bluuebunny/crossref_metadata_2025_split huggingface dataset:
Double-digest RAD-sequencing: do wet and dry protocol parameters impact biological results?
Authors: Cumer Tristan , Pouchon Charles , Boyer Frédéric , Yannic Glenn , Rioux Delphine , Bonin Aurélie , Capblancq Thibaut 
Published: October 2018

ABSTRACTNext-generation sequencing technologies have opened a new era of research in genomics. Among these, restriction enzyme-based techniques such as restriction-site associated DNA sequencing (RADseq) or double-digest RAD-sequencing (ddRADseq) are now widely used in many population genomics fields. From DNA sampling to SNP calling, both wet and dry protocols have been discussed in the literature to identify key parameters for an optimal loci reconstruction.The impact of these parameters on downstream analyses and biological results drawn from RADseq or ddRADseq data has however not been fully explored yet. In this study, we tackled this issue by investigating the effects of ddRADseq laboratory (i.e. wet protocol) and bioinformatics (i.e. dry protocol) settings on loci reconstruction and inferred biological signal at two evolutionary scale using two systems: a complex of butterfly species (Coenonympha sp.) and populations of Common beech (Fagus sylvatica).Results suggest an impact of wet protocol parameters (DNA quantity, number of PCR cycles during library preparation) on the number of recovered reads and SNPs, the number of unique alleles and individual heterozygosity. We also found that bioinformatic settings (i.e. clustering and minimum coverage thresholds) impact loci reconstruction (e.g. number of loci, mean coverage) and SNP calling (e.g. number of SNPs, heterozygosity). We however do not detect an impact of parameter settings on three types of analysis performed with ddRADseq data: measure of genetic differentiation, estimation of individual admixture, and demographic inferences. In addition, our work demonstrates the high reproducibility and low rate of genotyping inconsistencies of the ddRADseq protocol.Thus, our study highlights the impact of wet parameters on ddRADseq protocol with strong consequences on experimental success and biological conclusions. Dry parameters affects loci reconstruction and descriptive statistics but not biological conclusion for the two studied systems. Overall, this study illustrates, with others, the relevance of ddRADseq for population and evolutionary genomics at the inter- or intraspecific scales.
Insulin Receptor Deletion in S100a4-lineage cells accelerates age-related bone loss
Authors: Studentsova Valentina , Knapp Emma , Loiselle Alayna E. 
Published: October 2018

AbstractType I and Type II Diabetes dramatically impair skeletal health. Altered Insulin Receptor (IR) signaling is a common feature of both diseases, and insulin has potent bone anabolic functions. Several previous studies have demonstrated that loss of IR in bone cells results in disrupted bone homeostasis during early post-natal growth. Here we have deleted IR in S100a4-lineage cells (IRcKOS100a4) and assessed the effects on bone homeostasis at both young (15 weeks) and older adult (48 weeks) mice. S100a4-cre has previously been shown to target the perichondrium during bone development, and here we show that S100a4 is expressed by adult trabecular and cortical bone cells, and that S100a4-Cre effectively targets adult bone, resulting in efficient deletion of IR. Deletion of IR in S100a4-lineage cells does effect initial bone acquisition or homeostasis with no changes in cortical, trabecular or mechanical properties at 15-weeks of age, relative to wild type (WT) littermates. However, by 48-weeks of age, IRcKOS100a4 mice display substantial declines in trabecular bone volume, bone volume fraction and torsional rigidity, relative to age-matched WT controls. This work establishes the utility of using S100a4-cre to target bone and demonstrates that IR in S100a4-lineage cells is required for maintenance of bone homeostasis in adult mice.
A Sparse Additive Model for High-Dimensional Interactions with an Exposure Variable
Authors: Bhatnagar Sahir R , Lu Tianyuan , Lovato Amanda , Olds David L , Kobor Michael S , Meaney Michael J , O’Donnell Kieran , Yang Yi , Greenwood Celia MT 
Published: October 2018

