Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding the needle in a haystack: Mapping antifungal drug resistance in fungal pathogen by genomic approaches

Fungi are ubiquitous on earth and are essential for the maintenance of the global ecological equilibrium. Despite providing benefits to living organisms, they can also target specific hosts and inflict damage. These fungal pathogens are known to affect, for example, plants and mammals and thus reduce crop production necessary to sustain food supply and cause mortality in humans and animals. Designing defenses against these fungi is essential for the control of food resources and human health. As far as fungal pathogens are concerned, the principal option has been the use of antifungal agents, also called fungicides when they are used in the environment. Commercial antifungal agents and fungicides belong to several classes and affect several cellular functions (Fig 1) [1]. Fungicides outnumber medical antifungal agents by the number of chemical classes and by the number of licensed drugs [1]. Exposing plant and/or human pathogens to these agents results in a substantial fungal growth deficiency and eventually clearance of the pathogens in the infected sites or infected host. However, the same exposure imposes strong selective pressures for the development of resistance, which is ineluctably acquired over shortand long-term periods. Fungicides that are spread in the environment can also cause the selection of resistance in ubiquitous fungi that are also human pathogens. The transmission of environmentally acquired resistance to human is of concern because it reduces the limited number of possible treatments to a few alternatives. It is important to resolve resistance mechanisms because they may highlight not only novel biology but also may reveal novel strategies to counteract and combat resistance. Resistance mechanisms can be grouped in at least three general principles that include (i) decrease of effective intracellular drug concentration, (ii) alterations of the drug target, and (iii) compensatory mechanisms which decrease drug toxicity [2]. I will summarize here approaches that led to the understanding of antifungal resistance in plant and human fungal pathogens with a special focus on genome-wide studies.
THE EFFECT OF OUTPUT MONITORING ON SUPPLIER LOGISTICS PERFORMANCE AND FLEXIBILITY: THE MEDIATING EFFECTS OF BUYER-SUPPLIER INTEGRATION IN THE CONTEXT OF ESSENTIAL MEDICINES SUPPLY

This paper examines the relationship between output monitoring and supplier performance. It focuses specifically focuses on buyer-supplier exchange relationships in delivering essential medicines in Tanzania, and examines the mediation effects of buyer-supplier integration on supplier’s flexibility and supplier logistics performance. The resource-based view of the firm was used to frame the relationships by considering output monitoring efforts and the buyer-supplier integration as resources aimed to improve performance. A survey data of 111 public-owned health facilities selected randomly after stratifying them into hospitals, health centres and dispensaries was used for final analysis. Structural equation modelling using Smart PLS3 was employed to ascertain the relationship between output monitoring, buyer-supplier integration and supplier logistics performance and flexibility. The research hypotheses focused on both direct and indirect effects. The results revealed that the buyer-supplier integration partially mediates the effects of output monitoring on supplier logistics performance while fully mediating the effects of output monitoring on supplier flexibility. The results of the study imply that buyer-supplier integration is critical for multiple dimensions of performance improvement.
Anastomosis grouping and genetic diversity analysis of Rhizoctonia solani isolates causing wet root rot in chickpea

Rhizoctonia solani is considered as one of the most destructive soil-and-seed borne plant pathogens infecting various agricultural crops including chickpea. The 50 chickpea isolates of R. solani representing 10 different states of India were variable in hyphal anastomosis reactions and they were grouped into seven AGs as AG1, AG2-2, AG2-2LP, AG2-3, AG3, AG4 and AG5. Genetic diversity of the pathogen was determined by using molecular markers namely, simple sequence repeats (SSR) and rDNA internal transcribed spacer (ITS). A neighbor-joining tree constructed based on the profiles generated by SSR markers grouped the isolates into eight categories. This revealed 90% of genetic similarity among the isolates and partial correlation with reference to their geographical origin and AGs. The isolates were amplified with a set of primers ITS 1 and ITS 4 and they produced a specific band ≈650 bp. Low level of (7%) variability was observed in the nucleotide sequences of the ITS regions of these isolates. The phylogenic tree generated from bootstrap neighboring joint analysis grouped the Indian populations of R. solani into two categories. Keywords: Chickpea, wet root rot, genetic diversity, simple sequence repeats (SSR), internal transcribed spacer (ITS).
Community Assemblies and the Implementation of Local Economic Development Policy in Uganda

