Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of Gene Expression in Inflammatory Bowel Disease and Correlation with IBD Drugs: Screening by DNA Microarrays

Potential biomarkers for Crohn's disease (CD) and ulcerative colitis (UC) were identified from two sets of full thickness pathologic samples utilizing DermArray® and PharmArray® DNA microarrays relative to uninvolved (Un) colon or normal colon. Seven of the over-expressed genes were verified using quantitative RT-PCR (i.e., TMPT, FABP1, IFI27, LCN2, COL11A2, HXB, and metallothionein). By correlating gene expression profiles between inflammatory bowel disease (IBD) tissue samples and IBD drug-treated cell cultures it might be possible to identify new candidate molecular target genes for IBD therapy and drug discovery. Potential biomarkers for CaCo2 cell cultures, which are routinely used as a GI tract surrogate model for in vitro pharmacokinetic studies, treated with azathioprine, 5-aminosalicylic acid, metronidazole, and prednisone were also identified from another experiment. Metallothionein mRNA expression was found to be down-regulated in azathioprine-treated CaCo2 cells, and was coincidentally up-regulated in the CD sample, thus resulting in an anti-correlation. These results suggest that this new screening methodology is feasible, that metallothioneins might be biomarkers for azathioprine therapy in vivo in CD, and that azathioprine might mechanistically down-regulate metallothionein gene expression. Correlations were also observed between IBD samples and either metronidazole- or 5-aminosalicylic acid-treated CaCo2 cells. Similar comparisons of disease tissue samples in vivo vs drug-treated cell cultures in vitro might reveal new mechanistic insights concerning established or experimental drug therapies. This affordable in vitro methodology is promising for expanded studies of IBD and other diseases.
Ge-on-Si laser for silicon photonics

Ge-on-Si devices are explored for photonic integration. Importance of Ge in photonics has grown and through techniques developed in our group we demonstrated low density of dislocations (<1x10cm) and point defects Ge growth for photonic devices. The focus of this document will be exclusively on Ge light emitters. Ge is an indirect band gap material that has shown the ability to act like a pseudo direct band gap material. Through the use of tensile strain and heavy doping, Ge exhibits properties thought exclusive of direct band gap materials. Dependence on temperature suggests strong interaction between indirect bands, Δ and L, and the direct band gap at Γ. The behavior is justified through increase in photoluminescence on Ge. The range of efficient emission is to 120°C with the first band interaction, and above 400°C on the second band interaction. Low defect concentration in Ge is achieved through chemical vapor deposition at high vacuum (~1x10 mbar) in a two-step process. The high temperature growth and low concentration of particles permits epitaxial growth with low defect concentration. Chemical selectivity forbids Ge growth on oxide. Oxide trenches permit the growth on Si for a variety of shapes, without detrimentally affecting the strain of the Ge devices. Dopant concentration above intrinsic growth concentration, ~1x10cm phosphorus, have been achieved through a series of methods non-CMOS, spin-on dopant; and CMOS, implantation and delta doping. All the techniques explored use enhanced dopant diffusion
HIV, antiretroviral treatment, hypertension, and stroke in Malawian adults

Objective: To investigate HIV, its treatment, and hypertension as stroke risk factors in Malawian adults. Methods: We performed a case-control study of 222 adults with acute stroke, confirmed by MRI in 86%, and 503 population controls, frequency-matched for age, sex, and place of residence, using Global Positioning System for random selection. Multivariate logistic regression models were used for case-control comparisons. Results: HIV infection (population attributable fraction [PAF] 15%) and hypertension (PAF 46%) were strongly linked to stroke. HIV was the predominant risk factor for young stroke (≤45 years), with a prevalence of 67% and an adjusted odds ratio (aOR) (95% confidence interval) of 5.57 (2.43–12.8) (PAF 42%). There was an increased risk of a stroke in patients with untreated HIV infection (aOR 4.48 [2.44–8.24], p < 0.001), but the highest risk was in the first 6 months after starting antiretroviral therapy (ART) (aOR 15.6 [4.21–46.6], p < 0.001); this group had a lower median CD4+ T-lymphocyte count (92 vs 375 cells/mm3, p = 0.004). In older participants (HIV prevalence 17%), HIV was associated with stroke, but with a lower PAF than hypertension (5% vs 68%). There was no interaction between HIV and hypertension on stroke risk. Conclusions: In a population with high HIV prevalence, where stroke incidence is increasing, we have shown that HIV is an important risk factor. Early ART use in immunosuppressed patients poses an additional and potentially treatable stroke risk. Immune reconstitution inflammatory syndrome may be contributing to the disease mechanisms.
Robust speaker verification from GSM-transcoded speech based on decision fusion and feature transformation

