Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Association Studies Identify Candidate Genes for Coat Color and Mohair Traits in the Iranian Markhoz Goat

The Markhoz goat provides an opportunity to study the genetics underlying coat color and mohair traits of an Angora type goat using genome-wide association studies (GWAS). This indigenous Iranian breed is valued for its quality mohair used in ceremonial garments and has the distinction of exhibiting an array of coat colors including black, brown, and white. Here, we performed 16 GWAS for different fleece (mohair) traits and coat color in 228 Markhoz goats sampled from the Markhoz Goat Research Station in Sanandaj, Kurdistan province, located in western Iran using the Illumina Caprine 50K beadchip. The Efficient Mixed Model Linear analysis was used to identify genomic regions with potential candidate genes contributing to coat color and mohair characteristics while correcting for population structure. Significant associations to coat color were found within or near the ASIP, ITCH, AHCY, and RALY genes on chromosome 13 for black and brown coat color and the KIT and PDGFRA genes on chromosome 6 for white coat color. Individual mohair traits were analyzed for genetic association along with principal components that allowed for a broader perspective of combined traits reflecting overall mohair quality and volume. A multitude of markers demonstrated significant association to mohair traits highlighting potential candidate genes of POU1F1 on chromosome 1 for mohair quality, MREG on chromosome 2 for mohair volume, DUOX1 on chromosome 10 for yearling fleece weight, and ADGRV1 on chromosome 7 for grease percentage. Variation in allele frequencies and haplotypes were identified for coat color and differentiated common markers associated with both brown and black coat color. This demonstrates the potential for genetic markers to be used in future breeding programs to improve selection for coat color and mohair traits. Putative candidate genes, both novel and previously identified in other species or breeds, require further investigation to confirm phenotypic causality and potential epistatic relationships.
Identifying diffusion patterns of research articles on Twitter: A case study of online engagement with open access articles

The growing presence of research shared on social media, coupled with the increase in freely available research, invites us to ask whether scientific articles shared on platforms like Twitter diffuse beyond the academic community. We explore a new method for answering this question by identifying 11 articles from two open access biology journals that were shared on Twitter at least 50 times and by analyzing the follower network of users who tweeted each article. We find that diffusion patterns of scientific articles can take very different forms, even when the number of times they are tweeted is similar. Our small case study suggests that most articles are shared within single-connected communities with limited diffusion to the public. The proposed approach and indicators can serve those interested in the public understanding of science, science communication, or research evaluation to identify when research diffuses beyond insular communities.
HIERARCHICAL MARKOV DECISION PROCESS BASED ON DEVS FORMALISM

Markov decision processes (MDPs) have proven useful as models of stochastic planning and decision problems. To try to propose practical implementation of MDPs, hierarchical methods are often used in MDPs or reinforcement learning to delegate the optimization of the total problem to simpler hierarchical sub-problems. The goal of the paper is to propose a generic discrete-event based software Framework allowing to use hierarchical MDPs and reinforcement learning to solve planning or decision problems. The proposed approach has been validated using the ”grid world” typical MDP use case.
Relative impact of maternal depression and associated risk factors on offspring psychopathology

Background In general, mothers with depression experience more environmental and family risk factors, and lead riskier lifestyles, than mothers who are not depressed. Aims To test whether the exposure of a child to risk factors associated with mental health adds to the prediction of child psychopathology beyond exposure to maternal depression. Method In 7429 mother–offspring pairs participating in the Avon Longitudinal Study of Parents and Children in the UK, maternal depression was assessed when the children were aged 1.5 years; multiple risk factor exposures were examined between birth and 2 years of age; and DSM-IV-based externalising and internalising diagnoses were evaluated when the children were 7.5 years of age. Results Children of clinically depressed mothers were exposed to more risk factors associated with maternal mental health. Maternal depression increased diagnoses of externalising and internalising disorders, but a substantial portion of these associations was explained by increased risk factor exposure (41% for externalising and 37% for internalising disorders). At the same time, these risk exposures significantly increased the odds of both externalising and internalising diagnoses, over and above the influence of maternal depression. Conclusions Children of clinically depressed mothers are exposed to both maternal psychopathology and risks that are associated with maternal mental health. These results may explain why treating mothers with depression shows beneficial effects for children, but does not completely neutralise the increased risk of psychopathology and impairment.
Gamification : Using Game Design Elements in Non-Gaming Contexts

Gamification” is an informal umbrella term for the use of video game elements in non-gaming systems to improve user experience (UX) and user engagement. The recent introduction of ‘gamified’ applications to large audiences promises new additions to the existing rich and diverse research on the heuristics, design patterns and dynamics of games and the positive UX they provide. However, what is lacking for a next step forward is the integration of this precise diversity of research endeavors. Therefore, this workshop brings together practitioners and researchers to develop a shared understanding of existing approaches and findings around the gamification of information systems, and identify key synergies, opportunities, and questions for future research.
Mindfulness: Cognitive and Emotional Change

