Multiscale-temporal Feature Extraction and boundary confusion alleviation for VAG-based fine-grained multi-grade osteoarthritis deterioration monitoring.

BACKGROUND AND OBJECTIVE: Multi-grade osteoarthritis (OA) deterioration monitoring in the daily paradigm using Vibroarthrography (VAG) is very challenging due to two difficulties: (1) the composition of VAG signals is complex in the daily paradigm where friction is intensified because of weight-bearing movements. (2) VAG signal samples near the decision boundary of adjacent deterioration grades are easy to be misclassified. The majority of existing works only focus on the binary classification of OA, providing inadequate assistance in instructing physicians to develop treatment plans based on the presence or absence of OA. Thus, we propose a novel framework for fine-grained multi-grade OA deterioration monitoring in the daily paradigm. METHODS: We propose an end-to-end deep learning framework termed Fine-grained Multi-grade OA Deterioration Monitor (FMOADM), which consists of Multiscale-temporal Feature Extraction (MTFE) and Confusion-Free Master-Slave (CF-MS) Classification. Specifically, MTFE is adopted to extract multiscale-temporal discriminative features from the complicated VAG signals. And center loss is introduced by CF-MS to alleviate confusion at the boundary of adjacent deterioration grades in the feature space. Meanwhile, a master-slave structure is proposed for further fine-grained classification, where the master classifier integrates a channel attention mechanism and the slave classifier is designed to update MTFE parameters. As a result, the proposed method ensures fine-grained multi-grade OA monitoring performance via multiscale-temporal discriminative features and boundary confusion alleviation. RESULTS: Experimental results on the VAG-OA dataset demonstrate that our framework outperforms counterpart methods in the daily paradigm. The proposed framework achieved 78% in precision, obtaining an 8% improvement over the state-of-the-art method. CONCLUSION: The proposed framework benefits efficient multi-grade OA deterioration monitoring, empowering physicians to develop treatment plans based on fine-grained monitoring results. It takes knee joint health monitoring in daily activities a step further toward feasible.

Public trust in general practitioners and its association with primary care contracts: a cross-sectional study of community residents in China.

OBJECTIVES: This study aimed to assess the level of public trust in general practitioners (GPs) and its association with primary care contract services (PCCS) in China. STUDY DESIGN: Cross-sectional study. METHODS: Between September and December 2021, 4158 residents across eastern, central, and western China completed a structured self-administered questionnaire. Trust was assessed using the Chinese version of Wake Forest Physician Trust Scale. Multivariable linear regression models were established to identify predictors of trust. The effect size of PCCS on trust was estimated by the average treatment effect for the treated (ATT) through propensity score matching. RESULTS: The study participants had a mean Wake Forest Physician Trust Scale score of 36.82 (standard deviation = 5.45). Enrollment with PCCS (ß = 0.14, P < 0.01), Han ethnicity (ß = 0.03, P < 0.05), lower educational attainment (ß = -0.06, P < 0.01), higher individual monthly income (ß = 0.03, P < 0.05), better self-rated health (ß = 0.04, P < 0.05), chronic conditions (ß = 0.07, P < 0.01), and higher familiarity with primary care services (ß = 0.12, P < 0.01) and PCCS (ß = 0.21, P < 0.01) were associated with higher trust in GPs. The ATT of PCCS exceeded 1 (P < 0.05). CONCLUSIONS: PCCS are associated with higher levels of trust in GPs. PCCS may become an effective tool to attract public trust in GPs, although the relationship between the two may be bi-directional.

Virtual-State Model for Analyzing Electro-Optical Modulation in Ring Resonators.

