Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and Abundance of Latently Transcribed Varicella-Zoster Virus Genes in Human Ganglia

ABSTRACT In human ganglia latently infected with varicella-zoster virus (VZV), sequence analysis has revealed that five viral genes (VZV genes 21, 29, 62, 63, and 66) are transcribed. However, their comparative prevalence and abundance are unknown. Here, using real-time PCR, we analyzed 28 trigeminal ganglia from 14 humans for RNA corresponding to the five virus genes known to be transcribed in latently infected human ganglia. The most prevalent transcript found was VZV gene 63 (78%), followed by gene 66 (43%), gene 62 (36%), and gene 29 (21%). No gene 21 transcripts were detected in any of the 28 ganglia. VZV gene 63 RNA was also the most abundant (3,710 ± 6,895 copies per 1 μg of mRNA) transcript detected in latently infected human ganglia, followed by VZV gene 29 (491 ± 594), VZV gene 66 (117 ± 85), and VZV gene 62 (64 ± 38). Thus, the repeated detection and high abundance of VZV gene 63 transcripts in latently infected ganglia suggests that VZV gene 63 may be more important for the maintenance of virus latency than the less abundantly transcribed and randomly detected VZV genes 21, 29, 62, and 66.
Crowdsourcing as a Tool for Knowledge Acquisition in Spatial Planning

The term “crowdsourcing” was initially introduced by Howe in his article “The Rise of Crowdsourcing” [1]. During the last few years, crowdsourcing has become popular among companies, institutions and universities, as a crowd-centered modern “tool” for problem solving. Crowdsourcing is mainly based on the idea of an open-call publication of a problem, requesting the response of the crowd for reaching the most appropriate solution. The focus of this paper is on the role of crowdsourcing in knowledge acquisition for planning applications. The first part provides an introduction to the origins of crowdsourcing in knowledge generation. The second part elaborates on the concept of crowdsourcing, while some indicative platforms supporting the development of crowdsourcing applications are also described. The third part focuses on the integration of crowdsourcing with certain web technologies and GIS (Geographic Information Systems), for spatial planning applications, while in the fourth part, a general framework of the rationale behind crowdsourcing applications is presented. Finally, the fifth part focuses on a range of case studies that adopted several crowdsourcing techniques.
Adaptive robust control of the mobile manipulator based on neural network

This article describes an adaptive robust control algorithm of the mobile manipulator based on robust neural network to solve disturbances, nonlinearity, nonholonomic constraints between the mobile platform and the mounted manipulator. According to the nonlinear mapping ability of neural network and fast learning ability, neural network and robust control strategies are integrated into the adaptive control algorithm for the mobile manipulator with nonlinear and nonholonomic constraints. Basis on the mobile manipulator dynamics model and its modeling analysis, the controller can drive the trajectory tracking error of the mobile manipulator to converge to zero. The simulation results show that the algorithm has higher precision of trajectory tracking control and can achieve good control effect.
The Pisces-Cetus supercluster: a remarkable filament of galaxies in the 2dF Galaxy Redshift and Sloan Digital Sky surveys

The Pisces-Cetus supercluster (redshift z ∼ 0.06) is one of the richest nearby (z < 0.1) superclusters of galaxies, and emerges as a remarkable filament of galaxies at the edges of the two-degree Field Galaxy Redshift Survey (2dFGRS) and the ongoing Sloan Digital Sky Survey (SDSS). We explore the extent of the supercluster on the sky and in redshift space, and map the distribution of its constituent clusters and groups. We find evidence of enhanced star formation in galaxies belonging to groups in the supercluster compared to those in the field. This effect appears to be higher among the poorer groups than in the richer ones. In contrast, star formation is suppressed in the galaxies in rich clusters, which is consistent with previous studies. We identify two major filaments in this supercluster, consisting of 11 and 5 Abell clusters, including Abell 133 and Abell 85, respectively, and estimate the virial masses of the clusters from their velocity dispersions and optical surface brightness profiles. The lower limit to the masses of these filaments, amounting to the total virial mass of the constituent clusters, turns
Classification Based on SPACT and Visual Saliency

