Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Ranolazine on A1C and Glucose Levels in Hyperglycemic Patients With Non-ST Elevation Acute Coronary Syndrome

OBJECTIVE We determined the relationships between glycemia at randomization, concurrent antidiabetic therapy, and change in A1C and fasting plasma glucose (FPG) in patients with diabetes receiving standard treatment for diabetes and randomized to ranolazine or placebo within the MERLIN-TIMI-36 (MERLIN) study. Ranolazine is a novel first-in-class drug approved for treating angina pectoris. RESEARCH DESIGN AND METHODS Randomization and 4-month glycemic and antidiabetes drug usage data from MERLIN were analyzed using Spotfire and SAS version 9.1 software. RESULTS In patients with diabetes and A1C of ≥8–10% at randomization (n = 171), there was an absolute A1C reduction in the ranolazine group of 1.2% (95% CI −1.4 to −1.0), and the placebo-adjusted (n = 182) decrease in A1C by ranolazine was 0.59% (95% CI −0.99 to −0.20, P < 0.001). In patients with FPG of 150–400 mg/dl at randomization, ranolazine (n = 131) compared with placebo (n = 147) reduced FPG by 25.7 mg/dl (95% CI −43.3 to −8.1, P = 0.001). When changes in either A1C or FPG were correlated to A1C or FPG at randomization, the slopes were significantly steeper for ranolazine than placebo (A1C, P = 0.046; FPG, P < 0.001), indicating that lowering of A1C and FPG by ranolazine is related to hyperglycemia at randomization. Ranolazine, compared with placebo, was not associated with serious hypoglycemic events, associated with significant changes in concurrent antidiabetic therapy, or dependent on a history of angina. CONCLUSIONS Ranolazine, when added to concurrent antidiabetes treatment, lowers FPG and A1C in patients with cardiovascular disease and poorly controlled diabetes.
The association of adipokine levels in plasma and synovial fluid with the severity of knee osteoarthritis.

OBJECTIVE We sought to determine the association between plasma and SF levels of leptin and adiponectin in patients with knee OA. METHODS Plasma and SF levels of adipokines and soluble leptin receptor (sOB-R) were determined by ELISA in 96 patients with knee OA at different stages, according to Ahlback's classification. RESULTS Levels of adiponectin, leptin, sOB-R and free leptin in plasma and SF did not differ significantly across categories of OA severity. However, the ratio of SF to plasma leptin was significantly lower in the advanced OA stage compared with early stages of the disease (P = 0.02). After adjustment for sex and BMI, plasma leptin positively correlated with categories of OA severity (r = 0.23, P = 0.02), whereas SF/plasma leptin negatively correlated with OA stage (r = -0.27, P = 0.01). Cluster analysis showed that all men were included in one cluster and distributed in different stages of OA, whereas women formed three clusters with similar BMI, but those who were older and had the highest plasma leptin levels suffered from advanced OA. CONCLUSION Plasma leptin positively correlated with the severity of knee OA. The ratio of SF to plasma leptin might be a marker related to the severity of knee OA. Further studies should investigate whether similar associations exist in other joints affected by OA.
Calcium Extrusion from Mammalian Photoreceptor Terminals

Ribbon synapses of vertebrate photoreceptors constantly release glutamate in darkness. Transmitter release is maintained by a steady influx of calcium through voltage-dependent calcium channels, implying the presence of a mechanism that is able to extrude calcium at an equal rate. The two predominant mechanisms of intracellular calcium extrusion are the plasma membrane calcium ATPase (PMCA) and the Na+/Ca2+-exchanger. Immunohistochemical staining of retina sections revealed strong immunoreactivity for the PMCA in rod and cone terminals, whereas staining for the Na+/Ca2+-exchanger was very weak. The PMCA was localized to the plasma membrane along the sides of the photoreceptor terminals and was excluded from the base of the terminals where the active zones are located. The amplitude of a calcium-activated chloride current was used to monitor the intracellular calcium concentration. An upper limit for the time required to remove intracellular free calcium is obtained from two time constants measured for the calcium-activated chloride current tail currents: one of 50 msec and a second of 190 msec. Calcium extrusion was inhibited in the absence of intracellular ATP or in the presence of the PMCA inhibitor orthovanadate, but was unaffected by replacement of external Na+ with Li+. The data indicate that the PMCA, rather than the Na+/Ca2+-exchanger, is the predominant mechanism for calcium extrusion from photoreceptor synaptic terminals.
StrainSeeker: fast identification of bacterial strains from unassembled sequencing reads using user-provided guide trees

