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Abstract Myofibroblasts accumulation contributes to airway remodeling, with the mechanisms being poorly understood. It is steroid-insensitive and has not been therapeutically targeted in asthma. In this study, we explored the potential of yes-associated protein (YAP) as a therapeutic target for myofibroblasts formation in asthma, by revealing the novel role and mechanisms by which YAP activation in type II alveolar epithelial (ATII) cells promotes the fibroblast-to-myofibroblast transition in vitro and in vivo. By performing immunofluorescence staining, we showed that myofibroblasts were increased in the bronchial walls and alveolar parenchyma in clinical asthmatic and house dust mite (HDM)-induced mouse lung samples. This was accompanied by YAP overexpression and nuclear translocation in ATII cells, and connective tissue growth factor (CTGF) upregulation. In vitro, HDM or combination of rhIL-1β with rhTNF-α upregulated and activated YAP in human primary ATII cells and A549 cells, but not in the bronchial epithelial cells, BEAS-2B. This effect was mediated by F-actin polymerization and could be suppressed by pretreatment with latrunculin A but not budesonide. Inhibition of YAP/transcriptional coactivator with PDZ-binding motif (TAZ) in A549 cells by pretreatment with YAP/TAZ siRNA or verteporfin, but not budesonide, impaired the fibroblast-to-myofibroblast transition in vitro. In vivo, verteporfin partly or completely prevented HDM-induced bronchial or alveolar myofibroblast accumulation, and significantly suppressed CTGF expression and collagen deposition in mouse lungs, without profoundly affecting airway inflammation. Our results provide novel mechanistic insights into airway remodeling, and holds promise for the development of novel therapeutic strategies. Graphical abstract

Abstract Self-rectifying memristive devices have emerged as promising contenders for low-power in-memory computing, presenting numerous advantages. However, characterizing the functional behavior of passive crossbar arrays incorporating these devices remains challenging due to sophisticated parasitic currents stemming from rich memristive dynamic behavior. Conventional methods using read margin assessments to evaluate functional behavior in passive crossbars are hindered by the voltage divider effect from the pull-up resistor. In this study, we propose a novel performance metric, $$\Delta$$ Δ SC, harnessing sneak path currents to assess functional behavior. Through the application of a pair of negative rectification factors, $$\text {RF}_\text {n, L}$$ RF n, L and $$\text {RF}_\text {n, H}$$ RF n, H , we comprehensively delineate dynamic rectification behavior in both positive and negative bias regimes, as well as in low-resistance state and high-resistance state, deviating from conventional metrics such as on/off ratios, nonlinearity, and rectifying factors. Notably, $$\Delta$$ Δ SC provides a quantitative evaluation of the interaction between sneak path currents and read margin, demonstrating its efficacy and addressing a pivotal research gap in the field. For instance, employing self-rectifying BiFeO $$_3$$ 3 memristive cells featuring $$\text {RF}_\text {n, L}$$ RF n, L = 1.22E3 and $$\text {RF}_\text {n, H}$$ RF n, H = 9.27, we showcase the successful functional performance of a passive crossbar array, achieving $$\Delta$$ Δ SC < 2.19E−2, while ensuring a read margin > 0.

ABSTRACT Background and Aims Adolescent major depressive disorder (MDD) is prevalent globally but often goes unnoticed due to differences in symptoms compared to adult criteria. Analyzing the brain from a network perspective provides new insights into higher‐level brain functions and its pathophysiology. This study aimed to investigate changes in the topological organization of functional networks in adolescents with first‐episode, treatment‐naïve MDD. Method The study included 23 adolescents with depression and 27 matched healthy controls (HCs). Resting‐state functional MRI (rs‐fMRI) was conducted, and whole‐brain functional networks were constructed. Graph theory analysis was used to evaluate network topological properties. A machine‐learning multivariate diagnostic model was developed using network metrics associated with depression severity. Results Both the MDD and HC groups displayed small‐world topology, with male MDD patients showing reduced global clustering efficiency (Cp). The nodal Cp (NCp) and local efficiency (NLE) in the bilateral pallidum were significantly positively correlated with depression severity. In contrast, nodal efficiency (NE) in the left medial orbital superior frontal gyri (ORBsupmed) showed a negative correlation with disease severity. A machine‐learning multivariate model using regional network topological features produced an AUROC of 0.71 (95% CI: 0.54–0.92) and an F1 score of 0.65, successfully differentiating adolescent MDD from HCs. Conclusion Our findings suggest disruptions in small‐world topology in both global and local brain networks in adolescent depression. These abnormal nodal properties may serve as novel neural markers of the disorder.

