Miracle-dz/UniFAR-FTUs huggingface dataset:
National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases “chatter” – messages that are about influenza but that do not pertain to an actual infection – masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012–2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system’s influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.
Modifying the host range properties of retroviral vectors

Gene therapy protocols would be greatly facilitated by the availability of targetable injectable vectors which could deliver genes in vivo to specific target cells or to specific disease sites. Efforts to develop such retroviral vectors are therefore a high priority in gene therapy research. In this review, we describe the current state of our understanding of the structure and function of the retroviral envelope glycoprotein complex. We then discuss the results of the various strategies that have been devised to modify the host range of the retroviral envelope glycoproteins with a view to achieving retroviral vectors capable of delivering their genes in a highly specific manner to selected human target cells. The strengths and limitations of these strategies are examined. Copyright © 1999 John Wiley & Sons, Ltd.
Nanoacoustics: probing acoustic waves on the nanoscale

Two scanning acoustic probe microscopy (SAPM) techniques have been developed for the nanoscale analysis of the interaction of acoustic modes on the surface of a solid. The scanning acoustic force microscope (SAFM) and the scanning acoustic tunneling microscope (SATM) are extensions to the scanning probe microscopy world utilizing the non-linearity of the respective interaction-distance curves in order to detect high-frequency signals, like acoustic waves or electric fields. SAFM and SATM measurements are presented that give an overview of past and present work in this field. With SAPM, acoustic wave properties of arbitrarily polarized surface acoustic wave (SAW) modes can be measured with sub-wavelength resolution and unmatched amplitude sensitivity. SAW dispersion measurements, scattering and diffraction, SAW transducer studies, studies of the crystal anisotropy, as well as atomically resolved images of the oscillating lattice will be discussed.
Physics-based method to validate and repair flaws in protein structures

Significance Protocols for NMR determination of high-resolution protein structures in solution require, among other things, an accurate method with which to assess the quality of protein structures. It is important that such a validation method provide information as to where structural flaws are and how they can be repaired. So far, no generally accepted validation method with these characteristics appears to exist for evaluation of protein structures in solution. As an approach to find a solution to this long-standing problem, we developed a method to detect flaws in protein structures at the residue level and also to provide a way to repair these flaws. A method that makes use of information provided by the combination of 13Cα and 13Cβ chemical shifts, computed at the density functional level of theory, enables one to (i) validate, at the residue level, conformations of proteins and detect backbone or side-chain flaws by taking into account an ensemble average of chemical shifts over all of the conformations used to represent a protein, with a sensitivity of ∼90%; and (ii) provide a set of (χ1/χ2) torsional angles that leads to optimal agreement between the observed and computed 13Cα and 13Cβ chemical shifts. The method has been incorporated into the CheShift-2 protein validation Web server. To test the reliability of the provided set of (χ1/χ2) torsional angles, the side chains of all reported conformations of five NMR-determined protein models were refined by a simple routine, without using NOE-based distance restraints. The refinement of each of these five proteins leads to optimal agreement between the observed and computed 13Cα and 13Cβ chemical shifts for ∼94% of the flaws, on average, without introducing a significantly large number of violations of the NOE-based distance restraints for a distance range ≤ 0.5 Ǻ, in which the largest number of distance violations occurs. The results of this work suggest that use of the provided set of (χ1/χ2) torsional angles together with other observables, such as NOEs, should lead to a fast and accurate refinement of the side-chain conformations of protein models.
A Separate-and-Learn Approach to EM Learning of PCFGs

Wepropose a new approach to EM learning of PCFGs. We completely separate the process of EM learning from that of parsing, and for the former, we introduce a new EM algorithm called the graphical EM algorithm that runs on a new data structure called support graphs extracted from WFSTs (well formed substring tables) of various parsers. Learning experiments with PCFGs using two Japanese corpora indicate that our approach can signi(cid:12)cantly outperform the existing approaches using the Inside-Outside algo-rithm (Baker, 1979) and Stolcke’s EM algorithm (Stolcke, 1995).
Randomized Algorithms for Low-Rank Matrix Decomposition

Low-rank matrix factorization is one of the most useful tools in scientific computing and data analysis. The goal of low-rank factorization is to decompose a matrix into a product of two smaller matrices of lower rank that approximates the original matrix well. Such a decomposition exposes the low-rank structure of the data, requires less storage, and subsequent matrix operations require less computation. Classical methods for low-rank factorization of an m×n matrix into two matrices of rank k are O(mnk) and require at least k passes through the input matrix and assume random access. In modern applications, we deal more and more with matrices so large that accessing the data becomes the computational bottleneck, and the classical methods become unsuitable. In this literature review, we discuss several randomized methods for computing approximations to the best low-rank factorization, in only 1 or 2 passes over the input matrix. The remaining computation is polynomial in the desired rank k and at most sublinear in the original dimensions m×n. These methods provide a clear tradeoff between accuracy and computation time via the degree of sampling involved. We discuss the running time and bounds on the expected approximation error for each method. Finally, we present a unified framework for all methods, and compare them in terms of the computation-accuracy tradeoff, data access requirements and parallelizability.
Investment Returns and Inflation Models: Some Australian Evidence

