nils-herrmann/clean_scirepeval huggingface dataset:
The proximal promoter region of the human vascular endothelial growth factor gene has a G-quadruplex structure that can be targeted by G-quadruplex-interactive agents.

Previous studies on the functional analysis of the human vascular endothelial growth factor (VEGF) promoter using the full-length VEGF promoter reporter revealed that the proximal 36-bp region (-85 to -50 relative to transcription initiation site) is essential for basal or inducible VEGF promoter activity in several human cancer cells. This region consists of a polypurine (guanine) tract that contains four runs of at least three contiguous guanines separated by one or more bases, thus conforming to a general motif capable of forming an intramolecular G-quadruplex. Here, we show that the G-rich strand in this region is able to form an intramolecular propeller-type parallel-stranded G-quadruplex structure in vitro by using the electrophoretic mobility shift assay, dimethyl sulfate footprinting technique, the DNA polymerase stop assay, circular dichroism spectroscopy, and computer-aided molecular modeling. Two well-known G-quadruplex-interactive agents, TMPyP4 and Se2SAP, stabilize G-quadruplex structures formed by this sequence in the presence of a potassium ion, although Se2SAP is at least 10-fold more effective in binding to the G-quadruplex than TMPyP4. Between these two agents, Se2SAP better suppresses VEGF transcription in different cancer cell lines, including HEC1A and MDA-MB-231. Collectively, our results provide evidence that specific G-quadruplex structures can be formed in the VEGF promoter region, and that the transcription of this gene can be controlled by ligand-mediated G-quadruplex stabilization. Our results also provide further support for the idea that G-quadruplex structures may play structural roles in vivo and therefore might provide insight into novel methodologies for rational drug design.
State-of-the-art in biomedical literature retrieval for clinical cases: a survey of the TREC 2014 CDS track

Providing access to relevant biomedical literature in a clinical setting has the potential to bridge a critical gap in evidence-based medicine. Here, our goal is specifically to provide relevant articles to clinicians to improve their decision-making in diagnosing, treating, and testing patients. To this end, the TREC 2014 Clinical Decision Support Track evaluated a system’s ability to retrieve relevant articles in one of three categories (Diagnosis, Treatment, Test) using an idealized form of a patient medical record . Over 100 submissions from over 25 participants were evaluated on 30 topics, resulting in over 37k relevance judgments. In this article, we provide an overview of the task, a survey of the information retrieval methods employed by the participants, an analysis of the results, and a discussion on the future directions for this challenging yet important task.
A practical approach to sample-path simulation optimization

We propose solving continuous parametric simulation optimizations using a deterministic nonlinear optimization algorithm and sample-path simulations. The optimization problem is written in a modeling language with a simulation module accessed with an external function call. Since we allow no changes to the simulation code at all, we propose using a quadratic approximation of the simulation function to obtain derivatives. Results on three different queueing models are presented that show our method to be effective on a variety of practical problems.
Authenticated Group Key Agreement and Friends

hlany modern computing environments involve dynamic peer groups. Distributed Simdation, mtiti-user games, conferencing and replicated servers are just a few examples. Given the openness of today’s networks, communication among group members must be secure and, at the same time, efficient. This paper studies the problem of authenticated key agreement. in dynamic peer groups with the emphasis on efficient and provably secure key authentication, key confirmation and integrity. It begins by considering 2-party authenticateed key agreement and extends the restits to Group Dfi*Hehart key agreement. In the process, some new security properties (unique to groups) are discussed.
New Approaches to Fuzzy-Rough Feature Selection

There has been great interest in developing methodologies that are capable of dealing with imprecision and uncertainty. The large amount of research currently being carried out in fuzzy and rough sets is representative of this. Many deep relationships have been established, and recent studies have concluded as to the complementary nature of the two methodologies. Therefore, it is desirable to extend and hybridize the underlying concepts to deal with additional aspects of data imperfection. Such developments offer a high degree of flexibility and provide robust solutions and advanced tools for data analysis. Fuzzy-rough set-based feature (FS) selection has been shown to be highly useful at reducing data dimensionality but possesses several problems that render it ineffective for large datasets. This paper proposes three new approaches to fuzzy-rough FS-based on fuzzy similarity relations. In particular, a fuzzy extension to crisp discernibility matrices is proposed and utilized. Initial experimentation shows that the methods greatly reduce dimensionality while preserving classification accuracy.
Adding Attentiveness to the Neurons in Recurrent Neural Networks