Beck [1] developed cognitive therapy which led to broad therapeutic innovations and successful applications in the field of psychotherapy [2-4]. Mostly, by virtue of its linear nature and lack of empirical support from cognitive science, this clinically-driven model faced some limitations in practice [5]. To overcome the shortcomings, Teasdale and Barnard [6] experimentally investigated the link between cognition and emotion and came up with a non-linear/multilayer model (a science-driven model). As they suggested, information are processed in cognitive system at two qualitatively distinct levels, namely propositional level and implicational level. The former is linked with specific meanings behind language constructs (or words); the latter is associated with generic meanings behind language constructs (or words). In Beckian approach, it is the specific meanings that result in emotion production, whilst in the non-linear/multilayer model of cognition, in order to elicit emotional response, the generic, holistic meanings should be processed at implicational level [7].
Mixture Self-Paced Learning for Multi-view K-Means Clustering

In our daily life, there are more and more data characterized by multiple features. In multi-view setting, the clusters estimated using single view have some limitations, and the quality of single view clustering can be improved by means of multi-view clustering. Self-paced learning simulates human learning process which can gradually combine information of views into clustering task from easy to complex. In this paper, we first propose a new mixture self-paced learning regularizer. To recap the effectiveness of regularizer, we combine it with robust multi-view k-means clustering and propose a new selfpaced learning based multi-view k-means (SPLMKM) clustering method. As a non-trivial contribution, we present the solution based on alternating minimization strategy. The comparative experiments reveal the benefit of our proposed method.
eIMRT: a web platform for the verification and optimization of radiation treatment plans

The eIMRT platform is a remote distributed computing tool that provides users with Internet access to three different services: Monte Carlo optimization of treatment plans, CRT & IMRT treatment optimization, and a database of relevant radiation treatments/clinical cases. These services are accessible through a user‐friendly and platform independent web page. Its flexible and scalable design focuses on providing the final users with services rather than a collection of software pieces. All input and output data (CT, contours, treatment plans and dose distributions) are handled using the DICOM format. The design, implementation, and support of the verification and optimization algorithms are hidden to the user. This allows a unified, robust handling of the software and hardware that enables these computation‐intensive services. The eIMRT platform is currently hosted by the Galician Supercomputing Center (CESGA) and may be accessible upon request (there is a demo version at http://eimrt.cesga.es:8080/eIMRT2/demo; request access in http://eimrt.cesga.es/signup.html). This paper describes all aspects of the eIMRT algorithms in depth its user interface, and its services. Due to the flexible design of the platform, it has numerous applications including the intercenter comparison of treatment planning, the quality assurance of radiation treatments, the design and implementation of new approaches to certain types of treatments, and the sharing of information on radiation treatment techniques. In addition, the web platform and software tools developed for treatment verification and optimization have a modular design that allows the user to extend them with new algorithms. This software is not a commercial product. It is the result of the collaborative effort of different public research institutions and is planned to be distributed as an open source project. In this way, it will be available to any user; new releases will be generated with the new implemented codes or upgrades. PACS number: 87.55. kh
Privacy Preservation for Outsourced Medical Data With Flexible Access Control

Electronic medical records (EMRs) play an important role in healthcare networks. Since these records always contain considerable sensitive information regarding patients, privacy preservation for the EMR system is critical. Current schemes usually authorize a user to read one’s EMR if and only if his/her role satisfies the defined access policy. However, these existing schemes allow an adversary to link patients’ identities to their doctors. Therefore, classifications of patients’ diseases are leaked without adversaries actually seeing patients’ EMRs. To address this problem, we present two anonymous schemes. They not only achieve data confidentiality but also realize anonymity for individuals. The first scheme achieves moderate security, where adversaries choose attack targets before obtaining information from the EMR system. The second scheme achieves full security, where adversaries adaptively choose attack targets after interaction with the EMR system. We provide rigorous proof showing the security and anonymity of our schemes. In addition, we propose an approach in which EMR owners can search for their EMRs in an anonymous system. For a better user experience, we apply the online/offline approach to speed up data processing. Experimental results show that the time complexity for key generation and EMR encapsulation can be reduced to milliseconds.
Implementation of Neural Network to determine the New College Students

