nils-herrmann/clean_scirepeval huggingface dataset:
Evolution, antigenicity and pathogenicity of global porcine epidemic diarrhea virus strains
Venue: Virus Research

Emerging and re emerging coronaviruses cause morbidity and mortality in human and animal populations, resulting in serious public and animal health threats and economic losses. The ongoing outbreak of a highly contagious and deadly porcine epidemic diarrhea virus (PEDV) in Asia, the Americas and Europe is one example. Genomic sequence analyses of PEDV variants have revealed important insights into the evolution of PEDV. However, the antigenic variations among different PEDV strains are less explored, although they may contribute to the failure of PEDV vaccines in Asian countries. In addition, the evolution of PEDV results in variants with distinct genetic features and virulence differences; thus PEDV can serve as a model to explore the molecular mechanisms of coronavirus evolution and pathogenesis. In this article, we review the evolution, antigenic relationships and pathologic features of PEDV strains. This information and review of researches will aid in the development of strategies for control and prevention of PED.
Lactogenic immunity and vaccines for porcine epidemic diarrhea virus (PEDV) Historical and current concepts
Venue: Virus Research

Morbidity, mortality, and loss of productivity from enteric diseases in neonatal piglets cost swine producers millions of dollars annually. In 2013 2014, the porcine epidemic diarrhea virus (PEDV) outbreak led to $900 million to $1.8 billion in annual losses to US swine producers. Passive lactogenic immunity remains the most promising and effective way to protect neonatal suckling piglets from enteric diseases like PEDV. Protecting suckling piglets through lactogenic immunity is dependent on trafficking of pathogen specific IgA plasmablasts to the mammary gland and accumulation of secretory IgA (sIgA) antibodies in milk, defined as the gut mammary sIgA axis. Due to an impermeable placenta, piglets are born agammaglobulinic, and are highly susceptible to a plethora of infectious agents. They rely solely on colostrum and milk antibodies for maternal lactogenic immunity. Previous advances in the development of live and attenuated vaccines for another devastating diarrheal virus of pigs, transmissible gastroenteritis virus (TGEV) provide insights into the mechanisms of maternal immunity and piglet protection. In this chapter, we will review previous research on TGEV induced lactogenic immunity to provide a historical perspective on current efforts for PEDV control and vaccines in the swine industry. Identifying factors that influence lactogenic immunity and the gut mammary sIgA axis may lead to improved vaccine regimens for PEDV and other enteric pathogens in gestating swine and improved overall herd immunity, swine health and industry productivity.
Comparison of EoS Based and K Values Based Methods for Three Phase Thermal Simulation
Venue: Transport in Porous Media

This paper presents a detailed numerical analysis of two methods for phase behavior computations associated with thermal compositional reservoir simulation. Specifically, we analyze and compare the standard K values approach with an Equation of State (EoS) model. We study steam and steam solvent injection into a one dimensional reservoir saturated with heavy oil in order to identify the impact of using different methods to describe the phase behavior. We then present results for complex displacement processes in multidimensional reservoir models. The investigation shows the differences in the predictions between K values and EoS based simulations can vary from small to quite significant depending on the compositional interactions between the injected and resident fluids in the modeled displacement process.
Prediction drug target interaction networks based on semi supervised learning method
Venue: 2016 35th Chinese Control Conference (CCC)

Predicting interactions between drugs and target proteins is a crucial step to decipher many biological processes, and plays a critical role in drug discovery. In this work, we present an Improved Laplacian Regularized Least Square Method (ILRLS) for drug target interaction prediction. We predict unknown drug target interactions from chemical structure information, genomic sequence information simultaneously and drug protein interaction network space. We obtain the better achievement from enzymes, ion channels, nuclear receptors and GPCRs interaction networks. The result indicates that the method could play a complementary role to the existing prediction methods.
Prediction drug target interaction networks based on semi supervised learning method
Venue: CCC 2016

Predicting interactions between drugs and target proteins is a crucial step to decipher many biological processes, and plays a critical role in drug discovery. In this work, we present an Improved Laplacian Regularized Least Square Method (ILRLS) for drug target interaction prediction. We predict unknown drug target interactions from chemical structure information, genomic sequence information simultaneously and drug protein interaction network space. We obtain the better achievement from enzymes, ion channels, nuclear receptors and GPCRs interaction networks. The result indicates that the method could play a complementary role to the existing prediction methods.
Diffusion Convolutional Neural Networks
Venue: NIPS

We present diffusion convolutional neural networks (DCNNs) a new model for graph structured data. Through the introduction of a diffusion convolution operation, we show how diffusion based representations can be learned from graph structured data and used as an effective basis for node classification. DCNNs have several attractive qualities, including a latent representation for graphical data that is invariant under isomorphism, as well as polynomial time prediction and learning that can be represented as tensor operations and efficiently implemented on the GPU. Through several experiments with real structured datasets, we demonstrate that DCNNs are able to outperform probabilistic relational models and kernel on graph methods at relational node classification tasks.
Improving the quality of service for critical flows in Smart Grid using software defined networking
Venue: 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm)

