Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased heterosis in selfing populations of a perennial forb

Quantifying the effects of population bottlenecks and inbreeding on genetic variation underlying fitness in natural populations is central to understanding the potential limits to natural selection. One approach is to estimate heterosis in crosses between populations, thus revealing deleterious mutations that have become fixed within populations by random genetic drift. We estimated heterosis in selfing and outcrossing populations of Arabidopsis lyrata. We found massive heterosis in selfing populations, but strong heterosis even in outcrossing populations. Combined with other sources of information, our results suggest a common history of population bottlenecks, with possibly severe bottlenecks associated with the transition to selfing.
Predictive Value of 16-Slice Multidetector Spiral Computed Tomography to Detect Significant Obstructive Coronary Artery Disease in Patients at High Risk for Coronary Artery Disease: Patient- Versus Segment-Based Analysis

Background—In this study, we investigated the diagnostic value and limitations of multidetector computed tomography (MDCT)–based noninvasive detection of significant obstructive coronary artery disease (CAD) in a consecutive high-risk patient population with inclusion of all coronary segments. Methods and Results—In a prospective, blinded, standard cross-sectional technology assessment, a cohort of 33 consecutive patients with a positive stress test result underwent 16-slice MDCT and selective coronary angiography for the detection of significant obstructive CAD. We assessed the diagnostic accuracy of MDCT in a segment-based and a patient-based model and determined the impact of stenosis location and the presence of calcification on diagnostic accuracy in both models. Analysis of all 530 coronary segments demonstrated moderate sensitivity (63%) and excellent specificity (96%) with a moderate positive predictive value of 64% and an excellent negative predictive value (NPV) of 96% for the detection of significant coronary stenoses. Assessment restricted to either proximal coronary segments or segments with excellent image quality (83% of all segments) led to an increase in sensitivity (70% and 82%, respectively), and high specificities were maintained (94% and 93%, respectively). In a patient-based model, the NPV of MDCT for significant CAD was limited to 75%. Coronary calcification was the major cause of false-positive findings (94%). Conclusions—For all coronary segments included, 16-slice MDCT has moderate diagnostic value for the detection of significant obstructive coronary artery stenosis in a population with a high prevalence of CAD. The moderate NPV of patient-based detection of CAD suggests a limited impact on clinical decision-making in high-risk populations.
A lightweight key establishment in wireless sensor network based on elliptic curve cryptography

Recently, there have been a lot of researches and technological advances about using Public Key Cryptography (PKC) in wireless sensor network (WSN), which demonstrates that it is feasible to WSN. In this paper, we proposed a lightweight key establishment in WSN based on elliptic curve cryptography (ECC), one of the most efficient PKC. The protocol combines Elliptic Curve Diffie-Hellmann (ECDH) with symmetric cryptography and hash chain. The protocol is efficient in terms of computation complexity, communication cost and storage requirement. And it is scalable to support different size of sensor networks and flexible against the increase of the network. Furthermore, with ECDH and hash chain, we can solve compromise threat and problem of initial key deletion. Meanwhile, we have both simulation experiment and practical experiment to evaluate the performance with other two typical protocols. It turns out that our protocol is more efficient than other public key schemes.
Using Student Explanations as Models for Adapting Tutorial Dialogue

We describe a dialogue management approach that adapts dialogue strategies to a changing user model under the constraint that the approach be able to scale up rapidly for intelligent tutoring systems applications. Dialogue strategies are expressed as constraints on how to move through a student model that is composed of linked nodes. By orienting the dialogue to a linked model of the student’s likely beliefs and expressing the dialogue strategies as constraints on movement within the model, the approach facilitates adaptation of the dialogue strategy as the student model changes.
A comparison of the enrollment, persistence and transfer rates of white and minority students at Des Moines Area Community College

The role of the community college has been to unlock the doors of education opportunity for all individuals, and provide the entry point to higher education. Community colleges are the primary gateway to education for many of our minority and first generation students. Reflecting national trends, enrollment of minority students in Iowa's community colleges is increasing. The available transfer data demonstrate the low participation and transfer rates of minority students in community colleges and in higher education, in general, in Iowa. A detailed analysis of the number of minority students enrolling, and persisting in community colleges and transferring to four-year institutions is warranted. The purpose of this study was to determine if minority students enrolled at Des Moines Area Community College (DMACC) as probable transfer students transferred to Iowa State University at the same rate as White students, and if there were any differences in the enrollment, within-term course retention, fall-to-spring persistence, and after transfer success rates of male students and female students in each of the minority groups at DMACC. The Chi-square test was used to compare expected frequencies calculated from DMACC s White student population with actual frequencies for DMACC s minority population. Among the findings were: (a) with the exception of Hispanic students, most minority students enrolled in larger numbers than they exist in the population of the area; (b) Black and Asian students dropped courses at an alarmingly high rate during their first semester; (c) once minority students persisted past the first term of enrollment, the differences in persistence between minority and White students were minimal; (d) the transfer rate of Asian
ONTOLOGY BASED TEXT DOCUMENT SUMMARIZATION SYSTEM USING CONCEPT TERMS

