FDA, CDC, and NIH Co-sponsored Public Workshop Summary-Development Considerations of Antimicrobial Drugs for the Treatment of Gonorrhea.

There is an unmet need for developing drugs for the treatment of gonorrhea, due to rapidly evolving resistance of Neisseria gonorrhoeae against antimicrobial drugs used for empiric therapy, an increase in globally reported multidrug resistant cases, and the limited available therapeutic options. Furthermore, few drugs are under development. Development of antimicrobials is hampered by challenges in clinical trial design, limitations of available diagnostics, changes in and varying standards of care, lack of robust animal models, and clinically relevant pharmacodynamic targets. On April 23, 2021, the U.S. Food and Drug Administration; Centers for Disease Control and Prevention; and National Institute of Allergy and Infectious Diseases, National Institutes of Health co-sponsored a workshop with stakeholders from academia, industry, and regulatory agencies to discuss the challenges and strategies, including potential collaborations and incentives, to facilitate the development of drugs for the treatment of gonorrhea. This article provides a summary of the workshop.

Aster × chusanensis Growth and Phenolic Acid Composition under Different Cultivation Temperatures.

Plants of the Asteraceae family have been cultivated worldwide for economic, medicinal, and ornamental purposes, including genera such as Aster, Helianthus, and Cosmos. Numerous studies examined their secondary metabolites; however, those of Aster × chusanensis, which is a natural hybrid species in South Korea, are unclear, and optimized propagation methods should be identified. We analyzed phenolic acid concentrations in each part of Aster × chusanensis through HPLC. Further, we investigated the growth characteristics and secondary metabolite concentrations under various growth temperatures using division propagation, followed by growing at 20, 25, and 30 °C in a growth chamber. Chlorogenic acid was the primary compound, which was particularly high in the leaves. The growth characteristics did not differ significantly between temperatures, and 30 °C was most efficient for phenolic acid biosynthesis. Our results provide valuable information on optimized propagation and secondary metabolite concentrations under different temperatures of Aster × chusanensis.

Radiographic Findings Associated With Mild Hip Dysplasia in 3869 Patients Using a Deep Learning Measurement Tool.

Background: Hip dysplasia is considered one of the leading etiologies contributing to hip degeneration and the eventual need for total hip arthroplasty (THA). We validated a deep learning (DL) algorithm to measure angles relevant to hip dysplasia and applied this algorithm to determine the prevalence of dysplasia in a large population based on incremental radiographic cutoffs. Methods: Patients from the Osteoarthritis Initiative with anteroposterior pelvis radiographs and without previous THAs were included. A DL algorithm automated 3 angles associated with hip dysplasia: modified lateral center-edge angle (LCEA), Tönnis angle, and modified Sharp angle. The algorithm was validated against manual measurements, and all angles were measured in a cohort of 3869 patients (61.2 ± 9.2 years, 57.1% female). The percentile distributions and prevalence of dysplastic hips were analyzed using each angle. Results: The algorithm had no significant difference (P > .05) in measurements (paired difference: 0.3°-0.7°) against readers and had excellent agreement for dysplasia classification (kappa = 0.78-0.88). In 140 minutes, 23,214 measurements were automated for 3869 patients. LCEA and Sharp angles were higher and the Tönnis angle was lower (P < .01) in females. The dysplastic hip prevalence varied from 2.5% to 20% utilizing the following cutoffs: 17.3°-25.5° (LCEA), 9.4°-15.6° (Tönnis), and 41.3°-45.9° (Sharp). Conclusions: A DL algorithm was developed to measure and classify hips with mild hip dysplasia. The reported prevalence of dysplasia in a large patient cohort was dependent on both the measurement and threshold, with 12.4% of patients having dysplasia radiographic indices indicative of higher THA risk.

Explainable Deep-Learning-Based Gait Analysis of Hip-Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression.

Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional methods of assessing the likelihood of AIS progression are limited by variability and rely on static measurements. This study developed and validated machine learning models for classifying progressive and non-progressive scoliotic curves based on gait analysis using wearable inertial sensors. Gait data from 38 AIS patients were collected using seven inertial measurement unit (IMU) sensors, and hip-knee (HK) cyclograms representing inter-joint coordination were generated. Various machine learning algorithms, including support vector machine (SVM), random forest (RF), and novel deep convolutional neural network (DCNN) models utilizing multi-plane HK cyclograms, were developed and evaluated using 10-fold cross-validation. The DCNN model incorporating multi-plane HK cyclograms and clinical factors achieved an accuracy of 92% in predicting curve progression, outperforming SVM (55% accuracy) and RF (52% accuracy) models using handcrafted gait features. Gradient-based class activation mapping revealed that the DCNN model focused on the swing phase of the gait cycle to make predictions. This study demonstrates the potential of deep learning techniques, and DCNNs in particular, in accurately classifying scoliotic curve progression using gait data from wearable IMU sensors.

Synergizing breeding strategies via combining speed breeding, phenotypic selection, and marker-assisted backcrossing for the introgression of Glu-B1i in wheat.

Wheat is a major food crop that plays a crucial role in the human diet. Various breeding technologies have been developed and refined to meet the increasing global wheat demand. Several studies have suggested breeding strategies that combine generation acceleration systems and molecular breeding methods to maximize breeding efficiency. However, real-world examples demonstrating the effective utilization of these strategies in breeding programs are lacking. In this study, we designed and demonstrated a synergized breeding strategy (SBS) that combines rapid and efficient breeding techniques, including speed breeding, speed vernalization, phenotypic selection, backcrossing, and marker-assisted selection. These breeding techniques were tailored to the specific characteristics of the breeding materials and objectives. Using the SBS approach, from artificial crossing to the initial observed yield trial under field conditions only took 3.5 years, resulting in a 53% reduction in the time required to develop a BC2 near-isogenic line (NIL) and achieving a higher recurrent genome recovery of 91.5% compared to traditional field conditions. We developed a new wheat NIL derived from cv. Jokyoung, a leading cultivar in Korea. Milyang56 exhibited improved protein content, sodium dodecyl sulfate-sedimentation value, and loaf volume compared to Jokyoung, which were attributed to introgression of the Glu-B1i allele from the donor parent, cv. Garnet. SBS represents a flexible breeding model that can be applied by breeders for developing breeding materials and mapping populations, as well as analyzing the environmental effects of specific genes or loci and for trait stacking.

AI in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale.

BACKGROUND: The evaluation of gait function and severity classification of stroke patients are important to determine the rehabilitation goal and the level of exercise. Physicians often qualitatively evaluate patients' walking ability through visual gait analysis using naked eye, video images, or standardized assessment tools. Gait evaluation through observation relies on the doctor's empirical judgment, potentially introducing subjective opinions. Therefore, conducting research to establish a basis for more objective judgment is crucial. OBJECTIVE: To verify a deep learning model that classifies gait image data of stroke patients according to Functional Ambulation Category (FAC) scale. METHODS: Gait vision data from 203 stroke patients and 182 healthy individuals recruited from six medical institutions were collected to train a deep learning model for classifying gait severity in stroke patients. The recorded videos were processed using OpenPose. The dataset was randomly split into 80% for training and 20% for testing. RESULTS: The deep learning model attained a training accuracy of 0.981 and test accuracy of 0.903. Area Under the Curve(AUC) values of 0.93, 0.95, and 0.96 for discriminating among the mild, moderate, and severe stroke groups, respectively. CONCLUSION: This confirms the potential of utilizing human posture estimation based on vision data not only to develop gait parameter models but also to develop models to classify severity according to the FAC criteria used by physicians. To develop an AI-based severity classification model, a large amount and variety of data is necessary and data collected in non-standardized real environments, not in laboratories, can also be used meaningfully.

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations.

