Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel MicroRNAs and Targets Prediction in PRRS Virus Genome

In this paper, 4 microRNAs and their targets were predicted through a de novo prediction procedure in the genome of Porcine Reproductive and Respiratory Syndrome virus. Through clustalW analysis, these 4 microRNAs were recognized as novel PRRSV derivate microRNAs. Target prediction results showed PRRSV mir-1, 2 and 4 have total 16 targets in porcine 3’ UTR database. These targets could be divided into 4 groups, which related to germ cell development, immunity function, apoptosis and other cell function genes, respectively. The roles of these targets inhibition on virus infection were issued and the link of these suppressions to clinical syndrome was discussed. In conclusion, the results showed microRNAs were exploited by PRRSV to escape host anti-virus surveillance and help virus infection, and the consequences of individual target suppression were related closely to clinical syndromes. In conclusion, the results will deep the understanding on PRRSV infection and provide novel virus microRNAs as targets for counter virus infection in gene therapy era.
Growth, Congestion of Public Goods, and Second-Best Optimal Policy

This paper presents a general equilibrium endogenous growth model in which public spending is divided between public productive services and public consumption. A distinguishing feature of the model is the assumption that both components of public spending can be over used and, thus, congested by the private agents. We study the second-best dynamics of the model and prove that it is determinate. Moreover, we show that the optimal second-best policy could be not unique. Finally, the relationship between congestion and the optimal second-best policy, on the one hand, and congestion and the long run growth rate, on the other, is established.
Quantitative Risk Management: Concepts, Techniques, and Tools

This book provides the most comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management. Whether you are a financial risk analyst, actuary, regulator or student of quantitative finance, Quantitative Risk Management gives you the practical tools you need to solve real-world problems. Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book's methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives. Fully revised and expanded to reflect developments in the field since the financial crisis Features shorter chapters to facilitate teaching and learning Provides enhanced coverage of Solvency II and insurance risk management and extended treatment of credit risk, including counterparty credit risk and CDO pricing Includes a new chapter on market risk and new material on risk measures and risk aggregation
Comparative Transcriptome Analyses Reveal a Special Glucosinolate Metabolism Mechanism in Brassica alboglabra Sprouts

Brassica sprouts contain abundant phytochemicals, especially glucosinolates (GSs). Various methods have been used to enhance GS content in sprouts. However, the molecular basis of GS metabolism in sprouts remains an open question. Here we employed RNA-seq analysis to compare the transcriptomes of high-GS (JL-08) and low-GS (JL-09) Brassica alboglabra sprouts. Paired-end Illumina RNA-seq reads were generated and mapped to the Brassica oleracea reference genome. The differentially expressed genes were analyzed between JL-08 and JL-09. Among these, 1477 genes were up-regulated and 1239 down-regulated in JL-09 compared with JL-08. Enrichment analysis of these differentially expressed genes showed that the GS biosynthesis had the smallest enrichment factor and the highest Q-value of all metabolic pathways in Kyoto Encyclopedia of Genes and Genomes database, indicating the main metabolic difference between JL-08 and JL-09 is the GS biosynthetic pathway. Thirty-seven genes of the sequenced data were annotated as putatively involved in GS biosynthesis, degradation, and regulation, of which 11 were differentially expressed in JL-08 and JL-09. The expression level of GS degradation enzyme myrosinase in high-GS JL-08 was lower compared with low-GS JL-09. Surprisingly, in high-GS JL-08, the expression levels of GS biosynthesis genes were also lower than those in low-GS JL-09. As the GS contents in sprouts are determined by dynamic equilibrium of seed stored GS mobilization, de novo synthesis, degradation, and extra transport, the result of this study leads us to suggest that efforts to increase GS content should focus on either raising GS content in seeds or decreasing myrosinase activity, rather than improving the expression level of GS biosynthesis genes in sprouts.
Toward Synergistic Approaches to Knowledge and Information Visualization

Two fields of research are heavily interested in developing visualizations for helping users coping with complex tasks and ill-structured subject matter resources: knowledge visualization and information visualization. The goal of knowledge visualization is to assist students in learning and problem solving by providing tools for fostering externalized cognition. The goal of information visualization is to provide knowledge-based access to information resources and help users in making sense of the resources they are looking for in information retrieval. The contribution draws attention to digital concept maps as cognitive tools which may provide a basis for the development of synergistic approaches that may help visualizing, accessing, and managing both knowledge and information and foster resourcebased learning.
Ray Tracing With Adaptive Supersampling in Object Space

