Revisiting the Effect of Retirement on Cognition: Heterogeneity and Endowment

Since the seminal paper of Rohwedder and Willis (2010), the effect of retirement on cognition has drawn significant research interest from economists. Especially with ongoing policy discussions about public pension reforms and the increasing burden of dementia, it is indisputably an important research question with significant policy implications. Building on this growing literature, our paper makes two important contributions. First, we explicitly consider cognitive demands of jobs in studying hetereogeneity of the retirement effect. As the primary explanation for the potential adverse effect of retirement is that cognition is better maintained through mental exercise (Salthouse, 2006), by investigating the cognitive demands of the job one retires from we can directly test the hypothesized relationship. Second, we avoid biases associated with omitted variables, particularly by controlling for endowed cognitive ability. While endowed, genetic differences in cognitive ability is an important omitted variable that can explain individual differences in cognitive performance as well as selection into a particular type of job, this inherited characteristic has not been controlled for in the prior literature. Taking advantage of the polygenic risk score of cognition (Davies et al., 2015), we control for individual differences in genetic endowments in estimating the effect of retirement on cognition. We find supporting evidence for differential effects of retirement by cognitive demands of jobs after controlling for innate differences in cognition and educational attainment.

Pharmacokinetics of high-titer anti-SARS-CoV-2 human convalescent plasma in high-risk children

BACKGROUNDWhile most children who contract COVID-19 experience mild disease, high-risk children with underlying conditions may develop severe disease, requiring interventions. Kinetics of antibodies transferred via COVID-19 convalescent plasma early in disease have not been characterized.METHODSIn this study, high-risk children were prospectively enrolled to receive high-titer COVID-19 convalescent plasma (>1:320 anti-spike IgG; Euroimmun). Passive transfer of antibodies and endogenous antibody production were serially evaluated for up to 2 months after transfusion. Commercial and research ELISA assays, virus neutralization assays, high-throughput phage-display assay utilizing a coronavirus epitope library, and pharmacokinetic analyses were performed.RESULTSFourteen high-risk children (median age, 7.5 years) received high-titer COVID-19 convalescent plasma, 9 children within 5 days (range, 2-7 days) of symptom onset and 5 children within 4 days (range, 3-5 days) after exposure to SARS-CoV-2. There were no serious adverse events related to transfusion. Antibodies against SARS-CoV-2 were transferred from the donor to the recipient, but antibody titers declined by 14-21 days, with a 15.1-day half-life for spike protein IgG. Donor plasma had significant neutralization capacity, which was transferred to the recipient. However, as early as 30 minutes after transfusion, recipient plasma neutralization titers were 6.2% (range, 5.9%-6.7%) of donor titers.CONCLUSIONConvalescent plasma transfused to high-risk children appears to be safe, with expected antibody kinetics, regardless of weight or age. However, current use of convalescent plasma in high-risk children achieves neutralizing capacity, which may protect against severe disease but is unlikely to provide lasting protection.Trial registrationClinicalTrials.gov NCT04377672.FundingThe state of Maryland, Bloomberg Philanthropies, and the NIH (grants R01-AI153349, R01-AI145435-A1, K08-AI139371-A1, and T32-AI052071).

Paraneptunicella aestuarii gen. nov., sp. nov., a member of the family Alteromonadaceae isolated from seawater in East China Sea

