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Background and objectives: Obesity is a well-known cause of cardiovascular disease and metabolic disorders. Normal-weight obesity, where the body mass index(BMI) is within the normal range but the body fat percentage is high, also adversely impacts cardiovascular and metabolic health. This study explored the effects of whole-body circuit training using a weighted vest on serum resistin, insulin resistance, and cardiovascular disease risk factors in normal-weight obese women. Methods: Thirty-six normal-weight obese women were divided into three groups: Weighted Vest Circuit Training(WVCT)(n = 12), Body Weight Circuit Training(BWCT)(n = 12), and a Control group(CON)(n = 12). Participants in the WVCT and BWCT groups engaged in whole-body circuit training three times per week for eight weeks. Serum resistin, cardiovascular disease risk factors, and insulin resistance were measured before and after the intervention. Results: The study revealed significant and impactful findings. There were substantial improvements in body composition(Skeletal Muscle Mass: +7.5 %, p = 0.042, d = 0.80), Serum Resistin(-38.2 %, p = 0.001, d = 0.85), insulin resistance(HOMA-IR: 27.1 %, p

Abstract Background Recent studies have reported the inhomogeneity in the light emitted by dental light-curing units (LCUs). It is essential to understand how this uneven light distribution affects the physical properties of resin-based composites (RBCs) at various points across their surfaces. This study aimed to evaluate the effect of LCU beam’s inhomogeneity on the microhardness of RBCs with different shades and thicknesses. Methods Four body (A1B, A2B, A3B, and A4B), one dentin (A3D), and one enamel shade (A3E) of RBC (Filtek Z350 XT) were examined. The specimens were fabricated in four thicknesses (1, 2, 3, and 4 mm) and subjected to a 40-second light-curing. Vickers microhardness testing was performed at the center point, and 3 mm left and right from the center at the bottom surface of each sample. The LCU beam profile was characterized using a beam profiler, while irradiance after specimen passage was measured using a spectrometer. One-way analysis of variance (ANOVA) and Tukey’s post-hoc tests were used to analyze the effects of shades and thicknesses on irradiance and microhardness, respectively. One-way repeated-measures ANOVA was used to compare the microhardness across different points. Pearson’s correlation analysis examined the relationship between irradiance and microhardness. Results The beam profile of LCU revealed inhomogeneous light distribution. Light irradiance was decreased with both the increase in thickness and darker shade of the specimens (p

Abstract We present quad-layered reflective structural color filters generating vivid additive primary colors by controlling a mode number in a Fabry–Perot (FP) cavity and an anti-reflective (AR) coating layer, thus accomplishing high spectral contrast which is highly demanded in creating sharp colors. The reflection brightness of fabricated structural color filters is over 78% and a color gamut is comparable to the standard color gamut (sRGB). Higher-order resonant modes are exploited yielding a narrow passband with strong suppression of the reflection at shorter and longer wavelength ranges for a green color, while red and blue colors are produced by employing fundamental resonant modes. Besides, the structural color filters maintain both high brightness and high color purity at oblique incidence angles up to 40° due to a small angle of refraction by a cavity medium with high refractive index. Moreover, a large-scale fabrication is enabled owing to the simplicity of a device structure, where thin film deposition is used. The scheme presented in this work may open the door to a number of applications, such as reflective displays, imaging devices, colored photovoltaics, and decorations.

Summary: Extracellular vesicles (EVs) are membranous nanoparticles classified based on their size and surface markers, which can be specific to various cell origins. Here, we present a protocol for the isolation of pulmonary-specific EVs in mice. We describe steps for differential centrifugation, density gradient centrifugation, and commercially available polyethylene glycol(PEG)-based precipitation, employing pulmonary-specific EV-bound chemicals and antibodies. We then detail procedures for the characterization of these EVs through nanoparticle tracking analysis, flow cytometry, scanning electron microscopy, and transmission electron microscopy.For complete details on the use and execution of this protocol, please refer to Lee et al.1,2,3,4 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

PURPOSE This study sought to investigate the effects of passive warm-up on flexibility, exercise performance, and lactate oxidation rate in track and field athletes. METHODS A total of eight male athletes with more than three years of athlete experience were recruited as participants, and passive warm-up (PW) and active warm-up (AW) treatments were conducted in a single-group crossover study design. The participants performed thermal stimulation at 40°C for 20 minutes as a PW and performed a 60-70% HRmax cycle as an AW. Flexibility and exercise performance were measured after each treatment. Anaerobic power was measured using the Wingate test, and lactic acid concentration was measured. RESULTS Body temperature significantly increased in both PW and AW, and no significant difference was observed in exercise performance between treatments. Flexibility and lactic acid oxidation rate were significantly higher in PW than in AW. CONCLUSIONS In track and field sprinters, PW did not exhibit any significant difference in anaerobic power and exercise performance compared to AW even though no physical exercise was performed, and PW was effective in body temperature, lactic acid oxidation rate, and flexibility. PW suggests the possibility of replacing AW.

Abstract Background Recent advancements in positron emission tomograph (PET) using prostate specific membrane antigen (PSMA)-targeted radiopharmaceuticals have changed the standard of care for prostate cancer patients by providing more accurate information during staging of primary and recurrent disease. [68Ga]Ga-P16-093 is a new PSMA-PET radiopharmaceutical that demonstrated superior imaging performance in recent head-to-head studies with [68Ga]Ga-PSMA-11. To improve the availability of this new PSMA PET imaging agent, [18F]AlF-P16-093 was developed. The 18F-analog [18F]AlF-P16-093 has been synthesized manually at low activity levels using [18F]AlF2+ and validated in pre-clinical models. This work reports the optimization of the production of > 15 GBq of [18F]AlF-P16-093 using a custom automated synthesis platform. Results The sensitivity of the radiochemical yield of [18F]AlF-P16-093 to reaction parameters of time, temperature and reagent amounts was investigated using a custom automated system. The automated system is a low-cost, cassette-based system designed for 1-pot syntheses with flow-controlled solid phase extraction (SPE) workup and is based on the Raspberry Pi Zero 2 microcomputer/Python3 ecosystem. The optimized none-decay-corrected yield was 52 ± 4% (N = 3; 17.5 ± 2.2 GBq) with a molar activity of 109 ± 14 GBq/µmole and a radiochemical purity of 98.6 ± 0.6%. Run time was 30 min. A two-step sequence was used: SPE-purified [18F]F− was reacted with 80 nmoles of freeze-dried AlCl3·6H2O at 65 °C for 5 min followed by reaction with 160 nmoles of P16-093 ligand at 40 °C for 4 min in a 1:1 mixture of ethanol:0.5 M pH 4.5 NaOAc buffer. The mixture was purified by SPE (> 97% recovery). The final product formulation (5 mM pH 7 phosphate buffer with saline) exhibited a rate of decline in radiochemical purity of ~ 1.4%/h which was slowed to ~ 0.4%/h when stored at 4 °C. Conclusion The optimized method using a custom automated system enabled the efficient (> 50% none-decay-corrected yield) production of [18F]AlF-P16-093 with high radiochemical purity (> 95%). The method and automation system are simple and robust, facilitating further clinical studies with [18F]AlF-P16-093.

