Miracle-dz/UniFAR-FTUs huggingface dataset:
Phytase for food application

Phytase [myo-inositol(1,2,3,4,5,6)hexakisphosphate phosphohydrolase], a phytate-specific phosphatase, is already used as a supplement in diets for monogastric animals to improve phosphate utilisation from phytate [myo-inositol(1,2,3,4,5,6)hexakisphosphate], the major storage form of phosphate in plant seeds. In recent years, this class of enzymes has also been found increasingly interesting for use in processing and manufacturing of food for human consumption, particularly because the decline in food phytate results in an enhancement of mineral bioavailability. Different strategies could be applied to optimise phytate degradation during food processing and digestion in the human alimentary tract such as adjustment of more favourable conditions during food processing for the phytases naturally occurring in the raw material, addition of isolated phytases to the production process, use of raw material with a high intrinsic phytate-degrading activity either naturally present or introduced by genetic engineering and the use of recombinant food-grade microorganisms as carriers for phytate-degrading activity in the human gastrointestinal tract. Furthermore, phytases may find application in the production of functional foods or food supplements with health benefits. Last but not least, technological improvements are expected to occur due to phytate degradation during processing as shown for breadmaking, production of plant protein isolates, corn wet milling and the fractionation of cereal bran.
Control of feed intake and the efficiency of utilisation of feed by ruminants.

Appetite and the control of feed intake have been reviewed in the past, mostly by scientists immersed in the area and they usually cover a complex series of interactions at numerous levels of metabolic control. The concept developed here suggests that, although there are many factors which integrate to result in the voluntary feed intake, including behavioural, neural, environmental and metabolic factors; in any particular situations the over-riding control may be identified so that management strategies can overcome inappetence. It is often suggested that feed intake is the primary control of the level of production in animals on diets that have well balanced arrays of nutrients. However, this may not be the case where animals are consuming forages deficient in critical nutrients. In recent years it has become increasingly obvious that feed utilisation efficiency can be substantially altered and this often has a greater influence on productivity (and profitability) than increasing feed intake. However, where intake and feed conversion efficiency are improved simultaneously a compounding effect is observed. The concept developed below examines feed intake control as primarily being due to two influences, the first is food quality as indicated by digestibility, gut fill and the balance of nutrients that becomes available from the feed. The second is body temperature control which interacts with the heat of fermentation, heat increment of feeding, and climate heat stress. Acetate removal from the blood pool may be an over-riding factor in feed intake control and may be the primary feedback mechanism when the animal is suffering heat stress. In temperate areas the feeds tend to be higher in essential nutrients, leading to low heat generation in fermentative digestion and also in metabolism. In the hot climates, however, forage from tropical pastures or crop residues are often deficient in essential nutrients, particularly trace elements and nitrogen needed by the rumen microbes for efficient growth. It is postulated that a low P/E ratio that results from this translates into a high heat increment in the rumen and a high metabolic heat production in the animal, which at times interacts with climate to produce a heat stress which reduces feed intake. Fundamentally these differences infer that there should be two approaches to optimising feed intake which is suitable to either the tropics or temperate countries. The amount of protein required by ruminants under tropical conditions to maintain intake and productivity is therefore much higher than that for animals in temperate countries. However, when the rumen has a highly efficient microbial ecosystem the balance of nutrients absorbed will be the same in animals in both environments and then, feed intake is controlled largely by gut fill.
Diffuse Axonal Injury Lesion Segmentation Using Contouring Algorithm

Diffuse axonal injury (DAI) is a common type of brain damage induced by trauma. Accurate detection and quantification of DAI lesions is essential for assessment of a patient’s state and making a prognosis of a traumatic disease. This study is devoted to semiautomated segmentation of DAI lesions on T2*-weighted brain magnetic resonance (MR) images. A radiologist outlines the regions containing lesions, and the lesions are further automatically delineated inside these regions. A proposed algorithm for automated segmentation of DAI lesions inside specified regions is based on contouring algorithm which treats the image as a 3D topological map where a pixel’s intensity corresponds to its height. It builds isolines of an intensity function. Lesion contours are among these isolines. In order to distinguish true lesion contours from other closed contours obtained by contouring algorithm machine learning approach is exploited. A labeled training base with positive (lesions) and negative (non-lesions or lesion parts) examples of closed contours is used to train the classifier. The algorithm was evaluated with real T2*-weighted brain MRI images.
Homologous Gene Finding with a Hidden Markov Model