A Smart Grid system involves many applications, such as power grid state monitoring and control, demand response, distribution automation, distributed generation and microgrids. They will generate a large volume of data traffic over the grid with different quality of service (QoS) requirements, which present challenges to the existing network architecture and protocols. In this paper, we propose to develop a framework based on software defined networking to address the QoS issue in Smart Grid systems. We focus on providing better service for critical flows by dynamically setting up forwarding paths in the data plane. To that end, the control program will monitor the status of the network and direct critical flows over a better path by installing OpenFlow rules on the switches. We develop a path searching algorithm and implement it as a module for the Floodlight controller. The performance evaluations show that our approach can significantly improve the throughput obtained by the critical flows, compared with the shortest path routing algorithm used in current networks.
An Increase in the Omega 6/Omega 3 Fatty Acid Ratio Increases the Risk for Obesity
Venue: Nutrients

In the past three decades, total fat and saturated fat intake as a percentage of total calories has continuously decreased in Western diets, while the intake of omega 6 fatty acid increased and the omega 3 fatty acid decreased, resulting in a large increase in the omega 6/omega 3 ratio from 1:1 during evolution to 20:1 today or even higher. This change in the composition of fatty acids parallels a significant increase in the prevalence of overweight and obesity. Experimental studies have suggested that omega 6 and omega 3 fatty acids elicit divergent effects on body fat gain through mechanisms of adipogenesis, browning of adipose tissue, lipid homeostasis, brain gut adipose tissue axis, and most importantly systemic inflammation. Prospective studies clearly show an increase in the risk of obesity as the level of omega 6 fatty acids and the omega 6/omega 3 ratio increase in red blood cell (RBC) membrane phospholipids, whereas high omega 3 RBC membrane phospholipids decrease the risk of obesity. Recent studies in humans show that in addition to absolute amounts of omega 6 and omega 3 fatty acid intake, the omega 6/omega 3 ratio plays an important role in increasing the development of obesity via both AA eicosanoid metabolites and hyperactivity of the cannabinoid system, which can be reversed with increased intake of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) A balanced omega 6/omega 3 ratio is important for health and in the prevention and management of obesity.
Omega 3 Fatty Acid Status Enhances the Prevention of Cognitive Decline by B Vitamins in Mild Cognitive Impairment
Venue: Journal of Alzheimer's disease JAD

A randomized trial (VITACOG) in people with mild cognitive impairment (MCI) found that B vitamin treatment to lower homocysteine slowed the rate of cognitive and clinical decline. We have used data from this trial to see whether baseline omega 3 fatty acid status interacts with the effects of B vitamin treatment. 266 participants with MCI aged =70 years were randomized to B vitamins (folic acid, vitamins B6 and B12) or placebo for 2 years. Baseline cognitive test performance, clinical dementia rating (CDR) scale, and plasma concentrations of total homocysteine, total docosahexaenoic and eicosapentaenoic acids (omega 3 fatty acids) were measured. Final scores for verbal delayed recall, global cognition, and CDR sum of boxes were better in the B vitamin treated group according to increasing baseline concentrations of omega 3 fatty acids, whereas scores in the placebo group were similar across these concentrations. Among those with good omega 3 status, 33% of those on B vitamin treatment had global CDR scores >0 compared with 59% among those on placebo. For all three outcome measures, higher concentrations of docosahexaenoic acid alone significantly enhanced the cognitive effects of B vitamins, while eicosapentaenoic acid appeared less effective. When omega 3 fatty acid concentrations are low, B vitamin treatment has no effect on cognitive decline in MCI, but when omega 3 levels are in the upper normal range, B vitamins interact to slow cognitive decline. A clinical trial of B vitamins combined with omega 3 fatty acids is needed to see whether it is possible to slow the conversion from MCI to AD.
Use of Randomized Submaximal Glutamate Stimulus to Interpret Glial Effects on Neuronal Calcium Dynamics

Glutamate (GLU) binding to neurons can cause dynamic changes in intracellular calcium. We tested effects of a 3 group submaximal glutamate stimulus (250, 500 and 750 nanomolar GLU in randomized orders) on neurons in culture, and measured intracellular calcium dynamics in cultures high and low in glia at 8 and 9 days in vitro. Gliadepleted cultures responded to increasing GLU with synchronized dynamics, leading to a greater "area under the curve" (AUC) for intracellular calcium over time. The AUC determined if the neuron would respond dynamically to the next addition of glutamate. This observation was not displayed within cultures high in glia, where AUC returned to baseline with every GLU addition, regardless of order of addition. Furthermore, the 3 group stimulus resulted in decreasing average AUC, regardless of order. In contrast, for cultures depleted of glia, the deciding factor of a responding cell to dynamically respond to GLU additions depended on the ability of the cell to distribute the calcium load (AUC) of the prior addition. Determining how neurons respond and behave such as in the presence of functional or dysfunctional glia, may help our understanding of signal processing in the brain.
What does care farming provide for clients? The views of care farm staff