We introduce a new approach to two important problems in ray tracing: antialiasing and distributed light sources. For antialiasing, adaptive supersampling in object space (ASOS) combines the quality of supersampling with the speed of adaptive supersampling. In adaptive supersampling, the decision to partition a ray is taken in imagespace, which means that small or thin objects may be missed entirely. This is particularly problematic in an animation, where the intensity of such objects may appear to vary. ASOS is based on testing the proximity of a ray to the boundary of an object. If a primary ray is close to the boundary, it splits into 4 subrays, and the procedure continues re cursively with each subray. This splitting continues until the estimated error in pixel intensity is sufficiently small. ASOS also computes shadows from distributed light sources to any required precision . Our implementation includes spheres, polygons, disks, boxes, cones and cylinders and does not preclude other primitives.
TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths

As an essential form of knowledge representation, taxonomies are widely used in various downstream natural language processing tasks. However, with the continuously rising of new concepts, many existing taxonomies are unable to maintain coverage by manual expansion. In this paper, we propose TEMP, a self-supervised taxonomy expansion method, which predicts the position of new concepts by ranking the generated taxonomy-paths. For the first time, TEMP employs pre-trained contextual encoders in taxonomy construction and hypernym detection problems. Experiments prove that pre-trained contextual embeddings are able to capture hypernym-hyponym relations. To learn more detailed differences between taxonomy-paths, we train the model with dynamic margin loss by a novel dynamic margin function. Extensive evaluations exhibit that TEMP outperforms prior state-of-the-art taxonomy expansion approaches by 14.3% in accuracy and 15.8% in mean reciprocal rank on three public benchmarks.
Combining the Results of Rationality Studies: What Did We Know and When Did We Know It?

Numerous empirical literatures display conflicting results. This paper examines the conflicting results concerning the rationality of forecasts and expectations. Meta-analysis techniques are used to examine the empirical studies that tested the rationality hypothesis. What did we know, and when did we know it, about the rationality of forecasts? Our purpose is to systematically document how conclusions change over time and to investigate whether meta-analysis techniques might have been useful in shedding light on those findings. The results differ depending on which meta null is used, but there still is no definitive finding about the rationality of these forecasts.
Improvement of Structure Conservation Index with Centroid Estimators

RNAz, a support vector machine (SVM) approach for identifying functional non-coding RNAs (ncRNAs), has been proven to be one of the most accurate tools for this goal. Among the measurements used in RNAz, the Structure Conservation Index (SCI) which evaluates the evolutionary conservation of RNA secondary structures in terms of folding energies, has been reported to have an extremely high discrimination capability. However, for practical use of RNAz on the genome-wide search, a relatively high false discovery rate has unfortunately been estimated. It is conceivable that multiple alignments produced by a standard aligner that does not consider any secondary structures are not suitable for identifying ncRNAs in some cases and incur high false discovery rate. In this study, we propose C-SCI, an improved measurement based on the SCI applying gamma-centroid estimators to incorporate the robustness against low quality multiple alignments. Our experiments show that the C-SCI achieves higher accuracy than the original SCI for not only human-curated structural alignments but also low quality alignments produced by CLUSTAL W. Furthermore, the accuracy of the C-SCI on CLUSTAL W alignments is comparable with that of the original SCI on structural alignments generated with RAF for which 4.7-fold expensive computational time is required on average.
Impact of the Intel® Data Plane Development Kit (Intel® DPDK) on Packet Throughput in Virtualized Network Elements

Server consolidation using virtualization is a norm for IT data centers today. It is achieved by consolidating workloads running on multiple servers onto a single, virtualized server platform that can be managed centrally. The primary motivation for consolidation within IT data centers is that once multiple workloads have been consolidated onto a single virtual hardware platform, fewer physical servers will be required. This helps reduce IT management costs and lower power usage for server power and cooling by consolidating workloads running on multiple servers onto a single, virtualized server platform that can be managed centrally and decreases the amount of physical space required for server farms.
Enhancing the Tracking of Partials for the Sinusoidal Modeling of Polyphonic Sounds

This paper addresses the problem of tracking partials, i.e., determining the evolution over time of the parameters of a given number of sinusoids with respect to the analyzed audio stream. We first show that the minimal frequency difference heuristic generally used to identify continuities between local maxima of successive short-time spectra can be successfully generalized using the linear prediction formalism to handle modulated sounds such as musical tones with vibrato. The spectral properties of the evolutions in time of the parameters of the partials are next studied to ensure that the parameters of the partials effectively satisfy the slow time-varying constraint of the sinusoidal model. These two improvements are combined in a new algorithm designed for the sinusoidal modeling of polyphonic sounds. The comparative tests show that onsets/offsets of sinusoids as well as closely spaced sinusoids are better identified and stochastic components are better avoided.
An Externalist Process-Oriented Framework for Artificial Consciousness

