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A new type of label-free electrochemical immunosensor for the high-sensitivity determination of parathion was developed based on the oriented immobilization of nanobody (VHH9) on a gold nanoparticle-loaded polyvinyl alcohol/citric acid nanofiber membrane-modified electrode. The morphology characterization and assembly process of the modified materials were investigated using scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS). Under the optimum conditions, the label-free electrochemical immunosensor for parathion exhibited a linear range of 0.0015–6400 ng/mL and a low detection limit of 0.48 pg/mL, the signal response of which was 10 times higher than that of the randomly immobilized VHH9. The immunosensor possessed high selectivity, good repeatability and reusability (keeping above 90% of its initial activity after repeating 8 times), and stability (remaining 90% after 9 weeks of storage). Finally, the average recoveries of parathion from food samples were 93.76–105.73% with the coefficient of variation being 2.65–6.85%, showing good correlation with UPLC (R2 = 0.9950). Therefore, our nanobody immobilization protocol is simple and effective and proves the potential to be utilized as a promising candidate for sensing platform. [ABSTRACT FROM AUTHOR]

With SARS-CoV-2 N protein as a model target, a signal-enhanced LFIA based on Au@Pt nanoparticles (NPs) as labels is proposed. This Au@Pt NPs combined the distinguished localized surface plasma resonance (LSPR) effect of Au NPs and the ultrahigh peroxidase-like catalytic activity of Pt NPs. Au@Pt NPs could trigger substrate chromogenic reaction, generating a color signal orders of magnitude darker than their intrinsic color. In the detection, after the coloration of the strips, 3,3′,5,5′-tetramethylbenzidine (TMB) and H2O2 were added, and a dark blue chelate (OxTMB) was produced soon, enhancing the band color significantly. After the signal amplification, the naked-eye detection limit for N protein reached 40 pg/mL. The detection sensitivity enhanced more than 1000 times than that without signal amplification. Compared with mainstream LFIA requiring complex readout instruments, the Au@Pt-based LFIA achieved a comparable sensitivity using naked eyes detection. This point is crucial, especially for unprofessional users or low-resource areas. Hence, this signal-enhanced LFIA may serve as a sensitive, cost-effective, and user-friendly detection method. It can shorten the testing window period and help identify early infections. [ABSTRACT FROM AUTHOR]

Optogenetics as an emerging technology can eliminate spiral waves in myocardial tissue. The heat generated during illumination of myocardial tissue is an overlooked influence. Even small fluctuations in temperature may affect the action potentials of cardiomyocyte. In this paper, a minimal ventricular model and a simplified model of optogenetics are employed to study the effects of heat generation by illumination on elimination of spiral waves. The Luo-Rudy model and Channelrhodospin-2 light-sensitive ion channel model are used to validate our conclusions. We induce drift of spiral waves through inhomogeneities generated by discrete gradients of illumination. The inhomogeneity of temperature caused by gradient illumination can inhibit the elimination of spiral waves. Spiral waves in the myocardial medium can be induced to drift more efficiently by controlling temperature changes in the myocardial medium during illumination. We emphasized the importance of temperature factors in optogenetic experiments, hoping that our results could provide guidance for its clinical applications. [ABSTRACT FROM AUTHOR]

Background/Objectives: Childhood obesity, particularly in girls, is linked to early puberty onset, heightening risks for adult-onset diseases. Addressing childhood obesity and precocious puberty is vital to mitigate societal burdens. Despite existing costly and invasive medical interventions, introducing lifestyle-based alternatives is essential. Our study investigates alternate-day fasting's (ADF) impact on pubertal development in normal-weight and high-fat diet (HFD)-induced obese female mice. Methods: Four groups of female mice were utilized, with dams initially fed control chow during and before pregnancy. Post-parturition, two groups continued on control chow, while two switched to an HFD. Offspring diets mirrored maternal exposure. One control and one HFD group were subjected to ADF. Morphometry and hormone analyses at various time points were performed. Results: Our findings demonstrate that ADF in normal-weight mice led to reduced body length, weight, uterine, and ovarian weights, accompanied by delayed puberty and lower levels of sex hormones and growth hormone (GH). Remarkably, GH treatment effectively prevented ADF-induced growth reduction but did not prevent delayed puberty. Conversely, an HFD increased body length, induced obesity and precocious puberty, and altered sex hormones and leptin levels, which were counteracted by ADF regimen. Our data indicate ADF's potential in managing childhood obesity and precocious puberty. Conclusions: ADF reduced GH and sex hormone levels, contributing to reduced growth and delayed puberty, respectively. Therefore, parents of normal-weight children should be cautious about prolonged overnight fasting. ADF prevented HFD-induced obesity and precocious puberty, offering an alternative to medical approaches; nevertheless, further studies are needed for translation into clinical practice. [ABSTRACT FROM AUTHOR]

Osteoporosis (OP) is an osteometabolic disorder characterized by a lesser bone mineral density (BMD) and the disruption of bone tissue micro—architecture, resulting in a greater bone fragility and higher likelihoods of fractures. OP emerges once the bone mass declines faster than the body's capacity to replenish it, leading to a significant reduction in the strength of bone. OP impacts each and every bone throughout the body and provides no clinical signs until a fracture happens. Aging leads to the reduction in BMD, and the rate of percentage of fractures rises over time, usually causing mortality and morbidity. Numerous BMD evaluation methods are available, and they are used in a variety of settings by considering the location of the fracture. Dual-energy x-ray absorptiometry (DEXA/DXA) is recognized as the gold standard for predicting the fracture, since it is the most advanced, technologically affirmed, and also has excellent performance. According to the findings, most researchers do not endeavour the identification and the segmentation of low bone masses from DEXA images. Medical image segmentation supports in analyzing and visualizing the bone's low bone mass. The envisaged hybrid approach, that integrates GLCM for feature extraction and AlexNet for a low bone mass variation classification, provides segmented images that assist in categorizing bone health as normal, osteopenia, or osteoporosis. The developed algorithm's performance metrics, including Dice Co-efficient, Sensitivity, and Specificity, were 92.35, 90.26, and 92.42%, respectively. The Orthopedicians ascertained the efficacy of the outcomes rendered by the proposed algorithm. [ABSTRACT FROM AUTHOR]

