Determination of parabens in human urine by liquid chromatography coupled with electrospray ionization tandem mass spectrometry

A simple and sensitive method was developed for the simultaneous determination of methyl, ethyl,n-propyl,n-butyl, and benzyl parabens in human urine by liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS).</p

Stretchable elastic synaptic transistors for neurologically integrated soft engineering systems

Artificial synaptic devices that can be stretched similar to those appearing in soft-bodied animals, such as earthworms, could be seamlessly integrated onto soft machines toward enabled neurological functions. Here, we report a stretchable synaptic transistor fully based on elastomeric electronic materials, which exhibits a full set of synaptic characteristics. These characteristics retained even the rubbery synapse that is stretched by 50%. By implementing stretchable synaptic transistor with mechanoreceptor in an array format, we developed a deformable sensory skin, where the mechanoreceptors interface the external stimulations and generate presynaptic pulses and then the synaptic transistors render postsynaptic potentials. Furthermore, we demonstrated a soft adaptive neurorobot that is able to perform adaptive locomotion based on robotic memory in a programmable manner upon physically tapping the skin. Our rubbery synaptic transistor and neurologically integrated devices pave the way toward enabled neurological functions in soft machines and other applications

Study of Peeling of Single Crystal Silicon by Intense Pulsed Ion Beam

The surface peeling process induced by intense pulsed ion beam (IPIB) irradiation was studied. Single crystal silicon specimens were treated by IPIB with accelerating voltage of 350 kV current density of 130 A/cm2. It is observed that under smaller numbers of IPIB shots, the surface may undergo obvious melting and evaporation..

Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks

AbstractRecent studies demonstrated that the average heart rate (HR) can be measured from facial videos based on non-contact remote photoplethysmography (rPPG). However for many medical applications (e.g., atrial fibrillation (AF) detection) knowing only the average HR is not sufficient, and measuring precise rPPG signals from face for heart rate variability (HRV) analysis is needed. Here we propose an rPPG measurement method, which is the first work to use deep spatio-temporal networks for reconstructing precise rPPG signals from raw facial videos. With the constraint of trend-consistency with ground truth pulse curves, our method is able to recover rPPG signals with accurate pulse peaks. Comprehensive experiments are conducted on two benchmark datasets, and results demonstrate that our method can achieve superior performance on both HR and HRV levels comparing to the state-of-the-art methods. We also achieve promising results of using reconstructed rPPG signals for AF detection and emotion recognition.Abstract Recent studies demonstrated that the average heart rate (HR) can be measured from facial videos based on non-contact remote photoplethysmography (rPPG). However for many medical applications (e.g., atrial fibrillation (AF) detection) knowing only the average HR is not sufficient, and measuring precise rPPG signals from face for heart rate variability (HRV) analysis is needed. Here we propose an rPPG measurement method, which is the first work to use deep spatio-temporal networks for reconstructing precise rPPG signals from raw facial videos. With the constraint of trend-consistency with ground truth pulse curves, our method is able to recover rPPG signals with accurate pulse peaks. Comprehensive experiments are conducted on two benchmark datasets, and results demonstrate that our method can achieve superior performance on both HR and HRV levels comparing to the state-of-the-art methods. We also achieve promising results of using reconstructed rPPG signals for AF detection and emotion recognition

Learning Binary-Antithetical Information Bottleneck for Generalizable Face Anti-Spoofing

