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Background Sarcopenia negatively affects the short-term prognosis of hospitalized older adults. However, no evidence currently supports a direct relationship between sarcopenia and readmission among individuals who have experienced an acute stroke. Therefore, we investigated whether sarcopenia is associated with readmission after discharge. Methods This retrospective cohort study included patients who had experienced acute stroke. Sarcopenia was defined as the coexistence of low skeletal muscle mass index (SMI) and grip strength. We applied the log-rank test and Cox proportional hazards regression analysis to analyze whether sarcopenia, low SMI, and low grip strength were associated with readmission within 6 months. Results Among 228 included patients (mean age, 72.8 years; 146 males), the prevalence of sarcopenia was 24.6% (n=56; male 17.8%; female 36.6%). Cox proportional hazards regression analysis using the propensity score as a covariate revealed that sarcopenia (hazard ratio [HR]=7.21; 95% confidence interval [CI] 1.45–35.8; p=0.016) and low skeletal muscle mass (HR=7.40; 95% CI 1.14–48.1; p=0.036), but not low grip strength (HR=1.42; 95% CI 0.281–7.21; p=0.670), were significantly associated with readmission for stroke within 6 months. Conclusions Sarcopenia was negatively associated with readmission within 6 months of stroke onset in patients in Japan who had experienced an acute stroke. These findings suggest that the identification of sarcopenia may facilitate prognostic prediction from the acute stage and intervention(s) to prevent rehospitalization.

Abstract Introduction Physical activity is an important prognostic factor in patients undergoing hemodialysis. Walkability also affects physical activity. This study aimed to examine the effects of walkability on the physical activity of patients undergoing hemodialysis. Methods This multicenter study included 372 outpatients (69.1 ± 11.9 years, 229 males) undergoing hemodialysis at eight facilities in Japan. Patients were classified into two groups according to the walk score (WS) at home: WS ≥ 50 (walkable area group) and WS

With the development of robotics and wearable devices, there is a need for information processing under the assumption that an agent itself is mobile. Especially, understanding an acoustic environment around an agent is an important issue. In this paper, we solve a task in which a moving agent estimates the Direction of Arrival (DoA) of the surrounding sound sources. To this end, we propose a novel training method, Trajectory-based Direction Selection (TDS). In TDS, a mixture of binaural audio recorded by two agents and their trajectories are given as input to a network. Then, the network is trained to estimate the DoA of surrounding sounds that correspond to each agent’s trajectory separately. By corresponding the agent’s trajectory to the binaural audio with TDS, we can estimate DoAs of multiple sounds even with binaural audio as audio input, which has not been realized by sound-only methods. In simulated environments covering both single and multiple sources, our method outperforms existing DoA estimation methods.

Objectives: The aim of this study was to assess potential predictors of aortic events after an emergency surgery for acute type A aortic dissection, especially paying attention to the findings of computed tomography (CT) performed immediately after the surgery. Methods: Between January 2001 and December 2015, 72 patients, who were diagnosed as having Stanford type A acute aortic dissection with a patent false lumen in the descending thoracic aorta, survived the emergency operation, and had postoperative CT scan data, were included in this study (mean follow-up, 8.2 ± 3.8 years; range 0.8-17.4 years). From the CT scan data, the diameter of the false lumen (FL-D) and true lumen (TL-D) were measured, and the FL-D:TL-D ratio was calculated. Long-term outcomes of the FL-D > TL-D group (n = 30) and the FL-D TL-D group (92.0% and 88.6% vs 81% and 60.7%; log rank P

Learning cross-modal features is an essential task for many multimedia applications such as sound localization, audio-visual alignment, and image/audio retrieval. Most existing methods mainly focus on the semantic correspondence between videos and monaural sounds, and spatial information of sound sources has not been considered. However, sound locations are critical for understanding the sound environment. To this end, it is necessary to acquire cross-modal features that reflect the semantic and spatial relationship between videos and sounds. A video with stereo sound, which has become commonly used, provides the direction of arrival of each sound source in addition to the category information. This indicates its potential to acquire a desired cross-modal feature space. In this paper, we propose a novel self-supervised approach to learn a cross-modal feature representation that captures both the category and location of each sound source using stereo sound as input. For a set of unlabeled videos, the proposed method generates three kinds of audio-visual pairs: 1) perfectly matched pairs from the same video, 2) pairs from the same video but with the flipped stereo sound, and 3) pairs from a different video. The cross-modal feature encoder of the proposed method is trained on triplet loss to reflect the relationship between these three pairs ( $1>2>3$ ). We apply this method to cross-modal image/audio retrieval. Compared with previous audio-visual pretext tasks, the proposed method shows significant improvement in both real and synthetic datasets.

