Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Characterization of Puccinia striiformis f. sp. tritici Populations from Different Wheat Cultivars Using Simple Sequence Repeats

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most important fungal diseases affecting wheat (Triticum aestivum L.) worldwide. In this study, the genetic diversity and population structure of Pst isolates were analyzed using 15 microsatellite markers. Isolates were collected from five wheat cultivars with different levels of resistance from Yanting county and Fucheng district, Mianyang city, Sichuan province, China. The aim of this study was to investigate whether Pst populations are differentiated by wheat genotype or geographic origin. Seventy-six multilocus genotypes (MLGs) were identified from all 289 single uredinial isolates. In general, the genotypic diversity of Pst populations from five wheat cultivars in Fucheng was higher than that in Yanting. In addition, the genetic diversity was highest in the Pst populations from Mianmai 367, a cultivar considered to be highly resistant. The unweighted pair group method with arithmetic mean (UPGMA) phylogenetic tree, Bayesian clustering analysis, and minimum spanning network for the MLGs revealed two major genetic clusters based on geographical location. Greater differentiation was observed between the populations from the two sampling locations than between the populations from different hosts in the same location. The results suggest that geographic and environmental differences could partially explain the genetic differentiation of Pst more than wheat genotype. This study provides novel insight into the interactions between Pst populations and their hosts. The results could be helpful in designing more effective management strategies for stripe rust in wheat production.
Effect of Dihydroquercetin on Energy Metabolism in LPS-Induced Inflammatory Mice

This study investigated the effects and alterations of dihydroquercetin on the growth performance, nutriment metabolism, antioxidant and immune function, and energy substrate utilization in lipopolysaccharide-challenged mice. A total of 0, 50, and 200 mg/kg of dihydroquercetin were intragastrically administered once a day for 21 days. After the pretreatment with dihydroquercetin, each group was subjected to a lipopolysaccharide challenge (except for the control group). After lipopolysaccharide injection, food intake, body weight, metabolic indexes of blood and liver nutrients, blood inflammatory factors, and liver oxidative stress indexes were measured at 6, 12, 24, and 48 h, respectively. Indirect calorimetry analysis was performed by respiratory gas analysis for 48 h to calculate the energy substrate metabolism of carbohydrate, fat, and protein. Urinary nitrogen excretion was measured to evaluate the urinary protein metabolism to calculate the substrate utilization. The results showed that dihydroquercetin pretreatment can significantly increase the weight gain and average food intake and decrease the mortality rate in lipopolysaccharide-induced inflammation mice. Furthermore, dihydroquercetin pretreatment can alleviate the negative effects of lipopolysaccharides by increasing levels of superoxide dismutase and glutathione peroxidase and by decreasing the malondialdehyde and serum inflammatory cytokines (interleukin-1β, nuclear factor κB, and interleukin-6). Dihydroquercetin pretreatment also can relieve nutrient metabolic disorder by increasing blood glucose, serum total protein, and liver glycogen levels and reducing serum and liver triglycerides, serum cholesterol, serum lactate dehydrogenase, and serum urea nitrogen levels. Meanwhile, it increases the relative utilization of carbohydrate, reducing relative utilization of protein and lipid, alleviating the change in energy metabolism pattern from glucose-predominant to lipid-predominant caused by lipopolysaccharide stimulation. In addition, the degree of metabolic pattern transformation depends on the dose of dihydroquercetin supplement. Finally, according to principal component analysis, we found that the inflammation was strongest in the mice at 24 h and was subsequently relieved in the LPS-stimulated group, whereas in the dihydroquercetin-pretreated group, the inflammation was initially relieved. To summarize, dihydroquercetin pretreatment can improve energy metabolism disorder and attenuate the negative effects of lipopolysaccharide challenge in mice from the initial stage of inflammation.
Data Augmentation Techniques on Chilly Plants to Classify Healthy and Bacterial Blight Disease Leaves