An aerobic Gram-stain-negative, curved rod-shaped and non-spore-forming bacterial strain (NBU2194^T) was isolated from seawater collected in an intertidal zone in Ningbo, Zhejiang Province, PR China. It was motile though a single polar flagellum and grew at 20-42 °C (optimum, 30 °C), in 0-2.0 % NaCl (0 %, w/v) and at pH 5.0-9.0 (pH 6.0-7.0). The sole respiratory quinone was ubiquinone-8. The major cellular fatty acids were C_16 : 0, C_16 : 1  ω7c and/or C_16 : 1  ω6c. The polar lipids contained diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, one unidentified phospholipid and two unidentified aminophosphoglycolipids. A phylogenetic analysis based on 16S rRNA gene sequences and 65 genomic core genes showed that strain NBU2194^T formed a distinct lineage in the family Alteromonadaceae. The genome of strain NBU2194^T was 4 913 533 bp with a DNA G+C content of 43.9 mol% and coded 3895 genes, 12 rRNA genes and 47 tRNA genes. The average nucleotide identity, amino acid identity and digital DNA-DNA hybridization values between strain NBU2194^T and related species of Alteromonadaceae were below the threshold limit for prokaryotic species delineation. NBU2194^T could be distinguished from other genera in the family Alteromonadaceae based on phenotypic, chemotaxonomic and genomic characteristics. On the basis of the polyphasic taxonomic evidence collected in this study, strain NBU2194^T is considered to represent a novel genus and species in the family Alteromonadaceae, for which the name Paraneptunicella aestuarii is proposed. The type strain is NBU2194^T (=KCTC 82442^T=GDMCC 1.2217^T).

Integrated linkage-driven dexterous anthropomorphic robotic hand

Robotic hands perform several amazing functions similar to the human hands, thereby offering high flexibility in terms of the tasks performed. However, developing integrated hands without additional actuation parts while maintaining important functions such as human-level dexterity and grasping force is challenging. The actuation parts make it difficult to integrate these hands into existing robotic arms, thus limiting their applicability. Based on a linkage-driven mechanism, an integrated linkage-driven dexterous anthropomorphic robotic hand called ILDA hand, which integrates all the components required for actuation and sensing and possesses high dexterity, is developed. It has the following features: 15-degree-of-freedom (20 joints), a fingertip force of 34N, compact size (maximum length: 218 mm) without additional parts, low weight of 1.1 kg, and tactile sensing capabilities. Actual manipulation tasks involving tools used in everyday life are performed with the hand mounted on a commercial robot arm.

Though robotic hands capable of adaptive grasping have been developed, realizing integrated hands with higher degree of freedom (DOF) movement and technology compatibility remains a challenge. Here, the authors report integrated linkage-driven robotic hand with improved design and performance.

A Study on the Contribution of Medial and Lateral Longitudinal Foot Arch to Human Gait

This study aimed to investigate the contribution of medial longitudinal arch and lateral longitudinal arch in human gait and to study the correlation between foot features and gait characteristics. The foot arch plays a significant role in human movements, and understanding its contribution to spatiotemporal gait parameters is vital in predicting and rectifying gait patterns. To serve the objectives, the study developed a new foot feature measurement system and measured the foot features and spatiotemporal gait parameters of 17 young healthy subjects without any foot structure abnormality. The foot-feature parameters were measured under three movement conditions which were sitting, standing, and one-leg standing conditions. The spatiotemporal gait parameters were measured at three speeds which were fast, preferred, and slow speeds. The correlation study showed that medial longitudinal arch characteristics were found to be associated with temporal gait parameters while lateral longitudinal arch characteristics were found to be associated with spatial gait parameters. The developed system not only eases the burden of manual measuring but also secures accuracy of the collected data. Inviting variety of subjects including athletes and people with abnormal foot structures would extend the scope of this study in the future. The findings of this study break new ground in the field of the foot- and gait-related research work.Clinical Relevance-This study demonstrated that the medial longitudinal arch and lateral longitudinal arch characteristics were related to the temporal and spatial gait parameters, respectively. These underlying findings can be applied to investigate relationships between foot abnormality and gait characteristics.

Personal Identification Using Gait Spectrograms and Deep Convolutional Neural Networks

