Miracle-dz/UniFAR-FTUs huggingface dataset:
Postulation of Specific Disease Resistance Genes in Cereals: A Widely Used Method and Its Detailed Description

Cultivation of resistant varieties is an environmentally friendly and inexpensive method of crop protection. Numerous alleles of specific disease resistance occur in cereals and other crops, and knowledge of their presence in individual varieties has wide utilization in research and practice. Postulation based on phenotyping host—pathogen interactions and the gene-for-gene model is a common way of identifying these genes. The same technique and design of tests are used for postulating virulence when pathogen populations are studied. Powdery mildews caused by different formae speciales of Blumeria graminis (Bg) are important cereal diseases. In this contribution, experimental methods are described that use a model organism Bg f. sp. hordei, which can be employed for other cereal mildews and possibly rusts. It includes illustrations and a summary of our long-term practical experience. It also critically evaluates the benefits of leaf segment tests compared with screening whole plants.
Shiga Toxin-Producing Escherichia coli and Milk Fat Globules

Shiga toxin-producing Escherichia coli (STEC) are zoonotic Gram-negative bacteria. While raw milk cheese consumption is healthful, contamination with pathogens such as STEC can occur due to poor hygiene practices at the farm level. STEC infections cause mild to serious symptoms in humans. The raw milk cheese-making process concentrates certain milk macromolecules such as proteins and milk fat globules (MFGs), allowing the intrinsic beneficial and pathogenic microflora to continue to thrive. MFGs are surrounded by a biological membrane, the milk fat globule membrane (MFGM), which has a globally positive health effect, including inhibition of pathogen adhesion. In this review, we provide an update on the adhesion between STEC and raw MFGs and highlight the consequences of this interaction in terms of food safety, pathogen detection, and therapeutic development.
Well placement optimization

The amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells. For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator, is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms able to cope with the non-regularity of the objective function. The goal of this thesis was to develop an efficient methodology for determining optimal well locations and trajectories, that offers the maximum asset value using a technically feasible number of reservoir simulations.In this thesis, we show a successful application of the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) which is recognized as one of the most powerful derivative-free optimizers for continuous optimization. Furthermore, in order to reduce the number of reservoir simulations (objective function evaluations), we design two new algorithms. First, we propose a new variant of CMA-ES with meta-models, called the newlocal-meta-model CMA-ES (nlmm-CMA), improving over the already existing variant of the local-meta-model CMA-ES (lmm-CMA) on most benchmark functions, in particular for population sizes larger than the default one. Then, we propose to exploit the partial separability of the objective function in the optimization process to define a new algorithm called the partially separable local-meta-model CMA-ES (p-sep lmm-CMA), leading to an important speedup compared to the standard CMA-ES.In this thesis, we apply also the developed algorithms (nlmm-CMA and p-sep lmm-CMA) on the well placement problem to show, through several examples, a significant reduction of the number of reservoir simulations needed to find optimal well configurations. The proposed approaches are shown to be promising when considering a restricted budget of reservoir simulations, which is the imposed context in practice.Finally, we propose a new approach to handle geological uncertainty for the well placement optimization problem. The proposed approach uses only one realization together with the neighborhood of each well configuration in order to estimate its objective function instead of using multiple realizations. The approach is illustrated on a synthetic benchmark reservoir case, and is shown to be able to capture the geological uncertainty using a reduced number of reservoir simulations.
Why are college sports tourists traveling long distance? Antecedents of destination and team loyalty

