Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of the Glutathione Transferase Proteome of Drosophila melanogaster: Use of S- Substituted Glutathiones as Affinity Ligands

Glutathione transferases (GSTs) are a widely distributed super-family of enzymes involved in detoxification, catalyzing the conjugation of a great range of electrophilic compounds with the tripeptide glutathione (Boyland and Chasseaud, 1969). Much of the interest in insect GSTs has, for many years, been focused on their role in the development of resistance to many insecticides (Enayati et al., 2005; Motoyama and Dauterman, 1980). The enzymes are involved in resistance to most of the major classes of insecticide. These include organophosphates (e.g. Lewis & Sawicki, 1971; Oppenoorth et al., 1977); organochlorines, especially DDT (Clark & Shamaan, 1984, Tang & Tu, 1994), chitin synthesis inhibitors (Sonoda & Tsumuki, 2005), and pyrethroids by both direct (Yamamoto et al., 2009b) and indirect (Vontas et al., 2001) mechanisms. There are many instances in which detoxication of insecticides has been shown to be catalyzed by GSTs of the Delta or Epsilon classes (Lumjuan et al., 2005; Tang & Tu, 1994; Wei et al., 2001) but GSTs from Omega and Zeta classes (Yamamoto et al., 2009a; Yamamoto et al., 2009b) have also been reported as being involved in insecticide resistance.
Subjective adequacy of dialysis; a neglected concept in hemodialysis adequacy

Introduction: Dialysis adequacy is a predictive factor for mortality and hemodialysis outcomes. Objectives: Given the limited knowledge regarding dialysis adequacy, we sought to investigate the perceptions and experiences of patients as to subjective dialysis adequacy. Materials and Methods: This qualitative study was carried out using the conventional content analysis method. We enrolled 25 hemodialysis patients, their companions, nurses, and nephrologists residing in Mashhad, Iran. We selected the participants through purposive sampling. To collect the data, we used semi-structured interviews and recording field notes until reaching data saturation. The data was analyzed by using Hsieh and Shannon qualitative content analysis approach. Results: From the experiences of the patients regarding dialysis adequacy, five categories of inner wholeness, well-being, positive social interaction, effective self-empowerment and improvement of economic conditions were extracted. Conclusion: The obtained results can play a key role in implementation of effective interventions for models of hemodialysis clinical function to lower the rate of mortality and promote quality of life among dialysis patients.
Antioxidant activity, the content of total phenols and flavonoids in the ethanol extracts of Mentha longifolia (L.) Hudson dried by the use of different techniques

In this study, we have examined the yield of extracted substances obtained by means of extraction using 70 % ethanol (v/v), the content of total phenols and flavonoids, as well as the antioxidant activity of the extracts obtained from the samples of the herbs dried by means of different techniques. Wild mint Mentha longifolia (L.) Hudson was dried naturally in a laboratory oven at a temperature of 45 °C and in an absorptive low temperature condensation oven at 35°C. The highest yield of extracts was obtained from the naturally dried herbs and the lowest from the herbs dried in the low temperature condensation drying oven. The content of total phenols and flavonoids was determined by spectrophotometric methods with an FC reagent and by the complexation reaction with aluminium-chloride, respectively. The extract of the naturally dried herbs had the highest overall content of phenols (113.8±2.0 mg of gallic acid/g of the dry extract) and flavonoids (106.7±0.3 mg of rutin/g of the dry extract). The highest antioxidant activity determined by the FRAP and DPPH assay was determined in the extracts obtained from naturally dried herbs (2.76±0.15 mmol Fe2+/mg of the dry extract and EC50=0.022±0.001 mg/ml), while the lowest was obtained from the extracts of herbs dried in the laboratory oven (1.13±0.11 mmol Fe2+/mg of the dry extract and EC50=0.033±0.001 mg/ml). The HPLC-DAD analysis result show that the greatest content of phenolic compounds show extract obtained from naturally dried plant material. The dominant phenolic component in the all extracts is Kaempferol 3-O-glucoside. The content of all phenolic compound strongly depend on the drying conditions.
Biofabrication of Zinc oxide nanoparticles and their in-vitro cytotoxicity towards gastric cancer (MGC803) cell