Highlights We present the views of care farm staff about what a care farm offers its clients. The care farm was perceived to offer a homely, supportive environment. The setting was perceived to be inclusive and conducive to clients' personal growth. We consider links with wellbeing theory. Care farming may have benefits for staff wellbeing as well as clients.  Care farming in the UK can help the agricultural community to remain viable and facilitate public interaction with the natural environment. It can also be therapeutic because it can address a range of public health and service provision issues by engaging people in farming activities and improving their health, social and educational circumstances. This paper presents the findings from a UK qualitative study exploring what care farming staff feel are the aims and potential outcomes of the experience they provide with their clients. Fifteen care farming staff were interviewed, using a semi structured interview schedule. In summary the study findings show staff perceived that the care farm offered a homely, supportive environment where people can experience nature and sustainable food production. They perceived the care farm to be a place that provides an inclusive environment conducive to clients' personal growth; it enables them to connect with themselves, others and nature and to develop autonomy. People can be themselves at the care farm where they have the opportunity to learn about themselves and nature. We consider how the issues care farm staff identified are linked with well being theory.
Van Vleck correction generalization for complex correlators with multilevel quantization

Remote sensing with phased antenna arrays is based on measurement of the cross correlations between the signals from each antenna pair. Digital correlators have systematic errors due to the quantization losses. The correlation errors allow substantial abatement based on the assumption that the analog signals are stochastic processes sampled from a statistical distribution (usually the Gaussian) The correlation correction technique is named after Van Vleck who was the first to apply it to two level clipping quantizers. The correction is especially important for high correlation levels, e.g. in studies of solar radio emissions. We offer a generalized method that for every antenna pair inputs the quantized signals' covariance and standard deviations, and outputs high precision estimates of the analog correlation. Although correlation correction methods have been extensively investigated in the past, there are several problems that, as far as we know, have not been published yet. We consider a very general quantization scheme with arbitrary set of transition thresholds and output levels, and our correction method is designed for correlations obtained from signals with generally unequal standard deviations. We also provide a method for estimation of the analog standard deviation from the quantized one for subsequent use in the correlation correction. We apply the correction to the the complex valued analytic signals, overwhelmingly used in modern remote sensing systems with arrays of antennas. The approach is valid not only for analytic signals with the imaginary part being the Hilbert transform of the real one, but also for more general, circularly symmetric complex processes whose real and imaginary parts may have arbitrary relationships to each other. This work was motivated by the need for greater precision in analysis of data from the Murchison Widefield Array (MWA)
Community detection in social networks using user frequent pattern mining
Venue: Knowledge and Information Systems

Recently, social networking sites are offering a rich resource of heterogeneous data. The analysis of such data can lead to the discovery of unknown information and relations in these networks. The detection of communities including 'similar' nodes is a challenging topic in the analysis of social network data, and it has been widely studied in the social networking community in the context of underlying graph structure. Online social networks, in addition to having graph structures, include effective user information within networks. Using this information leads to enhance quality of community discovery. In this study, a method of community discovery is provided. Besides communication among nodes to improve the quality of the discovered communities, content information is used as well. This is a new approach based on frequent patterns and the actions of users on networks, particularly social networking sites where users carry out their preferred activities. The main contributions of proposed method are twofold: First, based on the interests and activities of users on networks, some small communities of similar users are discovered, and then by using social relations, the discovered communities are extended. The F measure is used to evaluate the results of two real world datasets (Blogcatalog and Flickr) demonstrating that the proposed method principals to improve the community detection quality.
Community detection in social networks
Venue: Wiley Interdiscip. Rev. Data Min. Knowl. Discov.

