Miracle-dz/UniFAR-FTUs huggingface dataset:
Prodrug defiance reveals logic-based strategies for treating bacterial resistance

Classifying the mechanisms of antibiotic failure has led to the development of new treatment strategies for killing bacteria. Among the currently described mechanisms – which include resistance, persistence and tolerance – we propose defiance as a subclass of antibiotic failure specific to prodrugs. Using locked antimicrobial peptides (AMP) that are activated by bacterial proteases as a prototypic prodrug, we observe that although treatment eliminates bacteria across the vast majority of environmental conditions (e.g., temperature, concentration of growth nutrients), bacteria spontaneously switch from susceptibility to defiance under conditions that alter the competing rates between bacterial proliferation and prodrug activation. To identify the determinants of this switch-like behavior, we model bacteria-prodrug dynamics as a multi-rate feedback system and identify a dimensionless quantity we call the Bacterial Advantage Heuristic (BAH) that perfectly classifies bacteria as either defiant or susceptible across a broad range of treatment conditions. By recognizing that the bacterial switch from susceptibility to defiance behaves analogously to electronic transistors, we construct prodrug logic gates (e.g., AND, OR, NOT, etc.) to allow assembly of an integrated 3-bit multi-prodrug circuit that kills defiant bacteria under all possible combinations of BAH values (i.e., 000, 001, …, 111) that represent a broad range of possible treatment conditions. Our study identifies a form of bacterial resistance specific to prodrugs that is described by a predictive dimensionless constant to reveal logic-based treatment strategies using multi-prodrug biological circuits.
Analyzing the Loss Functions in Training Convolutional Neural Networks with the Adam Optimizer for Classification of Imagess

The problem of analyzing and determining the loss functions used in training convolutional neural networks (CNN), which provide the highest accuracy of image classification, is considered. The loss functions implemented on the basis of the Keras and TensorFlow machine learning libraries, using which CNNs can be designed, trained, and applied in the Python language, are analyzed. The MNIST, EMNIST and CIFAR10 datasets are used as image sources for CNN training and validation purposes. These data sets are used to train CNNs having different architectures, for each of which the history of its training is preserved (information about the accuracy of image classification on training and validation samples after each training epoch). The evaluation results are then generalized. The highest achieved accuracy of image classification makes it possible to justify the expediency of using the relevant loss functions in training the CNNs.
Detection of E.Coli Strains Isolated from Water Sources and Diarrhea Cases by Random Amplified Polymorphic DNA in Basrah Governorate

One hundred samples of water and thirty of diarrheal cases were collected . Coliform bacteria was detected in most water samples by MPN to comparison with clinical coliform , tap water showed the higher rates of coliform than other sources . 23 different bacterial species from water and 3 from diarrhea were identified by 16SrDNA . E.coli 26 ( 35% ) as the prodomient species from water and 27 (79%) from diarrhea followed by K. pneumonia 11 (14.1%) and 4 (11.8%) respectively. Antibiotic susceptibility showed all E.coli isolates (100%) were MDR and the Ciprofloxacin was the more active antibiotic against isolates of water ( 59% ) and clinics ( 38% ) . In addition , the antibiotic susceptibility profiles showed the identical relatedness of 22 (53.65%) E.coli strains from water and diarrhea . Moreover , RAPD analysis showed 14 (34.14%) of E.coli strains from water and diarrhea were identical indicating to the fecal contamination of water.
GGraph: An Efficient Structure-Aware Approach for Iterative Graph Processing

