nils-herrmann/clean_scirepeval huggingface dataset:
Social support's relationship to correctional staff job stress, job involvement, job satisfaction, and organizational commitment

The literature suggests that social support, in general, is linked to positive outcomes among correctional staff, but the different types of social support may differ in their effects. Using survey data from staff working at a privately owned, maximum security prison for juveniles sentenced as adults, this study analyzes three intra organizational supports, administrative, supervisory, and coworker, and one extra organizational form, family/friends support, as antecedents of job stress, job involvement, job satisfaction, and organizational commitment. Personal variables serve as controls. Administrative and supervisory support are inversely related to job stress, while job involvement is affected positively by supervisory support and negatively by family/friends support. All three intra organizational forms of support are significant antecedents of job satisfaction and organizational commitment however family/friends support is not. These findings hold implications for improving job outcomes among correctional staff and for future research.
Chloroplast genomes: diversity, evolution, and applications in genetic engineering
Venue: Genome Biology

Chloroplasts play a crucial role in sustaining life on earth. The availability of over 800 sequenced chloroplast genomes from a variety of land plants has enhanced our understanding of chloroplast biology, intracellular gene transfer, conservation, diversity, and the genetic basis by which chloroplast transgenes can be engineered to enhance plant agronomic traits or to produce high value agricultural or biomedical products. In this review, we discuss the impact of chloroplast genome sequences on understanding the origins of economically important cultivated species and changes that have taken place during domestication. We also discuss the potential biotechnological applications of chloroplast genomes.
Genetic engineering of modular PKSs: from combinatorial biosynthesis to synthetic biology.
Venue: Natural product reports

Multienzyme polyketide synthases (PKSs) are molecular scale assembly lines which construct complex natural products in bacteria. The underlying modular architecture of these gigantic catalysts inspired, from the moment of their discovery, attempts to modify them by genetic engineering to produce analogues of predictable structure. These efforts have resulted in hundreds of metabolites new to nature, as detailed in this review. However, in the face of many failures, the heady days of imagining the possibilities for a truly 'combinatorial biosynthesis' of polyketides have faded. It is now more appropriate to talk about 'PKS synthetic biology' with its more modest goals of delivering specific derivatives of known structure in combination with and as a complement to synthetic chemistry approaches. The reasons for these failures will be discussed in terms of our growing understanding of the three dimensional architectures and mechanisms of these systems. Finally, some thoughts on the future of the field will be presented.
A Holistic Approach to Cross Channel Image Noise Modeling and Its Application to Image Denoising
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Modelling and analyzing noise in images is a fundamental task in many computer vision systems. Traditionally, noise has been modelled per color channel assuming that the color channels are independent. Although the color channels can be considered as mutually independent in camera RAW images, signals from different color channels get mixed during the imaging process inside the camera due to gamut mapping, tone mapping, and compression. We show the influence of the in camera imaging pipeline on noise and propose a new noise model in the 3D RGB space to accounts for the color channel mix ups. A data driven approach for determining the parameters of the new noise model is introduced as well as its application to image denoising. The experiments show that our noise model represents the noise in regular JPEG images more accurately compared to the previous models and is advantageous in image denoising.
Struggles for a global Internet constitution: protecting global communication structures against surveillance measures
Venue: Global Constitutionalism

In 2014, the UN Human Rights Committee published its Concluding Observations on the United States' fourth periodic report on the progress of the implementation of the International Covenant on Civil and Political Rights (UN Doc CCPR) in which also the US surveillance practices are criticised. The Committee's insistence on the right to privacy and its exterritorial effect is an important first step, but it is not comprehensive, as by remaining within the individual rights framework the UN Human Rights Committee fails to sufficiently take into account the systemic challenges in play. Developing a constitution of the Internet would necessitate not only protecting individual fundamental rights against state interference, but protecting communicative spheres by guaranteeing institutional autonomies and subjecting all social spheres to democratic control; this also requires opening up spaces for a critical public, including whistleblowers, and establishing a right to cryptography a crucial refraction in the polycentric panoptic schema.
Taming the Noise in Reinforcement Learning via Soft Updates
Venue: UAI

Model free reinforcement learning algorithms such as Q learning perform poorly in the early stages of learning in noisy environments, because much effort is spent on unlearning biased estimates of the state action function. The bias comes from selecting, among several noisy estimates, the apparent optimum, which may actually be suboptimal. We propose G learning, a new off policy learning algorithm that regularizes the noise in the space of optimal actions by penalizing deterministic policies at the beginning of the learning. Moreover, it enables naturally incorporating prior distributions over optimal actions when available. The stochastic nature of G learning also makes it more cost effective than Q learning in noiseless but exploration risky domains. We illustrate these ideas in several examples where G learning results in significant improvements of the learning rate and the learning cost.
Testing core creation in hydrodynamical simulations using the HI kinematics of field dwarfs

