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Abstract Malignant peripheral nerve sheath tumors (MPNSTs) are highly aggressive Schwann cell-derived sarcomas that are sporadic or associated with Neurofibromatosis 1 (NF1) gene mutations. Traditional therapies are usually ineffective for treating MPNSTs, so new targets need to be identified for the treatment of MPNSTs. In the present study, the role of the mitochondrial translocator protein (TSPO) in the regulation of cell proliferation and the cell cycle in MPNSTs was investigated. TSPO expression was lower in MPNSTs than in NFs. Loss-of-function experiments revealed that TSPO deficiency promoted MPNST cell growth, migration, and invasion and influenced the cell cycle in vitro and in vivo. In addition, TSPO depletion suppressed cell apoptosis by downregulating the expression of caspase-3, caspase-8, HSP60, p27, p53, and BCL-2 and suppressed the cell cycle by upregulating CDK1, CDK2, CCNB1 and CCNA2. Furthermore, CDK1 was determined to be an upstream target of TSPO-mediated regulation via RNA-seq, qPCR, and Western blotting. Specifically, depletion of CDK1 weakened the effect of TSPO deficiency on cell proliferation and migration. More importantly, CDK1 knockdown induced significant cell cycle arrest in the G2/M phase. In summary, TSPO deficiency regulates the cell cycle in MPNSTs by targeting CDK1, which may be an effective molecular target for prognosis evaluation and treatment.

Abstract Universities and colleges aim to provide students with a solid academic foundation. Quality instruction is one strategy for achieving the highest possible standard in the higher education system. Personalized teaching caters to each student by adapting the learning pace and method to their specific requirements. However, the present state of customized education in higher education resources prevents proper resources from being extracted due to a lack of multi-dimensional association analysis between students, circumstances, and materials. A hybrid personalized teaching system utilizing fuzzy association rules mining is the goal of this research to improve learning in higher education. Effective multi-dimensional association analysis among students, settings, and instructional materials is facilitated by the fuzzy association rules mining-based hybrid personalized teaching system (FARM-HPT). The proposed study conforms to AI standards, is based on fuzzy logic theories, and guarantees precise university-level resource discovery. The study builds on earlier work in data mining by presenting a new, learner-specific recommendation model for personalized teaching that uses FARM to ensure accurate resource recognition and efficient mining of instructional assets at the higher education level. This new approach generates fewer set comparisons and does them faster than the current standard. Focusing on experimental validation, the study shows that the FARM-HPT system can generate individualized lessons while overcoming the constraints of traditional information mining methods. These findings align with AI standards, which shows how important it is to validate new AI approaches using robust empirical evidence. The system ensures effective accuracy on various datasets: LFW (89.76%), JAOLAD (94.43%), OECD (95.43%) and OULAD (97.45%).

Abstract The construction models of smart cities and low-carbon cities are crucial for advancing global urbanization, enhancing urban governance, and addressing major urban challenges. Despite significant advancements in smart and low-carbon city research, a consensus on their coupling coordination remains elusive. This study employs mixed-method research, combining qualitative and quantitative analyses, to investigate the coupling coordination between urban smart performance (SCP) and low-carbon level (LCL) across 52 typical smart and low-carbon pilot cities in China. Independent evaluation models for SCP and LCL qualitatively assess the current state of smart and low-carbon city construction. Additionally, an Entropy–TOPSIS–Pearson correlation–Coupling coordination degree (ETPC) analysis model quantitatively examines their relationship. The results reveal that smart city initiatives in China significantly outperform low-carbon city development, with notable disparities in SCP and LCL between eastern, non-resource-based, and central cities versus western, resource-dependent, and peripheral cities. A strong positive correlation exists between urban SCP and overall LCL, with significant correlations in management, society, and economy, and moderate to weak correlations in environmental quality and culture. As SCP levels improve, the coupling coordination degree between the urban SCP and LCL systems also increases, driven primarily by economic, management, and societal factors. Conversely, the subsystems of low-carbon culture and environmental quality show poorer integration. Based on these findings, this study proposes an evaluation system for smart and low-carbon coupling coordination development, outlining pathways for future development from the perspective of urban complex systems.

This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO2 emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models such as LSTM and ARDL using Python showcases the proposed method's substantial reduction in prediction errors compared to singular reliance on electricity data, while also significantly reducing computational time in contrast to LSTM models. The findings establish a valuable reference for policymakers and researchers in refining carbon emission prediction methodologies and formulating effective carbon reduction policies.

The state of austenite grains has an essential role in the microstructure transformation and mechanical properties of alloy steel during heat treatment or subsequent hot working. The initial microstructure of alloy steel is inseparable from the nucleation and growth of austenite grains. Hence, in this work, high carbon low alloy steel with the initial microstructure of lamellar pearlite and grain boundary cementite was selected, and the effect of cementite dissolution on austenite grain growth was analyzed from the perspective of the phase transition dilatometer curve. Three nucleation sites in high carbon low alloy steel were found by multi-perspective characterization, forming two types of austenite grains: pearlite-austenite grain and cementite-austenite grain. Under cementite pinning and solute drag effect, the growth of the prior austenite grains was retarded during the isothermal temperatures process. The average size of prior austenite grains increased only from 65.1 μm (1050 °C) to 69.8 μm (1150 °C). This work provides a foundation for optimizing the prior austenite grains state by utilizing grain boundary cementite dissolution.