Many iterative graph processing systems have recently been developed to analyze graphs. Although they are effective from different aspects, there is an important issue that has not been addressed yet. A real-world graph follows the power-law property, in which a small number of vertices have high degrees (i.e., are connected to most other vertices in the graph). These vertices are called hot-vertices and usually require more iterations to converge. In the existing solutions, these hot-vertices may be allocated to many or even all graph partitions along with other vertices that are easy to converge. As the result, the partitions with hot-vertices have to be loaded repeatedly (and consequently the system suffers from high data access cost), although perhaps only a few vertices in these partitions are active. To cope with this issue, we develop an efficient open source graph partition manager, called GGraph, which can be integrated into the existing graph processing systems to efficiently support iterative graph processing, by taking into account the power-law property of the graph structure. It uses a novel graph repartitioning scheme with low overhead to dynamically partition the hot-vertices together, so as to avoid loading the inactive vertices in the same partition as the repeatedly processed hot-vertices. By such means, it not only enables less data access cost, but also enables the privileged processing of the hot-vertices. In order to further increase the convergence speed, a scheduling algorithm is further proposed in this work to prioritize the processing of the hot-vertices with low overhead. To demonstrate the efficiency of GGraph, we plug it into four state-of-the-art graph processing systems, i.e., Gemini, GraphChi, Chaos, and GridGraph, and experimental results show that GGraph improves their performance by up to 3.2 times, 3.8 times, 3.9 times, 3.5 times, respectively.
Structure investigation of a 6μm CaF2 crystal with synchrotron radiation

Two data sets containing altogether 131 Bragg intensities have been collected from a CaF2 crystal of 200 μm3 volume, which corresponds to a cube of about 6 μm edge length. The scattering power S of this crystal amounts to 1.3 × 1014. It is the smallest crystal ever used for an X-ray diffraction experiment. Crystal orientation and data collection were carried out at the storage ring DORIS II, HASYLAB, DESY, Hamburg, with the wavelength λ = 0.91 A. For the first data set all reflections up to sin θ/λ = 0.58 A-1 were collected, for the second only those reflections considered as observed by a prescan were measured up to sin θ/λ = 0.78 A-1. The refinements, carried out as a function of the degree of polarization K, converged to Rw(F) = 0.005 for K = 0.93 and Rw(F) = 0.031 for K = 0.94 for the first and second data set, respectively. For comparison, a 90 μm sphere was investigated with synchrotron radiation [sin θ/λ ≤ 0.91 A-1, Rw(F) = 0.020, K = 0.50] and with a normal X-ray tube [Mo Kα Rw(F) = 0.013]. The intensities are strongly affected by secondary extinction, in contrast to the 6 μm crystal. The following isotropic temperature factors were obtained for the 6 μm, 90 μm (synchrotron) and 90 μm (X-ray tube) data: B(Ca) = 0.38 (5), 0.64 (3), 0.610 (6); B(F) = 0.83 (4), 0.92 (3), 0.812 (8) A2. The TDS correction increased only the temperature factors from the data set measured with the X-ray tube owing to the difference in FWHM (0.03° for synchrotron radiation, 0.210° for the X-ray tube).
Cyclin-dependent kinase 8/β-catenin signalling contributes to tumourigenesis in prostate cancer and can be regulated by miR-216a-5p

Cyclin-dependent kinase 8 (CDK8), as an oncogene, contributes to carcinogenesis in several cancer types. However, the role of CDK8 in prostate cancer (PCa) has not been completely clarified. The present study was to investigate the molecular mechanisms underlying the progression of PCa.In human specimens, RT-qPCR and immunohistochemical (IHC) staining were used to detect the mRNA and protein expression of CDK8, respectively. In vitro experiments, MTT and colony formation assay, transwell assay, and Annexin V (FITC) double staining were performed to analyse cell proliferation, migration, invasion, and apoptosis, respectively.Up-regulation of CDK8 was observed in PCa tissues and associated with poor overall survival (OS) and disease-free survival (DFS). Knockdown of CDK8 by shRNA was able to repress cell proliferation and induce cell apoptosis in PC3 and DU145 cells, as well as reduce the protein expression of Wnt/-catenin pathway components. CDK8 was a direct target of miR-216a-5p, and overexpression of CDK8 reduced the antineoplastic activities of miR-216a-5p in vitro. Furthermore, miR-216a-5p could serve as an independent prognostic indicator for predicting the OS of PCa.miR-216a-5p/CDK8/-catenin signalling cascade modulates the aggression of PCa. miR-216a-5p and CDK8, as promising therapeutic targets, might facilitate the development of therapeutic strategies for the treatment of PCa.
Steady State Mean Square Analysis of Standard Maximum Versoria Criterion Based Adaptive Algorithm

