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Rationale: Some patients with interstitial lung disease (ILD) have a high mortality rate or experience acute exacerbation of ILD (AE-ILD) that results in increased mortality. Early identification of these high-risk patients and accurate prediction of the onset of these important events is important to determine treatment strategies. Although various factors that affect disease behavior among patients with ILD hinder the accurate prediction of these events, the use of longitudinal information may enable better prediction., Objectives: To develop a deep-learning (DL) model to predict composite outcomes defined as the first occurrence of AE-ILD and mortality using longitudinal data., Methods: Longitudinal clinical and environmental data were retrospectively collected from consecutive patients with ILD at two specialty centers between January 2008 and December 2015. A DL model was developed to predict composite outcomes using longitudinal data from 80% of patients from the first center, which was then validated using data from the remaining 20% patients and second center. The developed model was compared with the univariate Cox proportional hazard (CPH) model using the ILD gender-age-physiology (ILD-GAP) score and multivariate CPH model at the time of ILD diagnosis., Measurements and Main Results: AE-ILD was reported in 218 patients among the 1,175 patients enrolled, whereas 380 died without developing AE-ILD. The truncated concordance index (C-index) values of univariate/multivariate CPH models for composite outcomes within 12, 24, and 36 months after prediction were 0.789/0.843, 0.788/0.853, and 0.787/0.853 in internal validation, and 0.650/0.718, 0.652/0.756, and 0.640/0.756 in external validation, respectively. At 12 months after ILD diagnosis, the DL model outperformed the univariate CPH model and multivariate CPH model for composite outcomes within 12 months, with C-index values of 0.842, 0.840, and 0.839 in internal validation, and 0.803, 0.744, and 0.746 in external validation, respectively. Neutrophils, C-reactive protein, ILD-GAP score, and exposure to suspended particulate matter were strongly associated with the composite outcomes., Conclusions: The DL model can accurately predict the incidence of AE-ILD or mortality using longitudinal data.

This study presents a data-driven battery emulator using long short-term memory deep learning models to predict the charge-discharge behaviour of lithium-ion batteries (LIBs). This study aimed to reduce the economic costs and time associated with the fabrication of large-scale automotive prototype batteries by emulating their performance using smaller laboratory-produced batteries. Two types of datasets were targeted: simulation data from the Dualfoil model and experimental data from liquid-based LIBs. These datasets were used to accurately predict the voltage profiles from the arbitrary inputs of various galvanostatic charge-discharge schedules. The results demonstrated high prediction accuracy, with the coefficient of determination scores reaching 0.98 and 0.97 for test datasets obtained from the simulation and experiments, respectively. The study also confirmed the significance of state-of-charge descriptors and inferred that a robust model performance could be achieved with as few as five charge-discharge training datasets. This study concludes that data-driven emulation using machine learning can significantly accelerate the battery development process, providing a powerful tool for reducing the time and economic costs associated with the production of large-scale prototype batteries., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Amorphous silicon monoxide (a-SiO), which contains Si atoms with various valence states, has attracted much attention as a high-performance anode material for lithium (Li) ion batteries (LIBs). Although current experiments have provided some information during charge/discharge cycles, further investigation of structural changes at the atomic scale is needed. To investigate the lithiation process of a-SiO using first-principles simulations and machine learning techniques, we developed a computational code employing Bayesian optimization to efficiently identify stable sites for Li insertion in the large search-space of amorphous models to reproduce the actual lithiation process and compared this approach to the conventional random scheme by applying it to an a-SiO model previously generated with neural network potentials. The lithiation process based on Bayesian optimization resulted in lower formation energies compared to the conventional random scheme, indicating a more stable structure. During lithiation, Li atoms tended to enter the silicon (Si) phase after the SiO 2 phase, in agreement with experimental results. We analyzed the structural changes and observed significant differences in the structural evolution between the conventional and new schemes. Our study highlights the significant influence of the lithiation process on the structural transformation of a-SiO materials, which in turn affects the reversible capacity of the material. These findings will provide a framework for improving the performance and lifetime of a-SiO materials.

