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Dogs that had splenectomy are predisposed to fatal thrombotic conditions, and thrombocytosis is a risk factor for post-splenectomy hypercoagulability. However, in veterinary medicine, there are no specific therapeutic approaches for managing this hypercoagulability. This study aimed to determine the preventive effect of clopidogrel on post-operative hypercoagulability during the first 2 weeks post-splenectomy in dogs with splenic masses. This study included 12 dogs that had splenectomy. Seven dogs received no treatment (group A), and five were treated with clopidogrel (group B). Clopidogrel was loaded at 10 mg/kg on day 2 and continued at 2 mg/kg until day 14. Blood samples were collected on the day of surgery and 2, 7, and 14 days after splenectomy in both groups. In group B, thromboelastography (TEG) was performed on the same days. In group A, there was significant elevation of platelet counts on days 7 (p = 0.007) and 14 (p = 0.001) compared to day 0. In group B, the platelet counts were significantly elevated on day 7 (p = 0.032) but no significant difference was found on day 14 compared to day 0. Platelet counts on day 14 were significantly higher in group A than in group B (p = 0.03). The lower platelet counts were correlated with alterations in TEG parameters, and no significant differences were found in the K and α-angle values at all postoperative assessment points compared to day 0. Our study suggests that clopidogrel may reduce post-operative thrombocytosis and hypercoagulability in dogs that undergo splenectomy for splenic masses.

Abstract Alternative cleavage and polyadenylation within introns (intronic APA) generate shorter mRNA isoforms; however, their physiological significance remains elusive. In this study, we developed a comprehensive workflow to analyze intronic APA profiles using the mammalian target of rapamycin (mTOR)-regulated transcriptome as a model system. Our investigation revealed two contrasting effects within the transcriptome in response to fluctuations in cellular mTOR activity: an increase in intronic APA for a subset of genes and a decrease for another subset of genes. The application of this workflow to RNA-seq data from The Cancer Genome Atlas demonstrated that this dichotomous intronic APA pattern is a consistent feature in transcriptomes across both normal tissues and various cancer types. Notably, our analyses of protein length changes resulting from intronic APA events revealed two distinct phenomena in proteome programming: a loss of functional domains due to significant changes in protein length or minimal alterations in C-terminal protein sequences within unstructured regions. Focusing on conserved intronic APA events across 10 different cancer types highlighted the prevalence of the latter cases in cancer transcriptomes, whereas the former cases were relatively enriched in normal tissue transcriptomes. These observations suggest potential, yet distinct, roles for intronic APA events during pathogenic processes and emphasize the abundance of protein isoforms with similar lengths in the cancer proteome. Furthermore, our investigation into the isoform-specific functions of JMJD6 intronic APA events supported the hypothesis that alterations in unstructured C-terminal protein regions lead to functional differences. Collectively, our findings underscore intronic APA events as a discrete molecular signature present in both normal tissues and cancer transcriptomes, highlighting the contribution of APA to the multifaceted functionality of the cancer proteome.

Background: Koreoleptoxis globus is an endangered candidate species of snail in South Korean streams. This species primarily inhabits streams characterized by fast current velocities and coarse-particle streambed substrates. In this study, 12 types of probability distribution models, including exponential, normal, log-normal, logistic, Weibull, gamma, beta, and Gumbel, were used to quantitatively assess the physical microhabitat preferences of K. globus. The evaluation was based on data collected from 340 sampling units across 34 sites in South Korea between April 2024 and May 2024, focusing on variables such as water depth, current velocity, and streambed substrate. Results: The best-fitting probability distribution models for each physical environmental factor were identified as follows: 1) water depth, inverted log-normal distribution, 2) current velocity, beta distribution, and 3) streambed substrate, Weibull distribution. Optimal water depth preferences ranged from 31.2 cm to 53.7 cm. Current velocity preferences ranged from 57.8 cm s-1 to 88.8 cm s-1. Substrate preferences ranged from −7.4 Φm to −5.6 Φm. The mean values for these factors were water depth of 35.8 cm, current velocity of 63.0 cm s-1, and streambed substrate of −5.7 Φm. Mode values were water depth of 44.4 cm, current velocity of 75.6 cm s-1, and substrate of −6.7 Φm. Standard deviation values were water depth of 15.8 cm, current velocity of 21.4 cm s-1, and streambed substrate of 1.5 Φm. Conclusions: Overall, the beta and Weibull distribution models demonstrated a high degree of fit, likely owing to the inherent flexibility of these models. Beta distribution models were well suited for cases with negative skewness, whereas Weibull distribution models provided a better fit for cases with positive skewness. The physical habitat characteristics of K. globus were quantitatively demontrated to correspond to riffled areas. Further research is required to explore the interactions between physical environmental factors and the impact of habitat disturbance.

Freshwater crayfish are amongst the largest macroinvertebrates and play a keystone role in the ecosystems they occupy. Understanding the global distribution of these animals is often hindered due to a paucity of distributional data. Additionally, non-native crayfish introductions are becoming more frequent, which can cause severe environmental and economic impacts. Management decisions related to crayfish and their habitats require accurate, up-to-date distribution data and mapping tools. Such data are currently patchily distributed with limited accessibility and are rarely up-to-date. To address these challenges, we developed a versatile e-portal to host distributional data of freshwater crayfish and their pathogens (using Aphanomyces astaci, the causative agent of the crayfish plague, as the most prominent example). Populated with expert data and operating in near real-time, World of Crayfish™ is a living, publicly available database providing worldwide distributional data sourced by experts in the field. The database offers open access to the data through specialized standard geospatial services (Web Map Service, Web Feature Service) enabling users to view, embed, and download customizable outputs for various applications. The platform is designed to support technical enhancements in the future, with the potential to eventually incorporate various additional features. This tool serves as a step forward towards a modern era of conservation planning and management of freshwater biodiversity.

Abstract Solute carrier family (SLC) transporters are expressed in the digestive system and play important roles in maintaining physiological functions in the body. In addition, SLC transporters act as oncoproteins or tumor-suppressor proteins during the development, progression, and metastasis of various digestive system cancers. SLC22A18, a member of the SLC22 gene family, is an orphan transporter with an unknown endogenous substrate. Previous study revealed that SLC22A18 is downregulated in colorectal cancer tissues and that it acts as a suppressor in colorectal cancer, although the effects of SLC22A18 variants on colon cancer cell proliferation, migration, and invasion are unknown. Therefore, in this study, we identified SLC22A18 variants found in multiple populations by searching public databases and determined the in vitro effects of these missense variations on transporter expression and cancer progression. Our results indicated that three missense SLC22A18 variants—p.Ala6Thr, p.Arg12Gln, and p.Arg86His—had significantly lower cell expression than the wild type, possibly owing to intracellular degradation. Furthermore, these three variants caused significantly higher proliferation, migration, and invasion of colon cancer cells than the wild type. Our findings suggest that missense variants of SLC22A18 can potentially serve as biomarkers or prognostic tools that enable clinicians to predict colorectal cancer progression.

