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Lithium (Li) is an important resource that drives sustainable mobility and renewable energy. Its demand is projected to continue to increase in the coming decades. However, the risk of Li pollution has also emerged as a global concern. Here, we investigated the pollution characteristics, sources, exposure levels, and associated health risks of Li in the Jinjiang River basin, the largest area for Li2CO3production in China. Our results revealed the dominant role of Li extraction activities in the pollution of the river, with over 95% of dissolved Li in downstream river water being emitted from this source. Moreover, the Li concentration in aquatic plants (i.e., water hyacinth) and animals (i.e., fish) significantly increased from upstream to downstream areas, indicating a significant risk to local aquatic ecosystems. More importantly, our study found that local residents were suffering potential chronic noncarcinogenic health risks primarily from consuming contaminated water and vegetables. We also investigated the pollution characteristics of associated elements present in Li ores (e.g., Rb, Cs, Ni, and F–). By uncovering the remarkable impact of Li extraction activities on the Li content in ecosystems for the first time, our study emphasizes the importance of evaluating Li pollution from Li-related industrial activities, including mining, extraction, and recovery.

This article proposes a motion-robust video-based respiratory rate (RR) monitoring method. Specifically, the chest and abdomen regions of the human body are detected and tracked as the region of interest (ROI), which are further divided into patches. The dense inverse search (DIS) is then employed to calculate the optical flow within each patch. The phase signal of the optical flow is taken as the raw respiratory signal, which is denoised by a normalized least mean square (NLMS) adaptive filtering with the horizontal flow signal as the motion noise reference. Finally, a quality-guided RR pulse selection method in the time–frequency domain is designed to fuse the denoised RR pulses from all patches. The proposed approach is evaluated on the in-house BSIPL-RR dataset and the public COHFACE dataset. Experimental results indicate that this method outperforms the state-of-the-art (SOTA) comparison methods. This research effectively extends the scope of video-based RR measurements in real-world applications.

Video-based Photoplethysmography (VPPG) offers the capability to measure heart rate (HR) from facial videos. However, the reliability of the HR values extracted through this method remains uncertain, especially when videos are affected by various disturbances. Confronted by this challenge, we introduce an innovative framework for VPPG-based HR measurements, with a focus on capturing diverse sources of uncertainty in the predicted HR values. In this context, a neural network named HRUNet is structured for HR extraction from input facial videos. Departing from the conventional training approach of learning specific weight (and bias) values, we leverage the Bayesian posterior estimation to derive weight distributions within HRUNet. These distributions allow for sampling to encode uncertainty stemming from HRUNet's limited performance. On this basis, we redefine HRUNet's output as a distribution of potential HR values, as opposed to the traditional emphasis on the single most probable HR value. The underlying goal is to discover the uncertainty arising from inherent noise in the input video. HRUNet is evaluated across 1,098 videos from seven datasets, spanning three scenarios: undisturbed, motion-disturbed, and light-disturbed. The ensuing test outcomes demonstrate that uncertainty in the HR measurements increases significantly in the scenarios marked by disturbances, compared to that in the undisturbed scenario. Moreover, HRUNet outperforms state-of-the-art methods in HR accuracy when excluding HR values with $>$0.4 uncertainty. This underscores that uncertainty emerges as an informative indicator of potentially erroneous HR measurements. With enhanced reliability affirmed, the VPPG technique holds the promise for applications in safety-critical domains.

The video-based frequency measurement method for bridge cable vibration offers advantages such as speed, efficiency, and noncontact compared to traditional sensor-based methods. However, the presence of complex backgrounds in video images can affect the accuracy of cable frequency measurement. To address the problem, a novel phase-based frequency measurement method is proposed, which focuses on extracting cable edge vibration from background noise in the spatial and temporal domains. First, in the spatial domain, to process the vibration signals more precisely, each video sequence is divided into multiple subregions. To enhance the edge vibration within the subregions while initially suppressing background noise, the Otsu threshold segmentation method (OTSM) is employed for subregion categorization. Subsequently, the phase-based vibration estimation method is utilized to build the spatial domain vibration representation of the subregions based on the phase differences between adjacent frames while maintaining optical flow consistency. Then, the temporal vibrational waveforms are extracted, which may still include noise from the background edges. To restore the cable vibration, a combination of singular spectrum analysis and nonnegative matrix factorization (NMF) is further designed for characterizing cable vibrations and attenuating the noise in the temporal domain. Finally, the cable vibration restored from all subregions is synthesized, forming the ultimate cable signal. The proposed method has been evaluated through extensive testing in outdoor environments, and it has exhibited remarkable enhancements in measuring cable vibration frequencies when dealing with complex background interference compared to the existing methods.

