Human Posture Transition-Time Detection Based upon Inertial Measurement Unit and Long Short-Term Memory Neural Networks.

As human-robot interaction becomes more prevalent in industrial and clinical settings, detecting changes in human posture has become increasingly crucial. While recognizing human actions has been extensively studied, the transition between different postures or movements has been largely overlooked. This study explores using two deep-learning methods, the linear Feedforward Neural Network (FNN) and Long Short-Term Memory (LSTM), to detect changes in human posture among three different movements: standing, walking, and sitting. To explore the possibility of rapid posture-change detection upon human intention, the authors introduced transition stages as distinct features for the identification. During the experiment, the subject wore an inertial measurement unit (IMU) on their right leg to measure joint parameters. The measurement data were used to train the two machine learning networks, and their performances were tested. This study also examined the effect of the sampling rates on the LSTM network. The results indicate that both methods achieved high detection accuracies. Still, the LSTM model outperformed the FNN in terms of speed and accuracy, achieving 91% and 95% accuracy for data sampled at 25 Hz and 100 Hz, respectively. Additionally, the network trained for one test subject was able to detect posture changes in other subjects, demonstrating the feasibility of personalized or generalized deep learning models for detecting human intentions. The accuracies for posture transition time and identification at a sampling rate of 100 Hz were 0.17 s and 94.44%, respectively. In summary, this study achieved some good outcomes and laid a crucial foundation for the engineering application of digital twins, exoskeletons, and human intention control.

Machine Learning for Human Motion Intention Detection.

The gait pattern of exoskeleton control conflicting with the human operator's (the pilot) intention may cause awkward maneuvering or even injury. Therefore, it has been the focus of many studies to help decide the proper gait operation. However, the timing for the recognization plays a crucial role in the operation. The delayed detection of the pilot's intent can be equally undesirable to the exoskeleton operation. Instead of recognizing the motion, this study examines the possibility of identifying the transition between gaits to achieve in-time detection. This study used the data from IMU sensors for future mobile applications. Furthermore, we tested using two machine learning networks: a linearfFeedforward neural network and a long short-term memory network. The gait data are from five subjects for training and testing. The study results show that: 1. The network can successfully separate the transition period from the motion periods. 2. The detection of gait change from walking to sitting can be as fast as 0.17 s, which is adequate for future control applications. However, detecting the transition from standing to walking can take as long as 1.2 s. 3. This study also find that the network trained for one person can also detect movement changes for different persons without deteriorating the performance.

Performance Evaluation for Clinical Stroke Rehabilitation via an Automatic Mobile Gait Trainer.

This paper investigates the clinical efficacy of an automatic mobile trainer for gait training in stroke patients. Neuro-Developmental Treatment (NDT) is a rehabilitation method for stroke patients that enhances motor learning through repeated practice. Despite the proven effectiveness of therapist-assisted NDT, it is labor-intensive and demands health resources. Therefore, we developed automatic trainers based on NDT principles to perform gait training. This paper modifies the mobile trainer's intervention patterns to improve the subject's longitudinal gait symmetry, lateral pelvic displacement symmetry, and pelvic rotation. We first invited ten healthy subjects to test the modified trainer and then recruited 26 stroke patients to undergo the same gait training. Longitudinal symmetry, lateral symmetry, and pelvic rotation were assessed before, during, and after the intervention. Most subjects show improvements in longitudinal symmetry, lateral symmetry, and pelvic rotation after using the trainer. These results confirm the trainer's effectiveness of the modified intervention schemes in helping clinical gait rehabilitation for stroke patients.

Mechanically-Compliant Bioelectronic Interfaces through Fatigue-Resistant Conducting Polymer Hydrogel Coating.

Because of their distinct electrochemical and mechanical properties, conducting polymer hydrogels have been widely exploited as soft, wet, and conducting coatings for conventional metallic electrodes, providing mechanically compliant interfaces and mitigating foreign body responses. However, the long-term viability of these hydrogel coatings is hindered by concerns regarding fatigue crack propagation and/or delamination caused by repetitive volumetric expansion/shrinkage during long-term electrical interfacing. This study reports a general yet reliable approach to achieving a fatigue-resistant conducting polymer hydrogel coating on conventional metallic bioelectrodes by engineering nanocrystalline domains at the interface between the hydrogel and metallic substrates. It demonstrates the efficacy of this robust, biocompatible, and fatigue-resistant conducting hydrogel coating in cardiac pacing, showcasing its ability to effectively reduce the pacing threshold voltage and enhance the long-term reliability of electric stimulation. This study findings highlight the potential of its approach as a promising design and fabrication strategy for the next generation of seamless bioelectronic interfaces.