The Maximum Versoria Criterion (MVC) based adaptive algorithm has recently gained the attention of researchers because of its robustness against impulsive interference and reduced complexity. In this brief, the steady-state Excess Mean Square Error (EMSE) analysis of the standard MVC algorithm is done using the energy conservation relation. For the Gaussian noise case, quadratic equation that can provide the exact value of theoretical steady state EMSE is obtained without any assumptions other than the conventional ones and the Price’s theorem. For the non-Gaussian case, an approximate solution using Taylor’s expansion is derived. Experimental results validate the obtained theoretical findings.
New Method of Preparing a Stable and Functional Antibody-Polymer Nanoparticle Based on Deflocculation via Ultra-Sonication

Generally, when the part of PS nanoparticle surface is covered with proteins after the addition of limited amount of protein, the PS nanoparticles will flocculate surgically. The flocculators are induced through mixing and high collision frequency between the proteins on PS nanoparticles and the surface of other PS nanoparticles, and therefore, through rapid particle growth.
Assessment of Test Anxiety, Student Preferences and Performance on Different Examination Models in Umm AlQura University, Saudi Arabia

Background: Test anxiety is a special kind of anxiety-worry mixed with fear which arises in situations where an individual is being evaluated in an academic context. This anxiety may prevent effective use and communication of the information learned by students for examinations, resulting in poor academic performance. Factors such as examination types and socio-cultural context influence test anxiety in university students. Limited evidence is currently available on the roles played by these factors among nursing students in Saudi Arabia, where strong socio-cultural factors shape university education. Objectives: The study was designed to assess university students’ preferences for various examination types: quizzes, formal exam, Objective Structured Clinical Examination (OSCE), paper based written exam, oral exam, open book exam. The study also determined the association of test anxiety of the students and the results obtained from different examination types. Methods: This descriptive, cross-sectional study was conducted among 135 baccalaureate nursing students at the Umm Al-Qura University, Saudi Arabia. A pre-tested, semi-structured questionnaire was self-administered to the students, covering level of exam fairness, easiness, and the number of study hours required to attend the examinations. The test anxiety level of the students was evaluated using a Westside Test Anxiety Scale. Multivariate analysis of variance was performed to explore the association between test anxiety and socio-demographic characteristics of the students. Results: A total of 135 female nursing students were enrolled into the study. Their ages ranged from 20 to 22 years with mean age of 20.8 ± 0.63. Only 24 (17.8%) of them were married at the time of the study. Fifty-nine participants (43.7%) missed less than three theory lectures while 64 (47.4%) were absent for less than three times in the practical sessions. The most preferred examination type was monthly written examination 43 (31.9%) while 78 (57.8%) students had problematic test anxiety. The most frequent coping mechanisms adopted to manage test anxiety by study participants were psychological support and preparation for the examination (p=0.001 and p=0.031, respectively). No statistically significant association existed between test anxiety and academic performance (p=0.41). However, coping mechanisms such as preparing for the exam and psychological support had a statistically significant relationship with academic performance (p<0.0001 and 0.026, respectively). Conclusion: This study showed that test anxiety and preferences for particular test formats affect the students ability to demonstrate content knowledge. The results suggest the need for school support system to make testing outcomes more equitable for nursing students.
Different methodological approaches to natural vulnerability to erosion in southeastern Brazil