—Designing an automation system for the agriculture sector is difficult using machine learning approach. So many researchers proposed deep learning system which requires huge amount of data for training the system. The proposed system suggests that geometric transformations on the original dataset help the system to generate more images that can replicate the physical circumstances. This process is known as “Image Augmentation”. This enhancement of data helps the system to produce more accurate systems in terms of all metrics. In olden days when researchers work with machine learning techniques they used to implement traditional approaches which are a time consuming and expensive process. In deep learning, most of the operations are automatically taken care by the system. So, the proposed system applies neural style and to classify the images it uses the concept of transfer learning. The system utilizes the images available in the open source repository known as “Kaggle”, this majorly consists of images related to chilly, tomato and potato. But this system majorly focuses on chilly plants because it is most productive plant in the South Indian regions. Image augmentation creates new images in different scenarios using the existing images and by applying popular deep learning techniques. The model has chosen ResNet-50, which is a pre-trained model for transfer learning. The advantage of using pre-trained model lies in not to develop the model from scratch. This pre-trained model gives more accuracy with less number of epochs. The model has achieved an accuracy of “100%”.
A Possible Case of Varicella Zoster Virus (VZV) Meningoencephalitis in an Immunocompetent Host

The herpes zoster infection occurs in the United States, particularly targeting those who are immunocompromised, and can present with many manifestations including encephalitis. Instances of varicella zoster virus (VZV) encephalitis in immunocompetent patients have been rarely reported, but such diagnoses are becoming more frequent as detection of VZV has improved with the adoption of molecular diagnostic panels such as the BioFire Film Array meningitis panel (Salt Lake City, USA). Here, we present an interesting case of acute meningoencephalitis in an immunocompetent adult female without dermatomal neuralgia or cutaneous lesions attributable to VZV. Given many inconsistencies between the patient’s presentation and the positive polymerase chain reaction (PCR) result for VZV, we suspected our patient was infected with an undetected organism while possibly simultaneously shedding previously acquired VZV. As molecular diagnostic panels are increasingly used and have greatly improved detection of rarer etiologies of disease, we encourage clinicians to interpret results with caution.
A Lightweight ECC-based Three-Factor Mutual Authentication and Key Agreement Protocol for WSNs in IoT

—The Internet of Things (IoT) represents a giant ecosystem where many objects are connected. They collect and exchange large amounts of data at a very high speed. One of the main parts of IoT is the Wireless Sensor Network (WSN), which is deployed in various critical applications such as military surveillance and healthcare that require high levels of security and efficiency. Authentication is a primary security factor that ensures the legitimacy of data requests and responses in WSN. Moreover, sensor nodes are characterized by their limited resources, which raise the need for lightweight authentication schemes applicable in IoT environments. This paper presents an informal analysis of the security of X. Li et al.’s protocol, which is claimed to be efficient and resistant to various attacks. The analysis results show that the reviewed protocol does not provide user anonymity and it is vulnerable to session key disclosure attack, many-time pad attack, and insider attack. To address all these requirements, a new three-factor authentication protocol is presented, which guarantees higher security using Physically Unclonable Function (PUF) and Elliptic Curve Cryptography (ECC). This protocol does not only withstand the security weaknesses in X. Li et al.’s scheme but also provides smart card revocation and is resistant to cloning attack. In terms of both computational and communicational costs, results demonstrate that the proposed scheme provides higher efficiency in comparison with other related protocols, which makes it notably suitable for IoT environments.
Capacitive Memory Using GLAD Synthesized Annealed SnO2 Nanowires Array as a Dielectric

This paper presents the fabrication of as-deposited and annealed SnO<inf>2</inf> NWs via GLAD (glancing angle deposition) technique. On investigation, the 650 °C SnO<inf>2</inf> NWs show improvement in its crystallinity. The capacitive behavior of both the device was analyzed by evaluating the capacitance-voltage (CV) and conductance-voltage (G-V) characteristics for both asdeposited and annealed sample by varying frequencies. The annealed device showed improved capacitive memory with interface trap density (9.7×10<sup>9</sup> eV<sup>-1</sup>cm<sup>-2</sup>), charge storage density of 5.45×10<sup>10</sup> cm<sup>-2</sup> and memory window of ~ 0.89 V at ± 8V for 1MHz.
Fatores preditivos de incapacidades em pacientes com hanseníase

