the self-scattering cross section into the Born, the resonance and the classical regimes for the puffy dark matter. Then we observe that the resonance peaks for the puffy dark matter self-scattering and for the Sommerfeld enhancement effect have the same locations. Further, we find that for a large ratio between R χ (radius of a puffy dark matter particle) and 1/m ϕ (force range), the Sommerfeld enhancement factor approaches to 1 (no enhancement). Finally, for the puffy SIDM scenario to solve the small-scale problems, the values of the Sommerfeld enhancement factor are displayed in the allowed parameter regions.

Abstract Exploring the Venusian lower atmosphere is crucial for studying the atmospheric circulation, surface‐atmosphere interactions, and origin and evolution of the Venusian atmosphere and climate. In this study, we investigate the theoretical capabilities of a downward‐looking passive microwave sounder placed in low Venus orbit to measure the temperature, sulfur dioxide (SO2) and gaseous sulfuric acid (H2SO4(g)) profiles. A nonlinear iterative retrieval algorithm combining a radiative transfer (RT) model and an optimal‐estimation‐based inversion algorithm is established. With the RT model adapted to the Venusian atmosphere, simulations under different atmospheric conditions are performed to optimize the selection of frequency channels. The achievable altitude coverage, vertical resolution and corresponding expected precision of the temperature, SO2 and H2SO4(g) retrievals from the multi‐channel brightness temperature measurements are quantified via retrieval simulations. The temperature can be retrieved from the surface of Venus to an altitude of ∼61 km with a precision of 1–3.5 K and a vertical resolution of 6–15 km. A precision of 10%–35% is expected for SO2 in the ∼12–64 km altitude range and with a vertical resolution of 8–19 km. H2SO4(g) can be retrieved in the ∼36–54 km altitude range with a precision of 10%–30% and a vertical resolution of 6–13 km.

We aimed to investigate the expression and motor modulatory roles of several mechano-sensitive channels (MSCs) in human ureter. Human proximal ureters were obtained from eighty patients subjected to nephrectomy. Expression of MSCs at mRNA, protein and functional levels were examined. Contractions of longitudinal ureter strips were recorded in organ bath. A fluorescent probe Diaminofluoresceins was used to measure nitric oxide (NO). RT-PCR analyses revealed predominant expression of Piezo1 and TRPV2 mRNA in intact ureter and mucosa. Immunofluorescence assays indicate proteins of MSCs (Piezo1/Piezo2, TRPV2 and TRPV4) were mainly distributed in the urothelium. Ca2+ imaging confirmed functional expression of TRPV2, TRPV4 and Piezo1 in cultured urothelial cells. Specific agonists of Piezo1 (Yoda1, 3–300 μM) and TRPV2 (cannabidiol, 3–300 μM) attenuated the frequency of ureteral contractions in a dose-dependent manner while the TRPV4 agonist GSK1016790A (100 nM–1 μM) exerted no effect. The inhibitory effects of Piezo1 and TRPV2 agonists were significantly blocked by the selective antagonists (Dooku 1 for Piezo1, Tranilast for TRPV2), removal of the mucosa, and pretreatment with NO synthase inhibitor L-NAME (10 μM). Yoda1 (30 μM) and cannabidiol (50 μM) increased production of NO in cultured urothelial cells. Our results suggest that activation of Piezo1 or TRPV2 evokes NO production and release from mucosa that may mediate mechanical stimulus-induced reduction of ureter contractions. Our findings support the idea that targeting Piezo1 and TRPV2 channels may be a promising pharmacological strategy for ureter stone passage or colic pain relief.

BackgroundN-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is a neurotoxin widely used to induce PD models, but the effect of MPTP on the cells and genes of PD has not been fully elucidated.MethodsSingle-nucleus RNA sequencing was performed in the Substantia Nigra (SN) of MPTP mice. UMAP analysis was used for the dimensionality reduction visualization of the SN in the MPTP mice. Known marker genes highly expressed genes in each cluster were used to annotate most clusters. Specific Differentially Expressed Genes (DEGs) and PD risk genes analysis were used to find MPTP-associated cells. GO, KEGG, PPI network, GSEA and CellChat analysis were used to reveal cell type-specific functional alterations and disruption of cell-cell communication networks. Subset reconstruction and pseudotime analysis were used to reveal the activation status of the cells, and to find the transcription factors with trajectory characterized.ResultsInitially, we observed specific DEGs and PD risk genes enrichment in microglia. Next, We obtained the functional phenotype changes in microglia and found that IGF, AGRN and PTN pathways were reduced in MPTP mice. Finally, we analyzed the activation state of microglia and revealed a pro-inflammatory trajectory characterized by transcription factors Nfe2l2 and Runx1.ConclusionOur work revealed alterations in microglia function, signaling pathways and key genes in the SN of MPTP mice.

In June 2022, EU-OS came to the decision to make public a solubility data set of 100+K compounds obtained from several of the EU-OS proprietary screening compound collections. Leveraging on the interest of SLAS for screening scientific development it was decided to launch a joint EUOS-SLAS competition within the chemoinformatics and machine learning (ML) communities. The competition was open to real world computation experts, for the best, most predictive, classification model of compound solubility. The aim of the competition was multiple: from a practical side, the winning model should then serve as a cornerstone for future solubility predictions having used the largest training set so far publicly available. From a higher project perspective, the intent was to focus the energies and experiences, even if professionally not precisely coming from Pharma R&D; to address the issue of how to predict compound solubility. Here we report how the competition was ideated and the practical aspects of conducting it within the Kaggle framework, leveraging of the versatility and the open-source nature of this data science platform. Consideration on results and challenges encountered have been also examined.

