Does multidimensional daily information predict the onset of myopia? A 1-year prospective cohort study.

PURPOSE: This study aimed to develop an interpretable machine learning model to predict the onset of myopia based on individual daily information. METHOD: This study was a prospective cohort study. At baseline, non-myopia children aged 6-13 years old were recruited, and individual data were collected through interviewing students and parents. One year after baseline, the incidence of myopia was evaluated based on visual acuity test and cycloplegic refraction measurement. Five algorithms, Random Forest, Support Vector Machines, Gradient Boosting Decision Tree, CatBoost and Logistic Regression were utilized to develop different models and their performance was validated by area under curve (AUC). Shapley Additive exPlanations was applied to interpret the model output on the individual and global level. RESULT: Of 2221 children, 260 (11.7%) developed myopia in 1 year. In univariable analysis, 26 features were associated with the myopia incidence. Catboost algorithm had the highest AUC of 0.951 in the model validation. The top 3 features for predicting myopia were parental myopia, grade and frequency of eye fatigue. A compact model using only 10 features was validated with an AUC of 0.891. CONCLUSION: The daily information contributed reliable predictors for childhood's myopia onset. The interpretable Catboost model presented the best prediction performance. Oversampling technology greatly improved model performance. This model could be a tool in myopia preventing and intervention that can help identify children who are at risk of myopia, and provide personalized prevention strategies based on contributions of risk factors to the individual prediction result.

Reducing Power Line Interference from sEMG Signals Based on Synchrosqueezed Wavelet Transform.

Power line interference (PLI) is a major source of noise in sEMG signals. As the bandwidth of PLI overlaps with the sEMG signals, it can easily affect the interpretation of the signal. The processing methods used in the literature are mostly notch filtering and spectral interpolation. However, it is difficult for the former to reconcile the contradiction between completely filtering and avoiding signal distortion, while the latter performs poorly in the case of a time-varying PLI. To solve these, a novel synchrosqueezed-wavelet-transform (SWT)-based PLI filter is proposed. The local SWT was developed to reduce the computation cost while maintaining the frequency resolution. A ridge location method based on an adaptive threshold is presented. In addition, two ridge extraction methods (REMs) are proposed to fit different application requirements. Parameters were optimized before further study. Notch filtering, spectral interpolation, and the proposed filter were evaluated on the simulated signals and real signals. The output signal-to-noise ratio (SNR) ranges of the proposed filter with two different REMs are 18.53-24.57 and 18.57-26.92. Both the quantitative index and the time-frequency spectrum diagram show that the performance of the proposed filter is significantly better than that of the other filters.

[Research on muscle fatigue recognition model based on improved wavelet denoising and long short-term memory].

The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.

Design of Near-Infrared Nonfullerene Acceptor with Ultralow Nonradiative Voltage Loss for High-Performance Semitransparent Ternary Organic Solar Cells.

Semitransparent organic solar cells (ST-OSCs) are considered as one of the most valuable applications of OSCs and a strong contender in the market. However, the optical band gap of current high-performance ST-OSCs is still not low enough to achieve the optimal balance between power conversion efficiency (PCE) and average visible transmittance (AVT). An N-substituted asymmetric nonfullerene acceptor SN with over 40 nm bathochromically shifted absorption compared to Y6 was designed and synthesized, based on which the device with PM6 as donor obtained a PCE of 14.3 %, accompanied with a nonradiative voltage loss as low as 0.15 eV. Meanwhile, ternary devices with the addition of SN into PM6 : Y6 can achieve a PCE of 17.5 % with an unchanged open-circuit voltage and improved short-circuit current. Benefiting from extended NIR absorption and lowered voltage loss, ST-OSCs based on PM6 : SN : Y6 were fabricated and the optimized device demonstrated a PCE of 14.0 % at an AVT of 20.2 %, which is the highest PCE at an AVT over 20 %.

Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering.

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold.

Previous studies have used the anaerobic threshold (AT) to non-invasively predict muscle fatigue. This study proposes a novel method for the automatic classification of muscle fatigue based on surface electromyography (sEMG). The sEMG data were acquired from 20 participants during an incremental test on a cycle ergometer using sEMG sensors placed on the vastus rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), and gastrocnemius (GA) muscles of the left leg. The ventilation volume (VE), oxygen uptake (VO2), and carbon dioxide production (VCO2) data of each participant were collected during the test. Then, we extracted the time-domain and frequency-domain features of the sEMG signal denoised by the improved wavelet packet threshold denoising algorithm. In this study, we propose a new muscle fatigue recognition model based on the long short-term memory (LSTM) network. The LSTM network was trained to classify muscle fatigue using sEMG signal features. The results showed that the improved wavelet packet threshold function has better performance in denoising sEMG signals than hard threshold and soft threshold functions. The classification performance of the muscle fatigue recognition model proposed in this paper is better than that of CNN (convolutional neural network), SVM (support vector machine), and the classification models proposed by other scholars. The best performance of the LSTM network was achieved with 70% training, 10% validation, and 20% testing rates. Generally, the proposed model can be used to monitor muscle fatigue.