OBJETIVO:Analisar os fatores preditivos na evolucao do grau de incapacidade em pacientes com hanseniase. METODOS:Foram analisados dados de coorte retrospectiva, que acompanhou 595 pacientes com incapacidades, registrados em uma unidade de saude de Belo Horizonte (MG), de 1993 a 2003. Informacoes sociodemograficas e clinicas dos pacientes foram coletadas dos respectivos prontuarios. Comparou-se o grau de incapacidade na admissao e no final do tratamento por meio do teste de homogeneidade marginal. Para identificar os fatores associados a evolucao do grau de incapacidade foram utilizadas as analises univariada (teste qui-quadrado de tendencia linear) e multivariada pelo algoritmo Chi-square Automatic Interaction Detector. RESULTADOS:Dos casos com registro de grau de incapacidade na admissao e na alta, observou-se que 43,2% que tinham grau 1 na primeira avaliacao evoluiram para grau 0. Dos que apresentavam grau 2, 21,3% passaram a ter grau 0 e 20% passaram a grau 1. Na analise univariada as variaveis que se mostraram estatisticamente associadas a evolucao no grau de incapacidade foram: neurite, tempo ate a ocorrencia de neurite, numero de nervos acometidos, tipo de tratamento fisioterapico e maior dose de prednisona. Na analise multivariada, o principal fator que se associou a evolucao do grau de incapacidade foi o grau de incapacidade na admissao. CONCLUSOES:Os resultados mostraram a importância do diagnostico precoce de neuropatia, assim como da eficiente associacao das intervencoes medicamentosas e nao-medicamentosas por meio das tecnicas de prevencao de incapacidade e dosagens adequadas de corticoterapia.
Referral rate of chronic kidney disease patients to a nephrologist in the Region of Southern Denmark: results from KidDiCo

ABSTRACT Background Data on the referral rate of chronic kidney disease (CKD) patients to specialists are sparse. Investigating referral rates and characterizing patients with kidney disease not followed by a nephrologist are relevant for future measures in order to optimize public health and guideline implementation. Methods Data were extracted from the Kidney Disease Cohort of Southern Denmark (KidDiCo). Referral rates for all incident CKD patients below 60 mL/min/1.73 m² and referral rates according to the KDIGO guidelines based on glomerular filtration rates below 30 mL/min/1.73 m² were calculated. Information on contact with one of the nephrologist outpatient clinics in the Region of Southern Denmark was collected from the Danish National Patient Registry. The individual follow-up time for nephrology contact was 12 months. Additional data were accessed via the respective national databases. CKD patients on dialysis and kidney transplanted patients were excluded. Results A total of 3% of patients with an eGFR <60 mL/min/1.73 m²–16% of patients with an eGFR <30 mL/min/1.73 m² and 35% of patients with an eGFR <15 mL/min/1.73 m² were in contact with a nephrologist in the outpatient settings. Younger age, male sex, diabetes, hypertension, higher education and proximity to a nephrology outpatient clinic increased the chance of nephrology follow-up. Conclusion Only a small fraction of CKD patients are followed by a nephrologist. More studies should be performed in order to find out which patients will profit the most from renal referral and how to optimize the collaboration between nephrologists and general practitioners.
Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB

ABSTRACT A 16S rRNA gene database (http://greengenes.lbl.gov ) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria.
IL-6 and HSPA1A Gene Polymorphisms May Influence the Levels of the Inflammatory and Oxidative Stress Parameters and Their Response to a Chronic Swimming Training