This paper proposes an efficient approach for object classification. This method bases on bag-of-features classification framework and extends the limits of it. It applies modified spatial PACT as local feature descriptor, which can efficiently catch image patch's characteristic. In order to address the speed bottleneck of codebook creation, Extremely Randomized Clustering Forest is used to create discriminative visual codebook as well as classifier. The prior knowledge stored by the classifier is used to build saliency maps online. The saliency maps can bias the random sampling of sub-windows and improve the speed of classification. Through evaluation on PASCAL 2007 Visual Classification Challenge dataset set, the test results show that this object classification method has many advantages. It has comparable performances to state-of-the-art algorithms with short training and testing times. It has nearly no parameter to tune and it is easy to implement.
Feature selection using hierarchical feature clustering

One of the challenges in data mining is the dimensionality of data, which is often very high and prevalent in many domains, such as text categorization and bio-informatics. The high-dimensionality of data may bring many adverse situations to traditional learning algorithms. To cope with this issue, feature selection has been put forward. Currently, many efforts have been attempted in this field and lots of feature selection algorithms have been developed. In this paper we propose a new selection method to pick discriminative features by using information measurement. The main characteristic of our selection method is that the selection procedure works like feature clustering in a hierarchically agglomerative way, where each feature is considered as a cluster and the between-cluster and within-cluster distances are measured by mutual information and the coefficient of relevancy respectively. Consequently, the final aggregated cluster is the selection result, which has the minimal redundancy among its members and the maximal relevancy with the class labels. The simulation experiments on seven datasets show that the proposed method outperforms other popular feature selection algorithms in classification performance.
Online-Kommunikation im Lichte deliberativer Theorie: ein forschungsleitendes Modell zur Analyse von Online-Diskussionen

Empfohlene Zitierung / Suggested Citation: Frieß, Dennis: Online-Kommunikation im Lichte deliberativer Theorie: ein forschungsleitendes Modell zur Analyse von Online-Diskussionen. In: Henn, Philipp (Ed.) ; Frieß, Dennis(Ed.): Politische Online-Kommunikation: Voraussetzungen und Folgen des strukturellen Wandels der politischen Kommunikation. Berlin, 2016 (Digital Communication Research 3). ISBN 978-3-945681-03-9, 143-169. https://doi.org/10.17174/dcr.v3. 7
A quality risk model based on supply chain perspective

Supply chain is a dynamic system and there are a lot of uncertainties in supply chain operations. Quality risk transmits through the supply chain when quality issues are not managed and control appropriately. This may result in increasing Cost of Quality (COQ) and effecting customer satisfaction level. To reach a win-win situation, suppliers need to determine their quality risk management level by considering not only their current COQ, but also the potential future recalls, which affect other partners' interest. Based on the current literature, quantitative models which can simulate the risk transmission along supply chain are seldom used. The contribution of this paper is to quantify the quality risk and analyze risk transmission along supply chain. This paper provides mathematical models to simulate the risk transmission, thus helping suppliers to determine the optimal quality risk management level. In the end, we apply this model in a case company to validate its accuracy.
Who Cares? Generativity and the Moral Emotions, Part 3. Asocial Intuitionist “Ecology of Virtue”

Recent thinking in positive psychology has promoted the idea that positive and moral emotions facilitate prosocial (perhaps caring) behavior (Fredrickson, 2004; Haidt, 2003a). One prominent theory proposes that certain affective dispositions (e.g., empathy, gratitude) serve as “moral motives” for prosocial action, as they provide a link between other facets of moral character and prosocial action (McCullough, Kilpatrick, Emmons, and Larsen, 2001). Following this line of thought, and the social intuitionist model of moral motivation (SIM motivation) presented in Part 2, this article introduces (hypothesizes) a conceptual model of ten moral emotion-related capacities (virtues) that recent research suggests may be fundamental to a flourishing capacity for generative care: trust, love, elevation, empathy, compassion, gratitude, positive pride, guilt, forgiveness, and humility. The objectives of this article are, first, to describe this model; second, to suggest future directions for theoretical and empirical development of the model; and third, to discuss how it contributes to the emerging relational spirituality paradigm (Hall, 2004; Shults & Sandage, 2006). The article proposes that these moral affective capacities (MACs) represent one plausible model of the “virtues of acknowledged dependence” (MacIntyre, 1999), an “ecology of virtue” that describes the nature of a flourishing relational environment (Flanagan, 2007), and capacities that likely motivate and enable Christian kenotic love (Polkinghorne, 2001). The article concludes by looking ahead to future discussions of emotion and transformation in the relational spirituality paradigm, specifically the nature of the moral dilemma that persons encounter as they strive to develop a virtuous character (Leffel, 2007c).
Ribosome Dysfunction Is an Early Event in Alzheimer's Disease

