Miracle-dz/UniFAR-FTUs huggingface dataset:
Intracellular chloride modulates A‐type potassium currents in astrocytes

Application of the GABAA receptor agonist muscimol to astrocytes in situ or in vitro results in a receptor‐mediated Cl− current with a concomitant block of outward K+ currents. The effect on K+ current is largely selective for the inactivating A‐type current. Parallel experiments with various Cl− pipette concentrations show a significant reduction in A‐type current under low Cl− conditions with minimal effect on delayed current. In addition, lower Cl− conditions caused a depolarizing shift of steady‐state inactivation (V1/2, −68 to −57 mV) and activation (V1/2, −5.8 to 34 mV) kinetics of A‐type current only. Cl− had no effect on the time course of inactivation or reactivation kinetics, suggesting the Cl−‐mediated effect is largely on activation kinetics, indirectly affecting steady‐state inactivation. Muscimol application to astrocytes under perforated patch control (gramicidin) displayed a similar block of A‐type current to that of conventional whole cell patch at 40 or 20 mM pipette Cl− concentrations. With barium application under perforated patch conditions, the study of muscimol‐mediated Cl− current in isolation of the effect on K+ currents was possible. This allowed estimation of intracellular Cl− concentration from receptor current reversal information. The average intracellular Cl− concentration was found to be 29 ± 3.2 mM. The effect on activation kinetics and lack of effect on time course of inactivation or reactivation suggest that intracellular anion concentrations have an effect on the K+ channel voltage sensor region. Cl− may modulate K+ currents by altering membrane field potentials surrounding K+ channel proteins. GLIA 39:207–216, 2002. © 2002 Wiley‐Liss, Inc.
Physical and Tensile Properties of NiTi Alloy by Selective Electron Beam Melting

NiTi is characterized as a shape memory alloy that has found interesting applications from aerospace to biomedical engineering. The use of NiTi in biomedical applications is due to its excellent biocompatibility, shape memory and pseudoelastic properties. These properties make NiTi an excellent candidate for many functional designs in biomedical fields. However, difficulties in manufacturing and processing of this alloy are significant hindrance to widespread applications. Advances in additive manufacturing (AM) such as selective laser and electron beam techniques have provided opportunities in manufacturing complex shaped NiTi parts. In this research paper, we demonstrate manufacturing of NiTi parts using a selective electron beam melting (SEBM) technique. Complete evaluation of physical, chemical and mechanical properties was carried out to determine the suitability of SEBM process. Differential scanning calorimeter (DSC), X-ray diffraction (XRD), and metallographic analyses were employed for the thermal and structural characterizations. The obtained results suggest that it is imperative to, and challenging to control the additive manufacturing process in order to obtain the desired microstructures and avoid unwanted texture. An exhaustive heat treatment of the samples after SEBM process might also be necessary.
Chaotic properties and pattern competition during the learning phase of back propagation neural networks

This paper examines the chaotic behavior of Back Propagation neural networks during the training phase. The networks are trained using ordinary parameter values, while two different cases are considered. In the first one, the network does not meet desirable convergence within a pre-specified number of epochs. Chaotic behavior of this network is depicted by examining the values of the dominant Lyapunov exponents of the weight data series produced by additional training. For each training epoch, the data series representing input patterns producing the minimum absolute error in output during additional training, is also subjected to Lyapunov exponent investigation. The task of this investigation is to determine whether the network exhibits chaotic pattern competition of the best learned inputs. In the second case, the network is improved and desirable convergence is accomplished. Again, investigation focuses on the series of values representing input patterns that produce outputs with minimum absolute error. The results obtained from dominant Lyapunov exponent estimations show that chaotic pattern competition is still present, despite the fact that the network practically satisfies stability demands within predetermined accuracy limits. The best estimation series consist of the output values corresponding to the best learned input patterns. These series are examined using the theoretical tool of topological conjugacy, in addition to numerical verification of the results.
Generalized Closed World Reasoning in Description Logics with Extended Domain Closure

Generalized closed world reasoning allows for the assumption of a specified maximal set of negated atomic assertions retaining the consistency of an indefinite knowledge base. In this paper, a generalized closed world assumption (GCWA) is specified for the description logic ALCN and all of its sublanguages, provided that the terminological component is eliminable. In certain situations, by applying the GCWA, queries are often answered as intended by users. Opposed to queries with an epistemic operator K, querying with this approach provides the information which of the corresponding assertions can consistently be assumed to be true. Further, the GCWA can be applied locally.
Digital Watermarking for 3D Polygons using Multiresolution Wavelet Decomposition

