Search

Abstract Objective The genetic causes of a significant number of patients with cerebellar ataxia remain unsolved. Variations in the ITM2B gene, typically linked to dominantly inherited dementia, can sometimes present with cerebellar ataxia as an early symptom. This study aims to investigate the role of ITM2B variations in a Taiwanese cohort with unsolved cerebellar ataxia. Methods Genetic analysis of ITM2B was performed in 212 unrelated Taiwanese patients with unsolved cerebellar ataxia. Eight short tandem repeat markers flanking ITM2B were genotyped to analyze the associated haplotype. Affected carriers underwent comprehensive clinical evaluations. Results A heterozygous ITM2B variant, c.800G>T (p.(Ter267LeuextTer11)), was identified in three patients. Haplotype analysis demonstrated a shared haplotype linked to this variant in the three families, suggesting a founder effect. The three probands and additional three affected relatives presented with cerebellar ataxia and unsteady gait with an average onset age of 43.2 years. Most participants had no cognitive impairment at symptom onset but experienced memory decline, oculomotor disturbances, lower limb spasticity, and extensor plantar responses within 2–5 years. Magnetic resonance imaging and spectroscopy revealed progressive extension of white matter hyperintensity over periventricular and subcortical regions, subtle hippocampal atrophy, preserved cerebellar volumes, and decreased N‐acetylaspartate/creatine ratio over the vermis. Interpretation ITM2B mutations accounted for 1.4% of cerebellar ataxia cases in the Taiwanese cohort, with patients carrying ITM2B c.800G>T descending from a common ancestor. This study underscores the importance of considering ITM2B variations as a potential cause of cerebellar ataxia, even in the absence of dementia at the initial presentation.

This study investigated the influence of aggregate powder compositions (Ti–Al-Graphite/Ti–Al–TiC) and processing variables on microstructure, phase composition, and tribological properties of atmospheric plasma sprayed Ti–Al–C coatings. The coatings exhibited a distinct layered structure, with variations in phase composition and microstructure attributed to differences in agglomerated powder and spraying conditions. Incorporating TiC in the agglomerated powder significantly improved the hardness and bonding strength of the coatings. High-temperature friction and wear tests demonstrated superior wear resistance of the TiC-containing coatings compared to those with graphite. The formation of a protective oxide layer, consisting of TixAly and Ti2AlC phases, during frictional sintering oxidation led to enhanced wear resistance at elevated temperatures. The mechanisms of friction and wear in these coatings involved oxidative wear, adhesive wear, and fatigue wear. The results suggest that the composition of the Ti–Al–C coatings can be tailored to optimize their high-temperature tribological performance for various applications.

Abstract Argali stands as the largest species among wild sheep in Central and East Asia, with a concerning rate of decline estimated at 30%. The intraspecific taxonomy of argali remains contentious due to limited genomic data and unclear geographic separation. In this study, we constructed a chromosome-level genome assembly and annotation for the Tibetan argali (O. a. hodgsoni), together with population genomic resequencing of 32 individuals representing four subspecies. The contig-level genome was 2.64 Gb in size, with a contig N50 length of 71.69 Mb and an estimated genomic completeness of 96.01%. Using Hi-C sequencing data scaffolding, 99.90% of initially assembled sequences were mapped and oriented onto 28 pseudo-chromosomes except the Y chromosome. Annotation uncovered 21,564 protein-coding genes and 46.38% repeat sequences. The average coverage of the population resequencing data was 23.74 with mean mapping ratio up to of 97.19%. The high-quality genome assembly and annotation of the Tibetan argali, coupled with the high-depth population genomic data, will serve as a valuable genetic resource for studies on the taxonomy and conservation of argali.