Alzheimer's disease (AD) is a progressive and devastating disorder that is often preceded by mild cognitive impairment (MCI). In the present study, we report that in multiple cortical areas of MCI and AD subjects, there is a significant impairment in ribosome function that is not observed in the cerebellum of the same subjects. The impairment in ribosome function is associated with a decreased rate and capacity for protein synthesis, decreased ribosomal RNA and tRNA levels, and increased RNA oxidation. No alteration in the level of initiation factors was observed in the brain regions exhibiting impairments in protein synthesis. Together, these data indicate for the first time that impairments in protein synthesis may be one of the earliest neurochemical alterations in AD and directly demonstrate that the polyribosome complex is adversely affected early in the development of AD. These data have important implications for AD studies involving proteomics and studies analyzing proteolysis in AD, indicate that oxidative damage may contribute to decreased protein synthesis, and suggest a role for alterations in protein synthesis as a novel contributor to the onset and development of AD.
TDMA scheduling in wireless sensor networks using hybrid of genetic algorithm and particle swarm optimization

Today, wireless sensor networks encompass a wide volume of applications. In these networks, nodes consist of limited energy. Therefore, we must be able to save in their energy usage. Protocol TDMA in these networks is a suitable one for saving nodes energy. In this protocol, Time divided into time slots with equal length. Purpose of TDMA scheduling is time slots assignment into nodes in a way that minimize total number of time slots for sending their data packages and energy consumption. In this study, by hybrid of genetic algorithm and particle swarm optimization algorithm, this problem is optimized. Three algorithms are used for comparisons. It is shown that proposed algorithm is better than two algorithms on a total time and energy consumption.
From FNS to HEIV: a link between two vision parameter estimation methods

Problems requiring accurate determination of parameters from image-based quantities arise often in computer vision. Two recent, independently developed frameworks for estimating such parameters are the FNS and HEIV schemes. Here, it is shown that FNS and a core version of HEIV are essentially equivalent, solving a common underlying equation via different means. The analysis is driven by the search for a nondegenerate form of a certain generalized eigenvalue problem and effectively leads to a new derivation of the relevant case of the HEIV algorithm. This work may be seen as an extension of previous efforts to rationalize and interrelate a spectrum of estimators, including the renormalization method of Kanatani and the normalized eight-point method of Hartley.
A multicenter randomized controlled trial of tonsillectomy combined with steroid pulse therapy in patients with immunoglobulin A nephropathy

Background The study aim was, for the first time, to conduct a multicenter randomized controlled trial to evaluate the effect of tonsillectomy in patients with IgA nephropathy (IgAN). Methods Patients with biopsy-proven IgAN, proteinuria and low serum creatinine were randomly allocated to receive tonsillectomy combined with steroid pulses (Group A; n = 33) or steroid pulses alone (Group B; n = 39). The primary end points were urinary protein excretion and the disappearance of proteinuria and/or hematuria. Results During 12 months from baseline, the percentage decrease in urinary protein excretion was significantly larger in Group A than that in Group B (P < 0.05). However, the frequency of the disappearance of proteinuria, hematuria, or both (clinical remission) at 12 months was not statistically different between the groups. Logistic regression analyses revealed the assigned treatment was a significant, independent factor contributing to the disappearance of proteinuria (odds ratio 2.98, 95% CI 1.01–8.83, P = 0.049), but did not identify an independent factor in achieving the disappearance of hematuria or clinical remission. Conclusions The results indicate tonsillectomy combined with steroid pulse therapy has no beneficial effect over steroid pulses alone to attenuate hematuria and to increase the incidence of clinical remission. Although the antiproteinuric effect was significantly greater in combined therapy, the difference was marginal, and its impact on the renal functional outcome remains to be clarified.
Using an Path-Comparison Tracing Algorithm to Solve the most K Critical Paths Problem of Stochastic Networks

focus of this study is how to determine the most K Critical Paths (KCP) for stochastic network and we propose a Path Comparison Tracing Algorithms (PCTA) to solve this problem. In past studies, the well known Critical Path Method (CPM) has been the general method used for determining the critical path in deterministic networks but it is not proper to be used in stochastic network. Meanwhile, instead of solving single critical path in stochastic network, the KCP would be more practical for decision makers to face the real world cases. PCTA is based on the Label-Correcting Tracing Algorithm (LCTA) and modified Stochastic Dominated Operation (SDO) in our algorithms. Our numerical results were compared with results of the PERT and the Monte Carlo Simulation (MCS) of 20,000 samples and we concluded that our proposed approach is efficient for solving the KCP problem of stochastic networks.
A Retrospective Follow-up of Behaviorally Disordered Adolescents: Identifying Predictors of Treatment Outcome