IntroductionThe brain’s spontaneous neural activity can be recorded during rest using resting state functional magnetic resonance imaging (rs-fMRI), and intricate brain functional networks and interaction patterns can be discovered through correlation analysis. As a crucial component of rs-fMRI analysis, effective connectivity analysis (EC) may provide a detailed description of the causal relationship and information flow between different brain areas. It has been very helpful in identifying anomalies in the brain activity of depressed teenagers.MethodsThis study explored connectivity abnormalities in brain networks and their impact on clinical symptoms in patients with depression through resting state functional magnetic resonance imaging (rs-fMRI) and effective connectivity (EC) analysis. We first introduce some common EC analysis methods, discuss their application background and specific characteristics.ResultsEC analysis reveals information flow problems between different brain regions, such as the default mode network, the central executive network, and the salience network, which are closely related to symptoms of depression, such as low mood and cognitive impairment. This review discusses the limitations of existing studies while summarizing the current applications of EC analysis methods. Most of the early studies focused on the static connection mode, ignoring the causal relationship between brain regions. However, effective connection can reflect the upper and lower relationship of brain region interaction, and provide help for us to explore the mechanism of neurological diseases. Existing studies focus on the analysis of a single brain network, but rarely explore the interaction between multiple key networks.DiscussionTo do so, we can address these issues by integrating multiple technologies. The discussion of these issues is reflected in the text. Through reviewing various methods and applications of EC analysis, this paper aims to explore the abnormal connectivity patterns of brain networks in patients with depression, and further analyze the relationship between these abnormalities and clinical symptoms, so as to provide more accurate theoretical support for early diagnosis and personalized treatment of depression.

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

High dam discharge can lead to total dissolved gas (TDG) supersaturation in downstream rivers, causing fish to suffer from bubble trauma and even mortality. Focusing on the Datengxia hydropower station in the Xijiang River basin, we conducted in-situ experiments to explore the tolerance patterns of economic fish species, including Ctenopharyngodon idella, Hypophthalmichthys molitrix, and Cirrhinus molitorella, under the influence of TDG supersaturation at different compensation depths. Moreover, the development and recovery patterns of bubble trauma and the swimming ability of fish exposed to TDG supersaturated water were investigated. In-situ experiments showed that TDG supersaturation ranged from 112.2 % to 125.2 %, averaging 118.3 % at the experiment site. The results revealed that compensation depth is favorable in fish avoidance of TDG supersaturation. The survival rate of the experimental fish at the surface was lower than for those at the 0–3 m water depth. The survival rates of Ctenopharyngodon idella, Hypophthalmichthys molitrix, and Cirrhinus molitorella at the surface were only 30 %, 47.5 %, and 70 %, respectively, while at the 0–3 m water depth, the survival rates were 97.5 %, 87.5 %, and 87.5 %, respectively. Additionally, the survival rate of fish was related to their preferred water depth. The bubble trauma scores of the experimental fish in TDG supersaturated water significantly increased with exposure time and significantly decreased after recovery in freshwater. The relative and absolute critical swimming speed (Ucrit) of Ctenopharyngodon idella ranged from 10.91 to 12.98 BL/s and 83.3–102.9 cm/s respectively, and there were no significant changes in the Ucrit with increasing TDG supersaturation exposure.

