nils-herrmann/clean_scirepeval huggingface dataset:
Differential gene expression and alternation of patterns of DNA methylation in the multidrug resistant strain Escherichia coli ATCC BAA 196 caused by iodine containing nano micelle drug FS 1 that induces antibiotic resistance reversion
Year: 2020

Recent studies have demonstrated that antibiotic resistance in pathogenic bacteria could be reverted to the drug sensitive phenotype by the treatment with the newly introduced iodine containing nanomolecular complex, FS 1. Antibiotic resistance reversion has been verified as a promising therapeutic approach to combat multidrug resistant infections. The mechanisms of action, however, remain unclear. A collection strain, Escherichia coli ATCC BAA 196, showing an extended spectrum of resistance to beta lactam and aminoglycoside antibiotics was used in this study as a model organism. The FS 1 treated culture and the negative control variant were sequenced by PacBio RS II System following the SMRTbell 20 kb library preparation protocol. Total RNA samples of these strains were sequenced by Ion Torrent. It was shown that the treatment with FS 1 caused a profound gene expression alternation switching the bacterial metabolism to anaerobic respiration, increased anabolism, oxidative/acidic stress response and an inhibition of many nutrient uptake systems. All this leads to an increase in the susceptibility to antibiotics even when FS 1 is removed from the medium. The later fact implies an involvement of epigenetic mechanisms in gene regulation and antibiotic resistance reversion. This hypothesis was investigated by base call kinetic analysis in PacBio reads and DNA methylation profiling in the sequenced genomes. Several DNA motifs of adenosine and cytosine methylation were identified. While the numbers of methylated sites in chromosomes and plasmids of both genomes, NC and FS, were similar, the distribution of the methylated sites was different. It may explain the observed long lasting effect of the treatment of E. coli with FS 1 on antibiotic susceptibility of this model microorganism.
Optimization method for reducing network loss of dc distribution system with distributed resource
Venue: Photonic Network Communications
Year: 2018

With mature of the direct current (dc) distribution grid technology, the distributed energy is embraced as much as possible, and reducing network loss becomes an effective way to increase energy efficiency and system stability. The conventional reducing loss approaches include complex algorithms, loss modeling in power devices and adding hardware, which lead to costs and system response time increase and system stability reduce. Considering the Intermittent characteristics of distributed energy, for reducing network loss, the effective option is to control error and response speed directly through the upper control, when distributed energy accesses on dc distribution network, which is no need to change grid topologies, run cumbersome power flow algorithms, nor add additional equipment. A reducing network loss method is proposed based on this idea. The details are as follows: Firstly, the network loss formula is derived based on power flow calculation, and the network loss change rules are analyzed. Secondly, one optimal power flow mathematical model of dc distribution network is established, which takes the minimum network loss as the objective functions and conforms to constraints of system security and components operating limit. The tide optimization is solved by using the artificial bees (ABC) algorithm. Thirdly, the network loss reduction method is proposed in the dc distribution network by using master slave control through real time control instruction optimization. The node voltage, branch current and main voltage source converter power are precisely regulated to control the power flow; thus, the network loss of multiple distributed energy access dc distribution network can be reduced. Fourthly, when wind and solar energy is connected to the dc distribution network, a typical IEEE16 node case is demonstrated to verify the feasibility of the proposed method using software MATLAB/SIMULINK. Fifthly, evaluation and prospect are made for the research.
Detection and Compressed Classification in Twitter
Year: 2017

In this paper we introduce a novel information propagation method in Twitter, while maintaining a low computational complexity. It exploits the power of Compressive Sensing in conjunction with a Kalman filter to update the states of a dynamical system. The proposed method first employs Joint Complexity, which is defined as the cardinality of a set of all distinct factors of a given string represented by suffix trees, to perform topic detection. Then based on the inherent spatial sparsity of the data, we apply the theory of Compressive Sensing to perform sparsity based topic classification by recovering an indicator vector, while reducing significantly the amount of information from tweets, possessing limited power, storage, and processing capabilities, to a central server. We exploit datasets in various languages collected by using the Twitter streaming API and achieve better classification accuracy when compared with state of the art methods.
Uso de las tecnologias de la informacion y la comunicacion como competencia transversal en la formacion inicial de docentes
Venue: Revista Electronica Educare
Year: 2020

