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Abstract Oxidative stress and neuroinflammation contribute to schizophrenia (SCZ) pathology and may influence treatment efficacy. Matrix metalloproteinase 9 (MMP9) is a critical molecular node mediating the interaction between oxidative stress and inflammation, and so may influence treatment efficacy. Here we examined the associations of plasma MMP9 concentration with antipsychotic drug responses, clinical symptoms, and brain structure. A total of 129 healthy controls and 124 patients with SCZ were included in this study. Patients were monitored clinically during 8 weeks of antipsychotic treatment and classified as poor responders (n = 49) or good responders (n = 75). We then compared plasma MMP9 concentrations in healthy controls at baseline and both SCZ responder groups at baseline and after the 8-week antipsychotic treatment regimen. Cognitive function was also examined using the MATRICS Consensus Cognitive Battery. In addition, we extracted regional white matter density from magnetic resonance images of patients. Compared to healthy controls, plasma MMP9 levels were significantly elevated in poor responders at baseline and negatively correlated with both white matter density in the right superior temporal gyrus and the change in cognitive symptoms after treatment. Conversely, there was no significant difference in plasma MMP9 between good responders and healthy controls, and no associations of plasma MMP9 with cognitive symptoms or regional white matter density among good responders. Elevated plasma MMP9 is associated with poor antipsychotic drug efficacy and white matter deficits in SCZ patients, and so may be a useful biomarker to guide personalized treatment.

Addressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances in data assimilation techniques, the overwhelming volume and diversity of data, coupled with the lack of universally accepted models, underscore the limitations of these traditional approaches. Recently, deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate, multidimensional correlations. This approach resolves challenges previously deemed insurmountable, especially with lost, irregular, or noisy data through the design of specialized network architectures that adhere to physical invariants. However, gaps remain between academic advancements and their practical applications, including challenges in explainability and the computational costs associated with AI-driven solutions. Emerging technologies such as explainable artificial intelligence (XAI), AI for IT operations (AIOps), lifelong machine learning to mitigate catastrophic forgetting, and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment. In this perspective, we outline these challenges and opportunities, emphasizing the potential of innovative technologies to transform battery diagnostics, as demonstrated by our recent practice and the progress made in the field. This includes promising achievements in both academic and industry field demonstrations in modeling and forecasting the dynamics of multiphysics and multiscale battery systems. These systems feature inhomogeneous cascades of scales, informed by our physical, electrochemical, observational, empirical, and/or mathematical understanding of the battery system. Through data assimilation efforts, meticulous craftsmanship, and elaborate implementations—and by considering the wealth and spatio-temporal heterogeneity of available data—such AI-based intelligent learning philosophies have great potential to achieve better accuracy, faster training, and improved generalization.

Tracking performance and stability of path tracking is crucial for unmanned vehicles' navigational tasks. Researches on vehicle path tracking controllers primarily rely on dynamic models. In contrast, there are less designs and researches that focus on path tracking controller based on kinematic model. This scarcity may stem from the perceived inadequacy in the accuracy of vehicle kinematic models. This research introduces a novel method for modifying the traditional vehicle kinematic model by incorporating a front wheel steering angle modification function to enhance tracking performance of path tracking controllers based on kinematic models. In terms of control strategy selection, the study opted for nonlinear model predictive control with terminal cost. A controller which is founded on the modified model was used on a simulated vehicle on CarSim-Simulink platform to assess the tracking performance. The simulation was designed with a double-shift line reference trajectory and the initial speeds of the simulated vehicle are 5 m/s and 10 m/s, respectively. The results of the simulations indicate that employing a controller based on the modified model could reduce the peak tracking error by 76.45 % and 43.06 %, and the root mean square of the tracking error was reduced by 73.29 % and 36.06 %, respectively.

ObjectiveExplore the application effects of “Internet +” continuous emotional management training in depression patients and analyze the feasibility of innovative rehabilitation training methods.MethodsConveniently selected 100 discharged patients meeting the diagnostic criteria for depression from the Second Affiliated Hospital of Xinxiang Medical University between August 2022 and January 2023 as study subjects. They were divided into an intervention group and a control group according to their discharge time, with 50 patients in each group. The control group received routine monthly telephone follow-ups and “Internet +” health popularization. The intervention group received “Internet +” continuous emotional management training in addition to the control group’s protocol. Before and after the intervention, the Hamilton Depression Rating Scale (HAMD), the Chinese version of the Cognitive Emotion Regulation Questionnaire (CERQ-C), and the Social Adaptation Function Evaluation Scale (SAFE) were used to compare the differences in depression relief, cognitive emotion regulation levels, and social functions between the two groups.ResultsTwo patients in the control group and three patients in the intervention group were lost to follow-up during the intervention, Ultimately, 48 patients in the control group and 47 patients in the intervention group completed the study. Before the intervention, there were no statistically significant differences between the two groups in HAMD scores, CERQ-C subscale scores, and SAFE scores (P < 0.05). After the intervention, the intervention group had significantly lower HAMD and SAFE scores compared to the control group (P < 0.05). In the CERQ-C questionnaire, the intervention group scored lower in self-blame, rumination, catastrophizing, and blaming others but higher in acceptance, positive refocusing, refocusing on planning, positive reappraisal, and putting into perspective compared to the control group, with statistically significant differences (P < 0.05).Conclusion“Internet +” continuity emotion management training can reduce the severity of depression in post-discharge patients and enhance their emotional management and social adaptability. This approach is feasible in clinical practice.

Objectives: Neuregulin 1 (NRG1) is a risk gene for schizophrenia and involved in neurodevelopment and synaptic plasticity. Polymorphisms in NRG1 may affect psychotic symptoms in schizophrenia. This study investigated the effects of the single nucleotide polymorphism (SNP) rs6982890 on peripheral plasma NRG1 immunoreactivity, clinical symptoms and cognitive functions in schizophrenia patients. Material and methods: We recruited subjects from the Han population of northern China from 2010 to 2022. We first genotyped and analyzed 6 NRG1 SNPS in 1304 patients with schizophrenia and 871 healthy controls. Then, 91 patients with schizophrenia and 40 healthy controls were selected to detect the peripheral plasma NRG1 immunoreactivity by ELISA. Among them, 84 patients were divided into rs6982890 genotypes to analyze the correlation between NRG1 immunoreactivity and clinical symptoms. Results: Rs6982890 allelic frequencies were statistically significant between patients and controls. Baseline peripheral plasma NRG1 immunoreactivity in patients were significantly lower than controls. NRG1 immunoreactivity in patients were significantly increased after 8 weeks of antipsychotic treatment and significantly correlated with clinical symptoms and cognitive function. Genotyping of patients with SNP rs6982890 indicated NRG1 immunoreactivity in CC genotype increased significantly after treatment, while CT genotype had no significant change. Baseline NRG1 immunoreactivity with the CT genotype were significantly higher than CC genotype. Conclusions: NRG1 SNP rs6982890 is significantly associated with schizophrenia in the Han population of northern China, and it may affect the effect of antipsychotic drug treatment by regulating the peripheral plasma NRG1 immunoreactivity.