Background: First-time traumatic anterior shoulder dislocation (FASD) is a common trauma associated with shoulder dysfunction. Although several randomized controlled trials have compared conservative and surgical treatments for FASD, the comparative efficacy of these treatments is poorly understood. In this network meta-analysis (NMA), we compared the available evidence on the efficacy of various interventions in patients with FASD., Methods: We searched the Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE databases in March 2023. This NMA included randomized controlled trials comparing conservative and surgical treatments for FASD, including arthroscopic Bankart repair, arthroscopic lavage, external rotation (ER) immobilization, and internal rotation (IR) immobilization. The primary outcomes were redislocation rates, Western Ontario Shoulder Instability Index (WOSI) scores, and adverse events (AEs). We conducted random-effects NMA within the frequentist framework. To rank the treatments, the Surface Under the Cumulative Ranking curve was calculated using a Bayesian framework. We evaluated confidence in each outcome using the CINeMA tool., Results: Of the 2999 reviewed studies, 15 were included and analyzed. Regarding the primary outcomes, arthroscopic Bankart repair likely results in a large reduction in redislocation rates compared to IR immobilization (risk ratio [RR], 0.15; 95% confidence interval [CI], 0.07-0.33). Both arthroscopic lavage (RR, 0.47; 95% CI, 0.20-1.11) and ER immobilization (RR 0.70; 95% CI, 0.50-1.00) may reduce the redislocation rates slightly compared with IR immobilization. According to these results, arthroscopic Bankart repair ranked first in terms of reducing the redislocation rate, followed by arthroscopic lavage, ER and IR immobilization. Regarding the WOSI score, no substantial differences were observed in the WOSI scores among the four treatments. AEs showed that ER immobilization tended to cause greater shoulder stiffness than IR immobilization, and postoperative erythema, swelling, and adhesive capsulitis were observed after arthroscopic Bankart repair and lavage. However, a meta-analysis was not performed because the definitions of AEs differed between the studies., Conclusion: Arthroscopic Bankart repair showed a significant effect in reducing the redislocation rate compared to IR immobilization. Although both arthroscopic lavage and ER immobilization seemed to be effective in reducing the redislocation rates, it was not statistically significant. Moreover, these four treatments may result in little to no difference in disease-specific quality of life and there is no clear evidence of AEs., (Copyright © 2024 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)

Background: Shoulder and elbow pain are the common complaints associated with throwing injuries in baseball players. Prospective studies evaluating the effectiveness of stretching in increasing posterior shoulder flexibility or strengthening the external rotator muscles as preventive strategies for throwing injuries in baseball players have been published. However, there are limited reviews highlighting the role of preventive interventions for throwing injuries in baseball players. Therefore, this scoping review aimed to summarize the existing literature on preventive interventions for throwing injuries in baseball players., Methods: We searched the Cochrane Central Register of Controlled Trials, MEDLINE, and Scopus databases on 3 March 2023. Two independent reviewers screened the studies based on the eligibility criteria. We extracted existing literature on preventive interventions and effectiveness for throwing injuries in baseball players, and participants' characteristics and results were extracted from the studies. This scoping review was performed in accordance with the Extended Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement for Scoping Reviews., Results: The initial database search yielded 1170 articles, four of which met the eligibility criteria. Of the included studies, two were randomized controlled trials, and the remaining two were prospective cohort studies. Eligible studies targeted youth-to high school-level players and focused on preventive programs for throwing injuries that manifest in the upper extremities of baseball players. The outcome measure most commonly used to quantify the effectiveness of prevention programs was the incidence of shoulder and elbow injuries, although the definitions vary among studies. The prevention program included sleeper stretching, shoulder external rotation strengthening exercises, and comprehensive prevention programs (focusing on improving the elbow, shoulder, and hip range of motion; rotator cuff and periscapular muscle strength; posture; and lower extremity balance). Each prevention program was reported to have the potential to reduce the incidence of throwing injuries in baseball players, and two studies suggested that high compliance with the program led to a lower injury risk., Conclusion: This scoping review confirmed the presence of studies that examined preventive interventions for throwing injuries in baseball players. Preventive interventions that may reduce throwing injuries in baseball players include sleeper stretching, shoulder external rotation strengthening exercises, and comprehensive prevention programs. However, the characteristics of participants (eg, age, sports level, and position) and the definition of injury varied among previous studies. Injury profiles and prevention strategies may differ according to age, sport level, and position of players, and more studies are needed to prove this issue., (Copyright © 2024 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)

Efforts to optimize known materials and enhance their performance are ongoing, driven by the advancements resulting from the discovery of novel functional materials. Traditionally, the search for and optimization of functional materials has relied on the experience and intuition of specialized researchers. However, materials informatics (MI), which integrates materials data and machine learning, has frequently been used to realize systematic and efficient materials exploration without depending on manual tasks. Nonetheless, the discovery of new materials using MI remains challenging. In this study, we propose a method for the discovery of materials outside the scope of existing databases by combining MI with the experience and intuition of researchers. Specifically, we designed a two-dimensional map that plots known materials data based on their composition and structure, facilitating researchers' intuitive search for new materials. The materials map was implemented using an autoencoder-based neural network. We focused on the conductivity of 708 lithium oxide materials and considered the correlation with migration energy (ME), an index of lithium-ion conductivity. The distribution of existing data reflected in the materials map can contribute to the development of new lithium-ion conductive materials by enhancing the experience and intuition of material researchers., (© 2023. Springer Nature Limited.)