This study employs Latent Dirichlet Allocation (LDA) topic modelling methodology to analyze documents related to renewable energy laws and policies at the central level in China. The objective is to investigate the development and evolution of renewable energy policies in China and to gain insights into the national-level attitudes towards renewable energy development. The study consists of two phases: initially, renewable energy policy documents undergo keyword analysis using word clouds and keyword co-occurrence network analysis to elucidate the focal areas and their interconnections within the legal and policy texts. Subsequently, after determining the optimal number of topics for modelling based on topic perplexity and consistency results, the text undergoes data cleaning to isolate words with practical significance. These words are then incorporated into the LDA topic model to analyze the distribution and content of potential topics within the policies. Lastly, by linearly segmenting the time frame, changes in topic intensity over time are visually examined using heat maps. The findings indicate that energy policies have consistently prioritized “development” and emphasized the significance of “new energy” in renewable energy policies. Moreover, as renewable energy has progressed, governments and policymakers have come to acknowledge the importance of comprehensive energy planning, transitioning to clean energy sources, and regulating the electricity market. This growing awareness has led to efforts to strengthen policy and regulatory measures to foster renewable energy's sustainable development and utilization. In summary, this study highlights the effectiveness of the LDA topic model in analyzing renewable energy policies, advancing its adoption and furthering research in the field.

ABSTRACTCandida albicans, one of the most prevalent human fungal pathogens, causes diverse diseases extending from superficial infections to deadly systemic mycoses. Currently, only three major classes of antifungal drugs are available to treat systemic infections: azoles, polyenes, and echinocandins. Alarmingly, the efficacy of these antifungals against C. albicans is hindered both by basal tolerance toward the drugs and the development of resistance mechanisms such as alterations of the drug’s target, modulation of stress responses, and overexpression of efflux pumps. Thus, the need to identify novel antifungal strategies is dire. To address this challenge, we screened 3,049 structurally-diverse compounds from the Boston University Center for Molecular Discovery (BU-CMD) chemical library against a C. albicans clinical isolate and identified 17 molecules that inhibited C. albicans growth by >80% relative to controls. Among the most potent compounds were CMLD013360, CMLD012661, and CMLD012693, molecules representing two distinct chemical scaffolds, including 3-hydroxyquinolinones and a xanthone natural product. Based on structural insights, CMLD013360, CMLD012661, and CMLD012693 were hypothesized to exert antifungal activity through metal chelation. Follow-up investigations revealed all three compounds exerted antifungal activity against non-albicans Candida, including Candida auris and Candida glabrata, with the xanthone natural product CMLD013360 also displaying activity against the pathogenic mould Aspergillus fumigatus. Media supplementation with metallonutrients, namely ferric or ferrous iron, rescued C. albicans growth, confirming these compounds act as metal chelators. Thus, this work identifies and characterizes two chemical scaffolds that chelate iron to inhibit the growth of the clinically relevant fungal pathogen C. albicansIMPORTANCEThe worldwide incidence of invasive fungal infections is increasing at an alarming rate. Systemic candidiasis caused by the opportunistic pathogen Candida albicans is the most common cause of life-threatening fungal infection. However, due to the limited number of antifungal drug classes available and the rise of antifungal resistance, an urgent need exists for the identification of novel treatments. By screening a compound collection from the Boston University Center for Molecular Discovery (BU-CMD), we identified three compounds representing two distinct chemical scaffolds that displayed activity against C. albicans. Follow-up analyses confirmed these molecules were also active against other pathogenic fungal species including Candida auris and Aspergillus fumigatus. Finally, we determined that these compounds inhibit the growth of C. albicans in culture through iron chelation. Overall, this observation describes two novel chemical scaffolds with antifungal activity against diverse fungal pathogens.

Due to assembling, manufacturing errors, and wear, irregular rough surfaces inevitably exist in joints, which will increase the contact stiffness nonlinearity at the joint of the spatial developable mechanism, and the traditional contact force model is difficult to accurately predict the contact force change of irregular rough surface clearance. Aiming at the difficulty of accurately predicting the dynamic behavior of the spatial developable mechanism caused by rough joint clearance, an improved clearance contact force modeling method based on an uncoordinated contact model is proposed in this paper. The influence of rough peak distribution on the contact area is analyzed. The stiffness of the traditional Hertz model is modified based on the probability distribution density function of the rough peak, and an improved contact force model based on dynamic contact stiffness is established. Based on the Lagrange multiplier method, the dynamics model of spatial developable mechanism is constructed. Based on the model, the dynamic analysis of the spatial expansion mechanism is carried out, and the influence of the roughness of the contact surface and the size of the clearance on the dynamic characteristics of the system are explored; as well, the influence of different clearance parameters on the dynamic characteristics of the development mechanism is revealed. Through the analysis, this paper provides a theoretical basis for predicting the effect of a spatial revolute clearance joint on the dynamic characteristics of the mechanism and lays a good foundation for the manufacture and application of the mechanism.

