Miracle-dz/UniFAR-FTUs huggingface dataset:
The R-SNARE Motif of Tomosyn Forms SNARE Core Complexes with Syntaxin 1 and SNAP-25 and Down-regulates Exocytosis*

Tomosyn is a 130-kDa syntaxin-binding protein that contains a large N-terminal domain with WD40 repeats and a C-terminal domain homologous to R-SNAREs. Here we show that tomosyn forms genuine SNARE core complexes with the SNAREs syntaxin 1 and SNAP-25. In vitro studies with recombinant proteins revealed that complex formation proceeds from unstructured monomers to a stable four-helical bundle. The assembled complex displayed features typical for SNARE core complexes, including a profound hysteresis upon unfolding-refolding transitions. No stable complexes were formed between the SNARE motif of tomosyn and either syntaxin or SNAP-25 alone. Furthermore, both native tomosyn and its isolated C-terminal domain competed with synaptobrevin for binding to endogenous syntaxin and SNAP-25 on inside-out sheets of plasma membranes. Tomosyn-SNARE complexes were effectively disassembled by the ATPase N-ethylmaleimide-sensitive factor together with its cofactor α-SNAP. Moreover, the C-terminal domain of tomosyn was as effective as the cytoplasmic portion of synaptobrevin in inhibiting evoked exocytosis in a cell-free preparation derived from PC12 cells. Similarly, overexpression of tomosyn in PC12 cells resulted in a massive reduction of exocytosis, but the release parameters of individual exocytotic events remained unchanged. We conclude that tomosyn is a soluble SNARE that directly competes with synaptobrevin in the formation of SNARE complexes and thus may function in down-regulating exocytosis.
Summarizing Answer Graphs Induced by Keyword Queries

Keyword search has been popularly used to query graph data. Due to the lack of structure support, a keyword query might generate an excessive number of matches, referred to as "answer graphs", that could include different relationships among keywords. An ignored yet important task is to group and summarize answer graphs that share similar structures and contents for better query interpretation and result understanding. This paper studies the summarization problem for the answer graphs induced by a keyword query Q. (1) A notion of summary graph is proposed to characterize the summarization of answer graphs. Given Q and a set of answer graphs G, a summary graph preserves the relation of the keywords in Q by summarizing the paths connecting the keywords nodes in G. (2) A quality metric of summary graphs, called coverage ratio, is developed to measure information loss of summarization. (3) Based on the metric, a set of summarization problems are formulated, which aim to find minimized summary graphs with certain coverage ratio. (a) We show that the complexity of these summarization problems ranges from ptime to NP-complete. (b) We provide exact and heuristic summarization algorithms. (4) Using real-life and synthetic graphs, we experimentally verify the effectiveness and the efficiency of our techniques.
Evaluation of different feature sets for gait recognition using skeletal data from Kinect

Gait is a persons manner of walking. It is a biometric that can be used for identifying humans. Gait is an unobtrusive metric that can be obtained from distance, and this is its main strength compared to other biometrics. In this paper we construct and evaluate feature sets with the purpose of finding out the role of different types of features and body parts in the recognition process. The feature sets were constructed from skeletal images in three dimensions made with a Kinect sensor. The Kinect is a low-cost device that includes RGB, depth and audio sensors. In our work automated gait cycle extraction algorithm was performed on the Kinect recordings. Metrics like angles and distances between joints were aggregated within a gait cycle, and from those aggregations the different feature datasets were constructed. Multilayer perceptron, support vector machine with sequential minimal optimization and J48 algorithms were used for classification on these datasets. At the end we give conclusions on which groups of features and body parts gave the best recognition rates.
Trends in Coronary Revascularization in the United States From 2001 to 2009: Recent Declines in Percutaneous Coronary Intervention Volumes

Background— There is speculation that the volume of percutaneous coronary interventions (PCIs) has been decreasing over the past several years. Published studies of PCI volume have evaluated regional or hospital trends, but few have captured national data. This study describes the use of coronary angiography and revascularization methods in Medicare patients from 2001 to 2009. Methods and Results— This retrospective study used data from the Centers for Medicare & Medicaid Services from 2001 to 2009. The annual number of coronary angiograms, PCI, intravascular ultrasound, fractional flow reserve, and coronary artery bypass graft (CABG) surgery procedures were determined from billing data and adjusted for the number of Medicare recipients. From 2001 to 2009, the average year-to-year increase for PCI was 1.3% per 1000 beneficiaries, whereas the mean annual decrease for CABG surgery was 5%. However, the increase in PCI volume occurred primarily from 2001 to 2004, as there was a mean annual rate of decline of 2.5% from 2004 to 2009; similar trends were seen with diagnostic angiography. The use of intravascular ultrasound and fractional flow reserve steadily increased over time. Conclusions— This study confirms recent speculation that PCI volume has begun to decrease. Although rates of CABG have waned for several decades, all forms of coronary revascularization have been declining since 2004.
BuzzTrack: topic detection and tracking in email

