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Abstract Hand gesture recognition based on sparse multichannel surface electromyography (sEMG) still poses a significant challenge to deployment as a muscle–computer interface. Many researchers have been working to develop an sEMG-based hand gesture recognition system. However, the existing system still faces challenges in achieving satisfactory performance due to ineffective feature enhancement, so the prediction is erratic and unstable. To comprehensively tackle these challenges, we introduce a novel approach: a lightweight sEMG-based hand gesture recognition system using a 4-stream deep learning architecture. Each stream strategically combines Temporal Convolutional Network (TCN)-based time-varying features with Convolutional Neural Network (CNN)-based frame-wise features. In the first stream, we harness the power of the TCN module to extract nuanced time-varying temporal features. The second stream integrates a hybrid Long short-term memory (LSTM)-TCN module. This stream extracts temporal features using LSTM and seamlessly enhances them with TCN to effectively capture intricate long-range temporal relations. The third stream adopts a spatio-temporal strategy, merging the CNN and TCN modules. This integration facilitates concurrent comprehension of both spatial and temporal features, enriching the model’s understanding of the underlying dynamics of the data. The fourth stream uses a skip connection mechanism to alleviate potential problems of data loss, ensuring a robust information flow throughout the network and concatenating the 4 stream features, yielding a comprehensive and effective final feature representation. We employ a channel attention-based feature selection module to select the most effective features, aiming to reduce the computational complexity and feed them into the classification module. The proposed model achieves an average accuracy of 94.31% and 98.96% on the Ninapro DB1 and DB9 datasets, respectively. This high-performance accuracy proves the superiority of the proposed model, and its implications extend to enhancing the quality of life for individuals using prosthetic limbs and advancing control systems in the field of robotic human–machine interfaces.

Abstract We present a novel automatic preprocessing and ensemble learning technique for the segmentation of low-quality cell images. Capturing cells subjected to intense light is challenging due to their vulnerability to light-induced cell death. Consequently, microscopic cell images tend to be of low quality and it causes low accuracy for semantic segmentation. This problem can not be satisfactorily solved by classical image preprocessing methods. Therefore, we propose a novel approach of automatic enhancement preprocessing (AEP), which translates an input image into images that are easy to recognize by deep learning. AEP is composed of two deep neural networks, and the penultimate feature maps of the first network are employed as filters to translate an input image with low quality into images that are easily classified by deep learning. Additionally, we propose an automatic weighted ensemble learning (AWEL), which combines the multiple segmentation results. Since the second network predicts segmentation results corresponding to each translated input image, multiple segmentation results can be aggregated by automatically determining suitable weights. Experiments on two types of cell image segmentation confirmed that AEP can translate low-quality cell images into images that are easy to segment and that segmentation accuracy improves using AWEL.

Abstract Although novel techniques for avoiding incontinence during robot-assisted radical prostatectomy have been developed, long-term oncological outcomes are unknown. The objective of this study was to determine the long-term oncological outcomes and functional outcomes of novel nerve-sparing robot-assisted radical prostatectomy with endopelvic fascia preservation for a single surgeon. Data from 100 patients who underwent structure-preserving prostatectomies performed by a single surgeon were retrospectively analyzed. The median console time was 123 min. Bilateral nerve-sparing was performed in 43% of patients underwent, and 57% underwent unilateral nerve-sparing surgery. Most patients (96%) reached complete pad-zero urinary continence by one year after surgery. Satisfactory erectile function was achieved in 97% of patients who underwent bilateral nerve-sparing surgery, and 80% of patients who underwent unilateral nerve-sparing surgery. The surgical margin was positive for 25% of patients, and the biochemical recurrence-free rate at 5 years was 77%. The cancer-specific survival rate was 100% during the median follow-up period of 4.5 years. Clavien-Dindo grade III complications occurred in 1% of cases. The outcomes for novel nerve-sparing robot-assisted radical prostatectomy with endopelvic fascia preservation were similar to previously reported oncological outcomes, with satisfactory functional outcomes. This operative method may be useful for patients who are eligible for nerve-sparing surgery.

