Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of intelligence and education on the development of dementia. A test of the brain reserve hypothesis

Background. A number of recent epidemiological studies have shown that the prevalence and incidence of dementia are increased in population strata with low compared to high levels of education. This has been explained as a consequence of a greater ‘brain reserve capacity’ in people with a high level of education. Theoretically, however, brain reserve capacity is better reflected by intelligence than by level of education. Thus, the emergence of dementia will be better predicted by low pre-morbid intelligence than by low education. Methods. This prediction was tested in a population based sample of elderly subjects (N=2063; age range 65–84; Amsterdam Study of the Elderly) who were followed over 4 years. Dementia was diagnosed using the Geriatric Mental State examination (GMS). Pre-morbid intelligence was measured using the Dutch Adult Reading Test (DART), a short reading test which gives a good estimate of verbal intelligence, and is relatively insensitive to brain dysfunction. The effects of age, gender, occupational level, number of diseases affecting the central nervous system and family history of dementia or extreme forgetfulness were also examined. Results. Logistic regression analysis showed that low DART-IQ predicted incident dementia better than low level of education. A high occupational level (having been in charge of subordinates) had a protective effect. Conclusions. This result supports the brain reserve theory. It also indicates that low pre-morbid intelligence is an important risk factor for cognitive decline and dementia. Use of reading ability tests is to be preferred over years of education as estimator of pre-morbid cognitive level in (epidemiological) dementia research.
Optimal Bandwidth Assignment for Multiple Description Coding in Media Streaming

Multiple description coding (MDC) has been widely used in media streaming to address the bandwidth heterogeneity issue. That is, the video source encodes data into multiple descriptions. At the receiver end, the streaming quality is proportional to the number of descriptions received. In this paper, we investigate how to assign bandwidth for descriptions in order to make full use of available network bandwidth. We formulate the problem as an optimization problem. We then decompose the problem and address it by an iterative algorithm. We evaluate the algorithm through simulations. Our results show that arbitrary description rates may severely degrade system performance and an optimal solution can make efficient use of network bandwidth.
Molecular typing of Treponema pallidum isolates from Buenos Aires, Argentina: Frequent Nichols-like isolates and low levels of macrolide resistance

A total of 54 clinical samples, including genital lesion swabs, whole blood and cerebrospinal fluid from patients diagnosed with syphilis were collected in 2006 and in 2013 in Buenos Aires, Argentina. Treponemal DNA was detected in 43 of the analyzed samples (79.6%) and further analyzed using Sequencing-based molecular typing (SBMT) and Enhanced CDC-typing (ECDCT). By SBMT, 10 different Treponema pallidum subsp. pallidum (TPA) genotypes were found, of which six were related to the TPA SS14 strain, and four to the TPA Nichols strain. The 23S rRNA gene was amplified in samples isolated from 42 patients, and in six of them (14.3%), either the A2058G (four patients, 9.5%) or the A2059G (two patients, 4.8%) mutations were found. In addition to Taiwan, Madagascar and Peru, Argentina is another country where the prevalence of Nichols-like isolates (26.8%) is greater than 10%.
A fast two-stage classification method for high-dimensional remote sensing data

Classification for high-dimensional remotely sensed data generally requires a large set of data samples and enormous processing time, particularly for hyperspectral image data. In this paper, the authors present a fast two-stage classification method composed of a band selection (BS) algorithm with feature extraction/selection (FSE) followed by a recursive maximum likelihood classifier (MLC). The first stage is to develop a BS algorithm coupled with FSE for data dimensionality reduction. The second stage is to design a fast recursive MLC (RMLC) so as to achieve computational efficiency. The experimental results show that the proposed recursive MLC, in conjunction with BS and FSE, reduces computing time significantly by a factor ranging from 30 to 145, as compared to the conventional MLC.
When Does My Program Fail?

Oops! My program fails. Which are the circumstances under which this failure occurs? Answering this question is one of the first steps in debugging -- and a crucial one, as it helps characterizing, understanding, and classifying the problem. In this paper, we propose a technique to identify failure circumstances automatically. Given a concrete failure, we first compute the path condition leading to the failure and then use a constraint solver to identify, from the constraints in the path condition, the general failure conditions: "The program fails whenever the credit card number begins with 6, 5, and a non-zero digit. " A preliminary evaluation of the approach on real programs demonstrates its potential usefulness.
Energy-Efficient Topology Management With Interference Cancellation in Cooperative Wireless Ad Hoc Networks