Abstract In the last decade, prophages that possess the ability of lysogenic transformation have become increasingly significant. Their transfer and subsequent activity in the host have a significant impact on the evolution of bacteria. Here, we investigate the role of prophage phi456 with high spontaneous induction in the bacterial genome of Avian pathogenic Escherichia coli (APEC) DE456. The phage particles, phi456, that were released from DE456 were isolated, purified, and sequenced. Additionally, phage particles were no longer observed either during normal growth or induced by nalidixic acid in DE456Δphi456. This indicated that the released phage particles from DE456 were only phi456. We demonstrated that phi456 contributed to biofilm formation through spontaneous induction of the accompanying increase in the eDNA content. The survival ability of DE456Δphi456 was decreased in avian macrophage HD11 under oxidative stress and acidic conditions. This is likely due to a decrease in the transcription levels of three crucial genes—rpoS, katE, and oxyR—which are needed to help the bacteria adapt to and survive in adverse environments. It has been observed through animal experiments that the presence of phi456 in the DE456 genome enhances colonization ability in vivo. Additionally, the number of type I fimbriae in DE456Δphi456 was observed to be reduced under transmission electron microscopy when compared to the wild-type strain. The qRT-PCR results indicated that the expression levels of the subunit of I fimbriae (fimA) and its apical adhesin (fimH) were significantly lower in DE456Δphi456. Therefore, it can be concluded that phi456 plays a crucial role in helping bacterial hosts survive in unfavorable conditions and enhancing the colonization ability in DE456.

Smart cities were originally conceived to address a myriad of urban challenges arising from rapid urbanization, including energy scarcity, congestion, and environmental degradation. The Chinese government has made substantial efforts to advance smart city initiatives. However, the extent to which the integration of smart technologies contributes to urban sustainability, especially within a high-carbon urbanization paradigm, poses a critical question in light of escalating extreme weather events and worsening global challenges. Urgency is underscored in prioritizing low-carbon strategies within smart city frameworks. This paper presents a Multicriteria Decision Making Network (MCDN) approach to assess and rank the low-carbon levels (LCL) of 36 pilot smart cities in China. Findings reveal that overall LCL among these cities remains relatively modest, with significant disparities attributed to varying economic, social, institutional, cultural, and environmental contexts. The study also delves into the nexus between urban intelligence and LCL, highlighting a discernible positive correlation between a city's smartness and its low-carbon profile. Moreover, empirical evidence suggests that advancements in smart technologies are conducive, albeit to varying degrees, to enhancing urban LCL. In light of these findings, recommendations are made to fortify economic and social advancement, bolster management practices, and foster multi-stakeholder collaboration to propel the coordinated development of smart and low-carbon initiatives in China.

Sialadenitis and sialadenitis-induced sialopathy are typically caused by obstruction of the salivary gland ducts. Atrophy of the salivary glands in experimental animals caused by duct ligation exhibits a histopathology similar to that of salivary gland sialadenitis. Therefore, a variety of duct ligation/de-ligation models have been commonly employed to study salivary gland injury and regeneration. Duct ligation is mainly characterised by apoptosis and activation of different signaling pathways in parenchymal cells, which eventually leads to gland atrophy and progressive dysfunction. By contrast, duct de-ligation can initiate the recovery of gland structure and function by regenerating the secretory tissue. This review summarizes the animal duct ligation/de-ligation models that have been used for the examination of pathological fundamentals in salivary disorders, in order to unravel the pathological changes and underlying mechanisms involved in salivary gland injury and regeneration. These experimental models have contributed to developing effective and curative strategies for gland dysfunction and providing plausible solutions for overcoming salivary disorders.

Abstract In this paper, numerical simulations of single-jet impingement cooling and double-jet impingement cooling processes of heated L-shaped steel are carried out using the VOF model. The SIMPLEC pressure–velocity coupling algorithm and realizable k-ε model are used for the solution. The effects of jet position, water flow, and jet distance in the single-jet condition are analyzed in the simulations. The distributions of impact pressure, turbulence kinetic energy, and Nusselt number were obtained, as well as the variation of the peak values of these three factors with the jet position, water flow, and jet distance. The water flow rate is 3–11 L/min, and the jet distance is 5–25 cm. The effect of the distance between the two nozzles on the jet cooling uniformity under the dual jet condition was also analyzed. The distance between the two nozzles was 15–45 mm. The results showed that the variation of water flow rate had a greater effect on the ability of jet cooling compared with the jet position and jet distance, and the heat transfer efficiency also increased gradually with the increase of water flow, but the increased rate of heat transfer efficiency decreased gradually. When the flow rate increased from 3 to 11 L/min, the maximum instantaneous cooling rates at 1/4 of the thickness of the short side upper side, long side upper side, short side lower side, and long side lower side positions increased by 38.9%, 48.5%, 48.2%, and 32.9%, respectively. To ensure that the jet does not shift, the jet distance should be less than or equal to 10 cm. In the case of the double jet, the nozzle distance is 1.5 cm, and the cooling uniformity of the cooling area between the two nozzles is better. The peak Nusselt number in the cooling area of each part under the double jet cooling condition increased by 5%, 9.4%, 10.2%, and 13.3%, respectively, compared with the single jet.

Abstract Exposure of plants to ultraviolet-B (UV-B) radiation initiates transcriptional responses that modify metabolism, physiology and development to enhance viability in sunlight. Many of these regulatory responses to UV-B radiation are mediated by the photoreceptor UV RESISTANCE LOCUS 8 (UVR8). Following photoreception, UVR8 interacts directly with multiple proteins to regulate gene expression, but the mechanisms that control differential protein binding to initiate distinct responses are unknown. Here we show that UVR8 is phosphorylated at several sites and that UV-B stimulates phosphorylation at Serine 402. Site-directed mutagenesis to mimic Serine 402 phosphorylation promotes binding of UVR8 to REPRESSOR OF UV-B PHOTOMORPHOGENESIS (RUP) proteins, which negatively regulate UVR8 action. Complementation of the uvr8 mutant with phosphonull or phosphomimetic variants suggests that phosphorylation of Serine 402 modifies UVR8 activity and promotes flavonoid biosynthesis, a key UV-B-stimulated response that enhances plant protection and crop nutritional quality. This research provides a basis to understand how UVR8 interacts differentially with effector proteins to regulate plant responses to UV-B radiation.