Protein solubility is a critical parameter that determines the stability, activity, and functionality of proteins, with broad and far-reaching implications in biotechnology and biochemistry. Accurate prediction and control of protein solubility are essential for successful protein expression and purification in research and industrial settings. This study gathered information on soluble and insoluble proteins. In characterizing the proteins, they were mapped to STRING and characterized by functional and structural features. All functional/structural features were integrated to create a 5768-dimensional binary vector to encode proteins. Seven feature-ranking algorithms were employed to analyze the functional/structural features, yielding seven feature lists. These lists were subjected to the incremental feature selection, incorporating four classification algorithms, one by one to build effective classification models and identify functional/structural features with classification-related importance. Some essential functional/structural features used to differentiate between soluble and insoluble proteins were identified, including GO:0009987 (intercellular communication) and GO:0022613 (ribonucleoprotein complex biogenesis). The best classification model using support vector machine as the classification algorithm and 295 optimized functional/structural features generated the F1 score of 0.825, which can be a powerful tool to differentiate soluble proteins from insoluble proteins. [ABSTRACT FROM AUTHOR]

Objective: To compare therapeutic outcomes of predicted proliferative and nonproliferative hepatocellular carcinoma (HCC) after microwave ablation (MWA) using a previously developed imaging-based predictive model, the SMARS score. Methods: This multicenter retrospective study included consecutive 635 patients with unresectable HCC who underwent MWA between August 2013 and September 2020. Patients were stratified into predicted proliferative and nonproliferative phenotypes according to the SMARS score. Overall survival (OS) and recurrence-free survival (RFS) were compared between the predicted proliferative and nonproliferative HCCs before and after propensity score matching (PSM). OS and RFS were also compared between the two groups in subgroups of tumor size smaller than 30 mm and tumor size 30–50 mm. Results: The SMARS score classified 127 and 508 patients into predicted proliferative and nonproliferative HCCs, respectively. The predicted proliferative HCCs exhibited worse RFS but equivalent OS when compared with nonproliferative HCCs before (p < 0.001 for RFS; p = 0.166 for OS) and after (p < 0.001 for RFS; p = 0.456 for OS) matching. Regarding subgroups of tumor size smaller than 30 mm (p = 0.098) and tumor size 30–50 mm (p = 0.680), the OSs were similar between the two groups. However, predicted proliferative HCCs had worse RFS compared to nonproliferative HCCs in the subgroup of tumor size 30–50 mm (p < 0.001), while the RFS did not differ in the subgroup of tumor size smaller than 30 mm (p = 0.141). Conclusion: Predicted proliferative HCCs have worse RFS than nonproliferative ones after MWA, especially in tumor size larger than 30 mm. However, the phenotype of the tumor may not affect the OS. Critical relevance statement: Before performing microwave ablation for hepatocellular carcinoma, the tumor phenotype should be considered because it may affect the therapeutic outcome. Key Points: Proliferative hepatocellular carcinoma (HCC) may be identified using the SMARS score, an imaging-based predictive model. SMARS predicted proliferative HCCs have worse recurrence-free and equivalent overall survival compared to nonproliferative HCC after microwave ablation. Tumor phenotype should be considered before performing microwave ablation. [ABSTRACT FROM AUTHOR]

Synchronization phenomena are pervasive in coupled nonlinear systems across the natural world and engineering domains. Understanding how to dynamically identify the parameter space (or network structure) of coupled nonlinear systems in a synchronized state is crucial for the study of system synchronization. To address the challenge of achieving stable synchronization in coupled nonlinear systems, we develop a set of mathematical optimization techniques for dynamic learning of synchronization (DLS) inspired by machine learning. This technology captures the state differences between nodes within the system and dynamically adjusts weights, allowing coupled nonlinear systems to maintain a stable state of synchronization after appropriate weight adjustments. To enhance synchronization optimization, we use the master stability function (MSF) to demonstrate how DLS effectively adjusts networks into their synchronization regions. We introduce several variants of the DLS technique, including adaptive, supervised, and hybrid methods, effectively promoting synchronization in heterogeneous networks such as small-world, scale-free, and random networks. The efficacy of this technique is validated through its application to simple FitzHugh–Nagumo neural networks and complex Hodgkin–Huxley neuronal networks, examining its impact on both global and local synchronization. The DLS technique proposed in this study offers a new solution to synchronization problems in dynamic network environments, addressing the deficiencies in adaptability and flexibility of existing technologies and providing a fresh perspective for understanding and implementing synchronization phenomena in coupled nonlinear systems. [ABSTRACT FROM AUTHOR]

In cross-regional cultural interactions, the frequent and controversial practice of cultural borrowing has attracted attention. According to the different relative statuses and comparative directions of the cultural subjects, cultural borrowing can be divided into strong-to-weak (weak-to-strong) cultural borrowing between subjects with strong (weak) status and subjects with weak (strong) status in cultural interactions, as well as equal cultural borrowing between subjects with equal status in cultural interactions. After the concept and types of cultural borrowing are defined, a multisituation experimental method is used to confirm the internal mechanisms and effect boundaries of consumers’ cultural borrowing acceptance. The results of five experiments based on over a thousand subjects indicate that different types of cultural borrowing (strong-to-weak, equal, weak-to-strong) arouse consumers’ perceived cultural threat, thereby reducing their acceptance of cultural borrowing to varying degrees. The influence of different types of cultural borrowing on perceived cultural threat is moderated by the degree of incongruent use and reality of the presentation of the borrowed culture; the influence of perceived cultural threat on cultural borrowing acceptance is moderated by the positioning of the borrowing subjects and the degree of identity in the relationship. By exploring cultural borrowing and consumer acceptance issues in the field of consumption through a series of experiments, this study effectively reveals the differentiated impacts and underlying mechanisms of different types of cultural borrowing on consumers’ acceptance responses. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T2-weighted MR imaging (T2WI) for gastric cancer (GC). Methods: 112 patients with GCs undergoing gastric MRI were prospectively enrolled between Aug 2022 and Dec 2022. Axial DLSB-T2WI and BLADE-T2WI of stomach were scanned with same spatial resolution. Three radiologists independently evaluated the image qualities using a 5-scale Likert scales (IQS) in terms of lesion delineation, gastric wall boundary conspicuity, and overall image quality. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated in measurable lesions. T staging was conducted based on the results of both sequences for GC patients with gastrectomy. Pairwise comparisons between DLSB-T2WI and BLADE-T2WI were performed using the Wilcoxon signed-rank test, paired t-test, and chi-squared test. Kendall's W, Fleiss' Kappa, and intraclass correlation coefficient values were used to determine inter-reader reliability. Results: Against BLADE, DLSB reduced total acquisition time of T2WI from 495 min (mean 4:42 per patient) to 33.6 min (18 s per patient), with better overall image quality that produced 9.43-fold, 8.00-fold, and 18.31-fold IQS upgrading against BALDE, respectively, in three readers. In 69 measurable lesions, DLSB-T2WI had higher mean SNR and higher CNR than BLADE-T2WI. Among 71 patients with gastrectomy, DLSB-T2WI resulted in comparable accuracy to BLADE-T2WI in staging GCs (P > 0.05). Conclusions: DLSB-T2WI demonstrated shorter acquisition time, better image quality, and comparable staging accuracy, which could be an alternative to BLADE-T2WI for gastric cancer imaging. [ABSTRACT FROM AUTHOR]

