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Based on a Gaussian mixture type model of K components, we derive eigen selection procedures that improve the usual spectral clustering algorithms in high-dimensional settings, which typically act ...

Electrical neural interfaces serve as direct communication pathways that connect the nervous system with the external world. Technological advances in this domain are providing increasingly more powerful tools to study, restore, and augment neural functions. Yet, the complexities of the nervous system give rise to substantial challenges in the design, fabrication, and system-level integration of these functional devices. In this review, we present snapshots of the latest progresses in electrical neural interfaces, with an emphasis on advances that expand the spatiotemporal resolution and extent of mapping and manipulating brain circuits. We include discussions of large-scale, long-lasting neural recording; wireless, miniaturized implants; signal transmission, amplification, and processing; as well as the integration of interfaces with optical modalities. We outline the background and rationale of these developments and share insights into the future directions and new opportunities they enable.

The stress response can be triggered during the perioperative period by tension, fear, anesthesia, surgical trauma, and various postoperative adverse stimuli, leading to significant changes in the endocrine, metabolic, and immune systems of patients. In particular, immunosuppression induced by the stress response has adverse effects on the postoperative recovery of patients. As acupuncture stimulation-related technologies have been rapidly developed and applied in recent years, a number of basic and clinical studies have confirmed that acupoint stimulation can regulate the immune system via local, neurological, and endocrine pathways. Moreover, acupoint stimulation treatment has also been shown to reduce the adverse effects of immunosuppression by affecting the release of various cytokines, such as interleukin (IL) 1, IL-2, IL-4, IL-6, IL-10, IFN-γ, and tumor necrosis factor (TNF) β, immunoglobulins, complement proteins, and T cell markers such as CD3, CD4, and CD4/CD8 via the regulation of macrophages, neutrophils, NK cells, and endogenous opioids. In addition, acupuncture stimulation treatment during the perioperative period can also significantly decrease the amount of anesthetic required for anesthesia, effectively reduce nausea and vomiting, relieve post-operative pain, and accelerate the recovery of physiological functions. Therefore, acupuncture stimulation treatment has shown important potential for clinical applications.

BACKGROUND Hemorrhagic shock could induce acute lung injury (ALI), which is associated with cell hypoxia, lung tissue inflammation, free radical damage, and excessive cell apoptosis. Our previous studies demonstrated that hyperoxygenated solution could alleviate cell hypoxia. Furthermore, hydrogen-rich solution (HS) could relieve lung tissue inflammation, free radical damage and excessive cell apoptosis. Therefore we hypothesize that Hyperoxygenated Hydrogen-rich solution (HOHS) can protect the lung against ALI. MATERIALS AND METHODS SD rats were randomly divided into five groups (n = 6 at each time point in each group) and were exposed to Hemorrhagic shock induced ALI, and then treated with lactated Ringer's solution (LRS), hyperoxygenated solution, HS, and HOHS, respectively. The protective effects of these solutions were assessed using methods as follows: arterial blood samples were collected for blood gas analysis; Bronchoalveolar lavage fluid was collected for cell count and protein quantification; lung tissue samples were collected to measure wet/dry ratio, as well as levels of T-SOD, MDA, TNF-α, and IL-6; Caspase-3 and TUNEL-positive cells, and pathological changes were observed under light microscope; ALI was scored using the Smith scoring method; ultrastructural changes of lung tissues were further observed with transmission electron microscopy. RESULTS The results indicated that PaO2, PaCO2, and T-SOD increased in the three treatment groups (P

Gesture recognition has attracted great attention owing to its applications in many fields such as Human Computer Interaction. However, in video-based gesture recognition, some gesture-irrelevant factors like the background handicap the improvement of recognition rate. In this paper, we propose an effective 3D Convolutional Neural Network based method for large-scale gesture recognition using RGB-D video data. To obtain compact but with sufficient motion path information data for the network, the inputs are unified into 32-frame videos first. Then the optical flow images are constructed from the RGB videos frame by frame, to help with eliminating the disturbing background inside them. After that, the spatiotemporal features of de-background RGB and depth data are extracted with the C3D model (a 3D CNN model) respectively and blended together in the next stage according to the discriminant correlation analysis to boost the performance. Finally the classes are predicted with a linear SVM classifier. Our proposed method achieves 54.50% accuracy on the validation subset and 60.93% on the testing subset of the Chalearn LAP IsoGD dataset, both of which outperform our results (ranked 1st place) in the Chalearn LAP Large-scale Gesture Recognition Challenge.

Lithium-ion batteries (LIBs) are widely adopted power sources for portable digital products and electrical vehicles (EV). However, the high cost associated with raw materials and manufacturing and ...

Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making ‘go/no-go’ decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute and early-stage mTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.

Tele-Health services provide people with healthcare assistance and help to establish accessible and high-quality monitoring. Telematic healthcare administration offers direct aid through the Internet of Things (IoT), which is better than current methods involving physical presence. The IoT and sensor networks currently enable remote monitoring of an individual or community. There are different Energy Level (EL) estimation methods for aerobics using body-worn sensors; however, quantification of error estimation differences is lacking. In this article, the Internet of Things based Energy Level Estimation method (IoT-ELEM) has been proposed for determining energy rates. The latter two forms of calculation are accompanied by a list of behaviors and EL approximations. Likewise, the amount and location of accelerometer sensors have been examined to achieve maximal EL estimates in aerobic players. The device is built on a distributed design that gathers a person's physiological data from various sensors data and conducts a standardization phase. These results are all transmitted to the cloud for storing and further processing. The cloud device manages the obtained details and performs a data fusion procedure to display the EL of the user at all stages. The IoT-ELEM system has been evaluated and achieves a high accuracy ratio of 98.6%, a prediction ratio of 96.4%, a performance ratio of 97.8, a less error rate of 9.4%, and an F1 score ratio of 94.6%.