PurposeTinnitus is considered a neurological disorder affecting both auditory and nonauditory networks. This study aimed to investigate the structural brain covariance network in tinnitus patients and analyze its altered topological properties.MaterialsFifty three primary tinnitus patients and 67 age- and sex-matched healthy controls (HCs) were included. Gray matter volume (GMV) of each participant was extracted using voxel-based morphometry, a group-level structural covariance network (SCN) was constructed based on the GMV of each participant, and graph theoretic analyses were performed using graph analysis toolbox (GAT). The differences in the topological properties of SCN between both groups were compared and analyzed.ResultsBoth groups exhibited small-world attributes. Compared with HCs, tinnitus patients had significantly higher characteristic path length, lambda, transitivity, and assortativity (p

In this study, nano-Nb-modified AlSi10Mg composites were fabricated using laser powder bed fusion (LPBF). The effects of Nb addition on the microstructure and mechanical properties of the AlSi10Mg alloy were systematically investigated. Compared with the as-built AlSi10Mg alloy, the microstructure along the building direction of the AlSi10Mg–Nb composites changed from coarse columnar grains to ultrafine equiaxed grains. Moreover, the preferential grain growth orientation was significantly weakened. Grain refinement occurs due to the presence of abundant in-situ Al3Nb precipitates dispersed within the grain, serving as heterogeneous nucleation inoculants. Simultaneously, the precipitation of NbSi2 particles along the eutectic network also contributed to refining the structure of the eutectic network. As a result, the LPBF-processed AlSi10Mg–Nb samples demonstrated a very high ultimate tensile strength (∼560 MPa) and good ductility (∼7%), comparable to those of high-strength aluminum alloys. The underlying mechanisms for this strength enhancement are discussed based on the improved mechanical properties. This study provides a novel perspective for the fabrication of high-performance AlSi10Mg alloys using LPBF.

Abstract Background Iron deficiency anemia (IDA) is a common health problem worldwide. The objective of this study was to noninvasively and quantitatively evaluate early changes in left ventricular systolic function in patients with IDA using the left ventricular press–strain loop (LV-PSL). Methods Sixty-two patients with IDA were selected and divided into two groups based on hemoglobin (Hb) concentration: Group B with Hb > 9 g/dL and group C with 6 g/dL

Summary: Cervical lesions pose a significant threat to women’s health worldwide. Colposcopy is essential for screening and treating cervical lesions, but its effectiveness depends on the doctor’s experience. Artificial intelligence-based solutions via colposcopy images have shown great potential in cervical lesions screening. However, some challenges still need to be addressed, such as low algorithm performance and lack of high-quality multi-modal datasets. Here, we established a multi-modal colposcopy dataset of 2,273 HPV+ patients, comprising original colposcopy images, acetic acid reactions at 60s and 120s, iodine staining, diagnostic reports, and pathological results. Utilizing this dataset, we developed CerviFusionNet, a hybrid architecture that merges convolutional neural networks and vision transformers to learn robust representations. We designed a temporal module to capture dynamic changes in acetic acid sequences, which can boost the model performance without sacrificing inference speed. Compared with several existing methods, CerviFusionNet demonstrated excellent accuracy and efficiency.

Global climate change (GCC), characterized by warming, affects the hydrological conditions at the basin scale and whether harmful algal blooms (HABs) occur at the scale of river ecological systems. Research on HABs mainly focuses on oceans and lakes, and there is still less research on the effects of GCC on river-type HABs that differ from oceans and lakes in hydrodynamic, water temperature, and nutrient conditions. This study constructed a coupled model framework that includes the GCC model, downscaling model, hydrological model, hydrodynamic model, and eutrophication model, analyzing and exploring the effect of changes in the aquatic ecological environment caused by GCC on river-type HABs in the middle and lower reaches of the Hanjiang River (MLHR). Firstly, based on the three GCC models and statistical downscaling model in CMIP6, high-precision meteorological factors such as future precipitation and temperature were obtained. Secondly, a coupled model based on SWAT and MIKE21-ECOLab was used with the digital elevation model (DEM), land use, soil, meteorological, pollution source, and measured terrain data in the MLHR Basin, which was validated by observed data. Thirdly, there has not been a significant increase in Chl-a, and the impact of GCC has not fundamentally changed the temporal and spatial distribution of HABs. Fourthly, this study proposed to use 0.2 m/s (Corresponding discharge 1160 m3/s) as the hydrodynamic condition for preventing and controlling HABs in the Shayang section.