Aquaculture of marine macroalgae (i.e. seaweeds) such as the kelp Undaria pinnatifida is expected to contribute to future food and biomass production. Although macroalgal survival, biomass, and morphology are strongly affected by the density of individual plants in natural environments, little is known about the cultivation density (individuals per 1 m of cultivation rope) of macroalgae required to optimize aquaculture production, commercial profit (sales – labor expenses for processing), and quality as food. The present study examined the effect of increasing the cultivation density of U. pinnatifida from 10 to 200 individuals m-1 on survival rate, biomass production, profit, and morphological features related to quality as food. Survival rate was almost 100% in all treatments, indicating self-thinning did not occur. Biomass production increased with increasing density, suggesting that the maximum density possible is in excess of 200 individuals m-1. However, although profit rose with increasing density from 10 to 120 individuals m-1, it did not rise further if density was further increased. Moreover, some morphological features related to quality increased or decreased with increasing density. On balance, these results suggest that 80-120 individuals m-1 is an appropriate density range to optimize production of this species in terms of both profit and quality as food. However, only 10-30 individuals m-1 was the density best suited to enhance production of the sporophyll form, which is known to be a nutritious food both for humans and sea urchins.

The present study provides new insights into the growth of the brown algal cell wall by showing that cell wall polysaccharides play an important role in the process of growth, considering the physicochemical characteristic of young and old Cladosiphon okamuranus. To determine its structural variation in detail, the cell wall was sequentially fractionated into five fractions: hot water (HW), ammonium oxalate, hemicellulose-I (HC–I), HC-II, and cellulose, and analyzed physicochemically. Results showed that almost 80% of the total recovery cell wall from both young and old thalli was HW, and HC-I contained mainly fucoidan composed of Fucose, Glucuronic acid, and sulfate in molar ratios of 1.0:0.3:0.6~0.7 and 1.0:0.3:0.2~0.3, respectively. Fucoidan in HW was a highly sulfated matrix polysaccharide abundance in young thalli, while fucoidan in HC-I was rich in old thalli and functions as hemicellulose in land plants, crosslinking with cellulose and strengthening the cell wall. We found that HW and HC-I were particularly involved in the growth and strength of old thalli appeared to be due to the deposition of HC-I and the reduction in water content during the growth process.

Abstract Background Less experienced clinicians sometimes make misdiagnosis of hip fractures. We developed computer-aided diagnosis (CAD) system for hip fractures on plain X-rays using a deep learning model trained on a large dataset. In this study, we examined whether the accuracy of the diagnosis of hip fracture of the residents could be improved by using this system. Methods A deep convolutional neural network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0 were applied as frameworks, and an EfficientNet-B4 model (a pre-trained ImageNet model) was used. We handled the 5295 X-rays from the patients with femoral neck fracture or femoral trochanteric fracture from 2009 to 2019. We excluded cases in which the bilateral hips were not included within an image range, and cases of femoral shaft fracture and periprosthetic fracture. Finally, we included 5242 AP pelvic X-rays from 4851 cases. We divided these 5242 images into two images per image, and prepared 5242 images including fracture site and 5242 images without fracture site. Thus, a total of 10,484 images were used for machine learning. The accuracy, sensitivity, specificity, F-value, and area under the curve (AUC) were assessed. Gradient-weighted class activation mapping (Grad-CAM) was used to conceptualize the basis for the diagnosis of the fracture by the deep learning algorithm. Secondly, we conducted a controlled experiment with clinicians. Thirty-one residents;young doctors within 2 years of graduation from medical school who rotate through various specialties, were tested using 300 hip fracture images that were randomly extracted from the dataset. We evaluated the diagnostic accuracy with and without the use of the CAD system for each of the 300 images. Results The accuracy, sensitivity, specificity, F-value, and AUC were 96.1, 95.2, 96.9%, 0.961, and 0.99, respectively, with the correct diagnostic basis generated by Grad-CAM. In the controlled experiment, the diagnostic accuracy of the residents significantly improved when they used the CAD system. Conclusions We developed a newly CAD system with a deep learning algorithm from a relatively large dataset from multiple institutions. Our system achieved high diagnostic performance. Our system improved the diagnostic accuracy of residents for hip fractures. Level of evidence Level III, Foundational evidence, before-after study. Clinical relevance: high