Validity of a Newly-Designed Rectilinear Stepping Ergometer Submaximal Exercise Test to Assess Cardiorespiratory Fitness.

The maximum oxygen uptake (V̇O2 max), determined from graded maximal or submaximal exercise tests, is used to classify the cardiorespiratory fitness level of individuals. The purpose of this study was to examine the validity and reliability of the YMCA submaximal exercise test protocol performed on a newly-designed rectilinear stepping ergometer (RSE) that used up and down reciprocating vertical motion in place of conventional circular motion and giving precise measurement of workload, to determine V̇O2 max in young healthy male adults. Thirty-two young healthy male adults (32 males; age range: 20-35 years; height: 1.75 ± 0.05 m; weight: 67.5 ± 8.6 kg) firstly participated in a maximal-effort graded exercise test using a cycle ergometer (CE) to directly obtain measured V̇O2 max. Subjects then completed the progressive multistage test on the RSE beginning at 50W and including additional stages of 70, 90, 110, 130, and 150W, and the RSE YMCA submaximal test consisting of a workload increase every 3 minutes until the termination criterion was reached. A metabolic equation was derived from the RSE multistage exercise test to predict oxygen consumption (V̇O2) from power output (W) during the submaximal exercise test (V̇O2 (mL·min-1 )=12.4 ×W(watts)+3.5 mL·kg-1·min-1×M+160mL·min-1, R2= 0.91, standard error of the estimate (SEE) = 134.8mL·min-1). A high correlation was observed between the RSE YMCA estimated V̇O2 max and the CE measured V̇O2 max (r=0.87). The mean difference between estimated and measured V̇O2 max was 2.5 mL·kg-1·min-1, with an SEE of 3.55 mL·kg-1·min-1. The data suggest that the RSE YMCA submaximal exercise test is valid for predicting V̇O2 max in young healthy male adults. The findings show that the rectilinear stepping exercise is an effective submaximal exercise for predicting V̇O2 max. The newly-designed RSE may be potentially further developed as an alternative ergometer for assessing cardiorespiratory fitness and the promotion of personalized health interventions for health care professionals.

[Eyes-Brain-Hands Coordination Training System for Mental Retarded Children].

In order to help improving mental attention and sensory integration ability of mental retarded children, this paper proposes an interactive eyes-brain-hands coordination training system. This system realizes the principle of seeing, thinking and moving of hands by an interactive operation between the computer software custom icons and a touch control panel, so it can improve cognitive function and activity of daily living. The results show this training platform has a high degree of application and acceptance, and provides a portable training method for mental retarded children.

Regioisomeric thieno[3,4-d]thiazole-based A-Q-D-Q-A-type NIR acceptors for efficient non-fullerene organic solar cells.

This study explores the potential of regioisomeric quinoidal-resonance π-spacers in designing near-infrared (NIR) non-fullerene acceptors (NFAs) for high-performance organic solar cell devices. Adopting thienothiazole as the π-spacer, two new isomeric A-Q-D-Q-A NFAs, TzN-S and TzS-S, are designed and synthesized. Both NFAs demonstrate a broad spectral response extended to the NIR region. However, they exhibit different photovoltaic properties when they were mixed with the PCE10 donor to fabricate respective solar cells. The optimal device of TzS-S achieves a PCE of 10.75%, much higher than that of TzN-S based ones (6.13%). The more favorable energetic offset and better molecular packing contribute to the better charge generation and transport, which explains the relative superiority of TzS-S NFA. This work sheds new light on the regioisomeric effect of component materials for optoelectronic applications.

Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study.

OBJECTIVE: This study aimed to develop and evaluate machine-learning models for predicting the onset of overweight in adolescents aged 14‒17, utilizing easily collectible personal information. METHODS: This study was a one-year prospective cohort study. Baseline data were collected through anthropometric measurements and questionnaires, and the incidence of overweight was calculated one year later via anthropometric measurements. Predictive factors were selected through univariate analysis. Six machine-learning models were developed for predicting the onset of overweight. The SHapley Additive exPlanations (SHAP) was used for global and local interpretation of the models. RESULTS: Out of 1,241 adolescents, 204 (16.4%) were identified as overweight after one year. Nineteen features were associated with the overweight incidence in univariable analysis. Participants were randomly divided into a training group and a testing group in a 7:3 ratio. The Light Gradient Boosting Machine (LGBM) algorithm achieved outperformed other models, achieving the following metrics: Accuracy (0.956), Recall (0.812), Specificity (0.983), F1-score (0.855), AUC (0.961). Importance ranking revealed that the top 11 minimal feature set can maintain the stability of model performance. CONCLUSIONS: The onset of overweight in adolescents was accurately predicted using easily collectible personal information. The LGBM-based model exhibited superior performance. Oversampling technique notably improved model performance. The model interpretation technique provided innovative strategies for managing adolescent overweight/obesity.

