Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in Brief A Comparison of Human O 6-Methylguanine-DNA Methyltransferase Promoter Activity in Mer + and Mer ~ Cells 1

The activity of the human 06-methylguanine-DNA methyltransferase (MGMT) gene promoter was determined in eight human cell lines by measuring chloramphenicol acetyltransferase activity in a re porter gene system. MGMT promoter activities in cells that do not express MGMT (Mer ) fell within the range of activities seen in cells that do express MGMT (Mer4). The promoter region contains 11 po tential binding sites for the transcription factor Spl, but no correlation was seen between cellular Spl protein and MGMT promoter chloram phenicol acetyltransferase activity. Because Mer cells are not deficient in the factors needed for transcription of MGMT, we suggest that at least two mechanisms regulate MGMT expression. One suppresses MGMT mRNA and protein in Mer cells, and another regulates the levels of constitutive expression in Mer cells. Spl is not a limiting factor in MGMT expression.
An in situ test structure for simultaneously determining multimaterial properties of a film

We present a set of in-situ test structures for simultaneously determining residual stress (RS), Young's modulus (YM) and thermal expansion coefficient (TEC) of a film. Analytical models as functions of the displacement, geometry and material property of the test structures are theoretically derived for the task of extracting the film properties. This method utilizes available measurement apparatus and all the properties are identified and quantified on the same apparatus. The test structures consist of the measured film and the calibration film and are fabricated by a simple sacrificial-layer micromachining technique. The measured films made of undoped LPCVD polycrystalline silicon and the calibration film made of PECVD silicon nitride are used to demonstrate the effectiveness of the proposed method. The average calibrated residual stresses of undoped polysilicon films with deposition temperatures of 600°C and 620°C are 105± 5MPa and 240 ± 10MPa, respectively, and the corresponding Young's moduli are 170± 5GPa and 150 ± 5GPa. But the thermal expansion coefficient is approximately 2.7x 10-6 average.
Aluminium nitride: a promising and full CMOS compatible piezoelectric material for MOEMS applications

Aluminum nitride (AlN) is a promising piezoelectric material suitable for full CMOS compatible MEMS processes. Due to the transversal inverse piezoelectric effect the use of AlN enables quasistatic deformable mirrors by actively coupling lateral strain in micro machined membranes. In this work a fast and reliable way for reactive magnetron rf-sputtered aluminum nitride thin films with piezoelectric properties is shown. The thin AlN films were deposited on amorphous TiAl, SiO2 and silicon substrates using an industrial PVD cluster system. The morphologies of the deposited polycrystalline AlN films are characterized by X-ray diffraction measurements and SEM images of the layer surfaces. An enhanced texture coefficient is used to demonstrate the correlation between the X-ray diffraction pattern and the surface topology. High values of this enhanced texture coefficient will guarantee piezoelectric properties. Virtual powder X-ray diffraction experiments are used to determine the relative powder intensities required for texture coefficient evaluation. The transversal inverse piezoelectric coupling coefficient d31 is measured for tempered and untreated aluminum nitride thin films with high enhanced texture coefficients by quasistatic deflected wafer cantilevers.
Puromycin-sensitive aminopeptidase protects against aggregation-prone proteins via autophagy

A major function of proteasomes and macroautophagy is to eliminate misfolded potentially toxic proteins. Mammalian proteasomes, however, cannot cleave polyglutamine (polyQ) sequences and seem to release polyQ-rich peptides. Puromycin-sensitive aminopeptidase (PSA) is the only cytosolic enzyme able to digest polyQ sequences. We tested whether PSA can protect against accumulation of polyQ fragments. In cultured cells, Drosophila and mouse muscles, PSA inhibition or knockdown increased aggregate content and toxicity of polyQ-expanded huntingtin exon 1. Conversely, PSA overexpression decreased aggregate content and toxicity. PSA inhibition also increased the levels of polyQ-expanded ataxin-3 as well as mutant α-synuclein and superoxide dismutase 1. These protective effects result from an unexpected ability of PSA to enhance macroautophagy. PSA overexpression increased, and PSA knockdown or inhibition reduced microtubule-associated protein 1 light chain 3-II (LC3-II) levels and the amount of protein degradation sensitive to inhibitors of lysosomal function and autophagy. Thus, by promoting autophagic protein clearance, PSA helps protect against accumulation of aggregation-prone proteins and proteotoxicity.
Batalin-Tyutin Quantisation of the Spinning Particle Model