PurposeOn a home game weekend of collegiate football, thousands of fan visitors spend their weekend participating in college football games, showing significant potential of sports tourism businesses in the USA. Understanding the original personality of sport tourists and their travel motivation factors is imperative to develop appealing tourist attractions at the destinations of sports team fandoms. The purpose of the study is to examine the relationships among tourists' personality characteristics, their push and pulls motivations, satisfaction with travel experience, and loyalty toward destination and team using structural equation modeling.Design/methodology/approachThe author collected information from a total of 301 sports tourists who had traveled to a college football game within the past year, staying at least one night in a hotel or other lodging place. This study employed PASW Statistics 25.0 and structural equation modeling using LISREL 9.30 for data analyses.FindingsPersonality has a significant relationship with push factors, especially for socialization motivation, and pull factors (i.e. access and affordability, other attractions) had more significant effects on sports tourists' satisfaction than push factors. The findings indicate the key roles for the entertainment motivation in generating tourists' satisfaction and destination loyalty and important roles of destination loyalty in building team loyalty.Originality/valueThe present study provides an approach of sports tourists' travel behaviors and experiences in the sporting event tourism. The outcome of this research can help both sports event organizers and destination marketers to understand the motivations for sports game attendance and to develop marketing strategies and products/services for attracting various types of sports tourists to games.
Tuberkulosis paru sebagai penyebab tertinggi kasus pneumotoraks di bangsal paru RSUD Dr Moewardi (RSDM) Surakarta tahun 2009

Latar Belakang: Pneumotoraks merupakan suatu kegawatan di bagian paru. Pneumotoraks spontan merupakan jenis pneumotoraks yang paling sering terjadi. Tuberkulosis paru merupakan penyebab pneumotoraks spontan sekunder tertinggi di beberapa negara berkembang. Prevalensi TB paru yang masih tinggi di Indonesia merupakan faktor penyebab terjadinya PSS berhubungan dengan kasus TB paru. Metode: Merupakan penelitian deskriptif retrospektif dengan menggunakan total sampling. Sampel diambil dari catatan medis dan resume perawatan sejak 1 Januari 2009 – 31 Desember 2009. Hasil: Total 39 sampel kasus pneumotoraks terdiri 25 pasien laki-laki (64,10%) dan 14 pasien perempuan (35,90%) dengan rerata umur 49,13 tahun. Pneumotoraks spontan primer 3 pasien (7,69%) PSS 35 pasien (89,75%) dan 1 pasien pneumotoraks artifisial (2,56%). Penyebab PSS tertinggi tuberkulosis 18 pasien (46,15%), keganasan 13 pasien (33,33%), Pneumonia 3 pasien (7,69%), PPOK 1 pasien (2,56%). Tuberkulosis paru aktif terdapat 15 pasien (83,3%) terdiri TB paru BTA (+) 5 pasien (33,33%) dan TB paru BTA (-) 10 pasien (66,67%). Pneumotoraks paru kanan 25 pasien (64,11%) paru kiri 13 pasien (33,33%) dan 1 pasien (2,56%) pneumotoraks bilateral. Pengembangan paru sempurna terdapat 20 pasien (51,28%) mengembang sebagian 19 pasien (48,72%). Kesimpulan: Tuberkulosis paru di RSDM sebagai penyebab tertinggi kasus pneumotoraks. Hal ini terjadi akibat Indonesia adalah negara dengan kasus TB tertinggi ketiga di dunia. Kata kunci: Pneumotoraks, tuberkulosis, keganasan Singkatan: PSS = pneumotoraks spontan sekunder, PPOK = Penyakit paru obstruktif kronik.
Recombinant Pseudomonas Vaccine: Technological Aspects of Obtaining and Evaluating Quality Indicators

For an aim to prevent Pseudomonas aeruginosa, a candidate recombinant vaccine has been developed. This vaccine – (RPV) was based on two protective proteins of P. aeruginosa: the outer membrane protein F (OprF) and the recombinant truncated form of the Exotoxin A (toxoid) that were adsorbed on the aluminum hydroxide. The optimal immunization schedule for mice included two intraperitoneal administrations with a two-week interval. RPV promoted to increase survival rates in challenged immunized mice and stimulated humoral and innate immune responses. During preclinical studies, we confirmed the immunogenicity of the vaccine that had not pyrogenicity, acute and chronic toxicity, allergenicity and immunotoxicity. Keywords: Pseudomonas aeruginosa, outer membrane protein F (OprF), toxoid, Pseudomonas Recombinant Vaccine (PRV)
De las microconductas desviadas a la corrupción política en España / From Deviant Microbehavior to Political Corruption in Spain

