Miracle-dz/UniFAR-FTUs huggingface dataset:
Multicompartmental Pharmacokinetic Model of Tenofovir Delivery by a Vaginal Gel

Background Trials of a vaginal Tenofovir gel for pre-exposure prophylaxis (PrEP) for HIV have given conflicting results. Knowledge of concentrations of Tenofovir and its active form Tenofovir diphosphate, at putative sites of anti-HIV functioning, is central to understanding trial outcomes and design of products and dosage regimens. Topical Tenofovir delivery to the vaginal environment is complex, multivariate and non-linear; determinants relate to drug, vehicle, dosage regimen, and environment. Experimental PK methods cannot yield mechanistic understanding of this process, and have uncontrolled variability in drug sampling. Mechanistic modeling of the process could help delineate its determinants, and be a tool in design and interpretation of products and trials. Methods and Findings We created a four-compartment mass transport model for Tenofovir delivery by a gel: gel, epithelium, stroma, blood. Transport was diffusion-driven in vaginal compartments; blood concentration was time-varying but homogeneous. Parameters for the model derived from in vitro and in vivo PK data, to which model predictions gave good agreement. Steep concentration gradients occurred in stroma ≤8 hours after gel release. Increasing epithelial thickness delayed initial TFV delivery to stroma and its decline: tmax increased but AUC at 24 hours was not significantly altered. At 24 and 48 hours, stromal concentrations were 6.3% and 0.2% of Cmax. Concentrations in simulated biopsies overestimated stromal concentrations, as much as ∼5X, depending upon time of sampling, biopsy thickness and epithelial thickness. Conclusions There was reasonably good agreement of model predictions with clinical PK data. Conversion of TFV to TFV-DP was not included, but PK data suggest a linear relationship between them. Thus contrasts predicted by this model can inform design of gels and dosage regimens in clinical trials, and interpretation of PK data. This mass transport based approach can be extended to TFV conversion to TFV-DP, and to other drugs and dosage forms.
Monetary Union and the Assignment Problem

Proposals for the creation of an independent central bank in a European Monetary Union do not always take into account the interactions between monetary and fiscal policy. This analysis describes a system of two similar economies forming either a joint fiscal and monetary union or a simple monetary union. Policy authorities aim to achieve target values for inflation and the budget balance. If the monetary and fiscal authorities pursue their targets independently of each other, there is a risk that unstable interactions will be set up. The results indicate a hazard of the separation of monetary and fiscal management. Copyright 1995 by The editors of the Scandinavian Journal of Economics.
Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity

Semantic Textual Similarity (STS) is a foundational NLP task and can be used in a wide range of tasks. To determine the STS of two texts, hundreds of different STS systems exist, however, for an NLP system designer, it is hard to decide which system is the best one. To answer this question, an intrinsic evaluation of the STS systems is conducted by comparing the output of the system to human judgments on semantic similarity. The comparison is usually done using Pearson correlation. In this work, we show that relying on intrinsic evaluations with Pearson correlation can be misleading. In three common STS based tasks we could observe that the Pearson correlation was especially ill-suited to detect the best STS system for the task and other evaluation measures were much better suited. In this work we define how the validity of an intrinsic evaluation can be assessed and compare different intrinsic evaluation methods. Understanding of the properties of the targeted task is crucial and we propose a framework for conducting the intrinsic evaluation which takes the properties of the targeted task into account.
Lipid- and Polymer-Based Nanostructures for Cancer Theranostics

The relatively new field of nanotheranostics combines the advantages of in vivo diagnosis with the ability to administer treatment through a single nano-sized carrier, offering new opportunities for cancer diagnosis and therapy. Nanotheranostics has facilitated the development of nanomedicine through direct visualization of drug blood circulation and biodistribution. From a clinical perspective, nanotheranostics allows therapies to be administered and monitored in real time, thus decreasing the potential of under- or over-dosing and allowing for more personalized treatment regimens. Herein, we review recent development of nanotheranostics using lipid- and polymer-based formulations, with a particular focus on their applications in cancer research. Recent advances in nanotechnology aimed to combine therapeutic molecules with imaging agents for magnetic resonance imaging, radionuclide imaging, or fluorescence imaging are discussed.
q-deformed spin foam models of quantum gravity

