Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence database versioning for command line and Galaxy bioinformatics servers

Motivation: There are various reasons for rerunning bioinformatics tools and pipelines on sequencing data, including reproducing a past result, validation of a new tool or workflow using a known dataset, or tracking the impact of database changes. For identical results to be achieved, regularly updated reference sequence databases must be versioned and archived. Database administrators have tried to fill the requirements by supplying users with one-off versions of databases, but these are time consuming to set up and are inconsistent across resources. Disk storage and data backup performance has also discouraged maintaining multiple versions of databases since databases such as NCBI nr can consume 50 Gb or more disk space per version, with growth rates that parallel Moore's law. Results: Our end-to-end solution combines our own Kipper software package—a simple key-value large file versioning system—with BioMAJ (software for downloading sequence databases), and Galaxy (a web-based bioinformatics data processing platform). Available versions of databases can be recalled and used by command-line and Galaxy users. The Kipper data store format makes publishing curated FASTA databases convenient since in most cases it can store a range of versions into a file marginally larger than the size of the latest version. Availability and implementation: Kipper v1.0. 0 and the Galaxy Versioned Data tool are written in Python and released as free and open source software available at https://github.com/Public-Health-Bioinformatics/kipper and https://github.com/Public-Health-Bioinformatics/versioned_data, respectively; detailed setup instructions can be found at https://github.com/Public-Health-Bioinformatics/versioned_data/blob/master/doc/setup.md Contact: Damion.Dooley@Bccdc.Ca or William.Hsiao@Bccdc.Ca Supplementary information: Supplementary data are available at Bioinformatics online.
The role of two branched‐chain amino acid transporters in Staphylococcus aureus growth, membrane fatty acid composition and virulence

The branched‐chain amino acids (BCAAs) are vital to both growth and virulence of the human pathogen Staphylococcus aureus. In addition to supporting protein synthesis, the BCAAs serve as precursors for branched‐chain fatty acids (BCFAs), which are predominant membrane fatty acids, and, in association with the global regulatory protein CodY, the BCAAs are key co‐regulators of virulence factors. Despite these critical functions, S. aureus represses Leu and Val synthesis, instead preferring to acquire them from the extracellular milieu. We previously identified BrnQ1 as a BCAA transporter, yet a brnQ1 mutant remained capable of BCAA acquisition. Here, we describe BcaP as an additional BCAA transporter, and determine that it plays a secondary role to BrnQ1 during S. aureus growth in a chemically defined medium. Furthermore, membrane fatty acid composition analysis revealed that BrnQ1, and not BcaP, is required for transporting Leu and Val to be used for iso‐BCFA synthesis. Despite a predominant role for BrnQ1 in vitro, both BrnQ1 and BcaP are required for S. aureus fitness in vivo in a hematogenous spread infection model and a nasal colonisation model. These data demonstrate the importance of BrnQ1 and BcaP for growth, environmental adaptation and virulence of S. aureus.
Detecting People in Large Crowded Spaces using 3D Data from Multiple Cameras

Real time monitoring of large infrastructures has human detection as a core task. Since the people anonymity is a hard constraint in these scenarios, video cameras can not be used. This paper presents a low cost solution for real time people detection in large crowded environments using multiple depth cameras. In order to detect people, binary classifiers (person/notperson) were proposed based on different sets of features. It is shown that good classification performance can be achieved choosing a small set of simple feature.
Amendment to Trace and Revoke Systems with Short Ciphertexts

Traitor tracing is needed because some users in broadcast encryption system may give out their decryption keys to construct pirate decoders. Recently, Liu and Yuan described a trace and revoke systems with short ciphertexts. In this paper, we show that their scheme cannot achieve traitor tracing, since any receiver can decide whether the given ciphertext is well-formed or not so as to decide whether the system is now in normal broadcasting mode or in tracing mode. Thus, any user can construct decoders that will decrypt well-formed ciphertexts (ciphertexts for normal broadcasting) and refuse the badly-formed ciphertexts (ciphertexts for traitor tracing), so that traitors cannot be identified. In such case, innocent users will be framed. We provide an amendment to their scheme and render it useful in traitor tracing against both perfect and imperfect decoders.
Novelty detection and 3D shape retrieval using superquadrics and multi-scale sampling for autonomous mobile robots