Phragmites australis is a prevalent species in the Chongming Dongtan wetland and is capable of thriving in various tidal flat environments, including high salinity habitats. P. australis population displays inconsistent ecological performances, highlighting the need to uncover their survival strategies and mechanisms in tidal flats with diverse soil salinities. Upon comparing functional traits of P. australis at multiple tidal flats (low, middle, and high) and their responses to soil physicochemical properties, this study aimed to clarify the salt-tolerant strategy of P. australis and the corresponding mechanisms. These results showed that leaf characteristics, such as specific leaf area and leaf dry matter content, demonstrated more robust stability to soil salinity than shoot height and dry weight. Furthermore, as salt stress intensified, the activities of superoxide dismutase (SOD), catalase (CAT) and peroxisome (POD) in P. australis leaves at low tidal flat exhibited an increased upward trend compared to those at other tidal flats. The molecular mechanism of salt tolerance in Phragmites australis across various habitats was investigated using transcriptome sequencing. Weighted correlation network analysis (WGCNA) combined with differentially expressed genes (DEGs) screened out 3 modules closely related to high salt tolerance and identified 105 core genes crucial for high salt tolerance. Further research was carried out on the few degraded populations at low tidal flat, and 25 core genes were identified by combining WGCNA and DEGs. A decrease in the activity of ferroptosis marker gonyautoxin-4 and an increase in the content of Fe3+ in the degenerated group were observed, indicating that ferroptosis might participate in degradation. Furthermore, correlation analysis indicated a possible regulatory network between salt tolerance and ferroptosis. In short, this study provided new insights into the salt tolerance mechanism of P. australis population along tidal flats.

Abstract The mechanisms of acclimating to a nitrogen-fluctuating environment are necessary for the survival of aquatic cyanobacteria in their natural habitats, but our understanding is still far from complete. Here, the synthesis of phycobiliprotein is confirmed to be much earlier than that of photosystem components during recovery from nitrogen chlorosis and an unknown protein Ssr1698 is discovered to be involved in this synthetic process. The unknown protein is further identified as a c-type heme oxygenase (cHO) in tetrapyrrole biosynthetic pathway and catalyzes the opening of heme ring to form biliverdin IXα, which is required for phycobilin production and ensuing phycobiliprotein synthesis. In addition, the cHO-dependent phycobiliprotein is found to be vital for the growth of cyanobacterial cells during chlorosis and regreening through its nitrogen-storage and light-harvesting functions, respectively. Collectively, the cHO expressed preferentially during recovery from nitrogen chlorosis is identified in photosynthetic organisms and the dual function of this enzyme-dependent phycobiliprotein is proposed to be an important mechanism for acclimation of aquatic cyanobacteria to a nitrogen-fluctuating environment.

Abstract Lithium-ion batteries (LIBs) are crucial for the large-scale utilization of clean energy. However, because of the complexity and real-time nature of internal reactions, the mechanism of capacity decline in LIBs is still unclear. This has become a bottleneck restricting their promotion and application. Electrochemical impedance spectroscopy (EIS) contains rich electrochemical connotations and significant application prospects, and has attracted widespread attention and research on efficient energy storage systems. Compared to traditional voltage and current data, the state-of-health (SOH) estimation model based on EIS has higher accuracy. This paper categorizes EIS measurement methods based on different principles, introduces the relationship between LIBs aging mechanism and SOH, and compares the advantages of different SOH estimation methods. After a detailed analysis of the latest technologies, a review is given. The insights of this review can deepen the understanding of the relationship between EIS and the aging effect mechanism of LIBs, and promote the development of new energy storage devices and evaluation methods.

Effects of natural aging (NA) on artificial aging (AA) behavior of Al–5Mg–3Zn–1Cu alloy were studied from the perspectives of kinetics and microstructure evolution. The results show that short-term NA (24 h) has marginal effects on AA at any temperature, as well as long-term NA (>240 h) on low-temperature AA (120 °C), but long-term NA can accelerate aging kinetics and alleviate severe hardening ability attenuation at high temperatures (>150 °C). Analysis of the precipitates evolution demonstrates that such attenuation is primarily attributable to coarsening and reduction of intragranular precipitates and is also slightly related to the formation of precipitate-free zone (PFZ). Three main factors cause the unfavorable microstructure in NA-free alloy at high temperatures AA: (1) Insufficient nucleation of intragranular precipitates induced by weakened nucleation driving force, larger critical nucleation radius, and rapid vacancy annihilation; (2) The nucleated precipitates are prone to coarsening; (3) The precipitation near grain boundary is obstructed due to the depletion of vacancies and solutes. Clusters formed during NA can inhibit the unfavorable microstructure by delaying the annealing out of excess vacancies, promoting the nucleation of precipitates, and elevating the activation energy for precipitation coarsening, thereby enhancing the hardening potential. This work is instructive for the formulation of heat treatment strategies for Al–Mg–Zn–Cu alloys with high Mg/Zn ratios.

The epidemic of alcohol abuse affects millions of people worldwide. Relevant evidence supports the notion that the gut microbiota (GM) plays a crucial role in central nervous system (CNS) function, and its composition undergoes changes following alcohol consumption. Therefore, the purpose of this study was to investigate the effect of reconstructing the gut microbiota by fecal microbiota transplantation (FMT) on alcohol dependence. Here, we established an alcohol dependence model with C57BL/6J mice and proved that FMT treatment improved anxiety-like behavior and alcohol-seeking behavior in alcohol-dependent mice. Additionally, we found that the expression of the intestinal intercellular tight junction structure proteins ZO-1 and occludin was significantly increased after FMT. FMT repaired intestinal permeability in alcohol-dependent mice and decreased the levels of lipopolysaccharide (LPS) and proinflammatory factors. Moreover, the serotonin (5-hydroxytryptamine, 5-HT) content was significantly increased in alcohol-dependent mouse intestinal and brain tissues after receiving the fecal microbiome from healthy mice. 16S rRNA sequencing demonstrated that FMT markedly reshaped the composition of the gut microbiota and elicited changes in the intestinal barrier and 5-HT levels. Collectively, our results revealed that FMT has a palliative effect on alcohol dependence and explored the underlying mechanisms, which provides new strategies for the treatment of alcohol dependence.