ABSTRACT The development of stochastic investment models for actuarial and investment applications has become an important area of interest to actuaries. This paper reports the application of some techniques of modern time series and econometric analysis to Australian inflation, share market and interest rate data. It considers unit roots, cointegration and state space models. Some of the results from this analysis are not reflected in the published stochastic investment models.
Experimental Animal Models for Studies on the Mechanisms of Blast-Induced Neurotrauma

A blast injury is a complex type of physical trauma resulting from the detonation of explosive compounds and has become an important issue due to the use of improvised explosive devices (IED) in current military conflicts. Blast-induced neurotrauma (BINT) is a major concern in contemporary military medicine and includes a variety of injuries that range from mild to lethal. Extreme forces and their complex propagation characterize BINT. Modern body protection and the development of armored military vehicles can be assumed to have changed the outcome of BINT. Primary blast injuries are caused by overpressure waves whereas secondary, tertiary, and quaternary blast injuries can have more varied origins such as the impact of fragments, abnormal movements, or heat. The characteristics of the blast wave can be assumed to be significantly different in open field detonations compared to explosions in a confined space, such an armored vehicle. Important parameters include peak pressure, duration, and shape of the pulse. Reflections from walls and armor can make the prediction of effects in individual cases very complex. Epidemiological data do not contain information of the comparative importance of the different blast mechanisms. It is therefore important to generate data in carefully designed animal models. Such models can be selective reproductions of a primary blast, penetrating injuries from fragments, acceleration movements, or combinations of such mechanisms. It is of crucial importance that the physical parameters of the employed models are well characterized so that the experiments can be reproduced in different laboratory settings. Ideally, pressure recordings should be calibrated by using the same equipment in several laboratories. With carefully designed models and thoroughly evaluated animal data it should be possible to achieve a translation of data between animal and clinical data. Imaging and computer simulation represent a possible link between experiments and studies of human cases. However, in order for mathematical simulations to be completely useful, the predictions will most likely have to be validated by detailed data from animal experiments. Some aspects of BINT can conceivably be studied in vitro. However, factors such as systemic response, brain edema, inflammation, vasospasm, or changes in synaptic transmission and behavior must be evaluated in experimental animals. Against this background, it is necessary that such animal experiments are carefully developed imitations of actual components in the blast injury. This paper describes and discusses examples of different designs of experimental models relevant to BINT.
Visual Comparison of Hierarchically Organized Data

We provide a novel visualization method for the comparison of hierarchically organized data. Our technique visualizes a pair of hierarchies that are to be compared and simultaneously depicts how these hierarchies are related by explicitly visualizing the relations between matching subhierarchies. Elements that are unique to each hierarchy are shown, as well as the way in which hierarchy elements are relocated, split or joined. The relations between hierarchy elements are visualized using Hierarchical Edge Bundles (HEBs). HEBs reduce visual clutter, they visually emphasize the aforementioned splits, joins, and relocations of subhierarchies, and they provide an intuitive way in which users can interact with the relations. The focus throughout this paper is on the comparison of different versions of hierarchically organized software systems, but the technique is applicable to other kinds of hierarchical data as well. Various data sets of actual software systems are used to show how our technique can be employed to easily spot splits, joins, and relocations of elements, how sorting both hierarchies with respect to each other facilitates comparison tasks, and how user interaction is supported.
A Systematic Review of the Prevalence and Attribution of Human Papillomavirus Types among Cervical, Vaginal, and Vulvar Precancers and Cancers in the United States

