Miracle-dz/UniFAR-FTUs huggingface dataset:
Traumatic Brain Injury Imaging Research Roadmap

SUMMARY: The past decade has seen impressive advances in the types of neuroimaging information that can be acquired in patients with traumatic brain injury. However, despite this increase in information, understanding of the contribution of this information to prognostic accuracy and treatment pathways for patients is limited. Available techniques often allow us to infer the presence of microscopic changes indicative of alterations in physiology and function in brain tissue. However, because histologic confirmation is typically lacking, conclusions reached by using these techniques remain solely inferential in almost all cases. Hence, a need exists for validation of these techniques by using data from large population samples that are obtained in a uniform manner, analyzed according to well-accepted procedures, and correlated with closely monitored clinical outcomes. At present, many of these approaches remain confined to population-based research rather than diagnosis at an individual level, particularly with regard to traumatic brain injury that is mild or moderate in degree. A need and a priority exist for patient-centered tools that will allow advanced neuroimaging tools to be brought into clinical settings. One barrier to developing these tools is a lack of an age-, sex-, and comorbidities-stratified, sequence-specific, reference imaging data base that could provide a clear understanding of normal variations across populations. Such a data base would provide researchers and clinicians with the information necessary to develop computational tools for the patient-based interpretation of advanced neuroimaging studies in the clinical setting. The recent “Joint ASNR-ACR HII-ASFNR TBI Workshop: Bringing Advanced Neuroimaging for Traumatic Brain Injury into the Clinic” on May 23, 2014, in Montreal, Quebec, Canada, brought together neuroradiologists, neurologists, psychiatrists, neuropsychologists, neuroimaging scientists, members of the National Institute of Neurologic Disorders and Stroke, industry representatives, and other traumatic brain injury stakeholders to attempt to reach consensus on issues related to and develop consensus recommendations in terms of creating both a well-characterized normative data base of comprehensive imaging and ancillary data to serve as a reference for tools that will allow interpretation of advanced neuroimaging tests at an individual level of a patient with traumatic brain injury. The workshop involved discussions concerning the following: 1) designation of the policies and infrastructure needed for a normative data base, 2) principles for characterizing normal control subjects, and 3) standardizing research neuroimaging protocols for traumatic brain injury. The present article summarizes these recommendations and examines practical steps to achieve them.
Overlooked Risk for Chronic Kidney Disease after Leptospiral Infection: A Population-Based Survey and Epidemiological Cohort Evidence

Background Leptospirosis is the most widespread zoonosis. Chronic human infection and asymptomatic colonization have been reported. However, renal involvement in those with leptospira chronic exposure remains undetermined. Methods and Findings In 2007, a multistage sampling survey for chronic kidney disease (CKD) was conducted in a southern county of Taiwan, an area with a high prevalence of dialysis. Additionally, an independent cohort of 88 participants from a leptospira-endemic town was followed for two years after a flooding in 2009. Risks of CKD, stages of CKD, associated risk factors as well as kidney injury markers were compared among adults with anti-leptospira antibody as defined by titers of microscopic agglutination test (MAT). Of 3045 survey participants, the individuals with previous leptospira exposure disclosed a lower level of eGFR (98.3±0.4 vs 100.8±0.6 ml/min per 1.73 m2, P<0.001) and a higher percentage of CKD, particularly at stage 3a-5 (14.4% vs 8.5%), than those without leptospira exposure. Multivariable linear regression analyses indicated the association of leptospiral infection and lower eGFR (95% CI -4.15 to -1.93, P < 0.001). In a leptospiral endemic town, subjects with a MAT titer ≥400 showed a decreased eGFR and higher urinary kidney injury molecule–1 creatinine ratio (KIM1/Cr) level as compared with those having lower titers of MAT (P<0.05). Furthermore, two participants with persistently high MAT titers had positive urine leptospira DNA and deteriorating renal function. Conclusions and Significance Our data are the first to show that chronic human exposure of leptospirosis is associated significantly with prevalence and severity of CKD and may lead to deterioration of renal function. This study also shed light on the search of underlying factors in areas experiencing CKD of unknown aetiology (CKDu) such as Mesoamerican Nephropathy.
Buurtdynamiek en slachtofferschap van criminaliteit: Een studie naar de effecten van sociaal-economische stijging, daling en stabiliteit in Nederlandse buurten