Asymmetric Synthesis of S(IV) and S(VI) Stereogenic Centers.

Sulfur can form diverse S(IV) and S(VI) stereogenic centers, of which some have gained significant attention recently due to their increasing use as pharmacophores in drug discovery programs. The preparation of these sulfur stereogenic centers in their enantiopure form has been challenging, and progress made will be discussed in this Perspective. This Perspective summarizes different strategies, with selected works, for asymmetric synthesis of these moieties, including diastereoselective transformations using chiral auxiliaries, enantiospecific transformations of enantiopure sulfur compounds, and catalytic enantioselective synthesis. We will discuss the advantages and limitations of these strategies and will provide our views on how this field will develop.

Flexible Supercapacitors Based on Stretchable Conducting Polymer Electrodes.

Supercapacitors are widely used in various fields due to their high power density, fast charging and discharging speeds, and long service life. However, with the increasing demand for flexible electronics, integrated supercapacitors in devices are also facing more challenges, such as extensibility, bending stability, and operability. Despite many reports on stretchable supercapacitors, challenges still exist in their preparation process, which involves multiple steps. Therefore, we prepared stretchable conducting polymer electrodes by depositing thiophene and 3-methylthiophene on patterned 304 stainless steel (SS 304) through electropolymerization. The cycling stability of the prepared stretchable electrodes could be further improved by protecting them with poly(vinyl alcohol)/sulfuric acid (PVA/H2SO4) gel electrolyte. Specifically, the mechanical stability of the polythiophene (PTh) electrode was improved by 2.5%, and the stability of the poly(3-methylthiophene (P3MeT) electrode was improved by 7.0%. As a result, the assembled flexible supercapacitors maintained 93% of their stability even after 10,000 cycles of strain at 100%, which indicates potential applications in flexible electronics.

Design of adhesive conducting PEDOT-MeOH:PSS/PDA neural interface via electropolymerization for ultrasmall implantable neural microelectrodes.

Conducting polymers are emerging as promising neural interfaces towards diverse applications such as deep brain stimulation due to their superior biocompatibility, electrical, and mechanical properties. However, existing conducting polymer-based neural interfaces still suffer from several challenges and limitations such as complex preparation procedures, weak interfacial adhesion, poor long-term fidelity and stability, and expensive microfabrication, significantly hindering their broad practical applications and marketization. Herein, we develop an adhesive and long-term stable conducting polymer neural interface by a simple two-step electropolymerization methodology, namely, the pre-polymerization of polydopamine (PDA) as an adhesive thin layer followed by electropolymerization of hydroxymethylated 3,4-ethylenedioxythiophene (EDOT-MeOH) with polystyrene sulfonate (PSS) to form stable interpenetrating PEDOT-MeOH:PSS/PDA networks. As-prepared PEDOT-MeOH:PSS/PDA interface exhibits remarkably improved interfacial adhesion against metallic electrodes, showing 93% area retention against vigorous sonication for 20 min, which is one of the best tenacious conducting polymer interfaces so far. Enabled by the simple methodology, we can facilely fabricate the PEDOT-MeOH:PSS/PDA interface onto ultrasmall Pt-Ir wire microelectrodes (diameter: 10 µm). The modified microelectrodes display two orders of magnitude lower impedance than commercial products, and also superior long-term stability to previous reports with high charge injection capacity retention up to 99.5% upon 10,000,000 biphasic input pulse cycles. With these findings, such a simple methodology, together with the fabricated high-performance and stable neural interface, can potentially provide a powerful tool for both advanced neuroscience researches and cutting-edge clinical applications like brain-controlled intelligence.

Superior Gait Symmetry and Postural Stability among Yoga Instructors-Inertial Measurement Unit-Based Evaluation.

This study investigates gait symmetry and single-leg stance balance of professional yoga instructors versus age-matched typically developed controls using inertial measurement unit (IMU)-based evaluation. We recruited twenty-five yoga instructors and twenty-five healthy control subjects to conduct the walking experiments and single-leg stance tests. Kinematic data were measured by attaching IMUs to the lower limbs and trunk. We assessed the asymmetry of swing phases during the normal-walk and tandem-walk tests with eyes open and closed, respectively. The subjects subsequently conducted four single-leg stance tests, including a single-leg stance on both legs with eyes open and closed. Two balance indexes regarding the angular velocities of the waist and chest were defined to assess postural stability. The gait asymmetry indexes of yoga instructors were significantly lower than those of the typically developed controls. Similarly, the yoga instructors had better body balance in all four single-leg stance tests. This study's findings suggest that yoga improves gait asymmetry and balance ability in healthy adults. In the future, further intervention studies could be conducted to confirm the effect of yoga training.