Knowledge graph reasoning can deduce new facts and relationships, which is an important research direction of knowledge graphs. Most of the existing methods are based on end-to-end reasoning which cannot effectively use the knowledge graph, so consequently the performance of the method still needs to be improved. Therefore, we combine causal inference with reinforcement learning and propose a new framework for knowledge graph reasoning. By combining the counterfactual method in causal inference, our method can obtain more information as prior knowledge and integrate it into the control strategy in the reinforcement model. The proposed method mainly includes the steps of relationship importance identification, reinforcement learning framework design, policy network design, and the training and testing of the causal reinforcement learning model. Specifically, a prior knowledge table is first constructed to indicate which relationship is more important for the problem to be queried; secondly, designing state space, optimization, action space, state transition and reward, respectively, is undertaken; then, the standard value is set and compared with the weight value of each candidate edge, and an action strategy is selected according to the comparison result through prior knowledge or neural network; finally, the parameters of the reinforcement learning model are determined through training and testing. We used four datasets to compare our method to the baseline method and conducted ablation experiments. On dataset NELL-995 and FB15k-237, the experimental results show that the MAP scores of our method are 87.8 and 45.2, and the optimal performance is achieved.

The 1100 aluminum alloy has been widely used in many industrial fields due to its high specific strength, fracture toughness, excellent thermal conductivity, and corrosion resistance. In this study, the corrosion behavior of the homogenized and hot-extruded 1100 aluminum alloy in acid salt spray environment for different time was studied. The microstructure of the 1100 aluminum alloy before and after corrosion was characterized by an optical microscope (OM), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and a laser scanning confocal microscope (LSCM). The difference in corrosion resistance between the homogenized and extruded 1100 aluminum alloy was analyzed via the electrochemical method. The results indicate that after hot extrusion at 400 °C, the microstructure of the 1100 aluminum alloy changes from an equiaxed crystal structure with (111) preferentially distributed in a fibrous structure with (220) preferentially distributed. There was no obvious dynamic recrystallization occurring during extrusion, and the second-phase particles containing Al-Fe-Si were coarse and unevenly distributed. With the increase in corrosion time, corrosion pits appeared on the surface of the 1100 aluminum alloy, and a corrosion product layer was formed on the surface of the homogenized 1100 aluminum alloy, which reduced the corrosion rate. After 96 h of corrosion, the CPR of the extruded samples was 0.619 mm/a, and that of the homogenized samples was 0.442 mm/a. The corrosion resistance of the extruded 1100 aluminum alloy was affected by the microstructure and the second phase, and no protective layer of corrosion products was formed on the surface, resulting in a faster corrosion rate and deeper corrosion pits.

As more and more loads start to join the electricity market and the role of loads in power system balancing and regulation becomes more and more important, the study of the response characteristics of polymorphic loads becomes more and more important. This paper firstly reviews the aggregation methods for loads, introduces its parameter aggregation model using temperature-controlled loads as an example, and introduces the use of machine learning methods to first cluster and then aggregate. It then reviews methods for the study of load response characteristics, with an increasing number of neural networks and hybrid neural networks being introduced into the field. Finally, a review of load uncertainty is presented, introducing sources of load uncertainty and methods for coping with load uncertainty.

Abstract Background Research on aging is growing as the elderly make up a greater share of the population, focusing on reversing and inhibiting the aging process. The exhaustion and senescence of stem cells are the fundamental drivers behind aging. β-Carotene has been depicted to have many biological functions, and we speculate that it may have an anti-aging effect. Methods Firstly, the anti-aging property of β-carotene was investigated in vitro using mesenchymal stem cells (MSCs) induced by H2O2. The anti-aging effect was characterized using Western-bloting, confocal laser scanning microscopy, indirect immunofluorescence, and immunohistochemistry. The anti-aging property was also tested in vivo using aged mice. Results The in vitro experiment revealed that β-carotene could relieve the aging of MSCs, as evidenced by a series of aging marker molecules such as p16 and p21. β-Carotene appeared to inhibit aging by regulating the KAT7-P15 signaling axis. The in vivo experiment revealed that β-carotene treatment has significantly down-regulated the aging level of tissues and organs. Conclusions In this work, we explored the anti-aging effect of β-carotene in vivo and in vitro. The experimental results indicate that β-carotene may be an important potential anti-aging molecule, which can be used as a drug or in functional food to treat aging in the future.

Rosai–Dorfman disease (RDD) is a rare, benign, non-Langerhans cell histiocytic proliferative disease. RDD with central nervous system involvement is extremely rare. Surgical excision is generally regarded as the appropriate treatment of choice for this disease, especially when the lesion causes neurological compression. RDD can be accompanied by systemic symptoms, such as malaise, fever, weight change, leukocytosis, anemia, and hormonal disturbance, which may be challenging during general management. Little is known regarding peri-anesthesia management of this rare disease. We report a case of a patient in his 20s who had recurrent RDD and had general anesthesia with perioperative management. He was obese and hepatic insufficiency. This case report adds to the literature regarding the perioperative anesthetic management of RDD with central nervous system involvement.

BackgroundThis study aimed to explore the efficacy and safety of pembrolizumab combined with chemotherapy as neoadjuvant therapy in patients with resectable locally advanced head and neck squamous cell carcinomas (LA-HNSCCs).MethodsIn this prospective, single-arm, single-centre clinical trial, patients meeting the inclusion criteria were treated with preoperative neoadjuvant therapy with 200 mg pembrolizumab combined with 75 mg/m2 cisplatin and 175 mg/m2 paclitaxel. This was followed by surgery and postoperative adjuvant therapy. The primary endpoint was the postoperative pathological complete response (pCR) rate. All statistical analyses were performed using SPSS 26.ResultsA total of 22 patients were enrolled. The location of primary lesion showed: hypopharynx were 15 (68.2%), oropharynx were 6 (27.3%) and oral cavity was 1 (4.5%). The postoperative pCR rate, was 36.4% (8/22), and there was no delay to surgery due to adverse drug reactions. The rate of laryngeal function preservation was 90.9% (20/22). Delayed wound healing was the main surgical complication, with an incidence of 22.7% (5/22). The median follow-up time was 9.5 months, and only 1 patient (4.55%) suffered a regional recurrence.ConclusionPreoperative treatment with pembrolizumab and chemotherapy in resectable LA-HNSCC has a high pCR rate with no significant impact on surgical safety. This treatment was found to increase the rate of laryngeal function preservation. However, the effects of neoadjuvant immunotherapy on long-term prognosis in LA-HNSCCs require further study.