Magnetic nanoparticles (MNP) can generate localized thermal stress in response to an external ac magnetic field, offering unique opportunities for a wide range of biomedical applications. For example, hyperthermia cancer therapy, which raises the local temperature of tumors up to 43–45°C, can trigger apoptosis and disrupt cancer cells’ ability to repair DNA damage [1]. In neuroscience, temperature-sensitive ion channels can be thermally activated using MNP-induced heating, which allows for minimally invasive brain stimulation [2,3]. Compared to dielectric heating and ohmic heating, MNP-based magnetic heating offers superior specificity and minimal damage to the surrounding tissues since most biological systems are non-magnetic.

The article examines the role of social media in mitigating information asymmetry and coordination problems during COVID-19 epidemic crisis We use "Sisters-Fight-Epidemic" online volunteering project during the outbreak of COVID-19 in Wuhan, China, as a case to demonstrate how social media plays a role as a mechanism in linking multiple stakeholders and shaping their actions during the epidemic response We show that social media facilitates the self-organizing processes of volunteers and develops the emergency information networks, therefore enabling a relatively efficient relief responses to the needs of epidemic victims particularly female medical workers This article also identifies spontaneous online volunteering project as a new form of nonprofit organization and as a new emergent response group that can leverage the strengths of social media in disaster responses to enable effective coordination, initiate advocacy, and improve transparency of relief efforts

Background The waterborne pathogens Cryptosporidium spp., Giardia duodenalis, Enterocytozoon bieneusi and Cyclospora cayetanensis can cause intestinal diseases in humans. An understanding of their occurrence and transport in the environment is essential for accurate quantitative microbial risk assessment. Methods A total of 238 influent samples were collected from four wastewater treatment plants (WWTPs) and 88 samples from eight sewer locations in Guangzhou, China. PCR-based tools were used to detect and genetically characterize Cryptosporidium spp., G. duodenalis and E. bieneusi. Eimeria spp. and Cyclospora spp. were also analyzed to assess the sources of Cryptosporidium spp., G. duodenalis and E. bieneusi in wastewater. Results The overall occurrence rates in the WWTP and sewer samples were 14.3% (34/238) and 13.6% (12/88) for Cryptosporidium spp., 55.5% (132/238) and 33.0% (29/88) for G. duodenalis, 56.3% (134/238) and 26.1% (23/88) for E. bieneusi and 45.4% (108/238) and 47.7% (42/88) for Eimeria spp., respectively. Altogether, 11 Cryptosporidium species and genotypes, six G. duodenalis genotypes, 11 E. bieneusi genotypes and four C. cayetanensis were found, together with the presence of nine Eimeria species. The common occurrence of Cryptosporidium rat genotype IV, C. muris and Eimeria papillata and E. nieschulzi suggested that rodents were significant sources of the enteric pathogens detected in the wastewater samples. Conclusions While the dominant Cryptosporidium spp. detected in the raw wastewater sampled in this study are not pathogenic to humans, the widely detected G. duodenalis assemblage A and E. bieneusi genotypes D and Type IV are well-known zoonotic pathogens. Further studies are needed to monitor the occurrence of these waterborne pathogens in WWTPs to better understand their transmission and environmental transport in China. Graphical Abstract

Day-ahead multi-period scheduling can provide reasonable operation plans for various power sources and energy storage systems in distribution networks, so as to reduce costs and improve energy utilization. In this paper, the dynamic power flow optimization model of radial distribution network with wind/PV/energy storage is established firstly. The model takes the output of various power sources and the charge-discharge power of energy storage systems as decision variables, which belongs to nonconvex nonlinear programming problems. Then the second-order cone relaxation is applied to the power flow equation, and the problem is transformed into a second-order cone programming model which can be solved strictly and efficiently. Finally, the extended IEEE 30-node system is simulated, and the software Gurobi is used to obtain the optimal scheduling scheme, which verifies the effectiveness of this method. Test results show that the operating cost is reduced by adding wind/PV/energy storage into networks, and the consumption of renewable energy is increased by considering the punishment for wind and PV abandonment.

AimsThe aim of this study was to analyse the clinicopathological features and prognosis of human epidermal growth factor receptor-2 (HER2)-positive metaplastic squamous cell carcinoma (MSCC).MethodsFifty-eight patients with MSCC of the breast who were classified into 45 triple-negative and 13 HER2-positive subgroups diagnosed at the West China Hospital, Sichuan University, from 2004 to 2018, were enrolled. Clinicopathological features were collected and compared between HER2-positive MSCC, triple-negative MSCC, HER2-positive invasive breast carcinoma of no special type (NST) and triple-negative NST groups. In the prognostic survival analysis, HER2-positive MSCCs was compared with triple-negative MSCCs, HER2-positive NSTs and triple-negative NSTs.ResultsCompared with triple-negative MSCCs, more patients with Ki-67 low expression were in HER2-positive MSCCs (pConclusionIn conclusion, our study indicates that HER2-positive MSCC is an aggressive disease with unique clinicopathological characteristics. Both HER2-positive status and an SCC component are critical factors for poor prognosis. HER2-positive MSCC and triple-negative MSCC are distinct subgroups. Corresponding targeted therapy recommendations should be made for this HER2-positive MSCC group.