The aim of the study was to evaluate whether the most common polymorphisms in the IL-6 and HSP70 genes affect the circulating heat shock protein 70 (HSP70), as well as inflammatory and prooxidant-antioxidant parameters in healthy men undergoing chronic endurance training. The subjects were randomly assigned to a 12-week swimming training (ST group) or control group (CON). Fasting blood samples were collected pre- and post-study period to assessment: superoxide dismutase (SOD) and glutathione peroxidase (GPx) activities, serum levels of lipid hydroperoxides (LHs), tumor necrosis factor α (TNFα), and HSP70. Subjects were genotyped for IL-6-174G/C, HSPA1A + 190 G/C and HSPA1B + 1538 A/G single nucleotide polymorphisms (SNPs) by real-time PCR. After a 12-week study period, a decrease in TNFα, HSP70, and GPx was observed in the ST group, but not the CON group. IL-6 SNP affected serum TNFα levels (main effect of genotype). Higher TNFα levels (pre- and post-study period) was observed in CC CON than in other IL-6 genotypes of CON and ST groups. However, a post-training decrease in TNFα was observed in both GG and CC IL-6 genotypes of ST group. In turn, only GG IL-6 genotype of the ST group was related to a post-training decrease in HSP70 (main time and genotype interaction). Moreover, pre- and post-training LHs were lower in GG than GC/CC HSPA1A genotypes of the ST group (main genotype effect). In conclusion, polymorphisms within the IL-6 and HSPA1A genes seem to affect baseline levels of some inflammatory parameters and prooxidant-antioxidant status and/or their changes after chronic swimming training. However, the results should be confirmed in a study with a larger sample size, one that includes individuals with sedentary lifestyles.
Determinants of co-occurrences of diarrhea and fever among children under five years of age in South Sudan

ABSTRACT Children in developing countries continue to suffer a disproportionate burden of morbidities, including diarrhea and fever, which are major causes of mortality. These morbidities tend to co-occur due to shared or overlapping risk factors. This study examines the determinants of co-occurrences of diarrhea and fever among children under five years of age (n = 8,338) in South Sudan using the national representative sample drawn from the 2010 South Sudan Multiple Indicator Cluster Survey (MICS4). The results show more than 50% of children under five are affected either by diarrhea (19%) or fever (16%), or both ailments (16%). Multinomial logistic regression analysis shows that variables related to the individual (child’s age, breastfeeding status, uptake of vitamin A supplements), family (mother’s pregnancy status, family wealth index), hygiene (water treatment status, waste disposal practices), and environment (national state of residency) significantly influence comorbidities among children under five years relative to children with neither diarrhea nor fever in South Sudan.The study suggests the need for parents and caregivers to be well advised and equipped with customized sanitation and hygiene skills to combat infectious diseases, so that they can play a key role in reducing comorbidities and mortalities among children under five years of age in their communities.
Learning Human Driving Behaviors with Sequential Causal Imitation Learning

Learning human driving behaviors is an efficient approach for self-driving vehicles. Traditional Imitation Learning (IL) methods assume that the expert demonstrations follow Markov Decision Processes (MDPs). However, in reality, this assumption does not always hold true. Spurious correlation may exist through the paths of historical variables because of the existence of unobserved confounders. Accounting for the latent causal relationships from unobserved variables to outcomes, this paper proposes Sequential Causal Imitation Learning (SeqCIL) for imitating driver behaviors. We develop a sequential causal template that generalizes the default MDP settings to one with Unobserved Confounders (MDPUC-HD). Then we develop a sufficient graphical criterion to determine when ignoring causality leads to poor performances in MDPUC-HD. Through the framework of Adversarial Imitation Learning, we develop a procedure to imitate the expert policy by blocking π-backdoor paths at each time step. Our methods are evaluated on a synthetic dataset and a real-world highway driving dataset, both demonstrating that the proposed procedure significantly outperforms non-causal imitation learning methods.
Estimation of Pubertal Growth-Spurt Parameters in Children and Adolescents in Colombia: Comparison between Low and Moderate Altitudes

