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Backgrounds: Establishment of quantitative, objective evaluation tool for facial palsy has been a big issue for clinicians and researchers, and in an era of artificial intelligence, AI-driven video analysis can be considered a reasonable solution for this long-discussed issue. We introduced facial keypoint detection, which detects facial landmarks with 68 points, but existing models had been organized almost solely with images of healthy individuals and low accuracy was presumed in the prediction of asymmetric faces of facial palsy patients.The accuracy of existing model was assessed through applying it to the movies of 30 facial palsy patients. Qualitative review clearly showed its insufficiency; prone to detect patients' faces as symmetric, and unable to detect closure of the eyes. Thus, we decided to enhance the model through machine learning process of annotation (fine-tuning)., Methods: 1181 images extracted from the movies of 196 patients were enrolled in the training, and these images underwent manual correction of 68 keypoints. The annotated data was integrated into the previous model with a stack of two Hourglass networks combined with Channel Aggregation Block., Results: The post-annotation model showed improvement in normalized mean error from 0.026 to 0.018, and also qualitatively keypoint detection on each facial unit revealed improvements., Conclusion: Our strict control of the inter- and intra-annotator variability successfully fine-tuned the presenting model, and we consider our new model as one of promising solutions for objective assessment of facial palsy., (Copyright © 2024 by the American Society of Plastic Surgeons.)

The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research community. These requirements include credibility, reproducibility, and accessibility. Credibility assessment involves verification, validation, uncertainty quantification, and sensitivity analysis, ensuring the accuracy and realism of computational models. A proper credibility assessment requires a clear context of use and the questions that the study is intended to objectively answer. For reproducibility and accessibility, this article highlights the need for detailed documentation, user-friendly software packages, and standard input/output formats. Challenges in data and software sharing, including proprietary data and inconsistent file formats, are discussed. Recommended solutions to enhance accessibility include containerized environments and data-sharing hubs, along with following standards such as CDISC (Clinical Data Interchange Standards Consortium). By addressing challenges associated with credibility, reproducibility, and accessibility, virtual imaging trials can be positioned as a powerful and inclusive resource, advancing medical imaging innovation and regulatory science., (© 2024 American Association of Physicists in Medicine.)

Previously, the lack of a standard body part ontology has been identified as a critical deficiency needed to enable enterprise imaging. This whitepaper aims to provide a comprehensive assessment of anatomical ontologies with the aim of facilitating enterprise imaging. It offers an overview of the process undertaken by the Health Information Management Systems Society (HIMSS) and Society for Imaging Informatics in medicine (SIIM) Enterprise Imaging Community Data Standards Evaluation workgroup to assess the viability of existing ontologies for supporting cross-disciplinary medical imaging workflows. The report analyzes the responses received from representatives of three significant ontologies: SNOMED CT, LOINC, and ICD, and delves into their suitability for the complex landscape of enterprise imaging. It highlights the strengths and limitations of each ontology, ultimately concluding that SNOMED CT is the most viable solution for standardizing anatomy terminology across the medical imaging community., Competing Interests: Declarations. Competing Interests: The following authors declare competing interests: Dawn Cram—Global Product Manager, PaxeraHealth; Mitchell Goldburgh—Sr. Director, Industry Solution, NTT DATA Services; Jason Nagels—CEO, Founder, Nagels Consulting; Lawrence Yudkovitch—Sr. Product Manager, Cleerly; and Alexander J. Towbin—(a) author royalties (Elsevier), (b) funded travel (Merative), and (c) consultant (Applied Radiology), while Monief Eid declares none., (© 2024. The Author(s).)

Detailed literature search and writing is very important for the success of long research projects, publications and theses. Search engines provide significant convenience in research processes. However, conducting a comprehensive and systematic research on the web requires a long working process. In order to make literature searches effective, simple and comprehensive, various libraries and development tools have been created and made available. By using these development tools, research processes that may take days can be reduced to hours or even minutes. Literature review is not only necessary for academic studies, but it is a process that should be used and performed in every field where new approaches are adopted. Literature review is a process that gives us important ideas about whether similar studies have been conducted before, which methods have been used before and what has not been addressed in previous studies. It is also of great importance in terms of preventing possible copyright problems in future studies. The main purpose of this study is to propose an application that will facilitate, speed up and increase the efficiency of literature searches. In existing systems, literature searches are performed by browsing search sites or various article sites one by one and using the search tools provided by these sites. It is simple to use, allows the entire World Wide Web environment to be searched, and provides the user with the search findings. In this study, we have implemented an application that allows the crawling of the entire World Wide Web environment, is very simple to use, and quickly presents the crawl findings to the user., Competing Interests: Muhammed Ali Mutlu, a graduate of our Master’s Program, is employed by NTT DATA Business Solutions. He is currently working as Head of Data Platforms at NTT DATA Business Solutions., (© 2024 Mutlu et al.)

This SIIM-sponsored 2023 report highlights an industry view on artificial intelligence adoption barriers and success related to diagnostic imaging, life sciences, and contrasts. In general, our 2023 survey indicates that there has been progress in adopting AI across multiple uses, and there continues to be an optimistic forecast for the impact on workflow and clinical outcomes. This report, as in prior years, should be seen as a snapshot of the use of AI in imaging. Compared to our 2021 survey, the 2023 respondents expressed wider AI adoption but felt this was behind the potential. Specifically, the adoption has increased as sources of return on investment with AI in radiology are better understood as documented by vendor/client use case studies. Generally, the discussions of AI solutions centered on workflow triage, visualization, detection, and characterization. Generative AI was also mentioned for improving productivity in reporting. As payor reimbursement remains elusive, the ROI discussions expanded to look at other factors, including increased hospital procedures and admissions, enhanced radiologist productivity for practices, and improved patient outcomes for integrated health networks. When looking at the longer-term horizon for AI adoption, respondents frequently mentioned that the opportunity for AI to achieve greater adoption with more complex AI and a more manageable/visible ROI is outside the USA. Respondents focused on the barriers to trust in AI and the FDA processes., (© 2024. The Author(s).)