Squamous cell carcinoma of the head and neck (HNSCC) is a difficult-to-treat cancer and treatment is challenging due to recurrence or metastasis. Therefore, there is an urgent need to explore more effective targeted therapies to improve the clinical outcomes and survival of HNSCC patients. The nanomedicine is emerging as a promising strategy to achieve maximal anti-tumor effect in cancer therapy. In this review, we summarize some important signaling pathways and present the current and potential roles of various nanomaterial drug-delivery formulations in HNSCC treatment, aiming to understand the pathogenesis of HNSCC and further improve the therapeutic efficacy of nanomaterial HNSCC. This article seeks to highlight the exciting potential of novel nanomaterials for targeted cancer therapy in HNSCC and thus provide motivation for further research in this field.

Abstract Colon adenocarcinoma (COAD) is the second leading cause of cancer death, and there is still a lack of diagnostic biomarkers and therapeutic targets. In this study, bioinformatics analysis of the TCGA database was used to obtain RUNX1, a gene with prognostic value in COAD. RUNX1 plays an important role in many malignancies, and its molecular regulatory mechanisms in COAD remain to be fully understood. To explore the physiological role of RUNX1, we performed functional analyses, such as CCK-8, colony formation and migration assays. In addition, we investigated the underlying mechanisms using transcriptome sequencing and chromatin immunoprecipitation assays. RUNX1 is highly expressed in COAD patients and significantly correlates with survival. Silencing of RUNX1 significantly slowed down the proliferation and migratory capacity of COAD cells. Furthermore, we demonstrate that CDC20 and MCM2 may be target genes of RUNX1, and that RUNX1 may be physically linked to the deubiquitinating enzyme USP31, which mediates the upregulation of RUNX1 protein to promote transcriptional function. Our results may provide new insights into the mechanism of action of RUNX1 in COAD and reveal potential therapeutic targets for this disease.

Medium-duty and heavy-duty vehicles (MHDVs) are major contributors to air pollution and greenhouse gas emissions. California leads in transitioning towards zero-emission MHDVs through a comprehensive approach that includes financial incentive s for zero-emission vehicle (ZEV) purchases, improved infrastructure for battery charging and hydrogen refueling, and extensive public awareness campaigns. The state has enacted Advanced Clean Fleets (ACF) regulations, requiring all MHDVs—including box trucks, vans, work trucks, and tractors—to adopt ZEVs between 2035 and 2042. Consequently, thorough analysis and forecasting of market penetration for various MHDVs in the next decade are essential. This study introduces a purchase probability analysis-based dynamic discrete choice (PPADDC) model specifically designed for vehicle choice modeling in the MHDV sector. The model assesses the probabilities linked to sixteen decision factors that influence truck adoption from 2024 to 2040. Utilizing this model, we project market share and penetration under eight scenarios, reflecting our comprehensive understanding of the California market through observational, empirical, and mathematical perspectives. These scenarios differ in terms of vehicle cost, procurement incentives, and infrastructure development strategies. Our findings provide a strategic framework for achieving the ambitious ZEV targets set by the California Air Resources Board (CARB), illustrating that a combination of technological advancements, policy initiatives, and market incentives can significantly enhance ZEV adoption in the MHDV sector. This study not only confirms the practicality of California ZEV goals but also offers valuable insights for policymakers, industry stakeholders, and researchers working towards a more sustainable transportation future.

Abstract During drilling operations, naturally, fractured formations are prone to show serious mud losses, which hinder drilling and increase nonproductive time and costs. The influencing factors of dynamic fluid loss are important for optimizing drilling parameters, reducing drilling fluid loss, and protecting oil and gas reservoirs. In this study, we simulated the dynamic filtration loss of drilling fluid during drilling under formation conditions using commercial software CMG (Computer Modelling Group). The effects of filtration time, filtrate viscosity, pressure difference, internal filter cake permeability, and external filter cake permeability on filtration loss were investigated. The simulation results showed that the permeability of the external mud cake is an important factor to control the fluid loss, and the pressure consumed by the external mud cake with low permeability can account for more than 90% of the total pressure difference. When the permeability of the external mud cake is high, the permeability of the internal mud cake also has a significant influence on the dynamic fluid loss. Under formation conditions, the dynamic fluid loss of radial fluid loss is still proportional to the filtration time and pressure difference, and inversely proportional to the filtrate viscosity of drilling fluid. Under the simulated conditions, the pressure will quickly transfer to the boundary, and the formation pressure at the same position in the formation will gradually increase, while the increase is relatively small with a constant filtration rate. The results of this paper can be used to the real site for drilling optimization. This numerical analysis method can be easily applied to filtrate loss analysis, formation damage area calculation, and other radial flow-related study.

In the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the capabilities of traditional PHM approaches, which struggle to account for the interplay of multiple physical domains and scales. By harnessing technologies such as big data analytics, cloud computing, the Internet of Things (IoT), and deep learning, AI provides robust, data-driven solutions for capturing and predicting battery degradation. These advancements address long-standing limitations in battery prognostics, enabling more accurate and reliable performance assessments. The convergence of AI with Industry 4.0 technologies not only resolves existing challenges but also introduces innovative approaches that enhance the adaptability and precision of battery health management. This perspective highlights recent progress in battery PHM and explores the shift from traditional methods to AI-powered, data-centric frameworks. By enabling more precise and scalable monitoring and prediction of battery health, this transition marks a significant step forward in advancing the field.

Reducing carbon emissions and controlling air pollution is a dual challenge for China in addressing climate change. Analyzing the synergistic relationship between PM2.5 and CO2 in urban green spaces has become an important part of promoting pollution control. The study investigated the influence and synergistic relationship between the spatial pattern of community parks on PM2.5 and CO2 in Xi’an City, Shaanxi Province, through practical measurement and ENVI-met/Open Studio simulation calculations. The results showed that: (1) Within the sphere of influence, community parks exhibit a positive synergy varying with distance, peaking at 400 m and declining as 300 m > 500 m > 200 m > 100 m. (2) The green space rate, total edge (TE), and mean patch shape index (SHAPE_MN) positively influence the synergistic mitigation of PM2.5 and CO2, with a defined maximum impact boundary. The strongest synergistic reduction of PM2.5 and CO2 occurs at a green space rate of 85%, TE1200, and SHAPE1.2, with optimal influence boundaries of 300 m, 200 m, and 100 m, respectively. This conclusion demonstrates the key role of green space in community parks in the synergistic abatement and provides a scientific basis and practical guidance for the planning and design of urban green space under the goal of “dual-carbon”.

The introduction of artificial intelligence (AI) has triggered changes in modern dance education. This study investigates the application of diffusion-based modeling and virtual digital humans in dance instruction. Utilizing AI and digital technologies, the proposed system innovatively merges music-driven dance generation with virtual human-based teaching. It achieves this by extracting rhythmic and emotional information from music through audio analysis to generate corresponding dance sequences. The virtual human, functioning as a digital tutor, demonstrates dance movements in real time, enabling students to accurately learn and execute dance postures and rhythms. Analysis of the teaching outcomes, including effectiveness, naturalness, and fluidity, indicates that learning through the digital human results in enhanced user engagement and improved learning outcomes. Additionally, the diversity of dance movements is increased. This system enhances students’ motivation and learning efficacy, offering a novel approach to innovating dance education.