Human gait can serve as a useful behavioral trait for biometrics. Compared to fingerprint, face, and iris, the most commonly used physiological identifiers, gait can be unobtrusively monitored from a distance without requiring explicit involvement and physical restraint from people. Advances in wearable technology facilitate direct and faithful measurement of gait motions with easy-to-use and low-cost inertial sensors. This study aimed to propose an approach to using kinematic gait data collected with wearable inertial sensors for reliable personal identification. Sixty-nine individuals ranged in age from 24 to 62 years old participated in this study. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the feet, shanks, thighs, and posterior pelvis while walking. The gait spectrograms were acquired by applying time-frequency analyses to the lower body movement signals measured in one stride. Among each participant's 15 strides, 12 strides were used in the 4-fold cross validation of the deep convolutional neural network-based classifiers, and the remaining three strides were used to evaluate the classifiers. An accuracy of 99.69% was achieved by using the foot, shank, thigh, and pelvic spectrograms, and the accuracy was 96.89% using only the foot spectrograms. This study has the potential to be applied in behavior-based biometric technologies by confirming the feasibility of the proposed kinematic and spectrographic approaches in identifying personal behavioral characteristics.

Vision-based human joint angular velocity estimation during squat and walking on a treadmill actions

Elderly health monitoring, rehabilitation training, and sport supervision could benefit from continuous assessment of joint angle, and angular velocity to identify the joint movement patterns. However, most of the measurement systems are designed based on special kinematic sensors to estimate angular velocities. The study aims to measure the lower limb joint angular velocity based on a 2D vision camera system during squat and walking on treadmill action using deep convolution neural network (CNN) architecture. Experiments were conducted on 12 healthy adults, and six digital cameras were used to capture the videos of the participant actions in lateral and frontal view. The normalized cross-correlation (Cc_norm) analysis was performed to obtain a degree of symmetry of the ground truth and estimated angular velocity waveform patterns. Mean Cc_norm for angular velocity estimation by deep CNN model has higher than 0.90 in walking on the treadmill and 0.89 in squat action. Furthermore, joint-wise angular velocities at the hip, knee, and ankle joints were observed and compared. The proposed system gets higher estimation performance under the lateral view and the frontal view of the camera. This study potentially eliminates the requirement of wearable sensors and proves the applicability of using video-based system to measure joint angular velocities during squat and walking on a treadmill actions.

Gait-based Human Identification through Minimum Gait-phases and Sensors

The incredible pace at which the world's elderly population is growing will put severe burdens on current healthcare systems and resources. To alleviate this concern the health care systems must rely on the transformation of eldercare and old homes to use Ambient Assisted Living (AAL). Human identification is one of the most common and critical tasks for condition monitoring, human-machine interaction, and providing assistive services in such environments. Recently, human gait has gained new attention as a biometric for identification to achieve contactless identification from a distance robust to physical appearances. However, an important aspect of gait identification through wearables and image-based systems alike is accurate identification when limited information is available for example, when only a fraction of the whole gait cycle or only a part of the subject's body is visible. In this paper, we present a gait identification technique based on temporal and descriptive statistic parameters of different gait phases as the features and we investigate the performance of using only single gait phases for the identification task using a minimum number of sensors. Gait data were collected from 60 individuals through pelvis and foot sensors. Six different machine learning algorithms were used for identification. It was shown that it is possible to achieve high accuracy of over 95.5% by monitoring a single phase of the whole gait cycle through only a single sensor. It was also shown that the proposed methodology could be used to achieve 100% identification accuracy when the whole gait cycle was monitored through pelvis and foot sensors combined. The ANN was found to be more robust to less number of data features compared to SVM and was concluded as the best machine algorithm for the purpose.

Gait-based Frailty Assessment using Image Representation of IMU Signals and Deep CNN

Frailty is a common and critical condition in elderly adults, which may lead to further deterioration of health. However, difficulties and complexities exist in traditional frailty assessments based on activity-related questionnaires. These can be overcome by monitoring the effects of frailty on the gait. In this paper, it is shown that by encoding gait signals as images, deep learning-based models can be utilized for the classification of gait type. Two deep learning models (a) SS-CNN, based on single stride input images, and (b) MS-CNN, based on 3 consecutive strides were proposed. It was shown that MS-CNN performs best with an accuracy of 85.1%, while SS-CNN achieved an accuracy of 77.3%. This is because MS-CNN can observe more features corresponding to stride-to-stride variations which is one of the key symptoms of frailty. Gait signals were encoded as images using STFT, CWT, and GAF. While the MS-CNN model using GAF images achieved the best overall accuracy and precision, CWT has a slightly better recall. This study demonstrates how image encoded gait data can be used to exploit the full potential of deep learning CNN models for the assessment of frailty.