In this paper, we present a view of conscious perception that supposes a processual unity between the activity in the agent and the perceived event in the external world. It is a kind of radical externalism (both vehicle and content) that suggests that the boundary of the agent’s conscious mind are much larger than those of its body. We suggest a process-based approach as an explanation of ordinary perception and other variants of phenomenal experience such as illusions, memory, dreams, and mental imagery. This approach provides new insights into the problem of conscious representation in the brain and phenomenal consciousness. It is a form of anti-cranialism different from but related to other kinds of externalism. Eventually, we will sketch the relation between this model and the capability of developing new goals in an agent. Unity between the environment and the
Policy networks in British government

Policy is not made in the electoral arena or in the gladiatorial confrontations of Parliament, but in the netherworld of committees, civil servants, professions, and interest groups. This collection explores the private world of public policy. It provides a survey of the literature on the concept of policy networks and demonstrates its importance for understanding specific policy areas. The case studies cover policy-making in agriculture, civil nuclear power, youth employment, smoking, heart disease, sea defences, information technology, and exchange rate policy. Finally the editors attempt an overall assessment of the utility of the concept, focusing on such questions as why networks change, which interests dominate and benefit from networks, and the consequences of the present system for representative democracy. To describe policy networks is not to condone political oligopoly. Britain has witnessed the substitution of private government for public accountability. The analysis of policy networks draws attention to this erosion of representative democracy and exposes the private government of Britain to public gaze.
Provably robust digital watermarking

Copyright notification and enforcement, authentication, covert communication, and hybrid transmission are examples of emerging multimedia applications for digital watermarking methods, methods for embedding one signal (e.g., the digital watermark) within another 'host' signal to form a third, 'composite' signal. The embedding is designed to achieve efficient trade-offs among the three conflicting goals of maximizing information-embedding rate, minimizing distortion between the host signal and composite signal, and maximizing the robustness of the embedding. Quantization index modulation (QIM) methods are a class of watermarking methods that achieve provably good rate-distortion-robustness performance. Indeed, QIM methods exist that achieve performance within a few dB of capacity in the case of a (possibly colored) Gaussian host signal and an additive (possibly colored) Gaussian noise channel. Also, QIM methods can achieve capacity with a type of postprocessing called distortion compensation. This capacity is independent of host signal statistics, and thus, contrary to popular belief, the information-embedding capacity when the host signal is not available at the decoder is the same as the case when the host signal is available at the decoder. A low-complexity realization of QIM called dither modulation has previously been proven to be better than both linear methods of spread spectrum and nonlinear methods of low-bit(s) modulation against square-error distortion-constrained intentional attacks. We introduce a new form of dither modulation called spread-transform dither modulation that retains these favorable performance characteristics while achieving better performance against other attacks such as JPEG compression.
DNorm: disease name normalization with pairwise learning to rank

Motivation: Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text—the task of disease name normalization (DNorm)—compared with other normalization tasks in biomedical text mining research. Methods: In this article we introduce the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM. Our method is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. The technique is based on pairwise learning to rank, which has not previously been applied to the normalization task but has proven successful in large optimization problems for information retrieval. Results: We compare our method with several techniques based on lexical normalization and matching, MetaMap and Lucene. Our algorithm achieves 0.782 micro-averaged F-measure and 0.809 macro-averaged F-measure, an increase over the highest performing baseline method of 0.121 and 0.098, respectively. Availability: The source code for DNorm is available at http://www.ncbi. nlm.nih.gov/CBBresearch/Lu/Demo/DNorm, along with a web-based demonstration and links to the NCBI disease corpus. Results on PubMed abstracts are available in PubTator: http://www.ncbi. nlm.nih.gov/CBBresearch/Lu/Demo/PubTator Contact: zhiyong.lu@nih.gov
Unsupervised empirical Bayesian multiple testing with external covariates

In an empirical Bayesian setting, we provide a new multiple testing method, useful when an additional covariate is available, that influences the probability of each null hypothesis being true. We measure the posterior significance of each test conditionally on the covariate and the data, leading to greater power. Using covariate-based prior information in an unsupervised fashion, we produce a list of significant hypotheses which differs in length and order from the list obtained by methods not taking covariate-information into account. Covariate-modulated posterior probabilities of each null hypothesis are estimated using a fast approximate algorithm. The new method is applied to expression quantitative trait loci (eQTL) data.
Diagnostic Test for the Discrimination between Interictal Epileptic and Non-Epileptic Pathological EEG Events using Auto–Cross-Correlation Methods