Recurrent neural networks (RNNs) are capable of modeling the temporal dynamics of complex sequential information. However, the structures of existing RNN neurons mainly focus on controlling the contributions of current and historical information but do not explore the different importance levels of different elements in an input vector of a time slot. We propose adding a simple yet effective Element-wiseAttention Gate (EleAttG) to an RNN block (e.g., all RNN neurons in a network layer) that empowers the RNN neurons to have the attentiveness capability. For an RNN block, an EleAttG is added to adaptively modulate the input by assigning different levels of importance, i.e., attention, to each element/dimension of the input. We refer to an RNN block equipped with an EleAttG as an EleAtt-RNN block. Specifically, the modulation of the input is content adaptive and is performed at fine granularity, being element-wise rather than input-wise. The proposed EleAttG, as an additional fundamental unit, is general and can be applied to any RNN structures, e.g., standard RNN, Long Short-Term Memory (LSTM), or Gated Recurrent Unit (GRU). We demonstrate the effectiveness of the proposed EleAtt-RNN by applying it to the action recognition tasks on both 3D human skeleton data and RGB videos. Experiments show that adding attentiveness through EleAttGs to RNN blocks significantly boosts the power of RNNs.
1 Characteristic-based Clustering for Time Series Data

With the growing importance of time series clustering research, particularly for similarity searches amongst long time series such as those arising in medicine or finance, it is critical for us to find a way to resolve the outstanding problems that make most clustering methods impractical under certain circumstances. When the time series is very long, some clustering algorithms may fail because the very notation of similarity is dubious in high dimension space; many methods cannot handle missing data when the clustering is based on a distance metric. This paper proposes a method for clustering of time series based on their structural characteristics. Unlike other alternatives, this method does not cluster point values using a distance metric, rather it clusters based on global features extracted from the time series. The feature measures are obtained from each individual series and can be fed into arbitrary clustering algorithms, including an unsupervised neural network algorithm, self-organizing map, or hierarchal clustering algorithm. Global measures describing the time series are obtained by applying statistical operations that best capture the underlying characteristics: trend, seasonality, periodicity, serial correlation, skewness, kurtosis, chaos, nonlinearity, and self-similarity. Since the method clusters using extracted global measures, it reduces the dimensionality of the time series and is much less sensitive to missing or noisy data. We further provide a search mechanism to find the best selection from the feature set that should be used as the clustering inputs. The proposed technique has been tested using benchmark time series datasets previously reported for time series clustering and a set of time series datasets with known characteristics. The empirical results show that our approach is able to yield meaningful clusters. The resulting clusters are similar to those produced by other methods, but with some promising and interesting variations that can be intuitively explained with knowledge of the global characteristics of the time series.
Factors influencing a nurse's decision to question medication administration in a neonatal clinical care unit.

The aim of this study was to identify factors that influence nurse's decisions to question concerning aspects of medication administration within the context of a neonatal clinical care unit.



Medication error in the neonatal setting can be high with this particularly vulnerable population. As the care giver responsible for medication administration, nurses are deemed accountable for most errors. However, they are recognised as the forefront of prevention.  Therefore, this study focuses upon addressing the gap in knowledge around what nurses believe influences their decision to question.



A critical incident design was employed where nurses were asked to describe clinical incidents around their decision to question a medication issue. Nurses were recruited from a neonatal clinical care unit and participated in an individual digitally recorded interview.



One hundred and three nurses participated between December 2013-August 2014. Use of the constant comparative method revealed commonalities within transcripts. Thirty-six categories were grouped into three major themes: 'Working environment', 'Doing the right thing' and 'Knowledge about medications'.



Findings highlight factors that influence nurses' decision to question issues around medication administration. Nurses feel it is their responsibility to do the right thing and speak up for their vulnerable patients to enhance patient safety. Negative dimensions within the themes will inform planning of educational strategies to improve patient safety, whereas positive dimensions must be reinforced within the multidisciplinary team.


 TO CLINICAL PRACTICE
The working environment must support nurses to question and ultimately provide safe patient care. Clear and up to date policies, formal and informal education, role modelling by senior nurses, effective use of communication skills and a team approach can facilitate nurses to appropriately question aspects around medication administration.
Transmission model of endemic human malaria in a partially immune population

A new transmission model of human malaria in a partially immune population with three discrete delays is formulated for variable host and vector populations. These are latent period in the host population, latent period in the vector population and duration of partial immunity. The results of our mathematical analysis indicate that a threshold parameterR0 exists. ForR0 > 1, the expected number of mosquitoes infected from humansRhm should be greater than a certain critical valueR∗hm or should be less thanR∗hm whenR ∗ hm > 1, for a stable endemic equilibrium to exist. We deduce from model analysis that an increase in the period within which partial immunity is lost increases the spread of the disease. Numerically we deduce that treatment of the partially immune humans assists in reducing the severity of the disease and that transmission blocking vaccines would be effective in a partially immune population. Numerical simulations support our analytical conclusions and illustrate possible behaviour scenarios of the model.
An electro-fluid-dynamic simulator for the cardiovascular system.