Interfacial solar water evaporation is a reliable way to accelerate water evaporation and contaminant remediation. Embracing the recent advance in photothermal technology, a functional sponge was prepared by coating a sodium alginate (SA) impregnated sponge with a surface layer of reduced graphene oxide (rGO) to act as a photothermal conversion medium and then subsequently evaluated for its ability to enhance Pb extraction from contaminated soil driven by interfacial solar evaporation. The SA loaded sponge had a Pb adsorption capacity of 107.4 mg g−1. Coating the top surface of the SA sponge with rGO increased water evaporation performance to 1.81 kg m−2 h−1 in soil media under one sun illumination and with a wind velocity of 2 m s−1. Over 12 continuous days of indoor evaporation testing, the Pb extraction efficiency was increased by 22.0% under 1 sun illumination relative to that observed without illumination. Subsequently, Pb extraction was further improved by 48.9% under outdoor evaporation conditions compared to indoor conditions. Overall, this initial work shows the significant potential of interfacial solar evaporation technologies for Pb contaminated soil remediation, which should also be applicable to a variety of other environmental contaminants.

To predict the market dynamics of various zero-emission vehicle (ZEV) technologies, this study introduces a dynamic discrete vehicle choice model (VCM) that investigates the probabilities associated with 14 decision factors, applying these to the purchase of ZEVs from 2020 to 2040. Market share and penetration results are presented under eight scenarios, that vary by vehicle costs infrastructure development and incentive strategies. The findings suggest that in the early years, incentives alone may not generate significant market penetration of ZEVs before the infrastructure meets the basic convenience for daily use, especially for fuel cell vehicles (FCVs). However, in later years, incentives play a more important role in the market penetration of ZEVs under well-defined infrastructure networks. By 2040, battery electric vehicles (BEVs) are projected to dominate the market in California. Plug-in hybrid electric vehicles (PHEVs) and FCVs may experience a decline in market share due to improved charging convenience, which benefits the market penetration of BEVs. However, fuel cell plug-in hybrid electric vehicles (FC-PHEVs) could still be beneficial if accessible models are available, considering the limited availability of hydrogen refueling stations. The goal set by the California Air Resources Board (CARB) is achievable, but it requires a sustained combination of measures; no single effort can achieve it. These measures include technological improvements to reduce the cost of ZEVs, a wider range of models available for consumers to choose from based on their desired performance, the establishment of infrastructure (battery chargers and hydrogen dispensers), and attractive incentives aimed at promoting ZEV adoption. The proposed methodology can be adapted for other regions in the United States and globally by carefully examining the inputs for each decision factor at the desired scale.

Ion channels are widely present in cell membranes, serving as crucial pathways for the movement of ions enter and exit cells. Variations in the expression of ion channels are crucial for regulating cellular functions. Among the genes associated with leukemia, certain genes encode ion channels. When these ion channels experience dysfunction or changes in expression, they can impact the physiological functions and signal transduction of hematopoietic cells, thereby regulating leukemia cell proliferation, differentiation, invasion/migration, and apoptosis. This article will provide a comprehensive review of the research progress on the expression and function of various ion channels in leukemia, thoroughly exploring their roles and mechanisms in the onset and progression of the disease, providing new insights and ideas for identifying potential biomarkers and developing new treatment methods for leukemia, thereby promoting innovations in future leukemia diagnosis and therapy.

Research has demonstrated that urban green spaces play a crucial role in mitigating the severe urban heat island (UHI) effect. However, existing studies often suffer from limitations such as the neglect of the cooling effect of water bodies within the green spaces and incomplete considerations of the overall cooling effect. These limitations may lead to inaccuracies in the research findings. Therefore, the present study takes the city of Xi’an as a case study to explore the optimal green space size for achieving efficient cooling. The results indicate that (i) urban green spaces exhibit robust cooling effects, with variations observed among the various types; (ii) for community parks without water, and for street gardens, the optimal areas of these green spaces are 3.44 and 0.83 hectares, respectively; (iii) for community parks with water, the area of internal water bodies should ideally be maintained at around 29.43% of the total green space area in order to achieve an optimal cooling efficiency. In conclusion, this study introduces a new perspective and new optimization methods for urban green space planning, thereby offering scientific guidance to urban planners in formulating effective development and management policies and urban planning schemes.

With the widespread application of lithium-ion batteries (LIBs) energy storage stations in high-altitude areas, the impact of ambient pressure on battery thermal runaway (TR) behavior and venting flow characteristics have aroused wide research attention. This paper conducts a lateral heating experiment on 280 Ah lithium iron phosphate batteries (LFPs) and proposes a method for testing battery internal pressure using an embedded pressure sensor. This paper analyzes the battery characteristic temperature, internal pressure, chamber pressure, and gas components under different chamber pressures. The experiment is carried out in a N2 atmosphere using a 1000 L insulated chamber. At 40 kPa, the battery experiences two instances of venting, with a corresponding peak in temperature on the battery’s side of 136.3 °C and 302.8 °C, and gas generation rates of 0.14 mol/s and 0.09 mol/s, respectively. The research results indicate that changes in chamber pressure significantly affect the center temperature of the battery side (Ts), the center temperature of the chamber (Tc), the opening time of the safety valve (topen), the triggering time of TR (tTR), the time difference (Δt), venting velocity, gas composition, and flammable limits. However, the internal pressure and gas content of the battery are apparently unaffected. Considering the TR characteristics mentioned above, a safety assessment method is proposed to evaluate the TR behavior and gas hazard of the battery. The results indicate that the risk at 40 kPa is much higher than the other three chamber pressures. This study provides theoretical references for the safe use and early warning of energy storage LIBs in high-altitude areas.

The migration law of proppants in slick water during fracturing is of great significance for field fracturing. A large-scale visualized experimental device was utilized to test sand patterns for varying injection parameter combinations, and sensitivity parameters of proppant settlement are analyzed. Experimental results showed that when the viscosity of fluid is 5 mPa.s, proppants with 70–140 mesh and 8% sand ratio were used, the slick water for fracture initiation had a good sand-carrying capacity, and there was no sand bank formed at the entrance of the fracture but the spreading of sands in fractures was insufficient. When slick water with a lower viscosity of 10 mPa.s, the proppant of 40–70 mesh and 10% sand ratio were used, massive proppants were filled within the fractures, and a high sand bank was formed in the deep of the fracture, while a poor-filling effect appeared at the entrance of fracture. When the higher viscosity slick water of 20 mPa.s, proppants with 20–40 mesh and 20% sand ratio were used; with the growth of pump-in rate, the distance between the sand front and fracture entrance increased, the height of the bank is lower, and the balance height stayed the same for various fracturing fluid and proppant combination. The injection parameters affected the sand bank patterns and made diverse bank shapes, which made it essential to modify the fracturing fluid and proppant combination in the field to improve the conductivity at the entrance of the fracture.

Following field-based research and testing of residential buildings in Tibetan areas of western Sichuan, this study studied the interactions between and dynamic effects of geographic and meteorological elements. By quantifying the combined influence of geographical and meteorological elements on building orientation, the concept of the total orientation-based mean temperature difference was introduced, and a method for obtaining the optimum building orientation was provided based on an equation for calculating the average temperature difference by orientation. By quantifying the coupled influence of geographical and meteorological elements on building shape, an approach for optimizing building shape designs based on an equation for calculating the optimal building width was created. We found that a linear expression cannot be used to describe the quantitative relationship between the average indoor temperature and building orientation. When the horizontal solar radiation intensity was ≤6.44 MJ/m2 d, the primary consideration in building design was the shape coefficient. When the horizontal solar radiation intensity was >6.44 MJ/m2 d, the building width was the main evaluation index, and the shape coefficient was the auxiliary index. Based on the above findings, we propose a design scheme for optimizing the orientation and shape of buildings in major cities in western Sichuan.