Nisaea sediminum sp. nov., a heavy metal resistant bacterium isolated from marine sediment in the East China Sea

A Gram-negative, rod-shaped, motile and strictly aerobic bacterium, designated NBU1469^T, was isolated from marine sediment sampled on Meishan Island located in the East China Sea. Strain NBU1469^T grew optimally at temperature of 40 °C, NaCl concentration of 2.0% (w/v) and pH 7.5. Catalase and oxidase activities, H₂S production, nitrate reduction and hydrolysis of Tween 20 were positive. Indole, methyl red reaction, urease, hydrolysis of gelatin, starch, casein, Tweens 40, 60 and 80 were negative. The major cellular fatty acids were C_16:0, C_19:0 cyclo ω8c and summed feature 8 (C_18:1 ω7c and/or C_18:1 ω6c). The only respiratory quinone was ubiquinone-10 (Q-10). The major polar lipids were phosphatidylglycerol, two unidentified amino-phospholipids and two unidentified phospholipids. Comparative analysis of the 16S rRNA gene sequence showed highest similarities to the species with validated name Nisaea nitritireducens DR41_18^T (98.1%) and Nisaea denitrificans DR41_21^T (97.6%). Phylogenetic analyses indicated that strain NBU1469^T formed a distinct lineage with strains Nisaea nitritireducens DR41_18^T and Nisaea denitrificans DR41_21^T within the genus Nisaea. The average nucleotide identity and digital DNA-DNA hybridization values between strain NBU1469^T and related species of genus Nisaea were well below the threshold limit for prokaryotic species delineation. The DNA G + C content was 63.6%. Based on its phenotypic, chemotaxonomic and genotypic data, strain NBU1469^T is considered to be a representative of a novel species in the genus Nisaea, for which the name Nisaea sediminum sp. nov. is proposed. The type strain is NBU1469^T (=KCTC 82224 ^T =MCCC 1K04763^T).

Frailty Assessment Using Temporal Gait Characteristics and a Long Short-Term Memory Network

Faced with the rapidly aging world population, frailty has emerged as a major health burden among the elderly. This study aimed to investigate the feasibility of using temporal gait characteristics and a long short-term memory network for assessing frailty. Seventy-four community-dwelling elderly individuals participated in this study. The participants were categorized into three groups by their FRAIL scale: robust, pre-frail, and frail groups. The participants completed a 7-meter walking at the self-selected pace with a gyroscope on each foot. Analyzing the gyroscopic data produced seven temporal gait parameters per each gait cycle. Enumerating six consecutive values of each gait parameter produced the gait sequence features which were used as frailty predictors along with the demographic features. Five-fold cross-validation was applied to 70% of the data, and the remaining 30% were used as test data. An F₁-score of 0.931 was achieved in classifying the robust, pre-frail, and frail groups by the random forest model trained with age, sex, and the outputs of the long short-term memory network-based classifier that used the initial and terminal double-limb support, step, and stride times as inputs. The proposed approach of assessing frailty using the arrhythmic gait pattern of the elderly and machine learning technique is novel and promising. Pioneering a way that self-monitor frailty at home without any help from experts, the study can contribute toearly diagnosis of frailty and make timely medical intervention possible.

Triggering Growth via Growth Initiation Factors in Nature: A Putative Mechanism for in situ Cultivation of Previously Uncultivated Microorganisms