One of new student admission pathways at Universitas Negeri Surabaya (Unesa) is through the Indonesian National Public University Admission. This path is quite favourable for academic or vocational high school students who want to study at Unesa so that the number of participants for this selection program can reach up to ten thousand people every year. The large number of applicants makes the selection process more complex. Meanwhile, Unesa still uses the concept of weighting criteria in determining the result. One of the constraints in weighting process is the absence of optimal pattern or weight. This paper discusses a supervised learning approach to make determining process pattern which replaces manual weighting criteria. The supervised learning method used in this research was the Neural Network with multilayer perceptron. This research showed sufficient result which can be seen from the high accuracy rate (89.56%). The accuracy rate is enough to decide which participants who pass or fail the national admission. This system can be used as a prediction for the following years.
Study on Dielectric Property with Temperature Based on the Molecule Simulation Technique

Molecule simulation technique has introduced to research dielectric property in order to solve the difficulties in measuring dielectric property under different temperature and analyzed the relationship between microstructure and dielectric property. Firstly, 3D model of silicon dioxide was put in calculation of molecule dynamics simulation with a definitive temperature. Then constant temperature microstructure model of silicon dioxide has been obtained.Secondly, first-principles simulation has executed to computer this microstructure model. At last the dielectric property of silicon dioxide under this temperature was obtained. By changing temperature of simulation, we can study changes of dielectric property with temperature and analyze the microstructure changes related with dielectric property. In this paper, the relationship between different microscopic parameters for silicon dioxide was carried out. The authors found that Fermi energy and forbidden gap energy is inversely proportional to volume of cell. The law between dielectric constant and temperature was founded.
Alternative mRNA Splicing from the Glial Fibrillary Acidic Protein (GFAP) Gene Generates Isoforms with Distinct Subcellular mRNA Localization Patterns in Astrocytes

The intermediate filament network of astrocytes includes Glial fibrillary acidic protein (Gfap) as a major component. Gfap mRNA is alternatively spliced resulting in generation of different protein isoforms where Gfapα is the most predominant isoform. The Gfapδ isoform is expressed in proliferating neurogenic astrocytes of the developing human brain and in the adult human and mouse brain. Here we provide a characterization of mouse Gfapδ mRNA and Gfapδ protein. RT-qPCR analysis showed that Gfapδ mRNA and Gfapα mRNA expression is coordinately increased in the post-natal period. Immunohistochemical staining of developing mouse brain samples showed that Gfapδ is expressed in the sub-ventricular zones in accordance with the described localization in the developing and adult human brain. Immunofluorescence analysis verified incorporation of Gfapδ into the Gfap intermediate filament network and overlap in Gfapδ and Gfapα subcellular localization. Subcellular mRNA localization studies identified different localization patterns of Gfapδ and Gfapα mRNA in mouse primary astrocytes. A larger fraction of Gfapα mRNA showed mRNA localization to astrocyte protrusions compared to Gfapδ mRNA. The differential mRNA localization patterns were dependent on the different 3′-exon sequences included in Gfapδ and Gfapα mRNA. The presented results show that alternative Gfap mRNA splicing results in isoform-specific mRNA localization patterns with resulting different local mRNA concentration ratios which have potential to participate in subcellular region-specific intermediate filament dynamics during brain development, maintenance and in disease.
Do not neglect calcium: a systematic review and meta-analysis (meta-regression) of its digestibility and utilisation in growing and finishing pigs

Abstract Ca digestibility and utilisation in growing pigs are not well understood, and are usually neglected in diet formulation. This has implications not only for the accurate determination of its requirements but also for its interactions with other nutrients. A systematic review and meta-analysis (meta-regression) of published trials was carried out to quantify factors affecting Ca absorption and utilisation, and to derive an estimate of Ca endogenous excretion. The analysis was carried out on the data from forty studies, corresponding to 201 treatments performed on 1204 pigs. The results indicated that although Ca absorption and retention (g/kg of body weight per d) increased with increasing Ca intake (P<0·001), non-phytate-P intake (P<0·001) and exogenous phytase supplementation (P<0·001), these values decreased with increasing phytate-P intake (P<0·05). Interactions between exogenous phytase and Ca intake, indicating reduced efficacy of this enzyme (P<0·001), and between phytate-P intake and exogenous phytase, counteracting the direct negative effect of phytate-P (P<0·05) on Ca absorption and retention, were also detected. There were no effects of animal-related characteristics, such as pig genotype in Ca absorption and retention. The large amount of variance explained in Ca absorption (90 %) and retention (91 %) supported our choice of independent variables. Endogenous Ca losses obtained via linear regression were 239 mg/kg of DM intake (95 % CI 114, 364). These outcomes advance the current understanding of Ca digestibility and utilisation, and should contribute towards establishing requirements for digestible Ca. Consequently, pig diets will be more correctly formulated if digestible Ca values are used in estimating requirements for Ca.
Design Optimization at the Fluid-Level Synthesis for Safe and Low-Cost Droplet-Based Microfluidic Biochips

