bluuebunny/crossref_metadata_2025_split huggingface dataset:
Psychological models in sport psychology: A preliminary investigation
Authors: Collins Dave , Winter Stacy 
Published: November 2019

AbstractApplied psychology is characterised by a variety of theoretical models, informing distinct approaches to classification, explanation, and intervention in service‐delivery. Such theoretical or psychological models include behavioural, biological, cognitive, humanistic, psychodynamic, and social paradigms, with exposure to these models and attitude formation occurring within the structured university‐based stage of sport psychology development. It is, therefore, important for the sport psychological domain to investigate developing attitudes, given these models inform subsequent professional practice and decision making. Accordingly, the present study explored the attitudes of Stage‐1 sport psychology students through a modified form of the Maudsley Attitude Questionnaire (34 males, M age = 24.71 years, SD = 7.23 and 42 females, M age = 24.76 years, SD = 6.20). The questionnaire was designed to assess attitudes across eight psychological models (e.g. biological, cognitive) and four sport psychology issues (pre‐performance anxiety, a lack of confidence, depression, and eating disorders). Analyses of variance demonstrated significant main, model, and interaction effects. No one psychological model was endorsed by all respondents, with model endorsement varying signiﬁcantly as a function of the issue presented. Principal Axis Factoring revealed a large contribution attributable to cognitive–behavioural and ‘eclectic’ (mixed elements of social constructionism, biological, and psychodynamic) models. In contrast, the spiritual model represented low levels of participant endorsement and application. Investigation of Stage‐1 students can promote an evidence‐based understanding on currently developing attitudes and inform the development of sport psychology education, supervision of training routes, and subsequent professional delivery.
Improving the space-bandwidth product of structured illumination microscopy using a transillumination configuration
Authors: Joseph Joby , Faiz Kandankulangara P , Lahrberg Marcel , Tinguely Jean-Claude , Ahluwalia Balpreet Singh 
Published: November 2019

Abstract
Applying structured instead of plane illumination in widefield optical fluorescence microscopy can improve the spatial resolution beyond what is known as the Abbe limit. In general it is not only the resolution of an imaging system that is of interest but also its field of view (FOV). These two parameters are expressed in the space-bandwidth product (SBP). Here we introduce a modified structured illumination microscopy (SIM) approach that offers a larger SBP than any other available implementation. This is achieved through a transillumination geometry instead of the typical epifluorescent configuration. Compared to conventional SIM, the illumination path is decoupled from the objective lens by using a multi-mirror setup to generate the sinusoidal interference pattern for structured illumination in transmission mode. The spatial frequency of the illumination pattern can be controlled by changing the angle of the mirrors, achieving comparably fine patterns over a large FOV. In this work simulation results demonstrate the potential resolution improvement to be expected by the suggested implementation. Preliminary experimental results demonstrate phase-shifting ability and the stability of fringe frequencies over a large FOV of (

16 mm2) at different numerical apertures, fulfilling the prerequisites for SIM acquisition.
High-speed x-ray tomography of silo discharge
Authors: Stannarius Ralf , Martinez Diego Sancho , Börzsönyi Tamás , Bieberle Martina , Barthel Frank , Hampel Uwe 
Published: November 2019

Abstract
The outflow of granular materials from storage containers with narrow outlets is studied by means of ultrafast x-ray computed tomography (UFXCT). The used acquisition speed of this tomograph is high enough to allow high-speed recording of two horizontal cross sections (each of them at a rate of 1000 images per second) of the container during the discharge of material. Analyzing space-time plots that were generated from the tomograms, we retrieve velocity profiles and packing structures in the container. We compare hard spherical grains with soft, low-friction hydrogel spheres. Their flow profiles are qualitatively different. While the hard spheres form stagnant zones at the container side walls, the hydrogel spheres with extremely low friction coefficient flow in all regions of the container. Moreover, a shell-like positional arrangement of the soft spheres induced by the container walls is revealed. The results obtained for the flow field structure confirm earlier conclusions drawn from sequences of x-ray tomograms of clogged states.
Quantum limits of localisation microscopy
Authors: Bisketzi Evangelia , Branford Dominic , Datta Animesh 
Published: November 2019