Abstract We investigate generalizable face anti-spoofing (FAS) using information bottleneck theory. As generalizable FAS aims to detect spoofing in unseen scenarios, it has recently gained significant attention. Existing methods often use adversarial strategies or auxiliary modules to learn domain-invariant features by mining data relationships from distinct source domains. However, their learned feature space may still shift for unseen data due to the spurious correlations overfitted from training domains. Our rationale is that the problem of generalized pattern learning in FAS can be framed as a unified binary-antithetical information transition process, grounded in information bottleneck theory. Specifically, we leverage mutual-information optimization to preserve the instance-level spoof-aware information while compressing domain-related information modeled from the antithetical identity distribution. This enables the model to dynamically identify domain-agnostic, minimal sufficient representations that consistently describe the live/spoof distributions while mitigating spurious correlations through cross-identity compression. In light of this, we propose a novel learning framework for FAS, named Binary-Antithetical Information Bottleneck (BIB)-FAS, which is proven to be effectively generalized to unseen scenarios without using auxiliary information (e.g., domain labels) for training. Extensive cross-domain evaluations show that BIB-FAS significantly outperforms state-of-the-art methods. The code is available at: github.com/CV-AC/BIB-FAS.Abstract We investigate generalizable face anti-spoofing (FAS) using information bottleneck theory. As generalizable FAS aims to detect spoofing in unseen scenarios, it has recently gained significant attention. Existing methods often use adversarial strategies or auxiliary modules to learn domain-invariant features by mining data relationships from distinct source domains. However, their learned feature space may still shift for unseen data due to the spurious correlations overfitted from training domains. Our rationale is that the problem of generalized pattern learning in FAS can be framed as a unified binary-antithetical information transition process, grounded in information bottleneck theory. Specifically, we leverage mutual-information optimization to preserve the instance-level spoof-aware information while compressing domain-related information modeled from the antithetical identity distribution. This enables the model to dynamically identify domain-agnostic, minimal sufficient representations that consistently describe the live/spoof distributions while mitigating spurious correlations through cross-identity compression. In light of this, we propose a novel learning framework for FAS, named Binary-Antithetical Information Bottleneck (BIB)-FAS, which is proven to be effectively generalized to unseen scenarios without using auxiliary information (e.g., domain labels) for training. Extensive cross-domain evaluations show that BIB-FAS significantly outperforms state-of-the-art methods. The code is available at: github.com/CV-AC/BIB-FAS

Facial-Video-Based Physiological Signal Measurement : Recent advances and affective applications

AbstractMonitoring physiological changes [e.g., heart rate (HR), respiration, and HR variability (HRV)] is important for measuring human emotions. Physiological responses are more reliable and harder to alter compared to explicit behaviors (such as facial expressions and speech), but they require special contact sensors to obtain. Research in the last decade has shown that photoplethysmography (PPG) signals can be remotely measured (rPPG) from facial videos under ambient light, from which physiological changes can be extracted. This promising finding has attracted much interest from researchers, and the field of rPPG measurement has been growing fast. In this article, we review current progress on intelligent signal processing approaches for rPPG measurement, including earlier works on unsupervised approaches and recently proposed supervised models, benchmark data sets, and performance evaluation. We also review studies on rPPG-based affective applications and compare them with other affective computing modalities. We conclude this article by emphasizing the current main challenges and highlighting future directions.Abstract Monitoring physiological changes [e.g., heart rate (HR), respiration, and HR variability (HRV)] is important for measuring human emotions. Physiological responses are more reliable and harder to alter compared to explicit behaviors (such as facial expressions and speech), but they require special contact sensors to obtain. Research in the last decade has shown that photoplethysmography (PPG) signals can be remotely measured (rPPG) from facial videos under ambient light, from which physiological changes can be extracted. This promising finding has attracted much interest from researchers, and the field of rPPG measurement has been growing fast. In this article, we review current progress on intelligent signal processing approaches for rPPG measurement, including earlier works on unsupervised approaches and recently proposed supervised models, benchmark data sets, and performance evaluation. We also review studies on rPPG-based affective applications and compare them with other affective computing modalities. We conclude this article by emphasizing the current main challenges and highlighting future directions

Genome-wide copy number variant analysis for congenital ventricular septal defects in Chinese Han population

Background: Ventricular septal defects (VSDs) constitute the most prevalent congenital heart disease (CHD), occurs either in isolation (isolated VSD) or in combination with other cardiac defects (complex VSD). Copy number variation (CNV) has been highlighted as a possible contributing factor to the etiology of many congenital diseases. However, little is known concerning the involvement of CNVs in either isolated or complex VSDs. Methods: We analyzed 154 unrelated Chinese individuals with VSD by chromosomal microarray analysis. The subjects were recruited from four hospitals across China. Each case underwent clinical assessment to define the type of VSD, either isolated or complex VSD. CNVs detected were categorized into syndrom related CNVs, recurrent CNVs and rare CNVs. Genes encompassed by the CNVs were analyzed using enrichment and pathway analysis. Results: Among 154 probands, we identified 29 rare CNVs in 26 VSD patients (16.9 %, 26/154) and 8 syndrome-related CNVs in 8 VSD patients (5.2 %, 8/154). 12 of the detected 29 rare CNVs (41.3 %) were recurrently reported in DECIPHER or ISCA database as associated with either VSD or general heart disease. Fifteen genes (5 %, 15/285) within CNVs were associated with a broad spectrum of complicated CHD. Among these15 genes, 7 genes were in “abnormal interventricular septum morphology” derived from the MGI (mouse genome informatics) database, and nine genes were associated with cardiovascular system development (GO:0072538).We also found that these VSD-related candidate genes are enriched in chromatin binding and transcription regulation, which are the biological processes underlying heart development. Conclusions: Our study demonstrates the potential clinical diagnostic utility of genomic imbalance profiling in VSD patients. Additionally, gene enrichment and pathway analysis helped us to implicate VSD related candidate genes. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0163-4) contains supplementary material, which is available to authorized users