El objetivo de esta investigacion fue explorar como los maestros y maestras promueven el desarrollo de la competencia transversal uso de las tecnologias de la informacion y la comunicacion (TIC) en el futuro personal docente de educacion primaria. El estudio se llevo a cabo en una escuela normal rural del Estado de Mexico, usando el metodo de estudio de casos. Se realizaron entrevistas a trece docentes, asi como observaciones a sus clases impartidas. El analisis de datos se realizo con el programa Atlas.ti. Entre los hallazgos destaca que el uso de las TIC, por parte del profesorado, propicia que el estudiantado normalista se motive a emplearlas, no solo como medio de entretenimiento o socializacion, sino como un recurso didactico para aprender y ensenar temas relacionados con la educacion de primaria; se destaca que el uso de las TIC vuelve las clases mas llamativas, dinamicas y claras; favorece la interaccion social, la investigacion en Internet, el aprendizaje autorregulado y el desarrollo de redes para aprender. El estudiantado participante en este estudio demostro el logro de esta competencia en la elaboracion de sus trabajos academicos, busquedas de informacion en internet, aplicacion de software educativo para aprender matematicas, uso de redes sociales y diseno de blogs. Asimismo, se encontro que, no obstante, las politicas educativas a nivel nacional contemplan como meta la alfabetizacion digital de ninos, ninas y jovenes, en realidad no siempre se cuenta en las escuelas de educacion basica y normal con las condiciones y recursos tecnologicos para que tanto docentes como estudiantes adquieran, en forma optima, las competencias digitales necesarias para desenvolverse mejor en una sociedad basada en el conocimiento.
Kernel Based Least Squares Policy Iteration for Reinforcement Learning
Venue: IEEE Transactions on Neural Networks
Year: 2007

In this paper, we present a kernel based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel based least squares temporal difference algorithm called KLSTD Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large scale Markov decision problems (MDPs) Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.
Generating 3D dataset of Gait and Full body movement of children with Autism spectrum disorders collected by Kinect v2 camera
Year: 2020

Until now, a three dimensional dataset combines gait and body movement analysis of children with Autism Spectrum Disorders (ASD) in controlled environments has not been published. ASD mean disorders of neurodevelopment that last a lifetime which occurs in early childhood and usually associated with unusual movement and gait disturbances. Three dimensional gait features captured by Kinect v2 can assist clinicians in diagnosing, clinical decision making, and treatment planning of ASD. In this paper, Kinect v2 uses to build a 3D skeleton based gait dataset, which includes joints positions, the corresponding skeleton movement video, joints trajectories video captured by Kinect v2, and color videos captured by Samsung Note 9 rear camera. Besides building dataset, this paper classifies children with ASD from normal children by proposed system comprises four main stages: 1) dataset augmentation based on seven transformations to solve the problem of lack of ASD cases, 2) Extracting features that we think play an important role in classification, 3) Reducing data dimensions using Principal Component Analysis (PCA) and 4) Using Multilayer Perceptron (MLP) to classify data. Classification accuracy when using eleven features result from PCA and MLP is 95% with 0.7 seconds to build the model
Illiberal Constitutionalism: The Case of Hungary and Poland
Venue: German Law Journal
Year: 2019

This Article argues that, as far as Hungary and Poland are concerned, the use of term "illiberal constitutionalism" is justified. It also claims that, without denying that other states could also be considered illiberal democracies, Hungary and Poland display unique and distinctive features. These features include populist politics, which lead to the relativization of the rule of law and democracy principles, and human rights protection, which captured the constitution and constitutionalism by constitutionalizing populist nationalism, constitutional identity, and created new patrionalism and clientelism. All these features are supported by the ideological indoctrination of political constitutionalism. In the course of this process, formal and informal constitutional amendments are used, and a formal sense of constitutional democracy is maintained. Overturning these illiberal democracies by constitutional and legal means, at this time, seems doubtful, if not impossible.
CO2 Decreases the Activity of Locus Coeruleus Neurons in the Streptozotocin Induced model for Alzheimer's Disease
Year: 2020