A 1,2,3-Triazole Derivative of Quinazoline Exhibits Antitumor Activity by Tethering RNF168 to SQSTM1/P62.

Quinazoline and its derivatives have drawn much attention in the development of potential antitumor agents. Here, we synthesized a series of 1,2,3-triazole derivatives of quinazoline at the C6 position and evaluated for their cytotoxic activity in various human cancer cell lines. We found that compound 5a was the most cytotoxic to HCT-116 cells (IC50, 0.36 µM). Target profiling found that 5a directly binds to both the autophagy-associated protein SQSTM1/P62 and the E3 ligase RNF168, promoting their interaction. Consistently, 5a treatment induces a decrease in RNF168-mediated H2A ubiquitination and compromises homologous recombination-mediated DNA repair, thus increasing the sensitivity of HCT-116 to X-ray radiation. Moreover, 5a suppressed xenografted tumor growth in mice in a dose-dependent manner. Taken together, the 1,2,3-triazole derivative of quinazoline 5a may serve as a novel compound for tumor therapy based on its role in promoting a P62/RNF168 interaction.

Effects of UVA disappearance and presence on the acylated anthocyanins formation in grape berries.

Ultraviolet A (UVA), the major component of the UV, plays a crucial role in formatting the characteristics of color in wine grapes by influencing its anthocyanin composition and contents. Results showed that anthocyanin biosynthesis was suppressed by UVA screening and enhanced by irradiation. The acetylation and p-coumaroylation of anthocyanins were more pronounced and showed positive correlation with a* and negative correlation with L*, b*, C*, and h, thereby leading to changes in color. Weighted gene co-expression network analysis showed that two modules (red and turquoise) were significantly related to the acetylation and p-coumaroylation of peonidin. In addition, relative gene expression assays and correlation analysis also indicated that VvMYBA1 might influence anthocyanin accumulation by directly regulating VvOMT expression and increasing the flux to the vacuole through VvGST4. In conclusion, the results helped in improving our understanding of the role of UVA in skin color formation.

Superior gait performance and balance ability in Latin dancers.

Background: Latin dance consists of various fast and stability-challenging movements that require constant body adjustments to maintain proper posture and balance. Although human gaits are assumed to be symmetrical, several factors can contribute to asymmetrical behavior of the lower extremities in healthy adults. These include lower limb dominance, ground reaction forces, lower limb muscle power, foot placement angle, and range of joint motion. Gait impairment can lead to a high risk of falling, diminished mobility, and even cognition impairment. We hypothesized that Latin dancers might have a more symmetric gait pattern and better balance ability than healthy non-dancer controls. Methods: We investigated the impact of Latin dance training on gait behaviors and body balance. We recruited twenty Latin dancers and 22 normal healthy subjects to conduct walking experiments and one-leg stance tests, and we measured their kinematic data by inertial measurement units. We then defined four performance indexes to assess gait performance and body stability to quantify the potential advantages of dance training. Results: We found that the two gait asymmetric indexes during the walking test and the two performance indexes during the one-leg stance tests were better in Latin dancers compared with the healthy control group. The results confirmed the superiority of Latin dancers over the healthy control group in gait symmetry and balance stability. Our results suggest that Latin dancing training could effectively strengthen lower limb muscles and core muscle groups, thereby improving coordination and enhancing gait performance and balance. Conclusion: Latin dance training can benefit gait performance and body balance. Further studies are needed to investigate the effect of Latin dance training on gait and balance outcomes in healthy subjects and patients with gait disorders.

Integrating functional data analysis with case-based reasoning for hypertension prognosis and diagnosis based on real-world electronic health records.

BACKGROUND: Hypertension is the fifth chronic disease causing death worldwide. The early prognosis and diagnosis are critical in the hypertension care process. Inspired by human philosophy, CBR is an empirical knowledge reasoning method for early detection and intervention of hypertension by only reusing electronic health records. However, the traditional similarity calculation method often ignores the internal characteristics and potential information of medical examination data. METHODS: In this paper, we first calculate the weights of input attributes by a random forest algorithm. Then, the risk value of hypertension from each medical examination can be evaluated according to the input data and the attribute weights. By fitting the risk values into a risk curve of hypertension, we calculate the similarity between different community residents, and obtain the most similar case according to the similarity. Finally, the diagnosis and treatment protocol of the new case can be given. RESULTS: The experiment data comes from the medical examination of Tianqiao Community (Tongling City, Anhui Province, China) from 2012 to 2021. It contains 4143 community residents and 43,676 medical examination records. We first discuss the effect of the influence factor and the decay factor on similarity calculation. Then we evaluate the performance of the proposed FDA-CBR algorithm against the GRA-CBR algorithm and the CS-CBR algorithm. The experimental results demonstrate that the proposed algorithm is highly efficient and accurate. CONCLUSIONS: The experiment results show that the proposed FDA-CBR algorithm can effectively describe the variation tendency of the risk value and always find the most similar case. The accuracy of FDA-CBR algorithm is higher than GRA-CBR algorithm and CS-CBR algorithm, increasing by 9.94 and 16.41%, respectively.

