Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimates of genetic trends for daily milk yield and somatic cell score of primiparous Holstein cattle in Iran

ABSTRACT Genetic and permanent environmental correlations between milk yield and somatic cell score (SCS) were estimated. The data set used in this analyse contained a total of 204,438 test-day (TD) observations of milk yield and SCS from 22,903 primiparous daughters of 554 dairy Holstein sires. A multiple-trait random regression TD model was employed in the analyses using the Gibbs sampling method. Obtained results showed that daily heritabilities were highest in the second half of lactation and varied from 0.086 to 0.250 for milk yield and 0.041 to 0.086 for SCS. Undesirable genetic relationships with milk yield were found for SCS with genetic correlations ranging from −0.093 to 0.319 that the highest was estimated between SCS days in milk (DIM) 305 and milk DIM 5. Based on these results, we can conclude that the cows with high yield potential in the initial stage of lactation had the highest genetic potential for SCS at the end of lactation. This emphasizes the need for including udder health traits in the breeding goal. However results of this investigation, based on genetic trends, indicate that more emphasis is paid to increase milk yield of Holstein cows in Iran with no or a little emphasis on SCC.
In vitro studies of 3-O-Methylquercetin against phytopathogenic fungi of major cereals

Maize, wheat, and rice are major cereal crops in the world, which provide more caloric energy than any other type of cereal class. In recent years, the yield of these cereal grains has stagnated due to a myriad of constraints, including infestation by pests and pathogens. Plant pathogens, especially agricultural phytopathogenic fungi, may be resistant to the available chemical fungicides which may have detrimental effects and are expensive. This study investigated whether natural products isolated from Linzia glabra Steetz of family Asteraceae could be used as alternative antifungals for control of cereal diseases caused by agricultural fungi. A single pure fraction was separated and identified from L. glabra methanolic flower crude extract using high performance liquid chromatography and high resolution mass spectrometry methods. The pure fraction isolated was tested for inhibition of spore germination in vitro against Fusarium graminearum, Fusarium oxysporum, Fusarium verticillioides, Zymoseptoria tritici, Ustilago maydis, and Pyricularia oryzae. The fraction was identified as a flavonol, 3-O-Methylquercetin using high resolution mass spectrometry. Among the fungi tested, spores of Z. tritici were the most susceptible to 3-O-Methylquercetin (44% spore inhibition), followed by F. oxysporum (33% inhibition) at 27 μg/mL. Azoxystrobin, a standard commercialized fungicide, gave a complete inhibition of 100% against Z. tritici and 58% inhibition against F. oxysporum at 5 μg/mL. L. glabra contains flavonols with antifungal activity, but the modest and weak antifungal activity of 3-O-Methylquercetin compared to a standard such as azoxystrobin demonstrates the challenge to produce highly potent natural product fungicides. There is need for continued search for antifungals with new modes of action as solutions to challenges in agricultural production.
Efficient semi-numerical analysis of acoustic sensors using spectral domain methods—a review

Microacoustic sensors often represent a challenge in device modeling as coupled electromechanical fields and piezoelectric media are involved. Due to this complexity and because of the sometimes adverse ratios of characteristic dimensions to the device size (e.g., in the case of thickness shear mode resonators), the successful application of numerical models such as provided by the finite element method is not always possible in an efficient manner. In numerical electromagnetics and in the analysis of surface acoustic wave devices, the spectral domain method has been established as a useful semi-numerical tool allowing the computationally efficient implementation of device models. In recent years, we have successfully applied the method to several problems involving acoustic sensors. In this paper, the foundations of the method are reviewed and its application in the analysis of selected microacoustic sensors is discussed.
The complete mitogenome of Glyptothorax deqinensis (Siluriformes: Sisoridae)

