Miracle-dz/UniFAR-FTUs huggingface dataset:
Inference of Gene Regulatory Networks from Time Series Expression Data: A Data Mining Approach

The developments in large-scale monitoring of gene expression have made the reconstruction of gene regulatory networks (GRNs) feasible. Before one can infer the overall structures of GRNs, it is important to identify, for each gene in a network, which other genes can affect its expression and how they can affect it. Many existing methods to reconstructing GRNs are developed to generate hypotheses about the presence or absence of interactions between genes so that laboratory experiments can be performed afterwards for verification. Since, they are not intended to be used to predict if a gene has any interactions with other genes from an unseen sample. This makes statistical verification of the reliability of the discovered interactions difficult. In addition, some of them cannot make use of the temporal evidence in the data and also cannot take into account the directionality of regulation. For these reasons, we propose an effective data mining approach in this paper. For performance evaluation, it has been tested using real expression data. Experimental results show that it can be effective. The sequential associations discovered can reveal known gene regulatory relationships that could be used to infer the structures of GRNs
Regulation of proteolysis

The mechanisms of proteolysis remain to be fully defined. This review focuses on recent advances in our understanding of the ubiquitin-proteasome-dependent pathway, which is involved in the control of many major biological functions. The ubiquitinylation/deubiquitinylation system is a complex machinery responsible for the specific tagging and proof-reading of substrates degraded by the 26S proteasome, as well as having other functions. The formation of a polyubiquitin degradation signal is required for proteasome-dependent proteolysis. Several families of enzymes, which may comprise hundreds of members to achieve high selectivity, control this process. The substrates tagged by ubiquitin are then recognized by the 26S proteasome and degraded into peptides. In addition, the 26S proteasome also recognizes and degrades some non-ubiquitinylated proteins. In fact, there are multiple ubiquitin- or proteasome-dependent pathways. These systems presumably degrade specific classes of substrates and single proteins by alternative mechanisms and could be interconnected. They may also interfere or cooperate with other proteolytic pathways.
Variance compensation within the MLLR framework for robust speech recognition and speaker adaptation

The paper investigates the use of maximum likelihood linear regression (MLLR) for both speaker and environment adaptation. MLLR transforms the mean and variance parameters of a set of HMMs. A number of different types of linear transformations of the variances are examined including full, block diagonal, and diagonal transformation matrices. Experiments on large vocabulary speaker independent data sets are described. On all the data sets examined, the use of MLLR mean and variance compensation reduced the error rate compared to mean-only compensation. Furthermore, the use of a block diagonal or full transformation of the variances on the clean data task showed slight improvements over the diagonal case. However, when some environmental mismatch was present, there was no difference in performance between using multiple diagonal variance transformations and a more complex single variance transform.
Extending Manifold Leaning Algorithms by Neural Networks

Manifold learning algorithms have been recently reported superior to classical dimensionality reduction techniques, such as PCA or MDS, in their ability to discover a more meaningful low-dimensional embedding of the high-dimensional samples. However, most of them encounter the problem of extension to novel samples. In this paper, we propose a regression model to extend three well-known manifold learning algorithms, i.e. Isomap, LLE, and Laplacian Eigenmap to novel samples by neural networks. We first examine these algorithms, and then show that the nonlinear dimensionality reduction ability can be acquired by neural networks, thus the extension problem is easily addressed. This model is very flexible and still preserves the nonlinear nature of the manifold leaning algorithms. Experimental results of data visualization and classification are reported, which validate the feasibility of the proposed model.
A study on avian cellulitis in broiler chickens

Avian cellulitis in broiler chickens, especially on the thighs and abdominal wall, has been observed more frequently in recent years. In the present study, over a one-year period 98 broiler carcasses with cellulitis were diagnosed at slaughterhouse. The lesions were characterized by thickening and brown discoloration of the skin. A fibrinopurulent exudate with some caseation was seen in the subcutaneous tissues. Microscopically, hyperkeratosis, thickening of the dermis, infiltration of mononuclear cells and heterophils, along with fibrinocaseous exudates, were present. In 90 of the 98 (91.8%) broiler bacteriological samples E. coli was isolated, and in 82 (91.1%) of these samples it was the only bacterial species found. Serotyping results revealed that E. coli isolates were distributed among 6 different serotypes. The most prevalent serotype was O78 (52.2%). In addition, Staphylococcus aureus and Actinomyces pyogenes were isolated from 12 and 2 cases, respectively. This study confirms the frequent association of E. coli with cellulitis lesions in broiler chickens, along with isolation of S. aureus and A. pyogenes. The latter have not been reported in the previous studies.
Analysis of Different Similarity Measure Functions and their Impacts on Shared Nearest Neighbor Clustering Approach