3003 aluminum alloy exhibits commendable corrosion resistance, ease of processing, and good formability, rendering it extensively utilized across many industrial sectors. In this study, the corrosion behavior of 3003 aluminum alloy in a homogenized state and after hot extrusion deformation in an acidic salt spray environment for different times was studied. The microstructure of the 3003 aluminum alloy in the homogenized state and after hot extrusion was characterized using scanning electron microscopy (SEM), optical microscope (OM), laser scanning confocal microscope (LSCM) etc., while electrochemical methods were employed to study the difference in corrosion resistance between these two states. The results show that corrosion pits on the surface of the homogenized 3003 aluminum alloy increase with time, and corrosion extends along the second phase arrangement, while the hot extruded 3003 aluminum alloy mainly exhibits corrosion pit extension. The grain size of the homogenized 3003 aluminum alloy is larger than that of the hot extruded state, and the second phase is distributed in a reticular pattern. Hot extrusion deformation ensures not only a uniform distribution of the second phase in the 3003 aluminum alloy but also a reduced grain size, an increased grain boundary density, a heightened electrochemical activity in acidic environments, and an augmented pitting density. Compared with the homogenized 3003 aluminum alloy, the pitting density, maximum pitting depth, and weight loss of the hot extruded state are increased.

Surface layers with ultrafine grain (SUF) steels possess excellent crack arrestability. But the strength of the current SUF steels is low and the grain ultrarefinement mechanism is still in doubt. In this study, a high-strength SUF steel has been fabricated by the combination of adding microalloying elements and intermediate-billet controlled cooling method. Microstructures of the developed SUF steel are characterized by optical microscope, scanning electron microscope, electron back-scattered diffraction analyses, and transmission electron microscope. Tensile property and toughness are also determined. The results show that a microstructure dominated by bainitic ferrite forms in the surface layers of the intermediate billet after controlled cooling. A 3.5 mm-thick laminated and ultrafine-grained microstructure is generated after finishing rolling. Thickness ratio of the ultrafine-grained layers is 28.0%. At 2 mm thickness position from the surface, the average grain size is 2.0 μm. And the area fraction of recovered microstructure is 66.4%, which means dynamic recovery is the main grain ultrarefinement mechanism. The microstructure in the mid-thickness position is coarse-grained ferrite and pearlite. Fine and dispersed (Nb, V)C particles are distributed in the grains of ultrafine-grained layers and mid-thickness positions, respectively. The yield strength of the ultrafine-grained layers is 494 MPa, mainly caused by grain ultrarefinement. For the mid-thickness position, the yield strength is 424 MPa. The total strength of the developed SUF steel is larger than 440 MPa. An unexpected phenomenon in which toughness of the ultrafine-grained layers is lower than that of the mid-thickness position is mainly caused by delamination and low elongation.

The electroencephalogram (EEG) signals based on the Brian-computer Interface (BCI) equipment is weak, non-linear, non-stationary and time-varying, so an effective feature extraction method is the key to improving the recognition accuracy. Electrooculogram and electrocardiogram artifacts are common noises in the process of EEG signals acquisition, it seriously affects the extraction of useful information. This paper proposes a processing method on EEG signals by combing independent component analysis (ICA), wavelet transform (WT) and common spatial pattern (CSP). First, the independent component analysis algorithm is used to break the EEG signals into independent components; and then these independent components are decomposed by WT to obtain the wavelet coefficient of each independent source. The soft and hard compromise threshold function is used to process the wavelet packet coefficients. Then the CSP algorithm is used to extract the features of the denoised EEG data. Finally, four common classification algorithms are used for classification to verify the effectiveness of the improved algorithm. The experimental results show that the EEG signals processed by the proposed method has obvious advantages in identify and remove electrooculogram (EOG) and electrocardiogram (ECG) artifacts, meanwhile, it can preserve the neural activity that is missed in the noise component. Cross-comparison experiments also proved that the proposed method has higher classification accuracy than other algorithms.

Urban flood is one of the most frequent and deadly natural disasters in the world, seriously affecting urban sustainability and people's well-being in China. As the largest developing country in the world, China urgently needs to improve its urban flood resilience. Previous studies related to urban flood resilience are mostly focused on its assessment method and simulation. However, few studies directly aim to reveal the influencing factors of urban flood resilience and their inner relationships. In order to make a significant contribution to the long-term improvement of urban flood resilience in the context of global climate change and urbanization, it is crucial to explore the influencing mechanisms of urban flood resilience. This study aims to identify key influencing factors and their interactions on urban flood resilience in China. To this end, a conceptual framework based on Pressure-State-Response model and Social-Economic-Natural Complex Ecosystem theory (PSR-SENCE model) are established and 24 factors are identified within three dimensions. The relationships between the factors are tested using a fuzzy-DEMATEL method. The results reveal that factors in pressure and response dimensions have a greater impact on the whole system, while the factors in the state dimension are more influenced by the other two dimensions. The results identify 14 critical factors, with four detailed influence paths discussed among the different dimensions. Accordingly, the implications for improving urban flood resilience are discussed within the context of the key influencing paths. The study provides a theoretical basis and approach to directly explore how the factors influencing urban flood resilience and proposes specific impact paths and improvement implications.

In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; DEMATEL (Factual Decision Trial and Evaluation Laboratory) is inevitably influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. By combining the network analysis in complex network science, the model defines the global/local causal pathway and the causal pathway’s length in the causal network and takes the causal pathway contribution degree as an indicator to measure the approximate causal effects. The model includes steps such as causal network learning, causal pathway contribution degree calculation, and key influencing factor identification. The model uses the Fast Causal Inference (FCI) algorithm with prior knowledge to learn the global causal network of the complex system and uses the heuristic causal inference to calculate the causal pathway contribution degree. The heuristic method draws on the idea of complex network topology analysis and measures the influence degree between variables by the number and distance of causal pathways. The key influencing factors are finally identified according to the causal pathway contribution degree. Based on the SECOM dataset, we carried out simulation experiments and demonstrated the feasibility and effectiveness of the proposed method.