Abstract Glyptothorax deqinensis is a small endangered Sisoridae fish mainly distributed in the southwest of China. In the present study, the complete mitochondrial genome of G. deqinensis was firstly sequenced. The total length of the G. deqinensis mitogenome is 16,542 bp, including 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNA genes, and a control region. Among these genes, 28 are encoded by the H-strand and 9 by the L-strand. The information mitogenome obtained herein will provide useful tools for future studies on population genetic and phylogenetic analyses of this fish.
Cardiac function modulates endocardial cell dynamics to shape the cardiac outflow tract

Physical forces are important participants in the cellular dynamics that shape developing organs. During heart formation, for example, contractility and blood flow generate biomechanical cues that influence patterns of cell behavior. Here, we address the interplay between function and form during the assembly of the cardiac outflow tract (OFT), a crucial connection between the heart and vasculature that develops while circulation is underway. In zebrafish, we find that the OFT expands via accrual of both endocardial and myocardial cells. However, when cardiac function is disrupted, OFT endocardial growth ceases, accompanied by reduced proliferation and reduced addition of cells from adjacent vessels. The TGFβ receptor Acvrl1 is required for addition of endocardial cells, but not for their proliferation, indicating distinct regulation of these essential cell behaviors. Together, our results suggest that cardiac function modulates OFT morphogenesis by triggering endocardial cell accumulation that induces OFT lumen expansion and shapes OFT dimensions.
A typology of online care platforms for community-dwelling older adults in the Netherlands: A scoping review

Centre of Innovative Care and Technology (EIZT), Zuyd University of Applied Sciences, Henri Dunantstraat 2, 6419PB, Heerlen, The Netherlands; School CAPHRI, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Centre for Assistive Technology and Connected Healthcare (CATCH), University of Sheffield, Sheffield, United Kingdom; *Corresponding author: sarah.willard@zuyd.nl
Parallel ABC optimization algorithm for hydrothermal scheduling problems

This paper proposes a novel parallelization of ABC Algorithm to solve Short-term Hydro-Thermal Scheduling (SHTS) problem. Parallelization is a novel way of improving the convergence of the algorithm. In case of normal ABC algorithms accelerating operation or modification can be used for better convergence and diversity. But with large number of variables the non-linearity of the search space increases and these modifications are not sufficient to give enough diversity in the crowds of swarms for finding better solutions. However, parallelization of the algorithm increases the diversity in solutions and the search capability. The performance of the proposed algorithm is experimented on two short term multi reservoir cascaded hydrothermal systems. First test case is moderately large while the second test case is a considerably large one. Experimental results demonstrate the superior performance of the proposed algorithm as compared to other recently reported ones in terms of convergence and better quality of solutions.This paper proposes a novel parallelization of ABC Algorithm to solve Short-term Hydro-Thermal Scheduling (SHTS) problem. Parallelization is a novel way of improving the convergence of the algorithm. In case of normal ABC algorithms accelerating operation or modification can be used for better convergence and diversity. But with large number of variables the non-linearity of the search space increases and these modifications are not sufficient to give enough diversity in the crowds of swarms for finding better solutions. However, parallelization of the algorithm increases the diversity in solutions and the search capability. The performance of the proposed algorithm is experimented on two short term multi reservoir cascaded hydrothermal systems. First test case is moderately large while the second test case is a considerably large one. Experimental results demonstrate the superior performance of the proposed algorithm as compared to other recently reported ones in terms of convergence and better quality o.
Evaluation of machine translation systems and related procedures

Currently, the high volume of international information exchange involves a wide range of localities. As each locality comes with its own distinctive dialect, the need for an effective means of language translation is becoming more and more apparent. Among the concerns of information professionals is the capacity of an interested party to access web information offered in an unfamiliar language. Classified under the wide field of artificial intelligence, machine translation (MT) is an approach related to natural language processing. The machine translation technique involves the use of software for the conversion of documents or verbalized information from one natural language into another. Of late, a substantial number of procedures have been proposed for the fashioning of an efficient MT system. While these procedures were observed to be capable in certain areas, they were found wanting in others. The objectives of this endeavour are to (a) conduct a thorough investigation on machine translation and track its progress over recent decades, (b) examine the currently available machine translation procedures and systems and (c) offer an assessment on machine translation systems.
Can the digestible indispensable amino acid score methodology decrease protein malnutrition

