Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex Steroid Hormone Single-Nucleotide Polymorphisms, Pesticide Use, and the Risk of Prostate Cancer: A Nested Case–Control Study within the Agricultural Health Study

Experimental and epidemiologic investigations suggest that certain pesticides may alter sex steroid hormone synthesis, metabolism or regulation, and the risk of hormone-related cancers. Here, we evaluated whether single-nucleotide polymorphisms (SNPs) involved in hormone homeostasis alter the effect of pesticide exposure on prostate cancer risk. We evaluated pesticide–SNP interactions between 39 pesticides and SNPs with respect to prostate cancer among 776 cases and 1,444 controls nested in the Agricultural Health Study cohort. In these interactions, we included candidate SNPs involved in hormone synthesis, metabolism or regulation (N = 1,100), as well as SNPs associated with circulating sex steroid concentrations, as identified by genome-wide association studies (N = 17). Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Multiplicative SNP–pesticide interactions were calculated using a likelihood ratio test. We translated p-values for interaction into q-values, which reflected the false discovery rate, to account for multiple comparisons. We observed a significant interaction, which was robust to multiple comparison testing, between the herbicide dicamba and rs8192166 in the testosterone metabolizing gene SRD5A1 (p-interaction = 4.0 × 10−5; q-value = 0.03), such that men with two copies of the wild-type genotype CC had a reduced risk of prostate cancer associated with low use of dicamba (OR = 0.62 95% CI: 0.41, 0.93) and high use of dicamba (OR = 0.44, 95% CI: 0.29, 0.68), compared to those who reported no use of dicamba; in contrast, there was no significant association between dicamba and prostate cancer among those carrying one or two copies of the variant T allele at rs8192166. In addition, interactions between two organophosphate insecticides and SNPs related to estradiol metabolism were observed to result in an increased risk of prostate cancer. While replication is needed, these data suggest both agonistic and antagonistic effects on circulating hormones, due to the combination of exposure to pesticides and genetic susceptibility, may impact prostate cancer risk.
Unifying search algorithms for CSP

Ginsberg and McAllester [5] have shown that systematic and nonsystematic search algorithms can be quite close. In this paper, we go one step further in the direction of understanding the relationships between systematic and nonsystematic search algorithms by introducing the PLM model. We introduce a generic search algorithm for solving csp, and we show that search algorithms can be decomposed in primitives. A reduced set of primitives is sufficient to express almost all existing search algorithms. The knowledge of these primitives is of great help for comparing search algorithms as well as for configuring new search algorithms.
Improved Melody Recognition Performance of a Cochlear Implant Speech Processing Strategy Using Instantaneous Frequency Encoding Based on Teager Energy Operator

We present a speech processing strategy incorporating instantaneous frequency (IF) encoding for the enhancement of melody recognition performance of cochlear implants. For the IF extraction from incoming sound, we propose the use of a Teager energy operator (TEO), which is advantageous for its lower computational load. From time-frequency analysis, we verified that the TEO-based method provides proper IF encoding of input sound, which is crucial for melody recognition. Similar benefit could be obtained also from the use of a Hilbert transform (HT), but much higher computational cost was required. The melody recognition performance of the proposed speech processing strategy was compared with those of a conventional strategy using envelope extraction, and the HT-based IF encoding. Hearing tests on normal subjects were performed using acoustic simulation and a musical contour identification task. Insignificant difference in melody recognition performance was observed between the TEO-based and HT-based IF encodings, and both were superior to the conventional strategy. However, the TEO-based strategy was advantageous considering that it was approximately 35% faster than the HT-based strategy.
Coniferyl Aldehyde Attenuates Radiation Enteropathy by Inhibiting Cell Death and Promoting Endothelial Cell Function

Radiation enteropathy is a common complication in cancer patients. The aim of this study was to investigate whether radiation-induced intestinal injury could be alleviated by coniferyl aldehyde (CA), an HSF1-inducing agent that increases cellular HSP70 expression. We systemically administered CA to mice with radiation enteropathy following abdominal irradiation (IR) to demonstrate the protective effects of CA against radiation-induced gastrointestinal injury. CA clearly alleviated acute radiation-induced intestinal damage, as reflected by the histopathological data and it also attenuated sub-acute enteritis. CA prevented intestinal crypt cell death and protected the microvasculature in the lamina propria during the acute and sub-acute phases of damage. CA induced HSF1 and HSP70 expression in both intestinal epithelial cells and endothelial cells in vitro. Additionally, CA protected against not only the apoptotic cell death of both endothelial and epithelial cells but also the loss of endothelial cell function following IR, indicating that CA has beneficial effects on the intestine. Our results provide novel insight into the effects of CA and suggest its role as a therapeutic candidate for radiation-induced enteropathy due to its ability to promote rapid re-proliferation of the intestinal epithelium by the synergic effects of the inhibition of cell death and the promotion of endothelial cell function.
Pareto repeated weighted boosting search for multiple-objective optimisation

