Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of staple crop production during the Covid-19 outbreak. Evidence for women small producers in Senegal

ABSTRACT The Covid-19 outbreak has negatively affected the agri-food system especially in developing countries where most people rely on agriculture. Using primary data, we study the role of staple crop production as a coping strategy during the Covid-19 outbreak. Using a linear probability model, this article assesses the effect of income and food security deterioration perceived as a consequence of Covid-19 on rice investment propensity among 1320 women rice producers in Casamance, Senegal. Our results show that food security deterioration and especially income reduction due to Covid-19 are significantly and positively related to rice investment propensity. The presence of food retailers and the type of household livelihood, farm or nonfarm, dramatically drive the Covid-19 perception effects on rice investment propensity. While women small producers in villages with food retailers slightly increase their propensity to invest in rice, in remote villages lacking food retailers rice production represents an effective coping strategy for those women living in households that depend to a large extent on the market, i.e. those relying on nonfarm income. These results are supported by the strong correlation between limited physical access to the market, due to movement restrictions, and rice investment propensity, especially among households relying on nonfarm income.
Review of Escherichia coli O157:H7 Prevalence, Pathogenicity, Heavy Metal and Antimicrobial Resistance, African Perspective

Abstract Escherichia coli O157:H7 is an important food-borne and water-borne pathogen that causes hemorrhagic colitis and the hemolytic-uremic syndrome in humans and may cause serious morbidity and large outbreaks worldwide. People with bloody diarrhea have an increased risk of developing serious complications such as acute renal failure and neurological damage. The hemolytic-uremic syndrome (HUS) is a serious condition, and up to 50% of HUS patients can develop long-term renal dysfunction or blood pressure-related complications. Children aged two to six years have an increased risk of developing HUS. Clinical enteropathogenic Escherichia coli (EPEC) infections show fever, vomiting, and diarrhea. The EPEC reservoir is unknown but is suggested to be an asymptomatic or symptomatic child or an asymptomatic adult carrier. Spreading is often through the fecal-oral route. The prevalence of EPEC in infants is low, and EPEC is highly contagious in children. EPEC disease in children tends to be clinically more severe than other diarrheal infections. Some children experience persistent diarrhea that lasts for more than 14 days. Enterotoxigenic Escherichia coli (ETEC) strains are a compelling cause of the problem of diarrheal disease. ETEC strains are a global concern as the bacteria are the leading cause of acute watery diarrhea in children and the leading cause of traveler’s diarrhea. It is contagious to children and can cause chronic diarrhea that can affect the development and well-being of children. Infections with diarrheagenic E. coli are more common in African countries. Antimicrobial agents should be avoided in the acute phase of the disease since studies showed that antimicrobial agents may increase the risk of HUS in children. The South African National Veterinary Surveillance and Monitoring Programme for Resistance to Antimicrobial Drugs has reported increased antimicrobial resistance in E. coli. Pathogenic bacterial strains have developed resistance to a variety of antimicrobial agents due to antimicrobial misuse. The induced heavy metal tolerance may also enhance antimicrobial resistance. The prevalence of antimicrobial resistance depends on the type of the antimicrobial agent, bacterial strain, dose, time, and mode of administration. Developing countries are severely affected by increased resistance to antimicrobial agents due to poverty, lack of proper hygiene, and clean water, which can lead to bacterial infections with limited treatment options due to resistance.
Forensic Aspects of a Fatal Intoxication Involving Acetaminophen, Citalopram and Trazodone: A Case Report

We report the case of a young man, a former heroin addict, found dead at home by the Police Forces in an advanced state of decomposition. Numerous blisters and unpacked tablets of medications were found all over the bed and on the floor of the room. Multiple injuries to the face, left arm and neck of the deceased were noted. The latter damages were attributed to post-mortem dog bites, since no indications of a possible defense against the animal were observed. The autopsy findings were unremarkable. Toxicological investigations performed on peripheral blood and urine by gas chromatography-mass spectrometry (GC-MS) technique showed the presence of acetaminophen, citalopram and trazodone. Combined drug intoxication was proposed as the cause of death since acetaminophen and trazodone concentrations were comparable with the ones found in fatal cases. Moreover, citalopram concentration in peripheral blood was above the toxic range and in accordance with levels found in fatalities due to poly-drug intoxication.
Development of leanness index for SMEs