PURPOSE OF REVIEW: Patient-reported outcome measures (PROM) play a critical role in evaluating the success of treatment interventions for musculoskeletal conditions. However, predicting which patients will benefit from treatment interventions is complex and influenced by a multitude of factors. Artificial intelligence (AI) may better anticipate the propensity to achieve clinically meaningful outcomes through leveraging complex predictive analytics that allow for personalized medicine. This article provides a contemporary review of current applications of AI developed to predict clinically significant outcome (CSO) achievement after musculoskeletal treatment interventions. RECENT FINDINGS: The highest volume of literature exists in the subspecialties of total joint arthroplasty, spine, and sports medicine, with only three studies identified in the remaining orthopedic subspecialties combined. Performance is widely variable across models, with most studies only reporting discrimination as a performance metric. Given the complexity inherent in predictive modeling for this task, including data availability, data handling, model architecture, and outcome selection, studies vary widely in their methodology and results. Importantly, the majority of studies have not been externally validated or demonstrate important methodological limitations, precluding their implementation into clinical settings. A substantial body of literature has accumulated demonstrating variable internal validity, limited scope, and low potential for clinical deployment. The majority of studies attempt to predict the MCID-the lowest bar of clinical achievement. Though a small proportion of models demonstrate promise and highlight the utility of AI, important methodological limitations need to be addressed moving forward to leverage AI-based applications for clinical deployment.

Variability of the femoral mechanical-anatomical axis angle and its implications in primary and revision total knee arthroplasty.

Aims: Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Methods: Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL workflow was created to measure the FMAA and validated against human measurements. To reflect potential intramedullary guide placement during manual TKA, two different FMAAs were calculated either using a line approximating the entire diaphyseal shaft, and a line connecting the apex of the femoral intercondylar sulcus to the centre of the diaphysis. The proportion of FMAAs outside a range of 5.0° (SD 2.0°) was calculated for both definitions, and FMAA was compared using univariate analyses across sex, BMI, knee alignment, and femur length. Results: The algorithm measured 1,078 radiographs at a rate of 12.6 s/image (2,156 unique measurements in 3.8 hours). There was no significant difference or bias between reader and algorithm measurements for the FMAA (p = 0.130 to 0.563). The FMAA was 6.3° (SD 1.0°; 25% outside range of 5.0° (SD 2.0°)) using definition one and 4.6° (SD 1.3°; 13% outside range of 5.0° (SD 2.0°)) using definition two. Differences between males and females were observed using definition two (males more valgus; p < 0.001). Conclusion: We developed a rapid and accurate DL tool to quantify the FMAA. Considerable variation with different measurement approaches for the FMAA supports that patient-specific anatomy and surgeon-dependent technique must be accounted for when correcting for the FMAA using an intramedullary guide. The angle between the mechanical and anatomical axes of the femur fell outside the range of 5.0° (SD 2.0°) for nearly a quarter of patients.

Phase 1/2a Study of Rivoceranib, a Selective VEGFR-2 Angiogenesis Inhibitor, in Patients with Advanced Solid Tumors.

PURPOSE: This study aimed to report the results from an early-phase study of rivoceranib, an oral tyrosine kinase inhibitor highly selective for vascular endothelial growth factor receptor 2, in patients with advanced solid tumors. MATERIALS AND METHODS: In this open-label, single-arm, dose-escalating, multicenter three-part phase 1/2a trial, patients had advanced solid tumors refractory to conventional therapy. Part 1 evaluated the safety and pharmacokinetics of five ascending once-daily doses of rivoceranib from 81 mg to 685 mg. Part 2 evaluated the safety and antitumor activity of once-daily rivoceranib 685 mg. Part 3 was conducted later, due to lack of maximum tolerated dose determination in part 1, to evaluate the safety and preliminary efficacy of once-daily rivoceranib 805 mg in patients with unresectable or advanced gastric cancer. RESULTS: A total of 61 patients were enrolled in parts 1 (n=25), 2 (n=30), and 3 (n=6). In parts 1 and 2, patients were white (45.5%) or Asian (54.5%), and 65.6% were male. The most common grade ≥ 3 adverse events were hypertension (32.7%), hyponatremia (10.9%), and hypophosphatemia (10.9%). The objective response rate (ORR) was 15.2%. In part 3, dose-limiting toxicities occurred in two out of six patients: grade 3 febrile neutropenia decreased appetite, and fatigue. The ORR was 33%. CONCLUSION: The recommended phase 2 dose of rivoceranib was determined to be 685 mg once daily, which showed adequate efficacy with a manageable safety profile (NCT01497704 and NCT02711969).