Abstract
Localisation microscopy of multiple weak, incoherent point sources with possibly different intensities in one spatial dimension is equivalent to estimating the amplitudes of a classical mixture of coherent states of a simple harmonic oscillator. This enables us to bound the multi-parameter covariance matrix for an unbiased estimator for the locations in terms of the quantum Fisher information matrix, which we obtained analytically. In the regime of arbitrarily small separations we find it to be no more than rank two—implying that no more than two independent parameters can be estimated irrespective of the number of point sources. We use the eigenvalues of the classical and quantum Fisher information matrices to compare the performance of spatial-mode demultiplexing and direct imaging in localisation microscopy with respect to the quantum limits.
Identification of Genes That Result in High Mortality of Oryctes rhinoceros (Scarabaeidae: Coleoptera) When Targeted Using an RNA Interference Approach: Implications for Large Invasive Insects
Authors: Watanabe Shizu , Adams Brandi-Leigh , Kong Alexandra , Masang Nelson , Vowell Tomie , Melzer Michael 
Published: November 2019

Abstract
Coconut rhinoceros beetle, Oryctes rhinoceros (L., 1758), is a large scarab beetle native to Southeast Asia and a major pest of coconut (Cocos nucifera) and oil (Elaeis guineensis) palms in its invaded range. Few tools are available for coconut rhinoceros beetle management, particularly for an emerging haplotype with resistance to known strains of Oryctes rhinoceros nudivirus, the traditional biological control agent used in coconut rhinoceros beetle management programs. RNA interference (RNAi) represents an emerging tool for insect pest control that exploits an existing pathway for gene regulation in the target organism. In this study, we evaluated RNAi as a potential tool for coconut rhinoceros beetle management. Using transcriptome data generated from gut tissue of early instar larvae, we identified 24 RNAi target sequences that were either highly expressed or had demonstrated efficacy in other insect systems. Double-stranded (ds)RNAs ranging from 249 to 297 bp in length were generated for 23 of these target sequences and 150 ng were microinjected into coconut rhinoceros beetle 1st, 2nd, and 3rd instar larvae and adults. Five of these dsRNAs that targeted genes putatively encoding V-type ATPase, polyadenylate binding protein, and three forms of actin induced 30.8–100% mortality within 14 days post injection (dpi). Microinjection of 2nd instars with 10 and 100 ng of these same five dsRNAs induced 20–100% and 80–100% mortality at 7 and 14 dpi, respectively. These results indicate RNAi should be explored as a possible management option for coconut rhinoceros beetle. Coconut rhinoceros beetle may also represent a model species for using RNAi in the management of large invasive insect species.
Long-Term Trajectories of Body Weight, Diet, and Physical Activity From Midlife Through Late Life and Subsequent Cognitive Decline in Women
Authors: Wagner Maude , Grodstein Francine , Proust-Lima Cécile , Samieri Cécilia 
Published: November 2019

AbstractHealthy lifestyles are promising targets for prevention of cognitive aging, yet the optimal time windows for interventions remain unclear. We selected a case-control sample nested within the Nurses’ Health Study (starting year 1976, mean age = 51 years), including 14,956 women aged ≥70 years who were free of both stroke and cognitive impairment at enrollment in a cognitive substudy (1995–2001). Cases (n = 1,496) were women with the 10% worst slopes of cognitive decline, and controls (n = 7,478) were those with slopes better than the median. We compared the trajectories of body mass index (weight (kg)/height (m)2), alternate Mediterranean diet (A-MeDi) score, and physical activity between groups, from midlife through 1 year preceding the cognitive substudy. In midlife, cases had higher body mass index than controls (mean difference (MD) = 0.59 units, 95% confidence interval (CI): 0.39, 0.80), lower physical activity (MD = –1.41 metabolic equivalent of task–hours/week, 95% CI: –2.07, –0.71), and worse A-MeDi scores (MD = –0.16 points, 95% CI: –0.26, –0.06). From midlife through later life, compared with controls, cases had consistently lower A-MeDi scores but a deceleration of weight gain and a faster decrease in physical activity. In conclusion, maintaining a healthy lifestyle since midlife may help reduce cognitive decline in aging. At older ages, both deceleration of weight gain and a decrease in physical activity may reflect early signs of cognitive impairment.
Evolutionary history of a relict conifer, Pseudotaxus chienii (Taxaceae), in south-east China during the late Neogene: old lineage, young populations
Authors: Kou Yixuan , Zhang Li , Fan Dengmei , Cheng Shanmei , Li Dezhu , Hodel Richard G J , Zhang Zhiyong 
Published: November 2019