Background: A supervised physiotherapy program (SPP) is a standard regimen after surgical rotator cuff repair (RCR); however, the effect of a home-based exercise program (HEP), as an alternative, on postoperative functional recovery remains unclear. Therefore, the purpose of this meta-analysis was to compare the functional effects of SPP and HEP after RCR., Methods: We searched electronic databases including Central, Medline, and Embase in April 2022. The primary outcomes included the Constant score, American Shoulder and Elbow Surgeons score, University of California Los Angeles shoulder score, and pain score. Secondary outcomes included range of motion, muscle strength, retear rate, and patient satisfaction rate. A meta-analysis using random-effects models was performed on the pooled results to determine the significance., Results: The initial database search yielded 848 records, five of which met our criteria. Variables at 3 months after surgery were successfully analyzed, including the Constant score (mean difference, -8.51 points; 95% confidence interval [CI], -32.72 to 15.69; P=0.49) and pain score (mean difference, 0.02 cm; 95% CI, -2.29 to 2.33; P=0.99). There were no significant differences between the SPP and HEP. Other variables were not analyzed owing to the lack of data., Conclusions: Our data showed no significant differences between SSP and HEP with regard to the Constant and pain scores at 3 months after RCR. These results suggest that HEP may be an alternative regimen after RCR. Level of evidence: I.

Context: Wheelchair basketball athletes have increased upper limb stress, which frequently leads to shoulder pain., Objective: The purpose of this scoping review was to summarize the existing literature on shoulder pain in wheelchair basketball athletes., Methods: We searched PubMed, Cochrane Library, Scopus, and PEDro databases for studies performed between January 1990 and February 2021. Two independent reviewers screened the studies according to the inclusion and exclusion criteria. Participants' characteristics and results were extracted from the study., Results: The initial database search yielded 2455 articles, 11 of which met the inclusion criteria. This review included seven studies in its final analysis; among them, seven determined the prevalence and incidence of shoulder pain, five investigated the factors and mechanisms contributing to shoulder pain, and two reported the treatment and preventive measures. The prevalence of shoulder pain in wheelchair basketball players ranged from 38%-75%, and the incidence of shoulder pain was 14%. Factors/mechanisms included overuse, decreased trunk control, and driving posture. Treatment/preventive measures consisted of shoulder and scapular muscle strengthening and stretching exercises. However, the literature on treatment/prevention was scarce, and its effectiveness was unclear., Conclusion: This scoping review confirmed the high prevalence of shoulder pain in wheelchair basketball players; despite this, studies regarding treatment or preventive measures remain lacking. Further studies investigating the mechanism of onset of shoulder pain and the effectiveness of therapeutic and preventive measures are needed in the future.

Background: Although visual examination and palpation are used to assess shoulder motion in clinical practice, there is no consensus on shoulder motion under dynamic and static conditions. This study aimed to compare shoulder joint motion under dynamic and static conditions., Methods: The dominant arm of 14 healthy adult males was investigated. Electromagnetic sensors attached to the scapular, thorax, and humerus were used to measure three-dimensional shoulder joint motion under dynamic and static elevation conditions and compare scapular upward rotation and glenohumeral joint elevation in different elevation planes and angles., Results: At 120° of elevation in the scapular and coronal planes, the scapular upward rotation angle was higher in the static condition and the glenohumeral joint elevation angle was higher in the dynamic condition (P<0.05). In scapular plane and coronal plane elevation 90°- 120°, the angular change in scapular upward rotation was higher in the static condition and the angular change in scapulohumeral joint elevation was higher in the dynamic condition (P<0.05). No differences were found in shoulder joint motion in the sagittal plane elevation between the dynamic and static conditions. No interaction effects were found between elevation condition and elevation angle in all elevation planes., Conclusions: Differences in shoulder joint motion should be noted when assessing shoulder joint motion in different dynamic and static conditions. Level of evidence: Level III, diagnostic cross-sectional study.

Complex processes in science and engineering are often formulated as multistage decision-making problems. In this letter, we consider a cascade process, a type of multistage decision-making process. This is a multistage process in which the output of one stage is used as an input for the subsequent stage. When the cost of each stage is expensive, it is difficult to search for the optimal controllable parameters for each stage exhaustively. To address this problem, we formulate the optimization of the cascade process as an extension of the Bayesian optimization framework and propose two types of acquisition functions based on credible intervals and expected improvement. We investigate the theoretical properties of the proposed acquisition functions and demonstrate their effectiveness through numerical experiments. In addition, we consider suspension setting, an extension in which we are allowed to suspend the cascade process at the middle of the multistage decision-making process that often arises in practical problems. We apply the proposed method in a test problem involving a solar cell simulator, the motivation for this study., (© 2022 Massachusetts Institute of Technology.)