Background Fast, accurate and high-throughput detection of bacteria is in great demand. The present work was conducted to investigate the possibility of identifying both known and unknown bacterial strains from unassembled next-generation sequencing reads using custom-made guide trees. Results A program named StrainSeeker was developed that constructs a list of specific k-mers for each node of any given Newick-format tree and enables rapid identification of bacterial genomes within minutes. StrainSeeker has been tested and shown to successfully identify Escherichia coli strains from mixed samples in less than 5 minutes. StrainSeeker can also identify bacterial strains from highly diverse metagenomics samples. StrainSeeker is available at http://bioinfo.ut. ee/strainseeker. Conclusions Our novel approach can be useful for both clinical diagnostics and research laboratories because novel bacterial strains are constantly emerging and their fast and accurate detection is very important.
Characterizing Application Memory Error Vulnerability to Optimize Datacenter Cost via Heterogeneous-Reliability Memory

Memory devices represent a key component of datacenter total cost of ownership (TCO), and techniques used to reduce errors that occur on these devices increase this cost. Existing approaches to providing reliability for memory devices pessimistically treat all data as equally vulnerable to memory errors. Our key insight is that there exists a diverse spectrum of tolerance to memory errors in new data-intensive applications, and that traditional one-size-fits-all memory reliability techniques are inefficient in terms of cost. For example, we found that while traditional error protection increases memory system cost by 12.5%, some applications can achieve 99.00% availability on a single server with a large number of memory errors without any error protection. This presents an opportunity to greatly reduce server hardware cost by provisioning the right amount of memory reliability for different applications. Toward this end, in this paper, we make three main contributions to enable highly-reliable servers at low datacenter cost. First, we develop a new methodology to quantify the tolerance of applications to memory errors. Second, using our methodology, we perform a case study of three new dataintensive workloads (an interactive web search application, an in-memory key -- value store, and a graph mining framework) to identify new insights into the nature of application memory error vulnerability. Third, based on our insights, we propose several new hardware/software heterogeneous-reliability memory system designs to lower datacenter cost while achieving high reliability and discuss their trade-off. We show that our new techniques can reduce server hardware cost by 4.7% while achieving 99.90% single server availability.
A rule evaluation support method with learning models based on objective rule evaluation indexes

In this paper, we present a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indexes. Post-processing of mined results is one of the key issues to make a data mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective rule evaluation indexes and evaluations of a human expert for each rule. Since the method is needed more accurate rule evaluation models, we have compared learning algorithms to construct rule evaluation models with the actual meningitis data mining result and actual rule sets from UCI datasets. Then we show the availability of our adaptive rule evaluation support method.
An approximation algorithm for future wind scenarios

In power system operations, the success of stochastic optimization methods in helping to cope with increasing penetration of wind and solar power, rests on the effective construction of scenario trees that efficiently and accurately approximate the true probability space of the renewable power stochastic processes. In this work, by analyzing the statistical properties of wind turbines' power outputs which are recorded in Washington state (WA) for years 2012-2014, we identify the existing gaps in traditional modeling approaches and propose a possible solutions. The key idea we propose is to view scenario tree generation as a compression scheme of the wind power trajectories, sidestepping completely the model selection approach. We argue that to retain key features of the high order statistics, one can directly quantize realizations over the optimization horizon. We propose two approaches: one based on directly using scenarios in the time domain and an alternative one based on performing the quantization in the finite subspace of Morlet-wavelets. We compare the accuracy of the scenario trees with the prevalent approach based on an ARMA model selection step and show that the direct construction outperforms this approach when the complexity of the scenario tree is fixed.
Face recognition via semi-supervised discriminant local analysis

Semi-supervised learning approach is a fusion approach of supervised and unsupervised learning. Semi-supervised approach performs data learning from a limited number of available labelled training images along with a large pool of unlabelled data. Semi-supervised discriminant analysis (SDA) is one of the popular semi-supervised techniques. However, there is room for improvement. SDA resides in the illumination and local change of the face features. Hence, it is hardly to guarantee its performance if there are illumination and local changes on the images. This paper presents an improved version of SDA, termed as Semi-Supervised Discriminant Local Analysis (SDLA). In this proposed technique, a local descriptor is amalgamated with SDA. Hence, SDLA could possess the capabilities of both the local descriptor and SDA, in such a way that SDLA utilizes limited number of labelled training data and huge pool of unlabelled data to optimally capture local discriminant features of face data. The empirical results demonstrate that SDLA shows promising performance in both normal and makeup face authentication.
Extracting user preference from human natural interactions with physical items

With the help of pervasive computing, nowadays it is possible to gather user preference of physical items based on natural interaction, i.e., recognizing and recording user's actual choosing behavior by sensors, then analyzing the history to learn his/her current preference. In this paper, we propose a method based on this idea and implement a prototype to collect and apply the dietary preference. Preliminary user study shows its effectiveness.
Grid Economics: 10 Lessons from Finance

