Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of High Glucose on Stress-Induced Senescence of Nucleus Pulposus Cells of Adult Rats

Study Design In vitro cell culture model. Purpose We investigated the effect of diabetes mellitus (DM) on senescence of adult nucleus pulposus (NP) cells. Overview of Literature DM is a major public health issue worldwide, especially adult-onset (type 2) DM. DM is also thought to be an important etiological factor in disc degeneration. Hyperglycemia is considered to be a major causative factor in the development of DM-associated diseases through senescence. However, little is known about the effects of DM on senescence in adult NP cells. Methods Adult NP cells were isolated from 24-week-old rats, cultured, and placed in either 10% fetal bovine serum (FBS, normal control) and 10% FBS plus two different high glucose concentrations (0.1 M or 0.2 M; experimental conditions) for 1 or 3 days. We identified and quantified the occurrence of senescence in adult rat NP cells using senescence-associated-beta-galactosidase (SA-β-Gal) staining. We also investigated the expression of proteins related to the replicative senescence (p53-p21-pRB) and stress-induced premature senescence (p16-pRB) pathways. Results The mean SA-β-Gal-positive percentage was increased in adult rat NP cells treated with high glucose in a dose- and time-dependent manner. Both high glucose levels increased the expression of p16 and pRB proteins in adult rat NP cells. However, the levels of p53 and p21 proteins were decreased in adult rat NP cells treated with both high glucose concentrations. Conclusions The current study demonstrated that high glucose accelerated stress-induced senescence in adult rat NP cells in a dose- and time-dependent manner. Accelerated stress-induced senescence in adult NP cells could be an emerging risk factor for intervertebral disc degeneration in older patients with DM. These results suggest that strict blood glucose control is important in prevent or delaying intervertebral disc degeneration in older patients with DM.
Performance improvement of G.hn system based on bit mapping technique

A new specification for power line communication (PLC), named G.hn, has been developed recently for the existing-wire home networking. G.hn system specifies a coded modulation scheme based on quasi-cyclic low-density parity-check (QC-LDPC) codes and quadrature amplitude modulations (QAMs). In this paper, we present a bit mapping strategy to exploit the unequal error protection (UEP) property inherent in the irregular LDPC codes and high-order constellations, which can effectively improve the reliability of the whole coded modulation system with no penalty. The method to design the bit mapping scheme is also investigated via the analysis of extrinsic information transfer (EXIT) charts. Simulation results reveal that the appropriate bit mapping scheme can achieve considerable gains in the G.hn system.
Human ECG: nonlinear deterministic versus stochastic aspects

The authors discuss aspects of randomness and determinism in electrocardiographic signals. In particular, they take a critical look at attempts to apply methods of nonlinear time series analysis derived from the theory of deterministic dynamical systems. It is argued that deterministic chaos is not a likely explanation for the short-time variablity of the inter-beat interval times, except for certain pathologies. Conversely, densely sampled full ECG recordings possess properties typical of deterministic signals. In the last-mentioned case, methods of deterministic nonlinear time-series analysis can yield new insights.
Comparison of yield and genotyping performance of multiple displacement amplification and OmniPlex™ whole genome amplified DNA generated from multiple DNA sources