Cluster analysis of 3D head shapes plays a crucial role in the mass customization design of products related to the head. Head shapes exhibit variations across different races, and designing helmets exclusively for Chinese individuals cannot solely rely on or reference foreign head models. Currently, research on cluster analysis of Chinese head shapes is limited, especially concerning shape variances. To address this, we developed an improved k-medoids algorithm and integrated Cluster Validity Index as an assessment metric. This enabled us to cluster 339 Chinese young males aged 18 to 30 into 7 groups based on their head shapes. By comparing our improved algorithm to the traditional k-medoids method, we affirmed its superiority in achieving higher sample participation rates and reducing inter-cluster sample disparities. To simplify the helmet design and editing process, and to improve the efficiency of mass customization, we have developed a parametric modeling program for bicycle helmets based on the head shape clustering results. Results from the Helmet Fit Index and stress simulation analysis demonstrate that our approach significantly enhances helmet fit and wearer comfort.

High-entropy alloys (HEAs) have attracted considerable attention for their exceptional microstructures and properties. Discovering new HEAs with desirable properties is crucial, but traditional design methods are laborious and time-consuming. Fortunately, the emerging Machine Learning (ML) offers an efficient solution. In this study, composition-microhardness data pairs from various alloy systems were collected and expanded using a Generative Adversarial Network (GAN). These data pairs were converted into empirical parameter-microhardness pairs. Then Active Learning (AL) was employed to screen the Al-Co-Cr-Cu-Fe-Ni system and identify the eXtreme Gradient Boosting (XGBoost) as the optimal ML master model. Millions of data training iterations employing the XGBoost sub-model and accuracy evaluations using the Expected Improvement (EI) algorithm establish the relationship between HEA compositions and microhardness. The proposed sub-model aligns well with experimental data, wherein four Al-rich compositions exhibit ultra-high microhardness (>740 HV, with a maximum of ∼780.3 HV) and low density (

Ozone stands out as a crucial trace gas within the Earth’s atmosphere, exerting a substantial influence on climate change and air pollution. Tropospheric ozone plays an important role in the formation of photochemical smog, and its variations are associated with human activities. The utilization of satellite remote sensing technology for tropospheric ozone monitoring enables a quantitative analysis of its global and regional spatiotemporal characteristics. It also facilitates the investigation of the mechanisms involved in ozone formation within the troposphere. The significant progress in product accuracy and spatiotemporal resolution of ozone remote sensing products, including total ozone and vertical profiles, can be attributed to the extensive development of satellite remote sensing techniques. Nevertheless, the precision of tropospheric ozone products remains inadequate for contemporary scientific purposes, primarily because of faint signals in the lower atmosphere, the intricate nature of the underlying surface, and the existence of clouds and aerosols. This study places emphasis on the satellite remote sensing of tropospheric ozone, encompassing a comprehensive review of the advancements in satellite sensors and the characteristics and suitability of various retrieval algorithms. Moreover, this research delves into the possible utilization of satellite remote sensing for the provision of reliable tropospheric ozone observation data on a global and regional level.

Abstract Development of effective recycling strategies for cathode materials in spent lithium-ion batteries are highly desirable but remain significant challenges, among which facile separation of Al foil and active material layer of cathode makes up the first important step. Here, we propose a reaction-passivation driven mechanism for facile separation of Al foil and active material layer. Experimentally, >99.9% separation efficiency for Al foil and LiNi0.55Co0.15Mn0.3O2 layer is realized for a 102 Ah spent cell within 5 mins, and ultrathin, dense aluminum-phytic acid complex layer is in-situ formed on Al foil immediately after its contact with phytic acid, which suppresses continuous Al corrosion. Besides, the dissolution of transitional metal from LiNi0.55Co0.15Mn0.3O2 is negligible and good structural integrity of LiNi0.55Co0.15Mn0.3O2 is well-maintained during the processing. This work demonstrates a feasible approach for Al foil-active material layer separation of cathode and can promote the green and energy-saving battery recycling towards practical applications.

Petal abscission affects the growth, development, and economic value of plants, but the mechanism of ethylene-ROS-induced petal abscission is not clear. Therefore, we treated roses with different treatments (MOCK, ETH, STS, and ETH + STS), and phenotypic characteristics of petal abscission, changed ratio of fresh weight, morphology of cells in AZ and the expression of RhSUC2 were analyzed. On this basis, we measured reactive oxygen species (ROS) content in petals and AZ cells of roses, and analyzed the expression levels of some genes related to ROS production and ROS scavenging. Ethylene promoted the petal abscission of rose through decreasing the fresh weight of the flower, promoting the stacking and stratification of AZ cells, and repressing the expression of RhSUC2. During this process, ethylene induced the ROS accumulation of AZ cells and petals mainly through increasing the expressions of some genes (RhRHS17, RhIDH1, RhIDH-III, RhERS, RhPBL32, RhFRS5, RhRAC5, RhRBOHD, RhRBOHC, and RhPLATZ9) related to ROS production and repressing those genes (RhCCR4, RhUBC30, RhSOD1, RhAPX6.1, and RhCATA) related to ROS scavenging. In summary, ROS and related regulatory factors involved in ethylene induced petal abscission in roses.