Hyperlipidemia is associated with metabolic disorders, but the detailed mechanisms and related interventions remain largely unclear. As a functional food in Asian diets, Herba houttuyniae has been reported to have beneficial effects on health. The present research was to investigate the protective effects of Herba houttuyniae aqueous extract (HAE) on hyperlipidemia-induced liver and heart impairments and its potential mechanisms. Male C57BL/6J mice were administered with 200 or 400 mg/kg/day HAE for 9 days, followed by intraperitoneal injection with 0.5 g/kg poloxamer 407 to induce acute hyperlipidemia. HAE treatment significantly attenuated excessive serum lipids and tissue damage markers, prevented hepatic lipid deposition, improved cardiac remodeling, and ameliorated hepatic and cardiac oxidative stress induced by hyperlipidemia. More importantly, NF-E2 related factor (Nrf2)-mediated antioxidant and peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1&alpha, )-mediated mitochondrial biogenesis pathways as well as mitochondrial complex activities were downregulated in the hyperlipidemic mouse livers and hearts, which may be attributable to the loss of adenosine monophosphate (AMP)-activated protein kinase (AMPK) activity: all of these changes were reversed by HAE supplementation. Our findings link the AMPK/PGC-1&alpha, /Nrf2 cascade to hyperlipidemia-induced liver and heart impairments and demonstrate the protective effect of HAE as an AMPK activator in the prevention of hyperlipidemia-related diseases.

Gesture recognition has raised wide attention in computer vision owing to its many applications. However, the task of video-based large-scale gesture recognition yet faces many challenges, since many gesture-irrelevant factors like the background may disturb the recognition accuracy. To better recognize gestures with large-scale videos, we propose a method based on RGB-D data in this paper, where the “RGB-D” means RGB and depth data captured simultaneously by specific devices like Kinect. To learn gesture details better, we first use an adaptive frame unification strategy to unify the frame number of inputs, and then the RGB and depth data are sent to the C3D model to extract spatiotemporal features, respectively. In order to alleviate the interference of gesture-irrelevant factors, the saliency theory is also employed to generate auxiliary data. Next the features of these data are combined to boost the performance, which can also avoid unreasonable synthetic data, since the dimension of C3D features is uniform. Finally the performances of several classifiers are tested and the best one of SVM classifier is selected to output the ultimate accuracy. Our approach achieves 52.04% and 59.43% accuracy on the validation and testing subset of the Chalearn LAP IsoGD, respectively, both of which outperform our results in the chalearn LAP Large-scale Gesture Recognition Challenge as reported in ICPR 2016.

Background Premenstrual syndrome (PMS) is characterized by a series of emotional, physical and behavioral symptoms. Although PMS is related to dysfunctions of the central nervous system, the neuropathological mechanism of PMS still has not been clearly established. The aim of this study is to evaluate potential differences in both cortical thickness and subcortical volumes in PMS patients compared to healthy controls (HCs). Methods Twenty PMS patients and twenty HCs underwent a structural magnetic resonance imaging scan and clinical assessment. Cortical thickness and subcortical volumes were computed using the FreeSurfer image analysis suite. Relationships between cortical thickness/subcortical volumes and the daily rating of severity of problems (DRSP) score were then measured in patients. Results Compared to HCs, PMS patients exhibited reduced cortical thickness in the medial prefrontal cortex (MPFC), orbitofrontal cortex (OFC) and insula, and increased subcortical volumes of the amygdala, thalamus and pallidum. Furthermore, negative correlations were detected between the DRSP and cortical thickness in the anterior cingulate cortex and precuneus. Limitations The study is limited by a small sample size and narrow age range of participants. Conclusions Our findings indicate that the abnormal morphological changes are mainly implicated in emotional regulation and visceral perception in PMS patients. We hope that our study may contribute to a better understanding of PMS.

Broad spectrum absorption of semiconductor photocatalysts is an essential requirement to achieve the best values of solar energy utilization. Here, through precise surface state adjustment, coaxial tri-cubic Ag0/AgCl materials with distinct apparent colours (blue and fuchsia) were successfully fabricated. The reasons for the different colour generation of the Ag0/AgCl materials were investigated by performing corresponding spectrum analysis. It was revealed that Ag0/AgCl-blue and Ag0/AgCl-fuchsia crystals could efficiently boost the photon energy harvesting, spanning from the UV to near-infrared spectral region (250–800 nm), and achieved 2.6 and 5.4 times the wastewater degradation efficiency of AgCl-white. Simultaneously, these two fresh coloured candidates were demonstrated to have preferable photocatalytic CO2 photoreduction capability, with yields of ∼3.6 (Ag0/AgCl-fuchsia) and 2.6 (Ag0/AgCl-blue) times that of AgCl-white. It is expected that this work will provide a beneficial perspective for understanding the solar absorption feature at both the major structure modulation and particular surface state regulation level.