The promise of whole genome amplification (WGA) is that genomic DNA (gDNA) quantity will not limit molecular genetic analyses. Multiple displacement amplification (MDA) and the OmniPlex™ PCR‐based WGA protocols were evaluated using 4 and 5 ng of input gDNA from 60 gDNA samples from three tissue sources (mouthwash, buffy coat, and lymphoblast). WGA DNA (wgaDNA) yield and genotyping performance were evaluated using genotypes determined from gDNA and wgaDNA using the AmpFlSTR® Identifiler® assay and N=49 TaqMan® SNP assays. Short tandem repeat (STR) and SNP genotyping completion and concordance rates were significantly reduced with wgaDNA from all WGA methods compared with gDNA. OmniPlex wgaDNA exhibited a greater reduction in genotyping performance than MDA wgaDNA. Reduced wgaDNA genotyping performance was due to allelic (all protocols) and locus (OmniPlex) amplification bias leading to heterozygote and locus dropout, respectively, and %GC sequence content (%GC) was significantly correlated with TaqMan assay performance. Lymphoblast wgaDNA exhibited higher yield (OmniPlex), buffy coat wgaDNA exhibited higher STR genotyping completion (MDA), whereas mouthwash wgaDNA exhibited higher SNP genotyping discordance (MDA). Genotyping of wgaDNA generated from ≤5 ng gDNA, e.g., from archaeological, forensic, prenatal diagnostic, or pathology samples, may require additional genotyping validation with gDNA and/or more sophisticated analysis of genotypes incorporating observed reductions in genotyping performance. Hum Mutat 26(3), 262–270, 2005. © 2005 Wiley‐Liss, Inc.
Three dimensional numerical study on the efficiency of a core-shell InGaN/GaN multiple quantum well nanowire LED

This paper presents the development of 3D fully self-consistent FEM Poisson and drift-diffusion solver. We use this solver to investigate the current flow in the coreshell multiple quantum well LED. The current spreading effect was examined to determine the carrier distribution at the sidewall of core-shell nanowire LEDs. The results revealed that a larger aspect ratio by increasing the nanowire height could increase the nonpolar-active area volume and reduce the droop effect at the same current density. Making the current spreading length exceed a greater nanowire height is critical for reducing the droop effect. In addition, this paper presents a discussion on the influences of the spacing between each nanowire on corresponding nanowire diameters.
Postchallenge Glucose, A1C, and Fasting Glucose as Predictors of Type 2 Diabetes and Cardiovascular Disease

OBJECTIVE A1C has been proposed as a new indicator for high risk of type 2 diabetes. The long-term predictive power and comparability of elevated A1C with the currently used high-risk indicators remain unclear. We assessed A1C, impaired glucose tolerance (IGT), and impaired fasting glucose (IFG) as predictors of type 2 diabetes and cardiovascular disease (CVD) at 10 years. RESEARCH DESIGN AND METHODS This prospective population-based study of 593 inhabitants from northern Finland, born in 1935, was conducted between 1996 and 2008. An oral glucose tolerance test (OGTT) was conducted at baseline and follow-up, and A1C was determined at baseline. Those with a history of diabetes were excluded from the study. Elevated A1C was defined as 5.7–6.4%. Incident type 2 diabetes was confirmed by two OGTTs. Cardiovascular outcome was measured as incident CVD or CVD mortality. Multivariate log-binomial regression models were used to predict diabetes, CVD, and CVD mortality at 10 years. Receiver operating characteristic curves compared predictive values of A1C, IGT, and IFG. RESULTS Incidence of diabetes during the follow-up was 17.1%. Two of three of the cases of newly diagnosed diabetes were predicted by a raise in ≥1 of the markers. Elevated A1C, IGT, or IFG preceded diabetes in 32.8, 40.6, and 21.9%, respectively. CVD was predicted by an intermediate and diabetic range of 2-h glucose but only by diabetic A1C levels in women. CONCLUSIONS A1C predicted 10-year risk of type 2 diabetes at a range of A1C 5.7–6.4% but CVD only in women at A1C ≥6.5%.
Reasoning with Deep Learning: an Open Challenge

Building machines capable of performing automated reasoning is one of the most complex but fascinating challenges in AI. In particular, providing an effective integration of learning and reasoning mechanisms is a long-standing research problem at the intersection of many different areas, such as machine learning, cognitive neuroscience, psychology, linguistic, and logic. The recent breakthrough achieved by deep learning methods in a variety of AI-related domains has opened novel research lines attempting to solve this complex and challenging task.
Animating Scanned Human Models