Human hair segmentation is essential for face recognition and for achieving natural transformation of style transfer. However, it remains a challenging task due to the diverse appearances and complex patterns of hair in image. In this study, we propose a novel method utilizing diffusion-based generative models, which have been extensively researched in recent times, to effectively capture and to finely segment human hair. In diffusion-based models, an internal visual representation during the denoising process contains pixel-level rich information. Inspired by this aspect, we introduce diffusion-based models for segmenting fine-grained human hair. Specifically, we extract the representation from the diffusion-based models, which contains pixel-level semantic information, and then train a segmentation network using it. Particularly, to more finely segment human hair, our approach employs the representation from a text-to-image diffusion model, conditioned on text information, to extract more relevant information for human hair, thereby predicting detailed hair masks. To validate our method, we conducted experiments on three distinct hair-related datasets with unique characteristics: Figaro-1k, CelebAMask-HQ, and Face Synthetics. The experimental results show the improved performance of our proposed method across all three datasets, outperforming existing methods in terms of mIoU (mean intersection over union), accuracy, precision, and F1-score. This is particularly evident in its ability to accurately capture and finely segment human hair from background and non-hair elements. This demonstrates the effectiveness of our method in accurately and finely segmenting human hair with complex characteristics. Our research contributes not only to the fine-grained segmentation of human hair but also to the application of generative models in semantic segmentation tasks. We hope that the proposed method will be applied for detailed semantic segmentation in various fields in the future.

Abstract Intermediate-depth earthquakes are common in the double seismic structures of many subduction zones under high pressures (~1–4 GPa). Serpentine dehydration exhibits well-established links with double seismic zone earthquakes. Additionally, dehydration of several hydrous minerals including lawsonite and chlorite underlying the upper and lower layers, respectively, may be responsible for intermediate-depth earthquakes. Here, we present experimental evidence suggesting that chlorite dehydration can trigger intermediate-depth earthquakes at the lower plane (~700 °C). We conducted deformation experiments on chlorite peridotite under high-pressure (0.5–2.5 GPa) and high-temperature (500–750 °C) using a modified Griggs apparatus. Experiments revealed the presence of faults in samples that had undergone partial chlorite dehydration with the presence of the dehydration product Ca-amphibole along these faults. Our findings confirm, together with correlation studies between seismicity and mineral stability, that a part of intermediate-depth seismicity in the lower plane of double seismic zones can be attributed to chlorite dehydration.

The apple is a significant global fruit cultivated extensively worldwide. Fire blight, caused by Erwinia amylovora (Ea), poses a significant threat to global apple production. To control this disease, characterizing the virulence mechanisms/factors is imperative. Carbamoyl phosphate synthase is an essential enzyme in the biosynthesis of arginine and pyrimidine. However, the functions of this protein in Ea remains poorly understood. This study aimed to investigate the functions of the carbamoyl phosphate synthase large subunit in Ea (CarBEa). In a virulence assay using fruitlets, an Ea strain lacking CarBEa exhibited significantly reduced virulence on fruitlets. In the auxotrophy assay, this mutant failed to grow in minimal media lacking both arginine and pyrimidine, but growth was restored when both compounds were supplemented. The comparative proteomic analysis suggests that CarBEa is involved in diverse biological processes, including amino acid and nucleotide metabolism, and inorganic ion transport. Finally, we demonstrated that CarBEa is related to siderophore secretion/production by the chrome azurol S agar plate assay. This report provides valuable insights into the functions of carbamoyl phosphate synthase large subunit, which serves as a potential target for developing efficient anti-virulence substances to control fire blight.

The coexistence of carbon neutrality and capitalism can be realized if environmentally friendly management is proven to enhance profitability. The focus of green logistics practices (GLPs) is on maximizing energy efficiency in logistics services, potentially leading to cost reductions for companies. Such practices can be supported through public reports, such as Environmental, Social, and Governance (ESG) reports, and government policies, including government certification. The primary objective of this study is to investigate whether the financial efficiency of logistics companies can be improved through the implementation of GLPs and a relevant government policy. To achieve this objective, a two-stage analysis was conducted using Data Envelopment Analysis - Slack-Based Measure (DEA-SBM) and Tobit regression analysis. The DEA-SBM was employed to assess whether the financial efficiency of logistics companies adopting GLPs and a relevant government policy was superior to that of companies not adopting such practices. Additionally, Tobit regression analysis was employed to analyze the effects of GLPs, ESG reports, and a government policy on the financial efficiency of logistics companies. The findings of the study indicate the necessity for collaboration between the private and public sectors to implement GLPs in a financially positive manner. In conclusion, the research demonstrated that the implementation of GLPs, coupled with public disclosure through ESG reports or equivalents and a government policy, had a positive impact on the financial efficiency of logistics companies.

Intracellular assemblies play vital roles in maintaining cellular functions through structural recognition‐mediated interactions. The introduction of artificial structures has garnered substantial interest in modulating cellular functions via activation/inhibition interactions with biomacromolecules. However, the cellular uptake of these high‐molecular‐weight structures may limit their performance. Recently, intracellular chemical‐reaction‐induced self‐assembly has emerged as a promising strategy for generating in situ nanostructures with biofunctionalities for interacting with biomacromolecules. This approach addresses the challenge of synthetic reactions occurring in complex intracellular environments by utilizing diverse chemical reactions that respond to endogenous and exogenous stimuli. This review provides an overview of the latest advancements in intracellular chemical‐reaction‐induced self‐assembly techniques. It focuses on their responsiveness to specific endogenous conditions, such as redox environments and overexpressed enzymes. Additionally, the initiation of chemical reactions through exogenous stimuli, including chemical reagents and irradiation is explored. Polymerization‐induced hydrophobicity is highlighted, leading to self‐assembly into micro‐/nanostructures. These processes contribute to the in situ construction of synthetic materials with diverse morphologies, offering versatile functionalities for biological applications.