Protein undernutrition is a serious global problem that results in stunted growth, disease, and premature death in millions of people annually. Protein deficiency is caused by a deficiency in specific indispensable amino acids and efforts to alleviate protein malnutrition, therefore, need to focus on the provision of adequate quantities of digestible indispensable amino acids. The digestibility of amino acids is most correctly determined at the end of the small intestine, and digestibility values obtained using this approach are termed “ileal digestibility values. ” The total protein provision to an individual is based on the sum of digestible indispensable amino acids consumed in a meal. The protein quality of a single ingredient is less important than the quality of the mixed meal that is consumed. To assist in calculating the protein quality of meals for humans, the FAO recommends that a system called the “Digestible Indispensable Amino Acid Score” or DIAAS be used. This system is based on the ileal digestibility of each amino acid in individual protein foods, but allows for the calculation of the quality of a meal consisting of a number of protein foods. The system can then be used to assess the quality of total protein intake consumed as a meal. Comparison of this value to the requirements for indispensable amino acids by humans can be used to estimate an individual’s protein status.
Testosterone and Corticosterone Profiles and Body Condition of Calling and Non-calling Lithobates grylio

Anuran vocalization is one of the most energetically expensive behaviors recorded in vertebrates. To sustain this behavior, circulating hormones in male frogs must act to promote calling behavior while mobilizing energy reserves. We hypothesize that this is accomplished through the actions of testosterone (T) and corticosterone (CORT). T is thought to elicit calling, while CORT mobilizes energy reserves. Eventually, high CORT concentrations and low energy reserves suppress calling behavior. We evaluated this hypothesis in light of the reproductive ecology of the Pig Frog, Lithobates grylio. Plasma samples from calling and non-calling males were collected during four months of the reproductive season. Calling males were shown to have significantly higher concentrations of circulating T and high body conditions. Conversely, non-callers exhibited elevated CORT and low body conditions. There was no correlation between the two hormones, but circulating T concentrations did decrease over the season. Lastly, we found that CORT is positively correlated with circulating glucose (in non-callers); this finding is supported by CORT's known role in gluconeogenesis. Taken together, these data indicate that circulating hormones and available energy reserves likely influence calling behavior in L. grylio.
The Relationship between Mastery Experience and Teacher Self-Efficacy: A Meta-Analytic Review

The objective of the meta-analysis was to examine the likelihood and magnitude of the effect of the mastery experience and teacher self-efficacy relationship. The meta-analysis also aimed to identify the moderators of the relationship. A total of 39 original studies, involving 9,560 teachers was included in the meta-analysis. To identify the moderators, the study applied meta-regression procedure. The results supported the expectation that mastery experience positively influences teacher self-efficacy. In addition, differences in research design, school level, and culture impacted the variability of the relationship.
Determining Optimum Production Quantity on Multi-Product Home Textile Industry by Simulated Annealing

Production planning is a plan aimed at controlling the quantity of products produced. Production planning is very important to be carried out by the company so that the production will always be controlled. It is very difficult to plan production with a variety of product variations because each product certainly has a different demand value from its customers. This has become a complex problem so an algorithm is needed to overcome these problems. Simulated Annealing can produce optimal solutions more effectively and efficiently. Production costs generated by applying Simulated Annealing are Rp. 6,902,406,000, - for all types of products, which is better than existing condition.
A Discussion on Present Theories of Rubber Friction, with Particular Reference to Different Possible Choices of Arbitrary Roughness Cutoff Parameters