In this paper, we present a novel technique for animating the human model from whole body scanned data. In our method, the human model is represented with a layered model, its skeleton is generated automatically, and a simplified surface skin mapped to the underlying skeleton. We present methods for constructing and animating our models without manual intervention, a vertex blending technique is employed to create continuous and smooth deformation, and animation results are presented using motion capture data.
Recent advances in joint source-channel coding of video

It has previously been shown that for an image or video transmission scheme to be both efficient and robust in practice that a joint source-channel coding approach is necessary. In this work, we discuss efforts in the field of joint source-channel coding of images and video. We then present a general methodology for determining the optimal tradeoff between source and channel coding as a function of channel characteristics for transmission of video. Further reduction in the computational complexity of this methodology through the use of models is discussed. Results of the optimal rate allocation procedure are presented for selected source and channel coding schemes and as a function of channel parameters.
Micro fluidic device to control the position and to analyze the condition of C. elegans as a bioindicator

We proposed a micro fluidic device to control the position of Caenorhabditis elegans (C. elegans) and to analyze the condition of C. elegans as a bioindicator during its lifespan. In this device, we can filter C. elegans depending on size and control the position of C. elegans to culture and test C. elegans and to measure the capacitance change. The capacitance change depends on the body volume of C. elegans. Through the correlation with the capacitance change and the body volume of C. elegans, the body volume of C. elegans is assessed without imaging system. The proposed micro fluidic device was implemented using a PDMS layer and a glass substrate that were bonded together. The PDMS layer had a valve system and pattern rows to filter, culture and test C. elegans. A pair of electrodes is used to measure the capacitance change. In this study, we filtered C. elegans into the same size by filter and one C. elegans was introduced into a chamber row to be cultured and tested. And it was confirmed the correlation between the capacitance change and the body volume of C. elegans. And the proposed micro fluidic device will be applied to observe the effect of chemical or toxicant like heavy metal (Cd) on C. elegans as bioindicator.
Coordinated Direct and Relay Schemes for Two-Hop Communication in VANETS

In order to accommodate increasing need and offer communication with high performance, both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications are exploited. The advantages of static nodes and vehicular nodes are combined to achieve an optimal routing scheme. In this paper, we consider the communications between a static node and the vehicular nodes moving in an adjacent area of it. The adjacent area is defined as the zone where a vehicular can communicate with the static node within maximum two hops. We only consider single-hop and two-hop transmissions because these transmissions can be considered as building blocks to construct transmissions with a higher number of hops. Different cases in which an uplink or a downlink for the two-hop user combined with an uplink or a downlink for the single-hop user correspond to different CDR schemes. Using side information to intentionally cancel the interference, Network Coding (NC), CDR, overhearing and multi-way schemes aggregate communications flows in order to increase the performance of the network. We apply the mentioned schemes to a V2I network and propose novel schemes to optimally arrange and combine the transmissions.
PNrule: A New Framework for Learning Classifier Models in Data Mining (A Case-Study in Network Intrusion Detection)

Learning classifier models is an important problem in data mining. Observations from the real world are often recorded as a set of records, each characterized by multiple attributes. Associated with each record is a categorical attribute called class. Given a training set of records with known class labels, the problem is to learn a model for the class in terms of other attributes. The goal is to use this model to predict the class of any given set of records, such that certain objective function based on the predicted and actual classes is optimized. Traditionally, the goal has been to minimize the number of misclassified records; i.e. to maximize accuracy. Various techniques exist today to build classifier models[11]. Although no single technique is proven to be the best in all situations, techniques that learn rule-based models are especially popular in the domain of data mining. This can be contributed to the easy interpretability of the rules by humans, and competitive performance exhibited by rule-based models in many application domains.
Cap and Swaption Approximations in Libor Market Models with Jumps