BackgroundVancomycin pharmacokinetics are highly variable in patients with critical illnesses, and clinicians commonly use population pharmacokinetic (PPK) models based on a Bayesian approach to dose. However, these models are population-dependent, may only sometimes meet the needs of individual patients, and are only used by experienced clinicians as a reference for making treatment decisions. To assist real-world clinicians, we developed a deep learning–based decision-making system that predicts vancomycin therapeutic drug monitoring (TDM) levels in patients in intensive care unit. ObjectiveThis study aimed to establish joint multilayer perceptron (JointMLP), a new deep-learning model for predicting vancomycin TDM levels, and compare its performance with the PPK models, extreme gradient boosting (XGBoost), and TabNet. MethodsWe used a 977-case data set split into training and testing groups in a 9:1 ratio. We performed external validation of the model using 1429 cases from Kangwon National University Hospital and 2394 cases from the Medical Information Mart for Intensive Care–IV (MIMIC-IV). In addition, we performed 10-fold cross-validation on the internal training data set and calculated the 95% CIs using the metric. Finally, we evaluated the generalization ability of the JointMLP model using the MIMIC-IV data set. ResultsOur JointMLP model outperformed other models in predicting vancomycin TDM levels in internal and external data sets. Compared to PPK, the JointMLP model improved predictive power by up to 31% (mean absolute error [MAE] 6.68 vs 5.11) on the internal data set and 81% (MAE 11.87 vs 6.56) on the external data set. In addition, the JointMLP model significantly outperforms XGBoost and TabNet, with a 13% (MAE 5.75 vs 5.11) and 14% (MAE 5.85 vs 5.11) improvement in predictive accuracy on the inner data set, respectively. On both the internal and external data sets, our JointMLP model performed well compared to XGBoost and TabNet, achieving prediction accuracy improvements of 34% and 14%, respectively. Additionally, our JointMLP model showed higher robustness to outlier data than the other models, as evidenced by its higher root mean squared error performance across all data sets. The mean errors and variances of the JointMLP model were close to zero and smaller than those of the PPK model in internal and external data sets. ConclusionsOur JointMLP approach can help optimize treatment outcomes in patients with critical illnesses in an intensive care unit setting, reducing side effects associated with suboptimal vancomycin administration. These include increased risk of bacterial resistance, extended hospital stays, and increased health care costs. In addition, the superior performance of our model compared to existing models highlights its potential to help real-world clinicians.

Abstract Human induced pluripotent stem cells (iPSCs) hold great promise for personalized medicine, as they can be differentiated into specific cell types, especially mesenchymal stem cells (MSCs). Therefore, our study sought to assess the feasibility of deriving MSCs from teratomas generated from human iPSCs. Teratomas serve as a model to mimic multilineage human development, thus enriching specific somatic progenitors and stem cells. Here, we discovered a small, condensed mass of MSCs within iPSC‐generated teratomas. Afterward, we successfully isolated MSCs from this condensed mass, which was a byproduct of teratoma development. To evaluate the characteristics and cell behaviors of iPSC‐derived MSCs (iPSC‐MSCs), we conducted comprehensive assessments using qPCR, immunophenotype analysis, and cell proliferation‐related assays. Remarkably, iPSC‐MSCs exhibited an immunophenotype resembling that of conventional MSCs, and they displayed robust proliferative capabilities, similar to those of higher pluripotent stem cell‐derived MSCs. Furthermore, iPSC‐MSCs demonstrated the ability to differentiate into multiple lineages in vitro. Finally, we evaluated the therapeutic potential of iPSC‐MSCs using an osteochondral defect model. Our findings demonstrated that teratomas are a promising source for the isolation of condensed MSCs. More importantly, our results suggest that iPSC‐MSCs derived from teratomas possess the capacity for tissue regeneration, highlighting their promise for future therapeutic applications.

Beef consumers valued meat quality traits such as texture, tenderness, juiciness, flavor, and meat color that determining consumers’ purchasing decision. Most research on meat quality has focused on marbling, a key characteristic related to meat eating quality. However, other important traits such as meat texture, tenderness, and color have not much studied in cattle. Among these traits, meat tenderness and texture of cattle are among the most important factors affecting quality evaluation of consumers. Collagen is the main component of connective tissues.It greatly affects meat tenderness. The objective of this study was to determine significant variants and candidate genes associated with collagen contents trait (total collagen) through genome-wide association studies (GWAS). Phenotypic and genomic data from 135 Hanwoo were used. The BLUPF90 family program and GRAMMAR method for GWAS were applied in this study. A total of 73 potential single nucleotide polymorphisms (SNPs) showed significant associations with collagen content. They were located in or near 108 candidate genes. TMEM135 and ME3 genes were identified to have the most significant SNPs associated with collagen contents trait. Data indicated that these genes were related to collagen. Biological processes and pathways for the prediction of biological functions of candidate genes were confirmed. We found that candidate genes were involved in positive regulation of CREB transcription factor activity and actin cytoskeleton related to tenderness and texture of beef. Three genes (CRTC3, MYO1C and MYLK4) belonging to these biological functions were related to tenderness. These results provide a basis for improving genomic characteristics of Hanwoo for the production of tender beef. Furthermore, they could be used they could be used as an index to select desired traits for consumers.

This study introduces a deep-learning-based automatic sleep scoring system to detect sleep apnea using a single-lead electrocardiography (ECG) signal, focusing on accurately estimating the apnea–hypopnea index (AHI). Unlike other research, this work emphasizes AHI estimation, crucial for the diagnosis and severity evaluation of sleep apnea. The suggested model, trained on 1465 ECG recordings, combines the deep-shallow fusion network for sleep apnea detection network (DSF-SANet) and gated recurrent units (GRUs) to analyze ECG signals at 1-min intervals, capturing sleep-related respiratory disturbances. Achieving a 0.87 correlation coefficient with actual AHI values, an accuracy of 0.82, an F1 score of 0.71, and an area under the receiver operating characteristic curve of 0.88 for per-segment classification, our model was effective in identifying sleep-breathing events and estimating the AHI, offering a promising tool for medical professionals.

Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer’s disease, making early screening imperative for potential intervention and prevention of progression to Alzheimer’s disease (AD). Therefore, there is a demand for research to identify effective and easy-to-use tools for aMCI screening. While behavioral tests in virtual reality environments have successfully captured behavioral features related to instrumental activities of daily living for aMCI screening, further investigations are necessary to establish connections between cognitive decline and neurological changes. Utilizing electroencephalography with steady-state visual evoked potentials, this study delved into the correlation between behavioral features recorded during virtual reality tests and neurological features obtained by measuring neural activity in the dorsal stream. As a result, this multimodal approach achieved an impressive screening accuracy of 98.38%.