We numerically study Barrett–Crane models of Riemannian quantum gravity. We have extended the existing numerical techniques to handle q-deformed models and arbitrary spacetime triangulations. We present and interpret expectation values of a few selected observables for each model, including a spin–spin correlation function which gives insight into the behaviour of the models. We find the surprising result that, as the deformation parameter q goes to 1 through roots of unity, the limit is discontinuous.
Alemtuzumab: A new therapy for active relapsing–remitting multiple sclerosis

Alemtuzumab is a humanized monoclonal antibody directed against CD52 to deplete circulating T and B lymphocytes; lymphocyte depletion is followed by a distinctive pattern of T- and B-cell repopulation, changing the balance of the immune system. This review reports the efficacy and safety findings of the phase 2 CAMMS223 trial and the phase 3 CARE-MS I and II trials investigating alemtuzumab for the treatment of active relapsing–remitting MS. Alemtuzumab, administered intravenously, was shown to improve relapse rate versus subcutaneous interferon beta-1a in patients who were treatment-naive (CAMMS223 and CARE-MS I) or had relapsed on prior therapy (CARE-MS II), and to reduce sustained accumulation of disability (CAMMS223 and CARE-MS II). Important adverse events were infusion-associated reactions, serious infections and autoimmune events. A safety monitoring program allowed for early detection and management of autoimmune events. Recommendations for the monitoring of adverse events are made. Alemtuzumab’s mechanism of action, pharmacodynamics and opportunities for future research are discussed.
Determinants of Pro-Environmental Consumption: Multicountry Comparison Based upon Big Data Search

The Korean government has promoted a variety of environmental policies to revitalize pro-environmental consumption, and the government’s budget for this purpose has increased. However, there is a lack of quantitative data and analysis regarding the effects upon the pro-environmental consumption of education and changing public awareness of the environment. In addition, to improve pro-environmental consumption, the determinant and hindrance factors of pro-environmental consumption should be analyzed in advance. Accordingly, herein we suggest a pro-environmental consumption index that represents the condition of pro-environmental consumption based on big data queries and use the index to analyze determinants of and hindrances to pro-environmental consumption. To verify the reliability of the proposed indicator, we examine the correlation between the proposed indicator and Greendex, an existing survey-based indicator. In addition, we conduct an analysis of the determinants of pro-environmental consumption across 13 countries based upon the proposed indicator. The index is highest for Argentina and average for Korea. An analysis of the determinants shows that the levels of health expenditure, the ratio of the population aged over 65 years, and past orientation are signiﬁcantly negatively related to the pro-environmental consumption index, but the level of preprimary education is signiﬁcantly positively related with it. We also ﬁnd that high-GDP countries have a signiﬁcantly positive relationship between economy growth and pro-environmental consumption, but low-GDP countries do not have this relationship.
PARAMETRICS AND OPTIMIZATION USING ANSOFT HFSS