Arterial stiffness (AS) serves as a crucial indicator of arterial elasticity and function, typically requiring expensive equipment for detection. Given the strong correlation between AS and various photoplethysmography (PPG) features, PPG emerges as a convenient method for assessing AS. However, the limitations of independent PPG features hinder detection accuracy. This study introduces a feature selection method leveraging the interactive relationships between features to enhance the accuracy of predicting AS from a single-channel PPG signal. Initially, an adaptive signal interception method was employed to capture high-quality signal fragments from PPG sequences. 58 PPG features, deemed to have potential contributions to AS estimation, were extracted and analyzed. Subsequently, the interaction factor (IF) was introduced to redefine the interaction and redundancy between features. A feature selection algorithm (IFFS) based on the IF was then proposed, resulting in a combination of interactive features. Finally, the Xgboost model is utilized to estimate AS from the selected features set. The proposed approach is evaluated on datasets of 268 male and 124 female subjects, respectively. The results of AS estimation indicate that IFFS yields interacting features from numerous sources, rejects redundant ones, and enhances the association. The interaction features combined with the Xgboost model resulted in an MAE of 122.42 and 142.12 cm/sec, an SDE of 88.16 and 102.56 cm/sec, and a PCC of 0.88 and 0.85 for the male and female groups, respectively. The findings of this study suggest that the stated method improves the accuracy of predicting AS from single-channel PPG, which can be used as a non-invasive and cost-effective screening tool for atherosclerosis.

The video sensor-based method for bridge cable frequency measurement offers notable advantages in terms of speed, efficiency, and noncontact advantages compared with the conventional acceleration sensor approach. However, accurately measuring the cable’s microvibration under natural excitation remains a challenge for the video sensor method. To address the problem, a novel phase-based frequency measurement method, named EnhancedPVE, is proposed, which leverages the signal characteristics difference between cable signal and noise signal in the temporal and spatial domains without the necessity of cable identification. First, in the spatial domain, to preserve and enhance the cable phase while simultaneously attenuating the noise phase, a method combining bilateral filtering and phase-based motion amplification algorithm is designed based on the feature difference between cable edge phase and noise phase, which effectively enhances the discernibility of cable phase. Subsequently, in the time domain, to enhance the purity of the cable signal, the video image frame is partitioned into multiple subregions. Then, an innovative algorithm, named the maximum contribution combination (MCC) algorithm, is devised to determine the contribution degree of each subregion to the cable signal, which differs from the traditional phase average weighting approach. Finally, the cable vibration obtained from all subregions is synthesized, forming the ultimate cable signal. The EnhancedPVE method is evaluated in laboratory and extensive outdoor experiments. Compared with the state-of-the-art methods, EnhancedPVE exhibits substantial improvements on the cable microvibration frequency measurement under natural excitation.

Remote photoplethysmography (rPPG) enables heart rate (HR) measurement under stable illumination and low noise conditions. However, challenges arise due to rapid lighting changes, significant head movements, and vehicle vibrations during driving, impacting the recovery of clear rPPG signals and rendering denoising techniques alone inadequate in entirely eliminating noise interference. To address these challenges, we introduce an innovative approach, “quality-guided spectrum peak screening” (QSPS), for monitoring the driver’s HR during driving. First, we developed a signal evaluation framework for assessing signal quality across multiple facial regions following wavelet filtering. Subsequently, by leveraging quality information, a spectral peak screening algorithm is applied to signals from multiple regions, mitigating the impact of residual noise on rPPG. The precise HR is determined by integrating quality scores and short-term stability from various facial regions. Our method is employed in a commercially available driver monitoring system (DMS) equipped with a monochrome camera. Test results show QSPS’s robustness compared with established near-infrared (NIR)-based HR detection methods. In driving scenarios, QSPS achieves a mean absolute error (MAE) of 4.32 bpm, a root mean square error (RMSE) of 6.15 bpm, and a percentage of time with HR estimation errors smaller than 6 bpm of 68.8%. Furthermore, QSPS exhibits excellent performance during extended 10-min HR monitoring, with a Pearson correlation coefficient of 0.943 in the night test and 0.906 in the day test.