La corrupción política es percibida como un problema social en España. El objetivo de esta investigación es analizar cómo influye el factor cultural en la construcción de su percepción. La hipótesis principal es que existe una relación entre la percepción de microconductas desviadas y la percepción de la corrupción política con influencia de un nivel bajo de capital social. Se ha usado una metodología cuantitativa basada en una fuente de datos secundarios a través del estudio n. º 2826 del CIS, realizando análisis descriptivos e inferenciales. Tras controlar el efecto del bajo nivel de capital social en la sociedad española mediante regresión lineal simple, se verifica la existencia de una relación positiva asimétrica, en la cual la percepción de microconductas desviadas infiere en la percepción de la corrupción política.
Developmental Trends in the Application and Measurement of the Bidirectional Reflection Distribution Function

The bidirectional reflection distribution function (BRDF) is among the most effective means to study the phenomenon of light–object interaction. It can precisely describe the characteristics of spatial reflection of the target surface, and has been applied to aerial remote sensing, imaging technology, materials analysis, and computer rendering technology. This study provides a comprehensive review of the development of devices to measure the BRDF. We gathered research in the area by using the Web of Science Core Collection, and show that work on the BDRF has been ongoing in the last 30 years. We also describe some typical measurement devices for the BRDF proposed in the literature. Finally, we summarise outstanding problems related to BRDF measurement and propose directions of future research in the area.
Data mining toward literature analysis

With a rapid booming of the internet and data inflation,it is important and urgent to get the interesting knowledge and rules from the huge dataset.This article mainly aims at the bibliographical data,the analysis of mining problems based on these data,and shews a new coay to literature analysis and data mining,as is helpfal to researchers in further research work.
Hardware Efficient Massive MIMO Systems with Optimal Antenna Selection

An increase in the number of transmit antennas (M) poses an equivalent rise in the number of Radio Frequency (RF) chains associated with each antenna element, particularly in digital beamforming. The chain exhibits a substantial amount of power consumption accordingly. Hence, to alleviate such problems, one of the potential solutions is to reduce the number of RFs or to minimize their power consumption. In this paper, low-resolution Digital to Analogue Conversion (DAC) and transmit antenna selection at the downlink are evaluated to favour reducing the total power consumption and achieving energy efficiency in mMIMO with reasonable complexity. Antenna selection and low-resolution DAC techniques are proposed to leverage massive MIMO systems in free space and Close In (CI) path-loss models. The simulation results show that the power consumption decreases with antenna selection and low-resolution DAC. Then, the system achieves more energy efficiency than without low-resolution of DAC and full array utilization.
Time for Re-Evaluating the Human Carcinogenicity of Ethylenedithiocarbamate Fungicides? A Systematic Review

Background. In January 2021, the European Union ended the license of Mancozeb, the bestselling ethylenedithiocarbamate (EBDC) fungicide, because of some properties typical of human carcinogens. This decision contrasts the IARC classification of EBDC fungicides (Group 3, not classifiable as to human carcinogenicity). A systematic review of the scientific literature was conducted to explore the current evidence. Methods. Human and experimental studies of cancer and exposure to EBDC fungicides (Mancozeb, Maneb, Zineb, and others) and ethylene thiourea (ETU), their major metabolite, published in English as of December 2021, were retrieved using PubMed, the list of references of the relevant reports, and grey literature. Results. The epidemiological evidence of EBDC carcinogenicity is inadequate, with two studies each suggesting an association with melanoma and brain cancer and inconsistent findings for thyroid cancer. Experimental animal studies point at thyroid cancer in rats and liver cancer in mice, while multiple organs were affected following the long-term oral administration of Mancozeb. The mechanism of thyroid carcinogenesis in rats has also been shown to occur in humans. Genotoxic effects have been reported. Conclusions. The results of this systematic review suggest inadequate evidence for the carcinogenicity of EBDC fungicides from human studies and sufficient evidence from animal studies, with positive results on three out of ten key characteristics of carcinogens applying to humans as well. An IARC re-evaluation of the human carcinogenicity of EBDC fungicides is warranted.
Rate-Distortion Optimized Geometry Compression for Spinning LiDAR Point Cloud

