An Unsupervised Feature Learning Approach to Improve Automatic Incident Detection

Sophisticated automatic incident detection (AID) technology plays a key role in contemporary transportation systems. Though many papers were devoted to study incident classification algorithms, few study investigated how to enhance feature representation of incidents to improve AID performance. In this paper, we propose to use an unsupervised feature learning algorithm to generate higher level features to represent incidents. We used real incident data in the experiments and found that effective feature mapping function can be learnt from the data crosses the test sites. With the enhanced features, detection rate (DR), false alarm rate (FAR) and mean time to detect (MTTD) are significantly improved in all of the three representative cases. This approach also provides an alternative way to reduce the amount of labeled data, which is expensive to obtain, required in training better incident classifiers since the feature learning is unsupervised.Comment: The 15th IEEE International Conference on Intelligent Transportation Systems (ITSC 2012

Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching

Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches. However, it remains challenging to generate high-quality disparities for the inherently ill-posed regions. To tackle this problem, we propose a novel cascade CNN architecture composing of two stages. The first stage advances the recently proposed DispNet by equipping it with extra up-convolution modules, leading to disparity images with more details. The second stage explicitly rectifies the disparity initialized by the first stage; it couples with the first-stage and generates residual signals across multiple scales. The summation of the outputs from the two stages gives the final disparity. As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement. Moreover, it also benefits the training of the overall cascade network. Experimentation shows that our cascade residual learning scheme provides state-of-the-art performance for matching stereo correspondence. By the time of the submission of this paper, our method ranks first in the KITTI 2015 stereo benchmark, surpassing the prior works by a noteworthy margin.Comment: Accepted at ICCVW 2017. The first two authors contributed equally to this pape

Clinicopathologic Implications of Complement Genetic Variants in Kidney Transplantation

Genetic testing has uncovered rare variants in complement proteins associated with thrombotic microangiopathy (TMA) and C3 glomerulopathy (C3G). Approximately 50% are classified as variants of uncertain significance (VUS). Clinical risk assessment of patients carrying a VUS remains challenging primarily due to a lack of functional information, especially in the context of multiple confounding factors in the setting of kidney transplantation. Our objective was to evaluate the clinicopathologic significance of genetic variants in TMA and C3G in a kidney transplant cohort. We used whole exome next-generation sequencing to analyze complement genes in 76 patients, comprising 60 patients with a TMA and 16 with C3G. Ten variants in complement factor H (CFH) were identified; of these, four were known to be pathogenic, one was likely benign and five were classified as a VUS (I372V, I453L, G918E, T956M, L1207I). Each VUS was subjected to a structural analysis and was recombinantly produced; if expressed, its function was then characterized relative to the wild-type (WT) protein. Our data indicate that I372V, I453L, and G918E were deleterious while T956M and L1207I demonstrated normal functional activity. Four common polymorphisms in CFH (E936D, N1050Y, I1059T, Q1143E) were also characterized. We also assessed a family with a pathogenic variant in membrane cofactor protein (MCP) in addition to CFH with a unique clinical presentation featuring valvular dysfunction. Our analyses helped to determine disease etiology and defined the recurrence risk after kidney transplant, thereby facilitating clinical decision making for our patients. This work further illustrates the limitations of the prediction models and highlights the importance of conducting functional analysis of genetic variants particularly in a complex clinicopathologic scenario such as kidney transplantation

Modelling quality dynamics on business value and firm performance in big data analytics environment

Big data analytics have become an increasingly important component for firms across advanced economies. This paper examines the quality dynamics in big data environment that are linked with enhancing business value and firm performance. The study identifies that system quality (i.e., system reliability, accessibility, adaptability, integration, response time and privacy) and information quality (i.e., completeness, accuracy, format and currency) are key to enhance business value and firm performance in a big data environment. The study also proposes that the relationship between quality and firm performance is mediated by business value of big data. Drawing on the resource based theory and the information systems success literature, this study extends knowledge in this domain by linking system quality, information quality, business value and firm performance

Analyzing the mechanisms that facilitate the subtype-specific assembly of γ-aminobutyric acid type A receptors

Impaired inhibitory signaling underlies the pathophysiology of many neuropsychiatric and neurodevelopmental disorders including autism spectrum disorders and epilepsy. Neuronal inhibition is regulated by synaptic and extrasynaptic γ-aminobutyric acid type A receptors (GABAARs), which mediate phasic and tonic inhibition, respectively. These two GABAAR subtypes differ in their function, ligand sensitivity, and physiological properties. Importantly, they contain different α subunit isoforms: synaptic GABAARs contain the α1–3 subunits whereas extrasynaptic GABAARs contain the α4–6 subunits. While the subunit composition is critical for the distinct roles of synaptic and extrasynaptic GABAAR subtypes in inhibition, the molecular mechanism of the subtype-specific assembly has not been elucidated. To address this issue, we purified endogenous α1- and α4-containing GABAARs from adult murine forebrains and examined their subunit composition and interacting proteins using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and quantitative analysis. We found that the α1 and α4 subunits form separate populations of GABAARs and interact with distinct sets of binding proteins. We also discovered that the β3 subunit, which co-purifies with both the α1 and α4 subunits, has different levels of phosphorylation on serines 408 and 409 (S408/9) between the two receptor subtypes. To understand the role S408/9 plays in the assembly of α1- and α4-containing GABAARs, we examined the effects of S408/9A (alanine) knock-in mutation on the subunit composition of the two receptor subtypes using LC-MS/MS and quantitative analysis. We discovered that the S408/9A mutation results in the formation of novel α1α4-containing GABAARs. Moreover, in S408/9A mutants, the plasma membrane expression of the α4 subunit is increased whereas its retention in the endoplasmic reticulum is reduced. These findings suggest that S408/9 play a critical role in determining the subtype-specific assembly of GABAARs, and thus the efficacy of neuronal inhibition