With recent advances in parallel cooperative transmissions between multiple source-destination pairs, interference cancellation (IC) can be achieved to improve the capacity performance of wireless networks. However, from energy efficiency perspective, user cooperation may not be always appealing, since the increased data rate of one user comes at the price of the energy consumed by another user. In this paper, we study the potential benefits/drawbacks of cooperative communications on network-level issues, such as the capacity and the energy efficiency. We show that, in terms of network energy efficiency, cooperative communications do not always outperform non-cooperative communications, and cooperative communications should be dynamically applied in topology control to optimize the overall network energy efficiency. Specifically, we propose an energy-efficient topology control scheme by jointly considering the capacity and energy consumption of non-cooperative and cooperative communications. Simulation results are presented to show the effectiveness of the proposed scheme.
Novel numerical and computational strategies based on differential evolutionary strategy

Evolutionary algorithm for complex process optimization based on differential evolutionary strategy (DEACOP) that has a similar framework structure of scatter search is proposed. This algorithm not only retained the original algorithm's advantages, but also made improvements in three areas: above all, in order to maintain the diversity of the population, the set RefSet2 is selected from those individuals generated by Latin hypercube uniform sampling, according to minimum Euclidean distance to set RefSet1 is the highest. Furthermore, differential mutation with scaling factor and differential crossover strategy is introduced to replace linear combination method of evolutionary algorithm for complex-process optimization (EACOP). Finally, local search method is adopted to improve the trial solution generated at “go-beyond strategy” stages. The results show that the algorithm is able to use fewer adjustable parameters to complete to search and get feasible mathematical solution.
When arguments don ' t fit with verbs : Interdisciplinary approaches to metonymy and coercion METONYMY 2011

This paper discusses the computational linguistic account of ‘logical metonymy’ (Susan began the book, Mary enjoyed the movie) in which these constructions are analyzed as being interpreted by means of a lexicon-internal generative mechanism operating over information-rich lexical entries. It points out several empirical and theoretical problems with the theory, which it claims make it ultimately unworkable. The paper then suggests an alternative, pragmatic account of the logical metonymy phenomenon, where its interpretation is governed primarily by the operation of pragmatic inferential processes. It is argued that this provides a more explanatory, psychologically plausible account that is capable of avoiding the problems associated with the computational linguistic account.
Degree of Hearing Loss and Working Memory in Adults

The purpose of this study was to examine the relationship between hearing loss and working memory using an automated visual, verbal working memory task in adults with hearing loss. An exploratory, prospective, group design was used to evaluate the performance of 29 adults with varying hearing loss severity. Findings reported here are from a subset of data taken from a larger training study. Participants were assigned to one of two groups: Mild (PTA </= 40 dB HL) and Moderate -Severe (PTA >/ = 41dB HL). All participants also wore binaural hearing aids. Several measures were administered: a working memory test, a speech in noise test, a competing speaker test, and hearing aid verification. Overall, persons with more severe hearing losses had fewer errors during the working memory task than persons with mild hearing loss. Implications for use of an automated working memory task are discussed.
The Digital Divide in New Zealand: The Position of Maori and Pacific Peoples

Information and communication technology plays an ever-growing role in economic, political and social life and those who are unable to access such technologies are increasingly disadvantaged. Hence, what has come to be known as the 'digital divide' is of considerable significance. Here, indicators of that divide as it affects Maori and Pacific Islands peoples in Aotearoa/New Zealand are presented and discussed.
Microfinance Growth and Poverty Reduction in Bangladesh: What Does the Longitudinal Data Say?

This study investigates the long-term effects of microcredit programs, which have been operating in rural Bangladesh for over 20 years, on household income, expenditure, and poverty. The analysis shows that continuous participation in microcredit programs has helped participant households earn higher income and consume more, thereby lifting many of them out of poverty. Our estimates suggest that poverty reduction, in particular the reduction of extreme poverty, due to microcredit intervention accounts for more than 10 percent of the total reduction in extreme poverty in rural Bangladesh over the 2000-2010 decade.
Study of the Impact of Burst Assembly Algorithms in Optical Burst Switched Networks with Self-Similar Input Traffic

This work investigates the impact of burst assembly algorithms applied at the edge routers of optical burst switched (OBS) networks in terms of the self-similarity level observed at the output traffic in presence of self-similar input traffic. The algorithms under consideration are based on of time and burst size criteria, while both static and adaptive approaches are examined. This study is based on existing and new models developed in the OPNET simulation platform. In order to quantitatively assess the self-similarity level of the input and output traffic at the edge nodes, various Hurst parameter estimators have been applied. The performance impact of the burst assembly algorithms in terms of burst assembly delay and its jitter is also assessed. Our study has shown that the burst assembly mechanism at the OBS edge routers reduces the self-similarity level of the output traffic and that this reduction depends on the parameters of the algorithm. Our results reveal that the proposed adaptive burst assembly algorithm performs better comparing to its non-adaptive counterpart
MONaKA, a Novel Modulator of the Plasma Membrane Na,K-ATPase

