Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Interfering RNA-mediated Down-regulation of Caveolin-1 Differentially Modulates Signaling Pathways in Endothelial Cells*

Caveolin-1 is a scaffolding/regulatory protein that interacts with diverse signaling molecules in endothelial cells. To explore the role of this protein in receptor-modulated signaling pathways, we transfected bovine aortic endothelial cells (BAEC) with small interfering RNA (siRNA) duplexes to down-regulate caveolin-1 expression. Transfection of BAEC with duplex siRNA targeted against caveolin-1 mRNA selectively “knocked-down” the expression of caveolin-1 by ∼90%, as demonstrated by immunoblot analyses of BAEC lysates. We used discontinuous sucrose gradients to purify caveolin-containing lipid rafts from siRNA-treated endothelial cells. Despite the near-total down-regulation of caveolin-1 expression, the lipid raft targeting of diverse signaling proteins (including the endothelial isoform of nitric-oxide synthase, Src-family tyrosine kinases, Gαq and the insulin receptor) was unchanged. We explored the consequences of caveolin-1 knockdown on kinase pathways modulated by the agonists sphingosine-1 phosphate (S1P) and vascular endothelial growth factor (VEGF). siRNA-mediated caveolin-1 knockdown enhanced basal as well as S1P- and VEGF-induced phosphorylation of the protein kinase Akt and did not modify the basal or agonist-induced phosphorylation of extracellular signal-regulated kinases 1/2. Caveolin-1 knock-down also significantly enhanced the basal and agonist-induced activity of the small GTPase Rac. We used siRNA to down-regulate Rac expression in BAEC, and we observed that Rac knockdown significantly reduced basal, S1P-, and VEGF-induced Akt phosphorylation, suggesting a role for Rac activation in the caveolin siRNA-mediated increase in Akt phosphorylation. By using siRNA to knockdown caveolin-1 and Rac expression in cultured endothelial cells, we have found that caveolin-1 does not seem to be required for the targeting of signaling molecules to caveolae/lipid rafts and that caveolin-1 differentially modulates specific kinase pathways in endothelial cells.
A study of broadcasting over structured overlay

An efficient and scalable broadcast are two essential features of structured overlay networks which used in large scale distributed computing systems and in peer-to-peer networks. The existing broadcast based algorithms over the structured overlays experience certain issues due to a decentralised nature of such networks in which nodes join and leave the network dynamically. This causes poor routing efficiency and high maintenance bandwidth overhead. In this paper, we study routing efficiency and maintenance bandwidth overhead while broadcasting messages over the structured overlay networks in dynamic environment. We also propose an improved message broadcasting algorithm over the structured overlay networks. The simulation results show that although there is a trade off between routing efficiency and maintenance bandwidth, with optimisation we can improve one without severely effecting the other parameter.
The bus goes wireless: Routing-free data collection with QoS guarantees in sensor networks

We present the low-power wireless bus (LWB), a new communication paradigm for QoS-aware data collection in low-power sensor networks. The LWB maps all communication onto network floods by using Glossy, an efficient flooding architecture for wireless sensor networks. Therefore, unlike current solutions, the LWB requires no information of the network topology, and inherently supports networks with mobile nodes and multiple data sinks. A LWB prototype implemented in Contiki guarantees bounded end-to-end communication delay and duplicate-free, in-order packet delivery - key QoS requirements in many control and mission-critical applications. Experiments on two testbeds demonstrate that the LWB prototype outperforms state-of-the-art data collection and link layer protocols, in terms of reliability and energy efficiency. For instance, we measure an average radio duty cycle of 1.69 % and an overall data yield of 99.97% in a typical data collection scenario with 85 sensor nodes on Twist.
Electron Cyclotron Resonance Multiply Charged Ion Sources

We describe three ion sources working in our laboratory that deliver multiply charged ion beams. All of them are E.C. R. ion sources and are characterized by the fact that the electrons are emitted by the plasma itself and are accelerated to the adequate energy through electron cyclotron resonance (E.C.R.). They can work without interruption during several months in a quasi-continuous regime. (Duty cycle: <.5). Their charge state distributions (C.S.D.) depend on the size and the characteristics of the plasma. For the smallest source the performances are comparable to those of P.I. G. sources. For the biggest source, the C.S. D. tend towards those obtained by EBIS-type sources. Experimental results and guiding ideas concerning these three ion sources are reviewed.
A Tight Lower Bound for the Symbol Error Performance of the Uplink Sparse Code Multiple Access