Small cell carcinoma (SCC) in the nasal cavity and sinuses is extremely rare. The clinical data of 15 patients with primary SCC in nasal cavity and sinuses were analyzed retrospectively. All patients were treated with surgery, radiotherapy, and chemotherapy. Of the 15 patients, 2 patients are alive for more than 6 years, and 5 patients died after the median follow-up period (11 months). Most of our patients represent the later stage (73% presented at stage III or IV) and had surgery combined with radiotherapy and chemotherapy; however, nearly half of patients have tumor recurrence and/or distant metastasis. SCC of nasal cavity and sinuses often invades surrounding tissues, and the long-term curative rate is generally low. Early diagnosis and comprehensive treatment are key to improve survival. Although the overall survival time of SCC is not optimistic, it is still recommended that patients take comprehensive treatment.

In order to reduce the energy consumption and improve the economy of air conditioning system, theoretical analysis and energy consumption simulation are used in this paper to explore the adaptability of temperature and humidity independent control (THIC) air-conditioning system in green office buildings. On the basis of the air moisture content and working hours of staff in green office buildings, China was divided into four humidity-hour zones, and several typical cities were selected in each zone for the same building to carry out the analyses of energy consumption simulation and dynamic recovery period of different THIC air conditioning systems in office buildings. The results indicated that in Urumqi, as the representative city of humidity-hour zone I, green office buildings do not suit THIC system; THIC air conditioning system shows obvious advantages over traditional air conditioning in terms of energy saving and economy, and the energy saving rate of the most suitable THIC air conditioning system in Harbin, Beijing and Haikou, as the representative city of humidity-hour zone Ⅱ, zone Ⅲ and zone Ⅳ, reached 28.04%, 24.64%, and 24.42% respectively. Based on the number of humidity-hour, the most suitable THIC air conditioning systems for typical cities in different zone were raised, and a reference for the use of THIC air conditioning system in green office buildings was provided in this paper.

Objective An amelogenin-derived peptide has been shown to promote remineralization of demineralized enamel in an in vitro model of initial caries induced by pH cycling. The present study examines whether the peptide exerts similar effects within the complex oral environment in vivo. Design Specific pathogen-free Sprague-Dawley rats (n = 36) were infected with Streptococcus mutans, given ad libitum access to Diet 2000 and drinking water supplemented with sucrose (10%, w/v), and then randomly divided into three groups treated with 25 μM peptide solution, 1 g/L NaF or deionized water. Molar teeth were swabbed twice daily with the respective solutions for 24 days. Then animals were killed, their jaws were removed and caries lesions were analyzed using the quantitative light-induced fluorescence-digital (QLF-D) technique to measure changes in mineral content. To verify QLF-D results, caries were scored for lesion depth and size using the Keyes method, and analyzed using polarized light microscopy (PLM). Results Mineral gain was significantly higher in teeth treated with peptide or NaF than in teeth treated with water (p

Crohn’s disease (CD), a chronic inflammatory bowel disease, involved in brain structural and functional changes, including the amygdala. Amygdala is a key structure in the limbic system and its related circuits are implicated in processing of emotion, pain and sensory. However, limited study of the amygdala is elucidated in CD. This study mainly investigated altered functional connectivity (FC) of the amygdala in CD patients during resting-state. Magnetic resonance imaging scans were acquired from 42 CD patients and 35 healthy controls (HCs). Whole amygdala bilaterally were selected as regions of interest (ROIs). Voxel-based morphometry and FC methods were applied to investigate the differences of structure or intrinsic connectivity of the amygdala between the two groups, separately. Pearson correlations were performed to explore relationships between the clinical characteristics and neuroimaging findings in CD patients. Based on the whole amygdala bilaterally as ROIs, compared with HCs, CD patients showed no statistical differences of grey matter destiny but exhibited decreased FC between the amygdala and insula, parahippocampus, as well as anterior middle cingulate cortex/dorsal anterior cingulate cortex. CD patients had negative correlation between the disease duration and amygdala-insula connectivity. In the patient group, patients with higher anxiety or depression scores revealed increased FC of the amygdala with thalamus and orbitofrontal cortex. Our results reveal that aberrant FC of the amygdala may be involved in processing of visceral pain and sensation, and emotion in CD. These findings may further enhance the understanding of neural mechanisms of CD.

Manganese dioxide (MnO2 ) is a promising photo-thermo-electric-responsive semiconductor material for environmental applications, owing to its various favorable properties. However, the unsatisfactory environmental purification efficiency of this material has limited its further applications. Fortunately, in the last few years, significant efforts have been undertaken for improving the environmental purification efficiency of this material and understanding its underlying mechanism. Here, the aim is to summarize the recent experimental and computational research progress in the modification of MnO2 single species by morphology control, structure construction, facet engineering, and element doping. Moreover, the design and fabrication of MnO2 -based composites via the construction of homojunctions and MnO2 /semiconductor/conductor binary/ternary heterojunctions is discussed. Their applications in environmental purification systems, either as an adsorbent material for removing heavy metals, dyes, and microwave (MW) pollution, or as a thermal catalyst, photocatalyst, and electrocatalyst for the degradation of pollutants (water and gas, organic and inorganic) are also highlighted. Finally, the research gaps are summarized and a perspective on the challenges and the direction of future research in nanostructured MnO2 -based materials in the field of environmental applications is presented. Therefore, basic guidance for rational design and fabrication of high-efficiency MnO2 -based materials for comprehensive environmental applications is provided.

