Miracle-dz/UniFAR-FTUs huggingface dataset:
Occupational exposure to organic solvents and K-ras mutations in exocrine pancreatic cancer.

Occupational exposure to hydrocarbon solvents has been found to be associated with an increased risk of exocrine pancreatic cancer (EPC), the human tumor with the highest prevalence of K-ras mutations. Ras genes are critical DNA targets for chemical carcinogens. We analysed the relationship between past occupational exposure to hydrocarbon solvents and mutations in codon 12 of the K-ras gene in 107 incident cases of EPC. Information on occupational factors and life-style was obtained from personal interviews conducted during hospital stay. Occupational exposure to hydrocarbon solvents (aliphatic, aromatic, chlorinated, benzene, other organic solvents) was examined using two methods: expert assessment and the Finnish job-exposure matrix (Finjem). Exposure among K-ras mutated EPC cases (n = 83) was compared with that of K-ras wild-type EPC cases (n = 24). An association between K-ras mutations and solvent exposure was observed with Finjem but barely so with the expert assessment. Over 7-fold increased odds ratios (OR) were found for every group of solvents evaluated with Finjem (all P < 0.05). On the basis of the expert assessment, K-ras mutations were significantly associated only with exposure to benzene in men (OR = 7.07, P < 0.05). When requiring exposure to have occurred according to both the experts and Finjem, over 4-fold risks were obtained for aromatic, aliphatic, and for "any hydrocarbon solvent". A significantly higher proportion of cases with a mutation from glycine to valine (GGT-->GTT) or to aspartic acid (GGT-->GAT) were exposed to a hydrocarbon solvent. The results raise the possibility that hydrocarbon solvents might be involved in the pathogenesis of EPC, possibly through indirect modulation of K-ras activation. Since this is only the first study on occupational exposures and K-ras mutations in EPC, studies able to refute or to confirm the findings are required before public health implications, if any, are assessed.
Characterization of planar microstrip discontinuities

This paper proposes a solution to deal with the problem of the errors introduced by the presence of discontinuities in planar microstrip circuits in computing some physical quantities, like the distribution of the current, by the moment method. In this approach, we combine the Moment Method (MoM) with the method of generalized equivalent circuit (MCEG) [1] for analyzing step discontinuities in microstrip circuits. The interest of the method is the translation of an electromagnetic problem to an electric one and the use of a few number of subdomain trial functions in the base of entire domain trial functions in the moment method, generally one or two functions by interface to correct the mistake of the results at the discontinuities of the structure. Furthermore, the obtained results are conformed to theory with respect to the boundary conditions and they are reasonably in good agreement with previously published data.
Finding maximum independent sets in graphs arising from coding theory

New results are presented concerning binary correcting codes, such as deletion-correcting codes, transposition-correction codes, and codes for the Z-channel. These codes are important due to the possibility of packet loss and corruption on internet transmissions. It is known that the problem of finding the largest correcting codes can be reduced to a well-known combinatorial optimization problem on graphs, the maximum independent set problem. A general scheme of organizing a local search for the maximum independent set problem is discussed. Based on the developed heuristics, an exact branch-and-bound algorithm is proposed, which is able to find exact solutions for graphs with over 500 vertices within a reasonable time.
Infants’ Characteristics and Skills: Dissolving the Nature/Nurture Dichotomy

According to an emergent group of researchers, systemic, relational and evolutionary thought is needed to understand the development of organisms’ characteristics and competences. In this paper, we fi rst introduce the prevalent view on the biological basis of behavior, based on the New Synthesis of Modern Biology. Next, we present the critical view of those who defend Extended Evolutionary Synthesis. The existing debate between the diff erent perspectives is illustrated by studies on infants’ sensorial capacities, attachment, and neonatal imitation. Possible interpretations, based on the theoretical approaches presented, lead to a refl ection on the innate/acquired dichotomy: from the prevailing view, this dichotomy is overcome because the interaction between the innate and the acquired is being considered to explain the characteristics of living beings. A further refl ection on the developmental processes involved in the emergence of behavior, on the concepts of what is learning and what is innate leads to the dissolution of this dichotomy.
Efficient daily volatility forecasts and forecast revisions using intra-day data

