Miracle-dz/UniFAR-FTUs huggingface dataset:
Low vitamin D among HIV-infected adults: prevalence of and risk factors for low vitamin D Levels in a cohort of HIV-infected adults and comparison to prevalence among adults in the US general population.

BACKGROUND we explored serum 25-hydroxyvitamin D (25[OH]D) levels and associated factors for insufficiency or deficiency in an adult human immunodeficiency virus (HIV) cohort and compared 25(OH)D levels with those in the general US population. METHODS using baseline data from the Study to Understand the Natural History of HIV and AIDS in the Era of Effective Therapy (SUN), a prospective, observational cohort study of HIV-infected adults enrolled at 7 HIV specialty clinics in 4 US cities from March 2004 to June 2006, we estimated the prevalence of vitamin D insufficiency or deficiency (defined as 25(OH)D levels <30 ng/mL), standardized by age, race, and sex. Using multiple logistic regression, we examined risk factors for vitamin D insufficiency or deficiency. RESULTS among 672 SUN participants with baseline serum 25(OH)D determinations who were not receiving vitamin D supplements, 70.3% (95% confidence interval [CI], 68.1%-74.9%) were vitamin D insufficient or deficient, compared with 79.1% (95% CI, 76.7-81.3) of US adults. Factors associated with vitamin D insufficiency or deficiency included black race (adjusted odds ratio [aOR], 4.51; 95% CI, 2.59-7.85), Hispanic ethnicity (aOR, 2.78; 95% CI, 1.31-5.90), higher body mass index (aOR, 1.04; 95% CI, 1.00-1.09), hypertension (aOR, 1.88; 95% CI, 1.10-3.22), lack of exercise (aOR, 3.14; 95% CI, 1.80-5.47), exposure to efavirenz (aOR, 1.98; 95% CI, 1.18-3.34), higher exposure to ultraviolet light (aOR, .78; 95% CI, .71-.86), renal insufficiency (aOR, .55; 95% CI, .36-.83), and exposure to ritonavir (aOR, .56; 95% CI, .35-0.89). CONCLUSIONS similar to findings in US adults generally, vitamin D insufficiency or deficiency is highly prevalent among HIV-infected adults and is associated with known risk factors. Observed associations of vitamin D levels with renal insufficiency and with use of ritonavir- and efavirenz-containing regimens are consistent with both HIV-related and therapy-mediated alterations in vitamin D metabolism. Clinicians should consider screening all patients for vitamin D insufficiency or deficiency.
Dimension control method for the erection of structures on offshore floating dock

For the world's first Offshore Floating Dock(OFD), the dimension control system and method to aim at managing the dimension of offshore blocks in the process of erection are developed. This paper introduces the dimension control system and deals with the method of creating a simplified OFD model and of optimizing ballast-water plan to satisfy the demanded dimension of erected offshore plant blocks. A number of tests and successful application to the Jack & Saint Malo (JSM) offshore plant project was achieved and the results are also introduced.
Automatic extraction of knowledge from web documents

A large amount of digital information available is written as text documents in the form of web pages, reports, papers, emails, etc. Extracting the knowledge of interest from such documents from multiple sources in a timely fashion is therefore crucial. This paper provides an update on the Artequakt system which uses natural language tools to automatically extract knowledge about artists from multiple documents based on a predefined ontology. The ontology represents the type and form of knowledge to extract. This knowledge is then used to generate tailored biographies. The information extraction process of Artequakt is detailed and evaluated in this paper.
Model selection of polynomial kernel regression

Polynomial kernel regression is one of the standard and state-of-the-art learning strategies. However, as is well known, the choices of the degree of polynomial kernel and the regularization parameter are still open in the realm of model selection. The first aim of this paper is to develop a strategy to select these parameters. On one hand, based on the worst-case learning rate analysis, we show that the regularization term in polynomial kernel regression is not necessary. In other words, the regularization parameter can decrease arbitrarily fast when the degree of the polynomial kernel is suitable tuned. On the other hand,taking account of the implementation of the algorithm, the regularization term is required. Summarily, the effect of the regularization term in polynomial kernel regression is only to circumvent the " ill-condition" of the kernel matrix. Based on this, the second purpose of this paper is to propose a new model selection strategy, and then design an efficient learning algorithm. Both theoretical and experimental analysis show that the new strategy outperforms the previous one. Theoretically, we prove that the new learning strategy is almost optimal if the regression function is smooth. Experimentally, it is shown that the new strategy can significantly reduce the computational burden without loss of generalization capability.
Hierarchical Temporal Patterns and Interactive Aggregated Views for Pixel-Based Visualizations

