Miracle-dz/UniFAR-FTUs huggingface dataset:
Cannabinoid modulation of cortical adrenergic receptors and transporters

We previously reported that administration of the synthetic cannabinoid agonist WIN 55,212‐2 causes an increase in norepinephrine (NE) efflux in the frontal cortex (FC). The present study examined the expression levels of α2‐ and β1‐adrenergic receptors (ARs) as well as the norepinephrine transporter (NET) in the FC of rats following exposure to WIN 55,212‐2. Rats received systemic injection of WIN 55,212‐2 (3 mg/kg) acutely or for 7 days. Another group of rats received repeated WIN 55,212‐2 treatment followed by a period of abstinence. Control rats received vehicle injections. Rats were euthanized 30 min after the last WIN 55,212‐2 injection, the FC was microdissected, and protein extracts were probed for α2‐AR, β1‐AR, and NET. Results showed that β1‐AR expression was significantly decreased following repeated WIN 55,212‐2 treatment but significantly increased following a period of abstinence. α2‐AR expression showed no significant change in all groups examined. NET expression was significantly decreased following acute WIN 55,212‐2 treatment, with no changes following chronic administration or a period of abstinence. Alterations in NET may arise from modulation of cannabinoid receptors (CB1) that are localized to noradrenergic axon terminals as we demonstrate colocalization of CB1 receptor and NET in the same cortical axonal processes. The present findings support significant alterations in adrenergic receptor and NET expression in the FC after WIN 55,212 exposure that may underlie the reported changes in attention, cognition, and anxiety commonly observed after cannabinoid exposure. © 2009 Wiley‐Liss, Inc.
A numerical model for considering the bond-slip effect in axially loaded circular concrete-filled tube columns

This article introduces a numerical model that simulates the bond-slip behaviour in axially loaded circular concrete-filled tube columns without applying double nodes required in the use of the classical bond-link element. After calculating the nodal displacements of in-filled concrete, the deformation in the steel tube at each node has been found through the back-substitution technique from the first to the final steel element using a governing equation constructed on the basis of the force equilibrium and displacement compatibility at each node. Finally, correlation studies between analytical and experimental results are conducted to verify the efficiency and applicability of the introduced model.
Image feature and noise detection based on statistical hypothesis tests and their applications in noise reduction

In many video processing applications in the field of consumer electronics such as digital TV, it is well understood that the presence of a noise limits the performance of video enhancement functions due to the time-varying characteristics of the noise. The basic difficulty is that the noise and the signal are difficult to be distinguished. This paper proposes image feature and noise detection algorithms, which effectively distinguish the noise from the image feature or vice versa. Specifically, the proposed algorithms provide a way of measuring the degree of noise with respect to the degree of image feature. The fundamental idea behind the proposed algorithms is to derive a statistical measure to estimate the fact that a noise has a random characteristic whereas an image feature has a spatial correlation among the associated neighbor samples. With the proposed algorithms, many video enhancement algorithms such as noise reduction or sharpness enhancement can be adaptively performed although a time varying noise is presented.
Active Positive Semidefinite Matrix Completion: Algorithms, Theory and Applications

In this paper we provide simple, computationally efficient, active algorithms for completion of symmetric positive semidefinite matrices. Our proposed algorithms are based on adaptive Nystrom sampling, and are allowed to actively query any element in the matrix, and obtain a possibly noisy estimate of the queried element. We establish sample complexity guarantees on the recovery of the matrix in the max-norm and in the process establish new theoretical results, potentially of independent interest, on adaptive Nystrom sampling. We demonstrate the efficacy of our algorithms on problems in multi-armed bandits and kernel dimensionality reduction.
Tensile and interfacial properties of radially aligned CNT grown carbon fibers