Objectives: Predicting the prognosis of lung cancer is crucial for providing optimal medical care. However, a method to accurately predict the overall prognosis in patients with stage IV lung cancer, even with the use of machine learning, has not been established. Moreover, the inter-institutional generalizability of such algorithms remains unexplored. This study aimed to establish machine learning-based algorithms with inter-institutional generalizability to predict prognosis., Materials and Methods: This multicenter, retrospective, hospital-based cohort study included consecutive patients with stage IV lung cancer who were randomly categorized into the training and independent test cohorts with a 2:1 ratio, respectively. The primary metric to assess algorithm performance was the area under the receiver operating characteristic curve in the independent test cohort. To assess the inter-institutional generalizability of the algorithms, we investigated their ability to predict patient outcomes in the remaining facility after being trained using data from 15 other facilities., Results: Overall, 6,751 patients (median age, 70 years) were enrolled, and 1,515 (22 %) showed mutated epidermal growth factor receptor expression. The median overall survival was 16.6 (95 % confidence interval, 15.9-17.5) months. Algorithm performance metrics in the test cohort showed that the areas under the curves were 0.90 (95 % confidence interval, 0.88-0.91), 0.85 (0.84-0.87), 0.83 (0.81-0.85), and 0.85 (0.82-0.87) at 180, 360, 720, and 1,080 predicted survival days, respectively. The performance test of 16 algorithms for investigating inter-institutional generalizability showed median areas under the curves of 0.87 (range, 0.84-0.92), 0.84 (0.78-0.88), 0.84 (0.76-0.89), and 0.84 (0.75-0.90) at 180, 360, 720, and 1,080 days, respectively., Conclusion: This study developed machine learning algorithms that could accurately predict the prognosis in patients with stage IV lung cancer with high inter-institutional generalizability. This can enhance the accuracy of prognosis prediction and support informed and shared decision-making in clinical settings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Daichi Fujimoto reported receiving grants and personal fees from AstraZeneca KK and Boehringer Ingelheim Japan Inc. and personal fees from Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb Co., Ltd., Takeda Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co., Ltd., Chugai Pharmaceutical Co., Ltd., MSD KK, Eli Lilly Japan K.K., Kyowa Kirin, Daiichi Sankyo, and Novartis Pharma KK outside the submitted work. Hidetoshi Hayashi reported receiving grants from PAREXEL International Corp., Daiichi Sankyo Co., Ltd., Taiho Pharmaceutical Co., Ltd., MSD K.K., Ono Pharmaceutical Co., Ltd., SYNEOS HEALTH CLINICAL K.K., Covance Japan Inc., Sanofi K.K., Novartis Pharma K.K., Bristol Myers Squibb Company, Janssen Pharmaceutical K.K., Eisai Co., Ltd., Japan Clinical Research Operations, AstraZeneca K.K., Chugai Pharmaceutical Co., Ltd., Sysmex Corporation, Takeda Pharmaceutical Co., Ltd., IQVIA Services JAPAN K.K., GlaxoSmithKline K.K., EPS Corporation, Medical Research Support, PRA Health Science, Inc., Eisai., Inc., Nippon Boehringer Ingelheim Co., Ltd., and PPD-SNBL K.K. and personal fees from AstraZeneca K.K., Bristol Myers Squibb Company, Amgen, Inc., Chugai Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Ono Pharmaceutical Co., Ltd., and Pfizer Japan Inc outside the submitted work. Yukihiro Toi reported receiving personal fees from Bristol-Myers Squibb Company, Ono Pharmaceutical Co., Ltd., MSD K.K., AstraZeneca K.K., Chugai Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co., Ltd., Pfizer, Inc., and Kyowa Kirin Co., Ltd., outside the submitted work. Toshihide Yokoyama reported receiving personal fees from AstraZeneca K.K., Chugai Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Pfizer Japan Inc, Bristol-Myers Squibb Co., Ltd., Ono Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Ltd., Nippon Kayaku Co., Ltd., MSD, Novartis, and Merck Biopharma Co., Ltd. outside the submitted work. Terufumi Kato reported receiving grants from Abbvie, Amgen, AstraZeneca, BeiGene, BluePrint, Chugai, Daiichi-Sankyo, Eli Lilly, Haihe, Merck KGaA, MSD, Novartis, Pfizer, Regeneron, Takeda, and TurningPoint and personal fees from Amgen, AstraZeneca, BeiGene, Boehringer Ingelheim, Chugai, Daiichi-Sankyo, Eli Lilly, GlaxoSmithKline, Janssen, Merck KGaA, MSD, Novartis, Ono, Pfizer, Taiho, and Takeda outside the submitted work, and his wife is an employee of Eli Lilly. Teppei Yamaguchi reported receiving personal fees from Ono Pharmaceutical, Chugai Pharmaceutical, Eli Lilly, Taiho Pharmaceutical, AstraZeneca, MSD, Bristol-Meyers Squibb, and Novartis outside the submitted work. Kaoru Tanaka reported receiving personal fees from AstraZeneca, Merck Biopharma, Eisai, Bristol-Myers Squibb, Ono Pharmaceutical, MSD, Chugai Pharmaceutical, Takeda Pharmaceutical Co., Ltd., Taiho Pharmaceutical, and Novartis Pharma outside the submitted work. Satoru Miura reported receiving personal fees from AstraZeneca KK, Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb Co., Ltd., Taiho Pharmaceutical Co., Ltd., Chugai Pharmaceutical Co., Ltd., MSD K.K., Boehringer Ingelheim Japan Inc, Eli Lilly Japan K.K., Novartis Pharma K.K., Pfizer, Kyowa Hakko Kirin, Daiichi Sankyo, Abbive, Nippon Kayaku, AMGEN, Merck, and Takeda Pharmaceutical Co., Ltd. outside the submitted work. Motohiro Tamiya reported receiving personal fees from Pfizer, Chugai Pharmaceutical, Ono Pharmaceutical, MSD, Boehringer Ingelheim, AstraZeneca, Taiho Pharmaceutical, Eli Lilly, Bristol-Myers Squibb, and Amgen outside the submitted work. Motoko Tachihara reported receiving grants and personal fees from AstraZeneca K.K., Chugai Pharmaceutical Co., Ltd., and Eli Lilly Japan Co., Ltd., and personal fees from Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb Co., Ltd., MSD K.K., Novartis pharmaceuticals K.K., Takeda Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co., Ltd., Nippon Boehringer Ingelheim Co., Ltd., Daiichi Sankyo, Pfizer Japan, Inc., and Janssen Pharmaceutical K.K. Takehito Shukuya reported receiving grants and personal fees from AstraZeneca, Chugai Pharmaceutical, Boehringer Ingelheim, Novartis, and MSD and personal fees from Taiho Pharma, Daiichi-Sankyo, Ono Pharmaceutical, Bristol-Myers Squibb, Nippon Kayaku, Pfizer, Takeda Pharmaceutical Co., Ltd., and Eli Lilly outside the submitted work. Yuko Tsuchiya-Kawano reported receiving personal fees from Bristol-Myers Squibb, Taiho Pharmaceutical, Chugai Pharmaceutical, AstraZeneca, Kyowa Hakko Kirin, MSD, and Ono Pharmaceutical outside the submitted work. Yuki Sato reported receiving personal fees from AstraZeneca, MSD, Novartis, Chugai Pharmaceutical, Ono Pharmaceutical, Pfizer, Taiho Pharmaceutical, Nippon Kayaku, Bristol Myers Squibb, Eli Lilly, Takeda Pharmaceutical Co., Ltd., Kyowa Kirin, and Boehringer Ingelheim outside the submitted work. Satoshi Ikeda reported receiving personal fees from AstraZeneca, Bristol Myers Squibb, Ono Pharmaceutical, Chugai Pharmaceutical, Taiho Pharmaceutical, Boehringer Ingelheim, Eli Lilly, Takeda Pharmaceutical Co., Ltd., Daiichi-Sankyo, and Pfizer outside the submitted work. Shinya Sakata reported receiving personal fees from AstraZeneca, Chugai Pharmaceutical Co., Ltd., Taiho Pharmaceutical, and Takeda Pharmaceutical outside the submitted work. Takeshi Masuda reported receiving personal fees from MSD K.K., Eli Lilly Japan K.K., Chugai Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co., Ltd., AstraZeneca K.K., Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb Co., Ltd., and Kyowa Kirin outside the submitted work. Isamu Okamoto reported receiving personal fees from Nippon Boehringer Ingelheim Co., Ltd., AstraZeneca Plc., Eli Lilly Japan K.K., Takeda Pharmaceutical Co., Ltd., and Novartis Pharma K.K. outside the submitted work. Nobuyuki Yamamoto reported receiving grants from AstraZeneca K.K., MSD K.K., Amgen Astellas BioPharma, Eisai Co., Ltd., Sanofi K.K., Chugai Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Novartis Pharma K.K., Janssen Pharmaceutical K.K., Amgen K.K., Taiho Pharmaceutical Co., Ltd., Toppan USA, Inc., Nippon Boehringer Ingelheim Co., Ltd., and Daiichi Sankyo Co., Ltd. and personal fees from MSD K.K., Amgen K.K., Takeda Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Nippon Boehringer Ingelheim Co., Ltd., AstraZeneca K.K., Ono Pharmaceutical Co., Ltd., Chugai Pharmaceutical Co., Ltd., Novartis Pharma K.K., and Merck Biopharma Co., Ltd., outside the submitted work. The remaining authors declare no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Learning through perceptual training using the Gabor patch (GP) has attracted attention as a new vision restoration technique for myopia and age-related deterioration of visual acuity (VA). However, the task itself is monotonous and painful and requires numerous training sessions and some time before being effective, which has been a challenge for its widespread application. One effective means of facilitating perceptual learning is the empowerment of EEG alpha rhythm in the sensory cortex before neurofeedback (NF) training; however, there is a lack of evidence for VA., Methods: We investigated whether four 30-min sessions of GP training, conducted over 2 weeks with/without EEG NF to increase alpha power (NF and control group, respectively), can improve vision in myopic subjects. Contrast sensitivity (CS) and VA were measured before and after each GP training., Results: The NF group showed an improvement in CS at the fourth training session, not observed in the control group. In addition, VA improved only in the NF group at the third and fourth training sessions, this appears as a consolidation effect (maintenance of the previous training effect). Participants who produced stronger alpha power during the third training session showed greater VA recovery during the fourth training session., Discussion: These results indicate that enhanced pretraining alpha empowerment strengthens the subsequent consolidation of perceptual learning and that even a short period of GP training can have a positive effect on VA recovery. This simple protocol may facilitate use of a training method to easily recover vision., Competing Interests: MC was employed by Vie, Inc. TK was employed by ROHTO Pharmaceutical Co., Ltd. TI and SS were employed by the companies NTT Data Institute of Management Consulting, Inc. and Vie, Inc. The authors would like to declare that a patent associated with this research is pending and that products related to this research will be developed in the future. This does not alter our adherence to Frontiers' policies on sharing data and materials., (Copyright © 2024 Chang, Suzuki, Kurose and Ibaraki.)