In this modern world, due to the dramatic technological development huge amount of information is available in all over the places. So it is difficult to understand the main content of the document without reading the entire document. It takes time, based on the amount of information available in the document. By using the automatic summarization, these problems are solved. In this paper ontology based text summarization system using concept terms is introduced. Concepts are extracted using concept extraction algorithm. By using the ontology model the hierarchical representation is generated for the concept terms. Then by setting the concept depth the required summary is generated.
Impaired Spatial Learning and Memory after Sevoflurane–Nitrous Oxide Anesthesia in Aged Rats Is Associated with Down-Regulated cAMP/CREB Signaling

Neurocognitive deficits arising from anesthetic exposure have recently been debated, while studies have shown that the phosphorylation of cyclic AMP response element-binding protein (CREB) in the hippocampus is critical for long-term memory. To better understand the neural effects of inhalational anesthetics, we studied the behavioral and biochemical changes in aged rats that were exposed to sevoflurane (Sev) and nitrous oxide (N2O) for 4 h. Eighteen-month-old rats were randomly assigned to receive 1.3% sevoflurane and 50% nitrous oxide/50% oxygen or 50% oxygen for 4 h. Spatial learning and memory were tested with the Morris water maze 48 h after exposure, and the results showed that sevoflurane–nitrous oxide exposure induced a significant deficit in spatial learning acquisition and memory retention. Experiments revealed that the cAMP and pCREB levels in the dorsal hippocampus were decreased in rats with anesthetic exposure in comparison with control rats 48 h after anesthesia as well as 15 min after the probe trial, but there were no significant differences in CREB expression. Besides these, the current study also found the DG neurogenesis significantly decreased as well as neuronal loss and neuronal apoptosis increased in the hippocampus of rats exposed to Sev+N2O. The current study demonstrated that down-regulation of cAMP/CREB signaling, decrease of CREB-dependent neurogenesis and neuronal survival in the hippocampus contributed to the neurotoxicity and cognitive dysfunction induced by general anesthesia with sevoflurane–nitrous oxide.
Benchmarks in the Cloud: The mOSAIC Benchmarking Framework

A natural consequence of the pay-per-use business model of Cloud Computing, is that Cloud Users need to evaluate and compare different cloud providers in order to choose the best offerings in terms of trade-off among performance and prices. But this is, at the state of art, one of the main lack in the cloud environment: no real grants are offered by providers about the quality of the resources offered and no clear ways exists to compare two different offerings. Moreover the high dynamicity of cloud resources (virtual machines can be added or removed in few minutes) make evaluation of such systems really hard to be done. In this paper we propose a framework for building ad-hoc benchmark applications, whose behaviour is strictly related with user needs and which can be used to compare different provider. The framework is based on the mOSAIC project, which offers a deployable platform and an API for building provider-independent applications. Due to such independence we are able to directly compare different cloud offers. The paper proposes a detailed analysis of the state of art and of the problem of benchmarking in cloud environment, before detailing the proposed architecture and illustrating it briefly with an example.
Toward the design of a low cost reconfigurable real time augmented reality system

In this paper, we detail different step to partially design a low cost reconfigurable real time 3D graphic processor based on the soft core NIOS II CPU. Two major steps are presented in this paper. The first one is the design of a RTOS based on μClinux as OS and Xenomai as real time kernel. We also present already done steps to design a 3D graphic processor. The presented step is the analysis of the 3D graphic application to choose the most complex function for a hardware implementation.
Lévy noise improves the electrical activity in a neuron under electromagnetic radiation

As the fluctuations of the internal bioelectricity of nervous system is various and complex, the external electromagnetic radiation induced by magnet flux on membrane can be described by the non-Gaussian type distribution of Lévy noise. Thus, the electrical activities in an improved Hindmarsh-Rose model excited by the external electromagnetic radiation of Lévy noise are investigated and some interesting modes of the electrical activities are exhibited. The external electromagnetic radiation of Lévy noise leads to the mode transition of the electrical activities and spatial phase, such as from the rest state to the firing state, from the spiking state to the spiking state with more spikes, and from the spiking state to the bursting state. Then the time points of the firing state versus Lévy noise intensity are depicted. The increasing of Lévy noise intensity heightens the neuron firing. Also the stationary probability distribution functions of the membrane potential of the neuron induced by the external electromagnetic radiation of Lévy noise with different intensity, stability index and skewness papremeters are analyzed. Moreover, through the positive largest Lyapunov exponent, the parameter regions of chaotic electrical mode of the neuron induced by the external electromagnetic radiation of Lévy noise distribution are detected.
Mobile CRISPR/Cas-Mediated Bacteriophage Resistance in Lactococcus lactis