TransRPPG : Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection

Abstract3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from emergent 3D mask attacks. Recently, remote photoplethysmography (rPPG) has been developed as an intrinsic liveness clue for 3D mask PAD without relying on the mask appearance. However, the rPPG features for 3D mask PAD are still needed expert knowledge to design manually, which limits its further progress in the deep learning and big data era. In this letter, we propose a pure rPPG transformer (TransRPPG) framework for learning intrinsic liveness representation efficiently. At first, rPPG-based multi-scale spatial-temporal maps (MSTmap) are constructed from facial skin and background regions. Then the transformer fully mines the global relationship within MSTmaps for liveness representation, and gives a binary prediction for 3D mask detection. Comprehensive experiments are conducted on two benchmark datasets to demonstrate the efficacy of the TransRPPG on both intra- and cross-dataset testings. Our TransRPPG is lightweight and efficient (with only 547 K parameters and 763 M FLOPs), which is promising for mobile-level applications.Abstract 3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from emergent 3D mask attacks. Recently, remote photoplethysmography (rPPG) has been developed as an intrinsic liveness clue for 3D mask PAD without relying on the mask appearance. However, the rPPG features for 3D mask PAD are still needed expert knowledge to design manually, which limits its further progress in the deep learning and big data era. In this letter, we propose a pure rPPG transformer (TransRPPG) framework for learning intrinsic liveness representation efficiently. At first, rPPG-based multi-scale spatial-temporal maps (MSTmap) are constructed from facial skin and background regions. Then the transformer fully mines the global relationship within MSTmaps for liveness representation, and gives a binary prediction for 3D mask detection. Comprehensive experiments are conducted on two benchmark datasets to demonstrate the efficacy of the TransRPPG on both intra- and cross-dataset testings. Our TransRPPG is lightweight and efficient (with only 547 K parameters and 763 M FLOPs), which is promising for mobile-level applications

Expression of Regulatory Platelet MicroRNAs in Patients with Sickle Cell Disease

Background: Increased platelet activation in sickle cell disease (SCD) contributes to a state of hypercoagulability and confers a risk of thromboembolic complications. The role for post-transcriptional regulation of the platelet transcriptome by microRNAs (miRNAs) in SCD has not been previously explored. This is the first study to determine whether platelets from SCD exhibit an altered miRNA expression profile. Methods and Findings: We analyzed the expression of miRNAs isolated from platelets from a primary cohort (SCD = 19, controls = 10) and a validation cohort (SCD = 7, controls = 7) by hybridizing to the Agilent miRNA microarrays. A dramatic difference in miRNA expression profiles between patients and controls was noted in both cohorts separately. A total of 40 differentially expressed platelet miRNAs were identified as common in both cohorts (p-value 0.05, fold change>2) with 24 miRNAs downregulated. Interestingly, 14 of the 24 downregulated miRNAs were members of three families - miR-329, miR-376 and miR-154 - which localized to the epigenetically regulated, maternally imprinted chromosome 14q32 region. We validated the downregulated miRNAs, miR-376a and miR-409-3p, and an upregulated miR-1225-3p using qRT-PCR. Over-expression of the miR-1225-3p in the Meg01 cells was followed by mRNA expression profiling to identify mRNA targets. This resulted in significant transcriptional repression of 1605 transcripts. A combinatorial approach using Meg01 mRNA expression profiles following miR-1225-3p overexpression, a computational prediction analysis of miRNA target sequences and a previously published set of differentially expressed platelet transcripts from SCD patients, identified three novel platelet mRNA targets: PBXIP1, PLAGL2 and PHF20L1. Conclusions: We have identified significant differences in functionally active platelet miRNAs in patients with SCD as compared to controls. These data provide an important inventory of differentially expressed miRNAs in SCD patients and an experimental framework for future studies of miRNAs as regulators of biological pathways in platelets. © 2013 Jain et al