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Abstract Background SRSF1, a member of Serine/Arginine-Rich Splicing Factors (SRSFs), has been observed to significantly influence cancer progression. However, the precise role of SRSF1 in osteosarcoma (OS) remains unclear. This study aims to investigate the functions of SRSF1 and its underlying mechanism in OS. Methods SRSF1 expression level in OS was evaluated on the TCGA dataset, TAGET-OS database. qRT-PCR and Western blotting were employed to assess SRSF1 expression in human OS cell lines as well as the interfered ectopic expression states. The effect of SRSF1 on cell migration, invasion, proliferation, and apoptosis of OS cells were measured by transwell assay and flow cytometry. RNA sequence and bioinformatic analyses were conducted to elucidate the targeted genes, relevant biological pathways, and alternative splicing (AS) events regulated by SRSF1. Results SRSF1 expression was consistently upregulated in both OS samples and OS cell lines. Diminishing SRSF1 resulted in reduced proliferation, migration, and invasion and increased apoptosis in OS cells while overexpressing SRSF1 led to enhanced growth, migration, invasion, and decreased apoptosis. Mechanistically, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA) revealed that the biological functions of SRSF1 were closely associated with the dysregulation of the protein targeting processes, location of the cytosolic ribosome, extracellular matrix (ECM), and proteinaceous extracellular matrix, along with the PI3K-AKT pathway, Wnt pathway, and HIPPO pathway. Transcriptome analysis identified AS events modulated by SRSF1, especially (Skipped Exon) SE events and (Mutually exclusive Exons) MXE events, revealing potential roles of targeted molecules in mRNA surveillance, RNA degradation, and RNA transport during OS development. qRT-PCR confirmed that SRSF1 knockdown resulted in the occurrence of alternative splicing of SRRM2, DMKN, and SCAT1 in OS. Conclusions Our results highlight the oncogenic role of high SRSF1 expression in promoting OS progression, and further explore the potential mechanisms of action. The significant involvement of SRSF1 in OS development suggests its potential utility as a therapeutic target in OS.

Abstract The gut microbiota and microglia play critical roles in Alzheimer’s disease (AD), and elevated Bacteroides is correlated with cerebrospinal fluid amyloid-β (Aβ) and tau levels in AD. We hypothesize that Bacteroides contributes to AD by modulating microglia. Here we show that administering Bacteroides fragilis to APP/PS1-21 mice increases Aβ plaques in females, modulates cortical amyloid processing gene expression, and down regulates phagocytosis and protein degradation microglial gene expression. We further show that administering Bacteroides fragilis to aged wild-type male and female mice suppresses microglial uptake of Aβ1-42 injected into the hippocampus. Depleting murine Bacteroidota with metronidazole decreases amyloid load in aged 5xFAD mice, and activates microglial pathways related to phagocytosis, cytokine signaling, and lysosomal degradation. Taken together, our study demonstrates that members of the Bacteroidota phylum contribute to AD pathogenesis by suppressing microglia phagocytic function, which leads to impaired Aβ clearance and accumulation of amyloid plaques.

BackgroundCOVID-19, as a significant public health issue, has had a major impact on the mental health of people worldwide. Research shows a significant positive correlation between individuals’ risk perception levels and negative emotions during the outbreak of COVID-19. However, some studies also suggest that the relationship between the two is not significant. Therefore, we will conduct a meta-analysis to explore the relationship between risk perception and negative emotions from cultural, temporal, and individual psychological perspectives.MethodsSearches were conducted in the Web of Science, Pub Med, Google Scholar, PsycINFO, Scopus, and China National Knowledge Infrastructure databases, focusing on publications from January 2020 onwards, specifically targeting studies examining the relationship between risk perception and negative emotion during COVID-19.ResultsA total of 58 papers with 85 effect sizes were meta-analyzed using Comprehensive Meta-Analysis 3.0 software, with a combined sample of 83,948 individuals. Risk perception of COVID-19 showed a moderate positive correlation with negative emotions (r = 0.211, 95%CI [0.18, 0.24]). There was no moderating effect of tight-loose cultures on the relationship between risk perception of COVID-19 and negative emotions. However, the epidemic period, gender ratio, and measurement methods did have moderating effects on the relationship between risk perception of COVID-19 and negative emotions.ConclusionIn future research, we can further develop theories related to the risk perception of COVID-19 and negative emotions, and based on these, formulate interventions to promote people’s mental health.

Arsenic, a naturally occurring metal-like chemical element, is one of the 10 chemicals of major public concerns listed by the World Health Organization as harmful to the environment and human health. It can enter the human body through breathing, intaking food, drinking water, skin exposure, and other ways, and long-term exposure to arsenic can cause cancer of multiple organs and impaired function of multiple systems. Epigenetic mechanisms play an important role in arsenic-induced health effects, and research suggested that the carcinogenicity of arsenic may be associated with epigenetic changes. Previous studies focused on the effects of arsenic on DNA methylation modification. In recent years, research showed that 5-hydroxymethylcytosine (5-hmC), an intermediate of active demethylation of DNA, can act as a sensitive epigenetic mark and play a crucial role as a "bridge" between arsenic exposure and health effects. Based on the latest research progress on the role of DNA hydroxymethylation in the health effects associated with arsenic exposure, this article briefly described the relationship between the health effects of arsenic exposure and DNA hydroxymethylation, summarized the possible mechanisms of DNA hydroxymethylation in the health effects associated with arsenic exposure, and provided a scientific basis for preventing and treating the health effects associated with arsenic exposure.

Chronic myeloid leukemia (CML) is a common adult leukemia. Both the acute phase of the disease and the adverse effects of anti-cancer treatments can lead to a poor prognosis. The N6-methyladenine (m6A) modification plays an important regulatory role in various physiological and pathological processes. KIAA1429 is a known m6A regulator, but the biological role of KIAA1429 in CML is unclear. In this study, we observed that the m6A levels and KIAA1429 expression were significantly up-regulated in patients with blast phase CML. Notably, KIAA1429 regulated the total level of RNA m6A modification in the CML cells and promoted the malignant biological behaviors of CML cells, including proliferation, migration, and imatinib resistance. Inhibiting KIAA1429 in CML cells reduced the stability of RAB27B mRNA through the m6A/YTHDF1 axis, consequently inhibiting CML proliferation and drug efflux, ultimately increasing the sensitivity of CML cells to imatinib. Moreover, the knockdown of RAB27B also inhibited the proliferation and drug resistance of CML cells and promoted their apoptosis. Rucaparib, a recently developed anti-cancer agent, suppressed the expression of KIAA1429 and CML cell proliferation and promoted cell apoptosis. Rucaparib also inhibited the tumorigenesis of CML cells in vivo. The combined use of rucaparib and imatinib enhanced the sensitivity of CML cells to imatinib. Our study provides evidence that elevated KIAA1429 expression in the blast phase of CML enhances the stability of RAB27B mRNA through the m6A/YTHDF1 axis to up-regulate RAB27B expression, thereby promoting CML progression. Rucaparib exerts inhibitory effects on KIAA1429 expression and thus reduces CML progression.

