Heart rate (HR) is an important physiological signal that reflects the physical and emotional status of a person. Traditional HR measurements usually rely on contact monitors, which may cause ...inconvenience and discomfort. Recently, some methods have been proposed for remote HR estimation from face videos; however, most of them focus on well-controlled scenarios, their generalization ability into less-constrained scenarios (e.g., with head movement, and bad illumination) are not known. At the same time, lacking large-scale HR databases has limited the use of deep models for remote HR estimation. In this paper, we propose an end-to-end RhythmNet for remote HR estimation from the face. In RyhthmNet, we use a spatial-temporal representation encoding the HR signals from multiple ROI volumes as its input. Then the spatial-temporal representations are fed into a convolutional network for HR estimation. We also take into account the relationship of adjacent HR measurements from a video sequence via Gated Recurrent Unit (GRU) and achieves efficient HR measurement. In addition, we build a large-scale multi-modal HR database (named as VIPL-HR 1 ), which contains 2,378 visible light videos (VIS) and 752 near-infrared (NIR) videos of 107 subjects. Our VIPL-HR database contains various variations such as head movements, illumination variations, and acquisition device changes, replicating a less-constrained scenario for HR estimation. The proposed approach outperforms the state-of-the-art methods on both the public-domain and our VIPL-HR databases. 1 VIPL-HR is available at: http://vipl.ict.ac.cn/view_database.php?id=15.
With the wide deployment of the face recognition systems in applications from deduplication to mobile device unlocking, security against the face spoofing attacks requires increased attention; such ...attacks can be easily launched via printed photos, video replays, and 3D masks of a face. We address the problem of face spoof detection against the print (photo) and replay (photo or video) attacks based on the analysis of image distortion (e.g., surface reflection, moiré pattern, color distortion, and shape deformation) in spoof face images (or video frames). The application domain of interest is smartphone unlock, given that the growing number of smartphones have the face unlock and mobile payment capabilities. We build an unconstrained smartphone spoof attack database (MSU USSA) containing more than 1000 subjects. Both the print and replay attacks are captured using the front and rear cameras of a Nexus 5 smartphone. We analyze the image distortion of the print and replay attacks using different: 1) intensity channels (R, G, B, and grayscale); 2) image regions (entire image, detected face, and facial component between nose and chin); and 3) feature descriptors. We develop an efficient face spoof detection system on an Android smartphone. Experimental results on the public-domain Idiap Replay-Attack, CASIA FASD, and MSU-MFSD databases, and the MSU USSA database show that the proposed approach is effective in face spoof detection for both the cross-database and intra-database testing scenarios. User studies of our Android face spoof detection system involving 20 participants show that the proposed approach works very well in real application scenarios.
Most cationic vectors are difficult to avoid the fate of small interfering RNA (siRNA) degradation following the endosome-lysosome pathway during siRNA transfection. In this study, the endoplasmic ...reticulum (ER) membrane isolated from cancer cells was used to fabricate an integrative hybrid nanoplexes (EhCv/siRNA NPs) for improving siRNA transfection. Compared to the undecorated Cv/siEGFR NPs, the ER membrane-decorated EhCv/siRNA NPs exhibits a significantly higher gene silencing effect of siRNA in vitro and a better antitumor activity in nude mice bearing MCF-7 human breast tumor in vivo. Further mechanistic studies demonstrate that functional proteins on the ER membrane plays important roles on improving cellular uptake and altering intracellular trafficking pathway of siRNA. It is worth to believe that the ER membrane decoration on nanoplexes can effectively transport siRNA through the endosome-Golgi-ER pathway to evade lysosomal degradation and enhance the silencing effects of siRNA.
This study focuses on consumers' processing of online reviews as an empowering experience. We investigate how decision support information (i.e., online reviews) can lead to information overload and ...decision difficulty, and ultimately affect decision satisfaction. A series of three experimental studies with subjects from a professional marketing agency examine the influence of self-determined review quantity and perceived review quality on satisfaction, as well as the mediating effects of information overload and decision difficulty. Our results show that the effect of perceived review quality on information overload is enhanced when the consumer chooses to read more reviews. Individual characteristics are also relevant; consumers' product knowledge moderates the mediating effect of decision difficulty on satisfaction. This study contributes to the literature by (1) investigating how the characteristics of decision support information affect decision process and outcome, and (2) exploring the boundary conditions wherein online reviews can empower but also overload consumers under various decision contexts.
