Miracle-dz/UniFAR-FTUs huggingface dataset:
Adherence as a Predictor of Sexual Behaviors in People Living with HIV/AIDS during the First Year of Antiretroviral Therapy in Rural Cameroon: Data from Stratall ANRS 12110/ESTHER Trial

Objective This study aims to investigate the time pattern of inconsistence condom use (ICU) during the first year of antiretroviral therapy (ART) and its relationship with treatment adherence in naïve HIV-infected adult patients. Methods Data collection was nested within a longitudinal trial on HIV treatment. ICU was defined as reporting to have “never”, “sometimes” or “nearly always” used condoms with one’s main or casual partner(s) - either HIV-negative or of unknown HIV status in the three previous months. Adherence was defined as taking 100% of their ART prescribed doses in the 4 days before the visit and “not having interrupted treatment”, even once, for more than two consecutive days during the 4 previous weeks. Mixed logistic regression was used to study the relationship between adherence and ICU. Results Among the 459 patients enrolled, 212 (46%) during 334 visits reported to have had sexual intercourse at least once with their partner(s) – either HIV-negative or of unknown HIV status- during the first 12 months of ART. The proportion of ICU was 76%, 50% and 59% at month 0 (M0), month 6 (M6) and month 12 (M12), while 60% and 66% of patients were ART-adherent at M6 and M12, respectively. After adjustment for the frequency of sexual activity, type of sexual partner(s), perceived social class and desire for a child, patients adherent to ART were less likely to report ICU when compared with baseline (AOR [95% CI]: 0.38 [0.19–0.76]; P = 0.006). Conclusions Adherence to ART is associated with a lower risk of ICU but this result needs to be interpreted carefully. As adherence behaviors are not only determined by problems with the healthcare systems but also by social barriers encountered by patients in their daily life, counseling should not only be ART adherence-centered but also patient-centered, including sexual risk minimization and psychosocial support.
Cascaded Control Allocation in Overactuated Aircraft via Mixed Optimization

This paper presents the principle and application of a class of cascaded optimal control allocation approach for overactuated aircraft. The design is based on mixed optimized targets consisting of two parts: the tracking error of virtual control and the effectors’ error related to the preferred control vector. At each step, the unconstrained least square problem is solved through simple calculation to obtain the optimal solution. If any optimal commands exceed the position limit or rate limit, a scheme is designed to choose at most one effector set to its limit and the remaining virtual controls are redistributed among other unsaturated control effectors. The Simulation based on the ADMIRE model shows that the effectiveness of the proposed approach comparing with Cascaded Generalized Inverse and Fixed-Point Method.
Mechanisms of Diastolic Dysfunction in Cardiovascular Disease Vorticity is a marker of right ventricular diastolic dysfunction

X Brett E. Fenster, James Browning, Joyce D. Schroeder, Michal Schafer, Chris A. Podgorski, Jamie Smyser, Lori J. Silveira, J. Kern Buckner, and Jean R. Hertzberg Division of Cardiology, National Jewish Health, Denver, Colorado; Department of Mechanical Engineering, University of Colorado, Boulder, Colorado; Division of Radiology, National Jewish Health, Denver, Colorado; and Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, Colorado
A novel method for quantifying scanner instability in fMRI

A method was developed to quantify the effect of scanner instability on functional MRI data by comparing the instability noise to endogenous noise present when scanning a human. The instability noise was computed from agar phantom data collected with two flip angles, allowing for a separation of the instability from the background noise. This method was used on human data collected at four 3 T scanners, allowing the physiological noise level to be extracted from the data. In a “well‐operating” scanner, the instability noise is generally less than 10% of physiological noise in white matter and only about 2% of physiological noise in cortex. This indicates that instability in a well‐operating scanner adds very little noise to functional MRI results. This new method allows researchers to make informed decisions about the maximum instability level a scanner can have before it is taken off line for maintenance or rejected from a multisite consortium. This method also provides information about the background noise, which is generally larger in magnitude than the instability noise. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.
Path Loss Models Based on Stochastic Rays

Two-dimensional percolation lattices are applied to describe wireless propagation environment, and stochastic rays are employed to model the trajectories of radio waves. First, the authors derive the probability that a stochastic ray undergoes certain number of collisions at a specific spatial location. Three classes of stochastic rays with different constraint conditions are considered: stochastic rays of random walks and generic stochastic rays with two different anomalous levels. Subsequently, the authors obtain the closed-form formulation of mean received power of radio waves under non-line-of-sight conditions for each class of stochastic ray. Specifically, the determination of model parameters and the effects of lattice structures on the path loss are investigated. The theoretical results are validated by comparison with the experimental data.
Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Errors