Dl-3-n-butylphthalide is used to treat mild and moderate acute ischemic stroke. However, the precise underlying mechanism requires further investigation. In this study, we investigated the molecular mechanism of Dl-3-n-butylphthalide action by various means. We used hydrogen peroxide to induce injury to PC12 cells and RAW264.7 cells to mimic neuronal oxidative stress injury in stroke in vitro and examined the effects of Dl-3-n-butylphthalide. We found that Dl-3-n-butylphthalide pretreatment markedly inhibited the reduction in viability and reactive oxygen species production in PC12 cells caused by hydrogen peroxide and inhibited cell apoptosis. Furthermore, Dl-3-n-butylphthalide pretreatment inhibited the expression of the pro-apoptotic genes Bax and Bnip3. Dl-3-n-butylphthalide also promoted ubiquitination and degradation of hypoxia inducible factor 1α, the key transcription factor that regulates Bax and Bnip3 genes. These findings suggest that Dl-3-n-butylphthalide exhibits a neuroprotective effect on stroke by promoting hypoxia inducible factor-1α ubiquitination and degradation and inhibiting cell apoptosis.

The senescence of adipose stem cells (ASCs) impairs healthy adipose tissue remodeling, causing metabolic maladaptation to energy surplus. The intrinsic molecular pathways and potential therapy targets for ASC senescence are largely unclear. Here, we showed that visceral ASCs were prone to senescence that was caused by reactive oxygen species (ROS) overload, especially mitochondrial ROS. These senescent ASCs failed to sustain efficient glucose influx, pentose phosphate pathway (PPP) and redox homeostasis. We showed that CD90 silence restricted the glucose uptake by ASCs and thus disrupted their PPP and anti-oxidant system, resulting in ASC senescence. Notably, fibroblast growth factor 21 (FGF21) treatment significantly reduced the senescent phenotypes of ASCs by augmenting CD90 protein via glycosylation, which promoted glucose influx via the AKT-GLUT4 axis and therefore mitigated ROS overload. For diet-induced obese mice, chronic administration of low-dose FGF21 relieved their visceral white adipose tissue (VAT) dysfunction and systemic metabolic disorders. In particular, VAT homeostasis was restored in FGF21-treated obese mice, where ASC repertoire was markedly recovered, accompanied by CD90 elevation and anti-senescent phenotypes in these ASCs. Collectively, we reveal a molecular mechanism of ASC senescence by which CD90 downregulation interferes glucose influx into PPP and redox homeostasis. And we propose a FGF21-based strategy for healthy VAT remodeling, which targets CD90 glycosylation to correct ASC senescence and therefore combat obesity-related metabolic dysfunction.

Nowadays, evaporatively cooled office buildings commonly observed in dry hot areas in summer of China. However, few dedicated studies to record the local residents' thermal comfort and adaptability in these buildings. The contribution of adaptive comfort theory on thermal perception still remains unclear for optimizing office building design parameters. Hence, to deeper probe the adaptive thermal comfort of the related indoor environment, a field study of office buildings during summer considering evaporative cooling air conditioned (ECA) and naturally ventilated (NV) mode was conducted in Turpan, China. Based on 931 valid datasets collected from questionnaires, we found that the neutral temperature (Tn) of 28.4 °C in ECA group, 0.6 °C lower than NV group (29.0 °C). A lower air temperature (Ta) and higher humidity (RH)/air-velocity (Va) were expected in two modes, and Va has a stronger influence than RH on mean thermal sensation votes (MTSV). Meanwhile, occupants can adapt to current indoor environment through physiological, psychological and behavioral adjustments, while the clothing regulation had limited effect on MTSV unless the outdoor temperature exceeds 38 °C. Whether in ECA or NV mode, the predicted mean votes (PMV) model overestimated actual thermal sensation when operative temperature (Top) beyond 28 °C. Adaptive models were also proved varied from that in current standards, which indicated that they were not suitable for evaluating the studied buildings in Turpan. Above findings could suggest us a better understanding of the occupants' thermal adaptability, thereby providing the reference of design parameters revision and passive strategies for local newly/renovated buildings.

Nerve guidance conduits (NGCs) have attracted much attention due to their great necessity and applicability in clinical use for the peripheral nerve repair. Great efforts in recent years have been devoted to the development of high-performance NGCs using various materials and strategies. The present review provides a comprehensive overview of progress in the material innovation, structural design, advanced engineering technologies and multi functionalization of state-of-the-art nerve guidance conduits NGCs. Abundant advanced engineering technologies including extrusion-based system, laser-based system, and novel textile forming techniques in terms of weaving, knitting, braiding, and electrospinning techniques were also analyzed in detail. Findings arising from this review indicate that the structural mimetic NGCs combined with natural and synthetic materials using advanced manufacturing technologies can make full use of their complementary advantages, acquiring better biomechanical properties, chemical stability and biocompatibility. Finally, the existing challenges and future opportunities of NGCs were put forward aiming for further research and applications of NGCs.

A considerable chunk of population in China live in rural areas. To create a suitable living environment in winter, many of them resort to additional solar house to improve the indoor thermal environment. Most of the previous studies focus on how the additional solar house changes the environment, but few try to optimize the size of that house. Therefore, this paper carries out field measurement and numerical simulation to evaluate the indoor thermal environment of a traditional building of Danihe Village, an important residential settlement in Xi’an, northwestern China’s Shaanxi Province, and explores the most suitable size of the additional solar house for improving the indoor thermal environment of the building in winter. The results show that the width of the additional solar house should be controlled within the range of 600–2400 mm, which provides the residents indoor with the best thermal environment. The research results offer a guide for improving indoor thermal environment and reducing indoor energy cost in future.

Summary: Adipose-derived stem cells (ASCs) drive healthy visceral adipose tissue (VAT) expansion via adipocyte hyperplasia. Obesity induces ASC senescence that causes VAT dysfunction and metabolic disorders. It is challenging to restrain this process by biological intervention, as mechanisms of controlling VAT ASC senescence remain unclear. We demonstrate that a population of CX3CR1hi macrophages is maintained in mouse VAT during short-term energy surplus, which sustains ASCs by restraining their senescence, driving adaptive VAT expansion and metabolic health. Long-term overnutrition induces diminishment of CX3CR1hi macrophages in mouse VAT accompanied by ASC senescence and exhaustion, while transferring CX3CR1hi macrophages restores ASC reservoir and triggers VAT beiging to alleviate the metabolic maladaptation. Mechanistically, visceral ASCs attract macrophages via MCP-1 and shape their CX3CR1hi phenotype via exosomes; these macrophages relieve ASC senescence by promoting the arginase1-eIF5A hypusination axis. These findings identify VAT CX3CR1hi macrophages as ASC supporters and unravel their therapeutic potential for metabolic maladaptation to obesity.

