Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement of betacellulin levels in bovine serum, colostrum and milk.

Betacellulin, a member of the epidermal growth factor (EGF) family, was originally isolated and identified from the conditioned medium from a murine pancreatic beta-cell carcinoma cell line. Recently, we isolated bovine betacellulin from a growth factor enriched cheese whey extract, but there is no information on the presence of betacellulin in other biological fluids. We have cloned the cDNA for bovine betacellulin, produced recombinant betacellulin and shown that it has a similar potency to the purified native molecule in stimulating the proliferation of Balb/c3T3 fibroblasts. We have produced a polyclonal antiserum to bovine betacellulin which did not cross-react with EGF or transforming growth factor-alpha (TGF-alpha). The antibody was used in a homologous RIA that was able to detect betacellulin in pooled bovine colostrum sampled during the first 3 days after calving (2.30+/-0.11 ng/ml mean+/-s.e.m.; n=6), in bovine milk soluble fraction (1.93+/-0.64 ng/ml mean+/-s.e.m.; n=5) and in bovine cheese whey (2.59+/-0.16 ng/ml mean+/-s.e.m.; n=3). The betacellulin concentration in foetal bovine serum (FBS) (3.68+/-0.59 ng/ml mean+/-s.e.m.; n=6) greatly exceeded that of betacellulin in serum from male calves 1 and 5 weeks of age (0.53+/-0.15 ng/ml and 0.70+/- 0.09 ng/ml respectively; mean+/-s.e.m.; n=9). Betacellulin measured in the serum of these same animals when aged between 27 and 43 weeks was below the detection limits of the RIA. Sera from 10 out of 36 unmated heifers contained betacellulin levels within the detection limits of the assay (0.433+/-0.06 ng/ml mean+/-s.e.m.; n=10). The presence of betacellulin in bovine colostrum and milk suggests that it plays a role in the growth and development of the neonate and/or mammary gland function. The results also show that betacellulin is undetectable in the castrated adult male circulation. Additionally, although present in very low amounts, serum betacellulin could be under hormonal regulation in the female, since betacellulin was detected in sera from 27% of the unmated heifers examined in this study. The high levels of betacellulin detected in FBS relative to newborn and adult serum suggests a possible endocrine role for this growth factor in the bovine foetus.
Comparative gene prediction in human and mouse.

The completion of the sequencing of the mouse genome promises to help predict human genes with greater accuracy. While current ab initio gene prediction programs are remarkably sensitive (i.e., they predict at least a fragment of most genes), their specificity is often low, predicting a large number of false-positive genes in the human genome. Sequence conservation at the protein level with the mouse genome can help eliminate some of those false positives. Here we describe SGP2, a gene prediction program that combines ab initio gene prediction with TBLASTX searches between two genome sequences to provide both sensitive and specific gene predictions. The accuracy of SGP2 when used to predict genes by comparing the human and mouse genomes is assessed on a number of data sets, including single-gene data sets, the highly curated human chromosome 22 predictions, and entire genome predictions from ENSEMBL. Results indicate that SGP2 outperforms purely ab initio gene prediction methods. Results also indicate that SGP2 works about as well with 3x shotgun data as it does with fully assembled genomes. SGP2 provides a high enough specificity that its predictions can be experimentally verified at a reasonable cost. SGP2 was used to generate a complete set of gene predictions on both the human and mouse by comparing the genomes of these two species. Our results suggest that another few thousand human and mouse genes currently not in ENSEMBL are worth verifying experimentally.
Molecular Cloning and Characterization of GhlecRK, a Novel Kinase Gene with Lectin-like Domain from Gossypium hirsutum