Background The incidence of delay language development in early childhood is high, and the early recognition rate is low, which has a significant negative impact on the early childhood development of other dimensions. The family cognitive environment is a key factor affecting early childhood language development. Objective This study aimed to explore the influence of family cognitive environment on early childhood language development, and provide theoretical basis for promoting early childhood language development from the perspective of community intervention on family cognitive environment. Methods A retrospective case-control study was conducted on 4 307 children who were admitted to a community child healthcare department in Shanghai from 2018 to 2020. The Shanghai Pediatric Development Screening Scale Ⅱ (DenverⅡ) was used for developmental screening, which was conducted at the child's age of 1, 2, and 3 years old. A total of 172 children with delay language development were selected as the case group, and 516 children with normal language development were included in the control group, which was determined by the matching factor of age, with a ratio of the number of children in the case and control groups being 1∶3. The following information of children in both groups were collected, including children's basic birth characteristics, parental demographic characteristics, maternal pregnancy and childbirth characteristics, and family cognitive environment characteristics. The Logistic regression analysis was used to explore the influencing factors of delay early childhood language development. Results A total of 172 children with delayed early language development had an incidence rate of 3.99%, among them ,1-year-old, 2-year-old, and 3-year old children accounted for 33.14% (57 cases), 53.49% (92 cases), and 13.37% (23 cases), respectively. The proportions of male children, children with premature birth, and children with maternal education of high school and below were higher in the case group than those in the control group (P

Abstract This paper had conducted tensile shear tests on single-lap joints (SLJs)bonded structures of carbon fiber reinforced resin matrix (CFRP) composite laminates with different overlap lengths, overlap widths, overlap model, adherend material, and adhesive layer thicknesses under two environments: room temperature dry state (RTD) and elevated temperature wet state (ETW). The failure modes were observed, and load–displacement curves were obtained. The microscopic morphology of the fracture surface was observed by scanning electron microscope (SEM). At the same time, a finite element simulation model was established to simulate the damage initiation and evolution process between layers and in the adhesive layer, and analyze the distribution laws of peel stress and shear stress in the adhesive layer. Through the combination of test data and simulation results, the influences of geometric parameters, material parameters and environmental parameters on the structure were explored, and the joint failure mechanism was revealed. Finally, the ACO-BP neural network was used to optimize the geometric parameters through test data. The research results showed that the geometric parameters of the structure mainly affect the bearing capacity and failure type. Reducing the overlap length and increasing the overlap width within a certain range can weaken the peeling phenomenon, so that a smaller overlap area has a higher shear strength. The material parameters of the adherend mainly affect the stress distribution law and stress transfer process of the adhesive layer in the overlap area. The joint mainly bore shear stress and peel stress, and shear stress is the main cause of damage initiation. When the types of adherend materials are different, the stress distribution law shows obvious asymmetric offset. The lap model mainly affects the location distribution of the failure area, and the environmental parameters mainly affect the area proportion relationship of various fracture forms in the mixed failure mode.

The solid backfilling mining method has great advantages in handling coal-based solid waste and controlling surface subsidence, but its low backfilling efficiency, long succession time and high labour intensity constrain the development of green backfilling mining. Aiming at the endogenous driving force of solid backfilling technology upgrading, the urgent demand of industry development and the inevitable trend of mine intelligent construction, the solid intelligent backfilling method analysed by video AI algorithm is proposed. Initially, this paper examines the essence and challenges of intelligent solid backfilling methods, establishes the system framework for analyzing the intelligent solid backfilling mining method using video AI algorithms, elucidates the operational principles and implementation process of the video AI algorithm, and outlines the capabilities that can be achieved through this algorithm. It analyzes the influencing factors on key backfilling equipment under different geological conditions. The hydraulic support framework modeling was conducted using Creo to simulate its movement in various working conditions; corresponding control criteria and paths were provided, along with a designed control algorithm flow for key backfilling equipment across different processes. Based on the characteristics of video algorithms and their pros and cons, an image recognition algorithm is initially chosen; after training and adjusting the model, optimal algorithms and corresponding parameters are determined. Through an analysis of application effects on an coal mine fill surfaces, it is found that SVM evaluation indices outperform other algorithms, indicating excellent performance in discriminating working conditions with high accuracy and reliability. The research can realize the identification and regulation of abnormal working conditions of key backfilling equipment mechanism, improve backfilling efficiency, identify the positional parameters of the mechanism, and display the effect of backfilling space tamping, which can provide theoretical guidance for the research, development and application of solid intelligent backfilling and mining technology analyzed by video AI algorithm.

