Showing papers by "University of North Carolina at Charlotte published in 2015"

A global reference for human genetic variation.

Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

An integrated map of structural variation in 2,504 human genomes

Abstract: Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.

Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

Abstract: With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.

Identification of cell types from single-cell transcriptomes using a novel clustering method

Abstract: Motivation The recent advance of single-cell technologies has brought new insights into complex biological phenomena. In particular, genome-wide single-cell measurements such as transcriptome sequencing enable the characterization of cellular composition as well as functional variation in homogenic cell populations. An important step in the single-cell transcriptome analysis is to group cells that belong to the same cell types based on gene expression patterns. The corresponding computational problem is to cluster a noisy high dimensional dataset with substantially fewer objects (cells) than the number of variables (genes). Results In this article, we describe a novel algorithm named shared nearest neighbor (SNN)-Cliq that clusters single-cell transcriptomes. SNN-Cliq utilizes the concept of shared nearest neighbor that shows advantages in handling high-dimensional data. When evaluated on a variety of synthetic and real experimental datasets, SNN-Cliq outperformed the state-of-the-art methods tested. More importantly, the clustering results of SNN-Cliq reflect the cell types or origins with high accuracy. Availability and implementation The algorithm is implemented in MATLAB and Python. The source code can be downloaded at http://bioinfo.uncc.edu/SNNCliq.

Optical dielectric function of silver

Abstract: Using broadband spectroscopic ellipsometry, the authors determine the complex valued dielectric function of silver films from 0.05 eV (\ensuremath{\lambda}=25 \ensuremath{\mu}) to 4.14 eV (\ensuremath{\lambda} = 300 nm) with a statistical uncertainty of less than 1%. While several previous similar measurements exist, they span considerably shorter energy ranges and report partially inconsistent results. In view of the wide-ranging applications of silver in nanophotonics, plasmonics and optical metamaterials, we anticipate this paper to become a standard reference for many scientists and engineers.

Sex-based differences in immune function and responses to vaccination.

Abstract: Females typically develop higher antibody responses and experience more adverse reactions following vaccination than males. These differences are observed in response to diverse vaccines, including the bacillus Calmette-Guerin vaccine, the measles, mumps and rubella vaccine, the yellow fever virus vaccine and influenza vaccines. Sex differences in the responses to vaccines are observed across diverse age groups, ranging from infants to aged individuals. Biological as well as behavioral differences between the sexes are likely to contribute to differences in the outcome of vaccination between the sexes. Immunological, hormonal, genetic and microbiota differences between males and females may also affect the outcome of vaccination. Identifying ways to reduce adverse reactions in females and increase immune responses in males will be necessary to adequately protect both sexes against infectious diseases.

Equally Efficient Interlayer Exciton Relaxation and Improved Absorption in Epitaxial and Nonepitaxial MoS2/WS2 Heterostructures

Abstract: Semiconductor heterostructures provide a powerful platform to engineer the dynamics of excitons for fundamental and applied interests. However, the functionality of conventional semiconductor heterostructures is often limited by inefficient charge transfer across interfaces due to the interfacial imperfection caused by lattice mismatch. Here we demonstrate that MoS2/WS2 heterostructures consisting of monolayer MoS2 and WS2 stacked in the vertical direction can enable equally efficient interlayer exciton relaxation regardless the epitaxy and orientation of the stacking. This is manifested by a similar 2 orders of magnitude decrease of photoluminescence intensity in both epitaxial and nonepitaxial MoS2/WS2 heterostructures. Both heterostructures also show similarly improved absorption beyond the simple superimposition of the absorptions of monolayer MoS2 and WS2. Our result indicates that 2D heterostructures bear significant implications for the development of photonic devices, in particular those requestin...

Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing

Abstract: Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5 m, 1 m, and 8 m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5 m for localization and 0.9 m for tracking in the office environment. When using only the magnetism, Magicol consumes 9 $\times$ less energy in tracking compared to WiFi-based tracking.

Neuronal uptake and propagation of a rare phosphorylated high-molecular-weight tau derived from Alzheimer's disease brain.