With the acceleration of urbanization in China, urban problems have become increasingly serious, and have led to the concept of the Park City. This concept involves a consideration of the comprehensive value of green space in order to give full play to the maximum benefit of that green space and, thus, realize the sustainable development of the city. Therefore, an exploration of the optimal scale of urban green space will have theoretical and practical significance for the construction of the Park City and the rational allocation of urban land resources. Taking Xi'an as an example, the present paper comprehensively considers the socioeconomic and ecological benefits of urban green space, and calculates its optimal scale. The results indicate that: (i) in terms of the socioeconomic benefits alone, the per capita amount of green area in Xi’an needs to be larger than 11.08 m2, and there is no theoretical optimal size; (ii) in terms of ecological benefits alone, the per capita amount of green area in Xi’an needs to be greater than 22.98 m2; (iii) in terms of the combined socioeconomic and ecological benefits, the per capita amount of green area of Xi’an needs to be greater than 24.89 m2; (iv) the optimal scale for any city is more siginficantly impacted by the ecological effects than by the socioeconomic effects; (v) to satisfy the development and construction needs of various cities, with their individual characteristics, the optimal urban green space scale should be explored according to the target city’s developmental stage and land use situation. The methods and results of the present study are intended to provide a reference for the future construction of Park Cities in China, as well as the planning of green space systems in other cities globally, and to open new prospects for future research.

Abstract Direct conversion of low‐grade heat into electricity by thermal electrochemical cells is a promising strategy for energy generation. For stable heat‐to‐electricity conversion, maintaining a low‐grade heat induced temperature difference between the cell electrodes is essential. Here, a thermogalvanic cell consisting of a cellulose fiber‐based porous aerogel, a liquid electrolyte, a reduced graphene oxide light absorber, and carbon nanotube‐based electrodes is designed for low‐grade thermal energy harvesting and conversion. The low thermal conductivity of the porous cellulose aerogel enables limited heat transfer from the hot side to the cold side, and thermal energy management effectively reduces heat loss from the hot side to the environment. Thus, a sustainable temperature difference between the electrodes is maintained and a corresponding maximum power output of 6.94 mW m−2 is achieved under natural solar irradiation. The obtained thermal electrochemical cells are also integrated into an enclosed interfacial solar evaporation device to harvest the latent heat released from vapor condensation for electricity generation. In addition, the thermal electrochemical cells can be regenerated after 18 months of storage and show no performance degradation. This design thus offers a novel alternative strategy for practical low‐grade heat harvesting.

Abstract Background Postoperative pancreatic fistula (POPF) represents the most common complication following pancreaticoduodenectomy (PD). Predictive models are needed to select patients with a high risk of POPF. This study was aimed to establish an effective predictive nomogram for POPF following PD. Methods Consecutive patients who had undergone PD between January 2016 and May 2020 at a single institution were analysed retrospectively. A predictive nomogram was established based on a training cohort, and Lasso regression and multivariable logistic regression analysis were used to evaluate predictors. The predictive abilities of the predicting model were assessed for internal validation by the area under the receiver operating characteristic curve (AUC) and calibration plot using bootstrap resampling. The performance of the nomogram was compared with that of the currently used a-FRS model. Results A total of 459 patients were divided into a training cohort (n = 302) and a validation cohort (n = 157). No significant difference was observed between the two groups with respect to clinicopathological characteristics. The POPF rate was 16.56%. The risk factors of POPF POPF were albumin difference, drain amylase value on postoperative day 1, pancreas texture, and BMI, which were all selected into a nomogram. Nomogram application revealed good discrimination (AUC = 0.87, 95% CI: 0.81–0.94, P

The global annual incidence of brain tumors is approximately seven out of 100,000, accounting for 2% of all tumors. The mortality rate ranks first among children under 12 and 10th among adults. Therefore, the localization and segmentation of brain tumor images constitute an active field of medical research. The traditional manual segmentation method is time-consuming, laborious, and subjective. In addition, the information provided by a single-image modality is often limited and cannot meet the needs of clinical application. Therefore, in this study, we developed a multimodality feature fusion network, MM-UNet, for brain tumor segmentation by adopting a multi-encoder and single-decoder structure. In the proposed network, each encoder independently extracts low-level features from the corresponding imaging modality, and the hybrid attention block strengthens the features. After fusion with the high-level semantic of the decoder path through skip connection, the decoder restores the pixel-level segmentation results. We evaluated the performance of the proposed model on the BraTS 2020 dataset. MM-UNet achieved the mean Dice score of 79.2% and mean Hausdorff distance of 8.466, which is a consistent performance improvement over the U-Net, Attention U-Net, and ResUNet baseline models and demonstrates the effectiveness of the proposed model.

Background:Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an autoimmune disease with typical clinical features. Whether and how cerebral gray matter structural damage inherent to the disorder affects cognitive function in patients is still unclear. Therefore, this study aimed to explore the changes in cerebral gray matter volume and whether these alterations contribute to cognitive impairment and mood disorders.MethodsForty patients with anti-NMDAR encephalitis and forty healthy controls (HCs) matched for gender, age, and education were recruited. All participants underwent attention network tests (ANT), neuropsychological tests and magnetic resonance imaging (MRI). Voxel-based morphological analysis (VBM) and correlation analysis was performed on all participants. Finally, according to the course of disease, patients were divided into two groups: NMDARE_SD (short duration; course ≤ 2 years since diagnosis) and NMDARE_LD (long duration; course >2 years since diagnosis), to evaluate gray matter volume changes that differ as a function of disease course.ResultsCompared to HCs, patients with anti-NMDAR encephalitis showed decreased executive control ability and lower MoCA score, while increased anxiety and depression as reflected by HAMA and HAMD24 scores (all P < 0.05). In VBM analysis, patients showed decreased gray matter volume in bilateral thalamus, left medial prefrontal cortex (mPFC_L), left superior temporal gyrus (STG_L), and left rectus gyrus. In the analysis stratified by disease course, the NMDARE_LD group exhibited decreased gray matter volume in the left precuneus and right posterior cerebellar lobe compared to the NMDARE_SD group.ConclusionsPatients with anti-NMDAR encephalitis have cognitive, executive, and emotional dysfunction, and the sites of gray matter atrophy are concentrated in the thalamus, frontal lobe, and temporal lobe. These abnormalities may be involved in the process of cognitive and affective dysfunction.Patients with different courses of anti-NMDAR encephalitis have different brain atrophy sites. These results may help to clarify the contradiction between clinical and imaging manifestations of anti NMDAR encephalitis, which is worthy of further longitudinal studies.

N6-methyladenosine (m6A) has an important epitranscriptomic modification that controls cancer self-renewal and cell fate. The addition of m6A to mRNA is a reversible modification. The deposition of m6A is encoded by a methyltransferase complex involving three homologous factors, jargonized as “writers,” “erasers,” and “readers.” However, their roles in pancreatic adenocarcinoma (PAAD) are underexploited. With the use of The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases, we provided an mRNA signature that may improve the prognostic prediction of PAAD patients based on the genetic status of m6A regulators. PAAD patients with genetic alteration of m6A regulators had worse disease-free and overall survival. After comparing PAAD groups with/without genetic alteration of m6A regulators, we identified 196 differentially expressed genes (DEGs). Then, we generated a 16-mRNA signature score system through least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Multivariate cox regression analysis demonstrated that a high-risk score significantly correlates with poor prognosis. Moreover, time-dependent receiver operating characteristic (ROC) curves revealed it was effective in predicting the overall survival in both training and validation sets. PAH, ZPLD1, PPFIA3, and TNNT1 from our signature also exhibited an independent prognostic value. Collectively, these findings can improve the understanding of m6A modifications in PAAD and potentially guide therapies in PAAD patients.

Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal cancers due to frequently late diagnosis and futile treatment. It is a crucial necessity to determine the mechanisms of PDAC. Y-box Binding Protein 1 (YBX1), a highly conserved transcription factor, has been previously reported to play a role in various hallmarks of cancer. We show here that YBX1 is significantly overexpressed in PDAC and correlates with poor prognosis and reduced survival. In PDAC cell lines, YBX1 regulated cell-cycle progression, proliferation, and the expression of glycogen synthase kinase 3 beta (GSK3B) and cell-cycle-related proteins cyclin D1 and E1. Dual-luciferase reporter and chromatin immunoprecipitation (ChIP) assays established that YBX1 binds to the promoter of GSK3B, suggesting that YBX1 promotes pancreatic cancer cell growth through induction of GSK3B expression. These findings offer important insights into the mechanisms underlying pathologic proliferation in PDAC.

Summary: Despite great progress in battery safety modeling, accurately predicting the evolution of multiphysics systems is extremely challenging. The question on how to ensure safety of billions of automotive batteries during their lifetime remains unanswered. In this study, we overcome the challenge by developing machine learning techniques based on the recorded data that are uploaded to the cloud. Using charging voltage and temperature curves from early cycles that are yet to exhibit symptoms of battery failure, we apply data-driven models to both predict and classify the sample data by health condition based on the observational, empirical, physical, and statistical understanding of the multiscale systems. The best well-integrated machine learning models achieve a verified classification accuracy of 96.3% (exhibiting an increase of 20.4% from initial model) and an average misclassification test error of 7.7%. Our findings highlight the need for cloud-based artificial intelligence technology tailored to robustly and accurately predict battery failure in real-world applications.

As the popularity of electric vehicles (EVs) and smart grids continues to rise, so does the demand for batteries. Within the landscape of battery-powered energy storage systems, the battery management system (BMS) is crucial. It provides key functions such as battery state estimation (including state of charge, state of health, battery safety, and thermal management) as well as cell balancing. Its primary role is to ensure safe battery operation. However, due to the limited memory and computational capacity of onboard chips, achieving this goal is challenging, as both theory and practical evidence suggest. Given the immense amount of battery data produced over its operational life, the scientific community is increasingly turning to cloud computing for data storage and analysis. This cloud-based digital solution presents a more flexible and efficient alternative to traditional methods that often require significant hardware investments. The integration of machine learning is becoming an essential tool for extracting patterns and insights from vast amounts of observational data. As a result, the future points towards the development of a cloud-based artificial intelligence (AI)-enhanced BMS. This will notably improve the predictive and modeling capacity for long-range connections across various timescales, by combining the strength of physical process models with the versatility of machine learning techniques.

Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales.

ObjectiveTemporal lobe epilepsy (TLE) can be conceptualized as a network disease. However, the network characteristics in lateralization remain controversial.MethodsIn this study, resting-state functional MRI scans were acquired from 53 TLE patients [22 with left-side TLE (LTLE) and 31 with right-side TLE (RTLE)] and 37 matched healthy controls. We focused on the characteristics of static and dynamic functional connectivity, including static connectivity patterns and topological properties, as well as temporal properties of the dynamic connectivity state and the variability of the dynamic connectivity and network topological organization. Correlation analyses were conducted between abnormal static and dynamic properties and cognitive performances.ResultsThe static functional connectivity analysis presented a significantly decreased cortical-cortical connectivity pattern and increased subcortical-cortical connectivity pattern in RTLE. The global-level network in RTLE showed a significant decrease in global efficiency. The dynamic functional connectivity analysis revealed that RTLE patients exhibited aberrant connectivity states, as well as increased variability in the subcortical-cortical connectivity. The global-level network in RTLE revealed increased variance in global efficiency and local efficiency. The static or dynamic functional connectivity in LTLE did not show any significant abnormalities. The altered dynamic properties were associated with worsening cognitive performance in language and conceptual thinking by the TLE patients.ConclusionOur findings demonstrated the presence of abnormalities in the static and dynamic functional connectivity of TLE patients. RTLE patients exhibited more pronounced aberrant connectivity patterns and topological properties, which might represent a mechanism for reconfiguration of brain networks in RTLE patients. These observations extended our understanding of the pathophysiological network mechanisms of TLE.

The wind environment in residential areas can exert a direct or indirect influence on the spread of epidemics, with some scholars paying particular attention to the epidemic prevention and control of residential areas from the perspective of wind environments. As a result, it is urgent to re-examine the epidemic prevention response of residential spaces. Taking high-rise residential areas in Xi’an as an example, the article defines the air flow field area based on on-site wind environment measurements, crowd behavior annotation, and CFD simulation. Using the double-effect superposition of crowd behavior and risk space, the paper undertook a multiple identification strategy of epidemic prevention space. The identification methods and management and control strategies of epidemic prevention in high-rise residential areas are proposed. Additionally, the living environment of residential areas is optimized, and a healthy residential space is created. The transformation from concept and calls for action to space implementation is made to provide a reference for improving the space management and control capabilities in high-rise residential areas in China. The results of this study can be used as a guideline for future residential planning and design from the perspective of preventing airborne diseases.

Ocean acidification (OA) may interact with anthropogenic pollutants, such as heavy metals (HM), to represent a threat to marine organisms and ecosystems. Here, we perform a quantitative meta-analysis to examine the combined effects of OA and heavy metals on marine organisms. The results reveal predominantly additive interactions (67%), with a considerable proportion of synergistic interactions (25%) and a few antagonistic interactions (8%). The overall adverse effects of heavy metals on marine organisms were alleviated by OA, leading to a neutral impact of heavy metals in combination with OA. However, different taxonomic groups showed large variabilities in their responses, with microalgae being the most sensitive when exposed to heavy metals and OA, and having the highest proportion of antagonistic interactions. Furthermore, the variations in interaction type frequencies are related to climate regions and heavy metal properties, with antagonistic interactions accounting for the highest proportion in temperate regions (28%) and when exposed to Zn (52%). Our study provides a comprehensive insight into the interactive effects of OA and HM on marine organisms, and highlights the importance of further investigating the responses of different marine taxonomic groups from various geographic locations to the combined stress of OA and HM.

Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. Unfortunately, there are few reports on effective biomarkers for HCC metastasis. This study aimed to discover potential key genes of HCC, which could provide new insights for HCC metastasis. GEO (Gene Expression Omnibus) microarray and TCGA (The Cancer Genome Atlas) datasets were integrated to screen for candidate genes involved in HCC metastasis. Differentially expressed genes (DEGs) were screened, and then we performed enrichment analysis of Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction network was then built and analyzed utilizing STRING and Cytoscape, followed by the identification of 10 hub genes by cytoHubba. Four genes were associated with survival, their prognostic value was verified by prognostic signature analysis. Thymidylate synthase (TYMS) gene was identified as significant HCC metastasis-associated genes after mRNA expression validation and IHC analysis. TYMS silencing in HCC cells remarkedly inhibited growth and invasion. Finally, we found TYMS silencing dramatically decrease DNA synthesis and extracellular matrix (ECM) degradation, resulting in the inhibition of HCC metastasis, indicating TYMS had close associations with HCC development. These findings provided new insights into HCC metastasis and identified candidate gene prognosis signatures for HCC metastasis.