In speaker verification, a claimant may produce two or more utterances. Typically, the scores of the speech patterns extracted from these utterances are averaged and the resulting mean score is compared with a decision threshold. Rather than simply computing the mean score, we propose to compute the optimal weights for fusing the scores based on the score distribution of the independent utterances and our prior knowledge about the score statistics. More specifically, we use enrollment data to compute the mean scores of client speakers and impostors and consider them to be the prior scores. During verification, we set the fusion weights for individual speech patterns to be a function of the dispersion between the scores of these speech patterns and the prior scores. Experimental results based on the GSM-transcoded speech of 150 speakers from the HTIMIT corpus demonstrate that the proposed fusion algorithm can increase the dispersion between the mean speaker scores and the mean impostor scores. Compared with a baseline approach where equal weights are assigned to all scores, the proposed approach provides a relative error reduction of 19%.
A noisy self-organizing neural network with bifurcation dynamics for combinatorial optimization

The self-organizing neural network (SONN) for solving general "0-1" combinatorial optimization problems (COPs) is studied in this paper, with the aim of overcoming existing limitations in convergence and solution quality. This is achieved by incorporating two main features: an efficient weight normalization process exhibiting bifurcation dynamics, and neurons with additive noise. The SONN is studied both theoretically and experimentally by using the N-queen problem as an example to demonstrate and explain the dependence of optimization performance on annealing schedules and other system parameters. An equilibrium model of the SONN with neuronal weight normalization is derived, which explains observed bands of high feasibility in the normalization parameter space in terms of bifurcation dynamics of the normalization process, and provides insights into the roles of different parameters in the optimization process. Under certain conditions, this dynamical systems view of the SONN reveals cascades of period-doubling bifurcations to chaos occurring in multidimensional space with the annealing temperature as the bifurcation parameter. A strange attractor in the two-dimensional (2-D) case is also presented. Furthermore, by adding random noise to the cost potentials of the network nodes, it is demonstrated that unwanted oscillations between symmetrical and "greedy" nodes can be sufficiently reduced, resulting in higher solution quality and feasibility.
The Dynamics of Discrete-Time Computation, with Application to Recurrent Neural Networks and Finite State Machine Extraction

Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure.
Optical biosensor array based on natural ion channels

Ion channels are promising biomolecules for sensor applications. They combine high sensitivity with excellent selectivity. Presently the main problem consists in the integration of such ion channels into synthetic matrices but keeping them operative. In this study we report on the development of a microstructured polymer layer as matrix for an ion channel sensor array and an optical method for parallel detection. A thin PMMA layer was prepared by spin coating a gold surface. Small pores with diameter in the range of few micrometers were made by e-beam lithography. FTIR imaging spectroscopy and SPR imaging were used to characterize the quality of microstructured arrays.
Plagiarism Detection by using Karp-Rabin and String Matching Algorithm Together

In today word copying something from other sources and claiming it as an own contribution is a crime. We have also seen it is major problem in academic where students of UG, PG or even at PhD level copying some part of original documents and publishing on own name without taking proper permission from author or developer. Many software tools in exist to find out and assist the monotonous and time consuming task of tracing plagiarism, because identifying the owner of that whole text is practically difficult and impossible for markers. In our presentation we have focused on practical assignments (projects) as well as written document which is to be submitted by students in to college or university. Because of this crucial task and day by day increasing research in different fields, industry, academy people demanding such software to detect whether submitted articles, books, national or international papers are genuine or not. In this paper, our algorithm divides submitted articles in small pieces and scans it to compare with connected databases to the server on internet. Some existing work compares submitted articles with previously submitted articles i.e. with existing database.
Enzymes of phosphoinositide synthesis in secretory vesicles destined for the plasma membrane in Saccharomyces cerevisiae

CDP-diacylglycerol synthase, phosphatidylinositol synthase, and phosphatidylinositol kinase activities were associated with post-Golgi apparatus secretory vesicles destined for the plasma membrane of Saccharomyces cerevisiae. These results suggest that the plasma membrane is capable of synthesizing both CDP-diacylglycerol and phosphatidylinositol as well as phosphorylating phosphatidylinositol.
Hardware conversion of neural networks simulation models for neural processing accelerator implemented as FPGA-based SoC

The transition from a neural network simulation model to its hardware representation is a complex process, which touches computations precision, performance and effective architecture implementation issues. Presented neural processing accelerator involves neural network sectioning, precision reduction and weight coefficients parsing (arrangements) in order to increase efficiency and maximize FPGA hardware resources utilization. Particular attention has been devoted on to ANN conversion methods designed for a system based on neural processing units and related with this process redundant calculations and empty neurons generation. In addition, this paper describes the FPGA-based Neural Processing Accelerator architecture benchmark for real example implementation of a pattern recognition neural network.
STABILIZED1, a Stress-Upregulated Nuclear Protein, Is Required for Pre-mRNA Splicing, mRNA Turnover, and Stress Tolerance in Arabidopsis[W]