This paper proposes a main-auxiliary cooperative receiving coil (MA-coil) with a lower space occupation rate and a simple control based on the time-sharing working principle, which can effectively improve the anti-misalignment capability of a dynamic wireless charging (DWC) system. First, the structure and circuit topology of the MA-coil are designed. The two auxiliary coils (A-coil) are connected in reverse series and symmetrically placed on both sides of the main coil (M-coil). Second, the output performance of the MA-coil in the y-direction is calculated based on the time-sharing working principle. The A-coil works by itself and enhances the output power when side shift occurs. Third, the most suitable ratio of coil width wMand wAis determined. The anti-misalignment performance and the effective side shift range are compared through simulation between the MA-coil in this case and the square coil. Finally, an experimental prototype is built to verify the feasibility of the proposed structure, and experimental results obtained from the prototype are basically consistent with the theoretical analysis. The anti-misalignment capability of the MA-coil is more than 20% higher than that of the square coil.

Aiming at the problem that the amplitudes of false targets (FTs) are correlated among receivers in multistatic radar system, a multijammer cooperative deception jamming method based on periodic multiple phases modulation (PMPM) is proposed. A FT string with random fluctuations in amplitude can be generated after the signal undergoes PMPM. In the distributed jammer system, each jammer adopts PMPM jamming method with different parameters, and then jointly transmit beamforming to forward jamming signals. Finally, the amplitude of the FTs between receivers is jointly affected by the azimuth angle and the phase modulation coefficient, which destroys the amplitude “coherence” of the FTs between receivers, and eliminates the amplitude symmetry and high-order attenuation characteristics of the FT strings generated by traditional jamming method. We study the amplitude compensation and amplitude correlation performance of the proposed jamming method, derive the rank of the ratio matrix of amplitude correlation factors, and prove that FTs have dispersiveness in the amplitude ratio space. In order to obtain the optimal and stable jamming effect, a genetic algorithm based jamming modulation parameter optimization method is designed, which effectively drops the discrimination probability of physical targets (PTs) to 14% in the benchmark scenario. Simulation experiments demonstrate the feasibility and effectiveness of the jamming method.

Video-based Photoplethysmography (VPPG) can identify arrhythmic pulses during atrial fibrillation (AF) from facial videos, providing a convenient and cost-effective way to screen for occult AF. However, facial motions in videos always distort VPPG pulse signals and thus lead to the false detection of AF. Photoplethysmography (PPG) pulse signals offer a possible solution to this problem due to the high quality and resemblance to VPPG pulse signals. Given this, a pulse feature disentanglement network (PFDNet) is proposed to discover the common features of VPPG and PPG pulse signals for AF detection. Taking a VPPG pulse signal and a synchronous PPG pulse signal as inputs, PFDNet is pre-trained to extract the motion-robust features that the two signals share. The pre-trained feature extractor of the VPPG pulse signal is then connected to an AF classifier, forming a VPPG-driven AF detector after joint fine-tuning. PFDNet has been tested on 1440 facial videos of 240 subjects (50% AF absence and 50% AF presence). It achieves a Cohen's Kappa value of 0.875 (95% confidence interval: 0.840-0.910, $\mathbf{{P}}$<0.001) on the video samples with typical facial motions, which is 6.8% higher than that of the state-of-the-art method. PFDNet shows significant robustness to motion interference in the video-based AF detection task, promoting the development of opportunistic screening for AF in the community.