The Spinning Particle Model for anyon is analysed in the Batalin-Tyutin scheme of quantisation in extended phase space. Here additional degrees of freedom are introduced in the phase space such that all the constraints in the theory are rendered First Class that is commuting in the sense of Poisson Brackets. Thus the theory can be studied without introducing the Dirac Brackets which appear in the presence of non-commuting or Second Class constraints. In the present case the Dirac Brackets make the configuration space of the anyon non-canonical and also being dynamical variable dependent, poses problems for the quantisation programme. We show that previously obtained results, (e.g. gyromagnetic ratio of anyon being 2), are recovered in the Batalin-Tyutin variable independent sector in the extended space. The Batalin-Tyutin variable contributions are significant and are computable in a straightforward manner. The latter can beunderstood as manifestations of the non-commutative space-time in the enlarged phase space.
Proteome Profile of Swine Testicular Cells Infected with Porcine Transmissible Gastroenteritis Coronavirus

The interactions occurring between a virus and a host cell during a viral infection are complex. The purpose of this paper was to analyze altered cellular protein levels in porcine transmissible gastroenteritis coronavirus (TGEV)-infected swine testicular (ST) cells in order to determine potential virus-host interactions. A proteomic approach using isobaric tags for relative and absolute quantitation (iTRAQ)-coupled two-dimensional liquid chromatography-tandem mass spectrometry identification was conducted on the TGEV-infected ST cells. The results showed that the 4-plex iTRAQ-based quantitative approach identified 4,112 proteins, 146 of which showed significant changes in expression 48 h after infection. At 64 h post infection, 219 of these proteins showed significant change, further indicating that a larger number of proteomic changes appear to occur during the later stages of infection. Gene ontology analysis of the altered proteins showed enrichment in multiple biological processes, including cell adhesion, response to stress, generation of precursor metabolites and energy, cell motility, protein complex assembly, growth, developmental maturation, immune system process, extracellular matrix organization, locomotion, cell-cell signaling, neurological system process, and cell junction organization. Changes in the expression levels of transforming growth factor beta 1 (TGF-β1), caspase-8, and heat shock protein 90 alpha (HSP90α) were also verified by western blot analysis. To our knowledge, this study is the first time the response profile of ST host cells following TGEV infection has been analyzed using iTRAQ technology, and our description of the late proteomic changes that are occurring after the time of vigorous viral production are novel. Therefore, this study provides a solid foundation for further investigation, and will likely help us to better understand the mechanisms of TGEV infection and pathogenesis.
Simple tone discriminations are disrupted following experimental frontal traumatic brain injury in rats

Abstract Primary objective: To assess cognitive deficits in a rat model of brain injury. Research design: Cognitive deficits are some of the most pervasive and enduring symptoms of frontal traumatic brain injury (TBI) in human patients. In animal models, the assessment of cognitive deficits from TBI has primarily been limited to tests of spatial learning. Recently, simple discrimination performance has been shown to be sensitive to frontal brain damage. The current study provides a detailed characterization of deficits in a two-choice tone discrimination following a bilateral frontal controlled cortical impact injury. Methods and procedures: Rats were trained on a two-tone discrimination task in a standard operant chamber, then either a frontal brain injury was delivered or sham procedures performed. Following recovery, they were re-tested on the discrimination task and then tested on a reversal of the discrimination. Main outcomes and results: Frontal injury caused substantial deficits in responding and discrimination accuracy as well as an increase in side bias. Conclusions: Based on the outcomes seen in this study, discrimination and other operant tasks may provide a sensitive tool to assess the effect of therapeutic agents on cognitive deficits in animal models, which could lead to improved characterization of deficits and yield an improved assessment tool to aid in drug discovery.
A Deployment of Fine-Grained Sensor Network and Empirical Analysis of Urban Temperature