Today Grid technology is finding its way out of the academic incubator and into commercial environments. The interest in on-demand utility computing is larger then ever before in the industry and Grid tools are seen as an important enabler of this business model. The process of Grid commercialization inevitably brings financial issues to the fore. When multiple budget entities share resources, allocation decisions in resource management become also financial decisions. For the potentially large and complex systems enabled by Grids, equivalently robust financial engineering is needed. We draw parallels to the financial and commodity markets and outline 10 lessons learnt in the long history of asset management and decision-support for these markets. We show the relevance of these lessons for Grid commercialization and suggest specific issues that need to be resolved before (virtual) organizations can realize the theoretical value of Grid computing.
Natural language processing neural network for analogical inference

In this paper, we propose a novel neural network which can learn knowledge from natural language documents and can perform analogy. The conventional neural networks can use only the information the networks learned: knowledge acquisition has been a serious problem. The proposed network solves it by using a large scale dictionary named Google N-gram. In the preprocessing, natural language documents are analyzed by a Japanese dependency structure analyzer named Cabocha. The results are used in the network connection learning. In the analogy process, firing patterns of neurons are memorized in memory parts. When a similar firing pattern is appeared, a memorized pattern is retrieved. This process enables analogical inference. Three kinds of experiments were carried out using goo encyclopedia and Wikipedia as knowledge source. Superior performance of the proposed neural network has been confirmed.
Have Historical Climate Changes Affected Gentoo Penguin (Pygoscelis papua) Populations in Antarctica?

The West Antarctic Peninsula (WAP) has been suffering an increase in its atmospheric temperature during the last 50 years, mainly associated with global warming. This increment of temperature trend associated with changes in sea-ice dynamics has an impact on organisms, affecting their phenology, physiology and distribution range. For instance, rapid demographic changes in Pygoscelis penguins have been reported over the last 50 years in WAP, resulting in population expansion of sub-Antarctic Gentoo penguin (P. papua) and retreat of Antarctic Adelie penguin (P. adeliae). Current global warming has been mainly associated with human activities; however these climate trends are framed in a historical context of climate changes, particularly during the Pleistocene, characterized by an alternation between glacial and interglacial periods. During the last maximal glacial (LGM∼21,000 BP) the ice sheet cover reached its maximum extension on the West Antarctic Peninsula (WAP), causing local extinction of Antarctic taxa, migration to lower latitudes and/or survival in glacial refugia. We studied the HRVI of mtDNA and the nuclear intron βfibint7 of 150 individuals of the WAP to understand the demographic history and population structure of P. papua. We found high genetic diversity, reduced population genetic structure and a signature of population expansion estimated around 13,000 BP, much before the first paleocolony fossil records (∼1,100 BP). Our results suggest that the species may have survived in peri-Antarctic refugia such as South Georgia and North Sandwich islands and recolonized the Antarctic Peninsula and South Shetland Islands after the ice sheet retreat.
ANIMATED MESH SIMPLIFICATION BASED ON SALIENCY

ANIMATED MESH SIMPLIFICATION BASED ON SALIENCY METRICS Ahmet Tolgay M.S. in Computer Engineering Supervisors: Assoc. Prof. Dr. Uğur Güdükbay and Asst. Prof. Dr. Tolga K. Çapın July, 2008 Mesh saliency identifies the visually important parts of a mesh. Mesh simplification algorithms using mesh saliency as simplification criterion preserve the salient features of a static 3D model. In this thesis, we propose a saliency measure that will be used to simplify animated 3D models. This saliency measure uses the acceleration and deceleration information about a dynamic 3D mesh in addition to the saliency information for static meshes. This provides the preservation of sharp features and visually important cues during animation. Since oscillating motions are also important in determining saliency, we propose a technique to detect oscillating motions and incorporate it into the saliency based animated model simplification algorithm. The proposed technique is experimented on animated models making oscillating motions and promising visual results are obtained.
Multiscale Modeling of Chemical Vapor Deposition ( CVD ) Apparatus : Simulations and Approximations

We are motivated to compute delicate chemical vapor deposition (CVD) processes. Such processes are used to deposit thin films of metallic or ceramic materials, such as SiC or a mixture of SiC and TiC. For practical simulations and for studying the characteristics in the deposition area, we have to deal with delicate multiscale models. We propose a multiscale model based on two different software packages. The large scales are simulated with computational fluid dynamics (CFD) software based on the transportreaction model (or macroscopic model), and the small scales are simulated with ordinary differential equations (ODE) software based on the reactive precursor gas model (or microscopic model). Our contribution is to upscale the correlation of the underlying microscale species to the macroscopic model and reformulate the fast reaction model. We obtain a computable model and apply a standard CFD software code without losing the information of the fast processes. For the multiscale model, we present numerical results of a real-life deposition process.
Discrete Parameterization for Deforming Arbitrary Meshes