Many real-world problems involve time-oriented data. Time data is different from other kinds of data--explicitly harnessing the structures of time in visualizations can guide and support users’ visual analysis processes. State-of-the-art visualizations hardly take advantage of the structures of time to aid users in understanding and exploring the data. To bring more flexibility to the analysis process, we have developed interactive visual methods incorporating the structures of time within a pixel-based visualization called GROOVE (granular overview overlay). GROOVE uses different techniques to visualize time-oriented data by overlaying several time granularities in one visualization and provides interactive operators, which utilize the structures of time in different ways to capture and explore time-oriented data.
A Pitx transcription factor controls the establishment and maintenance of the serotonergic lineage in planarians

In contrast to adult vertebrates, which have limited capacities for neurogenesis, adult planarians undergo constitutive cellular turnover during homeostasis and are even able to regenerate a whole brain after decapitation. This enormous plasticity derives from pluripotent stem cells residing in the planarian body in large numbers. It is still obscure how these stem cells are programmed for differentiation into specific cell lineages and how lineage identity is maintained. Here we identify a Pitx transcription factor of crucial importance for planarian regeneration. In addition to patterning defects that are co-dependent on the LIM homeobox transcription factor gene islet1, which is expressed with pitx at anterior and posterior regeneration poles, RNAi against pitx results in islet1-independent specific loss of serotonergic (SN) neurons during regeneration. Besides its expression in terminally differentiated SN neurons we found pitx in stem cell progeny committed to the SN fate. Also, intact pitx RNAi animals gradually lose SN markers, a phenotype that depends neither on increased apoptosis nor on stem cell-based turnover or transdifferentiation into other neurons. We propose that pitx is a terminal selector gene for SN neurons in planarians that controls not only their maturation but also their identity by regulating the expression of the Serotonin production and transport machinery. Finally, we made use of this function of pitx and compared the transcriptomes of regenerating planarians with and without functional SN neurons, identifying at least three new neuronal targets of Pitx.
Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V. Database URL: http://219.223. 252.210:8080/SS/cdr.html
Multifrequency InSAR height reconstruction through maximum likelihood estimation of local planes parameters

In this paper, a technique that is able to reconstruct highly sloped and discontinuous terrain height profiles, starting from multifrequency wrapped phase acquired by interferometric synthetic aperture radar (SAR) systems, is presented. We propose an innovative unwrapping method, based on a maximum likelihood estimation technique, which uses multifrequency independent phase data, obtained by filtering the interferometric SAR raw data pair through nonoverlapping band-pass filters, and approximating the unknown surface by means of local planes. Since the method does not exploit the phase gradient, it assures the uniqueness of the solution, even in the case of highly sloped or piecewise continuous elevation patterns with strong discontinuities.
Contributions of mast cells and vasoactive products, leukotrienes and chymase, to dengue virus-induced vascular leakage

Dengue Virus (DENV), a flavivirus spread by mosquito vectors, can cause vascular leakage and hemorrhaging. However, the processes that underlie increased vascular permeability and pathological plasma leakage during viral hemorrhagic fevers are largely unknown. Mast cells (MCs) are activated in vivo during DENV infection, and we show that this elevates systemic levels of their vasoactive products, including chymase, and promotes vascular leakage. Treatment of infected animals with MC-stabilizing drugs or a leukotriene receptor antagonist restores vascular integrity during experimental DENV infection. Validation of these findings using human clinical samples revealed a direct correlation between MC activation and DENV disease severity. In humans, the MC-specific product, chymase, is a predictive biomarker distinguishing dengue fever (DF) and dengue hemorrhagic fever (DHF). Additionally, our findings reveal MCs as potential therapeutic targets to prevent DENV-induced vasculopathy, suggesting MC-stabilizing drugs should be evaluated for their effectiveness in improving disease outcomes during viral hemorrhagic fevers. DOI: http://dx.doi. org/10.7554/eLife.00481. 001
Random Sampling and Signal Reconstruction Based on Compressed Sensing

Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted as little as possible of the data by applying CS and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the structure and content of the information in the signal. In this paper, the signal is the sparse in the Fourier transform and random sparse sampling is advanced by programming random observation matrix for peak random base. The signal is successfully restored by the use of Bregman algorithm. The signal is described in the transform space, and a theoretical framework is established with a new signal description and processing. By making the case to ensure that the information loss, signal is sampled at much lower than the Nyquist sampling theorem requiring rate, but also the signal is completely restored in high probability. The random sampling has following advantages: alias-free, sampling frequency need not obey the Nyquist limit, and higher frequency resolution. So the random sampling can measure the signals which their frequencies component are close, and can implement the higher frequencies measurement with lower sampling frequency. Copyright © 2014 IFSA Publishing, S. L.
Design of Knowledge Discovery Agent based on Self Adaptive Components

Agents can be autonomous entities, deciding their next step without the interference of a user or they can be controllable ,serving as a me diary between the user and another agent, thereby having some amount of Artificial Intelligence. Knowledge discovering agents are a very interesting approach to software development in computing environments. An agent consists in a thread of execution that can migrate across the network. In this paper, we will employ a multi-agent for the search and extraction of data in a distributed environment. We will use an Integrator Agent in the proposed model on the Knowledge Discovery Agent(KDA). The KDA will address the inadequacy of other data mining tools in processing performance and efficiency when use for knowledge discovery. The Integrator Agent was developed based on CORBA architecture for search and extraction of data from heterogeneous servers in the distributed environment. Our experiment shows that the KDA generated essential association rules which can be practically explained for decision making purposes.
Clique of Functional Hubs Orchestrates Population Bursts in Developmentally Regulated Neural Networks

It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity.
Identification of Dopamine D1 Receptor Agonists and Antagonists under Existing Noise Compounds by TFS-based ANN and SVM

This paper describes classification and prediction for pharmacologically active classes of drugs under the presence of noise chemical compounds. Dopamine D1 receptor agonists (63 compounds), antagonists (169 compounds) and other drugs (696 compounds) were used for the work. Each drug molecule was characterized with Topological Fragment Spectra (TFS) reported by the authors. TFS-based artificial neural network (TFS/ANN) and support vector machine (TFS/SVM) were employed and evaluated for their classification and prediction abilities. It was concluded that the TFS/SVM works better than TFS/ANN in both the training and the prediction.
Fast best-effort pattern matching in large attributed graphs

We focus on large graphs where nodes have attributes, such as a social network where the nodes are labelled with each person's job title. In such a setting, we want to find subgraphs that match a user query pattern. For example, a "star" query would be, "find a CEO who has strong interactions with a Manager, a Lawyer,and an Accountant, or another structure as close to that as possible". Similarly, a "loop" query could help spot a money laundering ring. Traditional SQL-based methods, as well as more recent graph indexing methods, will return no answer when an exact match does not exist. This is the first main feature of our method. It can find exact-, as well as near-matches, and it will present them to the user in our proposed "goodness" order. For example, our method tolerates indirect paths between, say, the "CEO" and the "Accountant" of the above sample query, when direct paths don't exist. Its second feature is scalability. In general, if the query has nq nodes and the data graph has n nodes, the problem needs polynomial time complexity O(n n q), which is prohibitive. Our G-Ray ("Graph X-Ray") method finds high-quality subgraphs in time linear on the size of the data graph. Experimental results on the DLBP author-publication graph (with 356K nodes and 1.9M edges) illustrate both the effectiveness and scalability of our approach. The results agree with our intuition, and the speed is excellent. It takes 4 seconds on average fora 4-node query on the DBLP graph.
Nuclear F-actin: a functional component of baculovirus-infected lepidopteran cells?

Cellular functions known to involve actin are thought to occur in the cytoplasm. Even though actin has frequently been found in the nucleus, systems well-suited for studying the function of such nuclear actin are rare. We observed filamentous (F) actin within nuclei of IPLB-Sf-21 cells infected with Autographa californica M nuclear polyhedrosis virus (AcMNPV) as detected by laser confocal microscopy using fluorescent phalloidin probes. The nuclear F-actin co-localized with the major capsid protein of the virus during normal infections. Cytochalasin D, known to interfere with nucleocapsid morphogenesis, uncoupled the apparent co-localization of F-actin and the capsid protein and inhibited infectious progeny production. Inhibition was reversible throughout infection (even in the presence of a protein synthesis inhibitor) and the nuclear co-localization of F-actin and the capsid protein was re-established upon removal of the drug. These observations suggest that nuclear Factin plays a role in virus replication, and that AcMNPV-infected cells may constitute a useful system in which to expand our understanding of nuclear actin transport and function.
The black-legged kittiwake preen gland—an overlooked organ for depuration of fat-soluble contaminants?