AbstractBackground and AimsMany monotypic gymnosperm lineages in south-east China paradoxically remain in relict status despite long evolutionary histories and ample opportunities for allopatric speciation, but this paradox has received little attention and has yet to be resolved. Here, we address this issue by investigating the evolutionary history of a relict conifer, Pseudotaxus chienii (Taxaceae).MethodsDNA sequences from two chloroplast regions and 14 nuclear loci were obtained for 134 samples. The demographic history was inferred and the contribution of isolation by environment (IBE) in patterning genetic divergence was compared with that of isolation by distance (IBD).Key ResultsThree genetic clusters were identified. Approximate Bayesian computation analyses showed that the three clusters diverged in the late Pliocene (~3.68 Ma) and two admixture events were detected. Asymmetric gene flow and similar population divergence times (~ 3.74 Ma) were characterized using the isolation with migration model. Neither IBD nor IBE contributed significantly to genetic divergence, and the contribution of IBE was much smaller than that of IBD.ConclusionsThese results suggest that several monotypic relict gymnosperm lineages like P. chienii in south-east China did not remain in situ and undiversified for millions of years. On the contrary, they have been evolving and the extant populations have become established more recently, having insufficient time to speciate. Our findings provide a new perspective for understanding the formation and evolution of the relict gymnosperm flora of China as well as of the Sino-Japanese Flora.
Deep learning for mining protein data
Authors: Shi Qiang , Chen Weiya , Huang Siqi , Wang Yan , Xue Zhidong 
Published: November 2019

AbstractThe recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.
HypercubeME: two hundred million combinatorially complete datasets from a single experiment
Authors: Esteban Laura A , Lonishin Lyubov R , Bobrovskiy Daniil M , Leleytner Gregory , Bogatyreva Natalya S , Kondrashov Fyodor A , Ivankov Dmitry N 
Published: November 2019

Abstract

Motivation
Epistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a ‘combinatorially complete dataset’. So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets.

Results
We present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199 847 053 unique combinatorially complete genotype combinations of dimensionality ranging from 2 to 12. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data.

Availability and implementation
https://github.com/ivankovlab/HypercubeME.git.

Supplementary information
Supplementary data are available at Bioinformatics online.
An integrative approach for fine-mapping chromatin interactions
Authors: Jaroszewicz Artur , Ernst Jason 
Published: November 2019

Abstract

Motivation
Chromatin interactions play an important role in genome architecture and gene regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g. 5-25 kb), which is substantially coarser than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions.

Results
To predict the sources of Hi-C-identified interactions at a high resolution (e.g. 100 bp), we developed a computational method that integrates data from DNase-seq and ChIP-seq of TFs and histone marks. Our method, χ-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. χ-CNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show χ-CNN predictions enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological significance. χ-CNN provides an approach for analyzing important aspects of genome architecture and gene regulation at a higher resolution than previously possible.

Availability and implementation
χ-CNN software is available on GitHub (https://github.com/ernstlab/X-CNN).

Supplementary information
Supplementary data are available at Bioinformatics online.
Improved linking of motifs to their TFs using domain information
Authors: Baumgarten Nina , Schmidt Florian , Schulz Marcel H 
Published: November 2019

Abstract

Motivation
A central aim of molecular biology is to identify mechanisms of transcriptional regulation. Transcription factors (TFs), which are DNA-binding proteins, are highly involved in these processes, thus a crucial information is to know where TFs interact with DNA and to be aware of the TFs’ DNA-binding motifs. For that reason, computational tools exist that link DNA-binding motifs to TFs either without sequence information or based on TF-associated sequences, e.g. identified via a chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiment.
In this paper, we present MASSIF, a novel method to improve the performance of existing tools that link motifs to TFs relying on TF-associated sequences. MASSIF is based on the idea that a DNA-binding motif, which is correctly linked to a TF, should be assigned to a DNA-binding domain (DBD) similar to that of the mapped TF. Because DNA-binding motifs are in general not linked to DBDs, it is not possible to compare the DBD of a TF and the motif directly. Instead we created a DBD collection, which consist of TFs with a known DBD and an associated motif. This collection enables us to evaluate how likely it is that a linked motif and a TF of interest are associated to the same DBD. We named this similarity measure domain score, and represent it as a P-value. We developed two different ways to improve the performance of existing tools that link motifs to TFs based on TF-associated sequences: (i) using meta-analysis to combine P-values from one or several of these tools with the P-value of the domain score and (ii) filter unlikely motifs based on the domain score.