Lactococcus lactis is a biotechnological workhorse for food fermentations and potentially therapeutic products and is therefore widely consumed by humans. It is predominantly used as a starter microbe for fermented dairy products, and specialized strains have adapted from a plant environment through reductive evolution and horizontal gene transfer as evidenced by the association of adventitious traits with mobile elements. Specifically, L. lactis has armed itself with a myriad of plasmid-encoded bacteriophage defensive systems to protect against viral predation. This known arsenal had not included CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated proteins), which forms a remarkable microbial immunity system against invading DNA. Although CRISPR/Cas systems are common in the genomes of closely related lactic acid bacteria (LAB), none was identified within the eight published lactococcal genomes. Furthermore, a PCR-based search of the common LAB CRISPR/Cas systems (Types I and II) in 383 industrial L. lactis strains proved unsuccessful. Here we describe a novel, Type III, self-transmissible, plasmid-encoded, phage-interfering CRISPR/Cas discovered in L. lactis. The native CRISPR spacers confer resistance based on sequence identity to corresponding lactococcal phage. The interference is directed at phages problematic to the dairy industry, indicative of a responsive system. Moreover, targeting could be modified by engineering the spacer content. The 62.8-kb plasmid was shown to be conjugally transferrable to various strains. Its mobility should facilitate dissemination within microbial communities and provide a readily applicable system to naturally introduce CRISPR/Cas to industrially relevant strains for enhanced phage resistance and prevention against acquisition of undesirable genes.
EVALUATION OF EMOTIONAL COMPONENTS TO IMPROVE SSVEP-BCI

Brain-computer interface (BCI) provides a direct connection between the user’s brain signals and a computer, generating an alternative channel of communication that does not involve the traditional way as muscles and nerves. Recent decades have seen BCI applications as a novel and promising new channel of communication, control and entertainment for disabled and healthy people. However, BCI technology can be prone to errors due to the basic emotional state of the user: the performance of reactive and active BCIs decreases when user becomes stressed or bored, for example. Passive-BCI is a recent approach that fuses BCI technology with cognitive monitoring, providing valuable information about the user’s intentions, the situational interpretations and mainly the emotional state. In order to improve the accuracy of BCIs, subjects can perform simultaneous or sequential tasks typically used in two BCI approaches in a hybrid condition that combines both BCIs. In this work, a system composed of a passive-BCI co-working with a reactive-BCI, with the aim of improving the performance of the reactive-BCI is proposed. Thus the possibility of adjusting recognition characteristics of SSVEP-BCIs using a passive-BCI output is evaluated. Keywords— Emotional components, passive-BCI, reactive-BCI, SSVEP-based BCI, asymmetry index
Incorporation of a valency lexicon into a TectoMT pipeline

In this paper, we focus on the incorporation of a valency lexicon into TectoMT system for Czech-Russian language pair. We demonstrate valency errors in MT output and describe how the introduction of a lexicon influenced the translation results. Though there was no impact on BLEU score, the manual inspection of concrete cases showed some improvement.
Emotion detection in dialog systems: Applications, strategies and challenges

Emotion plays an important role in human communication and therefore also human machine dialog systems can benefit from affective processing. We present in this paper an overview of our work from the past few years and discuss general considerations, potential applications and experiments that we did with the emotional classification of human machine dialogs. Anger in voice portals as well as problematic dialog situations can be detected to some degree, but the noise in real life data and the issue of unambiguous emotion definition are still challenging. Also, a dialog system reacting emotionally might raise expectations with respect to its intellectual abilities that it can not fulfill.
Adaptable mission planning for kino-dynamic systems

Autonomous systems can perform tasks that are dangerous, monotonous, or even impossible for humans. To approach the problem of planning for Unmanned Aerial Vehicles (UAVs) we present a hierarchical method that combines a high-level planner with a low-level planner. We pose the problem of high-level planning as a Selective Traveling Salesman Problem (STSP) and select the order in which to visit our science sites. We then use a kino-dynamic path planner to create a large number of intermediate waypoints. This is a complete system that combines high and low level planning to achieve a goal. This paper demonstrates the benefits gained by adaptable high-level plans versus static and greedy plans.
GBTK: a toolkit for grid implementation of BLAST

