Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlations between growth and wool quality traits of genetically divergent Australian lambs in response to canola or flaxseed oil supplementation

The correlations between growth and wool traits in response to canola and flaxseed oil supplementation were evaluated in Australian prime lambs. Sixty dual-purpose prime lambs including purebred Merino and crossbred lambs were allocated to one of five treatments of lucerne hay basal diet supplemented with isocaloric and isonitrogenous wheat-based pellets. Treatments were: no oil inclusion (Control); 2.5% canola oil; 5% canola oil; 2.5% flaxseed oil and 5% flaxseed oil, with lamb groups balanced by breed and gender. Each lamb was daily supplemented with 1kg of pellets and had free access to lucerne hay and water throughout the 7-week feeding trial, after a 3-week adaptation. Individual animal basal and supplementary pellet feed intakes were recorded daily, while body conformation traits, body condition scores and liveweights were measured on days 0, 21, 35 and 49. The lambs were dye-banded on the mid-side and shorn before commencing the feeding trial and mid-side wool samples were collected from the same dye-banded area of each lamb at the end of the experiment. Correlations between wool quality traits and lamb performance were non-significant (P>0.05). Oil supplementation had no detrimental effect on lamb growth and wool quality traits (P > 0.05). Gender significantly affected wither height gain and fibre diameter. There were significant interactions between oil supplementation and lamb breed on chest girth. The correlations between clean fleece yield (CFY) and other wool quality traits were moderate ranging from 0.29 to 0.55. Moderate to high correlations between fibre diameter (FD) and other wool quality traits were detected (0.46–0.99) with the strongest relationship between FD and wool spinning fineness (SF). The relationship between CFY and wool comfort factor (CF) were positive, while negative relationships between CFY and the others were observed. A combination of 5% oil supplementation and genetics is an effective and strategic management tool for enhancing feed efficiency and growth performance without negative effects on wool quality in dual-purpose lamb production. This is a good outcome for dual-purpose sheep farmers. It essentially means the absorbed nutrients in supplemented lambs yielded good growth performance without any detrimental impact on wool quality; a win-win case of nutrient partitioning into the synthesis of muscle and wool without compromising either traits.
Agreement between a single-item measure of anxiety and depression and the Hospital Anxiety and Depression Scale: A cross-sectional study

Anxiety and depression can be heightened among individuals living with chronic diseases. Identifying these individuals is necessary for ensuring they are provided with adequate support. Traditional tools such as clinical interviews or symptom checklists are not always feasible to implement in practice. Robust single-item questions may be a useful alternative. This study aimed to measure agreement, sensitivity, specificity, positive predictive value and negative predictive value of a single-item question about anxiety and depression compared to the widely used Hospital Anxiety and Depression Scale (HADS). A cross-sectional survey of 2,811 people with cancer attending 19 treatment centres in Australia. Patients were approached in the waiting room prior to an outpatient clinic appointment and invited to complete a pen and paper survey. Participants completed the HADS as well as 2 single-items asking if they have felt anxious or depressed in the last week. The single-items for anxiety and depression each demonstrated moderate levels of sensitivity (0.78 for anxiety; 0.63 for depression) and specificity (0.75 for anxiety; 0.84 for depression) against the relevant HADS subscale. Positive predictive values were moderate (0.53 for anxiety and 0.52 for depression) while negative predictive values were high for both single-item questions (0.90 for anxiety and 0.89 for depression). The single-item measures of anxiety and depression may be useful to rule out individuals who do not require further psychological assessment or intervention for anxiety and depression. Further research is needed to explore whether these findings generalise to other chronic diseases.
Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator

Research on human motor adaptation has often focused on how people adapt to self-generated or externally-influenced errors. Trial-by-trial adaptation is a person's response to self-generated errors. Externally-influenced errors applied as catch-trial perturbations are used to calculate a person's perturbation adaptation rate. Although these adaptation rates are sometimes compared to one another, we show through simulation and empirical data that the two metrics are distinct. We demonstrate that the trial-by-trial adaptation rate, often calculated as a coefficient in a linear regression, is biased under typical conditions. We tested 12 able-bodied subjects moving a cursor on a screen using a computer mouse. Statistically different adaptation rates arise when sub-sets of trials from different phases of learning are analyzed from within a sequence of movement results. We propose a new approach to identify when a person's learning has stabilized in order to identify steady-state movement trials from which to calculate a more reliable trial-by-trial adaptation rate. Using a Bayesian model of human movement, we show that this analysis approach is more consistent and provides a more confident estimate than alternative approaches. Constraining analyses to steady-state conditions will allow researchers to better decouple the multiple concurrent learning processes that occur while a person makes goal-directed movements. Streamlining this analysis may help broaden the impact of motor adaptation studies, perhaps even enhancing their clinical usefulness.
The role of normative beliefs in the mediation of a school-based drug prevention program: A secondary analysis of the #Tamojunto cluster-randomized trial