NASICON-type LiZr 2 (PO 4 ) 3 (LZP) has attracted significant attention as a solid oxide electrolyte for all-solid-state Li-ion or Li-metal batteries owing to its high Li-ion conductivity, usability in all-solid-state batteries, and electrochemical stability against Li metal. In this study, we aim to improve the Li-ion conductivity of Li-rich NASICON-type LZPs doped with CaO and SiO 2 , i.e. , Li 1+ x +2 y Ca y Zr 2- y Si x P 3- x O 12 (0 ≤ x ≤ 0.3, 0 ≤ y ≤ 0.3) (LCZSP). Herein, a total of 49 compositions were synthesised, and their crystal structures, relative densities, and Li-ion conductivities were characterised experimentally. We confirmed the improvement in Li-ion conductivity by simultaneous replacement of Zr and P sites with Ca and Si ions, respectively. However, the intuition-derived determination of the composition exhibiting the highest Li-ion conductivity is technically difficult because the compositional dependence of the relative density and the crystalline phase of the sample is very complex. Bayesian optimisation (BO) was performed to efficiently discover the optimal composition that exhibited the highest Li-ion conductivity among the samples evaluated experimentally. We also optimised the composition of the LCZSP using multi-task Gaussian process regression after transferring prior knowledge of 47 compositions of Li 1+ x +2 y Y x Ca y Zr 2- x - y P 3 O 12 (0 ≤ x ≤ 0.376, 0 ≤ y ≤ 0.376) (LYCZP), i.e. , BO with transfer learning. The present study successfully demonstrated that BO with transfer learning can search for optimal compositions two times as rapid as the conventional BO approach. This approach can be widely applicable for the optimisation of various functional materials as well as ionic conductors., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Bayesian optimization (BO) is a popular method for expensive black-box optimization problems; however, querying the objective function at every iteration can be a bottleneck that hinders efficient search capabilities. In this regard, multifidelity Bayesian optimization (MFBO) aims to accelerate BO by incorporating lower-fidelity observations available with a lower sampling cost. In our previous work, we proposed an information-theoretic approach to MFBO, referred to as multifidelity max-value entropy search (MF-MES), which inherits practical effectiveness and computational simplicity of the well-known max-value entropy search (MES) for the single-fidelity BO. However, the applicability of MF-MES is still limited to the case that a single observation is sequentially obtained. In this letter, we generalize MF-MES so that information gain can be evaluated even when multiple observations are simultaneously obtained. This generalization enables MF-MES to address two practical problem settings: synchronous parallelization and trace-aware querying. We show that the acquisition functions for these extensions inherit the simplicity of MF-MES without introducing additional assumptions. We also provide computational techniques for entropy evaluation and posterior sampling in the acquisition functions, which can be commonly used for all variants of MF-MES. The effectiveness of MF-MES is demonstrated using benchmark functions and real-world applications such as materials science data and hyperparameter tuning of machine-learning algorithms., (© 2022 Massachusetts Institute of Technology.)

Background: Rotator cuff retear is a major concern after arthroscopic rotator cuff repair (ARCR); however, the effects of retear remain unclear. Therefore, the purpose of this study was to assess the clinical outcomes of postoperative retear and intact tendons after ARCR., Methods: We searched PubMed, Cochrane Library, Scopus, and PEDro databases for studies performed from January 2000 to June 2020. Clinical outcomes included the Constant score, American Shoulder and Elbow Surgeons (ASES) score, University of California Los Angeles shoulder (UCLA) score, pain score, range of motion, and muscle strength. Meta-analysis using random-effects models was performed on the pooled results to determine significance., Results: The initial database search yielded 3141 records. After removal of duplicates, 26 of which met the inclusion criteria. Patients in the retear group had significantly lower Constant score [- 8.51 points (95% CI, - 10.29 to - 6.73); P < 0.001], ASES score [- 12.53 points (95% CI, - 16.27 to - 8.79); P < 0.001], UCLA score [- 3.77 points (95% CI, - 4.72 to - 2.82); P < 0.001], and significantly higher pain score [0.56 cm (95% CI, 0.10 to 1.01); P = 0.02] than the intact group. In addition, the retear group had significantly lower flexion [- 10.46° (95% CI, - 19.86 to - 1.07); P = 0.03], abduction [- 14.84° (95% CI, - 28.55 to - 1.14); P = 0.03], and external rotation [- 7.22° (95% CI, - 13.71 to - 0.74); P = 0.03] range of motion, and flexion [- 1.65 kg·f (95% CI, - 2.29 to - 1.01); P < 0.001], abduction [- 1.87 kg·f (95% CI, - 3.02 to - 0.72); P = 0.001], and external rotation [- 1.66 kg·f (95% CI, - 3.25 to - 0.07); P = 0.04] muscle strength., Conclusion: Our results suggest that retear after ARCR leads to poor clinical outcomes after surgery., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2021 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.)

All-solid-state Li-ion batteries are of considerable interest as safer alternatives to Li-ion batteries containing flammable organic electrolytes. To date, however, achieving sufficient charging and discharging rates, in addition to capacity, at room temperature using these all-solid-state batteries has been challenging. To overcome these issues, material simulations and informatics investigations of a relatively new Na superionic conductor (NASICON)-type LiZr 2 (PO 4 ) 3 (LZP) electrolyte were conducted to elucidate its characteristics and material functions. The following thermodynamic and/or kinetic properties of NASICON-type Li-ion conductive oxides were investigated with respect to the crystal structure mainly using material simulation and informatics approaches: (1) the electrochemical stabilities of LZP materials with respect to Li metal and (2) Li-ion conductivities in the bulk and at the grain boundaries. An efficient materials informatics search method was employed to optimise the material functions of the LZP electrolyte via Bayesian optimisation. This study should promote the application of LZP in all-solid-state batteries for use in technologies such as mobile devices and electric vehicles and enable more complex composition and process control.