Abstract To address the phenomenon of many small and hard-to-detect objects in infrared and visible light images, we propose an object detection algorithm CDYL (Convolution to Fully Connect-ed-Deformable Convolution You only Look once) based on the CFC-DC (Convolution to Fully Connected-Deformable Convolution) module. The core operator of CDYL is CFC-DC, making our model not only have a large effective receptive field in infrared and visible light images, but also have adaptive spatial aggregation conditioned by input and task information. As a result, the CDYL reduces the strict inductive bias of traditional CNNs and has long-range dependence for large kernel convolution as well as adaptive spatial aggregation, deeply mining of edge and correlation information in images to enhance sensitivity to small objects, thereby improving performance in dense small object detection tasks. In order to improve the ability of the CFC-DC module to perceive the detailed information of the image, we use the Mish activation function, which has a wider minima which improves the generalization. The effectiveness as well as the generalization of CDYL is evaluated on an infrared image dataset and an UAV image dataset, and it is compared with other state-of-the-art object detection algorithms. Compared to the baseline network YOLOv8l, our model achieved a 3.0% improvement in mAP0.5 in infrared image detection tasks and a 1.1% improvement in mAP0.5 in visible light image detection tasks. The experimental results show that the proposed algorithm achieves superior average precision values on both infrared and visible light images, while maintaining a light weight. Code is publicly available at https://github.com/yangzhu1/CDYL .

Heterogeneous photocatalysis emerges as an exceptionally appealing technological avenue for the direct capture, conversion, and storage of renewable solar energy, facilitating the generation of sustainable and ecologically benign solar fuels and a spectrum of other pertinent applications. Heterogeneous nanocomposites, incorporating Covalent Triazine Frameworks (CTFs), exhibit a wide-ranging spectrum of light absorption, well-suited electronic band structures, rapid charge carrier mobility, ample resource availability, commendable chemical robustness, and straightforward synthetic routes. These attributes collectively position them as highly promising photocatalysts with applicability in diverse fields, including but not limited to the production of photocatalytic solar fuels and the decomposition of environmental contaminants. As the field of photocatalysis through the hybridization of CTFs undergoes rapid expansion, there is a pressing and substantive need for a systematic retrospective analysis and forward-looking evaluation to elucidate pathways for enhancing performance. This comprehensive review commences by directing attention to diverse synthetic methodologies for the creation of composite materials. And then it delves into a thorough exploration of strategies geared towards augmenting performance, encompassing the introduction of electron donor–acceptor (D-A) units, heteroatom doping, defect Engineering, architecture of Heterojunction and optimization of morphology. Following this, it systematically elucidates applications primarily centered around the efficient generation of photocatalytic hydrogen, reduction of carbon dioxide through photocatalysis, and the degradation of organic pollutants. Ultimately, the discourse turns towards unresolved challenges and the prospects for further advancement, offering valuable guidance for the potent harnessing of CTFs in high-efficiency photocatalytic processes.

Abstract The shortage and unevenness of fossil energy sources are affecting the development and progress of human civilization. The technology of efficiently converting material resources into energy for utilization and storage is attracting the attention of researchers. Environmentally friendly biomass materials are a treasure to drive the development of new‐generation energy sources. Electrochemical theory is used to efficiently convert the chemical energy of chemical substances into electrical energy. In recent years, significant progress has been made in the development of green and economical electrocatalysts for oxygen reduction reaction (ORR). Although many reviews have been reported around the application of biomass‐derived catalytically active carbon (CAC) catalysts in ORR, these reviews have only selected a single/partial topic (including synthesis and preparation of catalysts from different sources, structural optimization, or performance enhancement methods based on CAC catalysts, and application of biomass‐derived CACs) for discussion. There is no review that systematically addresses the latest progress in the synthesis, performance enhancement, and applications related to biomass‐derived CAC‐based oxygen reduction electrocatalysts synchronously. This review fills the gap by providing a timely and comprehensive review and summary from the following sections: the exposition of the basic catalytic principles of ORR, the summary of the chemical composition and structural properties of various types of biomass, the analysis of traditional and the latest popular biomass‐derived CAC synthesis methods and optimization strategies, and the summary of the practical applications of biomass‐derived CAC‐based oxidative reduction electrocatalysts. This review provides a comprehensive summary of the latest advances to provide research directions and design ideas for the development of catalyst synthesis/optimization and contributes to the industrialization of biomass‐derived CAC electrocatalysis and electric energy storage.

Accurate prediction of the prognosis of nasopharyngeal carcinoma (NPC) is important for treatment. Lymph nodes metastasis is an important predictor for distant failure and regional recurrence in patients with NPC. Traditionally, subjective radiological evaluation increases concerns regarding the accuracy and consistency of predictions. Radiomics is an objective and quantitative evaluation algorithm for medical images. This retrospective analysis was conducted based on the data of 729 patients newly diagnosed with NPC without distant metastases to evaluate the performance of radiomics pretreatment using magnetic resonance imaging (MRI)-determined metastatic lymph nodes models to predict NPC prognosis with three delineation methods. Radiomics features were extracted from all lymph nodes (ALN), largest lymph node (LLN), and largest slice of the largest lymph node (LSLN) to generate three radiomics signatures. The radiomics signatures, clinical model, and radiomics-clinic merged models were developed in training cohort for predicting overall survival (OS). The results showed that LSLN signature with clinical factors predicted OS with high accuracy and robustness using pretreatment MR-determined metastatic lymph nodes (C-index [95 % confidence interval]: 0.762[0.760–0.763]), providing a new tool for treatment planning in NPC.

Abstract The oil‐pressboard insulation on the valve side of the converter transformer withstands pulsating square wave voltage. The voltage contains lots of DC components and high‐order harmonics, which is likely to cause insulation failures. The partial discharge and space charge experiments of oil‐impregnated pressboard under DC superimposed harmonic voltage are conducted to study the effects of harmonic frequency and DC components on the discharge and charge accumulation and to explore the correlation mechanism of charge dynamic behaviour and discharge. Firstly, it is found that high‐order harmonic voltage promotes the partial discharge generation, but DC component suppresses the discharge. Moreover, the charge inside the pressboard is mainly injected in the same polarity and accumulates over time. The space charge density near the electrode increases with the increase of harmonic frequency, and the large amount of charge injection leads to electric field distortion. Meanwhile, the electric field direction changes faster, and the frequent charge injection and extraction behaviours make the charge recombination intensify, which promotes the discharge. Through the above analysis, the influence law of harmonic voltage on the failure of oil‐pressboard insulation is obtained, providing a theoretical basis for the structural optimisation and condition assessment of the converter transformer insulation.