Recently, much interest is being taken in methods to protect the copyright of digital data and preventing illegal duplication of it. However, in the area of CAD/CAM and CG, there are no effective ways to protect the copyright of 3D geometric models. As a first step to solve this problem, a new digital watermarking method for 3D polygonal models is introduced in this paper. Watermarking is one of the copyright protection methods where an invisible watermark is secretly embedded into the original data. The proposed watermarking method is based on wavelet transform (WT) and multiresolution representation (MRR) of the polygonal model. The watermark can be embedded in the large wavelet coefficient vectors at various resolution levels of the MRR. This makes the embedded watermark imperceptible and invariant to the affine transformation. And also makes the control of the geometric error caused by the watermarking reliable. First the requirements and features of the proposed watermarking method are discussed. Second the mathematical formulations of WT and MRR of the polygonal model are shown. Third the algorithm of embedding and extracting the watermark is proposed. Finally, the effectiveness of the proposed watermarking method is shown through several simulation results.
Boosting the ranking function learning process using clustering

As the Web continuously grows, the results returned by search engines are too many to review. Lately, the problem of personalizing the ranked result list based on user feedback has gained a lot of attention. Such approaches usually require a big amount of user feedback on the results, which is used as training data. In this work, we present a method that overcomes this issue by exploiting all search results, both rated and unrated, in order to train a ranking function. Given a small initial set of user feedback for some search results, we first perform clustering on all results returned by the search. Based on the clusters created, we extend the initial set of rated results, including new, unrated results. Then, we use a popular training method (Ranking SVM) to train a ranking function using the expanded set of results. The experiments show that our method approximates sufficiently the results of an "ideal" system where all results of each query should be rated in order to be used as training data, something that is not feasible in a real-world scenario.
A comparison of graphical design techniques for parallel, distributed software

textabstractWe have compared three graphical design techniques, OMT, ADL, and PARSE, on their suitability for the development of parallel/distributed applications. Our method has been to use all three of them in modeling one, existing, application: a backup facility running within the Andrew File System. We compare and analyze the outcomes on a number of important design aspects. Based on this, we draw conclusions on each individual technique and on graphical design techniques for parallel/distributed software in general.
Quantitative large scale gene expression profiling from human stem cell culture micro samples using multiplex pre-amplification

Abstract Transcriptional profiling is a powerful tool to study biological mechanisms during stem cell differentiation and reprogramming. Genome-wide methods like microarrays or next generation sequencing are expensive, time consuming, and require special equipment and bioinformatics expertise. Quantitative RT-PCR remains one of today’s most widely accepted and used methods for analyzing gene expression in biological samples. However, limitations in the amount of starting materials often hinder the quantity and quality of information that could be obtained from a given sample. Here, we present a fast 4-step workflow allowing direct, column-free RNA isolation from limited human pluripotent stem cell (hPSC) cultures that is directly compatible with subsequent reverse transcription, target specific multiplex pre-amplification, and standard SYBR-Green quantitative PCR (qPCR) analysis. The workflow delivers excellent correlations in normalized gene-expression data obtained from different samples of hPSCs over a wide range of cell numbers (500–50,000 cells). We demonstrate accurate and unbiased target gene quantification in limiting stem cell cultures which allows for monitoring embryoid body differentiation and induced pluripotent stem cell (iPSC) reprogramming. This method highlights a rapid and cost effective screening process, allowing reduction of culture formats and increase of processing throughputs for various stem cell applications.
Glass strengthening with an ultrafast laser

We performed laser-strengthening experiments on a number of glasses and glass-ceramics. The objective of this study was to identify the mechanism of strengthening and to establish the correlation between the residual stress and the apparent fracture-toughness increase. After the laser exposure, the stress was measured by interpreting the resulting birefringence pattern (Polscope™). The apparent fracture toughness was measured by a Double-Torsion method, which required a minimal number of samples to reliably detect the effect of laser treatment as compared with the four-point bending test. We discovered that the apparent fracture-toughness increase is proportional to the magnitude of laser induced stress. We also present the results on how the glass strengthening is influenced by the irradiation pattern and laser parameters.We performed laser-strengthening experiments on a number of glasses and glass-ceramics. The objective of this study was to identify the mechanism of strengthening and to establish the correlation between the residual stress and the apparent fracture-toughness increase. After the laser exposure, the stress was measured by interpreting the resulting birefringence pattern (Polscope™). The apparent fracture toughness was measured by a Double-Torsion method, which required a minimal number of samples to reliably detect the effect of laser treatment as compared with the four-point bending test. We discovered that the apparent fracture-toughness increase is proportional to the magnitude of laser induced stress. We also present the results on how the glass strengthening is influenced by the irradiation pattern and laser parameters.
Three‐Dimensional Left Ventricular Deformation in Hypertrophic Cardiomyopathy