Background: Caring for patients living with long term conditions is a long-term task. The augmenting use of social media (SM) supports for health information and communication has created new opportunities for new models of health-care. Objectives: This study aimed to explore the effectiveness of SM support for caregivers. Methods: This systematic review mainly includes research over the past decade on caregivers for chronic diseases, with the results focusing on SM support for caregivers. Results: This literature review found that SM-based interventions can meet caregivers’ needs and have a positive impact on caregiver burden (CB) of chronic diseases such as cancer, dementia and stroke. Experimental studies of health interventions for stroke caregivers (SCs) through WeChat interventions have been limited. Conclusion: More quantitative studies are needed in the future to explore the effectiveness of WeChat as an intervention in the burden of SCs.

In the actual operation process of PPP(Public-Private Partnership) projects, due to inaccurate estimation, lack of accuracy of submitted data and asymmetric information of all parties involved, problems such as asset falsification often occur when assets are formed. In order to solve this problem, we have properly integrated the concept of asset life-cycle management (ALM) into the whole life cycle of PPP projects, and established an ALM-PPP model to analyze the asset management of PPP projects. We select the construction phase of PPP project as the research scope, and take tracking audit as the core to audit the actual value of PPP project, so as to improve the management efficiency of PPP project and ensure the public interest.

Interpretability and stability are two important features that are desired in many contemporary big data applications arising in economics and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in the sense of controlling the fraction of wrongly discovered features which can enhance greatly the interpretability is still largely underdeveloped in the econometric settings. To this end, in this paper we exploit the general framework of model-X knockoffs introduced recently in Cand\`{e}s, Fan, Janson and Lv (2018), which is nonconventional for reproducible large-scale inference in that the framework is completely free of the use of p-values for significance testing, and suggest a new method of intertwined probabilistic factors decoupling (IPAD) for stable interpretable forecasting with knockoffs inference in high-dimensional models. The recipe of the method is constructing the knockoff variables by assuming a latent factor model that is exploited widely in economics and finance for the association structure of covariates. Our method and work are distinct from the existing literature in that we estimate the covariate distribution from data instead of assuming that it is known when constructing the knockoff variables, our procedure does not require any sample splitting, we provide theoretical justifications on the asymptotic false discovery rate control, and the theory for the power analysis is also established. Several simulation examples and the real data analysis further demonstrate that the newly suggested method has appealing finite-sample performance with desired interpretability and stability compared to some popularly used forecasting methods.

As the most important source, Internet word of mouth (IWOM) and online review, from which Internet consumers get product and service information, have become the key factors which influence consumers' purchase decision. On the other hand, nowadays, many online customers are glad to do such things as: helping other consumers in trouble, giving feedback about shopping experience or product suggestion to the E-vendors, recommending the product or service to others, and so on, which are known as customer citizenship behavior (CCB). So far, many scholars have studied IWOM and CCB separately, but research connecting IWOM and CCB is sparse. Do IWOM information receivers probably carry out CCBs? Can IWOM influence CCB? And how? Based on Social Cognitive Theory and Social Exchange Theory, the authors build a theoretical model about the effect of IWOM on CCB by the mediation of customer perceived value (CPV). Data are collected by means of self-report questionnaires after online practice, then the model and hypotheses are tested by Structured Equation Model software. The research results show that: Firstly, Internet Word Of Mouth has a positive impact on Customer Perceived Value, and CPV positively influences Customer Citizenship Behavior. Secondly, Customer Perceived Value, as a mediator of Internet Word Of Mouth and Customer Citizenship Behavior, plays an indispensable and crucial role in this model. But Internet Word Of Mouth has no significantly direct effect on Customer Citizenship Behavior.

With the development of modern e-commerce, shopping online has completely changed the traditional commodity marketing model and consumer habits. More and more consumers use the online medium to distribute unfavorable experiences. Many previous studies have confirmed that negative word-of-mouth has a greater impact on consumers than positive word-of-mouth. However, there are few studies explaining how negative word-of-mouth affect consumers. To fill the existing research gap, this research proposed and empirically tested a negative online word-of-mouth on consumer's purchase intention model, investigating the influence of communicator characteristics, information features and receiver characteristics on trust and consumer's purchase intention. We conducted a survey in the form of a questionnaire and the empirical results showed that negative online word-of-mouth communicator's relationship strength with receiver, recipients' trust tendency and product involvement, significantly influence receivers' purchase intention, while the quantity, quality and information intensity of negative online word-of-mouth, positively affect receivers' trust in word-of-mouth, which further influences their purchase intention. Finally, this research proposed some substantive suggestions on the impact of negative online word-of-mouth on consumer's purchase intention.