We have cloned and characterized mouse and human variants of MONaKA, a novel protein that interacts with and modulates the plasma membrane Na,K-ATPase. MONaKA was cloned based on its sequence homology to the Drosophila Slowpoke channel-binding protein dSlob, but mouse and human MONaKA do not bind to mammalian Slowpoke channels. At least two splice variants of MONaKA exist; the splicing is conserved perfectly between mouse and human, suggesting that it serves some important function. Both splice variants of MONaKA are expressed widely throughout the CNS and peripheral nervous system, with different splice variant expression ratios in neurons and glia. A yeast two-hybrid screen with MONaKA as bait revealed that it binds tightly to the β1 and β3 subunits of the Na,K-ATPase. The association between MONaKA and Na,K-ATPase β subunits was confirmed further by coimmunoprecipitation from transfected cells, mouse brain, and cultured mouse astrocytes. A glutathione S-transferase-MONaKA fusion protein inhibits Na,K-ATPase activity from whole brain or cultured astrocytes. Furthermore, transfection of MONaKA inhibits 86Rb+ uptake via the Na,K-ATPase in intact cells. These results are consistent with the hypothesis that MONaKA modulates brain Na,K-ATPase and may thereby participate in the regulation of electrical excitability and synaptic transmission.
Interference Tolerance Signaling Using TDD Busy Tone Concept

This paper presents a fully decentralized link adaptation algorithm to manage interference in an ad hoc network operating in TDD mode. The algorithm, called busy tone interference tolerance signaling, is of low complexity and easy to implement. Two functions are explored to set the busy tone signal power dependent on the level of tolerable interference, and their performance is compared with a system with fixed transmit powers. Results show that an appropriately chosen function for setting the busy-tone exhibits significantly enhanced power efficiency, implying that battery life can be extended, while providing a similar capacity than a fixed power system.
RNA-Seq Analysis of Mycobacterium avium Non-Coding Transcriptome

Deep sequencing was implemented to study the transcriptional landscape of Mycobacterium avium. High-resolution transcriptome analysis identified the transcription start points for 652 genes. One third of these genes represented leaderless transcripts, whereas the rest of the transcripts had 5′ UTRs with the mean length of 83 nt. In addition, the 5′ UTRs of 6 genes contained SAM-IV and Ykok types of riboswitches. 87 antisense RNAs and 10 intergenic small RNAs were mapped. 6 intergenic small RNAs, including 4.5S RNA and rnpB, were transcribed at extremely high levels. Although several intergenic sRNAs are conserved in M. avium and M. tuberculosis, both of these species have unique intergenic sRNAs. Moreover, we demonstrated that even conserved small RNAs are regulated differently in these species. Different sets of intergenic sRNAs may underlie differences in physiology between conditionally pathogenic M. avium and highly specialized pathogen M. tuberculosis.
Smart inertial sensor-based navigation system for flexible snake robot

The position and orientation of a robot during the navigation is a big challenge to the researchers. The received signal strength information (RSSI) of Wifi or Bluetooth or RFID is the available way to solve this issue for the localization researches. But the obstacles in the environment are very big challenge for this technology. RSSI should be vulnerable depends on the obstacle, if it is dynamic environment then there is nothing to say about this signal. Thus inertial sensors like accelerometer and rate gyro are chosen for flexible snake robot localization in planar surface navigation. This paper describes the methods of navigation positioning system using inertial sensor and finally, perform the experiment with the flexible snake robot for indoor position and orientation.
The Set Classification Problem and Solution Methods

This paper focuses on developing classification algorithms for problems in which there is a need to predict the class based on multiple observations (examples) of the same phenomenon (class). These problems give rise to a new classification problem, referred to as set classification, that requires the prediction of a set of instances given the prior knowledge that all the instances of the set belong to the same unknown class. This problem falls under the general class of problems whose instances have class label dependencies. Four methods for solving the set classification problem are developed and studied. The first is based on a straightforward extension of the traditional classification paradigm whereas the other three are designed to explicitly take into account the known dependencies among the instances of the unlabeled set during learning or classification. A comprehensive experimental evaluation of the various methods and their underlying parameters shows that some of them lead to significant gains in performance.
Robust Gesture Recognition using a Prediction-Error-Classification Approach