Abstract Background Physical activity is an essential parameter associated with the prognosis of hemodialysis patients. Physical activity has regional characteristics; for example, snow removal is imperative in snowbelts during the winter. This study investigated the effects of removing snow on the physical activity levels of hemodialysis patients living in a snowbelt. Methods This study retrospectively examined 28 patients who underwent hemodialysis (age 69.6 ± 15.4 years, 19 males) in Uonuma Kikan Hospital between December 2019 and March 2020. Physical activity was assessed using the International Physical Activity Questionnaire. Patients were divided into a snow remover group and a non-snow remover group. We examined whether there were differences in physical or mental functioning and physical activity between the two groups. We also investigated the factors that influence high levels of physical activity among hemodialysis patients using multivariate linear regression analysis. Results The snow remover group had significantly greater grip strength, higher sit-to-stand test scores and gait speed, and a lower prevalence of frailty than the non-snow remover group. The level of physical activity was also significantly higher in the snow remover group. One of the patients in the snow remover group was frail. The univariate linear regression analysis showed that physical activity was related to age, grip strength, sit-to-stand test score, gait speed, and snow removal. In the multivariate linear regression analysis, snow removal and grip strength were significantly associated with a high level of physical activity. Conclusions Among hemodialysis patients living in a snowbelt, the snow remover group had significantly better physical functioning and a higher level of physical activity.

Hand segmentation is a crucial task in first-person vision. Since first-person images exhibit strong bias in appearance among different environments, adapting a pre-trained segmentation model to a new domain is required in hand segmentation. Here, we focus on appearance gaps for hand regions and backgrounds separately. We propose (i) foreground-aware image stylization and (ii) consensus pseudo-labeling for domain adaptation of hand segmentation. We stylize source images independently for the foreground and background using target images as style. To resolve the domain shift that the stylization has not addressed, we apply careful pseudo-labeling by taking a consensus between the models trained on the source and stylized source images. We validated our method on domain adaptation of hand segmentation from real and simulation images. Our method achieved state-of-the-art performance in both settings. We also demonstrated promising results in challenging multi-target domain adaptation and domain generalization settings.

The important role of vegetated ecosystems in the sequestration of carbon has gained strong interest across a wide variety of disciplines. With evidence growing of the potential for macroalgae ecosystems to capture carbon, there is burgeoning interest in applying newfound knowledge of carbon capture rates to better understand the potential for carbon sequestration. Seaweed farms are expected to play a significant role in carbon capture; advocates for the expansion of seaweed farms are increasing in many countries. In general, seaweed farms are expected to be highly productive, although whether they are autotrophic or heterotrophic ecosystems and hence potential exporters of carbon, is under debate. Therefore, we present our investigation of three seaweed farms, two in northern Japan and one in southern Japan. We examine the frequency of autotrophic days and compare potential rates of carbon capture of the seaweed farms with two natural macroalgae ecosystems and one degraded site. We estimated potential carbon capture rates by calculating the net ecosystem productivity from continuous recordings of dissolved oxygen concentrations under natural environmental conditions. The net ecosystem production rates for the natural ecosystems in Arikawa Bay and Omura Bay were equivalent to 0.043 and 0.054 [g C m-2 d-1] m-1, respectively. Whereas, for the degraded ecosystem in Tainoura Bay, it was -0.01 [g C m-2 d-1] m-1. We reveal that the Undaria pinnatifida farm in Matsushima Bay experience autotrophy more often than natural ecosystems, although for seaweed farms producing U. pinnatifida in Hirota Bay and Cladospihon okamuranus at Bise Point, autotrophy was less frequently observed. Nevertheless, up to 14.1 g C m-2 (0.110 g C m-2 d-1) was captured by the production of U. pinnatifida and 3.6 g C m-2 (0.034 g C m-2 d-1) was captured by C. okamuranus, and the total yield of carbon captured during 2021 production season for these farms was 43,385 kg C.