Abstract: Tau pathology is known to spread in a hierarchical pattern in Alzheimer's disease (AD) brain during disease progression, likely by trans-synaptic tau transfer between neurons. However, the tau species involved in inter-neuron propagation remains unclear. To identify tau species responsible for propagation, we examined uptake and propagation properties of different tau species derived from postmortem cortical extracts and brain interstitial fluid of tau-transgenic mice, as well as human AD cortices. Here we show that PBS-soluble phosphorylated high-molecular-weight (HMW) tau, though very low in abundance, is taken up, axonally transported, and passed on to synaptically connected neurons. Our findings suggest that a rare species of soluble phosphorylated HMW tau is the endogenous form of tau involved in propagation and could be a target for therapeutic intervention and biomarker development.

Mindfulness Meditation-Based Pain Relief Employs Different Neural Mechanisms Than Placebo and Sham Mindfulness Meditation-Induced Analgesia.

Abstract: Mindfulness meditation reduces pain in experimental and clinical settings. However, it remains unknown whether mindfulness meditation engages pain-relieving mechanisms other than those associated with the placebo effect (e.g., conditioning, psychosocial context, beliefs). To determine whether the analgesic mechanisms of mindfulness meditation are different from placebo, we randomly assigned 75 healthy, human volunteers to 4 d of the following: (1) mindfulness meditation, (2) placebo conditioning, (3) sham mindfulness meditation, or (4) book-listening control intervention. We assessed intervention efficacy using psychophysical evaluation of experimental pain and functional neuroimaging. Importantly, all cognitive manipulations (i.e., mindfulness meditation, placebo conditioning, sham mindfulness meditation) significantly attenuated pain intensity and unpleasantness ratings when compared to rest and the control condition ( p p = 0.032) and pain unpleasantness ( p p = 0.030) and pain unpleasantness ( p = 0.043) ratings more than sham mindfulness meditation. Mindfulness-meditation-related pain relief was associated with greater activation in brain regions associated with the cognitive modulation of pain, including the orbitofrontal, subgenual anterior cingulate, and anterior insular cortex. In contrast, placebo analgesia was associated with activation of the dorsolateral prefrontal cortex and deactivation of sensory processing regions (secondary somatosensory cortex). Sham mindfulness meditation-induced analgesia was not correlated with significant neural activity, but rather by greater reductions in respiration rate. This study is the first to demonstrate that mindfulness-related pain relief is mechanistically distinct from placebo analgesia. The elucidation of this distinction confirms the existence of multiple, cognitively driven, supraspinal mechanisms for pain modulation. SIGNIFICANCE STATEMENT Recent findings have demonstrated that mindfulness meditation significantly reduces pain. Given that the “gold standard” for evaluating the efficacy of behavioral interventions is based on appropriate placebo comparisons, it is imperative that we establish whether there is an effect supporting meditation-related pain relief above and beyond the effects of placebo. Here, we provide novel evidence demonstrating that mindfulness meditation produces greater pain relief and employs distinct neural mechanisms than placebo cream and sham mindfulness meditation. Specifically, mindfulness meditation-induced pain relief activated higher-order brain regions, including the orbitofrontal and cingulate cortices. In contrast, placebo analgesia was associated with decreased pain-related brain activation. These findings demonstrate that mindfulness meditation reduces pain through unique mechanisms and may foster greater acceptance of meditation as an adjunct pain therapy.

Piezo-phototronic Effect Enhanced UV/Visible Photodetector Based on Fully Wide Band Gap Type-II ZnO/ZnS Core/Shell Nanowire Array.

Abstract: A high-performance broad band UV/visible photodetector has been successfully fabricated on a fully wide bandgap ZnO/ZnS type-II heterojunction core/shell nanowire array. The device can detect photons with energies significantly smaller (2.2 eV) than the band gap of ZnO (3.2 eV) and ZnS (3.7 eV), which is mainly attributed to spatially indirect type-II transition facilitated by the abrupt interface between the ZnO core and ZnS shell. The performance of the device was further enhanced through the piezo-phototronic effect induced lowering of the barrier height to allow charge carrier transport across the ZnO/ZnS interface, resulting in three orders of relative responsivity change measured at three different excitation wavelengths (385, 465, and 520 nm). This work demonstrates a prototype UV/visible photodetector based on the truly wide band gap semiconducting 3D core/shell nanowire array with enhanced performance through the piezo-phototronic effect.