This work presents the initial studies and the proposal for a cardiovascular system electro-fluid-dynamic simulator to be applied in the development of left ventricular assist devices (LVADs). The simulator, which is being developed at University Sao Judas Tadeu and at Institute Dante Pazzanese of Cardiology, is composed of three modules: (i) an electrical analog model of the cardiovascular system operating in the PSpice electrical simulator environment; (ii) an electronic controller, based on laboratory virtual instrumentation engineering workbench (LabVIEW) acquisition and control tool, which will act over the physical simulator; and (iii) the physical simulator: a fluid-dynamic equipment composed of pneumatic actuators and compliance tubes for the simulation of active cardiac chambers and big vessels. The physical simulator (iii) is based on results obtained from the electrical analog model (i) and physiological parameters.
Pronunciation Problems with Vowels by Japanese Speakers of English : Insights from a Contrastive Analysis

This paper aims to compare the differences between English and Japanese vowels in order to explain why it is difficult for Japanese speakers to pronounce English vowels. Since Japanese has only five vowels, and each vowel covers more than one English vowel, Japanese learners of English often mispronounce English vowels by substituting them with Japanese vowels. In addition, Japanese speakers are not aware of how they should move their mouths, lips, and jaws when they speak English because Japanese does not require large facial expression or movement when speaking. It is very important for learners to recognize the differences because recognizing is the first step to learn pronunciation of foreign language. In order to teach Japanese speakers to pronounce English vowels correctly, teachers should also be aware of these differences and incorporate this knowledge in teaching.
The Kanerva Machine: A Generative Distributed Memory

We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva’s sparse distributed memory, it has a robust distributed reading and writing mechanism. The memory is analytically tractable, which enables optimal on-line compression via a Bayesian update-rule. We formulate it as a hierarchical conditional generative model, where memory provides a rich data-dependent prior distribution. Consequently, the top-down memory and bottom-up perception are combined to produce the code representing an observation. Empirically, we demonstrate that the adaptive memory significantly improves generative models trained on both the Omniglot and CIFAR datasets. Compared with the Differentiable Neural Computer (DNC) and its variants, our memory model has greater capacity and is significantly easier to train.
A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies

We introduce a framework for the optimal extraction of flat clusterings from local cuts through cluster hierarchies. The extraction of a flat clustering from a cluster tree is formulated as an optimization problem and a linear complexity algorithm is presented that provides the globally optimal solution to this problem in semi-supervised as well as in unsupervised scenarios. A collection of experiments is presented involving clustering hierarchies of different natures, a variety of real data sets, and comparisons with specialized methods from the literature.
STAR : a Smart Tourist Agenda Recommender

In this paper we claim that the new generation of Webbased systems should offer an intelligent and flexible support to their users, coupled with the ability to provide personalized solutions. We think that prolem solving techniques could be exploited to face this challenge. In particular, we present STAR, a Web-based system that exploits the configuration technology in order to support a tourist in organizing a personalized agenda for a tour in a city. We describe the system architecture and the knowledge representation supporting this task. A first prototype of STAR is being implemented and it is instantiated on the city of Torino (Italy).
Analysis and Comparison: Full Adder Block in Submicron Technology

The reduction in the operating voltage play a majorrole in improving the performance of the integratedcircuits.Apart from that lesser power consumption, reducedarea and smaller size of transistors are also the vital factors inthe design criteria and fabrication of the systems. This articleapproaches towards the increasing performance of the systemsby comparing different types of adder circuits. In this article, anew circuit has been designed using the TG technology. Basedon different parameters like average power consumption anddelay, it has been observed that the Carry look-ahead adderand Carry bypass adder consumes more power. TheComparative analysis of TG based 8-bit different AdderDesigns using 180nm technology using TANNER tool has beenconsidered.
Mechanisms of Cancer Induction by Tobacco-Specific NNK and NNN

