Miracle-dz/UniFAR-FTUs huggingface dataset:
A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery

Functional relationships between proteins that do not share global structure similarity can be established by detecting their ligand-binding-site similarity. For a large-scale comparison, it is critical to accurately and efficiently assess the statistical significance of this similarity. Here, we report an efficient statistical model that supports local sequence order independent ligand–binding-site similarity searching. Most existing statistical models only take into account the matching vertices between two sites that are defined by a fixed number of points. In reality, the boundary of the binding site is not known or is dependent on the bound ligand making these approaches limited. To address these shortcomings and to perform binding-site mapping on a genome-wide scale, we developed a sequence-order independent profile–profile alignment (SOIPPA) algorithm that is able to detect local similarity between unknown binding sites a priori. The SOIPPA scoring integrates geometric, evolutionary and physical information into a unified framework. However, this imposes a significant challenge in assessing the statistical significance of the similarity because the conventional probability model that is based on fixed-point matching cannot be applied. Here we find that scores for binding-site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version. Efficient statistical analysis makes it possible to apply SOIPPA to genome-based drug discovery. Consequently, we have applied the approach to the structural genome of Mycobacterium tuberculosis to construct a protein–ligand interaction network. The network reveals highly connected proteins, which represent suitable targets for promiscuous drugs. Contact: lxie@sdsc.edu
Interorganizational Networks: A Case Study of Industrial Location

The recent interest in the literature on interorganizational relations is briefly reviewed and the so-called 'political economy' perspective is suggested as a helpful approach in conceptualizing the environments of organizations as a network in continuous negotia tions. A case study is presented of the negotiations between the British Government and the Ford Motor Company over the siting of a major engine plant in South Wales. The case study highlights the importance of choice and manipulation of environments; the extent to which organizations can control their environments; the importance of under standing organizational power; and the market position of the organization.
Activation of multiple antibiotic resistance and binding of stress-inducible promoters by Escherichia coli Rob protein

Multiple antibiotic resistance in Escherichia coli can be mediated by induction of the SoxS or MarA protein, triggered by oxygen radicals (in the soxRS regulon) or certain antibiotics (in the marRAB regulon), respectively. These small proteins (SoxS, 107 residues; MarA, 127 residues) are homologous to the C terminus of the XylS-AraC family of proteins and are more closely related to a approximately 100-residue segment in the N terminus of Rob protein, which binds the right arm of the replication origin, oriC. We investigated whether the SoxS-MarA homology in Rob might extend to the regulation of some of the same inducible genes. Overexpression of Rob indeed conferred multiple antibiotic resistance similar to that known for SoxS and MarA (against chloramphenicol, tetracycline, nalidixic acid, and puromycin), as well as resistance to the superoxide-generating compound phenazine methosulfate. The Rob-induced antibiotic resistance depended only partially on the micF antisense RNA that down-regulates the OmpF outer membrane porin to limit antibiotic uptake. Similar antibiotic resistance was conferred by expression of a Rob fragment containing only the N-terminal 123 residues that constitute the SoxS-MarA homology. Both intact Rob and the N-terminal fragment activated expression of stress genes (inaA, fumC, sodA) but with a pattern distinct from that found for SoxS and MarA. Purified Rob protein bound a DNA fragment containing the micF promoter (50% bound at approximately 10(-9) M Rob) as strongly as it did oriC, and it bound more weakly to DNA containing the sodA, nfo, or zwf promoter (50% bound at 10(-8) to 10(-7) M). Rob formed multiple DNA-protein complexes with these fragments, as seen previously for SoxS. These data point to a DNA-binding gene activator module used in different protein contexts.
Graph Clustering for Keyword Search

Keyword search on data represented as graphs, is receiving lot of attention in recent years. Initial versions of keyword search systems assumed that the graph is memory resident. However, there are applications where the graph can be much larger than the available memory. This led to the development of search algorithms which search on a smaller memory resident summary graph (supernode graph), and fetch parts of the original graph from the disk, only when required. In this scenario, good clustering of nodes into supernodes, when constructing the summary graph, is a key to ecient search. In this paper, we address the issue of graph clustering for keyword search, using a technique based on random walks. We propose an algorithm, which we call Modied Nibble clustering algorithm, that improves upon the Nibble algorithm proposed earlier. We outline several policies that can improve its performance. Then, we compare our algorithm with two graph clustering algorithms proposed earlier, EBFS and kMetis. Our performance metrics include edge compression, keyword search performance, and the time and space overheads for clustering. Our results show that Modied Nibble outperforms EBFS uniformly, and outperforms kMetis in some settings. Further, the memory requirements of our algorithm are much lower than that of kMetis, making it practical even with a very large number of nodes, unlike kMetis.
Rutgers Filtering Work at TREC 2002: Adaptive and Batch