This study explores the integration of concept bottleneck models (CBMs) with knowledge distillation (KD) while preserving the locality characteristics of the CBM. Although KD proves effective in model compression, compressed models often lack interpretability in their decision-making process. We enhance comprehensive explainability by maintaining CBMs’ inherent interpretability through our novel approach to knowledge distillation. We introduce visual concept knowledge distillation (VICO-KD), which transfers both explicit and implicit visual concepts from the teacher to the student model while preserving the local interpretability of the CBM, enabling accurate classification and clear visualization of evidence. VICO-KD demonstrates superior performance on benchmark datasets compared to Vanilla-KD, ensuring the student model learns visual concepts while maintaining the local interpretation capabilities of the teacher CBM. Our methodology shows competitive performance against existing concept models, and the student model, trained via VICO-KD, exhibits enhanced performance compared to the teacher during interventions. This study highlights the effectiveness of combining a CBM with KD to improve both interpretability and explainability in compressed models while maintaining locality properties.

Transformer-based time series forecasting models use patch tokens for temporal patterns and variate tokens to learn covariates’ dependencies. While patch tokens inherently facilitate self-supervised learning, variate tokens are more suitable for linear forecasters as they help to mitigate distribution drift. However, the use of variate tokens prohibits masked model pretraining, as masking an entire series is absurd. To close this gap, we propose LSPatch-T (Long–Short Patch Transfer), a framework that transfers knowledge from short-length patch tokens into full-length variate tokens. A key implementation is that we selectively transfer a portion of the Transformer encoder to ensure the linear design of the downstream model. Additionally, we introduce a robust frequency loss to maintain consistency across different temporal ranges. The experimental results show that our approach outperforms Transformer-based baselines (Transformer, Informer, Crossformer, Autoformer, PatchTST, iTransformer) on three public datasets (ETT, Exchange, Weather), which is a promising step forward in generalizing time series forecasting models.

ABSTRACT Mitogen-activated protein kinase (MAPK) pathways are fundamental to the regulation of biological processes in eukaryotic organisms. The basidiomycete Cryptococcus neoformans, known for causing fungal meningitis worldwide, possesses five MAPKs. Among these, Cpk1, Hog1, and Mpk1 have established roles in sexual reproduction, stress responses, and cell wall integrity. However, the roles of Cpk2 and Mpk2 are less understood. Our study elucidates the functional interplay between the Cpk1/Cpk2 and Mpk1/Mpk2 MAPK pathways in C. neoformans. We discovered that CPK2 overexpression compensates for cpk1Δ mating deficiencies via the Mat2 transcription factor, revealing functional redundancy between Cpk1 and Cpk2. We also found that Mpk2 is phosphorylated in response to cell wall stress, a process regulated by the MAPK kinase (MAP2K) Mkk2 and MAP2K kinases (MAP3Ks) Ssk2 and Ste11. Overexpression of MPK2 partially restores cell wall integrity in mpk1Δ by influencing key cell wall components, such as chitin and the polysaccharide capsule. Contrarily, MPK2 overexpression cannot restore thermotolerance and cell membrane integrity in mpk1Δ. These results suggest that Mpk1 and Mpk2 have redundant and opposing roles in the cellular response to cell wall and membrane stresses. Most notably, the dual deletion of MPK1 and MPK2 restores wild-type mating efficiency in cpk1Δ mutants via upregulation of the mating-regulating transcription factors MAT2 and ZNF2, suggesting that the Mpk1 and Mpk2 cooperate to negatively regulate the pheromone-responsive Cpk1 MAPK pathway. Our research collectively underscores a sophisticated regulatory network of cryptococcal MAPK signaling pathways that intricately govern sexual reproduction and cell wall integrity, thereby controlling fungal development and pathogenicity.IMPORTANCEIn the realm of fungal biology, our study on Cryptococcus neoformans offers pivotal insights into the roles of specific proteins called mitogen-activated protein kinases (MAPKs). Here, we discovered the cryptic functions of Cpk2 and Mpk2, two MAPKs previously overshadowed by their dominant counterparts Cpk1 and Mpk1, respectively. Our findings reveal that these “underdog” proteins are not just backup players; they play crucial roles in vital processes like mating and cell wall maintenance in C. neoformans. Their ability to step in and compensate when their dominant counterparts are absent showcases the adaptability of C. neoformans. This newfound understanding not only enriches our knowledge of fungal MAPK mechanisms but also underscores the intricate balance and interplay of proteins in ensuring the organism’s survival and adaptability.

Magnetic microrobots and nanoparticles offer unique capabilities for medical applications by granting unprecedented access to intricate and delicate anatomical structures. Nevertheless, an intuitive manipulation approach remains a challenge due to their small size and limited feedback. This study presents a new method for magnetic microrobot and nanoparticle control that employs virtual reality (VR), creating an immersive and realistic view of the bodily anatomy and the microrobot within. This study compares three manipulation modes: a traditional mode using 2D displays, VR mode using a VR headset and touch controllers, and VR autonomous programmed maneuvering. It is shown that VR‐assisted modes reduce the manipulation time by improving spatial awareness. Nanoparticles in a VR environment are also manipulated. The proposed method will find applications in terms of intuitive, immersive microrobots or magnetic nanoparticle control in complex biological environments, thus in many medical procedures and for drug and cell delivery. The immersive nature of this approach enhances the ability of the user to perceive and understand complex anatomical structures, facilitating better navigation within delicate environments; both dexterity and spatial awareness are improved. Thus, it is shown how microrobots and magnetic nanoparticles can be controlled using immersive simulations to improve both visualization and manipulation.

IntroductionPrevious research has highlighted the duality of self-consciousness, which simultaneously plays adaptive and maladaptive roles. This study aims to develop a measure that categorically distinguishes between different types of self-consciousness styles based on the Regulatory Focus Theory (RFT) and examines their relationship with mental health-related indicators.MethodsData were gathered through an online mental health survey conducted at a University Student Counseling Center in Seoul. The study involved exploratory factor analysis, confirmatory factor analysis, and reliability and validity analysis, which resulted in the development of a 14-question Self-Consciousness Type Scale (SCTS).ResultsBoth exploratory and confirmatory factor analyses validated the two-factor structure of the SCTS. The fit indices of the final model indicated a good fit, with high internal consistency for both sub-factors. Convergent and discriminant validity were confirmed through correlations between the sub-scales. Cluster analysis identified four distinct subtypes of self-consciousness styles: Growth-oriented, Defensive, Ambivalent, and Low-focus self-consciousness. Group difference analysis revealed significant differences in mental health-related variables among the subtypes, supporting the 2 × 2 model of prevention-focused and promotion-focused self-consciousness.DiscussionThe findings support the SCTS as a valid measurement tool capable of distinguishing four distinct types of self-consciousness, aligning with the multidimensional model of self-consciousness. The study’s limitations and implications were discussed based on the results, emphasizing the potential applications of the SCTS in mental health research and practice.