The study investigated the contribution of Community Assemblies (CA) in the implementation of the Local Economic Development Policy(LED). Two of the core assemblies studied were; Barazas and Neighbourhood Assemblies in Kayonza Sub County, Kayunga district from the period 2012 –2016. The study assessed the role of Barazas in LED and the contribution of Neighbourhood Assemblies in LED. It also identified the factors affecting effectiveness of community spaces in LED. The study used a qualitative descriptive design based on interviews, observations, document and literature review carried out in the district of Kayunga in Kayonza subcounty. Findings indicated that community assemblies play a positive role in the implementation of LED, in the awareness creation, monitoring and evaluation of government programmes, citizen mobilisation, as fora for engagement and introduction of government programmes. Community assemblies were accountability and feedback sharing forums and supported identification of local problems, acted as information centres as well as informed policy changes through recommendations. However, their work was affected by lack of sustainability, politics of public policy, limited citizens’ participation, conflicting policies, poor coordination, human and capital resources, institutional challenges and limited follow up on the concern raised by the citizens through these assemblies. The study recommended participatory planning, coordination, implementation, monitoring and evaluation of government programmes. Similarly, the contribution of community assemblies ought to be recognised and enhanced to realise full economic emancipation, since this is the way to go to recognise an empowered citizenry.
BOARD COMPOSITION, PROCESS, AND ACTIVISM: EVIDENCE WITHIN AMERICAN FIRMS

This study provided an empirical test of the effects of compositional and process variables on board activism. The attributes examined were functional area knowledge, independence, duality, and effort norms. The findings from this study provided support for the positive relationships between both functional area knowledge and effort norms and board activism. The strong relationship between effort norms and activism indicates the importance of process variables in board research and the need for additional research using process variables in a board context giving further insight into board activism and governance. Additionally, the relationship between functional area knowledge and board activism highlights how boards ability to meet cognitive demands is critical in board performance. These results as well as future research directions are examined in the discussion.
Biocontrol of rhizome soft rot ( Pectobacterium carotovorum ) on valerian by Pseudomonas spp . under in vitro and greenhouse conditions

Pectobacterium carotovorum is dreadful causal agent of potato and several agricultural products soft rot and able to damage crops seriously. Recently rhizome soft rot of valerian (Valeriana officinalis) has been reported in Iran. Actually, there are no efficient bactericides used to protect valerian against Pectobacterium. Biological control could be an interesting approach to manage this disease. In this study, the potential of forty isolates of Pseudomonas from Valerian rhizosphere for biological control of P. carotovorum were investigated. Sixteen bacterial isolates showed inhibitory effects against P. carotovorum. They exhibited antimicrobial activity and prevented the growth of pathogen significantly. Isolates 1 and 3 with 3.43 and 3.83 cm had maximum inhibition zone, respectively. Based on the results of in vitro experiments, seven isolates from the Pseudomonas genus were selected and studied on valerian growth index under greenhouse condition. Valerian transplants were treated with the isolates. Good prevention of the disease was obtained using the rhizosphere antagonists. The results revealed that two isolates, 1 and 3, were able significantly to increase the fresh and dry weight of root. Detection of phlD gene using PCR procedure indicated that these two isolates had phlD gene.
Prenatal and postnatal lipid-based nutrient supplementation and cognitive, social-emotional, and motor function in preschool-aged children in Ghana: a follow-up of a randomized controlled trial