The relatively high mass-specific strength and stiffness of carbon fibers (CFs) has established CF reinforced plastics (CFRPs) as the benchmark material for next-generation aerospace structures. While CFRPs with radially-grown aligned carbon nanotubes (CNTs), termed fuzzy fiber reinforced plastics (FFRPs), have exhibited enhanced interand intralaminar mechanical properties on model FRP systems, these results have not been replicated for aerospace-grade CFRP due to challenges in manufacturing. This thesis reports a scaled (weavevs. tow-level) manufacturing method of fuzzy woven CFRPs designed to yield dense and aligned CNT coverage on the fibers, and retain the fiber tensile and interface properties. These challenges were explored through mechanical testing, in addition to numerical reactive computational fluid dynamics (CFD) CNT growth models. Single fiber tensile tests for fuzzy fibers from aerospace-grade CF weaves showed no reduction in tensile strength compared to baseline (as received) fibers. Continuously monitored single fiber composite fragmentation testing revealed a 34% decrease in fiber-matrix interfacial shear strength (IFSS) for sized (polymer coating on fibers) fuzzy fibers, attributed to thermally induced sizing transformations during CNT growth, whereas the fuzzy de-sized fibers exhibited no reduction in IFSS. The CFD model demonstrated gas depletion trends correlated to the areas of substandard growth and a high sensitivity to the surface-to-volume ratio of the porous woven substrate. Retained CF properties supports this facile, scaled manufacturing method's ability to disperse CNTs uniformly on CF weaves to create a laminate-level fuzzy CFRP towards enhanced mechanical and multifunctional properties. With continued CNT growth modeling efforts, further scaling of this fuzzy CFRP architecture could be integrated into commercial manufacturing processes. Thesis Supervisor: Brian L. Wardle Title: Professor of Aeronautics and Astronautics
Real-Time Energy Management for Diesel Heavy Duty Hybrid Electric Vehicles

In this paper, a fuzzy-tuned equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for a conceptual diesel engine-equipped heavy duty hybrid electric vehicle (HEV). In the HEV, two electric motors/generators are mounted on the turbocharger shaft and engine shaft, respectively, which can improve fuel efficiency by capturing and storing energy from both regenerative braking and otherwise wasted engine exhaust gas. The heavy duty HEV frequently involved in duty cycles characterized by start-stop events, especially in off-road applications, whose dynamics is analyzed in this paper. The on-line optimization problem is formulated as minimizing a cost function in terms of weighted fuel power and electric power. In the cost function, a cost factor is defined for both improving energy transmission efficiency and maintaining the battery energy balance. To deal with the nonexplicit relationship between HEV fuel economy, battery state of charge (SOC), and control variables, the cost factor is fuzzy tuned using expert knowledge and experience. In relation to the fuel economy, the air-fuel ratio is an important factor. An online search for capable optimal variable geometry turbocharger (VGT) vane opening and exhaust gas recirculation (EGR) valve opening is also necessary. Considering the exhaust emissions regulation in diesel engine control, the boundary values of VGT and EGR actuators are identified by offline design-of-experiment tests. An online rolling method is used to implement the multivariable optimization. The proposed method is validated via simulation under two transient driving cycles, with the fuel economy benefits of 4.43% and 6.44% over the nonhybrid mode, respectively. Compared with the telemetry equivalent consumption minimization strategy, the proposed F-ECMS shows better performance in the sustainability of battery SOC under driving conditions with the rapid dynamics often associated with off-road applications.
A Parallel Algorithm for Transitive Closure

We present a parallel algorithm for the problem of computing the transitive closure for an acyclic digraph D with n vertices and m edges. We use the BSP/CGM model of parallel computing. Our algorithm uses O(log p) rounds of communications with p processors, where p n, and each processor has O(mn p ) local memory. The local computation of each processor is equal to the product of the number of edges and vertices of D that are stored in p.
Evidence that Human Serum with High Levels of Estrogens Interferes with Immune System Both by Decreasing CD25 Expression on T-Cell and by Increasing Interleukin-2 Availability