Accurate inspection of subtle defects on apple surfaces is necessary in agricultural engineering. However, existing methods often rely on expensive equipment and encounter difficulty in detecting small defect areas effectively. To address this challenge, we introduce the Subtle Surface Defects in Apples (SSDA) dataset, a custom dataset specifically collected and annotated for two defect types: scratches and pest damage. For benchmarking, we propose a heatmap-based anchor-free (HAFREE) detector, a novel end-to-end object detection architecture designed to localize subtle defects on apple surfaces. Unlike prior methods that rely on anchor boxes, HAFREE employs a heatmap-based approach to represent defects as keypoints using two-dimensional Gaussian heatmaps. We introduce a multiscale feature fusion block, targeting small objects by incorporating local and global contextual information. To mitigate overfitting, we also implement a patch training strategy incorporating full images and cropped patches during training as a regularizer. The proposed method achieves a mAP50 of 50.05% on the SSDA dataset, outperforming one- and two-stage anchor-based detectors and previous anchor-free approaches. Code is available at: https://github.com/nbngochan/HAFREE.

We report the annealing time-dependent microstructure–mechanical properties relationship of cold-rolled and annealed Fe–6Mn-0.05C–3Ni-1.5Al (wt.%) steel, containing Ni and Al to form NiAl B2 precipitates. The microstructures of the investigated materials after cold-rolling and intercritical annealing show a mixture of lath and equiaxed zones consisting of a triplex matrix phase (ferrite, austenite, and tempered martensite) with B2 precipitates. Recovery of the deformed martensite in the cold-rolled microstructure during annealing resulted in the formation of sub-boundaries and a continuous transition from partially-recrystallized tempered martensite to recrystallized ferrite. The investigated materials showed a decrease in strength and an increase in ductility and strain-hardening rate with increasing annealing time. A decrease in the fraction of tempered martensite, an increase in the fraction of recrystallized ferrite/austenite grains, and enhanced transformation-induced plasticity (TRIP) kinetics with increasing annealing time led to enhanced ductility and strain hardening of the materials. The mixed presence of less stable equiaxed austenite and more stable lath austenite resulted in sustained TRIP effect during tensile deformation and superior strain hardening capacity of the specimen annealed for 24 h. This study provides a novel microstructure design solution for medium Mn steels, and further optimization of the composition and processing will lead to the development of medium Mn steels with superior mechanical performance.

Multimodal emotion recognition has emerged as a promising approach to capture the complex nature of human emotions by integrating information from various sources such as physiological signals, visual behavioral cues, and audio-visual content. However, current methods often struggle with effectively processing redundant or conflicting information across modalities and may overlook implicit inter-modal correlations. To address these challenges, this paper presents a novel multimodal emotion recognition framework which integrates audio-visual features with viewers’ EEG data to enhance emotion classification accuracy. The proposed approach employs modality-specific encoders to extract spatiotemporal features, which are then aligned through contrastive learning to capture inter-modal relationships. Additionally, cross-modal attention mechanisms are incorporated for effective feature fusion across modalities. The framework, comprising pre-training, fine-tuning, and testing phases, is evaluated on multiple datasets of emotional responses. The experimental results demonstrate that the proposed multimodal approach, which combines audio-visual features with EEG data, is highly effective in recognizing emotions, highlighting its potential for advancing emotion recognition systems.

During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWDT, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction.

In many machine learning applications, there are many scenarios when performance is not satisfactory by single classifiers. In this case, an ensemble classification is constructed using several weak base learners to achieve satisfactory performance. Unluckily, the construction of the ensemble classification is empirical, i.e., to try an ensemble classification and if performance is not satisfactory then discard it. In this paper, a challenging analytical problem of the estimation of ensemble classification using the prediction performance of the base learners is considered. The proposed formulation is aimed at estimating the performance of ensemble classification without physically developing it, and it is derived from the perspective of probability theory by manipulating the decision probabilities of the base learners. For this purpose, the output of a base learner (which is either true positive, true negative, false positive, or false negative) is considered as a random variable. Then, the effects of logical disjunction-based and majority voting-based decision combination strategies are analyzed from the perspective of conditional joint probability. To evaluate the forecasted performance of ensemble classifier by the proposed methodology, publicly available standard datasets have been employed. The results show the effectiveness of the derived formulations to estimate the performance of ensemble classification. In addition to this, the theoretical and experimental results show that the logical disjunction-based decision outperforms majority voting in imbalanced datasets and cost-sensitive scenarios.

The development of low-cost and low-power gas sensors for reliable NO2 gas detection is important due to the highly toxic nature of NO2 gas. Herein, initially, SnO2 nanowires (NWs) were synthesized through a simple vapor–liquid–solid growth mechanism. Subsequently, different amounts of SnO2 NWs were composited with MoS2 nanosheets (NSs) to fabricate SnO2 NWs/MoS2 NS nanocomposite gas sensors for NO2 gas sensing. The operation of the sensors in self-heating mode at 1–3.5 V showed that the sensor with 20 wt.% SnO2 (SM-20 nanocomposite) had the highest response of 13 to 1000 ppb NO2 under 3.2 V applied voltage. Furthermore, the SM-20 nanocomposite gas sensor exhibited high selectivity and excellent long-term stability. The enhanced NO2 gas response was ascribed to the formation of n-n heterojunctions between SnO2 NWs and MoS2, high surface area, and the presence of some voids in the SM-20 composite gas sensor due to having different morphologies of SnO2 NWs and MoS2 NSs. It is believed that the present strategy combining MoS2 and SnO2 with different morphologies and different sensing properties is a good approach to realize high-performance NO2 gas sensors with merits such as simple synthesis and fabrication procedures, low cost, and low power consumption, which are currently in demand in the gas sensor market.