BackgroundIn hypertrophic cardiomyopathy, ejection fraction is normal or increased, and force-length relations are reduced. However, three-dimensional (3D) motion and deformation in vivo have not been assessed in this condition. We have reconstructed the 3D motion of the left ventricle (LV) during systole in 7 patients with hypertrophic cardiomyopathy (HCM) and 12 normal volunteers by use of magnetic resonance tagging. Methods and ResultsTransmural tagging stripes were automatically tracked to subpixel resolution with an active contour model. A 3D finite-element model was used to interpolate displacement information between short- and long-axis slices and register data on a regional basis. Displacement and strain data were averaged into septal, posterior, lateral, and anterior regions at basal, midventricular, and apical levels. Radial motion (toward the central long axis) decreased slightly in patients with HCM, whereas longitudinal displacement (parallel to the long axis) of the base toward the apex was markedly reduced: 7.5±2.5mm (SD) versus 12.5±2.0 mm, P<. 001. Circumferential and longitudinal shortening were both reduced in the septum (P<.01 at all levels). The principal strain associated with 3D maximal contraction was slightly depressed in many regions, significantly in the basal septum (−0.18±0.05 versus −0.22±0.02, P<.05) and anterior (−0.20±0.05 versus −0.23±0.02, P<.05) walls. In contrast, LV torsion (twist of the apex about the long axis relative to the base) was greater in HCM patients (19.9±2.4° versus 14.6±2.7°, P<.01). ConclusionsHCM patients had reduced 3D myocardial shortening on a regional basis; however, LV torsion was increased.
Sample Stabilization For Tomography Experiments In Presence Of Large Plant Uncertainty

A new low emittance lattice storage ring is under construction at the ESRF. In this new instrument, an upgraded end station for ID31 beamline must allow to position the samples along complex trajectories with a nanometer precision. In order to reach these requirements, samples have to be mounted on high precision stages, combining a capability of large stroke, spin motion, and active rejection of disturbances. First, the end station will be presented with the associated requirements. However, the precision is limited by thermal expansion and various imperfections that are not actively compensated. Our approach is to add a Nano Active Stabilization System (NASS) which is composed of a 6DoF Stewart platform and a 6 DoF metrology system. A 3D model of the end station updated with experimental data is developed. As the mass of the samples may vary by up to two orders of magnitudes, robust control strategies are required to address such plant uncertainty. The proposed control strategy are presented and applied on the developed model by conducting time domain simulations of tomography experiment in presence of instrumentation noise and system uncertainty.
A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries

In this paper, we proposed not only an extraction methodology of multiple feature vectors from ultrasound images for carotid arteries (CAs) and heart rate variability (HRV) of electrocardiogram signal, but also a suitable and reliable prediction model useful in the diagnosis of cardiovascular disease (CVD). For inventing the multiple feature vectors, we extract a candidate feature vector through image processing and measurement of the thickness of carotid intima-media (IMT). As a complementary way, the linear and/or nonlinear feature vectors are also extracted from HRV, a main index for cardiac disorder. The significance of the multiple feature vectors is tested with several machine learning methods, namely Neural Networks, Support Vector Machine (SVM), Classification based on Multiple Association Rule (CMAR), Decision tree induction and Bayesian classifier. As a result, multiple feature vectors extracted from both CAs and HRV (CA+HRV) showed higher accuracy than the separative feature vectors of CAs and HRV. Furthermore, the SVM and CMAR showed about 89.51% and 89.46%, respectively, in terms of diagnosing accuracy rate after evaluating the diagnosis or prediction methods using the finally chosen multiple feature vectors. Therefore, the multiple feature vectors devised in this paper can be effective diagnostic indicators of CVD. In addition, the feature vector analysis and prediction techniques are expected to be helpful tools in the decisions of cardiologists.
Optical parameter extraction and error analysis of terahertz time domain spectrum detection

Terahertz time-domain spectroscopy (THz-TDS) is a spectral detection method. The information of the material is measured through the broadband terahertz pulse carrying the medium information (such as amplitude and phase). The ceramic matrix composites and silica gel materials were tested with the detection method of transmission. The optical parametric models of the material were established, and the values of the refractive index and the absorption coefficient were extracted. The curves of the refractive index and the absorption coefficient with frequency were plotted. The refractive index of the ceramic matrix composites with different density are respectively convergent to a constant of 1.11, 1.14 and 1.16, and the refractive index of silica gel with different thickness is 2.1, which is not dependent to frequency. While the frequency dependence of the absorption coefficient is evident, and the absorption of samples with different material properties is significantly different. Based on the Gaussian error theory, the errors of the optical parameters are simulated and modeled. The experimental results show that there are several error sources in the optical parameters of the ceramic matrix composites with density of 2.8 g/cm3. The standard deviation of the refractive index and the absorption coefficient are obviously related to the frequency, and the standard deviation is in the order of 0.001, which is of great significance to the precise extraction of the physical parameters such as the refractive index and the absorption coefficient.
Variational calculus based planning for deliberative agentes