Abstract Background Recent studies have reported that Integrin alpha 2 (ITGA2) plays an essential role in tumor cell proliferation, invasion, metastasis, and angiogenesis. An abnormally expressed ITGA2 correlates with unfavorable prognoses in multiple types of cancer. However, the specific mechanism of how ITGA2 contributes to tumorigenesis remains unclear. Methods The GEPIA web tool was used to find the clinical relevance of ITGA2 in cancer, and this significance was verified using Western blotting analysis of paired patient tissues and immunohistochemistry of the pancreatic cancer tissue. Functional assays, such as the MTS assay, colony formation assay, and transwell assay, were used to determine the biological role of ITGA2 in human cancer. The relationship between ITGA2 and programmed death-ligand 1 (PD-L1) was examined using Western blot analysis, RT-qPCR assay, and immunohistochemistry. The protein-protein interaction between ITGA2 and STAT3 was detected via co-immunoprecipitation. Results Our study showed that ITGA2 was markedly overexpressed in several malignant tumor cells and clinical tissues. Blocking ITGA2 inhibited the proliferation and invasion ability of cancer cells significantly, whereas overexpressed ITGA2 increased the degree of those processes considerably. Additionally, the RNA-seq assay indicated that ITGA2 transcriptionally regulated the expression of PD-L1 in pancreatic cancer. We also demonstrated that ITGA2 interacted with STAT3 and up-regulated the phosphorylation of STAT3; this interaction might involve the mechanism of ITGA2 inducing PD-L1 expression in cancer cells. Our results suggest that ITGA2 plays a critical role in cancer cell progression and the regulation of PD-L1 by activating the STAT3 pathway. Conclusions We identified a novel mechanism by which ITGA2 plays a critical role in modulating cancer immune response by transcriptionally increasing the expression of PD-L1 in cancer cells. Thus, targeting ITGA2 is an effective method to enhance the efficacy of checkpoint immunotherapy against cancer.

Hepatocellular carcinoma (HCC) is a malignancy with one of the worst prognoses. Long noncoding RNA (lncRNA) are emerging as an important regulator of gene expression and function, leading to the development of cancer. The aim of this study was to determine the relationship between lncRNA and HCC and to further guide clinical therapy. lncRNA in HCC and adjacent tissues were screened, and the correlation between lncRNA‐PDPK2P expression in liver tissues and the pathological characteristics and severity of HCC was assessed. The effects of PDPK2P on HCC proliferation, apoptosis, metastasis, and invasion were also systematically investigated via CCK‐8 assay, flow cytometry, scratch wound healing, and transwell assay, respectively. The relationship between PDPK2P and PDK1 was verified by RNA pull‐down, rescue experiments and western blot. lncRNA‐PDPK2P was highly expressed in HCC tissues with a distinct positive correlation between PDPK2P and PDK1, and the upregulation was clinically associated with a larger tumor embolus, low differentiation, and poor survival. Mechanistically, lncRNA‐PDPK2P interacted with PDK1 and promoted HCC progression through the PDK1/AKT/caspase 3 signaling pathway. lncRNA‐PDPK2P can promote HCC progression, suggesting it may be a clinically valuable biomarker and serve as a molecular target for the diagnosis, prognosis, and therapy of hepatocellular carcinoma.

Abstract Background Sepsis is a common critical condition caused by the body’s overwhelming response to certain infective agents. Many biomarkers, including the serum lactate level, have been used for sepsis diagnosis and guiding treatment. Recently, the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) recommended the Sequential Organ Failure Assessment (SOFA) and the quick SOFA (qSOFA) rather than lactate for screening sepsis and assess prognosis. Here, we aim to explore and compare the prognostic accuracy of the lactate level, the SOFA score and the qSOFA score for mortality in septic patients using the public Medical Information Mart for Intensive Care III database (MIMIC III). Methods The baseline characteristics, laboratory test results and outcomes for sepsis patients were retrieved from MIMIC III. Survival was analysed by the Kaplan-Meier method. Univariate and multivariate analysis was performed to identify predictors of prognosis. Receiver operating characteristic curve (ROC) analysis was conducted to compare lactate with SOFA and qSOFA scores. Results A total of 3713 cases were initially identified. The analysis cohort included 1865 patients. The 24-h average lactate levels and the worst scores during the first 24 h of ICU admission were collected. Patients in the higher lactate group had higher mortality than those in the lower lactate group. Lactate was an independent predictor of sepsis prognosis. The AUROC of lactate (AUROC, 0.664 [95% CI, 0.639–0.689]) was significantly higher than that of qSOFA (AUROC, 0.547 [95% CI, 0.521–0.574]), and it was similar to the AUROC of SOFA (AUROC, 0.686 [95% CI, 0.661–0.710]). But the timing of lactate relative to SOFA and qSOFA scores was inconsistent. Conclusion Lactate is an independent prognostic predictor of mortality for patients with sepsis. It has superior discriminative power to qSOFA, and shows discriminative ability similar to that of SOFA.

Mass marketing of battery-electric vehicles (EVs) will require that car buyers have high confidence in the performance, reliability and safety of the battery in their vehicles. Over the past decade, steady progress has been made towards the development of advanced battery diagnostic and prognostic technologies using data-driven methods that can be used to inform EV owners of the condition of their battery over its lifetime. The research has shown promise for accurately predicting battery state of health (SOH), state of safety (SOS), cycle life, the remaining useful life (RUL), and indicators of cells with high risk of failure (i.e., weak cells). These methods yield information about the battery that would be of great interest to EV owners, but at present it is not shared with them. This paper is concerned with the present status of the information available on the battery with a focus on data-driven diagnostic and prognostic approaches, and how the information would be generated in the future for the millions of EVs that will be on the road in the next decade. Finally, future trends and key challenges for the prognostics and health management of the batteries in real-world EV applications are presented from four perspectives (cloud-edge interaction, full-scale diagnosis, artificial intelligence and electronic health reports) are discussed.

Three-dimensional landscape patterns are an effective means to study the relationship between landscape pattern evolution and eco-environmental effects. This paper selects six districts in Xi'an as the study area to examine the spatial distribution characteristics of the three-dimensional architectural landscape in the city's main urban area using three-dimensional information on the buildings in 2020 with the support of GIS. In this study, two new architectural landscape indices-landscape height variable coefficient and building rugosity index-were employed in landscape pattern analysis, whilst a system of rigorous and comprehensive three-dimensional architectural landscape metrics was established using principal component analysis. A mathematical model of weighted change of landscape metrics based on the objective weighting method was applied to carry out scale analysis of the landscape patterns. Spatial statistical analysis and spatial autocorrelation analysis were conducted to comprehensively study the differentiation of three-dimensional architectural landscape spatial patterns. The results show that the characteristic scale of the three-dimensional landscape pattern in Xi'an's main urban area is around 8 km. Moreover, the three-dimensional landscape of the buildings in this area is spatially positively correlated, exhibiting a high degree of spatial autocorrelation whilst only showing small spatial differences. The layout of the architectural landscape pattern is disorderly and chaotic within the second ring, whilst the clustering of patch types occurs near the third ring. Moreover, the building density in the Beilin, Lianhu, and Xincheng districts is large, the building height types are rich, and the roughness of the underlying surface is high, such that these are key areas to be improved through urban renewal. The height, volume, density, morphological heterogeneity, and vertical roughness of the architectural landscape vary amongst functional areas within the study area. This paper is the first to apply the study of spatial heterogeneity of three-dimensional landscape patterns to Xi'an. It does so in order to provide a quantitative basis for urban landscape ecological design for urban renewal and the rational planning of built-up areas, which will promote the sustainable development of the city's urban environment.