Ring resonators play a crucial role in optical communication and quantum technology applications. However, these devices lack a simple and intuitive theoretical model to describe their electro-optical modulation. When the resonance frequency is rapidly modulated, the filtering and modulation within a ring resonator become physically intertwined, making it difficult to analyze the complex physical processes involved. We address this by proposing an analytical solution for electro-optic ring modulators based on the concept of a "virtual state." This approach equates a lightwave passing through a dynamic ring modulator to one excited to a virtual state by a cumulative phase and then returning to the real state after exiting the static ring. Our model simplifies the independent analysis of the intertwined physical processes, enhancing its versatility in analyzing various incident signals and modulation formats. Experimental results, including resonant and detuning modulation, align with the numerical simulation of our model. Notably, our findings indicate that the dynamic modulation of the ring resonator under detuning driving approximates phase modulation.

Mechanistic insight into the competition between interfacial and bulk reactions in microdroplets through N2O5 ammonolysis and hydrolysis.

Reactive uptake of dinitrogen pentaoxide (N2O5) into aqueous aerosols is a major loss channel for NOx in the troposphere; however, a quantitative understanding of the uptake mechanism is lacking. Herein, a computational chemistry strategy is developed employing high-level quantum chemical methods; the method offers detailed molecular insight into the hydrolysis and ammonolysis mechanisms of N2O5 in microdroplets. Specifically, our calculations estimate the bulk and interfacial hydrolysis rates to be (2.3 ± 1.6) × 10-3 and (6.3 ± 4.2) × 10-7 ns-1, respectively, and ammonolysis competes with hydrolysis at NH3 concentrations above 1.9 × 10-4 mol L-1. The slow interfacial hydrolysis rate suggests that interfacial processes have negligible effect on the hydrolysis of N2O5 in liquid water. In contrast, N2O5 ammonolysis in liquid water is dominated by interfacial processes due to the high interfacial ammonolysis rate. Our findings and strategy are applicable to high-chemical complexity microdroplets.

Vibroarthrography-based Knee Lesions Location via Multi-Label Embedding Learning.

Vibration arthrography (VAG) signals are widely utilized for knee pathology recognition due to their non-invasive and radiation-free nature. While most studies focus on determining knee health status, few have examined using VAG signals to locate knee lesions, which would greatly aid physicians in diagnosis and patient monitoring. To address this, we propose using Multi-Label classification (MLC) to efficiently locate different types of lesions within a single input. However, current MLC methods are not suitable for knee lesion location due to two major issues: 1) the positive-negative imbalance of pathological labels in knee pathology recognition is not considered, leading to poor performance, and 2) sparse label correlations between different lesions cannot be effectively extracted. Our solution is a label autoencoder incorporating a pre-trained model (PTM-LAE). To mitigate the positive-negative disequilibrium, we propose a pre-trained feature mapping model utilizing focal loss to dynamically adjust sample weights and focus on difficult-to-classify samples. To better explore the correlations between sparse labels, we introduce a Factorization-Machine-based neural network (DeepFM) that combines higher-order and lower-order correlations between different lesions. Experiments on our collected VAG data demonstrate that our model outperforms state-of-the-art methods.

Molecular Insights into the Spontaneous Generation of Cl2O in the Reaction of ClONO2 and HOCl at the Air-Water Interface.

Chemical processes involving chlorine nitrate (ClONO2) at the surface of stratospheric aerosols are crucial to ozone depletion. Herein, we show a reaction route for the formation of Cl2O, which is a source of stratospheric chlorine, in the ClONO2 + HOCl reaction at the air-water interface. Our ab initio molecular dynamics (AIMD) simulations show that the (ClONO2)Cl···O(HOCl) halogen bond plays a key role in the reaction and is the main interaction between ClONO2 and HOCl both at the air-water interface and in the bulk liquid water. Furthermore, metadynamics-based AIMD simulations reveal two pathways: (i) The OCl fragment of HOCl binds to the Cl atom in ClONO2, resulting in the formation of Cl2O and NO3-. Simultaneously, the remaining hydrogen atom is transferred to a water molecule to form H3O+. (ii) HOCl acts as a bridge for Cl atom transfer from ClONO2 to the O atom of a water molecule, and this water molecule transfers one of its H atoms to another water molecule, forming two HOCl molecules, NO3-, and H3O+. Free-energy calculations show that the former is the energetically more favorable process. More importantly, the free-energy barrier for Cl2O formation at the air-water interface is only ∼0.8 kcal/mol, and the reaction is exothermic. These findings provide insights into the importance of fundamental chlorine chemistry and the broader implications of the aerosol air-water interface for atmospheric chemistry.