The Early Cretaceous Yamama Formation of X oilfield, deposited in a semi-restricted setting, holds considerable oil reserves. However, the reservoir is extremely heterogeneous and is poorly studied. Integrating outcrops, cores, cast thin sections, regular or special core analysis, wireline logging data from six wells, and seismic data, this study provides an improved understanding of reservoir petrophysical characteristics and geological controlling factors including sedimentation, diagenesis, and sequence. The results showed that eight lithologies are developed in the Yamama Formation, of which packstone and wackstone are dominant. The physical properties span a wide range, with porosity mainly distributed between 10% and 25%, and the permeability mainly distributed between 0.1 mD and 1 mD. Nine types of pores are developed, with moldic pores, micropores, and skeletal pores being the most developed. The reservoir has six types of microstructures, of which the poorly sorted with mega-throat represent the best reservoir. The Yamama Formation was mainly deposited in a lagoon, along with five other facies, such as supratidal flat, patchy reef, back shoal, shoal, and open shelf. Six types of diageneses are developed, with dissolution during the penecontemporaneous stage being the most beneficial to the reservoir and cementation being the most destructive. Three sequences were recognized in the Yamama Formation. It concluded that the hydrodynamics in semi-restricted depositional setting is weak overall and does not have the potential to develop large-scale high-quality reservoirs. A wide range of bioclasts were selectively dissolved to form a large number of secondary pores. Sediments rich in Algae, Bacinella, and peloids tend to form moldic pores, skeletal pores, and intergranular pores, respectively, which are prone to be favorable reservoirs. Controlled by the coupling of sedimentation and diagenesis driven by sequence, the reservoir is extremely heterogenous.

Rare earth elements (REEs) are a new type of material resource which have attracted significant attention in recent years. REEs have emerged as essential metals in modern-day technology due to their unique functions. The long-term, large-scale mining and utilization of rare earths has caused serious environmental pollution and constitutes a global health issue, which has raised concerns regarding the safety of human health. However, the toxicity profile of suspended particulate matter in REEs in the environment, which interacts with the human body, remains largely unknown. Studies have shown that REEs can enter the human body through a variety of pathways, leading to a variety of organ and system dysfunctions through changes in genetics, epigenetics, and signaling pathways. Through an extensive literature search and critical analysis, we provide a comprehensive overview of the available evidence, identify knowledge gaps, and make recommendations for future research directions.

Antibiotics cannot be effectively removed by traditional wastewater treatment processes, and have become widespread pollutants in various environments. In this study, a Z-type heterojunction photo-catalyst Pg-C3N4 (PCN)/Nitrogen doped biochar (N-Biochar)/BiVO4 (NCBN) for the degradation of norfloxacin (NOR) was prepared by the hydrothermal method. The specific surface area of the NCBN (42.88 m2/g) was further improved compared to BiVO4 (4.528 m2/g). The photo-catalytic performance of the catalyst was investigated, and the N-Biochar acted as a charge transfer channel to promote carrier separation and form Z-type heterojunctions. Moreover, the NCBN exhibited excellent performance (92.5%) in removing NOR, which maintained 70% degradation after four cycles. The main active substance of the NCBN was •O2−, and the possible degradation pathways are provided. This work will provide a theoretical basis for the construction of heterojunction photo-catalysts.

Abstract We present a comprehensive study on the self-interaction cross-section of puffy dark matter (DM) particles, which have a significant intrinsic size compared to their Compton wavelength. For such puffy DM self-interaction cross-section in the resonant and classical regimes, our study demonstrates the significance of the Yukawa potential and the necessity of partial wave analysis: (i) Due to the finite-size effect of puffy DM particles, the new Yukawa potential of puffy DM is found to enlarge the Born-effective regime for the self-interaction cross-section, compared with the point-like DM; (ii) Our partial wave analysis shows that depending on the value of the ratio between R χ (radius of a puffy DM particle) and 1/m ϕ (force range), the three regimes (Born-effective, resonant and classical) for puffy DM self-interaction cross-section can be very different from the point-like DM; (iii) We find that to solve the small-scale anomalies via self-interacting puffy DM, the Born-effective and the resonant regimes exist for dwarf galaxies, while for the cluster and Milky Way galaxy the non-Born regime is necessary.

Abstract Embryonic tissues undergoing shape change draw mechanical input from extraembryonic substrates. In avian eggs, the early blastoderm disk is under the tension of the vitelline membrane (VM). Here we report that the chicken VM characteristically downregulates tension and stiffness to facilitate stage-specific embryo morphogenesis. Experimental relaxation of the VM early in development impairs blastoderm expansion, while maintaining VM tension in later stages resists the convergence of the posterior body causing stalled elongation, failure of neural tube closure, and axis rupture. Biochemical and structural analysis shows that VM weakening is associated with the reduction of outer-layer glycoprotein fibers, which is caused by an increasing albumen pH due to CO2 release from the egg. Our results identify a previously unrecognized potential cause of body axis defects through mis-regulation of extraembryonic tissue tension.

Based on the hail data of the China Meteorological Administration, the spatial and temporal distributions of hail disaster eents in China during 2010-2020 are studied by using the statistical method and GIS technology. The result is shown below. (1) The number of stations and the days of hails in China increase, while the number of severe hails decreases during 2010-2020. (2) The seasonal change is significant for the number of stations and the days of hails with the maximum in summer and the minimum in winter. In terms of monthly variation, hail occurs most often in June and least often in November. In southwest and central China, the monthly variation of the number of stations and the days of hails is bimodal. The monthly distribution of severe hail is similar to that of hail, but it occurs least often in December. The severe hail occurs most frequently in southwest China. (3) For diurnal variation, hail or severe hail mainly occurrs from afternoon to evening. The peak appears 1-hour later in northwest China than in other areas. (4) In terms of spatial distribution, the number of stations and the days of hail or severe disaster in Northwest, Southwest, and North China are significantly higher than those in the other regions, and the proportion of severe hail in Northeast and South China is higher than that in the other regions. The annual average days of hail or severe hail are distributed in multi-zone bands, and the multi-hail area extends along the mountains, with the largest value located in the Yunnan-Guizhou Plateau, followed by the mountains of Qinling, Yinshan, Daxing 'anling, and Tianshan. In addition, the low-incidence area of eastern China in spring has become a high-incidence area of hail. (5)From the elevation distribution, hail is most likely to occur at the altitude of 1.5-2 km, and severe hail is most likely to occur at the altitude of 1-1.5 km. In winter, hail is mainly below 2 km. With the rise of temperature, hail gradually increases in the high-altitude areas, and the proportion of hailstones in the high-altitude areas reaches the maximum in September.

