Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical efficacy and safety of evolocumab for low-density lipoprotein cholesterol reduction

Multiple categories of medications have been developed to manage lipid profiles and reduce the risk of cardiovascular events in patients with heart disease. However, currently marketed medications have not solved the problems associated with preventing and treating cardiovascular diseases completely. A substantial population of patients cannot take advantage of statin therapy due to statin intolerance, heart failure, or kidney hemodialysis, suggesting a need for additional effective agents to reduce low-density lipoprotein cholesterol (LDL-C) levels. Proprotein convertase subtilisin/kexin type 9 (PCSK9) was discovered in 2003 and subsequently emerged as a novel target for LDL-C-lowering therapy. Evolocumab is a fully human monoclonal immunoglobulin G2 (IgG2) directed against human PCSK9. By inactivating PCSK9, evolocumab upregulates LDL receptors causing increased catabolism of LDL-C and the consequent reduction of LDL-C levels in blood. Overall, evolocumab has had notable efficacy, with LDL-C reduction ranging from 53% to 75% in monotherapy and combination therapies, and is associated with minor adverse effects. However, studies regarding the ability of evolocumab to reduce mortality as well as long-term safety concerns are limited. The fact that the drug was introduced at a cost much higher than the existing medications and shows a low incremental mortality benefit suggests that many payers will consider evolocumab to have an unfavorable cost–benefit ratio.
Natural Scene Classification Based on Integrated Topic Simplex

We present a novel model named Integrated Latent Topic Model (ILTM), to learn and recognize natural scene category. Unlike previous work, which considered the discrepancy and common property separately among all categories, Our approach combines universal topics from all categories with specific topics from each category. As a result, the model is implemented to produce a few but specific topics and more generic topics among categories, and each category is represented in a different topics simplex, which correlates well with human scene understanding. We investigate the classification performance with variable scene category tasks. The experiments have shown our model outperforms latent-space methods with less training data.
Geographical Job Scheduling in Data Centers with Heterogeneous Demands and Servers

The fast proliferation of cloud computing promotes the rapid development of large-scale commercial data centers. Tens or even hundreds of geographically distributed data centers have been deployed for better reliability and quality of services. This brings huge energy consumption for data centers. Previous research has proved that the geographical load balancing technique can achieve significant energy cost savings for geographically distributed data centers. However, existing methods for geographical load balancing often assume data centers with homogeneous servers, and workloads with single-dimension or uniform resource demands. This is an over-simplification in reality, especially when modern data centers are typically constructed from a variety of server classes. In this paper, we systematically study the problem of job scheduling for geographically distributed data centers to embrace the heterogeneity of underlying platforms and workloads. We develop a novel distributed algorithm to solve the problem efficiently based on the alternating direction method of multipliers. Extensive evaluations based on real-life data center topology, traffic traces, and electricity price data show high efficiency and efficacy of our method.
A Phase-Shifted Control for Wireless Power Transfer System by Using Dual Excitation Units

Wireless power transfer (WPT) technology can provide intelligent robots with a flexible, robust, and safe power supply, especially in very harsh environments including high humidity and high temperature. To meet increasing power requirement for robotic applications, this paper proposes a novel method to increase system power transfer capability without increasing voltage and current stress, realized by using dual excitation units at the primary side. On this basis, this paper proposes a phase-shifted control method for output power regulation which can keep efficiency high. At the same time, the system is proved to have a better output robust characteristic by analysis under the condition of parameter variation. Finally, experimental results show the proposed dual excitation units (DEU)-WPT system can increase output power by at least three times compared to classical WPT system, and the efficiency is improved by 9%.
Cognitive users with useful vacations

A cognitive user (CU) operates as a secondary user of a cognitive channel. Before transmission CU samples the channel until it estimates that the channel can be accessed successfully. When CU transmits a packet, it may nevertheless be unsuccessful because its estimate was wrong; CU knows about its failure and stops the ongoing transmission sometime before the transmission ends. Then CU restarts sensing the channel. In this paper we derive the first and second moments of the effective transmission time of a packet which experiences additional delay which is similar to a “vacation time” in queues, though it differs from conventional vacation time models because such “sampling vacations” are beneficial to the future transmission. Using an on-off model of channel occupancy, our analysis shows that the duration of the sensing and the packet's length can both be tuned to optimize for throughput, energy, interference, quality of service (QoS) and average response time of CUs.
Binary Feature Classification for Word Disambiguation in Statistical Machine Translation