Abstract War is an extreme form of collective human behaviour characterized by coordinated violence. We show that this nature of war is substantiated in the temporal patterns of conflict occurrence that obey power law. The focal metric is the interconflict interval (ICI), the interval between the end of a conflict in a dyad (i.e. a pair of states) and the start of the subsequent conflict in the same dyad. Using elaborate statistical tests, we confirmed that ICI samples compiled from the history of interstate conflicts from 1816 to 2014 followed a power-law distribution. We then demonstrate that the power-law properties of ICIs can be explained by a hypothetical model assuming an information-theoretic formulation of the Clausewitz thesis on war: the use of force is a means of interstate communication. Our findings help us to understand the nature of wars between regular states, the significance of which has increased since the Russian invasion of Ukraine in 2022.

To identify a biomarker for the early diagnosis of enzootic bovine leukosis (EBL) caused by bovine leukemia virus (BLV), we investigated the expression of a microRNA, bta-miR-375, in cattle serum. Using quantitative reverse-transcriptase PCR analysis, we measured bta-miR-375 levels in 27 samples from cattle with EBL (EBL cattle), 45 samples from animals infected with BLV but showing no clinical signs (NS cattle), and 30 samples from cattle uninfected with BLV (BLV negative cattle). In this study, we also compared the kinetics of bta-miR-375 with those of the conventional biomarkers of proviral load (PVL), lactate dehydrogenase (LDH), and thymidine kinase (TK) from the no-clinical-sign phase until EBL onset in three BLV-infected Japanese black (JB) cattle. Bta-miR-375 expression was higher in NS cattle than in BLV negative cattle (P < 0.05) and greater in EBL cattle than in BLV negative and NS cattle (P < 0.0001 for both comparisons). Receiver operating characteristic curves demonstrated that bta-miR-375 levels distinguished EBL cattle from NS cattle with high sensitivity and specificity. In NS cattle, bta-miR-375 expression was increased as early as at 2 months before EBL onset-earlier than the expression of PVL, TK, or LDH isoenzymes 2 and 3. These results suggest that serum miR-375 is a promising biomarker for the early diagnosis of EBL.

Abstract This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods.

Abstract Serum fatty acids (FAs) exist in the four lipid fractions of triglycerides (TGs), phospholipids (PLs), cholesteryl esters (CEs) and free fatty acids (FFAs). Total fatty acids (TFAs) indicate the sum of FAs in them. In this study, four statistical analysis methods, which are independent component analysis (ICA), factor analysis, common principal component analysis (CPCA) and principal component analysis (PCA), were conducted to uncover food sources of FAs among the four lipid fractions (CE, FFA, and TG + PL). Among the methods, ICA provided the most suggestive results. To distinguish the animal fat intake from endogenous fatty acids, FFA variables in ICA and factor analysis were studied. ICA provided more distinct suggestions of FA food sources (endogenous, plant oil intake, animal fat intake, and fish oil intake) than factor analysis. Moreover, ICA was discovered as a new approach to distinguish animal FAs from endogenous FAs, which will have an impact on epidemiological studies. In addition, the correlation coefficients between a published dataset of food FA compositions and the loading values obtained in the present ICA study suggested specific foods as serum FA sources. In conclusion, we found that ICA is a useful tool to uncover food sources of serum FAs.

Bovine leukemia virus (BLV) is the causative agent of enzootic bovine leukosis (EBL), a malignant B cell lymphoma. However, the mechanisms of BLV-associated lymphomagenesis remain poorly understood. Here, after deep sequencing, we performed comparative analyses of B cell microRNAs (miRNAs) in cattle infected with BLV and those without BLV. In BLV-infected cattle, BLV-derived miRNAs (blv-miRNAs) accounted for 38% of all miRNAs in B cells. Four of these blv-miRNAs (blv-miR-B1-5p, blv-miR-B2-5p, blv-miR-B4-3p, and blv-miR-B5-5p) had highly significant positive correlations with BLV proviral load (PVL). The read counts of 90 host-derived miRNAs (bta-miRNAs) were significantly down-regulated in BLV-infected cattle compared to those in uninfected cattle. Only bta-miR-375 had a positive correlation with PVL in BLV-infected cattle and was highly expressed in the B cell lymphoma tissue of EBL cattle. There were a few bta-miRNAs that correlated with BLV tax/rex gene expression; however, BLV AS1 expression had a significant negative correlation with many of the down-regulated bta-miRNAs that are important for tumor development and/or tumor suppression. These results suggest that BLV promotes lymphomagenesis via AS1 and blv-miRNAs, rather than tax/rex, by down-regulating the expression of bta-miRNAs that have a tumor-suppressing function, and this downregulation is linked to increased PVL.