Purpose This paper aims to discuss the concept of leanness and provide an effective assessment tool for measuring leanness of small and medium enterprises (SMEs). Design/methodology/approach A hybrid interpretive structural modelling–analytical network process (ISM-ANP) approach is used to develop leanness index for SMEs. The parameters for leanness are extracted from the literature survey, and the inter-relationships among them are identified through ISM approach. Further, the ANP tool is used to derive the weights of the parameters, and the leanness index is developed for SMEs. Findings A leanness assessment model is developed, which considers the interdependencies among leanness parameters. Continuous improvement, Just in Time and active management participation, respectively, get first, second and third ranks for leanness measurement in SMEs. Research limitations/implications This study is based on the expert’s opinion, and it may tend to be biased. However, future study will be performed as empirical research to catch more explicit concept and more insights of leanness in context of SME sector. Practical implications This paper provides guidelines to the managers of SMEs for measuring the leanness index and planning for future. This leanness index gives information regarding the degree of lean adoption in the organization. Originality/value The proposed model has been developed by the expert opinion of academicians and practitioners. The proposed model can provide guidelines and directions for managers for leanness assessment in SME context.
Green Technology for Pork Loin Wet Curing—Unconventional Use of Cow and Soy Milk Treated with Non-Thermal Atmospheric Plasma

This study was conducted to evaluate the possibility of using plasma-activated cow and soy milk powders as a substitute for sodium nitrite for wet curing of pork meat (m. longissimus thoracis et lumborum). Pork loin slices were cured for 4 d at refrigerate conditions in four brines: water + salt (NC group), water + salt + sodium nitrite (PC group), water + salt + plasma-activated cow milk powder (B1 group), and water + salt + plasma-activated soy milk powder (B2 group). Importantly, brines from groups PC, B1, and B2 were characterized by the same concentration of NO2− ions (200 ppm). Results show that samples from B1 and B2 groups had significantly (p < 0.05) higher values of redness, nitrosylhemochrome content, and lower values of thiobarbituric acid reactive substances (TBARS) compared to samples from the NC group. At the same time, the groups cured with alternative curing agents were characterized by lower residual nitrite content with regard to groups cured with NaNO2. No significant differences (p ≥ 0.05) were found in pH and shear force values among the treatments. Finally, the aroma profile of the samples from groups B1 and B2 was similar to the aroma profile of the samples from the PC group (the aroma differed by a maximum of 1.73% in the case of brine containing plasma-activated cow milk powder) but differed significantly from the NC group (the aroma differed in 97.21%). Due to the higher nitrite depletion in the final product, while maintaining the quality parameters similar to traditionally cured pork loins, both alternative curing agents can be recommended, with a predominance of plasma-treated soy milk.
Hsa-mir-135a Shows Potential as A Putative Diagnostic Biomarker in Saliva and Plasma for Endometriosis

Endometriosis is a chronic disease characterized by the implantation and proliferation of endometrial tissue outside of the uterine cavity. The nonspecific nature of the symptoms and the lack of sensitive, noninvasive diagnostic methods often lead to a significant delay in diagnosis, highlighting the need for diagnostic biomarkers. The correlation of circulating miRNAs with altered inflammatory signals seen in patients with endometriosis has raised the possibility that miRNAs can serve as biomarkers for the disease. In our study, we analyzed miRNA expression in saliva of women with and without endometriosis using a FireFly custom multiplex circulating miRNA assay. This focused panel included 28 human miRNAs, 25 of which have been previously found to be differentially expressed either in plasma, serum, and/or blood of women with endometriosis, compared to controls. We found that hsa-mir-135a was expressed significantly higher in the saliva of women with endometriosis, independent of disease stage and menstrual cycle phase. We confirmed that hsa-mir-135a also showed significantly elevated expression in the plasma of endometriosis patients. This indicates that hsa-mir-135a is a putative noninvasive biomarker of endometriosis in both saliva and plasma, but further validation studies are required to assess its clinical value as a biomarker.
PS+: A Simple yet Effective Framework for Fast Training on Parameter Server