Study Design: Retrospective diagnostic study., Objective: To automatically detect osteolytic bone metastasis lesions in the thoracolumbar region using conventional computed tomography (CT) scans, we developed a new deep learning (DL)-based computer-aided detection model., Summary of Background Data: Radiographic detection of bone metastasis is often difficult, even for orthopedic surgeons and diagnostic radiologists, with a consequent risk for pathologic fracture or spinal cord injury. If we can improve detection rates, we will be able to prevent the deterioration of patients' quality of life at the end stage of cancer., Materials and Methods: This study included CT scans acquired at Tokyo Medical and Dental University (TMDU) Hospital between 2016 and 2022. A total of 263 positive CT scans that included at least one osteolytic bone metastasis lesion in the thoracolumbar spine and 172 negative CT scans without bone metastasis were collected for the datasets to train and validate the DL algorithm. As a test data set, 20 positive and 20 negative CT scans were separately collected from the training and validation datasets. To evaluate the performance of the established artificial intelligence (AI) model, sensitivity, precision, F1-score, and specificity were calculated. The clinical utility of our AI model was also evaluated through observer studies involving six orthopaedic surgeons and six radiologists., Results: Our AI model showed a sensitivity, precision, and F1-score of 0.78, 0.68, and 0.72 (per slice) and 0.75, 0.36, and 0.48 (per lesion), respectively. The observer studies revealed that our AI model had comparable sensitivity to orthopaedic or radiology experts and improved the sensitivity and F1-score of residents., Conclusion: We developed a novel DL-based AI model for detecting osteolytic bone metastases in the thoracolumbar spine. Although further improvement in accuracy is needed, the current AI model may be applied to current clinical practice., Level of Evidence: Level III., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

[Purpose] The aim of this study was to develop a novel wearable surface electromyograph called NOK, and compare its reliability and validity to an existing electromyograph. [Participants and Methods] The study participants were 23 healthy university students (Seven males and 16 females; age 20.3 ± 1.1 years [mean ± standard deviation]; height 162.0 ± 6.7 cm; weight 58.4 ± 10.1 kg) who all gave informed written consent. The newly developed electromyograph (NOK) features a rubberized skin contact surface that requires no electrodes and allows the acquisition of up to 10 channels of muscle waveforms on a portable personal computer. After measuring maximal isometric elbow extension and flexion, we examined muscle waveforms during isometric contractions of elbow joint flexion and extension at approximately 50% of maximal voluntary contraction using both NOK and Delsys electromyographs and compared the results of the two devices. [Results] We found a significant moderate correlation between the measurements by the two devices for biceps and triceps. The measurements by the two devices also showed strong measure-retest reliability. Systematic errors were observed for elbow flexion and extension in the two measurements, indicating limited agreement between the two measurement methods. [Conclusion] Although the new device also has high repeatability and reliability, it is unsuitable for analyzing detailed muscle activity. However, since it can measure up to 10 channels of muscle activity, it is expected to be used in the rehabilitation and sports field in the future., Competing Interests: None., (2024©by the Society of Physical Therapy Science. Published by IPEC Inc.)

Real-world evidence for clinical outcomes and treatment patterns in patients with hormone receptor-positive(HR+)and human epidermal growth factor receptor 2-negative(HER2-)early breast cancer(EBC)in Japan is limited. We aimed to provide recent evidence in this population using the National Database of Health Insurance Claims and Specific Health Check-ups of Japan(NDB). Adults ≥20 years old who were diagnosed with HR+/HER2- breast cancer and underwent breast resection surgery were followed up. Patient characteristics and treatment patterns were evaluated. Durations of overall post-operative endocrine therapy(ET)and luteinizing hormone-releasing hormone(LH-RH)agonist therapy, and time to metastasis/recurrence after surgery were analyzed using Kaplan-Meier method. Overall, 294,904 patients were included. Cyclophosphamide and tamoxifen were the most common peri-operative chemotherapeutic and ET drugs. Median(95% confidence interval[CI])duration of post-operative ET and LH-RH agonist therapy was 5.01(5.01-5.01)years and 2.13 (2.12-2.14)years, respectively. Five-year cumulative rate(95% CI)of any recurrence was 8.6%(8.5-8.7), visceral metastasis being the most common. Nation-wide treatment patterns were described, which were consistent with guideline recommendations for patients with HR+, HER2- EBC. Further discussion is required to delay metastasis/recurrence and improve clinical outcomes(Fig. 1: Plain language summary of the study).