The high frequency structure simulator (HFSS) is widely recognized as the tool that brought the power of the finite element method to three-dimensional (3-D) RF and microwave design. Finite element analysis allows complicated 3-D structures such as transitions, filters, couplers and antennas to be simulated accurately by computing the underlying electromagnetic fields. OptimetricsTM is a powerful new capability in Ansoft HFSS that speeds the design process and allows users to perform parametric analysis, optimization, sensitivity analysis and other design studies from an easy-to-use interface. With this new capability, dozens of design variations can be performed quickly and effortlessly, components can be optimized to minimize any userdefined cost function and design of experiments studies can be automated to derive sensitivities and uncertainties as a function of manufacturing tolerances. Optimetrics provides integrated parametrics and optimization capabilities by exploiting the macro scripting language in the simulator. An existing feature of Ansoft HFSS is its ability to record macro commands whenever the software is run. This capability allows any simulator session to be replayed by simply rerunning the associated macro file. Modifying the macros modifies the operations that the HFSS performs and allows quantities such as geometry, materials, boundary conditions, sources and frequencies to be varied. The smart parametrics and optimization engine in Optimetrics are made possible by having a convenient interface to generate macro commands. At the start of a session, the user creates a nominal problem and defines the independent parameters to be varied. The dependent variables to be computed in a parametric analysis or the cost function to be minimized in optimization is then defined. These dependent variables and cost functions can be of any quantity capable of being computed in the simulator. Field values, S parameters, frequency response, eigenmode data, impedance and antenna metrics are available at the click of a button. The simulator performs the requested computations, providing the output in convenient table format in the case of parametric analysis or in terms of optimal design specification in the case of optimization. The need for the user to work with the macro commands has been largely eliminated. A user interface has been created that automatically and seamlessly creates HFSS macro commands for most of the operations involved in parametrics and optimization applications. In addition, only a single nominal project is needed, greatly simplifying the input requirements for the user.
Synopsis Data Structures for XML Databases: Models, Issues, and Research Perspectives

Due to the lack of efficient native XML database management systems, XML data manipulation and query evaluation may be resource-consuming, and represent a bottleneck for several computationally intensive applications. To overcome the above limitations, a possible solution consists in computing synopsis data structures from XML databases, i.e. compressed representations providing a "succinct" description of the original databases while ensuring low computational overhead and high accuracy for many XML processing tasks. Specifically, these data structures are very useful for both selectivity estimation and approximate query answering purposes. On the other hand, while synopsis data structures have been widely applied to relational as well as multidimensional data, a full usage for XML data is still lacking. Inspired by these considerations, in this paper we discuss the models and issues of synopsis data structures for XML databases, and we complete our analysis by selecting and discussing future perspectives for this research field.
Combining probability forecasts

Summary. Linear pooling is by far the most popular method for combining probability forecasts. However, any non‐trivial weighted average of two or more distinct, calibrated probability forecasts is necessarily uncalibrated and lacks sharpness. In view of this, linear pooling requires recalibration, even in the ideal case in which the individual forecasts are calibrated. Towards this end, we propose a beta‐transformed linear opinion pool for the aggregation of probability forecasts from distinct, calibrated or uncalibrated sources. The method fits an optimal non‐linearly recalibrated forecast combination, by compositing a beta transform and the traditional linear opinion pool. The technique is illustrated in a simulation example and in a case‐study on statistical and National Weather Service probability of precipitation forecasts.
p120RasGAP Is a Mediator of Rho Pathway Activation and Tumorigenicity in the DLD1 Colorectal Cancer Cell Line

KRAS is mutated in ∼40% of colorectal cancer (CRC), and there are limited effective treatments for advanced KRAS mutant CRC. Therefore, it is crucial that downstream mediators of oncogenic KRAS continue to be studied. We identified p190RhoGAP as being phosphorylated in the DLD1 CRC cell line, which expresses a heterozygous KRAS G13D allele, and not in DKO4 in which the mutant allele has been deleted by somatic recombination. We found that a ubiquitous binding partner of p190RhoGAP, p120RasGAP (RasGAP), is expressed in much lower levels in DKO4 cells compared to DLD1, and this expression is regulated by KRAS. Rescue of RasGAP expression in DKO4 rescued Rho pathway activation and partially rescued tumorigenicity in DKO4 cells, indicating that the combination of mutant KRAS and RasGAP expression is crucial to these phenotypes. We conclude that RasGAP is an important effector of mutant KRAS in CRC.
Eigenvector-based clustering using aggregated similarity matrices