Temperature in an urban area exhibits a complicated pattern due to complexity of infrastructure. Despite geographical proximity, structures of a group of buildings and streets affect changes in temperature. To investigate the pattern of fine-grained distribution of temperature, we installed a densely distributed sensor network called UScan. In this paper, we describe the system architecture of UScan as well as experience learned from installing 200 sensors in downtown Tokyo. The field experiment of UScan system operated for two months to collect long-term urban temperature data. To analyze the collected data in an efficient manner, we propose a lightweight clustering methodology to study the correlation between the pattern of temperature and various environmental factors including the amount of sunshine, the width of streets, and the existence of trees. The analysis reveals meaningful results and asserts the necessity of fine-grained deployment of sensors in an urban area.
Hybrid distributed video coding with frame level coding mode selection

Distributed video coding (DVC), a new video coding paradigm based on Slepian-Wolf and Wyner-Ziv theories, is a promising solution for implementing low-power and low-cost distributed wireless video sensors since most of the computation load is moved from the encoder to the decoder. It has been showed that there is still room to improve the coding efficiency of the current DVC codec. In this paper, we propose a hybrid coding structure with frame-level coding mode selection (CMS) to allow the DVC encoder to flexibly choose channel coding or entropy coding to code each band. The experimental results show the significant improvement on the rate-distortion (R-D) performance with only slight increase in the encoding complexity compared to the DISCOVER codec. The proposed DVC system performs comparably to H.264 No Motion with much lower encoding complexity.
Reduction Techniques for Exemplar-Based Learning Algorithms

Exemplar-based learning algorithms are often faced with the problem of deciding which instances or other exemplars to store for use during generalization. Storing too many exemplars can result in large memory requirements and slow execution speed, and can cause an oversensitivity to noise. This paper has two main purposes. First, it provides a survey of existing algorithms used to reduce the number of exemplars retained in exemplar-based learning models. Second, it proposes six new reduction algorithms called DROP1-5 and DEL that can be used to prune instances from the concept description. These algorithms and 10 algorithms from the survey are compared on 31 datasets. Of those algorithms that provide substantial storage reduction, the DROP algorithms have the highest generalization accuracy in these experiments, especially in the presence of noise.
Lithium Promotes Adult Neural Progenitor Differentiation Via GSK3β- Dependent Signaling Pathways

Lithium is one of the standard drugs in the treatment of bipolar disorder, although its molecular modes of ac- tion are not well understood. It is a potent inhibitor of the multifunctional enzyme Glycogen Synthase Kinase 3s (GSK3s), which also plays a central role in neurogenesis via the developmental Wnt signaling pathway. In the present study, we analyzed the influence of lithium on GSK3s signaling in adult neural progenitor cells (NPCs) from the rat sub- ventricular zone. Protein expression patterns of NPCs cultured in the presence of 20 mM lithium chloride were compared to those of untreated cells. A proteomic approach based on two-dimensional gel electrophoresis and mass spectrometry showed changes in several GSK3s-related proteins. We demonstrate the inhibition of GSK3s by stabilization and nuclear transfer of its downstream target, s-catenin. Moreover, the phosphorylated (=inhibited) GSK3s protein was strongly enri- ched in the nuclear fraction of lithium-treated cells. The fraction of cells differentiated into astroglia increased moderately, and the fraction of cells differentiated into neurons increased strongly, as shown by immunostaining. In conclusion, li- thium promotes NPC differentiation, mainly to neurons, via GSK3s-related pathways.
Evaluation of Features for Leaf Classification in Challenging Conditions

Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (Conv Net) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and Conv Net features yields state-of-the art performance with an average accuracy of 97.3%±0:6% compared to traditional features which obtain an average accuracy of 91.2%±1:6%. Further experiments show that this combined classification approach consistently outperforms the best set of traditional features by an average of 5.7% for all of the evaluated condition variations.
Indoor Scene Classification: A Comparative Study of Feature Detectors and Local Descriptors

Indoor scene classification is a challenging problem in computer vision. In order to achieve an accurate solution for this task, a model that can exploit the discriminating information between different scene categories is necessary. In this paper, local feature extraction is suggested with supervised classification techniques for scene recognition in indoor environments. A comparative study between several feature detectors: SIFT, SURF, FAST, ORB, MSER, BRISK, and several local descriptors: SIFT, SURF, ORB, BRISK is presented. Two different classifiers, SVM and k-NN, are used for classification. The different techniques have been tested using the MIT dataset for indoor scenes and all the corresponding performance of each combination has been reported. Upon inspecting obtained results, it is observed that the combination of MSER detector with ORB as descriptor using SVM performs the best, as they give a combination of relatively high accuracy and low complexity; in terms of average execution time and memory space.
On computing temporal aggregates with range predicates

Computing temporal aggregates is an important but costly operation for applications that maintain time-evolving data (data warehouses, temporal databases, etc.) Due to the large volume of such data, performance improvements for temporal aggregate queries are critical. Previous approaches have aggregate predicates that involve only the time dimension. In this article we examine techniques to compute temporal aggregates that include key-range predicates as well (range-temporal aggregates). In particular we concentrate on the SUM aggregate, while COUNT is a special case. To handle arbitrary key ranges, previous methods would need to keep a separate index for every possible key range. We propose an approach based on a new index structure called the Multiversion SB-Tree, which incorporates features from both the SB-Tree and the Multiversion B+--tree, to handle arbitrary key-range temporal aggregate queries. We analyze the performance of our approach and present experimental results that show its efficiency. Furthermore, we address a novel and practical variation called functional range-temporal aggregates. Here, the value of any record is a function over time. The meaning of aggregates is altered such that the contribution of a record to the aggregate result is proportional to the size of the intersection between the record's time interval and the query time interval. Both analytical and experimental results show the efficiency of our result.
Par-eXpress: A Tool for Analysis of Sequencing Experiments With Ambiguous Assignment of Fragments in Parallel

With new high-throughput and low-cost sequencing technologies, an increasing amount of genetic data is becoming available to researchers. While the analysis of this vast amount of data has great potential for future scientific advances, it becomes imperative to exploit parallelism in order to process this data efficiently. In this paper, we address probabilistic assignment of ambiguously mapped fragments. This is a very significant, but time consuming, process for downstream analysis of genomic data. We develop a distributed-memory parallel version of a popular probabilistic fragment assignment tool, eXpress. In our experiments, we show that our approach achieves significant speedups over eXpress without decreasing its accuracy. The speedup we achieve increases as the number of iterations and/or data size increases.
Rough-granular approach for impulse fault classification of transformers using cross-wavelet transform

A novel approach based on information granulation using Rough sets for impulse fault identification of transformers has been proposed. It is found that the location and type of fault within a transformer winding can be classified efficiently by the features extracted from cross-wavelet spectra of current waveforms, obtained from impulse test. Results show that the proposed methodology can localize the fault within 5% of the winding length with a high degree of accuracy. The basic concepts of feature extraction using cross-wavelet transform and the method of classification of those features by rough-granular method are also explained.
DISTANCE BETWEEN THE POINTS OF MAXIMUM PRESSURE FOR SITTING SUBJECTS