Macrocyclic conformations play a crucial role in regulating their properties. Our understanding of the determinants to control macrocyclic conformation interconversion is still in its infancy. Here we present a macrocycle, octamethyl cyclo[4](1,3-(4,6)-dimethylbenzene)[4]((4,6-benzene)(1,3-dicarboxylate) (OC-4), that can exist at 298 K as two stable atropisomers with C2v and C4v symmetry denoted as C2v-OC-4 and C4v-OC-4, respectively. Heating induces the efficient stepwise conversion of C2v- to C4v-OC-4 via a Cs-symmetric intermediate (Cs-OC-4). It differs from the typical transition state-mediated processes of simple C–C single bond rotations. Hydrolysis and further esterification with a countercation dependence promote the generation of C2v- and Cs-OC-4 from C4v-OC-4. In contrast to C2v-OC-4, C4v-OC-4 can bind linear guests to form pseudo-rotaxans, or bind C60 or C70 efficiently. The present study highlights the differences in recognition behavior that can result from conformational interconversion, as well as providing insights into the basic parameters that govern coupled molecular rotations. The three-dimensional conformation of macrocycles determine their chemical and recognition properties and the factors that influence the interconversion between macrocyclic forms are poorly understood. Here the authors describe a macrocycle existing as two stable atropisomers and the mechanism behind the thermally-induced transition between them. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma is a serious illness with a high rate of mortality. A high dose of theranostic drugs with efficient diagnostic and therapeutic capabilities should be required. Chemo-photothermal therapy is presently recognized as a secure method of cancer treatment that specifically targets tumour tissue or cells. Additionally, the success of cancer therapy is increased by the use of targeted nanoparticles. The current study aims to investigate the interaction between phototherapy and the anti-hepatocellular carcinoma treatment combination HA-GEM-MoS2/ZnO nanocomposites (NCs) loaded with gemcitabine and molybdenum disulphide. NCs were synthesized and characterized using FT-IR, XRD, TEM, and DLS analyses. The present investigation shows that the synthesized HA-MoS2/ZnO nanocomposites were elongated spherical in shape and their sizes ranged from 62.3 to 75.7 nm according to the estimation using XRD results, which is consistent with TEM findings. Further, HA-MoS2/ZnO nanocomposites could effectively encapsulate the GEM, showing dual pH and thermal triggered drug release behaviour. The result of cell uptake tests clearly demonstrated improved cellular uptake of synthesized nanocomposites following HA and GEM-loaded NCs in hepatocellular carcinoma cell lines. In addition, combination therapies caused the highest incidence of cell death in hepatocellular carcinoma, according to cytotoxicity experiments and showed a good compatibility. In vitro studies prove that HA-GEM-MoS2/ZnO nanocomposites enhanced tumour treatment that combines chemotherapy and photothermal therapy to remove the tumour and prevent relapses. Still, no studies have been done to see if gemcitabine-encapsulated HA-MoS2/ZnO NCs inhibit human hepatocellular carcinoma cell. Hence, the current study can give a new paradigm for the diagnosis and treatment of cancer and the outcome may be helpful to improve the quality of cancer patient's life. [ABSTRACT FROM AUTHOR]

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Allergic asthma is a complex inflammatory disorder predominantly orchestrated by T helper 2 (Th2) lymphocytes. The anti-inflammatory protein Clara Cell 10-kDa (CC10), also known as secretoglobin family 1A member 1 (SCGB1A1), shows promise in modulating respiratory diseases. However, its precise role in asthma remains unclear. This study examines the potential of CC10 to suppress allergic asthma inflammation, specifically assessing its regulatory effects on Th2 cell responses and dendritic cells (DCs). Lower CC10 levels in asthma were observed and correlated with increased IgE and lymphocytes. Cc10−/− mice exhibited exacerbated allergic airway inflammation marked by increased inflammatory cell infiltration, Th2 cytokines, serum antigen-specific IgE levels, and airway hyperresponsiveness (AHR) in house dust mite (HDM)-induced models. Conversely, recombinant CC10 significantly attenuated these inflammatory responses. Intriguingly, CC10 did not directly inhibit Th cell activation but significantly downregulated the population of CD11b+CD103− DCs subsets in lungs of asthmatic mice and modulated the immune activation functions of DCs through NF-κB signaling pathway. The mixed lymphocyte response assay revealed that DCs mediated the suppressive effect of CC10 on Th2 cell responses. Collectively, CC10 profoundly mitigates Th2-type allergic inflammation in asthma by modulating lung DC phenotype and functions, highlighting its therapeutic potential for inflammatory airway conditions and other related immunological disorders. [ABSTRACT FROM AUTHOR]

Many older patients with COVID-19 likely have co-morbid osteoporosis. We investigated the clinical outcomes of COVID-19 patients with osteoporosis. This was a retrospective cohort study using national claims data from Korea encoded in the common data model. Patients aged ≥ 50 years diagnosed with COVID-19 infection between January 2020 and April 2022 were included and stratified into two groups according to a history of osteoporosis. Clinical outcomes of COVID-19 infection were analyzed using logistic regression analysis after large-scale propensity score stratification. Of the 597,011 patients with COVID-19 included in the study, 105,172 had a history of osteoporosis. In patients with a history of osteoporosis, the odds of mortality decreased (odds ratio [OR] 0.82, P < 0.002), whereas most clinical outcomes of COVID-19 did not exhibit differences compared to those without such a history. Osteoporosis patients with a history of fractures showed increased odds of pneumonia, hospitalization, major adverse cardiac events, venous thromboembolism, and mortality, compared to patients without osteoporosis (ORs 1.34–1.58, P < 0.001 to P = 0.001). Our study suggests that patients with severe osteoporosis who have experienced fractures have an elevated risk of severe complications with COVID-19, while osteoporosis patients without fractures who have sought medical attention have a lower risk of mortality. [ABSTRACT FROM AUTHOR]