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges. Conventional strategies always exhibit the insufficiencies in overall coverage, analytical efficiency, and degree of automation, and the results highly rely on the personal knowledge and experience. The goal of this work was to establish a software-aided approach, by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) and in-house high-definition MS2 library, to enhance the identification of prototypes and metabolites of the compound formulae in vivo, taking Sishen formula (SSF) as a template. Seven different MS2 acquisition methods were compared, which demonstrated the potency of a hybrid scan approach (namely high-definition data-independent/data-dependent acquisition (HDDIDDA)) in the identification precision, MS1 coverage, and MS2 spectra quality. The HDDIDDA data for 55 reference compounds, four component drugs, and SSF, together with the rat bio-samples (e.g., plasma, urine, feces, liver, and kidney), were acquired. Based on the UNIFI™ platform (Waters), the efficient data processing workflows were established by combining mass defect filtering (MDF)-induced classification, diagnostic product ions (DPIs), and neutral loss filtering (NLF)-dominated structural confirmation. The high-definition MS2 spectral libraries, dubbed in vitro-SSF and in vivo-SSF, were elaborated, enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples. Consequently, 118 prototypes and 206 metabolites of SSF were identified, with the identification rate reaching 80.51% and 79.61%, respectively. The metabolic pathways mainly involved the oxidation, reduction, hydrolysis, sulfation, methylation, demethylation, acetylation, glucuronidation, and the combined reactions. Conclusively, the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner.

Abstract Although 3D reconstruction has been widely used in many fields as a key component of environment perception, existing technologies still have the potential for further improvement in 3D scene reconstruction. We propose an improved reconstruction algorithm based on the MVSNet network architecture. To glean richer pixel details from images, we suggest deploying a DE module integrated with a residual framework, which supplants the prevailing feature extraction mechanism. The DE module uses ECA-Net and dilated convolution to expand the receptive field range, performing feature splicing and fusion through the residual structure to retain the global information of the original image. Moreover, harnessing attention mechanisms refines the 3D cost volume's regularization process, bolstering the integration of information across multi-scale feature volumes, consequently enhancing depth estimation precision. When assessed our model using the DTU dataset, findings highlight the network's 3D reconstruction scoring a completeness (comp) of 0.411 mm and an overall quality of 0.418 mm. This performance is higher than that of traditional methods and other deep learning-based methods. Additionally, the visual representation of the point cloud model exhibits marked advancements. Trials on the Blended MVS dataset signify that our network exhibits commendable generalization prowess.

Abstract Background This study aimed to evaluate the T2W hypointense ring and T2-FLAIR mismatch signs in gliomas and use these signs to construct prediction models for glioma grading and isocitrate dehydrogenase (IDH) mutation status. Methods Two independent radiologists retrospectively evaluated 207 glioma patients to assess the presence of T2W hypointense ring and T2-FLAIR mismatch signs. The inter-rater reliability was calculated using the Cohen’s kappa statistic. Two logistic regression models were constructed to differentiate glioma grade and predict IDH genotype noninvasively, respectively. Receiver operating characteristic (ROC) analysis was used to evaluate the developed models. Results Of the 207 patients enrolled (119 males and 88 females, mean age 51.6 ± 14.8 years), 45 cases were low-grade gliomas (LGGs), 162 were high-grade gliomas (HGGs), 55 patients had IDH mutations, and 116 were IDH wild-type. The number of T2W hypointense ring signs was higher in HGGs compared to LGGs (p

Abstract Classical metabolomic and new metabolic network methods were used to study the developmental features of autism spectrum disorder (ASD) in newborns (n = 205) and 5-year-old children (n = 53). Eighty percent of the metabolic impact in ASD was caused by 14 shared biochemical pathways that led to decreased anti-inflammatory and antioxidant defenses, and to increased physiologic stress molecules like lactate, glycerol, cholesterol, and ceramides. CIRCOS plots and a new metabolic network parameter, $$\dot{{{\boldsymbol{V}}}}\!$$ V ° net , revealed differences in both the kind and degree of network connectivity. Of 50 biochemical pathways and 450 polar and lipid metabolites examined, the developmental regulation of the purine network was most changed. Purine network hub analysis revealed a 17-fold reversal in typically developing children. This purine network reversal did not occur in ASD. These results revealed previously unknown metabolic phenotypes, identified new developmental states of the metabolic correlation network, and underscored the role of mitochondrial functional changes, purine metabolism, and purinergic signaling in autism spectrum disorder.