A novel gene encoding a lectin-like protein kinase was cloned from the upland cotton (Gossypium hirsutum) through cDNA library screening. This gene (named as Ghlecrk; GenBank accession number: AY487461) had a total length of 2233 bp with an open reading frame of 1926 bp, and encoded a predicted polypeptide of 641 amino acids with a molecular weight of 71.16 kDa. The GhLecRK protein shared 73, 65, 64 and 59% identity with other lectin-like kinase proteins isolated from A. thaliana (At3g53810, At2g37710, At3g55550) and Populus nigra (PnLPK) at amino acid level, respectively. Southern blot analysis showed that GhLecRK belonged to a multi-copy gene family. Expression patterns revealed that GhLecRK was enriched in the developing boll (six days post anthesis, 6DPA) and shoot, but low in the root and stem and no expression in the leaf. The domains analysis showed that GhlecRK protein possessed many activating sites/domains including ATP-binding sites, a transmembrane region, a lectin-like domain and a kinase domain. These results indicate that GhlecRK is a lectin-like membrane protein that may play an important role in the phase of fiber development.
Intraseasonal Variability of Equatorial Indian Ocean Zonal Currents

New satellite and in situ observations show large intraseasonal (10–60 day) variability of surface winds and upper-ocean current in the equatorial Indian Ocean, particularly in the east. An ocean model forced by the Quick Scatterometer (QuikSCAT) wind stress is used to study the dynamics of the intraseasonal zonal current. The model has realistic upper-ocean currents and thermocline depth variabilities on intraseasonal to interannual scales. The quality of the simulation is directly attributed to the accuracy of the wind forcing. At the equator, moderate westerly winds are punctuated by strong 10–40-day westerly wind bursts. The wind bursts force swift, intraseasonal (20–50 day) eastward equatorial jets in spring, summer, and fall. The zonal momentum balance is between local acceleration, stress, and pressure, while nonlinearity deepens and strengthens the eastward current. The westward pressure force associated with the thermocline deepening toward the east rapidly arrests eastward jets and, subsequently, generates (weak) westward flow. Thus, in accord with direct observations in the east, the spring jet is a single intraseasonal event, there are intraseasonal jets in summer, and the fall jet is long lived but strongly modulated on an intraseasonal scale. The zonal pressure force is almost always westward in the upper 120 m, and changes sign twice a year in the 120–200-m layer. Transient eastward equatorial undercurrents in early spring and late summer are associated with semiannual Rossby waves generated at the eastern boundary following thermocline deepening by the spring and fall jets. An easterly wind stress is not necessary to generate the undercurrents. Experiments with a single westerly wind burst forcing show that apart from the intraseasonal response, the zonal pressure force and current in the east have an intrinsic 90-day time scale that arises purely from equatorial adjustment.
Obesity and Its Relationship with Hypertension among Adults 50 Years and Older in Jinan, China

Background The relationship between obesity and hypertension varies with geographical area, race and definitions of obesity. Our study aimed to investigate the prevalence of obesity using standard Chinese criteria based on the body mass index (BMI) and the waist circumference (WC) and to examine the association between obesity and hypertension among middle-aged and elderly people in Jinan city. Methods This cross-sectional study examined 1,870 subjects from the blocks randomly selected from among the 6 communities of Jinan, China in 2011–2012. The Student's t-test was used to compare numerical data, and the χ2 test was used to compare categorical data. Multivariate logistic regression analyses were performed to assess the effects of general and central obesity on hypertension after adjusting for age or for education level, smoking, alcohol consumption, and continuous age. Results The prevalence of general obesity among people age 50 years and older was 21.1% (17.0% for males and 23.1% for females), and the prevalence of central obesity was 77.8% for men and 78.7% for women. For men, compared with a normal BMI, the ORs and 95% CIs for overweight and general obesity were 1.853 (1.252, 2.744) and 3.422 (1.894, 6.182), respectively, after adjusting for age, smoking, alcohol consumption and educational level. Compared with a normal WC, the ORs and 95% CIs for central obesity were 2.334 (1.573, 3.465) and 2.318 (1.544, 3.479), respectively, for men. For women, compared with a normal BMI, the ORs and 95% CIs were 1.942 (1.473, 2.599) and 4.011 (2.817, 5.712), respectively, after adjusting for age, smoking, alcohol consumption and educational level. Compared with a normal WC, the ORs and 95% CIs for central obesity were 2.488 (1.865, 3.319) and 2.379 (1.773, 3.192), respectively, for women. Conclusions The relationship between hypertension and general obesity was stronger than the relationship between hypertension and either overweight or central obesity in both genders.
Divergent Mitochondrial and Endoplasmic Reticulum Association of DMPK Splice Isoforms Depends on Unique Sequence Arrangements in Tail Anchors