Premenstrual syndrome (PMS) is a menstrual cycle-related disorder. Although the precise pathophysiology is not fully understood, it is increasingly believed that the central nervous system plays a vital role in the development of PMS. The aim of this study is to elucidate specific functional connectivity between the thalamus and cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 20 PMS patients and 21 healthy controls (HCs). Seed-based functional connectivity between the thalamus and six cortical regions of interest, including the prefrontal cortex (PFC), posterior parietal cortex, somatosensory cortex, motor cortex/supplementary motor area, temporal and occipital lobe, was adopted to identify specific thalamocortical connectivity in the two groups. Correlation analysis was then used to examine relationships between the neuroimaging findings and clinical symptoms. Activity in distinct cortical regions correlated with specific sub-regions of the thalamus in the two groups. Comparison between groups exhibited decreased prefrontal-thalamic connectivity and increased posterior parietal-thalamic connectivity in the PMS patients. Within the PMS group, the daily record of severity of problems (DRSP) score negatively correlated with the prefrontal-thalamic connectivity. Our findings may provide preliminary evidence for abnormal thalamocortical connectivity in PMS patients and may contribute to a better understanding of the pathophysiology of PMS.

Power and reproducibility are key to enabling refined scientific discoveries in contemporary big data applications with general high-dimensional nonlinear models. In this paper, we provide theoretical foundations on the power and robustness for the model-free knockoffs procedure introduced recently in Cand\`{e}s, Fan, Janson and Lv (2016) in high-dimensional setting when the covariate distribution is characterized by Gaussian graphical model. We establish that under mild regularity conditions, the power of the oracle knockoffs procedure with known covariate distribution in high-dimensional linear models is asymptotically one as sample size goes to infinity. When moving away from the ideal case, we suggest the modified model-free knockoffs method called graphical nonlinear knockoffs (RANK) to accommodate the unknown covariate distribution. We provide theoretical justifications on the robustness of our modified procedure by showing that the false discovery rate (FDR) is asymptotically controlled at the target level and the power is asymptotically one with the estimated covariate distribution. To the best of our knowledge, this is the first formal theoretical result on the power for the knockoffs procedure. Simulation results demonstrate that compared to existing approaches, our method performs competitively in both FDR control and power. A real data set is analyzed to further assess the performance of the suggested knockoffs procedure., Comment: 37 pages, 6 tables, 9 pages supplementary material

We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent for classifying uncorrupted test data points. Furthermore, under stronger conditions, we derive detailed asymptotic properties for the popular $k$-nearest neighbour ($k$nn), support vector machine (SVM) and linear discriminant analysis (LDA) classifiers. One consequence of these results is that the knn and SVM classifiers are robust to imperfect training labels, in the sense that the rate of convergence of the excess risks of these classifiers remains unchanged; in fact, our theoretical and empirical results even show that in some cases, imperfect labels may improve the performance of these methods. On the other hand, the LDA classifier is shown to be typically inconsistent in the presence of label noise unless the prior probabilities of each class are equal. Our theoretical results are supported by a simulation study., Comment: 44 pages, 7 figures

Background Functional brain abnormalities have been noted in premenstrual syndrome (PMS). However, the brain structural alterations related to PMS remain unclear. This study aimed to identify possible abnormalities in gray matter (GM) volumes and structural covariance patterns among PMS patients. Methods Structural magnetic resonance imaging data were obtained from 20 PMS patients and 20 healthy controls. Voxel-based morphometry (VBM) analysis was applied to examine GM volumes changes between the two groups. Receiver operating characteristic (ROC) curve was used to investigate the most reliable biomarker for distinguishing PMS patients from health controls based on the intergroup differences. Correlation analysis was then performed to assess relationships between the daily rating of severity of problems (DRSP) and abnormal brain regions. Finally, the regions identified from VBM analysis were served as seeds to characterize the whole-brain structural covariance patterns. Results Compared with healthy controls, PMS patients showed increased GM volumes in the precuneus/posterior cingulate cortex (precuneus/PCC) and thalamus, and decreased GM volumes in the insula. The precuneus/PCC exhibited the highest classification power by ROC analysis and positively correlated with the DRSP. Moreover, different patterns of structural covariance in the two groups were mainly located in the dorsolateral prefrontal cortex, anterior cingulate cortex, angular gyrus and hippocampus. Limitations This study is limited by a small sample and narrow age range of participants. Conclusions Our findings may provide preliminary evidence for brain morphology alterations in PMS patients and contribute to a better understanding of the pathophysiology of PMS.

TiO2 is an abundant and environmentally benign material, but has a wide band gap, which greatly confines its applications in photocatalysis. Doping and modifying the material composition are both generally used to change and control the photocatalytic activity of semiconductors. Herein, we describe a method and resulting activity of depositing Ce-/S-codoped TiO2 nanoparticles (NPs) on water-soluble sulfonated graphene (SGE) sheets, which guarantees a direct contact and satisfactory electron transfer between the semiconductor and graphene. The Ce/S–TiO2 NPs are homogeneously fixed on the surface of SGE sheets with an average particle size of ∼7 nm. The resulting composite showed noticeable activity in photodegrading methyl orange (κ = 0.425 h−1). This improved performance can be attributed to the synergistic effects of Ce- and S-codoping toward TiO2 and the composite action between TiO2 NPs and SGE. This type of novel composite is expected to stimulate the development of doped and graphene-involved photocatalysts for addressing environmental problems.