ABSTRACT Background Adequate nutrition is necessary for brain development during pregnancy and infancy. Few randomized controlled trials of supplementation during these periods have measured later developmental outcomes. Objective Our objective was to investigate the effects of provision of prenatal and postnatal lipid-based nutrient supplements (LNS) on child development at preschool age. Methods We conducted a follow-up study of 966 children aged 4–6 y in 2016, born to women who participated in the International Lipid-Based Nutrient Supplements-DYAD trial conducted in Ghana in 2009–2014, representing 79% of eligible children. Women ≤20 weeks of gestation were randomized to daily LNS or multiple micronutrient (MMN) capsules during pregnancy through 6 mo postpartum or iron and folic acid (IFA) capsules during pregnancy and calcium placebo capsules during 6 mo postpartum. Children in the LNS group received LNS from 6 to 18 mo. Primary outcomes of this follow-up study were (1) a cognitive factor score based on a test battery adapted from several standard tests, 2) fine motor score (9-hole pegboard test), and (3) social-emotional difficulties (Strengths and Difficulties Questionnaire; SDQ). Eight secondary outcomes were calculated in specific domains (e.g., language, SDQ prosocial). Analysis was by a complete case intention to treat in a 2-group comparison: LNS compared with non-LNS (MMN + IFA). Results Children in the LNS group had significantly lower social-emotional difficulties z-scores than children in the non-LNS group (adjusted for child age β = −0.12, 95% CI: −0.25, 0.02, P = 0.087; fully adjusted β = −0.16, 95% CI: −0.29, −0.03, P = 0.013). The effect of LNS on social-emotional difficulties score was larger among children living in households with lower home environment scores (P-interaction = 0.081). No other outcomes differed between the 2 intervention groups. Conclusions Provision of LNS during the first 1000 d of development improved behavioral function, particularly for children from low nurturing and stimulation households, but did not affect cognition at preschool age in this setting. Trial Registration: clinicaltrials.gov, Identifier NCT00970866.
Carbon Paste Gold Nanoparticles Sensor for the Selective Determination of Dopamine in Buffered Solutions

�1 universal buffer solution pH 7.4 is introduced. The sensor is based on a carbon paste CP electrode modified with gold nanoparticles. In a mixture of DA, AA, and UA, the sensor shows high selective response toward DA and no response for AA or UA. The effect of various experimental parameters including time of deposition of gold nanoparticles on the CP electrode, pH, and scan rate on the voltammetric response of DA was investigated. At the optimum conditions, the concentration of DA was determined using differential pulse voltammetry in a linear range of 1.0 10 �7 to 5.0 10 �6 mol L �1 and 5.0 10 �6 to 1.3 10 �4 mol L �1 with correlation coefficients of 0.9995 and 0.9988 and a detection limit of 5.9 10 �9 and 8.2 10 �8 mol L �1 , respectively. The modified electrode can be used for the determination of DA spiked into human serum samples, and excellent recovery results were obtained over a wide concentration range of DA. Moreover, validation parameters, such as reproducibility, sensitivity, and recovery were evaluated successfully in the determination of DA in diluted human urine.
A Decentralized Power Management and Sliding Mode Control Strategy for Hybrid AC/DC Microgrids including Renewable Energy Resources

This paper presents a new decentralized robust strategy to improve small and large-signal stability and power-sharing of hybrid AC/DC microgrids and improve its performance for nonlinear and unbalanced loads. In addition to the sliding mode controller for DC/DC converters, for the sake of improving power sharing and regulating active and reactive powers injected by distributed energy resources, and moreover, controlling harmonic and negative-sequence current in the presence of nonlinear and unbalanced loads, two separate controllers for positive sequence power control and negative sequence current control are designed based on the sliding mode control and Lyapunov function theory, respectively. The theoretical concept of the proposed robust control strategy, including mathematical modeling of microgrid components, basic theorems, controller design procedure, and robustness and closed loop stability analysis are outlined. Also, this direct power/current/voltage control scheme is governed by a new hybrid AC/DC hierarchical control scheme that exploits a harmonic virtual impedance loop and voltage compensation scheme. To show the effectiveness of the proposed robust control scheme, offline time-domain simulations are done on a hybrid AC/DC wind/photovoltaic/fuel-cell microgrid with nonlinear and unbalanced loads in MATLAB/Simulink environment, and the results are experimentally verified by OPAL-RT real-time digital simulator.
Application of Direct and Surrogate-Based Optimization to Two-Dimensional Benchmark Aerodynamic Problems: A Comparative Study