The phenomenon in which the response of a neuronal network to a weak signal is significantly enhanced in moderate noise is known as stochastic resonance (SR). Most of the previous studies on the transmission of signals by networks have been based on static synaptic connections, whereas dynamic synaptic connections modified by spike-time-dependent plasticity (STDP) are the basis of learning and memory in the nervous system. In this paper, we explore the phenomenon of SR in a neuronal network consisting of different ratios of excitatory vertebral neurons and inhibitory interneurons. The equivalent circuit method was employed to assess the average energy efficiency of the network. The differences in signal response before and after the introduction of STDP were compared for purely excitatory, purely inhibitory and excitatory-inhibitory networks, respectively. It was found that excitatory STDP promotes the network's response to weak signals, while inhibitory STDP has the opposite effect. The introduction of the inhibitory STDP makes the inhibitory network insensitive to the modulation of the coupling strength and increases its robustness. Furthermore, in the excitatory-inhibitory network, we found that STDP had little effect on the overall signalling of the network, and that the network's response to weak signals was more stable. Our findings contribute to the understanding of the importance of excitatory-inhibitory balance in ensuring accurate transmission and processing of information and provide new insights into the role of STDP in neuronal information processing. [ABSTRACT FROM AUTHOR]

Face anti-spoofing aims at detecting whether the input is a real photo of a user (living) or a fake (spoofing) image. As new types of attacks keep emerging, the detection of unknown attacks, known as Zero-Shot Face Anti-Spoofing (ZSFA), has become increasingly important in both academia and industry. Existing ZSFA methods mainly focus on extracting discriminative features between spoofing and living faces. However, the nature of the spoofing faces is to trick anti-spoofing systems by mimicking the livings, therefore the deceptive features between the known attacks and the livings, which have been ignored by existing ZSFA methods, are essential to comprehensively represent the livings. Therefore, existing ZSFA models are incapable of learning the complete representations of living faces and thus fall short of effectively detecting newly emerged attacks. To tackle this problem, we propose an innovative method that effectively captures both the deceptive and discriminative features distinguishing between genuine and spoofing faces. Our method consists of two main components: a two-against-all training strategy and a semantic autoencoder. The two-against-all training strategy is employed to separate deceptive and discriminative features. To address the subsequent invalidation issue of categorical functions and the dominance disequilibrium issue among different dimensions of features after importing deceptive features, we introduce a modified semantic autoencoder. This autoencoder is designed to map all extracted features to a semantic space, thereby achieving a balance in the dominance of each feature dimension. We combine our method with the feature extraction model ResNet50, and experimental results show that the trained ResNet50 model simultaneously achieves a feasible detection of unknown attacks and comparably accurate detection of known spoofing. Experimental results confirm the superiority and effectiveness of our proposed method in identifying the living with the interference of both known and unknown spoofing types. [ABSTRACT FROM AUTHOR]

COVID-19 is an ongoing pandemic disease. To make more accurate diagnosis on COVID-19 than existing approaches, this paper proposed a novel method combining DenseNet and optimization of transfer learning setting (OTLS) strategy. Preprocessing was used to enhance, crop, and resize the collected chest CT images. Data augmentation method was used to increase the size of training set. A composite learning factor (CLF) was employed which assigned different learning factor to three types of layers: frozen layers, middle layers, and new layers. Meanwhile, the OTLS strategy was proposed. Finally, precomputation method was utilized to reduce RAM storage and accelerate the algorithm. We observed that optimization setting "201-IV" can achieve the best performance by proposed OTLS strategy. The sensitivity, specificity, precision, and accuracy of our proposed method were 96.35 ± 1.07, 96.25 ± 1.16, 96.29 ± 1.11, and 96.30 ± 0.56, respectively. The proposed DenseNet-OTLS method achieved better performances than state-of-the-art approaches in diagnosing COVID-19. [ABSTRACT FROM AUTHOR]

Transmission of weak signals in neural networks is crucial for understanding the functionality of brain. In this work, stochastic resonance (SR) in the three neuron FitzHugh–Nagumo (FHN) motifs and its small-world network with higher order motif interactions are studied. Simulation results show that a single motif induces SR and responds better to high-frequency weak signal. Stronger coupling strength within the motif increases the firing rate of the output neurons, resulting in a more pronounced resonance. Considering only the connections within the motif, a higher in-degree of the output neuron or a shorter minimum path length between input and output neurons will lead to a better response to weak signals. SR phenomena can also be observed in small-world networks composed of these motif. Increasing whether the motif coupling or node coupling strength enhances the firing rate of output neurons, amplifying the response. There is a very strong correlation between firing rate of output neurons and response. Our results may provide insights into the propagation of weak signals in higher order networks and the selection of appropriate network topology. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontology (GO) terms are related to the determination of PPIs, suggesting the special patterns on the GO terms of proteins in PPIs. In this study, we explored the special GO term patterns on human PPIs, trying to uncover the underlying functional mechanism of PPIs. The experimental validated human PPIs were retrieved from STRING database, which were termed as positive samples. Additionally, we randomly paired proteins occurring in positive samples, yielding lots of negative samples. A simple calculation was conducted to count the number of positive samples for each GO term pair, where proteins in samples were annotated by GO terms in the pair individually. The similar number for negative samples was also counted and further adjusted due to the great gap between the numbers of positive and negative samples. The difference of the above two numbers and the relative ratio compared with the number on positive samples were calculated. This ratio provided a precise evaluation of the occurrence of GO term pairs for positive samples and negative samples, indicating the latent GO term patterns for PPIs. Our analysis unveiled several nuclear biological processes, including gene transcription, cell proliferation, and nutrient metabolism, as key biological functions. Interactions between major proliferative or metabolic GO terms consistently correspond with significantly reported PPIs in recent literature. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Post-acute sequelae of COVID-19 (PASC) is a persistent complication of severe acute respiratory syndrome coronavirus 2 infection that includes symptoms, such as fatigue, cognitive impairment, and respiratory distress. These symptoms severely affect the quality of life of patients after their recovery from COVID-19. In this study, a group of machine learning algorithms analyzed the whole blood RNA-seq data from patients with different PASC levels. The purpose of this analysis was to identify the gene markers associated with PASC and the special expression patterns for different PASC levels. By comparing the quality of life of patients after the acute phase of COVID-19 and before the disease, samples in the dataset were divided into three groups, namely, "Better," "The Same," and "Worse." Each patient was represented by the expression levels of 58,929 genes. The machine learning-based workflow included six feature-ranking algorithms, incremental feature selection (IFS), and four classification algorithms. The feature ranking algorithms were in charge of assessing feature importance, whereas IFS with classification algorithms were used to extract essential genes and to construct efficient classifiers and classification rules. The expression of top genes in the results was associated with the immune response to viral infection, which is supported by the published literature. For example, patients with low CCDC18 expression and high CPED1 expression had good quality of life, whereas those with low CDC16 expression had poor quality of life. [ABSTRACT FROM AUTHOR]