ABSTRACT. This study investigates the problem of discriminating between interictal epileptic and non-epileptic pathological EEG states that present with episodic loss of consciousness. The proposed method is based on a novel algorithm that produces auto-correlated coefficients from an appropriate, selected EEG segment. The characteristic features of these coefficients are based on a prototype spectral Variation of an extended spectrum in which alpha, beta, and gamma activities are included. The statistical processing of the results of this method shows that it may be characterized as accurate for recognition purposes.
Increased Risk of Dementia in Patients with Tension-Type Headache: A Nationwide Retrospective Population-Based Cohort Study

Purpose The association between primary headaches, including tension-type headache (TTH) as one of the most common primary headache disorders, and dementia remains controversial. In this nationwide, population-based, retrospective, cohort study, we explored the potential association between TTH and dementia and examined sex, age, and comorbidities as risk factors for dementia. Methods Using the Taiwan National Health Insurance Research Database (NHIRD) claims data, the sample included 13908 subjects aged ≥20 years with newly-diagnosed TTH in 2000–2006. The non-TTH group included 55632 randomly selected sex- and age-matched TTH-free individuals. All subjects were followed until dementia diagnosis, death, or the end of 2011. Patients with dementia, including vascular and non-vascular (including Alzheimer’s) subtypes, were identified using International Classification of Diseases Ninth Revision, Clinical Modification codes. Multivariate Cox proportional hazards regression models were used to assess the risk of dementia and dementia-associated risk factors, such as migraine and other medical comorbidities. Results During the average follow-up of 8.14 years, the incidence density rates of dementia were 5.30 and 3.68/1,000 person-years in the TTH and non-TTH groups, respectively. Compared with the non-TTH group, the risks of dementia were 1.25 (95% confidence interval [CI], 1.11–1.42) and 1.13 (95% CI, 1.01–1.27) times higher in the women and >65-year-old TTH group, respectively. TTH patients with comorbidities had a higher risk of dementia. TTH patients had a greater risk of non-vascular dementia (hazard ratio, 1.21; 95% CI, 1.09–1.34) than the non-TTH group. Conclusion TTH patients have a future risk of dementia, indicating a potentially linked disease pathophysiology that warrants further study. The association between TTH and dementia is greater in women, older adults, and with comorbidities. Clinicians should be aware of potential dementia comorbidity in TTH patients.
High-Frequency Stimulation of the Subthalamic Nucleus Selectively Reverses Dopamine Denervation-Induced Cellular Defects in the Output Structures of the Basal Ganglia in the Rat

High-frequency stimulation (HFS) of the subthalamic nucleus (STN) is now recognized as an effective treatment for advanced Parkinson's disease, but the molecular basis of its effects remains unknown. This study examined the effects of unilateral STN HFS (2 hr of continuous stimulation) in intact and hemiparkinsonian awake rats on STN neuron metabolic activity and on neurotransmitter-related gene expression in the basal ganglia, by means of in situhybridization histochemistry and immunocytochemistry. In both intact and hemiparkinsonian rats, this stimulation was found to induce c-fos protein expression but to decrease cytochrome oxidase subunit I mRNA levels in STN neurons. STN HFS did not affect the dopamine lesion-mediated overexpression of enkephalin mRNA or the decrease in substance P in the ipsilateral striatum. The lesion-induced increases in intraneuronal glutamate decarboxylase 67 kDa isoform (GAD67) mRNA levels on the lesion side were reversed by STN HFS in the substantia nigra, partially antagonized in the entopeduncular nucleus but unaffected in the globus pallidus. The stimulation did not affect neuropeptide or GAD67 mRNA levels in the side contralateral to the dopamine lesion or in intact animals. These data furnish the first evidence that STN HFS decreases the metabolic activity of STN neurons and antagonizes dopamine lesion-mediated cellular defects in the basal ganglia output structures. They provide molecular substrate to the therapeutic effects of this stimulation consistent with the current hypothesis that HFS blocks STN neuron activity. However, the differential impact of STN HFS on the effects of dopamine lesion among structures receiving direct STN inputs suggests that this stimulation may not cause simply interruption of STN outflow.
Uncorrelated Energy Spread and Longitudinal Emittance of A Photoinjector Beam

Longitudinal phase space properties of a photoinjector beam are important in many areas of high-brightness beam applications such as bunch compression, transverse to-longitudinal emittance exchange, and high-gain free-electron lasers. In this paper, we discuss both the rf and the space charge contributions to the uncorrelated energy spread of the beam generated from a laser-driven rf gun. We compare analytical expressions for the uncorrelated energy spread and the longitudinal emittance with numerical simulations and recent experimental results.