The objective of this thesis is to examine the predictive performance of begin-of-the-day volatility in forecasting end-of-the-day volatility using intra-day data; to search for an efficient timing window to make our forecast and to assess the process of forecast revisions during the day. For this we develop three methods that make use of diurnal patterns: the average seasonal over the last K days, an exponentially weighted average seasonal and a Flexible Fourier Form alternatively we build a method on Mincer-Zarnowitz bias adjustments. As the actual volatility is an unobserved variable several high-frequency volatility estimators: Realized Variance, Bipower variation, Realized Range, Two Time Scales and Kernel estimation are proposed to base ex-ante forecasts and measure ex-post forecasting performance. We conclude that begin-of-the-day volatility is a highly predictive measure for end-of-the-day volatility and can be improved by scaling and bias adjustments. The assessment of efficient timing samples leads us to believe that roughly the first 15-20 min are most informative, with the remark that omitting the first 0-10 minutes of return data could be beneficial as noise prevails during this interval. Furthermore, correlations between lagged and current volatility forecast revisions is found to be small yet statistically significant, implying our models inherited some build in smoothing property.
Face Recognition Hybrid Algorithm Based on Improved PCA

As it is simple and convenient to operate face recognition system, and this kind of system is without invasive, making face recognition technology become an important research direction of biometric identification. Face recognition technology involves digital image processing, pattern recognition, computer vision, neural networks and other research areas, has important research values. PCA algorithm is a common method for face feature extraction and recognition. In this paper, an improved PCA algorithm for face recognition is designed for the defect of PCA, traditional PCA algorithm combines with the processing of enhancing local mean and standard deviation of image. The improved PCA algorithm mixed with LDA and 2DPCA algorithm respectively, expands the application scope of PCA algorithm. Experimental results on ORL face database show that the methods designed in this paper have good recognition performance, have certain robustness on the uneven illumination and expression changing human faces.
UNGULATE POPULATION MODELS WITH PREDATION: A CASE STUDY WITH THE NORTH AMERICAN MOOSE'

In this article I examine moose-wolf interactions over a broad spectrum of moose densities with the primary objective to test empirically whether wolf predation can regulate moose numbers. I also present four conceptual models of moose population dy- namics and outline their specific predictions. Based on 27 studies where moose were the dominant prey species, the functional and numerical responses of wolves to changing moose density were derived using an hyperbolic, Michaelis-Menten function. Per capita killing rate was strongly related (P = .01) to moose density, as was the density of wolves (P < .0 1). Killing rate plateaued at 3.36 moose wolf (100 days) I when predators were fully satiated. The asymptotic value for wolf density was 5 8.7 animals/ 1 000 km2. Wolf predation rate, as derived from the total predator response, proved to be density dependent from 0 to 0.65 moose/km2, and inversely density dependent at higher moose densities. Predator: prey ratios reflected wolf predation rate poorly because they did not integrate the functional response. An empirical model based on these results suggests that moose would stabilize at 2.0 moose/km2 in the absence of predators, and at - 1.3 moose/km2 in the presence of a single predator, the wolf. Density-dependent food competition creates these two high- density equilibrium conditions. If moose productivity is diminished through either dete- riorating habitat quality or bear-induced early calf mortality, then a low-density equilibrium (0.2-0.4 moose/km2) is predicted. The model also suggests that when a low equilibrium develops, a "predator pit" is absent or extremely shallow, thus arguing against the appro- priateness of a predation-food two-state model. Further research on the density relationship of bear predation, on the effect of alternate prey on wolf total response, and on the regulatory impact of food competition at high moose densities, is required for a full understanding of moose demography.
Betweenness Centrality Measure in Dynamic Networks by Habiba