Abstract Although the overall survival of hepatocellular carcinoma (HCC) patients has been significantly improved, prognostic clinical evaluation remains a substantial problem owing to the heterogeneity and complexity of tumor. A reliable and accurate predictive biomarker may assist physicians in better monitoring of patient treatment outcomes and follow the overall survival of patients. Accumulating evidence has revealed that DTNBP1 plays functional roles in cancer prognosis. Therefore, the expression and function of DTNBP1in HCC was systematically investigated in our study. The expression and prognostic value of DTNBP1 were investigated using the data from Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) cohorts and clinical samples. A series of cellular function assays were performed to elucidate the effect of DTNBP1 on cellular proliferation, apoptosis and metastasis. Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment and Protein–protein interaction (PPI) network construction were performed to screen the genes with highest interaction scores with DTNBP1. Finally, the underlying mechanism was also analyzed using Gene Set Enrichment Analysis (GSEA) and confirmed using RT-qPCR and western blotting. DTNBP1 was upregulated in many types of cancers, especially in HCC. The DTNBP1 expression levels is associated with clinicopathologic variables and patient survival status. The differential expression of DTNBP1 could be used to determine the risk stratification of patients with HCC. DTNBP1 deficiency inhibited cell proliferation and metastasis, but promoted cell apoptosis. Mechanistically, DTNBP1 regulated the cell cycle progression through affecting the expression of cell cycle-related genes such as CDC25A, CCNE1, CDK2, CDC20, CDC25B, CCNB1, and CDK1. DTNBP1, which regulates the cell cycle progression, may be used as a prognostic marker for HCC.

BackgroundNeoadjuvant chemoimmunotherapy (NCIO) is more effective than neoadjuvant immunotherapy alone for pathological response in non-small cell lung cancer (NSCLC) patients, but the processes for determining patient suitability for its implementation are not clear. We aimed to identify the most relevant factors and build a convenient model to select NSCLC patients who would benefit most from NCIO.Methods We retrospectively collected the clinical data of patients with locally advanced NSCLC who received NCIO followed by surgery at our institution between January 2019 and July 2022.ResultsA total of 101 eligible stage IIB-IIIC NSCLC patients were included. After NCIO, all patients successfully underwent surgical resection. A total of 46.53% (47/101) of patients achieved pathological complete response (pCR), and 70.30% (71/101) achieved major pathologic response (MPR). Tumor regression rate (adjusted odds ratio OR = 12.33), PD-L1 expression (adjusted odds ratio (OR) = 9.66), pembrolizumab/nab-paclitaxel–based regimens (adjusted OR = 4.92), and comorbidities (adjusted OR = 0.16) were independently associated with pCR rate (all P < 0.05). Tumor regression rate (adjusted OR = 8.45), PD-L1 expression (adjusted OR = 5.35), and presence of squamous cell carcinoma (adjusted OR = 7.02) were independently associated with MPR rate (all P < 0.05). We established and validated an easy-to-use clinical model to predict pCR (with an area under the curve [AUC] of 0.848) and MPR (with an AUC of 0.847). Of note, the present study showed that CD4+ T-cell count/rate and total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) levels in the peripheral blood of pre-NCIO patients were also significantly correlated with pathological response in univariate analyses.ConclusionsThe tumor regression rate, PD-L1 expression, pembrolizumab/nab-paclitaxel–based regimens, presence of squamous cell carcinoma, and comorbidities were the main influential factors for incidence of pCR/MPR in patients with stage IIB-IIIC NSCLC in the present study. Through predictive models, we can predict who will benefit most from NCIO prior to the emergence of clinical outcomes in locally advanced NSCLC.

BackgroundThere are trillions of microbiota in our intestinal tract, and they play a significant role in health and disease via interacting with the host in metabolic, immune, neural, and endocrine pathways. Over the past decades, numerous studies have been published in the field of gut microbiome and disease. Although there are narrative reviews of gut microbiome and certain diseases, the whole field is lack of systematic and quantitative analysis. Therefore, we outline research status of the gut microbiome and disease, and present insights into developments and characteristics of this field to provide a holistic grasp and future research directions.MethodsAn advanced search was carried out in the Web of Science Core Collection (WoSCC), basing on the term “gut microbiome” and its synonyms. The current status and developing trends of this scientific domain were evaluated by bibliometric methodology. CiteSpace was used to perform collaboration network analysis, co-citation analysis and citation burst detection.ResultsA total of 29,870 articles and 13,311 reviews were retrieved from the database, which involve 42,900 keywords, 176 countries/regions, 19,065 institutions, 147,225 authors and 4,251 journals. The gut microbiome and disease research is active and has received increasing attention. Co-cited reference analysis revealed the landmark articles in the field. The United States had the largest number of publications and close cooperation with other countries. The current research mainly focuses on gastrointestinal diseases, such as inflammatory bowel disease (IBD), ulcerative colitis (UC) and Crohn’s disease (CD), while extra-intestinal diseases are also rising, such as obesity, diabetes, cardiovascular disease, Alzheimer’s disease, Parkinson’s disease. Omics technologies, fecal microbiota transplantation (FMT) and metabolites linked to mechanism would be more concerned in the future.ConclusionThe gut microbiome and disease has been a booming field of research, and the trend is expected to continue. Overall, this research field shows a multitude of challenges and great opportunities.