The homology search problem and the gene finding problem are two fundamental problems in bioinformatics. The homology search problem is to find the homologous regions of two biological sequences; the gene finding problem is to find all the genes in both strands of a genomic sequence. Recently, gene finding research has demonstrated that homology search results can be used to improve the accuracy of gene finding. By combining the two problems, we define a new problem called the homologous gene finding problem. The homologous gene finding problem is to find homologous genes of a query gene in a target genomic sequence. Consequently, we present a new homologous gene finding algorithm in this thesis. We borrow the idea of gene mapping and alignment algorithms, and apply existing seed-based homology search algorithms and hidden Markov model-based (HMM-based) gene finding algorithms to solve the homologous gene finding problem. After we find high-scoring segment pairs (HSPs) between the query gene and the target genomic sequence, we locate target regions that we believe contain a gene homologous to the query gene. Then, we extend existing HMM-based gene finding algorithms to find homologous gene candidates. To improve the accuracy of homologous gene finding, we train a HMM to be biased toward the query gene. We also introduce a new coding sequence (CDS) length penalty as a measure of how the CDS lengths of the query gene and its homologous gene vary to further improve the accuracy. We use the new CDS length penalty together with our enhanced Viterbi algorithm and our flexible finish condition to improve the speed of homologous gene fining without harming the accuracy. Finally, we use protein alignment to pick and rank the best homologous gene candidates. In this thesis, we also describe several experiments to evaluate and support our homologous gene finding algorithm.
Comparisons of effectiveness and safety between bivalirudin and heparin with tirofiban in ST-segment elevation myocardial infarction treated with percutaneous coronary intervention

Objective: To compare the effectiveness and safety between bivalirudin and heparin with tirofiban in patients with acute myocardial infarction by percutaneous coronary intervention. Methods: A randomized controlled, open-label experiment was performed. 260 cases of hospitalized patients with acute ST-segment elevation myocardial infarction (STEMI) who accepted emergency PCI were enrolled in the study. They were randomly divided into bivalirudin group (129 cases), heparin plus tirofiban group (131 cases). In Bivalirudin group, they were given intravenous injection 0.75 mg/kg for the first dose. Then they were given 1.75 mg/(kg·h) continuous intravenous infusion until PCI surgery was completed. 1.75 mg/(kg·h) intravenous infusion was followed after the surgery and sustained with an average time of 190 min. Patients were followed-up for 30 days. Results: A total of 259 patients completed 30 days of follow-up (99.6%). 14 cases (10.9%) in the bivalirudin group and 32 cases (24.4%) in heparin plus tirofiban group showed 30 days of net adverse clinical events (RR, 0.45; 95% CI, 0.19~0.75; P=0.004). Two major adverse cardiac and cerebrovascular events (a given group bivalirudin 5.4%, heparin plus tirofiban 9.2%; P=0.25), stent thrombosis (1.6% vs 3.1%; P=0.42), acquired platelet less disease (0.8% vs 0; P=0.32) and the incidence of acute stent thrombosis (0 vs 0.8%; P=0.25) showed statistical significance. Conclusion: In emergency PCI treated STEMI patients, compared with heparin combined tirofibantreatment, bivalirudin (1.75 mg/kg/h) postoperative continuous with the average of 190 min) can decrease the incidence of net adverse clinical events in 30 days.
A New Algorithm Based On Reinforcement Learning For Regressor Selection In The Least Square Estimation

Structure identification means determining the model and it’s details, like the number of regressor in the least square estimation. This is one of the most important problems in system modeling and identification. In this paper a new algorithm based on reinforcement learning is presented for structure identification in least square estimation. The results of the new algorithm are compared with that of the subset selection methods and the steepest descent. Online structure identification is one of the advantages of the proposed method. KeywordsStructure Identification; Least Square Estimation; Reinforcement Learning
Integrating Gender and Nutrition within Agricultural Extension Services Technical Note October 2016 Methods for Teaching and Evaluating Food-Based Dietary Guidelines

Food-Based Dietary Guidelines (FBDG) are established in several countries around the world to address the nutrition concerns of populations (FAO, 2016). FBDGs inform the public about consuming nutritious foods and living a healthy life (FAO, 2016). However, the methods and strategies to educate the public, especially those living in rural communities, and their evaluation are limited. Therefore, the purpose of this technical note is to two-fold: 1) to assist health professionals and non-health professionals educate the public about understanding and using FBDGs, and 2) to provide organizations an overview of methods to evaluate these teaching strategies for their effectiveness in changing community members’ dietary behaviors.
Spectrometric Technique for a Potable Gamma-ray Detector using a LabVIEW Software