Under the trend of building green and comfortable development, effective control of building energy consumption has become one of the problems that countries are actively facing to solve. People’s demand for residential buildings has changed from the past survival type to a comfortable and livable type. The high level of heating energy consumption is worthy of in-depth study. In order to reduce energy consumption, realize the mapping of energy-saving concepts in buildings, and understand the energy consumption of different building materials and the influence of external factors on human thermal comfort, this book has conducted research on building thermal comfort based on energy-saving concepts. First of all, this article introduces the concept and application mode of energy-saving concepts in buildings and the concept of thermal comfort and the SET index of standard effective temperature, including the two-node model and the algorithm involved in the Fanger heat balance equation. In the experimental part, a model based on the concept of energy saving was designed to predict and analyze the energy consumption and thermal comfort effects of the building. In the analysis part, a comprehensive analysis of the effects of temperature, humidity, wind speed, and gender on thermal comfort, methods to improve thermal comfort, cumulative load changes with the heat transfer coefficient of windows, and the effects of windows of different materials on energy consumption was performed. At the same temperature, the wind speed is different, and the degree of heat sensation is also different. When the wind speed is 0.18 m/s and the temperature is 28°C, the thermal sensation is 0.32, and the human sensation is close to neutral. When the wind speed increases to 0.72 m/s, the heat sensation drops to −0.45, and the human body feels neutral and cool. It can be seen that the increase in wind speed has a certain compensation effect on the thermal sensation of the human body. When the wind speed does not change, increase the air temperature. For example, when the wind speed is 0.72 m/s, the temperature is 28°C, and the thermal sensation is −0.45, and when the temperature is increased to 29°C, the thermal sensation is 0.08, which shows that the temperature is improving the thermal sensation of the human body which has a certain offsetting effect. By studying the thermal comfort of buildings based on energy-saving concepts, it is possible to obtain the effect of external factors on thermal comfort, thereby optimizing building materials and using building materials with lower heat transfer coefficients to reduce heating energy consumption.

Buildings will generate considerable resource and energy consumption, environmental impact, and costs throughout their entire life cycle. Life cycle assessment and life cycle cost methods have become widely used building sustainability evaluation methods internationally due to their long-term comprehensive evaluation perspectives and scientific quantitative calculation methods. In response to energy consumption management issues in green buildings, a questionnaire survey was conducted to investigate the concepts of sustainable development, green environmental protection, and energy consumption management of construction enterprise personnel. Through the survey, it can be seen that corporate executives generally have a sense of sustainable development and pay more attention to sustainable competition and the green development of enterprises. Approximately 90% of executives understand energy consumption models. Only 10% of employees do not understand, but they are acquired by the company’s frontline employees. The result of the feedback is just the opposite. Frontline workers pay little attention to the long-term development of the company. Only about 5% of employees are proficient in the energy management system, but the proportion of completely unknown is as high as 20%, indicating that the overall energy management awareness of Chinese enterprises still needs to be strengthened. Secondly, through design experiments, the energy-saving management effect of the energy consumption model was observed and analyzed. From the power saving and load test, we found that the monitoring of the energy consumption model is helpful for companies to choose more energy-efficient building circuit designs, achieving an average annual energy-saving effect of about 8%, so as to achieve the purpose of energy saving.

In view of the pollution problems related to using oil-based drilling fluids, we prepared a new environmentally friendly synthetic fluid, namely, NSF, for use in synthetic-based drilling fluid instead. We used heavy hydrocarbons from Daqing as raw material in our preparation and employed few-steps refining techniques, such as hydrodesulfurization, hydrodearomatization, and hydrogenation. After the treatment, the sulfur and aromatic hydrocarbon contents in the NSF were 1,000,000 mg/L water-soluble fraction; therefore, its toxicity is

Abstracts Background Pancreatic ductal adenocarcinoma is one of the leading causes of cancer-related death worldwide. Immune checkpoint blockade therapy, including anti-PD-1 and anti-PD-L1, is a new therapeutic strategy for cancer treatment but the monotherapy with PD-L1 inhibitors for pancreatic cancer is almost ineffective for pancreatic cancer. Thus, exploring the regulatory mechanism of PD-L1 in cancer cells, especially in pancreatic cancer cells, is one of the key strategies to improving cancer patient response to PD-L1 blockade therapy. Histone acetyltransferase 1(HAT1) is a classic type B histone acetyltransferase and the biological role of HAT1 in pancreatic cancer is unclear. Methods The clinical relevance of HAT1 was examined by the GEPIA web tool, Western blotting and immunohistochemistry of pancreatic cancer tissue microarray slides. Tumor cell motility was investigated by MTS assay, colony formation assay and xenografts. The relationship between HAT1 and PD-L1 was examined by Western blot analysis, RT-qPCR and immunohistochemistry. Results HAT1 was upregulated in PDAC and associated with poor prognosis in PDAC patients. The knockdown of HAT1 decreased the proliferation of pancreatic cancer cells in vivo and in vitro. Strikingly, we showed that HAT1 transcriptionally regulated PD-L1, and this process was mainly mediated by BRD4 in pancreatic cancer. The knockdown of HAT1 improved the efficacy of immune checkpoint blockade by decreasing the PD-L1. Conclusions The recognition of HAT1 in regulating tumor cell proliferation and cancer immunity indicated that HAT1 might be employed as a new diagnostic and prognostic marker and a predictive marker for pancreatic cancer therapy, especially in immune checkpoint blockade therapy. Targeting HAT1 highlights a novel therapeutic approach to overcome immune evasion by tumor cells.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal tumor types worldwide. BET inhibitors display anti-tumor activity in pancreatic cancer, however the cells often develop resistance after a long-term treatment and the underlying molecular basis is not fully understood. Methods Drug screening assay in Fructose-1, 6-biphosphatase (FBP1) knockdown or overexpressing pancreatic cancer cells was performed. Tumor cell motility, FBP1 protein and mRNA changes were investigated after BET inhibitors treatment. The interaction between TRIM28 and FBP1 after BET inhibitors treatment was examined by Co-immunoprecipitation (IP) and GST pull-down. The relationship between FBP1 and c-Myc was examined by western blot, RT-qPCR and immunohistochemistry (IHC). Results The expression of FBP1 protein increased the sensitivity of pancreatic cancer cells to JQ1. Furthermore, we showed that JQ1 stabilized FBP1 protein level by disrupting the interaction between FBP1 and TRIM28 in pancreatic cancer cells. Moreover, we demonstrated that FBP1 promoted c-Myc degradation through disrupting the ERK-c-Myc axis. Conclusions FBP1 modulates the sensitivity of pancreatic cancer cells to BET inhibitors by decreasing the expression of c-Myc. These findings highlight FBP1 could be used as a therapeutic niche for patient-tailored therapies.

Abstract Pheochromocytomas (PCCs) and paragangliomas (PGLs) are the most heritable endocrine tumors. Genetic testing for 12 driver susceptibility genes is recommended in all PCC and PGL cases. However, detection of somatic mutations in PCC and PGL remains unrealizable for genetic diagnosis and preoperative assessment. We compared the serum exosomal DNA and tumor tissue DNA from patients or mice with PCC or PGL and found double-stranded DNA (dsDNA) fragments in the circulating exosomes of patients with PCC or PGL. Exosomal dsDNA shared the same mutations in the susceptibility genes with that of the parent tumor cells. Moreover, our research showed that serum-derived exosomal dsDNA in PCC and PGL was highly consistent with the paired tumor genome. Our findings provide the first definitive evidence of the presence of exosomal dsDNA that can be used as a noninvasive genetic marker in one of the most effective somatic mutation screens for the diagnosis and preoperative assessment of PCCs and PGLs.