Microbial rhodopsins are photoreceptive membrane proteins, which are used as molecular tools in optogenetics. Here, a machine learning (ML)-based experimental design method is introduced for screening rhodopsins that are likely to be red-shifted from representative rhodopsins in the same subfamily. Among 3,022 ion-pumping rhodopsins that were suggested by a protein BLAST search in several protein databases, the ML-based method selected 65 candidate rhodopsins. The wavelengths of 39 of them were able to be experimentally determined by expressing proteins with the Escherichia coli system, and 32 (82%, p = 7.025 × 10 -5 ) actually showed red-shift gains. In addition, four showed red-shift gains >20 nm, and two were found to have desirable ion-transporting properties, indicating that they would be potentially useful in optogenetics. These findings suggest that data-driven ML-based approaches play effective roles in the experimental design of rhodopsin and other photobiological studies. (141/150 words).

Background: Several risk factors for postoperative retear after arthroscopic rotator cuff repair (ARCR) have been cited in a large number of reports; various combinations of these seem to be present in the clinical setting., Purpose: Using a combination model for decision tree analysis, we aimed to investigate the combination of risk factors that affect postoperative retear the most., Methods: A total of 286 patients who underwent magnetic resonance (MR) imaging at 6 months after surgery were included in this study. Based on the structural integrity of the MR images taken 6 months after surgery, the patients were divided into a healed group (intact tendon, 254 patients) and a retear group (ruptured tendon, 32 patients). Using univariate and decision tree analyses, we selected a combination of 11 risk factors that drastically affected postoperative retear., Results: The mean age was 64.9 ± 7.1 years, and the mean symptom duration was 9.7 ± 11.6 months. The tear was small/medium in 177 patients and large/massive in 109 patients. The technique for surgical repair was single row in 42 patients, double row in 60 patients, and suture bridging in 216 patients. On univariate analysis, both groups had significant differences in the anteroposterior (AP) tear size (P < .0001), mediolateral tear size (P < .0001), hyperlipidemia (P = .0178), global fatty degeneration index (P < .0001), supraspinatus fatty degeneration stage (P < .0001), and critical shoulder angle (CSA) (P = .0015). All of these 5 risk factors, except for mediolateral tear size, were selected as candidates for the decision tree analysis. Eight combination patterns were determined to have prediction probabilities that ranged from 4.3% to 86.1%. In particular, the combination of an AP tear size of ≥40 mm, hyperlipidemia, and a CSA of ≥37° affected retear after ARCR the most., Conclusions: Decision tree analysis lead to the extraction of different retear factor combinations, which were divided into 5 retear groups. The worst combination was of AP tear size ≥40 mm, hyperlipidemia, and CSA ≥37°, and the prediction probability of this combination was 86.2%. Therefore, our data may offer a new index for the prediction of retear after ARCR., (Copyright © 2020 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)

Testing under what conditions a product satisfies the desired properties is a fundamental problem in manufacturing industry. If the condition and the property are respectively regarded as the input and the output of a black-box function, this task can be interpreted as the problem called level set estimation (LSE): the problem of identifying input regions such that the function value is above (or below) a threshold. Although various methods for LSE problems have been developed, many issues remain to be solved for their practical use. As one of such issues, we consider the case where the input conditions cannot be controlled precisely-LSE problems under input uncertainty. We introduce a basic framework for handling input uncertainty in LSE problems and then propose efficient methods with proper theoretical guarantees. The proposed methods and theories can be generally applied to a variety of challenges related to LSE under input uncertainty such as cost-dependent input uncertainties and unknown input uncertainties. We apply the proposed methods to artificial and real data to demonstrate their applicability and effectiveness.

In this letter, we study an active learning problem for maximizing an unknown linear function with high-dimensional binary features. This problem is notoriously complex but arises in many important contexts. When the sampling budget, that is, the number of possible function evaluations, is smaller than the number of dimensions, it tends to be impossible to identify all of the optimal binary features. Therefore, in practice, only a small number of such features are considered, with the majority kept fixed at certain default values, which we call the working set heuristic . The main contribution of this letter is to formally study the working set heuristic and present a suite of theoretically robust algorithms for more efficient use of the sampling budget. Technically, we introduce a novel method for estimating the confidence regions of model parameters that is tailored to active learning with high-dimensional binary features. We provide a rigorous theoretical analysis of these algorithms and prove that a commonly used working set heuristic can identify optimal binary features with favorable sample complexity. We explore the performance of the proposed approach through numerical simulations and an application to a functional protein design problem.