Video-based vibration measurement technology offers the benefits of noncontact measurement and high automation. By mounting a camera on an unmanned aerial vehicle (UAV), it enables the assessment of vibrations in objects that are inaccessible with traditional fixed camera systems, including large buildings and elevated mechanical equipment. However, existing video-based vibration measurement methods are challenging with UAV-mounted airborne cameras. In this article, we present an anti-disturbance video-based vibration measurement method that enables an airborne camera to accurately measure the vibrations of a target structure despite the presence of airflow-induced shaking. To address the global nature of UAV disturbances and the localized nature of target vibrations in the spatial domain, we propose a vibration extraction framework that leverages regional adjacent-frame phase differences (PDs). Furthermore, we design a baseline correction algorithm named piecewise empirical mode decomposition (PEMD) to suppress disturbances in the temporal domain by utilizing the trend characteristics of UAV disturbances in the PD sequence. Experiments are conducted in laboratory and outdoor settings, revealing that the correlation coefficient between the spectrums measured by the proposed method and the ground truth can reach 0.97. The results demonstrate that the proposed method outperforms existing state-of-the-art approaches with higher accuracy.

Objective: The tiny change of skin color, caused by a heartbeat, can be captured with consumer-level cameras by using the imaging photoplethysmography (iPPG) technique, offering a noncontact way of extracting pulse signals. Pulse signals have been demonstrated to contain information on human physiological characteristics and have been used for blood pressure (BP) estimation in recent years. According to BP-related cardiovascular knowledge, this article presents a new method for BP estimation based on the iPPG pulse signals, featured by incorporating cardiovascular characteristics including heart rate (HR), stroke volume (SV), the elasticity of vessel walls (EVW), and peripheral vascular resistance (PVR). Correlations between the systolic BP (SBP), diastolic BP (DBP), pulse pressure (PP), and cardiovascular characteristics are extracted, which facilitates the selection of pulse features consistent with BP properties. Based on the selected features, two Bayesian neural network (BNN) models are constructed for the estimation of SBP and DBP, respectively, where the machine learning (ML) uncertainty of the estimation is also evaluated. This method is uncalibrated which means it can work without additional information except for the videos from the camera. The proposed method has been tested on 220 patients with a history of cardiovascular diseases. Errors of the BP estimation are 9 ± 13 (MAE ± STD) mmHg for SBP, 7 ± 10 (MAE ± STD) mmHg for DBP, and the ML uncertainty of the estimation indicates the reliability of the proposed method.

Coronary artery disease (CAD) is highly prevalent worldwide but is challenging to identify due to its hidden symptoms. Facial videos provide a possible means for CAD screening as facial features and skin color changes are associated with CAD risk. However, opposite test results of CAD may be drawn from facial features and skin color changes. A neural network, VideoCAD, is proposed in this work to address this problem. It consists of two modules, PulseCAD and ImageCAD. PulseCAD predicts whether CAD was present based on the facial features in a video frame, and ImageCAD performs CAD prediction based on the pulse-related features in skin color changes. VideoCAD quantifies the uncertainties of the two modules’ predictions before selecting the prediction with lower uncertainty as the final result. VideoCAD is evaluated on 1200 video samples of 200 subjects (50% CAD). Its predictions are in substantial agreement with the ground truths, with both the sensitivity (SE) and specificity (SP) of $>0.88$ . It also outperforms the state-of-the-art methods that only consider the face or pulse features, achieving a >20% increase in Cohen’s Kappa coefficient. This technique may hold promise to achieve opportunistic screening for occult CAD in the community.

ABSTRACTRecently, Convolutional Neural Network (CNN) has achieved some success in synthetic aperture radar (SAR) image classification. This outstanding performance mainly depends on a large number of training samples, but achieved satisfactory classification results with limited training samples remains a challenge. To address this problem, we propose a dual-scale Siamese densely connected network with Markov Random Fields (DS-SDCNet-MRF) for single-polarization SAR image classification. First, the Siamese densely connected network (SDCNet) is proposed to fully extract discriminative features under limited samples. Then, the proposed DS-SDCNet is constructed with two SDCNets of different scales to produce complementary classification results. Among them, the large-scale SDCNet has a better classification result in the homogeneous region, while the small-scale SDCNet tends to provide good detail preservation. Finally, an improved MRF model which combined the category probability information is proposed to further improve the classification performance. Experimental results on simulated and real single-polarization SAR data demonstrate that the proposed method achieves more encouraging classification performance than the current state-of-the-art classification methods with limited samples.