Introduction: Technology Enhanced Learning (TEL) can provide the tools to safely master minimally invasive surgery (MIS) skills in patient-free environments and receive immediate objective feedback without the constant presence of an instructor. However, TEL-based systems tend to work isolated from one another, focus on different skills, and fail to provide contents without a sound pedagogical background., Objective: The objective of this descriptive study is to present in detail EASIER, an innovative TEL platform for surgical and interventional training, as well as the results of its validation., Methods: EASIER provides a Learning Management System (LMS) for institutions and content creators that can connect and integrate TEL "external assets" (virtual reality simulators, augmented box trainers, augmented videos, etc.) addressing different skills. The platform integrates all skills under an Assessment Module that measures skills' progress in different courses. Finally, it provides content creators with a pedagogical model to scaffold contents while retaining flexibility to approach course design with different training philosophies in mind. Three courses were developed and hosted in the platform to validate it with end-users in terms of usability, performance, learning results in the courses and student self-perception on learning., Results: In total 111 volunteers completed the validation. The study was limited due to the COVID-19 pandemic, which limited access to external assets (virtual reality simulators). Nevertheless, usability was rated with 73.1 in the System Usability Scale. Most positive aspects on performance were easiness to access the platform, easiness to change the configuration and not requiring additional plug-ins to use the platform. The platform was rated above average in the six scales of the User Experience Questionnaire. Overall, student results improved significantly across the three courses (p < 0.05)., Conclusions: This study provides, within its limitations, evidence on the usefulness of the EASIER platform for distance learning of MIS skills. Results show the potential impact of the platform and are an encouraging boost for the future, especially in the aftermath of the COVID-19 pandemic., Competing Interests: Declaration of Competing Interest Project EASIER (588404-EPP-1–2017-1-ES-EPPKA2-KA) is funded with support from the European Commission. The European Commission’s support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein., (Copyright © 2023 Elsevier B.V. All rights reserved.)

This research examines the feasibility of using synchronization signals broadcasted by currently deployed fifth generation (5G) cellular networks to determine the position of a static receiver. The main focus lies on the analysis of synchronization among the base stations of a real 5G network in Milan, Italy, as this has a major impact on the accuracy of localization based on time of arrival measurements. Understanding such properties, indeed, is fundamental to characterize the clock drifts and implement compensation strategies as well as to identify the direct communication beam. The paper shows how the clock errors, i.e., inaccurate synchronization, among 5G base stations exhibit a significant bias, which is detrimental for precise cellular positioning. By compensating the synchronization errors of devices' clocks, we demonstrate that it is in principle possible to localize a static user with an accuracy of approximately 8-10 m in non-obstructed visibility conditions, for urban and rural scenarios, using the deployed 5G network operating at 3.68 GHz and relying on broadcast signals as defined by 5G Release 15 standard. This work has been funded by the European Space Agency (ESA) Navigation Innovation and Support Program (NAVISP) Element 2 pillar which aims at improving the competitiveness of the industry of the participating States in the global Positioning, Navigation and Timing (PNT) market., (© 2023. Springer Nature Limited.)

Background: Depression is frequently seen among pregnant women. This is called antenatal depression (AND). Aim: Our aim was to identify clusters of AND and its core symptoms. Methods: The Patient Health Questionnaire-9 (PHQ-9), Pregnancy-Unique Quantification of Emesis and Nausea (PUQE-24), and Nausea and Vomiting of Pregnancy Quality of Life Questionnaire (NVP-QOL) were distributed to 382 pregnant women with a gestational age of 10 to 13 weeks who were attending antenatal clinics. The two PHQ-9 subscale scores were entered into a 2-step cluster analysis. The PHQ-9 items' capacity to identify AND were examined in terms of the area under curve (AUC) of a receiver operating characteristic (ROC) analysis. The selected symptom items were examined for their diagnostic capability in terms of the graded response model (GRM) in the item response theory (IRT) analysis. Results: Three clusters emerged. Cluster 3 scored highly in the scores of the two PHQ-9 subscales and the two emesis scales. In the ROC, five items showed an AUC > 0.80. The GRM identified four items with high information: 'loss of interest', 'depressed mood', 'self-esteem', and 'poor concentration'. Conclusions: The core symptoms of antenatal depression were four non-somatic symptoms; particularly, 'depressed mood' and 'loss of interest'. AND did not exist alone, but was accompanied by nausea and vomiting. Hence, we propose a new category: emesis-depression complex among pregnant women.

Insomnia is associated with adverse outcomes in women in the perinatal period; thus, the assessment of insomnia is important for pregnant women. The Insomnia Severity Index (ISI) is an instrument used globally to assess the severity of insomnia. However, its factor structure and structural invariance for pregnant women have not been studied. Therefore, we aimed to conduct factor analyses to search for the best model to fit its structural invariance. A cross-sectional study with the ISI was conducted at one hospital and five clinics in Japan from January 2017 to May 2019. A set of questionnaires was administered on two occasions with a one-week interval. The study included 382 pregnant women ranging in gestational age from 10 to 13 weeks. One week later, 129 participants answered the retest. After exploratory and confirmatory factor analyses, the measurement and structural invariance between parity and two time points was tested. The two-factor structure model showed an acceptable fit for the ISI in pregnant women ( χ 2 (12) = 28.516, CFI = 0.971, RMSEA = 0.089). The model also showed satisfactory measurement and structure invariance between parity and time points. The findings indicate that the ISI's use would be appropriate for pregnant women as a two-factor subscale of "severity" and "impact", regardless of the parity or time point. The ISI's factor structure may vary by subject; hence, it is necessary to confirm the measurement and structural invariance of the subject for whom the ISI will be used. Furthermore, interventions that focus not only on total scores and cutoff points but also on the phenomenon of subscales should be considered.

Introduction: A framework that extracts oncological outcomes from large-scale databases using artificial intelligence (AI) is not well established. Thus, we aimed to develop AI models to extract outcomes in patients with lung cancer using unstructured text data from electronic health records of multiple hospitals., Methods: We constructed AI models (Bidirectional Encoder Representations from Transformers [BERT], Naïve Bayes, and Longformer) for tumor evaluation using the University of Miyazaki Hospital (UMH) database. This data included both structured and unstructured data from progress notes, radiology reports, and discharge summaries. The BERT model was applied to the Life Data Initiative (LDI) data set of six hospitals. Study outcomes included the performance of AI models and time to progression of disease (TTP) for each line of treatment based on the treatment response extracted by AI models., Results: For the UMH data set, the BERT model exhibited higher precision accuracy compared to the Naïve Bayes or the Longformer models, respectively (precision [0.42 vs. 0.47 or 0.22], recall [0.63 vs. 0.46 or 0.33] and F1 scores [0.50 vs. 0.46 or 0.27]). When this BERT model was applied to LDI data, prediction accuracy remained quite similar. The Kaplan-Meier plots of TTP (months) showed similar trends for the first (median 14.9 [95% confidence interval 11.5, 21.1] and 16.8 [12.6, 21.8]), the second (7.8 [6.7, 10.7] and 7.8 [6.7, 10.7]), and the later lines of treatment for the predicted data by the BERT model and the manually curated data., Conclusion: We developed AI models to extract treatment responses in patients with lung cancer using a large EHR database; however, the model requires further improvement., (© 2022. The Author(s).)