Locus Coeruleus (LC) is an important chemosensitive nucleus and affected by neurodegenerative diseases like Alzheimer's disease (AD) LC dysfunction in AD may account for the respiratory problems observed in patients.  To test the electrophysiological properties of LC neurons in a model for sporadic AD.  AD was induced in rats (6 7 weeks) by intracerebroventricular injection of streptozotocin (STZ; 2 mg/kg) 14 days following injection, LC neurons were recorded using the patch clamp technique and tested for CO 2 chemosensitivity (10% CO 2 pH 7.0)  When exposure hypercapnic condition, most LC neurons ~60% exhibited a blunted spike discharge to current injections in comparison to baseline responses. The minority of cells either increased spiking ~20% or did not respond ~20% to CO 2 Within cells that were inhibited by CO 2 current evoked spike discharge at baseline condition had the same magnitude in control and STZ rats. Responses in both groups decreased significantly when exposed to 10% CO 2 (bsl vs. 10% CO 2 CTL, p=0.003, n=8 and STZ, p=0.001, n=9) In the STZ group, this CO 2 induced decrease in spike discharge was more pronounced when compared to control (CTL vs. STZ, p=0.038) suggesting greater sensitivity to hypercapnia. There was no difference in resting membrane potential and input resistance (R i cell membrane resistance) between groups at baseline and CO 2 However, although there was no difference between groups values, we found a significant decrease of R i within the STZ group when exposed to hypercapnia (bsl, 126.5 14.9 M vs. 10% CO 2 98.4 8.2 M; p=0.01) indicating opening of ion channels. The current voltage relationship of the cell membrane showed a significant CO 2 induced decrease of the steady state current (bsl vs. 10% CO 2 CTL, p=0.002, and STZ, p=0.001) The magnitude was similar in both groups. This result would paradoxically favor increased excitability in neurons. Analysis of action potential (AP) parameters (AP threshold, AP peak, upstroke slope, peak to anti peak) also showed no difference between groups. However, within the STZ group spike threshold was significantly shifted to more positive potentials under increased CO 2 39.6 1.9 mV vs 34.8 2.2 mV, p=0.01) This shift in spike threshold would explain the blunted spike discharge of LC neurons in the STZ group during hypercapnic conditions.  In summary, our data suggest that the majority of LC neurons in adult rats are inhibitedunder CO 2 exposure. Further, the STZ treated group exhibits a greater sensitivity to CO 2 likely due to an increased spike threshold and opening of additional, yet unidentified membrane channels. Decreased excitability of LC neurons may be an underlying mechanism for the breathing disturbances observed in patients with AD.  CO2,neurons,adult rats, locus coeruleus Neurons, streptozotocin Induced model, Alzheimer's Disease
More than Collection Development: Using Local Citation Analysis to Begin a Career in Business Librarianship
Year: 2020

This study shares the results of a citation analysis performed on the publications of the faculty of the College of Business at a large public university in the southern United States for the years 2014 2018. The study examined articles produced by the faculty over the last five years, as well as their cited references and citing articles, in an attempt to establish a list of core journals for business and uncover potential gaps in coverage. Implications for collection development are addressed, as are the benefits for early career librarians or those unfamiliar with the publishing behaviors of a newly assigned subject discipline.
BACK INJECTION MOLDING WITH ADDITIVE MANUFACTURED MOLD INSERTS USING ALL INKJET PRINTED SUBSTRATES
Year: 2017

Back injection molding is a well known process in the plastics industry. For example, the fabrication of decorated interior parts in the automotive industry is one of many fields of application. In the last few years, the additive manufacturing process rapidly expanded in injection molding. For instance, it is used for producing injection mold inserts made of metal or plastic. This technique is called Rapid Tooling. The aim of this paper is to investigate the back injection molding process with additive manufactured mold inserts made of Fullcure RGD 720 and to verify if this technique is feasible. Therefore, a substrate with all inkjet printed conductor traces was placed in the mold insert. The injection molding was done with a standard injection molding machine. The conductive traces were printed with a standard inkjet printer manufactured by Epson. To generate additional information about the additive manufacturing material Fullcure RGD 720 a differential scanning calorimetry (DSC) was done.
Design Phase Delay Control for Soft Starting 3 Phase Induction Motor
Year: 2020

Induction Motor is one type of electric machine that is widely used both in household consumers and industrial players. However, induction motors have drawbacks, namely starting currents that can reach 5 7 times the nominal current, of course this can lead to reduced life time of other electrical equipment and the possibility of shutdown or trip is very large. This research proposes a new modeling to create a soft starting system to reduce the 3 phase induction motor start current. This system was built with the help of a Phase Delay Control power electronics circuit with the main component, Triac, which functions to regulate the input voltage of a 3 phase induction motor and a contactor whose function is to move the 3 phase induction motor power supply from the soft starting system to the PLN source directly if, The input voltage is the same as the nominal voltage. The output of these processes is the Triac firing angle, so during the starting process, the induction motor will receive an input voltage in stages from the smallest to reach its nominal voltage. Thus the results of testing the soft starting system that is designed to have a maximum current value at the start of 2.45 A or 77.58% lower than the start current using the DOL method with a value of 10.93 A at no load conditions and 7.41 A or 34.19% lower than the motor starting current with the DOL method with a value of 11.31 A when the motor is loaded.
Therapeutic implication of vascular endothelial growth factor receptor 1 (VEGFR 1) targeting in cancer cells and tumor microenvironment by competitive and non competitive inhibitors
Venue: Pharmacological research
Year: 2018