Results
We demonstrate the functionality of MASSIF on several human ChIP-seq datasets, using either motifs from the HOCOMOCO database or de novo identified ones as input motifs. In addition, we show that both variants of our method improve the performance of tools that link motifs to TFs based on TF-associated sequences significantly independent of the considered DBD type.

Availability and implementation
MASSIF is freely available online at https://github.com/SchulzLab/MASSIF.

Supplementary information
Supplementary data are available at Bioinformatics online.
BRM: a statistical method for QTL mapping based on bulked segregant analysis by deep sequencing
Authors: Huang Likun , Tang Weiqi , Bu Suhong , Wu Weiren 
Published: November 2019

Abstract

Motivation
Bulked segregant analysis by deep sequencing (BSA-seq) has been widely used for quantitative trait locus (QTL) mapping in recent years. A number of different statistical methods for BSA-seq have been proposed. However, determination of significance threshold, the key point for QTL identification, remains to be a problem that has not been well solved due to the difficulty of multiple testing correction. In addition, estimation of the confidence interval is also a problem to be solved.

Results
In this paper, we propose a new statistical method for BSA-seq, named Block Regression Mapping (BRM). BRM is robust to sequencing noise and is applicable to the case of low sequencing depth. Significance threshold can be reasonably determined by taking multiple testing correction into account. Meanwhile, the confidence interval of QTL position can also be estimated.

Availability and implementation
The R scripts of our method are open source under GPLv3 license at https://github.com/huanglikun/BRM.

Supplementary information
Supplementary data are available at Bioinformatics online.
Doubly functional graphical models in high dimensions
Authors: Qiao Xinghao , Qian Cheng , James Gareth M , Guo Shaojun 
Published: November 2019

SummaryWe consider estimating a functional graphical model from multivariate functional observations. In functional data analysis, the classical assumption is that each function has been measured over a densely sampled grid. However, in practice the functions have often been observed, with measurement error, at a relatively small number of points. We propose a class of doubly functional graphical models to capture the evolving conditional dependence relationship among a large number of sparsely or densely sampled functions. Our approach first implements a nonparametric smoother to perform functional principal components analysis for each curve, then estimates a functional covariance matrix and finally computes sparse precision matrices, which in turn provide the doubly functional graphical model. We derive some novel concentration bounds, uniform convergence rates and model selection properties of our estimator for both sparsely and densely sampled functional data in the high-dimensional large-$p$, small-$n$ regime. We demonstrate via simulations that the proposed method significantly outperforms possible competitors. Our proposed method is applied to a brain imaging dataset.
Expertise, Emotion and Specialization in the Development of Persistent Burglary
Authors: Meenaghan Amy , Nee Claire , Van Gelder Jean-Louis , Vernham Zarah , Otte Marco 
Published: November 2019

AbstractThis article describes a qualitative analysis of interviews conducted among 70 experienced residential burglars regarding the reasons for getting involved in and maintaining criminal behaviour. Themes emerging reflected an interaction between skill-development and affect, which played a key role in the initiation and continuation of burglary-related behaviour. Early participation in burglary seemed to be strongly influenced by the desire for excitement. Over time this diminished and was replaced by habitual engagement in burglary. With respect to the actual commission of offences, automatic decision-making appeared to be characteristic of the entire decision-chain, from initial thoughts to the commission of the burglary. Implications of the interaction between affect, cognition and expertise on diversification, specialization and desistance from crime are discussed.
Examining the how of Plural Policing: Moving from Normative Debate to Empirical Enquiry
Authors: Nøkleberg Martin 
Published: November 2019

AbstractThe networked and plural nature of policing suggests that agencies are often involved in extensive exchanges of expertise, resources and knowledge. However, the network structure and distribution of power between various policing actors can vary considerably. This highlights the importance of developing sound analytical perspectives that can help unpack the complexities behind the linkages. Applying the network perspective, this article underlines the value of utilizing analytical tools and approaches drawn from social network analysis, such as brokerage and homophily, to empirically assess the roles of agencies and their contribution to plural policing. This, in turn, shows how, in the mixed economy of policing, as well as being understood in terms of the normative debates that often figure in the current literature, relational phenomena also require more sophisticated empirical approaches.