Predict Market Share with Users’ Online Activities Data: An Initial Study on Market Share and Search Index of Mobile Phone

Acquiring accurate and timely market share information is very important for producers to arrange producing plan and design marketing strategy. However the high cost and long period of collecting survey data in survey-based method make it much difficult to easily get latest market shares data. Recently, the emerging online web systems provide users with new and convenient ways of searching, learning, experiencing and buying products. The users’ activities data captured by these web systems can reflect users’ buying intentions and behaviours very well, and contain very valuable information for predicting market shares. In this study, the correlation between Google search index and market shares of mobile phones is analyzed with time series analysis technology. The experiment result shows the statistically significant relationships exist between search index and market shares. This indicates the easily got search index data with low cost has the power of timely forecasting market shares. This study opens a door to apply users’ online activities data to accurately and timely predict market shares, which will bring many benefits to producers and customers

Antioxidant and Antihypertensive Activity Egg White Powder Produced by Pan Drying at Different Temperature and Drying Time

Antioxidant and antihypertensive (ACE-Inhibitors) are commonly known as bioactive molecules in foodstuff. Both molecules can be obtained naturally or through processing and preservation of egg white of poultry eggs. One way of preserving the egg white with drying method is by pan drying method. The objective of this study was to determine an appropriate temperature and drying time to produce high yield of antioxidant and antihypertensive activity. The materials used for this study were 900 eggs which were obtained from the same farm. That amount was calculated based on the number of experimental units required to run the experiment with the total number of treatment (3 x 3) with 4 replications for each treatment combination giving 25 chicken eggs for each treatment. The experiment was carried out using a 3x3 factorial arrangement according to completely randomized design. The first factor was drying temperature, i.e. 45oC, 50oC, and 55oC and the second factor was drying time, i.e. 30h, 39h, and 48h. The results showed that high antioxidant activity was found on egg white which was dried at temperature of 45oC for 39 hours which reached 26.85%. However, antihypertensive activity was optimum at 50oC and drying for 48 hours, which was up to 75.06%. Drying the egg white using appropriate temperature and time may improve the antioxidant and antihypertensive activities

Characterization of extended co-culture of non-typeable Haemophilus influenzae with primary human respiratory tissues

Non-typeable Haemophilus influenzae (NTHi) are human-adapted Gram-negative bacteria that comprise part of the normal flora of the human upper airway, but are also responsible for a number of mucosal infections such as otitis media and bronchitis. These infections often recur and can become chronic. To characterize the effect of long-term co-culture of NTHi with human tissues, we infected primary respiratory epithelial cells grown at the air–liquid interface with three NTHi strains over a range of 1–10 days. Scanning and transmission electron microscopy of tissues confirmed that intact NTHi were persisting paracellularly, while organisms observed in intracellular vacuoles appeared degraded. Furthermore, the apical surface and tight junctions of the infected tissues were undisturbed, with high transepithelial electrical resistances, while the basal cell layer displayed more junctional disorganization and wider intercellular spaces than the uninfected control tissues. Although the tissues elaborated the cytokine profile reported for NTHi-caused otitis media in vivo, there was little change in the dynamics of cytokine secretion over the time points tested. Finally, we report that NTHi strains released outer membrane vesicles (OMVs) during extended co-culture with the tissues, and show that these OMVs directly interact with host cell membranes

Isolation and Characterization of Multi-Protein Complexes Enriched in the K-Cl Co-transporter 2 From Brain Plasma Membranes

Kcc2 plays a critical role in determining the efficacy of synaptic inhibition, however, the cellular mechanisms neurons use to regulate its membrane trafficking, stability and activity are ill-defined. To address these issues, we used affinity purification to isolate stable multi-protein complexes of K-Cl Co-transporter 2 (Kcc2) from the plasma membrane of murine forebrain. We resolved these using blue-native polyacrylamide gel electrophoresis (BN-PAGE) coupled to LC-MS/MS and label-free quantification. Data are available via ProteomeXchange with identifier PXD021368. Purified Kcc2 migrated as distinct molecular species of 300, 600, and 800 kDa following BN-PAGE. In excess of 90% coverage of the soluble N- and C-termini of Kcc2 was obtained. In total we identified 246 proteins significantly associated with Kcc2. The 300 kDa species largely contained Kcc2, which is consistent with a dimeric quaternary structure for this transporter. The 600 and 800 kDa species represented stable multi-protein complexes of Kcc2. We identified a set of novel structural, ion transporting, immune related and signaling protein interactors, that are present at both excitatory and inhibitory synapses, consistent with the proposed localization of Kcc2. These included spectrins, C1qa/b/c and the IP3 receptor. We also identified interactors more directly associated with phosphorylation; Akap5, Akap13, and Lmtk3. Finally, we used LC-MS/MS on the same purified endogenous plasma membrane Kcc2 to detect phosphorylation sites. We detected 11 sites with high confidence, including known and novel sites. Collectively our experiments demonstrate that Kcc2 is associated with components of the neuronal cytoskeleton and signaling molecules that may act to regulate transporter membrane trafficking, stability, and activity