During the SARS period in 2003 and COVID-19 pandemic period in 2020, unexpected severe particulate matter pollution occurred in northern China, although the anthropogenic activities and associated emissions have assumed to be reduced dramatically. This anomalistic increase in PM2.5pollution raises a question about how source emissions impact the air quality during these pandemic periods. In this study, we investigated the stable Cu and Si isotopic compositions and typical source-specific fingerprints of PM2.5and its sources. We show that the primary PM2.5emissions (PM2.5emitted directly from sources) actually had no reduction but redistribution during these pandemic periods, rather than the previous thought of being greatly reduced. This finding provided critical evidence to interpret the anomalistic PM2.5increase during the pandemic periods in north China. Our results also suggested that both the energy structure adjustment and stringent regulations on primary emissions should be synergistically implemented in a regional scale for clean air actions in China.

Background: Xuezhikang capsule, which contains cholesterol synthase inhibitors and a large number of natural statins, is put in the clinical application of lipid-lowering and so on. However, the specific use of dose, lipid-lowering effect and the relationship between metabolites are to be further studied. Introduction: Metabonomics is the study of the relationship between the change of quantity and physiological changes from metabolites. At present, metabolomics has been widely used in drug development and testing. In this study, we developed a metabolomic method based on gas chromatographymass spectrometry (GC-MS) to find out hyperlipemia-related substances, and study the lipid-lowering mechanism of Xuezhikang. Methods: Fifty SD rats (220 ± 20 g) were given a high-fat diet. After four-weeks modeling, they were randomly divided into semi-control groups, high fat group, simvastatin intervention group and Xuezhikang intervention group (0.23, 0.69, 1.15 mg/kg, low, medium, high), each dosage in eight rats. The control group (rest eight rats) were given a normal diet, and no specific treatment. The rats were sacrificed at the end of the experiment. Results: The biochemical and body weight indexes of the normal control group and the high fat group were significantly different (P <0.05), which indicated that the model of hyperlipidemia was established success. There was a significant difference (P <0.05) between the Xuezhikang intervention group and high-fat control group (P <0.05), and hyperlipemia metabolomics related markers, oxalic acid, butyric acid, mannitol, glucose, glucuronic acid were found. Glucuronic acid and non-binding bilirubin combined with bilirubin, combined with some of the liver harmful substances, play a detoxification effect. Conclusion: The results of metabonomics showed that the high-fat group and the control group have significant differences. Mannose, glucose content is relatively stable, lipid metabolism in high-fat group stearic acid, palmitic acid levels decreased, suggesting that high-fat diet disorders rat body lipid metabolism. It is worth mentioning that the experimental evaluation of rats, such as biochemical indicators and pathological results are prompted to model success, Xuezhikang intervention effect is more significant, consistent with the expected.

ABSTRACTRecently, convolutional neural networks (CNNs) have been widely used for synthetic aperture radar (SAR) image classification because of their powerful feature extraction ability and high performance. However, extracting discriminative features with limited training samples is still a challenge. Moreover, some samples may be image edge samples, which often contain multiple image categories, thus deteriorate classification accuracy. To address these issues, we propose a novel classification framework, named dense-connected global covariance network (DGCNet) with edge sample constraint (ESC). First, a dense-connected sub-network was designed, which can connect different convolutional layers of conventional CNN to strengthen feature propagation, encourage feature reuse, and alleviate gradient vanishing problem. Then, a global covariance pooling layer was introduced to fully exploit the second-order information of deep features and reduce the number of training parameters. Finally, an ESC strategy was integrated into DGCNet to further improve the classification performance by assigning a smaller weight to edge samples than non-edge samples during the training process. Experimental results on two datasets demonstrated that the proposed method achieves better classification results than several popular classification methods with limited training samples.

Introduction: Epilepsy is one of the most common neurological diseases in clinical practice. The combined application of metabolomics technology plays a great advantage in the screening of biomarkers. Methods: In this study, Wistar rats were used as experimental subjects to model intractable epilepsy and to detect the metabolic changes of small molecules in plasma. UPLC-MS/MS was used to determine the small molecules in rat plasma. UPLC HSS C18 (2.1mmx100mm, 1.7 μm) column was used for separation, column temperature of 40°C. The initial mobile phase was acetonitrile -0.3% formic acid with gradient elution, the flow rate was 0.3 mL/min, total running time 4.0 min. Quantitative analysis was performed with multi-response monitoring (MRM). Results: Compared to the control group, the L-Alanine and L-Arginine decreased in the Epilepsy group (p<0.05); while Cytosine, Adenosine, L-Tyrosine, Citric acid, Fructose increased (p<0.05). Conclusion: In the screening of epilepsy biomarkers using metabolomics, various amino acids that lead to increased energy production and neurotransmitter imbalance play an important role in epileptic seizures.