Malignant peripheral nerve sheath tumor in the kidney of a dog.

A 12-year-old castrated male poodle presented with vomiting and diarrhea. Ultrasonography and computed tomography revealed a protruding mass at the caudal pole of the left kidney. Grossly, the poorly circumscribed abnormal mass was 1.6 × 1.8 × 1.9 cm in size and had multifocal dark-red foci. Microscopically, it was composed of densely or loosely packed variable-sized short spindle or ovoid cells. These neoplastic cells showed high pleomorphism, mitotic figures, and invasive tendency to the adjacent tissue. Immunohistochemically, the neoplastic spindle cells expressed vimentin, S100, neuron-specific enolase, nerve growth factor receptor, and laminin. Therefore, the mass was diagnosed as a malignant peripheral nerve sheath tumor (MPNST). To our knowledge, this is the first report of primary renal MPNST in a dog.

Enhanced mono-aromatics production by the CH4-assisted pyrolysis of microalgae using Zn-based HZSM-5 catalysts.

This study presents the catalytic pyrolysis of microalgae, Chlorella vulgaris (C. vulgaris), using pure CH4 and H2-rich gas evolved from CH4 decomposition on three different HZSM-5 catalysts loaded with Zn, Ga, and Pt, aimed specifically at producing high-value mono-aromatics such as benzene, toluene, ethylbenzene, and xylene (BTEX). In comparison with that for the typical inert N2 environment, a pure CH4 environment increased the bio-oil yield from 32.4 wt% to 37.4 wt% probably due to hydrogen and methyl radical insertion in the bio-oil components. Furthermore, the addition of bimetals further increased bio-oil yield. For example, ZnPtHZ led to a bio-oil yield of 47.7 wt% in pure CH4. ZnGaHZ resulted in the maximum BTEX yield (6.68 wt%), which could be explained by CH4 activation, co-aromatization, and hydrodeoxygenation. The BTEX yield could be further increased to 7.62 wt% when pyrolysis was conducted in H2-rich gas evolved from CH4 decomposition over ZnGaHZ, as rates of aromatization and hydrodeoxygenation were relatively high under this condition. This study experimentally validated that the combination of ZnGaHZ and CH4 decomposition synergistically increases BTEX production using C. vulgaris.

Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty.

BACKGROUND: The radiographic assessment of bone morphology impacts implant selection and fixation type in total hip arthroplasty (THA) and is important to minimize the risk of periprosthetic femur fracture (PFF). We utilized a deep-learning algorithm to automate femoral radiographic parameters and determined which automated parameters were associated with early PFF. METHODS: Radiographs from a publicly available database and from patients undergoing primary cementless THA at a high-volume institution (2016 to 2020) were obtained. A U-Net algorithm was trained to segment femoral landmarks for bone morphology parameter automation. Automated parameters were compared against that of a fellowship-trained surgeon and compared in an independent cohort of 100 patients who underwent THA (50 with early PFF and 50 controls matched by femoral component, age, sex, body mass index, and surgical approach). RESULTS: On the independent cohort, the algorithm generated 1,710 unique measurements for 95 images (5% lesser trochanter identification failure) in 22 minutes. Medullary canal width, femoral cortex width, canal flare index, morphological cortical index, canal bone ratio, and canal calcar ratio had good-to-excellent correlation with surgeon measurements (Pearson's correlation coefficient: 0.76 to 0.96). Canal calcar ratios (0.43 ± 0.08 versus 0.40 ± 0.07) and canal bone ratios (0.39 ± 0.06 versus 0.36 ± 0.06) were higher (P < .05) in the PFF cohort when comparing the automated parameters. CONCLUSIONS: Deep-learning automated parameters demonstrated differences in patients who had and did not have early PFF after cementless primary THA. This algorithm has the potential to complement and improve patient-specific PFF risk-prediction tools.