Background: The construction industry is an important productive sector worldwide. However, the industry is also responsible for high numbers of work-related accidents, which highlights the necessity for improving safety management on construction sites. In parallel, technological applications such as machine learning (ML) are used in many productive sectors, including construction, and have proved significant in process optimizations and decision-making. Thus, advanced studies are required to comprehend the best way of using this technology to enhance construction site safety., Objective: This research developed a systematic literature review using ten scientific databases to retrieve relevant publications and fill the knowledge gaps regarding ML applications in construction accident prevention., Methods: This study examined 73 scientific articles through bibliometric research and descriptive analysis., Results: The results showed the publications timeline and the most recurrent journals, authors, institutions, and countries-regions. In addition, the review discovered information about the developed models, such as the research goals, the ML methods used, and the data features. The research findings revealed that USA and China are the leading countries regarding publications. Also, Support Vector Machine - SVM was the most used ML method. Furthermore, most models used textual data as a source, generally related to inspection reports and accident narratives. The data approach was usually related to facts before an accident (proactive data)., Conclusion: The review highlighted improvement proposals for future works and provided insights into the application of ML in construction safety management.

Background: Many pregnant women experience impairments in social, occupational, or other important functioning. Aim: This study aimed to confirm measurement and structural invariance of the Sheehan Disability Scale (SDS) and its validity during early pregnancy. Design: Longitudinal study with two observations. Methods: Questionnaires were distributed to pregnant women attending antenatal clinics at gestational weeks 10-13. Of 382 respondents, 129 responded to the SDS again 1 week later. Results: Confirmatory factor analysis shows good fit with the data: χ 2 / df = 0, comparative fit index (CFI) = 1.000, standardized root mean square residual (SRMR) = 0, and root mean square error of approximation (RMSEA) = 0.718. There is acceptable configural, measurement, and structural invariance of the factor structure between primiparas and multiparas as well as between two observation occasions. The Pregnancy-Unique Quantification of Emesis and Nausea, Patient Health Questionnaire-9, and Insomnia Severity Index subscales explain 47% of the variance in SDS scores. Conclusion: Perinatal health care professionals should pay more attention to the difficulties and disabilities that pregnant women face.

Computational methods from reinforcement learning have shown promise in inferring treatment strategies for hypotension management and other clinical decision-making challenges. Unfortunately, the resulting models are often difficult for clinicians to interpret, making clinical inspection and validation of these computationally derived strategies challenging in advance of deployment. In this work, we develop a general framework for identifying succinct sets of clinical contexts in which clinicians make very different treatment choices, tracing the effects of those choices, and inferring a set of recommendations for those specific contexts. By focusing on these few key decision points, our framework produces succinct, interpretable treatment strategies that can each be easily visualized and verified by clinical experts. This interrogation process allows clinicians to leverage the model's use of historical data in tandem with their own expertise to determine which recommendations are worth investigating further e.g. at the bedside. We demonstrate the value of this approach via application to hypotension management in the ICU, an area with critical implications for patient outcomes that lacks data-driven individualized treatment strategies; that said, our framework has broad implications on how to use computational methods to assist with decision-making challenges on a wide range of clinical domains., (© 2022. The Author(s).)

Melanoma is the cutaneous neoplasm responsible for more patient deaths in all countries. BRAF mutations are the most common driver mutation and with the development of molecular targeted therapy, the precise knowledge of BRAF status has become increasingly important. Evaluation of BRAF mutation status has routinely been performed by polymerase chain reaction, a time consuming and expensive technique. Immunohistochemistry has been suggested as a cheaper alternative, but it has not gained general acceptance. A retrospective observational study in a cohort of 106 patients with invasive melanoma was conducted in order to develop and evaluate a machine learning approach to predict BRAF status using clinical and histologic variables. We compared the performance of different common machine learning algorithms and use SHapley Additive exPlanations (SHAP) to explain individual predictions and extract medical insights to define a heuristic model to estimate BRAF mutation probability. The Extreme Gradient Boosting algorithms obtained the best performance. Interpretability of models shows that the most important variables to estimate BRAF mutation probability are: age, Breslow thickness, and Breslow density. Based in this interpretation and medical knowledge, a simplify heuristic model is proposed to predict BRAF status using only 7 variables and obtain a performance of 0.878 of area under the curve. We propose a heuristic model that could be used by clinicians to obtain a good estimator of BRAF mutation probability., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: The challenges of an aging population worldwide are the increased number of people needing medical and nursing care and inadequate medical resources. Information and communication technologies have progressed remarkably, leading to innovations in various areas. 5G communication systems are capable of high-capacity, high-speed communication with low latency and are expected to transform medicine. We aimed to report a demonstration experiment of telerehabilitation and telemedicine using a mobile ultrasound system in a depopulated area in a mountainous terrain, where 32% of the population are 65 years or older., Methods: At the core hospital, a physician or physical therapist remotely performed ultrasonography or rehabilitation on a subject in a clinic. Five general residents participated in the telerehabilitation as subjects. The delay time and video quality transmitted with 5G and long-term evolution (LTE) communication systems were compared. The physician or physical therapist subjectively evaluated the quality and delay of the transmitted images and subject acceptability., Results: Of seven physical therapists, six and three responded that the video quality was "good" for telerehabilitation with 5G/4K resolution and LTE, respectively. Five physical therapists and one physical therapist reported that the delay time was "acceptable" with 5G/4K resolution and LTE, respectively. For telemedicine using a mobile ultrasound system, the responses for 5G were "the delay was acceptable" and "rather acceptable." In contrast, both respondents' responses for LTE were "not acceptable.", Conclusions: Multiple high-definition images can be transmitted with lower latency in telerehabilitation and telemedicine using mobile ultrasound imaging systems with a 5G communication system. These differences affected the subjective evaluation of the doctors and physical therapists., (© The Author(s) 2022.)

Purpose: The purpose of this study was to clarify whether the novel lateral transfer assist robot facilitates easier transfers compared with a wheelchair in post-stroke hemiparesis patients., Methods: This cross-sectional study enrolled 20 post-stroke hemiparesis patients, and the task difficulty of transfers was compared between a wheelchair and lateral transfer assist robot. All participants were asked to transfer from either wheelchair or lateral transfer assist robot to a platform table and back. The primary outcome was the transfer score of the Functional Independence Measure. The secondary outcome was the time required for transfer., Results: The transfer score of the Functional Independence Measure was significantly higher with lateral transfer assist robot than with wheelchair ( p  < .001). The transfer times from these devices to a platform table and back showed no significant differences (to device from platform table: 7.8 s, lateral transfer assist robot vs 7.6 s, wheelchair, p  > .05: device to platform table: 7.1 s, lateral transfer assist robot vs 8.0 s, wheelchair, p > .05)., Conclusions: Transfer with a lateral transfer assist robot is easier than with wheelchair and facilitates independence in post-stroke hemiparesis patients.IMPLICATIONS FOR REHABILITATIONTransfer skill influences the functional independence and quality of life of a wheelchair userA novel structural mobility device-the lateral transfer assist robot (LTAR)-can facilitate transfersThe LTAR could improve the degree of independence for transfers than the wheelchair, without any time loss, in post-stroke hemiparesis patientsThe LTAR could potentially reduce the risk for falls in various medical and care facilities.