We derive a tight lower bound for symbol error rate (SER) performance of the sparse code multiple access (SCMA) uplink operated over Rayleigh fading channels. In particular, based on the working principle of message passing algorithm receiver and the special structure of the sparse binary matrix, which is used to control the collisions of SCMA system, the system can be simplified to a single user single input multiple output (SU-SIMO) system employing maximal ratio combining (MRC) reception. In this way, the average SER lower bound of SCMA is obtained by calculating the average SER of SU-SIMO system employing MRC reception. Our results show that the derived lower bound is not only an efficient means of analyzing the SER performance, but also is tight, especially in the high signal-to-noise ratio regime.
Design and Fabrication of a MEMS Pressure Sensor and Developing a Release Protocol for MEMS . by Kaustubh

Bhate Kaustubh, Ramesh. Design and Fabrication of a MEMS Pressure Sensor and Developing a Release Protocol for MEMS (Under the guidance of Dr. Paul Franzon and Dr. Christine Grant) This presents the design and fabrication of a MEMS Pressure Sensor for application in the Textile Industry. The transducer element in the sensor is a piezoresistive device. The Pressure Sensor layout is made in Cadence-Virtuoso and a 3-D Model is simulated in MEMCAD. Calculated and simulated results are compared. Processing steps are carried out in the NCSU Cleanroom to fabricate the device. A part of this thesis also attempts to develop a Release Protocol for MEMS devices made in the SUMMIT process. SUMMIT chips are released by wet etching in HF followed by rinsing in Methanol. The released device is then observed under Optical Microscope for results. Cantilever beams are also designed in SUMMIT process to be tested electrically when they come back after being fabricated at Sandia National Laboratories. Design and Fabrication of a MEMS Pressure Sensor and Developing a Release Protocol for MEMS. by Kaustubh Ramesh Bhate A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree in Master of Science in Electrical Engineering. Department of Electrical and Computer Engineering
Uncertainty Measures for Improving Exemplar-Based Source Separation

This work studies the use of observation uncertainty measures for improving the speech recognition performance of an exemplar-based source separation based front end. To generate the observation uncertainty estimates for the enhanced features, we propose the use of heuristic methods based on the sparse representation of the noisy signal in the exemplar-based source separation algorithm. The effectiveness of the proposed measures is evaluated in a large vocabulary noisy speech recognition task. The best proposed measure achieved relative error reductions up to 18 % over the baseline feature enhancement method without uncertainty measures. Index Terms: robustness, speech recognition, source separation, observation uncertainties
What are the Effects of Severe Visual Impairment on the Cortical Organization and Connectivity of Primary Visual Cortex?

The organization and connections of the primary visual area (V1) were examined in mice that lacked functional rods (Gnat−/−), but had normal cone function. Because mice are nocturnal and rely almost exclusively on rod vision for normal behaviors, the Gnat−/− mice used in the present study are considered functionally blind. Our goal was to determine if visual cortex is reorganized in these mice, and to examine the neuroanatomical connections that may subserve reorganization. We found that most neurons in V1 responded to auditory, or some combination of auditory, somatosensory, and/or visual stimulation. We also determined that cortical connections of V1 in Gnat−/− mice were similar to those in normal animals, but even in normal animals, there is sparse input from auditory cortex (AC) to V1. An important observation was that most of the subcortical inputs to V1 were from thalamic nuclei that normally project to V1 such as the lateral geniculate (LG), lateral posterior (LP), and lateral dorsal (LD) nuclei. However, V1 also received some abnormal subcortical inputs from the anterior thalamic nuclei, the ventral posterior, the ventral lateral and the posterior nuclei. While the vision generated from the small number of cones appears to be sufficient to maintain most of the patterns of normal connectivity, the sparse abnormal thalamic inputs to VI, existing inputs from AC, and possibly abnormal inputs to LG and LP may be responsible for generating the alterations in the functional organization of V1.
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries in Wavelet Domain

This paper proposes a novel hybrid image denoising method based on wavelet transform and sparse and redundant representations model which is called signal-scale wavelet K-SVD algorithm (SWK-SVD). In wavelet domain, mutiscale features of images and sparse prior of wavelet coefficients are achieved in a natural way. This gives us the motivation to build sparse representations in wavelet domain. Using K-SVD algorithm, we obtain adaptive and over-complete dictionaries by learning on image approximation and high-frequency wavelet coefficients respectively. This leads to a state-of-art denoising performance both in PSNR and visual effects with strong noise.
Speaker verification with GSM coded telephone speech