The majority of recent hydrodynamical simulations indicate the creation of central cores in the mass profiles of low mass halos, a process that is attributed to star formation related baryonic feedback. Core creation is regarded as one of the most promising solutions to potential issues faced by the lambda cold dark matter (LambdaCDM) cosmology on small scales. For example, the reduced dynamical mass enclosed by cores can explain the low rotational velocities measured for nearby dwarf galaxies, thus possibly lifting the seeming contradiction with the LambdaCDM expectations (the so called "too big to fail" problem) Here we test core creation as a solution of cosmological issues by using a sample of dwarfs with measurements of their atomic hydrogen (HI) kinematics extending to large radii. Using the NIHAO hydrodynamical simulation as an example, we show that core creation can successfully reproduce the kinematics of dwarfs with small kinematic radii, R 1.5 kpc. However, the agreement with observations becomes poor once galaxies with kinematic measurements extending beyond the core region, R 1.5 4 kpc, are considered. This result illustrates the importance of testing the predictions of hydrodynamical simulations that are relevant for cosmology against a broad range of observational samples. We would like to stress that our result is valid only under the following set of assumptions: i) that our sample of dwarfs with HI kinematics is representative of the overall population of field dwarfs, ii) that there are no severe measurement biases in the observational parameters of our HI dwarfs (e.g. related to inclination estimates) and iii) that the HI velocity fields of dwarfs are regular enough to allow the recovery of the true enclosed dynamical mass.
India Biodiversity Portal: An integrated, interactive and participatory biodiversity informatics platform
Venue: Biodiversity data journal

This paper describes a growing biodiversity platform, launched in 2008, which organizes knowledge on the biodiversity of India. The main objective and originality of the India Biodiversity Portal (IBP) is to aggregate curated biodiversity data of different kinds (e.g. distribution maps, temporal distribution or life history) in an integrated platform where amateurs and experts can easily interact. New information Since its launch, the platform has seen an exceptional increase in both user activity and biodiversity data. Currently the portal has descriptions of over 20,400 species, and has aggregated approximately 1,280,000 observations covering more than 30,000 species, which already constitutes a unique source of information for scientists and stakeholders in conservation. Over 8500 users have registered on the portal. The amount of data generated and to be generated in the next few years by this portal will certainly help the effective implementation of biodiversity conservation and management in one of the most ecologically diverse countries in the world.
Toxicity of macrolide antibiotics on isolated heart mitochondria: a justification for their cardiotoxic adverse effect
Venue: Xenobiotica; the fate of foreign compounds in biological systems

Macrolides belong to the polyketide class of natural products. These products are a group of drugs (typically antibiotics) which their activity stems from the presence of a macrolide ring. Antibiotic macrolides are used to treat infections caused by Gram positive bacteria and Haemophilus influenzae infections such as respiratory tract and soft tissue infections. Macrolides, mainly erythromycin and clarithromycin, rarely show QT prolongation, as their infamous adverse reaction which can lead to torsades de pointes. Electrophysiological studies showed that macrolides prolonging the QT interval inhibit the rapid component of the delayed rectifier K+ current (IKr) through the block of potassium channels encoded by the human ether a go go related gene (HERG) Other studies suggest that increased ROS generation alters the kinetics of hERG K+ conductance. 2. In our study, rat cardiomyocytes were isolated with collagen perfusion technique. Finally, mitochondria isolated from cardiomyocytes were exposed to erythromycin, azithromycin and clarithromycin for their probable toxicity effects. 3. Our results demonstrated that macrolides induced reactive oxygen species formation, mitochondrial membrane permeabilization and mitochondrial swelling and finally cytochrome c release in cardiomyocyte mitochondria. 4. These findings suggested that the toxicity of heart mitochondria is a starting point for cardiotoxic effects of macrolides including QT prolongation, torsades de pointes and arrhythmia.
Challenging Issues of Video Surveillance System Using Internet of Things in Cloud Environment

In video surveillance system, traditional systems are susceptible to environmental variation i.e. change in light, motion in the background due to water, fluctuation or reflection of light etc. This paper focuses on the challenging issues, application areas, freely available resources (dataset, tools) and benefits of video surveillance system, risk occurred in visual surveillance system. Here, we are exploring the study of major challenges along with application area. This paper also presents some basic steps of proposed framework using Internet of Things in cloud (IoTC) environment. Such surveillance systems can be developed according to suitability and requirement of society, army, navigation, robotics, healthcare, transportation, social media, etc.
Hand Pose Regression via a Classification Guided Approach
Venue: ACCV