Energy absorption and consumption are essential for the activity of single neurons and neuronal networks. The synchronization mode transition and energy dependence in a delay-coupled FitzHugh-Nagumo (FHN) neuronal system driven by chaotic activity are investigated in this paper. With the change of chaotic current intensity, it was found that the synchronization mode of coupled neurons undergoes synchronous state, transition state, anti-phase state, alternating asynchronous and anti-phase state, and chaotic current-induced chaotic state. The Hamiltonian energy is much dependent on the synchronization mode of coupled neurons. The synchronization mode and the Hamiltonian energy of coupled neurons can be modulated by chaotic current intensity, coupling strength and time delay. The introduction of the time delay induces the system to become bistable state. Chaotic current as an external force induced transitions between the synchronous and anti-phase states. Coupling strength is an intrinsic property of the system and can change the properties of the bistable state. Furthermore, the synchronous and anti-phase states appear intermittently with the increasing of time delay. A chained neuronal network is used to prove that the synchronization mode transition of the system of multiple neurons is similar to the two neurons. The results of this paper might help one to understand the intrinsic energy alteration mechanisms of neuronal synchronization. [ABSTRACT FROM AUTHOR]

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: The aim of this study was to investigate the anti-tumor effect of resveratrol (RSV) on glioblastoma (GBM) and its specific mechanism in improving the inflammatory response of the tumor microenvironment. The tumor microenvironment of GBM is highly neuroinflammatory, inducing tumor immunosuppression. Therefore, ameliorating the inflammatory response is an important focus for anti-tumor research. Methods: The anti-tumor effect of RSV on GBM was demonstrated through in vitro cellular assays, including CCK-8, EdU, PI staining, Transwell, wound healing assay, and flow cytometry. Potential mechanisms of RSV's anti-GBM effects were identified through network pharmacological analysis. In addition, the relationship of RSV with the JAK2/STAT3 signaling pathway and the inflammasome NLRP3 was verified using Western blot. Results: RSV significantly inhibited cell viability in GBM cell lines LN-229 and U87-MG. Furthermore, it inhibited the proliferation and invasive migration ability of GBM cells, while promoting apoptosis. Network pharmacological analysis revealed a close association between the anti-GBM effects of RSV and the JAK/STAT signaling pathway, as well as inflammatory responses. Western blot analysis confirmed that RSV inhibited the over-activation of the inflammasome NLRP3 through the JAK2/STAT3 signaling pathway. Partial reversal of RSV's inhibition of inflammasome NLRP3 was observed with the addition of the JAK/STAT agonist RO8191. Conclusions: In vitro, RSV can exert anti-tumor effects on GBM and improve the inflammatory response in the GBM microenvironment by inhibiting the activation of the JAK2/STAT3 signaling pathway. These findings provide new insights into potential therapeutic targets for GBM. [ABSTRACT FROM AUTHOR]

AlCrCuFeMnx (x=0, 0.5, 1, 1.5, and 2) high-entropy alloys were prepared using the vacuum arc melting technology. The microstructure and mechanical properties of AlCrCuFeMnx were analyzed and tested by XRD, SEM, TEM, nanoindentation, and electronic universal testing. The results indicate that the AlCrCuFeMnx high-entropy alloy exhibits a dendritic structure, consisting of dendrites with a BCC structure, interdendrite regions with an FCC structure, and precipitates with an ordered BCC structure that form within the dendrite. Manganese (Mn) has a strong affinity for dendritic, interdendritic, and precipitate structures, allowing it to easily enter these areas. With an increase in Mn content, the size of the precipitated nanoparticles in the dendritic region initially increases and then decreases. Similarly, the area fraction initially decreases and then increases. Additionally, the alloy's strength and wear resistance decrease, while its plasticity increases. The AlCrCuFeMn1.5 alloy boasts excellent mechanical properties, including a hardness of 360 HV and a wear rate of 2.4×10-5 mm³ ·N-1·mm-1. It also exhibits impressive yield strength, compressive strength, and deformation rates of 960 MPa, 1,700 MPa, and 27.5%, respectively. [ABSTRACT FROM AUTHOR]

Stochastic resonance and energy consumption are significant for information processing and transmission in the neural system. In this paper, we constructed an excitatory–inhibitory cortical neuronal network to investigate the response of the system to weak signals and the corresponding energy consumption. The findings indicate that the excitability of neurons modulates the performance of signal response. Furthermore, the performance of signal response exhibits a bell-shaped dependence on ion channel noise, which is a typical manifestation of the stochastic resonance phenomenon. Stochastic resonance also exists in the network with increasing noise at different excitatory coupling strengths and inhibitory coupling strengths. Furthermore, it is found that the neuronal system obtains optimal transmission of the weak signal at a lower energy consumption. It illustrates that there is a certain economy and efficiency in the signal transmission. At weak inhibitory coupling strength, an optimal excitatory coupling strength exists to allow the neuronal network to make the optimal transmission of the weak signal. However, the phenomenon of double resonant peaks occurs at strong inhibitory coupling strength, which is due to the balance of excitatory and inhibitory synaptic currents. Finally, we demonstrated the robustness of the results to network topology and initial conditions. The results of this paper may contribute to the understanding of signal transmission and its energy consumption in cortical networks. [ABSTRACT FROM AUTHOR]

Amongst all types of cancer, breast cancer has become one of the most common cancers in the UK threatening millions of people's health. Early detection of breast cancer plays a key role in timely treatment for morbidity reduction. Compared to biopsy, which takes tissues from the lesion for further analysis, image-based methods are less time-consuming and pain-free though they are hampered by lower accuracy due to high false positivity rates. Nevertheless, mammography has become a standard screening method due to its high efficiency and low cost with promising performance. Breast mass, as the most palpable symptom of breast cancer, has received wide attention from the community. As a result, the past decades have witnessed the speeding development of computer-aided systems that are aimed at providing radiologists with useful tools for breast mass analysis based on mammograms. However, the main issues of these systems include low accuracy and require enough computational power on a large scale of datasets. To solve these issues, we developed a novel breast mass classification system called DF-dRVFL. On the public dataset DDSM with more than 3500 images, our best model based on deep random vector functional link network showed promising results through five-cross validation with an averaged AUC of 0.93 and an average accuracy of 81.71 % . Compared to sole deep learning based methods, average accuracy has increased by 0.38. Compared with the state-of-the-art methods, our method showed better performance considering the number of images for evaluation and the overall accuracy. [ABSTRACT FROM AUTHOR]