The water quality index (WQI) is an important tool for evaluating the water quality status of lakes. In this study, we used the WQI to evaluate the spatial water quality characteristics of Dianchi Lake. However, the WQI calculation is time-consuming, and machine learning models exhibit significant advantages in terms of timeliness and nonlinear data fitting. We used a machine learning model with optimized parameters to predict the WQI, and the light gradient boosting machine achieved good predictive performance. The machine learning model trained based on the entire Dianchi Lake water quality data achieved coefficient of determination (R2), mean square error, and mean absolute error values of 0.989, 0.228, and 0.298, respectively. In addition, we used the Shapley additive explanations (SHAP) method to interpret and analyse the machine learning model and identified the main water quality parameter that affects the WQI of Dianchi Lake as NH4+-N. Within the entire range of Dianchi Lake, the SHAP values of NH4+-N varied from −9 to 3. Thus, in future water environmental governance, it is necessary to focus on NH4+-N changes. These results can provide a reference for the treatment of lake water environments. HIGHLIGHTS The water quality of Dianchi Lake at the entry point is better than that at the exit point.; An interpretable machine learning model for predicting water quality index has been proposed.; NH4+-N is the main parameter affecting the water quality of Dianchi Lake.;

Background: Acute lung injury (ALI) significantly impacts the survival rates in intensive care units (ICU). Releasing a lot of pro-inflammatory mediators during the progression of the disease is a core feature of ALI, which may lead to uncontrolled inflammation and further damages the tissues and organs of patients. This study explores the potential therapeutic mechanisms of Dexmedetomidine (Dex) in ALI. Methods: In present study, cecal ligation puncture (CLP)-established ALI model mice and lipopolysaccharide (LPS)-stimulated RAW264.7 cell line were established to discover the influence of Dex. The evaluation of lung injury in vivo using histopathology, TUNEL assay, and analysis of inflammatory factors in bronchoalveolar lavage fluid (BALF) and serum. The receptor for advanced glycation end products (RAGE)/Caspase-11-dependent pyroptosis-related proteins and macrophage polarization markers were analyzed using western blot, immunofluorescence, and flow cytometry. Finally, the mechanism of Dex in macrophages was further verified in vitro. Results: In vivo, Dex alleviated lung injury and decreased TUNEL-positive cell expression in CLP group. Dex decreased tumor necrosis factor-alpha (TNF-α), interleukin (IL)-6 and IL-17A levels in BALF and serum, while increasing IL-10 expression. Dex treatment decreased the protein levels of RAGE, caspase-11, IL-1β and Gasdermin-D (GSDMD) in both in cells and in mice. Dex also down-regulated the synthesis of inducible nitric oxide synthase (iNOS) of classical activation phenotype (M1) markers, and up-regulated the synthesis of CD206 and Arg-1 of alternate activation phenotype (M2) markers. Conclusions: Dex treatment can inhibit inflammation and reduce lung injury caused by CLP. It could be associated with mediating M1 and M2 polarization and suppressing RAGE/Caspase-11-depended pyroptosis.