Post-curing is the process of applying extra light to complete the polymerization process of 3D printing. The mechanical properties of light-cured three-dimensional (3D) printed resin can be improved by decreasing the oxygen concentrations during post-curing, and nitrogen-saturated post-curing has been applied for this purpose. This study aimed to evaluate and compare the color stability of 3D-printed resin crowns that were post-cured in both normal air and nitrogen-saturated conditions. Crowns were fabricated with a 3D printer and post-cured in normal air (control group; air) or nitrogen-saturated conditions (experimental group; nitrogen). The specimens in each group were subdivided into four subgroups, each exposed to different discoloration agents: distilled water, coffee, wine, and curry. Post-immersion color changes were measured using a digital spectrophotometer and analyzed using repeated-measures ANOVA. Fourier transform infrared (FT-IR) spectroscopy evaluated the degree of conversion of resin over immersion times for both post-curing conditions. Upon comparing the effects of post-curing conditions, a significant difference between the control and experimental groups in terms of immersion time in the wine and curry subgroups was found. FT-IR analysis showed a significant difference in the degree of conversion between the air and nitrogen groups from 10 to 300 s. These findings suggest that nitrogen-saturated post-curing can potentially enhance the conversion rate of 3D-printed resin crowns, thereby improving their color stability.

This paper highlights the significance of safety and reliability in modern industries, particularly in sectors like petroleum and LNG, where safety valves play a critical role in ensuring system safety under extreme conditions. To enhance the reliability of these valves, this study aims to develop a deep learning-based prognostics and health management (PHM) model. Past empirical methods have limitations, driving the need for data-driven prediction models. The proposed model monitors safety valve performance, detects anomalies in real time, and prevents accidents caused by system failures. The research focuses on collecting sensor data, analyzing trends for lifespan prediction and normal operation, and integrating data for anomaly detection. This study compares related research and existing models, presents detailed results, and discusses future research directions. Ultimately, this research contributes to the safe operation and anomaly detection of pilot-operated cryogenic safety valves in industrial settings.

Since the foot-and-mouth disease (FMD) outbreak in South Korea in 2010–2011, vaccination policies utilizing inactivated FMD vaccines composed of types O and A have been implemented nationwide. However, because type Asia1 occurred in North Korea in 2007 and intermittently in neighboring countries, the risk of type Asia1 introduction cannot be ruled out. This study evaluated the antigen yield and viral inactivation kinetics of the recombinant Asia1 Shamir vaccine strain (Asia1 Shamir-R). When Asia1 Shamir-R was proliferated in shaking flasks (1 L), a 2 L bioreactor (1 L), and a wave bioreactor (25 L), the antigen yields were 7.5 μg/mL, 5.2 μg/mL, and 3.8 μg/mL, respectively. The optimal FMDV inactivation conditions were 2 mM BEI at 26 °C and 1.0 mM BEI at 37 °C. There was no antigen loss due to BEI treatment, and only a decrease in antigen levels was observed during storage. The sera from pigs immunized with antigen derived from a bioreactor exhibited a neutralizing antibody titer of approximately 1/1000 against Asia1 Shamir and Asia1/MOG/05 viruses; therefore, Asia1 Shamir-R is expected to provide sufficient protection against both viruses. If an FMD vaccine production facility is established, this Asia1 Shamir-R can be employed for domestic antigen banks in South Korea.

Abstract Objective To evaluate the performance of a deep learning model for hippocampal sclerosis classification on the clinical dataset and suggest plausible visual interpretation for the model prediction. Methods T2‐weighted oblique coronal images of the brain MRI epilepsy protocol performed on patients were used. The training set included 320 participants with 160 no, 100 left and 60 right hippocampal sclerosis, and cross‐validation was implemented. The test set consisted of 302 participants with 252 no, 25 left and 25 right hippocampal sclerosis. As the test set was imbalanced, we took an average of the accuracy achieved within each group to measure a balanced accuracy for multiclass and binary classifications. The dataset was composed to include not only healthy participants but also participants with abnormalities besides hippocampal sclerosis in the control group. We visualized the reasons for the model prediction using the layer‐wise relevance propagation method. Results When evaluated on the validation of the training set, we achieved multiclass and binary classification accuracy of 87.5% and 88.8% from the voting ensemble of six models. Evaluated on the test sets, we achieved multiclass and binary classification accuracy of 91.5% and 89.76%. The distinctly sparse visual interpretations were provided for each individual participant and group to suggest the contribution of each input voxel to the prediction on the MRI. Significance The current interpretable deep learning‐based model is promising for adapting effectively to clinical settings by utilizing commonly used data, such as MRI, with realistic abnormalities faced by neurologists to support the diagnosis of hippocampal sclerosis with plausible visual interpretation.

Salmonella enterica serovar Typhimurium isolate HJL777 is a virulent bacterial strain in pigs. The high rate of salmonella infection are at high risk of non-typhoidal salmonella gastroenteritis development. Salmonellosis is most common in young pigs. We investigated changes in gut microbiota and biological function in piglets infected with salmonella via analysis of rectal fecal metagenome and intestinal transcriptome using 16S rRNA and RNA sequencing. We identified a decrease in Bacteroides and increase in harmful bacteria such as Spirochaetes and Proteobacteria by microbial community analysis. We predicted that reduction of Bacteroides by salmonella infection causes proliferation of salmonella and harmful bacteria that can cause an intestinal inflammatory response. Functional profiling of microbial communities in piglets with salmonella infection showed increasing lipid metabolism associated with proliferation of harmful bacteria and inflammatory responses. Transcriptome analysis identified 31 differentially expressed genes. Using gene ontology and Innate Immune Database analysis, we identified that BGN, DCN, ZFPM2 and BPI genes were involved in extracellular and immune mechanisms, specifically salmonella adhesion to host cells and inflammatory responses during infection. We confirmed alterations in gut microbiota and biological function during salmonella infection in piglets. Our findings will help prevent disease and improve productivity in the swine industry.

Abstract Spontaneous brain activity, measured with resting state fMRI (R-fMRI), is correlated among regions that are co-activated by behavioral tasks. It is unclear, however, whether spatial patterns of spontaneous activity within a cortical region correspond to spatial patterns of activity evoked by specific stimuli, actions, or mental states. The current study investigated the hypothesis that spontaneous activity in motor cortex represents motor patterns commonly occurring in daily life. To test this hypothesis 15 healthy participants were scanned while performing four different hand movements. Three movements (Grip, Extend, Pinch) were ecological involving grip and grasp hand movements; one control movement involving the rotation of the wrist was not ecological and infrequent (Shake). They were also scanned at rest before and after the execution of the motor tasks (resting-state scans). Using the task data, we identified movement-specific patterns in the primary motor cortex. These task-defined patterns were compared to resting-state patterns in the same motor region. We also performed a control analysis within the primary visual cortex. We found that spontaneous activity patterns in the primary motor cortex were more like task patterns for ecological than control movements. In contrast, there was no difference between ecological and control hand movements in the primary visual area. These findings provide evidence that spontaneous activity in human motor cortex forms fine-scale, patterned representations associated with behaviors that frequently occur in daily life.