A guided stochastic search algorithm, known as the repeated weighted boosting search (RWBS), offers an effective means for solving the difficult single-objective optimisation problems with non-smooth and/or multi-modal cost functions. Compared with other global optimisation solvers, such as the genetic algorithms (GAs) and adaptive simulated annealing, RWBS is easier to implement, has fewer algorithmic parameters to tune and has been shown to provide similar levels of performance on many benchmark problems. This contribution develops a novel Pareto RWBS (PRWBS) algorithm for multiple objective optimisation applications. The performance of the proposed PRWBS algorithm is compared with the well-known non-dominated sorting GA (NSGA-II) for multiple objective optimisation on a range of benchmark problems, and the results obtained demonstrate that the proposed PRWBS algorithm offers a competitive performance whilst retaining the benefits of the original RWBS algorithm.
Applying Theory-Driven Approaches to Understanding and Modifying Clinicians ' Behavior : What Do We . Know ?

Obiective: Despite major recent research advances, large gaps exist between accepted mental health knowledge and clinicians' real-world . practices. Although hundreds of studies have successfully utilized basic behavioral science theories to understand, predict, and change patients' health behaviors, the extent to which these theories-most notably the theory of reasoned action (TRA) and its extension, the theory of planned behavior (TPB)-have been applied to understand and change clinician behavior is unclear. This article reviews the applica. tion of theory-driven approaches to understanding and changing clinician behaviors. Methods: MEDLINE and PsycINFO databases were searched, along with bibliographies, textbooks on health behavior or public health, and references from experts, to find article titles that describe theory-driven approaches (TRA or TPB) to understanding and modifying health professionals' behavior. Results: A total of 19 articles that detailed 20 studies described the use ofTRA or TPB and clinicians' behavior. Eight articles describe the use of TRA or TPB with physicians, four relate to nurses, three relate to pharmacists, and two relate to health workers. Only two articles applied TRA or TPB to mental health clinicians. The body of work shows that different constructs of TRA or TPB predict intentions and behavior among different groups of clinicians and for different behaviors and guidelines. Conclusions: The number of studies on this topic is extremely limited, but they offer a rationale and a direction for future research as well as a theoretical basis for increasing the specificity and efficiency of clinician-targeted interventions.
A three factor model of followership, part 2: research on the three factor model and its application to team roles

Purpose The purpose of this three-part paper is to point out that while there is an extensive body of theory and research on leadership, less attention has been given to other roles, particularly follower roles. The authors outlined a three factor model of leadership and suggested it could be applied to followership. In the second part of the paper, the authors present empirical evidence on the three factor model of leadership and its application to the full range of team roles, including follower, co-worker and leader roles. In the third part of the paper the authors present and discuss further evidence specifically on follower behaviour. Design/methodology/approach In this part of the paper, the authors present evidence on the internal reliability and external validity of three self-assessment instruments, two on leadership behaviour and one on team role behaviours. Evidence is provided from a diverse group of managers, mainly in the UK public sector. Individuals completed a variety of self-assessment instruments and, in some cases, had 360 degree assessments completed on them, and provided evidence on contextual variables. The methodology involves looking at the degree of correlation, and its statistical significance, between variables. Findings The three self-assessment instruments, two on leadership and one on team roles, were found to possess satisfactory levels of both internal reliability and external validity, consistent with the three factor model. These findings lent support to the three factor model of leadership, to its extension and application to followership, and to the reliability and validity of the three self-assessment instruments. Research limitations/implications Effective organisations need effective followers and effective leaders. Moreover, the skills of the effective leader develop out of and build on those of the effective follower. The research was based mainly on individuals in the public sector in the UK. It would be useful to extend such research to other contexts. Practical implications Given that the skills of the effective leader develop out of and build on those of the effective follower, the training of effective followers is seen to underpin that of effective leaders. Social implications The finding that the skills of the effective leader develop out of build on those of the effective follower challenges the widely held pre-occupation with leadership, the idea that leadership is qualitatively different from and superior to followership. Thus it challenges the cult of leadership. Originality/value This paper is the first published attempt to successfully apply the three factor model of leadership to team roles in general, including follower, co-worker and leader roles. In the third part of this paper, the three factor model is applied specifically to follower roles. Ways of measuring leader and follower roles are developed and used to identify follower behaviours, as well as to make possible the identification of behaviours valued when used by followers, based on 360 degree assessments.
A recommendation approach for digital TV systems based on multimodal features