The chloroquine resistance transporter, PfCRT, is an essential factor during intraerythrocytic development of the human malaria parasite Plasmodium falciparum. PfCRT resides at the digestive vacuole of the parasite, where hemoglobin taken up by the parasite from its host cell is degraded. PfCRT can acquire several mutations that render PfCRT a drug transporting system expelling compounds targeting hemoglobin degradation from the digestive vacuole. The non-drug related function of PfCRT is less clear, although a recent study has suggested a role in oligopeptide transport based on studies conducted in a heterologous expression system. The uncertainty about the natural function of PfCRT is partly due to a lack of a null mutant and a dearth of functional assays in the parasite. Here, we report on the generation of a conditional PfCRT knock-down mutant in P. falciparum. The mutant accumulated oligopeptides 2 to at least 8 residues in length under knock-down conditions, as shown by comparative global metabolomics. The accumulated oligopeptides were structurally diverse, had an isoelectric point between 4.0 and 5.4 and were electrically neutral or carried a single charge at the digestive vacuolar pH of 5.2. Fluorescently labeled dipeptides and live cell imaging identified the digestive vacuole as the compartment where oligopeptides accumulated. Our findings suggest a function of PfCRT in oligopeptide transport across the digestive vacuolar membrane in P. falciparum and associated with it a role in nutrient acquisition and the maintenance of the colloid osmotic balance. IMPORTANCE The chloroquine resistance transporter, PfCRT, is important for the survival of the human malaria parasite Plasmodium falciparum. It increases the tolerance to many antimalarial drugs, and it is essential for the development of the parasite within red blood cells. While we understand the role of PfCRT in drug resistance in ever increasing detail, the non-drug resistance functions are still debated. Identifying the natural substrate of PfCRT has been hampered by a paucity of functional assays to test putative substrates in the parasite system and the absence of a parasite mutant deficient for the PfCRT encoding gene. By generating a conditional PfCRT knock-down mutant, together with comparative metabolomics and uptake studies using fluorescently labeled oligopeptides, we could show that PfCRT is an oligopeptide transporter. The oligopeptides were structurally diverse and were electrically neutral or carried a single charge. Our data support a function of PfCRT in oligopeptide transport.

Aim: The Nausea and Vomiting of Pregnancy Quality of Life (NVP QOL) Questionnaire is a self-report measure of health-related QOL for nausea and vomiting during pregnancy. This study determines the best fitting factor structure for the NVP QOL Questionnaire and explores its measurement invariance in terms of observation time and parity., Methods: A test-retest study of pregnant women was conducted at Gestational Weeks (GWs) 10-13 (T1: N  = 381) and 1 week later (T2: n  = 128) at one hospital and five clinics with the NVP QOL and the Pregnancy-Unique Quantification of Emesis and Nausea (PUQE). Exploratory and confirmatory factor analyses were performed to compare different factor structure models and evaluate measurement invariance of the best fitting model between two time points and between primiparas and multiparas. Concurrent validity of the NVP QOL was clarified by correlations with the PUQE, Sheehan Disability Scale, and other scales., Results: The one-factor model had the best fit. This factor structure model was acceptable up to the factor invariance level for two time points and up to the factor mean level for primiparas versus multiparas. Correlations between NVP QOL, PUQE, and Sheehan Disability Scale scores were strong. Women with higher NVP QOL scores were more likely to lose weight, have lower daily fluid intake, have reduced fluid and food intake since pregnancy began, and receive outpatient or inpatient treatment., Conclusion: The one-factor structure and measurement invariance of the NVP QOL at different times and parities were demonstrated, suggesting that the NVP QOL can be used to evaluate primiparas and multiparas in a longitudinal study., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Psychiatry and Clinical Neurosciences Reports published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.)

Eldercare programs such as health consultations and physiotherapy that improve the well-being and extend the life expectancy of people in rural or sparsely populated areas is a socially important though costly problem. We ran a pilot project to test the effectiveness potential of telerehabilitation using markerless motion capture technology integrated in a fast and low-latency IMT-2020 (5G) mobile network. Accelerating technological innovations and the surge in advances of telehealth will greatly impact conventional home visit or outpatient rehabilitation services, working in concert with or even supplanting them, given the potential lower cost and better utilization of time.

Objectives: Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology., Methods: We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool., Results: 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data., Conclusion: Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Leo Anthony Celi is the Editor-in Chief of PLOS Digital Health., (Copyright: © 2022 Crowson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Quantum annealing has gained considerable attention because it can be applied to combinatorial optimization problems, which have numerous applications in logistics, scheduling, and finance. In recent years, with the technical development of quantum annealers, research on solving practical combinatorial optimization problems using them has accelerated. However, researchers struggle to find practical combinatorial optimization problems, for which quantum annealers outperform mathematical optimization solvers. Moreover, there are only a few studies that compare the performance of quantum annealers with the state-of-the-art solvers, such as Gurobi and CPLEX. This study determines that quantum annealing demonstrates better performance than the solvers in that the solvers take longer to reach the objective function value of the solution obtained by the quantum annealers for the break minimization problem in a mirrored double round-robin tournament. We also explain the desirable performance of quantum annealing for the sparse interaction between variables and a problem without constraints. In this process, we demonstrate that this problem can be expressed as a 4-regular graph. Through computational experiments, we solve this problem using our quantum annealing approach and two-integer programming approaches, which were performed using the latest quantum annealer D-Wave Advantage, and Gurobi, respectively. Further, we compare the quality of the solutions and the computational time. Quantum annealing was able to determine the exact solution in 0.05 seconds for problems with 20 teams, which is a practical size. In the case of 36 teams, it took 84.8 s for the integer programming method to reach the objective function value, which was obtained by the quantum annealer in 0.05 s. These results not only present the break minimization problem in a mirrored double round-robin tournament as an example of applying quantum annealing to practical optimization problems, but also contribute to find problems that can be effectively solved by quantum annealing., Competing Interests: The authors have declared that no competing interests exist.

Over the last few years, the financial sector has undergone a digital revolution that has had a severe impact on different related areas such as, the entities, the cybersecurity of systems, regulations and, of course, customers. SOTER project takes the challenge providing a complete set of tools to enhance the cybersecurity levels by implementing, in addition to non-technological tools, an innovative onboarding process has been implemented with the goals of increasing security, improving the user experience and integrity in the sector, and facilitating the customer entry into the digital marketplace by combining a set of breakthrough technologies. The cybersecurity research plays a crucial role in the conception and implementation of the onboarding process and for this kind of processes it has to be studied as an individual area by itself, as it is necessary to analyze the possible threats that can affect them, their origin and the solution(s) that can be taken to address them. Therefore, the SOTER project presents a fully digital onboarding process, innovative, adaptable to both current and future market needs and to possible changes in regulation, based on the most advanced technologies available today, and above all guaranteeing the cybersecurity of both entities and end users., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 García MK et al.)

Processing of sensory information is embedded into ongoing neural processes which contribute to brain states. Electroencephalographic microstates are semi-stable short-lived power distributions which have been associated with subsystem activity such as auditory, visual and attention networks. Here we explore changes in electrical brain states in response to an audiovisual perception and memorization task under conditions of auditory distraction. We discovered changes in brain microstates reflecting a weakening of states representing activity of the auditory system and strengthening of salience networks, supporting the idea that salience networks are active after audiovisual encoding and during memorization to protect memories and concentrate on upcoming behavioural response., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Korn, Krylova, Heck, Häußinger, Stark, Alizadeh, Jamalabadi, Walter, Galuske and Munk.)