Background: Several systematic reviews have reported on the conservative treatment of full-thickness rotator cuff tears; however, clinical results of this treatment still remain determined., Methods: PubMed, Cochrane Library, PEDro, and CINAHL databases were systematically searched for randomized clinical trials and observational studies. Two independent researchers reviewed a total of 2,981 articles, 28 of which met the criteria for inclusion in the study. Clinical outcome measures included Constant score, visual analog scale score for pain, range of motion, and short-form 36 questionnaires. The meta-analysis used a linear mixed model weighted with the variance of the estimate., Results: The meta-analysis showed a significant improvement after surgery. Pain score is 26.2 mm (1 month) to 26.4 mm (3 months), and 24.8 mm (12 months) (P<0.05); active abduction: 153.2º (2 months), 159.0º (6 months), 168.1º (12 months) (P<0.05); Constant score: 67.8 points (2 months) to 77.2 points (12 months) (P<0.05); short-form 36 questionnaires "vitality" section: 57.0 points (6 months) to 70.0 points (12 months) (P<0.05)., Conclusions: Our data confirmed the effectiveness of conservative treatment in patients with full-thickness rotator cuff tears 12 months post-intervention. The results suggest that conservative treatment for patients with full-thickness rotator cuff tears should be the first line of treatment before considering surgery., Competing Interests: Conflict of interest None., (Copyright © 2020 Korean Shoulder and Elbow Society.)

Distance metric learning has been widely used to obtain the optimal distance function based on the given training data. We focus on a triplet-based loss function, which imposes a penalty such that a pair of instances in the same class is closer than a pair in different classes. However, the number of possible triplets can be quite large even for a small data set, and this considerably increases the computational cost for metric optimization. In this letter, we propose safe triplet screening that identifies triplets that can be safely removed from the optimization problem without losing the optimality. In comparison with existing safe screening studies, triplet screening is particularly significant because of the huge number of possible triplets and the semidefinite constraint in the optimization problem. We demonstrate and verify the effectiveness of our screening rules by using several benchmark data sets.

In this study, an efficient method for estimating material parameters based on the experimental data of precipitate shape is proposed. First, a computational model that predicts the energetically favorable shape of precipitate when a d-dimensional material parameter (x) is given is developed. Second, the discrepancy (y) between the precipitate shape obtained through the experiment and that predicted using the computational model is calculated. Third, the Gaussian process (GP) is used to model the relation between x and y. Finally, for identifying the "low-error region (LER)" in the material parameter space where y is less than a threshold, we introduce an adaptive sampling strategy, wherein the estimated GP model suggests the subsequent candidate x to be sampled/calculated. To evaluate the effectiveness of the proposed method, we apply it to the estimation of interface energy and lattice mismatch between MgZn 2 ([Formula: see text]) and α-Mg phases in an Mg-based alloy. The result shows that the number of computational calculations of the precipitate shape required for the LER estimation is significantly decreased by using the proposed method.

Objective: Although clinical outcomes after arthroscopic rotator cuff repair are generally favorable, postoperative return to work is also an important issue. This study aimed to assess clinical outcomes and clarify the factors affecting return to work in patients who had undergone arthroscopic rotator cuff repair., Methods: In total, 63 patients who had undergone arthroscopic rotator cuff repair were included in this study. Clinical assessment was performed using Japanese Orthopaedic Association (JOA) scores, along with measurements of pain, range of motion, muscle strength, cuff integrity, and fatty infiltration. Depending on their return-to-work status at the final follow-up, subjects were assigned to either the complete return group (the patient returned to work) or the incomplete return group (the patient had quit or had changed their occupation at final follow-up). Various clinical parameters affecting the return to work outcome were examined through univariate and multivariate analyses., Results: Of the 63 subjects, 42 belonged to the complete return group and 21 belonged to the incomplete return group. Therefore, the working capability recovery rate following arthroscopic rotator cuff repair was 66.7%. Both groups showed significant improvement from their preoperative status, but there were statistically significant differences in JOA scores between the groups at 9 and 12 months postoperatively (P <0.01). Multivariate stepwise logistic regression analysis showed that heavy work and female sex were significantly adversely associated with return to work (P <0.05)., Conclusion: The working capability recovery rate following arthroscopic rotator cuff repair was 66.7%, and the preoperative factors affecting recovery of working capability were heavy work and female sex., Competing Interests: CONFLICT OF INTEREST: There are no conflicts of interest related to this study., (©2019 The Japanese Association of Rehabilitation Medicine.)