There are several applications for which it is important to both detect and communicate changes in data models. For instance, in some mobile robotics applications (e.g. surveillance) a robot needs to detect significant changes in the environment (e.g. a layout change) which it may achieve by comparing current data provided by its sensors with previously acquired data (e.g. map) of the environment. This often constitutes an extremely challenging task due to the large amounts of data that must be compared in real-time. This paper proposes a framework to detect, and represent changes through a compact model. The main steps of the procedure are: multi-scale sampling to reduce the computation burden; change detection based on Gaussian mixture models; fitting superquadrics to detected changes; and refinement and optimization using the split and merge paradigm. Experimental results in various real and simulated scenarios demonstrate the approach's feasibility and robustness with large datasets.
Neural responses to salient visual stimuli

The neural mechanisms involved in the selective processing of salient or behaviourally important stimuli are uncertain. We used an aversive conditioning paradigm in human volunteer subjects to manipulate the salience of visual stimuli (emotionally expressive faces) presented during positron emission tomography (PET) neuroimaging. Increases in salience, and conflicts between the innate and acquired value of the stimuli, produced augmented activation of the pulvinar nucleus of the right thalamus. Furthermore, this pulvinar activity correlated positively with responses in structures hypothesized to mediate value in the brain–right amygdala and basal forebrain (including the cholinergic nucleus basalis of Meynert). The results provide evidence that the pulvinar nucleus of the thalamus plays a crucial modulatory role in selective visual processing, and that changes in perceptual salience are mediated by value–dependent plasticity in pulvinar responses.
Parylene-coated NMOSFET-embedded cantilevers for surface stress sensing in liquid environment

This paper reports a parylene-coated nMOSFET (n-type metal-oxide-semiconductor field-effect transistor)-embedded cantilever, which is suitable for the detection of chemical or biological molecules in liquid phase based on the surface stress sensing principle. The silicon cantilevers are configured along <110> direction, and the channels of the embedded-nMOSFETs are perpendicular to the cantilevers. The fabrication process based upon POST-CMOS (complementary MOS) technology for making these devices on silicon-on-insulator (SOI) wafers is reported. The functionality of the sensor is preliminarily proven by the detection of 1-octanethiol in absolute ethanol.
Impact Evaluation of Uncorrected Background on the Minimum Uncertainty of the "White" Mixtures Analysis Using Tikhonov Regularization and the Partial Least Squares Method

The paper reports the Tikhonov regularization and the partial least squares-based analysis of "white" mixtures which spectra were disturbed by uncorrected various-profile background. The dependences of the minimum relative mean-square error of analysis on the background norm were obtained by different methods based on estimating the optimal regularization parameter and compared to each other. The upper limit of the minimum uncertainty of the multicomponent quantitative analysis was established. In the case of low background, the partial least-squares method was found to yield the best predictions performance. If the background intensity increases, the prediction uncertainty of this method also increases being larger than the minimal achievable value.
On Virtualization of Reconfigurable Hardware in Distributed Systems

In the design of next-generation distributed and high-performance computing systems, Reconfigurable Processing Elements (RPEs) such as FPGAs and multi-core heterogeneous computers will play an important role. FPGAs are well-known for their programmability, power efficiency, reasonably high performance, functional flexibility, and their scalability. However, proper virtualization schemes at higher software abstraction layers to utilize these RPEs are under research. In this paper, we propose a virtualization framework for distributed computing systems that supports RPEs. First, we present various scenarios in terms of use-cases to discuss the utilization of RPEs in distributed computing systems. Secondly, we propose a scheme to virtualize RPEs in distributed systems. Based on various virtualization levels, we provide a general model for a computing node which incorporates both General Purpose Processors (GPPs) and RPEs. Thirdly, we present a typical application task model. Finally, we present a case study of a large-scale application from the bioinformatics domain, which demands different types of processing elements in a distributed computing system.
A Novel Network Coding Scheme for Efficient Multicast and Unicast in Ad-hoc Networks