Background: The increasing prevalence of autism spectrum disorder (ASD) highlights the need for objective diagnostic markers and a better understanding of its pathogenesis. Metabolic differences have been observed between individuals with and without ASD, but their causal relevance remains unclear. Methods: Bidirectional two-sample Mendelian randomization (MR) was used to assess causal associations between circulating plasma metabolites and ASD using large-scale genome-wide association study (GWAS) datasets—comprising 1091 metabolites, 309 ratios, and 179 lipids—and three European autism datasets (PGC 2015: n = 10,610 and 10,263; 2017: n = 46,351). Inverse-variance weighted (IVW) and weighted median methods were employed, along with robust sensitivity and power analyses followed by independent cohort validation. Results: Higher genetically predicted levels of sphingomyelin (SM) (d17:1/16:0) (OR, 1.129; 95% CI, 1.024–1.245; p = 0.015) were causally linked to increased ASD risk. Additionally, ASD children had higher plasma creatine/carnitine ratios. These MR findings were validated in an independent US autism cohort using machine learning analysis. Conclusion: Utilizing large datasets, two MR approaches, robust sensitivity analyses, and independent validation, our novel findings provide evidence for the potential roles of metabolomics and circulating metabolites in ASD diagnosis and etiology.

Abstract Peripheral blood metabolomics was used to gain chemical insight into the biology of treatment-refractory Major Depressive Disorder with suicidal ideation, and to identify individualized differences for personalized care. The study cohort consisted of 99 patients with treatment-refractory major depressive disorder and suicidal ideation (trMDD-SI n = 52 females and 47 males) and 94 age- and sex-matched healthy controls (n = 48 females and 46 males). The median age was 29 years (IQR 22–42). Targeted, broad-spectrum metabolomics measured 448 metabolites. Fibroblast growth factor 21 (FGF21) and growth differentiation factor 15 (GDF15) were measured as biomarkers of mitochondrial dysfunction. The diagnostic accuracy of plasma metabolomics was over 90% (95%CI: 0.80–1.0) by area under the receiver operator characteristic (AUROC) curve analysis. Over 55% of the metabolic impact in males and 75% in females came from abnormalities in lipids. Modified purines and pyrimidines from tRNA, rRNA, and mRNA turnover were increased in the trMDD-SI group. FGF21 was increased in both males and females. Increased lactate, glutamate, and saccharopine, and decreased cystine provided evidence of reductive stress. Seventy-five percent of the metabolomic abnormalities found were individualized. Personalized deficiencies in CoQ10, flavin adenine dinucleotide (FAD), citrulline, lutein, carnitine, or folate were found. Pathways regulated by mitochondrial function dominated the metabolic signature. Peripheral blood metabolomics identified mitochondrial dysfunction and reductive stress as common denominators in suicidal ideation associated with treatment-refractory major depressive disorder. Individualized metabolic differences were found that may help with personalized management.

The friction pair gap affects not only the temperature increase of the multi-disc wet clutch, but also the efficiency of the helicopter transmission system. Consequently, a rotational–axial engagement and disengagement-coupled kinetic model of a wet multi-disc clutch considering asperity contact, hydrodynamic lubrication, spline resistance, and a separating spring model are developed in this paper. The effects of operating conditions on the dynamic characteristics of the wet clutch are investigated. Further, the gap deviation coefficient is proposed to characterize the dynamic behavior of the friction pair gap. As the control oil pressure increases from 1.3 MPa to 1.7 MPa, the gap deviation coefficient increases by 8.6%. Moreover, as the rotation speed increases from 888 rpm to 2488 rpm and the lubricant oil temperature increases from 25 °C to 85 °C, the gap deviation coefficient decreases by 1.1% and 4.44%, respectively. Therefore, an appropriate increase in lubricant oil temperature and rotation speed can facilitate the friction pair gap to be more uniform. These results are useful for the development of optimal control strategies for aviation wet clutch systems.

This study explores memristor-based true random number generators (TRNGs) through their evolution and optimization, stemming from the concept of memristors first introduced by Leon Chua in 1971 and realized in 2008. We will consider memristor TRNGs coming from various entropy sources for producing high-quality random numbers. However, we must take into account both their strengths and weaknesses. The comparison with CMOS-based TRNGs will serve as an illustration that memristor TRNGs stand out due to their simpler circuits and lower power consumption— thus leading us into a case study involving electroless YMnO3 (YMO) memristors as TRNG entropy sources that demonstrate good security properties by being able to produce unpredictable random numbers effectively. The end of our analysis sees us pinpointing challenges: post-processing algorithm optimization coupled with ensuring reliability over time for memristor-based TRNGs aimed at next-generation security applications.