Neighbourhood dynamics and crime victimization: A study on the effects of socio-economic improvement, decline, and stability in Dutch neighbourhoods Changes in neighbourhood status primarily result from the selective in- and out-migration of income groups. In turn, these changes are related to the chance of becoming the victim of a crime in the local community. Drawing from Shaw and McKay’s (1942) social disorganization theory, we argue that victimization is not only more likely in disadvantaged neighbourhoods but also in neighbourhoods where improvements are taking place. These neighbourhoods are assumed to suffer from social instability caused by the strong influx of new residents, and from social heterogeneity that is caused by the simultaneous presence of high-income and low-income groups. Indeed, results from multilevel analyses on victimization survey data from the Politiemonitor Bevolking 1999 for over seventy thousand respondents show that strong socio-economic improvement of neighbourhoods is related to higher victimization risk for theft, violence and vandalism. City-level characteristics (e.g. population size) were also associated with victimization, independent of individual and neighbourhood characteristics. This study thereby adds to social disorganization theory in two ways: 1) Social disorganization is not only dependent upon socio-economic composition of neighbourhoods, but also upon socio-economic dynamics. 2) To understand differentiation in victimization, social processes within neighbourhoods as well as the larger social context are relevant.
Noise Reduction and Increased VAD Accuracy Using Spectral Subtraction

paper shows that performing voice activity detection (VAD) on the output of a spectral subtraction noised reduced signal increases the accuracy of the VAD and reduces the VAD sensitivity to fixed thresholds. An initial VAD decision is used to control the noise estimate update in the spectral subtraction algorithm. The more accurate VAD after the first spectral subtraction is then used to reprocess the original noisy speech again via spectral subtraction to reduce the noise while not attenuating the speech. Auditory masking thresholds were used to weight the spectral subtraction to avoid the introduction of musical noise artifacts. Energy thresholds were used to detect voiced frames of speech recorded inside a car at an 8kHz sampling rate and combined with four different noise conditions. The received noise and the speech were combined to produce inputs to the algorithm at 0, 5, and 10 dB SNR where it was shown that the VAD accuracy consistently increased after spectral subtraction. However, if the VAD and spectral subtraction were iterated more then twice on the signal, then the VAD accuracy started to decrease. Visual inspection of the clean speech was used to determine which frames should be classified as voice and used to determine the accuracy of the VAD algorithm. The VAD accuracy was only increased by a few percent in each case, but this small improvement makes a big difference when using the resulting decisions to control the noise estimate of the spectral subtraction algorithm in order to avoid attenuating the speech. Modifications of the fixed offset for detecting voice had less of an effect when the VAD operated on the signal after spectral subtraction and compared to VAD processing on the original signal, which can be attributed to the reduced variance in the noise. Objective speech quality measures show that the algorithm removes a large amount of the stationary noise in a hands-free environment of an automobile with relatively minimal speech distortion.
Designing an Information System for Pension Fund Management

This paper discusses the problems of Ukrainian new pension system, non-state pension funds in particular. The paper is concerned with development of mathematical methods and models that would form a base of management information systems. The new pension system has been lawfully ratified only three years ago and is still developing, that is why this paper is very important.
SIW‐based interdigital bandpass filter with harmonic suppression