The vascular endothelial growth factor receptor 1 (VEGFR 1) is a tyrosine kinase receptor for VEGF A, VEGF B, and placental growth factor (PlGF) ligands that is expressed in endothelial, myelomonocytic and tumor cells. VEGF B and PlGF exclusively bind to VEGFR 1, whereas VEGF A also binds to VEGFR 2. At variance with VEGFR 2, VEGFR 1 does not play a relevant role in physiological angiogenesis in the adult, while it is important in tumor associated angiogenesis. VEGFR 1 and PlGF are expressed in a variety of tumors, promote invasiveness and contribute to resistance to anti VEGF A therapy. The currently approved antiangiogenic therapies for the treatment of a variety of solid tumors hamper VEGF A signaling mediated by both VEGFR 2 and VEGFR 1 [i.e. the monoclonal antibody (mAb) anti VEGF A bevacizumab, the chimeric molecule aflibercept and several small molecule tyrosine kinase inhibitors] or exclusively by VEGFR 2 (i.e. the mAb anti VEGFR 2 ramucirumab) However, molecules that interfere with VEGF A/VEGFR 2 signaling determine severe adverse effects due to inhibition of physiological angiogenesis and their efficacy is hampered by tumor infiltration of protumoral myeloid cells. Blockade of VEGFR 1 may exert anti tumor activity by multiple mechanisms: a) inhibition of tumor associated angiogenesis; b) reduction of myeloid progenitor mobilization and tumor infiltration by VEGFR 1 expressing M2 macrophages, which contribute to tumor progression and spreading; c) inhibition of invasiveness, vasculogenic mimicry and survival of VEGFR 1 positive tumor cells. As a consequence of these properties, molecules targeting VEGFR 1 are expected to produce less adverse effects and to counteract resistance towards anti VEGF A therapies. More interestingly, selective VEGFR 1 inhibition might enhance the efficacy of immunotherapy with immune checkpoint inhibitors. In this review, we will examine the experimental evidence available so far that supports targeting VEGFR 1 signal transduction pathway for cancer treatment by competitive inhibitors that prevent growth factor interaction with the receptor and non competitive inhibitors that hamper receptor activation without affecting ligand binding.
What role can health policy and systems research play in supporting responses to COVID 19 that strengthen socially just health systems?
Venue: Health policy and planning
Year: 2020

research play in supporting responses to COVID 19 that strengthen socially just health systems? Lucy Gilson Bruno Marchal, Irene Ayepong, Edwine Barasa, Jean Paul Dossou Asha George, Ryan Guinaran, Daniel Maceira, Sassy Molyneux, NS Prashanth, Helen Schneider Yusra Shawar, Jeremy R Shiffman, Kabir Sheikh, Neil Spicer, Sara Van Belle and Eleanor Whyle Section Editor Health Policy Processes, Health Policy and Systems Division, University of Cape Town, South Africa, Section Editor Health Systems Research, Health Policy and Planning and Institute of Tropical Medicine, Belgium, Ghana Health Service, Ghana, Kenya Medical Research Institute Wellcome Trust Research Programme, Nairobi, Kenya, Centre de Recherche en Reproduction Humaine et en Demographie (CERRHUD) Benin, Health Systems Global and University of the Western Cape, South Africa, Benguet State University, The Philippines, University of Buenos Aires, Argentina, Kenya Medical Research Institute Wellcome Trust Research Programme, Kilifi, Kenya and Oxford University, UK, Institute of Public Health, Bengaluru, India, University of the Western Cape, South Africa, Section Editor Health Policy Processes, Health Policy and Planning and Johns Hopkins University, USA, Section Editor Health Systems Research, Health Policy and Planning and Alliance for Health Policy and Systems Research, Geneva, Switzerland, Section Editor Health Systems Research, Health Policy and Planning and London School of Hygiene and Tropical Medicine, UK, Institute of Tropical Medicine, Belgium and University of Cape Town, South Africa
Transformation of Teacher Education and Talent Training Reform in Local Normal College
Year: 2010

With the profound change of macro environment of society and economy,the teacher education in China enters a significant shifting and development period.But,to local normal university,how to face such challenges? In this text,we think,as the most obvious advantages and features,the teacher education of local normal college should constantly improve training system,adjust the structure of teacher education,emphasis on curriculum system optimization and integration,construction of a new structure on practice teaching,and speed up reform on talent training in local normal college.
Customer engagement through omnichannel retailing: The effects of channel integration quality
Venue: Industrial Marketing Management
Year: 2019

While many retailers have turned to omnichannel retailing to remain competitive, engaging customers across channels has become one of the biggest challenges they face. Drawing on social exchange theory, we proposed and tested a research model of customer engagement in the context of omnichannel retailing. Structural equation modeling was employed to test the research model with customers of two emerging omnichannel retailers, Apple (n 269) and Kroger (n 221) The results showed that channel integration quality dimensions (including breadth of channel service choice, transparency of channel service configuration, content consistency, and process consistency) positively influenced customer engagement which in turn led to positive word of mouth and repurchase intention. The research model was examined using both high involvement products (e.g. Apple) and low involvement products (e.g. Kroger) despite the varying effects of channel integration quality on customer engagement. This study adds to the growing body of knowledge on customer engagement vis a vis omnichannel retailing and provides retailers with actionable insights into engaging customers across channels.