Association of educational environment with the prevalence of myopia: a cross-sectional study in central China.

Purposes: This study was to estimate the prevalence of myopia among primary school students in Hefei, China, and evaluate the association of educational environment with myopia. Methods: This study was a cross-sectional study, and recruited primary school students in grades 1-6. Children underwent a stepwise ophthalmic examination, which included visual acuity and objective cycloplegic refraction to identify children with myopia. Under the guidance of parents, children completed a questionnaire, including gender, region, grade and several indicators related to education. The study analyzed the risk factors by using a logistic regression and assessed feature importance by using a random forest algorithm. Results: A total of 3,596 primary school students were involved in this analysis, and the overall prevalence of myopia was 27.1%. Gender, grade, education level of the father, education level of the mother, academic level of children, hours of homework per day on weekends, number of after-school tutoring per week and frequency of extracurricular reading were significantly associated with myopia. There was no significant association between the amount of homework per day on school days and myopia after adjusting for covariates. In terms of educational environment, the top 3 factors were academic level of children, homework on weekends and after-school tutoring. Conclusions: Educational environment with high educational loads was associated with the high prevalence of myopia. Reducing the burden of studying, especially that after class, was an effective way to prevent myopia.

Solution Processed Semi-Transparent Organic Solar Cells Over 50% Visible Transmittance Enabled by Silver Nanowire Electrode with Sandwich Structure.

Photovoltaic windows with easy installation for the power supply of household appliances have long been a desire of energy researchers. However, due to the lack of top electrodes that offer both high transparency and low sheet resistance, the development of high-transparency photovoltaic windows for indoor lighting scenarios has lagged significantly behind photovoltaic windows where privacy issues are involved. Addressing this issue, this work develops a solution-processable transparent top electrode using sandwich structure silver nanowires, realizing high transparency in semi-transparent organic solar cells. The wettability and conducting properties of the electrode are improved by a modified hole-transport layer named HP. The semi-transparent solar cell exhibits good see-through properties at a high average visible transmittance of 50.8%, with power conversion efficiency of 7.34%, and light utilization efficiency of 3.73%, which is the highest without optical modulations. Moreover, flexible devices based on the above-mentioned architecture also show excellent mechanical tolerance compared with Ag electrode counterparts, which retains 94.5% of their original efficiency after 1500 bending cycles. This work provides a valuable approach for fabricating solution-processed high transparency organic solar cells, which is essential in future applications in building integrated photovoltaics.

Theory-Guided Material Design Enabling High-Performance Multifunctional Semitransparent Organic Photovoltaics without Optical Modulations.

Semitransparent organic photovoltaics (ST-OPVs) have drawn great attention for promising applications in building-integrated photovoltaics, providing additional power generation for daily use. A previously proposed strategy, "complementary NIR absorption," is widely applied for high-performance ST-OPVs. However, rational material design toward high performance has not been achieved. In this work, an external quantum efficiency (EQE) model describing this strategy is developed to explore the full potential of material design on ST-OPV performance. Guided by the model, a novel nonfullerene acceptor (NFA), ATT-9, is designed and synthesized, which possesses optimal bandgap for ST-OPVs, achieving a record short-circuit current density of 30 mA cm-2 and a power conversion efficiency of 13.40%, the highest value among devices based on NFAs with bandgaps lower than 1.2 eV. It is notworthy that, at such a low bandgap, the energy loss of the device is only 0.58 eV, which is attributed to the low energetic disorder confirmed by an ultralow Urbach energy of 21.6 meV. Benefiting from the optimal bandgap and low energy loss, the ATT-9-based ST-OPV achieves a high light utilization efficiency of 3.33% without optical modulations, and meanwhile shows excellent thermal insulation, exceeding the commercial 3M heat-insulating window film, demonstrating the outstanding application prospects of multifunctional ST-OPVs.

[Inflammatory myofibroblastic tumor of the prostate: a case report and review of the literature].

