Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of multilocus linkage disequilibrium

Linkage disequilibrium (LD) in the human genome, often measured as pairwise correlation between adjacent markers, shows substantial spatial heterogeneity. Congruent with these results, studies have found that certain regions of the genome have far less haplotype diversity than expected if the alleles at multiple markers were independent, while other sets of adjacent markers behave almost independently. Regions with limited haplotype diversity have been described as “blocked” or “haplotype blocks. ” In this article, we propose a new method that aims to distinguish between blocked and unblocked regions in the genome. Like some other approaches, the method analyses haplotype diversity. Unlike other methods, it allows for adjacent, distinct blocks and also multiple, independent single nucleotide polymorphisms (SNPs) separating blocks. Based on an approximate likelihood model and a parsimony criterion to penalize for model complexity, the method partitions a genomic region into blocks relatively quickly, and simulations suggest that its partitions are accurate. We also propose a new, efficient method to select SNPs for association analysis, namely tag SNPs. These methods compare favorably to similar blocking and tagging methods using simulations. Genet. Epidemiol. © 2005 Wiley‐Liss, Inc.
MiRNA-128 regulates the proliferation and neurogenesis of neural precursors by targeting PCM1 in the developing cortex

During the development, tight regulation of the expansion of neural progenitor cells (NPCs) and their differentiation into neurons is crucial for normal cortical formation and function. In this study, we demonstrate that microRNA (miR)-128 regulates the proliferation and differentiation of NPCs by repressing pericentriolar material 1 (PCM1). Specifically, overexpression of miR-128 reduced NPC proliferation but promoted NPC differentiation into neurons both in vivo and in vitro. In contrast, the reduction of endogenous miR-128 elicited the opposite effects. Overexpression of miR-128 suppressed the translation of PCM1, and knockdown of endogenous PCM1 phenocopied the observed effects of miR-128 overexpression. Furthermore, concomitant overexpression of PCM1 and miR-128 in NPCs rescued the phenotype associated with miR-128 overexpression, enhancing neurogenesis but inhibiting proliferation, both in vitro and in utero. Taken together, these results demonstrate a novel mechanism by which miR-128 regulates the proliferation and differentiation of NPCs in the developing neocortex. DOI: http://dx.doi. org/10.7554/eLife.11324. 001
Application of imperfect speech recognition to navigation and editing of audio documents

In this paper we describe work in progress; using transcripts of audio files, generated with speech recognition software, to navigate and manipulate audio information. Three applications are being developed. The first is to find and extract speech segments from a longer recording. The second is to support navigation in a recorded lecture; both truncated transcript segments in display bars, and full transcripts are used here. The third application is a novel audio editor, permitting rearrangement of spoken text by direct manipulation of its transcript. The intended use of this software is to allow speakers to amend recorded lectures.
A Note on Low Density Parity Check Codes for Erasures and Errors

We analyze low density parity check codes that correct both errors and erasures using a simple decoding scheme. Our framework unifies previous analyses for low density parity check codes and erasure codes. The result is a general class of codes that corrects both errors and erasures, with corresponding simple linear time encoding and decoding algorithms and provable performance guarantees. We also describe how these codes can be applied to yield a new fast algorithm for the setting of of Gaussian noise, and leave several open questions regarding the construction of these codes and the associated decoding algorithms.
A case for dynamic view management

Materialized aggregate views represent a set of redundant entities in a data warehouse that are frequently used to accelerate On-Line Analytical Processing (OLAP). Due to the complex structure of the data warehouse and the different profiles of the users who submit queries, there is need for tools that will automate and ease the view selection and management processes. In this article we present DynaMat, a system that manages dynamic collections of materialized aggregate views in a data warehouse. At query time, DynaMat utilizes a dedicated disk space for storing computed aggregates that are further engaged for answering new queries. Queries are executed independently or can be bundled within a multiquery expression. In the latter case, we present an execution mechanism that exploits dependencies among the queries and the materialized set to further optimize their execution. During updates, DynaMat reconciles the current materialized view selection and refreshes the most beneficial subset of it within a given maintenance window. We show how to derive an efficient update plan with respect to the available maintenance window, the different update policies for the views and the dependencies that exist among them.
Recognition of Low-Resolution Logos in Vehicle Images Based on Statistical Random Sparse Distribution

Traditional image recognition approaches can achieve high performance only when the images have high resolution and superior quality. A new vehicle logo recognition (VLR) method is proposed to treat low-resolution and poor-quality images captured from urban crossings in intelligent transport system, and the proposed approach is based on statistical random sparse distribution (SRSD) feature and multiscale scanning. The SRSD feature is a novel feature representation strategy that uses the correlation between random sparsely sampled pixel pairs as an image feature and describes the distribution of a grayscale image statistically. Multiscale scanning is a creative classification algorithm that locates and classifies a logo integrally, which alleviates the effect of propagation errors in traditional methods by processing the location and classification separately. Experiments show an overall recognition rate of 97.21% for a set of 3370 vehicle images, which showed that the proposed algorithm outperforms classical VLR methods for low-resolution and inferior quality images and is very suitable for on-site supervision in ITSs.
Efficient Multi-Dimensional Mapping Using QAM Constellations for BICM-ID