This present report describes the green approach for the preparation of zinc oxide nanoparticles (ZnO NPs) using Murraya keenigii leaf extract by an eco-friendly approach. The produced ZnO NPs were characterized by using techniques such as X-Ray Diffraction (XRD), Fourier Transform Infrared (FTIR), Ultra Violet-Visible (UV-Vis), Energy Dispersive Spectroscopy (EDS) and Transmission Electron Microscopy (TEM). TEM and Dynamic Light Scattering (DLS) results have confirmed the formation of spherical ZnO NPs with average size of 20 nm. Further, the cytotoxicity results of the synthesized ZnO NPs showed their excellent biocompatibility towards gastric cancer (MGC803) cell lines, extending their scope of applications in biomedicine.
Positive pressure induced channeled suction cups

Leaking in water pipe is a critical issue in Middle Eastern countries such as Kuwait where water is scarce. In-pipe robots can be dispatched to discover the network and inspect the inner surface of the pipe. This thesis describes the design and characterization of suction pads for in-pipe robots. The suction cups are made of hyperelastic materials that contain air channels inside. Instead of using conventional suction technique that is prone to leaking and loosing adhesion force, the suction cup is actuated by compressed air or liquid, which deforms the geometry of the cup to achieve suction. In order to verify the geometry changes during actuation, a uniaxial tension test was done on the elastic material used to acquire the material properties and the suction cup designs were then simulated using FEA in ABAQUS. Next, prototypes were designed in SolidWorks and fabricated using soft lithography techniques. To measure the suction force, pressure measurements were taken both inside the prototype channels and underneath the suction cup. Results show that a Positive Pressure Actuated Suction-cup (PPAS) of diameter forty millimeters can achieve suction force up to 68 N. As more air is inserted into PPAS, the suction force also increases. The concept has proved to be a feasible solution for pipe inspection robot. Thesis Supervisor: Sangbae Kim Title: Assistant Professor of Mechanical Engineering
MINING DATA FOR TRAFFIC DETECTION SYSTEM USING GPS_ ENABLE MOBILE PHONE IN MOBILE CLOUD INFRASTRUCTURE

The increasing need for traffic detection system has become a vital area in both developing and developed countries. However, it is more important to get the accurate and valuable data to give the better result about traffic condition. For this reason, this paper proposes an approach of tracking traffic data as cheap as possible in terms of communication, computation and energy efficient ways by using mobile phone network. This system gives the information of which vehicles are running on which location and how much speed for the Traffic Detection System. The GPS sensor of mobile device will be mainly utilized to guess a user’s transportation mode, then it integrates cloud environment to enhance the limitation of mobile device, such as storage, energy and computing power. This system includes three main components: Client Interface, Server process and Cloud Storage. Some tasks are carried out on the Client. Therefore, it greatly reduces the bottleneck situation on Server side in efficient way. Most of tasks are executed on the Server and history data are stored on the Cloud Storage. Moreover, the paper mainly uses the distance based clustering algorithm in grouping mobile devices on the same bus to get the accurate data.
Decoding the Sense of Touch. Neural Representation and Processing of Tactile Sensory Input in the Neocortex