ABSTRACT Myotonic dystrophy protein kinase (DMPK) is a Ser/Thr-type protein kinase with unknown function, originally identified as the product of the gene that is mutated by triplet repeat expansion in patients with myotonic dystrophy type 1 (DM1). Alternative splicing of DMPK transcripts results in multiple protein isoforms carrying distinct C termini. Here, we demonstrate by expressing individual DMPKs in various cell types, including C2C12 and DMPK −/− myoblast cells, that unique sequence arrangements in these tails control the specificity of anchoring into intracellular membranes. Mouse DMPK A and C were found to associate specifically with either the endoplasmic reticulum (ER) or the mitochondrial outer membrane, whereas the corresponding human DMPK A and C proteins both localized to mitochondria. Expression of mouse and human DMPK A—but not C—isoforms in mammalian cells caused clustering of ER or mitochondria. Membrane association of DMPK isoforms was resistant to alkaline conditions, and mutagenesis analysis showed that proper anchoring was differentially dependent on basic residues flanking putative transmembrane domains, demonstrating that DMPK tails form unique tail anchors. This work identifies DMPK as the first kinase in the class of tail-anchored proteins, with a possible role in organelle distribution and dynamics.
Automatic Threshold Search for Heat Map Based Feature Selection: A Cancer Dataset Analysis

—Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modiﬁcation allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classiﬁcation performance that is very competitive against current techniques.
Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation

Segmentation of histopathology sections is an ubiquitous requirement in digital pathology and due to the large variability of biological tissue, machine learning techniques have shown superior performance over standard image processing methods. As part of the GlaS@MICCAI2015 colon gland segmentation challenge, we present a learning-based algorithm to segment glands in tissue of benign and malignant colorectal cancer. Images are preprocessed according to the Hematoxylin-Eosin staining protocol and two deep convolutional neural networks (CNN) are trained as pixel classifiers. The CNN predictions are then regularized using a figure-ground segmentation based on weighted total variation to produce the final segmentation result. On two test sets, our approach achieves a tissue classification accuracy of 98% and 94%, making use of the inherent capability of our system to distinguish between benign and malignant tissue.
Collaboratively Regularized Nearest Points for Set Based Recognition

Set based recognition has been attracting more and more attention in recent years, benefitting from two facts: the difficulty of collecting sets of images for recognition fades quickly, and set based recognition models generally outperform the ones for single instance based recognition. In this paper, we propose a novel model called collaboratively regularized nearest points (CRNP) for solving this problem. The proposal inherits the merits of simplicity, robustness, and high-efficiency from the very recently introduced regularized nearest points (RNP) method on finding the set-to-set distance using the l2-norm regularized affine hulls. Meanwhile, CRNP makes use of the powerful discriminative ability induced by collaborative representation, following the same idea as that in sparse recognition for classification (SRC) for image-based recognition and collaborative sparse approximation (CSA) for set-based recognition. However, CRNP uses l2-norm instead of the expensive l1-norm for coefficients regularization, which makes it much more efficient. Extensive experiments on five benchmark datasets for face recognition and person re-identification demonstrate that CRNP is not only more effective but also significantly faster than other state-of-the-art methods, including RNP and CSA.
Improving spoken language understanding using word confusion networks

