Miracle-dz/UniFAR-FTUs huggingface dataset:
NAPS: a residue-level nucleic acid-binding prediction server

Nucleic acid-binding proteins are involved in a great number of cellular processes. Understanding the mechanisms underlying these proteins first requires the identification of specific residues involved in nucleic acid binding. Prediction of NA-binding residues can provide practical assistance in the functional annotation of NA-binding proteins. Predictions can also be used to expedite mutagenesis experiments, guiding researchers to the correct binding residues in these proteins. Here, we present a method for the identification of amino acid residues involved in DNA- and RNA-binding using sequence-based attributes. The method used in this work combines the C4.5 algorithm with bootstrap aggregation and cost-sensitive learning. Our DNA-binding model achieved 79.1% accuracy, while the RNA-binding model reached an accuracy of 73.2%. The NAPS web server is freely available at http://proteomics.bioengr. uic.edu/NAPS.
Type inclusion constraints and type inference

We present a general algorithm for solving systems of inclusion constraints over type expressions. The constraint language includes function types, constructor types, and liberal intersection and union types. We illustrate the application of our constraint solving algorithm with a type inference system for the lambda calculus with constants. In this system, every pure lambda term has a (computable) type and every term typable in the Hindley/Milner system has all of its Hindley/Milner types. Thus, the inference system is an extension of the Hindley/Milner system that can type a very large set of lambda terms.
Using processor affinity in loop scheduling on shared-memory multiprocessors

The authors consider a new dimension to the problem of loop scheduling on shared-memory multiprocessors: communication overhead caused by accesses to nonlocal data. It is shown that traditional algorithms for loop scheduling, which ignore the location of data when assigning iterations to processors, incur a significant performance penalty on modern shared-memory multiprocessors. The authors propose a loop scheduling algorithm that attempts to simultaneously balance the workload, minimize synchronization, and colocate loop iterations with the necessary data. They compare the performance of this algorithm to that of other known algorithm using four representative applications on a Silicon Graphics multiprocessor workstation, a BBN Butterfly, and a Sequent Symmetry, and they show that the algorithm offers substantial performance improvements, up to a factor of 3 in some cases. They conclude that loop scheduling algorithms for shared-memory multiprocessors cannot afford to ignore the location of data, particularly in light of the increasing disparity between processor and memory speeds. <<ETX>>
Accurate Pinyin-English Codeswitched Language Identification

Pinyin is the most widely used romaniza-tion scheme for Mandarin Chinese. We consider the task of language identiﬁcation in Pinyin-English codeswitched texts, a task that is signiﬁcant because of its application to codeswitched text input. We create a codeswitched corpus by extracting and automatically labeling existing Mandarin-English codeswitched corpora. On language identiﬁ-cation, we ﬁnd that SVM produces the best result when using word-level segmentation, achieving 99.3% F1 on a Weibo dataset, while a linear-chain CRF produces the best result at the letter level, achieving 98.2% F1. We then pass the output of our models to a system that converts Pinyin back to Chinese characters to simulate codeswitched text input. Our method achieves the same level of performance as an oracle system that has perfect knowledge of token-level language identity. This result demonstrates that Pinyin identiﬁcation is not the bottleneck towards developing a Chinese-English codeswitched Input Method Editor, and future work should focus on the Pinyin-to-Chinese character conversion step.
Climate Warming May Facilitate Invasion of the Exotic Shrub Lantana camara

Plant species show different responses to the elevated temperatures that are resulting from global climate change, depending on their ecological and physiological characteristics. The highly invasive shrub Lantana camara occurs between the latitudes of 35°N and 35°S. According to current and future climate scenarios predicted by the CLIMEX model, climatically suitable areas for L. camara are projected to contract globally, despite expansions in some areas. The objective of this study was to test those predictions, using a pot experiment in which branch cuttings were grown at three different temperatures (22°C, 26°C and 30°C). We hypothesized that warming would facilitate the invasiveness of L. camara. In response to rising temperatures, the total biomass of L. camara did increase. Plants allocated more biomass to stems and enlarged their leaves more at 26°C and 30°C, which promoted light capture and assimilation. They did not appear to be stressed by higher temperatures, in fact photosynthesis and assimilation were enhanced. Using lettuce (Lactuca sativa) as a receptor plant in a bioassay experiment, we also tested the phytotoxicity of L. camara leachate at different temperatures. All aqueous extracts from fresh leaves significantly inhibited the germination and seedling growth of lettuce, and the allelopathic effects became stronger with increasing temperature. Our results provide key evidence that elevated temperature led to significant increases in growth along with physiological and allelopathic effects, which together indicate that global warming facilitates the invasion of L. camara.
Collective Violence: A Research Agenda and Some Strategic Considerations