This single-institution cross-sectional study aimed to grasp the prevalence and features of neurocognitive dysfunction in HIV-infected hemophilia patients in Japan. We conducted neuropsychological tests and medical examinations in 56 HIV-infected hemophilia patients who received outpatient treatment at the AIDS Clinical Center, National Center for Global Health and Medicine. A total of 388 HIV-infected non-hemophilia patients who received outpatient treatment at the same institution were included as a control group. To investigate sites responsible for neurocognitive dysfunction in HIV-infected hemophilia patients using brain FDG-PET/CT scans, the accumulation of FDG in each brain region was compared. Approximately 50% of HIV-infected hemophilia patients had neurocognitive dysfunction. The prevalence of asymptomatic neurocognitive impairment was high (34%). Neurocognitive dysfunction was associated with educational level in HIV-infected hemophilia patients. In the symptomatic group, hemophilic arthropathy and history of cerebrovascular disorders were associated with neurocognitive dysfunction. Left temporal lobe function was reduced in the symptomatic group.

Multidrug-resistant enterococci are considered crucial drivers for the dissemination of antimicrobial resistance determinants within and beyond a genus. These organisms may pass numerous resistance determinants to other harmful pathogens, whose multiple resistances would cause adverse consequences. Therefore, an understanding of the coexistence epidemiology of resistance genes is critical, but such information remains limited. In this study, our first objective was to determine the prevalence of principal resistance phenotypes and genes among Enterococcus faecalis isolated from retail chicken domestic products collected throughout Japan. Subsequent analysis of these data by using an additive Bayesian network (ABN) model revealed the co-appearance patterns of resistance genes and identified the associations between resistance genes and phenotypes. The common phenotypes observed among E. faecalis isolated from the domestic products were the resistances to oxytetracycline (58.4%), dihydrostreptomycin (50.4%), and erythromycin (37.2%), and the gene tet(L) was detected in 46.0% of the isolates. The ABN model identified statistically significant associations between tet(L) and erm(B), tet(L) and ant(6)-Ia, ant(6)-Ia and aph(3')-IIIa, and aph(3')-IIIa and erm(B), which indicated that a multiple-resistance profile of tetracycline, erythromycin, streptomycin, and kanamycin is systematic rather than random. Conversely, the presence of tet(O) was only negatively associated with that of erm(B) and tet(M), which suggested that in the presence of tet(O), the aforementioned multiple resistance is unlikely to be observed. Such heterogeneity in linkages among genes that confer the same phenotypic resistance highlights the importance of incorporating genetic information when investigating the risk factors for the spread of resistance. The epidemiological factors that underlie the persistence of systematic multiple-resistance patterns warrant further investigations with appropriate adjustments for ecological and bacteriological factors.

Because antimicrobial resistance in food-producing animals is a major public health concern, many countries have implemented antimicrobial monitoring systems at a national level. When designing a sampling scheme for antimicrobial resistance monitoring, it is necessary to consider both cost effectiveness and statistical plausibility. In this study, we examined how sampling scheme precision and sensitivity can vary with the number of animals sampled from each farm, while keeping the overall sample size constant to avoid additional sampling costs. Five sampling strategies were investigated. These employed 1, 2, 3, 4 or 6 animal samples per farm, with a total of 12 animals sampled in each strategy. A total of 1,500 Escherichia coli isolates from 300 fattening pigs on 30 farms were tested for resistance against 12 antimicrobials. The performance of each sampling strategy was evaluated by bootstrap resampling from the observational data. In the bootstrapping procedure, farms, animals, and isolates were selected randomly with replacement, and a total of 10,000 replications were conducted. For each antimicrobial, we observed that the standard deviation and 2.5-97.5 percentile interval of resistance prevalence were smallest in the sampling strategy that employed 1 animal per farm. The proportion of bootstrap samples that included at least 1 isolate with resistance was also evaluated as an indicator of the sensitivity of the sampling strategy to previously unidentified antimicrobial resistance. The proportion was greatest with 1 sample per farm and decreased with larger samples per farm. We concluded that when the total number of samples is pre-specified, the most precise and sensitive sampling strategy involves collecting 1 sample per farm.