In statistical machine translation, there are many source words that can present different translations. The usual approach to disambiguating these words is to use a target language model. These models are based on local phenomena and in many cases are not capable of properly translating some of these words. To deal with this problem, a new approach is proposed that is based on the so-called naive Bayesian classifier. After a process of automatic selection of ambiguous words, an adequate binary feature set that maximizes the information gain is chosen. A Bayesian classifier can be used to choose the most adequate translation of a word, using this feature set. Experimental results on a parallel corpus (approximately 650,000 Spanish-Catalan sentence pairs) show the benefits that the proposed method can achieve.
The Interplay between Feature-Saliency and Feedback Information in Visual Category Learning Tasks

What is the role of feedback information in different visual category learning (VCL) scenarios? To address this question we tested participants' performance in VCL tasks in which stimuli varied in three feature dimensions, one of which was relevant for the task and the other two were irrelevant. The relevant feature could be identified based on trial-by-trial feedback. In one condition the task relevant and irrelevant features were highly-salient. In the second condition all features had low-visual-saliency. Feedback information was also manipulated: In the high-information condition the task relevant feature could be identified by the information provided in each trial whereas in the mid-information condition the feedback was ambiguous and information from several learning trials was required in order to confidently identify the relevant feature. Surprisingly, our data shows that mid- and high-information feedback are similarly effective in high-saliency VCL tasks. In contrast, in low-saliency VCL tasks, mid-information feedback impairs learning. We suggest that VCL can be done effectively either when feedback is ambiguous or in low-saliency conditions, but not in scenarios when both challenges occur concurrently.
New strategy for the synthesis of chemically modified RNA constructs exemplified by hairpin and hammerhead ribozymes

The CuAAC reaction (click chemistry) has been used in conjunction with solid-phase synthesis to produce catalytically active hairpin ribozymes around 100 nucleotides in length. Cross-strand ligation through neighboring nucleobases was successful in covalently linking presynthesized RNA strands with high efficiency (trans-ligation). In an alternative strategy, intrastrand click ligation was employed to produce a functional hammerhead ribozyme containing a novel nucleic acid backbone mimic at the catalytic site (cis-ligation). The ability to synthesize long RNA strands by a combination of solid-phase synthesis and click ligation is an important addition to RNA chemistry. It is compatible with a plethora of site-specific modifications and is applicable to the synthesis of many biologically important RNA molecules.
Compact Mathematical Programs For DEC-MDPs With Structured Agent Interactions

To deal with the prohibitive complexity of calculating policies in Decentralized MDPs, researchers have proposed models that exploit structured agent interactions. Settings where most agent actions are independent except for few actions that affect the transitions and/or rewards of other agents can be modeled using Event-Driven Interactions with Complex Rewards (EDI-CR). Finding the optimal joint policy can be formulated as an optimization problem. However, existing formulations are too verbose and/or lack optimality guarantees. We propose a compact Mixed Integer Linear Program formulation of EDI-CR instances. The key insight is that most action sequences of a group of agents have the same effect on a given agent. This allows us to treat these sequences similarly and use fewer variables. Experiments show that our formulation is more compact and leads to faster solution times and better solutions than existing formulations.
A Hybrid Model for Enhancing Lexical Statistical Machine Translation (SMT)

The interest in statistical machine translation systems increases currently due to political and social events in the world. A proposed Statistical Machine Translation (SMT) based model that can be used to translate a sentence from the source Language (English) to the target language (Arabic) automatically through efficiently incorporating different statistical and Natural Language Processing (NLP) models such as language model, alignment model, phrase based model, reordering model, and translation model. These models are combined to enhance the performance of statistical machine translation (SMT). Many implementation tools have been used in this work such as Moses, Gizaa++, IRSTLM, KenLM, and BLEU. Based on the implementation, evaluation of this model, and comparing the generated translation with other implemented machine translation systems like Google Translate, it was proved that this proposed model has enhanced the results of the statistical machine translation, and forms a reliable and efficient model in this field of research.
Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories

We propose a Markov process model for spike-frequency adapting neural ensembles that synthesizes existing mean-adaptation approaches, population density methods, and inhomogeneous renewal theory, resulting in a unified and tractable framework that goes beyond renewal and mean-adaptation theories by accounting for correlations between subsequent interspike intervals. A method for efficiently generating inhomogeneous realizations of the proposed Markov process is given, numerical methods for solving the population equation are presented, and an expression for the first-order interspike interval correlation is derived. Further, we show that the full five-dimensional master equation for a conductance-based integrate-and-fire neuron with spike-frequency adaptation and a relative refractory mechanism driven by Poisson spike trains can be reduced to a two-dimensional generalization of the proposed Markov process by an adiabatic elimination of fast variables. For static and dynamic stimulation, negative serial interspike interval correlations and transient population responses, respectively, of Monte Carlo simulations of the full five-dimensional system can be accurately described by the proposed two-dimensional Markov process.
Cell Proliferation Signal Strength To Differentially Modulate T Surfactant Protein A Integrates Activation