Hydrogen (H2) gas, with its high calorimetric combustion energy and cleanness, is a green source of energy and an alternative to fossil fuels. However, it has a small kinetic diameter, with high diffusivity and a highly explosive nature. Hence, the reliable detection of H2 gas is essential in various fields such as fuel cells. Herein, we decorated ZnO nanoparticles (NPs) with Pd noble metal NPs, using UV irradiation to enhance their H2 gas-sensing performance. The synthesized materials were fully characterized in terms of their phases, morphologies, and chemical composition. Then, the sensing layer was deposited on the electrode-patterned glass substrate to make a transparent sensor. The fabricated transparent gas sensor was able to detect H2 gas at various temperatures and humidity levels. At 250 °C, the sensor exhibited the highest response to H2 gas. As a novelty of the present study, we successfully detected H2 gas in mixtures of H2/benzene and H2/toluene gases. The enhanced H2 gas response was related to the catalytic effect of Pd, the formation of heterojunctions between Pd and ZnO, the partial reduction of ZnO to Zn in the presence of H2 gas, and the formation of PdHx. With a high performance in a high response, good selectivity, and repeatability, we believe that the sensor developed in this study can be a good candidate for practical applications where the detection of H2 is necessary.

Parking structures taller than 8 m in height are difficult to design because both fire and seismic resistance must be considered, whereas structures shorter than this threshold are simpler to design and are subject to fewer construction restrictions. Therefore, to optimize the design of these low parking structures, we propose a novel wide, long-span, steel-concrete composite beam that can be applied to reduce their story height. We investigated flexural and shear-bonding performance of the developed composite beams based on experiments, as well as theoretical and analytical research. Seven flexural beams were loaded at two points prior to monotonic bending tests, and four shear bonding beams were tested using compressive loading schemes. The results of the flexural tests indicated that the flexural strength of the wide composite-steel beams increased by approximately 20% as steel thickness increased by 3 mm (from 6 to 9 mm). Shear-bonding tests showed that all specimens without shear connectors exhibited insufficient shear-bonding strength to resist the mechanical bond mechanism; therefore, they should be reinforced with two or more flat bars. As a result, the novel composite beam system can be applied to reduce construction costs and resources by decreasing floor height in systems with long-span beams.

This study investigated the effect of heat treatment on the microstructure and impact toughness property of AISI D2 manufactured with direct energy deposition (DED) and compared the results with conventional wrought material. The fracture crack propagation behavior was examined in connection with microstructures through fracture surface analysis. AISI D2 manufactured with DED had a eutectic structure that turned into a net-type carbide after heat treatment, and Cr-rich needle-type secondary carbide was observed. Impact toughness of DED AISI D2 measured 2.0 J/cm2 in the as-built sample and 1.1 J/cm2 in the heat-treated sample. Compared to a wrought heat-treated AISI D2, DED AISI D2 had relatively low impact toughness. DED AISI D2 and wrought material had different crack propagation mechanisms. In DED AISI D2, the eutectic structure and net-type carbide boundary were identified as the major microstructural factor decreasing impact toughness.

In this study, an electromagnetic coil system was developed for semi-autonomous manipulation of microrobots in three-dimensional (3D) space with sequential programming. Cylindrical-shaped cores were used for the electromagnetic coils; the coils were arranged in a hemispherical configuration to allow open access to the workspace for imaging tools. The system consisted of four main parts: the electromagnetic coils, an electronic control unit, optical cameras, and a direct-manipulation graphical user interface (GUI). The system was capable of producing a magnetic field of up to 18 mT with a maximum current of 8 A current. Using this system, magnetic force and torque can be applied for semi-autonomous manipulation of magnetic objects, with different time intervals, by instantly inputting the required fields and gradients via the GUI. Sequential programming of the magnetic field allowed for better controllability and enhanced the repeatability of the system. The versatility of the system was demonstrated by moving a magnetic guidewire, micromagnet, and nanoparticles to their intended targets to assess the potential application of the system in biomedical fields.

Abstract The ATP-binding cassette subfamily 4 (ABCA4), a transporter, is localized within the photoreceptors of the retina, and its genetic variants cause retinal dystrophy. Despite the clinical importance of the ABCA4 transporter, a few studies have investigated the function of each variant. In this study, we functionally characterized ABCA4 variants found in Korean patients with Stargardt disease or variants of the ABCA4 promoter region. We observed that four missense variants—p.Arg290Gln, p.Thr1117Ala, p.Cys1140Trp, and p.Asn1588Tyr—significantly decreased ABCA4 expression on the plasma membrane, which could be due to intracellular degradation. There are four major haplotypes in the ABCA4 proximal promoter. We observed that the H1 haplotype (c.-761C>A) indicated significantly increased luciferase activity compared to that of the wild-type, whereas the H3 haplotype (c.-1086A>C) indicated significantly decreased luciferase activity (P T in the H2 haplotype exhibited significantly increased luciferase activity compared with that of the wild-type. Two transcription factors, GATA-2 and HLF, were found to function as enhancers of ABCA4 transcription. Our findings suggest that ABCA4 variants in patients with Stargardt disease affect ABCA4 expression. Furthermore, common variants of the ABCA4 proximal promoter alter the ABCA4 transcriptional activity, which is regulated by GATA-2 and HLF transcription factors.

Inflammatory bowel disease is a common condition in cats, characterized by recurring gastrointestinal signs with histologic evidence of intestinal inflammation. A 9-month-old neutered male Sphynx cat was presented with a 5-week history of vomiting and hematochezia. Conservative patient management with a therapeutic gastrointestinal formula, antibiotics, and antiemetics resulted in a positive response to treatment, with relapse of signs when the medications were discontinued. A new finding of marked eosinophilia and basophilia was identified 3 months after the initial presentation. Colonoscopy revealed cecal erosions and a surgical biopsy with histopathology confirmed a diagnosis of lymphocytic-plasmacytic and eosinophilic enterocolitis. For this diagnosis, the patient was treated with prednisolone, tylosin, and metronidazole. Antibiotics were gradually tapered as the cat showed clinical improvement. The patient showed resolution of the gastrointestinal signs, and the numbers of eosinophils and basophils returned within the reference range 8 weeks after the treatment began. Basophilia and eosinophilia has been reported in conjunction with feline T-cell lymphoma. However, marked basophilia accompanying eosinophilia is extremely rare in cats with inflammatory bowel disease. We herein provide clinical details, including ultrasonography, endoscopy, histopathology, and disease course of feline lymphocytic-plasmacytic and eosinophilic enterocolitis with marked basophilia and eosinophilia. This case highlights the importance of considering enteritis as potential diagnoses when eosinophilia and basophilia are concurrently observed in cats.

Asthma is a chronic inflammatory disease of the airways characterized by varying and recurrent symptoms, reversible airway obstruction, and bronchospasm. In this paper, clinical important studies on asthma published between March 2021 and February 2022 were reviewed. A study on the relationship between asthma and chronic rhinosinusitis, bronchiectasis, and hormone replacement therapy was published. A journal on the usefulness of fractional exhaled nitric oxide for the prediction of severe acute exacerbation was also introduced. Studies on the effect of inhaler, one of the most important treatments for asthma, were published. Studies on the control of severe asthma continued. Phase 2 and 3 studies of new biologics were also published. As the coronavirus disease 2019 (COVID-19) pandemic has been prolonged, many studies have explored the prevalence and mortality of COVID-19 infection in asthma patients.