Objectives: To describe prevalence and estimated attribution of human papillomavirus (HPV) types in U.S. cervical, vaginal, and vulvar precancers and cancers. Methods: U.S. studies reporting HPV typing for cervical intraepithelial neoplasia (CIN), vulvar intraepithelial neoplasia (VIN), and vaginal intraepithelial neoplasia (VaIN) and/or invasive cancers of those sites were gathered from the PubMed database (http://www.ncbi.nlm.nih.gov/sites/entrez/). Selected studies had PCR testing data for ≥10 cases for a disease endpoint. Analytic methods augmented prior reviews of cervical disease with an updated and expanded analysis (including vulvar and vaginal disease), new selection criteria for specimens, and adjustment for histologic type, where possible, among pooled cancer cases. In addition, for analyses of estimated attribution of HPV types, we incorporated accounting methods for lesions infected with multiple HPV types. Results: Data from 22 U.S. studies meeting review eligibility criteria were tabulated. Following adjustment for the presence of multiple HPV types in a single specimen, the top two HPV types contributing to disease were CIN 1 (HPV 16/66; 15.3%), CIN 2/3 (HPV 16/31; 61.9%), cervical cancer (HPV 16/18; 79.2%), VIN 1 (HPV 6/11; 41.7%), VIN 3 (HPV 16/18; 84.0%), vulvar cancer (HPV 16/33; 55.5%), VaIN 3 (HPV 16/18; 65.1%), and vaginal cancer (HPV 16/18; 72.7%). Conclusions: The HPV type distribution and proportion of cases testing positive for any HPV type were observed to vary among U.S. cervical, vulvar, and vaginal neoplasias and by grade of disease. Adjustment for the presence of multitype HPV infections can have an important effect on the estimated attribution of HPV types to disease, particularly for types other than HPV 16. (Cancer Epidemiol Biomarkers Prev 2008;17(7):1611–22)
Genetic analysis of historic western Great Lakes region wolf samples reveals early Canis lupus/lycaon hybridization

The genetic status of wolves in the western Great Lakes region has received increased attention following the decision to remove them from protection under the US Endangered Species Act. A recent study of mitochondrial DNA has suggested that the recovered wolf population is not genetically representative of the historic population. We present microsatellite genotype data on three historic samples and compare them with extant populations, and interpret published genetic data to show that the pre-recovery population was admixed over a century ago by eastern wolf (Canis lycaon) and grey wolf (Canis lupus) hybridization. The DNA profiles of the historic samples are similar to those of extant animals in the region, suggesting that the current Great Lakes wolves are representative of the historic population.
Efficient Inhibition of HIV Replication in the Gastrointestinal and Female Reproductive Tracts of Humanized BLT Mice by EFdA

Background The nucleoside reverse transcriptase inhibitor (NRTI) 4'-ethynyl-2-fluoro-2'-deoxyadenosine (EFdA) in preclinical development exhibits improved safety and antiviral activity profiles with minimal drug resistance compared to approved NRTIs. However, the systemic antiviral efficacy of EFdA has not been fully evaluated. In this study, we utilized bone marrow/liver/thymus (BLT) humanized mice to investigate the systemic effect of EFdA treatment on HIV replication and CD4+ T cell depletion in the peripheral blood (PB) and tissues. In particular, we performed a comprehensive analysis of the female reproductive tract (FRT) and gastrointestinal (GI) tract, major sites of transmission, viral replication, and CD4+ T cell depletion and where some current antiretroviral drugs have a sub-optimal effect. Results EFdA treatment resulted in reduction of HIV-RNA in PB to undetectable levels in the majority of treated mice by 3 weeks post-treatment. HIV-RNA levels in cervicovaginal lavage of EFdA-treated BLT mice also declined to undetectable levels demonstrating strong penetration of EFdA into the FRT. Our results also demonstrate a strong systemic suppression of HIV replication in all tissues analyzed. In particular, we observed more than a 2-log difference in HIV-RNA levels in the GI tract and FRT of EFdA-treated BLT mice compared to untreated HIV-infected control mice. In addition, HIV-RNA was also significantly lower in the lymph nodes, liver, lung, spleen of EFdA-treated BLT mice compared to untreated HIV-infected control mice. Furthermore, EFdA treatment prevented the depletion of CD4+ T cells in the PB, mucosal tissues and lymphoid tissues. Conclusion Our findings indicate that EFdA is highly effective in controlling viral replication and preserving CD4+ T cells in particular with high efficiency in the GI and FRT tract. Thus, EFdA represents a strong potential candidate for further development as a part of antiretroviral therapy regimens.
Parallel DBSCAN with Priority R-tree

According to the efficiency bottleneck of algorithm DBSCAN, we present P-DBSCAN, a novel parallel version of this algorithm in distributed environment. By separating the database into several parts, the computer nodes carry out clustering independently; after that, the sub-results will be aggregated into one final result. P-DBSCAN achieves good results and much better efficiency than DBSCAN. Experiments show that, P-DBSCAN accelerates more than 40% on one PC, and 60% on two PCs. In addition, the parallel algorithm has much better scalability than DBSCAN, so that it can be used for clustering analysis in huge databases.
Prediction of diseases by cascading clustering and classification