Since the early study by Grosch in 1963 it has been known that rubber friction shows generally two maxima with respect to speed—the first one attributed to adhesion, and another at higher velocities attributed to viscoelastic losses. The theory of Klüppel and Heinrich and that of Persson suggests that viscoelastic losses crucially depend on the “multiscale” aspect of roughness and in particular on truncation at fine scales. In this study, we comment a little on both theories, giving some examples using Persson’s theory on the uncertainties involved in the truncation of the roughness spectrum. It is shown how different choices of Persson’s model parameters, for example the high-frequency cutoff, equally fit experimental data on viscoelastic friction, hence it is unclear how to rigorously separate the adhesive and the viscoelastic contributions from experiments.
Topological power plants as embedded microgrids for the market and grid integration of distributed energy resources

Coordinating flexibility from distributed energy resources (DER) poses a challenge as it becomes increasingly relevant for individual profitability and market integration as well as system and local needs (ancillary services). This paper introduces Topological Power Plants as a holistic integration approach for DER networks aiming at efficient market participation as well as at providing ancillary services. A distinguishing feature is the discussion of the approach by means of specific use cases in connection with corresponding role distributions. After outlining the concept, the developed modelling framework is described and preliminary results from field tests in southern Germany are evaluated.
CONTENT-AWARE IMAGE COMPRESSION WITH CONVOLUTIONAL NEURAL NETWORKS

Traditional image compression algorithms treat all image regions equally, regardless of their content, often resulting in reconstructed images that do not correlate well with human perception. Content-aware compression, on the other hand, prioritizes image regions that are more relevant to the interpretation of an image and encodes them at a higher bitrate, i.e. without loss or with less loss, than the rest of the image. Our paper explores the multi-structure region of interest (MS-ROI) model, a convolutional neural network, which enables the localization of multiple regions of interest (ROIs) in an image. The localization is expressed as a corresponding saliency map, which identifies the relevance of individual image regions and provides a saliency value for each pixel of the given image. This information is then used to guide the compression. The saliency values are discretized into multiple levels and more important levels are encoded with a higher quality factor Q than the less important ones, allowing for most of the reduction in image resolution to occur in non-salient image regions. Because the generated saliency maps produce soft boundaries between salient and non-salient image regions, smooth transitions between these regions are achieved. The obtained image is then encoded further using the standard JPEG algorithm with a uniform Q factor, resulting in the final image of the standard JPEG format. Our model was trained on the Caltech-101 image dataset and its performance was tested on two other image datasets. Presented are the obtained saliency maps for several images, as well as the results of contentaware compression, which are compared to the standard JPEG compression at different Q factors. For an objective comparison and evaluation of the quality of the obtained images, various standard quality metrics were used, i.e. mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and multi-scale structural similarity index (MS-SSIM).
Clustering-based Sales Forecasting in a Forklift Distributor

Sales forecasting refers to the prediction of future demand based on past data. A vast literature on sales forecasting has accumulated due to its vital role in balancing demand and supply. Among these, data mining has emerged as a powerful tool to facilitate sales forecasting. In this study, we use data mining methods for accurate and reliable sales forecasts in a forklift distributor company. Monthly sales data for 100 different types of forklifts between 1998 and 2016 are used. The proposed forecasting methodology includes three steps. First, products with similar sales patterns are determined using hierarchical clustering. Dynamic time warping is applied to calculate the similarities among product sales data. Second, features are extracted and selected for each cluster. In addition to the features adopted from the literature, four new features are proposed to characterize intermittency. Multivariate adaptive regression splines model is used for feature selection. Third, support vector regression is used to predict future sales of each product cluster. Finally, the performance of the proposed approach is evaluated according to forecasting error and complexity. The numerical analysis shows that the proposed approach gives reasonable accuracy with less complexity.
Naive Bayesian Automatic Classification of Railway Service Complaint Text Based on Eigenvalue Extraction