Abstract Objective Expand genetic screening for atypical Type I sialidosis (ST‐1) could address its underdiagnosed in both progressive myoclonic ataxia (PMA) and ataxia patients. To evaluate the potential founder effect of mutation in the population. Methods We enrolled 231 patients with PMA or ataxia from the First Affiliated Hospital of Fujian Medical University. Through Whole Exome Sequencing and Sanger sequencing, we identified the causative gene in patients. Haplotype analysis was employed to explore a potential founder effect of the NEU1 c.544A>G mutation. Results A total of 31 patients from 23 unrelated families were genetically diagnosed with ST‐1. A significant 80.6% of these patients were homozygous for the c.544A>G mutation. We discovered six different NEU1 variants, including two novel mutations: c.951_968del and c.517T>G. The mean age of onset was 18.0 ± 7.1 years. The clinical spectrum of ST‐1 featured ataxia and myoclonus as the most common initial symptoms. Over 40% suffered from controlled generalized tonic–clonic seizures. Mobility and independence varied greatly across the cohort. Cherry‐red spots were rare, occurring in just 9.5% (2/21) of patients. Brain MRIs were typically unremarkable, except for two patients with unusual findings. EEGs showed diffuse paroxysmal activity in 17 patients. The c.544A>G mutation in NEU1 is a founder variant in Fujian, with a unique haplotype prevalent in East Asians. Interpretation ST‐1 should be suspected in patients with PMA or ataxia in Southeast China, even without macular cherry‐red spots and seizures, and the premier test could be a variant screening of the founder variant NEU1 c.544A>G.

Abstract Hypertension is a primary risk factor for the onset of cardiocerebrovascular diseases, leading to increased mortality. In the early stages of hypertension, changes in the diameter of the retinal arteries and veins are closely observed. Therefore, the automatic segmentation of these arteries and veins in retinal fundus images is crucial for hypertension monitoring. However, current deep learning based methods still fail to generate semantically consistent segmentation result, especially for narrow blood vessel segments. Moreover, the pixels near the vessel edge are prone to be missclassified by current methods, struggling to obtain edge-preserving segmentation results. These critical issues severely hinder downstream applications, which require accurate measurement of artery/vein diameter. First, to alleviate the issue of lacking semantic consistency for vessel segments, we propose a long-range spatial dependency modeling module to learn to model the long-range spatial dependency. On this basis, a multi-level edge guided spatial aggregation module is further presented to enhance the ability to accurately classify the edge pixels, generating edge-preserving results. Extensive experimental results on the widely used DRIVE dataset and our constructed dataset (HFC-50) show the superiority of the proposed model over state-of-the-art methods. Our method achieves balanced accuracy of 95.7% on DRIVE dataset, outperforming all state-of-art methods. Finally, in order to directly demonstrate the benefit of more accurate retinal artery/vein vessel segmentation for the measurement of the ratio of artery and vein, we quantitatively evaluate the computed artery/vein ratio for hypertension patients, with the artery/vein segmentation results generated by our proposed method.

Abstract Massive multiple‐input multiple‐output and non‐orthogonal multiple access (MIMO‐NOMA) with low‐resolution analog‐to‐digital converters (ADCs) have been widely considered for the next‐generation wireless communication systems. However, the performance of the system including power‐scaling law has not been well investigated for the practical low complexity receivers. Employing the additive quantization noise model, we derive asymptotic approximate expressions of the spectrum efficiency for the system with group successive interference cancellation (GSIC) receivers over Rician fading channels. Based on these approximations, we conduct a unified asymptotic analysis for the system with linear, SIC, and GSIC receivers. The analysis reveals the transmission power can be scaled by the number of antennas for the system with GSIC receivers and shows the effects of crucial parameters including the number of groups, resolution bits, and antennas on the performance. Given a quality of service, the minimum data transmission power is also calculated for each user and the corresponding approximate power allocation is derived. The asymptotic analysis and the accuracy of the power allocation approximation are then verified by simulation results. Numerical results also demonstrate that high spectrum efficiency and energy efficiency can be achieved by the system with medium‐resolution ADCs and low complexity maximum ratio combining‐GSIC receivers with a small number of groups.