Point cloud is a major representation format of 3D objects and scenes. It has been increasingly applied in various applications due to the rapid advances in 3D sensing and rendering technologies. In the field of autonomous driving, point clouds captured by spinning Light Detection And Ranging (LiDAR) devices have become an informative data source for road environment perception and intelligent vehicle control. On the other hand, the massive data volume of point clouds also brings huge challenges to point cloud transmission and storage. Therefore, establishing compression frameworks and algorithms that conform to the characteristics of point cloud data has become an important research topic for both academia and industry. In this paper, a geometry compression method dedicated to spinning LiDAR point cloud was proposed taking advantage of the prior information of the LiDAR acquisition procedure. Rate-distortion optimizations were further integrated into the coding pipeline according to the characteristics of the prediction residuals. Experimental results obtained on different datasets show that the proposed method consistently outperforms the state-of-the-art G-PCC predictive geometry coding method with reduced runtime at both the encoder and decoder sides.
Investigation of Warpage for Multi-Die Fan-Out Wafer-Level Packaging Process

This paper focuses on characterizing the evolution of warpage, effects of epoxy molding compound (EMC), and effects of carrier 2 (the second carrier in the process) of 12 inch RDL-first multi-die fan-out wafer-level packaging (FOWLP) during the manufacturing process. The linear viscoelasticity properties of EMC and polyimide (PI) were characterized using dynamic mechanical analysis (DMA) in the frequency domain at different temperatures. , The elastic and viscoelastic model were used for PI and EMC, the finite element analyses (FEA) of the cured structure were carried out and the results were compared with the test results. The viscoelastic properties of the EMC in the FEA could predict the wafer warpage more accurately. The FEA and experiments were used to investigate the evolution of warpage. The molding had a great influence on the warpage. The effects of the EMC and carrier 2 were also investigated with FEA. The wafer warpage could be reduced by lowering the thickness of the EMC, increasing the thickness of carrier 2, and selecting EMC and carrier 2 with a matched coefficient of thermal expansion (CTE).
Comparison of Several Features of Building Detection in Remote Sensing Image

AdaBoost algorithm based on Haar features and Local Binary Patterns (LBP)features are widely used in machine learning method, and deep learning algorithm is a hot research field in recent years. Remote sensing image recognition meets the needs of human beings in the social development of Information Technology Intelligent. Combined machine learning method with the three features, a certain number of buildings in the remote sensing image were trained and performance test, and then the test results were evaluated, respectively. The results show that the features performance of the deep learning algorithm is better than the other two kinds of features, and the accuracy can meet the requirements of the application of remote sensing image interpretation.
Identification of ICU Patients with High Nutritional Risk after Abdominal Surgery Using Modified NUTRIC Score and the Association of Energy Adequacy with 90-Day Mortality

For patients undergoing abdominal surgery, malnutrition further increases the susceptibility to infection, surgical complications, and mortality. However, there is no standard tool for identifying high-risk groups of malnutrition or exact criteria for the optimal target of nutrition supply. We aimed to identify the nutritional risk in critically ill patients using modified Nutrition Risk in the Critically Ill (mNUTRIC) scores and assessing the relationship with clinical outcomes. Furthermore, we identified the ideal target of energy intake during the acute postoperative period. A prospective observational study was conducted. mNUTRIC scores and the average calories prescribed and given were calculated. To identify the high-risk group of malnutrition, receiver operating characteristic curves were plotted. The ideal target of energy adequacy and predisposing factors of 90-day mortality were assessed using multiple logistic regression analyses. A total of 206 patients were analyzed. The cutoff value for mNUTRIC score predicting 90-day mortality was 5 (Area under the curve = 0.7, 95% confidence interval (Cl) 0.606–0.795, p < 0.001). A total of 75 patients (36.4%) were classified in the high mNUTRIC group (mNUTRIC ≥ 5) and had a significantly higher postoperative complication and longer length of surgical intensive care unit stay. High mNUTRIC scores (odds ratio = 2.548, 95% CI 1.177–5.514, p = 0.018) and energy adequacy less than 50% (odds ratio = 6.427, 95% CI 1.674–24.674, p = 0.007) were associated with 90-day mortality.
Soil loss estimation and prioritization using geographic information systems and the RUSLE model: a case study of the Anger River Sub-basin, Western Ethiopia