This paper is based on the premise that current empirical analyses of collective violence are largely inadequate, and takes as its primary objective a detailed specification of alternative methodological strategies. Quantitative research on violence is cited for problems of measurement and causal inference, which stem from attempts to represent important theoretical concepts (relative deprivation, resource mobilization) at levels of analysis other than those at which they are formulated. Alternative strategies for empirical investigation of violence include timeseries analyses of individual perceptions and data on organized groups, as well as studies of crowd dynamics. Methods for linking the latter type of qualitative approach to quantitative ecological analyses are indicated. In general, the conclusions strongly suggest a theoretical and empirical reorientation toward “disaggregated” approaches to collective violence, and away from global conceptual and cross-national methodological efforts. Although consideration is limited to the determinants of domestic violence, the concluding section outlines the potential relevance of international linkages and the outcomes of violence to these central concerns.
Patterns of Adjustment to Partner Loss in Old Age: The Widowhood Adaptation Longitudinal Study

The present longitudinal study aims to explain emotional and social loneliness experienced by older adults (N=99) during two-and-a-half years of widowhood. Utilization of multilevel analysis and a “visual” cluster analysis with prescribed classification criteria enabled us to search for average adaptational developments as well as individual variability in the adjustment process. Results were interpreted within the theory of mental incongruity. Adjustment to loneliness appears to develop along different individual-specific curves. About 30 percent of the bereaved had not adapted in two-and-a-half years to their loss in terms of emotional loneliness. Presence of favorable opportunities such as good health and high self-esteem as well as coping efforts like social behavior resulted in lower levels of emotional and social loneliness. It is concluded that the adjustment process among older bereaved does not exist. Moreover, including measures of cognitions and attitudes that are related to the relational needs and desires of widow(er)s may enlarge our knowledge of how older adults adapt to partner death.
Identification of a heat shock cognate protein 70 gene in Chinese soft-shell turtle (Pelodiscus sinensis) and its expression profiles under thermal stress

The heat shock cognate protein 70 (Hsc70) is a member of a 70-kDa heat shock protein (HSP70) family that functions as molecular chaperones. In this study, a novel Hsc70 gene from Chinese soft-shelled turtle (Pelodiscus sinensis) (tHsc70) was identified. The tHsc70 full-length complementary DNA (cDNA) is 2 272 bp long with a 1 941-bp open reading frame (ORF) encoding 646 amino acids. Three characteristic signature regions of the HSP70 family, two major domains of an adenosine triphosphate (ATP)/guanosine triphosphate (GTP) binding domain (ABD), and a substrate-binding domain (SBD) were present in the predicted tHsc70 amino acid sequence. The tHsc70 gene was expressed in Escherichia coli BL21 and the expression product reacted with the anti-Hsc70 mouse monoclonal antibody by Western blotting. Homology analysis revealed that tHsc70 shared identity from 53.9% to 87.7% at the nucleotide level, and 49.1% to 99.5% at the amino acid level with the known Hsc70s. Phylogenetic analysis showed that tHsc70 was clustered together with the Hsc70 gene of another reptile species (Alligator mississippiensis). The tHsc70 was expressed in the liver, lung, heart, and skeletal muscle. The expression patterns of tHsc70 messenger RNA (mRNA) differed among different tissues under different durations of heat stress at 40 °C. Adaptation at 25 °C for 1 h after heat stress was also different among tissues and length of heat stress. Irrespective of different profiles of expression under heat stress, tHsc70 may play roles in protecting turtles from thermal stress.
Data Analysis using Computational Topology and Geometric Statistics