Diagnosis of the disease is one of the application areas where data mining techniques helps in the extraction of knowledge from medical database. Recently, researchers have been investigating the effect of cascading more than one technique showing enhanced results in the diagnosis of the disease. This paper proposes a hybrid model using K-means as a preprocessing algorithm. The proposed model is developed in four stages. In the initial stage, datasets selected from the UCI repository is cleaned by deleting all the instances with missing values. In the second stage Best First search algorithm and Correlation based feature selection (CFS) are used in a cascaded fashion for relevant feature selection In the third stage the resultant dataset (binary class datasets) is then clustered into two segments using K-means and incorrectly clustered samples are eliminated to get final samples. Finally, the correctly clustered samples from the previous stage is trained with 12 different classifiers to build the final classifier model, using Stratified 10 fold cross validation. Experimental results proved that cascaded K-means clustering and classification with CFS and Best First as a Feature selection method showed enhanced classification accuracy on an average of 95% and above on 5 different medical datasets with all 12 classifiers.
Presence and progression of non‐motor symptoms in relation to uric acid in de novo Parkinson's disease

Uric acid (UA) has been studied extensively as a valuable biomarker of Parkinson's disease (PD), but its relationship with non‐motor symptoms (NMS) in de novo PD has been poorly investigated. Our aim was to evaluate the usefulness of baseline serum UA as a marker of NMS progression in newly diagnosed PD.
Material Characterization on the Fracture of Filament-Reinforced Composites

The mixed mode fracture of unidirectional composites is investigated by application of the strain energy density theory. Two analytical models are used. The first assumes that the composite is an ideal homogeneous anisotropic body, and the second assumes that failure takes place in a layer of matrix material whose edges are bonded to two orthotropic plates. The expressions for the strain energy density factor, S, are derived for both models, and brittle fracture is assumed to occur when the S-factor in an element of material ahead of the crack reaches a critical value, Sc . Results are presented for E-glass fiber reinforced plastics, stainless steel fiber reinforced aluminum and graphite fiber epoxy materials and compared with available experimental data. The latter model is preferred as it accounts for the effect of nonhomogeneity that is inherent in the filament-reinforced composites.
Advances on genetic rat models of epilepsy

Considering the suitability of laboratory rats in epilepsy research, we and other groups have been developing genetic models of epilepsy in this species. After epileptic rats or seizure-susceptible rats were sporadically found in outbred stocks, the epileptic traits were usually genetically-fixed by selective breeding. So far, the absence seizure models GAERS and WAG/Rij, audiogenic seizure models GEPR-3 and GEPR-9, generalized tonic-clonic seizure models IER, NER and WER, and Canavan-disease related epileptic models TRM and SER have been established. Dissection of the genetic bases including causative genes in these epileptic rat models would be a significant step toward understanding epileptogenesis. N-ethyl-N-nitrosourea (ENU) mutagenesis provides a systematic approach which allowed us to develop two novel epileptic rat models: heat-induced seizure susceptible (Hiss) rats with an Scn1a missense mutation and autosomal dominant lateral temporal epilepsy (ADLTE) model rats with an Lgi1 missense mutation. In addition, we have established episodic ataxia type 1 (EA1) model rats with a Kcna1 missense mutation derived from the ENU-induced rat mutant stock, and identified a Cacna1a missense mutation in a N-Methyl-N-nitrosourea (MNU)-induced mutant rat strain GRY, resulting in the discovery of episodic ataxia type 2 (EA2) model rats. Thus, epileptic rat models have been established on the two paths: ‘phenotype to gene’ and ‘gene to phenotype’. In the near future, development of novel epileptic rat models will be extensively promoted by the use of sophisticated genome editing technologies.
Quantification and Localization of Intracellular Free Mg2+ in Bovine Chromaffin Cells

Magnesium is an essential element for all living systems. The quantification of free intracellular Mg2+ concentration ([Mg2+]i) is of utmost importance since changes in its basal value may be an indication of different pathologies due to abnormalities of Mg2+ metabolism. In this work we used 31P NMR and fluorescence spectroscopy to determine the resting [Mg2+]i in bovine chromaffin cells, a neuron-like cellular model, as well as confocal laser scanning microscopy to study the free Mg2+ spatial distribution in these cells. 31P NMR spectroscopy did not prove to be effective for the determination of [Mg2+]i in this particular case due to some special morphological and physiological properties of this cell type. A basal [Mg2+]i value of 0.551 ± 0.008 mM was found for these cells using fluorescence spectroscopy and the Mg2+-sensitive probe furaptra; this value falls in the concentration range reported in the literature for neurons from different sources. This technique proved to be an accurate and sensitive tool to determine the [Mg2+]i. lntraceilular free Mg2+ seems to be essentially localized in the nucleus and around it, as shown by confocal microscopy with the Mg2+-sensitive probe Magnesium Green. It was not possible to derive any conclusion about free Mg2+ localization inside the chromaffin granules and/or in the cytoplasm due to the lack of sufficient spatial resolution and to probe compartmentalization.