Hand pose estimation from single depth image has achieved great progress in recent years, however, up to data methods are still not satisfying the application requirements like in human computer interaction. One possible reason is that existing methods try to learn a general regression function for all types of hand depth images. To handle this problem, we propose a novel "divide and conquer" method, which includes a classification step and a regression step. At first, a convolutional neural network classifier is used to classify the input hand depth image into different types. Then, an effective and efficient multiway cascaded random forest regressor is used to estimate the hand joints' 3D positions. Experiments demonstrate that the proposed method achieves state of the art performance on challenging dataset. Moreover, the proposed method can be easily combined with other regression method.
Three Dimensional Human Neuro Spheroid Model of Alzheimer's Disease Based on Differentiated Induced Pluripotent Stem Cells
Venue: PloS one

The testing of candidate drugs to slow progression of Alzheimer's disease (AD) requires clinical trials that are lengthy and expensive. Efforts to model the biochemical milieu of the AD brain may be greatly facilitated by combining two cutting edge technologies to generate three dimensional (3D) human neuro spheroid from induced pluripotent stem cells (iPSC) derived from AD subjects. We created iPSC from blood cells of five AD patients and differentiated them into 3D human neuronal culture. We characterized neuronal markers of our 3D neurons by immunocytochemical staining to validate the differentiation status. To block the generation of pathologic amyloid b peptides (Ab) the 3D differentiated AD neurons were treated with inhibitors targeting b secretase (BACE1) and g secretases. As predicted, both BACE1 and g secretase inhibitors dramatically decreased Ab generation in iPSC derived neural cells derived from all five AD patients, under standard two dimensional (2D) differentiation conditions. However, BACE1 and g secretase inhibitors showed less potency in decreasing Ab levels in neural cells differentiated under 3D culture conditions. Interestingly, in a single subject AD1, we found that BACE1 inhibitor treatment was not able to significantly reduce Ab42 levels. To investigate underlying molecular mechanisms, we performed proteomic analysis of 3D AD human neuronal cultures including AD1. Proteomic analysis revealed specific reduction of several proteins that might contribute to a poor inhibition of BACE1 in subject AD1. To our knowledge, this is the first iPSC differentiated 3D neuro spheroid model derived from AD patients' blood. Our results demonstrate that our 3D human neuro spheroid model can be a physiologically relevant and valid model for testing efficacy of AD drug.
Basis compensation in non negative matrix factorization model for speech enhancement
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

In this paper, we propose a basis compensation algorithm for non negative matrix factorization (NMF) models as applied to supervised single channel speech enhancement. In the proposed framework, we use extra free basis vectors for both the clean speech and noise during the enhancement stage in order to capture the features which are not included in the training data. Specifically, the free basis vectors of the clean speech are obtained by exploiting a priori knowledge based on a Gamma distribution. The free bases of the noise are estimated using a regularization approach, which enforces them to be orthogonal to the clean speech and noise basis vectors estimated during the training stage. Experimental results show that the proposed NMF algorithm with basis compensation provides better performance in speech enhancement than the benchmark algorithms.
Production and consumption based approaches for the environmental Kuznets curve using ecological footprint

We examine the environmental Kuznets curve (EKC) hypothesis using the ecological footprint (EF) a more comprehensive indicator of environmental degradation, for 11 countries covering the 1971 2007 period. We test the EKC hypothesis using a traditional quadratic function from both the supply and consumption side, adding several explicative variables: urbanisation, petrol price and industrialisation for the supply side; biocapacity, life expectancy and energy use for the consumption side. We perform an autoregressive distributed lagged modelling in order to study both short and long run periods. We find that there is no stable relationship between the environment and economic development in the long run. For the short run analysis, the EKC hypothesis is supported for no one, we rather find an increasing relationship between growth and environment. Results for explicative variables are mixed: for the production side approach, industrialisation appears to have a positive impact on EF for Sweden, but a negative one for Portugal and Spain. For the consumption side approach, energy use seems to have a positive impact on EF for Argentina and Colombia whereas biocapacity and life expectancy have a positive and negative impact, respectively, on EF for Paraguay. Lastly, biocapacity has a positive impact on EF for Canada but negative for Norway, likely depending on levels of biocapacity and ecological reserve.
Interactionist Labeling

This article critically reviews prior labeling theory research concerning juvenile delinquency and crime, and proposes a new study using a recent data set. The labeling perspective is outlined as it was originally presented, and the theoretical elaborations that have taken place since are highlighted. Distinctions are made between formally applied criminal justice labels and the informal labels that are applied by educational institutions, significant others, and parental figures. An interactionist labeling model is presented to explain levels of juvenile delinquency among a nationally representative sample of American adolescents: the first three waves of the National Longitudinal Study of Adolescent Health (Add Health) Finally, negative binomial regression models are estimated to better explain the dynamic relationship between labels and delinquency. Consistent with labeling theory, formal labeling significantly increased future delinquency.
Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System
Venue: Int. J. Syst. Dyn. Appl.