A self-microemulsifying excipient formulation for increasing the bioavailability of a drug which includes an emulsion including an oil or other lipid material, a surfactant, and a hydrophilic co-surfactant. A method for making a drug delivery system for increasing the bioavailability of a drug by emulsifying at least one drug with a self-microemulsifying excipient comprising an oil or other lipid material, a surfactant, and a hydrophilic co-surfactant and drugs formulated thereby.
Control of a cascaded H-bridge multilevel converter for grid connection of photovoltaic systems

In this paper a control method for cascaded H-bridge multilevel converters for grid connection of photovoltaic systems is analyzed. The use of the multilevel converter introduces a series of advantages: improved power quality (lower current ripple), smaller filters, reduced switching frequency, no need of boost dc-dc stage and possible elimination of step-up transformer, all of which have a positive impact on the system efficiency. However it requires a more sophisticated control method specially due to dc-link voltage drifts produced by circulating power in the converter. The proposed control method is based on traditional voltage oriented control with a cascaded dc-link voltage control and grid current control loop. Two extra stages are added to this traditional control scheme, one to introduce maximum power point tracking for each module or string, and another to control the dc-link voltages drift due to circulating power in the converter. The latter is performed by the addition of a simple feedforward control strategy directly in the modulation stage. The proposed control method is modular and easy to adapt for any number of converters in series. Simulation results are presented to support the theoretical analysis.
Periodic Limb Movements in Complex Sleep Apnea Syndrome

Introduction: The development of repetitive central apneas during application of CPAP in patients initially showing obstructive apneas, a condition called complex sleep apnea syndrome (CompSAS), implies respiratory control instability. Respiratory control is known to be transiently destabilized by arousals. We hypothesized that PLMS and PLM- related arousals would be more frequent in patients with CompSAS compared to those with obstructive sleep apnea (OSA) and could account for some of the respiratory instability. Methodology: Comparative retrospective review of patients studied in our Sleep Disorders Center. Results: 88 patients with CompSAS were compared with 112 patients with OSA. Total arousal index (TAI), respiratory- related arousal index (RRAI), and PLM-related arousal index (PLMAI) were similar during the diagnostic polysomnogra- phy of CompSAS and OSA patients. Following CPAP application, patients with CompSAS had a higher TAI (27.2 (15.5 - 39.9) vs. 16.6 (10.7 - 26.5); p� 0.001; median (interquartile range)), but a lower PLMI (0 (0 - 21.1) vs. 12.6 (0 - 36.2); p=0.009) and PLMAI (0 (0 - 1.3) vs. 0.8 (0 - 4.2); p=0.004) than patients with OSA. Conclusion: PLMS and PLM-related arousals are not more common in CompSAS than in OSA patients and do not appear to play a significant role in destabilizing respiratory control in CompSAS patients.
Multi-population genetic algorithm quality assessment implementing intuitionistic fuzzy logic

Intuitionistic fuzzy logic has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of S. cerevisiae fed-batch cultivation model parameters obtained using multi-population genetic algorithm (MpGA). Performances of the examined algorithm have been tested before and after the application of the procedure for purposeful model parameters genesis for three different values of generation gap which is the most sensitive genetic algorithms parameter toward convergence time. Results obtained after the implementation of intuitionistic fuzzy logic for the algorithm performance assessment have been compared and MpGA with GGAP = 0.1 after the purposeful model parameters genesis procedure application has been distinguished as the fastest and the most reliable one.
Intrusion Detection A Text Mining Based Approach

Intrusion Detection is one of major threats for organization. The approach of intrusion detection using text processing has been one of research interests which is gaining significant importance from researchers. In text mining based approach for intrusion detection, system calls serve as source for mining and predicting possibility of intrusion or attack. When an application runs, there might be several system calls which are initiated in the background. These system calls form the strong basis and the deciding factor for intrusion detection. In this paper, we mainly discuss the approach for intrusion detection by designing a distance measure which is designed by taking into consideration the conventional Gaussian function and modified to suit the need for similarity function. A Framework for intrusion detection is also discussed as part of this research.