Background: In patients with rotator cuff tears, Shoulder36 (Sh36) was compared to the Simple Shoulder Test (SST) to determine a broader use of Sh36 worldwide., Methods: Sh36, SST, "Constant score," and the Japanese Orthopaedic Association score (JOA) were used to evaluate 230 patients (male, 116; female, 114) during the first visit, analyzed by staff blinded to the study. Pearson's correlation coefficient was used for the statistical analysis., Results: The correlation coefficient between the Constant score and each domain in Sh36 was as follows: r = 0.68 (vs. pain, p < 0.01); r = 0.69 (vs. range of motion, p < 0.01); r = 0.74 (vs. muscle strength, p < 0.01); r = 0.62 (vs. general health, p < 0.01); r = 0.66 (vs. ability of daily living, p < 0.01); and r = 0.65 (vs. ability for sports, p < 0.01). The correlation coefficient between the JOA and each domain in Sh36 was as follows: r = 0.76 (vs. pain, p < 0.01); r = 0.73 (vs. range of motion, p < 0.01); r = 0.78 (vs. muscle strength, p < 0.01); r = 0.68 (vs. general health, p < 0.01); r = 0.71 (vs. ability of daily living, p < 0.01); and r = 0.70 (vs. ability for sports, p < 0.01). The correlation coefficient between the SST and each domain in Sh36 was as follows: r = 0.73 (vs. pain, p < 0.01); r = 0.70 (vs. range of motion, p < 0.01); r = 0.75 (vs. muscle strength, p < 0.01); r = 0.67 (vs. general health, p < 0.01); r = 0.69 (vs. ability of daily living, p < 0.01); and r = 0.64 (vs. ability for sports, p < 0.01)., Conclusion: A strong association exists between the SST and each domain in the Sh36, in patients with rotator cuff tears ( r = 0.64-0.73), suggesting the usefulness of Sh36 for patient-based scoring.

The light-dependent ion-transport function of microbial rhodopsin has been widely used in optogenetics for optical control of neural activity. In order to increase the variety of rhodopsin proteins having a wide range of absorption wavelengths, the light absorption properties of various wild-type rhodopsins and their artificially mutated variants were investigated in the literature. Here, we demonstrate that a machine-learning-based (ML-based) data-driven approach is useful for understanding and predicting the light-absorption properties of microbial rhodopsin proteins. We constructed a database of 796 proteins consisting of microbial rhodopsin wildtypes and their variants. We then proposed an ML method that produces a statistical model describing the relationship between amino-acid sequences and absorption wavelengths and demonstrated that the fitted statistical model is useful for understanding colour tuning rules and predicting absorption wavelengths. By applying the ML method to the database, two residues that were not considered in previous studies are newly identified to be important to colour shift.

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration., (Copyright © 2017. Published by Elsevier Inc.)

Triple-negative (TN) breast cancer (BC) patients have limited treatment options and poor prognosis even after extant treatments and standard chemotherapeutic regimens. Linking TN patients to clinically known phenotypes with appropriate treatments is vital. Location-specific sequence variants are expected to be useful for this purpose by identifying subgroups within a disease population. Single gene mutational signatures have been widely reported, with related phenotypes in literature. We thoroughly survey currently available mutations (and mutated genes), linked to BC phenotypes, to demonstrate their limited performance as sole predictors/biomarkers to assign phenotypes to patients. We then explore mutational combinations, as a pilot study, using The Cancer Genome Atlas Research Network mutational data of BC and three machine learning methods: association rules (limitless arity multiple procedure), decision tree and hierarchical disjoint clustering. The study results in a patient classification scheme through combinatorial mutations in Phosphatidylinositol-4,5-Bisphosphate 3-Kinase and tumor protein 53, being consistent with all three methods, implying its validity from a diverse viewpoint. However, it would warrant further research to select multi-gene signatures to identify phenotypes specifically and be clinically used routinely., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Graph-based approaches have been most successful in semisupervised learning. In this paper, we focus on label propagation in graph-based semisupervised learning. One essential point of label propagation is that the performance is heavily affected by incorporating underlying manifold of given data into the input graph. The other more important point is that in many recent real-world applications, the same instances are represented by multiple heterogeneous data sources. A key challenge under this setting is to integrate different data representations automatically to achieve better predictive performance. In this paper, we address the issue of obtaining the optimal linear combination of multiple different graphs under the label propagation setting. For this problem, we propose a new formulation with the sparsity (in coefficients of graph combination) property which cannot be rightly achieved by any other existing methods. This unique feature provides two important advantages: 1) the improvement of prediction performance by eliminating irrelevant or noisy graphs and 2) the interpretability of results, i.e., easily identifying informative graphs on classification. We propose efficient optimization algorithms for the proposed approach, by which clear interpretations of the mechanism for sparsity is provided. Through various synthetic and two real-world data sets, we empirically demonstrate the advantages of our proposed approach not only in prediction performance but also in graph selection ability.

Canonical correlation analysis (CCA) is a classical dimensionality reduction technique for two sets of variables that iteratively finds projection directions with maximum correlation. Although CCA is still in vital use in many practical application areas, recent real-world data often contain more complicated nonlinear correlations that cannot be properly captured by classical CCA. In this paper, we thus propose an extension of CCA that can effectively capture such complicated nonlinear correlations through statistical dependency maximization. The proposed method, which we call least-squares canonical dependency analysis (LSCDA), is based on a squared-loss variant of mutual information, and it has various useful properties besides its ability to capture higher-order correlations: for example, it can simultaneously find multiple projection directions (i.e., subspaces), it does not involve density estimation, and it is equipped with a model selection strategy. We demonstrate the usefulness of LSCDA through various experiments on artificial and real-world datasets., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

We propose a multiple incremental decremental algorithm of support vector machines (SVM). In online learning, we need to update the trained model when some new observations arrive and/or some observations become obsolete. If we want to add or remove single data point, conventional single incremental decremental algorithm can be used to update the model efficiently. However, to add and/or remove multiple data points, the computational cost of current update algorithm becomes inhibitive because we need to repeatedly apply it for each data point. In this paper, we develop an extension of incremental decremental algorithm which efficiently works for simultaneous update of multiple data points. Some analyses and experimental results show that the proposed algorithm can substantially reduce the computational cost. Our approach is especially useful for online SVM learning in which we need to remove old data points and add new data points in a short amount of time.