Preterm and small-for-gestational-age (SGA) neonates are vulnerable groups that are susceptible to various microbial infections. Vγ9Vδ2-T cells are critical components of the host immune system and have been demonstrated to play an important role in the defense against viral infection in adults. However, the characteristics of Vγ9Vδ2-T cells in children, especially the preterm and SGA populations, are poorly understood. Here, we examined the frequency and antiviral function of Vγ9Vδ2-T cells in neonates, including preterm, SGA and full-term babies. When compared to adults, neonates had a significantly lower percentage of Vγ9Vδ2-T cells in the blood. Upon influenza virus stimulation, neonatal Vγ9Vδ2-T cells, especially from preterm and SGA babies, showed markedly decreased and delayed antiviral cytokine responses than those of adults. In addition, the antiviral responses of neonatal Vγ9Vδ2-T cells were positively correlated with gestational age and birth weight. Finally, a weaker expansion of Vγ9Vδ2-T cells by isopentenyl pyrophosphate (IPP) was shown in neonates than the expansion in adults. Our data suggest that the depressed antiviral activity and decreased frequency of Vγ9Vδ2-T cells may likely account for the high susceptibility to microbial infection in neonates, particularly in preterm and SGA babies. Improving Vγ9Vδ2-T-cell function of neonates may provide a new way to defend against virus infection.

The gesture recognition has raised attention in computer vision owing to its many applications. However, video-based large-scale gesture recognition still faces many challenges, since many factors like background may disturb the accuracy. To achieve gesture recognition with large-scale videos, we propose a method based on RGB-D data. To learn gesture details better, the inputs are expanded into 32-frame videos first, and then the RGB and depth videos are sent to the C3D model to extract spatiotemporal features respectively. Next these features are combined to boost the performance, which can also avoid unreasonable synthetic data due to the uniform dimension of C3D features. Our approach achieves 49.2% accuracy on the validation subset of the Chalearn LAP IsoGD Database just with a linear SVM classifier. It also outperforms the baseline and other methods in the challenge and wins the first place at 56.9% on testing set.

This paper proposes the design and analysis of a novel micro-scale three dimensional (3D) helix inductor based on focused ion beam (FIB) technology, which is used for terahertz applications. The structure of the inductor includes two parts: the metallic helix and the coplanar waveguide (CPW). It has extremely small scale and can be integrated with other parts of on-chip circuits easily. In addition, fabricating such 3D helix inductors with high precision is much more convenient than other traditional planar spiral inductors. Good performance of quality factor and inductance value have been observed from the simulation results using High Frequency Structure Simulator (HFSS).

Large-scale precision matrix estimation is of fundamental importance yet challenging in many contemporary applications for recovering Gaussian graphical models. In this paper, we suggest a new approach of innovated scalable efficient estimation (ISEE) for estimating large precision matrix. Motivated by the innovated transformation, we convert the original problem into that of large covariance matrix estimation. The suggested method combines the strengths of recent advances in high-dimensional sparse modeling and large covariance matrix estimation. Compared to existing approaches, our method is scalable and can deal with much larger precision matrices with simple tuning. Under mild regularity conditions, we establish that this procedure can recover the underlying graphical structure with significant probability and provide efficient estimation of link strengths. Both computational and theoretical advantages of the procedure are evidenced through simulation and real data examples., to appear, The Annals of Statistics (2016)

Muscle atrophy occurs in several pathologic conditions such as diabetes and chronic obstructive pulmonary disease (COPD), as well as after long-term clinical administration of synthesized glucocorticoid, where increased circulating glucocorticoid accounts for the pathogenesis of muscle atrophy. Others and we previously reported mitochondrial dysfunction in muscle atrophy-related conditions and that mitochondria-targeting nutrients efficiently prevent kinds of muscle atrophy. However, whether and how mitochondrial dysfunction involves glucocorticoid-induced muscle atrophy remains unclear. Therefore, in the present study, we measured mitochondrial function in dexamethasone-induced muscle atrophy in vivo and in vitro, and we found that mitochondrial respiration was compromised on the 3rd day following after dexamethasone administration, earlier than the increases of MuRF1 and Fbx32, and dexamethasone-induced loss of mitochondrial components and key mitochondrial dynamics proteins. Furthermore, dexamethasone treatment caused intracellular ATP deprivation and robust AMPK activation, which further activated the FOXO3/Atrogenes pathway. By directly impairing mitochondrial respiration, FCCP leads to similar readouts in C2C12 myotubes as dexamethasone does. On the contrary, resveratrol, a mitochondrial nutrient, efficiently reversed dexamethasone-induced mitochondrial dysfunction and muscle atrophy in both C2C12 myotubes and mice, by improving mitochondrial function and blocking AMPK/FOXO3 signaling. These results indicate that mitochondrial dysfunction acts as a central role in dexamethasone-induced skeletal muscle atrophy and that nutrients or drugs targeting mitochondria might be beneficial in preventing or curing muscle atrophy.

Integrated watershed management is required to ensure the reasonable use of resources and reconcile interactions among natural and human systems. In the present study, an interval fuzzy multiobjective programming (IFMOP) method was used to solve an integrated watershed management problem. Based on system analysis, an IFMOP model suitable for a lake watershed system {IFMOPLWS} was developed and applied to the Lake Qionghai watershed in China. Scenario analysis and an interactive approach were used in the solution process. In this manner, various system components were incorporated into one framework for holistic consideration and optimization. Integrality and uncertainty, as well as the multiobjective and dynamic characteristics of the watershed system, were well addressed. Using two scenarios, two planning schemes were generated. Agriculture, tourism, macroeconomics, cropland use, water supply, forest coverage, soil erosion, and water pollution were fully interpreted and compared to identify a preferable planning alternative for local agencies. This study showed that the IFMOPLWS is a powerful tool for integrated watershed management planning and can provide a solid base for sustainable watershed management.