Behaviorally disordered adolescents are a heterogeneous group. Determining which students enrolled in particular treatment programs are likely to be successful is a difficult task. This retrospective follow-up systematically investigated behaviorally disordered youth and identified factors associated with successful completion of a comprehensive residential and day treatment program. Results suggest that, for the 70 subjects studied, attendance, day or residential status, and prior adjudication were related to treatment outcomes. A dditionally, for a subset of 34 subjects directly interviewed and assessed at follow-up, measures of academic achievement were unrelated to outcomes.
VariantSpark: applying Spark-based machine learning methods to genomic information

Genomic information is increasingly being used for medical research, giving rise to the need for efficient analysis methodology able to cope with thousands of individuals and millions of variants. Catering for this need, we developed VariantSpark, a framework for applying machine learning algorithms in MLlib to genomic variant data using the efficient in-memory Spark compute engine. We demonstrate a speedup compared to our earlier Hadoop-based implementation as well as a published Spark-based approach using the ADAM framework. Adapting emerging methodologies for fast efficient analysis of large diverse data volumes to process genomic information, will be the cornerstone for precision genome medicine, which promises tailored healthcare for diagnosis and treatment.
Triangular Patch Antennas for Dual Mode 802 . 11 a , b WLAN Application

The analysis of equiangular triangular patch antennas is conducted to determine the mutual coupling under the constraint of small interelement spacing. The results show that the optimum location is near the midpoint along the edge of the antenna and not at the vertices. A four element diversity antenna is designed that has dual band (2.45;5.25, 5.8 GHz) and dual linear polarization capabilities.
An Efficient Algorithm for the Piecewise-Smooth Model with Approximately Explicit Solutions

An efficient approach to image segmentation that approximates the piecewise smooth (PS) functional in Vese and Chan (2002) with explicit solutions is presented. By rendering some rational constraints on the initial conditions and the final solutions of the PS functional, the authors propose two novel formulations which can be approximated to be the explicit solutions of the evolution partial differential equations (PDEs) of the PS model, in which only one PDE needs to be solved efficiently. Furthermore, an energy term that regularises the level set function to be a signed distance function is incorporated into our evolution formulation, and the time-consuming re-initialisation is avoided. Experiments on synthetic and real images show that the method is more efficient than both the PS model and the local binary fitting (LBF) model, while having similar segmentation accuracy as the LBF model.
Enhancement on the predictive power of the prediction model for human genomic DNA methylation

DNA methylation is an important type of epigenetic modification that plays an instrumental role in organogenesis, cellular differentiation, suppression of deleterious elements, and carcinogenesis. In addition to the experimentbased approaches, computational prediction provides guidance in an effective, fast and cheap way to the genomewide DNA methylation profiling. In this paper, we describe the development of support vector machine-based models for the prediction of the CpG island methylation. The features used for prediction include those that have been previously demonstrated effective (e.g., CpG island specific attributes, DNA sequence composition patterns, DNA structure patterns, distribution patterns of functional and evolutionarily conserved elements, and histone methylation status) as well as those that have not been extensively explored but are likely to contribute additional information from a biological point of view (e.g., nucleosome positioning propensities, gene functions, and histone acetylation status). Statistical tests were performed to identify the features that are significantly correlated with the methylation status of CpG islands, and principal component analysis was subsequently performed to decorrelate the selected features. The CpG island methylation profile data from the Human Epigenetic Project were used to train, validate and test our predictive models. Specifically, the models were trained and validated by using the data of the CD4 lymphocyte, and were then further tested for generalizability using the data of the other 11 tissues and cell types. The experiments showed that (1) an eight-dimensional feature space that was selected via the principal component analysis and that combines all categories of information was effective for predicting the CpG island methylation status, (2) by incorporating the information regarding the nucleosome positioning, gene functions, and histone acetylation, the model could achieve a higher specificity and accuracy than the existing model while maintaining a comparable sensitivity, (3) the histone modification information contributed significantly to the prediction, without which the performance of the model deteriorated, especially in terms of sensitivity, and, (4) the predictive models generalized well to different tissues and cell types, no matter whether the histone modification information was incorporated or not.