In this paper we investigate the impact on the performance of Speaker Veriication (SV) systems of the signal and channel coding in GSM cellular telephone networks. In this study only the eeects of the codec are investigated. This is done by transcoding the signals in an existing speech corpus, recorded in the xed network, to GSM. We compared text dependent SV performance of systems trained with A-law speech and tested with A-law and GSM speech, as well as systems trained with GSM speech and tested with GSM speech. All SV systems compared were based on continuous density Gaussian mixtures HMM models, diiering in acoustic resolution. We have compared several parameter representations derived from FFT and LPC based spectral estimates. It is shown that (and why) LPC based estimates are to be preferred. It is also shown that it pays to extend the analysis bandwidth to the full 4 kHz ooered by the digital telephone network. The major conclusion of our research is that the impact of GSM coding on the parameter representations is marginal and can eeectively be ignored.
Depth Image Dimension Reduction Using Deep Belief Networks

Nowadays 3D images are becoming a widely used tool in many computer vision tasks, such as scene segmentation or object recognition. A common problem when using 3D images, is that often the size of the corresponding cloud of points is very big and therefore hard to process, making the subsequent algorithms computationally expensive. In this work, we address this problem by introducing a pre-processing step in order to reduce the number of points needed to represent an object in 3D by reducing the number of pixels in the depth image. In order to achieve this we use state-of-the-art techniques for data dimension reduction; Deep Belief Networks based on RBM greedy layer wise pre-training. In this work we demonstrate that the role of the structure of the network is not as important as the underlying methods used to learn the parameters of the network. Most importantly, we prove in this work the strategic role of the fine tuning and the RBM pre-training steps in several aspects.
Narrative Generates a Learning Spiral in Education: Recognition, Reflection, and Reconstruction.

The narrative form is everywhere. It can be as common as our daily stories and as significant as a great novel. Narrating can be a process of self-assessment and introspection around a certain theme. In this sense it is important in education. In this paper I argue that people learn not only by listening to narrative but also by teaching others through narrative and by reflection, which is really a form of narrative where we tell stories to ourselves. I propose a model of narrative learning adapted from the work of Mary Catherine Bateson (1994) that involves recognition of experience, reflection, and reconstruction, which are interrelated to each other as a spiral. In this paper I will first describe my understanding of the importance of narrative and my own experience with narrative learning, then I will describe the narrative learning spiral model. I claim that narrative is ubiquitous and that it is essential for learning.
Validation of a new HPLC-UV method for determination of the antibiotic linezolid in human plasma and in bronchoalveolar lavage.

A rapid and selective HPLC-UV method was developed for the quantification of linezolid (LNZ) in human plasma and bronchoalveolar lavage (BAL) at the concentrations associated with therapy. Plasma samples were extracted by solid-phase extraction followed by evaporation to dryness and reconstitution in mobile phase solution. The chromatographic separation was carried out on a C18 column with an isocratic mobile phase consisting of dihydrogen phosphate buffer 50 mm (pH 3.5) and acetonitrile (60:40 v/v). The detection was performed using a photodiode array. Under these conditions, a single chromatographic run could be completed within 12 min. The method was validated by estimating the precision and the accuracy for inter- and intra-day analysis in the concentration range of 25-25600 ng/mL. The method was linear over the investigated range with all the correlation coefficients R > 0.999. The intra- and inter-day precision was within 8.90% and the accuracy ranged from -4.76 to +5.20%. This rapid and sensitive method was fully validated and could be applied to pharmacokinetic study for the determination of LNZ levels in human plasma and BAL samples.
Environmental Determinants of Chronic Disease and Medical Approaches: Recognition, Avoidance, Supportive Therapy, and Detoxification

The World Health Organization warns that chronic, noncommunicable diseases are rapidly becoming epidemic worldwide. Escalating rates of neurocognitive, metabolic, autoimmune and cardiovascular diseases cannot be ascribed only to genetics, lifestyle, and nutrition; early life and ongoing exposures, and bioaccumulated toxicants may also cause chronic disease. Contributors to ill health are summarized from multiple perspectives—biological effects of classes of toxicants, mechanisms of toxicity, and a synthesis of toxic contributors to major diseases. Healthcare practitioners have wide-ranging roles in addressing environmental factors in policy and public health and clinical practice. Public health initiatives include risk recognition and chemical assessment then exposure reduction, remediation, monitoring, and avoidance. The complex web of disease and environmental contributors is amenable to some straightforward clinical approaches addressing multiple toxicants. Widely applicable strategies include nutrition and supplements to counter toxic effects and to support metabolism; as well as exercise and sweating, and possibly medication to enhance excretion. Addressing environmental health and contributors to chronic disease has broad implications for society, with large potential benefits from improved health and productivity.
The outburst properties of AM CVn stars

We briefly summarise the observational properties of ultra-compact binaries called AM CVn stars. We analyse their outbursts originating from the thermal-viscous instability in helium accretion disc. We present our preliminary results in applying the model of Dwarf Novae outbursts to helium discs. We can calculate models of outbursts of reasonable amplitude of 2 mag with a constant α parameter throughout the calculation. Setting the mass transfer rate close to its upper critical value produces model lightcurves that resemble short superoutbursts (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
A Game-Based Approach for Collecting Semantic Annotations of Music