Inertial measurement unit sensor-based gait analysis in adults and older adults: A cross-sectional study.

BACKGROUND: Gait assessment has been used in a wide range of clinical applications, and gait velocity is also a leading predictor of disease and physical functional aspects in older adults. RESEARCH QUESTION: The study aim to examine the changes in IMU-based gait parameters according to age in healthy adults aged 50 and older, to analyze differences between aging patients. METHODS: A total of 296 healthy adults (65.32 ± 6.74 yrs; 83.10 % female) were recruited. Gait assessment was performed using an IMU sensor-based gait analysis system, and 3D motion information of hip and knee joints was obtained using magnetic sensors. The basic characteristics of the study sample were stratified by age category, and the baseline characteristics between the groups were compared using analysis of variance (ANOVA). Pearson's correlation analysis was used to analyze the relationship between age as the dependent variable and several measures of gait parameters and joint angles as independent variables. RESULTS: The results of this study found that there were significant differences in gait velocity and both terminal double support in the three groups according to age, and statistically significant differences in the three groups in hip joint angle and knee joints angle. In addition, it was found that the gait velocity and knee/hip joint angle changed with age, and the gait velocity and knee/hip joint angle were also different in the elderly and adult groups. CONCLUSIONS: We found changes in gait parameters and joint angles according to age in healthy adults and older adults and confirmed the difference in gait velocity and joint angles between adults and older adults.

Innate antiviral responses against Shaan virus infection in HEK293, A549 and MARC-145 cells and limited role of viperin against Shaan virus replication.

The Shaan virus is a new paramyxovirus species recently isolated from an insectivorous bat. Therefore, its replication characteristics remain unclear. We used transcriptome analysis and molecular experiments to examine host cell responses in human A549, HEK293, and monkey MARC-145 cell lines infected with the Shaan virus (ShaV/B16-40). Transcriptome data showed that Shaan virus infection induced innate immune responses associated with defense mechanisms against viral infection in all infected host cells. In real-time RT-PCR, IFN-α, -ß and -λ1 were significantly upregulated in response to infection with Shaan virus in A549 and HEK-293 cells. However, the expression of IFN-α and -λ1 did not change in MARC-145 infected cells, while IFN-ß significantly increased compared to the control in all the infected cell lines. In DEG analysis, the viperin expression pattern by Shaan virus infection varied depending on the host cell types or their origins. Viperin was highly induced at the RNA level by Shaan virus infection, and viperin protein expression was detected by western blotting. Although viperin, an ISG, has broad inhibitory effects on a range of viral pathogens, viperin knockdown or knock-in in the infected cells indicated that this protein did not markedly affect Shaan virus replication. Interestingly, these effects were independent of CMPK2 expression, which is beneficial for the antiviral effects of viperin. Therefore, the present results suggest that Shaan virus might have a strategy to evade the antiviral effect of viperin or not be significantly affected by viperin.

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified Using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers.