Bit-interleaved coded modulation with iterative decoding (BICM-ID) offers improved error performance over additive white Gaussian noise and fading channels if it uses a wisely designed signal mapping. Furthermore, error performance of BICM-ID can be significantly improved by employing multi-dimensional (MD) modulation. However, suitable MD mappings are obtained by computer search techniques, except for MD modulations that use small constellations, e.g., binary phase shift keying, quadrature phase shift keying, and 8-ary phase shift keying as basic modulation. The alphabet size of MD modulations increases exponentially as the order of the basic modulation increases and computer searching becomes intractable. In this paper, we propose a systematic mapping method for MD modulations. The innovation of our proposed method is that it generates MD mappings using 16- and 64-quadrature amplitude modulation very efficiently. The presented numerical results show that the proposed method improves the bit error rate of BICM-ID.
Effective and adaptive technological solution to block spam E-mails

The internet E-mail infrastructure has become famous and widely used means of communication for personal, business and academic purposes just because it is fast, cheap and very efficient. People are using this E-mail infrastructure for their day-to-day work. The users are receiving many unwanted E-mails from the unknown senders. These unwanted E-mails are called as spam E-mails. In this paper technological solution for blocking spam E-mail is discussed. This technological solution consists of combination of origin based filters (OBF) and content based filters (CBF) which is adaptive in nature. The CBF Filter contains two components machine learning based classifier (MLC) and semantic similarity with edge based classifier (SSC). This technological solution is tested on the standard dataset such as Enron, LingSpam along with personal E-mail messages (PEM). The results empirically prove the strength of this solution.
Multiple-Locus Variable-Number Tandem Repeat Analysis of Neisseria meningitidis Yields Groupings Similar to Those Obtained by Multilocus Sequence Typing

ABSTRACT We identified many variable-number tandem repeat (VNTR) loci in the genomes of Neisseria meningitidis serogroups A, B, and C and utilized a number of these loci to develop a multiple-locus variable-number tandem repeat analysis (MLVA). Eighty-five N. meningitidis serogroup B and C isolates obtained from Dutch patients with invasive meningococcal disease and seven reference strains were analyzed using MLVA and multilocus sequence typing (MLST). MLVA, based on eight VNTR loci with limited variability in the number of repeats, yielded clustering of the strains similar to that obtained by MLST, with congruence between both methods amounting to 69%. The ability to recognize clonal complexes makes MLVA a valuable high-throughput method to serve as a tool complementary to MLST. Four highly variable VNTR loci were used in a second assay to analyze N. meningitidis serogroup C strains collected during an outbreak of meningococcal disease in The Netherlands. Typing based on the latter VNTR loci enabled differentiation of isolates with identical MLST sequence types and grouped epidemiologically related strains.
Balneotherapy Together with a Psychoeducation Program for Benzodiazepine Withdrawal: A Feasibility Study

Benzodiazepines should be prescribed on a short-term basis, but a significant proportion of patients (%) use them for more than 6 months, constituting a serious public health issue. Indeed, few strategies are effective in helping patients to discontinue long-term benzodiazepine treatments. The aim of this study was to assess the feasibility and the impact of a program including cognitive behavioural therapy, psychoeducation, and balneotherapy in a spa resort to facilitate long-term discontinuation of benzodiazepines. We conducted a prospective multicentre cohort study. Patients with long-term benzodiazepine use were recruited with the aim of anxiolytic withdrawal by means of a psychoeducational program and daily balneotherapy during 3 weeks. The primary efficacy outcome measure was benzodiazepine use 6 months after the program, compared to use at baseline. A total of 70 subjects were enrolled. At 6 months, overall benzodiazepine intake had decreased by 75.3%, with 41.4% of patients completely stopping benzodiazepine use. The results also suggest a significantly greater improvement in anxiety and depression symptoms among patients who discontinued benzodiazepines compared to patients who only reduced their use. Our findings suggest that balneotherapy in association with a psychoeducative program is efficient in subjects with benzodiazepine addiction.
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields

We apply stochastic average gradient (SAG) algorithms for training conditional random fields (CRFs). We describe a practical implementation that uses structure in the CRF gradient to reduce the memory requirement of this linearly-convergent stochastic gradient method, propose a non-uniform sampling scheme that substantially improves practical performance, and analyze the rate of convergence of the SAGA variant under non-uniform sampling. Our experimental results reveal that our method often significantly outperforms existing methods in terms of the training objective, and performs as well or better than optimally-tuned stochastic gradient methods in terms of test error.
Symptomatic Therapy and Rehabilitation in Primary Progressive Multiple Sclerosis

