Miracle-dz/UniFAR-FTUs huggingface dataset:
É possível defender a ciência contra as ideologias?

This paper aims to identify the possibility of an objective science, free of ideologies, in the sense commonly attributed to the physical sciences. Ideologies can be used primarily in two different ways: to reinforce individual interests or beliefs; or confront opposing ideological assumptions. Thus, even if a field of science can be built on skewed ideologies or theoretical assumptions, the rigorous tests by which the theories are subjected bring the theories closer to the truth and it can lead to the rejection of those contrary to the available evidence. The truth proves to be an essential value, but it is not the only one, for the practice of science.
The Mechanism Underlying the Extreme Sensitivity of Duck to Aflatoxin B1

Most metabolites of aflatoxin B1 (AFB1), especially exo-AFB1-8,9-epoxide (AFBO), can induce the production of reactive oxygen species (ROS) to vary degrees, causing oxidative stress and liver damage, and ultimately induce liver cancer in humans and animals. Duck is one of the most sensitive animals to AFB1, and severe economic losses are caused by duck AFB1 poisoning every year, but the exact mechanism of this high sensitivity is still unclear. This review highlights significant advances in our understanding of the AFB1 metabolic activation, like cytochrome P450s (CYPs), and AFB1 metabolic detoxification, like glutathione S-transferases (GSTs) in poultry. In addition, AFB1 may have other metabolic pathways in poultry, such as the mutual conversion of AFB1 and aflatoxicol (AFL) and the process of AFBO to produce AFB1-8,9-dihydrodiol (AFB1-dhd) and further metabolize it into detoxification substances. This review also summarized some exogenous regulatory substances that can alleviate AFB1-induced oxidative stress.
Land grabbing, power configurations and trajectories of China’s investments in Argentina

ABSTRACT This paper analyses the display of the power relations involved in the trajectory of China’s land grabbing in Argentina. Through a study of the failed agri-food project of Beidahuang Group in Rio Negro, it shows the centrality of the dynamic configurations of material, discursive and institutional power surrounding Chinese investments to both facilitate land grabbing and condition its outcomes. Drawing on a neo-Gramscian approach, three configurations of power are identified in interaction with a logic of agricultural accumulation with hegemonic aspirations: the alignment of power relations; the challenges and the misalignment of these forces. Consequently, the project suspension results from a change in power relations caused by reactions from ‘below’ and ‘above’, although this does not affect the global primacy of agribusiness.
Reverse Regulation Between sFRP 5 and Dkk 1 Gene in Early Stage Lung Adenocancer

Purpose: We aimed to examine the expression levels of the genes of APC (Adenomatous Polyposis Coli) 1, APC 2, Dkk (Dickkopf related protein) 1, Dkk -3, sFRP (Secreted frizzled-related protein) -2, sFRP-4, and sFRP-5 genes which play a role in the Wnt signaling pathway in lung adenocarcinoma and adjacent normal lung tissues, and to evaluate their relationship with clinical-pathological factors.Materials and methods: Between 2011 and 2018, the expression levels of the relevant genes in formalin-fixed paraffin-embedded tumor and adjacent intact lung tissue samples of 57 patients who were operated for lung adenocarcinoma were determined by Real-time PCR analysis. Results: The expression levels of the Dkk-1 gene in the tumor tissue, especially in stage I-II, were statistically significantly suppressed compared to normal tissue (p <0.025 ). Although Dkk-1 gene expression was suppressed in the tumor tissue of patients with early-stage lung adenocarcinoma, the level of expression of the sFRP-5 gene was found to be statistically significantly higher (p<0.039). Conclusion: In our study, between the sFRP-5 and Dkk-1 genes, known as the extracellular antagonist of the Wnt signaling pathway was found the reverse regulation. sFRP-5 gene was found as having an oncogenic role in adenocarcinoma development. Reverse regulation between these genes in early-stage lung adenocarcinoma may shed light on the mechanisms associated with the development of carcinogenesis. For that reason, clinically, this relationship needs to research in a larger series of pure adenocarcinoma and normal human lung tissues, separated by its stage, for potential therapeutic target or prognostic its significance.
Semantic Segmentation of Cabbage in the South Korea Highlands with Images by Unmanned Aerial Vehicles