Our study aimed to verify whether serum with high estrogen levels, obtained from infertile women who underwent multiple follicular development, was able to interfere with peripheral blood mononuclear cells proliferative response to phytohemoagglutinin +/— interleukin-2, recombinant interleukin-2 alone and phytohemoagglutinin-stimulated CD25 expression. Blood samples from 10 women were collected at the onset (low estrogen doses), during multiple follicular development (intermediate estrogen doses), and on the day preceding the human chorionic gonadotropin administration (high estrogen doses). Serum with high estrogen doses reduced the proliferative response to phytohemoagglutinin alone and plus recombinant interleukin-2, while increased the proliferative response to recombinant interleukin-2 alone, and reduced the CD25 expression in nonblastic and total lymphocytes. These effects were inhibited by the estrogen receptor antagonist ICI182,780. Inconclusion, estrogens exert a dual effect on immune system: (1) an inhibition of cell-mediated immunity through a reduction of interleukin-2 receptors; (2) an increased proliferative response to interleukin-2.
ExonImpact: Prioritizing Pathogenic Alternative Splicing Events

Alternative splicing (AS) is a closely regulated process that allows a single gene to encode multiple protein isoforms, thereby contributing to the diversity of the proteome. Dysregulation of the splicing process has been found to be associated with many inherited diseases. However, among the pathogenic AS events, there are numerous “passenger” events whose inclusion or exclusion does not lead to significant changes with respect to protein function. In this study, we evaluate the secondary and tertiary structural features of proteins associated with disease‐causing and neutral AS events, and show that several structural features are strongly associated with the pathological impact of exon inclusion. We further develop a machine‐learning‐based computational model, ExonImpact, for prioritizing and evaluating the functional consequences of hitherto uncharacterized AS events. We evaluated our model using several strategies including cross‐validation, and data from the Gene‐Tissue Expression (GTEx) and ClinVar databases. ExonImpact is freely available at http://watson.compbio. iupui.edu/ExonImpact.
A Unified Representation for Morphological, Syntactic, Semantic, and Referential Annotations

This paper reports on the SYN-RA (SYNtax-based Reference Annotation) project, an on-going project of annotating German newspaper texts with referential relations. The project has developed an inventory of anaphoric and coreference relations for German in the context of a unified, XML-based annotation scheme for combining morphological, syntactic, semantic, and anaphoric information. The paper discusses how this unified annotation scheme relates to other formats currently discussed in the literature, in particular the annotation graph model of Bird and Liberman (2001) and the pie-in-the-sky scheme for semantic annotation.
Functional role of DNA mismatch repair gene PMS2 in prostate cancer cells

DNA mismatch repair (MMR) enzymes act as proofreading complexes that maintains genomic integrity and MMR-deficient cells show an increased mutation rate. MMR has also been shown to influence cell signaling and the regulation of tumor development. MMR consists of various genes and includes post-meiotic segregation (PMS) 2 which is a vital component of mutL-alpha. In prostate, the functional role of this gene has never been reported and in this study, our aim was to investigate the effect of PMS2 on growth properties of prostate cancer (PCa) cells. Previous studies have shown PMS2 to be deficient in DU145 cells and this lack of expression was confirmed by Western blotting whereas normal prostatic PWR-1E and RWPE-1 cells expressed this gene. PMS2 effects on various growth properties of DU145 were then determined by creating stable gene transfectants. Interestingly, PMS2 caused decreased cell proliferation, migration, invasion, and in vivo growth; and increased apoptosis as compared to vector control. We further analyzed genes affected by PMS2 expression and observe the apoptosis-related TMS1 gene to be significantly upregulated whereas anti-apoptotic BCL2A1 was downregulated. These results demonstrate a functional role for PMS2 to protect against PCa progression by enhancing apoptosis of PCa cells.
Simulating energy transfer between nanocrystals and organic semiconductors

Recent trends in renewable energy made silicon based photovoltaics the undisputed leader. Therefore, technologies that enhance, instead of compete with, silicon based solar cells are desirable. One such technology is the use of organic semiconductors and noncrystalline semiconductors for photon up- and down-conversion. However, the understanding of energy transfer in these hybrid systems required to effectively engineer devices is missing. In this thesis, I explore and explain the mechanism of energy transfer between noncrystalline semiconductors and organic semiconductors. Using a combination of density functional calculations, molecular dynamics, and kinetic theory, I have explored the geometry, morphology, electronic structure, and coarse grained kinetics of these system. The result is improved understanding of the transfer mechanism, rate, and the device structure needed for efficient devices. I have also looked at machine learning inspired algorithm for acceleration of density functional theory methods. By training machine learning models on DFT data, a much improved initial guess can be made, greatly accelerating DFT optimizations. Generating and examining this data set also revealed a remarkable degree of structure, that perhaps can be further exploited in the future.
Nanocomposites of Chalcogenide and their Applications