Multiple sclerosis (MS) is an autoimmune inflammatory demyelinating disease of the central nervous system and a major cause of chronic neurological disability in young adults. Primary progressive MS (PPMS) constitutes about 10% of cases, and is characterized by a steady decline in function with no acute attacks. The rate of deterioration from disease onset is more rapid than relapsing remitting and secondary progressive MS types. Multiple system involvement at onset and rapid early progression have a worse prognosis. PPMS can cause significant disability and impact on quality of life. Recent studies are biased in favour of relapsing remitting patients as treatment is now available for them and they are more likely to be seen at MS clinics. Since prognosis for PPMS is worse than other types of MS, the focus of rehabilitation is on managing disability and enhancing participation, and application of a “neuropalliative” approach as the disease progresses. This chapter presents the symptomatic treatment and rehabilitation for persons with MS, including PPMS. A multidisciplinary approach optimizes the intermediate and long-term medical, psychological and social outcomes in this population. Restoration and maintenance of functional independence and societal reintegration, and issues relating to quality of life are addressed in rehabilitation processes.
Automatically Identifying Known Software Problems

Re-occurrence of the same problem is very common in many large software products. By matching the symptoms of a new problem to those in a database of known problems, automated diagnosis and even self-healing for re-occurrences can be (partially) realized. This paper exploits function call stacks as highly structured symptoms of a certain class of problems, including crashes, hangs, and traps. We propose and evaluate algorithms for efficiently and accurately matching call stacks by a weighted metric of the similarity of their function names, after first removing redundant recursion and uninformative (poor discriminator) functions from those stacks. We also describe a new indexing scheme to speed queries to the repository of known problems, without compromising the quality of matches returned. Experiments conducted using call stacks from actual product problem reports demonstrate the improved accuracy (both precision and recall) resulting from our new stack-matching algorithms and removal of uninformative or redundant function names, as well as the performance and scalability improvements realized by indexing call slacks. We also discuss how call-stack matching can be used in both self-managing (or autonomic systems) and human "help desk" applications.
Sequencing of complete mitochondrial genome of brown algal Saccharina sp. ye-W

Abstract The complete sequence (37 657 bp) of the mitochondrial DNA (mtDNA) of the Saccharina sp. ye-W was determined using Illumina sequencing data. The genome contains 38 protein-coding genes (PCG), three ribosomal RNA (rRNA), 25 transfer RNA (tRNA) genes that are typical of Saccharina mtDNA. Phylogenetic analysis based on the mitochondrial genomes of brown algae indicated that Saccharina sp. ye-W and Saccharina longissima, Saccharina japonica are the most closely related species, which strongly supports their close phylogenetic affinity.
Overview of the TREC 2006 Enterprise Track

The goal of the enterprise track is to conduct experiments with enterprise data — intranet pages, email archives, document repositories — that reflect the experiences of users in real organizations, such that for example, an email ranking technique that is effective here would be a good choice for deployment in a real multi-user email search application. This involves both understanding user needs in enterprise search and development of appropriate IR techniques. The enterprise track began in TREC 2005 as the successor to the web track, and this is reflected in the tasks and measures. While the track takes much of its inspiration from the web track, the foci are on search at the enterprise scale, incorporating non-web data and discovering relationships between entities in the organization. As a result, we have created the first test collections for multi-user email search and expert finding. This year the track has continued using the W3C collection, a crawl of the publicly available web of the World Wide Web Consortium performed in June 2004. This collection contains not only web pages but numerous mailing lists, technical documents and other kinds of data that represent the day-to-day operation of the W3C. Details of the collection may be found in the 2005 track overview (Craswell et al., 2005). Additionally, this year we began creating a repository of information derived from the collection by participants. This data is hosted alongside the W3C collection at NIST. There were two tasks this year, email discussion search and expert search, and both represent refinements of the tasks initially done in 2005. NIST developed topics and relevance judgments for the email discussion search task this year. For expert search, rather than relying on found data as last year, the track participants created the topics and relevance judgments. Twenty-five groups took part across the two tasks.
Ecophylogenetics: advances and perspectives