The purpose of this paper was to develop an analysis software for energy spectrum of gamma-ray sources that can check food and water for radiation contamination and radiation dose. The handheld radiation detector is composed of a scintillator, a silicon photomultiplier (SiPM) and a signal processing circuit. The detector is an instrumentation device to measure and count the signal generated at nuclear detecting sensors. Radioactive elements have prospective characteristics for the energy spectrum. So a developed graphical program (LabVIEW, NI) based on PC was used to collect and analysis the gamma-ray energy by detector. The results have been checked spectrum of Cs using 6 mm * 6 mm*50 mm scintillator coupled to a SiPM.
Detection, identification and phylogenetic analysis of lactic acid bacteria isolated from Tarkhineh, Iranian fermented cereal product, by amplifying the 16s rRNA gene with universal primers and differentiation using rep-PCR

Tarkhineh is a traditional fermented cereal based food which is produced in different rural areas in Kermanshah province, Iran. The aims of this study were to identify the predominant lactic acid bacteria isolates. A total of 75 Gram-positive and catalase-negative isolates were subjected to grouping and identifying using the carbohydrates fermentation profiles and 16S rDNA sequencing, respectively. The sequencing results showed that the majority of the strains belonged to the genus of Lactobacillus, and were dispersed into five species: L. plantarum (25.33%), L. fermentum (22.66%), L. pentosus (12%), L. brevis (10.66%), and L. diolivorans (1.33%). Minor populations of the other genera, included: Enterococcus (faecium and faecalis) (18.66%), Leuconostoc citreum (5.33%) and Pediococcus (acidilactici and pentosaceus) (3.99%). All of the strains of L. plantarum and L. brevis were subjected to rep-PCR typing. High intra-species diversity was found among L. plantarum and L. brevis isolates. It was concluded that poly-phasic strategies, based on molecular techniques, are necessary for reliable and accurate studying of the microbial composition of fermented products.
TOURISM GOLF REPEAT CHOICE BEHAVIOUR IN THE ALGARVE: A MIXED LOGIT APPROACH

This paper focuses on the behavioural intention of repeater golf tourists in the Algarve. The analysis employs a questionnaire undertaken in 2004 to ascertain the significant characteristics of the returning golf playing tourists. A random parameter logit model is used to analyse which characteristics (e.g. individual characteristics, motivations, tripographic variables and the most quoted attributes of golf destinations) are associated with the probability of golf tourists playing in Algarve to return to this Portuguese tourist region, taking into account uncontrolled heterogeneity of the data. This model outperforms the standard logit model. Some policy implications are presented.
Schwarzschild-de-Sitter Solution in Quantum Gauge Theory of Gravity

We use the theory based on the gravitational gauge group G to obtain a spherical symmetric solution of the field equations for the gravitational potentials on a Minkowski space-time. The gauge group G is defined and then we introduce the gauge-covariant derivative Dμ. The strength tensor of the gravitational gauge field is also obtained and a gauge-invariant Lagrangian including the cosmological constant is constructed. A model whose gravitational gauge potentials Aμα(x) have spherical symmetry, depending only on the radial coordinate r is considered and an analytical solution of these equations, which induces the Schwarzschild-de-Sitter metric on the gauge group space, is then determined. All the calculations have been performed by GR Tensor II computer algebra package, running on the Maple V platform, along with several routines that we have written for our model.
Modeling the Combined Effects of Temperature, pH, and Sodium Chloride and Sodium Lactate Concentrations on the Growth Rate of Lactobacillus plantarum ATCC 8014

Nowadays, microorganisms with probiotic or antimicrobial properties are receiving major attention as alternative resources for food preservation. Lactic acid bacteria are able to synthetize compounds with antimicrobial activity against pathogenic and spoilage flora. Among them, Lactobacillus plantarum ATCC 8014 has exhibited this capacity, and further studies reveal that the microorganism is able to produce bacteriocins. An assessment of the growth of L. plantarum ATCC 8014 at different conditions becomes crucial to predict its development in foods. A response surface model of the growth rate of L. plantarum was built in this study as a function of temperature (4, 7, 10, 13, and 16°C), pH (5.5, 6.0, 6.5, 7.0, and 7.5), and sodium chloride (0, 1.5, 3.0, 4.5, and 6.0%) and sodium lactate (0, 1, 2, 3, and 4%) concentrations. All the factors were statistically significant at a confidence level of 90% . When temperature and pH increased, there was a corresponding increase in the growth rate, while a negative relationship was observed between NaCl and Na-lactate concentrations and the growth parameter. A mathematical validation was carried out with additional conditions, demonstrating an excellent performance of the model. The developed model could be useful for designing foods with L. plantarum ATCC 8014 added as a probiotic.
TRANSMISSION LINES FAULT DETECTION USING DISCRETE WAVELET TRANSFORM AND ARTIFICIAL NEURAL NETWORK ALGORITHM