Most microorganisms resist cultivation under standard laboratory conditions. On the other hand, cultivating microbes in a membrane-bound device incubated in nature (in situ cultivation) can be an effective approach to overcome this limitation. In the present study, we applied in situ cultivation to isolate diverse previously uncultivated marine sponge-associated microbes and comparatively analyzed this method's efficiencies with those of the conventional method. Then, we attempted to investigate the key and previously unidentified mechanism of growing uncultivated microorganisms by in situ cultivation focusing on growth triggering via growth initiation factor. Significantly more novel and diverse microbial types were isolated via in situ cultivation than by standard direct plating (SDP). We hypothesized that some of environmental microorganisms which resist cultivation are in a non-growing state and require growth initiation factors for the recovery and that these can be provided from the environment (in this study from marine sponge). According to the hypothesis, the effect of the sponge extract on recovery on agar medium was compared between strains derived from in situ and SDP cultivation. Adding small amounts of the sponge extracts to the medium elevated the colony-formation efficiencies of the in situ strains at the starvation recovery step, while it showed no positive effect on that of SDP strains. Conversely, specific growth rates or saturated cell densities of all tested strains were not positively affected. These results indicate that, (1) the sponge extract contains chemical compounds that facilitate recovery of non-growing microbes, (2) these substances worked on the in situ strains, and (3) growth initiation factor in the sponge extract did not continuously promote growth activity but worked as triggers for regrowth (resuscitation from non-growing state).

Application of in situ cultivation in marine microbial resource mining

Microbial communities in marine habitats are regarded as underexplored reservoirs for discovering new natural products with potential application. However, only a few microbes in nature can be cultivated in the laboratory. This has led to the development of a variety of isolation and cultivation methods, and in situ cultivation is one of the most popular. Diverse in situ cultivation methods, with the same basic principle, have been applied to a variety of environmental samples. Compared with conventional approaches, these new methods are able to cultivate previously uncultured and phylogenetically novel microbes, many with biotechnological potential. This review introduces the various in situ cultivation methods for the isolation of previously uncultured microbial species and their potential for marine microbial resource mining. Furthermore, studies that investigated the key and previously unidentified mechanisms of growing uncultivated microorganisms by in situ cultivation, which will shed light on the understanding of microbial uncultivability, were also reviewed.

Accessing previously uncultured marine microbial resources by a combination of alternative cultivation methods

Few microbes can grow under laboratory conditions, highlighting the fact that the majority of microbes in environment are still uncultured and untapped resources. This study used alternative cultivation methods, diffusion chambers (DC), dilution-to-extinction culture (DTE) and modified agar preparation step (PS media) to cultivate previously uncultured marine bacterial species. These methods were applied to samples from a coastal intertidal zone, and the results were compared with those from standard direct plating (SDP) cultivation. Among the strains isolated with DC, DTE and PS media methods, 28%, 48% and 33% were novel species, respectively, while the SDP method resulted in the isolation of only 9% of novel species. Most isolates were unique to the method used for their cultivation. This implies that each method is selective in its own way, which is different from SDP, thus able to access species that are difficult to obtain using conventional approaches. Comparing the diversity showed that 75 genera were recovered by the alternative methods, 2.7 times higher than that of the SDP cultivation, which constituted 45% of total diversity from culture-independent sequencing. We conclude that combining alternative cultivation methods represents a highly promising key for accessing 'microbial dark matter'.

Classifying the Risk of Cognitive Impairment Using Sequential Gait Characteristics and Long Short-Term Memory Networks

Cognitive impairment in the elderly causes a significant decline in the quality of life and a substantial economic burden on society. Yet, diagnosing cognitive impairment is apt to be missed or delayed due to its assessment being laborious. This study aimed to propose a new approach of classifying the risk of cognitive impairment in the elderly using sequential gait characteristics and machine learning techniques. A total of 108 community-dwelling elderly individuals participated in this study. The participants were categorized into three groups based on their scores of the mini-mental state examination. Each participant completed both the usual- and fast-paced walking on the straight path with two gyroscopes on each foot. By analyzing the foot sagittal angular velocity signals, the temporal gait parameters were extracted from each gait cycle. Five classical machine learning models and a long short-term memory network were respectively employed to produce the classifiers that used the time-consecutive temporal gait parameters as predictors of cognitive impairment. Five-fold cross-validation was applied to 70% of the data in each group, and the remaining 30% were used as test data. An F1-score of 0.974 was achieved in classifying the three groups by the long short-term memory network-based classifier that used the double-limb support, stance, step, and stride times at usual-paced walking and the double- and single-limb support, stance, and stride times at fast-paced walking as inputs. The proposed approach would pave the way for earlier diagnosis of cognitive impairment in non-clinical settings without professional help, which can facilitate more timely intervention.