Recently, matrix such as polymer, graphene and carbon nanotubes (CNTs) for the semiconductor nanoparticles plays a promising role due to their better structural, functional properties and broad range of applications in every field. This chapter reviews the metal chalcogenide semiconductor nanocomposites, their properties and applications. Furthermore, the importance of aforementioned matrix and their role in superior properties of metal chalcogenide nano composites have been discussed.
ThermoMechanical Characterization of Calotropis gigantea Stem Powder-Filled Jute Fiber-Reinforced Epoxy Composites

ABSTRACT Filler performs key tasks in enhancing the strength of the composites. Calotropis gigantea is a weed waste typically called White Madar, widely grown in Asian countries. It is ground to powder and used as filler. Thus the present work deals with the development of three different jute fiber reinforced epoxy composite filled without and with various weight proportion of Calotropis gigantea stem powder namely 5 and 10 weight percentage using conventional hand lay-up technique. The developed composites are tested for its mechanical properties as per ASTM standards and thermal stability using the Thermo Gravimetric Analyzer. The composite which is filled with 10 weight percentage Calotropis gigantea powder filler showed superior properties in both thermal and mechanical characteristics due to its higher filler content which possess constituents like cellulose, lignin, etc. Scanning Electron Microscopy helped to analyze the fiber pullout, fractured interface, filler distribution, voids of the composites.
Multiplicative interaction in network meta‐analysis

Meta‐analysis of a set of clinical trials is usually conducted using a linear predictor with additive effects representing treatments and trials. Additivity is a strong assumption. In this paper, we consider models for two or more treatments that involve multiplicative terms for interaction between treatment and trial. Multiplicative models provide information on the sensitivity of each treatment effect relative to the trial effect. In developing these models, we make use of a two‐way analysis‐of‐variance approach to meta‐analysis and consider fixed or random trial effects. It is shown using two examples that models with multiplicative terms may fit better than purely additive models and provide insight into the nature of the trial effect. We also show how to model inconsistency using multiplicative terms. Copyright © 2014 John Wiley & Sons, Ltd.
The Paradox of Enrichment in Metaecosystems

The paradox of enrichment has been studied almost exclusively within communities or metacommunities, without explicit nutrient dynamics. Yet local recycling of materials from enriched ecosystems may affect the stability of connected ecosystems. Here we study the effect of nutrient, detritus, producer, and consumer spatial flows—combined with changes in regional enrichment—on the stability of a metaecosystem model. We considered both spatially homogeneous and heterogeneous enrichment. We found that nutrient and detritus spatial flows are destabilizing, whereas producer or consumer spatial flows are either neutral or stabilizing. We noticed that detritus spatial flows have only a weak impact on stability. Our study reveals that heterogeneity no longer stabilizes well-connected systems when accounting for explicit representation of nutrient dynamics. We also found that intermediate consumer diffusion could lead to multiple equilibria in strongly enriched metaecosystems. Stability can emerge from a top-down control allowing the storage of materials into inorganic form, a mechanism never documented before. In conclusion, local enrichment can be stabilized if spatial flows are strong enough to efficiently redistribute the local excess of enrichment to unfertile ecosystems. However, high regional enrichment can be dampened only by intermediate consumer diffusion rates.
Lipoprotein(a) plasma levels and the risk of cancer: the PRIME study