ABSTRACTA conceptual paradigm for onset of a new disease is often considered to be the result of changes in entire biological networks whose states are affected by a complex interaction of genetic and environmental factors. However, when modelling a relevant phenotype as a function of high dimensional measurements, power to estimate interactions is low, the number of possible interactions could be enormous and their effects may be non-linear. In this work, we introduce a method calledsailfor detecting non-linear interactions with a key environmental or exposure variable in high-dimensional settings which respects the strong or weak heredity constraints. We prove that asymptotically, our method possesses the oracle property, i.e., it performs as well as if the true model were known in advance. We develop a computationally efficient fitting algorithm with automatic tuning parameter selection, which scales to high-dimensional datasets. Through an extensive simulation study, we show thatsailoutperforms existing penalized regression methods in terms of prediction accuracy and support recovery when there are non-linear interactions with an exposure variable. We applysailto detect non-linear interactions between genes and a prenatal psychosocial intervention program on cognitive performance in children at 4 years of age. Results show that individuals who are genetically predisposed to lower educational attainment are those who stand to benefit the most from the intervention. Our algorithms are implemented in an R package available on CRAN (https://cran.r-project.org/package=sail).
Exploratory and prospective model of stent restenosis after Percutaneous Coronary Intervention using Multivariate Gaussian Subspatial Regression
Authors: Vicente-Palacios Victor , Vicente-Tavera Santiago , Dorado-Díaz P. Ignacio , Sanchez-Puente Antonio , Sampedro-Gómez Jesús , Galindo Purificación , Gómez Itziar , San Román José A. , Fernández-Avilés Francisco , Sánchez Pedro L. 
Published: October 2018

A new statistical analysis methodology, “Multivariate Gaussian Subspatial Regression” (MGSR), has been applied to randomized clinical trial data collected from percutaneous coronary intervention (PCI) patients, which combines the descriptive quality of Factorial Techniques and the predictive power of Gaussian Processes.This model has been built from 3 different quantitative coronary angiographic core-lab measures of the same lesion from 2 separate angiograms (at baseline before PCI, at baseline immediately after PCI and at 12 months follow-up). Measurements of the pre-PCI variables of a patient are mapped to the factorial plane and predictions are visualized as regions of interest in this plane.MGSR makes it possible to detect patients at risk of coronary stent restenosis or patients in whom ruling out the disease, in a graphical way; avoiding unnecessary, costly and possibly risky treatments for patients with no complications predicted; and advising to closely follow patients at risk. In addition, the model recovers missing values regardless of the variables, and once fitted, it corrects itself when more dependent variables are included.MGSR software is freely available online at https://github.com/victorvicpal/MGSR.
sEMG-based shoulder-elbow composite motion pattern recognition and control methods for upper limb rehabilitation robot
Authors: Zhang Xiufeng , Dai Jitao , Li Xia , Li Huizi , Fu Huiqun , Pan Guoxin , Zhang Ning , Yang Rong , Xu Jianguang 
Published: October 2018

Purpose
This paper aims to develop a signal acquisition system of surface electromyography (sEMG) and use the characteristics of (sEMG) signal to interference action pattern.

Design/methodology/approach
This paper proposes a fusion method based on combining the coefficient of AR model and wavelet coefficient. It improves the recognition rate of the target action. To overcome the slow convergence speed and local optimum in standard BP network, the study presents a BP algorithm which combine with LM algorithm and PSO algorithm, and it improves the convergence speed and the recognition rate of the target action.

Findings
Experiments verify the effectiveness of the system from two aspects the target motion recognition rate and the corresponding reaction speed of the robotic system.

Originality/value
The study developed a signal acquisition system of sEMG and used the characteristics of (sEMG) signal to interference action pattern. The myoelectricity integral values are presented to determine the starting point and end point of target movement, which is more effective than using single sample point amplitude method.
Structural change and poverty elimination
Authors: Lin Justin Yifu 
Published: October 2018

Purpose
The United Nations adopted 17 goals for sustainable development, which has been known as the 17 SDGs. Knowing how to achieve these goals will be very important for many countries. The first of the 17 is no poverty. The purpose of this paper is to analyze how to realize no poverty in UN’s SDGs by focusing on structural changes based on the New Structural Economics.

Design/methodology/approach
This paper explains the relationship between structural changes and people’ s income in both rural and urban areas, and then introduces how to eliminate poverty from a New Structural Economics’ perspective. Finally, it discusses what to do to make these changes a reality.

Findings
To reduce and eventually eliminate poverty, increasing personal income becomes the first step. From national perspective, structural changes are related to an income increase. In rural and urban areas alike, the structural changes will usually be accompanied by new technologies and job opportunities, which will help people improve their incomes.

Originality/value
This paper explains relationship between structural changes and poverty elimination. How to increase people’s income is also discussed according to New Structural Economics. This paper’s findings may well be valuable for research on poverty elimination in the future.
How entrepreneurial learning impacts one’s intention towards entrepreneurship
Authors: Zhang Feng , Wei Liqun , Sun Hongyi , Tung Lo Choi 
Published: October 2018

Purpose
Although entrepreneurial learning is widely believed as an important factor in shaping one’s entrepreneurial intention, research finds little consistent results on the direct effect of entrepreneurial learning on one’s entrepreneurial intention. To solve the conflicted effects of entrepreneurial learning, the purpose of this paper is to explore how entrepreneurial learning may impact individual entrepreneurial intention.