Although the rational use of water resources through damming rivers can improve economic benefits, it inevitably changes the connectivity of rivers and the hydraulic characteristics of water reduced river reaches, posing a threat to fish habitats. Habitat assessment can provide a reference for assessing the ecological impact of hydropower development and the effectiveness of habitat restoration. However, previous evaluation methods only considered the number of habitats, which may mislead the evaluation results. In this article, we proposed a novel fish habitat assessment model based on the landscape pattern index method and circuit theory, which additionally considers the spatial pattern and connectivity of habitat patches. This model is applied to the Anshunchang reach of the Dadu River, and the target fish is Schizothorax prenanti. We obtained a suitable ecological discharge range for this river reach from 163.0 m3/s to 217.4 m3/s. To improve the suitability of fish habitats, we have also attempted multiple restoration measures. Comparing different habitat restoration measures, it was ultimately found that the river dredging scenario had the best ecological improvement effect, with a maximum improvement rate of 9.87 %. The habitat assessment model in this article can help reservoir managers evaluate fish habitats and formulate habitat restoration plans.

Multi-view stereo based on learning is a critical task in three-dimensional reconstruction, enabling the effective inference of depth maps and the reconstruction of fine-grained scene geometry. However, the results obtained by current popular 3D reconstruction methods are not precise, and achieving high-accuracy scene reconstruction remains challenging due to the pervasive impact of feature extraction and the poor correlation between cost and volume. In addressing these issues, we propose a cascade deep residual inference network to enhance the efficiency and accuracy of multi-view stereo depth estimation. This approach builds a cost-volume pyramid from coarse to fine, generating a lightweight, compact network to improve reconstruction results. Specifically, we introduce the omni-dimensional dynamic atrous spatial pyramid pooling (OSPP), a multiscale feature extraction module capable of generating dense feature maps with multiscale contextual information. The feature maps encoded by the OSPP module can generate dense point clouds without consuming significant memory. Furthermore, to alleviate the issue of feature mismatch in cost volume regularization, we propose a normalization-based 3D attention module. The 3D attention module aggregates crucial information within the cost volume across the dimensions of channel, spatial, and depth. Through extensive experiments on benchmark datasets, notably DTU, we found that the OD-MVSNet model outperforms the baseline model by approximately 1.4% in accuracy loss, 0.9% in completeness loss, and 1.2% in overall loss, demonstrating the effectiveness of our module.

Abstract Background and purpose Ki-67 labeling index (LI) is an important indicator of tumor cell proliferation in glioma, which can only be obtained by postoperative biopsy at present. This study aimed to explore the correlation between Ki-67 LI and apparent diffusion coefficient (ADC) parameters and to predict the level of Ki-67 LI noninvasively before surgery by multiple MRI characteristics. Methods Preoperative MRI data of 166 patients with pathologically confirmed glioma in our hospital from 2016 to 2020 were retrospectively analyzed. The cut-off point of Ki-67 LI for glioma grading was defined. The differences in MRI characteristics were compared between the low and high Ki-67 LI groups. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of each ADC parameter in predicting the Ki-67 level, and finally a multivariate logistic regression model was constructed based on the results of ROC analysis. Results ADCmin, ADCmean, rADCmin, rADCmean and Ki-67 LI showed a negative correlation (r = − 0.478, r = − 0.369, r = − 0.488, r = − 0.388, all P