Railways have developed rapidly in China for several decades. The hardware of railways has already reached the world's leading level, but the level of service of these railways still has room for improvement. The railway management department receives a large number of passenger complaints every year and records them in text, which needs to be classified and analyzed. The text of railway complaints includes characteristics spanning wide business coverage, various events, serious colloquialisms, interference and useless information. When using the direct classification via traditional text categorization, the classification accuracy is low. The key to the automatic classification of such text lies in an eigenvalue extraction. The more accurate the eigenvalue extraction, the higher the accuracy of text classification. In this paper, the TFIDF algorithm, TextRank algorithm and Word2vec algorithm are selected to extract text eigenvalues, and a railway complaint text classification method is constructed with a naive Bayesian classifier. The three types of eigenvalue extraction algorithms are compared. The TF-IDF algorithm, based on eigenvalue extraction, achieves the highest automatic text classification accuracy.
Influence of Different Glycoproteins and of the Virion Core on SERINC5 Antiviral Activity

Host plasma membrane protein SERINC5 is incorporated into budding retrovirus particles where it blocks subsequent entry into susceptible target cells. Three accessory proteins encoded by diverse retroviruses, HIV-1 Nef, EIAV S2, and MLV Glycogag, each independently disrupt SERINC5 antiviral activity, by redirecting SERINC5 from the site of virion assembly on the plasma membrane to an internal RAB7+ endosomal compartment. Pseudotyping retroviruses with particular glycoproteins, e.g., the vesicular stomatitis glycoprotein (VSV G), renders the infectivity of particles resistant to inhibition by virion-associated SERINC5. To better understand viral determinants for SERINC5-sensitivity, the effect of SERINC5 was assessed using HIV-1, MLV, and M-PMV virion cores, pseudotyped with glycoproteins from Arenavirus, Coronavirus, Filovirus, Rhabdovirus, Paramyxovirus, and Orthomyxovirus genera. Infectivity of particles, pseudotyped with HIV-1, amphotropic-MLV, or influenza virus glycoproteins, was decreased by SERINC5, whether the core was provided by HIV-1, MLV, or M-PMV. Particles generated by all three cores, and pseudotyped with glycoproteins from either avian leukosis virus-A, human endogenous retrovirus K (HERV-K), ecotropic-MLV, HTLV-1, Measles morbillivirus, lymphocytic choriomeningitis mammarenavirus (LCMV), Marburg virus, Ebola virus, severe acute respiratory syndrome-related coronavirus (SARS-CoV), or VSV, were insensitive to SERINC5. In contrast, particles pseudotyped with M-PMV, RD114, or rabies virus (RABV) glycoproteins were sensitive to SERINC5, but only with particular retroviral cores. Resistance to SERINC5 by particular glycoproteins did not correlate with reduced SERINC5 incorporation into particles or with the route of viral entry. These findings indicate that some non-retroviruses may be sensitive to SERINC5 and that, in addition to the viral glycoprotein, the retroviral core influences sensitivity to SERINC5. IMPORTANCE The importance of SERINC5 for inhibition of retroviruses is underscored by convergent evolution among three non-monophyletic retroviruses, each of which encodes a structurally unrelated SERINC5 inhibitor. One of these retroviruses causes tumors in mice, a second anemia in horses, and a third causes AIDS. SERINC5 is incorporated into retrovirus particles where it blocks entry into target cells, via a mechanism that is dependent on the viral glycoprotein. Here we demonstrate that retroviruses pseudotyped with glycoproteins from several non-retroviruses are also inhibited by SERINC5, suggesting that enveloped viruses other than retroviruses may also be inhibited by SERINC5. Additionally, we found that sensitivity to SERINC5 is determined by the retrovirus core, as well as by the glycoprotein. By better understanding how SERINC5 inhibits viruses we hope to extend fundamental understanding of virus replication and of the native role of SERINC5 in cells, and perhaps to advance the development of new antiviral strategies.