CEC’s Standards for Classifying the Evidence Base of Practices in Special Education

Abstract: As an initial step toward improving the outcomes of learners with disabilities, special educators have formulated guidelines for identifying evidence-based practices. We describe the Council of Exceptional Children’s new set of standards for identifying evidence-based practices in special education and how they (a) were systematically vetted by expert special education researchers through a Delphi study, (b) demonstrated adequate inter-rater reliability in a pilot study, (c) delineate specific criteria in many areas and provide flexibility to tailor other criteria, (d) provide an integrated set of standards for classifying the evidence base of practices based on findings from both group comparison and single-subject studies, and (e) can be applied by independent special education researchers. We conclude by noting limitations to the standards, briefly comparing these new standards with other evidence-based practice standards, and providing recommendations for future research and for refining the standards.

Invention as a combinatorial process: evidence from US patents

Abstract: Invention has been commonly conceptualized as a search over a space of combinatorial possibilities. Despite the existence of a rich literature, spanning a variety of disciplines, elaborating on the recombinant nature of invention, we lack a formal and quantitative characterization of the combinatorial process underpinning inventive activity. Here, we use US patent records dating from 1790 to 2010 to formally characterize invention as a combinatorial process. To do this, we treat patented inventions as carriers of technologies and avail ourselves of the elaborate system of technology codes used by the United States Patent and Trademark Office to classify the technologies responsible for an invention's novelty. We find that the combinatorial inventive process exhibits an invariant rate of ‘exploitation’ (refinements of existing combinations of technologies) and ‘exploration’ (the development of new technological combinations). This combinatorial dynamic contrasts sharply with the creation of new technological capabilities—the building blocks to be combined—that has significantly slowed down. We also find that, notwithstanding the very reduced rate at which new technologies are introduced, the generation of novel technological combinations engenders a practically infinite space of technological configurations.

Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval

Abstract: Automatic analysis of histopathological images has been widely utilized leveraging computational image-processing methods and modern machine learning techniques. Both computer-aided diagnosis (CAD) and content-based image-retrieval (CBIR) systems have been successfully developed for diagnosis, disease detection, and decision support in this area. Recently, with the ever-increasing amount of annotated medical data, large-scale and data-driven methods have emerged to offer a promise of bridging the semantic gap between images and diagnostic information. In this paper, we focus on developing scalable image-retrieval techniques to cope intelligently with massive histopathological images. Specifically, we present a supervised kernel hashing technique which leverages a small amount of supervised information in learning to compress a 10 $\thinspace$ 000-dimensional image feature vector into only tens of binary bits with the informative signatures preserved. These binary codes are then indexed into a hash table that enables real-time retrieval of images in a large database. Critically, the supervised information is employed to bridge the semantic gap between low-level image features and high-level diagnostic information. We build a scalable image-retrieval framework based on the supervised hashing technique and validate its performance on several thousand histopathological images acquired from breast microscopic tissues. Extensive evaluations are carried out in terms of image classification (i.e., benign versus actionable categorization) and retrieval tests. Our framework achieves about 88.1% classification accuracy as well as promising time efficiency. For example, the framework can execute around 800 queries in only 0.01 s, comparing favorably with other commonly used dimensionality reduction and feature selection methods.

Wide-Area Image Geolocalization with Aerial Reference Imagery

Abstract: We propose to use deep convolutional neural networks to address the problem of cross-view image geolocalization, in which the geolocation of a ground-level query image is estimated by matching to georeferenced aerial images. We use state-of-the-art feature representations for ground-level images and introduce a cross-view training approach for learning a joint semantic feature representation for aerial images. We also propose a network architecture that fuses features extracted from aerial images at multiple spatial scales. To support training these networks, we introduce a massive database that contains pairs of aerial and ground-level images from across the United States. Our methods significantly out-perform the state of the art on two benchmark datasets. We also show, qualitatively, that the proposed feature representations are discriminative at both local and continental spatial scales.