Prophage sequences are present in most bacterial genomes and account for up to 20% of its host genome. Integration of temperate phages may have an impact on the expression of host genes, while some prophages could turn into the lytic cycle and affect bacterial host biological characteristics. We investigated the role of spontaneous induction prophages in avian pathogenic Escherichia coli (APEC), which is the causative agent of avian colibacillosis in poultry, and considered a potential zoonotic bacterium related to the fact it serves as an armory of extraintestinal pathogenic E. coli. We found that APEC strain DE458 had a high spontaneous induction rate in vivo and in vitro. The released phage particles, phi458, were isolated, purified, and sequenced, and the deletion mutant, DE458Δphi458, was constructed and characterized. Biofilm formation of DE458Δphi458 was strongly decreased compared to that of the wild-type strain (p

Remaining useful life shows extraordinary function in guiding the timely replacement of supercapacitors that reach the service life limit, which has great significance to the security and stability of the energy storage system. In order to more accurately predict the remaining useful life of supercapacitors so as to ensure the reliability of the whole supercapacitor bank, a temporal convolutional network is used. Among them, a residual block can solve the problems of gradient explosion and gradient disappearance, which are widespread in the recurrent neural network. Early stopping technology is used to avoid overfitting, and the Adam algorithm was used to optimize the process of parameter adjustment of the temporal convolutional network. The stability and accuracy of the model prediction were verified by using the capacity attenuation dataset of supercapacitors under different experimental conditions. Meanwhile, to verify the generalization ability of the model, the datasets of supercapacitors at different working conditions without training are input into the temporal convolutional network model. Simulation shows that the temporal convolutional network model exhibits strong robustness and high accuracy in predicting the remaining useful life of supercapacitors.

One of the key issues limiting the application of Al-Mg-Zn-Cu alloys in the automotive industry is forming at a low cost. Isothermal uniaxial compression was accomplished in the range of 300–450 °C, 0.001–10 s−1 to study the hot deformation behavior of an as-cast Al-5.07Mg-3.01Zn-1.11Cu-0.01Ti alloy. Its rheological behavior presented characteristics of work-hardening followed by dynamic softening and its flow stress was accurately described by the proposed strain-compensated Arrhenius-type constitutive model. Three-dimensional processing maps were established. The instability was mainly concentrated in regions with high strain rates or low temperatures, with cracking being the main instability. A workable domain was determined as 385–450 °C, 0.001–0.26 s−1, in which dynamic recovery (DRV) and dynamic recrystallization (DRX) occurred. As the temperature rose, the dominant dynamic softening mechanism shifted from DRV to DRX. The DRX mechanisms transformed from continuous dynamic recrystallization (CDRX), discontinuous dynamic recrystallization (DDRX), and particle-stimulated nucleation (PSN) at 350 °C, 0.1 s−1 to CDRX and DDRX at 450 °C, 0.01 s−1, and eventually to DDRX at 450 °C, 0.001 s−1. The eutectic T-Mg32(AlZnCu)49 phase facilitated DRX nucleation and did not trigger instability in the workable domain. This work demonstrates that the workability of as-cast Al-Mg-Zn-Cu alloys with low Zn/Mg ratios is sufficient for hot forming.

Small ubiquitin-related modifiers (SUMO) represent a class of ubiquitin-like proteins that are conjugated, like ubiquitin, by a set of enzymes to form cellular regulatory proteins, and play key roles in the control of cell proliferation, differentiation, and apoptosis. We found that ginkgolic acid (GA) can significantly reduce cell vitality in a dose- and time-dependent manner and can also accelerate cyto-apoptosis in both Tca8113 and Cal-27 cells. Migration and wound-healing assays were executed to determine the anti-migration effect of GA in oral squamous cell carcinoma (OSCC) cell lines. GA represses transforming growth factor-β1 (TGF-β1)-induced epithelial-mesenchymal transition (EMT) markers in OSCC cell lines. This investigation is the first evidence that GA suppresses TGF-β1-induced SUMOylation of SMAD4. We show that GA affects the phosphorylation of SMAD2/3 protein and the release of SMAD4. In the xenograft mouse model, the OSCC progression was reduced by GA, effectively suppressing the growth of tumors. In addition, siSMAD4 improved cell migration and viability, which was inhibited by GA in Tca8113 cells. GA suppresses tumorigenicity and tumor progression of OSCC through inhibition of TGF-β1-induced enhancement of SUMOylation of SMAD4. Thus, GA could be a promising therapeutic for OSCC. Keywords: small ubiquitin-like modifiers, oral squamous cell carcinoma, ginkgolic acid, proliferation, migration

Lithium-ion batteries stand out from other clean energy sources because of their high energy density and small size. With the increasing application scope and scale of lithium-ion batteries, real-time and accurate monitoring of its state of health plays an important role in ensuring the healthy and stable operation of an energy storage system. Due to the interaction of various aging reactions in the aging process of lithium-ion batteries, the capacity attenuation shows no regularity. However, the traditional monitoring method is mainly based on voltage and current, which cannot reflect the internal mechanism, so the accuracy is greatly reduced. Recently, with the development of electrochemical impedance spectroscopy, it has been possible to estimate the state of health quickly and accurately online. Electrochemical impedance spectroscopy can measure battery impedance in a wide frequency range, so it can reflect the internal aging state of lithium-ion batteries. In this paper, the latest impedance spectroscopy measurement technology and electrochemical impedance spectroscopy based on lithium-ion battery health state estimation technology are summarized, along with the advantages and disadvantages of the summary and prospects. This fills the gap in this aspect and is conducive to the further development of this technology.

The energy consumption of data center cooling systems accounts for a large proportion of total energy consumption. The optimization of airflow organization is one of the most important methods to improve the energy efficiency of cooling systems. The adjustment scale of many current air flow organization methods, however, is too large and does not support the data center’s refined operation. In this paper, a new type of air supply terminal device is proposed, and it could adaptively adjust according to the power of servers in the rack for cold air redistribution. In addition, the corresponding regulation strategy is proposed. A CFD model is established according to field investigation of a real data center in Shanghai to investigate the adjustment range and the energy saving potential of the device. The simulation results indicate that the device can suppress the local hot spots caused by excessive server power to some extent and greatly improve the uniformity of servers exhaust temperature. The case study shows that the device can save energy consumption by 20.1% and 4.2% in mitigating local hot spots compared with reducing supply air temperature and increasing supply air flowrate.