Emphysematous cystitis is an extremely rare, complicated urinary tract infection with the presence of gas in the bladder wall and lumen caused by gas-producing bacterial infections. A 7-year-old spayed female pomeranian dog was presented with a 3-day history of hematuria and pollakiuria (case 1), and a 9-year-old spayed female jindo dog was presented with a 4-day history of intermittent hematuria (case 2). Imaging modalities, including radiography, ultrasonography, and computed tomography, and bacterial culture tests were used for the diagnosis. Emphysematous cystitis due to Escherichia coli infection with the extension of gas into multiple locations was identified in both cases. Based on the results of antibiotic susceptibility testing, systemic antibiotics were initiated. Both animals had an excellent response to antibiotic treatment, and the clinical signs of the gas collection were completely resolved within ~1 month after treatment initiation. This response was sustained without recurrence in the follow-up period. This case report describes clinical details of extremely rare canine cases of emphysematous cystitis with the extension of gas into multiple locations and evaluates the clinical efficacy of antibiotic therapy.

Abstract The need for high‐resolution wind resource maps is increasing with the increase in the supply and development of wind power. Many physical downscaling models have been developed and applied to make these maps. However, as the existing models require extensive computations and time, statistical models with higher efficiency are being studied. Statistical models such as regression and machine learning models can quickly calculate wind resource maps, but they have a problem of low accuracy. This study proposes a machine learning model with new topography‐derived variables to interpret the physical characteristics of the wind. As the shape of topography, which was unable to be interpreted in previous studies, can be considered with new derived variables, a significant performance improvement was identified. The analysis was conducted using 1 km Weather Research and Forecasting (WRF) results and ERA5 reanalysis data from South Korea. Two Weibull distribution parameter maps were calculated and used as input and output data. Three collections of derived variables were devised and compared. Therefore, the multi‐resolution topography data showed the highest improvements with approximately 15% reduction in root mean square error (RMSE) for both the linear regression and machine learning models. In particular, the land area showed a decrease of 20%. The best proposed models showed an RMSE of 7% and 8% for two Weibull parameters. The results are expected to serve as a reference for continuing research and utilization of statistical models.

This study proposed a novel V-bracing system equipped with a laterally layered friction damper (LLFD), which can supplement the shortcomings of conventional vibration control systems and is applicable to existing reinforced concrete (R/C) buildings. A material test was used to evaluate the material performance and energy dissipation capacity of this LLFD. Pseudo-dynamic testing was conducted on two-story frame specimens based on an existing R/C building with non-seismic details to verify the seismic retrofitting effects of applying the LLFD V-bracing system to existing R/C frames, i.e., the restoring force characteristics, energy dissipation capacity, and seismic response control capacity. Based on the results of the material and pseudo-dynamic tests, restoring characteristics were proposed for the non-linear dynamic analysis of a building (frame specimen) retrofitted with the LLFD V-bracing system. A non-linear dynamic analysis was conducted based on the proposed restoring force characteristics, and the results obtained were compared with the pseudo-dynamic test results. Finally, for evaluating the commercialization potential of the LLFD V-bracing system, a non-linear dynamic analysis was conducted on an existing R/C building with non-seismic details retrofitted with the system. The seismic retrofitting effect was verified by examining the seismic response load and displacement characteristics, energy dissipation capacity, and damper load and displacement response before and after seismic retrofitting. The study results showed that the R/C frame (building) with non-seismic details exhibited shear failure at a design basis earthquake scale of 200 cm/s2; however, light seismic damage could be expected for a frame (building) retrofitted with the LLFD V-bracing system. At a maximum considered earthquake scale of 300 cm/s2, insignificant seismic damage was also anticipated, thereby verifying the validity of the newly developed LLFD V-bracing system.

Dogs with sialocele often have concurrent hypercortisolism or are receiving long-term glucocorticoid treatment. However, their association has not been investigated. This retrospective matched case–control study investigated the association between hypercortisolism, long-term glucocorticoid treatment, and sialocele in dogs. We retrospectively reviewed the records from 1 January 2018 to 31 December 2022. Records of 19 dogs diagnosed with sialocele were investigated for hypercortisolism and long-term glucocorticoid treatment. Two age- and breed-matched controls for each sialocele dog (38 dogs) were investigated for the same concurrent diseases. Logistic regression analysis was used. The odds of sialocele in dogs with hypercortisolism were 15.56 times those of dogs without hypercortisolism (p = 0.02; 95% CI: 1.54–156.79). The odds of sialocele in dogs with long-term glucocorticoid treatment (median, 8 months; range, 5–13) were 7.78 times those of dogs without long-term glucocorticoid treatment (p = 0.03; 95% CI: 1.23–49.40). No associations were found between age, sex, body weight, and the presence of sialocele. The results indicate that sialocele was significantly associated with hypercortisolism and long-term glucocorticoid treatment in dogs. Therefore, dogs with hypercortisolism or receiving long-term glucocorticoid therapy should be screened for possible sialocele. Additionally, dogs with sialocele should be identified for concurrent hypercortisolism and prolonged glucocorticoid exposure.

In the face of increasing irregular temperature patterns and climate shifts, the need for accurate power consumption prediction is becoming increasingly important to ensure a steady supply of electricity. Existing deep learning models have sought to improve prediction accuracy but commonly require greater computational demands. In this research, on the other hand, we introduce DelayNet, a lightweight deep learning model that maintains model efficiency while accommodating extended time sequences. Our DelayNet is designed based on the observation that electronic series data exhibit recurring irregular patterns over time. Furthermore, we present two substantial datasets of electricity consumption records from South Korean buildings spanning nearly two years. Empirical findings demonstrate the model’s performance, achieving 21.23%, 43.60%, 17.05% and 21.71% improvement compared to recurrent neural networks, gated-recurrent units, temporal convolutional neural networks and ARIMA models, as well as greatly reducing model complexity and computational requirements. These findings indicate the potential for micro-level power consumption planning, as lightweight models can be implemented on edge devices.