Design/methodology/approach
Drawing on the theory of planned behavior (TPB), the authors proposed and tested on a three mediating effect model, in which entrepreneurial learning is associated with entrepreneurial intention through attitude, subjective norm and perceived behavioral control toward entrepreneurship. Moreover, the moderating role of prior exposure to entrepreneurship was proposed and tested. Based on a sample of 200 university students who have taken entrepreneurial courses in Hong Kong, the hierarchical regressions and moderated mediation tests were used to test the hypotheses.

Findings
The authors find that the positive relationship between entrepreneurial learning and entrepreneurial intention is significantly mediated by attitude, subjective norm and perceived behavioral control toward entrepreneurship; and the mediating effects of entrepreneurial learning on entrepreneurial intention via attitudes and perceived behavioral control respectively, is moderated by exposure to entrepreneurship.

Originality/value
Contributing to the literature of entrepreneurship education, this study identifies individuals who exposed to the same entrepreneurship education may perform differently in entrepreneurial learning. The findings also help us to better understand the mechanism through which and under which context one’s entrepreneurial learning may enhance his/her entrepreneurial intention.
Community participation framework for post-disaster resettlement and its practical application in Pakistan
Authors: Jamshed Ali , Rana Irfan Ahmad , Khan Masood Ali , Agarwal Nikhil , Ali Ahsan , Ostwal Mayank 
Published: October 2018

Purpose
The purpose of this paper is to propose a practical framework for community participation in post-disaster resettlement.

Design/methodology/approach
The proposed framework has been tested in two model villages (MVs) of Punjab, Pakistan. Primary data were collected through household surveys, focus group discussions and expert interviews. A survey with 67 households was conducted for obtaining qualitative data regarding community participation in post-disaster resettlement.

Findings
The first MV (Ittehad MV) was resettled by the local NGO, and the second (Basti Meera Mullan) by the provincial government. Results indicate that community participation significantly varied in selected MVs. NGOs have achieved positive realizations due to effective community involvement in resettlement efforts, whereas the governmental approach lacked in proactive community participation.

Practical implications
This framework can be used for other disasters, by refining and incorporating disaster relevant components. This research will be highly useful for disaster managers, private developers and NGOs engaged in resettling disaster-affected population.

Social implications
The proposed framework can help disaster-affected communities to resettle according to their terms. This can only be attained if affected communities will proactively participate in resettlement planning process.

Originality/value
This original framework is exclusively designed to attain sustainability for post-disaster settlement through community participation.
Development and customization of in-house developed OCR and its evaluation
Authors: S. Rajeswari , Magapu Sai Baba 
Published: October 2018

Purpose
The purpose of this paper is to develop a text extraction tool for scanned documents that would extract text and build the keywords corpus and key phrases corpus for the document without manual intervention.

Design/methodology/approach
For text extraction from scanned documents, a Web-based optical character recognition (OCR) tool was developed. OCR is a well-established technology, so to develop the OCR, Microsoft Office document imaging tools were used. To account for the commonly encountered problem of skew being introduced, a method to detect and correct the skew introduced in the scanned documents was developed and integrated with the tool. The OCR tool was customized to build keywords and key phrases corpus for every document.

Findings
The developed tool was evaluated using a 100 document corpus to test the various properties of OCR. The tool had above 99 per cent word read accuracy for text only image documents. The customization of the OCR was tested with samples of Microfiches, sample of Journal pages from back volumes and samples from newspaper clips and the results are discussed in the summary. The tool was found to be useful for text extraction and processing.

Social implications
The scanned documents are converted to keywords and key phrases corpus. The tool could be used to build metadata for scanned documents without manual intervention.

Originality/value
The tool is used to convert unstructured data (in the form of image documents) to structured data (the document is converted into keywords, and key phrases database). In addition, the image document is converted to editable and searchable document.
Considering online consumer reviews to predict movie box-office performance between the years 2009 and 2014 in the US
Authors: Hu Ya-Han , Shiau Wen-Ming , Shih Sheng-Pao , Chen Cho-Ju 
Published: October 2018

Purpose
The purpose of this paper is to combine basic movie information factors, external factors and review factors, to predict box-office performance and identify the most crucial factor of influence for box-office performance.