Purpose: There is no approved targeted therapy for chordoma at present. Although several preclinical studies have implied the potential applicability of CDK4/6 inhibitor for this rare tumor, no clinical evidence has been documented so far. The purpose of this study was to elucidate the therapeutic efficacy of CDK4/6 inhibitor for chordoma. Methods: The next generation sequencing (as for whole-exome sequencing, WES assay) and immunohistochemical (IHC) staining of the chordoma tissue from a patient with an advanced lesion were performed before treatment. Then, the patient was treated with Palbociclib for 4 months until progression occurred in the 5th month. Surgical resection was implemented and the tumor tissue was obtained postoperatively for assessment of molecular alterations. Results: Molecular features of the tumor before medical treatment suggested applicability of CDK4/6 inhibitor and the patient showed partial response (PR) according to Choi Criteria after 4 months treating with Palbociclib until progression occurred. Then, a drastic molecular alteration of the tumor as represented by emergence of dramatic E2F amplification, which is known to induce CDK4/6 independent cell-cycle entry and progression after treatment, was detected. The findings in this patient demonstrated tumor evolution under drug pressure. Conclusion: The findings of the present study suggest the feasibility of Palbociclib for the clinical treatment of chordoma, and imply the necessity of combination therapies rather single drug administration due to the quick resistance of the tumor to Palbociclib treatment. [ABSTRACT FROM AUTHOR]

This paper presents a reconfigurable satellite ground microgravity simulation system based on a parallel mechanism, which allows cxsfor adjustable gravity coefficients and can simulate three-dimensional space movement with fast response and high accuracy. Firstly, the parallel motion platform and parallel six-dimensional force sensor designed specifically for the microgravity simulation system serve as the mechanical structure of the system. Secondly, a control system for simulating microgravity has been proposed, which includes a data acquisition component and a motion control component. Thirdly, a novel microgravity simulation algorithm, which can adjust the gravity coefficient and is based on the constant variation method, was proposed to establish the mapping relationship between the six-dimensional external force and displacement. Finally, the six-dimensional force sensor is statically calibrated and demonstrated excellent measurement performance. After implementing gravity compensation through surface polynomial fitting, the motion platform for microgravity simulation can react within 0.15 s upon detection of a force signal by the sensor, with a response error of less than 3%. The ground microgravity simulation system based on parallel mechanisms has been successfully applied to test the tolerance capability of reconfigurable satellite docking interfaces. [ABSTRACT FROM AUTHOR]

Brain tumor is a life-threatening disease with fast growth rate, which makes its detection a critical task. However, low contrast and noise content in brain magnetic resonance images (MRI) hampers the screening of brain tumor. Therefore, contrast enhancement of these images are necessary to obtain a more definitive imaging for tumor detection. This paper presents an optimized enhancement model for processing Brain MRI by employing morphological filters in coherence with human visual system (HVS) system. The HVS coherence in response of filtering process is incorporated by combination of top-hat and bottom-hat morphological operators using logarithmic image processing model. Application of morphological filter requires selection of structuring element of requisite shape and size to ensure precision in brain tumor detection. This process is challenging as brain tumors (in MRI) may vary rigorously in size and morphology with each case or stages of tumor. Herein, this constraint has been resolved by using a disk-shaped structuring element whose order (size) is optimized using particle swarm optimization algorithm. The enhancement results are quantitatively evaluated using image quality measurement parameters like contrast improvement index, average signal to noise ratio, peak signal to noise ratio and measure of enhancement. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, and recent epidemiological studies suggested type 2 diabetes mellitus (T2DM) is an independent risk factor for the development of AF. Zinc finger and BTB (broad-complex, tram-track and bric-a-brac) domain containing 16 (Zbtb16) serve as transcriptional factors to regulate many biological processes. However, the potential effects of Zbtb16 in AF under T2DM condition remain unclear. Here, we reported that db/db mice displayed higher AF vulnerability and Zbtb16 was identified as the most significantly enriched gene by RNA sequencing (RNA-seq) analysis in atrium. In addition, thioredoxin interacting protein (Txnip) was distinguished as the key downstream gene of Zbtb16 by Cleavage Under Targets and Tagmentation (CUT&Tag) assay. Mechanistically, increased Txnip combined with thioredoxin 2 (Trx2) in mitochondrion induced excess reactive oxygen species (ROS) release, calcium/calmodulin-dependent protein kinase II (CaMKII) overactivation, and spontaneous Ca2+ waves (SCWs) occurrence, which could be inhibited through atrial-specific knockdown (KD) of Zbtb16 or Txnip by adeno-associated virus 9 (AAV9) or Mito-TEMPO treatment. High glucose (HG)-treated HL-1 cells were used to mimic the setting of diabetic in vitro. Zbtb16-Txnip-Trx2 signaling-induced excess ROS release and CaMKII activation were also verified in HL-1 cells under HG condition. Furthermore, atrial-specific Zbtb16 or Txnip-KD reduced incidence and duration of AF in db/db mice. Altogether, we demonstrated that interrupting Zbtb16-Txnip-Trx2 signaling in atrium could decrease AF susceptibility via reducing ROS release and CaMKII activation in the setting of T2DM. [ABSTRACT FROM AUTHOR]

Molecular folding regulation with environmental stimuli is critical in living and artificial molecular machine systems. Herein, we described a macrocycle, cyclo[4] (1,3-(4,6-dimethyl)benzene)[4](1,3-(4,6-dimethyl)benzene)(4-pyridine). Under 298 K, it has three stable stiff atropisomers with names as 1 (Cs symmetry), 2 (Cs symmetry), and 3 (C4v symmetry). At 393 K, 1 can reversibly transform into 2, but at 473 K, it can irrevocably transform into 3. At 338 K, 3 and (PhCN)2PdCl2 complex to produce the metal-organic cage 4. Only at 338 K does the combination of 1 or 2 and (PhCN)2PdCl2 create a gel-like structure. Heating both gels to 473 K transforms them into 4. In addition to offering a thermally accelerated method for modifying self-assembled systems using macrocyclic building blocks, this study also has the potential to develop the nanoscale transformation material with a thermal response. While heat modification is ubiquitous in biologic systems, it remains a significant challenge in artificial systems. Here the authors demonstrate thermal regulation of conversion between different rigid macrocycle atropisomers and related self-assemblies. [ABSTRACT FROM AUTHOR]