Aims To investigate the mediating effects of normative beliefs of drug use on the effects of the #Tamojunto school-based prevention program (Unplugged). Design Secondary analysis of a cluster randomized controlled trial. Setting Brazil. Participants: A total of 6,391 adolescents (12.68 y.o) from 72 public schools in 6 Brazilian cities. Intervention: Schools were assigned to an experimental condition (#Tamojunto curriculum) or a control condition (no prevention program). Measurements: Baseline data were collected prior to program implementation, and follow-up data were collected 9 and 21 months later. The substances examined were alcohol (including binge drinking), tobacco, marijuana and inhalants. Five in-parallel mediation models evaluated whether the positive and negative beliefs were mediators of the likely effects of the intervention on drug use. Findings Lack of evidences regarding differences in normative beliefs or drug use were found between the intervention and control groups. However, there was a clear association between negative drug beliefs and lower consumption (i.e. OR = 0.78; 95% CI 0.70; 0.87, for cannabis use) as well as between positive drug beliefs and higher consumption (i.e. OR = 1.77; 95% CI 1.56; 2.02, for cannabis use) independent of the assigned group. Conclusions These results suggest that there is a lack of evidence that the program impact the normative beliefs, as proposed by the theoretical model of the program, suggesting that modifications are needed to produce the intended effect of the program. Negative normative beliefs seem to be a potential protective factor for drug use, but the program’s effect itself on drug use via normative beliefs was not found to be statistically significant. Program activities intended to affect normative beliefs should be improved.
Comparison of single sourcing (with lot streaming) and dual-sourcing

Dual-sourcing is a strategy of ordering the material required to process a lot (order) from two suppliers, instead of a single supplier. This strategy has been well-studied in the literature and has been shown to reduce lead time. However, the additional ordering cost incurred for dual-sourcing makes this strategy unattractive. In this paper, we compare it to an alternative strategy of sourcing the required material from a single supplier, but by permitting its delivery in two partial shipments (referred to here as single-sourcing (with lot streaming)). We consider the case when the supplier lead time is stochastic and its distribution depends on the size of the lot processed. We show that single-sourcing (with lot streaming): (1) is less prone to stockouts than dual-sourcing, (2) incurs lower expected lead time for a given stockout risk when the time between orders in dual-sourcing is large enough, and (3) results in lower inventory levels given the same lead time performance and allowable stockout risk when the supplier’s processing times are significantly smaller than those of the manufacturer. The risk of delay in the arrival of the first sublot is smaller for dual-sourcing, however, this advantage decreases when time between orders is increased.
The economic burden of Lyme disease and the cost-effectiveness of Lyme disease interventions: A scoping review

Introduction While Lyme disease (LD) is mostly treatable, misdiagnosed or untreated LD can result in debilitating sequelae and excessive healthcare usage. The objective of this review was to characterize the body of literature on the economic burden of Lyme disease (LD) and the cost-effectiveness of LD interventions, such as antibiotic treatment and vaccination. Methods We followed Joanna Briggs Institute scoping review methodologies. We systematically searched terms related to LD, economic evaluations, costs, and cost-effectiveness in Medline, Embase, PsycInfo, Cochrane Library, and the grey literature up to November 2017. We included primary economic evaluations conducted in North America and Europe, reporting LD-related costs or cost-effectiveness of human interventions. Two reviewers screened articles and charted data independently. Costs were standardized to 2017 United States dollars (USD). Results We screened 923 articles, and included 10 cost-effectiveness analyses (CEA) and 11 cost analyses (CA). Three CEAs concluded LD vaccination was likely cost-effective only in endemic areas (probability of infection ≥1%). However, LD vaccination is not currently available as an intervention in the US or Europe. Six studies assessed economic burden from a societal perspective and estimated significant annual national economic impact of: 735,550 USD for Scotland (0.14 USD per capita, population = 5.40M), 142,562 USD in Sweden (0.014 USD per capita, 9.96M), 40.88M USD in Germany (0.51 USD per capita, 80.59M), 23.12M USD in the Netherlands (1.36 USD per capita, 17.08M), and up to 786M USD in the US (2.41 USD per capita, 326.63M). Conclusions Lyme disease imposes an economic burden that could be considered significant in the US and other developed countries to justify further research efforts in disease control and management. Societal costs for Lyme disease can be equally impactful as healthcare costs, but are not fully understood. Economic literature from countries with historically high incidence rates or increasing rates of Lyme disease are limited, and can be useful for future justification of resource allocation.
Identification of calnexin as a diacylglycerol acyltransferase-2 interacting protein