In plants, many gene transcripts are very unstable, which is important for the tight control of their temporal and spatial expression patterns. To identify cellular factors controlling the stability of unstable mRNAs in plants, we used luciferase imaging in Arabidopsis thaliana to isolate a recessive mutant, stabilized1-1 (sta1-1), with enhanced stability of the normally unstable luciferase transcript. The sta1-1 mutation also causes the stabilization of some endogenous gene transcripts and has a range of developmental and stress response phenotypes. STA1 encodes a nuclear protein similar to the human U5 small ribonucleoprotein–associated 102-kD protein and to the yeast pre-mRNA splicing factors Prp1p and Prp6p. STA1 expression is upregulated by cold stress, and the sta1-1 mutant is defective in the splicing of the cold-induced COR15A gene. Our results show that STA1 is a pre-mRNA splicing factor required not only for splicing but also for the turnover of unstable transcripts and that it has an important role in plant responses to abiotic stresses.
The Numerical Analysis of Spiral Heat Exchanger

The lumped parameter method was used to establish a mathematical model for the spiral heat exchanger with complex structures and specific heat transfer. By implementing the finite difference method, the differential equation was discretized, and then the steady temperature field of the spiral heat exchanger and the dynamic response of temperature can be obtained under boundary conditions and initial conditions. And the essential correctness and measurement accuracy of the model was proved by fluent. The results show that this model can simulate the temperature field and the dynamic response of it well.
A Reversible Data Hiding Scheme Based on Side Match Vector Quantization

Many researchers have studied reversible data hiding techniques in recent years and most have proposed reversible data hiding schemes that guarantee only that the original cover image can be reconstructed completely. Once the secret data are embedded in the compression domain and the receiver wants to store the cover image in a compression mode to save storage space, the receiver must extract the secret data, reconstruct the cover image, and compress the cover image again to generate compression codes. In this paper, we present a reversible data hiding scheme based on side match vector quantization (SMVQ) for digitally compressed images. With this scheme, the receiver only performs two steps to achieve the same goal: extract the secret data and reconstruct the original SMVQ compression codes. In terms of the size of the secret data, the visual quality, and the compression rate, experimental results show that the performance of our proposed scheme is better than those of other information hiding schemes for VQ-based and SMVQ-based compressed images. The experimental results further confirm the effectiveness and reversibility of the proposed scheme
Image realization steganography with LCS based mapping

Image Realization Steganography aims in devising techniques that realize a secret message from a cover image using a suitable cover-to-secret mapping with low embedding rates. This paper presents an Image Realization Steganography technique which maps a secret image as a Longest Common Subsequence of a given cover image. The cover image being a string of high degree of randomness and a very large length as compared to the secret image, the length of the LCS between them is expected to be close to the length of the binary string equivalent of the secret image. This enables the proposed technique to realize a secret image with very little or no modification of the cover. Experimental results validate this fact strongly. The results also indicate that any given cover image can be used to realize a secret image as large as itself thereby establishing the high hiding capacity of the technique.
Reductions in Workload and Reporting Time by Use of Methicillin-Resistant Staphylococcus aureus Screening with MRSASelect Medium Compared to Mannitol-Salt Medium Supplemented with Oxacillin

ABSTRACT Methicillin-resistant Staphylococcus aureus (MRSA) is a significant pathogen in both nosocomial and community settings, and screening for carriers is an important infection control practice in many hospitals. In this retrospective study, we demonstrate that the implementation of an MRSA screening protocol using a selective chromogenic medium (MRSASelect) reduced the workload for this screening test by 63.7% overall and by 12.6% per specimen and reduced the turnaround time for reporting by an average of 1.33 days for all MRSA screening specimens, 1.97 days for MRSA-positive specimens, and 1.3 days for MRSA-negative specimens compared to standard mannitol-salt agar supplemented with 6 mg of oxacillin/liter.
Cytokines and Other Mediators in Alopecia Areata

Alopecia areata, a disease of the hair follicles with multifactorial etiology and a strong component of autoimmune origin, has been extensively studied as far as the role of several cytokines is concerned. So far, IFN-γ, interleukins, TNF-α, are cytokines that are well known to play a major role in the pathogenesis of the disease, while several studies have shown that many more pathways exist. Among them, MIG, IP-10, BAFF, HLA antigens, MIG, as well as stress hormones are implicated in disease onset and activity. Within the scope of this paper, the authors attempt to shed light upon the complexity of alopecia areata underlying mechanisms and indicate pathways that may suggest future treatments.
Learning Dynamical System Modulation for Constrained Reaching Tasks