Through millions of years of evolution, bones have developed a complex and elegant hierarchical structure, utilizing tropocollagen and hydroxyapatite to attain an intricate balance between modulus, strength, and toughness. In this study, continuous fiber silk composites (CFSCs) of large size are prepared to mimic the hierarchical structure of natural bones, through the inheritance of the hierarchical structure of fiber silk and the integration with a polyester matrix. Due to the robust interface between the matrix and fiber silk, CFSCs show maintained stable long-term mechanical performance under wet conditions. During in vivo degradation, this material primarily undergoes host cell-mediated surface degradation, rather than bulk hydrolysis. We demonstrate significant capabilities of CFSCs in promoting vascularization and macrophage differentiation toward repair. A bone defect model further indicates the potential of CFSC for bone graft applications. Our belief is that the material family of CFSCs may promise a novel biomaterial strategy for yet to be achieved excellent regenerative implants.

Abstract In this study, a structural adhesive was used to bond unidirectional prepreg and fiber fabric in a single lap joint. The mechanical properties of the structural adhesive were investigated under room temperature dry state (RTD) and elevated temperature wet state (ETW, 71 ℃/85% RH), and different adhesive layer thicknesses (0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm). The fracture surfaces of the bonded joints were examined using scanning electron microscopy (SEM), and finite element simulations were conducted to observe the failure modes and failure paths. Additionally, the specimens were immersed in water and hydraulic oil, and their tensile shear strength was tested to evaluate their liquid sensitivity. The experimental results indicated that with increasing adhesive layer thickness, the strength of the specimens decreased by 21% in the RTD and by 52% in the ETW. The strength differences between different environments were minimal for adhesive layer thicknesses of 1 mm and 1.5 mm. The shear strength of the specimens decreased after immersion in water and hydraulic oil, with reductions of 43.78% and 39.21%, compared to the room temperature dry respectively. SEM observations of the bonded joint sections revealed that the primary failure modes were adherend failure and adhesive layer failure. Finite element simulations indicated that fiber tearing and crack initiation occurred in stress concentration areas during loading, leading to structural failure.

Abstract With the development of industry and modern manufacturing, nondegradable low-density polyethylene (LDPE) has been widely used, posing a rising environmental hazard to natural ecosystems and public health. In this study, we isolated a series of LDPE-degrading fungi from landfill sites and carried out LDPE degradation experiments by combining highly efficient degrading fungi in pairs. The results showed that the mixed microorganisms composed of Alternaria sp. CPEF-1 and Trametes sp. PE2F-4 (H-3 group) had a greater degradation effect on heat-treated LDPE (T-LDPE). After 30 days of inoculation with combination strain H-3, the weight loss rate of the T-LDPE film was approximately 154% higher than that of the untreated LDPE (U-LDPE) film, and the weight loss rate reached 0.66 ± 0.06%. Environmental scanning electron microscopy (ESEM) and Fourier transform infrared spectroscopy (FTIR) were used to further investigate the biodegradation impacts of T-LDPE, including the changes on the surface and depolymerization of the LDPE films during the fungal degradation process. Our findings revealed that the combined fungal treatment is more effective at degrading T-LDPE than the single strain treatment, and it is expected that properly altering the composition of the microbial community can help lessen the detrimental impact of plastics on the environment.