IntroductionMild cognitive impairment (MCI) is often described as an intermediate stage of the normal cognitive decline associated with aging and dementia. There is a growing interest in various non-pharmacological interventions for MCI to delay the onset and inhibit the progressive deterioration of daily life functions. Previous studies suggest that cognitive training (CT) contributes to the restoration of working memory and that the brain-computer-interface technique can be applied to elicit a more effective treatment response. However, these techniques have certain limitations. Thus, in this preliminary study, we applied the neurofeedback paradigm during CT to increase the working memory function of patients with MCI.MethodsNear-infrared spectroscopy (NIRS) was used to provide neurofeedback by measuring the changes in oxygenated hemoglobin in the prefrontal cortex. Thirteen elderly MCI patients who received CT-neurofeedback sessions four times on the left dorsolateral prefrontal cortex (dlPFC) once a week were recruited as participants.ResultsCompared with pre-intervention, the activity of the targeted brain region increased when the participants first engaged in the training; after 4 weeks of training, oxygen saturation was significantly decreased in the left dlPFC. The participants demonstrated significantly improved working memory compared with pre-intervention and decreased activity significantly correlated with improved cognitive performance.ConclusionOur results suggest that the applications for evaluating brain-computer interfaces can aid in elucidation of the subjective mental workload that may create additional or decreased task workloads due to CT.

Foot-and-mouth disease (FMD) is a highly contagious viral infection causing acute and severe vesicular lesions in cattle and pigs, which has prompted global vaccination policies. This study presents a technique for enhancing antigen yield in SAT1 BOT and SAT3 ZIM by treatment with calcium chloride (CaCl2). We tested changes in cell viability in BHK-21 suspension cells treated with varying concentrations of CaCl2. The optimal CaCl2 concentration was determined based on antigen yield. The timing of CaCl2 supplementation relative to FMD virus inoculation was tested. Finally, the optimal medium for antigen production was identified. We observed a concentration-dependent decrease in BHK-21 cell viability at >7.5 mM CaCl2. A CaCl2 concentration of 3 mM yielded the most antigens. CaCl2 supplementation relative to FMD virus infection was optimal 2 h before or with viral inoculation. CD-BHK 21 medium supplemented with CaCl2 was the most productive medium. Specifically, SAT1 BOT and SAT3 ZIM showed improved antigen production in CD-BHK 21 medium with 3 mM CaCl2, while Provero-1 and Cellvento BHK-200 media showed no significant enhancement. Overall, CaCl2 supplementation enhanced FMD antigen productivity. This study provides a useful framework for enhancing antigen production efficiently in the FMD vaccine industry.

South Korea has experienced outbreaks of foot-and-mouth disease (FMD) of serotypes O and A, leading to nationwide vaccination with a bivalent vaccine. Since the FMD virus (FMDV) Asia1 group-V genotype occurred in North Korea in 2007, an Asia1/MOG/05 vaccine strain belonging to the Asia1 group-V genotype was developed using a genetic recombination method (Asia1/MOG/05-R). This study aimed to evaluate the antigen productivity and viral inactivation kinetics of Asia1/MOG/05-R to assess its commercial viability. The antigen yield of Asia1/MOG/05-R produced in flasks and bioreactors was approximately 4.0 μg/mL. Binary ethylenimine (BEI) inactivation kinetics of Asia1/MOG/05-R showed that 2 mM and 1.0 mM BEI treatment at 26 °C and 37 °C, respectively, resulted in a virus titer −7 TCID50/mL within 24 h, meeting the inactivation kinetics criteria. During incubation at 26 °C and 37 °C, 10% antigen loss occurred, but not due to BEI treatment. When pigs were inoculated twice with the Asia1/MOG/05-R antigen, the virus neutralization titer increased to approximately 1:1000; therefore, it can sufficiently protect against Asia1/MOG/05-R and Asia1 Shamir viruses. The Asia1/MOG/05-R will be useful as a vaccine strain for domestic antigen banks.

Herein, the effects of the precursor solution’s acidity level on the crystal structure, morphology, nucleation, and growth of WO3·nH2O and WO3 nanostructures are reported. Structural investigations on WO3·nH2O using X-ray diffraction and Fourier–transform infrared spectroscopy confirm that the quantity of hydrate groups increases due to the interaction between H+ and water molecules with increasing HCl volume. Surface analysis results support our claim that the evolution of grain size, surface roughness, and growth direction on WO3·nH2O and WO3 nanostructures rely on the precursor solution’s pH value. Consequently, the photocurrent density of a WO3 photoanode using a HCl-5 mL sample achieves the best results with 0.9 mA/cm2 at 1.23 V vs. a reversible hydrogen electrode (RHE). We suggest that the improved photocurrent density of the HCl-5 mL sample is due to the efficient light absorption from the densely grown WO3 nanoplates on a substrate and that its excellent charge transport kinetics originate from the large surface area, low charge transfer resistance, and fast ion diffusion through the photoanode/electrolyte interface.

Microscopic origins of thermopower are investigated to design efficient thermoelectric devices. Here, the authors report thermopower and phonon puddles in the charge density wave states in 1T-TaS2 by scanning thermoelectric microscopy.

Polyalkenoate cement (PAC) is a promising material for regenerative hard tissue therapy. The ionically rich glass component of PAC encourages bioactive interaction via. the release of essential ions. However, PAC bioactivity is restricted owing to (i) structurally inherent cationic network formers and (ii) surface bacterial biofilm formation. These two factors cause a deficiency in ion release, further complicated by secondary infections and premature therapeutic failure. Here, a multivalent zwitterionic network modifier (mZM) is presented for upregulation of ionic exchange and bioactivity enhancement. By introducing a non-zero charged mZM into PACs, an increase in the proportion of non-bridging oxygen occurs. The network modification promotes ion channel formation, causing a multiple-fold increase in ion release and surface deposition of hydroxy-carbonate apatite (ca. 74%). Experiments ex vivo and animal models also demonstrate the efficient remineralization ability of the mZM. Furthermore, divalent cationic interaction results in bacterial biofilm reduction (ca. 68%) while also influencing a shift in the biofilm species composition, which favors commensal growth. Therefore, PAC modification with mZM offers a promising solution for upregulation of bioactivity, even aiding in customization by targeting site-specific regenerative therapy in future applications.

Ginseng is an important medicinal plant cultivated in East Asia for thousands of years. It is typically cultivated in the same field for 4 to 6 years and is exposed to a variety of pathogens. Among them, ginseng root rot is the main reason that leads to the most severe losses. In this study, endophytic bacteria were isolated from healthy ginseng, and endophytes with antagonistic effect against ginseng root rot pathogens were screened out. Among the 17 strains, three carried antagonistic effect, and were resistant to radicicol that is a mycotoxin produced by ginseng root rot pathogens. Finally, Bacillus velezensis CH-15 was selected due to excellent antagonistic effect and radicicol resistance. When CH-15 was inoculated on ginseng root, it not only inhibited the mycelial growth of the pathogen, but also inhibited the progression of disease. CH-15 also carried biosynthetic genes for bacillomycin D, iturin A, bacilysin, and surfactin. In addition, CH-15 culture filtrate significantly inhibited the growth and conidial germination of pathogens. This study shows that endophytic bacterium CH-15 had antagonistic effect on ginseng root rot pathogens and inhibited the progression of ginseng root rot. We expected that this strain can be a microbial agent to suppress ginseng root rot.