In this paper we propose three methods of measuring betweenness of individuals in networks which are best modeled as graphs with explicit time ordering on their edges. The betweenness centrality index is one of the basic measure in the analysis of social networks, but most of the work done for measuring the betweenness index of individuals is based on the aggregate representation of the network. Many network problems are based on fundamental relationship involving time. We incorporate the time factor in the aggregate graph representation of social networks to create dynamic networks. We define and measure the betweenness in this dynamic framework. We compare the three betweenness with the standard betweenness measure for the same network. We show that by incorporating the exact times of interactions among individuals in a network, we can better study the betweenness of individuals in the underlying network.
Neural Network Techniques for Improved Intrusion Detection in Communication Systems

In this paper we discuss the main research approaches in the development of automated and systematic methods for intrusion detection. In this critical overview, the key concept underlying the presented intrusion detection systems is that they involve pattern analysis techniques to discover consistent and useful patterns of system features that describe program and user behaviour, and the set of relevant system features to compute and recognize anomalies and known intrusions. The derived patterns compose the inputs of classification systems, which are based on statistical, structural and machine learning pattern recognition techniques. The second goal of this paper is to present a conceptual framework for systematically applying neural network techniques in intrusion detection problems, derived from the constraints put by the critical overview of pattern analysis based intrusion detection systems. The novelty of this work lies on the attempt to define a systematic and overall conceptual framework for applying neural network techniques in the design of such systems by identifying all underlying factors.
Mathematical Model of Bursting in Dissociated Purkinje Neurons

In vitro, Purkinje cell behaviour is sometimes studied in a dissociated soma preparation in which the dendritic projection has been cleaved. A fraction of these dissociated somas spontaneously burst. The mechanism of this bursting is incompletely understood. We have constructed a biophysical Purkinje soma model, guided and constrained by experimental reports in the literature, that can replicate the somatically driven bursting pattern and which hypothesises Persistent Na+ current (INaP) to be its burst initiator and SK K+ current (ISK) to be its burst terminator.
Neuroevolution through Augmenting Topologies Applied to Evolving Neural Networks to Play Othello

Many different approaches to game playing have been suggested including alpha-beta search, temporal difference learning, genetic algorithms, and coevolution. Here, a powerful new algorithm for neuroevolution, Neuro-Evolution for Augmenting Topologies (NEAT), is adapted to the game playing domain. Evolution and coevolution were used to try and develop neural networks capable of defeating an alpha-beta search Othello player. While standard evolution outperformed coevolution in experiments, NEAT did develop an advanced mobility strategy. Also we demonstrated the need for protection of long-term strategies in coevolution. NEAT established its potential to enter the game playing arena and illustrated the necessity of the mobility strategy in defeating a powerful positional player in Othello.
Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time

Printing from an NTSC source and conversion of NTSC source material to high-definition television (HDTV) format are some of the applications that motivate superresolution (SR) image and video reconstruction from low-resolution (LR) and possibly blurred sources. Existing methods for SR image reconstruction are limited by the assumptions that the input LR images are sampled progressively, and that the aperture time of the camera is zero, thus ignoring the motion blur occurring during the aperture time. Because of the observed adverse effects of these assumptions for many common video sources, this paper proposes (i) a complete model of video acquisition with an arbitrary input sampling lattice and a nonzero aperture time, and (ii) an algorithm based on this model using the theory of projections onto convex sets to reconstruct SR still images or video from an LR time sequence of images. Experimental results with real video are provided, which clearly demonstrate that a significant increase in the image resolution can be achieved by taking the motion blurring into account especially when there exists large interframe motion.
Alpine Cold Vegetation Response to Climate Change in the Western Nyainqentanglha Range in 1972–2009