Clustering of high dimensional data is often performed by applying Singular Value Decomposition (SVD) on the original data space and building clusters from the derived eigenvectors. Often no single eigenvector separates the clusters. We propose a method that combines the self-similarity matrices of the eigenvector in such a way that the concepts are well separated. We compare it with a K-Means approach on public domain data sets and discuss when and why our method outperforms the K-Means on SVD method.
Refinement Relations on Partial Specifications

Refinement Relations on Partial Specifications Shiva Nejati Master of Science Graduate Department of Computer Science University of Toronto 2003 We discuss the problem of refining partial specifications of software systems at different levels of abstraction. In general, refinement is the process of deriving an implementation from a specification and verifying the correctness of the derivation. Recently, partial specifications have been advocated for describing software systems mainly because they do not impose a commitment to all decisions made at initial stages of software development life-cycle. Using finite-state transition systems with partial transitions and propositions as our modeling formalism, we define a refinement relation that is insensitive to finite stuttering. We refer to our proposed refinement relation as stuttering refinement relation. We, then, present a logical characterization of this refinement relation and describe an algorithm for computing it.
Congressional Primary Elections

1. Why Study Congressional Primaries? 2. Primary Elections and the "Democratic Experiment". 3. The Strategic Context of Congressional Primaries. 4. Open Seat Primaries. 5. Challenger Primaries. 6. Incumbent Primaries. 7. Race, Redistricting, and Primary Elections. 8. Primary Reform.
Adaptive Sliding-Mode-Observer-Based Fault Reconstruction for Nonlinear Systems With Parametric Uncertainties

In this paper, a class of nonlinear systems with uncertain parameters is considered. A novel adaptive law is designed to identify unknown parameters under the assumption that the time derivative of some of the outputs is measurable. Then, a sliding-mode observer is proposed to estimate the system state variables. By using the inherent features of sliding-mode observers, a fault-reconstruction scheme is proposed which can be implemented online. The proposed reconstruction signal can approximate the fault signal to any required accuracy even in the presence of uncertain parameters. A simulation example for a magnetic-levitation system is given to illustrate the feasibility and effectiveness of the proposed scheme.
An X to Ka-Band fully-integrated stacked power amplifier in 45 nm CMOS SOI technology

An X to Ka-Band fully-integrated power amplifier (PA) designed with 8 stacked transistors is implemented in a 45 nm CMOS SOI technology. The stacked configuration allows the PA to deliver high output power over a wide bandwidth while each transistor is biased under low drain-source voltage. At 18 GHz, the PA under a bias supply of 9.6 V measures a saturated output power PSAT and linear output power P1dB of 27 dBm (0.5 Watt) and 24.5 dBm, respectively, with a peak power-added efficiency PAE of 11.8%. For the frequencies measured from 10 to 32 GHz, the PSAT, and P1dB are above 23.9 dBm and 18.8 dBm, respectively.
Exact Multiple-Control Toffoli Network Synthesis With SAT Techniques

Synthesis of reversible logic has become a very important research area in recent years. Applications can be found in the domain of low-power design, optical computing, and quantum computing. In the past, several approaches have been introduced that synthesize reversible networks with respect to a given function. Most of these methods only approximate a minimal network representation. In this paper, exact algorithms for the synthesis of multiple-control Toffoli networks are presented, i.e., algorithms that guarantee to find a network with the minimal number of gates. Our iterative algorithms formulate the synthesis problem as a sequence of decision problems. The decision problems are encoded as Boolean satisfiability (SAT) or SAT modulo theory (SMT) instances, respectively. As soon as one of these instances becomes satisfiable, a Toffoli network representation for the given function has been found. We show that choosing the encoding for synthesis is crucial for the resulting runtimes. Furthermore, we discuss the principal limits of the SAT and SMT approaches. To overcome these limits, we propose a method using problem-specific knowledge during synthesis. In addition, better embeddings to make irreversible functions reversible are considered. For the resulting synthesis problems, an improvement is presented that reduces the overall runtime by automatically setting the constant inputs to their optimal values. Experimental results on a large set of benchmarks demonstrate the differences between three exact synthesis algorithms. In addition, a comparison with the best-known heuristic results is provided. In summary, the results show that, for some benchmarks, the heuristic approaches have already found the minimal network, while for other benchmarks, significantly smaller networks exist.
ESCHERICHIA COLI PROTEIN CODING SEQUENCE DETECTION BY ANALYSIS OF FREE ENERGY PERIODICITY