Design/methodology/approach
Five movie genres and first-week movie reviews found on IMDb were collected. The movie reviews were quantified using sentiment analysis tools SentiStrength and Stanford CoreNLP, in which quantified data were combined with basic movie information and external environment factors to predict movie box-office performance. A movie box-office performance prediction model was then developed using data mining (DM) technologies with M5 model trees (M5P), linear regression (LR) and support vector regression (SVR), after which movie box-office performance predictions were made.

Findings
The results of this paper showed that the inclusion of movie reviews generated more accurate prediction results. Concerning movie review-related factors, the one that exhibited the greatest effect on box-office performance was the number of movie reviews made, whereas movie review content only displayed an effect on box-office performance for specific movie genres.

Research limitations/implications
Because this paper collected movie data from the IMDb, the data were limited and primarily consisted of movies released in the USA; data pertaining to less popular movies or those released outside of the USA were, thus, insufficient.

Practical implications
This paper helps to verify whether the consideration of the features extracted from movie reviews can improve the performance of movie box-office.

Originality/value
Through various DM technologies, this paper shows that movie reviews enhanced the accuracy of box-office performance predictions and the content of movie reviews has an effect on box-office performance.
Quality-time-complexity universal intelligence measurement
Authors: Liu Jing , Pan Zhiwen , Xu Jingce , Liang Bing , Chen Yiqiang , Ji Wen 
Published: October 2018

Purpose
With the development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more autonomous and smart. Therefore, there is a growing demand for developing a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method.

Design/methodology/approach
This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents.

Findings
By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment.

Originality/value
This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.
Positioning of wine tourism websites across different country winescapes
Authors: Cassar Mario , Caruana Albert , Vella Joseph 
Published: October 2018

PurposeThis paper aims to investigate the extent to which wine tour-operating companies are successfully using their websites to communicate persuasive and meaningful differences to customers in their quest to clearly position their product offering.Design/methodology/approachText data are collected from 250 websites that promote wine tourism in five different countries’ winescapes. Lexical, regression and hierarchical clustering analyses are used.FindingsLexical analysis using DICTION can distinguish among websites from different countries’ winescapes, and French wines obtain the best mean review scores from customers, while US, Napa Valley, websites obtain the lowest scores. DICTION dimensions allow for meaningful clusters and can also predict TripAdvisor’s mean review scores.Practical implicationsThere is a need to pay better attention in the development of website content and the critical role that both syntax and semantics can play in facilitating the use of a firm’s website, specifically in terms of clear positioning.Originality/valueThe study uses lexical analyses of website narratives to understand the current positioning of firms.
Tool wear in dry turning and wet turning with non-toxic cutting fluid
Authors: Ozimina Dariusz , Madej Monika , Kowalczyk Joanna , Ozimina Ewa , Plaza Stanislaw 
Published: October 2018

Purpose
This study aims to determine the properties of a new non-toxic cutting fluid and compared with cutting fluid based on mineral oil.

Design/methodology/approach
The tool wear was measured under dry and wet cutting conditions. The non-toxic cutting fluid was compared with cutting fluid based on mineral oil. The experiments were carried out using CTX 310 ECO numerical control lathe. The wear of the cutting tools was measured by means of stereo zoom microscopy (SX80), while the elements were identified through scanning electron microscopy (JSM 7100F). The workpiece surface texture was studied using a Talysurf CCI Lite non-contact 3D profiler. The contact wetting angle was established with a KSV CAM 100 tester.

Findings
The non-toxic cutting fluid has reached comparable coefficient of friction with a coolant containing mineral oil. The use of the non-toxic cutting fluid with low foaming tendency resulted in lower wear.

Practical implications
Machining processes require that cutting fluids be applied to reduce the tool wear and improve the quality of the workpiece surface. Cutting fluids serve numerous purposes such as they act as coolants and lubricants, remove chips and temporarily prevent corrosion of the product.

Originality/value
The investigations discussed in this paper have contributed to the development of non-toxic and environmentally friendly manufacturing because of the use of cutting fluid containing zinc aspartate and its comparison with commonly used cutting fluid.
The perceptually-inspired model of tactile texture sensor based on the inverse-magnetostrictive effect
Authors: Wan Lili , Wang Bowen , Wang Xiaodong , Huang Wenmei , Weng Ling 
Published: October 2018

Purpose
The purpose of this study is to develop an output model to extract surface microstructure characteristics of different objects, so as to predict the response of the output voltage obtained from tactile texture sensor.

Design/methodology/approach
The model is based on the consideration of the inverse-magnetostrictive effect, the flexure mode, the linear constitutive equations and the strain principle.

Findings
This research predicts and investigates the effect of the texture properties on the tactile texture sensor output characteristics.

Originality/value
The surface texture characteristic is regarded to be important information to evaluate and recognize the object.