Identifying agricultural fields that grow cabbage in the highlands of South Korea is critical for accurate crop yield estimation. Only grown for a limited time during the summer, highland cabbage accounts for a significant proportion of South Korea’s annual cabbage production. Thus, it has a profound effect on the formation of cabbage prices. Traditionally, labor-extensive and time-consuming field surveys are manually carried out to derive agricultural field maps of the highlands. Recently, high-resolution overhead images of the highlands have become readily available with the rapid development of unmanned aerial vehicles (UAV) and remote sensing technology. In addition, deep learning-based semantic segmentation models have quickly advanced by recent improvements in algorithms and computational resources. In this study, we propose a semantic segmentation framework based on state-of-the-art deep learning techniques to automate the process of identifying cabbage cultivation fields. We operated UAVs and collected 2010 multispectral images under different spatiotemporal conditions to measure how well semantic segmentation models generalize. Next, we manually labeled these images at a pixel-level to obtain ground truth labels for training. Our results demonstrate that our framework performs well in detecting cabbage fields not only in areas included in the training data but also in unseen areas not included in the training data. Moreover, we analyzed the effects of infrared wavelengths on the performance of identifying cabbage fields. Based on the results of our framework, we expect agricultural officials to reduce time and manpower when identifying information about highlands cabbage fields by replacing field surveys.
The Role of Renewable Energy and Ecological Footprint on Economic Growth in Francophone African Countries in presence of Institutions

This paper targets to examine the impact of renewable energy and ecological footprint on economic growth in 14 selected French-speaking countries in Africa. The study contributes to the ongoing debate in the literature on environment growth-nexus by providing evidence that economic growth emerges with environmental degradations and can be improved when there is a robust institutional framework. The present research used the generalized method of moments (GMM) to assess a dynamic growth model with data from 2007 to 2015. The results demonstrate that renewable energy is significant and negatively related to economic growth, which implies that renewable energy sources lower the per capita income growth in these countries. Meanwhile, the ecological footprint is positive and statistically significant in impacting economic growth in the long run. For institutions, we find that voice and accountability, political stability, and the rule of law are positive and statistically significant in influencing economic growth. Consequently, it is recommended that policymakers in this region develop dual policies that raise institutions' quality with minimal emissions of greenhouse gases.
Validating a partial-credit scoring approach for multiple-choice science items: an application of fundamental ideas in science

ABSTRACT This study provides a partial-credit scoring (PCS) approach to awarding students’ performance on multiple-choice items in science education. The approach is built on fundamental ideas, the critical pieces of students’ understanding and knowledge to solve science problems. We link each option of the items to several specific fundamental ideas to capture their mastery patterns when an option is selected. Using these mastery patterns to order the options of items and accordingly assign credits to students, we measure students’ cognitive proficiency without including additional measures (e.g. times of trial) to the test or requiring extra support (e.g. technology). Using many-facet Rasch analysis, we find that the ordered options students selected were aligned with their ability measures. The PCS yields robust psychometric quality; compared to the dichotomous scoring of multiple-choice items, it generates better item fit and separation parameters. Besides, this PCS approach helps address construct validity by modelling student responses at the option level to reflect students’ mastery of fundamental ideas.
Morphological Influence of Precursor Composition Characteristics During Ceramic Membrane Fabrication