This paper proposes a novel approach for fault discovery of three-phase transmission line, which is primarily based upon the discrete wavelet transformation (DFT) and back propagation neural network algorithm. Three phase currents of most effective one end are measured, and discrete features are extracted using discrete wavelet transform. Those features are then used as inputs to the back propagation neural network algorithm. The training data set for back propagation neural network algorithm is obtained by way of simulating the ten extraordinary kinds of faults the use of diverse values of fault inception angles and fault resistances, so that the actual consequences may be received. The proposed back propagation neural network algorithm employs twenty inputs and best one output for classifying the faults. The distinctiveness of the proposed approach is that all the features, data used in developing the algorithm are normalized, so that the method may be used for any system with none significant changes. The simulation of the three-phase transmission line network and discrete wavelet transformation analysis are achieved inside the toolboxes of MATLAB®, and back propagation neural network algorithm codes also are written in MATLAB®.
Impacts of Public Expenditure on Poverty in Bangladesh: An Empirical Analysis

Public expenditure and government intervention have very important impacts on people’s living in Bangladesh. Especially, the poor segment of the population are affected by government’s activities in many ways. Bangladesh Government spends a lot of public resources in some thrust sectors of the economy such as education, health, infrastructure building, power and energy etc in order to provide valuable public services to all people. However, the country has been suffering from rampant poverty for a long period of time despite the fact that successive governments in Bangladesh spend a big amount of public funds in poverty reduction activities and programs. This study seeks to investigate the relationship of public expenditure and poverty reduction in the Bangladesh perspective through an econometric analysis of time series data by the OLS estimation technique. Our empirical findings show that public expenditure in Bangladesh does not have any meaningful and significant impact in poverty reduction.
Research on High-Accuracy Gait Recognition Technique based on Parallel Genetic Algorithm

In the field of sports science, gait recognition has a wide range of applications. However, compared to other biometric technologies, it has the problem of low recognition accuracy. Therefore, a technique combining the parallel genetic algorithm based on hierarchical fair competition with neural network ensemble is proposed to be applied in gait recognition. First, parallel genetic algorithm is adopted to increase individual diversity, and only appropriate features are selected to train each neural network candidate. Then, a set of individual neural networks are selected to complete the neural network ensemble so as to minimize the generalization error and maximize the negative correlation. Experiments on the CASIA gait database demonstrate the effectiveness of the proposed gait recognition method. The results show that the proposed method has a higher recognition rate than the existing methods.
Role of NF-KB in Loss of Skeletal Muscle Mass in Cancer Cachexia and its Therapeutic Targets

12.00 12.00 Normal 0 7.8 磅 0 2 false false false EN-US ZH-CN X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:普通表格; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:. 0001pt; mso-pagination:widow-orphan; font-size:10.5pt; mso-bidi-font-size:11.0pt; font-family:"Century","serif"; mso-ascii-font-family:Century; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:宋体; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Century; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-font-kerning:1.0pt;} Cancer cachexia is a type of cancer metabolic syndrome characterized by wasting of energy storage tissue of the body such as loss of skeletal muscle or fat body mass . It is one of the most common forms of cachexia. Researchers have shown that around 50%–80% cancer patients are cachectic and about 22% cancer related deaths are due to cachexia. Recent evidences confirm that skeletal muscle loss is induced by different mediator based mechanisms such as cytokines, tumor-derived factors, TNF-alpha receptors, androgen receptors, negative modulator of growth factors, and ubiquitin-proteasome pathways. These mediators interact with their unique receptors on skeletal muscles and activate nuclear factor-kappa-B (NF-κB) which is a transcription factor essential for atrophy based muscle protein degradation. Inhibition of NF-κB ameliorates cancer-induced muscle loss are suggestive of a new set of drug targets for clinical intervention in cancer cachexia. Here we summarize the recent advances concerning involvement of NF-κB in loss of skeletal muscle mass in cancer cachexia and its therapeutic targets. Future experimental efforts may focus on discovering of new drugs that act as potential therapeutic targets in cancer cachexia against the myriad of signaling pathways to precisely understand the mechanisms of loss of skeletal muscle mass and adipose tissue which could demonstrates significant improvement in treatment outcome, functional status and quality of life of the patients. Normal 0 7.8 磅 0 2 false false false EN-US ZH-CN X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:普通表格; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:. 0001pt; mso-pagination:widow-orphan; font-size:10.5pt; mso-bidi-font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-font-kerning:1.0pt;}
Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fault diagnosis are given to demonstrate the efficiency of our proposed method.