Although the number of patients with osteoporosis is increasing worldwide, diagnosis and treatment are presently inadequate. In this study, we developed a deep learning model to predict bone mineral density (BMD) and T-score from chest X-rays, which are one of the most common, easily accessible, and low-cost medical imaging examination methods. The dataset used in this study contained patients who underwent dual-energy X-ray absorptiometry (DXA) and chest radiography at six hospitals between 2010 and 2021. We trained the deep learning model through ensemble learning of chest X-rays, age, and sex to predict BMD using regression and T-score for multiclass classification. We assessed the following two metrics to evaluate the performance of the deep learning model: (1) correlation between the predicted and true BMDs and (2) consistency in the T-score between the predicted class and true class. The correlation coefficients for BMD prediction were hip = 0.75 and lumbar spine = 0.63. The areas under the curves for the T-score predictions of normal, osteopenia, and osteoporosis diagnoses were 0.89, 0.70, and 0.84, respectively. These results suggest that the proposed deep learning model may be suitable for screening patients with osteoporosis by predicting BMD and T-score from chest X-rays.

Heat stress is known to induce photoinhibition, leaf senescence, and nutrient remobilization in terrestrial plants with apical growth, however, its effect on blade erosion and associated-changes in chemical compositions has rarely been studied in marine macroalgae with intercalary growth such as kelp. The present study examined the combined effects of ocean warming (23 and 26°C), irradiance (30 and 180 μmol m–2 s–1), and nutrient enrichment (enriched and non-enriched) on photosystem II maximum efficiency (Fv/Fm) in the kelp Eisenia bicyclis. It also investigated the effect of ocean warming on the kelp’s relative growth rates based on five morphological parameters and three chemical compositions (carbon, nitrogen, and phlorotannnins). A warming effect on photoinhibition (i.e., decline in Fv/Fm) was only detected under the higher irradiance combined with nutrient-enrichment condition. Under this condition, elevated temperature decreased relative growth rates to negative values, indicating occurrence of apical blade erosion. Temperature elevation also caused increases in nitrogen and phlorotannin contents within the whole body, but not carbon content. Moreover, nitrogen content in the meristems at 26°C was higher than that at 23°C, although such a difference was not observed with phlorotannin content. These results suggested that heat-induced apical blade erosion promoted nitrogen accumulation in the meristems, located in the lower part of the blade, in E. bicyclis.

Gonad size, color, texture and taste of Mesocentrotus nudus sea urchins collected from a barren can be improved by a short-term cage culture while being fed fresh Saccharina japonica kelp during May-July. We investigated the effect of S. japonica feeding during May-July on the improvement of gonad flavor in M. nudus collected from a barren. After feeding, we analyzed the odor-active volatile organic compounds (VOCs) from the gonads using gas chromatography-mass spectrometry and GC-sniffing analyses and compared to those from the gonads of wild sea urchins from an Eisenia kelp bed (fishing ground) and a barren. A total of 48 VOCs were detected from the gonads of cultured and wild sea urchins. Of them, a larger number of odor-active compounds were detected in the gonads of cultured sea urchins (25) than in those from the Eisenia bed (14) and the barren (6). Dimethyl sulfide from the gonads of sea urchins from the barren was described as having a strong, putrid odor. Sea urchin-like aromas were attributed to 2-butanol, 2-ethylhexanol, benzaldehyde and ethylbenzene from the gonads of cultured sea urchin and those of the Eisenia bed. Kelp feeding decreased the putrid odor from dimethyl sulfide, and enhanced pleasant, sweet aromas.

Heatwaves under global warming have negative impacts on ecosystem primary producers. This warming effect may be synergized or antagonized by local environments such as light and nutrient availability. However, little is known about the interactive effects of warming, irradiance, and nutrients on physiology of marine macroalgae, which are dominant in coastal ecosystems. The present study examined the combined effects of warming (23 and 26 °C), irradiance (30 and 150 µmol photon m−2 s−1), and nutrients (enriched and non-enriched) on specific growth rate (SGR) and biochemical compositions of the canopy-forming marine macroalga Sargassum fusiforme. The negative effect of warming on SGR and ratio of chlorophyll (Chl) c to Chl a was antagonized by decreased irradiance. Moreover, the negative effect of temperature elevation on carbon content was antagonized by nutrient enrichment. These results suggest that the effect of warming on the growth and carbon accumulation of this species can be mitigated by decreased irradiance and nutrient enrichment.