This research project envisions using a lunar observation platform to measure the full-wave (0.2~100 μm) and shortwave (0.2~4.3 μm) radiation of the Earth, achieving an accurate estimation of the overall radiation budget of the Earth. Based on the lunar platform, the system analyzes Earth’s radiation characteristics and geometric attributes, as well as the sampling properties of observation times. Informed by these analyses, an Earth-facing optical radiation measurement system tailored to these specifications is designed. The optical system adopts an off-axis three-mirror configuration with a secondary image plane, incorporating a field stop at the primary image plane to effectively suppress solar stray light, scattered lunar surface light, and background radiation from the instrument itself, ensuring the satisfactory signal-to-noise ratio, detection sensitivity, and observation duration of the instrument. At the same time, stringent requirements are imposed for the surface treatments of instrument components and temperature control accuracy to further ensure accuracy. Simulation analyses confirm that the design satisfies requirements, achieving a measurement accuracy of better than 1% across the entire optical system. This Moon-based Earth-radiation measurement system, with capabilities for Earth-pointing tracking, radiation energy detection, and stray-light suppression, furnishes a more comprehensive dataset, helping to advance our understanding of the mechanisms driving global climate change

Background and aimsHyperthyroidism is an endocrine disease with multiple etiologies and manifestations. Heart failure (HF) is a common, costly, and deadly medical condition in clinical practice. Numerous studies have suggested that abnormal thyroid function can induce or aggravate the development of heart disease. However, no study has demonstrated a causal relationship between hyperthyroidism and heart failure. Therefore, the purpose of this study was to explore the causal link between hyperthyroidism and HF.MethodsSummary data for genetically predicted hyperthyroidism were obtained from a genetic association study. The data examined for genetically determined all-cause heart failure came from 218,208 individuals from the FinnGen Consortium. Two-sample Mendelian randomization (MR) analysis was used to estimate the causal link between hyperthyroidism and heart failure. Statistical analyses were conducted using the inverse variance-weighted, weighted median, simple median, weighted mode, MR-PRESSO (number of distribution = 5000), MR-Egger, and leave-one-out.ResultsThe results of the inverse-variance weighted analysis indicated a causal association between hyperthyroidism and an increased risk of all-cause heart failure (IVW: β=0.048, OR=1.049, 95%CI: [1.013 to 1.087], P=0.007). Similarly, the weighted median approach demonstrated a positive correlation between hyperthyroidism and all-cause heart failure (OR=1.049, [95% CI, 1.001-1.100]; P=0.044). Additionally, no horizontal pleiotropy or heterogeneity was observed. The leave-one-out analysis revealed that the majority of the SNP-driven associations were not influenced by a single genetic marker.ConclusionOur study observed a causal relationship between hyperthyroidism and all-cause heart failure. Hyperthyroidism may associate with heart failure genetically.

The hypothesis of paternal origins of health and disease (POHaD) indicates that paternal exposure to adverse environment could alter the epigenetic modification in germ line, increasing the disease susceptibility in offspring or even in subsequent generations. p,p′-Dichlorodiphenyldichloroethylene (p,p′-DDE) is an anti-androgenic chemical and male reproductive toxicant. Gestational p,p′-DDE exposure could impair reproductive development and fertility in male offspring. However, the effect of paternal p,p′-DDE exposure on fertility in male offspring remains uncovered. From postnatal day (PND) 35 to 119, male rats (F0) were given 10 mg/body weight (b.w.) p,p′-DDE or corn oil by gavage. Male rats were then mated with the control females to generate male offspring. On PND35, the male offspring were divided into 4 groups according whether to be given the high-fat diet (HF): corn oil treatment with control diet (C-C), p,p′-DDE treatment with control diet (DDE-C), corn oil treatment with high-fat diet (C-HF) or p,p′-DDE treatment with high-fat diet (DDE-HF) for 35 days. Our results indicated that paternal p,p′-DDE exposure did not affect the male fertility of male offspring directly, but decreased sperm quality and induced testicular apoptosis after the high-fat diet treatment. Further analysis demonstrated that paternal exposure to p,p′-DDE and pre-pubertal high-fat diet decreased sperm Igf2 DMR2 methylation and gene expression in male offspring. Hence, paternal exposure to p,p′-DDE and pre-pubertal high-fat diet increases the susceptibility to male fertility impairment and sperm Igf2 DMR2 hypo-methylation in male offspring, posing a significant implication in the disease etiology.

Abstract Expanding the application scenarios of wood-derived biochar guided by the conversion of traditional energy to new energy shows great promise as a field. As thrilling energy conversion apparatus, zinc-air batteries (ZABs) require cathode catalysts with high oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) activities and stability. Herein, two-dimensional nickel-iron hydroxide nanosheets were creatively assembled in N-doped wood-derived biochar (NiFe-LDH@NC) by an in-situ growth method. The categorized porous organization in wood-derived biochar facilitates the rapid seepage of electrolytes and rapid diffusion of reaction gases. The unique interfacial structure of biochar and NiFe-LDH accelerates electron transfer during oxygen electrocatalysis, and endows NiFe-LDH@NC with first-class catalytic activity and durability for ORR and OER. The ZAB derived from NiFe-LDH@NC showed elevated discharge productivity and cycle endurance, making it promising for viable applications. This work provided a convenient way for the conversion of wood-derived biochar to high-value added electrocatalysts. Graphical Abstract

A type of spherical AgBr nanoparticles was synthesized using the aerosol-assisted precipitation method. The influence of solution mixing mode and polyvinylpyrrolidone on the morphology and photocatalytic activity of AgBr was investigated. The results demonstrate that both the aerosol-assisted precipitation method and the addition of polyvinylpyrrolidone surfactant effectively reduce the particle size of AgBr, leading to an enhancement in its specific surface area. Furthermore, the presence of polyvinylpyrrolidone surfactant promotes uniformity in shape and reduces size distribution variability among AgBr particles. These characteristics contribute to an improved photocatalytic performance, thereby expanding the potential applications for AgBr nanoparticles.

ABSTRACTBifidobacterium longum subsp. infantis (B. infantis) utilizes oligosaccharides secreted in human milk as a carbohydrate source. These human milk oligosaccharides (HMOs) integrate the nitrogenous residue N-acetylglucosamine (NAG), although HMO nitrogen utilization has not been described to date. Herein, we characterize the B. infantis nitrogen utilization phenotype on two NAG-containing HMO species, LNT and LNnT. This was characterized through in vitro growth kinetics, incorporation of isotopically labeled NAG nitrogen into the proteome, as well as modulation of intracellular 2-oxoglutarate levels while utilizing HMO nitrogen. Further support is provided by comparative transcriptomics and proteomics that identified global regulatory networks deployed during HMO nitrogen utilization. The aggregate data demonstrate that B. infantis strains utilize HMO nitrogen with the potential to significantly impact fundamental and clinical studies, as well as enable applications.