The natural vulnerability addresses the processes of morphogenesis and pedogenesis integrating information related to the rocky substrate, soils, geomorphology, vegetation. The study of natural variability is essential for understanding ecosystems in the face of change processes in the environment. This work aimed to analyze the natural vulnerability to the occurrence of erosion, adopting a method based on Crepani and Analysis Hierarchical Process (AHP) method in the Rio Claro Watershed São Paulo Brasil. Four information plans were used in the 1:50000 scale, being: geomorphology, pedology, geology and vegetation/anthropic intervention. The Natural Vulnerability to Erosion Chart is classified into five classes (Very Low, Low, Average, High, and Very High). Comparing the charts generated by the two methods based on Crepani et al. (2001) and Saaty (1980), there was a difference in the Very Low and High vulnerability classes. The most evident class in both charts is the average vulnerability, corresponding 94.55km2 of the area in Crepani Method chart, and 93.34km2 in AHP Method chart, followed by the “Low” class corresponding of 55.56km2 and 76.67% respectively the Crepani and AHP methods. For the "High" class there was a decrease of Brazilian Journal of Development Braz. J. of Develop. , Curitiba, v. 6, n.6, p.36755-36775 jun. 2020. ISSN 2525-8761 36757 48.28% when compared to the two cards, the "Very high" class suffered the least change (0.09%), while the "Very low" class increased above 100%. The methods employed allowed the generation of results that show the reality of the study area, it is worth mentioning that the Crepani method is more generic because it considers the similarity of the information plans for the vulnerability analysis, whereas the AHP method presents the advantage of hierarchizing both information plans and their classes, highlighting the relative importance of natural vulnerability to erosion.
Churn Analysis Using Deep Learning: Customer Classification from a Practical Point of View

The business relevance of customer churn analysis is increasing due to the growing availability of corresponding data and intensifying competition. Here, especially the predictive accuracy of modeling approaches is in the focus of researchers and practitioners alike, with deep neural networks recently becoming an attractive method due to their high performance in a variety of fields. However, from a practical point of view, other factors such as the ease of application and model interpretability are also to be considered. These aspects are generally viewed as shortcomings of deep neural networks. Therefore, a novel framework for the application of deep learning in churn analysis is developed and tested in a practical setting. It is shown, that a less complex application procedure and more easily interpretable prediction modeling can be achieved.
How managerial coaching enables thriving at work. A sequential mediation

The psychological state in which an individual experiences a form of vitality and a sense of learning at work is known as thriving at work. Since the new millennium, empirical research is evident that thriving (employeesâ€™ sustainability) is critical for organizational sustainability. However, this human dimension of sustainability is understudied, and little is known about how individual characteristics and managers promote employee thriving at work. To address the gap, this pioneering study investigates the work context and individual differences in promoting thriving at work. The intervening mechanism of self-efficacy and prosocial motivation in the association between managerial coaching and thriving at work has been examined using a sequential mediation approach. Data has been analyzed using a Hayesâ€™ PROCESS Model 6 (based on 1,000 bootstrap resampling) with an actual sample of 221 respondents. Our results provide support for our hypothesized model. The study finds a direct association between managerial coaching and self-efficacy. It is concluded that self-efficacy is directly related to prosocial motivation, hence enhanced employee thriving at work. It is also found that self-efficacy and prosocial motivation play a vital role in explaining the association between managerial coaching and thriving at work
Preventive and Curative Actions by Meshed Bipolar HVDC-Overlay-Systems

With the introduction of high voltage direct current (HVDC)-system, especially the voltage source converter (VSC)-based implementation, new degrees of freedom have been made accessible for system operation and for ensuring system security where they can be utilized either in the form of preventive or curative actions. This work presents an optimization model meant to determine a set of preventive and curative relief measures. Thereby, both methods are not calculated in a successive approach, but the optimization of the curative actions is nested into the preventive optimization. By doing so, a feedback can be given in cases the preventive optimization is evolving in regions where no feasible curative actions can be provided. A numerical case study is conducted, where the calculation of the curative set-points is performed with a linearized power flow model. Thereby, an effect is shown, where preventive insecure contingencies appear as curative feasible as the linearized model does not show any violated constraints.
Game Theoretic-Based Distributed Charging Strategy for PEVs in a Smart Charging Station