Summary: To investigate whole-slide-level prediction in the field of artificial intelligence identification of dMMR/pMMR from hematoxylin and eosin (H&E) in colorectal cancer (CRC), we established a segmentation-based dMMR/pMMR deep learning detector (SPEED). Our model was approximately 1,700 times faster than that of the classification-based model. For the internal validation cohort, our model yielded an overall AUC of 0.989. For the external validation cohort, the model exhibited a high performance, with an AUC of 0.865. The human‒machine strategy further improved the model performance for external validation by an AUC up to 0.988. Our whole-slide-level prediction model provided an approach for dMMR/pMMR detection from H&E whole slide images with excellent predictive performance and less computer processing time in patients with CRC.

For the light relativistic dark matter (DM) boosted by high energy cosmic ray, its scattering cross section with the nucleon is sensitively dependent on the momentum-transfer and such an dependence is caused by the mediator in the scattering. For puffy DM particle with a size, the momentum-transfer dependence can also arise from the DM radius effect. All these momentum-transfer dependences should be considered. In this note we study the direct detection limits on the cosmic ray-boosted puffy DM for a simplified model with a light mediator. For comparison, we first re-derive the direct detection limits on the cosmic ray-boosted point-like DM. We display the limits on various planes of parameters and find that the limits for the cosmic ray-boosted puffy DM are stronger than for the point-like DM.

Abstract Background Macrophage-like cells (MLCs) located at the ILM were observed in live human retinas using adaptive optics optical coherence tomography (OCT) as well as clinically-used OCT. The study aimed to quantitatively analyzing MLCs at the vitreoretinal interface (VRI) in diabetic retinopathy (DR) using en face OCT and swept-source optical coherence tomography angiography (SS-OCTA). Methods 190 DR eyes were included in the study, with 70 proliferative diabetic retinopathy (PDR) eyes and 120 non- proliferative diabetic retinopathy (NPDR) eyes. Sixty-three eyes from normal subjects were included as controls. MLCs were visualized in a 5 μm en face OCT slab above the VRI centered on the fovea. Mann-Whitney U test and Kruskal-Wallis H test were used to compare the OCTA parameters and the MLC parameters among groups. We evaluated the MLC density among groups on binarized images after image processing. We also investigated the relationship between MLC density and other OCT parameters including retina thickness and vessel density (VD). Results The MLC density significantly increased in PDR eyes (PDR vs. NPDR, 8.97 (8.40) cells/mm2 vs.6.14 (8.78) cells/mm2, P = 0.013; PDR vs. normal, 8.97 (8.40) cells/mm2vs. 6.48 (6.71) cells/mm2, P = 0.027) and diabetic macular edema (DME) eyes (DME vs. without DME, 8.94 (8.26) vs.6.09 (9.00), P = 0.005). After adjusting for age and gender, MLC density in NPDR eyes negatively correlated to VD of deep capillary plexus (DCP) (P = 0.01). Conclusions SS-OCTA is a non-invasive and simple method for the characterization of MLCs at the VRI. PDR and DME are two factors that increase MLC density. MLC density also correlated with VD.

Layer segmentation of Optical Coherence Tomography (OCT) images is an important step in diagnosing retinal diseases. However, the presence of some artifacts and noise in OCT images often leads to unsatisfactory layer segmentation results. Especially when the number of layers to be segmented is particularly large, the boundaries between layers are indistinguishable, which poses a great challenge to automatic and accurate segmentation. To solve these problems, we propose a novel multi-task dual boundary-aware network to improve the retinal layer segmentation performance in OCT images. Specifically, based on the hierarchical relationship between retinal layers, we design a dual boundary representation method to encode the bidirectional boundary information between layers. Then we design a multi-task architecture and a novel consistency loss to utilize the boundary representation to make the segmentation more accurate. For evaluation, we have built a large-scale OCT layer segmentation dataset with 1,200 images. The comprehensive experimental results show that our method achieves superior performance over other state-of-the-art algorithms.

The background harmonic fluctuation has a significant impact on the results of harmonic contribution quantification. However, the existing methods, especially those applied for multiple harmonic sources scenario, usually assume that the background harmonics remain constant, which is inconsistent with the actual operation of the power grid. Thus, a method based on minimum impedance fluctuation is proposed to quantify the harmonic contribution in the multiple harmonic sources scenario. First, the harmonic impedance is roughly estimated and used to calculate the unmeasurable background harmonics; Second, the measured harmonics are sequenced according to the size of background harmonics, and divided into several segments to suppress the adverse effect of background harmonic fluctuation; Finally, the actual harmonic impedance is solved by minimizing the impedance fluctuation function with measured harmonic data in each segment, and the actual harmonic contribution of sources are quantified. Compared with the existing methods, the proposed method can reduce the adverse impact of background harmonic fluctuation and obtain more accurate results due to the preprocess of background harmonics. The effectiveness of the proposed method is verified by case studies.