The rapid development of new technologies has made the acquisition and processing of big data much easier and more accessible to various domains including construction management. This trend has resulted in numerous new technical or management issues in the field, as well as increased research needs. Thus, it is very necessary to invest and assess the past, present, and possible future developments of construction management-related studies. This paper provides a comprehensive bibliometric analysis of the articles published in this field over the previous two decades. The seven most popular research themes were identified and discussed with the data adopted in the related studies, including modern technology, waste management, performance management, risk management, project management, knowledge management, and organization management. Typical research data, research approaches, and future research directions were discussed. Emerging topics such as smart technology, sustainability, resilience, and human factors are suggested to be further explored in the domain. The review conducted in this study can provide some insights into what has been done and what can be achieved in future research in the construction management domain towards a data-rich era.

Social media data have been gradually regarded as a prospective social sensor in the transportation domain for capturing road conditions. Most existing social media data-based sensors (SMDbSs) of road conditions, however, rely heavily on lexicon-based methods for information extraction and provide coarse-grained location information. Hence, this work newly devises an SMDbS based on joint relation extraction and entity recognition for sensing road conditions from social media data, which eliminates the reliance on lexicon-based methods and offers finer-grained location information in comparison with existing SMDbSs. This SMDbS development consists of four major steps, including data collection and annotation, data cleansing, two-stage information extraction, and model verification. A tweet dataset in Lexington city is exploited to demonstrate this SMDbS, which shows satisfactory information extraction performance. This study would help facilitate social media data to be an extra information source in the transportation domain.

Patients with germline neurofibromatosis type 1 (NF1) microdeletions frequently exhibit hereditary syndromes such as cardiovascular anomalies and have an increased risk of malignant peripheral nerve sheath tumors (MPNSTs). This study aimed to identify the genes codeleted with SUZ12 that are related to MPNST. We used differential gene expression and enrichment analyses to analyze the SUZ12-mutant and SUZ12-wild-type gene expression profiles in the GSE118186 and GSE66743 datasets in Gene Expression Omnibus (GEO). PPI network analysis combined with MPNST patient survival analysis was used to identify ADCY1, which catalyzes the conversion of ATP to cAMP, as a key gene. Moreover, chromatin immunoprecipitation sequencing (ChIP-Seq) showed that the distribution of H3K27me3 in the ADCY1 promoter region and gene body was significantly reduced in SUZ12-mutant cells. To verify the role of ADCY1 in SUZ12 mutation, we used RNA interference and plasmid transfection to interfere with SUZ12 expression in plexiform neurofibroma (pNF) and MPNST cell lines and then treated the cells with forskolin, IBMX and H89. ERK phosphorylation was accelerated and prolonged after siRNA transfection, especially in ipNF05.5 cells, and the intensity and duration of ERK activation were reduced after SUZ12 overexpression. Importantly, the level of p-ERK was consistent with that of Rap1-GTP. Moreover, H89 completely blocked Rap1 activation and the changes in the p-ERK level after SUZ12 siRNA transfection. In conclusion, our findings suggested that SUZ12 loss potentiates the effects of NF1 mutations by amplifying Ras signaling through the ADCY1/cAMP/Rap1/ERK pathway and that SUZ12 may serve as a therapeutic and prognostic biomarker in NF1-associated neurofibromas.

Aluminum alloy components typically have structural characteristics such as large size and complex shape, making the in situ non-destructive detection of internal residual stress in these structures a challenge that the manufacturing sector has tried to solve. Ultrasonic longitudinal critically refracted (LCR) waves have shown good sensitivity to normal stress in the horizontal direction and could be used to detect the distribution of internal residual stress in components, offering an advantage not shared by other detection methods. In this study, we investigated the propagation mode of LCR waves in a 2A14 aluminum alloy component and established the characterization model of the average normal stress of LCR waves in different depth ranges. The blocking effect of LCR waves by a groove with a depth equal to twice the wavelength was analyzed and experimentally verified using a machined aluminum alloy test specimen. Then, the propagation depths of LCR waves in the aluminum alloy at different frequencies were determined. A load test on a cantilever beam based on the stress depth distribution model was designed, and the stress characterization model and LCR waves’ propagation depth were further verified by the self-developed LCR wave stress detection system. The test results showed that the LCR wave could accurately detect the depth distribution of stress and could serve as a useful tool for evaluating the depth distribution of normal stress inside aluminum alloy components.

The state of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs). Recently, the SOH estimation method based on electrochemical impedance spectroscopy (EIS) has been proven effective. In response to different practical applications, two models for SOH estimation are proposed in this paper. Aiming at based on the equivalent circuit model (ECM) method, a variety of ECMs are proposed. Used EIS to predict the ECM, the results show that the improved method ensures the correctness of the ECM and improves the estimation results of SOH. Aiming at a data-driven algorithm, proposes a convolution neural network (CNN) to process EIS data which can not only extract the key points but also simplifies the complexity of manual feature extraction. The bidirectional long short-term memory (BiLSTM) model was used for serial regression prediction. Moreover, the improved Particle Swarm Optimization (IPSO) algorithm is proposed to optimize the model. Comparing the improved model (IPSO-CNN-BiLSTM) with the traditional PSO-CNN-BiLSTM, CNN-BiLSTM and LSTM models, the prediction results are improved by 13.6%, 93.75% and 94.8%, respectively. Besides that, the two proposed methods are 27% and 35% better than the existing gaussion process regression (GPR) model, which indicates that the proposed improved methods are more flexible for SOH estimation with higher precision.

Abstract Background Escherichia coli is an important pathogen that causes diarrhea in both humans and animals. To determine the relationships between putative virulence factors and pathotypes or host taxa, many molecular studies on diarrhea-associated E. coli have been reported. However, little is known regarding genome-wide variation of E. coli from animal hosts. In this study, we performed whole genome sequencing of 127 E. coli isolates from sheep and swine with diarrhea in China. We compared isolates to explore the phylogenomic relatedness based on host origin. We explored the relationships of putative virulence factors across host taxa and pathotypes. Antimicrobial resistance was also tested. Results The E. coli genomes in this study were diverse with clear differences in the SNP, MLST, and O serotypes. Seven putative virulence factors (VFs) were prevalent (> 95%) across the isolates, including Hcp, csgC, dsdA, feoB, fepA, guaA, and malX. Sixteen putative VFs showed significantly different distributions (P