Opportunistic spectrum access (OSA) that allows secondary users to independently search for and exploit instantaneous spectrum availability is considered. The design objective is to maximize the throughput of a secondary user while limiting the probability of colliding with primary users. Integrated in the joint design are three basic components: a spectrum sensor that identifies spectrum opportunities, a sensing strategy that determines which channels in the spectrum to sense, and an access strategy that decides whether to access based on potentially erroneous sensing outcomes. This joint design is formulated as a constrained partially observable Markov decision process (POMDP), and a separation principle is established. The separation principle reveals the optimality of myopic policies for the design of the spectrum sensor and the access strategy, leading to closed-form optimal solutions. Furthermore, it decouples the design of the sensing strategy from that of the spectrum sensor and the access strategy, and reduces the constrained POMDP to an unconstrained one. Numerical examples are provided to study the tradeoff between sensing time and transmission time, the interaction between the physical layer spectrum sensor and the MAC layer sensing and access strategies, and the robustness of the ensuing design to model mismatch.
A Better Good-Turing Estimator for Sequence Probabilities

We consider the problem of estimating the probability of an observed string drawn i.i. d. from an unknown distribution. The key feature of our study is that the length of the observed string is assumed to be of the same order as the size of the underlying alphabet. In this setting, many letters are unseen and the empirical distribution tends to overestimate the probability of the observed letters. To overcome this problem, the traditional approach to probability estimation is to use the classical Good-Turing estimator. We introduce a natural scaling model and use it to show that the Good-Turing sequence probability estimator is not consistent. We then introduce a novel sequence probability estimator that is indeed consistent under the natural scaling model.
mRNA Expression Levels in Failing Human Hearts Predict Cellular Electrophysiological Remodeling: A Population-Based Simulation Study

Differences in mRNA expression levels have been observed in failing versus non-failing human hearts for several membrane channel proteins and accessory subunits. These differences may play a causal role in electrophysiological changes observed in human heart failure and atrial fibrillation, such as action potential (AP) prolongation, increased AP triangulation, decreased intracellular calcium transient (CaT) magnitude and decreased CaT triangulation. Our goal is to investigate whether the information contained in mRNA measurements can be used to predict cardiac electrophysiological remodeling in heart failure using computational modeling. Using mRNA data recently obtained from failing and non-failing human hearts, we construct failing and non-failing cell populations incorporating natural variability and up/down regulation of channel conductivities. Six biomarkers are calculated for each cell in each population, at cycle lengths between 1500 ms and 300 ms. Regression analysis is performed to determine which ion channels drive biomarker variability in failing versus non-failing cardiomyocytes. Our models suggest that reported mRNA expression changes are consistent with AP prolongation, increased AP triangulation, increased CaT duration, decreased CaT triangulation and amplitude, and increased delay between AP and CaT upstrokes in the failing population. Regression analysis reveals that changes in AP biomarkers are driven primarily by reduction in I, and changes in CaT biomarkers are driven predominantly by reduction in I and SERCA. In particular, the role of I is pacing rate dependent. Additionally, alternans developed at fast pacing rates for both failing and non-failing cardiomyocytes, but the underlying mechanisms are different in control and heart failure.
Vibration Weld Strength of Polypropylene-based Nanocomposites

Nanocomposites present interesting mechanical properties owing to the high surface-area-to-volume ratio of the dispersed reinforcement. The vibration weldability of these new composites has not yet been reported in the literature. This research examines the effect of the vibration-welding parameters of weld pressure and target meltdown on the strength of butt welds made from two polypropylene-based nanocomposites. The nanocomposites were made by melt-compounding two levels of organoclay with polypropylene. The results of unreinforced polypropylene are shown for reference. All welded assemblies were assessed by tensile testing. The results show that 3-6% of organoclay reinforcement causes a significant decrease in weld strength.
Differential and long‐lasting alterations of high‐voltage activated calcium currents in CA1 and dentate granule neurons after status epilepticus

The effects on high‐voltage activated (HVA) calcium currents were examined in hippocampal CA1 cells and dentate gyrus (DG) granule neurons, 2 days (short‐term; ST) and 2–3 months (long‐term; LT) after electrically induced, limbic electrographic and behavioural seizures in rats. Whole‐cell voltage‐clamp recordings in dissociated CA1 neurons of LT rats showed a decrease in the sustained HVA calcium current amplitude and a faster inactivation of the current both in rats that had experienced a status epilepticus (post‐SE rats) and those in which the stimulation did not lead to SE (non‐SE rats). In CA1 neurons of LT–SE rats this resulted in a reduced Ca2+ entry through the HVA channels. Perforated‐patch voltage‐clamp recordings in dissociated DG granule neurons of LT–SE rats showed an increased sustained HVA current amplitude compared to controls and non‐SE rats, leading to an increased Ca2+ entry via HVA calcium channels. Two days after SE, we observed an increased Ca2+ entry for a defined depolarization, although the change in HVA current amplitude and inactivation rate did not reach significance. We also observed a decrease in calbindin‐D28k staining in DG post‐SE neurons, but this change was not associated with a change in HVA current inactivation. The opposite changes in neuronal Ca2+ entry through HVA channels in CA1 vs. DG cells depended strongly on whether rats had experienced SE and later spontaneous seizure activity. These changes are likely to contribute to regionally different effects on local network excitability.
A layered classification for malicious function identification and malware detection