Immunological approaches are gaining attention as a convenient and economical method for sex-sorting mammalian spermatozoa. A monoclonal antibody (WholeMom™) has previously been reported to cause agglutination of Y-chromosome-bearing spermatozoa in frozen-thawed semen for gender preselection. However, its usefulness for gender preselection in fresh semen and subsequent in vitro fertilization (IVF) after freeze-thawing has not been reported. This study investigated the in vitro development of cattle embryos produced from fresh bull semen pre-treated with WholeMom™ monoclonal antibody. Results showed that antibody-treated, non-agglutinated spermatozoa (presumably X-chromosome-bearing spermatozoa) could fertilize cattle oocytes in vitro. However, embryos generated from non-agglutinated (enriched in X-chromosome-bearing spermatozoa) had a lower (p < 0.05) ability to cleave (66.4 ± 2.5% vs. 75.1 ± 3.3%) than those of non-treated control sperm. Nevertheless, the percentage of blastocysts developed from cleaved embryos did not differ (p > 0.05) between the groups (34.8 ± 3.7% vs. 35.8 ± 3.4%). Duplex PCR of blastocysts, using a bovine-specific universal primer pair and a Y-chromosome-specific primer pair, showed a sex ratio of 95.8% females from sex-sorted spermatozoa, which was higher than those of non-treated control spermatozoa (46.4%). In conclusion, the results of the present study suggest that monoclonal antibody-based enrichment of X- chromosome-bearing spermatozoa can be applied to fresh bull semen without compromising their post-fertilization early embryonic development to the blastocyst stage. Future studies should investigate the term development and sex ratio of calves from antibody-treated spermatozoa. [ABSTRACT FROM AUTHOR]

Introduction: Psoriasis, an incurable chronic inflammatory disease, affects over 6 million people in China. Ixekizumab, a monoclonal antibody against interleukin-17A, has demonstrated efficacy and safety for the treatment of moderate-to-severe plaque psoriasis, although limited data are available regarding its use in routine clinical practice in China. We investigated the real-world application of ixekizumab in China. Methods: Adults (≥ 18 years) with moderate-to-severe plaque psoriasis prescribed ixekizumab in routine clinical practice were enrolled in this prospective, observational, single-arm, multicenter, post-marketing surveillance study. The primary endpoint was the safety of ixekizumab at week 12. The effectiveness of ixekizumab, based on the Psoriasis Area and Severity Index (PASI) and Dermatology Life Quality Index (DLQI), was assessed as a secondary endpoint. Results: In total, 666 patients were enrolled; 663 were included in the safety analysis, and 612 in the effectiveness analysis. At least one adverse event (AE) was reported by 42.7% (283/663) of patients, most of which were mild (242/283, 85.5%), and 32.7% (217/663) of patients reported AEs related to study treatment. The most frequently reported AEs were injection site reactions. AEs led to discontinuation in five patients (0.8%). Only three patients had a serious AE. Mean ± standard deviation (SD) change from baseline in PASI score was reduction in 10.79 ± 9.55 at week 2 and 16.80 ± 12.15 at week 12. At week 2, 63.7% of patients achieved PASI 50. At week 12, 93.2%, 77.4%, and 45.1% of patients achieved PASI 75, PASI 90, and PASI 100, respectively. Mean ± SD change from baseline in DLQI was reduction in 5.91 ± 6.27 at week 2 and 9.76 ± 7.16 at week 12. DLQI 0/1 was achieved by 19.8% and 59.9% of patients at week 2 and 12, respectively. Conclusion: Ixekizumab was well tolerated and effective in real-world clinical practice in Chinese adults with moderate-to-severe plaque psoriasis. [ABSTRACT FROM AUTHOR]

With the continuous development of the Internet of Vehicles(IoV), Vehicle Edge Computing(VEC) has become a key technology for computational resource scheduling, but more and more smart devices are connected to the internet, which makes it difficult for traditional Vehicle Edge Networks(VEN) to deal with tasks in time. In this paper, in order to cope with the challenges of the large number of devices accessing the internet, we propose a vehicle-assisted computation offloading algorithm based on proximal policy optimization(VCOPPO) for User Equipment(UE) tasks, and it combines dynamic parked vehicles incentives mechanism and computational resource allocation strategy by using road vehicles and parked vehicles as edge servers. Firstly, a non-convex optimization problem combining VEN utility and task processing delay is formulated, subject to the constraints of the residual energy and the transmission rate of the task. Secondly, the proposed VCOPPO is used to solve the formulated non-convex optimization problem, and we use stochastic policy to obtain the optimal computation offloading decisions and resource allocation schemes. Finally, the experimental results have shown that the proposed VCOPPO has an excellent performance in network reward and task processing delay respectively, and it can effectively schedule and allocate computational resources. Compared with using Dueling Deep Q Network(Dueling DQN), Deep Q Network(DQN) and Q-learning methods, the proposed VCOPPO improves the network reward by 31%, 18% and 91%, reduces the delay in task processing by 78%, 63% and 74%, respectively. [ABSTRACT FROM AUTHOR]

The classical microbubble generator of the venturi nozzle type is still used in a wide range of industries despite its relatively low performance. In this study, for overcoming the relatively low performances and maintaining the existing advantages, the cylindrical venturi type of microbubble generators with screw structures and flared diffusers are proposed using conventional 3D printers. In the experimental facility, flow rates and pressure drop were measured. Using high speed images, the performances were deduced for prepared microbubble generators. Based on experimental data, the effect of screw structures for performances of microbubble generators was identified. Furthermore, the flared diffuser to the diffuser of regular venturi nozzle with constant diverging angle were compared. Additionally, synergies are obtained by the screw structure in the flare diffuser. In conclusion, the performances of the venturi type microbubble generator through optimal designed screw structures by 39.4 % were improved. [ABSTRACT FROM AUTHOR]

As individuals are involved in self-quantification, increasing fields are applying self-quantification with technical support, and a clear understanding of the influencing mechanism and effect boundary of self-quantification is indispensable. Focusing on the field of physical fitness, under different goal settings, the influencing mechanisms of self-quantification on an individual's participation performance (outcome, experience, persistence intention) and behavioral decision-making (category selection, novelty-seeking) are analyzed and confirmed. The results show that when there is no goal, self-quantification will enhance outcome salience, thus enhancing the individual's participation outcome and selection for highly healthy categories; self-quantification will reduce perceived enjoyment, thus damaging participation experience and novelty-seeking, but does not affect the persistence intention. When there is a goal, self-quantification will enhance perceived certainty, thus reducing the individual's participation outcome and selection for highly healthy categories; self-quantification will enhance perceived enjoyment, thus improving the participation experience, persistence intention, and novelty-seeking. Compared to descriptive analysis of phenomena, through laboratory and field experiments, this research confirms the influencing mechanism and effect boundary of self-quantification on an individual's activity participation performance in real situations. This provides a theoretical basis for understanding the targeted behavioral decision-making patterns of individuals in different types of self-quantification activities. [ABSTRACT FROM AUTHOR]