A novel configuration of interdigital bandpass filter based on the substrate integrated waveguide (SIW) technology is proposed. In addition to the interdigital resonators in SIW that determine the main response/characteristics of the filter, narrowing the width of the SIW at the center of the filter and additional vias at its input and output parts act as two additional control mechanisms to achieve the desired filter response. Moreover, dumbbells are etched to the ground side of the microstrip feeding sections at both ends of the filter to improve its harmonic suppression. A prototype filter is designed and fabricated for verification. The measured results are in good agreement with the simulations, and the filter exhibits very good harmonic suppression. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:66–69, 2015
Interaction of the Deubiquitinating Enzyme Ubp2 and the E3 Ligase Rsp5 Is Required for Transporter/Receptor Sorting in the Multivesicular Body Pathway

Protein ubiquitination is essential for many events linked to intracellular protein trafficking. We sought to elucidate the possible involvement of the S. cerevisiae deubiquitinating enzyme Ubp2 in transporter and receptor trafficking after we (this study) and others established that affinity purified Ubp2 interacts stably with the E3 ubiquitin ligase Rsp5 and the (ubiquitin associated) UBA domain containing protein Rup1. UBP2 interacts genetically with RSP5, while Rup1 facilitates the tethering of Ubp2 to Rsp5 via a PPPSY motif. Using the uracil permease Fur4 as a model reporter system, we establish a role for Ubp2 in membrane protein turnover. Similar to hypomorphic rsp5 alleles, cells deleted for UBP2 exhibited a temporal stabilization of Fur4 at the plasma membrane, indicative of perturbed protein trafficking. This defect was ubiquitin dependent, as a Fur4 N-terminal ubiquitin fusion construct bypassed the block and restored sorting in the mutant. Moreover, the defect was absent in conditions where recycling was absent, implicating Ubp2 in sorting at the multivesicular body. Taken together, our data suggest a previously overlooked role for Ubp2 as a positive regulator of Rsp5-mediated membrane protein trafficking subsequent to endocytosis.
HPCG: Preliminary Evaluation and Optimization on Tianhe-2 CPU-only Nodes

HPCG has become a new metric for the design and ranking of HPC. By incorporating a local symmetric Gauss-Seidel preconditioned, HPCG implements the Conjugate Gradient method to solve a sparse linear system. HPCG performs poorly with irregular memory access and may consume a great deal of MPI resources when it is executed on supercomputers. This paper focuses on optimizing SpMV and the Gauss-Seidel preconditioned, the two most important kernels in HPCG. By evaluating the performance impacts of several representative sparse matrix formats, ELLPACK is selected due to its suitability for SIMD, resulting in a speedup of 2.3x for the SpMV kernel. Multi-coloring is performed for Gauss-Seidel, resulting in a speedup of 7.3x over the reference implementation. The CG convergence rate may also be improved after multi-coloring. Our experimental results show that our optimization process works well on supercomputers, achieving 6.5 Gflops on a CPU-only node. This has boosted the total HPCG Gflops by about 7x, giving rise to 80,151 Gflops on 8192 CPU-only Tianhe-2 nodes.
Sequence‐Based, in situ detection of chromosomal abnormalities at high resolution

We developed single copy probes from the draft genome sequence for fluorescence in situ hybridization (scFISH) which precisely delineate chromosome abnormalities at a resolution equivalent to genomic Southern analysis. This study illustrates how scFISH probes detect cryptic and subtle abnormalities and localize the sites of chromosome rearrangements. scFISH probes are substantially shorter than conventional recombinant DNA‐derived probes, and Cot1 DNA is not required to suppress repetitive sequence hybridization. In this study, 74 single copy sequence probes (>1,500 bp) have been developed from ≥100 kb genomic intervals associated with either constitutional or acquired disorders. Applications of these probes include detection of congenital microdeletion syndromes on chromosomes 1, 4, 7, 15, 17, 22 and submicroscopic deletions involving the imprinting center on chromosome 15q11.2q13. We demonstrate how hybridization with multiple combinations of probes derived from the Smith‐Magenis syndrome interval on chromosome 17 identified a patient with an atypical, proximal deletion breakpoint. A similar multi‐probe hybridization strategy has also been used to delineate the translocation breakpoint region on chromosome 9 in chronic myelogenous leukemia. Probes have also been designed to hybridize to multiple cis paralogs, both enhancing the chromosomal target size and detecting chromosome rearrangements, for example, by splitting and separating a family of related sequences flanking an inversion breakpoint on chromosome 16 in acute myelogenous leukemia. These novel strategies for rapid and precise characterization of cytogenetic abnormalities are feasible because of the sequence‐defined properties and dense euchromatic organization of single copy probes. © 2003 Wiley‐Liss, Inc.
Notes on the EM Algorithm