Digital TV content providers are becoming widespread, with hundreds of programs available each day. The information overload makes difficult for the user to find programs of interest. To help the user, Recommendation Systems (RS) are a popular path. However, applying RS to some environments is not easy, either due to the lack or insufficiency of data to create accurate recommendations. In Digital TV domain, the main information available to make recommendations is the Electronic Program Guide (EPG) that is limited, containing only reduced textual data, making difficult to get an accurate recommendation using standard techniques. In this work we introduce a multimodal approach to recommend Digital TV programs, combining EPG text and visual information. We experimentally demonstrated that using multimodal features improved accuracy when compared with RS standard approaches.
Simple Ant Routing Algorithm

A mobile ad-hoc network has limited and scarce resources and thus routing protocols in such environments must be kept as simple as possible. This paper presents a MANET routing protocol, inspired in insect societies' biological models, the simple ant routing algorithm (SARA), which provides a simple and efficient routing solution. SARA uses a controlled neighbour broadcast route discovery procedure, aimed at reducing the routing overhead of existing solutions. In this controlled neighbour broadcast strategy, every node collects routing information received from its neighbours and updates its own routing information accordingly, but only one of them is responsible for forwarding this information. The selection of the node which will be responsible for this task is made. Simulation results have shown that, besides reducing the overhead incurred by the routing protocol, SARA also provides a solution to detect early congestion link situations and tries to re-route the traffic through alternative routes (if available).
In-game tactic adaptation for interactive computer games

Computer controlled characters in games typically lack the ability to adapt to situations that were not envisaged by the game artificial intelligence (AI) programmer. Consequently game characters tend to be lifeless and susceptible to repetitive and predictable behaviour. The focus of this work is to provide practical approaches to in-game learning and thus produce NPCs that are more believable and responsive to the human player. In this paper, we present an approach to online tactic learning and adaptation for autonomous agents in virtual environments. The design of the tactic adaptation architecture was influenced by commercial and theoretical approaches to the organisation and control of NPC behaviour in FPS games. The system is applied to Unreal Tournament using the GameBots API and evaluated in FPS game scenarios. The results show that the architecture is capable of successfully adapting NPC tactic selection and illustrate how a multi instance approach to organising tactic libraries can enhance adaptation.
P-CUBE: Block Type Programming Tool for Visual Impairments

We developed P-CUBE, which is an education tool for programming mobile robots, and confirmed the system operation. Elementary school and junior high school students have recently begun to learn programming using PCs. Students who are taught how to create programs are often required to use an interface of a personal computer. This method keeps visually impaired and younger children from learning programming. Furthermore, the cost associated with PCs is often prohibitive. In an attempt to address these problems, we have developed P-CUBE, which is a new programming education tool. P-CUBE consists of cubic blocks and a program mat. The user creates a program by laying out surface gap blocks on the program mat based on the structure of the algorithm. RFID readers are arranged in the programming mat, and the information of the RFID tags in the blocks is read in order to specify the location of each block. P-CUBE is considered to be nearly indestructible because of its simple structure and is expected to be used as an educational toy for students.
A novel approach for content based image retrieval using hybrid filter techniques

In general the users are in need to retrieve images from a collection of database images from variety of domains. In earlier phase this need was satisfied by retrieving the relevant images from different database simply. Where there is a bottleneck that the images retrieved was not relevant much to the user query because the images were not retrieved based on content where another drawback is that the manual searching time is increased. To avoid this (CBIR) is developed it is a technique for retrieving images on the basis of automatically -derived features such as colour, texture and shape of images. To provide a best result in proposed work we are implementing high level filtering where we are using the Anisotropic Morphological Filters, hierarchical Kaman filter and particle filter proceeding with feature extraction method based on color and gray level feature and after this the results were normalized.
Constraints on bimetric gravity from Big Bang nucleosynthesis

Bimetric gravity is a ghost-free and observationally viable extension of general relativity, exhibiting both a massless and a massive graviton. The observed abundances of light elements can be used to constrain the expansion history of the Universe at the period of Big Bang nucleosynthesis. Applied to bimetric gravity, we readily obtain constraints on the theory parameters which are complementary to other observational probes. For example, the mixing angle between the two gravitons must satisfy θ . 18◦ in the graviton mass range mFP & 10−16 eV/c, representing a factor of two improvement compared with other cosmological probes.
Joint Determination of Product and Labor Market Policies in a Model of Rent Creation and Division