Clustering is a technique of grouping data with analogous data content. In recent years, Density based clustering algorithms especially SNN clustering approach has gained high popularity in the field of data mining. It finds clusters of different size, density, and shape, in the presence of large amount of noise and outliers. SNN is widely used where large multidimensional and dynamic databases are maintained. A typical clustering technique utilizes similarity function for comparing various data items. Previously, many similarity functions such as Euclidean or Jaccard similarity measures have been worked upon for the comparison purpose. In this paper, we have evaluated the impact of four different similarity measure functions upon Shared Nearest Neighbor (SNN) clustering approach and the results were compared subsequently. Based on our analysis, we arrived on a conclusion that Euclidean function works best with SNN clustering approach in contrast to cosine, Jaccard and correlation distance measures function.
A Hardware-Software System for Tomographic Reconstruction

We present the design of a hardware–software system for the reconstruction of tomographic images. In a systematic approach we developed the parallel processor array, a reconfigurable hardware controller and processing kernel, and the software control up to the integration into a graphical user interface. The processor array acting as a hardware accelerator, is constructed using theoretical results and methods of application-specific hardware design. The reconfigurability of the system allows one to utilize a much wider realm of algorithms than the three reconstruction algorithms implemented so far. In the paper we discuss the system design at different levels from algorithm transformations to board development.
Development of an Immunoassay for Endogenous Digitalislike Factor

Recently, attempts to purify and identify a circulating inhibitor of the sodium pump have been successful. Based on the outcome of mass spectral analysis of purified inhibitor, we raised in rabbits antibodies to conjugates of the commercially available cardenolide ouabain and used them in the development of an indirect enzyme-linked immunosorbent assay (ELISA) for endogenous digitalislike factor (EDLF). Antisera obtained were of high antibody titer (l:2xlO6) and showed full cross-reactivity with purified EDLF. The antisera were highly specific for ouabain and structurally related cardenolides but showed no cross-reactivity with numerous endogenous steroids and peptides. At each step in the purification of EDLF, inhibition of the sodium pump and immunologic cross-reactivity were inseparable. The ELISA as developed had a working range of 5-2,000 fimol, with an IC50 of 80 fmol/well. Using solid-phase extraction and the ELISA, we determined the circulating level of EDLF in plasma from normal human volunteers to be 138±43 fmol/ml, whereas patients on total parenteral nutrition for at least 1 week had a circulating level of 108 ±17 fmol/ml, suggesting that the circulating factor was of endogenous origin. The ELISA developed appears to measure a naturally occurring counterpart to the cardenolides that could play a role in modulating the sodium pump and thereby cellular electrolyte homeostasis. (Hypertension 1991;17:936-943)
Aging and Religious Participation: Reconsidering the Effects of Health

This study examined the relationship between aging and religious participation, focusing on changes in older people's formal participation in church life, informal or private religious practices, and attitudes towards participation in the church. Survey data from a random sample of older persons in a medium-sized northeastern city included both objective and subjective measures of health. Contrary to earlier studies which relied on subjective health as the sole indicator of health status, multiple regression analyses showed that health, especially as measured by health-related, functional limitations, serves an important mediating role. Older people experience greater functional limitations and, in turn, are less likely to engage in various church activities. These findings have important implications for the activity-disengagement debate within social gerontology and also for the concern in the sociology of religion with the role of religion in older people's lives.
Comparison of Portable and Dark Room Autokinetic Movement

A portable apparatus for producing autokinetic (AK) movement is described and compared with the standard dark room method. Use of the portable apparatus results in a smaller amount and expanse of apparent movement, but the test-retest reliabilities are of the same order of magnitude as in the dark room method. The portable apparatus provides several advantages to the AK researcher. It is easy to handle; it can readily be disassembled; it eliminates the ventilation and light-proofing problems inherent in achieving a totally darkened room. This apparatus automatically measures the latency of onset of AK movement. The importance of controlling or measuring the latency of onset of AK movement is demonstrated.
Title Decision Fusion with Channel Errors in DistributedDecode-Then-Fuse Sensor Networks

Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modulation, reception mode and communication channel into account. The average bit error rate (BER) is employed to characterize such an uncertainty. Further, the detection performance is analyzed under both non-identical and identical local detection performance indices. In addition, the performance of the proposed method is compared with the existing optimal and suboptimal LRT fusion rules. The results show that the proposed fusion rule is more robust compared to these existing ones.
Induction of Host Matrix Metalloproteinases by Borrelia burgdorferi Differs in Human and Murine Lyme Arthritis