Microbulbifer hainanensis sp. nov., a moderately halopilic bacterium isolated from mangrove sediment

A new bacterium was successfully isolated from a mangrove sediment sample in Haikou City, Hainan Province, China. The organism is a Gram-negative, rod-shaped, non-motile and strictly aerobic bacterium, named NBU-8HK146^T. Strain NBU-8HK146^T was able to grow at temperatures of 10-40 °C, at salinities of 0-11% (w/v) and at pH 5.5-9.5. Veoges-Proskauer, methyl red reaction and hydrolysis of Tween 20 were negative. Catalase and oxidase activities, H₂S production, hydrolysis of starch, casein, Tweens 40, 60 and 80 were positive. The major cellular fatty acids were C_16:0, iso-C_15:0 and summed feature 9. The major respiratory quinone was ubiquinone-8 (Q-8). The major polar lipids were phosphatidylethanolamine, phosphatidylglycerol and two unidentified glycolipids. According to 16S rRNA gene sequence similarities, strain NBU-8HK146^T shared 98.0%, 97.9%, 97.7%, 97.6% and 97.3% similarities to the species with validated name Microbulbifer taiwanensis CC-LN1-12^T, Microbulbifer rhizosphaerae Cs16b^T, Microbulbifer marinus Y215^T, Microbulbifer donghaiensis CN85^T and Microbulbifer aggregans CCB-MM1^T, respectively. Phylogenetic analyses indicated that strain NBU-8HK146^T formed a distinct lineage with strains Microbulbifer taiwanensis CC-LN1-12^T and Microbulbifer marinus Y215^T. Both digital DNA-DNA hybridization values (19.5-22.7%) and average nucleotide identity values (73.2-78.9%) between strain NBU-8HK146^T and related species of genus Microbulbifer were below the species delineation cutoffs. The DNA G+C content was 58.9 mol%. Many proteins involving in the adaption of osmotic stress in the salt environment of mangrove were predicted in genome of strain NBU-8HK146^T. From phenotypic, genotypic, phylogenetic and chemotaxonomic characteristics, strain NBU-8HK146^T can be regarded as a new Microbulbifer species for which the name Microbulbifer hainanensis. The type strain is NBU-8HK146^T (= KCTC 82226^T = MCCC 1K04737^T).

Probing sedimentation non-ideality of particulate systems using analytical centrifugation

Analytical centrifugation is a versatile technique for the quantitative characterization of colloidal systems including colloidal stability. The recent developments in data acquisition and evaluation allow the accurate determination of particle size, shape anisotropy and particle density. High precision analytical centrifugation is in particular suited for the study of particle interactions and concentration-dependent sedimentation coefficients. We present a holistic approach for the quantitative determination of sedimentation non-ideality via analytical centrifugation for polydisperse, plain and amino-functionalized silica particles spanning over one order of magnitude in particle size between 100 nm and 1200 nm. These systems typically behave as neutral hard spheres as predicted by auxiliary lattice Boltzmann simulations. The extent of electrostatic interactions and their impact on sedimentation non-ideality can be quantified by the repulsion range, which is the ratio of the Debye length and the average interparticle distance. Experimental access to the repulsion range is provided through conductivity measurements. With the experimental repulsion range at hand, we estimate the effect of polydispersity on concentration-dependent sedimentation properties through a combination of lattice Boltzmann and Brownian dynamics simulations. Finally, we determine the concentration-dependent sedimentation properties of charge-stabilized, fluorescently-labeled silica particles with a nominal particle size of 30 nm and reduced interparticle distance, hence an elevated repulsion range. Overall, our results demonstrate how the influence of hard-sphere type and electrostatic interactions can be quantified when probing sedimentation non-ideality of particulate systems using analytical centrifugation even for systems exhibiting moderate sample heterogeneity and complex interactions.