Mapping the field of family business research: past trends and future directions

Abstract: Receiving increasing attention over the past decade by scholars worldwide, family business research has developed in diverse directions. Due to the numerous challenges family businesses face in their development and continuation, research has generated a wide ranging spectrum of the subjects explored within it and a large body of knowledge as a result. Based on the results of the by far largest bibliometric analyses in the field, this paper elaborates and clarifies the fragmented state of family business research. By analyzing virtually all existing family business-related writings, the most influential publications are highlighted, and changes in citation patterns before and after the year 2000 are discussed. Here, five topical clusters are identified which reflect the tracks family business research follows. With these clusters as a basis, the paper concludes by identifying avenues for future research.

Short and sparse text topic modeling via self-aggregation

Abstract: The overwhelming amount of short text data on social media and elsewhere has posed great challenges to topic modeling due to the sparsity problem. Most existing attempts to alleviate this problem resort to heuristic strategies to aggregate short texts into pseudo-documents before the application of standard topic modeling. Although such strategies cannot be well generalized to more general genres of short texts, the success has shed light on how to develop a generalized solution. In this paper, we present a novel model towards this goal by integrating topic modeling with short text aggregation during topic inference. The aggregation is founded on general topical affinity of texts rather than particular heuristics, making the model readily applicable to various short texts. Experimental results on real-world datasets validate the effectiveness of this new model, suggesting that it can distill more meaningful topics from short texts.

Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts

Abstract: Majority of the load forecasting literature has been on point forecasting, which provides the expected value for each step throughout the forecast horizon. In the smart grid era, the electricity demand is more active and less predictable than ever before. As a result, probabilistic load forecasting, which provides additional information on the variability and uncertainty of future load values, is becoming of great importance to power systems planning and operations. This paper proposes a practical methodology to generate probabilistic load forecasts by performing Quantile Regression Averaging (QRA) on a set of sister point forecasts. There are two major benefits of the proposed approach: 1) it can leverage the development in the point load forecasting literature over the past several decades; and 2) it does not rely so much on high quality expert forecasts, which are rarely achievable in load forecasting practice. To demonstrate the effectiveness of the proposed approach and make the results reproducible to the load forecasting community, we construct a case study using the publicly available data from the Global Energy Forecasting Competition 2014. Comparing with the benchmark methods that utilize the variability of a selected individual forecast, the proposed approach leads to dominantly better performance as measured by the pinball loss function and the Winkler score.

Query Specific Rank Fusion for Image Retrieval

Abstract: Recently two lines of image retrieval algorithms demonstrate excellent scalability: 1) local features indexed by a vocabulary tree, and 2) holistic features indexed by compact hashing codes Although both of them are able to search visually similar imageseffectively, their retrieval precision may vary dramatically among queries Therefore, combining these two types of methods is expected to further enhance the retrieval precision However, the feature characteristics and the algorithmic procedures of these methods are dramatically different, which is very challenging for the feature-level fusion This motivates us to investigate how to fuse the ordered retrieval sets, ie, the ranks of images, given by multiple retrieval methods, to boost the retrieval precision without sacrificing theirscalability In this paper, we model retrieval ranks as graphs of candidate images and propose a graph-based query specific fusion approach, where multiple graphs are merged and reranked by conducting a link analysis on a fused graph The retrieval quality of an individual method is measured on-the-fly by assessing the consistency of the top candidates’ nearest neighborhoods Hence, it iscapable of adaptively integrating the strengths of the retrieval methods using local or holistic features for different query images This proposed method does not need any supervision, has few parameters, and is easy to implement Extensive and thorough experiments have been conducted on four public datasets, ie, the UKbench , Corel-5K , Holidays and the large-scale San Francisco Landmarks datasets Our proposed method has achieved very competitive performance, including state-of-the-art results on several data sets, eg, the N-S score 383 for UKbench

Three-Dimensional Blood-Brain Barrier Model for in vitro Studies of Neurovascular Pathology.