— Achieving higher throughput values in wireless ad-hoc networks is essential to support many applications. Network coding was found to be the most promising and effective approach for this purpose. However current network coding schemes, mainly relying on COPE, were able to improve the throughput gain in case of unicast flows only, while failing to achieve similar gain in case of the multicast scenario. With the notable flourish of conference-based and multimedia streaming applications that are mainly dependent on multicast flows, it became crucial to formulate a new network coding scheme that is able to handle both unicast and multicast flows with the same efficiency. In this paper, we introduce a novel network coding scheme to efficiently handle multicast flows simultaneously with the unicast ones with the same high efficiency and hence achieve a real high throughput gains. It even provides the option to favor one type of flow over the other when needed. The proposed scheme depends on graph theory to model packets and nodes in the network. A smart algorithm is then introduced to discover all feasible coding options in a way that smartly avoids the draw backs of previous schemes. Extensive simulation studies report the success of the proposed scheme to deliver nearly double of the COPE throughput gain in case of multicast flows while still delivering on par gain in unicast case.
Current prospects and future challenges for nasal vaccine delivery

ABSTRACT Nasal delivery offers many benefits over traditional approaches to vaccine administration. These include ease of administration without needles that reduces issues associated with needlestick injuries and disposal. Additionally, this route offers easy access to a key part of the immune system that can stimulate other mucosal sites throughout the body. Increased acceptance of nasal vaccine products in both adults and children has led to a burgeoning pipeline of nasal delivery technology. Key challenges and opportunities for the future will include translating in vivo data to clinical outcomes. Particular focus should be brought to designing delivery strategies that take into account the broad range of diseases, populations and healthcare delivery settings that stand to benefit from this unique mucosal route.
Neonatal symptomatic thromboembolism in Germany: two year survey

AIMS To determine the incidence of neonatal thromboembolism in Germany. METHODS Diagnostic imaging techniques, therapeutic modalities, and short term outcome were evaluated in a prospective nationwide two year case registry study. RESULTS The reported incidence of symptomatic neonatal thromboembolism, diagnosed in most cases with Doppler ultrasonography, was 5.1 per 100 000 births, with a total of 79 cases registered: renal venous thrombosis (n=35); venous thrombosis (n=25); and arterial vascular occlusion (n=19). Fifty seven of 79 thromboses were associated with additional risk factors (central line n=25, asphyxia n=13, septicaemia n=11, dehydration n=6, maternal diabetes n=2, cardiac disease n=1). Inherited thrombophilia was also diagnosed in seven out of 35 cases investigated. Twenty three children received supportive treatment: 42 received heparin and in 13 neonates thrombolytic agents were administered. Most neonates (91%) survived; seven died. CONCLUSION Controlled multicentre studies are needed to obtain more information on treatment efficacy.
. ) Ontology of Multimodal Discourse Ontology Programming Interface ( OPI ) OPI Generator Audiovisual Toolkit Feature Extractor Annotate ! Feature Visualizer Meeting BrowserComparison Tool KronoBase Persistent Triple Store

We present a set of annotations of hierarchical topic segmentations and action item subdialogues collected over 65 meetings from the ICSI and ISL meeting corpora, designed to support automatic meeting understanding and analysis. We describe an architecture for representing, annotating, and analyzing multi-party discourse, including: an ontology of multimodal discourse, a programming interface for that ontology, and an audiovisual toolkit which facilitates browsing and annotating discourse, as well as visualizing and adjusting features for machine learning tasks.
You Are Known by How You Vlog: Personality Impressions and Nonverbal Behavior in YouTube