The aim of this experiment was to determine the effects of cellulase and xylanase additives on the fermentation quality, chemical composition of silage maize. In the experiment, cellulase (0, 0.25, 0.5, 1.0 g·kg−1) and xylanase (0, 0.25, 0.5, 1.0 g·kg−1) in different concentrations were applied alone or in combination on silage materials. After 60 d ensiling at room temperature, the results showed that cellulase and xylanase have positive effects on silage quality and chemical composition of silage. Cellulase increased contents of water-soluble carbohydrate, crude protein and crude fat while decreased contents of ammonia nitrogen and total nitrogen, neutral detergent fiber and acid detergent fiber. For xylanase, it increased the crude protein and ether extract content. Interactive effects were observed in CP and organic acids. Therefore, the adding cellulase and xylanase improved fermentation quality and nutrition value of silage maize. According to the comprehensive evaluation of the membership function, the recommended adding concentration of cellulase is 0.5 g·kg−1 alone. When combined with xylanase, the concentration of both cellulase and xylanase were 0.5 g·kg−1 and 0.25 g·kg−1, respectively.

Biochar materials have garnered attention as potential catalysts for peroxymonosulfate (PMS) activation due to their cost-effectiveness, notable specific surface area, and advantageous structural properties. In this study, a suite of plantain-derived biochar (MBB-400, MBB-600, and MBB-800), possessing a well-defined pore structure and a substantial number of uniformly distributed active sites (oxygen vacancy, OVs), was synthesized through a facile calcination process at varying temperatures (400, 600, and 800 °C). These materials were designed for the activation of PMS in the degradation of sulfamethoxazole (SMX). Experimental investigations revealed that OVs not only functioned as enriched sites for pollutants, enhancing the opportunities for free radicals (•OH/SO4•−) and surface-bound radicals (SBRs) to attack pollutants, but also served as channels for intramolecular charge transfer leaps. This role contributed to a reduction in interfacial charge transfer resistance, expediting electron transfer rates with PMS, thereby accelerating the decomposition of pollutants. Capitalizing on these merits, the MBB-800/PMS system displayed a 61-fold enhancement in the conversion rate for SMX degradation compared to inactivated MBB/PMS system. Furthermore, the MBB-800 exhibited less cytotoxicity towards rat pheochromocytoma (PC12) cells. Hence, the straightforward calcination synthesis of MBB-800 emerges as a promising biochar catalyst with vast potential for sustainable and efficient wastewater treatment and environmental remediation.

We investigated biostimulation as an effective strategy for enhancing the degradation efficiency of recalcitrant organic compounds, with MSC14 (a novel polycyclic aromatic hydrocarbon degrading bacterium Pantoea dispersa MSC14) as the study material. Here, we investigated the impact of sodium gluconate on MSC14-mediated degradation of B[a]p. This study focused on the application of sodium gluconate, a biostimulant, on MSC14, targeting Benzo[a]pyrene (B[a]p) as the model pollutant. In this study, the novel PAHs-degrading bacterium P. dispersa MSC14 demonstrated the capability to degrade 24.41% of B[a]p after 4 days. The addition of the selected sodium gluconate stimulant at a concentration of 4 g/L stimulated MSC14 to degrade 54.85% of B[a]p after 16 h. Intermediate metabolites were analyzed using gas chromatography-mass spectrometry to infer the degradation pathway. The findings indicated that sodium gluconate promoted the intracellular transport of B[a]p by MSC14, along with the secretion of biosurfactants, enhancing emulsification and solubilization capabilities for improved B[a]p dissolution and degradation. Further analysis through transmission electron microscopy (TEM) and scanning electron microscopy (SEM) revealed the formation of a biofilm by MSC14 and an increase in flagella as a response to B[a]p stress. Transcriptome profiling elucidated the interplay of quorum sensing systems, chemotaxis systems, and flagellar systems in the degradation mechanism. Additionally, the study uncovered the molecular basis of B[a]p transport, degradation pathways, metabolic changes, and genetic regulation. In summary, the addition of sodium gluconate promotes the degradation of B[a]p by P. dispersa MSC14, offering the advantages of being rapid, efficient, and cost-effective. This research provides an economically viable approach for the remediation of petroleum hydrocarbon pollution, with broad potential applications.

The study of the relationship between sustainable built environment and user perception has often taken a single perspective, displaying neither a holistic view of the relationship nor a systematic and refined grasp of the research content previously. This has resulted in a biased understanding of the two research objects and their respective measurement methods and made it difficult to develop synergies. In this context, this paper summarises the current research hotspots and trends in the relationship between sustainable built environment and user perception through CiteSpace quantitative analyses such as keyword co-occurrence networks, emergent word detection, and disciplinary re-clustering, and takes a multidisciplinary perspective to focus on relevant research in public health, environmental science, and architecture and urban design, such as the progress of research between sustainable built environment and users in physical activity, environmental cognition, and image perception. The results show that in the environmental science field, environmental cognition is the core, based on the study of users’ direct perception of sustainable built environment and its externalisation in the expression of behaviours. The public health field and the architecture and urban design field are dominated by the study of perceptual outcomes. There is a trend towards big data as a measurement tool for research subjects in all fields with a multidisciplinary perspective and the inclusion of more disciplines can produce more meaningful research results. The study provides a framework for research into the relationship between the two from a broader perspective and provides guidance for future multidisciplinary research, with implications for the construction of high-quality human-centred urban spatial environments.

This paper proposes a supervised deep neural network model for accomplishing highly efficient image quality assessment (IQA) for adaptive optics (AO) images. The AO imaging systems based on ground-based telescopes suffer from residual atmospheric turbulence, tracking error, and photoelectric noise, which can lead to varying degrees of image degradation, making image processing challenging. Currently, assessing the quality and selecting frames of AO images depend on either traditional IQA methods or manual evaluation by experienced researchers, neither of which is entirely reliable. The proposed network is trained by leveraging the similarity between the point spread function (PSF) of the degraded image and the Airy spot as its supervised training instead of relying on the features of the degraded image itself as a quality label. This approach is reflective of the relationship between the degradation factors of the AO imaging process and the image quality and does not require the analysis of the image’s specific feature or degradation model. The simulation test data show a Spearman’s rank correlation coefficient (SRCC) of 0.97, and our method was also validated using actual acquired AO images. The experimental results indicate that our method is more accurate in evaluating AO image quality compared to traditional IQA methods.