Background: To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy. Methods: Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction. Results: RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D'Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score. Conclusions: AI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification. [ABSTRACT FROM AUTHOR]

Purpose: For the drop-induced shock mitigation system with magnetorheological energy absorbers (MREAs), the time lag of MREA has a nonnegligible influence on the shock mitigation performance. This paper proposed a minimum transmitted load control method considering time lag (MTL-T) is proposed. Methods: First, the influence of a time lag on the shock mitigation performance is analyzed, and the relationship between the theoretical controllable force and the actual controllable force of the MREA with a time lag is given. Then, taking the controllable force of traditional MTL control method as the theoretical value and the "soft-landing" as the control target, the control algorithm of MTL-T control method is derived. Finally, the simulation is conducted for the shock mitigation system with variable parameters controlled by MTL-T, and the comparative performance of MTL-T control method and other methods is given. Results: The theoretical analysis shows that MTL-T control method can make the shock mitigation system with a time lag achieve the control target of "soft-landing". The advantages of MTL-T control method are shown by the comparative performance of the MTL-T control method and other methods. Conclusions: Once the parameters of the system and the impact intensity are obtained, the MTL-T can be determined. The adaptability of MTL-T control method is verified. Compared with other control method, the MTL-T has a small peak transmitted load while making full use of MREA. [ABSTRACT FROM AUTHOR]

COVID-19 has triggered 6.42 million death tolls, and more than 586 million confirmed positive cases until 10/Aug/2022. CT-based diagnosis methods need special expert knowledge, and the labeling procedure is tedious. We first propose a 12-layer CNN-based backbone network. Then, we utilize the Swish activation function to replace traditional ReLU. The multiple-way data augmentation is utilized to enhance the training set. Our model is named Swish-based CNN (SCNN). A web app is developed based on the proposed SCNN model. The SCNN model performs better than the ReLU-based backbone network and LReLU-based backbone network, indicating the effectiveness of the Swish function. The SCNN model achieves a sensitivity of 94.50 ± 1.06, a specificity of 95.25 ± 0.59, and an accuracy of 94.88 ± 0.65. It performs better than ten state-of-the-art COVID-19 diagnosis methods. Our SCNN model is promising in diagnosing COVID-19. The developed web app can help the users upload their own images and give the prediction results. [ABSTRACT FROM AUTHOR]

COVID-19 has caused over 6.35 million deaths and over 555 million confirmed cases till 11/July/2022. It has caused a serious impact on individual health, social and economic activities, and other aspects. Based on the gray-level co-occurrence matrix (GLCM), a four-direction varying-distance GLCM (FDVD-GLCM) is presented. Afterward, a five-property feature set (FPFS) extracts features from FDVD-GLCM. An extreme learning machine (ELM) is used as the classifier to recognize COVID-19. Our model is finally dubbed FECNet. A multiple-way data augmentation method is utilized to boost the training sets. Ten runs of tenfold cross-validation show that this FECNet model achieves a sensitivity of 92.23 ± 2.14, a specificity of 93.18 ± 0.87, a precision of 93.12 ± 0.83, and an accuracy of 92.70 ± 1.13 for the first dataset, and a sensitivity of 92.19 ± 1.89, a specificity of 92.88 ± 1.23, a precision of 92.83 ± 1.22, and an accuracy of 92.53 ± 1.37 for the second dataset. We develop a mobile app integrating the FECNet model, and this web app is run on a cloud computing-based client–server modeled construction. This proposed FECNet and the corresponding mobile app effectively recognize COVID-19, and its performance is better than five state-of-the-art COVID-19 recognition models. [ABSTRACT FROM AUTHOR]

Transparent and FAIR disclosure of meta-information about healthcare data and infrastructure is essential but has not been well publicized. In this paper, we provide a transparent disclosure of the process of standardizing a common data model and developing a national data infrastructure using national claims data. We established an Observational Medical Outcome Partnership (OMOP) common data model database for national claims data of the Health Insurance Review and Assessment Service of South Korea. To introduce a data openness policy, we built a distributed data analysis environment and released metadata based on the FAIR principle. A total of 10,098,730,241 claims and 56,579,726 patients' data were converted as OMOP common data model. We also built an analytics environment for distributed research and made the metadata publicly available. Disclosure of this infrastructure to researchers will help to eliminate information inequality and contribute to the generation of high-quality medical evidence. [ABSTRACT FROM AUTHOR]

Thermal growth oxide (TGO) is produced when a thermal barrier coating (TBC) works, and the generated TGO is nonuniform at the peak and valley of the TBC interface. The aim of this paper is to clarify whether the nonuniform TGO thickness model is better than the uniform thickness model in TBC simulation. First, finite element models of TGO with nonuniform and uniform thickness are established based on the TBC tube experiments. Then, the vertical stress perpendicular to the TC interface morphology and the corresponding shear stress are analyzed based on the stress transformation equations. Finally, the fatigue life of the TBC is analyzed by nonuniform and uniform TGO thickness models. The results show that the area of the local maximum of the vertical stress and the shear stress for different TGO thicknesses is 4-10 µm from the TC interface morphology peak, which can be considered the risk area for fatigue failure of the TC interface. The risk area in the uniform TGO thickness model is consistent with that of the nonuniform thickness model. The fatigue life prediction error of the TBC using the nonuniform TGO thickness model is 29.53%, which is smaller than that of the uniform thickness model, proving the need to consider the nonuniform thickness of the TGO in TBC simulations. The fatigue life prediction result is verified by different TBC experiments. The conclusions of this study can provide guidance for TBC simulation analysis in the future. [ABSTRACT FROM AUTHOR]

In this paper, a new hysteresis model with resistor–capacitor (RC) operator for magnetorheological dampers (MRD) is proposed, which takes the excitation current, frequency, and amplitude as input variables. A hysteresis model based on RC operator is established, and the influence of each parameter in the model on the nonlinear behavior of MRD is given. The parameters are fitted based on the test result of an MRD with double rod. The fitting result of parameters shows that the amplitude and frequency of the excitation also affect the parameter values of the model. Base on this, the new hysteresis model is proposed. The parameter fitting results of the proposed model is good. The accuracy of the proposed model and the ability of the model to predict the nonlinear behavior of MRD has been validated by comparing with the experimental data and the RC operator-based model. The comparison results show that the proposed model has higher description accuracy and stronger prediction ability for nonlinear characteristics of MRD. [ABSTRACT FROM AUTHOR]