Triacylglycerol synthesis is catalyzed by acyl CoA:diacylglycerol acyltransferase-2 (DGAT2). DGAT2 is an integral membrane protein that is localized to the endoplasmic reticulum and interacts with lipid droplets. Using BioId, a method to detect proximal and interacting proteins, we identified calnexin as a DGAT2-interacting protein. Co-immunoprecipitation and proximity ligation assays confirmed this finding. We found that calnexin-deficient mouse embryonic fibroblasts had reduced intracellular triacylglycerol levels and fewer large lipid droplets (>1.0 μm2 area). Despite the alterations in triacylglycerol metabolism, in vitro DGAT2 activity, localization and protein stability were not affected by the absence of calnexin.
The integration of pharmacophore-based 3D QSAR modeling and virtual screening in safety profiling: A case study to identify antagonistic activities against adenosine receptor, A2A, using 1,897 known drugs

Safety pharmacology screening against a wide range of unintended vital targets using in vitro assays is crucial to understand off-target interactions with drug candidates. With the increasing demand for in vitro assays, ligand-and structure-based virtual screening approaches have been evaluated for potential utilization in safety profiling. Although ligand based approaches have been actively applied in retrospective analysis or prospectively within well-defined chemical space during the early discovery stage (i.e., HTS screening and lead optimization), virtual screening is rarely implemented in later stage of drug discovery (i.e., safety). Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2aR). The resulting models, obtained from 268 chemically diverse compounds, were used to test a set of 1,897 chemically distinct drugs, simulating the real-world challenge of safety screening when presented with novel chemistry and a limited training set. Due to the unique requirements of safety screening versus discovery screening, the limitations of 3D QSAR methods (i.e., chemotypes, dependence on large training set, and prone to false positives) are less critical than early discovery screen. We demonstrated that 3D QSAR modelling can be effectively applied in safety assessment prior to in vitro assays, even with chemotypes that are drastically different from training compounds. It is also worth noting that our model is able to adequately make the mechanistic distinction between agonists and antagonists, which is important to inform subsequent in vivo studies. Overall, we present an in-depth analysis of the appropriate utilization and interpretation of pharmacophore-based 3D QSAR models for safety screening.
The effects and mechanism of peiminine-induced apoptosis in human hepatocellular carcinoma HepG2 cells

Peiminine is a compound that is isolated from Bolbostemma paniculatum (Maxim) Franquet (Cucurbitaceae family), which has demonstrated antitumor activities. Its precise molecular mechanisms underlying antitumor activity remain elusive. In this study, peiminine-induced apoptosis towards human hepatocellular carcinoma and its molecular mechanisms were investigated. MTT assay was employed to assess anticancer effects of peiminine at concentrations of 2, 4, 6, 8, 10, 12, and 14 μg/ml after 24, 48, or 72 h. Nuclear staining and flow cytometry were carried out to further assess apoptosis. Mitochondrial membrane potential evaluation and Western blot analysis were performed to investigate the mechanism of peiminine-induced apoptosis. Peiminine reduced the viability of HepG2 cells in a time- and dose-dependent manner and had an IC50 of 4.58 μg/mL at 24h. Flow cytometry assessment indicated that peiminine markedly increased the cell number of apoptotic cells and the mitochondrial membrane potential dose-dependently in HepG2 cells. The results of Western blotting showed the expression of Bcl-2, procaspase-3, procaspase-8, procaspase-9, and PARP1 decreased in HepG2 cells treated with peiminine, while the expression of Bax, caspase-3, caspase-8, caspase-9, and cleaved PARP1 increased. The result suggest taht peiminine can induce apoptosis in human hepatocellular carcinoma HepG2 cells through both extrinsic and intrinsic apoptotic pathways.
Automatic Certificate Management Environment (ACME)

Public Key Infrastructure X.509 (PKIX) certificates are used for a number of purposes, the most significant of which is the authentication of domain names. Thus, certification authorities (CAs) in the Web PKI are trusted to verify that an applicant for a certificate legitimately represents the domain name(s) in the certificate. Today, this verification is done through a collection of ad hoc mechanisms. This document describes a protocol that a CA and an applicant can use to automate the process of verification and certificate issuance. The protocol also provides facilities for other certificate management functions, such as certificate revocation. RFC EDITOR: PLEASE REMOVE THE FOLLOWING PARAGRAPH: The source for this draft is maintained in GitHub. Suggested changes should be submitted as pull requests at https://github.com/ietf-wg-acme/acme [1]. Instructions are on that page as well. Editorial changes can be managed in GitHub, but any substantive change should be discussed on the ACME mailing list (acme@ietf.org).
Orthogonality-Promoting Dictionary Learning via Bayesian Inference