Walking-in-Place Characteristics-Based Geriatric Assessment Using Deep Convolutional Neural Networks

The world population is aging, and this phenomenon is expected to continue for the next decades. This study aimed to propose a simple and reliable method that can be used for daily in-home monitoring of frailty and cognitive dysfunction in the elderly based on their walking-in-place characteristics. Fifty-four community-dwelling elderly people aged 65 years or older participated in this study. The participants were categorized into the robust and the non-robust groups according to the FRAIL scale. The mini-mental state examination was used to classify the cognitive impairment and the non-cognitive impairment groups. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the foot, shank, thigh, and posterior pelvis while each participant was walking in place for 20 seconds. The walking-in-place spectrograms were acquired by applying time-frequency analysis to the lower body movement signals measured in one stride. Four-fold cross-validation was applied to 80% of the total samples and the remaining 20% were used as test data. The deep convolutional neural network-based classifiers trained with the walking-in-place spectrograms enabled to categorize the robust and the non-robust groups with 94.63% accuracy and classify the cognitive impairment and the non-cognitive impairment groups with 97.59% accuracy. This study suggests that the walking-in-place spectrograms, which can be obtained without spacious experimental space, cumbersome equipment, and laborious processes, are effective indicators of frailty and cognitive dysfunction in the elderly.

Deep Generative Models-Based Anomaly Detection for Spacecraft Control Systems

A spacecraft attitude control system provides mechanical and electrical control to achieve the required functions under various mission scenarios. Although generally designed to be highly reliable, mission failure can occur if anomalies occur and the attitude control system fails to properly orient and stabilize the spacecraft. Because accessing spacecraft to directly repair such problems is usually infeasible, developing a continuous condition monitoring model is necessary to detect anomalies and respond accordingly. In this study, a method for detecting anomalies and characterizing failures for spacecraft attitude control systems is proposed. Herein, features are extracted from multidimensional time-series data of a simulation of the attitude control system. Then, the artificial neural network learning algorithms based on two types of generation models are applied. A Bayesian optimization algorithm with a Gaussian process is used to optimize the hyperparameters for the neural network to improve the performance. The performance is evaluated based on the reconstruction error through the algorithm using the newly generated data not used for learning as input data. Results show that the detection performance depends on the operating characteristics of each submode in the operation scenarios and type of generation model. The diagnostic results are monitored to detect anomalies in operation modes and scenarios.

Multiple Classification of Gait Using Time-Frequency Representations and Deep Convolutional Neural Networks

Human gait has served as a useful barometer of health. Existing studies for automatic categorization of gait have been limited to a binary classification of pathological and non-pathological gait and provided low accuracy in multi-classification. This study aimed to propose a novel approach that can multi-classify gait with no visually discernible difference in characteristics. Sixty-nine participants without gait disturbance were recruited. Twenty-nine of the participants were semi-professional athletes, and 19 were ordinary people. The remaining 21 participants were people with subtle foot deformities. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the foot, shank, thigh, and posterior pelvis while walking. The gait spectrograms were acquired by applying time-frequency analyses to the lower body movement signals measured in one stride and used to train the deep convolutional neural network-based classifiers. Four-fold cross-validation was applied to 80% of the total samples and the remaining 20% were used as test data. The foot, shank, and thigh spectrograms enabled complete classification of the three groups even without requiring a sophisticated process for feature engineering. This is the first study that employed the spectrographic approach in gait classification and achieved reliable multi-classification of gait without observable differences in characteristics using the deep convolutional neural networks.

Repair versus nonrepair of medial meniscus posterior root tear: A systematic review of patients' selection criteria, including clinical and radiographic outcomes

BACKGROUND

The general consensus regarding a rational choice among various treatment strategies for medial meniscus posterior root tears (MMPRTs) has yet to be clearly established. The purpose of this systematic review was to analyze patient selection criteria based on index arthrosis, as well as clinical and radiological outcomes after repair or nonrepair treatment in patients with MMPRTs.