The vertical profiles, spatiotemporal distribution, and trends of temperature and humidity in the middle atmosphere are significant for numerical weather prediction and the analysis of global climate change. To better design and apply spaceborne microwave spectrometer on the new generation Fengyun satellite, the sensitivities of the spectrometers at 22.235, 50–60, 118.75, 183.31, 325.153, 380.33, 424.77, 448.0, and 556.94 GHz are analyzed, respectively. Qpack2 included in the atmospheric radiative transfer simulator is used. The results show that the retrieval accuracy of the 50.0–60.0 GHz spectrometer is obviously better than other spectrometers, and the effective detection height (EDH) is the highest, reaching 0.233 hPa. The humidity profiles bigger than 500 hPa are well detected by the seven channels of the Humidity and Temperature Profiler or 22.0–32.0 GHz spectrometer. The retrieval accuracy is better than 6%, which greatly improves the retrieval performance of humidity profiles in the lower troposphere over the sea. In addition, this frequency band is not affected by errors in sea surface temperature or wind speed. The humidity profiles over the sea and land in the middle atmosphere under clear-sky and cloudy-sky conditions are well detected by the 183.31 and 556.94 GHz spectrometer. The EDH can be increased to 1.14 hPa by the 556.94 GHz spectrometer. In the design and application of future spaceborne microwave spectrometers for temperature and humidity profiles detection, the spectrometers mentioned previously are good candidates, and the parameter configuration of these spectrometers can be used as a reference.

Abstract Extensive studies of the tumor microenvironment (TME) in the last decade have reformed the view of cancer as a tumor cell-centric disease. The tumor microenvironment, especially termed the "seed and soil" theory, has emerged as the key determinant in cancer development and therapeutic resistance. The TME mainly consists of tumor cells, stromal cells such as fibroblasts, immune cells, and other noncellular components. Within the TME, intimate communications among these components largely determine the fate of the tumor. The pivotal roles of the stroma, especially cancer-associated fibroblasts (CAFs), the most common component within the TME, have been revealed in tumorigenesis, tumor progression, therapeutic response, and tumor immunity. A better understanding of the function of the TME sheds light on tumor therapy. In this review, we summarize the emerging understanding of stromal factors, especially CAFs, in cancer progression, drug resistance, and tumor immunity with an emphasis on their functions in epigenetic regulation. Moreover, the importance of epigenetic regulation in reshaping the TME and the basic biological principles underpinning the synergy between epigenetic therapy and immunotherapy will be further discussed.

Soil microorganisms are vital components of the karst terrestrial ecosystem. However, their responses to the vegetation succession on karst mountain peaks remain unclear as to whether soil microbial diversity and community compositions change with vegetation succession. We investigated the diversity and community compositions of soil bacteria and fungi and associated environmental factors along a vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G) on karst mountain peaks. The results indicated that soil organic carbon and total nitrogen generally increased, and soil pH changed in the range of 8.19–8.44 and slightly declined with vegetation succession. Overall, there was an increase in microbial biomass along the vegetation succession, with the dominant phyla of bacteria, including Proteobacteria, Acidobacteria, Actinobacteria, Choroflexi, Gemmatiomnadates, Bacteroidetes, and Planctomycetes, and the dominated phyla of fungi, including Basidiomycota and Ascomycota. Notably, both the bacterial and fungal community compositions were different among the three successional stages. Spearman’s correction analysis showed that soil organic carbon and total nitrogen had stronger and more significant influences on the soil microbial community compositions compared to soil water content, pH, and C:N ratio. Overall, our results provide evidence for the changes and influencing factors of the microbial community with the succession vegetation on karst mountain peaks.

In China, Fusarium pseudograminearum has emerged as a major pathogen causing Fusarium crown rot (FCR) and caused significant losses. Studies on the pathogen’s properties, especially its mating type and trichothecene chemotypes, are critical with respect to disease epidemiology and food/feed safety. There are currently few available reports on these issues. This study investigated the species composition, mating type idiomorphs, and trichothecene genotypes of Fusarium spp. causing FCR in Henan, China. A significant shift in F. pseudograminearum-induced FCR was found in the present study. Of the 144 purified strains, 143 were F. pseudograminearum, whereas only 1 Fusarium graminearum was identified. Moreover, a significant trichothecene-producing capability of F. pseudograminearum strains from Henan was observed in this work. Among the 143 F. pseudograminearum strains identified, F. pseudograminearum with a 15ADON genotype was found to be predominant (133 isolates), accounting for 92.36% of all strains, followed by F. pseudograminearum with a 3ADON genotype, whereas only one NIV genotype strain was detected. Overall, a relatively well-balanced 1:1 ratio of the F. pseudograminearum population was found in Henan. To the best of our knowledge, this is the first study that has examined the Fusarium populations responsible for FCR across the Henan wheat-growing region.

Additional Affiliations [...]