Mechanistic Insights into the Reactive Uptake of Chlorine Nitrate at the Air-Water Interface.

It is well-known that the aqueous-phase processing of chlorine nitrate (ClONO2) plays a crucial role in ozone depletion. However, many of the physical and chemical properties of ClONO2 at the air-water interface or in bulk water are unknown or not understood on a microscopic scale. Here, the solvation and hydrolysis of ClONO2 at the air-water interface and in bulk water at 300 K were investigated by classical and ab initio molecular dynamics (AIMD) simulations combined with free energy methods. Our results revealed that ClONO2 prefers to accumulate at the air-water interface rather than in the bulk phase. Specifically, halogen bonding interactions (ClONO2)Cl···O(H2O) were found to be the predominant interactions between ClONO2 and H2O. Moreover, metadynamics-biased AIMD simulations revealed that ClONO2 hydrolysis is catalyzed at the air-water interface with an activation barrier of only ∼0.2 kcal/mol; additionally, the difference in free energy between the product and reactant is only ∼0.1 kcal/mol. Surprisingly, the near-barrierless reaction and the comparable free energies of the reactant and product suggested that the ClONO2 hydrolysis at the air-water interface is reversible. When the temperature is lowered from 300 to 200 K, the activation barrier for the ClONO2 hydrolysis at the air-water interface is increased to ∼5.4 kcal/mol. These findings have important implications for the interpretation of experiments.

Willingness to maintain contracts with family doctors among Chinese residents: results from one national cross-sectional study and a meta-analysis of 25 studies.

Background: A number of studies have investigated the influencing factors regarding the renewal of contracts associated with Family Doctor Contract Services (FDCS) in different regions of China since it was officially implemented in 2009; however, none of the previous studies have been considered using a nationally representative sample in combination with a meta-analysis. Methods: A multistage stratified sampling method was used to investigate participants' socio-demographic characteristics, health status, understanding, use, and evaluation of the FDCS, and their willingness to renew contracts in Eastern, Central, and Western China from September to November 2021. We searched the PubMed, Ovid Medline, CNKI, VIP, Wanfang, and SinoMed databases to retrieve previous studies related to the willingness of Chinese residents to renew contracts with their family doctor (FD), and a meta-analysis was performed to systematically summarize the willingness to maintain contracts and influencing factors. Results: Among 2,394 residents, 2,122 (88.64%) were willing to renew their contracts. The mixed-effect logistic regression model results demonstrated that residents who (1) preferred primary health service institutions, (2) had a better knowledge of FDCS, (3) were more willing to visit primary health service after signing the contract with FDs, (4) were not intending to change FDs, (5) were satisfied with FDCS, and (6) trusted in FDs reported a higher level of willingness to maintain contracts with FDs. Our meta-analysis confirmed that older age, being married, having chronic diseases, choosing primary medical institutions for the first contact, having a good knowledge of FDCS/FDs, being satisfied with FDCS and the medical skills of FDs, and trusting FDs were all positively associated with residents' willingness to renew contracts (p < 0.05). Conclusion: The willingness of consumers to maintain contracts with FDs in China varies in different areas. Giving priority services to groups of high need contributed to an improved rate of renewal. We suggest that in order to continue to increase annual contract renewal, it is necessary to strengthen consumer awareness through effective marketing and continue to work toward meeting consumer expectations, thereby increasing confidence and trust in FDCS.

Generation of a coherent distributed RF array with a strong positive correlation.