Effect of Cold Shock Pretreatment Combined with Perforation-Mediated Passive Modified Atmosphere Packaging on Storage Quality of Cucumbers.

This study evaluated the application of cold shock combined with perforation-mediated passive modified atmosphere packaging technology (CS-PMAP) for cucumber preservation through physicochemical, sensory, and nutritional qualities. The effectiveness of CS-PMAP in maintaining the quality of fresh cucumbers was studied; cucumbers were pretreated with cold shock and then packed into perforated polyethylene bags (bag size of 20 × 30 cm; film thickness of 0.07 mm; and two holes in each bag with a diameter of 6 mm), while the cucumbers without cold shock were considered as the control. Storage of the samples was performed at (13 ± 2) °C for 20 days to determine the quality changes in terms of gas composition, weight loss, skin color, texture, total soluble solids (TSS), ascorbic acid, malondialdehyde (MDA), and volatile organic compounds (VOCs). The CS-PMAP showed a significant improvement in maintaining firmness, TSS, ascorbic acid, and flavor profile of cucumbers; the control samples without cold shock showed higher weight loss and MDA levels. Results of this study confirmed that CS-PMAP has potential use in the storage of cucumbers.

3D Printing of Stretchable, Adhesive and Conductive Ti3C2Tx-Polyacrylic Acid Hydrogels.

Stretchable, adhesive, and conductive hydrogels have been regarded as ideal interfacial materials for seamless and biocompatible integration with the human body. However, existing hydrogels can rarely achieve good mechanical, electrical, and adhesive properties simultaneously, as well as limited patterning/manufacturing techniques posing severe challenges to bioelectronic research and their practical applications. Herein, we develop a stretchable, adhesive, and conductive Ti3C2Tx-polyacrylic acid hydrogel by a simple pre-crosslinking method followed by successive direct ink writing 3D printing. Pre-polymerization of acrylic acid can be initiated by mechanical mixing with Ti3C2Tx nanosheet suspension, leading to the formation of viscous 3D printable ink. Secondary free radical polymerization of the ink patterns via 3D printing can achieve a stretchable, adhesive, and conductive Ti3C2Tx-polyacrylic acid hydrogel. The as-formed hydrogel exhibits remarkable stretchability (~622%), high electrical conductivity (5.13 S m-1), and good adhesion strength on varying substrates. We further demonstrate the capability of facilely printing such hydrogels into complex geometries like mesh and rhombus patterns with high resolution and robust integration.

Effect of nickel-based electrocatalyst size on electrochemical carbon dioxide reduction: A density functional theory study.

The electrochemical carbon dioxide (CO2) reduction reaction (CO2RR) used for converting higher-value chemicals is a promising solution to mitigate CO2 emissions. Nickel (Ni)-based catalysts have been identified as a potential candidate for CO2 activation and conversion. However, in the CO2RR, the size effect of the Ni-based electrocatalysts has not been well explored. Herein, the single Ni atom and the Ni4 cluster doped nitrogen-doped carbon nanotube (Ni@CNT and Ni4@CNT), and the Ni (110) facet were designed to explore the size effect in the CO2RR by using density functional theory (DFT) calculations. The results show that carbon monoxide (CO) is produced on the Ni@CNT with a free energy barrier of 0.51 eV. The reduction product of CO2 on the Ni4@CNT and Ni(110) facet is methane (CH4) in both cases, via different reaction pathways, and the Ni(110) facet is a more efficient electrocatalyst with a low overpotential of 0.27 V when compared to Ni4@CNT (0.50 V). The rate-determining step (RDS) is the formation of *CHO on the Ni4@CNT (The "*" represents the catalytic surface), while the *COH formation is the RDS on the Ni(110) facet. Meanwhile, the Ni(110) facet also has the highest selectivity of CH4 among the three catalysts. The CO2 reduction product changes from CO to CH4 with the increasing size of the Ni-based catalysts. These results demonstrate that the catalytic activity and selectivity of CO2RR highly depend on the size of the Ni-based catalysts.