Games based on human computation are a valuable tool for collecting semantic information about images. We show how to transfer this idea into the music domain in order to collect high-quality semantic information about songs. We present Listen Game, a online, multiplayer game that measures the semantic relationship between music and words. In the normal mode, a player sees a list of semantically related words (e.g., instruments, emotions, usages, genres) and is asked to pick the best and worst word to describe a song. In the freestyle mode, a user is asked to suggest a new word that describes the music. Each player receives realtime feedback about the agreement amongst all players. We show that we can use the data collected during a two-week pilot study of Listen Game to learn a supervised multiclass labeling (SML) model. We show that this SML model can annotate a novel song with meaningful words and retrieve relevant songs from a database of audio content.
The afterglow of GRB 021004: surfing on density waves

We present a model for the early optical afterglow of GRB 021004. This burst had one of the earliest detected optical afterglows, allowing for a dense optical sampling. The lightcurve was peculiar, since bright bumps were superimposed to the regular power-law decay observed in many other events. We show that, given their time scale and shape, the bumps are likely due to the interaction of the fireball with moderate density enhancements in the ambient medium. The enhancements have a density contrast of order 10, modifying only slightly the dynamics of the fireball, which therefore surfs on them rather than colliding into them. A relativistic reverse shock does not develop. Since the interaction takes place when the fireball is still hyper-relativistic it is not possible to understand if the overdensities are localized in clumps or are spherically symmetric around the GRB progenitor. The monotonic decrease of the contrast of successive rebrightenings suggests however the presence of clumps embedded in a uniform environment. Such an interpretation, complemented by the detection of several high velocity absorption systems in the optical spectrum, strongly suggests that GRB 021004 exploded within the remnant of a previous explosion.
Sentinel Lymph Node Detection in Patients with Oral Cancer by MR Lymphography using Superparamagnetic Iron Oxide

The purpose of this study was to examine the feasibility of interstitial MR lymphography using Superparamagnetic Iron Oxide (SPIO) to detect sentinel lymph nodes (SLNs) in head and neck cancer. For two patients with cT2N0 squamous cell carcinoma of the tongue, the submucosal injection of SPIO as well as sentinel lymph node navigation surgery (SLNNS) was performed before undergoing surgical treatment. SPIO was used for MR lymphography and Tc-99m phytate for SPECT. We compared the images by both modalities. Berlin blue stain was also performed postoperatively to verify if SLNs had the uptake of SPIO. The lymph nodes with MR signal attenuation and those with hot spots were probed to be anatomically identical. All the lymph nodes detected as a SLN by a gamma-probe contained the blue granules from SPIO. These data suggest that SPIO can be a novel tracer for performing SLNNS and SLN biopsy (SLNB) in oral cancer patients with cN0 neck.
SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization

In this paper we present a supervised hyperspectral image segmentation algorithm based on a convex formulation of a marginal maximum a posteriori segmentation with hidden fields and structure tensor regularization: Segmentation via the Constraint Split Augmented Lagrangian Shrinkage by Structure Tensor Regularization (SegSALSA-STR). This formulation avoids the generally discrete nature of segmentation problems and the inherent NP-hardness of the integer optimization associated. We extend the Segmentation via the Constraint Split Augmented Lagrangian Shrinkage (SegSALSA) algorithm [1] by generalizing the vectorial total variation prior using a structure tensor prior constructed from a patch-based Jacobian [2]. The resulting algorithm is convex, time-efficient and highly parallelizable. This shows the potential of combining hidden fields with convex optimization through the inclusion of different regularizers. The SegSALSA-STR algorithm is validated in the segmentation of real hyperspectral images.
An ASIP approach for adaptive AVC Motion Estimation

A new algorithm and an adapted hardware architecture of an ASIP are proposed in this paper. When compared with other hardware ASIP implementations, this architecture significantly speeds up the motion estimation procedure and substantially decreases the memory requirements. Moreover, it also makes use of significantly fewer memory accesses, still maintaining its coding quality performances in what concerns both the obtained bit rate and PSNR. As a consequence, the proposed algorithm proves to be specially adequate to be implemented in most embedded systems with restricted computational and power resources that are often adopted by portable and battery supplied devices.
Peer-to-peer computing for mobile networks - information discovery and dissemination

This book presents research results on data dissemination in mobile networks and peer-to-peer systems for mobile devices. The main focus is a novel resource-sharing mechanism for mobile devices that introduces a new paradigm of information-sharing cooperation among mobile devices not necessarily connected to the internet. The book is geared to the needs of researchers and practitioners in industry, and is also suitable for advanced-level students in computer science and electrical engineering.