Tobacco use is a major public health problem worldwide. Tobacco-related cancers cause millions of deaths annually. Although several tobacco agents play a role in the development of tumors, the potent effects of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and N'-nitrosonornicotine (NNN) are unique. Metabolically activated NNK and NNN induce deleterious mutations in oncogenes and tumor suppression genes by forming DNA adducts, which could be considered as tumor initiation. Meanwhile, the binding of NNK and NNN to the nicotinic acetylcholine receptor promotes tumor growth by enhancing and deregulating cell proliferation, survival, migration, and invasion, thereby creating a microenvironment for tumor growth. These two unique aspects of NNK and NNN synergistically induce cancers in tobacco-exposed individuals. This review will discuss various types of tobacco products and tobacco-related cancers, as well as the molecular mechanisms by which nitrosamines, such as NNK and NNN, induce cancer.
Approximate polytope membership queries

We consider an approximate version of a fundamental geometric search problem, polytope membership queries. Given a convex polytope P in REd, presented as the intersection of halfspaces, the objective is to preprocess P so that, given a query point q, it is possible to determine efficiently whether q lies inside P subject to an error bound ε. Previous solutions to this problem were based on straightforward applications of classic polytope approximation techniques by Dudley (1974) and Bentley et al. (1982). The former yields minimum storage, and the latter yields constant query time. A space-time tradeoff can be obtained by interpolating between the two. We present the first significant improvements to this tradeoff. For example, using the same storage as Dudley, we reduce the query time from O(1/ε(d-1)/2) to O(1/ε(d-1)/4). Our approach is based on a very simple algorithm. Both lower bounds and upper bounds on the performance of the algorithm are presented.
 To establish the relevance of our results, we introduce a reduction from approximate nearest neighbor searching to approximate polytope membership queries. We show that our tradeoff provides significant improvements to the best known space-time tradeoffs for approximate nearest neighbor searching. Furthermore, this is achieved with constructions that are much simpler than existing methods.
Toward An Understanding of the Behavioral Intention to Use Mobile Banking Services

Although millions of dollars have been spend on building mobile banking systems, report on mobile banking systems has shown that potential users may not use the systems in spite of their availability. There is a need for research to identify the factors that determine users’ acceptance of mobile banking. According to the technology acceptance model (TAM), perceived ease of use and perceived usefulness constructs are believed to be fundamental in determining the acceptance and use of various IT. These beliefs, however, may not fully explain the user’s behavior toward the newly emerging mobile banking. Using the technology acceptance model (TAM) as a theoretical framework, this study introduces “perceived credibility” as a new factor that reflects the user’s security and privacy concern in the acceptance of mobile banking, and examines the effect of computer self-efficacy on intention to use mobile banking. Data collected from 267 users in Taiwan were tested against the extended TAM using the structural equation modeling approach. The results strongly support the extended TAM in predicting users’ intention to adopt mobile banking systems, and demonstrate the significant effect of computer self-efficacy on behavioral intention through perceived ease of use, perceived usefulness, and perceived credibility.
Transition-based Dependency Parsing with Selectional Branching

We present a novel approach, called selectional branching, which uses confidence estimates to decide when to employ a beam, providing the accuracy of beam search at speeds close to a greedy transition-based dependency parsing approach. Selectional branching is guaranteed to perform a fewer number of transitions than beam search yet performs as accurately. We also present a new transition-based dependency parsing algorithm that gives a complexity of O(n) for projective parsing and an expected linear time speed for non-projective parsing. With the standard setup, our parser shows an unlabeled attachment score of 92.96% and a parsing speed of 9 milliseconds per sentence, which is faster and more accurate than the current state-of-the-art transitionbased parser that uses beam search.
Proving Infinitary Formulas

The infinitary propositional logic of here-and-there is important for the theory of answer set programming in view of its relation to strongly equivalent transformations of logic programs. We know a formal system axiomatizing this logic exists, but a proof in that system may include infinitely many formulas. In this note we describe a relationship between the validity of infinitary formulas in the logic of here-and-there and the provability of formulas in some finite deductive systems. This relationship allows us to use finite proofs to justify the validity of infinitary formulas.
Design and DfT of a High-Speed Area-Efficient Embedded Asynchronous FIFO

Embedded First-In First-Out (FIFO) memories are increasingly used in many IC designs. We have created a new full-custom embedded ripple-through FIFO module with asynchronous read and write clocks. The implementation is based on a micropipeline architecture and is at least a factor two smaller than SRAM-based and standard-cell-based counterparts. This paper gives an overview of the most important design features of the new FIFO module and describes its test and design-for-test approach.