We present BuzzTrack, an email client extension that helps users deal with email overload. This plugin enhances the interface to present messages grouped by topic, instead of the traditional approach of organizing email in folders. We discuss a clustering algorithm that creates the topic-based grouping, and a heuristic for labeling the resulting clusters to summarize their contents. Lastly, we evaluate the clustering scheme in the context of existing work on topic detection and tracking (TDT) for news articles: Our algorithm exhibits similar performance on emails as current work on news text. We believe BuzzTrack's organization structure, which can be obtained at no cost to the end user, will be helpful for managing the massive amounts of email that land in the inbox every day.
Dissolution of Cellose with Ionic Liquids

We report here initial results that demonstrate that cellulose can be dissolved without activation or pretreatment in, and regenerated from, 1-butyl-3-methylimidazolium chloride and other hydrophilic ionic liquids. This may enable the application of ionic liquids as alternatives to environmentally undesirable solvents currently used for dissolution of this important bioresource.
Group interpersonal psychotherapy for depression in rural Uganda: a randomized controlled trial.

CONTEXT Despite the importance of mental illness in Africa, few controlled intervention trials related to this problem have been published. OBJECTIVES To test the efficacy of group interpersonal psychotherapy in alleviating depression and dysfunction and to evaluate the feasibility of conducting controlled trials in Africa. DESIGN, SETTING, AND PARTICIPANTS For this cluster randomized, controlled clinical trial (February-June 2002), 30 villages in the Masaka and Rakai districts of rural Uganda were selected using a random procedure; 15 were then randomly assigned for studying men and 15 for women. In each village, adult men or women believed by themselves and other villagers to have depressionlike illness were interviewed using a locally adapted Hopkins Symptom Checklist and an instrument assessing function. Based on these interviews, lists were created for each village totaling 341 men and women who met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for major depression or subsyndromal depression. Interviewers revisited them in order of decreasing symptom severity until they had 8 to 12 persons per village, totaling 284. Of these, 248 agreed to be in the trial and 9 refused; the remainder died or relocated. A total of 108 men and 116 women completed the study and were reinterviewed. INTERVENTION Eight of the 15 male villages and 7 of the 15 female villages were randomly assigned to the intervention arm and the remainder to the control arm. The intervention villages received group interpersonal psychotherapy for depression as weekly 90-minute sessions for 16 weeks. MAIN OUTCOME MEASURES Depression and dysfunction severity scores on scales adapted and validated for local use; proportion of persons meeting DSM-IV major depression diagnostic criteria. RESULTS Mean reduction in depression severity was 17.47 points for intervention groups and 3.55 points for controls (P<.001). Mean reduction in dysfunction was 8.08 and 3.76 points, respectively (P<.001). After intervention, 6.5% and 54.7% of the intervention and control groups, respectively, met the criteria for major depression (P<.001) compared with 86% and 94%, respectively, prior to intervention (P =.04). The odds of postintervention depression among controls was 17.31 (95% confidence interval, 7.63-39.27) compared with the odds among intervention groups. Results from intention-to-treat analyses remained statistically significant. CONCLUSIONS Group interpersonal psychotherapy was highly efficacious in reducing depression and dysfunction. A clinical trial proved feasible in the local setting. Both findings should encourage similar trials in similar settings in Africa and beyond.
Predicting variability in biological control of a plant—pathogen system using stochastic models

A stochastic model for the dynamics of a plant–pathogen interaction is developed and fitted to observations of the fungal pathogen Rhizoctonia solani (Kühn) in radish (Raphanus sativus L.), in both the presence and absence of the antagonistic fungus Trichoderma viride (Pers ex Gray). The model incorporates parameters for primary and secondary infection mechanisms and for characterizing the time–varying susceptibility of the host population. A parameter likelihood is developed and used to fit the model to data from microcosm experiments. It is shown that the stochastic model accounts well for observed variability both within and between treatments. Moreover, it enables us to describe the time evolution of the probability distribution for the variability among replicate epidemics in terms of the underlying epidemiological parameters for primary and secondary infection and decay in susceptibility. Consideration of profile likelihoods for each parameter provides strong evidence that T. viride mainly affects primary infection. By using the stochastic model to study the dependence of the probability distribution of disease levels on the primary infection rate we are therefore able to predict the effectiveness of a widely used biological control agent.
First indications that northern bottlenose whales are sensitive to behavioural disturbance from anthropogenic noise