Abstract Background As the COVID-19 (Coronavirus disease 2019) pandemic is prolonged, psychological responses to the pandemic have changed, and a new scale to reflect these changes needs to be developed. In this study, we attempt to develop and validate the COVID-19 Impact Scale (CIS) to measure the psychological stress responses of the COVID-19 pandemic, including emotional responses and difficulty with activities of daily living. Methods We recruited 2152 participants. Participants completed the CIS, the Fear of COVID-19 Scale (FCV-19S), and other mental health related measures. The factor structure, reliability, and validity of the CIS were analyzed. In addition, the validity of the scale was confirmed by its relationships to the existing measures assessing fear of COVID-19, depression, anxiety, subjective well-being, and suicidal ideation. Results Using exploratory factor analysis (N 1 = 1076), we derived a one-factor structure. In confirmatory factor analysis (N 2 = 1076), the one-factor model showed good to excellent fitness. The CIS was positively correlated with depression, anxiety, suicidal ideation, fear of COVID-19 and negatively correlated with subjective well-being. The FCV-19S did not show significant correlations with subjective well-being or suicidal ideation, and FCV-19S’s explanatory powers on depression and anxiety were lower than those of the CIS. Conclusions These results support that the CIS is a valid assessment of emotional problems and deterioration of the quality of life caused by the COVID-19 pandemic. Finally, the limitations of this study and future research directions are discussed.

A novel three-dimensional two-step numerical model was developed and analyzed for a tandem process with a reverse-polarity hot wire. Magneto-hydrodynamic analysis was conducted and the free surface of the molten pool was tracked to investigate the arc and molten pool behaviors of the proposed process, respectively. The results indicated that a change in electromagnetic force by the hot wire reduced arc deflection, which increased the arc velocity in the co-axial direction of electrodes, thereby increasing the arc pressure and arc shear force acting on the molten pool. Consequently, the molten pool in the novel tandem process was wider and deeper than that in the conventional tandem process.

Owing to their large surface area, two-dimensional (2D) semiconducting nanomaterials have been extensively studied for gas-sensing applications in recent years. In particular, the possibility of operating at room temperature (RT) is desirable for 2D gas sensors because it significantly reduces the power consumption of the sensing device. Furthermore, RT gas sensors are among the first choices for the development of flexible and wearable devices. In this review, we focus on the 2D MXenes used for the realization of RT gas sensors. Hence, pristine, doped, decorated, and composites of MXenes with other semiconductors for gas sensing are discussed. Two-dimensional MXene nanomaterials are discussed, with greater emphasis on the sensing mechanism. MXenes with the ability to work at RT have great potential for practical applications such as flexible and/or wearable gas sensors.

To understand human emotional states, local activities in various regions of the cerebral cortex and the interactions among different brain regions must be considered. This paper proposes a hierarchical emotional context feature learning model that improves multichannel electroencephalography (EEG)-based emotion recognition by learning spatiotemporal EEG features from a local brain region to a global brain region. The proposed method comprises a regional brain-level encoding module, a global brain-level encoding module, and a classifier. First, multichannel EEG signals grouped into nine regions based on the functional role of the brain are input into a regional brain-level encoding module to learn local spatiotemporal information. Subsequently, the global brain-level encoding module improved emotional classification performance by integrating local spatiotemporal information from various brain regions to learn the global context features of brain regions related to emotions. Next, we applied a two-layer bidirectional gated recurrent unit (BGRU) with self-attention to the regional brain-level module and a one-layer BGRU with self-attention to the global brain-level module. Experiments were conducted using three datasets to evaluate the EEG-based emotion recognition performance of the proposed method. The results proved that the proposed method achieves superior performance by reflecting the characteristics of multichannel EEG signals better than state-of-the-art methods.

Endometriosis is a gynecological disease prevalent in women of reproductive age, and it is characterized by the ectopic presence and growth of the eutopic endometrium. The pathophysiology and diagnostic biomarkers of endometriosis have not yet been comprehensively determined. To discover molecular markers and pathways underlying the pathogenesis of endometriosis, we identified differentially expressed genes (DEGs) in three Gene Expression Omnibus microarray datasets (GSE11691, GSE23339, and GSE7305) and performed gene set enrichment analysis (GSEA) and protein–protein interaction (PPI) network analyses. We also validated the identified genes via immunohistochemical analysis of tissues obtained from patients with endometriosis or healthy volunteers. A total of 118 DEGs (79 upregulated and 39 downregulated) were detected in each dataset with a lower (fold change) FC cutoff (log2|FC| > 1), and 17 DEGs (11 upregulated and six downregulated) with a higher FC cutoff (log2|FC| > 2). KEGG and GO functional analyses revealed enrichment of signaling pathways associated with inflammation, complement activation, cell adhesion, and extracellular matrix in endometriotic tissues. Upregulation of seven genes (C7, CFH, FZD7, LY96, PDLIM3, PTGIS, and WISP2) out of 17 was validated via comparison with external gene sets, and protein expression of four genes (LY96, PDLIM3, PTGIS, and WISP2) was further analyzed by immunohistochemistry and western blot analysis. Based on these results, we suggest that TLR4/NF-κB and Wnt/frizzled signaling pathways, as well as estrogen receptors, regulate the progression of endometriosis. These pathways may be therapeutic and diagnostic targets for endometriosis.

Objective:The general public recognizes the connections between obesity, family history of diabetes (FHD), and diabetes, but remains unaware of synergistic effects that occur due to their combination. In this study, we investigate the synergistic effects of event diabetes between snoring, FHD, and obesity.Materials and Methods:Study subjects were selected among participants in the Korean Genome and Epidemiology Study Ansan- Ansung cohort, an ongoing prospective population-based study. Ansan-Ansung cohort initiated in 2001 and 2002 and has been followed biennially. At baseline, the initial cohort of 10.030 subjects, aged 40 to 69 years. A total of 5.759 participants were included in this study. The relationships between diabetes and snoring, FHD, and obesity were estimated using Cox hazard regression models after adjusting for confounding factors. Snoring, obesity and FHD were significantly associated with diabetes in both males and females after adjustments for covariates. However, males were significantly more likely to have FHD and obesity.Results:A synergistic effect between these two risk factors showed that being female was associated with the all group after adjustment for covariates. Moreover, there was a high risk of having two risk factors among female patients, while males showed no such tendency. A significant synergistic effect between these three risk factors was seen in females. An exposure group with these three factors had higher diabetes risk than other groups [hazard ratio (HR): 6.27 (95% confidence interval (CI): 2.93-13.41] p

Today, resistance gas sensors which are mainly realized from metal oxides are among the most used sensing devices. However, generally, their sensing temperature is high and other materials with a lower operating temperature can be an alternative to them. Graphene and its derivatives with a 2D structure are among the most encouraging materials for gas-sensing purposes, because a 2D lattice with high surface area can maximize the interaction between the surface and gas, and a small variation in the carrier concentration of graphene can cause a notable modulation of electrical conductivity in graphene. However, they show weak sensing performance in pristine form. Hence, doping, and in particular N doping, can be one of the most promising strategies to enhance the gas-sensing features of graphene-based sensors. Herein, we discuss the gas-sensing properties of N-doped graphene and its derivatives. N doping can induce a band gap inside of graphene, generate defects, and enhance the conductivity of graphene, all factors which are beneficial for sensing studies. Additionally, not only is experimental research reviewed in this review paper, but theoretical works about N-doped graphene are also discussed.