In this work, we present a coherent distributed radio frequency (RF) array, discover and quantitatively describe the strong positive correlation between reconstructed signals for the first time. Eight replicable parallel receivers are connected to the phase-locked common trunk link via eight optical couplers spaced 1 km apart. The forward and backward signals at each receiver, extracted from two ports of optical couplers, are recovered to RF signals separately and then mixed to achieve upward frequency conversion. The link delay jitter is counteracted by wavelength-tuning of the optical carrier. With the long-term stability of point-to-multipoint fiber-optic RF dissemination effectively improved, the coherent distributed array is generated, and further the relative frequency stability between signals at different receivers is studied. The proposed correlation coefficient at 103 s is ∼0.8 and shows a slight downward trend with the increase of averaging time based on our experimental results.

Anomalous water transport in narrow-diameter carbon nanotubes.

Carbon nanotubes (CNTs) mimicking the structure of aquaporins support fast water transport, making them strong candidates for building next-generation high-performance membranes for water treatment. The diffusion and transport behavior of water through CNTs or nanoporous graphene can be fundamentally different from those of bulk water through a macroscopic tube. To date, the nanotube-length-dependent physical transport behavior of water is still largely unexplored. Herein, on the basis of molecular dynamics simulations, we show that the flow rate of water through 0.83-nm-diameter (6,6) and 0.96-nm-diameter (7,7) CNTs exhibits anomalous transport behavior, whereby the flow rate increases markedly first and then either slowly decreases or changes slightly as the CNT length l increases. The critical range of l for the flow-rate transition is 0.37 to 0.5 nm. This anomalous water transport behavior is attributed to the l-dependent mechanical stability of the transient hydrogen-bonding chain that connects water molecules inside and outside the CNTs and bypasses the CNT orifice. The results unveil a microscopic mechanism governing water transport through subnanometer tubes, which has important implications for nanofluidic manipulation.

Microscopic Insight into Water Desalination through Nanoporous Graphene: The Influence of the Dipole Moment.

Nanoporous graphene membranes with controllable pore size and chemical functionality may be one of the most desirable materials for water desalination. Herein, we investigate desalination performance of hydrogen-functionalized nanoporous graphene membranes. The charge values on hydrogen atoms (qH) and carbon atoms at the pore rim are systematically adjusted. For qH > 0, the flow rate decreases as qH increases, whereas for qH < 0, the flow rate tends to increase first and then decrease with increasing qH, yielding a peak at ∼ -0.2 e. Moreover, nanopores with large dipole moments at the rim have little effect on the salt rejection. The calculated oxygen and hydrogen density maps, the potential of mean force for water molecule and salt ion passage through the nanopores, and the coordination number unveil the mechanisms underlying water desalination in nanoporous graphene. This work may inspire the design and improvement of two-dimensional membranes for water desalination.

Perinatal exposure to low-level PBDE-47 programs gut microbiota, host metabolism and neurobehavior in adult rats: An integrated analysis.

Polybrominated diphenyl ethers (PBDEs), a major class of flame retardants, have been extensively applied in plastics, electrical equipment, textile fabrics, and so on. Early-life exposure to PBDEs is correlated to neurobehavioral deficits in adulthood, yet the underlying mechanism has not been fully understood. Increasing evidence has demonstrated that gut microbiota dysbiosis and serum metabolites alterations play a role in behavioral abnormalities. However, whether their perturbation is implicated in PBDEs-induced neurotoxicity remains unclear. Here, we sought to explore the effects of developmental exposure to environmentally relevant levels of 2, 2', 4, 4'-tetrabromodiphenyl ether (PBDE-47), a major congener in human samples, on gut microbiota and serum metabolic profile as well as their link to neurobehavioral parameters in adult rats. The open field test showed that gestational and lactational exposure to PBDE-47 caused hyperactivity and anxiety-like behavior. Moreover, 16S rRNA sequencing of fecal samples identified a distinct community composition in gut microbiota following PBDE-47 exposure, manifested as decreased genera Ruminococcaceae and Moraxella, increased families Streptococcaceae and Deferribacteraceae as well as genera Escherichia-Shigella, Pseudomonas and Peptococcus. Additionally, the metabolomics of the blood samples based on liquid chromatography-mass spectrometry revealed a significant shift after PBDE-47 treatment. Notably, these differential serum metabolites were mainly involved in amino acid, carbohydrate, nucleotide, xenobiotics, and lipid metabolisms, which were further validated by pathway analysis. Importantly, the disturbed gut microbiota and the altered serum metabolites were associated with each other and with neurobehavioral disorders, respectively. Collectively, these results suggest that gut microbiota dysbiosis and serum metabolites alterations potentially mediated early-life low-dose PBDE-47 exposure-induced neurobehavioral impairments, which provides a novel perspective on understanding the mechanisms of PBDE-47 neurotoxicity.