In this work, the influence of inorganic low-cost precursor compositions (viz. kaolin, feldspar, saw dust, sodium metasilicate and boric acid) has been investigated on the morphological characteristics (such as average pore size and porosity) of the ceramic membranes that were fabricated using dry compaction method and saw dust as the pore forming agent. In order to do so, kaolin to feldspar ratio has been varied from 0.48 – 2.05 and binder composition has been varied from 10 – 15 wt. %. For an inorganic precursor formulation of kaolin 38.77 wt. %, feldspar 23.03 wt. %, saw dust 8.19 wt. %, sodium metasilicate 15 wt. % and boric acid 15 wt. %, the sub-micron range low-cost ceramic membranes (95.8 nm average pore size and 13.95% porosity) have been achieved as a key novelty. With minor variations in the precursor composition, the microfiltration membranes could be converted to ultrafiltration membranes without undergoing any complex surface reactions or polymeric coatings. An empirical model has been as well developed to quantify the variation of dependent variables on the membrane characteristics.
ASSESSMENT OF FALL RISK AMONG ELDERLY IN HOME ENVIRONMENT IN AL-HASA

Paramasivan Mani, Nowaf Alanazi, Senthil Vadivu, Mohammed AL Sultan, Omar Almahri, Ibrahim Alnaim and Ahmed Almulhim King Saud Bin Abdulaziz University for Health Sciences, King Abdulla International Research Centre (KAIMRC), Saudi Arabia. Manuscript Info Abstract . Manuscript History Received: 05 February 2021 Final Accepted: 10 March 2021 Published: April 202
A Model of Psychological Mechanisms of Inclusive Music-Making: Empowerment of Marginalized Young People

Adopting a mixed-methods research design, this study explored the psychological experiences of marginalized young people participating in inclusive music projects, with attention to inter- and intra-personal outcomes and underlying mechanisms. We worked with four different music projects, aimed at young people from lower socio-economic backgrounds or those with special educational needs and/or disabilities. With a total sample of 134 young people, parents, and creative practitioners, we used quantitative analyses of retrospective surveys to assess staff members’ perceptions of changes evident in individual young people (n1(total) = 99, n1(female) = 39, n1(male) = 59, n1(non-binary) = 1, mean age = 15.59). We performed thematic analysis on eleven semi-structured focus group discussions conducted with the young people (n2 = 26), their parents (n3 = 14), and staff members (n3 = 21), and 82 session reports completed by the staff members after each session. Growth over time in both intra-personal and inter-personal dimensions of functioning was observed, with qualitative data illuminating possible environmental and psychological mechanisms via two overarching themes of “Self-Development” and “Social Acknowledgement”. These results are collated in the model of youth empowerment, and its relevance to marginalized groups of young people is highlighted through promotion of active agency and empowerment. The model is interpreted in the light of several psychological theories of well-being, particularly self-determination theory and access-awareness-agency model, and implications for future work are discussed.
Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs

Abstract Performing online monitoring for short horizon data is a challenging, though cost effective benefit. Self-starting methods attempt to address this issue adopting a hybrid scheme that executes calibration and monitoring simultaneously. In this work, we propose a Bayesian alternative that will utilize prior information and possible historical data (via power priors), offering a head-start in online monitoring, putting emphasis on outlier detection. For cases of complete prior ignorance, the objective Bayesian version will be provided. Charting will be based on the predictive distribution and the methodological framework will be derived in a general way, to facilitate discrete and continuous data from any distribution that belongs to the regular exponential family (with Normal, Poisson and Binomial being the most representative). Being in the Bayesian arena, we will be able to not only perform process monitoring, but also draw online inference regarding the unknown process parameter(s). An extended simulation study will evaluate the proposed methodology against frequentist based competitors and it will cover topics regarding prior sensitivity and model misspecification robustness. A continuous and a discrete real data set will illustrate its use in practice. Technical details, algorithms, guidelines on prior elicitation and R-codes are provided in appendices and supplementary material. Short production runs and online phase I monitoring are among the best candidates to benefit from the developed methodology.
Quantification of anaerobic thermophilic endospores in marine sediment by microcalorimetry, and its use in bioprospecting for gas and oil