The microstructure, texture, and mechanical properties of as-extruded and cold-rolled Mg–8Li–3Al–2Zn-1Gd-0.2Zr (LAZ832-1Gd-0.2Zr) alloys were investigated with the aim of strengthening the alloy by combining hot extrusion and cold rolling. Cold rolling does not change the phase composition of as-extruded LAZ832-1Gd-0.2Zr alloy, but introduces a larger number of dislocations and twins into alloy matrix. Cold rolling leads to the weakening of the original {0001} basal and {112‾0}-{011‾0} prismatic textures in α-Mg matrix, as well as the α and γ fiber textures in β-Li matrix. But new slip systems that are favorable for cold rolling are activated, resulting in the formation of {033‾8} pyramidal and new prismatic ({112‾0}⟨101‾4⟩ and {101‾0}⟨21‾1‾10⟩) texture components. Cold rolling further strengthens LAZ832-1Gd-0.2Zr alloy on the basis of hot extrusion, mainly through work hardening effect caused by introducing a large number of dislocations into α-Mg matrix. When the rolling reduction is 45%, the yield strength and tensile strength of LAZ832-1Gd-0.2Zr alloy are increased to 269.8 ± 4.0 and 308.6 ± 4.1 MPa, respectively, and the elongation is higher than 15%.

Abstract MicroRNAs (miRNAs) are 18–25 nucleotides long, single-stranded, non-coding RNA molecules that regulate gene expression. They play a crucial role in maintaining normal cellular functions and homeostasis in organisms. Studies have shown that miR-124-3p is highly expressed in brain tissue and plays a significant role in nervous system development. It is also described as a tumor suppressor, regulating biological processes like cancer cell proliferation, apoptosis, migration, and invasion by controlling multiple downstream target genes. miR-124-3p has been found to be involved in the progression of various kidney diseases, including diabetic kidney disease, calcium oxalate kidney stones, acute kidney injury, lupus nephritis, and renal interstitial fibrosis. It mediates these processes through mechanisms like oxidative stress, inflammation, autophagy, and ferroptosis. To lay the foundation for future therapeutic strategies, this research group reviewed recent studies on the functional roles of miR-124-3p in renal diseases and the regulation of its downstream target genes. Additionally, the feasibility, limitations, and potential application of miR-124-3p as a diagnostic biomarker and therapeutic target were thoroughly investigated.

Abstract Membranous nephropathy (MN) is a leading cause of nephrotic syndrome in adults and is associated with high rates of end-stage renal disease. Early detection and precise interventions are crucial for improving patient prognosis and quality of life. However, the current diagnosis primarily relies on renal biopsies and traditional biomarkers, which have limitations. Additionally, targeted therapeutic strategies are lacking. Exosomes, small vesicles that facilitate intercellular communication, have emerged as potential noninvasive diagnostic markers due to their stability, diverse cargo, and rapid detectability. They also hold promise as carriers for gene and drug delivery, presenting innovative opportunities in renal disease prognosis and treatment. However, research on exosomes in the context of idiopathic membranous nephropathy (IMN) remains limited, with a focus on exploring urinary exosomes as IMN markers. In this review, we summarize the current status of MN diagnosis and treatment, highlight the fundamental characteristics of exosomes, and discuss recent advancements in their application to IMN diagnosis and therapy. We provide insights into the clinical prospects of exosomes in IMN and acknowledge potential challenges. This article aims to offer forward-looking insights into the future of exosome-mediated IMN diagnosis and treatment, indicating a revolutionary transformation in this field. Graphical Abstract

Abstract Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7–84.1%), 69.9% (95% CI 69.8–69.9%), 86.5% (95% CI 81.6–91.3%), 61.5% (95% CI 56.6–66.4%), 53.2% (95% CI 47.7–58.7%), 65.9% (95% CI 60.2–71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.

Abstract Industrial CO2 electrolysis via electrochemical CO2 reduction has achieved progress in alkaline solutions, while the same reaction in acidic solution remains challenging because of severe hydrogen evolution side reactions, acid corrosion, and low target product selectivity. Herein, an industrial acidic CO2 electrolysis to pure HCOOH system is realized in a proton-exchange-membrane electrolyzer using an acid-tolerant Bi-based metal-organic framework guided by a Pourbaix diagram. Significantly, the Faradaic efficiency of HCOOH synthesis reaches 95.10% at a large current density of 400 mA/cm2 with a high CO2 single-pass conversion efficiency of 64.91%. Moreover, the proton-exchange-membrane device also achieves an industrial-level current density of 250 mA/cm2 under a relatively low voltage of 3.5 V for up to 100 h with a Faradaic efficiency of 93.5% for HCOOH production, which corresponds to an energy consumption of 200.65 kWh/kmol, production rate of 12.1 mmol/m2/s, and an energy conversion efficiency of 38.2%. These results will greatly aid the contemporary research moving toward commercial implementation and success of CO2 electrolysis technology.