This paper develops formulas for pricing caps and swaptions in LIBOR market models with jumps. The arbitrage-free dynamics of this class of models were characterized in Glasserman and Kou (1999) in a framework allowing for very general jump processes. For computational purposes, it is convenient to model jump times as Poisson processes; however, the Poisson property is not preserved under the changes of measure commonly used to derive prices in the LIBOR market model framework. In particular, jumps cannot be Poisson under both a forward measure and the spot measure, and this complicates pricing. To develop pricing formulas, we approximate the dynamics of a forward rate or swap rate using a scalar jump-diffusion process with time-varying parameters. We develop an exact formula for the price of an option on this jump-diffusion through explicit inversion of a Fourier transform. We then use this formula to price caps and swaptions by choosing the parameters of the scalar diffusion to approximate the arbitrage-free dynamics of the underlying forward or swap rate. We apply this method to two classes of models: one in which the jumps in all forward rates are Poisson under the spot measure, and one in which the jumps in each forward rate are Poisson under its associated forward measure. Numerical examples demonstrate the accuracy of the approximations.
Principal Curves and Surfaces

Abstract : Principal curves are smooth one dimensional curves that pass through the middle of a p dimensional data set. They minimize the distance from the points, and provide a non-linear summary of the data. The curves are non- parametric and their shape is suggested by the data. Similarly, principal surfaces are two dimensional surfaces that pass through the middle of the data. The curves and surfaces are found using an iterative procedure which starts with a liner summary such as the usual principal component line or plate. Each successive iteration is a smooth or local average of the p dimensional points, where local is based on the projections of the points onto the curve or surface of the previous iteration. A number of linear techniques, such as factor analysis and errors in variables regression, end up using the principal components as their estimates (after a suitable scaling of the co-ordinates). Principal curves and surfaces can be viewed as the estimates of non-linear generalizations of these procedures. Principal Curves (or surfaces) have a theortical definition for distributions: they are the Self Consistent curves. A curve is self consistent if each point on the curve is the conditional mean of the points that project there. The main theorem proves that principal curves are critical values of the expected squared distance between the points and the curve. Linear principal components have this property as well; in fact, we prove that if a principal curve is straight, then it is a principal component. These results generalize the usual duality between conditional expectation and distance minimization. We also examine two sources of bias in the procedures, which have the satisfactory property of partially cancelling each other.
Self-Similar Evolution of Gravitational Clustering. II. N-Body Simulations of the n = –2 Spectrum

The power spectrum P(k) ∝ kn with n = -2 is close to the shape of the measured galaxy spectrum on small scales. Unfortunately, this spectrum has proven rather difficult to simulate. Furthermore, two-dimensional simulations have suggested a breakdown of self-similar scaling for spectra with n < -1 due to divergent contributions from the coupling of long wave modes. This paper is the second (numerical) part of our investigation into nonlinear gravitational clustering of scale-free spectra, in particular, to test the scaling of the n = -2 spectrum. Using high-resolution N-body simulations, we find that the n = -2 power spectrum displays self-similar scaling. The phase shift of Fourier modes of the density show a dual scaling, self-similar scaling at early times and a scaling driven by the bulk velocity at late times. The second scaling was shown analytically to be a kinematical effect that does not affect the growth of clustering. Thus our analytical and N-body results verify that self-similarity in gravitational clustering holds for -3 < n < 1. The N-body spectrum is also compared with analytic fitting formulae, which are found to slightly underestimate the power in the nonlinear regime. The asymptotic shape of the spectrum at high k is a power law with the same slope as predicted by the stable clustering hypothesis.
A Graph-Based Approach to String Regeneration

The string regeneration problem is the problem of generating a fluent sentence from a bag of words. We explore the Ngram language model approach to string regeneration. The approach computes the highest probability permutation of the input bag of words under an N-gram language model. We describe a graph-based approach for finding the optimal permutation. The evaluation of the approach on a number of datasets yielded promising results, which were confirmed by conducting a manual evaluation study.
Delta Delta Delta