Apigenin, identified as 4′, 5, 7-trihydroxyflavone, is a natural flavonoid compound that has many interesting pharmacological activities and nutraceutical potential including anti-inflammatory and ...antioxidant functions. Chronic, low-grade inflammation and oxidative stress are involved in both the initiation and progression of hypertension and hypertension-induced cardiac hypertrophy. However, whether or not apigenin improves hypertension and cardiac hypertrophy through modulating NADPH oxidase-dependent reactive oxygen species (ROS) generation and inflammation in hypothalamic paraventricular nucleus (PVN) has not been reported. This study aimed to investigate the effects of apigenin on hypertension in spontaneously hypertensive rats (SHRs) and its possible central mechanism of action. SHRs and Wistar-Kyoto (WKY) rats were randomly assigned and treated with bilateral PVN infusion of apigenin or vehicle (artificial cerebrospinal fluid) via osmotic minipumps (20 μg/h) for 4 weeks. The results showed that after PVN infusion of apigenin, the mean arterial pressure (MAP), heart rate, plasma norepinephrine (NE), Beta 1 receptor in kidneys, level of phosphorylation of PKA in the ventricular tissue and cardiac hypertrophy, perivascular fibrosis, heart level of oxidative stress, PVN levels of oxidative stress, interleukin 1β (IL-1β), interleukin 6 (IL-6), iNOS, monocyte chemotactic protein 1 (MCP-1), tyrosine hydroxylase (TH), NOX2 and NOX4 were attenuated and PVN levels of interleukin 10 (IL-10), superoxide dismutase 1 (Cu/Zn-SOD) and the 67-kDa isoform of glutamate decarboxylase (GAD67) were increased. These results revealed that apigenin improves hypertension and cardiac hypertrophy in SHRs which are associated with the down-regulation of NADPH oxidase-dependent ROS generation and inflammation in the PVN.
Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of ...them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal versus nominal and holistic versus local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.
Automatic face recognition is now widely used in applications ranging from deduplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about ...face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person's face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient and rather robust face spoof detection algorithm based on image distortion analysis (IDA). Four different features (specular reflection, blurriness, chromatic moment, and color diversity) are extracted to form the IDA feature vector. An ensemble classifier, consisting of multiple SVM classifiers trained for different face spoof attacks (e.g., printed photo and replayed video), is used to distinguish between genuine (live) and spoof faces. The proposed approach is extended to multiframe face spoof detection in videos using a voting-based scheme. We also collect a face spoof database, MSU mobile face spoofing database (MSU MFSD), using two mobile devices (Google Nexus 5 and MacBook Air) with three types of spoof attacks (printed photo, replayed video with iPhone 5S, and replayed video with iPad Air). Experimental results on two public-domain face spoof databases (Idiap REPLAY-ATTACK and CASIA FASD), and the MSU MFSD database show that the proposed approach outperforms the state-of-the-art methods in spoof detection. Our results also highlight the difficulty in separating genuine and spoof faces, especially in cross-database and cross-device scenarios.
In the past decade, renewable energy has been a hot pursuit in scientific and industrial communities because of the fast depletion of fossil fuels and increasing concern about the environment. To ...efficiently utilize and largely deploy the intermittent renewable energy, high-performance electrochemical energy storage devices are desperately needed. As a result, tremendous effort has been devoted to this field with remarkable achievements. Despite this progress, fossil fuels, like petroleum, will still play an indispensable role in our energy structure in the foreseeable future. Besides, petroleum is becoming increasingly heavier and the heavy oil results in more and more low-value by-products such as asphalt and petroleum coke during the oil refinery processes. In this context, how to harmonize the deployment of renewable energy and value-added utilization of these abundant and low-cost by-products from the petroleum industry represents a significant challenge. Considering their high carbon content and versatile tunability, one viable solution may be the controllable conversion of asphalt and/or petroleum coke into functional carbon materials for energy storage applications. In this article, we summarize the recent progress of carbon materials derived from heavy oil by-products and their utilization as electrode materials for energy storage devices. At first, we give a brief introduction to the features and advantages of heavy oil by-products compared to biomass and polymers as the precursors of carbon materials. Then, the typical methods of constructing functional carbon materials from these by-products are discussed in detail. After that, their performance as electrode materials for lithium-ion batteries, sodium-ion batteries, and supercapacitors is elaborately presented. Finally, the possible challenges and future perspectives are analyzed based on our knowledge to end this review.
Recent progress in constructing carbon materials using heavy oil-derived by-products for energy storage has been summarized.
Addictive substances are known to increase dopaminergic signaling in the mesocorticolimbic system. The origin of this dopamine (DA) signaling originates in the ventral tegmental area (VTA), which ...sends afferents to various targets, including the nucleus accumbens, the medial prefrontal cortex, and the basolateral amygdala. VTA DA neurons mediate stimuli saliency and goal-directed behaviors. These neurons undergo robust drug-induced intrinsic and extrinsic synaptic mechanisms following acute and chronic drug exposure, which are part of brain-wide adaptations that ultimately lead to the transition into a drug-dependent state. Interestingly, recent investigations of the differential subpopulations of VTA DA neurons have revealed projection-specific functional roles in mediating reward, aversion, and stress. It is now critical to view drug-induced neuroadaptations from a circuit-level perspective to gain insight into how differential dopaminergic adaptations and signaling to targets of the mesocorticolimbic system mediates drug reward. This review hopes to describe the projection-specific intrinsic characteristics of these subpopulations, the differential afferent inputs onto these VTA DA neuron subpopulations, and consolidate findings of drug-induced plasticity of VTA DA neurons and highlight the importance of future projection-based studies of this system.
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