This study investigated novel quantitative traits loci (QTLs) associated with the control of grain shape and size as well as grain weight in rice. We employed a joint-strategy multiple GAPIT (Genome Association and Prediction Integrated Tool) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK)), Fixed and random model Circulating Probability Uniform (FarmCPU), Settlement of MLM Under Progressive Exclusive Relationship (SUPER), and General Linear Model (GLM)]-High-Density SNP Chip DNA Markers (60,461) to conduct a Genome-Wide Association Study (GWAS). GWAS was performed using genotype and grain-related phenotypes of 143 recombinant inbred lines (RILs). Data show that parental lines (Ilpum and Tung Tin Wan Hein 1, TTWH1, Oryza sativa L., ssp. japonica and indica, respectively) exhibited divergent phenotypes for all analyzed grain traits), which was reflected in their derived population. GWAS results revealed the association between seven SNP Chip makers and QTLs for grain length, co-detected by all GAPIT models on chromosomes (Chr) 1-3, 5, 7, and 11, were qGL1-1BFSG (AX-95918134, Chr1: 3,820,526 bp) explains 65.2-72.5% of the phenotypic variance explained (PVE). In addition, qGW1-1BFSG (AX-273945773, Chr1: 5,623,288 bp) for grain width explains 15.5-18.9% of PVE. Furthermore, BLINK or FarmCPU identified three QTLs for grain thickness independently, and explain 74.9% (qGT1Blink, AX-279261704, Chr1: 18,023,142 bp) and 54.9% (qGT2-1Farm, AX-154787777, Chr2: 2,118,477 bp) of the observed PVE. For the grain length-to-width ratio (LWR), the qLWR2BFSG (AX-274833045, Chr2: 10,000,097 bp) explains nearly 15.2-32% of the observed PVE. Likewise, the major QTL for thousand-grain weight (TGW) was detected on Chr6 (qTGW6BFSG, AX-115737727, 28,484,619 bp) and explains 32.8-54% of PVE. The qTGW6BFSG QTL coincides with qGW6-1Blink for grain width and explained 32.8-54% of PVE. Putative candidate genes pooled from major QTLs for each grain trait have interesting annotated functions that require functional studies to elucidate their function in the control of grain size, shape, or weight in rice. Genome selection analysis proposed makers useful for downstream marker-assisted selection based on genetic merit of RILs.

All-Solid-State Synaptic Transistors with Lithium-Ion-Based Electrolytes for Linear Weight Mapping and Update in Neuromorphic Computing Systems.

Neuromorphic computing, an innovative technology inspired by the human brain, has attracted increasing attention as a promising technology for the development of artificial intelligence systems. This study proposes synaptic transistors with a Li1-xAlxTi2-x(PO4)3 (LATP) layer to analyze the conductance modulation linearity, which is essential for weight mapping and updating during on-chip learning processes. The high ionic conductivity of the LATP electrolyte provides a large hysteresis window and enables linear weight update in synaptic devices. The results demonstrate that optimizing the LATP layer thickness improves the conductance modulation and linearity of synaptic transistors during potentiation and degradation. A 20 nm-thick LATP layer results in the most nonlinear depression (αd = -6.59), whereas a 100 nm-thick LATP layer results in the smallest nonlinearity (αd = -2.22). Additionally, a device with the optimal 100 nm-thick LATP layer exhibits the highest average recognition accuracy of 94.8% and the smallest fluctuation, indicating that the linearity characteristics of a device play a crucial role in weight update during learning and can significantly affect the recognition accuracy.

Evidence for the circulation of genotype 4 Eurasian avian-like lineage swine H1N1 influenza A viruses on Korean swine farms, obtained using a newly developed one-step multiplex RT-qPCR assay.

Genotype 4 (G4) Eurasian avian-like lineage swine H1N1 influenza A viruses, which are reassortants containing sequences from the pandemic 2009 H1N1 virus lineage, triple-reassortant-lineage internal genes, and EA-lineage external genes, have been reported in China since 2013. These have been predominant in pig populations since 2016 and have exhibited pandemic potential. In this study, we developed a one-step multiplex RT-qPCR assay targeting the M, HA1, and PB2 genes to detect G4 and related EA H1N1 viruses, with detection limits of 1.5 × 101 copies/µL and 1.15 × 10-2 ng/µL for the purified PCR products and RNA templates, respectively. The specificity of the detection method was confirmed using various influenza virus subtypes. When the one-step multiplex RT-qPCR assay was applied to swine respiratory samples collected between 2020 and 2022 in Korea, a virus related to G4 EA H1N1 strains was detected. Phylogenetic analysis based on portions of all eight genome segments showed that the positive sample contained HA, NA, PB2, NS, and NP genes closely related to those of G4 EA H1N1 viruses, confirming the ability of our assay to accurately detect G4 EA H1N1 viruses in the field.

Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study.

BACKGROUND: The rise in single-person households has resulted in social problems like loneliness and isolation, commonly known as "death by loneliness." Various factors contribute to this increase, including a desire for independent living and communication challenges within families due to societal changes. Older individuals living alone are particularly susceptible to loneliness and isolation due to limited family communication and a lack of social activities. Addressing these issues is crucial, and proactive solutions are needed. It is important to explore diverse measures to tackle the challenges of single-person households and prevent deaths due to loneliness in our society. OBJECTIVE: Non-face-to-face health care service systems have gained widespread interest owing to the rapid development of smart home technology. Particularly, a health monitoring system must be developed to manage patients' health status and send alerts for dangerous situations based on their activity. Therefore, in this study, we present a novel health monitoring system based on the auto-mapping method, which uses real-time position sensing mats. METHODS: The smart floor mats are operated as piezo-resistive devices, which are composed of a carbon nanotube-based conductive textile, electrodes, main processor circuit, and a mat. The developed smart floor system acquires real-time position information using a multiconnection method between the modules based on the auto-mapping algorithm, which automatically creates a spatial map. The auto-mapping algorithm allows the user to freely set various activity areas through floor mapping. Then, the monitoring system was evaluated in a room with an area of 41.3 m2, which is embedded with the manufactured floor mats and monitoring application. RESULTS: This monitoring system automatically acquires information on the total number, location, and direction of the mats and creates a spatial map. The position sensing mats can be easily configured with a simple structure by using a carbon nanotube-based piezo-resistive textile. The mats detect the activity in real time and record location information since they are connected through auto-mapping technology. CONCLUSIONS: This system allows for the analysis of patients' behavior patterns and the management of health care on the web by providing important basic information for activity patterns in the monitoring system. The proposed smart floor system can serve as the foundation for smart home applications in the future, which include health care, intelligent automation, and home security, owing to its advantages of low cost, large area, and high reliability.

EwaldSolidSolution: A High-Throughput Application to Quickly Sample Stable Site Arrangements for Ionic Solid Solutions.

Data-driven materials design of ionic solid solutions often requires sampling (meta)stable site arrangements among the massive number of possibilities, which has been hampered by the lack of relevant methods. Herein, we develop a quick high-throughput sampling application for site arrangements of ionic solid solutions. Given the Ewald Coulombic energies for an initial site arrangement, EwaldSolidSolution updates the modified parts of the energy with varying sites only, which can be exhaustively estimated by using massively parallel processing. Given two representative examples of solid electrolytes, Li10GeP2S12 and Na3Zr2Si2PO12, EwaldSolidSolution successfully calculates the Ewald Coulombic energies of 211,266,225 (235,702,467) site arrangements for Li10GeP2S12 (Na3Zr2Si2PO12) with 216 (160) ion sites per unit cell in 1223.2 (1187.9) seconds: 0.0057898 (0.0050397) milliseconds per site arrangement. The computational cost is enormously saved in comparison with an existing application, which estimates the energy of a site arrangement on the second timescale. The positive correlations between the Ewald Coulombic energies and those estimated by density functional theory calculations show that (meta)stable samples are easily revealed by our computationally inexpensive algorithm. We also reveal that the different-valence nearest-neighbor pairs are distinctively formed in the low-energy site arrangements. EwaldSolidSolution will boost the materials design of ionic solid solutions by attracting broad interest.