Abstract: Blood–brain barrier (BBB) pathology leads to neurovascular disorders and is an important target for therapies. However, the study of BBB pathology is difficult in the absence of models that are simple and relevant. In vivo animal models are highly relevant, however they are hampered by complex, multi-cellular interactions that are difficult to decouple. In vitro models of BBB are simpler, however they have limited functionality and relevance to disease processes. To address these limitations, we developed a 3-dimensional (3D) model of BBB on a microfluidic platform. We verified the tightness of the BBB by showing its ability to reduce the leakage of dyes and to block the transmigration of immune cells towards chemoattractants. Moreover, we verified the localization at endothelial cell boundaries of ZO-1 and VE-Cadherin, two components of tight and adherens junctions. To validate the functionality of the BBB model, we probed its disruption by neuro-inflammation mediators and ischemic conditions and measured the protective function of antioxidant and ROCK-inhibitor treatments. Overall, our 3D BBB model provides a robust platform, adequate for detailed functional studies of BBB and for the screening of BBB-targeting drugs in neurological diseases.

Learning Binary Codes for Maximum Inner Product Search

Abstract: Binary coding or hashing techniques are recognized to accomplish efficient near neighbor search, and have thus attracted broad interests in the recent vision and learning studies. However, such studies have rarely been dedicated to Maximum Inner Product Search (MIPS), which plays a critical role in various vision applications. In this paper, we investigate learning binary codes to exclusively handle the MIPS problem. Inspired by the latest advance in asymmetric hashing schemes, we propose an asymmetric binary code learning framework based on inner product fitting. Specifically, two sets of coding functions are learned such that the inner products between their generated binary codes can reveal the inner products between original data vectors. We also propose an alternative simpler objective which maximizes the correlations between the inner products of the produced binary codes and raw data vectors. In both objectives, the binary codes and coding functions are simultaneously learned without continuous relaxations, which is the key to achieving high-quality binary codes. We evaluate the proposed method, dubbed Asymmetric Inner-product Binary Coding (AIBC), relying on the two objectives on several large-scale image datasets. Both of them are superior to the state-of-the-art binary coding and hashing methods in performing MIPS tasks.

Physical Activity Levels in College Students With Chronic Ankle Instability.

Abstract: Context: Ankle sprains are the most common orthopaedic pathologic condition, and more concerning is the high percentage of persons who develop chronic ankle instability (CAI). Researchers have repo...

Quadriceps neural alterations in anterior cruciate ligament reconstructed patients: A 6‐month longitudinal investigation

Abstract: The purpose of this investigation was to evaluate differences in quadriceps corticospinal excitability, spinal-reflexive excitability, strength, and voluntary activation before, 2 weeks post and 6 months post-anterior cruciate ligament reconstruction (ACLr) This longitudinal, case-control investigation examined 20 patients scheduled for ACLr (11 females, 9 males; age: 209 ± 44 years; height:1724 ± 75 cm; weight:762 ± 118 kg) and 20 healthy controls (11 females, 9 males; age:217 ± 37 years; height: 1737 ± 99 cm; weight: 761 ± 197 kg) Maximal voluntary isometric contractions (MVIC), central activation ratio (CAR), normalized Hoffmann spinal reflexes, active motor threshold (AMT), and normalized motor-evoked potential (MEP) amplitudes at 120% of AMT were measured in the quadriceps muscle at the specific time points ACLr patients demonstrated bilateral reductions in spinal-reflexive excitability compared with controls before surgery (P = 002) and 2 weeks post-surgery (P ≤ 0001) ACLr patients demonstrated higher AMT at 6 months post-surgery (P ≤ 0001) in both limbs No MEP differences were detected Quadriceps MVIC and CAR were lower in both limbs of the ACLr group before surgery and 6 months post-surgery (P ≤ 005) compared with controls Diminished excitability of spinal-reflexive and corticospinal pathways are present at different times following ACLr and occur in combination with clinical deficits in quadriceps strength and activation Early rehabilitation strategies targeting spinal-reflexive excitability may help improve postoperative outcomes, while later-stage rehabilitation may benefit from therapeutic techniques aimed at improving corticospinal excitability

Five-factor model personality traits as predictors of perceived and actual usage of technology

Abstract: Understanding the adoption and use of technology is extremely important in the field of information systems. Not surprisingly, there are several conceptual models that attempt to explain how and wh...