Periodontal disease is a highly prevalent disease worldwide that seriously affects people’s oral health, including gingivitis and periodontitis. Although the current treatment of periodontal disease can achieve good control of inflammation, it is difficult to regenerate the periodontal supporting tissues to achieve a satisfactory therapeutic effect. In recent years, due to the good tissue regeneration ability, the research on Mesenchymal stromal/stem cells (MSCs) and MSC-derived exosomes has been gradually deepened, especially its ability to interact with the microenvironment of the body in the complex immunoregulatory network, which has led to many new perspectives on the therapeutic strategies for many diseases. This paper systematically reviews the immunomodulatory (including bone immunomodulation) properties of MSCs and their role in the periodontal inflammatory microenvironment, summarizes the pathways and mechanisms by which MSCs and MSC-EVs have promoted periodontal regeneration in recent years, lists potential areas for future research, and describes the issues that should be considered in future basic research and the direction of development of “cell-free therapies” for periodontal regeneration.

The imbalanced soil nutrient status caused by the long-term monoculture of flue-cured tobacco are a concern. The tobacco–maize relay intercropping, widely used in Yunnan, may improve soil nutrients by enhancing the soil microbial community, but this remains unexplored. This study employed high-throughput sequencing technology to examine soil microbial diversity under tobacco monoculture and tobacco–maize relay intercropping, using the varieties Hongda and K326, respectively. The results indicated that tobacco–maize relay intercropping significantly enhanced root biomass compared to tobacco monoculture, with no significant effect on aboveground biomass. This intercropping treatment also significantly improved soil physicochemical properties, including soil pH, total phosphorus, available phosphorus, and available potassium, which was associated with an increase in the soil microbial community (as indicated by the Chao1 and Shannon indices). Specifically, the abundance of arbuscular mycorrhizal fungi, Nitrospira, and Acidobacteria increased, but the abundance of Chloroflexi decreased. Therefore, these findings suggest that tobacco-maize relay intercropping can improve soil physicochemical properties and enhance soil nutrient supply.

Under hygrothermal environments, the structural stability and strength of all-fiber composite aircraft are significantly affected during long-term flight use. The wing skin, as a critical structural component, plays a vital role in bearing and transmitting aerodynamic loads. Therefore, it is crucial to investigate the structural compressive stability and strength of the wing skin throughout the aircraft's entire life cycle under these conditions. This study employs a real wing carbon fiber foam sandwich structure to investigate the compressive stability and strength of the wing skin structure of a new energy aircraft under actual flight conditions, specifically during the entire process of the room temperature dry state (RTD) and elevated temperature wet state (ETW). Initially, three-point bending tests were conducted on carbon fiber reinforced polymer (CFRP) laminates, foam cores, and CFRP reinforced foam sandwich structures. The CFRP laminates fully rebounded after bending damage in both the RTD and ETW environments. While CFRP reinforced foam sandwich structures also rebounded fully in the RTD environment, their rebound performance diminished in hygrothermal conditions due to the thermoplastic mobility of the foam cores, resulting in only weak rebound capabilities. In hygrothermal environments, the thermoplastic mobility of the foam core leads to diminished resilience after bending damage, resulting in only weak rebound capabilities. Subsequently, compressive instability tests were conducted on the wing skin foam sandwich structure. The results indicated that the basic test study effectively predicted the structural test outcomes. Structural components in the RTD environment exhibited overall flexural instability under compressive load, with damage morphology resembling a circular curve; the damaged specimens fully rebounded after unloading. Conversely, specimens in the ETW environment displayed localized instability, characterized by a wrinkled damage profile, resulting in only weak rebound capabilities after unloading.