Although experimental studies have shown lipoprotein(a) antiangiogenic and antitumoral effects, the association of lipoprotein(a) levels with cancer in population studies remains elusive and poorly documented. The aim of this study was to analyse the relationship between lipoprotein(a) plasma levels and the incidence of cancer over 10 years of follow-up. Data from two French centres of the PRIME cohort were used, representing 5237 men aged 50–59 years and free from a history of cancer at baseline. Data on medical history, socioeconomic and lifestyle factors were obtained by questionnaire. Lipoprotein(a) plasma levels were analysed from fasting blood samples collected at baseline. The relationship between lipoprotein(a) levels and first incident cancer was studied using the multivariate Cox proportional hazards models for all-site and the main-site-specific cancers, adjusted for various potential confounders including age, centre, smoking status and alcohol consumption. During follow-up, 456 new cancers were identified. No significant association was found between lipoprotein(a) and the all-site or main-site-specific cancers (hazard ratios for quartiles 2–4 vs. 1, respectively: 1.24, 1.11, 1.29, P=0.23). However, a higher risk seemed to be observed for highest lipoprotein(a) levels in all sites, lung, colorectal or tobacco/alcohol-related cancers. For prostate cancer, the lowest risk was observed for the highest levels of lipoprotein(a) (P=0.12). In conclusion, no evident association was found between the lipoprotein(a) levels and the incidence of cancer. Nevertheless, a higher cancer risk seemed to be observed for the highest lipoprotein(a) levels. Further research focusing on the lipoprotein(a) qualitative structure, that is, apolipoprotein(a) polymorphism could help clarify this highly complex relation.
Aurothiomalate inhibits the expression of mPGES-1 in primary human chondrocytes

Objectives: Microsomal prostaglandin E synthase-1 (mPGES-1) is a terminal enzyme in the production of prostaglandin E2 (PGE2) and its expression is upregulated during inflammation. mPGES-1 is considered as a potential drug target for the treatment of arthritis to reduce adverse effects related to the current non-steroidal anti-inflammatory drugs (NSAIDs). Our aim was to study the expression of mPGES-1 in primary human chondrocytes and whether the expression is affected by clinically used antirheumatic drugs. Method: Primary human chondrocytes were isolated from cartilage samples obtained from patients undergoing total knee replacement surgery. Expression of mPGES-1 was studied by quantitative real-time polymerase chain reaction (PCR) and Western blot analysis. PGE2 levels were measured by enzyme-linked immunosorbent assay (ELISA). Results: mPGES-1 expression in primary human chondrocytes was enhanced when the cells were exposed to interleukin-1β (IL-1β) and mPGES-1 protein levels continued to increase up to the 96-h follow-up. Aurothiomalate inhibited mPGES-1 expression and PGE2 production in a dose-dependent manner, as did the anti-inflammatory steroid dexamethasone. Other disease-modifying antirheumatic drugs (DMARDs) studied (sulfasalazine, methotrexate, and hydroxychloroquine) did not alter mPGES-1 expression. Conclusions: The results introduce aurothiomalate as the first, and so far the only, DMARD found to be able to inhibit mPGES-1 expression. The effect is likely involved in the mechanisms of action of this gold-containing DMARD in rheumatic diseases. The results are implicated in the regulatory mechanisms of mPGES-1 expression, which are under intensive research.
An Artificial Neural Net Viterbi Decoder

The Viterbi algorithm is a maximum likelihood means for decoding convolutional codes and has thus played an important role in applications ranging from satellite communications to cellular telephony. In the past, Viterbi decoders have usually been implemented using digital circuits. The speed of these digital decoders is directly related to the amount of parallelism in the design. As the constraint length of the code increases, parallelism becomes problematic due to the complexity of the decoder. In this paper an artificial neural network (ANN) Viterbi decoder is presented. The ANN decoder is significantly faster than comparable digital-only designs due to its fully parallel architecture. The fully parallel structure is obtained by implementing most of the Viterbi algorithm using analog neurons as opposed to digital circuits. Several modifications to the ANN decoder are considered, including an analog/digital hybrid design that results in an extremely fast and efficient decoder. The ANN decoder requires one-sixth the number of transistors required by the digital decoder. The connection weights of the ANN decoder are either +1 or -1, so weight considerations in the implementation are eliminated. This, together with the design's modularity and local connectivity, makes the ANN Viterbi decoder a natural fit for VLSI implementation. Simulation results are provided to show that the performance of the ANN decoder matches that of an ideal Viterbi decoder.