A natural language spoken dialog system includes a large vo-cabulary automatic speech recognition (ASR) engine, whose output is used as the input of a spoken language understanding component. Two challenges in such a framework are that the ASR component is far from being perfect and the users can say the same thing in very different ways. So, it is very important to be tolerant to recognition errors and some amount of orthographic variability. In this paper, we present our work on developing new methods and investigat-ing various ways of robust recognition and understanding of an utterance. To this end, we exploit word-level confusion networks (sausages), obtained from ASR word graphs (lat-tices) instead of the ASR 1-best hypothesis. Using sausages with an improved conﬁdence model, we decreased the call-type classiﬁcation error rate for AT&T’s How May I Help You SM ( HMIHY SM ) natural dialog system by 38%.
Tyrosine, Cysteine, and S-Adenosyl Methionine Stimulate In Vitro [FeFe] Hydrogenase Activation

Background [FeFe] hydrogenases are metalloenzymes involved in the anaerobic metabolism of H2. These proteins are distinguished by an active site cofactor known as the H-cluster. This unique [6Fe–6S] complex contains multiple non-protein moieties and requires several maturation enzymes for its assembly. The pathways and biochemical precursors for H-cluster biosynthesis have yet to be elucidated. Principal Findings We report an in vitro maturation system in which, for the first time, chemical additives enhance [FeFe] hydrogenase activation, thus signifying in situ H-cluster biosynthesis. The maturation system is comprised of purified hydrogenase apoprotein; a dialyzed Escherichia coli cell lysate containing heterologous HydE, HydF, and HydG maturases; and exogenous small molecules. Following anaerobic incubation of the Chlamydomonas reinhardtii HydA1 apohydrogenase with S-adenosyl methionine (SAM), cysteine, tyrosine, iron, sulfide, and the non-purified maturases, hydrogenase activity increased 5-fold relative to incubations without the exogenous substrates. No conditions were identified in which addition of guanosine triphosphate (GTP) improved hydrogenase maturation. Significance The in vitro system allows for direct investigation of [FeFe] hydrogenase activation. This work also provides a foundation for studying the biosynthetic mechanisms of H-cluster biosynthesis using solely purified enzymes and chemical additives.
Grid-Based Galaxy Morphology Analysis for the National Virtual Observatory

As part of the development of the National Virtual Observatory (NVO), a Data Grid for astronomy, we have developed a prototype science application to explore the dynamical history of galaxy clusters by analyzing the galaxies' morphologies. The purpose of the prototype is to investigate how Grid-based technologies can be used to provide specialized computational services within the NVO environment. In this paper we focus on the key enabling technology components, particularly Chimera and Pegasus which are used to create and manage the computational workflow that must be present to deal with the challenging application requirements. We illustrate how the components interplay with each other and can be driven from a special purpose application portal.
Transcriptome analysis using next generation sequencing reveals molecular signatures of diabetic retinopathy and efficacy of candidate drugs