The main idea of this paper consists on doing gesture recognition by means of prediction. Taking into account that a signal predictor will predict accurately future values of gestures of its class and inaccurately the values of the others, we can use the prediction error to classify the gestures. These predictors are implemented using neuro fuzzy systems. We call this approach prediction-error-classification approach (PEC) and this idea represents a different approach to solve the problem of gesture recognition in real time using inexpensive accelerometers. To validate this approach we have studied the impact of the number of training samples in the prediction error using cross-validation. We have also studied the impact of the number of training samples in the recognition rate, using again cross-validation. And to test the robustness and applicability in a real situation of this approach, we have repeated all the tests with a more realistic experiment.
Freewheeling diode reverse-recovery failure modes in IGBT applications

In this paper, reverse-recovery failure modes in modern fast power diodes are investigated. By the aid of semiconductor device simulation tools, a better view is obtained for the physical process, and operating conditions at which both diode snappy recovery and dynamic avalanching occur during the recovery period in modern high-frequency power electronic applications. The work presented here confirms that the reverse-recovery process can by expressed by means of diode capacitance effects which influence the reverse-recovery characteristics. The paper also shows that the control of the carrier gradient and the remaining stored charge in the drift region during the recovery phase influence both failure modes and determine if the diode exhibits soft, snappy, or dynamic avalanche recovery characteristics.
Symbolic Reachability Graph Generation for High-level Models based on Synthesized Operators

This paper introduces a new technique for generating Binary Decision Diagrams (BDDs) representing high-level model’s underlying state/transition systems. The obtained decision diagram may serve as input for various analysis methods such as symbolic (probabilistic) model checking and/or Markovian performance and reliability analysis. As usual the proposed technique makes use of partitioned symbolic reachability analysis. However, contrary to existing techniques it neither relies on pregenerated symbolic representations of transition relations, nor does it make use of standard BDD-manipulating algorithms. Instead, symbolic reachability analysis is carried out by means of customized BDD-algorithms directly synthesized from high-level models to be analyzed. Overall the presented approach yields the core of a new tool bench for the symbolic analysis of state-based system descriptions. The tool bench is implemented on top of the Eclipse Modeling Framework and exploits Java Emitter Templates for code synthesis. Standard benchmark models show that for generating high-level models underlying state/transition systems significant improvements with respect to CPU time and memory consumption can be realized, ultimately allowing the verification of larger and much more complex systems.
p-cycle based multicast protection — A new ILP formulation

Fast development of the Internet including increasing number of various streaming services, e.g., IPTV, gaming, distance learning, triggers the need to deploy robust methods to protect critical streaming sessions. Since the most effective way to provide streaming services in computer networks is multicasting, in this paper we focus on the problem multicast session protection. We propose to use the p-cycles approach to provide 100% protection against a single link failure. The key contribution of this paper is a new ILP (Integer Linear Programming) model for joint optimisation of working and spare capacity for link failure recovery problem in multicast flow protected by p-cycles proposed in [1]. The main idea of our approach is to model multicast sessions using predefined candidate trees, i.e., during the optimisation, one among several multicast candidate trees is chosen to realise each session. According to extensive numerical experiments, our new ILP model provides results very close comparing the previous ILP formulation (in some case even less than 3% worse), but the execution time is significantly lower.
α2(I) collagen gene regulation by protein kinase C signaling in human dermal fibroblasts

We investigated the mechanisms by which protein kinase C (PKC) regulates the expression of the α2(I) collagen gene in normal dermal fibroblasts. Reduction of PKC-α activity by treatment with Gö697-6 or by overexpression of a dominant negative (DN) mutant form decreased α2(I) collagen gene expression. This decrease required a sequence element in the collagen promoter that contains Sp1/Sp3 binding sites. Reduction of PKC-δ activity by rottlerin or overexpression of DN PKC-δ also decreased α2(I) collagen gene expression. This effect required a separate sequence element containing Sp1/Sp3-binding sites and an Ets-binding site. In both cases, point mutations within the response elements abrogated the response to PKC inhibition. Forced overexpression of Sp1 rescued the PKC inhibitor-mediated reduction in collagen protein expression. A DNA affinity precipitation assay revealed that inhibition of PKC-δ by rottlerin increased the binding activity of endogenous Fli1 and decreased that of Ets1. On the other hand, TGF-β1, which increased the expression of PKC-δ, had the opposite effect, increasing the binding activity of Ets1 and decreasing that of Fli1. Our results suggest that PKC-δ is involved in the regulation of the α2(I) collagen gene in the presence or absence of TGF-β. Alteration of the balance of Ets1 and Fli1 may be a novel mechanism regulating α2(I) collagen expression.