Monitoring deterioration of knee osteoarthritis using vibration arthrography in daily activities.

BACKGROUND AND OBJECTIVE: Pathological recognition of knee joint using vibration arthrography (VAG) is increasingly becoming prevailed, due to the non-invasive and non-radiative benefits. However, knee joint health monitoring using VAG signals is a difficult problem, since VAG signals are contaminated by strong motion artifacts (MA) caused by knee movements during daily activities, such as squatting. So far few works have investigated this problem. Existing studies mainly focused on clinical diagnosis of knee disorders for 2-class (normal/abnormal) classification using VAG signals, which are less contaminated by MA in the scene when subjects perform knee extension and flexion movements in seated position. The purpose of this study is to propose a framework to monitor knee joint health during daily activities. METHODS: In this paper, a general framework is designed to monitor knee joint health, which consists of VAG enhancement, feature extraction and fusion, and classification. VAG enhancement aims to remove MA and irrelevant components of knee joint pathologies in raw VAG signals. Distinctive features from enhanced VAG signals are obtained in feature extraction and fusion. Classification can not only distinguish whether the knee joint is normal or abnormal, but also distinguish the grade of deterioration of knee osteoarthritis. RESULTS: 813 VAG signals from VAG-OA dataset, which is currently the largest VAG dataset, have been collected from medical cases in Xijing Hospital of the Fourth Military Medical University during daily activities. Experimental results on VAG-OA dataset showed that the accuracy of 2-class (normal/abnormal) classification was 95.9% with sensitivity 98.1% and specificity 93.3%. For 5-class classification based on deterioration grades of osteoarthritis (OA), we obtained accuracy 74.4%, sensitivity 52.6% and specificity 78.3%. CONCLUSION: The VAG-OA dataset can be used not only for knee joint health monitoring but also for clinical diagnosis. The designed framework on VAG-OA dataset has high classification accuracy, which is of great value to monitor knee joint health using VAG signals during daily activities. The results also demonstrate that the designed framework significantly outperforms the baselines and several state-of-the-art methods.

Alleviating Feature Confusion in Cross-Subject Human Activity Recognition via Adversarial Domain Adaptation Strategy.

Sensor-based Human Activity Recognition (HAR) plays an important role in health care. However, great individual differences limit its application scenarios and affect its performance. Although general domain adaptation methods can alleviate individual differences to a certain extent, the performance of these methods is still not satisfactory, since the feature confusion caused by individual differences tends to be underestimated. In this paper, for the first time, we analyze the feature confusion problem in cross-subject HAR and summarize it into two aspects: Confusion at Decision Boundaries (CDB) and Confusion at Overlapping (COL). The CDB represents the misclassification caused by the feature located near the decision boundary, while the COL represents the misclassification caused by the feature aliasing of different classes. In order to alleviate CDB and COL to improve the stability of trained model when processing the data from new subjects, we propose a novel Adversarial Cross-Subject (ACS) method. Specifically, we design a parallel network that can extract features from both image space and time series simultaneously. Then we train two classifiers adversarially, and consider both features and decision boundaries to optimize the distribution to alleviate CDB. In addition, we introduce Minimum Class Confusion loss to reduce the confusion between classes to alleviate COL. The experiment results on USC-HAD dataset show that our method outperforms other generally used cross-subject methods.

End-to-End Versatile Human Activity Recognition with Activity Image Transfer Learning.