OBJECTIVE: To discuss the diagnosis and treatment of inflammatory myofibroblastic tumor (IMT) of the prostate. METHODS: We retrospectively analyzed the clinical data of a case of IMT of the prostate and reviewed relevant literature. The patient was a 62-year-old man, who was twice referred to the local hospital for recurrence of dysuria. He was diagnosed as with benign prostatic hyperplasia (BPH) and twice underwent transurethral resection of the prostate (TURP). But frequent recurrence of dysuria followed postoperatively, for which he came to our hospital and received another TURP and transurethral resection of the bladder tumor (TURBT). Pathological findings showed chronic inflammation of the bladder, papillary hyperplasia with mild dysplasia in regional urothelial and IMT of the prostate. Six months later, the patient came again to our hospital for recurrence of dysuria. Computed tomography revealed a large mass in the bladder. RESULTS: The patient underwent laparoscopic bladder radical resection and ileal conduit diversion, simultaneously with pelvic lymph node dissection. The pathological examination confirmed it to be an IMT of the prostate with the bladder but not regional lymph nodes involved. An 11-month follow-up showed neither local recurrence nor distant metastasis. CONCLUSION: IMT of the prostate is a rare borderline lesion, whose diagnosis is very difficult and mainly depends on pathological findings. IMT of the prostate is liable to recur and even invade the bladder. Radical resection is recommended for patients with large and recurrent tumors, and close follow-up is strongly warranted.

Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology.

Surface electromyography (sEMG) has great potential in investigating the neuromuscular mechanism for knee pathology. However, due to the complex nature of neural control in lower limb motions and the divergences in subjects' health and habits, it is difficult to directly use the raw sEMG signals to establish a robust sEMG analysis system. To solve this, muscle synergy analysis based on non-negative matrix factorization (NMF) of sEMG is carried out in this manuscript. The similarities of muscle synergy of subjects with and without knee pathology performing three different lower limb motions are calculated. Based on that, we have designed a classification method for motion recognition and knee pathology diagnosis. First, raw sEMG segments are preprocessed and then decomposed to muscle synergy matrices by NMF. Then, a two-stage feature selection method is executed to reduce the dimension of feature sets extracted from aforementioned matrices. Finally, the random forest classifier is adopted to identify motions or diagnose knee pathology. The study was conducted on an open dataset of 11 healthy subjects and 11 patients. Results show that the NMF-based sEMG classifier can achieve good performance in lower limb motion recognition, and is also an attractive solution for clinical application of knee pathology diagnosis.

NMR Quantification of Halogen-Bonding Ability To Evaluate Catalyst Activity.

Quantification of halogen-bonding abilities is described for a series of benzimidazolium-, imidazolium- and bis(imidazolium) halogen-bond donors (XBDs) using 31P NMR spectroscopy. The measured Δδ(31P) values correlate with calculated activation free energy ΔG‡ and catalytic activity for a Friedel-Crafts indole addition. This rapid method also serves as a sensitive indicator for Brønsted acid impurities.

Safety, Survival, and Efficacy of Preserving Left Colonic Artery in Rectal Cancer Surgery: A Meta-Analysis and Review.

Purpose: This study analyzes the effect, long-term survival rate, and complications about preserving the left colonic artery (LCAP) in rectal cancer surgery. Methods: Relevant articles were systematically retrieved from multiple electronic databases, for example, EMBASE, BioMed Central, PubMed, Web of Science, and Cochrane. The time for retrieving was from the establishment of the database to December 31, 2018. Evaluated endpoints were effect of LCAP on the curative effect of rectal neoplasms, such as operation time, the amount of bleeding during the operation, root lymph nodes positive number, and the related complications (anastomotic leakage, etc.), postoperative urinary retention, 5-year survival rate, and recurrence differences in rates. Results: Totally 12 studies were included in this review. The meta-analysis showed that LCAP has less operation time and lower anastomotic leakage incidence. Intraoperative bleeding, root lymph nodes, and other complications did not show any significant difference. Conclusions: LCAP in radical rectal cancer surgery ensures both the radical resection of the tumor and the safety of the operation. So it can provide a new approach to the management of blood vessels and lymph nodes.

Mechanical analyses on the digital behaviour of the Tokay gecko (Gekko gecko) based on a multi-level directional adhesion model.

This paper proposes a multi-level hierarchical model for the Tokay gecko (Gekko gecko) adhesive system and analyses the digital behaviour of the G. gecko under macro/meso-level scale. The model describes the structures of G. gecko's adhesive system from the nano-level spatulae to the sub-millimetre-level lamella. The G. gecko's seta is modelled using inextensible fibril based on Euler's elastica theorem. Considering the side contact of the spatular pads of the seta on the flat and rigid substrate, the directional adhesion behaviour of the seta has been investigated. The lamella-induced attachment and detachment have been modelled to simulate the active digital hyperextension (DH) and the digital gripping (DG) phenomena. The results suggest that a tiny angular displacement within 0.25° of the lamellar proximal end is necessary in which a fast transition from attachment to detachment or vice versa is induced. The active DH helps release the torque to induce setal non-sliding detachment, while the DG helps apply torque to make the setal adhesion stable. The lamella plays a key role in saving energy during detachment to adapt to its habitat and provides another adhesive function which differs from the friction-dependent setal adhesion system controlled by the dynamic of G. gecko's body.