Mathematical scientists of diverse backgrounds are being asked to apply the techniques of their specialty to data which is greater in both size and complexity than that which has been studied previously. Large, high-dimensional data sets, for which traditional linear methods are inadequate, pose challenges in representation, visualization, interpretation and analysis. A common finding is that these massive data sets require the development of new theory and that these advances are dependent on increasing technical sophistication. Two such data-analytic techniques that have recently developed independently of each other have come to the fore, namely, Geometric Statistics and Computational Topology. Although the former uses geometric arguments, while the latter uses algebraic-topological arguments, and hence they appear disparate, there is substantial commonality and overlap just as in the more traditional overlap between geometry and topology. Thus the purpose of this workshop is to bring together these two research directions and explore their overlap, particularly in the service of statistical data analysis. A standard paradigm assumes that the data comes from some underlying geometric structure, such as a curved submanifold or a singular algebraic variety. The observed data is obtained as a random sample from this space, and the objective is to statistically recover features of the underlying space and/or the distribution that generated the sample. In Geometric Statistics one uses the underlying Riemannian structure to recover quantitative information concerning the probability distribution and/or functionals thereof. The idea is to extend statistical estimation techniques to functions over Riemannian manifolds, utilizing spectral methods adapted to the Riemannian structure. One then considers the magnitude of the statistical accuracy of these estimators. Considerable progress has been achieved in terms of optimal estimation in the minimax sense. These ideas have far reaching implications in the analysis of high-dimensional data such as, for example, in astronomy, biomechanics, medical imaging, microwave engineering and texture analysis. In Computational Topology, one attempts to recover more qualitative global features of the underlying data instead, such as connectedness, or the number of holes, or the existence of obstructions to certain constructions, based upon the random sample. In other words, one hopes to recover the underlying topology. An advantage of topology is that it is stable under deformations and thus insensitive to errors introduced in the sampling. A combinatorial construction such as the alpha complex or the Čech complex converts the discrete data into an object for which it is possible to compute the topology. However, it is quickly apparent that such a
Particle Swarm Optimization: A Stochastic Approximation Approach

Recently, much progress has been made on particle swarm optimization (PSO). A number of works have been devoted to analyzing the convergence of the underlying algorithms. Nevertheless, in most cases, certain rather simpliﬁed hypotheses are used. For example, it often assumes that the swarm has only one particle. In addition, more often than not, the variables and the points of attraction are assumed to remain constant throughout the optimization process. In reality, such assumptions are often violated. Moreover, up to now, not much is known regarding the convergence rates of particle swarm. In this paper, we develop a class of PSO algorithms, and analyze asymptotic properties of the algorithms using stochastic approximation methods. We introduce four coeﬃcients and rewrite the PSO procedure as a stochastic approximation type recursive algorithm. Then we analyze its convergence using weak convergence method. It is proved that a suitably scaled sequence of swarms converge to the solution of an ordinary diﬀerential equation. We also establish certain stability results. Moreover, convergence rates are ascertained by using weak convergence method. A centered and scaled sequence of the estimation errors is shown to have a diﬀusion limit. Furthermore, we demonstrate that our PSO algorithms perform much better than the traditional PSOs on some test functions for optimization problems.
Coupled ocean-atmosphere nested modeling of the Adriatic Sea during winter and spring 2001

[1] Realistic simulations of the Adriatic Sea for over 125 days are conducted using the Navy Coastal Ocean Model with atmospheric forcing provided by the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS is a registered trademark of the Naval Research Laboratory (COAMPS™)). In two separate simulations of the Adriatic, a nested 2-km-resolution ocean model is forced by the inner (4-km) and outer (36-km) nests of the atmospheric model. Two meteorological stations and two acoustic Doppler current profiler observation sites are used to evaluate modeled atmosphere and ocean velocity fields for 28 January–4 June 2001. Modeled/observed correlations of atmospheric 10-m velocity are greater than 0.85 for both resolution models. Oceanic 5- and 25-m current fluctuations from both simulations generally match the magnitude and orientation of the observations. The 4-km-resolution atmospheric model is differentiated from the 36-km-resolution model by its ability to resolve the small-scale flow structures of the “bora” wind and by its better agreement with observed wind velocity statistics. The ocean simulation forced by the 4-km-resolution model is distinguished from the one forced by the 36-km-resolution model by its ability to reproduce the expected double-gyre circulation in the northern Adriatic and by its ability to better capture the magnitude and shape of the observed depth-dependent velocity correlation with wind at the deeper site. Though the 36-km forced ocean model agrees better with many observed velocity statistics, the 4-km forced ocean model produces the highest correlations with observations (exceeding 0.78) at subsurface depths that are most strongly correlated with winds.
Internet negotiation patterns and an appraisal of data mining from a managerial perspective : a case study approach