This thesis presents studies on how tactile sensory input is represented and processed in the neocortex. We used an experimental setup with electrical activation of cutaneous receptors for innocuous touch in the second digit of the forepaw of anesthetized rats. The activation consisted of a set of eight reproducible spatiotemporal patterns. Using cell-attached and whole-cell patch clamp in vivo, we measured the response in neocortical neurons. The studies provided support for a distributed model of cortical processing, where ensembles of neurons in the primary somatosensory cortex are significantly better than single neurons at decoding different types of tactile stimulations, but individual neurons are at the same time able to separate between single pulse stimulations of adjacent receptive fields. We also showed that the intracellular response to identical cutaneous stimulation can be classified as one out of a finite set of typical responses for the neuron. Our data hence suggested that tactile sensory processing is dependent not only on the direct afferent input, but also on its internal state and projections from other parts of the nervous system. The cerebellum has connections to the neocortex and is therefore an important regulator of internal states. The cerebellar output cells are in part driven by tactile input from sensorimotor signals in spinocerebellar systems and as part of the dissertation we also studied the synaptic plasticity of those inputs. The study showed that the connection between the spinal cord and deep cerebellar nuclei neurons have a low or non-existent medium-term plasticity in the adult nervous system. Our analysis suggests that neocortical sensory processing is mediated through parallel streams of overlapping network activity, each with its own local state variation over time. In order to increase the sensitivity of tactile processing, differentiation in neuronal activity is enforced, causing neurons to respond differently to the same afferent input.
NASE MORE.indd

Sažetak U ovom radu prikazan je Voith-Schneiderov porivni uređaj kao jedan od čestih propulzijskih uređaja na trajektima, tegljačima i ostalim brodovima gdje su veliki zahtjevi za manevarskim osobinama. Uz opis samog uređaja dan je i pregled sila na lopaticama koje rezultiraju propulzijom broda te pregled materijala za lopatice. Ključne riječi: porivni uređaj, lopatica, vijak, sile na lopatici, putanja lopatice, materijali lopatica.
Changes in Return to Higher Education in Poland 1998-2005

In the article private rate of return to higher education in the 1998-2005 period is considered. The model is based on a comparative advantage theory. Extended Mincerian wage equation is used to account for a non-random decision to undertake studies at university level. The estimate of private rate of return in Poland is roughly 9%, and it is among the highest in Europe. In addition, the unexpected rise in rate of return is observed. Moreover, positive relationship between graduation and the obtained wages was found. This change has been linked to labour market transformation and Skill Biased Technical Change. Also the influence of financing tertiary education is considered.
279-288 JMB13-01027.fm

Phosphorylation and dephosphorylation of proteins trigger many critical events involved in cellular response, such as regulation of enzymatic activity, protein conformational change, protein-protein interaction, and cellular localization. Any malfunction of protein phosphorylation leads to a diseased state such as diabetes, cancer, and even neurodegenerative diseases. In order to comprehend the molecular view of the complex biological processes of these diseases in depth, very sensitive and detailed analytical methods are necessary for identification of the phosphorylated residues in a protein. As part of these efforts, phosphoproteomics has been developed and applied for the elucidation of neurodegenerative diseases. In this review, we present a brief summary of phosphoproteomics approaches that are now routinely used in biomedical research, and describe the biomedical application of phosphoproteomics especially in Alzheimer’s and other neurodegenerative diseases.
Metal Artifact Reduction in Cone-Beam X-Ray CT via Ray Profile Correction

In computed tomography (CT), metal implants increase the inconsistencies between the measured data and the linear attenuation assumption made by analytic CT reconstruction algorithms. The inconsistencies give rise to dark and bright bands and streaks in the reconstructed image, collectively called metal artifacts. These artifacts make it difficult for radiologists to render correct diagnostic decisions. We describe a data-driven metal artifact reduction (MAR) algorithm for image-guided spine surgery that applies to scenarios in which a prior CT scan of the patient is available. We tested the proposed method with two clinical datasets that were both obtained during spine surgery. Using the proposed method, we were not only able to remove the dark and bright streaks caused by the implanted screws but we also recovered the anatomical structures hidden by these artifacts. This results in an improved capability of surgeons to confirm the correctness of the implanted pedicle screw placements.
Feature Selection for Gene Expression Data Based on Hilbert-Schmidt Independence Criterion