• N is a positive integer specifying the number of states in the model. Without loss of generality, we will take theN ’th state to be a special state, the final or stopstate. • Σ is a set of output symbols, for example Σ = {a, b} • Θ is a vector of parameters. It contains three types of parameters: – πj for j = 1 . N is the probability of choosing state j as an initial state. Note that ∑N j=1 πj = 1. – aj,k for j = 1 . (N − 1), k = 1 . N , is the probability of transitioning from state j to state k. Note that for allj, ∑N k=1 aj,k = 1. – bj(o) for j = 1 . (N − 1), ando ∈ Σ, is the probability of emitting symbol o from statej. Note that for allj, ∑ o∈Σ bj(o) = 1. Thus it can be seen that Θ is a vector ofN + (N − 1)N + (N − 1)|Σ| parameters.
Biotechnology R&D: Policy Options to Ensure Access and Benefits for the Poor

The majority of biotech research and almost all of the commercialization of genetically engineered crops has been done by private firms based in industrialized countries. The dominance of the private sector in biotechnology research and product development has raised concern in developing countries that their farmers – particularly poor farmers – may not benefit from biotechnology either because it is not available or is too expensive. This paper examines the consequences of the emergence of a few large companies as the leaders in the commercialization of biotechnology and analyses a number of concerns about who benefits from biotech research. It reviews the status of crop biotechnology research worldwide and analyses the influence of intellectual property rights and market concentration on the development and diffusion of new technology. The paper explores the potential of public-private partnerships recommends policy measures and investments that could focus more biotechnology research on the problems of the poor and alleviate some of the concerns about the impacts of biotechnology. This paper was prepared as background material for the 2003 issue of The State of Food and Agriculture, which has the theme “Agricultural Biotechnology: Meeting the Needs of the Poor? ” Several companion papers are also available in the ESA Working Paper series.
On matching sketches with digital face images

This paper presents an efficient algorithm for matching sketches with digital face images. The algorithm extracts discriminating information present in local facial regions at different levels of granularity. Both sketches and digital images are decomposed into multi-resolution pyramid to conserve high frequency information which forms the discriminating facial patterns. Extended uniform circular local binary pattern based descriptors use these patterns to form a unique signature of the face image. Further, for matching, a genetic optimization based approach is proposed to find the optimum weights corresponding to each facial region. The information obtained from different levels of Laplacian pyramid are combined to improve the identification accuracy. Experimental results on sketch-digital image pairs from the CUHK and IIIT-D databases show that the proposed algorithm can provide better identification performance compared to existing algorithms.
A Novel One-Step Fabricated, Droplet-Based Electrochemical Sensor for Facile Biochemical Assays