The expansion of the web and emergence of a large number of social networking sites (SNS) have empowered users to easily interconnect on a shared platform. A social network can be represented by a graph consisting of a set of nodes and edges connecting these nodes. The nodes represent the individuals/entities, and the edges correspond to the interactions among them. The tendency of people with similar tastes, choices, and preferences to get associated in a social network leads to the formation of virtual clusters or communities. Detection of these communities can be beneficial for numerous applications such as finding a common research area in collaboration networks, finding a set of likeminded users for marketing and recommendations, and finding protein interaction networks in biological networks. A large number of community detection algorithms have been proposed and applied to several domains in the literature. This paper presents a survey of the existing algorithms and approaches for the detection of communities in social networks. We also discuss some of the applications of community detection. WIREs Data Mining Knowl Discov 2016, 6:115 135. doi: 10.1002/widm.1178
Novel viral genomes identified from six metagenomes reveal wide distribution of archaeal viruses and high viral diversity in terrestrial hot springs.
Venue: Environmental microbiology

Limited by culture dependent methods the number of viruses identified from thermophilic Archaea and Bacteria is still very small. In this study we retrieved viral sequences from six hot spring metagenomes isolated worldwide, revealing a wide distribution of four archaeal viral families, Ampullaviridae, Bicaudaviridae, Lipothrixviridae and Rudiviridae. Importantly, we identified 10 complete or near complete viral genomes allowing, for the first time, an assessment of genome conservation and evolution of the Ampullaviridae family as well as Sulfolobus Monocaudavirus 1 (SMV1) related viruses. Among the novel genomes, one belongs to a putative thermophilic virus infecting the bacterium Hydrogenobaculum, for which no virus has been reported in the literature. Moreover, a high viral diversity was observed in the metagenomes, especially among the Lipothrixviridae, as indicated by the large number of unique contigs and the lack of a completely assembled genome for this family. This is further supported by the large number of novel genes in the complete and partial genomes showing no sequence similarities to public databases. CRISPR analysis revealed hundreds of novel CRISPR loci and thousands of novel CRISPR spacers from each metagenome, reinforcing the notion of high viral diversity in the thermal environment.
Is it getting hot in here? Adjustment of hydraulic parameters in six boreal and temperate tree species after 5 years of warming.
Venue: Global change biology

Global temperatures (T) are rising, and for many plant species, their physiological response to this change has not been well characterized. In particular, how hydraulic parameters may change has only been examined experimentally for a few species. To address this, we measured characteristics of the hydraulic architecture of six species growing in ambient T and ambient +3.4 degC T plots in two experimentally warmed forest sites in Minnesota. These sites are at the temperate boreal ecotone, and we measured three species from each forest type. We hypothesized that relative to boreal species, temperate species near their northern range border would increase xylem conduit diameters when grown under elevated T. We also predicted a continuum of responses among wood types, with conduit diameter increases correlating with increases in the complexity of wood structure. Finally, we predicted that increases in conduit diameter and specific hydraulic conductivity would positively affect photosynthetic rates and growth. Our results generally supported our hypotheses, and conduit diameter increased under elevated T across all species, although this pattern was driven predominantly by three species. Two of these species were temperate angiosperms, but one was a boreal conifer, contrary to predictions. We observed positive relationships between the change in specific hydraulic conductivity and both photosynthetic rate (P 0.080) and growth (P 0.012) Our results indicate that species differ in their ability to adjust hydraulically to increases in T. Specifically, species with more complex xylem anatomy, particularly those individuals growing near the cooler edge of their range, appeared to be better able to increase conduit diameters and specific hydraulic conductivity, which permitted increases in photosynthesis and growth. Our data support results that indicate individual's ability to physiologically adjust is related to their location within their species range, and highlight that some wood types may adjust more easily than others.
Definition, existence, stability and uniqueness of the solution to a semilinear elliptic problem with a strong singularity at u 0

In this paper we consider a semilinear elliptic equation with a strong singularity at $u=0$ namely \displaystyle u\geq 0 \mbox{in \Omega, \displaystyle div ,A(x) D u F(x,u) \mbox{in} \Omega, u 0 \mbox{on} \partial \Omega, with $F(x,s) a Carath\'eodory function such that 0\leq F(x,s)\leq \frac{h(x)\Gamma(s)\mbox{ a.e. x\in\Omega, \forall s>0, with $h$ in some $L^r(\Omega) and \Gamma$ a $C^1([0,\infty[ function such that \Gamma(0)=0$ and \Gamma'(s)>0$ for every $s>0$ We introduce a notion of solution to this problem in the spirit of the solutions defined by transposition. This definition allows us to prove the existence and the stability of this solution, as well as its uniqueness when $F(x,s) is nonincreasing in $s$
Local existence and uniqueness of strong solutions to the Navier Stokes equations with nonnegative density

In this paper, we consider the initial boundary value problem to the nonhomogeneous incompressible Navier Stokes equations. Local strong solutions are established, for any initial data \rho_0, u_0)\in (W^{1,\gamma} \cap L^\infty)\times H_{0,\sigma}^1$ with \gamma>1$ and if \gamma\geq2$ then the strong solution is unique. The initial density is allowed to be nonnegative, and in particular, the initial vacuum is allowed. The assumption on the initial data is weaker than the previous widely used one that \rho_0, u_0)\in (H^1 \cap L^\infty \times(H_{0,\sigma}^1 \cap H^2) and no compatibility condition is required.