This year at TREC 2002 we participated in the adaptive ltering sub-task of the ltering track with some models for training a Rocchio classier. Results were poorer than average on the utility type measures. Using simple feature selection produced better than average results on an F-type measure. The key to our approach was the use of pseudojudgments, and an approach to threshold updating. We also participated in the batch ltering sub-task of the ltering track and investigated the use of rank based feature selection techniques in conjunction with a very simple classication rule.
Sparsity adjusted information gain for feature selection in sentiment analysis

The widespread use of social media and the internet are emerging trends that offer an additional interaction channel for companies to better understand customer sentiments about their brands and products. Sentiment analysis uses text data from social media such as customer comments and reviews, which has the nature of high dimensionality. Without selection, typically there are at least thousands of features (words or phrases) that can be extracted from a text corpus, among which there are many redundant or irrelevant features for sentiment classification task. Thus, it is critical to select a compact yet effective set of features to avoid the complex classifier design and slow running time of classification process. However, very few of existing metrics is able to improve efficacy of feature selection by addressing the issue of sparsity of feature matrix for text data, i.e., many features may appear only in a few documents. In this paper, an improved feature selection metric known as sparsity adjusted information gain (SAIG) is proposed, which modifies the conventional information gain metric and aims to adjust the feature ranking scores according to the sparsity of the feature vector. It is able to use less features to obtain a targeted performance level. The experiment results show that SAIG is able to improve the performance of sentiment classification.
Early Life Adversity and Adult Biological Risk Profiles

Objectives To determine whether there is a relationship between early life adversity (ELA) and biological parameters known to predict health risks and to examine the extent to which circumstances in midlife mediate this relationship. Methods We analyzed data on 1180 respondents from the biomarker subsample of the second wave of the National Survey of Midlife Development in the United States. ELA assessments were based on childhood socioeconomic disadvantage (i.e., on welfare, perceived low income, and less educated parents) and other stressors (e.g., parental death, parental divorce, and parental physical abuse). The outcome variable was cumulative allostatic load (AL), a marker of biological risk. We also incorporate information on adult circumstances, including than following: education, social relationships, and health behaviors. Results Childhood socioeconomic adversity and physical abuse were associated with increased AL (B = 0.094, standard error = 0.041, and B = 0.263, standard error = 0.091 respectively), with nonsignificant associations for parental divorce and death with AL. Adult education mediated the relationship between socioeconomic ELA and cumulative AL to the point of nonsignificance, with this factor alone explaining nearly 40% of the relationship. The association between childhood physical abuse and AL remained even after adjusting for adult educational attainments, social relationships, and health behaviors. These associations were most pronounced for secondary stress systems, including inflammation, cardiovascular function, and lipid metabolism. Conclusions The physiological consequences of early life socioeconomic adversity are attenuated by achieving high levels of schooling later on. The adverse consequences of childhood physical abuse, on the other hand, persist in multivariable-adjusted analysis.
Leadership And Motivating Missions: A Model For Organizations From Science Literature

The phenomenon of motivating missions linked with high levels of performance and leadership excellence is a topic cited in both popular and academic literature. The author of this paper posits a model of leadership applied to examples in hospitality organizations from literature in the areas of biology, chemistry, and physics that explain the mechanics of the systems approach to managing organizations. Future empirical studies done by other researchers will advance the model presented in the paper to additional levels of development.
Suppressed T Cell and Macrophage Function in the "Reeler" (rl/rl) Mutant, a Murine Strain with Elevated Cerebellar Norepinephrine Concentration

The effects of neurochemical alterations in specific brain regions on the immune system were examined in reeler (rl/rl) mice, a neurologic mutant strain having an abnormally high concentration of cerebellar norepinephrine (NE). Following immunization with sheep red blood cells, lower numbers of IgM-producing B cells were found in rl/rl mice than in B6C3Fea/a controls. Interleukin-1 (IL-1) production by splenic macrophages from rl/rl mice was reduced compared to B6C3F3a/a controls, as was the proliferative response of splenic T lymphocytes from rl/rl mice activated with an anti-CD3 monoclonal antibody. Levels of IL-4, interferon-gamma and IL-2 produced by splenic T lymphocytes from rl/rl mice were also lower than those of B6C3Fea/a controls. Rl/rl mice do not have an intrinsic defect in the ability to produce IgM, as lipopolysaccharide activated splenic lymphocytes from rl/rl mice produced levels of IgM similar to those of controls. This suggests that defective function in the T lymphocyte and/or macrophage population rather than in the B cell population may underlie the defect in IgM production. No significant alterations were observed in basal splenic levels of NE or neuropeptides in rl/rl mice relative to controls. The reeler mouse model shows that alterations in immune function are present in a strain with inherited alterations in cerebellar noradrenergic innervation and NE concentration.
Inherent details in self-regulated learning