It shall also be the purpose of Delta Delta Delta to promote and develop mutually beneficial relationships between the fraternity and the colleges and universities where the fraternity has established chapters, to develop qualities of unselfish leadership among its members, and to encourage them to assume, with integrity and devotion to moral and democratic principles, the highest responsibilities of college women.
Power optimized partial product reduction interconnect ordering in parallel multipliers

When designing the reduction tree of a parallel multiplier, we can exploit a large intrinsic freedom for the interconnection order of partial products. The transition activities vary significantly for different internal partial products. In this work we propose a method for generation of power-efficient parallel multipliers in such a way that its partial products are connected to minimize activity. The reduction tree is designed progressively. A simulated annealing optimizer uses power cost numbers from a specially implemented probabilistic gate-level power estimator and selects a power-efficient solution for each stage of the reduction tree. VHDL simulation using ModelSim shows a significant reduction in the overall number of transitions. This reduction ranges from 15% up to 32% compared to randomly generated reduction trees and is achieved without any noticeable area or performance overhead.
Understanding the Adoption of Ada: A Field Study Report

Abstract : In 1983, the U.S. Department of Defense (DOD) established a policy requiring the use of Ada for the development of all new DOD mission-critical computer applications. A multi-industry field study was conducted with seven business units from DOD contractors that have made decisions about the adoption and use of Ada. This report examines the extent to which the Ada adoption behavior of these contractors is influenced by their expectations of the technological opportunity provided by Ada, market demand for Ada, and appropriability conditions in the product market of firms. Findings indicate contractor decisions about adopting Ada are influenced both by the technical merits of the language and the economic impact on the firm. Keywords: Ada programming language; Appropriability conditions; Costs of adoption; Market demand.
Behind Schedule: Psychosocial Aspects of Delayed Puberty in the Competitive Female Gymnast

Research on the psychosocial aspects of maturational timing has not addressed the experience of the late-developing gymnast. Studies to date have used conceptual and timing frameworks incommensurate with the experience of the athlete who reaches puberty in late adolescence. The role of environmental influences, including the support group, in a gymnast’s experience of puberty is salient. In order for sports practitioners to help late maturing female gymnasts deal with their personal and physical development, a reconceptualization of the related psychosocial parameters is needed.
Effects of spatial correlation and associated parameters on the capacity of MIMO fading channels

The effect of spatial correlation on the capacity of multiple-input multiple-output (MIMO) fading channel with uniform linear arrays (ULAs) at the transmitter and receiver is investigated. The situations when the channel is known and unknown at the transmitter are considered. The effects of antenna spacing, angle spread, and number of antennas on the capacity are also investigated. Extensive Monte Carlo computer simulations are carried out. The results showed that the spatial correlation may severely degrade the capacity of MIMO systems. The degradation in the capacity can be reduced by increasing the separation distance between the antenna elements. However, increasing the antenna spacing beyond /spl ap/ 2/spl lambda/ has no effect on the capacity. Small angle spread leads to higher correlation between the antenna elements and consequently lower capacity.
Enhancing Clustering Technique to Plan Social Infrastructure Services

This article deals with social infrastructure planning problems in urban city. Each facility must serve minimum pre-specified level of demand. The objective is to minimize the distance traveled by users to reach the facilities this means also to maximize the accessibility to facilities. A location model that captures the above features is formulated and different solution methods are tested. Clustering in spatial data mining is to group similar objects based on their connectivity, distance, or their relative density in space. In real word, there exist many physical obstacles such as rivers, lakes, highways and mountains, and their presence may affect the result of clustering significantly. In this paper, we study the problem of clustering in the presence of obstacles to solve location of public service facility problem. In this paper, CSPOD-DBSCAN algorithm (Clustering with short path Obstructed Distance - Density- Based Spatial Clustering of Applications with Noise) is developed in the spirit of DBSCAN clustering algorithms. This algorithm is Density-based clustering algorithm using Dijkstra algorithm to calculate obstructed short path distance. The application of this algorithm is illustrated through a case study involving the location of schools in the districts of Mecca in Saudi Arabia.