In distributed training, workers collaboratively refine the global model parameters by pushing their updates to the Parameter Server and pulling fresher parameters for the next iteration. This introduces high communication costs for training at scale, and incurs unproductive waiting time for workers. To minimize the waiting time, existing approaches overlap communication and computation for deep neural networks. Yet, these techniques not only require the layer-by-layer model structures, but also need significant efforts in runtime profiling and hyperparameter tuning. To make the overlapping optimization simple and generic, in this article, we propose a new Parameter Server framework. Our solution decouples the dependency between push and pull operations, and allows workers to eagerly pull the global parameters. This way, both push and pull operations can be easily overlapped with computations. Besides, the overlapping manner offers a different way to address the straggler problem, where the stale updates greatly retard the training process. In the new framework, with adequate information available to workers, they can explicitly modulate the learning rates for their updates. Thus, the global parameters can be less compromised by stale updates. We implement a prototype system in PyTorch and demonstrate its effectiveness on both CPU/GPU clusters. Experimental results show that our prototype saves up to 54% less time for each iteration and up to 37% fewer iterations for model convergence, achieving up to 2.86× speedup over widely-used synchronization schemes.
Association between Immediate Postoperative Radiographic Findings and Failed Internal Fixation for Trochanteric Fractures: Systematic Review and Meta-Analysis

Failed internal fixations for trochanteric fractures have a strong negative impact owing to increased postoperative mortality and high medical costs. However, evidence on the prognostic value of postoperative radiographic findings for failed internal fixations is limited. We aimed to clarify the association between comprehensive immediate postoperative radiographic findings and failed internal fixation using relative and absolute risk measures. We followed the meta-analysis of observational studies in epidemiology guidelines and the Cochrane handbook. We searched specific databases in November 2021. The outcomes of interest were failed internal fixation and cut-out. We pooled the odds ratios and 95% confidence intervals using a random-effects model and calculated the number needed to harm for each outcome. Thirty-six studies involving 8938 patients were included. The certainty of evidence in the association between postoperative radiographic findings and failed internal fixation or cut-out was mainly low or very low except for the association between intramedullary malreduction on the anteromedial cortex and failed internal fixation. Moderate certainty of evidence supported that intramedullary malreduction on the anteromedial cortex was associated with failed internal fixation. Most postoperative radiographic findings on immediate postoperative radiographs for trochanteric fractures were uncertain as prognostic factors for failed internal fixations.
Towards Analytic Solutions of Step‐Wise Safe Switching for Known Affine‐Linear Models

In the present work we establish conditions which guarantee safe transitions for the closed‐loop system produced by the application of the Step‐Wise Safe Switching control approach to an affine linear system when the nonlinear description of the plant is known. These conditions are based on the local Input to State Stability (ISS) properties of the nonlinear system around the plant’s nominal operating points.
A High-Fat Diet Modifies Brain Neurotransmitter Profile and Hippocampal Proteome and Morphology in an IUGR Pig Model

Intrauterine Growth Restriction (IUGR) hinders the correct growth of the fetus during pregnancy due to the lack of oxygen or nutrients. The developing fetus gives priority to brain development (“brain sparing”), but the risk exists of neurological and cognitive deficits at short or long term. On the other hand, diets rich in fat exert pernicious effects on brain function. Using a pig model of spontaneous IUGR, we have studied the effect on the adult of a long-term high-fat diet (HFD) on the neurotransmitter profile in several brain areas, and the morphology and the proteome of the hippocampus. Our hypothesis was that animals affected by IUGR (born with low birth weight) would present a different susceptibility to an HFD when they become adults, compared with normal birth-weight animals. Our results indicate that HFD affected the serotoninergic pathway, but it did not provoke relevant changes in the morphology of the hippocampus. Finally, the proteomic analysis revealed that, in some instances, NBW and LBW individuals respond to HFD in different ways. In particular, NBW animals presented changes in oxidative phosphorylation and the extracellular matrix, whereas LBW animals presented differences in RNA splicing, anterograde and retrograde transport and the mTOR pathway.
The Cell-Wall β-d-Glucan in Leaves of Oat (Avena sativa L.) Affected by Fungal Pathogen Blumeria graminis f. sp. avenae