An increasing interest in understanding human perception in social media has led to the study of the processes of personality self-presentation and impression formation based on user profiles and text blogs. However, despite the popularity of online video, we do not know of any attempt to study personality impressions that go beyond the use of text and still photos. In this paper, we analyze one facet of YouTube as a repository of brief behavioral slices in the form of personal conversational vlogs, which are a unique medium for self-presentation and interpersonal perception. We investigate the use of nonverbal cues as descriptors of vloggers' behavior and find significant associations between automatically extracted nonverbal cues for several personality judgments. As one notable result, audio and visual cues together can be used to predict 34% of the variance of the Extraversion trait of the Big Five model. In addition, we explore the associations between vloggers' personality scores and the level of social attention that their videos received in YouTube. Our study is conducted on a dataset of 442 YouTube vlogs and 2,210 annotations collected using Amazon's Mechanical Turk.
Outcome Measures in Functional Urology : towards evaluation through patient reported outcome measures ?

COlOFOn 't Hoen, LA Outcome Measures in Functional Urology: towards evaluation through patient reported outcome measures? Door lisette Aimée 't Hoen geboren te Rotterdam COnTEnTS 1. General introduction 7 Part I: Additional treatment options for therapy-refractory patients in functional urology. 2. Breaking the vicious circle: Onabotulinum toxin A in children with therapy-refractory dysfunctional voiding. 21 3. Intermittent sacral neuromodulation for idiopathic urge urinary incontinence in women: a pilot study.
Automatic emotion recognition using prosodic parameters

This paper presents the experiments made to automatically identify emotion in an emotional speech database for Basque. Three different classifiers have been built: one using spectral features and GMM, other with prosodic features and SVM and the last one with prosodic features and GMM. 86 prosodic features were calculated and then an algorithm to select the most relevant ones was applied. The first classifier gives the best result with a 98.4% accuracy when using 512 mixtures, but the classifier built with the best 6 prosodic features achieves an accuracy of 92.3% in spite of its simplicity, showing that prosodic information is very useful to identify emotions.
Verbal compounding and causativity in Mandarin Chinese

meaning in 打 dǎ +A (e.g. 打硬 dǎyìng ‘dǎ + hard = harden’) and 打 dǎ +V (打折 dǎzhé ‘dǎ + snap = snap’) constructions (cf. 4.5.1.1). 96 This sense of 拍 phah4, which is lacking in Mandarin Chinese (拍 pāi), is found also in other Sinitic languages, e.g. the Min dialect of Xiamen (福建厦门), 拍 p’aʔ 32 and the Yue dialect of Haikang (广东海康), p’a 55 (cf. HFDC 1999). 97 See also 台文/華文線上辭典 ‘Taiwanese / Chinese on-line dictionary’ (TCOD): http://203.64. 42.21/iug/Ungian/SoannTeng/chil/Taihoa.asp 98 On the progressive abstractive generalization of meaning of 打dǎ in Chinese and related examples, cf. 4.5. 1.1.
Comparison of a function-based model and a meaning-based model of quality of life in oncology: multidimensionality examined

Within psychosocial oncology, investigation of quality of life remains hindered by the lack of a clear definition of the quality-of-life construct. Although conceptualizations of quality of life can vary widely in different measures, those who develop the tests explicitly acknowledge their underlying models only rarely. In addition, few comparative studies have explored differences between models. This article compares the "functional living" and "experience of illness" models of quality of life by comparing two quality-of-life measures used in oncology. The authors discuss important differences between the resulting pictures of quality of life that each measure produced in the same group of 38 patients. The "experience of illness" measure indicated greater dissatisfaction on the pain and depression items, information, and global quality-of-life than did the "functional living" measure. No significant correlations between the two measures were found for the three non-function-specific psychosocial domains.
Relevance Driven Visualization of Financial Performance Measures