IntroductionGranulomatous mastitis (GM) is a chronic inflammatory breast disease. In recent years, the role of Corynebacterium in GM onset has received more and more attention. This study aims to detect the dominant bacterium in GM patients and analyze the association between clinical characteristics and infectious factors.MethodsIn this study, 88 samples from 44 GM patients, six acute lactation mastitis (ALM) patients, and 25 non-inflammatory breast disease (NIB) patients were divided into a GM pus group, a GM tissue group, an ALM pus group, and a NIB tissue group; then, 16S ribosomal DNA sequencing was used to explore their microbiota. The clinical data of all 44 GM patients were also retrospectively collected and analyzed to determine their relationship with infection.ResultsThe median age of the 44 GM patients was 33 years, and 88.6% of patients had primary-onset cases, while 11.4% were recurrences; additionally, 89.5% of patients were postpartum and 10.5% were nulliparous. The serum prolactin level was abnormal in nine patients (24.3%). Samples from 15 GM patients (34.1%) had a Corynebacterium abundance of >1% (1.08–80.08%), with eight (53.3%) displaying an abundance of >10%. Corynebacterium was the only genus with significant differences between the GM pus group and the other three groups (p < 0.05). Corynebacterium kroppenstedtii was the predominant Corynebacterium species. Among clinical characteristics, a statistical difference in breast abscess formation was observed according to Corynebacterium abundance in Corynebacterium-positive and- negative patients (p < 0.05).DiscussionThis study explored the relationship between Corynebacterium infection and GM, compared the clinical characteristics between Corynebacterium-positive and- negative patients, and provided support for the role of Corynebacterium species-in particular, C. kroppenstedtii-in the pathogenesis of GM. The detection of Corynebacterium can predict GM onset, especially in patients with high prolactin levels or a history of recent lactation.

Cyclic AMP (cAMP) is a ubiquitous second messenger that transduces signals from cellular receptors to downstream effectors. Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis, devotes a considerable amount of coding capacity to produce, sense, and degrade cAMP. Despite this fact, our understanding of how cAMP regulates Mtb physiology remains limited. Here, we took a genetic approach to investigate the function of the sole essential adenylate cyclase in Mtb H37Rv, Rv3645. We found that a lack of rv3645 resulted in increased sensitivity to numerous antibiotics by a mechanism independent of substantial increases in envelope permeability. We made the unexpected observation that rv3645 is conditionally essential for Mtb growth only in the presence of long-chain fatty acids, a host-relevant carbon source. A suppressor screen further identified mutations in the atypical cAMP phosphodiesterase rv1339 that suppress both fatty acid and drug sensitivity phenotypes in strains lacking rv3645. Using mass spectrometry, we found that Rv3645 is the dominant source of cAMP under standard laboratory growth conditions, that cAMP production is the essential function of Rv3645 in the presence of long-chain fatty acids, and that reduced cAMP levels result in increased long-chain fatty acid uptake and metabolism and increased antibiotic susceptibility. Our work defines rv3645 and cAMP as central mediators of intrinsic multidrug resistance and fatty acid metabolism in Mtb and highlights the potential utility of small molecule modulators of cAMP signaling.

Abstract Acute myeloid leukemia (AML) is a complex hematologic malignancy. Survival rate of AML patients is low. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play important roles in AML tumorigenesis and progression. However, the relationship between lncRNAs and biological characteristics of AML, as well as how lncRNAs influence the prognosis of AML patients, remain unclear. In this study. In this study, Pearson correlation analysis was used to identify lncRNAs related to m6A regulatory genes, namely m6A-related lncRNAs. And we analyzed their roles and prognostic values in AML. m6A-related lncRNAs associated with patient prognosis were screened using univariate Cox regression analysis, followed by systematic analysis of the relationship between these genes and AML clinicopathologic and biologic characteristics. Furthermore, we examined the characteristics of tumor immune microenvironment (TIME) using different IncRNA clustering models. Using LASSO regression, we identified the risk signals related to prognosis of AML patients. We then constructed and verified a risk model based on m6A-related lncRNAs for independent prediction of overall survival in AML patients. Our results indicate that risk scores, calculated based on risk-related signaling, were related to the clinicopathologic characteristics of AML and level of immune infiltration. Finally, we examined the expression level of TRAF3IP2-AS1 in patient samples through real-time polymerase chain reaction analysis and in GEO datasets, and we identified a interaction relationship between SRSF10 and TRAF3IP2-AS1 through in vitro assays. Our study shows that m6A-related lncRNAs, evaluated using the risk prediction model, can potentially be used to predict prognosis and design immunotherapy in AML patients.

At present, Chinese family businesses have ushered in the peak period of generational inheritance, so the issue of family business inheritance has become more and more important. The generational inheritance of family businesses is not something that can be accomplished in a short period of time, but requires a long process. There are many influencing factors, and how to choose an effective inheritance model for family businesses has always been the focus of academic circles and practitioners. At present, there is no unified conclusion about the choice of family business generational inheritance model and the factors that affect the model choice. By sorting out a large number of domestic and foreign literature, this paper summarizes the predecessors’ perspectives on the inheritance model, discusses various driving factors that may affect the current inheritance model. By further analyzes the current influencing factors on the inheritance model, this paper discusses that inheritance intention of successor may be a research perspective that has not been valued, thus proposing an expandable direction in the field of family business inheritance research.

In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing prognostic models to predict progression-free survival (PFS) in patients with LARC. Patients with LARC diagnosed between March 2014 and May 2016 were included in this retrospective study. A radiomic signature based on extracted MRI features and clinical prognostic models based on clinical features were constructed in the training cohort to predict 3-year PFS. C-indices were used to evaluate the predictive accuracies of the radiomic signature, clinical prognostic models, and integrated prognostic model (iPostM). In total, 166 consecutive patients were included (110 vs. 56 for training vs. validation). Eleven radiomic features were filtered out to construct the radiomic signature, which was significantly related to PFS. The MRI feature-derived radiomic signature exhibited better prognostic performance than the clinical prognostic models (P = 0.007 vs. 0.077). Then, we proposed an iPostM that combined the radiomic signature with tumor regression grade. The iPostM achieved the highest C-indices in the training and validation cohorts (0.942 and 0.752, respectively), outperforming other models in predicting PFS (all P < 0.05). Decision curve analysis and survival curves of the validation cohort verified that iPostM demonstrated the best performance and facilitated risk stratification. Therefore, iPostM provided the most reliable prognostic prediction for PFS in patients with LARC.

In this work, hydroxylated g-C3N4 is synthesized by one-step hydrothermal method and the mechanism of activating PMS mediated by OH-C3N4/PMS charge transfer surface complex is proposed and verified for the first time. Based on the dual roles of PMS as surface complexing ligand and free radical precursor, PMS is activated effectively on the surface of OH-C3N4. The degradation mechanism of pollutants formed by C3N4-OOSO3− charge transfer surface complex under visible light irradiation is explored by radical quenching experiments and EPR spectrum.