Schools play a central role in the transmission of many respiratory infections. Heterogeneous social contact patterns associated with the social structures of schools (i.e., classes/grades) are likely to influence the within-school transmission dynamics, but data-driven evidence on fine-scale transmission patterns between students has been limited. Using a mathematical model, we analyzed a large-scale dataset of seasonal influenza outbreaks in Matsumoto city, Japan, to infer social interactions within and between classes/grades from observed transmission patterns. While the relative contribution of within-class and within-grade transmissions to the reproduction number varied with the number of classes per grade, the overall within-school reproduction number, which determines the initial growth of cases and the risk of sustained transmission, was only minimally associated with class sizes and the number of classes per grade. This finding suggests that interventions that change the size and number of classes, e.g., splitting classes and staggered attendance, may have a limited effect on the control of school outbreaks. We also found that vaccination and mask-wearing of students were associated with reduced susceptibility (vaccination and mask-wearing) and infectiousness (mask-wearing), and hand washing was associated with increased susceptibility. Our results show how analysis of fine-grained transmission patterns between students can improve understanding of within-school disease dynamics and provide insights into the relative impact of different approaches to outbreak control., Competing Interests: Competing interest statement: A.E. received a research grant from Taisho Pharmaceutical Co., Ltd., (Copyright © 2021 the Author(s). Published by PNAS.)

Background: The severity of nausea and vomiting of pregnancy (NVP) correlates with pregnancy complications. This study aimed to confirm the measurement and structural invariance of the 24 h Pregnancy-Unique Quantification of Emesis and Nausea (PUQE-24) regarding parity and observation time among pregnant women during the first trimester., Methods: Questionnaires including the PUQE-24 and the Health-Related Quality of Life for Nausea and Vomiting during Pregnancy (NVP-QOL) questionnaire were distributed to pregnant women from 10 to 13 weeks of gestation who were attending antenatal clinics. There were 382 respondents, and of these, 129 responded to the PUQE-24 again one week later., Results: Confirmatory factor analysis of this single factor model showed a good fit with the data: CFI = 1.000. The PUQE-24 factor and NVP-QOL factor were strongly correlated ( r = 82). Configural, measurement, and structural invariance of the factor structure of the PUQE items were confirmed between primiparas and multiparas as well as at the test and retest observation occasions., Conclusion: The findings suggested that using the PUQE-24 among pregnant women in the first trimester was robust in its factor structure. The PUQE-24 may be a promising tool as an easy and robust measure of the severity of nausea and vomiting among pregnant women.

In this study, a novel single-piece thin multi-layer tungsten resistive heater was successfully fabricated using additive manufacturing and tested as an electrothermal thruster. The heater has 12 resistive layers, with each layer having a thickness and height of 0.15 and 81 mm, respectively, and can provide high heating efficiency. A single-piece or monolithic heater was manufactured via additive manufacturing technique, which drastically improved its reliability and decreased its manufacturing cost. In the heating and thrust measurement tests that used nitrogen gas as a propellant, the heater reached a gas temperature of ∼2000 K at a 140-A heater current without experiencing any failure. The tungsten-heater resistance linearly increased with an increase in temperature due to the temperature dependence of tungsten's resistivity. The specific impulse and thrust increased with the heater temperature in accordance with the theoretical prediction. Even including a voltage drop due to a contact resistance, the achieved heater efficiency reached 63% at a 100-A heater current even without a thermal insulation around the thruster. The heater efficiency decreased with an increase in the heater temperature due to heat loss to the surroundings. The heat-loss analysis indicated that both thermal conduction and radiation heat losses were crucial for improving the heater performance at a high-temperature operation of over 2000 K.

Background : COVID-19 has caused significant disruptions in supply chains. It has increased the demand for products and decreased the supply of raw materials. This has interrupted many production processes. The emerging technologies of Industry 4.0 have the potential to streamline supply chains by improving time-sensitive customized solutions during this emergency. Purpose : The study identifies the core technologies of Industry 4.0 and the role and impact of these technologies in managing the disruption caused by the COVID-19 outbreak in strengthening the supply chain resilience. Design/methodology/approach: An extensive literature review using the "Preferred Reporting Items for Systematic Review and Meta-Analysis" method was carried out on the impact of the COVID-19 pandemic on supply chains and Industry 4.0 technologies. The study was undertaken by selecting keywords validated by experts, and a search was conducted in the Scopus, ProQuest, and Google Scholar databases. Publications from the leading journals on these topics were selected. The bibliographical search resulted in 1484 articles, followed by multiple layers of filtering. Finally, the most pertinent articles were selected for review, and a total of 42 articles were analyzed. Findings: The findings of the study showed that the majority of the articles emphasized the digitalization of supply chain management, acknowledging the fundamentals, applications, and prospects, revealing the drivers and challenges of Industry 4.0 technologies to manage disruptions. Most of the authors identified IoT, big data, cloud computing, additive manufacturing, and blockchain to maintain the supply chain resilience. Originality/value: Existing literature on epidemics lacks the basics and practices of utilizing Industry 4.0 technologies in the supply chain recovery process. To fill this research gap, the study summarizes the potential of Industry 4.0 technologies to lessen supply chain disruptions caused by COVID-19. The study findings are valuable for policymakers and practitioners and contribute to supply chain management studies., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Reza MNH et al.)

This study examined exercise intervention effects on older adults' brain structures and function. Brain data were analyzed from 47 healthy adults between 61 and 82 years of age who, in a previous study, showed cognitive improvement following a 3-month intervention. The participants were assigned to a motor exercise intervention group (n = 24), performing exercise training programs for a 12-week period, or a waiting control group (n = 23), abstaining from any exercise program. Structural analysis of the frontal cortex and hippocampus revealed increased gray matter volume and/or thickness in several prefrontal areas in the intervention group and reduced hippocampal gray matter volume in the control group. Importantly, the volume increase in the middle frontal sulcus in the intervention group was associated with a general cognitive improvement after the intervention. Functional analysis showed that the prefrontal functional connectivity during a working memory task differently changed in response to the intervention or waiting in the two groups. The functional connectivity decreased in the intervention group, whereas the corresponding connectivity increased in the control group, which was associated with maintaining cognitive performance. The current longitudinal findings indicate that short-term exercise intervention can induce prefrontal plasticity associated with cognitive performance in older adults., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful tool for such quantitative modeling. However, whether semantic representations in the brain revealed by the word vector-based models actually capture our perception of semantic information remains unclear, as there has been no study explicitly examining the behavioral correlates of the modeled brain semantic representations. To address this issue, we compared the semantic structure of nouns and adjectives in the brain estimated from word vector-based brain models with that evaluated from human behavior. The brain models were constructed using voxelwise modeling to predict the functional magnetic resonance imaging (fMRI) response to natural movies from semantic contents in each movie scene through a word vector space. The semantic dissimilarity of brain word representations was then evaluated using the brain models. Meanwhile, data on human behavior reflecting the perception of semantic dissimilarity between words were collected in psychological experiments. We found a significant correlation between brain model- and behavior-derived semantic dissimilarities of words. This finding suggests that semantic representations in the brain modeled via word vectors appropriately capture our perception of word meanings., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: This study was funded by NTT Data Corp. NM and MK are employees of NTT Data Corp.

In this paper, some collision avoidance systems based on MEC in a VANET environment are proposed and investigated. Micro services at edge are considered to support service continuity in vehicle communication and advertising. This considered system makes use of cloud and edge computing, allowing to switch communication from edge to cloud server and vice versa when possible, trying to guarantee the required constraints and balancing the communication among the servers. Simulation results were used to evaluate the performance of three considered mechanisms: the first one considering only edge with load balancing, the second one using edge/cloud switching and the third one using edge with load balancing and collision avoidance advertising.