Droplet-based Digital Microfluidic Biochips (or DMFBs) are now being the prime platform for a number of point of care diagnostics, clinical studies, and sample preparations. Several design optimization methods exist at the fluid-level that automate the tasks of a DMFB. However, in high frequency applications, its performance heavily deteriorates in the order of degrading electrodes producing incorrect outcomes. Vague results may also be generated if cross contamination during droplet routing is not avoided. We define a DMFB to be safe if it is capable of handling reliability and also free from cross contamination. Alongside, as the fluid-level of DMFBs comprises several tasks that altogether introduces design cycles and leads to higher cost, a low-cost platform is urgently required. This paper proposes a fluid-level design for DMFBs that considers the above facts together. A graph model has been used to tackle them. An exact algorithm is presented. The obtained results are validated with several benchmarks.
Formal Verification of Smart Contracts Based on Users and Blockchain Behaviors Models

Blockchain technology has attracted increasing attention in recent years. One reason of this new trend is the introduction of on-chain smart contracts enabling the implementation of decentralized applications in trust-less environments. Along with its adoption, attacks exploiting smart contract vulnerabilities are inevitably growing. To counter these attacks and avoid breaches, several approaches have been explored such as documenting vulnerabilities or model checking using formal verification. However, these approaches fail to capture the Blockchain and users behavior properties. In this paper, we propose a novel formal modeling approach to verify a smart contract behavior in its execution environment. We apply this formalism on a concrete smart contract example and analyze its breaches with a statical model checking approach.
Genome-wide pleiotropy and shared biological pathways for resistance to bovine pathogens

Host genetic architecture is a major factor in resistance to pathogens and parasites. The collection and analysis of sufficient data on both disease resistance and host genetics has, however, been a major obstacle to dissection the genetics of resistance to single or multiple pathogens. A severe challenge in the estimation of heritabilities and genetic correlations from pedigree-based studies has been the confounding effects of the common environment shared among relatives which are difficult to model in pedigree analyses, especially for health traits with low incidence rates. To circumvent this problem we used genome-wide single-nucleotide polymorphism data and implemented the Genomic-Restricted Maximum Likelihood (G-REML) method to estimate the heritabilities and genetic correlations for resistance to 23 different infectious pathogens in calves and cows in populations undergoing natural pathogen challenge. Furthermore, we conducted gene-based analysis and generalized gene-set analysis to understand the biological background of resistance to infectious diseases. The results showed relatively higher heritabilities of resistance in calves than in cows and significant pleiotropy (both positive and negative) among some calf and cow resistance traits. We also found significant pleiotropy between resistance and performance in both calves and cows. Finally, we confirmed the role of the B-lymphocyte pathway as one of the most important biological pathways associated with resistance to all pathogens. These results both illustrate the potential power of these approaches to illuminate the genetics of pathogen resistance in cattle and provide foundational information for future genomic selection aimed at improving the overall production fitness of cattle.
Influence of hepatic ammonia removal on ureagenesis, amino acid utilization and energy metabolism in the ovine liver

The mass transfers of O2, glucose, NH3, urea and amino acids across the portal-drained viscera (PDV) and the liver were quantified, by arterio–venous techniques, during the last 4 h of a 100 h infusion of 0 (basal), 150 or 400 μmol NH4HCO3/min into the mesenteric vein of three sheep given 800 g grass pellets/d and arranged in a 3 × 3 Latin-square design. Urea irreversible loss rate (ILR) was also determined by continuous infusion of [14C]urea over the last 52 h of each experimental period. PDV and liver movements of glucose, O2 and amino acids were unaltered by NH4HCO3 administration, although there was an increase in PDV absorption of non-essential amino acids (P = 0·037) and a trend for higher liver O2 consumption and portal appearance of total amino acid-N, glucogenic and non-essential amino acids at the highest level of infusion. PDV extraction of urea-N (P = 0·015) and liver removal of NH3 (P < 0·001), release of urea-N (P = 0·002) and urea ILR (P = 0·001) were all increased by NH4HCO3 infusion. Hepatic urea-N release (y) and NH3 extraction(x) were linearly related (R2 0·89), with the slope of the regression not different from unity, both for estimations based on liver mass transfers (1·16; SE 0·144; PB ≠ 1 = 0·31) AND [14C]UREA (0·97; se 0·123; Pb ≠ 1 = 0·84). The study indicates that a sustained 1·5 or 2·4-fold increase in the basal NH3 supply to the liver did not impair glucose or amino acid supply to non-splanchnic tissues; nor were additional N inputs to the ornithine cycle necessary to convert excess NH3 to urea. Half of the extra NH3 removed by the liver was, apparently, utilized by periportal glutamate dehydrogenase and aspartate aminotransferase for sequential glutamate and aspartate synthesis and converted to urea as the 2-amino moiety of aspartate.