Most birds preen their feathers with an oily excrete from the uropygial (preen) gland. This oily excrete contains persistent organic pollutants (POPs), which make the preen gland a potential route of depuration of POPs in birds. Black-legged kittiwakes (Rissa tridactyla) were studied during two periods of high energy demand: incubation and chick-rearing. A rather high concentration of POPs in preen gland tissue indicates that the preen gland secrete is an excretory pathway for POPs in kittiwakes. The similarity in the POP profile detected in this study of liver, preen gland and feathers suggests that POPs found in the feathers are excreted through the preen gland. The finding also indicates that excretion of POPs through the preen gland is compound unspecific. This qualitative study should be followed up by a new quantitative study to determine the importance of excretion of POPs through the preen gland.
A Self-Mixing Laser Sensor Design With an Extended Kalman Filter for Optimal Online Structural Analysis and Damping Evaluation

We have developed a new algorithm based on the extended Kalman filter in order to improve the resolution of a self-mixing (SM) optical displacement sensor. This noncontact sensor, which provides vibration measurement with a very high accuracy, can be used for online quality control, for example, measuring the damping of excited mechanical structures. This SM sensor subject to weak feedback has been tested in comparison with a commercial vibrometer in order to measure the frequency response function (FRF) of a plate with a passive damping to be characterized, and to show the efficiency of a damping treatment.
Soft Maps Between Surfaces

The problem of mapping between two non‐isometric surfaces admits ambiguities on both local and global scales. For instance, symmetries can make it possible for multiple maps to be equally acceptable, and stretching, slippage, and compression introduce difficulties deciding exactly where each point should go. Since most algorithms for point‐to‐point or even sparse mapping struggle to resolve these ambiguities, in this paper we introduce soft maps, a probabilistic relaxation of point‐to‐point correspondence that explicitly incorporates ambiguities in the mapping process. In addition to explaining a continuous theory of soft maps, we show how they can be represented using probability matrices and computed for given pairs of surfaces through a convex optimization explicitly trading off between continuity, conformity to geometric descriptors, and spread. Given that our correspondences are encoded in matrix form, we also illustrate how low‐rank approximation and other linear algebraic tools can be used to analyze, simplify, and represent both individual and collections of soft maps.
Hyper-minimisation of deterministic weighted finite automata over semifields

Hyper-minimisation of deterministic nite automata is a recently introduced state reduction technique that allows a nite change in the recognised language. A generalisation of this lossy compression method to the weighted setting over semi elds is presented, which allows the recognised formal power series to di er for nitely many input strings. First, the structure of hyper-minimal deterministic weighted nite automata is characterised in a similar way as in classical weighted minimisation and unweighted hyper-minimisation. Second, an e cient minimisation algorithm, which runs in time O(n logn), is derived from this characterisation.
VAMP3, syntaxin-13 and SNAP23 are involved in secretion of matrix metalloproteinases, degradation of the extracellular matrix and cell invasion

Cellular remodeling of the extracellular matrix (ECM), an essential component of many physiological and pathological processes, is dependent on the trafficking and secretion of matrix metalloproteinases (MMPs). Soluble NSF attachment protein receptor (SNARE)-mediated membrane traffic has documented roles in cell-ECM interactions and the present study specifically examines SNARE function in the trafficking of MMPs during ECM degradation. Using the invasive human fibrosarcoma cell line HT-1080, we demonstrate that a plasma membrane SNARE, SNAP23, and an endosomal v-SNARE, VAMP3 (also known as cellubrevin), partly colocalize with MMP2 and MMP9, and that inhibition of these SNAREs using dominant-negative SNARE mutants impaired secretion of the MMPs. Inhibition of VAMP3, SNAP23 or syntaxin-13 using dominant-negative SNARES, RNA interference or tetanus toxin impaired trafficking of membrane type 1 MMP to the cell surface. Consistent with these observations, we found that blocking the function of these SNAREs reduced the ability of HT-1080 cells to degrade a gelatin substrate in situ and impaired invasion of HT-1080 cells in vitro. The results reveal the importance of VAMP3, syntaxin-13 and SNAP23 in the trafficking of MMP during degradation of ECM substrates and subsequent cellular invasion.