Electroencephalogram EEG signals based Brain Computer Interface BCI is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information MI including Minimum Redundancy Maximal Relevance MRMR and maximal Relevance MaxRel. Adaptive Neuro Fuzzy Inference System ANFIS classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.
A discriminative subject specific spatio spectral filter selection approach for EEG based motor imagery task classification
Venue: Expert Syst. Appl.

A subject specific spatio spectral filter selection approach is presented.CFIS handles non stationary EEG signals.Subject wise results show superior performance over existing algorithms.S CFIS and SS CFIS achieve improvement of 15 18% over CSP and 2% over BSSFO. Motor imagery tasks generate event related synchronization and de synchronization in certain subject specific frequency ranges of the subject's ElectroEncephaloGraphy (EEG) signals. The selection of frequency ranges for each subject is important for obtaining better classification accuracy of motor imagery based Brain Computer Interface (BCI) Further, the spatial filters extracted corresponding to the selected spectral ranges also influence the classification accuracy. In this paper, a subject specific spatio spectral filter selection approach using a cognitive fuzzy inference system for classification of the motor imagery tasks in a two step approach is presented. The cognitive fuzzy inference system (CFIS) employs an evolving interval type 2 system to classify the non stationary features. The classifier employs a meta cognitive sequential algorithm to determine both the structure and parameters of the CFIS. In the first step, the CFIS classifier is used to find the desired spectral filters by eliminating those frequency bands that do not affect the classification performance. In the second step, CFIS is used to eliminate those spatial filters which do not affect the performance. The performance of CFIS based spatio spectral scheme has been evaluated using two publicly available BCI competition data sets and compared with other existing algorithms like FBCSP, DCSP and BSSFO. The results indicate that the proposed approach outperforms the CSP method by approximately 15 18% and other algorithms like FBCSP, DCSP by 8 10% Compared to a recently proposed algorithm BSSFO, it achieves an improvement of 2% but is simpler in comparison to BSSFO. The main impact of the work is its ability to handle non stationarity using interval type 2 sets and provide good classification performance. In general, the proposed CFIS algorithm can be applied in the field of expert and intelligent systems where it is necessary to deal with non stationary signals.
A City Called Heaven: Chicago and the Birth of Gospel Music

A City Called Heaven: Chicago and the Birth of Gospel Music. By Robert M. Marovich.  Until now the city has been well covered with full histories on jazz, blues, rhythm and blues, and soul but was bereft of such a history on the city's singular role in developing gospel music.Marovich laudably explains the full social context of the rise of gospel music in Chicago, notably the Great Migration, which brought a large number of African Americans to the North and to Chicago from World War I through the next three decades. This migration formed the seedbed from which gospel music would emerge and flourish.The author goes over familiar ground in laying out the development of gospel music in the city, which was primarily the work of Thomas Andrew Dorsey. He began his career in gospel in the late 1920s by publishing his gospel song sheets and peddling them to churches. I was always under the impression that Dorsey had grown up in the church, then went into recording dirty blues (Hokum) with Tampa Red, and then returned to the church to promote his gospel music. The way that Marovich presents the story is that Dorsey never left the church, and, while active in blues, he was still engaged in creating gospel music.The new gospel music that Dorsey was composing was animated by the 1920s blues and jazz music that he performed and knew well. This gospel music eventually spread throughout all the African American churches from the most conservative old line AME churches with their European hymns to the Church of God in Christ (GOG1C) or Pentecostal churches, which had already been creating a more African American style of church music.This book may surprise many readers who are only familiar with the solo and quartet variety of recorded gospel, as personified by Mahalia Jackson and the Roberta Martin Singers, two of the most significant gospel artists who came out of Chicago. A large focus of this study is on the adoption of gospel music by the churches and the formation of gospel choirs, much of which was never recorded in the 1930s and 1940s. Notable churches the author covers are Pilgrim Baptist Church, Ebenezer Baptist Church, First Church of Deliverance, Greater Harvest Baptist Church, and All Nations Pentecostal Church.