Screening and activity assessing of phosphorus availability improving microorganisms associated with bamboo rhizosphere in subtropical China.

Phosphorus-solubilizing microorganisms (PSMicros) play vital roles in helping plants to resist phosphorus (P) deficiency in soils, while their activities may vary with site conditions. The present study investigated the microbial diversity and subsequently screened PSMicro strains from rhizosphere soils at five bamboo forests in subtropical China, among which four were developed in a same stand. The activities of the screened PSMicros were also assessed. The results showed great variation in microbial diversity among different forests. Concomitantly, a total of 52 PSMicro strains were isolated and identified to 10 bacterial genera and 4 fungal genera, with different forest rhizosphere soils containing different PSMicros and/or showing different abundances for a certain PSMicro genus, despite some PSMicros would not grow readily on plates. Different, and even the same microbial genera isolated across the five forests, varied significantly in the amount of P that they solubilized from the medium, which ranged from 18.5 to 581.33 mg L-1 . Among the isolated PSMicros, species of Bacillus, Kluyvera, Buttiauxella, Meyerozyma and Penicillium were preponderant to liberate P from organic and inorganic P pools. This will have implications for biotechnological exploitation of microbes to alleviate P limitation in agricultural and natural systems with a sustainable green ecological approach.

Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units.

This paper develops Deep Neural Network (DNN) models that can recognize stroke gaits. Stroke patients usually suffer from partial disability and develop abnormal gaits that can vary widely and need targeted treatments. Evaluation of gait patterns is crucial for clinical experts to make decisions about the medication and rehabilitation strategies for the stroke patients. However, the evaluation is often subjective, and different clinicians might have different diagnoses of stroke gait patterns. In addition, some patients may present with mixed neurological gaits. Therefore, we apply artificial intelligence techniques to detect stroke gaits and to classify abnormal gait patterns. First, we collect clinical gait data from eight stroke patients and seven healthy subjects. We then apply these data to develop DNN models that can detect stroke gaits. Finally, we classify four common gait abnormalities seen in stroke patients. The developed models achieve an average accuracy of 99.35% in detecting the stroke gaits and an average accuracy of 97.31% in classifying the gait abnormality. Based on the results, the developed DNN models could help therapists or physicians to diagnose different abnormal gaits and to apply suitable rehabilitation strategies for stroke patients.

Online Gait Detection with an Automatic Mobile Trainer Inspired by Neuro-Developmental Treatment.

This paper demonstrates the development of an automatic mobile trainer employing inertial movement units (IMUs). The device is inspired by Neuro-Developmental Treatment (NDT), which is an effective rehabilitation method for stroke patients that promotes the relearning of motor skills by repeated training. However, traditional NDT training is very labor intensive and time consuming for therapists, thus, stroke patients usually cannot receive sufficient rehabilitation training. Therefore, we developed a mobile assisted device that can automatically repeat the therapists' intervention and help increase patient training time. The proposed mobile trainer, which allows the users to move at their preferred speeds, consists of three systems: the gait detection system, the motor control system, and the movable mechanism. The gait detection system applies IMUs to detect the user's gait events and triggers the motor control system accordingly. The motor control system receives the triggering signals and imitates the therapist's intervention patterns by robust control. The movable mechanism integrates these first two systems to form a mobile gait-training device. Finally, we conducted preliminary tests and defined two performance indexes to evaluate the effectiveness of the proposed trainer. Based on the results, the mobile trainer is deemed successful at improving the testing subjects' walking ability.

Synthesis and cytotoxic activity of chalcone analogues containing a thieno[2,3-d]pyrimidin-2-yl group as the A-ring or B-ring.

Many natural or synthetic chalcones have potential anti-tumor activity. Here, we synthesized two series of chalcone analogues containing a thieno[2,3-d]pyrimidin-2-yl group and evaluated for their cytotoxic activity towards cultured human lung cancer A549 and colorectal HCT-116 cells. Among them, compound 8d was the most cytotoxic against HCT-116 cells, with an IC50 value of 2.65 µM. Analyses of the phenotypic changes induced by this compound found a dose-dependent accumulation of HCT-116 cells in sub-G1 phase, indicating that compound 8d might induce apoptosis. Furthermore, we found that 8d triggered mitochondrial membrane potential depolarization, promoted reactive oxygen species formation in HCT-116 cells, and increased the percentage of early and late apoptotic cells. Finally, immunoblotting indicated that 8d increased PARP-1 and caspases 3, 7 and 9 cleavage. These data suggest that compound 8d induces apoptosis via the mitochondrial death pathway.