A simple, novel concept for the one-step fabrication of a low-cost, easy-to-use droplet-based electrochemical (EC) sensor is described, in which the EC reagents are contained in a droplet and the droplet assay is operated on a simple planar surface instead of in a complicated closed channel/chamber. In combination with an elegant carbon electrode configuration, screen-printed on a widely available polyethylene terephthalate (PET) substrate, the developed sensor exhibits a stable solution-restriction capacity and acceptable EC response, and thus can be used directly for the detection of different analytes (including ascorbic acid (AA), copper ions (Cu2+), 2′-deoxyguanosine 5′-triphosphate (dGTP) and ferulic acid (FA)), without any pretreatment. The obtained, acceptable linear ranges/detection limits for AA, Cu2+, dGTP and FA are 0.5–10/0.415 mM, (0.0157–0.1574 and 0.1574–1.5736)/0.011 mM, 0.01–0.1/0.008 mM and 0.0257–0.515/0.024 mM, respectively. Finally, the utility of the droplet-based EC sensor was demonstrated for the determination of AA in two commercial beverages, and of Cu2+ in two water samples, with reliable recovery and good stability. The applicability of the droplet-based sensor demonstrates that the proposed EC strategy is potentially a cost-effective solution for a series of biochemical sensing applications in public health, environmental monitoring, and the developing world.
Computer-Aided Algorithm Design: Automated Tuning, Configuration, Selection, and Beyond

In this talk, I will introduce computer-aided algorithm design and discuss its main ingredients: design patterns, which provide ways of structuring potentially large spaces of candidate algorithms, and meta-algorithmic optimisation procedures, which are used for ﬁnding good designs within these spaces. After explaining how this algorithm design approach differs from and complements related approaches in program synthesis, genetic programming and so-called hyperheuristics, I will illustrate its success using examples from our own work in SAT-based software veriﬁcation (Hutter et al. 2007), timetabling (Chiarandini, Fawcett, and Hoos 2008) and mixed integer programming (Hutter, Hoos, and Leyton-Brown 2010). Furthermore, I will argue why this approach can be expected to be particularly useful and effective for building better solvers for rich and diverse classes of combinatorial problems, such as planning and scheduling. Finally, I will outline out how programming by optimisation — a design paradigm that emphasises the automated construction of performance-optimised algorithm by means of searching large spaces of alternative designs — has the potential to transform the design of high-performance algorithm from a craft that is based primarily on experience and intuition into a principled and highly effective engineering effort.
User Interface of the Quick Pad Tagger

More and more domain specific applications in the internet make use of Natural Language Processing (NLP) tools (e. g. Information Extraction systems). The output quality of these applications relies on the output quality of the used NLP tools. Often, the quality can be increased by annotating a domain specific corpus. However, annotating a corpus is a time consuming and exhaustive task. To reduce the annotation time we present a custom Graphical User Interface for different annotation layers.
Influence of Chain Stiffness, Grafting Density and Normal Load on the Tribological and Structural Behavior of Polymer Brushes: A Nonequilibrium-Molecular-Dynamics Study

We have performed coarse-grained molecular-dynamics simulations on both flexible and semiflexible multi-bead-spring model polymer brushes in the presence of explicit solvent particles, to explore their tribological and structural behaviors. The effect of stiffness and tethering density on equilibrium-brush height is seen to be well reproduced within a Flory-type theory. After discussing the equilibrium behavior of the model brushes, we first study the shearing behavior of flexible chains at different grafting densities covering brush and mushroom regimes. Next, we focus on the effect of chain stiffness on the tribological behavior of polymer brushes. The tribological properties are interpreted by means of the simultaneously recorded density profiles. We find that the friction coefficient decreases with increasing persistence length, both in velocity and separation-dependency studies, over the stiffness range explored in this work.
Reliable diagnosis of acute abdominal pain with conformal prediction

Medical decision support is an area in which a lot of machine learning research has been conducted and several diagnostic and prognostic systems have been developed. The majority of these systems only produce bare predictions, without any indication of how reliable each of these predictions is. An indication of this kind however, is highly desirable especially in the medical field. In this paper we address this problem with the use of a recently developed technique, called conformal prediction, for accompanying the predictions of traditional machine learning algorithms with measures of their accuracy and reliability. We apply conformal prediction based on a Neural Network classifier to the problem of acute abdominal pain diagnosis and obtain predictions which have a high level of accuracy and are complemented with well-calibrated and practically useful confidence measures.