METHODS

A systematic electronic search was performed with established medical databases. Data from the selected studies which were assessed using the modified Coleman methodology score were analyzed in terms of index arthrosis and degree of lower limb alignment, functional and radiologic outcomes after meniscus repair, partial meniscectomy, and conservative treatment.

RESULTS

In total, 17 studies and 655 patients (665 cases) were enrolled in this study, of which 42% (279 cases) underwent MMPRT repair and 58% (386 cases) were treated using a nonrepair strategy. The mean age and the mean follow-up period were 54.7 years and 32.5 months in the repair group, respectively, and 57.0 years and 49.3 months in the nonrepair group, respectively. Based on the clinical data available in this study, most of the MMPRT repairs were performed in patients with mild arthrosis, mild varus alignment, and mild chondral injury. Although data were limited, the percentage of patients with mild chondral injury was only 40% in the nonrepair group, implying that the nonrepair group may have more advanced arthrosis at the baseline. Based on the available Lysholm score across the studies, good functional outcomes were obtained in the repair group, whereas the results of the nonrepair treatment exhibited fair functional outcomes that were somewhat heterogenous. The radiologic outcomes of the mean 5 years' follow-up study showed that arthritic change could not be prevented by either nonrepair or repair treatment.

CONCLUSIONS

In general, MMPRT repair led to significant improvement in clinical outcomes. On the contrary, the nonrepair group also showed symptomatic relief in some selected cases, despite the somewhat heterogenous results. Given the subgroup analysis for the functional results reported in this review, strict patient selection is important to obtain satisfactory clinical outcomes, regardless of the treatment option selected.

Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

BACKGROUND

A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier.

METHODS

The study included consecutively discharged patients between 1st of January 2017 and 31st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals.

RESULTS

The study included 45,388 inpatient episodes. The models' performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping.

CONCLUSION

The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.

Deep Neural Network-Based Gait Classification Using Wearable Inertial Sensor Data

Human gait has been regarded as a useful behavioral biometric trait for personal identification and authentication. This study aimed to propose an effective approach for classifying gait, measured using wearable inertial sensors, based on neural networks. The 3-axis accelerometer and 3-axis gyroscope data were acquired at the posterior pelvis, both thighs, both shanks, and both feet while 29 semi-professional athletes, 19 participants with normal foot, and 21 patients with foot deformities walked on the 20-meter straight path. The classifier based on the gait parameters and fully connected neural network was developed by applying 4-fold cross-validation to 80% of the total samples. For the test set that consisted of the remaining 20% of the total samples, this classifier showed an accuracy of 93.02% in categorizing the athlete, normal foot, and deformed foot groups. Using the same model validation and evaluation method, up to 98.19% accuracy was achieved from the convolutional neural network-based classifier. This classifier was trained with the gait spectrograms obtained from the time-frequency domain analysis of the raw acceleration and angular velocity data. The classification based only on the pelvic spectrograms exhibited an accuracy of 94.25% even without requiring a time-consuming and resource-intensive process for feature engineering. The notable performance and practicality in gait classification achieved by this study suggest potential applicability of the proposed approaches in the field of biometrics.

The Impact of Rate Formulations on Stochastic Molecular Motor Dynamics

Cells are complex structures which require considerable amounts of organization via transport of large intracellular cargo. While passive diffusion is often sufficiently fast for the transport of smaller cargo, active transport is necessary to organize large structures on the short timescales necessary for biological function. The main mechanism of this transport is by cargo attachment to motors which walk in a directed fashion along intracellular filaments. There are a number of models which seek to describe the motion of motors with attached cargo, from detailed microscopic to coarse phenomenological descriptions. We focus on the intermediate-detailed discrete stochastic hopping models, and explore how cargo transport changes depending on the number of motors, motor interaction, system constraints and rate formulations, which are derived from common thermodynamic assumptions. We find that, despite obeying the same detailed balance constraint, the choice of rate formulation considerably affects the characteristics of the overall motion of the system, with one rate formulation exhibiting novel behavior of loaded motor groups moving faster than a single unloaded motor.