Self-regulated learning (SRL) has become a pivotal construct in contemporary accounts of effective academic learning. I examine several areas of theory and empirical research, which are not prominently cited in educational psychology's research into SRL, that reveal new details of what SRL is and how students develop productive SRL. I interpret findings from these investigations to suggest that nondeliberative, knowledge-based elements are inherent in the processes of SRL, and in learning more generally. Several topics for future research are sketched based on an assumption that learning effectively by oneself will remain a goal of education and can be an especially revealing context in which to research SRL.
The faculty of language

Rev Soc Bras Fonoaudiol. 2007;12(4):343-5 (1) Pós-graduanda em Lingüística e Língua Portuguesa pela Pontifícia Universidade Católica de Minas Gerais – PUC-Minas – Belo Horizonte (MG), Brasil; Professora da Pontifícia Universidade Católica de Minas Gerais – PUCMinas – Belo Horizonte (MG), Brasil. Endereço para correspondência: Denise Brandão de Oliveira e Britto. R. Desembargador Jorge Fontana, 250/1201, bloco 1, Belvedere, Belo Horizonte – MG, CEP 30320-670. E-mail: denisebrandao@pucminas.br The faculty of language
A Wideband CMOS LC-VCO Using Variable Inductor

This paper proposes a wide-range tunable CMOS voltage controlled oscillator (VCO). VCO uses an on-chip variable inductor and switched capacitors as variable elements. The VCO was fabricated using a standard 0.18 mum CMOS process with five metal layers. The oscillation frequency can be tuned from 1.28 GHz to 2.75 GHz with tuning range of 72 %.
Active surveillance with selective delayed intervention: using natural history to guide treatment in good risk prostate cancer.

PURPOSE This article reviews the data supporting an approach of active surveillance with selective delayed intervention for good risk localized prostate cancer. The challenge is to identify those patients who are not likely to experience significant progression, while offering radical therapy to those who are at risk. MATERIALS AND METHODS A prospective phase 2 study of active surveillance with selective delayed intervention was initiated in 1995. Patients were treated initially with surveillance, while those who had a prostate specific antigen (PSA) doubling time (DT) of 2 years or less, or grade progression on re-biopsy were offered radical intervention. The remainder were closely monitored. RESULTS The cohort consisted of 299 patients with good risk prostate cancer or intermediate risk prostate cancer in men older than 70 years. Median PSA DT was 7.0 years and 35% of the men had a PSA DT of greater than 10 years. The majority of patients remain on surveillance. At 8 years overall actuarial survival was 85% and disease specific survival was 99%. CONCLUSIONS Most men with favorable risk prostate cancer will die of unrelated causes. The approach of active surveillance with selective delayed intervention based on PSA DT represents a practical compromise between radical therapy in all, which results in overtreatment in patients with indolent disease, and watchful waiting with palliative therapy only, which results in under treatment in those with aggressive disease. Results at 8 years are favorable. Longer followup will be required to confirm the safety of this approach in men with long (greater than 15-year) life expectancy.
Unique and Overlapping Transcriptional Roles of Arylhydrocarbon Receptor Nuclear Translocator (Arnt) and Arnt2 in Xenobiotic and Hypoxic Responses*

Arnt and the homologous Arnt2 share a high degree of sequence similarity and are believed to function as obligate common partners for a number of basic helix-loop-helix (bHLH)-PAS transcription factors including arylhydrocarbon receptor (AhR) and HIFα. Genetic disruption of both Arnt and Arnt2 demonstrated both unique and overlapping functions in response to environmental stimuli and during mouse development. Either stably or transiently expressed Arnt/Arnt2 wild type and various mutants or chimeric constructs in Hepa1-c4 cells exhibit similar levels of hypoxic response element-driven reporter gene expression and the induction of endogenous Glut-1 through binding with HIFα in response to hypoxia. In contrast, we observed clear functional differences in the ability of Arnt and Arnt2 to induce xenobiotic response element-driven reporter and endogenous CYP1A1 gene expression. In contrast with Arnt, Arnt2 was practically incapable of interacting with ligand-activated AhR to induce the expression of target genes for xenobiotic-metabolizing enzymes in response to xenobiotics. The differential binding of AhR by Arnt and Arnt2 can be ascribed to a single His/Pro amino acid difference in the PASB region of Arnt and Arnt2, suggesting that the PASB/PASB interaction between bHLH-PAS transcription factors plays a selective role for their specific partner molecule.
Metastability-based Feedback Method for Enhancing FPGA-based TRNG