The composite structure wick has been proven to significantly enhance the heat transfer performance of vapor chambers. Aluminum vapor chambers offer advantages such as being lightweight and cost-effective, meeting the requirements for lightweight cooling in fields like aviation. This study presented two novel composite porous wick structures with regular-shaped pores, designed and controlled via selective laser melting (SLM): the vertical square pore composite wick structure and the round-vertical square pore composite wick structure. A water-cooling test platform was built to investigate the effects of parameters such as wick structure thickness, round pore diameter, and round pore depth on the heat transfer performance and thermal resistance of the vapor chambers. The results indicate that the optimal thickness for the vertical square pore composite wick structure was 1.5 mm, yielding a minimum thermal resistance of 0.04 K/W. When the thickness was below 1.5 mm, it caused a reduction in the density of the vaporized core and insufficient liquid transport capacity. Conversely, when the thickness exceeded 1.5 mm, the primary limiting factor for heat transfer shifts to vapor-liquid flow resistance. Furthermore, the round-vertical square pore composite wick structure can provide further improvements in vapor-liquid transport within the structure.

Hypermetabolism is a prominent characteristic of ALS patients. Aberrant activation of AMPK, an energy sensor regulated by adiponectin, is known to cause TDP-43 mislocalization, an early event in ALS pathogenesis. This study aims to evaluate the association between key energy mediators and clinical severity in ALS patients. We found that plasma adiponectin levels were significantly higher in ALS patients with ALSFRS-R scores below 38 compared to controls (p = 0.047). Additionally, adiponectin concentration was inversely correlated with ALSFRS-R scores (p = 0.021). Immunofluorescence staining of PBMCs revealed negative associations between AMPK activation, TDP-43 mislocalization, and ALSFRS-R scores. We then examined the hypothesis that adiponectin may activate the AMPK-TDP-43 axis in motor neurons. Our results demonstrated that adiponectin treatment of NSC34 cells and HiPSC-MNs induced AMPK activation and TDP-43 mislocalization in an adiponectin receptor-dependent manner. Collectively, these findings suggest that elevated plasma adiponectin may enhance AMPK activation, leading to TDP-43 mislocalization in both PBMCs and motor neurons of ALS patients. This highlights the potential involvement of the adiponectin-AMPK-TDP-43 axis in the dysregulated energy balance observed in ALS.

Sprayed fireproofing materials in underground coal mine roadways are limited due to their poor adhesion, easy-to-crack, easy-to-fall off and poor sealing effect. This study therefore develops a fireproofing spraying material with fly ash as aggregate, ordinary silicate cement, sulphur-aluminate cement and gypsum as base material, and re-dispersible latex powder(VAE)as additive. Specifically, we conducted the uniaxial compressive strength test and used coal rock multiphase multi-field triaxial dynamic seepage experimental system, and scanning electron microscopy to study the effects of dispersible latex powder on the adhesive, cementation, mechanical, and blocking properties of the spray material. The results show that adding dispersible latex powder could promote the formation of filamentary bonding bridges in the hydration product, and realize the riveting and bridging of the binder bridges with the hydration products to form a dense network structure. This improves the cracking resistance of the spray material and enhances the material's adhesion, sealing and cementing properties when combined with coal. Adding 1 % VAE spraying material can fully encapsulate the coal body, forming a dense coating on the surface of the coal body, effectively isolating the air from contact with the coal body, thus preventing spontaneous combustion of coal. The proposed materials are useful in preventing spontaneous combustion of coal at the top of the roadway.