Soil erosion is a major environmental problem that affects people's livelihoods and environmental health, particularly in developing countries. The present study aimed to identify soil erosion hotspot areas and prioritization in the Anger River Sub-basin for conservation planning. The Revised Universal Soil Loss Equation model and geospatial technologies were adopted to identify soil erosion hotspot areas and prioritization of the sub-watershed for conservation planning. Key parameters such as rainfall data, soil data, slope length and steepness factor, cover management, and conservation practices were used to estimate potential soil erosion risk in the sub-watershed. The results showed that the annual soil loss rate in the Anger River Sub-basin ranged between 0 and 932.6 t/ha/year with a mean annual soil loss of 83.7 t/ha/year. About 1,140.7 km2 (43.6%) and 220.6 km2 (8.4%) were categorized under very severe and severe soil loss types, respectively. The mid- and upstream areas, as well as the steeper parts of the sub-watershed, were highly exposed to soil erosion. This research provides tangible evidence in the decision-making process for soil and water conservation practices at a sub-watershed scale. Moreover, further research should be conducted at a micro-watershed scale to minimize the effects of soil erosion on the health and sustainability of the watershed.
Prediction and Elucidation of Triglycerides Levels Using a Machine Learning and Linear Fuzzy Modelling Approach

Introduction Triglycerides are lipids composed of fatty acids that provide energy to the cell. These compounds are delivered to the body's cells via lipoproteins found in the bloodstream. Increased blood triglyceride levels have been associated with high-fat or high-carbohydrate diets. Generally, increased triglyceride levels occur in conjunction with other symptoms that are difficult to notice and recognize. Objectives The study's goal was to develop and predict the model that could be used to explain the relationship between triglycerides and waist circumference, high-density lipoprotein (HDL), and hypertension status by determining the relationship between triglycerides and waist circumference, HDL, and hypertension status. This model was developed using qualitative predictor variables and incorporated data bootstrapping multilayer perceptron neural networks and fuzzy linear regression. Materials and procedures. This was a public health study that combined retrospective data analysis with methodology development. The medical records of patients who attended outpatient clinics at Hospital Universiti Sains Malaysia (USM) were collected and analyzed. This was to provide a more extensive illustration of the methods developed. Screening and selection of patient data were necessary following the inclusion and exclusion criteria. The patient's medical record was used to obtain triglycerides, high-density lipoprotein (HDL), waist circumference, and hypertension status. Due to the critical nature of the variable, it was chosen to aid the clinical expert. The R-Studio software was used to develop the associated syntax for the hybrid model, which would define the association between the examined variables. The purpose of this study is to create a technique for the clinical trial design that utilizes bootstrapping, Qualitative Predictor Variables (QPV), Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Fuzzy Regression (FR). All analyses were performed using the newly introduced R syntax. The research developed a fuzzy linear model that increased modelling performance by incorporating clinically significant factors and validated variables via Multilayer Perceptron (MLP). Conclusion The proposed technique for modelling and prediction appeared to be the ideal combination of bootstrap, Multilayer Feed Forward (MLFF) neural network, and fuzzy linear regression. The created syntax is currently being evaluated and validated clinically. For modelling and prediction, the proposed technique looked to be the best, as it incorporated bootstrap, MLFF neural network, and fuzzy linear regression. The established syntax is now being utilized in the clinic to evaluate and validate the outcome. In terms of variable selection, modelling, and model validation, this strategy was superior to earlier approaches for fuzzy regression modelling.