Visual data analysis has received a lot of research interest in recent years, and a wide variety of new visualization techniques and applications have been developed to improve insight into the various application domains. In financial data analysis, however, analysts still primarily rely on a set of statistical performance parameters in combination with traditional line charts in order to evaluate assets and to make decisions, and information visualization is only very slowly entering this important domain. In this paper, we analyze some of the standard statistical measures for technical financial data analysis and demonstrate cases where they produce insufficient and misleading results that do not reflect the real performance of an asset. We propose a technique for visualizing financial time series data that eliminates these inadequacies, offering a complete view on the real performance of an asset. The technique is enhanced by relevance and weighting functions according to the users' preferences in order to emphasize specific regions of interest. Based on these principles we redefine some of the standard performance measures. We apply our technique on real world financial data sets and combine it with higher-level financial analysis techniques such as performance/risk analysis, dominance evaluation, and efficiency curves in order to show how traditional techniques from economics can be improved by modern visual data analysis techniques.
A Novel Modular Permanent Magnet Drive System Design

With improvement in magnetic property of soft magnetic composites (SMC), electrical machines can be fabricated with more flexible structure at low cost, which results in more options of the drive system design different from the conventional technology, for example, 3D machine structure compared with the 2D structure enforced by the lamination sheets. In this paper, a modular permanent drive system based on the independent machine module and drive circuit are proposed. With independent control, the potential of fault tolerant capability of the drive system is discussed as well. The experiment results from the prototype system will be provided for the verification
Evaluating large-scale knowledge resources across languages

This paper presents an empirical evaluation in a multilingual scenario of the semantic knowledge present on publicly available large-scale knowledge resources. The study covers a wide range of manually and automatically derived large-scale knowledge resources for English and Spanish. In order to establish a fair and neutral comparison, the knowledge resources are evaluated using the same method on two Word Sense Disambiguation tasks (Senseval-3 English and Spanish Lexical Sample Tasks). First, this study empirically demonstrates that the combination of the knowledge contained in these resources surpass the most frequent sense classi er for English. Second, we also show that this large-scale topical knowledge acquired from one language can be successfully ported to other languages.
Two-way TOA with limited dead reckoning for GPS-free vehicle localization using single RSU

Road safety applications in Vehicular Ad Hoc Networks (VANETs) necessitate highly accurate vehicle localization techniques. Unfortunately, Global Positioning System (GPS)-based localization techniques do not only suffer from the lack of ubiquitous GPS coverage but also are susceptible to high localization errors (10 m to 30 m). Consequently, such techniques are not suitable for road safety applications. Recently, GPS-free localization based on vehicle communication with installed roadside units (RSUs) has emerged as a more accurate alternative. However, existing GPS-free techniques require the vehicle to communicate with two RSUs in order to achieve high localization accuracy. Such techniques either increase the system cost due to the high number of needed RSUs or delay the localization decisions. In this paper, we propose a GPS-free localization framework that uses two-way time of arrival with partial use of dead reckoning to locate the vehicles based on communication with a single RSU. Our results show that the accuracy of the proposed framework is 13.1% to 20.1% better than the techniques that use 1 RSUs while completely depending on dead reckoning, and only 3% to 8.9% worse than those techniques that use 2 RSUs on both sides on the road.
Comments on the computation of interval Routh approximants

In Bandyopadhyay et al. (1994, 1997), the Routh approximation method was extended to derive reduced-order interval models for linear interval systems. In this paper, the authors show that: 1) interval Routh approximants to a high-order interval transfer function depend on the implementation of interval Routh expansion and inversion algorithms; 2) interval Routh expansion algorithms cannot guarantee the success in generating a full interval Routh array; 3) some interval Routh approximants may not be robustly stable even if the original interval system is robustly stable; and 4) an interval Routh approximant is in general not useful for robust controller design because its dynamic uncertainties (in terms of robust frequency responses) do not cover those of the original interval system.