PurposeWhile observational studies have identified obesity as a potential risk factor for gastric cancer, the causality remains uncertain. This study aimed to evaluate the causal relationship between obesity and gastric cancer and identify the shared molecular signatures linking obesity to gastric cancer.MethodsA two-sample Mendelian randomization (MR) analysis was conducted using the GWAS data of body fat percentage (exposure, n = 331,117) and gastric cancer (outcome, n = 202,308). Bioinformatics and meta-analysis of multi-omics data were performed to identify key molecules mediating the causality. The meta-analysis of the plasma/serum proteome included 1,662 obese and 3,153 gastric cancer patients. Obesity and gastric cancer-associated genes were identified using seven common gene ontology databases. The transcriptomic data were obtained from TCGA and GEO databases. The Bioinformatic findings were clinically validated in plasma from 220 obese and 400 gastric cancer patients across two hospitals. Finally, structural-based virtual screening (SBVS) was performed to explore the potential FDA-approved drugs targeting the identified mediating molecules.ResultsThe MR analysis revealed a significant causal association between obesity and gastric cancer (IVW, OR = 1.37, 95% CI:1.12-1.69, P = 0.0028), without pleiotropy or heterogeneity. Bioinformatic and meta-analysis of multi-omics data revealed shared TNF, PI3K-AKT, and cytokine signaling dysregulation, with significant upregulation of AKT1, IL-6, and TNF. The clinical study confirmed widespread upregulation of systemic inflammatory markers in the plasma of both diseases. SBVS identified six novel potent AKT1 inhibitors, including the dietary supplement adenosine, representing a potentially preventive drug with low toxicity.ConclusionObesity causally increases gastric cancer, likely mediated by persistent AKT1/IL-6/TNF upregulation. As a potential AKT1 inhibitor, adenosine may mitigate the obesity-to-gastric cancer transition. These findings could inform preventive drug development to reduce gastric cancer risk in obesity.

Plant diseases and pests have always been major contributors to losses that occur in agriculture. Currently, the use of deep learning-based convolutional neural network models allows for the accurate identification of different types of plant diseases and pests. To enable more efficient identification of plant diseases and pests, we design a novel network architecture called Dise-Efficient based on the EfficientNetV2 model. Our experiments demonstrate that training this model using a dynamic learning rate decay strategy can improve the accuracy of plant disease and pest identification. Furthermore, to improve the model’s generalization ability, transfer learning is incorporated into the training process. Experimental results indicate that the Dise-Efficient model boasts a compact size of 13.3 MB. After being trained using the dynamic learning rate decay strategy, the model achieves an accuracy of 99.80% on the Plant Village plant disease and pest dataset. Moreover, through transfer learning on the IP102 dataset, which represents real-world environmental conditions, the Dise-Efficient model achieves a recognition accuracy of 64.40% for plant disease and pest identification. In light of these results, the proposed Dise-Efficient model holds great potential as a valuable reference for the deployment of automatic plant disease and pest identification applications on mobile and embedded devices in the future.

Macau, recognized as a global tourism hub and the world’s most densely populated region, provides a unique environment conducive to methicillin-resistant Staphylococcus aureus (MRSA) transmission in healthcare and community settings, posing a significant public health concern both locally and globally. The epidemiology and molecular characteristics of MRSA in the distinct city of Macau remain largely unelucidated. This five-year longitudinal study (2017–2022) examined the local prevalence and molecular typing of MRSA in Macau, with future MRSA type distribution predicted through ARIMA modeling. We subsequently analyzed the epidemiological characteristics of MRSA, including specimen source, clinical department, collection year, season, patient age, sex, and the annual number of tourists. Comprehensive antibiotic resistance profiles of the strains were also assessed. Of 504 clinically isolated S. aureus strains, 183 (36.3%) were identified as MRSA by the cefoxitin disk diffusion method and validated through multi-locus sequence typing (MLST). The MRSA detection rate showed an upward trend, increasing from 30.1% in 2017 to 45.7% in 2022. SCCmec type IV was predominant (28.9%), followed by types II (25.4%), III (22.1%), and V (22.1%). The primary sources of MRSA isolates were sputum (39.2%) and secretions (25.6%). Older age emerged as a risk factor for MRSA infection, whereas no significant associations were found with seasonal variations, gender, or the annual number of tourists. Despite displaying universal resistance to cefoxitin, oxacillin, and benzylpenicillin, MRSA isolates in Macau remained fully sensitive to vancomycin, tigecycline, quinupristin, nitrofurantoin, and linezolid. Continuous surveillance and analysis of MRSA distribution in Macau could provide invaluable insights for the effective management of MRSA prevention and control measures within healthcare settings.