We find that, across OECD countries, there is a strong link between the rate of self employment and the intensity of both product- and labor-market regulations. The sensitivity of the self-employment rate to regulation appears greater in terms of statistical significance and magnitude than the sensitivity of unemployment. We then develop a model of rent creation and division featuring product and labor market policies. The calibrated model accounts broadly for these features of the data. We also use the model to look at the effect of labor market and product market regulations on the different constituencies in the economy.
UNDERSPECIFIED FEATURE MODELS FOR PRONUNCIATION VARIATIO N IN ASR

In the 1990s, several studies showed that if we could just predict correctly when to include alternate pronunciation s of words in ASR lexica, we could greatly reduce error rates for conversational speech tasks (i.e., Switchboard). But i t is clear that the field has thus far failed to reach that potential. Many scholars model pronunciation variation via a substitution of one phonetic sequence for another (either b y replacing entries in a pronunciation lexicon, or dynamical ly modifying phonetic sequences in response to contextual fac tors such as speaking rate). In 1999, Ostendorf called for th e community to move beyond the “beads-on-a-string” model of pronunciation, and outlined some promising directions for research. In this paper, I continue the argument that we should move away from phonetic representations, and examine how we might model phonetic variation through phonological features. By expressing pronunciation varia tion in terms of partially underspecified phonological feature bundles, we might be able to better model lexical access. I also review some recent approaches that integrate some of these concepts.
Learning Words from Images and Speech

Previous research has shown that an efficient acoustic model can be trained from wordlevel annotations alone [1]. Here, we explore the possibility of learning both an acoustic model and a word/image association from multi-modal co-occurrences between speech and pictures alone (a task known as cross-situational learning [2]). Our work is inspired by the observation that infants achieve spontaneously this kind of correspondence during their first year of life, e.g. with pictures of natural kinds like animals [3].
Sum-Product decoding of convolutional code for wireless LAN standard

This paper proposes decoding methods for the convolutional code for the wireless Local Area Network (LAN) standard by using sum-product algorithm (SPA). The code is non-systematic. For this code, the conventional SPA decoding cannot provide good decoding performance. This paper shows the reason. The reason is that the conventional SPA uses a Tanner graph including information bits, however, no received signals for information bits are available for the code. This paper proposes two-step SPA decoding method (Proposed Method 1: PM1). The PM1 is as follows. (1) Only parity bits are decoded by SPA. (2) With decoded parity bits, information bits are decoded by SPA. The PM1 can provide much better decoding performance in comparison to the conventional SPA. Furthermore, this paper proposes the combined use of PM1 and method of [4] (Proposed Method 2: PM2). The PM2 provides good performance near BCJR performance. At BER=10−5, PM2 performance is only 0.7[dB] inferior to BCJR performance.
Performance Enhancement of Intrusion Detection using Neuro-Fuzzy Intelligent System

This research work aims at developing hybrid algorithms using data mining techniques for the effective enhancement of anomaly intrusion detection performance. Many proposed algorithms have not addressed their reliability with varying amount of malicious activity or their adaptability for real time use. The study incorporates a theoretical basis for improvement in performance of IDS using K-medoids Algorithm, Fuzzy Set Algorithm, Fuzzy Rule System and Neural Network techniques. Also statistical significance of estimates has been looked into for finalizing the best one using DARPA network traffic datasets.
Religiousness and cognitive emotion regulation strategies in adolescence

Human religiousness is a complex and multidimensional reality embedded in basic human needs and connected with people’s desires, anxieties, and hopes. The aim of the paper is to assess religiousness, considered in terms of meaning and cognitive emotion regulation strategies, in adolescents. The religious meaning system is a multidimensional construct encompassing convictions, beliefs, emotional connotations, and the rules of worship. In situations of negative events or unpleasant experiences, young people resort to various coping strategies. Cognitive emotion regulation strategies concern relatively stable conscious ways of regulating emotions, which consist in mentally handling the incoming information that evoke emotions. The study concerned young people aged 13 to 16 years (N = 130). Religiousness was assessed using D. Krok’s Religious Meaning System Questionnaire (RMSQ) and cognitive coping strategies were measured using the Cognitive Emotion Regulation Questionnaire (CERQ) by N. Garnefski and V. Kraaij. The results show that there are relations between religiousness, understood in terms of the young participants’ meaning system, and adaptive as well as maladaptive emotion regulation strategies. The results of the study are discussed in the light of theory and research.