While functional regression models have received increasing attention recently, most existing approaches assume both a linear relationship and a scalar response variable. We suggest a new method, "Functional Response Additive Model Estimation" (FRAME), which extends the usual linear regression model to situations involving both functional predictors, $X_j(t)$, scalar predictors, $Z_k$, and functional responses, $Y(s)$. Our approach uses a penalized least squares optimization criterion to automatically perform variable selection in situations involving multiple functional and scalar predictors. In addition, our method uses an efficient coordinate descent algorithm to fit general nonlinear additive relationships between the predictors and response. We develop our model for novel forecasting challenges in the entertainment industry. In particular, we set out to model the decay rate of demand for Hollywood movies using the predictive power of online virtual stock markets (VSMs). VSMs are online communities that, in a market-like fashion, gather the crowds' prediction about demand for a particular product. Our fully functional model captures the pattern of pre-release VSM trading prices and provides superior predictive accuracy of a movie's post-release demand in comparison to traditional methods. In addition, we propose graphical tools which give a glimpse into the causal relationship between market behavior and box office revenue patterns, and hence provide valuable insight to movie decision makers., Published in at http://dx.doi.org/10.1214/14-AOAS781 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

A sparse representation is known as a very powerful tool to solve image reconstruction problem such as denoising and the single image super-resolution. In the sparse representation, it is assumed that an image patch or data can be approximated by a linear combination of a few bases selected from a given dictionary. A single overcomplete dictionary is usually learned with training patches. Dictionary learning methods almost are concerned about building a general over-complete dictionary on the assumption that the bases in dictionary can represent everything. However, using more appropriate dictionary, the sparse representation of patch can obtain better results. In this paper, we propose a classification-and-reconstruction approach with multiple dictionaries. Before learning dictionary for reconstruction, some representative bases can be used to classify all training patches from database and multiple dictionaries for reconstruction can be learned by classified patches respectively. In reconstruction phase, the patch of input image can be classified and the adaptive dictionary can be selected to use. We demonstrate that the proposed classification-and-reconstruction approach outperforms existing sparse representation with the single dictionary.

Train operation energy consumption occupies a large proportion of whole rail transport resource consumption. Aiming at improving energy utilization, current research is mainly about saving energy while security and time are limiting conditions. However, actual train operation is a complex process which has strict requirements on safety, energy consumption, precise parking and some other factors. This paper summarizes train control optimization research, analyzes train running characteristics and influential factors, and introduces train traction computing method. Multi-objective Particle Swarm Optimization with inertia weight algorithm is applied to study train operation optimization. With energy-saving, punctuality and precise parking being optimization goals, train operation optimization models and fitness evaluation functions are established. Through optimization, optimal operating condition sequence and corresponding condition conversion points are obtained. Effectiveness of the proposed algorithm is verified by offline simulations, and the results present good optimization performance.

Since city is the economic centre and huge emission source of CO 2 , development model of it affects whether low-carbon economy (LCE) could be accomplished smoothly. Low-carbon city (LCC), based on the method of LCE, is in the making as the most valuable means to realize LCE. In this paper, with LCE method as the guide, evaluation index system was established. Different from the previous researches, indices screen out has the same variable, namely equivalent of CO 2 , in order to set the stage for the further study of modeling and optimization, striving to reflect the development level and providing scientific evidence for decision support.

On the basis of traditional assessment indices and combining practical indices summarized from the practical experience of LCC development home and abroad, a controlling index system was build comprising three aspects such as structure emission reduction, technology emission reduction and supervision emission reduction. The indices selected in this paper are of high practical application and operation, and could provide a basis for controlling approach of reducing GHG and saving energies.

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is poorly understood. In a seminal paper, Bickel and Levina [Bernoulli 10 (2004) 989--1010] show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as poor as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as poorly as the random guessing. Thus, it is important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample $t$-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure., Comment: Published in at http://dx.doi.org/10.1214/07-AOS504 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

In conjunction with socioeconomic development in watersheds, increasingly challenging problems, such as scarcity of water resources and environmental deterioration, have arisen. Watershed management is a useful tool for dealing with these issues and maintaining sustainable development at the watershed scale. The complex and uncertain characteristics of watershed systems have a great impact on decisions about countermeasures and other techniques that will be applied in the future. An optimization method based on scenario analysis is proposed in this paper as a means of handling watershed management under uncertainty. This method integrates system analysis, forecast methods, and scenario analysis, as well as the contributions of stakeholders and experts, into a comprehensive framework. The proposed method comprises four steps: system analyses, a listing of potential engineering techniques and countermeasures, scenario analyses, and the optimal selection of countermeasures and engineering techniques. The proposed method was applied to the case of the Lake Qionghai watershed in southwestern China, and the results are reported in this paper. This case study demonstrates that the proposed method can be used to deal efficiently with uncertainties at the watershed level. Moreover, this method takes into consideration the interests of different groups, which is crucial for successful watershed management. In particular, social, economic, environmental, and resource systems are all considered in order to improve the applicability of the method. In short, the optimization method based on scenario analysis proposed here is a valuable tool for watershed management.