Abstract Targeting ferroptosis-related pathway is a potential strategy for treatment of lung cancer (LC). Consequently, exploration of ferroptosis-related markers is important for treating LC. We collected LC clinical data and mRNA expression profiles from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained through FerrDB database. Expression analysis was performed to obtain differentially expressed FRGs. Diagnostic and prognostic models were constructed based on FRGs by LASSO regression, univariate, and multivariate Cox regression analysis, respectively. External verification cohorts GSE72094 and GSE157011 were used for validation. The interrelationship between prognostic risk scores based on FRGs and the tumor immune microenvironment was analyzed. Immunocytochemistry, Western blotting, and RT-qPCR detected the FRGs level. Eighteen FRGs were used for diagnostic models, 8 FRGs were used for prognostic models. The diagnostic model distinguished well between LC and normal samples in training and validation cohorts of TCGA. The prognostic models for TCGA, GSE72094, and GSE157011 cohorts significantly confirmed lower overall survival (OS) in high-risk group, which demonstrated excellent predictive properties of the survival model. Multivariate Cox regression analysis further confirmed risk score was an independent risk factor related with OS. Immunoassays revealed that in high-risk group, a significantly higher proportion of Macrophages_M0, Neutrophils, resting Natural killer cells and activated Mast cells and the level of B7H3, CD112, CD155, B7H5, and ICOSL were increased. In conclusion, diagnostic and prognostic models provided superior diagnostic and predictive power for LC and revealed a potential link between ferroptosis and TIME.

Accurately grasping the evolution characteristics of the water-conducting fracture zone in the mining overburden is an important basis for preventing the occurrence of water inrush disasters in the working face, especially for the high strength mining conditions in the western mines. Taking the 201 west wing area of Yushuwan Coal Mine as the background, taking the uniaxial compressive strength of each sandstone layer, RQD, and thickness of sandstone layer as the system cluster analysis indexes, this paper proposes a geological generalization method for thick bedrock, systematically studies the influence of different bedrock layer thickness on the development height, fracture angle, frequency, and joint strain energy evolution law of the water-conducting fracture zone, and determines the quantitative relationship between the height of the water-conducting fracture zone and the total joint strain energy. The research results show that: the development height of the hydraulic fracture zone simulated based on the geological generalization method presents a log-positive correlation increasing trend with the increase of the bedrock thickness, and the increasing rate gradually decreases. This law is verified by comparing with the measured results of the adjacent mines; with the increase of the thickness of bedrock, the development frequency of vertical cracks in the water-conducting fracture zone increases significantly, while the horizontal cracks in the water-conducting fracture zone increase, which indicates that the increase of the thickness of bedrock has a certain inhibition effect on the initiation and expansion of horizontal cracks in the water-conducting fracture zone; the development height of the water-conducting fracture zone is closely related to the energy release degree of the overlying rock. It increases exponentially with the increase of the maximum strain energy and the total strain energy of the joint, and the increasing rate gradually increases.

The underground working environment of coal mines is complex, with unfavorable factors such as low brightness of artificial light sources, high dust content, and high water vapor density. This leads to difficulties in extracting features and low accuracy of object recognition and positioning when existing object detection algorithms are applied to coal mines. An object detection algorithm for low illumination environments in coal mines is proposed, which consists of an low-light mine image enhancement module LMIENet and a object detection module. The image enhancement module is used to improve the image quality of the original image, restore various image information, and then use a target detection network to perform specific target detection on the enhanced image, effectively improving the accuracy of detection. In the image enhancement module, a lightweight enhancement parameter prediction network is designed with reference to the zero reference depth curve estimation algorithm, and the pixel level enhancement parameter matrix is calculated for image quality enhancement and brightness adjustment of low light images. The network implicitly measures the image enhancement effect through the designed non-reference loss function, and guides the network to conduct unsupervised learning, Enable the network to adaptively enhance the image quality of the original image without relying on paired datasets. In the object detection module, the YOLO v8n object detection model is adopted, which has a lightweight model size and high flexibility to avoid excessive overall model complexity; Using Focal EIoU Loss to improve regression loss, accelerate model convergence, and improve model detection accuracy. The experimental results show that compared with classic object detection algorithms such as Faster R–CNN, SSD, RetinaNet, etc., the proposed algorithm performs well on the self-made coal mine object detection dataset, and is effective in object detection in low light environments mAP@0.5 reaches 98.0%, mAP@0.5∶0.95 reaches 64.8%, and the running time of a single frame image in the experimental environment is only 11 ms, which is superior to other comparison methods. It is proven that the proposed algorithm can effectively achieve object detection in low illumination and complex environments in coal mines, with short time consumption and high computational efficiency.