Millions of new malicious programs are produced by the mature industry of malware production. These programs have tremendous challenges on the signature‐based antivirus products. Machine learning techniques are applicable for detecting unknown malicious programs without knowing their signatures. In this paper, a layered classification method is developed to detect malwares with a two‐layer framework. The low‐level‐classifier is employed to identify whether the programs perform any malicious functions according to the API‐calls of the programs; the up‐level‐classifier is applied to detect malwares according to the function identification. A hybrid structure called Type‐Function, constituting of the classification results of low‐level‐classifier and up‐level‐classifier, is proposed to describe the malware. This method is compared with Naive Bayes, decision tree, and boosting using a comprehensive test dataset containing 16,135 malwares and 1800 benign programs. The experiments demonstrate that our method outperforms other algorithms in terms of detection accuracy. Moreover, the Type‐Function structure is proved as an unprejudiced and effective method for malware description. Copyright © 2011 John Wiley & Sons, Ltd.
The effect of substrate roughness characteristics on wettability and on the mechanical properties of adhesive j oints loaded at high strain rates

This study describes the influence of the surface characteristics of a substrate on the joint strength in the case of a metal/polymer/metal sandwich. The objective was to evaluate the impact resistance of various adhesively bonded joints subjected to shear loading. The first part is devoted to the evaluation of the shear strength of adhesive joints at high strain rates using the Hopkinson pressure bar technique. The macroscopic approach enables us to compare different adhesive joints. The second part deals with the characterization of the substrate surface using roughness and wettability measurements, and this investigation shows the influence of substrate roughness characteristics on wettability and on the mechanical properties of adhesive joints. The micro-geometry of the substrate surface needs to be taken into account if one is interested in the influence of the roughness. We have tested adhesive joints that were made using different sheets under high strain rates. The surface of sheet was either untr.
Connectives in Quantum and other Cumulative Logics

Cumulative logics are studied in an abstract setting, i.e., without connectives, very much in the spirit of Makinson's early work. A powerful representation theorem characterizes those logics by choice functions that satisfy a weakening of Sen's property alpha, in the spirit of the author's "Nonmonotonic Logics and Semantics" (JLC). The representation results obtained are surprisingly smooth: in the completeness part the choice function may be defined on any set of worlds, not only definable sets and no definability-preservation property is required in the soundness part. For abstract cumulative logics, proper conjunction and negation may be defined. Contrary to the situation studied in "Nonmonotonic Logics and Semantics" no proper disjunction seems to be definable in general. The cumulative relations of KLM that satisfy some weakening of the consistency preservation property all define cumulative logics with a proper negation. Quantum Logics, as defined by Engesser and Gabbay are such cumulative logics but the negation defined by orthogonal complement does not provide a proper negation.
Advantages and challenges of integration: opportunities for integrating early childhood development and nutrition programming

A growing body of evidence supports the notion that integrated programs addressing nutrition and stimulation provide stronger impacts on nutritional and developmental outcomes than either intervention alone. When translating evidence into practice, several advantages and challenges for integration can be noted. Combined interventions may be more efficient than separate interventions, because they are intended for the same population and make use of the same facilities, transportation, and client contacts. In addition, for families, particularly for those most at risk, combined interventions can also lead to increased access to services. However, in order for integrated nutrition and early childhood development interventions to be successful, a variety of challenges must be addressed. These include workload of staff and supervisors, communication and coordination among different ministries and among staff in different sectors, and common language and measurement. It must be acknowledged at both the national and community levels that comprehensive, integrated care addressing both the physical and developmental needs of the child is key to promoting optimal health, growth, and development for children.
With raised eyebrows or the eyebrows raised ? A Neural Network Approach to Grammar Checking for Definiteness

In this paper, we use a feature model of the semantics of plural determiners to present an approach to grammar checking for definiteness. Using neural network techniques, a semantics -- morphological category mapping was learned. We then applied a textual encoding technique to the 125 occurences of the relevant category in a 10 000 word narrative text and learned a surface -- semantics mapping. By applying the learned generation function to the newly generated representations, we achieved a correct category assignment in many cases (87 %). These results are considerably better than a direct surface categorization approach (54 %), with a baseline (always guessing the dominant category) of 60 %. It is discussed, how these results could be used in multilingual NLP applications.
Personalized influence maximization on social networks