DNA microarrays are capable of measuring expression levels of thousands of genes, even the whole genome, in a single experiment. Based on this, they have been widely used to extend the studies of cancerous tissues to a genomic level. One of the main goals in DNA microarray experiments is to identify a set of relevant genes such that the desired outputs of the experiment mostly depend on this set, to the exclusion of the rest of the genes. This is motivated by the fact that the biological process in cell typically involves only a subset of genes, and not the whole genome. The task of selecting a subset of relevant genes is called feature (gene) selection. Herein, we propose a feature selection algorithm for gene expression data. It is based on the Hilbert-Schmidt independence criterion, and partly motivated by Rank-One Downdate (R1D) and the Singular Value Decomposition (SVD). The algorithm is computationally very fast and scalable to large data sets, and can be applied to response variables of arbitrary type (categorical and continuous). Experimental results of the proposed technique are presented on some synthetic and well-known microarray data sets. Later, we discuss the capability of HSIC in providing a general framework which encapsulates many widely used techniques for dimensionality reduction, clustering and metric learning. We will use this framework to explain two metric learning algorithms, namely the Fisher discriminant analysis (FDA) and closed form metric learning (CFML). As a result of this framework, we are able to propose a new metric learning method. The proposed technique uses the concepts from normalized cut spectral clustering and is associated with an underlying convex optimization problem.
METHOD FOR ASSESSMENT OF TRANSPORT MEANS FAILURES REPAIRS

An analysis of experimental tests and a literature survey of the issue revealed that the problems connected with assessment of costs and effectiveness of transport means repairs has a significant influence on their operation efficiency. Operating factors affecting the elements of technical objects cause negative changes in their features resulting in failures. The failures are referred to as events which significantly impair the vehicle operation efficiency, safety and reliability. On the basis of experimental tests concerning times of failures occurrence, it was found that the set of failures can be divided into subsets of primary and secondary failures. The tests results revealed that the most frequent cause of secondary failures occurrence was improperly performed primary repairs. Primary failures are not related to each other and they appear randomly. However, secondary failures are related to each other as their occurrence is conditioned by the primary failure and results from an inappropriate performance of the successive secondary failure. Basing on an analysis of the author’s own tests results, it was established that failures of transport means used in real transport systems are the effect of impact of various forcing factors. Some failures result from the natural wear of machine elements, whereas, other failures can be caused by inappropriate repair of the previous damage. Thus, there occur the so called secondary failures in a short period of time. In connection with this, high efficiency of the repair performance is of primary importance as it largely affects efficiency of transport tasks accomplishment. In this article, there has been discussed a method developed for assessment of repair costs connected with elimination of secondary failures and its influence on the studied transport system operation efficiency. Familiarity with available technologies for repairs of primary failures as well as with costs of work and costs connected with technical equipment of the repair stations enables specification of the lowest costs necessary for reduction of the number of secondary failures and the average time of their repairs.
F AKE N EWS I DENTIFICATION CS 229: M ACHINE L EARNING : G ROUP 621

Due to recent events in American Politics, fake news, or maliciously-fabricated media has taken a central role in American political discourse. A litany of verticals, spanning national security, education and social media are currently scrambling to find better ways to tag and identify fake news with the goal of protecting the public from misinformation. Our goal is to develop a reliable model that classifies a given news article as either fake or true. Our model is designed to emulate the functionality of the BS Detector, a popular extension for Chrome that automatically flags articles, websites and content as BS. Our simple models give us accuracy of up to 82%.
Distinct Heritable Influences Underpin In-Group Love and Out-Group Derogation