ABSTRACT Matrix metalloproteinases (MMPs) are induced from host tissues in response to Borrelia burgdorferi. Upregulation of MMPs may play a role in the dissemination of the organism through extracellular matrix tissues, but it can also result in destructive pathology. Although mice are a well-accepted model for Lyme arthritis, there are significant differences compared to human disease. We sought to determine whether MMP expression could account for some of these differences. MMP expression patterns following B. burgdorferi infection were analyzed in primary human chondrocytes, synovial fluid samples from patients with Lyme arthritis, and cartilage tissue from Lyme arthritis-susceptible and -resistant mice by using a gene array, real-time PCR, an enzyme-linked immunosorbent assay, and immunohistochemistry. B. burgdorferi infection significantly induced transcription of MMP-1, -3, -13, and -19 from primary human chondrocyte cells. Transcription of MMP-10 and tissue inhibitor of metalloprotease 1 was increased with B. burgdorferi infection, but protein expression was only minimally increased. The synovial fluid levels of MMPs from patients with high and low spirochete burdens were consistent with results seen in the in vitro studies. B. burgdorferi-susceptible C3H/HeN mice infected with B. burgdorferi showed induction of MMP-3 and MMP-19 but no other MMP or tissue inhibitor of metalloprotease. As determined by immunohistochemistry, MMP-3 expression was increased only in chondrocytes near the articular surface. The levels of MMPs were significantly lower in the more Lyme arthritis-resistant BALB/c and C57BL/6 mice. Differences between human and murine Lyme arthritis may be related to the lack of induction of collagenases, such MMP-1 and MMP-13, in mouse joints.
Detecting rare structural variation in evolving microbial populations from new sequence junctions using breseq

New mutations leading to structural variation (SV) in genomes—in the form of mobile element insertions, large deletions, gene duplications, and other chromosomal rearrangements—can play a key role in microbial evolution. Yet, SV is considerably more difficult to predict from short-read genome resequencing data than single-nucleotide substitutions and indels (SN), so it is not yet routinely identified in studies that profile population-level genetic diversity over time in evolution experiments. We implemented an algorithm for detecting polymorphic SV as part of the breseq computational pipeline. This procedure examines split-read alignments, in which the two ends of a single sequencing read match disjoint locations in the reference genome, in order to detect structural variants and estimate their frequencies within a sample. We tested our algorithm using simulated Escherichia coli data and then applied it to 500- and 1000-generation population samples from the Lenski E. coli long-term evolution experiment (LTEE). Knowledge of genes that are targets of selection in the LTEE and mutations present in previously analyzed clonal isolates allowed us to evaluate the accuracy of our procedure. Overall, SV accounted for ~25% of the genetic diversity found in these samples. By profiling rare SV, we were able to identify many cases where alternative mutations in key genes transiently competed within a single population. We also found, unexpectedly, that mutations in two genes that rose to prominence at these early time points always went extinct in the long term. Because it is not limited by the base-calling error rate of the sequencing technology, our approach for identifying rare SV in whole-population samples may have a lower detection limit than similar predictions of SNs in these data sets. We anticipate that this functionality of breseq will be useful for providing a more complete picture of genome dynamics during evolution experiments with haploid microorganisms.
Virtual smartphone over IP

The number of smartphone users and mobile application offerings are growing rapidly. A smartphone is often expected to offer PC-like functionality. In this paper, we present Virtual Smartphone over IP system that allows users to create virtual smartphone images in the mobile cloud and to customize each image to meet different needs. Users can easily and freely tap into the power of the data center by installing the desired mobile applications remotely in one of these images. Because the mobile applications are controlled remotely, they are not constrained by the limit of processing power, memory and battery life of a physical smartphone.
Capillary electrophoresis for the detection of PMP22 gene duplication: Study in Mexican patients

Charcot‐Marie‐Tooth (CMT) disease is the most common inherited disorder of the human peripheral nerve, with an estimated overall prevalence of 17‐40/10 000 [1]. The typical phenotype presents peroneal muscular atrophy and pes cavus [2]. CMT is usually divided into two large types, about two‐thirds of the patients have CMT type 1 (CMT1), that affects the layer of myelin (demyelination). In type 2 (CMT2) the nerve fibers are affected (axonal). CMT diseases have autosomal dominant, autosomal recessive, and X‐linked inheritance [1]. The most frequent subtype is 1A (CMT1A) with autosomal dominant transmission, secondary in most cases to a tandem duplication of a 1.5 Mb DNA fragment on chromosome 17p11.2‐p12 [4–7]. In this region, the codification of the peripheral myelin protein 22 (PMP22) takes place. The severity of the disease varies among patients, even within the same family, from almost no symptoms to severe foot‐drop and sensory loss. The PMP22 gene has four exons and is regulated by two promoters located toward the extreme 5′. The origin of the duplication that causes the disease is an uneven exchange of the chromatids during the meiosis. This unequal recombination occurs between two regions that limit the PMP22 gene, described as REP places of 24 kb, proximal and distal [3, 4].
Can Monolingual Embeddings Improve Neural Machine Translation?