Endospores of anaerobic thermophilic bacteria (thermospores) are ubiquitous in cold marine sediments. These misplaced thermophiles likely originate from warm environments and are delivered to the seafloor by passive dispersal through the water column. The few studies of the abundance of thermospores that exist have only quantified a subset of the anaerobic metabolic types possibly present and the data density has been too low to address patterns of dispersal. Here, we introduce isothermal microcalorimetry as a fast tool to quantify the abundance of thermospores in environmental samples. We developed and tested the method on samples from the water column and sediment of Aarhus Bay, Denmark. The thermospores were stimulated to germinate and grow by heating the samples to 50°C inside an isothermal microcalorimeter and the metabolic heat production was followed across the exponential growth phase. The number of germinated thermospores was then calculated by dividing the total heat production rate by the cell specific heat production rate, empirically determined in parallel experiments. The abundance of thermospores in the water column of Aarhus Bay was 1.2 × 103−3.6 × 103 cells × mL−1 seawater. The abundance of thermospores in sediment in Aarhus Bay was 1.1 × 106−6.1 × 106 cells × mL−1 sediment and their half‐life was ∼70 yr. As application example we used isothermal microcalorimetry to test if thermospores were aggregated at a pockmark in Limfjorden (Denmark) with seepage of thermogenic gas and oil.
A gravitational torque-compensated 2-DOF planar robotic arm design and its active control

Serial robot manipulators have their servo motors with reduction gears on the link joints. When it comes to hyper-redundant robots, this kind of joint actuation mechanism cannot be implemented since this makes hyper-redundant robots too heavy. Instead, cable driven mechanisms are preferred. However, the positioning accuracy is negatively affected by the cables. This paper addresses the positioning accuracy problem of cable driven hyper-redundant robots by employing a 2-DOF robotic arm whose modules are counter-balanced. While the actuators connected to the base actively do most of the work using cables and springs, light and compact actuators connected to the links produce precise motion. The method will result in compact, light and precise hyper-redundant robotic arms. The above-mentioned procedure governed by a control software including a 2D simulator developed is experimentally proved to be a feasible method to compensate the gravitational torque successfully.
Cyclopentadione-aniline conjugate suppresses proliferation and induces apoptosis in liver cancer cells via up-regulation of p38 phosphorylation

Purpose: To investigate the effect of cyclopentadione-aniline conjugate (CAC) on proliferation of liver cancer cells. Methods: MTT assay and flow cytometry were used for the determination of the effect of CAC on cell proliferation and apoptosis. Western blotting was used to measure the influence of CAC on the expressions of various proteins, while Matrigel-coated Transwell assay was used for assessment of cell invasion. Results: CAC inhibited proliferation of liver cancer cells in a concentration-dependent manner. The degree of proliferation of HepG2 cells was 98, 89, 76, 66, 51, 42 or 36 %, on treatment with 0.25, 0.5, 1.0, 1.5, 2.0, 2.5 or 3.0 μM CAC, respectively. In H4TG cells, treatment with 0.25, 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 μM CAC decreased proliferation of cells to 99, 91, 79, 70, 54, 46 and 40 %, respectively. Apoptosis was induced in 34.56, 37.37 and 52.98 % cells, on treatment with 2.0, 2.5 and 3.0 μM CAC, respectively. The invasive potential of HepG2 cells was significantly decreased by CAC (p < 0.05). Marked decreases were observed in Bcl-2, MMP-2, MMP-9, c-ERK1/2 and phospho-Akt levels in CACtreated HepG2 cells. CAC treatment markedly upregulated Bax and phospho ERK1/2, but significantly downregulated phospho PI3K, phospho mTOR and phospho Akt in HepG2 cells (p < 0.05). However, the level of phospho p38 was decreased in CAC-treated cells. Conclusion: These results demonstrate that CAC inhibits proliferation of liver cancer cells via apoptosis induction. Thus, CAC can potentially be used for the treatment of liver cancer in humans.
Importance of considering non-starch polysaccharide content of poultry diets