The green macroalga Ulva prolifera has a number of variants, some of which are asexual (independent from sexual variants). Although it has been harvested for food, the yield is decreasing. To meet market demand, developing elite cultivars is required. The present study investigated the genetic stability of asexual variants, genotype (hsp90 gene sequences) and phenotype variations across a temperature gradient (10–30 °C) in an apomictic population. Asexual variants were collected from six localities in Japan and were isolated as an unialgal strain. The hsp90 gene sequences of six strains were different and each strain included multiple distinct alleles, suggesting that the strains were diploid and heterozygous. The responses of growth and sporulation versus temperature differed among strains. Differences in thermosensitivity among strains could be interpreted as the result of evolution and processes of adaptation to site-specific environmental conditions. Although carbon content did not differ among strains and cultivation temperatures, nitrogen content tended to increase at higher temperatures and there were differences among strains. A wide variety of asexual variants stably reproducing clonally would be advantageous in selecting elite cultivars for long-term cultivation. Using asexual variants as available resources for elite cultivars provides potential support for increasing the productivity of U. prolifera.

Abstract The power scaling of laser devices can contribute to the future of humanity. Giant microphotonics have been advocated as a solution to this issue. Among various technologies in giant microphotonics, process control of microdomains with quantum mechanical calculations is expected to increase the optical power extracted per unit volume in gain media. Design of extensive variables influencing the Gibbs energy of controlled microdomains in materials can realize desired properties. Here we estimate the angular momentum quantum number of rare-earth ions in microdomains. Using this process control, we generate kilowatt-level laser output from orientation-controlled microdomains in a laser gain medium. We also consider the limitations of current samples, and discuss the prospects of power scaling and applications of our technology. This work overturns at least three common viewpoints in current advanced technologies, including material processing based on magnetohydrodynamics, grain-size control of transparent polycrystals in fine ceramics, and the crystallographic symmetry of laser ceramics in photonics.

Sea urchin gonads are a delicious seafood item of high commercial value. Our past studies have revealed that the gonads of the sea urchin Mesocentrotus nudus fed the basal frond portion of fresh Saccharina kelp (BS) or the sporophylls of fresh Undaria (SU) during May–July are of high-quality. The present study investigated the flavor and taste of BS and SU gonads in comparison with those from non-fed M. nudus (NF) using gas chromatography–mass spectrometry (GC–MS) and gas chromatography (GC)-sniffing techniques, and a taste-sensing system. Data of the estimated intensity of taste (EIT) were compared with assessment of gonads from M. nudus collected from an Eisenia bed (fishing ground) and a barren in July. Gonads from both BS and SU released pleasant green, sour, and fruity aromas characteristic of butyl acetate, which are here recognized essential flavor components of high-quality gonads. The gonads of BS and SU had a strong umami taste compared to those of NF, and the Eisenia bed and the barren. The most marketable M. nudus gonads were assessed to be those with green and fruity aromas from butyl acetate, sweet aroma from benzaldehyde, umami EIT > 13.8, bitterness EIT < 3.1, and without any unpleasant sulfurous odor from sulfur-containing compounds.

Transverse emittance growth due to space-charge-induced resonance was studied for high-power beam operation in the J-PARC main ring synchrotron. The turn-by-turn beam sizes were measured near the structure resonance at ν_{x}=21 and near the nonstructure resonance at ν_{x}=22. A clear difference in the emittances was observed. The emittance was observed to grow by 5 times at the operating tune ν_{x}=21.10, and by 1.8 times at ν_{x}=22.05 in the first 100 turns. The results show the advantage of tuning in the region of ν_{x}=22 for high intensity beam operation. The turn-by-turn emittance varied depending on the horizontal tune in the region of ν_{x}=21. This indicates that the mechanism of emittance growth is related to the tune spread distribution. The results were satisfactorily reproduced by the simulations of the beam dynamics, including the space-charge effect incorporated via the particle-in-cell algorithm. To reveal the mechanism of the emittance growth, the space-charge effects on the resonances were evaluated by simulations as well as analytical calculations.

Understanding how humans subjectively look at and evaluate images is an important task for various applications in the field of multimedia interaction. While it has been repeatedly pointed out that eye movements can be used to infer the internal states of humans, not many successes have been reported concerning image understanding. We investigate the possibility of image preference estimation based on a person’s eye movements in a supervised manner in this paper. A dataset of eye movements is collected while the participants are viewing pairs of natural images, and it is used to train image preference label classifiers. The input feature is defined as a combination of various fixation and saccade event statistics, and the use of the random forest algorithm allows us to quantitatively assess how each of the statistics contributes to the classification task. We show that the gaze-based classifier had a higher level of accuracy than metadata-based baseline methods and a simple rule-based classifier throughout the experiments. We also present a quantitative comparison with image-based preference classifiers and discuss the potential and limitations of the gaze-based preference estimator.