The Tibetan Plateau is regarded as one of the most climatic-sensitive regions all over the world. Long-term remote sensing data enable us to monitor spatial-temporal change in this area. The vegetation changes of the western Nyainqentanglha region were detected by using RS and GIS techniques. And the vegetation coverage was derived by the NDVI-SMA (spectral mixture analysis) methods. An incensement of vegetation was observed in the mountain areas during 1972–2009 with a mean vegetation coverage of 24.87%, 35.89%, and 42.88% in 30/09/1972, 14/09/1991, and 30/08/2009, respectively. The vegetation fraction increased by 18% in the period of 1972–2009. The bin with the elevation between 4400 and 5200 m had the highest vegetation coverage. This may be the result of the mountain effect. Alpine vegetation had a trend to increase and expand to higher altitudes with the climate change in the past 40 years. The variation appears to be associated with an increase in mean temperature of 0.05°C per year and an increase in precipitation of 1.83 mm per year in the growing season of the past four decades. The results provide further evidence of alpine ecosystem change due to climate change in the central Tibetan Plateau.
Racial identity development among college students : an examination of five students' perspectives

approved: This research sought to "get at" students' understandings of their own racial identity development. The limited degree with which participants have been heard and honored in research activities points to the need to inquire about their experiences first-hand, respect their stories, and consider their voices among, or in spite of, the current models that exist. My research efforts drew upon an interpretive design and were enlightened by a variety of theoretical perspectives, mainly critical theory and postmodern feminism. My goals throughout the process were twofold: (a) challenge the dominant discourse and current research as it relates to our knowledge of racial identity development, and (b) employ research methods that are truly liberating in their incorporation of participants' voices and perspectives. In this way, I consider both the findings and research process equally important in this dissertation. The primary data collection method employed within this design was a dialogic interview process with five student participants. I facilitated individual dialogues and group dialogues with the student participants where we discussed a variety of topics including campus climate for students of color, common shared experiences, personal identity issues, and emergent issues and themes. Dialogues were not "structured" or "standardized" in any way, but were primarily emergent. I employed a basic thematic analysis method, with participants individually and collectively involved throughout the dialogues in the development, confirmation, and rejection of themes as they relate to their own dialogues. As a result of the analyses processes, five themes were identified. In drawing on a cross-case analysis of the participants' stories, these five students' stories appear to challenge the dominant discourse and traditional research in terms of the linearity, universality, and predictability of current racial identity development models. These students' stories reflect a more holistic, fluid, cognitive, and complex process, one that has shared experiences, yet defies the "development" of all-purpose models. Given that we as educators tend to find comfort with and rely upon the predictability and familiarity of traditional developmental models, the implications for being effective and honest in our interaction with students are far-reaching and warrant considerable attention. © by Jessica White April 20, 2001 All Rights Reserved Racial Identity Development Among College Students: An Examination of Five Students' Perspectives
The Effect of Different Mixing Methods on the pH and Solubility of Mineral Trioxide Aggregate and Calcium-Enriched Mixture

Introduction: The aim of this study was to evaluate the effect of different mixing techniques on the pH and solubility of mineral trioxide aggregate (MTA) and calcium-enriched mixture (CEM). Methods and Materials: Five samples were prepared from each biomaterial with different mixing techniques including hand-, amalgamator- or ultrasonic-mixing and were then placed in pre-weighted plastic tubes to determine their pH values. Each tube was then incubated in 10 mL deionized distilled water for 1 h at 37ºC. An electrode was placed in the fluid in each flask at 24ºC and the pH was recorded. In the next stage, six samples from each mixing technique/material were separately placed in glass bottles containing 50 mL of distilled water at 37ºC for 1 h and were let dry for 1 h at 37ºC. The samples’ weights were measured and recorded twice. The procedure was repeated at 1-, 7- and 21-day intervals. Data were analyzed with the repeated measures ANOVA (for solubility) and two-way ANOVA (for pH) and then the post-hoc Tukey’s test was done. Results: The pH of the materials was not significantly affected by mixing methods. (P=0.8 for CEM and P=0.1 for MTA). The solubility of all test groups was within the acceptable range (=3%). However, the solubility of CEM at 1- and 21-day intervals was significantly different (P=0.03 for 1 day and P=0.001 for 21 days). Different mixing techniques had significant effects on the solubility of MTA at the three time points (P=0.004, 0.003 and 0.002 for 1-, 7- and 21-day intervals, respectively). Conclusion: The pH of biomaterials was not influenced by the mixing technique and their solubility was within the acceptable range.
The Correlation Analysis of Two Common Polymorphisms in STAT6 Gene and the Risk of Asthma: A Meta-Analysis