Negotiation on the Internet is a new business activity that emerged with the development of the Internet and the World Wide Web. In order to study the use of software tools in cross-cultural Internet negotiations, a project named InterNeg was initiated. The INSPIRE negotiation support system of this project collected data of Internet negotiations by allowing participants to negotiate a mock case. Empirical research was conducted on these data by applying different data mining methods. This is because there were few former studies and hypotheses, and the variable number is large and they are not obviously correlated. Three data mining methods were applied to find hidden behavior patterns of Internet negotiations: Tree Rule Induction (TRI), Artificial Neural Networks (ANN) and Logistic Regression Analysis (IRA). The results showed that the numbers of offers sent, especially during the early and middle negotiation stages, are positively related to reaching agreements, while sending offers at last minute has low chance to get the compromise. Other factors, such as gender and interval time between offers, can also affect Internet negotiation results. Comparisons of results from different data mining, especially on their prediction accuracies, were also conducted. The results revealed that the TRI method enjoys the highest prediction accuracy while consuming least processing time. The ANN method has the lowest prediction accuracy. Our research results also indicated that the layer number and hidden unit number in the layers could not affect the ANN method's prediction accuracy.
A Survey on Methods for Modeling and Analyzing Integrated Biological Networks

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics” technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.
A Common Genetic Determinism for Sensitivities to Soil Water Deficit and Evaporative Demand: Meta-Analysis of Quantitative Trait Loci and Introgression Lines of Maize1[W][OA]

Evaporative demand and soil water deficit equally contribute to water stress and to its effect on plant growth. We have compared the genetic architectures of the sensitivities of maize (Zea mays) leaf elongation rate with evaporative demand and soil water deficit. The former was measured via the response to leaf-to-air vapor pressure deficit in well-watered plants, the latter via the response to soil water potential in the absence of evaporative demand. Genetic analyses of each sensitivity were performed over 21 independent experiments with (1) three mapping populations, with temperate or tropical materials, (2) one population resulting from the introgression of a tropical drought-tolerant line in a temperate line, and (3) two introgression libraries genetically independent from mapping populations. A very large genetic variability was observed for both sensitivities. Some lines maintained leaf elongation at very high evaporative demand or water deficit, while others stopped elongation in mild conditions. A complex architecture arose from analyses of mapping populations, with 19 major meta-quantitative trait loci involving strong effects and/or more than one mapping population. A total of 68% of those quantitative trait loci affected sensitivities to both evaporative demand and soil water deficit. In introgressed lines, 73% of the tested genomic regions affected both sensitivities. To our knowledge, this study is the first genetic demonstration that hydraulic processes, which drive the response to evaporative demand, also have a large contribution to the genetic variability of plant growth under water deficit in a large range of genetic material.
INTRON-LIKE ELEMENTS IN INTERGENIC REGIONS OF Caenorhabditis elegans AND Drosophila melanogaster

Pentamer frequency distributions in non-coding portions of the the Caenorhabditis elegans genome can be shown to be consistently correlated over chromosomal distances and on different chromosomes. This finding prompted us to perform a statistical analysis of pentamer usage in sets of experimentally confirmed introns and exons from C.elegans and Drosophila melanogaster. Principal component analysis reveals that in either species a set of preferred oligos (a “vocabulary”) characterises both introns and frequent intergenic elements. The vocabularies extracted for the two species exhibit interesting symmetry properties and are in part overlapping. A cross-correlation in oligonucleotide usage between C. elegans and D. melanogaster is present in non-coding regions not related by significant sequence similarity. INTRODUCTION It is a well known fact that, in eukaryotic genomes, oligonucleotide preferences differ between exons and introns. As early as 1986, Claverie and Bougueleret (1986), using the sequence data available at that time, compiled separate tables for the frequencies of overlapping oligonucleotides (1-9 bp long, shifted by 1 bp) in intron and exon sequences, and successfully constructed discriminant profiles on the basis of differences in pentamer or hexamer statistical distributions. In commenting their results, they suggest that “intron and exon sequences have a distinctive statistical ‘colour’ or ‘taste’ irrespective of the underlying translation reading frame”. In the nineties, on the other hand, in-phase hexamer usage (Claverie, Sauvaget and Bougueleret, 1990; Fickett and Tung, 1992) became part of the standard toolbox for “ab initio” gene-predicting