Pulmonary surfactant lipoproteins lower the surface tension at the alveolar–airway interface of the lung and participate in host defense. Previous studies reported that surfactant protein A (SP-A) inhibits lymphocyte proliferation. We hypothesized that SP-A– mediated modulation of T cell activation depends upon the strength, duration, and type of lymphocyte activating signals. Modulation of T cell signal strength imparted by different activating agents ex vivo and in vivo in different mouse models and in vitro with human T cells shows a strong correlation between strength of signal (SoS) and functional effects of SP-A interactions. T cell proliferation is enhanced in the presence of SP-A at low SoS imparted by exogenous mitogens, speciﬁc Abs, APCs, or in homeostatic proliferation. Proliferation is inhibited at higher SoS imparted by different doses of the same T cell mitogens or indirect stimuli such as LPS. Importantly, reconstitution with exogenous SP-A into the lungs of SP-A 2 / 2 mice stimulated with a strong signal also resulted in suppression of T cell proliferation while elevating baseline proliferation in unstimulated T cells. These signal strength and SP-A–dependent effects are mediated by changes in intracellular Ca 2+ levels over time, involving extrinsic Ca 2+ -activated channels late during activation. These effects are intrinsic to the global T cell population and are manifested in vivo in naive as well as memory phenotype T cells. Thus, SP-A appears to integrate signal thresholds to control T cell proliferation. The Journal of Immunology , 2012, 188: 000–000. octylglucoside and 5 mM Tris, pH 7.4. Extracted SP-Awas then passed over a polymyxin B-agarose column to reduce endotoxin contamination. SP-A preparations had ﬁnal endotoxin concentrations of , 0.01 pg/mg SP-A as determined by the Limulus amebocyte lysate assay according to the manufacturers’ instruc-tions (QCL-1000; BioWhittaker [Lonza], Walkersville, MD). In some experiments, SP-Awas labeled according to the standard Molecular Probes protocol, and density of labeling was calculated using an extinction co- efﬁcient of 239,000 for AF647 and 72,000 for SP-A. Although no active TGF- b was detected in the SP-A preparations used for this study, we did ﬁnd varying amounts of inactive (“total”) TGF- b ranging from ∼ 1.6–2.8 pg/ m g of puriﬁed SP-A using both a bioassay (CCL64 cells) and an ELISA (R&D Systems). A CCL64 mink lung epithelial cell line with a PAI1 lu-ciferase reporter system was used to determine biological activity of TGF- b (diluted to contain , 0.4% FBS during assay) using either puriﬁed human platelet-derived or recombinant TGF- b (Peprotech or R&D Systems) as a reference standard.
The multi-parameter combination mind-evolutionary-based machine learning and its application

Based on mind-evolutionary-based machine learning (MEBML), four conceptions: degree of interaction, individual similartaxis, group similartaxis and dead band space are defined in this paper. Consequently, the multi-parameter combination mind-evolutionary-based machine learning (MPCMEBML) is established. MPCMEBML can solve efficiently the searching problem of the multi-dimension parameter space. In MDPMEML, the whole parameter space is separated and combined into several sub parameter spaces and the global billboard is endowed with the interactive function. Not every single group searches the whole parameter spaces. Information exchange will supplement the insufficient knowledge that not every group can get by itself. The new fuzzy logic controller is constructed by using MPCMEBML. Simulation results show that MPCMEBML is possessed of a strong ability of searching in the multi-dimension parameter space and the new FLC has good performance.
Adopting SAFe to Scale Agile in a Globally Distributed Organization

Large software development organizations adopting agile methods need solutions and models to help scale agile to fit their needs. During recent years, several frameworks for scaling agile have been created by consultants, including the Scaled Agile Framework (SAFe), Large-scale Scrum (LeSS) and Disciplined Agile Delivery (DAD). However, research on how these frameworks are adopted in practice is seriously lacking. In this paper we describe how Comptel, a globally distributed software development company, adopted the SAFe framework in two business lines. Based on eleven interviews we present why and how the organization adopted SAFe, and discuss related challenges and success factors. The comparison of the two adoptions showed that investing in SAFe trainings, engaging people and change agents, hiring a coach, investing in a full-time release train engineer, preparing well for the first planning event and continuously improving and customizing SAFe led to good results.
Computational aeroacoustic characterization of different orifice geometries under grazing flow conditions