ABSTRACT The CCAAT-binding heme activator protein (HAP) complex, comprising the DNA-binding heterotrimeric complex Hap2/3/5 and transcriptional activation subunit HapX, is a key regulator of iron homeostasis, mitochondrial functions, and pathogenicity in Cryptococcus neoformans, which causes fatal meningoencephalitis. However, its role in the development of human fungal pathogens remains unclear. To elucidate the role of the HAP complex in C. neoformans development, we constructed hap2Δ, hap3Δ, hap5Δ, and hapXΔ mutants and their complemented congenic MATα H99 and MATa YL99a strains. The HAP complex plays a conserved role in iron utilization and stress responses in cells of both mating types. Deletion of any of the HAP complex components markedly enhances filamentation during bisexual mating. However, the Hap2/3/5 complex, but not HapX, is crucial in repressing pheromone production and cell fusion and is thus a critical repressor of sexual differentiation of C. neoformans. Interestingly, deletion of the heterotrimeric complex transcriptionally regulated both positive and negative regulators in the pheromone-responsive Cpk1 mitogen-activated protein kinase (MAPK) pathway. Chromatin immunoprecipitation-quantitative PCR analysis revealed that the HAP complex physically bound to the CCAAT motif of the CRG1 and GPA2 promoter regions. Notably, the HAP complex was differentially localized depending on the mating type in basal conditions; it was enriched in the nuclei of MATα cells but diffused in the cytoplasm of MATa cells. Interestingly, however, a portion of the HAP complex in both mating types relocalized to the cell membrane during mating. In conclusion, the Hap2/3/5 heterotrimeric complex and HapX play major and minor roles, respectively, in repressing the sexual development of C. neoformans in association with the Cpk1 MAPK pathway. IMPORTANCE Cryptococcus neoformans isolates are of two mating types: MATα strains, which are predominant, and MATa strains, isolated from the sub-Saharan African region, where cryptococcosis is most abundant and severe. Here, we demonstrated the function of the CCAAT-binding HAP complex (Hap2/3/5/X) as a transcriptional repressor of Cpk1 pathway-related genes in cells of both mating types. Deletion of any HAP complex component markedly enhanced filamentation without affecting normal sporulation. In particular, deletion of the DNA-binding HAP complex components (Hap2/3/5), but not HapX, markedly enhanced pheromone production and cell fusion efficiency, validating its repressive role in the early stage of mating in C. neoformans. The HAP complex regulates the expression of both negative and positive mating regulators and is thus crucial for the regulation of the Cpk1 MAPK pathway during mating. This study provides insights into the complex signaling networks governing the sexual differentiation of C. neoformans.

In recent industrial applications, machine learning technology is proving useful in preventing equipment failures in advance through early failure diagnosis. In particular, we show that different domains can be linked through adversarial learning with data available in different working conditions to facilitate the training of the model, as it is impractical to acquire data for all conditions in real-world applications. Nevertheless, the initial failure is a difficult problem to diagnose because it does not show a significant difference from the normal data between different conditions. Moreover, if only the domain discriminator is judged when adapting the domain, it tends to easily cause misclassification, so the reliability of the detection result needs to be improved. In this study, we propose a new learning method that improves classification performance by sharing the classification characteristics of the classifier for each task with the target domain characteristic generator. The proposed mechanism uses spatial attention to extract the focused partial information of the feature generator and discriminator, and further enhances task-specific features using the attention mechanism between the two extracted information types. Addresses the challenge of implementing both domain adaptation and classification. Extensive experimentation demonstrates efficiency and improved classification performance on benchmark and real-world application datasets. In real machine cases, the classification accuracy is improved by almost 4%. In addition, the negative impact on false alarms was lowered by increasing the classification accuracy of minimum failures. Convincingly demonstrate model effectiveness by performing an empirical analysis of the method through ablation analysis and visualization.

Phosphatases are key components in cellular signalling networks. Here, the authors present a systematic functional analysis of phosphatases of the fungal pathogen Cryptococcus neoformans, revealing roles in virulence, stress responses, O-mannosylation, retromer function and other processes.

Abstract High strength SiC whisker-reinforced Ti3SiC2 composites (SiCw/Ti3SiC2) with an improved thermal conductivity and mechanical properties were fabricated by spark plasma sintering. The bending strength of 10 wt% SiCw/Ti3SiC2 was 635 MPa, which was approximately 50% higher than that of the monolithic Ti3SiC2 (428 MPa). The Vickers hardness and thermal conductivity (k) also increased by 36% and 25%, respectively, from the monolithic Ti3SiC2 by the incorporation of 10 wt% SiCw. This remarkable improvement both in mechanical and thermal properties was attributed to the fine-grained uniform composite microstructure along with the effects of incorporated SiCw. The SiCw/Ti3SiC2 can be a feasible candidate for the in-core structural application in nuclear reactors due to the excellent mechanical and thermal properties.

Resistive-based gas sensors built through the combination of semiconducting metal oxides and conducting polymers (CPs) are widely used for the detection of diverse gaseous components. In light of the great potential of each of these components, electrospun CPs produced by a facile electrospinning method can offer unique opportunities for the fabrication of sensitive gas sensors for diverse gaseous compounds due to their large surface area and favorable nanomorphologies. This review focuses on the progress achieved in gas sensing technology based on electrospun CPs. We offer numerous examples of CPs as gas sensors and discuss the parameters affecting their sensitivity, selectivity, and sensing mechanism. This review paper is expected to offer useful insights into potential applications of CPs as gas sensing systems.

In this paper, we addressed the challenges in sorting high-yield apple cultivars that traditionally relied on manual labor or system-based defect detection. Existing single-camera methods failed to uniformly capture the entire surface of apples, potentially leading to misclassification due to defects in unscanned areas. Various methods were proposed where apples were rotated using rollers on a conveyor. However, since the rotation was highly random, it was difficult to scan the apples uniformly for accurate classification. To overcome these limitations, we proposed a multi-camera-based apple sorting system with a rotation mechanism that ensured uniform and accurate surface imaging. The proposed system applied a rotation mechanism to individual apples while simultaneously utilizing three cameras to capture the entire surface of the apples. This method offered the advantage of quickly and uniformly acquiring the entire surface compared to single-camera and random rotation conveyor setups. The images captured by the system were analyzed using a CNN classifier deployed on embedded hardware. To maintain excellent CNN classifier performance while reducing its size and inference time, we employed knowledge distillation techniques. The CNN classifier demonstrated an inference speed of 0.069 s and an accuracy of 93.83% based on 300 apple samples. The integrated system, which included the proposed rotation mechanism and multi-camera setup, took a total of 2.84 s to sort one apple. Our proposed system provided an efficient and precise solution for detecting defects on the entire surface of apples, improving the sorting process with high reliability.