Latent Dirichlet allocation (LDA) obtains essential information from data by using Bayesian inference. It is applied to knowledge discovery via dimension reducing and clustering in many fields. However, its generalization error had not been yet clarified since it is a singular statistical model where there is no one-to-one mapping from parameters to probability distributions. In this paper, we give the exact asymptotic form of its generalization error and marginal likelihood, by theoretical analysis of its learning coefficient using algebraic geometry. The theoretical result shows that the Bayesian generalization error in LDA is expressed in terms of that in matrix factorization and a penalty from the simplex restriction of LDA's parameter region. A numerical experiment is consistent with the theoretical result., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Vehicle positioning is becoming an important issue related to Intelligent Transportation Systems (ITSs). Novel vehicles and autonomous vehicles need to be localized under different weather conditions and it is important to have a reliable positioning system to track vehicles. Satellite navigation systems can be a key technology in providing global coverage and providing localization services through many satellite constellations such as GPS, GLONASS, Galileo and so forth. However, the modeling of positioning and localization systems under different weather conditions is not a trivial objective especially considering different factors such as receiver sensitivity, dynamic weather conditions, propagation delay and so forth. This paper focuses on the use of simulators for performing different kinds of tests on Global Navigation Satellite System (GNSS) systems in order to reduce the cost of the positioning testing under different techniques or models. Simulation driven approach, combined with some specific hardware equipment such as receivers and transmitters can characterize a more realistic scenario and the simulation can consider other aspects that could be complex to really test. In this work, the main contribution is the introduction of the Troposphere Collins model in a GNSS simulator for VANET applications, the GPS-SDR-SIM software. The use of the Collins model in the simulator allows to improve the accuracy of the simulation experiments throughout the reduction of the receiver errors.

It remains to be determined how different inputs for memory-encoding, such as the use of paper notebooks or mobile devices, affect retrieval processes. We compared three groups of participants who read dialogues on personal schedules and wrote down the scheduled appointments on a calendar using a paper notebook (Note), an electronic tablet (Tablet), or a smartphone (Phone). After the retention period for an hour including an interference task, we tested recognition memory of those appointments with visually presented questions in a retrieval task, while scanned with functional magnetic resonance imaging. We obtained three major results. First, the duration of writing down schedules was significantly shorter for the Note group than the Tablet and Phone groups, and accuracy was much higher for the Note group in easier (i.e., more straightforward) questions. Because the input methods were equated as much as possible between the Note and Tablet groups, these results indicate that the cognitive processes for the Note group were deeper and more solid. Second, brain activations for all participants during the retrieval phase were localized in the bilateral hippocampus, precuneus, visual cortices, and language-related frontal regions, confirming the involvement of verbalized memory retrieval processes for appointments. Third, activations in these regions were significantly higher for the Note group than those for the Tablet and Phone groups. These enhanced activations for the Note group could not be explained by general cognitive loads or task difficulty, because overall task performances were similar among the groups. The significant superiority in both accuracy and activations for the Note group suggested that the use of a paper notebook promoted the acquisition of rich encoding information and/or spatial information of real papers and that this information could be utilized as effective retrieval clues, leading to higher activations in these specific regions., Competing Interests: TI and TY were employed by the company NTT Data Institute of Management Consulting, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Umejima, Ibaraki, Yamazaki and Sakai.)

Purpose: Location visualization is essential for locating people/objects, improving efficiency, and preventing accidents. In hospitals, Wi-Fi, Bluetooth low energy (BLE) Beacon, indoor messaging system, and similar methods have generally been used for tracking, with Wi-Fi and BLE being the most common. Recently, nurses are increasingly using mobile devices, such as smartphones and tablets, while shifting. The accuracy when using Wi-Fi or BLE may be affected by interference or multipath propagation. In this research, we evaluated the positioning accuracy of geomagnetic indoor positioning in hospitals., Materials and Methods: We compared the position measurement accuracy of a geomagnetic method alone, Wi-Fi alone, BLE beacons alone, geomagnetic plus Wi-Fi, and geomagnetic plus BLE in a general inpatient ward, using a geomagnetic positioning algorithm by GiPStech. The existing Wi-Fi infrastructure was used, and 20 additional BLE beacons were installed. Our first experiment compared these methods' accuracy for 8 test routes, while the second experiment verified a combined geomagnetic/BLE beacon method using 3 routes based on actual daily activities., Results: The experimental results demonstrated that the most accurate method was geomagnetic/BLE, followed by geomagnetic/Wi-Fi, and then geomagnetic alone., Discussion: The geomagnetic method's positioning accuracy varied widely, but combining it with BLE beacons reduced the average position error to approximately 1.2 m, and the positioning accuracy could be improved further. We believe this could effectively target humans (patients) where errors of up to 3 m can generally be tolerated., Conclusion: In conjunction with BLE beacons, geomagnetic positioning could be sufficiently effective for many in-hospital localization tasks., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

A Sleeping Beauty is a publication that is apparently unrecognized by citation for some period of time before experiencing a burst of recognition. Various reasons, including resistance to new ideas, have been attributed to such delayed recognition. We study this phenomenon in the special case of co-citations, which represent new ideas generated through the combination of existing ones. Using relatively stringent selection criteria derived from the work of others, we analyze a very large dataset of over 940 million unique co-cited article pairs, and identify 1,196 cases of delayed co-citations. We further classify these 1,196 cases with respect to amplitude, rate of citation, and disciplinary origin., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zhao, Korobskiy and Chacko.)

Background: Medical terminologies are commonly used in medicine. For instance, to answer a pharmacovigilance question, pharmacovigilance specialists (PVS) search in a pharmacovigilance database for reports in relation to a given drug. To do that, they first need to identify all MedDRA terms that might have been used to code an adverse reaction in the database, but terms may be numerous and difficult to select as they may belong to different parts of the hierarchy. In previous studies, three tools have been developed to help PVS identify and group all relevant MedDRA terms using three different approaches: forms, structured query-builder, and icons. Yet, a poor usability of the tools may increase PVS' workload and reduce their performance. This study aims to evaluate, compare and improve the three tools during two rounds of formative usability evaluation., Methods: First, a cognitive walkthrough was performed. Based on the design recommendations obtained from this evaluation, designers made modifications to their tools to improve usability. Once this re-engineering phase completed, six PVS took part in a usability test: difficulties, errors and verbalizations during their interaction with the three tools were collected. Their satisfaction was measured through the System Usability Scale. The design recommendations issued from the tests were used to adapt the tools., Results: All tools had usability problems related to the lack of guidance in the graphical user interface (e.g., unintuitive labels). In two tools, the use of the SNOMED CT to find MedDRA terms hampered their use because French PVS were not used to it. For the most obvious and common terms, the icons-based interface would appear to be more useful. For the less frequently used MedDRA terms or those distributed in different parts of the hierarchy, the structured query-builder would be preferable thanks to its great power and flexibility. The form-based tool seems to be a compromise., Conclusion: These evaluations made it possible to identify the strengths of each tool but also their weaknesses to address them before further evaluation. Next step is to assess the acceptability of tools and the expressiveness of their results to help identify and group MedDRA terms.