There is an urgent need for reliable and effective models to study air pollution health effects on human lungs. Here, we report the utilization of human pluripotent stem cell (hPSC) induction models for human lung progenitor cells (hLPs) and alveolar type 2 epithelial cell-like cells (ATLs) for the toxicity assessment of benzo(a)pyrene, nano-carbon black, and nano-SiO2, as common air pollutants. We induced hPSCs to generate ATLs, which recapitulated key features of human lung type 2 alveolar epithelial cells, and tested the induction models for cellular uptake of nanoparticles and toxicity evaluations. Our findings reveal internalization of nano-carbon black, dose-dependent uptake of nano-SiO2, and interference with surfactant secretion in ATLs exposed to benzo(a)pyrene/nano-SiO2. Thus, hLP and ATL induction models could facilitate the evaluation of environmental pollutants potentially affecting the lungs. In conclusion, this is one of the first studies that managed to adopt hPSC pulmonary induction models in toxicology studies.

Two efficient synthetic methods for preparation of 3-(trifluoromethyl)cyclobutane-1-carboxylic acid are reported starting from readily available 4-oxocyclobutane precursors. These cyclobutanones can be converted to their CF3carbinols upon treatment with TMSCF3and a fluoride source. The bis-carboxylate system 9was deoxygenated by treatment of Bu3SnH and provided desired compound 1upon decarboxylation. In the monocarboxylate system 15, the triflate could be efficiently eliminated; subsequent hydrogenation afforded cis-1.

Nanosized magnetite is a highly toxic material due to its strong ability to generate reactive oxygen species in vivo, and the presence of magnetite NPs in the brain has been linked with aging and neurodegenerative diseases such as Alzheimer’s disease. Recently, magnetite pollution nanoparticles (NPs) were found to be present in the human brain, heart, and blood, which raises great concerns about the health risks of airborne magnetite NPs. Here, we report the abundant presence and chemical multifingerprints (including high-resolution structural and elemental fingerprints) of magnetite NPs in the urban atmosphere. We establish a methodology for high-efficiency retrieving and accurate quantification of airborne magnetite NPs. We report the occurrence levels (annual mean concentration 75.5 ± 33.2 ng m–3in Beijing with clear season variations) and the pollution characteristics of airborne magnetite NPs. Based on the chemical multifingerprints of the NPs, we identify and estimate the contributions of the major emission sources for airborne magnetite NPs. We also give an assessment of human exposure risks of airborne magnetite NPs. Our findings support the identification of airborne magnetite NPs as a threat to human health.

Since the implementation of the “Air Pollution Prevention and Control Action Plan” (APPCAP) in 2013, the air quality in China has been greatly improved but still much exceeded the WHO guideline limit. Here we employed a novel approach, two-dimensional Si fingerprints, including stable Si isotopic composition (δ30Si and Si abundance (Si%), to investigate the annual variations in both primary and secondary sources of PM2.5in Beijing during the APPCAP period (2013–2017). The δ30Si and Si% values were used as tracers to reflect the variations in primary and secondary sources, respectively. For primary sources, the mean δ30Si value of PM2.5in 2015–2017 (>−0.5‰) was significantly more positive than that of 2013 (−1.24‰), indicating a dramatic decline in the contribution of 30Si-depleted sources (i.e., coal burning and industrial emission). For secondary sources, the mean Si% of PM2.5increased from 1.2% in 2013 to 4.6% in 2017, suggesting a large decrease in the secondary aerosol contribution from 83% to 42%. It is worth noting that we found the 30Si-depleted sources showed a rebound trend during 2015–2017. This study reveals the responses of anthropogenic emission sources under strong regulation policies and provides a reference for future policymaking in Beijing and other polluted regions and countries.