This paper introduces a robust mathematical formalism for the definition of deliberative agents implemented using a case-based reasoning system. The concept behind deliberative agents is introduced and the case-based reasoning model is described using this analytical formalism. Variational calculus is used during the reasoning process to identify the problem solution. The agent may use variational calculus to generate plans and modify them at execution time, so they can react to environmental changes in real time.
Auction Approaches for Resource Allocation in Wireless Systems: A Survey

As wireless systems evolve with new mobile technologies, they tend to become complicated in terms of architectures and managements. Auction theory, as a subfield of economics and business management, has been introduced to provide an interdisciplinary technology for radio resource allocation (e.g., subchannels, time slots, and transmit power levels) in the wireless systems. By using various auction approaches, such radio resources are efficiently allocated among users and providers of services in the systems. Participants (i.e., users and providers) of an auction have their own strategies that follow the incentives and rules brought by the auction. Auction methods are widely employed in areas such as cognitive radio, cellular networks, and wireless mesh networks. This paper gives a comprehensive survey of recent auction approaches (i.e., auction-based applications and mechanisms) applied in wireless and mobile systems. First, auction theory and different types of auction are introduced. The motivation of using auction in wireless systems is given. Then, the reviews of auction approaches applied in the single-hop and multi-hop wireless networks are provided. Finally, the open research issues are discussed.
Isoflurane pre-treatment before cardiopulmonary bypass alleviates neutrophil accumulation in dog lungs

Objective This study investigated the effect of isoflurane pre-treatment on cardiopulmonary bypass (CPB)-related lung injury. Methods Twelve dogs were randomly divided into two groups of six each. In one group, 1.0 minimum alveolar concentration (MAC) of isoflurane was administered for 30 min before CPB, while the control group received no anaesthetic. Both groups then underwent 100 min of mild hypothermic CPB with 60-min aortic cross clamping. Haemodynamic parameters, respiratory mechanics and alveolar arterial oxygen difference (AaDO2) were measured during the experiment. One hundred and fifty minutes after CPB, lung tissue samples from the non-dependent and dependent portions of the left and right lungs were harvested for polymorphonulear leukocyte (PMNs) counts. Results Following CPB, within the control group, pulmonary vascular resistance (PVR) was significantly increased at 60, 120 and 180 min after declamping, AaDO2 deteriorated at 180 min post-declamping, and dynamic lung compliance (DLC) was reduced dramatically after declamping. Isoflurane pre-treatment before CPB significantly reduced PVR compared to the controls. AaDO2 was impaired at 180 min after declamping and DLC was decreased after declamping within the isoflurane group. No differences in AaDO2 and DLC were found between the isoflurane and control groups. At 180 min after declamping, the PMN count in both the non-dependent and dependent regions of the isoflurane pre-treated lungs was significantly lower than that of the controls. Conclusions Our results suggest that 30-min pre-treatment with 1.0 MAC isoflurane before CPB caused a reduction in PMN accumulation in the dog lungs, inhibition of increases in PVR, and it did not affect AaDO2 in the early post-CPB stage.
Hypertext Writing and Document Reuse: The Role of a Semantic Net

SUMMARY When document components are classified and then recombined during document reuse, a semantic net may serve as the classification language. A theory of analogical inheritance, applied to this semantic net, guides the reorganization of document components. Authors index paragraphs from various sources with node-link-node triples from a semantic net and then use programs to traverse the semantic net and generate various outlines. The program examines node and link names in deciding which path to take. This paper describes how these techniques helped the author to reuse parts of an existing book to write a new one.
Elaborating Requirements Using Model Checking and Inductive Learning

The process of Requirements Engineering (RE) includes many activities, from goal elicitation to requirements specification. The aim is to develop an operational requirements specification that is guaranteed to satisfy the goals. In this paper, we propose a formal, systematic approach for generating a set of operational requirements that are complete with respect to given goals. We show how the integration of model checking and inductive learning can be effectively used to do this. The model checking formally verifies the satisfaction of the goals and produces counterexamples when incompleteness in the operational requirements is detected. The inductive learning process then computes operational requirements from the counterexamples and user-provided positive examples. These learned operational requirements are guaranteed to eliminate the counterexamples and be consistent with the goals. This process is performed iteratively until no goal violation is detected. The proposed framework is a rigorous, tool-supported requirements elaboration technique which is formally guided by the engineer's knowledge of the domain and the envisioned system.