This paper presents a novel and efficient method to enhance the randomness of a Programmable Delay Line (PDL)-based True Random Number Generator (TRNG) by introducing Metastability-based Feedback scheme. As a principal tool for the security of sensor network, random number generator is one of important security primitives. At the CHES2011 conference, a new method of generating random numbers by inducing metastability using precise PDL and PI (proportional-Integral) control has been proposed. As the proposed TRNG could not achieve sufficient randomness on its own, a filtering scheme was used for higher entropy. Unfortunately, the intrinsic characteristics of filters made the throughput of the TRNG decrease by approximately 50\%. To preserve the original throughput and attain a high level of randomness, we present a simple solution that analyzes the probability of outputs and assign long time to metastable state. The proposed scheme shows high randomness in the NIST randomness test suite without any of the throughput loss caused by filtering.
Optimized Next-Generation Sequencing Genotype-Haplotype Calling for Genome Variability Analysis

The accurate estimation of nucleotide variability using next-generation sequencing data is challenged by the high number of sequencing errors produced by new sequencing technologies, especially for nonmodel species, where reference sequences may not be available and the read depth may be low due to limited budgets. The most popular single-nucleotide polymorphism (SNP) callers are designed to obtain a high SNP recovery and low false discovery rate but are not designed to account appropriately the frequency of the variants. Instead, algorithms designed to account for the frequency of SNPs give precise results for estimating the levels and the patterns of variability. These algorithms are focused on the unbiased estimation of the variability and not on the high recovery of SNPs. Here, we implemented a fast and optimized parallel algorithm that includes the method developed by Roesti et al and Lynch, which estimates the genotype of each individual at each site, considering the possibility to call both bases from the genotype, a single one or none. This algorithm does not consider the reference and therefore is independent of biases related to the reference nucleotide specified. The pipeline starts from a BAM file converted to pileup or mpileup format and the software outputs a FASTA file. The new program not only reduces the running times but also, given the improved use of resources, it allows its usage with smaller computers and large parallel computers, expanding its benefits to a wider range of researchers. The output file can be analyzed using software for population genetics analysis, such as the R library PopGenome, the software VariScan, and the program mstatspop for analysis considering positions with missing data.
Some methodological problems in estimating incidence and prevalence in child sexual abuse research

Research into child sexual abuse is fraught with methodological problems. This paper provides an overview of some common aspects of them. Definitional inconsistencies are frequent, including that of child sexual abuse itself and what age limits define a child. Other methodological difficulties include determining the number of children sexually abused in any period of time, choice of sample, the type and number of questions asked, factors such as publicity associated with the historical period, and estimates of the rate of increase of child sexual abuse over time. It is suggested that greater consistency, particularly of definition, would assist comparative conclusions to be made with less ambiguity and greater validity.
Towards computer-assisted photo-identification of humpback whales

This paper describes current work on a photo-id system for humpback whales. Individuals of this species can be uniquely identified by the light and dark pigmentation patches on their tails (flukes). We developed an interface that assists the user in segmenting the animal's tail from the sea and fitting an affine invariant coordinate grid to it. A numerical feature vector capturing the patch-distribution with respect to the grid is then automatically extracted and used to match the individual against a database of similarly processed images.
Correlated Variations in the Parameters That Regulate Dendritic Calcium Signaling in Mouse Retinal Ganglion Cells

The amplitude and time course of stimulus-evoked second messenger signals carried by intracellular changes in free calcium ([Ca]free) depend on the total influx of Ca2+, the fraction bound to endogenous buffer and the rate of extrusion. Estimates of the values of these three parameters in proximal dendrites of 15 mouse α retinal ganglion cells were made using the “added buffer” method and found to vary greatly from one experiment to the next. The variations in the measured parameters were strongly correlated across the sample of cells. This reduced the variability in the amplitude and time course of the dendritic Ca2+ signal and suggests that the expression of Ca2+ channels, binding proteins and extrusion mechanisms is homeostatically coordinated to maintain the amplitude and kinetics of the Ca2+ signal within a physiologically appropriate range.
Jazz Melody Generation from Recurrent Network Learning of Several Human Melodies

Recurrent (neural) networks have been deployed as models for learning musical processes, by computational scientists who study processes such as dynamic systems. Over time, more intricate music has been learned as the state of the art in recurrent networks improves. One particular recurrent network, the Long Short-Term Memory (LSTM) network shows promise as a module that can learn long songs, and generate new songs. We are experimenting with using two LSTM modules to cooperatively learn several human melodies, based on the songs’ harmonic structures, and the feedback inherent in the network. We show that these networks can learn to reproduce four human melodies. We then introduce two harmonizations, constructed by us, that are given to the learned networks. i.e. we supply a reharmonization of the song structure, so as to generate new songs. We describe the reharmonizations, and show the new melodies that result. We also use a different harmonic structure from an existing jazz song not in the training set, to generate a new melody.