In this paper we combine kinesthetic demonstrations and dynamical systems to enable a humanoid robot to imitate constrained reaching gestures directed toward a target. Using a learning algorithm based on Gaussian mixture regression, the task constraints are extracted from several demonstrations. Those constraints take the form of desired velocity profiles for the end-effector and joint angle variables, with associated covariance matrices describing the variations allowed around the desired profiles. Those constraints are then used to modulate a dynamical system which has the reaching target as attractor. This way, the reaching trajectory can be reshaped in order to satisfy the constraints of the task, while preserving the adaptability and robustness provided by the dynamical system. In particular, the system can adapt to changes in the initial conditions and to target displacements occurring during the movement execution. We first evaluate the potential of this method on experiments involving the Hoap3 humanoid robot putting an object into a box. We then show how a manipulation tasks can be executed as sequences of such constrained reaching movement. This is illustrated on a packaging task performed by the robot
Predicting relevant documents for enterprise communication contexts

Sharing information in communication sessions enhances end user collaboration. However, today with documents indexed on keywords and on meta-data such as author, date, title, etc., it is left to the user to bring the right information or documents for sharing in communication sessions. In enterprises, typically users search contents of communication units that surround the shared documents to get appropriate documents. Further, the results a user expects also depends on the current communication context such as the current conference call or current event they are attending. Real-time prediction of the documents needed for information sharing in communication applications would enhance collaboration by providing enterprise users with the right content at the right time. In this work, we present an algorithm for predicting relevant documents for an enterprise user in any context. We propose a novel context progression tree algorithm that can not only search relevant documents but also merges threads based on their similarity of topic of discussion. We prove search properties on these trees and present results from our implementation on users in a real enterprise deployment.
Solid Particle Erosion of Thermoplastic Resins Reinforced by Short Fibers

Experiments were carried out to study the effects of matrix materials, reinforcement fibers, impact angle and particle velocity on the solid particle erosion behavior of thermoplastic resins reinforced by short fibers. All of the previous research on the erosion of fiber-reinforced plastics deals with steady state erosion, and no work has been done on the initial process of erosion. So special attention was focussed on an incubation period of erosion. Two types of fiber-reinforced plastics were used for erosion tests. One was a new type of thermoplastic polyimide (New-TPI) resin reinforced by short glass or carbon fibers. The other was polyetheretherketone (PEEK) reinforced by short glass or carbon fibers. The fiber-reinforced plastics with different fiber contents were utilized as test materials to examine the effect of fiber content. Initial and eroded surfaces of test specimens were observed with a scanned electron microscope and an optical microscope. The results of the experiments show that the mass of an incubation period is strongly dependent on initial surface roughness. The erosion rate at a steady state depends on the matrix material and on the volume content of fibers, regardless of fiber type.
Separate universes in loop quantum cosmology: framework and applications

I present a streamlined review of how the separate universe approach to cosmological perturbation theory can be used to study the dynamics of long-wavelength scalar perturbations in loop quantum cosmology (LQC), and then use it to calculate how long-wavelength curvature perturbations evolve across the LQC bounce assuming a constant equation of state. A similar calculation is possible for tensor modes using results from a complementary approach to cosmological perturbation theory in LQC based on an effective Hamiltonian constraint. An interesting result is that the tensor-to-scalar ratio can be suppressed or amplified by quantum gravity effects during the bounce, depending on the equation of state of the matter field dominating the dynamics. In particular, if the equation of state lies between − 1/3 and 1, the value of the tensor-to-scalar ratio will be suppressed during the bounce, in some cases significantly.
Effect of the substrate surface topology and temperature on the structural properties of ZnO layers obtained by plasma enhanced chemical vapour deposition

In this work thin ZnO layers were grown by metal-organic PECVD (RF 13.56 MHz) on Si wafers. Zn acetylacetonate was used as a precursor and oxygen as oxidant. A system for dosed injection of the precursor and oxidant into the plasma reactor was developed. The influence of the substrate surface topology and temperature on the structural properties of the deposited layers was studied. ZnO and graphite powder dispersions were used to modify the silicon wafers before starting the deposition process of the layers. Some of the ZnO layers were deposited on the back, unpolished, side of Si wafers. Depositions at 400 °C were performed to examine the effect of the substrate temperatures on the layer growth. The film structure was examined by XRD and SEM. The results show that all layers are crystalline with hexagonal wurtzite structure. The crystallites are preferentially oriented along the c-axis direction perpendicular to the substrate surfaces. ZnO layers deposited on thin ZnO seed films and clean Si surface exhibit well-developed grain structures and more c-axis preferred phase with better crystal quality than that of the layers deposited on graphite seed layer or rough, unpolished Si wafer.