With the popularity of smart phones, non-contact video-based vital sign monitoring using a camera has gained increased attention over recent years. Especially, imaging photoplethysmography (IPPG), a technique for extracting pulse waves from videos, conduces to monitor physiological information on a daily basis, including heart rate, respiration rate, blood oxygen saturation, and so on. The main challenge for accurate pulse wave extraction from facial videos is that the facial color intensity change due to cardiovascular activities is subtle and is often badly disturbed by noise, such as illumination variation, facial expression changes, and head movements. Even a tiny interference could bring a big obstacle for pulse wave extraction and reduce the accuracy of the calculated vital signs. In recent years, many novel approaches have been proposed to eliminate noise such as filter banks, adaptive filters, Distance-PPG, and machine learning, but these methods mainly focus on heart rate detection and neglect the retention of useful details of pulse wave. For example, the pulse wave extracted by the filter bank method has no dicrotic wave and approaching sine wave, but dicrotic waves are essential for calculating vital signs like blood viscosity and blood pressure. Therefore, a new framework is proposed to achieve accurate pulse wave extraction that contains mainly two steps: 1) preprocessing procedure to remove baseline offset and high frequency random noise; and 2) a self-adaptive singular spectrum analysis algorithm to obtain cyclical components and remove aperiodic irregular noise. Experimental results show that the proposed method can extract detail-preserved pulse waves from facial videos under realistic situations and outperforms state-of-the-art methods in terms of detail-preserving and real time heart rate estimation. Furthermore, the pulse wave extracted by our approach enabled the non-contact estimation of atrial fibrillation, heart rate variability, blood pressure, as well as other physiological indices that require standard pulse wave.

To investigate the effects of Ganjiang granule (GG) on cecal microflora and serum biochemical components in rats with constipation-predominant irritable bowel syndrome (IBS–C).

ABSTRACTIn this letter, a pointwise approach using stereo-graph cuts (SGC) with spatio-temporal information is proposed for synthetic aperture radar (SAR) image change detection. The SGC is based on stereo-graph, which is designed to connect the local maximum pixels on two SAR images and can be used to capture the spatio-temporal information. With the support of stereo-graph, a novel SGC energy function is presented to quantify the spatio-temporal information and implicitly measure the difference information between the SAR images. Therefore, the change detection results can be obtained by minimizing the energy function with graph cuts technology. Experimental results on real SAR datasets confirm the validity of the proposed approach in which SGC and spatio-temporal information offer great contributions on improving the robustness and accuracy of detection.

Particulate air pollution caused by human activities has drawn global attention due to its potential health risks. Considering the inevitable contact of inhaled airborne fine particulate matter (PM) with plasma, the hematological effects of PM are worthy of study. In this study, the potential effect of PM on hematological homeostasis through triggering the crosstalk of the kallikrein-kinin system (KKS), complement, and coagulation systems in plasma was investigated. The ex vivo, in vitro, and in vivo KKS activation assays confirmed that PM samples could efficiently cause the cascade activation of key zymogens in the KKS, wherein the particles coupled with lipopolysaccharide attachment provided substantial contribution. The binding of Hageman factor XII (FXII) with PM samples and its subsequent autoactivation initiated this process. The crucial elements in the complement cascade, including complement 3 (C3) and complement 5 (C5), and coagulation system (prothrombin) were also found to be actively induced by PM exposure, which was regulated by the interplay of KKS activation. The data provided solid evidence on hematological effects of airborne PM through inducing the activation of the KKS, complement, and coagulation systems, which would be valuable in the risk assessment on air-pollution-related cardiovascular diseases.

Magnetic particles (MPs), with magnetite (Fe3O4) and maghemite (γ-Fe2O3) as the most abundant species, are ubiquitously present in the natural environment. MPs are among the most applied engineered particles and can be produced incidentally by various human activities. Identification of the sources of MPs is crucial for their risk assessment and regulation, which, however, is still an unsolved problem. Here, we report a novel approach, hierarchical classification-aided stable isotopic fingerprinting, to address this problem. We found that naturally occurring, incidental, and engineered MPs have distinct Fe and O isotopic fingerprints due to significant Fe/O isotope fractionation during their generation processes, which enables the establishment of an Fe–O isotopic library covering complex sources. Furthermore, we developed a three-level machine learning model that not only can distinguish the sources of MPs with a high precision (94.3%) but also can identify the multiple species (Fe3O4or γ-Fe2O3) and synthetic routes of engineered MPs with a precision of 81.6%. This work represents the first reliable strategy for the precise source tracing of particles with multiple species and complex sources.