Dictionary Learning (DL) plays a crucial role in numerous machine learning tasks. It targets at finding the dictionary over which the training set admits a maximally sparse representation. Most existing DL algorithms are based on solving an optimization problem, where the noise variance and sparsity level should be known as the prior knowledge. However, in practice applications, it is difficult to obtain these knowledge. Thus, non-parametric Bayesian DL has recently received much attention of researchers due to its adaptability and effectiveness. Although many hierarchical priors have been used to promote the sparsity of the representation in non-parametric Bayesian DL, the problem of redundancy for the dictionary is still overlooked, which greatly decreases the performance of sparse coding. To address this problem, this paper presents a novel robust dictionary learning framework via Bayesian inference. In particular, we employ the orthogonality-promoting regularization to mitigate correlations among dictionary atoms. Such a regularization, encouraging the dictionary atoms to be close to being orthogonal, can alleviate overfitting to training data and improve the discrimination of the model. Moreover, we impose Scale mixture of the Vector variate Gaussian (SMVG) distribution on the noise to capture its structure. A Regularized Expectation Maximization Algorithm is developed to estimate the posterior distribution of the representation and dictionary with orthogonality-promoting regularization. Numerical results show that our method can learn the dictionary with an accuracy better than existing methods, especially when the number of training signals is limited.
METODOS DE DESARROLLO DE SOFTWARE: EL DESAFIO PENDIENTE DE LA ESTANDARIZACION

This paper describes the evolution of software development methods. The focus is the disagreement about how programmers must make software. The issue is: How is software development considered as a engineering process or as a human centered process. Unified Model Language (UML) is considered as a standard notation for software development. At present, UML is considered as basis for a Monumental Methodology (which include a lot of rules and practices) - RUP. We mentioned the reactions to these Monumental methodologies: The Agile Methodologies. Which is characteristic of an immature state of software development as a discipline. There is not only disagreements in terminology, approach, and details of different methods, there is not even a commonly accepted classification scheme. The following structure is based on the evolution of the underlying concepts and the key distinctions that reflects the paradigmatic shifts the philosophy of methods.
An empirical evaluation of the effectiveness of JML assertions as test oracles

Test oracles remain one of the least understood aspects of the modern testing process. An oracle is a mechanism used by software testers and software engineers for determining whether a test has passed or failed. One widely-supported approach to oracles is the use of runtime assertion checking during the testing activity. Method invariants, preand postconditions help detect bugs during runtime. While assertions are supported by virtually all programming environments, are used widely in practice, and are often assumed to be effective as test oracles, there are few empirical studies of their efficacy in this role. In this thesis, we present the results of an experiment we conducted to help understand this question. To do this, we studied seven of the core Java classes that had been annotated by others with assertions in the Java Modeling Language, used the muJava mutation analysis tool to create mutant implementations of these classes, exercised them with input-only (i.e., no oracle) test suites that achieve branch coverage, and used a machine learning tool, Weka, to determine which annotations were effective at “killing” these mutants. We also evaluate how effective the “null oracle” (in our case, the Java runtime system) is at catching these bugs. The results of our study are interesting, and help provide software engineers with insight into situations in which assertions can be relied upon to find bugs, and situations in which assertions may need to be augmented with other approaches to test oracles.
Group-Based Susceptible-Infectious-Susceptible Model in Large-Scale Directed Networks

Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.
A Vision-Based Neural Network Controller for the Autonomous Landing of a Quadrotor on Moving Targets

Time constraints is the most critical factor that faces the first responders’ teams for search and rescue operations during the aftermath of natural disasters and hazardous areas. The utilization of robotic solutions to speed up search missions would help save the lives of humans who are in need of help as quickly as possible. With such a human-robot collaboration, by using autonomous robotic solutions, the first response team will be able to locate the causalities and possible victims in order to be able to drop emergency kits at their locations. This paper presents a design of vision-based neural network controller for the autonomous landing of a quadrotor on fixed and moving targets for Maritime Search and Rescue applications. The proposed controller does not require prior information about the target location and depends entirely on the vision system to estimate the target positions. Simulations of the proposed controller are presented using ROS Gazebo environment and are validated experimentally in the laboratory using a Parrot AR Drone system. The simulation and experimental results show the successful control of the quadrotor in autonomously landing on both fixed and moving landing platforms.