SUMMARY The contribution of dietary fibre in feed ingredients is still inadequately accounted for during feed formulation, despite extensive evidence of its impact on bird performance, litter quality and digestibility of other nutrients, and its high level in plant-derived feed ingredients. True dietary fibre is defined as the total quantity of non-starch polysaccharides (NSP) and lignin. In contrast to other nutrients, NSP vary substantially in composition, both among different feed ingredients and among batches of the same ingredient. Consequently, NSP display variable characteristics in final feeds. NSP have traditionally been recognised as anti-nutritive, through their impact on digesta viscosity and as a nutrient diluent in poultry rations. However, more recently, there is growing interest in their capacity to stimulate gastrointestinal function, improve gut health and act as a source of energy, partially via prebiotic properties. Recent developments in methods for measuring NSP have increased precision when estimating the dietary fibre component of feed ingredients and diets, yet there is still a lack of application of NSP values in both research and industry diets for poultry. Determining the fate of fibre post-ingestion is challenging, due to the complexity of its structural components and limited understanding about how fibre interacts with other dietary nutrients within the intestinal tract environment. The aim of this review is to highlight the importance of considering NSP values when formulating poultry rations, based on evidence from scientific studies presenting the direct impact dietary NSP has on gastrointestinal health and bird performance.
Modelling the impacts of climate change on potential cultivation area and water deficit in five Mediterranean crops

Aim of study: To assess the impacts of climate change on local agriculture with a high resolution in a Mediterranean region with a diversity of climates.Area of study: Catalonia (NW Spain). Material and methods: Based on historical meteorological records and a regionalization of the RCP4.5 model created by the Catalan Meteorological Service, the Papadakis agro-climate classification was calculated for two climate scenarios. The changes in agro-climatic suitability and irrigation needs of five typical Mediterranean crops (alfalfa, almond, barley, olive and orange) were analysed. Main results: In the 2031-2050 climate scenario, over 15% of the study area will no longer be adequate for non-irrigated almond or olive, at locations in which they have been traditionally rainfed crops. If irrigation is provided, orange is likely to become agro-climatically suited for the entire Catalan coastline. Were the current crop distribution maintained, irrigation needs may increase on average 16% in the study area in the future scenario.Research highlights: High-resolution GIS data may be combined with Papadakis’ classical method to compare different climate scenarios and detect risks and opportunities for local and regional agriculture.
Machine learning based decline curve analysis for short-term oil production forecast

Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific models to fit production data for production forecasting. Various decline curve models have been applied for unconventional wells, including the Arps model, stretched exponential model, Duong model, and combined capacitance-resistance model. However, it is not straightforward to determine which model should be used, as multiple models may fit a dataset equally well but provide different forecasts, and hastily selecting a model for probabilistic DCA can underestimate the uncertainty in a production forecast. Data science, machine learning, and artificial intelligence are revolutionizing the oil and gas industry by utilizing computing power more effectively and efficiently. We propose a data-driven approach in this paper to performing short term predictions for unconventional oil production. Two states of the art level models have tested: DeepAR and used Prophet time series analysis on petroleum production data. Compared with the traditional approach using decline curve models, the machine learning approach can be regarded as” model-free” (non-parametric) because the pre-determination of decline curve models is not required. The main goal of this work is to develop and apply neural networks and time series techniques to oil well data without having substantial knowledge regarding the extraction process or physical relationship between the geological and dynamic parameters. For evaluation and verification purpose, The proposed method is applied to a selected well of Midland fields from the USA. By comparing our results, we can infer that both DeepAR and Prophet analysis are useful for gaining a better understanding of the behavior of oil wells, and can mitigate over/underestimates resulting from using a single decline curve model for forecasting. In addition, the proposed approach performs well in spreading model uncertainty to uncertainty in production forecasting; that is, we end up with a forecast which outperforms the standard DCA methods.