This paper presents the results of applying direct and surrogate-based optimization (SBO) algorithms to two-dimensional aerodynamic benchmark problems, both involving transonic flow, one invisvid and the other viscous. The direct optimization methods used in this study are the adjoint-based FUN3D and Stanford University Unstructured solvers. The SBO algorithms include the SurroOpt framework, which exploits approximation-based models, the multi-level optimization (MLO) algorithm, which relies on physics-based models, as well as the adjoint-enhanced MLO algorithm. The results demonstrate that direct optimization and the approximation-based methods are able to yield designs that are comparable to those obtained with high-dimensional shape parameterization methods. Physics-based SBO shows a rapid design improvement at a low computational cost compared to the direct and the approximation-based SBO techniques, which indicates that—for certain problems—derivative-free methods may be competitive to adjoint-based algorithms when embedded in surrogate-assisted frameworks. On the other hand, global search approaches, while more expensive, exhibit the potential to produce the best quality results.
Implementation of Rural Infrastructure Development in Pohuwato Regency

This study aims to determine the role of the village government in improving the welfare of the people of Bulili Village, Duhiadaa District, Pohuwato Regency. The research approach uses descriptive research. Informants were the village head, village head, BPD chairman, LPM Chair, village treasurer and Bulili village community leaders. The results showed that the Bulili Village Government in improving community welfare carried out three programs such as: 1) an increase in agricultural production programs planning assistance that would improve the economy of Bulili villagers such as providing rice seeds, corn seeds, tractor assistance, hansplayer assistance (tengki), assistance fertilizer, and PNPM assistance, 2) program to increase livestock production, namely the collaboration of district governments, sub-district governments and village governments in the form of providing assistance for productive superior cow and productive goat seeds to cattle farmers in the Bulili village community formed by Bulili village government . 3) development of village livestock agribusiness, which is to form a group of livestock farmers who are given the assistance of productive cow and goat seeds to each group to be managed.
Drone-based land-cover mapping using a fuzzy unordered rule induction algorithm integrated into object-based image analysis

ABSTRACT Land-cover maps provide essential data for a wide range of practical and small-scale applications. A number of data sources appropriate for land-cover extraction are available. Among these, images captured using unmanned aerial vehicles (UAVs) are low cost, have very high resolution, and can be acquired at any time with few restrictions. Over the past two decades, various classification techniques have been developed to extract land-cover features from UAV images, and object-based image analysis (OBIA) is the preferred technique based on the recent literature. This study presents a novel method that integrates the fuzzy unordered rule induction algorithm (FURIA) into OBIA to achieve accurate land-cover extraction from UAV images. The images were segmented using a multiresolution segmentation algorithm with an optimized scale parameter. The scale parameter was optimized using a novel approach that integrated feature space optimization into the plateau objective function. During the classification stage, significant features were selected via random forest, and rule sets were developed using FURIA. For comparison, result of the proposed approach was compared with those of decision tree (DT) rules and the Support Vector Machine (SVM) classification method. The results of this study indicate that the proposed method outperforms DT and SVM with an overall accuracy of 91.23%. A transferability evaluation showed that FURIA achieved accurate classification results on different UAV image subsets captured at different times. The findings suggest that fuzzy rules are more appropriate than conventional crisp rules for land-cover extraction from UAV images.
Rapid biofunctionalization of magnetic beads with function-spacer-lipid constructs