English. Neural machine translation (NMT) recently redefined the state of the art in machine translation, by introducing deep learning architecture that can be trained end-to-end. One limitation of NMT is the difficulty to learn representations of rare words. The most common solution is to segment words into subwords, in order to allow for shared representations of infrequent words. In this paper we present ways to directly feed a NMT network with external word embeddings trained on monolingual source data, thus enabling a virtually infinite source vocabulary. Our preliminary results show that while our methods do not seem effective under large-data training conditions (WMT En-De), they instead show great potential for the typical low-resourced data scenario (IWSLT EnFr). By leveraging external embeddings learned on Web crawled English texts, we were able to improve a word-level En-Fr baseline trained on 200,000 sentence pairs by up to 4 BLEU points. Italiano. La traduzione automatica con reti neurali (neural machine translation, NMT) ha ridefinito recentemente lo stato dell’arte nella traduzione automatica introducendo un’architettura di deep learning che può essere addestrata interamente, dall’input all’output. Una limitazione della NMT è comunque la difficoltà di apprendere rappresentazioni di parole poco frequenti. La soluzione più adottata consiste nel segmentare le parole in sottoparole, in modo da consentire rappresentazioni condivise per parole poco frequenti. In questo lavoro presentiamo dei metodi per fornire ad una rete word embedding esterni addestrati su testi nella lingua sorgente, consentendo quindi un vocabolario virtualmente illimitato sulla lingua di input. I nostri risultati preliminari mostrano che i nostri metodi, pur non sembrando efficaci sotto condizioni di addestramento con molti dati (WMT EnDe), risultano invece promettenti per scenari di addestramento con poche risorse (IWSLT En-Fr). Sfruttando word embedding appresi da testi inglesi estratti dal Web, siamo riusciti a migliorare un sistema NMT basato a parole e addestrato su 200.000 coppie di frasi fino a 4 punti
Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium

We study a multiagent learning problem where agents can either learn via repeated interactions, or can follow the advice of a mediator who suggests possible actions to take. We present an algorithm that each agent can use so that, with high probability, they can verify whether or not the mediator's advice is useful. In particular, if the mediator's advice is useful then agents will reach a correlated equilibrium, but if the mediator's advice is not useful, then agents are not harmed by using our test, and can fall back to their original learning algorithm. We then generalize our algorithm and show that in the limit it always correctly verifies the mediator's advice.
On the choice of data sources to improve content discoverability via textual feature optimization

A large portion of the audience of video content items on the web currently comes from keyword-based search and/or tag-based navigation. Thus, the textual features of this content (e.g., the title, description, and tags) can directly impact the view count of a particular content item, and ultimately the advertisement generated revenue. More importantly, the textual features can generally be optimized to attract more search traffic. This study makes progress on the problem of automating tag selection for online video content with the goal of increasing viewership. It brings two key insights: first, based on evidence that existing tags for YouTube videos can be improved by an automated tag recommender, even for a sample of well curated movies, it explores the impact of using information mined from repositories created by different production modes (e.g., peer- and expert-produced); second, this study performs a preliminary characterization of the factors that impact the quality of the tag recommendation pipeline for different input data sources.
Detecting Good Abandonment in Mobile Search

Web search queries for which there are no clicks are referred to as abandoned queries and are usually considered as leading to user dissatisfaction. However, there are many cases where a user may not click on any search result page (SERP) but still be satisfied. This scenario is referred to as good abandonment and presents a challenge for most approaches measuring search satisfaction, which are usually based on clicks and dwell time. The problem is exacerbated further on mobile devices where search providers try to increase the likelihood of users being satisfied directly by the SERP. This paper proposes a solution to this problem using gesture interactions, such as reading times and touch actions, as signals for differentiating between good and bad abandonment. These signals go beyond clicks and characterize user behavior in cases where clicks are not needed to achieve satisfaction. We study different good abandonment scenarios and investigate the different elements on a SERP that may lead to good abandonment. We also present an analysis of the correlation between user gesture features and satisfaction. Finally, we use this analysis to build models to automatically identify good abandonment in mobile search achieving an accuracy of 75%, which is significantly better than considering query and session signals alone. Our findings have implications for the study and application of user satisfaction in search systems.