Techniques for deforming polygonal meshes are demonstrated by using two-dimensional lattices of control points or functions for pasting features. The deformations use a shape-preserving parametrization that embeds the mesh’s vertices in a normalized two-dimensional space while preserving shape consistency for non-flat surfaces. A discrete smoothing used for the parametrization has inefficient iterative calculations, which is unsuitable for manipulations of dense meshes, and an initial approximation for the smoothing is therefore proposed in order to reduce the number of iterations. The approximation uses a graph-searching algorithm and a discrete normalization whose computational costs are negligible in comparison with that of the iterative calculations.
Virtual compensated sliding mode control of non-minimum phase nonlinear systems with uncertainty

The paper studies the robust control of non-minimum phase nonlinear system based on the differential geometry theory. The nonlinear system could be expressed as input-output dynamics with the unstable internal modes, through nonlinear coordinate transformation. Virtual controller is introduced to stabilize zero dynamic, and the sliding mode controller, which is robust to the parameter perturbations of the closed-loop system. Simulations show that the virtual stabilized sliding mode control scheme guarantee nice result, and is strong robust to parameter perturbations.
Forecasting Supply Chain Demand Using Machine Learning Algorithms

Managing supply chains in today’s complex, dynamic, and uncertain environment is one of the key challenges affecting the success of the businesses. One of the crucial determinants of effective supply chain management is the ability to recognize customer demand patterns and react accordingly to the changes in face of intense competition. Thus the ability to adequately predict demand by the participants in a supply chain is vital to the survival of businesses. Demand prediction is aggravated by the fact that communication patterns between participants that emerge in a supply chain tend to distort the original consumer’s demand and create high levels of noise. Distortion and noise negatively impact forecast quality of the participants. This work investigates the applicability of machine learning (ML) techniques and compares their performances with the more traditional methods in order to improve demand forecast accuracy in supply chains. To this end we used two data sets from particular companies (chocolate manufacturer and toner cartridge manufacturer), as well as data from the Statistics Canada manufacturing survey. A representative set of traditional and ML-based forecasting techniques have been applied to the demand data and the accuracy of the methods was compared. As a group, Machine Learning techniques outperformed traditional techniques in terms of overall average, but not in terms of overall ranking. We also found that a support vector machine (SVM) Réal Carbonneau HEC Montréal, Canada Rustam Vahidov Concordia University, Canada Kevin Laframboise Concordia University, Canada Forecasting Supply Chain Demand Using Machine Learning Algorithms DOI: 10.4018/978-1-60960-818-7.ch6. 9
Extrinsic calibration for wide-baseline RGB-D camera network

In the recent years, color and depth camera systems have attracted intensive attention because of its wide applications in image-based rendering, 3D model reconstruction, and human tracking and pose estimation. These applications often require multiple color and depth cameras to be placed with wide separation so as to capture the scene objects from different prospectives. The difference in modality and the wide baseline make calibration a challenging problem. In this paper, we present an algorithm that simultaneously and automatically calibrates the extrinsics across multiple color and depth cameras across the network. Rather than using the standard checkerboard, we use a sphere as a calibration object to identify the correspondences across different views. We experimentally demonstrate that our calibration framework can seamlessly integrate different views with wide baselines that outperforms other techniques in the literature.
Multiple features for image retrieval in distributed datacenter

The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose the scalable image retrieval framework which can efficiently support content similarity search in the distributed environment. Its key idea is to integrate image fusion features into distributed hash tables (DHTs) by exploiting the property of the locality sensitive hashing (LSH). Thus, the images with similar content are most likely gathered into the same node without the knowledge of any global information. To the best of our knowledge, there is less comprehensive study on large-scale CBIR with fusion features in the distributed environment.
Modeling crowds as single-minded entities

Inspired by Gustave Lebon's idea of crowds as single-minded entities, we present a novel approach to describe the behavior of a crowd as a single entity, based on the global movement of the entire aggregate of people conforming the crowd. The present work significantly differs from existing literature where the behavior of single individuals within the crowd are the building blocks to describe crowd behavior. A bidimensional neural gas network is implemented to learn the topology of the physical environment in an unsupervised fashion, then a self-organizing map and a Bayesian network are used to describe the behavior of the crowd as a single entity. Experiments were conducted using footage from New York Grand Central Station to test the accuracy of our model to learn and identify different behaviors of the crowd. Results show high accuracy to identify behaviors under usual circumstances and low but consistently increasing accuracy over time on less common cases.