This paper deals with the numerical prediction of the aeroacoustic behavior of orifices under grazing flow conditions. A hybrid computational aeroacoustics approach is adopted where the steady, incompressible, mean flow over the orifice is obtained from a RANS simulation. In a next step, the mean flow variables are used to solve the linearized Navier–Stokes equations (LNSE), using a Runge–Kutta Discontinuous Galerkin (RKDG) method. In this way, the linear interaction mechanisms between the aerodynamic and acoustic fluctuations are studied which enables an aeroacoustic characterization of the orifice. A methodology is presented involving a virtual impedance tube and two computations for each geometrical configuration: one with the presence of a mean flow and one for a quiescent medium. This allows to isolate the contribution of the mean flow to the orifice impedance. The method is verified against theoretical models and experimental data from literature, and is used to study the influence of orifice geometry variations, such as the orifice length, the plate thickness and the edge rounding, on the mean flow contribution to the impedance.
Calcium Secretion Coupling at Calyx of Held Governed by Nonuniform Channel–Vesicle Topography

Phasic transmitter release at synapses in the mammalian CNS is regulated by local [Ca2+] transients, which control the fusion of readily releasable vesicles docked at active zones (AZs) in the presynaptic membrane. The time course and amplitude of these [Ca2+] transients critically determine the time course and amplitude of the release and thus the frequency and amplitude tuning of the synaptic connection. As yet, the spatiotemporal nature of the [Ca2+] transients and the number and location of release-controlling Ca2+ channels relative to the vesicles, the “topography” of the release sites, have remained elusive. We used a time-dependent model to simulate Ca2+ influx, three-dimensional buffered Ca2+ diffusion, and the binding of Ca2+ to the release sensor. The parameters of the model were constrained by recent anatomical and biophysical data of the calyx of Held. Comparing the predictions of the model with previously measured release probabilities under a variety of experimental conditions, we inferred which release site topography is likely to operate at the calyx: At each AZ one or a few clusters of Ca2+ channels control the release of the vesicles. The distance of a vesicle to the cluster(s) varies across the multiple release sites of a single calyx (ranging from 30 to 300 nm; average ∼100 nm). Assuming this topography, vesicles in different locations are exposed to different [Ca2+] transients, with peak amplitudes ranging from 0.5 to 40 μm (half-width ∼400 μsec) during an action potential. Consequently the vesicles have different release probabilities ranging from <0.01 to 1. We demonstrate how this spatially heterogeneous release probability creates functional advantages for synaptic transmission.
Structuring uncertainty and variability in forest dynamics models - Application to coexistence of silver fir and Norway Spruce in mountain forests

Practitioners and decision-makers need information on how to conserve mixed Fir and Spruce forests in mountains. To answer this question, we modelled demographic (growth, mortality, recruitment) and allometric functions from field data for the two species. These functions have been implemented in a forest dynamics model to improve the knowledge on natural dynamics of the two species and to test for silvicultural practices. Parameter estimation was performed within a hierarchical Bayesian framework. Estimation permitted to quantify and underline species differences : Fir, the relatively shade-tolerant species compared to Spruce, was less sensitive to self-thinning, had a higher growth and a higher probability of recruitment at low light than Spruce. Models took into account observation errors and spatio-temporal process variability. Individual variability (or so-called intra-specific variability) and temporal variability (interannual and intraindividual variability) were significant and tended to equalize species fitness, inverting species hierarchy locally in space or sporadically in time. First simulations with simulator Samsara2 showed a better behavior of the model when including process variability. Variability led to patterns for stand structuration (tree heights and diameters) and to patterns for basal-area evolution with time which were much more realistic than without variability. Running simulations on a mixed Fir and Spruce stand, we observed a competitive exclusion of Spruce by Fir. Equalizing mechanisms associated to variability were not able to blur the competitive advantage of Fir on Spruce. Nevertheless, transient dynamics of the system was slow and apparent coexistence of the two species lasted for several hundred years.
The role of spoken language dialogue interaction in intelligent environments

An Intelligent Environment is a physical space that becomes augmented with computation, communication and digital content, thus transcending the limits of direct human perception. Spoken dialogue is a key factor for user-friendly human-computer interaction. This article details how to integrate Spoken Dialogue Systems into Intelligent Environments. We will outline research areas and future trends including assistive, adaptive and proactive system design, dialogue management and system-environment interaction.
Temperature dependence of mechanical power output in mammalian (rat) skeletal muscle

Force‐velocity data at different temperatures (range, 10–35 degrees C) from intact fibre bundles are analysed to determine the temperature dependence of the maximal mechanical power output of fast and slow rat muscles. At 35 degrees C, the maximal mechanical power was approximately 250 kW m(−3) (=250 microW mm(−3)) in fast (probably an underestimate) and approximately 100 kW m(−3) in slow muscle. Within the more physiological temperature range (25–35 degrees C), the temperature coefficient (Q10) of maximum power was 2–2.5. In both muscles, the maximal power at 10 degrees C was only about 3–5% of that at 35 degrees C, the decrease being particularly pronounced at temperatures below 20 degrees C (Q10 of 5–7).