The automated and efficient classification of astronomical spectra is an important research issue in the era of large sky surveys. Most current studies on automatic spectral classification primarily focus on specific data sets and demonstrate outstanding performance. However, the diversity in spectra poses formidable challenges for these classification models, as they exhibit limited capability to generalize across more comprehensive data sets. In response to these challenges, we pioneer a method called the multiscale partial convolution net (MSPC-Net), which amalgamates partial, large kernel, and grouped convolution to facilitate multilabel spectral classification. By harnessing the capabilities of partial convolution, MSPC-Net can effectively reduce the number of model parameters, accelerate the training process, and mitigate the overfitting issue. Integrating large kernel and grouped convolution empowers the model to capture local and global features simultaneously, enhancing its overall classification efficacy. To rigorously evaluate the model’s performance, we generate ten different data sets sourced from the Sloan Digital Sky Survey and Large Sky Area Multi-Object Spectroscopic Telescope. These data sets encompass stellar class, stellar subclass, and full classification, providing a comprehensive assessment across various application scenarios. The experimental results reveal that MSPC-Net consistently outperforms the other models across different data sets, especially demonstrating superior performance in the last two data sets with full classification. Consequently, MSPC-Net is poised to find extensive applications in the detailed classification for large-scale sky survey projects. This work not only addresses the challenges of generalization in spectral classification but also contributes significantly to the advancement of robust models for astronomical research.

Transition metal sulfides (TMSs) are considered as attractive materials in the areas of energy storage because of their unique redox properties, excellent electronic conductivity, as well as environmental friendliness. However, poor cyclic stability and limited electrochemical active sites hinder their further application. To address this issue, a flower-like hierarchical CuCo2S4 structure is constructed by a two-step hydrothermal method. In this nanostructure, CuCo2S4 grows outward to form a tightly bound hierarchical structure on the nickel foams (NFs). This oriented structure can provide more laminar gaps for electrolyte ion diffusion, exposing more reaction sites to increase the ion transport efficiency between the layers, reducing the ion transport resistance and improving the reaction kinetics. Thus, the CuCo2S4 electrode exhibits excellent energy storage performance, exhibiting a high specific capacity of 1415.6 F g−1 at 1 A g−1. After 10,000 cycles of 10 A g−1, it still has 91.9% of the initial performance. In addition, an asymmetrical supercapacitor (ASC) was constructed by choosing CuCo2S4 as the anode and RGO as the cathode, which has the maximum energy density (61.8 Wh Kg−1) at 812.1 W Kg−1 and significant cycling endurance (92.05% retention) at 10,000 turns. Briefly, the researchers successfully constructed an array of CuCo2S4 flower-like hierarchical nanostructures and confirmed their potential application in supercapacitors.

Abstract Electromagnetic (EM) metamaterial is a composite material with EM stealth properties, which is constructed by artificially reverse engineering metal split resonance rings (SRR). However, the greatest limitation of EM metamaterials is that they can only stealth at a fixed and lower frequency of EM waves, and modern processing techniques still cannot meet the accuracy requirements to fabric nano‐size structural unit. Nano‐sized and even ultra‐small SRR at molecular level are promising arrays to realize the ability of EM stealth function at a higher frequency, although it has proven challenging to synthesize long, straight, connected molecular SRR, and also difficult to arrange those molecular SRR into a strict array. Here, the study overcomes this challenge and demonstrates that the fabric of polypyrrole molecular SRR achieves an ultra‐small inner diameter of 2.49 Å and realizes the arrays arrangement at molecular level. Furthermore, the study exploits the EM stealth function and verifies that such arrays of molecular SRR with 2.49 Å have the ability to reach high‐performance EM stealth in the range of 106–1016 Hz. This design concept opens a pathway for developing new metamaterials with broadband EM wave stealth and also serves the wider range of new applications.

Abstract Introduction To design and evaluate a deep learning model based on ultra-widefield images (UWFIs) that can detect several common fundus diseases. Methods Based on 4574 UWFIs, a deep learning model was trained and validated that can identify normal fundus and eight common fundus diseases, namely referable diabetic retinopathy, retinal vein occlusion, pathologic myopia, retinal detachment, retinitis pigmentosa, age-related macular degeneration, vitreous opacity, and optic neuropathy. The model was tested on three test sets with data volumes of 465, 979, and 525. The performance of the three deep learning networks, EfficientNet-B7, DenseNet, and ResNet-101, was evaluated on the internal test set. Additionally, we compared the performance of the deep learning model with that of doctors in a tertiary referral hospital. Results Compared to the other two deep learning models, EfficientNet-B7 achieved the best performance. The area under the receiver operating characteristic curves of the EfficientNet-B7 model on the internal test set, external test set A and external test set B were 0.9708 (0.8772, 0.9849) to 1.0000 (1.0000, 1.0000), 0.9683 (0.8829, 0.9770) to 1.0000 (0.9975, 1.0000), and 0.8919 (0.7150, 0.9055) to 0.9977 (0.9165, 1.0000), respectively. On a data set of 100 images, the total accuracy of the deep learning model was 93.00%, the average accuracy of three ophthalmologists who had been working for 2 years and three ophthalmologists who had been working in fundus imaging for more than 5 years was 88.00% and 94.00%, respectively. Conclusion High performance was achieved on all three test sets using our UWFI multidisease classification model with a small sample size and fast model inference. The performance of the artificial intelligence model was comparable to that of a physician with 2–5 years of experience in fundus diseases at a tertiary referral hospital. The model is expected to be used as an effective aid for fundus disease screening.

Abstract In the minimal supersymmetric standard model (MSSM) extended by a singlet superfield, when the coupling between the singlet sector and the MSSM sector is tiny, the singlet sector can be a quasi dark sector with supersymmetry (SUSY). We investigate the cosmological phenomena in this scenario and obtain the following observations: (i) In the parameter space solving the small cosmological scale anomalies via self-interacting singlino dark matter (SIDM), a first-order phase transition (FOPT) can readily happen but requires rather light dark matter below MeV; (ii) The corresponding parameter space indicated by FOPT and SIDM can be partially covered by detecting the phase-transition gravitational waves (GWs) at the near-future projects, such as LISA, TianQin and Taiji. Therefore, the recently developed GW astronomy could be a novel probe to such a SUSY scenario.