Transfer learning is a common solution to address cross-domain identification problems in Human Activity Recognition (HAR). Most existing approaches typically perform cross-subject transferring while ignoring transfers between different sensors or body parts, which limits the application scope of these models. Only a few approaches have been made to design a versatile HAR approach (cross-subject, cross-sensor and cross-body-part). Unfortunately, these existing approaches depend on complex handcrafted features and ignore the inequality of samples for positive transfer, which will hinder the transfer performance. In this paper, we propose a framework for versa-tile cross-domain activity recognition. Specifically, the proposed framework allows end-to-end implementation by exploiting adaptive features from activity image instead of extracting handcrafted features. And the framework uses a two-stage adaptation strategy consisting of pretraining stage and re-weighting stage to perform knowledge transfer. The pretraining stage ensures transferability of the source domain as well as separability of the target domain, and the re-weighting stage rebalances the contribution of the two domain samples. These two stages enhance the ability of knowledge transfer. We evaluate the performance of the proposed framework by conducting comprehensive experiments on three public HAR datasets (DSADS, OPPORTUNITY, and PAMAP2), and the experimental results demonstrate the effectiveness of our framework in versatile cross-domain HAR.

PPG-based Biometric Identification: Discovering and Identifying a New User.

The convenience of Photoplethysmography (PPG) signal acquisition from wearable devices makes it becomes a hot topic in biometric identification. A majority of studies focus on PPG biometric technology in a verification application rather than an identification application. Yet, in the identification application, it is an inevitable problem in discovering and identifying a new user. However, so far few works have investigated this problem. Existing approaches can only identify trained old users. Their identification model needs to be retrained when a new user joins, which reduces the identification accuracy. This work investigates the approach and performance of identifying both old users and new users on a deep neural network trained only by old users. We used a deep neural network as a feature extractor, and the distance of the feature vector to discover and identify a new user, which avoids retraining the identification model. On the BIDMC data set, we achieved an accuracy of more than 99% for old users, an accuracy of more than 90% for discovering a new user, and an average accuracy of about 90% for identifying a new user. Our proposed approach can accurately identify old users and has feasibility in discovering and identifying a new user without retraining in the identification application.

Mechanisms of Acid-Promoted N2 and N2O Generation from NH2NO and NH2NO2.

The effects of water, formic acid, and nitric acid on N2 and N2O generation from NH2NO and NH2NO2 are studied using high-level quantum-chemical calculations. It is shown that the reaction barriers from isolated NH2NO and NH2NO2 are 35.5 and 38.1 kcal/mol, respectively. When the NH2NO and NH2NO2 reactions are examined in the presence of water, formic acid, or nitric acid, the energy barriers are different. The mechanisms of these reactions are revealed through reaction pathway calculations. The isomerization of intermediate HNNOH and HNNOOH molecules, which follows a group rotation mechanism, plays a key role in reactions of isolated NH2NO and NH2NO2. However, the presence of water, formic acid, or nitric acid changes the isomerization mechanism substantially. HNNOH or HNNOOH and the catalyzing molecule form a doubly hydrogen-bonded prereactive complex, which, in turn, facilities hydrogen atom migration (this is denoted as the hydrogen atom migration mechanism). This study demonstrates the feasibility of N2 or N2O generation from NH2NO and NH2NO2 in the presence of water, formic acid, or nitric acid.

Janus Graft Block Copolymers: Design of a Polymer Architecture for Independently Tuned Nanostructures and Polymer Properties.

The graft-through synthesis of Janus graft block copolymers (GBCPs) from branched macromonomers composed of various combinations of homopolymers is presented. Self-assembly of GBCPs resulted in ordered nanostructures with ultra-small domain sizes down to 2.8 nm (half-pitch). The grafted architecture introduces an additional parameter, the backbone length, which enables control over the thermomechanical properties and processability of the GBCPs independently of their self-assembled nanostructures. The simple synthetic route to GBCPs and the possibility of using a variety of polymer combinations contribute to the universality of this technique.