Continuous efforts were made in detecting cracks in images. Varied CNN models were developed and tested for detecting or segmenting crack regions. However, most datasets used in previous works contained clearly distinctive crack images. No previous methods were validated on blurry cracks captured in low definitions. Therefore, this paper presented a framework of detecting the regions of blurred, indistinct concrete cracks. The framework divides an image into small square patches which are classified into crack or non-crack. Well-known CNN models were employed for the classification and compared with each other with experimental tests. This paper also elaborated on critical factors—the patch size and the way of labeling patches—which had considerable influences on the training performance. Furthermore, a series of post-processes for measuring crack lengths were introduced. The proposed framework was tested on the images of bridge decks containing blurred thin cracks and showed reliable performance comparable to practitioners.

This work discusses the challenges of multi-label image classification and presents a novel Efficient Shuffle Net (EffShuffNet) based on a convolutional neural network (CNN) architecture to address these challenges. Multi-label classification is difficult as the complexity of prediction increases with the number of labels and classes, and current multi-model approaches require optimized deep learning models which increase computational costs. The EffShuff block divides the input feature map into two parts and processes them differently, with one half undergoing a lightweight convolution and the other half undergoing average pooling. The EffShuff transition component shuffles the feature maps after lightweight convolution, resulting in a 57.9% reduction in computational cost compared to ShuffleNetv2. Furthermore, we propose EffShuff-Dense architecture, which incorporates Dense connection to further emphasize low-level features. In experiments, the EffShuffNet achieved 96.975% accuracy in age and gender classification, which is 5.83% higher than the state-of-the-art, while EffShuffDenseNet was even better with 97.63% accuracy. Additionally, the proposed models were found to have better classification performance with smaller model sizes in fine-grained image classification experiments.

Summary: Novel gas sensors that work at room temperature are attracting attention due to their low energy consumption and stability in the presence of toxic gases. However, the development of sensing characteristics at room temperature is still a primary challenge. Diverse reaction pathways and low adsorption energy for gas molecules are required to fabricate a gas sensor that works at room temperature with high sensitivity, selectivity, and efficiency. Therefore, we enhanced the gas sensing performance at room temperature by constructing hybridized nanostructure of 1D–2D hybrid of SnSe2 layers and SnO2 nanowire networks and by controlling the back-gate bias (Vg = 1.5 V). The response time was dramatically reduced by lowering the energy barrier for the adsorption on the reactive sites, which are controlled by the back gate. Consequently, we believe that this research could contribute to improving the performance of gas sensors that work at room temperature.

Clinically, fungal pneumonia rarely occurs in adults, and invasive fungal infections can cause substantial morbidity, and mortality due to sepsis and septic shock. In the present study, we have designed peptides that exhibit potent antifungal activities against fluconazole-resistant Candida albicans in physiological monovalent, and divalent ionic buffers, with minimal fungicidal concentrations ranging from 16 to 32 µM. None of these tested peptides resulted in the development of drug resistance similar to fluconazole. Among them, the PS1-2 peptide did not induce stimulation of macrophages by C. albicans, and it exerted antifungal and anti-inflammatory effects against C. albicans-induced intratracheal infection, in an acute lung injury mouse model. PS1-2 is likely a novel therapeutic agent for the control, and prevention of drug-resistant C. albicans infection, and our findings may be useful for designing antimicrobial peptides to combat fungal infection.

The brain is more sensitive to stress than other organs and can develop many diseases under excessive stress. In this study, we developed a method to improve the accuracy of emotional stress recognition using multi-channel electroencephalogram (EEG) signals. The method combines a three-dimensional (3D) convolutional neural network with an attention mechanism to build a 3D convolutional gated self-attention neural network. Initially, the EEG signal is decomposed into four frequency bands, and a 3D convolutional block is applied to each frequency band to obtain EEG spatiotemporal information. Subsequently, long-range dependencies and global information are learned by capturing prominent information from each frequency band via a gated self-attention mechanism block. Using frequency band mapping, complementary features are learned by connecting vectors from different frequency bands, which is reflected in the final attentional representation for stress recognition. Experiments conducted on three benchmark datasets for assessing the performance of emotional stress recognition indicate that the proposed method outperforms other conventional methods. The performance analysis of proposed methods confirms that EEG pattern analysis can be used for studying human brain activity and can accurately distinguish the state of stress.

Renewable energy supply is essential for carbon neutrality; however, technologies aiming to optimally utilize renewable energy sources remain insufficient. Seasonal variability in renewable energy is a key issue, which many studies have attempted to overcome through operating systems and energy storage. Currently, hydrogen is the only technology that can solve this seasonal storage problem. In this study, the amount of hydrogen required to circumvent the seasonal variability in renewable energy supply in Korea was quantified. Spatiotemporal analysis was conducted using renewable energy resource maps and power loads. It was predicted that 50% of the total power demand in the future will be met using solar and wind power, and a scenario was established based on the solar-to-wind ratio. It was found that the required hydrogen production differed by approximately four-times, depending on the scenarios, highlighting the importance of supplying renewable energy at an appropriate ratio. Spatially, wind power was observed to be unsuitable for the physical transport of hydrogen because it has a high potential at mountain peaks and islands. The results of this study are expected to aid future hydrogen research and solve renewable energy variability problems.

Background Overactivation of inflammatory cells, including macrophages and neutrophils, is associated with acute lung injury. BMS-470539 is a selective agonist of melanocortin 1 receptor, which triggers the inhibition of proinflammatory responses, suppressing neutrophil infiltration and protecting tissue. This study evaluated the effects of BMS-470539 on lipopolysaccharide-induced acute lung injury in a mouse model. Methods Mice received a subcutaneous injection of saline or BMS-470539 (18.47 mg/kg) 1 hour before an intratracheal injection of saline or lipopolysaccharide (20 μg). Mice were sacrificed to analyze the severity of pulmonary edema (lung wet-to-dry weight [W/D] ratio) and inflammatory responses (level of leukocytes, polymorphonuclear neutrophils [PMNs] and tumor necrosis factor alpha [TNF-α] in bronchoalveolar lavage fluid [BALF]), and neutrophil infiltration (myeloperoxidase activity). TNF-α activation was also measured in neutrophils from bone marrow. Survival was investigated in a second-hit sepsis mouse model. Results BMS-470539 improved sepsis-induced pulmonary edema, as demonstrated by a decreased W/D ratio (5.76%±0.83% to 3.81%±0.86%, P

Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R2 was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management.