The enterochromaffin-like (ECL) cells of the gastric mucosa in animals play an important role in gastric acid secretion. They contain few granules and numerous secretory vesicles and microvesicles. They operate under the control of circulating gastrin. In the present study, we conducted an immunoelectron microscopic study for histamine (HA) in the ECL cells of rats given the proton pump inhibitor lansoprazole (LP), which is known to induce hypergastrinemia. The pre-embedding indirect immunoperoxidase procedure utilized a mouse monoclonal antibody AHA-2 against glutaraldehyde-conjugated HA. Rats received LP (50 microg/kg per day, subcutaneously) over a period of a month, and developed hypertrophy of the ECL cells in the stomach. It was clearly demonstrated that HA was located to a much higher degree in the cytoplasm of ECL cells of LP-treated rats than in normal rats. HA immunoreactivity was observed in the cores of the granules and secretory vesicles of the ECL cells in all the rats, but in the LP-treated rats it was observed in the cores of the newly developed vacuoles as well. These results may suggest that HA may be actively generated in the cytoplasm of the hypertrophic ECL cells of LP-treated rats. Also suggested in the present study is that HA is instrumental in the transformation of granules into secretory vesicles and in their consequent enlargement, and that vacuoles are formed by the fusion of large secretory vesicles. Furthermore, the finding that relatively little HA immunoreactivity existed in the vacuoles may suggest that the vacuoles actively degrade superfluous secretory products (for example, HA) through enhanced autophagocytosis and/or oxidative stress. Another possibility may be that the membrane-bounded structure regarded as the vacuoles in this study might actually be an invagination structure produced as a result of successive series of exocytosis through which the secretory vesicles actively and rapidly release HA.

Green tea polyphenols (TEA) are known to exhibit antioxidative activity as well as tumor-suppressing activity. In order to examine the tumor-suppressing activity of TEA against adult T-cell leukemia (ATL), we cultivated peripheral blood T lymphocytes of ATL patients (ATL PBLs), an HTLV-I-infected T-cell line (KODV) and healthy controls (normal PBLs) for 3 days in the presence of TEA and its main constituent, epigallocatechin-3-gallate (EGCg), to measure cell proliferation and apoptosis, and to quantitate mRNAs of HTLV-I pX and beta-actin genes of the cultured cells. Growth of ATL PBLs was significantly inhibited by 9-27 microg/ml of TEA and EGCg, in contrast to minimal growth inhibition of T cells of normal PBLs. Inhibition of KODV was intermediate between ATL PBLs and normal PBLs. The ATL PBLs and KODV treated with 27 microg/ml of either TEA or EGCg induced apoptotic DNA fragmentation, producing terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL)-positive cells, while the normal PBLs treated with the same concentration of TEA or EGCg produced a negligibly small number of TUNEL-positive cells, in which apoptotic DNA fragmentation was not detectable. Expression of HTLV-I pX mRNA was suppressed more than 90% in ATL PBLs by treatment with 3-27 microg/ml of either TEA or EGCg, while expression of beta-actin mRNA was much less suppressed by treatment with the same concentration of TEA or EGCg. These results indicate that TEA and EGCg inhibit growth of ATL PBLs, as well as HTLV-I-infected T-cells, by suppressing HTLV-I pX gene expression and inducing apoptotic cell death.

Perinuclear clear cytoplasm observed in the light microscopic specimens of the tumor cells of smooth muscle origin is in general, understood as the artefact caused by the formalin fixation. However, the precise mechanisms of the histogenesis of clear cytoplasm are still not clear. We observed the clear cytoplasm directly by mean of the electron microscopy of materials detached from light microscopic specimen. Furthermore, we observed the light microscopic specimens made by varying types of methods, examining whether the clear cytoplasm was present or not. The electron microscopy of materials detached from light microscopic specimens revealed the band-like defects of cytoplasm along the long axes of tumor cells. These defects were thought to result from the falling off of cytoplasm. The 1 mu section of the epon embedded block derived from the paraffin embedded block for light microscopic specimen presented no clear cytoplasm, suggesting that the cytoplasm falls off at the procedure of deparaffinization and staining. Although the specimen of conventional frozen section showed no clear cytoplasm, the specimen made by the frozen sectioning after formalin fixation revealed clear cytoplasm. Consequently, it is thought that the fixation of the tissue before the sectioning makes the cytoplasm fragile, thereafter, the cytoplasm falls off at the procedure of deparaffinization and staining.