Abstract Specific emitter identification task often encounters the problem of channel inconsistency between the collected signal and the signal to be recognised, that is, different channel environments would cause inconsistency in the sample distribution, which would result in a significant decrease in recognition accuracy; the same problem is encountered in transfer learning. The authors focus on an important category of methods in transfer learning called domain adaptation to solve the above problems. Motivated by the idea of the Fisher discriminant and metric learning, a novel domain adaptation method called the multi‐discrepancy deep adaptation network is proposed. In contrast to the current approach of reducing the discrepancies between the source and target domain distributions without considering category information, the basic idea of the proposed method is to increase interclass discrepancies between the source and target domains while decreasing interclass discrepancies. To adjust the weights of interclass and interclass discrepancies, a dynamic balancing factor that adaptively adjusts the weights between the interclass variance and interclass variance according to the latest trends of both variances is introduced, which is effective in improving performance. Dynamic balancing and learning weight factors, which are used to adjust the training weights of interclass and interclass discrepancies during the training process are introduced. The authors applied the network to a specific emitter identification problem under different channels on the ORACLE radio frequency Fingerprinting Data set and demonstrated a maximum domain adaptability increase of 15.4%, which outperformed existing domain adaptation methods.

The transmission line maintenance is usually carried out by live-line maintenance, and equipotential live-line maintenance is one of the primary forms. In this process, the linemen need to enter the high potential with the help of conductive gloves or a potential transfer rod. This process is called the potential transfer. Because this process is accompanied by sizeable potential transfer current and transient energy, it may threaten linemen’s safety. Hence, the research on this process is of great significance. This paper improves the traditional transient model by introducing the arc model. The finite element method calculates the capacitance distribution between conductors by establishing the three-dimensional models of different postures and positions that the lineman may adopt. Then, considering the influence of the arc model on the process of the potential transfer, the potential transfer current and transient energy under different linemen’s postures and positions are calculated. Finally, the optimal mode of potential transfer operation for linemen in the process of potential transfer is proposed, which provides a theoretical reference for the safety protection of personnel in the process of the potential transfer.

Measurement(s) distributed global population and water withdrawal Technology Type(s) mathematical statistics and analysis

Abstract Background Several miRNAs are now known to have clear connections to the pathogenesis of asthma. The present study focused on the potential role of miR-3934 during asthma development. Methods miR-3934 was detected as a down-regulated miRNA in basophils by sequencing analysis. Next, the expression levels of miR-3934 in peripheral blood mononuclear cells of 50 asthma patients and 50 healthy volunteers were examined by RT-qPCR methods. The basophils were then treated with AGEs and transfected with miR-3934 mimics. The apoptosis levels were examined by flow cytometry assay; and the expression levels of cytokines were detected using the ELISA kits. Finally, the Western blot was performed to examined the expression of key molecules in the TGF-β/Smad signaling pathway. Results miR-3934 was down-regulated in the basophils of asthmatic patients. The expression of the pro-inflammatory cytokines IL-6, IL-8 and IL-33 was enhanced in basophils from asthmatic patients, and this effect was partially reversed by transfection of miR-3934 mimics. Furthermore, receiver operating characteristics analysis showed that miR-3934 levels can be used to distinguish asthma patients from healthy individuals. miR-3934 partially inhibited advanced glycation end products-induced increases in basophil apoptosis by suppressing expression of RAGE. Conclusion Our results indicate that miR-3934 acts to mitigate the pathogenesis of asthma by targeting RAGE and suppressing TGF-β/Smad signaling.

Hypoxia can promote tumor escape from immune surveillance and immunotherapy. Here, the authors show that hypoxia induces T and NK cell dysfunction through HIF1α-mediated epigenetic suppression of effector gene expression, conferring resistance to anti-PD1 blockade in triple negative breast cancer models.

Bisphenol F (BPF), BPS and BPAF are gaining popularity as main substitutes to BPA, but there is no clear evidence that these compounds disrupt glycemic homeostasis in the same way. In this study, four bisphenols were administered to C57BL/6 J mice, and showed that the serum insulin was elevated in the BPA and BPS exposed mice, whereas BPF exposed mice exhibited lower serum insulin and higher blood glucose. BPF decreased oxidized glutathione/reduced glutathione ratio (GSSG/GSH) and N6–methyladenosine (m6A) levels, which was responsible for pancreatic apoptosis in mice. Additionally, the downregulation of Nrf2 and the aberrant regulation of the p53-lncRNA H19 signaling pathway further increased miR-200 family in the BPF-exposed pancreas. The miR-200 family directly suppressed Mettl14 and Xiap by targeting their 3' UTR, leading to islet apoptosis. Antioxidant treatment not only elevated m6A levels and insulin contents but also suppressed the miR-200 family in the pancreas, ultimately improving BPF-induced hyperglycemia. Taken together, miR-200 family could serve as a potential oxidative stress-responsive regulator in the pancreas. And moreover, we demonstrated a novel toxicological mechanism in that BPF disrupted the Keap1-Nrf2 redox system to upregulate miR-141/200b/c which controlled pancreatic insulin production and apoptosis via Mettl14 and Xiap, respectively. As the major surrogates of BPA in various applications, BPF was also diabetogenic, which warrants attention in future research.