In this paper, we study a new problem on social network influence maximization. The problem is defined as, given a target user $w$, finding the top-k most influential nodes for the user. Different from existing influence maximization works which aim to find a small subset of nodes to maximize the spread of influence over the entire network (i.e., global optima), our problem aims to find a small subset of nodes which can maximize the influence spread to a given target user (i.e., local optima). The solution is critical for personalized services on social networks, where fully understanding of each specific user is essential. Although some global influence maximization models can be narrowed down as the solution, these methods often bias to the target node itself. To this end, in this paper we present a local influence maximization solution. We first provide a random function, with low variance guarantee, to randomly simulate the objective function of local influence maximization. Then, we present efficient algorithms with approximation guarantee. For online social network applications, we also present a scalable approximate algorithm by exploring the local cascade structure of the target user. We test the proposed algorithms on several real-world social networks. Experimental results validate the performance of the proposed algorithms.
The PCR Revolution: The road from qualitative to quantitative assay: What is next?

INTRODUCTION 110 PRE-PCR STEPS 111 INVENTIONS MADE IN “ABSOLUTE” QUANTIFICATION ASSAYS 112 HOW THE RELATIVE QUANTIFICATION STRATEGY CHANGED 114 WHAT ABOUT PCR EFFICIENCY? 115 ASSAY VARIANCE AND HOW TO PERFORM A PROPER NORMALIZATION 116 EXPRESSION PROFILING, qPCR BIOINFORMATICS, AND STATISTICAL ANALYSIS 117 WHAT IS NEXT IN REAL-TIME PCR? 118 GENE EXPRESSION AND MORE – THE SYSTEM BIOLOGY IDEA 120 CONCLUSION 124 REFERENCES 124
Dach2-Hdac9 signaling regulates reinnervation of muscle endplates

Muscle denervation resulting from injury, disease or aging results in impaired motor function. Restoring neuromuscular communication requires axonal regrowth and endplate reinnervation. Muscle activity inhibits the reinnervation of denervated muscle. The mechanism by which muscle activity regulates muscle reinnervation is poorly understood. Dach2 and Hdac9 are activity-regulated transcriptional co-repressors that are highly expressed in innervated muscle and suppressed following muscle denervation. Dach2 and Hdac9 control the expression of endplate-associated genes such as those encoding nicotinic acetylcholine receptors (nAChRs). Here we tested the idea that Dach2 and Hdac9 mediate the effects of muscle activity on muscle reinnervation. Dach2 and Hdac9 were found to act in a collaborative fashion to inhibit reinnervation of denervated mouse skeletal muscle and appear to act, at least in part, by inhibiting denervation-dependent induction of Myog and Gdf5 gene expression. Although Dach2 and Hdac9 inhibit Myog and Gdf5 mRNA expression, Myog does not regulate Gdf5 transcription. Thus, Myog and Gdf5 appear to stimulate muscle reinnervation through parallel pathways. These studies suggest that manipulating the Dach2-Hdac9 signaling system, and Gdf5 in particular, might be a good approach for enhancing motor function in instances where neuromuscular communication has been disrupted. Summary: Dach2 and Hdac9 work in a collaborative fashion to inhibit the reinnervation of denervated mouse skeletal muscle by suppressing the denervation-dependent expression of Myog and Gdf5.
Learning Optimal Augmented Bayes Networks

Naive Bayes is a simple Bayesian classifier with strong independence assumptions among the attributes. This classifier, desipte its strong independence assumptions, often performs well in practice. It is believed that relaxing the independence assumptions of a naive Bayes classifier may improve the classification accuracy of the resulting structure. While finding an optimal unconstrained Bayesian Network (for most any reasonable scoring measure) is an NP-hard problem, it is possible to learn in polynomial time optimal networks obeying various structural restrictions. Several authors have examined the possibilities of adding augmenting arcs between attributes of a Naive Bayes classifier. Friedman, Geiger and Goldszmidt define the TAN structure in which the augmenting arcs form a tree on the attributes, and present a polynomial time algorithm that learns an optimal TAN with respect to MDL score. Keogh and Pazzani define Augmented Bayes Networks in which the augmenting arcs form a forest on the attributes (a collection of trees, hence a relaxation of the stuctural restriction of TAN), and present heuristic search methods for learning good, though not optimal, augmenting arc sets. The authors, however, evaluate the learned structure only in terms of observed misclassification error and not against a scoring metric, such as MDL. In this paper, we present a simple, polynomial time greedy algorithm for learning an optimal Augmented Bayes Network with respect to MDL score.