In addition to the structural and storage functions of the (1,3; 1,4)-β-d-glucans (β-d-glucan), the possible protective role of this polymer under biotic stresses is still debated. The aim of this study was to contribute to this hypothesis by analyzing the β-d-glucans content, expression of related cellulose synthase-like (Csl) Cs1F6, CslF9, CslF3 genes, content of chlorophylls, and β-1,3-glucanase content in oat (Avena sativa L.) leaves infected with the commonly occurring oat fungal pathogen, Blumeria graminis f. sp. avenae (B. graminis). Its presence influenced all measured parameters. The content of β-d-glucans in infected leaves decreased in all used varieties, compared to the non-infected plants, but not significantly. Oats reacted differently, with Aragon and Vaclav responding with overexpression, and Bay Yan 2, Ivory, and Racoon responding with the underexpression of these genes. Pathogens changed the relative ratios regarding the expression of CslF6, CslF9, and CslF3 genes from neutral to negative correlations. However, changes in the expression of these genes did not statistically significantly affect the content of β-d-glucans. A very slight indication of positive correlation, but statistically insignificant, was observed between the contents of β-d-glucans and chlorophylls. Some isoforms of β-1,3-glucanases accumulated to a several-times higher level in the infected leaves of all varieties. New isoforms of β-1,3-glucanases were also detected in infected leaves after fungal infection.
ECONOMIC GLOBALIZATION AND UNEMPLOYMENT: THE EXPERIENCE OF ARAB COUNTRIES

The present study seeks to examine the impact of international trade policies on unemployment for 10 Arab countries over the period of 1991 to 2014. In addition to various trade policies measurements, the present study takes into consideration various variables that represent macroeconomic distortion policies; a country’s economic size; the quality of government institutions; and labor market regulation. Using different proxies for trade policies, the results of the Pooled Mean Group (PMG) shows that trade liberalization policies appear to decrease unemployment rates over time. The finding supports contemporary calls to participate in international trade to facilitate job creation. However, further studies are required to identify the specific channels through which trade policies can affect unemployment in the Arab region.
A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems

Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE ( εmax ), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of εmax , and (b) separately re-parameterizing εmax in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art εmax –static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three εmax –static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with in-situ measurements and was found to outperform the three εmax –static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m−2 d−1) by 24.2% and 35.4% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7% and 20% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIRv) were used to re-parameterize the εmax , respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of εmax and indicates great potential for improving daily agroecosystem GPP estimates at a global scale.
Hybrid CNN Model for Classification of Rumex Obtusifolius in Grassland

Rumex obtusifolius Linnaeus (R. obtu. L.) is one of the vital broad-leaved weeds in grassland that needs removal. It affects dairy products and reduces their quality. Hand-removal methods are costly and time-consuming. Chemical treatment using herbicides has a negative impact on crops and causes environmental pollution. In smart farming, weeding is performed by using computer vision to recognize the weeds efficiently and effectively. Conventional machine learning (ML)-based algorithms face challenges, especially in identifying the weeds in real-world data due to a lack of features. Deep learning (DL) approaches use self-learning to extract all potential features that assist in classifying malignant weed species accurately. Recently, single deep learning methods achieved high performance in identifying well-separated and illumination but suffered from misclassification in more sophisticated cases such as overlapping and partial occlusion leaves. This paper presents a hybrid Convolutional Neural Network (CNN) model of three state-of-the-art CNNs to classify Rumex obtusifolius. The proposed model utilizes convolutional neural networks to extract features and classify images. The framework of the proposed method comprises three paramount stages to accomplish the classification key idea, including the data preparation phase, pre-processing phase, and classification phase. A hybrid model of three CNN extractor networks is used as the backbone in the classification stage. Our tested data is real-world data that includes multi-circumstances (overlap, occlusion, various illuminations, etc.) acquired from nature. The first extractor is the Visual Graphics Group-16 (VGG-16) for well-separated leaves and non-complicated issues. The second extractor is Residential Energy Services Network-50 (ResNet-50), to overcome complex real-world issues. The third extractor is Inception-v3 to solve the illumination problem. Therefore, combining three networks into one model improves the discriminatory ability to extract additional useful features. The proposed model has been tested using two benchmark datasets for Rumex weed plants. Both of these datasets were captured in real-world environments. The first dataset consists of 900 samples, while the second dataset consists of 677 samples. Each dataset is individually tested in the proposed model to evaluate the classification accuracy using a set of standard evaluation metrics including accuracy, precession, recall, True-Positive Rate (TPR), False-Positive Rate (FPR), and F1-score. The total averages of the proposed model on both datasets are 97.51%, 97.4%, 94.45%, and 95.9% on the accuracy, recall, precision, and F1-score, respectively.