Free energy calculation for binding between the ShineDalgarno 3’ end of the 16S rRNA and initiation sites in E.coli suggest the region is used as an landing site for construction of the ribosome around mRNA. Research indicates that there is a continued role for Shine-Dalgarno interaction within coding sequences – a mechanism that is still unclear. The consistency of rRNA/mRNA interactions within coding sequences provides a basis for predicting the location of coding sequences within the genome, and therefore may in fact connect directly to translational efficiency. This crude periodicity model for protein prediction compares well to the popular GeneMark hidden Markov model. A few obvious refinements to the crude model should provide a very effective protein prediction program and aid in transgenic production design.
NEGATIVELY DEPENDENT BOUNDED RANDOM VARIABLE PROBABILITY INEQUALITIES AND THE STRONG LAW OF LARGE NUMBERS

Let X 1 , … , X n be negatively dependent uniformly bounded random variables with d.f. F ( x ) . In this paper we obtain bounds for the probabilities P ( | ∑ i = 1 n X i | ≥ n t ) and \varepsilon } \right)$" xmlns:mml="http://www.w3. org/1998/Math/MathML"> P ( | ξ ˆ p n − ξ p | > ϵ ) where ξ ˆ p n is the sample p th quantile and ξ p is the p th quantile of F ( x ) . Moreover, we show that ξ ˆ p n is a strongly consistent estimator of ξ p under mild restrictions on F ( x ) in the neighborhood of ξ p . We also show that ξ ˆ p n converges completely to ξ p .
The Development of Personalized Writing Assistant for Electronic Discharge Summaries Based on Named Entity Recognition

Named entity recognition (NER) is one of the fundamental tasks in natural language processing, with a high utilization value in the medical domain. The electronic discharge summary is a comprehensive clinical document, with the important legal effect especially in medical disputes, which contains patients' relevant information during hospitalization. Current main writing mode of electronic discharge summary in China is typing along with copying/pasting or modifying on some existing template files with fixed forms, which inevitably leads to writing inefficiency and transcription errors. In order to solve this problem, this paper intelligently analyses some potential writing style using NER and designs a personalized writing assistant scheme to improve efficiency and reduce errors. The NER model trained by Chinese discharge summaries and rich features set has a good performance. The writing assistant monitors some key words typed in the writing process and timely extracts structural information from electronic medical record database as candidate inputs for the writer.
Mining Opponent Behavior: A Champion of RoboCup Coach Competition

Opponent modeling is one of the most attractive and practical arenas in multi agent system (MAS) for predicting and identifying the future behaviors of opponent. This paper introduces an approach towards opponent modeling in RoboCup Soccer Coach Simulation. In this scene, an autonomous coach agent is able to identify the weaknesses or patterns of the opponent by analyzing the opponent's past games and advising own players. To gain this goal, we introduce a 3-tier learning architecture. At first, by gathering data from the environment, sequential events of the players are identified. Then the weaknesses or patterns of the opponent are predicted using statistical calculations. Eventually, by comparing the opponent patterns with the rest of team's behavior, a model of the opponent is constructed. According to this architecture, coach models the opponent and to simplify pattern recognition, provides an appropriate strategy to play against the opponent. This structure is tested in RoboCup Soccer Coach Simulation and MRLCoach was the champion at Iran Open 2006