Although northern bottlenose whales were the most heavily hunted beaked whale, we have little information about this species in its remote habitat of the North Atlantic Ocean. Underwater anthropogenic noise and disruption of their natural habitat may be major threats, given the sensitivity of other beaked whales to such noise disturbance. We attached dataloggers to 13 northern bottlenose whales and compared their natural sounds and movements to those of one individual exposed to escalating levels of 1–2 kHz upsweep naval sonar signals. At a received sound pressure level (SPL) of 98 dB re 1 μPa, the whale turned to approach the sound source, but at a received SPL of 107 dB re 1 μPa, the whale began moving in an unusually straight course and then made a near 180° turn away from the source, and performed the longest and deepest dive (94 min, 2339 m) recorded for this species. Animal movement parameters differed significantly from baseline for more than 7 h until the tag fell off 33–36 km away. No clicks were emitted during the response period, indicating cessation of normal echolocation-based foraging. A sharp decline in both acoustic and visual detections of conspecifics after exposure suggests other whales in the area responded similarly. Though more data are needed, our results indicate high sensitivity of this species to acoustic disturbance, with consequent risk from marine industrialization and naval activity.
Wrap-Up: a Trainable Discourse Module for Information Extraction

The vast amounts of on-line text now available have led to renewed interest in information extraction (IE) systems that analyze unrestricted text, producing a structured representation of selected information from the text. This paper presents a novel approach that uses machine learning to acquire knowledge for some of the higher level IE processing. Wrap-Up is a trainable IE discourse component that makes intersentential inferences and identifies logical relations among information extracted from the text. Previous corpus-based approaches were limited to lower level processing such as part-of-speech tagging, lexical disambiguation, and dictionary construction. Wrap-Up is fully trainable, and not only automatically decides what classifiers are needed, but even derives the feature set for each classifier automatically. Performance equals that of a partially trainable discourse module requiring manual customization for each domain.
A Virtual Driving System for Enhancing Efficient Driving Style

This paper proposes a virtual driving system for enhancing efficient driving style. We compared fuel economies and levels of consumable wear on three driving simulation test (none, visual, visual+auditory). The test results show that higher fuel economy can be achieved and consumable wear be reduced when they received feedback from the proposed system compared to driving without eco-driving training.
Watershed Transformation Based Identification of the Combustion Region in an Oxy-coal Flame Image

To meet the increasingly stringent standards on pollutant emissions, oxy-coal combustion technologies are being proposed for both existing and new coal-fired power plants. However, there is lack of research to characterize this new type of combustion flame due to the complicity of its structure and background noise. This paper presents a new computer algorithm to identify the combustion region in oxy-coal flame images through the combination of image enhancement, wavelet denoising, and watershed transformation. The algorithm overcomes the limitations of the thresholding method which often wrongly detects the bright background and ignores the dark combustion region. Results on oxy-coal flame images obtained on an oxy-coal combustion test facility are reported to demonstrate the effectiveness of the algorithm proposed.
Automated Mineral Detection in Visible/Near-Infrared Spectra for Focus-of-Attention

Introduction: Hyperspectral systems collect huge volumes of multidimensional data. To identify materials within the spectra, these data must be processed and analyzed. This analysis is time consuming, costly and often requires an expert to robustly identify target compositions by comparison to spectral libraries. On Earth, this may necessitate field checking of targets in a given scene and knowledge of scene-specific parameters e.g. [1, 2 and references therein, 3]. For planetary missions, where global datasets are collected, these data analysis costs restrict rapid classification to only a subset of an entire mission dataset, reducing mission science return. It is of foremost importance that the amount of science return for a given payload be maximized by suitable information technology. We are developing a series of algorithms to automate the processing and analysis of hyperspectral images to identify constituent spectra. While an ultimate goal would be to accurately classify the composition of every pixel on a planet’s surface, this is made difficult by the fact that most pixels are complex mixtures of n materials, which may or may not be represented in library (training) data. We instead focus on the identification of specific important mineral compositions within pixels in the data. Specific targeting is a frequent application of hyperspectral imaging, e.g., minerals associated with acid mine drainage, hydrocarbons, or mineral exploration. For Mars, high priority targets include minerals associated with the presence of water (e.g., carbonates, sulfates, evaporates). Data downlink restrictions from planetary missions also highlight the need for robust mineral detection algorithms. For example, both OMEGA and CRISM will map only ~5% of the Mars surface at full spatial and spectral resolution. While some targets are preselected for full resolution study (e.g., landing sites), other high priority targets on Mars will be selected in response to observations made by the instruments in a multispectral survey mode. The challenge is to create mineral detection algorithms that can be utilized to analyze any and all image cubes (x, y, λ) for a selected instrument. Such algorithms can help ensure that priority targets are not overlooked in these datasets. We are developing automated supervised algorithms that will rapidly classify hyperspectral data and identify geologically important minerals. These detectors will be tunable to a range of hyperspectral systems, providing a mechanism to search all data for targets of interest. We have focused on the specific application of building detectors to identify minerals in VIS/NIR hyperspectral data collected at Mars. Initially, as an analog to OMEGA and CRISM, we have analyzed data from JPL’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; 17 m/pixel, 224 bands over 400-2500 nm [4]) instrument.
Transient stability analysis of series unbalanced conditions using dedicated mixed-signal hardware