Pre-crash injury risk (IR) assessment is essential for guiding efforts toward active vehicle safety. This work aims to conduct crash severity assessment using pre-crash information and establish the intrinsic mechanism of IR with proper interpretation methods. The impulse–momentum theory is used to propose novel a priori formulations of several severity indicators, including velocity change (ΔV), energy equivalent speed (EES), crash momentum index (CMI), and crash severity index (CSI). Six IR models based on different machine learning methods were applied to a fusion dataset containing 24,082 vehicle-level samples. Prediction results indicate that the pre-crash indicators (PCIs) are more influential than the commonly used basic crash information because the average accuracy of six models can be improved by 14.35% after utilizing PCIs. Furthermore, the features’ importance and their marginal effects are interpreted based on parameter estimation, Shapley additive explanation value, and partial dependence. The ΔV, EES, and CMI are identified as the determinant indicators of the potential IR, and their partial distributions are significantly influenced by the crash type and impact position. Based on partial dependence probabilities, the study establishes decision thresholds for PCIs for each severity category for different impact positions, which can serve as a useful reference for developing targeted safety strategies. These results suggest that the proposed method can effectively improve pre-crash IR assessment, which can be readily transferred to safety-related modeling in an active traffic management system.

Lunar habitat design is a complex endeavor characterized by complicated task-composition, numerous internal iterations, and intense task-coupling. The design process of assembled building in terrestrial construction and the system composition of lunar habitats have been constructed. However, there is insufficient experience to fully understand lunar habitat design missions and likewise there is insufficient coordination between architects and various disciplines. The task flow for sequencing optimization can be determined using a design structure matrix (DSM), which is widely used in engineering. The DSM can reveal necessary interfaces within the lunar habitat system as per relevant interface variables and processes. By decomposing the lunar habitat design process, an initial activity-based DSM is established in the present study. Informational interactions between each design task and its respective intensity are statistically investigated to clarify them across the four dimensions of energy, space, materials, and information. A sequencing algorithm is applied to optimize the design process. Finally, 20 design tasks of lunar habitat design are clarified among four phases: Pre-planning, spatial design, environmental design, and optimization. Related disciplines should coordinate in the design process according to the optimization results, and use the optimized task coupling relationship to build the requirement model and design model in an orderly manner to improve the design efficiency.

This work investigates a self-masking technology for roughening the surface of light-emitting diodes (LEDs). The carbonized photoresist with a naturally nano/micro-textured rough surface was used as a mask layer. After growing the Si3N4 passivation layer on LEDs, the texture pattern of the mask layer was transferred to the surface of the passivation layer via reactive ion beam (RIE) dry etching, resulting in LEDs with nano-textured surfaces. This nano-textured surface achieved by self-masking technology can alleviate the total internal reflection at the top interface and enhance light scattering, thereby improving the light extraction efficiency. As a result, the wall-plug efficiency (WPE) and external quantum efficiency (EQE) of rough-surface LEDs reached 53.9% and 58.8% at 60 mA, respectively, which were improved by 10.3% and 10.5% compared to that of the flat-surface Si3N4-passivated LED. Additionally, at the same peak, both LEDs emit a wavelength of 451 nm at 350 mA. There is also almost no difference between the I–V characteristics of LEDs before and after roughening. The proposed self-masking surface roughening technology provides a strategy for LEE enhancement that is both cost-effective and compatible with conventional fabrication processes.

A transfer radiometer (TR) applied to an on-orbit radiometric benchmark transfer chain has been developed, which can achieve the high-precision transformation of power and radiance responsivity and transmit the radiance responsivity traced to the cryogenic radiometer to remote sensors, such as an imaging spectrometer, so that the on-orbit remote sensors can achieve the high accuracy calibration of 10−3 magnitude. Radiance comparison experiments between the TR and the radiance standard of the National Institute of Metrology (NIM) were carried out to demonstrate the absolute accuracy of the TR radiance measurement. At 780.0 nm and 851.9 nm, the relative measurement uncertainties of the TR filter-free channel were 0.24% (k = 1). Additionally, the radiance measurement results of the TR were consistent with those of the NIM radiance meter, and the radiance measurement results’ relative differences between the TR and the NIM radiance meter were approximately 0.04% at 780.0 nm and 851.9 nm. The relative measurement uncertainties of TR 780.4 nm and 851.8 nm filter channels were 0.89% (k = 1) and 0.84% (k = 1), respectively. Additionally, the radiance measurement results of the TR 780.4 nm and 851.8 nm filter channels were consistent with the radiances of the integrating sphere source calibrated by the NIM at 780.4 nm and 851.8 nm; the relative differences between the radiances measured by the two TR filter channels and the radiances of the integrating sphere source itself were better than 0.56%. This proved that the TR could measure the monochromatic source radiance with a measurement uncertainty of 0.24% and measure the broadband source radiance with a measurement uncertainty better than 0.89%. The TR can be applied to the radiometric benchmark transfer chain to improve the measurement precision of on-orbit remote-sensing instruments.

To analyze the influence of the distance between tunnels at the bridge-tunnel junction in the mountainous area on the driving safety under crosswind, a vehicle dynamics model is established by Carsim software. Under the conditions of different driving speeds (60km/h, 80km/h and 100km/h), different wind scales (level 6-9) and three alignment combinations of the tunnels (tunnel-straight road-tunnel, tunnel-curving road-tunnel, tunnel-the combination of straight and curves road-tunnel), the vehicle's lateral offset and lateral force coefficient are used as evaluation indexes to study the influence of distance between tunnels on driving safety. When the distance is small, the lateral offset and lateral force coefficient of vehicle driving on the bridge-tunnel junction increase firstly and then decrease as the distance increases. But the changes of the lateral offset and lateral force coefficient are not synchronized, and the peak value of the lateral force coefficient occurs earlier than the lateral offset. Finally, the minimum safety spacing of each condition and speed limits for different distances are proposed. Under the condition of the straight-tunnel, 20m distance between tunnels cannot ensure the driving safety when the wind scale reaches level 9, and the road should be closed. Under the conditions of the curve and the combination of straights and curves, driving safety cannot be maintained at each spacing at wind scale level 8 and 9, and the road should be closed. However, when the wind speed is level 6 or 7, speed limits can be adopted to ensure driving safety. The results suggest that vehicles' lateral offset and lateral force coefficient can be effectively reduced by reducing the driving speed or increasing the distance between tunnels, so the risk of accidents caused by the crosswind can be reduced.

People often use tools for tasks, and sometimes there is uncertainty about whether a given task can be completed with a given tool. This project explored whether, when, and how people’s optimism about successfully completing a task with a given tool is affected by the contextual salience of a better or worse tool. In six studies, participants were faced with novel tasks. For each task, they were assigned a tool but also exposed to a comparison tool that was better or worse in utility (or sometimes similar in utility). In some studies, the tool comparisons were essentially social comparisons, because the tool was assigned to another person. In other studies, the tool comparisons were merely counterfactual rather than social. The studies revealed contrast effects on optimism, and the effect worked in both directions. That is, worse comparison tools boosted optimism and better tools depressed optimism. The contrast effects were observed regardless of the general type of comparison (e.g., social, counterfactual). The comparisons also influenced discrete decisions about which task to attempt (for a prize), which is an important finding for ruling out superficial scaling explanations for the contrast effects. It appears that people fail to exclude irrelevant tool-comparison information from consideration when assessing their likelihood of success on a task, resulting in biased optimism and decisions.