Drones, also known as unmanned aerial vehicles (UAVs) and sometimes referred to as ‘Mobile IoT’ or ‘Flying IoT’, are widely adopted worldwide, with their market share continuously increasing. While drones are generally harnessed for a wide range of positive applications, recent instances of drones being employed as lethal weapons in conflicts between countries like Russia, Ukraine, Israel, Palestine, and Hamas have demonstrated the potential consequences of their misuse. Such misuse poses a significant threat to cybersecurity and human lives, thereby highlighting the need for research to swiftly and accurately analyze drone-related crimes, identify the responsible pilot, and establish when and what illegal actions were carried out. In contrast to existing research, involving limited data collection and analysis of the drone, our study focused on collecting and rigorously analyzing data without restrictions from the remote controller used to operate the drone. This comprehensive approach allowed us to unveil essential details, including the pilot’s account information, the specific drone used, pairing timestamps, the pilot’s operational location, the drone’s flight path, and the content captured during flights. We developed methodologies and proposed artifacts to reveal these specifics, which were supported by real-world data. Significantly, this study is the pioneering digital forensic investigation of remote controller devices. We meticulously collected and analyzed all internal data, and we even employed reverse engineering to decrypt critical information files. These achievements hold substantial significance. The outcomes of this research are expected to serve as a digital forensic methodology for drone systems, thereby making valuable contributions to numerous investigations.

Decision tree algorithm (DT) is a commonly used data mining method for classification and regression. DT repeatedly divides a dataset into pure subsets based on impurity measurements such as entropy and Gini. Then relatively “pure” partitions consisting of observations with the (almost) same class are obtained. Gini index is one of the representative indices for measuring the impurity of data. However, the Gini index does not take into account distances between classes. If the distances between classes are considered when measuring impurity, the decision tree algorithm can distinguish clearly observations with different classes. To the end, a new decision tree algorithm based on Rao-Stirling index is proposed considering distances between classes. Rao-Stirling index considers distances between classes in such a way that weights more to pairs of references in more distant classes when measuring data impurity. Experimental results indicate that the proposed method is superior in terms of accuracy, implying that considering the distances between classes can help improve accuracy in DT.

In this paper, we propose an accelerated sparse recovery algorithm based on inexact alternating direction of multipliers. We formulate a sparse recovery problem with a concave regularizer and solve it with the relaxed and accelerated alternating method of multipliers (R-A-ADMM). We introduce learnable parameters to optimize the algorithm with given data sets. The derived algorithm is an accelerated version of LISTA-AT that controls the threshold for each entry according to the previously recovered estimate. Numerical results show that the proposed Accel-LISTA-AT algorithm converges much faster and recovers the sparse signals with lower mean squared errors than the other learning-based sparse recovery algorithms.

Abstract Osteoarthritis (OA) causes serious changes in the metabolic and signaling pathways of chondrocytes, including the mitogen-activated protein kinase (MAPK) pathway. However, the role of sprouty RTK signaling antagonist 4 (SPRY4), an inhibitor of MAPK, in the human cartilage tissues and chondrocytes remains to be understood. Here, using SPRY4 gene delivery into healthy and degenerated chondrocytes, we elucidated the role of SPRY4 in preventing chondrocyte hypertrophy. In addition to using the human cartilage tissues with the destabilization of the medial meniscus (DMM) model in Sprague-Dawley (SD) rats, the role of SPRY4 in cartilage tissues and chondrocytes was explored through their molecular and histological analyses. In order to determine the effects of SPRY4 on healthy human chondrocyte hypertrophy, small interfering RNA (siRNA) was used to knock down SPRY4. Lentiviral transduction of SPRY4 into degenerated human chondrocytes allowed us to investigate its ability to prevent hypertrophy. SPRY4 expression levels were higher in healthy human cartilage tissue and chondrocytes than in degenerated human cartilage tissues and hypertrophy-induced chondrocytes. The knockdown of SPRY4 in healthy chondrocytes caused an increase in hypertrophy, senescence, reactive oxygen species (ROS) production, and extracellular matrix (ECM) protease expression. However, all these factors decreased upon overexpression of SPRY4 in degenerated chondrocytes via regulation of the MAPK signaling pathway. We conclude that SPRY4 is a crucial indicator of osteoarthritis (OA) severity and could play an important role in preventing OA in the cartilage by inhibiting chondrocyte hypertrophy.

In this study, a low-cost continuous wave (CW) radar system with 3D-printed high-gain horn antennas called VitRad is proposed for human vital sign detection. The CW radar consists of 3D-printed high-gain horn antennas, commercially available low-cost surface-mounting devices, and monolithic ICs. The CW radar system operates at a frequency band of 5.8 GHz, and the backscattered I/Q data are collected using a digital storage oscilloscope (DSO). The data is processed on MATLAB to determine vital sign information such as respiratory and heartbeat rates. It is demonstrated that the proposed CW radar system for vital-sign monitoring can effectively measure respiratory and heartbeat rates.

Foot-and-mouth disease (FMD) is a highly infectious disease affecting cloven-hoofed animals and causes significant economic losses to the livestock industry. The Type O PanAsia-2 (O PA-2) vaccine strain is protective against a wide range of serotype O FMD virus (FMDV) strains in East Asia, and A22 Iraq/24/64 (A22 IRQ) is the most widely used vaccine strain in FMD vaccine antigen banks. The aim of this study was to produce antigens from O PA-2 and A22 IRQ viruses using a 100 L bioreactor and evaluate the protective efficacy of varying antigen concentrations in pigs. More than 2 μg/mL of the antigen was recovered from the O PA-2 and A22 IRQ virus-infected supernatants. Further, inactivation of O PA-2 and A22 IRQ by binary ethyleneimine revealed that the viral titers decreased below 10−7 TCID50/mL within 13 h and 9 h, respectively. The O PA-2 and A22 IRQ vaccines, containing 10 μg and 5 μg of antigen, respectively, provided protection against homologous viruses in pigs. This is the first report demonstrating that the antigens obtained from the pilot-scale production of O PA-2 and A22 IRQ are viable candidate vaccines. These results will pave the way for industrial-scale FMD vaccine production in South Korea.