Context-specific information, including differences in geographical areas, such as distinct altitudes, can be important to explain variations in physical growth. We aimed to compare the estimation of maximum growth velocity and pubertal growth-spurt parameters of children and adolescents living at low and moderate altitudes in Colombia. A cross-sectional study, including a representative cohort of 30.305 (51% boys) children and adolescents aged 1–18 years from Colombia, was performed. The heights were measured with standardized techniques. The Preece–Baines growth model was used to estimate the mathematical and biological parameters of the height-growth velocities and growth spurts for both sexes. The altitudes were categorized as low (18 to 564 m above sea level) or moderate (2420 to 2640 m above sea level). There were no differences in final height (h1), peak height velocity size (hθ), age at peak height velocity (APHV), or peak height velocity PHV (cm/y) between the subjects living in both altitudes (p > 0.05). The APHV was estimated at 12.75 ± 0.75 years in the boys and at 10.05 ± 0.65 years in the girls. The girls reached the APVH 2.70 years earlier than the boys. Regarding the PHV, the boys reached higher growth velocity, which was 6.85 ± 0.55 cm/y. In conclusion, there were no significant differences in final height, peak height, APHV, or PHV between the children and adolescents living at distinct altitudes in Colombia. The PHV occurred approximately 3 years earlier in the girls than in the boys. Furthermore, the girls’ estimated PHV, APHV, and final height were lower than those of the boys. This study allows additional insight into pubertal growth-spurt parameters and also provides a valuable reference database for the assessment of Colombian children and adolescents.
Optimization of the Carbonization Parameter of Exhausted Coffee Husk (ECH) as Biochar for Pb and Cu Removal Based on Energy Consumption

Recent studies on agricultural waste as a potential precursor of biochar for heavy metals removal from aqueous solution had not considered exhausted coffee husk (ECH) as the potential one. It is well-known that the carbonization process influences the removal performance of biochar, particularly removal efficiency (RE). However, previous studies rarely considered the energy consumption during the carbonization process. The major objective of this study is to investigate the optimum carbonization parameter on ECH biochar for removal of ion Pb and Cu from economic stand point. The ECH biochar was produced at the different heating temperature (300 – 600°C) and heating time (30 – 120 minutes). In regard to specific cost of bio-sorption, the results showed that 500°C was the optimum heating temperature of ECH biochar for the Pb removal, while 600°C was the optimum one for the Cu removal. Furthermore, the heating time experimental outcomes suggested that the optimum heating time were 30 minutes for Pb removal and 120 minutes done Cu removal. Key words: Biochar, carbonization temperature, carbonization time, exhausted coffee husk, specific energy cost
Experimental Studies on Rock Thin-Section Image Classification by Deep Learning-Based Approaches

Experimental studies were carried out to analyze the impact of optimizers and learning rate on the performance of deep learning-based algorithms for rock thin-section image classification. A total of 2634 rock thin-section images including three rock types—metamorphic, sedimentary, and volcanic rocks—were acquired from an online open-source science data bank. Four CNNs using three different optimizer algorithms (Adam, SGD, RMSprop) under two learning-rate decay schedules (lambda and cosine decay modes) were trained and validated. Then, a systematic comparison was conducted based on the performance of the trained model. Precision, f1-scores, and confusion matrix were adopted as the evaluation indicators. Trials revealed that deep learning-based approaches for rock thin-section image classification were highly effective and stable. Meanwhile, the experimental results showed that the cosine learning-rate decay mode was the better option for learning-rate adjustment during the training process. In addition, the performance of the four neural networks was confirmed and ranked as VGG16, GoogLeNet, MobileNetV2, and ShuffleNetV2. In the last step, the influence of optimization algorithms was evaluated based on VGG16 and GoogLeNet, and the results demonstrated that the capabilities of the model using Adam and RMSprop optimizers were more robust than that of SGD. The experimental study in this paper provides important practical value for training a high-precision rock thin-section image classification model, which can also be transferred to other similar image classification tasks.
BUSINESS PROCESS REENGINEERING: CONCEPTS CAUSES AND EFFECT

Some people made a wrong concept about Business Process Reengineering (BPR). Some were misunderstanding about the BPR term. In other way, so many researches were introduced to describe a better definition about BPR. The thinking about concepts, causes, and effect of BPR will make a new perception about the term of BPR itself as a better methodology instead of the other Quality Management Methodology such as Total Quality Management (TQM), Just In Time (JIT), etc. This paper will mention the context of BPR in some of case study's journal.