Background Chronic myeloid leukemia (CML) is a malignant hyperplastic tumor of the bone marrow originating from pluripotent hematopoietic stem cells. The advent of tyrosine kinase inhibitors (TKIs) has greatly improved the survival rate of patients with CML. However, TKI-resistance leads to the disease recurrence and progression. This study aimed to identify immune-related genes (IRGs) associated with CML progression. Methods We extracted the gene’s expression profiles from the Gene Expression Omnibus (GEO). Bioinformatics analysis was used to determine the differentially expressed IRGs of CML and normal peripheral blood mononuclear cells (PBMCs). Functional enrichment and gene set enrichment analysis (GSEA) were used to explore its potential mechanism. Hub genes were identified using Molecular Complex Detection (MCODE) and the CytoHubba plugin. The hub genes’ diagnostic value was evaluated using the receiver operating characteristic (ROC). The relative proportions of infiltrating immune cells in each CML sample were evaluated using CIBERSORT. Quantitative real-time PCR (RT-qPCR) was used to validate the hub gene expression in clinical samples. Results A total of 31 differentially expressed IRGs were identified. GO analyses revealed that the modules were typically enriched in the receptor ligand activity, cytokine activity, and endopeptidase activity. KEGG enrichment analysis of IRGs revealed that CML involved Th17 cell differentiation, the NF-kappa B signaling pathway, and cytokine-cytokine receptor interaction. A total of 10 hub genes were selected using the PPI network. GSEA showed that these hub genes were related to the gamma-interferon immune response, inflammatory response, and allograft rejection. ROC curve analysis suggested that six hub genes may be potential biomarkers for CML diagnosis. Further analysis indicated that immune cells were associated with the pathogenesis of CML. The RT-qPCR results showed that proteinase 3 (PRTN3), cathepsin G (CTSG), matrix metalloproteinase 9 (MMP9), resistin (RETN), eosinophil derived neurotoxin (RNase2), eosinophil cationic protein (ECP, RNase3) were significantly elevated in CML patients’ PBMCs compared with healthy controls. Conclusion These results improved our understanding of the functional characteristics and immune-related molecular mechanisms involved in CML progression and provided potential diagnostic biomarkers and therapeutic targets.

In this paper, we present the design and implementation of a two-element interferometer operating in the millimeter-wave band (39.5–40 GHz) for observing solar radio emissions through nulling interference. The system is composed of two 50 cm aperture Cassegrain antennas installed on a common equatorial mount, with a separation of 230 wavelengths. The cross-correlation of the received signals effectively cancels out the quiet solar component of the high flux density (∼3000 sfu) that reduces the detection limit due to atmospheric fluctuations. The system performance is as follows: the noise factor of the analog front end in the observation band is less than 2.1 dB, system sensitivity is approximately 12.4 K (∼34 sfu) with an integration time constant of 0.1 ms (default), the frequency resolution is 153 kHz, and the dynamic range is ≥30 dB. Through actual testing, the nulling interferometer observes a quiet Sun with a low level of output fluctuations (up to 50 sfu) and has a significantly lower radiation flux variability (up to 190 sfu) than an equivalent single-antenna system, even under thick cloud cover. As a result, this new design can effectively improve observation sensitivity by reducing the impact of atmospheric and system fluctuations during observation.

Geographically neural network weighted regression is an improved model of GWR combined with a neural network. It has a stronger ability to fit nonlinear functions, and complex geographical processes can be modeled more fully. GNNWR uses the distance metric of Euclidean space to express the relationship between sample points. However, except for spatial location features, geographic entities also have many diverse attribute features. Incorporating attribute features into the modeling process can make the model more suitable for the real geographical process. Therefore, we proposed a spatial-attribute proximities deep neural network to aggregate data from the spatial feature and attribute feature, so that one unified distance metric can be used to express the spatial and attribute relationships between sample points at the same time. Based on GNNWR, we designed a spatial and attribute neural network weighted regression (SANNWR) model to adapt to this new unified distance metric. We developed one case study to examine the effectiveness of SANNWR. We used PM2.5 concentration data in China as the research object and compared the prediction accuracy between GWR, GNNWR and SANNWR. The results showed that the “spatial-attribute” unified distance metric is useful, and that the SANNWR model showed the best performance.

Phosphorus is one of the main causes of water eutrophication. Hard biochar is considered a promising phosphate adsorbent, but its application is limited by its textural properties and low adsorption capacity. Here, an adhesion approach in a mixed suspension containing egg white is proposed for preparing the hybrid material of Mg/Al-layered double hydroxide (LDH) and almond shell biochar (ASB), named L-AE or L-A (with or without egg white). Several techniques, including XRD, SEM/EDS, FTIR and N2 adsorption/desorption, were used to characterize the structure and adsorption behavior of the modified adsorbents. The filament-like material contained nitrogen elements at a noticed level, indicating that egg white was the crosslinker that mediated the formation of the L-AE hybrid material. The L-AE had a higher phosphate adsorption rate with a higher equilibrium adsorption capacity than the L-A. The saturation phosphate adsorption capacity of L-AE was nearly three times higher than that of L-A. Furthermore, the number of surface groups and the density of the positively charged surface sites follow the ASB < L-A < L-AE order, which is consistent with their phosphate adsorption performance. The study may offer an efficient approach to improving hard biochar’s adsorption performance in wastewater treatment.

In this study, a novel “on-off-enhanced on” approach to highly sensitive rapid sensing of biothiols was developed, based on competitive modulation of gold nanoclusters (AuNCs) and Hg2+ ions. In our approach, the AuNCs were encapsulated into a zeolite imidazole framework (ZIF) for predesigned competitive aggregation-induced luminescence (AIE) emission. To readily operate this approach, the Hg2+ ions were selected as mediators to quench the fluorescence of AuNCs. Then, due to the stronger affinities between the interactions of Hg2+ ions with -SH groups in comparison to the AuNCs with -SH groups, the quenched probe of AuNCs@ZIF-8/Hg2+ displayed enhanced fluorescence after the Hg2+ ions were competitively interacted with -SH groups. Based on enhanced fluorescence, the probe for AuNCs@ZIF-8/Hg2+ had a sensitive and specific response to trace amounts of biothiols. The developed fluorescence strategy had limit of quantification (LOQ) values of 1.0 μM and 1.5 μM for Cys and GSH molecules in serum, respectively. This competitive AIE strategy provided a new direction for developing biological probes and a promising method for quantifying trace amounts of biothiols in serum. It could promote progress in disease diagnosis.