To investigate the factors affecting water quality in coastal regions with sea dike constructions, surface water outside a sea dike was monitored for six years from 2015 to 2020 in the Saemangeum region of Korea. Statistical analyses of the six years of high-frequency measurements revealed that the water quality in this system was predominantly governed by natural processes followed by pollutant inputs as the secondary influencing factor. Severe dissolved oxygen (DO) depletion was observed in the surface waters during warm periods, probably owing to the advection of DO-depleted water from elsewhere to the surface layer. Based on the apparent oxygen utilization (AOU)–pH relationship (r = 0.52, n = 1837), the maximum AOU (180 µM) led to a pH decrease from 8.04 to 7.50, which was considerably lower than the estimated value of 7.72. This extra pH drop was probably due to a reduction in the buffering capacity associated with increased CO2 in the water column originating from the atmosphere and in situ production, as well as local water column redox reactions associated with benthic inputs of reduced chemical species. Overall, persistent DO depletion with ongoing eutrophication/hypoxia could accelerate ocean acidification in Korean coastal waters, which could be more acute in coastal regions with artificial coastal constructions.

Predicting student dropout from universities is an imperative but challenging task. Numerous data-driven approaches that utilize both student demographic information (e.g., gender, nationality, and high school graduation year) and academic information (e.g., GPA, participation in activities, and course evaluations) have shown meaningful results. Recently, pretrained language models have achieved very successful results in understanding the tasks associated with structured data as well as textual data. In this paper, we propose a novel student dropout prediction framework based on demographic and academic information, using a pretrained language model to capture the relationship between different forms of information. To this end, we first formulate both types of information in natural language form. We then recast the student dropout prediction task as a natural language inference (NLI) task. Finally, we fine-tune the pretrained language models to predict student dropout. In particular, we further enhance the model using a continuous hypothesis. The experimental results demonstrate that the proposed model is effective for the freshmen dropout prediction task. The proposed method exhibits significant improvements of as much as 9.00% in terms of F1-score compared with state-of-the-art techniques.

Abstract This study aimed to investigate the effects of low-intensity pulsed ultrasound (LIPUS) on replacement root resorption after replantation of avulsed teeth stored in a dry condition in dogs. A total of 73 premolar roots from four male mongrel dogs were intentionally avulsed with forceps and divided into four groups—HN, HL, DN, and DL—according to storage conditions and whether or not they received LIPUS treatment. Thirty-eight roots were kept in Hanks’ Balanced Salt Solution for 30 min (HN and HL groups), whereas the remaining 35 roots were left to dry in the air for an hour (DN and DL groups) prior to replantation. Following replantation, the roots in the HL and DL groups (21 and 18 roots, respectively) received a 20-min daily LIPUS treatment for 2 weeks. The animals were euthanized 4 weeks after the operation. Micro-computed tomography images were acquired for each root and the amount of replacement root resorption was measured three-dimensionally. Histological assessments were also carried out. There was significantly less replacement root resorption for the roots in the DL group compared to the DN group (p

Atmospheric aerosol particles are complex mixtures having various physicochemical properties. To predict the role and characteristics of such complex aerosol particles in air pollution and related atmospheric chemistry, our knowledge of the number and types of phases in complex aerosol particles should be improved. However, most studies on the phase behavior of aerosol particles have been conducted in the laboratory and have not used real-world aerosol particles. In this study, using a combination of optical microscopy and poke-and-flow technique, we investigated the number and types of phases of actual aerosol particles of particulate matter < 2.5 µm (PM2.5) collected on heavily polluted days in Seosan, South Korea in winter 2020–2021. From the microscopic observations at 293 K, it showed that the PM2.5 particles exist in a single liquid phase at relative humidity (RH) >∼85%, a liquid-liquid phase at ∼70% < RH

In addition to direct solar illumination to the photovoltaic cell, the albedo effect provides a unique opportunity to enhance energy harvesting. However, conventional solar cells are inefficient albedo energy harvesters because the rear side of the cell is usually blocked by a thick substrate or metal contact. In this study, structurally thin active layers of GaAs thin‐film solar cells covered with transparent Ag nanowires that enable bifacial solar cell operations are fabricated. The GaAs bifacial thin‐film solar cell is fabricated by bonding the active regions of the solar cell on colorless polyimide sheets with highly transparent epoxy adhesives. The fabricated bifacial solar cells without any anti‐reflection coatings demonstrated power conversion efficiencies of 8.54 and 6.78% at the front and rear sides, respectively, under AM 1.5 G irradiance conditions. The increase in energy harvesting by the GaAs bifacial thin‐film solar cells was estimated, which showed approximately a 72% increase in short‐circuit current density (JSC) when the solar cells operated on a high‐reflection‐coated ground. Positioning the solar cells on snow or sand (deserts and beaches) increased JSC by ≈44 and 31%, respectively, compared to monofacial solar cells.

Foot-and-mouth disease (FMD) vaccines must be produced in a biosafety level 3 facility, so the FMD virus (FMDV) must be completely inactivated after amplification. The inactivation kinetics of FMDV during vaccine antigen production were assessed by evaluating whether the viral titer dropped below 10−7 TCID50/mL within 24 h of binary ethyleneimine (BEI) treatment. This study dealt with four FMD vaccine candidate strains for the efficacy of BEI treatment at different concentrations and temperatures to determine the optimal inactivation condition of each virus. Two domestic isolates, O/SKR/Boeun/2017 (O BE) and A/SKR/Yeoncheon/2017 (A YC), and two recombinant viruses, PAK/44/2008 (O PA-2) and A22/Iraq/24/64 (A22 IRQ), were investigated. The O BE and A22 IRQ required 2 mM BEI at 26 °C and 0.5 mM BEI at 37 °C for complete inactivation. The O PA-2 and A YC required 2 mM BEI at 26 °C and 1 mM BEI at 37 °C. Crucially, the yield of FMD virus particles (146S) in the viral infection supernatant was higher (>4.0 µg/mL) than those previously reported; additionally, there was little antigen loss, even after 24 h of treatment with 3 mM BEI. Overall, it is considered economical to produce FMD vaccines using these four kinds of viruses; therefore, these candidate strains will be prioritized for the manufacture of FMD vaccines in South Korea.

Hydrogels are widely used in stem cell therapy due to their extensive tunability and resemblance to the extracellular matrix (ECM), which has a three-dimensional (3D) structure. These features enable various applications that enhance stem cell maintenance and function. However, fast and simple hydrogel fabrication methods are desirable for stem cells for efficient encapsulation and to reduce adverse effects on the cells. In this study, we present a one-pot double-crosslinked hydrogel consisting of polyethylene glycol (PEG) and collagen, which can be prepared without the multi-step sequential synthesis of each network, by using bio-orthogonal chemistry. To enhance the adipogenic differentiation efficiency of adipose-derived stem cells (ADSCs), we added degradable components within the hydrogel to regulate matrix stiffness through cell-mediated degradation. Bio-orthogonal reactions used for hydrogel gelation allow rapid gel formation for efficient cell encapsulation without toxic by-products. Furthermore, the hybrid network of synthetic (PEG) and natural (collagen) components demonstrated adequate mechanical strength and higher cell adhesiveness. Therefore, ADSCs grown within this hybrid hydrogel proliferated and functioned better than those grown in the single-crosslinked hydrogel. The degradable elements further improved adipogenesis in ADSCs with dynamic changes in modulus during culture and enabled the retrieval of differentiated cells for potential future applications.