Ecophylogenetics can be viewed as an emerging fusion of ecology, biogeography and macroevolution. This new and fast‐growing field is promoting the incorporation of evolution and historical contingencies into the ecological research agenda through the widespread use of phylogenetic data. Including phylogeny into ecological thinking represents an opportunity for biologists from different fields to collaborate and has provided promising avenues of research in both theoretical and empirical ecology, towards a better understanding of the assembly of communities, the functioning of ecosystems and their responses to environmental changes. The time is ripe to assess critically the extent to which the integration of phylogeny into these different fields of ecology has delivered on its promise. Here we review how phylogenetic information has been used to identify better the key components of species interactions with their biotic and abiotic environments, to determine the relationships between diversity and ecosystem functioning and ultimately to establish good management practices to protect overall biodiversity in the face of global change. We evaluate the relevance of information provided by phylogenies to ecologists, highlighting current potential weaknesses and needs for future developments. We suggest that despite the strong progress that has been made, a consistent unified framework is still missing to link local ecological dynamics to macroevolution. This is a necessary step in order to interpret observed phylogenetic patterns in a wider ecological context. Beyond the fundamental question of how evolutionary history contributes to shape communities, ecophylogenetics will help ecology to become a better integrative and predictive science.
Western blotting of total lysate of Helicobacter pylori in cases of atrophic body gastritis.

BACKGROUND Atrophic body gastritis is considered the first important step in the histogenesis of gastric carcinoma, a multistep process starting from chronic gastritis and progressing through chronic atrophic gastritis, intestinal metaplasia, and dysplasia. Helicobacter pylori is involved in the induction of atrophic body gastritis, but documentation of H. pylori infection is difficult because of the progressive disappearance of the bacterium. Our study aimed to detect past H. pylori infection in patients with atrophic body gastritis. METHODS We used Western blot analyses of whole bacterial protein lysate of 2 different strains to probe sera from 143 patients. All sera were analyzed by ELISA (Bio-Rad), and results of gastric histology were available for all patients. RESULTS Among 111 patient sera previously classified as negative for H. pylori infection by ELISA, 106 (95.5%) were positive when assayed by immunoblotting. CONCLUSIONS Commercial diagnostic reagent sets may fail to detect H. pylori infection. Western blotting of whole bacterial protein extracts could provide the basis of a noninvasive serology tool able to assess previous infection with H. pylori in patients with atrophic body gastritis.
Comparison of two phonetic approaches to language identification

This paper presents two unsupervised approaches to Automatic Language Identification (ALI) based on a segmental preprocessing. In the Global Segmental Model approach, the language system is modeled by a Gaussian Mixture Model (GMM) trained with auto-matically detected segments. In the Phonetic Differentiated Model approach, an unsupervised detection vowel/non vowel is performed and the language model is defined with two GMMs, one to model the vowel segments and a second one to model the others segments. For each approach, no labeled data are required. GMMs are initialized using an efficient data-driven variant of the LBG algorithm: the LBG-Rissanen algo-rithm.With 5 languages from the OGI MLTS corpus and in a closed set identification task, we reach 85 % of correct identification with each system using 45 second duration utterances for the male speakers. We increase this performance (91%) when we merge the two systems.
A novel second-order feedforward approach for tracking control of a class of motion systems with flexible modes

Jerk derivative feedforward control is proposed and used in recent literatures. However, such feedforward control scheme is restricted to the minimum-phase system which has constrained static gains in the flexible modes. This paper presents a novel form of feedforward controller for a wider class of flexible motion systems, even to be non-minimum phase, as long as the summation of DC gains of flexible modes is positive. The newly proposed feedforward scheme gives superior performance over existing feedforward schemes. Especially, the tracking error can be almost eliminated, when the plant's lowest frequency anti-resonance is outside the main frequency band of the reference trajectory. In other cases, this novel controller still gives adequate tracking performance, while other feedforward controllers are malfunctioned.
Human Evaluation of Machine Translation Through Binary System Comparisons

We introduce a novel evaluation scheme for the human evaluation of different machine translation systems. Our method is based on direct comparison of two sentences at a time by human judges. These binary judgments are then used to decide between all possible rankings of the systems. The advantages of this new method are the lower dependency on extensive evaluation guidelines, and a tighter focus on a typical evaluation task, namely the ranking of systems. Furthermore we argue that machine translation evaluations should be regarded as statistical processes, both for human and automatic evaluation. We show how confidence ranges for state-of-the-art evaluation measures such as WER and TER can be computed accurately and efficiently without having to resort to Monte Carlo estimates. We give an example of our new evaluation scheme, as well as a comparison with classical automatic and human evaluation on data from a recent international evaluation campaign.
Generational and Transgenerational Issues of the Japanese American Internment : A Phenomenological Study

This study utilized a qualitative/phenomenological research methodology to examine the generational and transgenerational issues of five identified Japanese American families. To be included in this study, families were identified to contain at least one member who was interned during World War II or who had parents, grandparents, or great-grandparents who were interned. Semistructured interviews, including Adlerian lifestyle assessments, were conducted with the 28 research informants who represented the second, third, and fourth generations of their families.