This article investigates the charging problem of plug-in electric vehicles (PEVs) in a smart charging station (SCS) under a new interaction mechanism that allows the interactions among PEVs. The target is to coordinate the charging strategies of all PEVs such that the energy cost of SCS is minimized without compromising a set of constraints for PEVs and SCS. To this end, we first construct a non-cooperative game framework, in which each player (i.e., PEV) expects to minimize its cost by choosing the optimal charging strategy over the entire charging horizon. Then, the existence and optimality of Nash equilibrium (NE) for the formulated non-cooperative game is provided. Moreover, to find the unique generalized Nash equilibrium (GNE), we propose a distributed GNE-seeking algorithm based on the Newton fixed-point method. And a fast alternating direction multiplier method (fast-ADMM) framework is applied to determine the best response of PEVs. The convergence of the proposed distributed GNE-seeking algorithm and PEVs’ best response are also provided with theoretical analysis. Simulations are presented at last to validate the effectiveness of the proposed algorithm.
Hyperbolic type convexity and some new inequalities

In this paper, we introduce and study the concept of hyperbolic type convexity functions and their some algebraic properties. We obtain Hermite-Hadamard type inequalities for this class of functions. In addition, we obtain some reﬁnements of the Hermite-Hadamard inequality for functions whose ﬁrst derivative in absolute value, raised to a certain power which is greater than one, respectively at least one, is hyperbolic convexity. Moreover, we compare the results obtained with both H¨older, H¨older-˙I¸scan inequalities and power-mean, improved-power-mean integral inequalities.
Attribute discretization method based on rough set theory and information entropy

This paper analyzed the current attribute discretization methods,and put forwards a cut point set computing algorithm.Then defined the cut information entropy,and used it as the importance measurement of cut point.Based on the above method,put forward an attribute discretization algorithm based on the rough set theory and information entropy.In the experiments,detected the effectiveness of the method and compared with other methods.The results show that the method is effective when the sample and attributes number increasing to very large.
A Comparison of Front-End Amplifiers for Tetrapolar Bioimpedance Measurements

Many commercial benchtop impedance analyzers are incapable of acquiring accurate tetrapolar measurements, when large electrode contact impedances are present, as in bioimpedance measurements using electrodes with micrometer-sized features. External front-end amplifiers can help overcome this issue and provide high common-mode rejection ratio (CMRR) and input impedance. Several discrete component-based topologies are proposed in the literature. In this article, these are compared with new alternatives with regard to their performance in measuring known loads in the presence of electrode contact impedance models, to emulate tetrapolar bioimpedance measurements. These models are derived from bipolar impedance measurements taken from the electrodes of a tetrapolar bioimpedance sensor. Comparison with other electrode models used in the literature established that this is a good and challenging model for bioimpedance front-end amplifier evaluation. Among the examined amplifiers, one of the best performances is achieved with one of the proposed topologies based on a custom front-end with no external resistors (AD8066/AD8130). Under the specific testing conditions, it achieved an uncalibrated worst-case absolute measurement deviation of 4.4% magnitude and 4° at 20 Hz, and 2.2% and 7° at 1 MHz accordingly with loads between $10~\Omega $ and 10 $\text{k}\Omega $ . Finally, the practical use of the front-end with the impedance analyzer is demonstrated in the characterization of the bioimpedance sensor, in saline solutions of varying conductivities (2.5–20 mS/cm) to obtain its cell constant. This article serves as a guide for evaluating and choosing front-end amplifiers for tetrapolar bioimpedance measurements both with and without impedance analyzers for practical/clinical applications and material/sensor characterization.
Arbitration Patterns: Resolving conflicts one network at a time

Economic aspects of modern engineering lead to more frequent inclusion of mixed-effort networks in system architecture. Which pose the challenge of allowing for a degree of guaranteed delivery while keeping the network schedule dynamic. Introducing an Arbiter as role or component can prevent bus contention, through prioritizing specific categories of telegrams. This publication examines different approaches to arbitration of shared resources. Most commonly bus systems and networks. We propose a pattern called Registration Arbiter for controlling access to the shared medium by passively monitoring the medium, as well as further candidate patterns. The target audience are Embedded System Architects. We examine this pattern in the context of Industrial Control Systems.