This study proposes a sensor data process and motion control method for a mobile platform essential for transporting finished products or subsidiary materials in a smart factory. We developed a system that recognizes a fiducial marker printed on the work clothes worn by a worker, estimates the worker’s location, and follows the worker using the estimated location. To overcome the limitations of simulation-based research, gait data on a two-dimensional plane were derived through a human gait model and an error model according to the distance between the image sensor and the reference marker. The derived gait data were defined as the localization result for the worker, and a Kalman filter was used to robustly address the uncertainty of the localization result. A virtual spring-damper system was applied to follow the Mecanum wheel-based mobile platform workers. The performance of the proposed algorithm was demonstrated through comparative simulations with existing methods.

JPEG compression is one of the major image compression methods and is widely used on the Internet. In addition, identifying traces of JPEG compression and double JPEG compression (DJPEG) is crucial in the image forensics field. Therefore, JPEG compression detection and DJPEG compression detection are two of the popular image authentication methods. Many feature-based JPEG detection methods have been proposed for that purpose, and there have been outstanding improvements in DJPEG detection with the development of deep learning. A number of anti-forensics of JPEG detection that counter feature-based detectors have been proposed but only a few techniques that counter DJPEG have been researched. This paper explores whether JPEG reconstruction methods, including restoration and anti-forensics of JPEG detection, can deceive JPEG and DJPEG detectors. We demonstrate that existing anti-forensics of JPEG detection can deceive both JPEG and DJPEG detectors well but perform poorly in non-aligned cases and degrade the image quality. We propose a convolutional neural network (CNN) based anti-forensics method to improve the performance of anti-forensics so that they can proficiently deceive JPEG and DJPEG detectors with higher image quality. Moreover, we explore the generalization algorithm to handle the real scenario.

NADPH oxidase is an enzyme that generates reactive oxygen species from oxygen and NADPH and is highly conserved in eukaryotes. In Fusarium graminearum, a series of different Nox enzymes have been identified. NoxA is involved in sexual development and ascospore production and, like NoxB, also contributes to pathogenicity. Both NoxA and NoxB are regulated by the subunit NoxR, whereas NoxC is usually self-regulated by EF-hand motifs found on the enzyme. In this study, we characterized another NADPH oxidase in F. graminearum, FgNoxD. In the FgNoxD deletion mutant, vegetative growth and conidia production were reduced, while sexual development was totally abolished. The FgNoxD deletion mutant also showed reduced resistance to cell wall perturbing agents; cell membrane inhibitors; and osmotic, fungicide, cold, and extracellular oxidative stress, when compared to the wild type. Moreover, in comparison to the wild type, the FgNoxD deletion mutant exhibited reduced virulence against the host plant. The FgNoxD deletion mutant produced less deoxynivalenol than the wild type, and the Tri5 and Tri6 gene expression was also downregulated. In conclusion, our findings show that FgNoxD is involved in the survival against various stresses, conidiation, sexual development, and virulence, highlighting this enzyme as a new target to control the disease caused by F. graminearum.

Comparison of spontaneous canine cancers and human cancers may illuminate future therapeutic avenues. Here, genomic analyses of these tumors highlights a convergence on PI3K-Akt oncogenic pathways.

Deep neural networks (DNNs) have been making progress in many ways. DNNs are typically used to model complex nonlinearity of high-dimensional data in regression or classification problems. As DNNs contain additional hidden layers, they generally improve performance but increase the number of parameters to train, thereby extending the learning time. Many studies, such as those employing Dropout and regularization methods, have undertaken to solve these problems. The method proposed in this paper is an additive ensemble neural networks (AENNs), by which a boosting mechanism of an ensemble methodology is applied to the neural networks instead of regularization techniques. That is, the model by AENNs is obtained by sequentially combining several simple shallow network models. Experiments showed that AENNs yield better results than conventional DNNs and machine learning methods for regression and classification problems, thereby alleviating the troublesome model complexity issue.

In this paper, we propose element-wise adaptive threshold methods for learned iterative shrinkage thresholding algorithms. The threshold for each element is adapted in such a way that it is set to be smaller when the previously recovered estimate or the current one-step gradient descent at that element has a larger value. This adaptive threshold gives a lower misdetection probability of the true support, which speedups the convergence to the optimal solution. We show that the proposed element-wise threshold adaption method has better convergence rate than the existing non-adaptive threshold methods. Numerical results show that the proposed neural network has the best recovery performance among the tested algorithms. In addition, it is robust to the sparsity mismatch, which is very desirable in the case of unknown signal sparsity.

As IoT devices are always connected to mobile devices or other computing devices via the Internet, clever malwares targeting IoT devices or other computing devices connected to IoT devices are emerging. Therefore, effective IoT security research is needed to respond to hacking attacks by these kinds of malware. This paper studied the method of identifying and analyzing malware combined with social engineering from the perspective of digital forensics. The paper classified and analyzed intelligent malware characteristics and proposed a method of quickly identifying and analyzing the malware that secretly intruded into the devices installed with Android, Linux OS, using digital forensics techniques. Moreover, this paper proved its effectiveness by applying this investigation method to two actual malware cases. The research outcomes will be useful in responding to increasingly clever malware attacking IoT devices.

We report on the fabrication of TiO2 nanosphere (NS) monolayer arrays for the enhancement of light extraction quantum efficiency of GaN-based light-emitting diodes (LEDs). The fabricated TiO2 NSs monolayer arrays were composed of different phases of anatase (An-) and amorphous (Am-) TiO2. The arrays were transferred onto the topmost layer of LED chips via the facile icing transfer method. The LED chips covered with Am-TiO2 NS monolayer arrays showed 3.0- times enhanced light output power intensity compared with reference LED chips at a fixed injection current of 100 mA. The enhanced light extraction of LED chips by an Am-TiO2 NS monolayer can be attributed to a high transmittance (91.1%) in visible and increased light extraction probability of photons generated in LEDs, resulting from the enhanced light coupling efficiency by reduced total internal reflection (TIR). Finite-difference time-domain (FDTD) simulation results also agreed well with the experimentally observed results. Based on the experimental and theoretical results, our suggested Am- and An-TiO2 NS arrays can be considered a very facile and effective method to improve the device performance of various visible LED chips.