This paper examines the use of the symmetrical components theory for the representation of unbalanced conditions, when using the concept of analog emulation for highspeed power system analysis. This concept has been proven and validated through the realization of a mixed-signal hardware platform dedicated to power system studies. Computations related to the grid are handled by analog electronics, while the models of the network components are implemented on a digital processing core. Balanced operation is natively supported, while unbalanced operation is handled with the symmetrical component theory. Particular focus is given on series unbalanced operations, such as the opening of a single-phase circuit breaker. A hardware architecture solution for the complete unbalanced conditions representation is described. The proposed approach has shown significant computational speed benefits compared to traditional digital solutions.
Cloning and Characterization of a 2-Cys Peroxiredoxin in the Pine Wood Nematode, Bursaphelenchus xylophilus, a Putative Genetic Factor Facilitating the Infestation

The pine wood nematode, Bursaphelenchus xylophilus, is an invasive plant parasitic nematode and a worldwide quarantine pest. An indigenous species in North America and the causal agent of pine wilt disease, B. xylophilus has devastated pine production in Southeastern Asia including Japan, China, and Korea since its initial introduction in the early 1900s. The reactive oxygen species (ROS) is the first line of defense utilized by host plants against parasites, while nematodes, counteractively, employ antioxidants to facilitate their infection. Peroxiredoxins (Prxs) are a large class of antioxidants recently found in a wide variety of organisms. In this report, a gene encoding a novel 2-cysteine peroxiredoxin protein in B. xylophilus was cloned and characterized. The 2-cysteine peroxiredoxin in B. xylophilus (herein refers to as “BxPrx”) is highly conserved in comparison to 2-cysteine peroxiredoxins (Prx2s) in other nematodes, which have two conserved cysteine amino acids (Cp and Cr), a threonine-cysteine-arginine catalytic triad, and two signature motifs (GGLG and YF) sensitive to hydrogen peroxide. In silico assembly of BxPrx tertiary structure reveals the spatial configuration of these conserved domains and the simulated BxPrx 3-dimensional structure is congruent with its presumed redox functions. Although no signal peptide was identified, BxPrx was abundantly expressed and secreted under the B. xylophilus cuticle. Upon further analysis of this leader-less peptide, a single transmembrane α-helix composed of 23 consecutive hydrophobic amino acids was found in the primary structure of BxPrx. This transmembrane region and/or readily available ATP binding cassette transporters may facilitate the transport of non-classical BxPrx across the cell membrane. Recombinant BxPrx showed peroxidase activity in vitro reducing hydrogen peroxide using glutathione as the electron donor. The combined results from gene discovery, protein expression and distribution profiling (especially the “surprising” presence under the nematode cuticle), and recombinant antioxidant activity suggest that BxPrx plays a key role in combating the oxidative burst engineered by the ROS defense system in host plants during the infection process. In summary, BxPrx is a genetic factor potentially facilitating B. xylophilus infestation.
Combining Odometry and Visual Loop-Closure Detection for Consistent Topo-Metrical Mapping

We address the problem of simultaneous localization and mapping (SLAM) by combining visual loop-closure detection with metrical information given by a robot odometry. The proposed algorithm extends a purely appearance-based loop-closure detection method based on bags of visual words [A. Angeli, D. Filliat, S. Doncieux and J.-A. Meyer, IEEE Transactions On Robotics, Special Issue on Visual SLAM 24 (2008) 1027–1037], which is able to detect when the robot has returned back to a previously visited place. An efficient optimization algorithm is used to integrate odometry information and to generate a consistent topo-metrical map much more usable for global localization and path planning. The resulting algorithm which only requires a monocular camera and robot odometry data, is real-time, incremental (i.e. it does not require any a priori information on the environment), and can be easily embedded on medium platforms.
Asymmetric Effects of FOREX Intervention Using Intraday Data: Evidence from Peru

Asymmetric effects of Central Bank foreign exchange (forex) intervention have not been extensively studied in the literature, even though in practice Central Bank's motives for purchasing and for selling foreign currency may differ. This paper studies asymmetric effects of Central Bank interventions under the premise that policy authorities view depreciations and appreciations as having asymmetric implications. Using undisclosed intraday data for Peru from 2009 to 2011, this paper shows that Central Bank interventions in the foreign exchange market have a signifcant and asymmetric effect on interbank exchange rates. Specifically, central bank intervention is more effective in reducing the interbank exchange rate than in raising it.