Background Several studies have reported that the GT dinucleotide repeat length polymorphism and the G2964A polymorphism in signal transducer and activator of transcriptional factor 6 gene are associated with asthma susceptibility, but others have conflicting results. Our meta-analysis aimed to elucidate the emerging paradigms. Methods We searched PUBMED, EMBASE, ISI web of knowledge, Chinese National Knowledge Infrastructure, and Wanfang databases. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were used to evaluate the strength of association. We applied Bonferroni step-down and Benjamini-Hochberg step-up methods to adjust the values for multiple comparisons. Results A total of 12 individual studies in 11 articles were included in the meta-analysis. For GT repeat polymorphism, the S allele had approximately 45% increased risk of asthma (S vs. L: OR = 1.45, 95% CI = 1.22–1.71, P UNCORRECTED <0.001, PBon <0.001, PFDR <0.001). Further analysis indicated that GT13 and GT14 contributed to asthma risk, whereas GT15 and GT16 were protective (GT13 vs. GT15: OR = 1.38, 95% CI = 1.16–1.65, P UNCORRECTED = 0.001, PBon = 0.005, PFDR = 0.002). Similar results were obtained in the subgroup analysis of Asian population. G2964A polymorphism analysis showed that the AA genotype moderately increased the risk of asthma by 47% compared with the GG genotype (OR = 1.47, p = 0.068) in Chinese population, whereas the 2964A allele moderately increased the risk of asthma in Chinese population by 18% (2964A vs. 2964G: OR = 1.18, p = 0.08). However, none of the associations reached statistically significant levels particularly after correction for multiple testing. Conclusions This meta-analysis suggests that S allele (GT13 and GT14) of the GT repeat polymorphism confers significant risks to asthma. However, the G2964A polymorphism does not have an association with the susceptibility to asthma.
Variants Near MC4R Are Associated With Obesity and Influence Obesity-Related Quantitative Traits in a Population of Middle-Aged People: Studies of 14,940 Danes

OBJECTIVE— Variants downstream of the melanocortin-4 receptor gene (MC4R) have been reported to associate with obesity. We examined rs17782313, rs17700633, rs12970134, rs477181, rs502933, and rs4450508 near MC4R for association with obesity-related quantitative traits, obesity, and type 2 diabetes in Danish individuals. RESEARCH DESIGN AND METHODS— The variants were investigated for association with obesity-related quantitative traits in 5,807 population-based sampled individuals, obesity in 14,940 individuals, and type 2 diabetes in 8,821 individuals. RESULTS— The minor risk alleles of rs17782313, rs17700633, and rs12970134 were associated with BMI (effect per allele 0.25 kg/m2, P = 0.01; 0.23, P = 0.01; and 0.31, P = 7 × 10−4, respectively), waist circumference (0.67 cm, P = 0.006; 0.53, P = 0.02; and 0.85, P = 3 × 10−4), and body weight (1.04 kg, P = 6 × 10−4; 0.71, P = 0.01; and 1.16, P = 8 × 10−5). In case-control studies of obesity defined by BMI, the minor C-allele of rs17782313 was associated with overweight/obesity and obesity (odds ratio [OR] 1.09, P = 0.006 and OR 1.12, P = 0.003, respectively). Similarly, the minor A-allele of rs17700633 was associated with overweight/obesity and obesity (1.12, P = 8 × 10−5 and 1.16, P = 2 × 10−5), and the minor A-allele of rs12970134 was also associated with overweight/obesity and obesity (1.13, P = 2 × 10−5 and 1.15, P = 6 × 10−5). rs477181, rs502933, and rs4450508 were not significantly associated with obesity in the Danish population. The frequency of the minor risk alleles of rs17782313 and rs12970134 was higher among patients with type 2 diabetes than among glucose-tolerant individuals (OR 1.08, P = 0.08 and 1.08, P = 0.06, respectively); however, these borderline associations were abolished after adjustment for BMI. CONCLUSIONS— rs17782313, rs17700633, and rs12970134 near MC4R associate with measures of obesity in Danish individuals.