In order to meet the high-accuracy calibration requirements of satellite remote sensing instruments, a transfer radiometer for an on-orbit radiometric benchmark transfer chain has been developed, which provides vital technical support for realizing the radiometric calibration uncertainty of the order of 10−3 for remote sensing instruments. The primary role of the transfer radiometer is to convert from the spectral power responsivity traceable to a cryogenic radiometer to the spectral radiance responsivity and transfer it to the imaging spectrometer. At a wavelength of 852.1 nm, the combined uncertainty of the radiance measurement comparison experiment between the transfer radiometer and a radiance meter is 0.43% (k = 1), and the relative deviation of the measurements between the transfer radiometer and the radiance meter is better than 0.36%, which is better than the combined uncertainty of the radiance measurement comparison experiment. This demonstrates that the transfer radiometer can achieve radiance measurements with a measurement uncertainty better than 0.3% (k = 1).

In this paper, a high-density gas–liquid discharge plasma is obtained combined with nanosecond pulse voltage and a floating electrode. The discharge images, the waveforms of pulse voltage and discharge current, and the optical emission spectra are recorded. Gas temperature and electron density are calculated by the optical emission spectra of N2 (C3Πu → B3Πg) and the Stark broadening of Hα, respectively. The emission intensities of N2 (C3Πu → B3Πg), N2+ (B2Σ → X2Π), OH (A2Σ → X2Π), O (3p5P → 3s5S0), He (3d3D → 3p3P20), gas temperature, and electron density are acquired by optical emission spectra to discuss plasma characteristics varying with spatial distribution, discharge gap, and gas flow rate. The spatial distributions of discharge characteristics, including gas temperature, electron density, and emission intensities of N2 (C3Πu → B3Πg), N2+ (B2Σ → X2Π), OH (A2Σ → X2Π), O (3p5P → 3s5S0), and He (3d3D → 3p3P20), are presented. It is found that a high-density discharge plasma with the electron density of 2.2 × 1015 cm−3 and low gas temperature close to room temperature is generated. While setting the discharge gap distance at 10 mm, the discharge area over liquid surface has the largest diameter of 20 mm; under the same conditions, electron density is in the order of 1015 cm−3, and gas temperature is approximately 330 K. In addition, the discharge plasma characteristics are not kept consistent in the axial direction, in which the emission intensities of N2+ (B2Σ → X2Π), N2 (C3Πu → B3Πg), OH (A2Σ → X2Π), and gas temperature increased near the liquid surface. As the discharge gap is enlarged, the gas temperature increases, whereas the electron density remains almost constant. Moreover, as the gas flow rate was turned up, the electron density increased and the gas temperature was kept constant at 320 K.

As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition.

We herein reported the first case of successfully synthesizing homologous Ti(IV) − based metal−organic frameworks (MOFs) junction, comprising NH2-MIL-125 core and NTU-9 layer, via a MOF-on-MOF strategy. Benefiting from the core-shell structural advantages and the homophase type II junction effect, the obtained NH2-MIL-125@NTU-9 hybrids exhibit accelerated reaction kinetics and enhanced electron transfer behaviour in the solar-driven CO2 reduction. The quantum efficiency of formate production at 430 nm (0.67%) and product selectivity (94.47%) over NH2-MIL-125@NTU-9 far surpass those of the pure NH2-MIL-125 (0.13%, 88.56%) and NTU-9 (0.16%, 89.26%), which is among the highest performances of MOFs reported previously.

Immune thrombocytopenia (ITP) is an acquired bleeding disease due to immune-mediated destruction of antilogous platelets and ineffective thrombopoiesis. Although the etiology of ITP remains unknown, genetic variants are thought to predispose individuals to the disease. Several candidate gene analyses have identified several loci that increased ITP susceptibility, but no systematic genetic analysis on a genome-wide scope. To extend the genetic evidence and to identify novel candidates of ITP, we performed a pooling genome-wide association study (GWAS) by IlluminaHumanOmniZhongHua-8 combining pathway analysis in 200 ITP cases and 200 controls from Chinese Han population (CHP). The results revealed that 4 novel loci (rs117503120, rs5998634, rs4483616, and rs16866133) were strongly associated with ITP (P

In this paper, we aim to reveal the connection between the predictability and prediction accuracy of stock closing price changes with different data frequencies. To find out whether data frequency will affect its predictability, a new information-theoretic estimator Plz, which is derived from the Lempel–Ziv entropy, is proposed here to quantify the predictability of five-minute and daily price changes of the SSE 50 index from the Chinese stock market. Furthermore, the prediction method EEMD-FFH we proposed previously was applied to evaluate whether financial data with higher sampling frequency leads to higher prediction accuracy. It turns out that intraday five-minute data are more predictable and also have higher prediction accuracy than daily data, suggesting that the data frequency of stock returns affects its predictability and prediction accuracy, and that higher frequency data have higher predictability and higher prediction accuracy. We also perform linear regression for the two frequency data sets; the results show that predictability and prediction accuracy are positive related.

Summary: DNA replication-coupled (RC) nucleosome assembly is mediated by histone chaperones and is fundamental for epigenetic inheritance and maintenance of genomic integrity. The mechanisms that promote this process are only partially understood. Here, we show that the histone chaperone FACT (facilitates chromatin transactions), consisting of Spt16 and Pob3, promotes newly synthesized histone H3-H4 deposition. We describe an allele of Spt16 (spt16-m) that has a defect in binding to H3-H4 and impairs their deposition onto DNA. Consistent with a direct role for FACT in RC nucleosome assembly, spt16-m displays synthetic defects with other histone chaperones associated with this process, CAF-1 and Rtt106. Importantly, we show that FACT physically associates with Rtt106 and that the acetylation of H3K56, a mark on newly synthesized H3, modulates this interaction. Therefore, FACT collaborates with CAF-1 and Rtt106 in RC nucleosome assembly. : Yang et al. show that mutation of Spt16, a FACT subunit, impairs the replication-coupled histone H3-H4 deposition. Mechanistically, FACT binds H3-H4 and cooperates with other histone chaperones, CAF-1 and Rtt106, to participate in replication-coupled nucleosome assembly.

Taxi demand can be divided into pick-up demand and drop-off demand, which are firmly related to human’s travel habits. Accurately predicting taxi demand is of great significance to passengers, drivers, ride-hailing platforms and urban managers. Most of the existing studies only forecast the taxi demand for pick-up and separate the interaction between spatial correlation and temporal correlation. In this paper, we first analyze the historical data and select three highly relevant parts for each time interval, namely closeness, period and trend. We then construct a multi-task learning component and extract the common spatiotemporal feature by treating the taxi pick-up prediction task and drop-off prediction task as two related tasks. With the aim of fusing spatiotemporal features of historical data, we conduct feature embedding by attention-based long short-term memory (LSTM) and capture the correlation between taxi pick-up and drop-off with 3D ResNet. Finally, we combine external factors to simultaneously predict the taxi demand for pick-up and drop-off in the next time interval. Experiments conducted on real datasets in Chengdu present the effectiveness of the proposed method and show better performance in comparison with state-of-the-art models.