KODETM Technology is based on novel water-dispersible self-assembling molecules, called a functionspacer-lipids or KODETM constructs (Figure 1) that are able to coat virtually any biological or non-biological surface with almost any biological or non-biological material [1-10]. The primary coating method of live cells, organisms, bacteria and viruses or solid surfaces (glass, metals, plastics, etc.) is achieved by simple contact with a solution containing one or more FSL KODETM constructs. Upon contact the FSLs spontaneously and harmlessly create a stable and novel surface coating. Essentially the spontaneous self-assembling process is driven by the need of the constructs to “exclude water”. Because the constructs are able to bind to virtually any surface, be it hydrophobic or hydrophilic the mechanisms of action are multiple and complex and include hydrophobic interactions (via lipid tail), hydrophilic interactions (via the head group and spacer), micelle entrapment, encapsulation, bi/multi layer assembly, and other factors such as hydrogen bonding, van der Waals forces, electrostatic and ionic interactions and combinations of all the above on complex surfaces.
Interactive comment on “ Development of a parameterization of black carbon aging for use in general circulation models ” by N . Oshima

The authors present a simple parameterization for the ageing of black carbon (BC) from hydrophobic to hydrophilic. The authors derive their parameterization from many case studies performed with the box-model MADRID-BC, and arrive to a simple formula that describes the characteristic ageing time of BC τBC as inversely proportional to the mass condensation rate VBC . They fit their model results to derive the coefficient of proportionality between τBC and VBC . In this parameterization only condensation is considered as an ageing process, and coagulation, cloud processing and photochemistry are neglected.
Application of Data Mining Techniques to Predict Urinary Fistula Surgical Repair Outcome: The Case of Addis Ababa Fistula Hospital, Addis Ababa, Ethiopia

Background: Maternal outcomes are good in most countries of the developed world while the same is not true in many developing countries. The likelihood of the occurrence of incontinence after successful surgical repair makes predicting urinary fistula surgical repair outcome is important for decision making during treatment and follow up. Therefore, this research is aimed to apply data mining techniques to build a model that can assist in predicting surgical outcome of urinary fistula repair based on clinical assessments done just before surgical repair. Methods: The six-step hybrid knowledge discovery process model is used as a framework for the overall activities in the study. 15961 instances that have undergone urinary fistula repair in Addis Ababa Fistula Hospital are used for both predictive association rule extraction and predictive model building. Apriori algorithm is used to extract association rules while classification algorithms J48, PART, Naive Bayes and multinomial logistic regression are used to build predictive models. Support and confidence are used as interestingness measure for association rules while area under the WROC and ROC curve for each specific outcome is sequentially used to compare performances of models from the predictive algorithms. Results: Predictive association rules from Apriori have shown frequent co-occurrence of less severity of injury with cured outcome. The predictive model from PART-M2-C0.05-Q1 scheme has shown an area under WROC curve of 0.742. Area under the ROC curve for residual outcome (ROCResidual=0.822) from this algorithm is better than Naive Bayes and logistic, while the areas under the ROC curves for the other outcomes are greater than the model from J48. Conclusion: Predictive model is developed with the use of PART-M2-C0.05-Q1. The predictive association rules and predictive model built with the use of data mining techniques can assist in predicting urinary fistula surgical repair outcome. Therefore, it is better in detecting residual outcome than the logistic regression model.
Bidirectional Nonnegative Deep Model and Its Optimization in Learning

Nonnegative matrix factorization (NMF) has been successfully applied in signal processing as a simple two-layer nonnegative neural network. Projective NMF (PNMF) with fewer parameters was proposed, which projects a high-dimensional nonnegative data onto a lower-dimensional nonnegative subspace. Although PNMF overcomes the problem of out-of-sample of NMF, it does not consider the nonlinear characteristic of data and is only a kind of narrow signal decomposition method. In this paper, we combine the PNMF with deep learning and nonlinear fitting to propose a bidirectional nonnegative deep learning (BNDL) model and its optimization learning algorithm, which can obtain nonlinear multilayer deep nonnegative feature representation. Experiments show that the proposed model can not only solve the problem of out-of-sample of NMF but also learn hierarchical nonnegative feature representations with better clustering performance than classical NMF, PNMF, and Deep Semi-NMF algorithms.