We describe the implementation of GBTK (Grid BLAST Tool Kit), a lightweight application specific grid framework for BLAST, for geographically distributed high performance computing (HPC) systems. This framework was built on the concept of synchronized Web services using RPC (remote procedure calls) encoded as XML (extensible Markup Language).
Design of a contactless battery charger for cellular phone

In this paper, the design of a contactless charger for a 3.3 W lithium-ion battery of a cellular phone is presented. In this charger, the primary core of the transformer is in the charger unit and the secondary core is in the telephone. The gap (3 mm) between them is the thickness of the two plastic cases. The transformer core design for the maximum coupling coefficient and the maximum magnetizing inductance with the size constraint on the secondary side is presented. A half-bridge series resonant converter is used to compensate the leakage inductance and to achieve ZVS operation. Voltage gain, current gain analysis, and design procedure are presented. For the battery charging control, an infrared LED is used and its performance is verified from the hardware experiments.
Coreference Resolution with ILP-based Weighted Abduction

This paper presents the first systematic study of the coreference resolution problem in a general inference-based discourse processing framework. Employing the mode of inference called weighted abduction, we propose a novel solution to the overmerging problem inherent to inference-based frameworks. The overmerging problem consists in erroneously assuming distinct entities to be identical. In discourse processing, overmerging causes establishing wrong coreference links. In order to approach this problem, we extend Hobbs et al. (1993)’s weighted abduction by introducing weighted unification and show how to learn the unification weights by applying machine learning techniques. For making large-scale processing and parameter learning in an abductive logic framework feasible, we employ a new efficient implementation of weighted abduction based on Integer Linear Programming. We then propose several linguistically motivated features for blocking incorrect unifications and employ different large-scale world knowledge resources for establishing unification via inference. We provide a large-scale evaluation on the CoNLL-2011 shared task dataset, showing that all features and almost all knowledge components improve the performance of our system.
Use of Renin-Angiotensin System Inhibitors Is Associated with Reduction of Fracture Risk in Hemodialysis Patients

Background Patients with chronic kidney disease, especially those undergoing dialysis treatment and having secondary hyperparathyroidism, have a high risk of bone fracture. The renin-angiotensin system (RAS) is associated with osteoclastic bone resorption. We aimed to examine whether the use of RAS inhibitors reduces the incidence of fracture in hemodialysis patients. Methods and Findings This was a multicenter, 3-year, prospective, observational study. From 2008 to 2011, maintenance hemodialysis patients with secondary hyperparathyroidism (N = 3,276) treated with angiotensin converting enzyme inhibitor (ACEI)/angiotensin II receptor blocker (ARB) at baseline were followed for a mean of 2.7 years. The association between the use of ACEI/ARB and hospitalization rate owing to fracture was examined by using Cox regression models. Effect modifications by the severity of secondary hyperparathyroidism (intact parathyroid hormone [iPTH] level), sex, and systolic blood pressure were also examined. The incidence proportion of fracture-related hospitalization was 5.42% throughout the observation period. ACEI/ARB use was associated with a lower rate of fracture-related hospitalization (adjusted hazard ratio, 0.65; 95% confidence interval [CI], 0.45–0.92). This association was not significantly affected by sex (P = 0.56) or systolic blood pressure levels (P = 0.87). The hazard ratios adjusted by iPTH levels were qualitatively different, but not statistically significant (P = 0.11): 0.77 (95% CI, 0.42–1.39), 0.38 (95% CI, 0.20–0.73), 0.59 (95% CI, 0.29–1.21), and 1.29 (95% CI, 0.58–2.42) for the first, second, third and fourth quartiles of iPTH, respectively. Conclusions Use of RAS inhibitors is associated with a lower rate of fracture-related hospitalization in hemodialysis patients with secondary hyperparathyroidism. Trial Registration ClinicalTrials.gov NCT00995163
Neuro-predictive process control using online controller adaptation

This paper proposes a technique of integrating neural networks with conventional controller structures, for the predictive control of complex process systems. In the developed method, a baseline conventional controller, e.g. a PI controller, is used to control the process. In addition, a recurrent neural network is used in the form of a multi-step-ahead predictor (MSP) to model the process dynamics. Utilizing the MSP capabilities of recurrent neural networks, the parameters of the conventional controller can be tuned by a backpropagation-like approach, to achieve acceptable regulation and stabilization of the controlled process variables. The advantage of such a formulation is the effective online adaptation of the controller parameters while the process is in operation, and the tracking of the different operating regimes and variations in process characteristics. The developed method is applied for the stabilization and transient control of U-tube steam generator water level.