Purpose To define gene expression changes associated with diabetic retinopathy in a mouse model using next generation sequencing, and to utilize transcriptome signatures to assess molecular pathways by which pharmacological agents inhibit diabetic retinopathy. Methods We applied a high throughput RNA sequencing (RNA-seq) strategy using Illumina GAIIx to characterize the entire retinal transcriptome from nondiabetic and from streptozotocin-treated mice 32 weeks after induction of diabetes. Some of the diabetic mice were treated with inhibitors of receptor for advanced glycation endproducts (RAGE) and p38 mitogen activated protein (MAP) kinase, which have previously been shown to inhibit diabetic retinopathy in rodent models. The transcripts and alternatively spliced variants were determined in all experimental groups. Results Next generation sequencing-based RNA-seq profiles provided comprehensive signatures of transcripts that are altered in early stages of diabetic retinopathy. These transcripts encoded proteins involved in distinct yet physiologically relevant disease-associated pathways such as inflammation, microvasculature formation, apoptosis, glucose metabolism, Wnt signaling, xenobiotic metabolism, and photoreceptor biology. Significant upregulation of crystallin transcripts was observed in diabetic animals, and the diabetes-induced upregulation of these transcripts was inhibited in diabetic animals treated with inhibitors of either RAGE or p38 MAP kinase. These two therapies also showed dissimilar regulation of some subsets of transcripts that included alternatively spliced versions of arrestin, neutral sphingomyelinase activation associated factor (Nsmaf), SH3-domain GRB2-like interacting protein 1 (Sgip1), and axin. Conclusions Diabetes alters many transcripts in the retina, and two therapies that inhibit the vascular pathology similarly inhibit a portion of these changes, pointing to possible molecular mechanisms for their beneficial effects. These therapies also changed the abundance of various alternatively spliced versions of signaling transcripts, suggesting a possible role of alternative splicing in disease etiology. Our studies clearly demonstrate RNA-seq as a comprehensive strategy for identifying disease-specific transcripts, and for determining comparative profiles of molecular changes mediated by candidate drugs.
Robust Semi-Automatic Depth Map Generation in Unconstrained Images and Video Sequences for 2D to Stereoscopic 3D Conversion

We describe a system for robustly estimating synthetic depth maps in unconstrained images and videos, for semi-automatic conversion into stereoscopic 3D. Currently, this process is automatic or done manually by rotoscopers. Automatic is the least labor intensive, but makes user intervention or error correction difficult. Manual is the most accurate, but time consuming and costly. Noting the merits of both, a semi-automatic method blends them together, allowing for faster and accurate conversion. This requires user-defined strokes on the image, or over several keyframes for video, corresponding to a rough estimate of the depths. After, the rest of the depths are determined, creating depth maps to generate stereoscopic 3D content, with Depth Image Based Rendering to generate the artificial views. Depth map estimation can be considered as a multi-label segmentation problem: each class is a depth. For video, we allow the user to label only the first frame, and we propagate the strokes using computer vision techniques. We combine the merits of two well-respected segmentation algorithms: Graph Cuts and Random Walks. The diffusion from Random Walks, with the edge preserving of Graph Cuts should give good results. We generate good quality content, more suitable for perception, compared to a similar framework.
An active approach to spoken language processing

State of the art data-driven speech and language processing systems require a large amount of human intervention ranging from data annotation to system prototyping. In the traditional supervised passive approach, the system is trained on a given number of annotated data samples and evaluated using a separate test set. Then more data is collected arbitrarily, annotated, and the whole cycle is repeated. In this article, we propose the active approach where the system itself selects its own training data, evaluates itself and re-trains when necessary. We first employ active learning which aims to automatically select the examples that are likely to be the most informative for a given task. We use active learning for both selecting the examples to label and the examples to re-label in order to correct labeling errors. Furthermore, the system automatically evaluates itself using active evaluation to keep track of the unexpected events and decides on-demand to label more examples. The active approach enables dynamic adaptation of spoken language processing systems to unseen or unexpected events for nonstationary input while reducing the manual annotation effort significantly. We have evaluated the active approach with the AT&T spoken dialog system used for customer care applications. In this article, we present our results for both automatic speech recognition and spoken language understanding.
Event detection in Twitter: A machine-learning approach based on term pivoting

The large number of messages on Twitter posted each day provide rich insights into real-world events and public opinion. However, it is difficult to automatically distinguish tweets referring to such events from everyday chatter, and subsequently to distinguish significant events affecting many people from insignificant events. We apply a term-pivot approach to event detection from the Twitter stream. In order to filter out noisy and mundane events, we train a machine learning classifier on several rich features, and rank the events based on classifier confidence. After training and re-training the classifier using manually annotated data, we obtain an F score of 0.79. However, a baseline that only takes into account the frequency of the tweets that refer to an event yields a better F score of 0.86. We argue that performance is highly related to the definition of what makes a significant event, and that human understanding of this concept is not uniform.