In-group favoritism has often been conceptualized as the flip side of out-group derogation. Whereas research has dissociated these attitudes at the phenotypic level, it is currently unknown whether such dissociation is also evident at the biological level. Here, using an adult German twin sample, which provided ratings on patriotism, nationalism, and prejudice, we tested whether common or distinct heritable influences best explained variation in in-group love and out-group derogation. Results indicated that independent genetic effects accounted for individual differences in in-group love (i.e., patriotism) and out-group derogation (i.e., prejudice). In addition, we observed that nationalism showed common genetic links to both patriotism and prejudice, albeit through distinct pathways. These findings suggest that in-group sentiment is complex at the genetic level as well as at the behavioral level. Future work is recommended to further delineate the specific biological processes underlying these heritable effects.
Predicting intermittent network device failures based on network metrics from multiple datasources

Predicting failures in networking has been a subject for a long time. Time series data and syslog are now often used to monitor the state of the network. SURFnet has been collecting this data in searchable database clusters for over two years. We set out to determine to what extent these two datasets can be used to predict network device failures. This enables network operators to take pre-emptive measures to prevent an imminent failure. Using experiments, we proved a relation between the dataset containing the syslog device events and the dataset containing the time-series network metrics.
Biosynthesis and Size of Silver Nanoparticles Using Aspergillus niger ATCC 16404 as antibacterial activity

Nanoparticles are used nowadays in many fields of sciences such as chemistry, physics, medicine and biology. Nanotechnologies can solve a numerous problems. Metalbased nanoparticles are derived from silver, iron, titanium and a variety of other metals and their oxides pal et al.,(2007). Biosynthetic and environment friendly technology for the synthesis of silver nanoparticles (silver NPs) are believed to be nontoxic, biosafe, and biocompatible and have been used as antimicrobial and fillings in medical materials . a number of physical and chemical strategies were employed for the synthesis of AgNPs Inoue et al.,2010. However, concern has been raised on the toxicity of chemical agents used in AgNPs synthesis. Thus, it is essential to develop a green approach for AgNPs production without using hazardous substances to the human health and environment. Compared with the traditional synthetic methods, biological systems provide a novel idea for the production of nano-materials Bansal et al., (2011). Up to now, several microorganisms from bacteria to fungi have been reported to synthesize inorganic materials either intraor extracellularly, and thus to be potentially utilized as eco-friendly nanofactories Mohanpuria, et al.,( 2008).
Enhanced Linear Perspective using Adaptive Intrinsic Camera Parameters

Monocular depth cues allow us to perceive depth from two-dimensional (2-D) images, and linear perspective is one of the most important monocular depth cues. In order to insert improved depth perception into the images, we propose a new method for enhancing linear perspective based on adaptive intrinsic camera parameters in this paper. The threedimensional (3-D) warping technique is utilized to rearrange textures according to their depth values. Using directional dilation and in-paint methods, we fill holes generated by the 3-D warping operation. Experimental results show that the proposed algorithm can enhance linear perspective and improve depth perception of 2-D images without considerable distortion.
ILLUSTRATION OF SEMI-SUPERVISED FEATURE SELECTION USING EFFECTIVE FRAMEWORKS

Semisupervised feature selection is a supervised feature jobs as well as method that uses the unlabeled data for guiding that the small quantity of labeled dataset with a huge quantity of unlabeled dataset. Semisupervised feature selection is drop between the unsupervised selection and supervised selection. Feature selections have been playing an essential task in the different studies as well as application area of machine learning. In this paper we have explored a three-different level framework for semi-supervised feature selection. Which are mainly feature selection methods center on discovering relevant features for optimizing high-dimensional data. In this paper, we have shown that the relevance need three essential frameworks which provide an efficient feature selection in the semi-supervised context. In the constraint selection framework they select pair wise constraints which can be extracted from the labeled part of data. The Relevance analysis framework shows original utilized realization which competently merges the direct of the restricted geometrical construction of unlabeled data with a selected constraint from the first framework. It allows us to verify the set of relevant features. For the CSFSR and efficiency framework, is to find out and supply the redundant features from the relevant ones which can be chosen from the second framework. It also shows the comparison between third framework and prims algorithm for better feature selection. Result of this proposed system is efficiency with statistical and graphical view.