More than one hundred studies have used the mainland Chinese version of the MATRICS Consensus Cognitive Battery (MCCB) to assess cognition in schizophrenia, but the results of these studies, the quality of the reports, and the strength of the evidence provided in the reports have not been systematically assessed. We identified 114 studies from English-language and Chinese-language databases that used the Chinese MCCB to assess cognition in combined samples of 7394 healthy controls (HC), 392 individuals with clinical high risk for psychosis (CHR-P), 4922 with first-episode schizophrenia (FES), 1549 with chronic schizophrenia (CS), and 2925 with schizophrenia of unspecified duration. The mean difference (MD) of the composite MCCB T-score (−13.72) and T-scores of each of the seven cognitive domains assessed by MCCB (−14.27 to −7.92) were significantly lower in individuals with schizophrenia than in controls. Meta-analysis identified significantly greater cognitive impairment in FES and CS than in CHR-P in six of the seven domains and significantly greater impairment in CS than FES in the reasoning and problem-solving domain (i.e., executive functioning). The only significant covariate of overall cognitive functioning in individuals with schizophrenia was a negative association with the severity of psychotic symptoms. These results confirm the construct validity of the mainland Chinese version of MCCB. However, there were significant limitations in the strength of the evidence provided about CHR-P (small pooled sample sizes) and the social cognition domain (inconsistency of results across studies), and the quality of many reports (particularly those published in Chinese) was rated ‘poor’ due to failure to report sample size calculations, matching procedures or methods of handling missing data. Moreover, almost all studies were cross-sectional studies limited to persons under 60 with at least nine years of education, so longitudinal studies of under-educated, older individuals with schizophrenia are needed.

ABSTRACTThe convolutional neural network has been widely used in synthetic aperture radar (SAR) image classification, for it can learn discriminative features from massive amounts of data. However, it is short of distinctive learning mechanisms for different regions in SAR images. In this letter, a novel architecture called multi-depth convolutional neural network (Multi-depth CNN) is proposed which can select different levels of features for classification. Differing from classical convolutional neural network, Multi-depth CNN adopts a piecewise back-propagation method to optimize the network. Meanwhile, compared with classical convolutional neural network, the proposed network can reduce the training time effectively. Experimental results on two datasets demonstrate that the proposed network can achieve better classification accuracy compared with some state-of-art algorithms.

To compare the effects of pulsating and static cupping on non-specific neck pain and local skin microcirculation blood perfusion, which is a pilot study.

Highlights: [•] New fuzzy reasoning method combining logic system with reasoning model is proposed. [•] The new method contains the full implication inference method as its particular case. [•] The previous principles for the full implication inference method are improved. [•] Unified forms of the new method are obtained. [•] Excellent reversibility properties of the new method are verified. [ABSTRACT FROM AUTHOR]

Soot, mainly derived from incomplete combustion of fossil fuel and biomass, exists ubiquitously in different environmental matrixes. To study the detrimental effects of soot on climate, air quality, and human health, accurate quantification of soot is an important prerequisite. However, until now, quantification of soot in environmental media, especially in carbonaceous media, is still very challenging. Here, we report a matrix-free laser desorption/ionization mass spectrometry (LDI-MS) method for in situ imaging of soot particles in size-segregated aerosol samples collected on filter membranes. A series of round-shaped sample spots in filter membranes were selected and subjected to MS imaging analysis, enabling direct in situ quantification of soot without solvent extraction or separation. Especially, the MS imaging with serial sample spots can overcome the problems of sweet-spot in LDI-MS and inhomogeneous distribution of soot in the filter membrane, thus greatly improving the precision of quantification. The limit of detection of soot was 4 ng/m2and the recovery was 84.4–126%. By using this method, we found that a higher soot content was present in larger-sized particulate matter than smaller-sized particles, suggesting that aerosol soot was mainly derived from primary emission sources. Furthermore, this method also shows the potential to analyze nitrate and sulfate species in PM2.5. To the best of our knowledge, it is the first method capable of simultaneous analysis of inorganic salts and soot in air samples. It represents a novel strategy for in situ quantification of aerosol soot with the advantages of high specificity, high sensitivity, separation-, solvent- and matrix-free.