Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential neuroprotective potential of CRMP2 peptide aptamers conjugated to cationic, hydrophobic, and amphipathic cell penetrating peptides

The microtubule-associated axonal specification collapsin response mediator protein 2 (CRMP2) is a novel target for neuroprotection. A CRMP2 peptide (TAT-CBD3) conjugated to the HIV transactivator of transcription (TAT) protein’s cationic cell penetrating peptide (CPP) motif protected neurons in the face of toxic levels of Ca2+ influx leaked in via N-methyl-D-aspartate receptor (NMDAR) hyperactivation. Here we tested whether replacing the hydrophilic TAT motif with alternative cationic (nona-arginine (R9)), hydrophobic (membrane transport sequence (MTS) of k-fibroblast growth factor) or amphipathic (model amphipathic peptide (MAP)) CPPs could be superior to the neuroprotection bestowed by TAT-CBD3. In giant plasma membrane vesicles (GPMVs) derived from cortical neurons, the peptides translocated across plasma membranes with similar efficiencies. Cortical neurons, acutely treated with peptides prior to a toxic glutamate challenge, demonstrated enhanced efflux of R9-CBD3 compared to others. R9-CBD3 inhibited N-methyl-D-aspartate (NMDA)-evoked Ca2+ influx to a similar extent as TAT-CBD3 while MTS-CBD3 was ineffective which correlated with the ability of R9- and TAT-CBD3, but not MTS-CBD3, to block NMDAR interaction with CRMP2. Unrestricted Ca2+ influx through NMDARs leading to delayed calcium dysregulation and neuronal cell death was blocked by all peptides but MAP-CBD3. When applied acutely for 10 min, R9-CBD3 was more effective than TAT-CBD3 at neuroprotection while MTS- and MAP-CBD3 were ineffective. In contrast, long-term (>24 h) treatment with MTS-CBD3 conferred neuroprotection where TAT-CBD3 failed. Neither peptide altered surface trafficking of NMDARs. Neuroprotection conferred by MTS-CBD3 peptide is likely due to its increased uptake coupled with decreased efflux when compared to TAT-CBD3. Overall, our results demonstrate that altering CPPs can bestow differential neuroprotective potential onto the CBD3 cargo.
Caching strategies to improve disk system performance

I/O subsystem manufacturers attempt to reduce latency by increasing disk rotation speeds, incorporating more intelligent disk scheduling algorithms, increasing I/O bus speed, using solid-state disks, and implementing caches at various places in the I/O stream. In this article, we examine the use of caching as a means to increase system response time and improve the data throughput of the disk subsystem. Caching can help to alleviate I/O subsystem bottlenecks caused by mechanical latencies. This article describes a caching strategy that offers the performance of caches twice its size. After explaining some basic caching issues, we examine some popular caching strategies and cache replacement algorithms, as well as the advantages and disadvantages of caching at different levels of the computer system hierarchy. Finally, we investigate the performance of three cache replacement algorithms: random replacement (RR), least recently used (LRU), and a frequency-based variation of LRU known as segmented LRU (SLRU). <<ETX>>
Intense photoluminescence and optical gain in rare-earth doped Polycrystalline Sapphire: a new bulk media for high-power phosphors and high-energy lasers

Conventional materials engineering approaches for polycrystalline ceramic gain media rely on optically isotropic crystals with high equilibrium solubility of luminescent rare-earth (RE) ions. Crystallographic optical symmetry is traditionally relied upon to avoid scattering losses caused by refractive index mismatch at grain boundaries in randomly oriented anisotropic crystals and high-equilibrium RE-solubility is needed to produce sufficient photoluminescence (PL) for amplification and oscillation. These requirements exclude materials such as polycrystalline sapphire/alumina that have significantly superior thermo-mechanical properties (Rs~19,500Wm-1), because it possesses 1) uxiaxial optical properties that at large grain sizes, result in significant grain boundary scattering, and 2) a very low (~10-3%) RE equilibrium solubility that prohibits suitable PL. I present new materials engineering approaches operating far from thermodynamic equilibrium to produce a bulk Nd:Al2O3 medium with optical gain suitable for amplification/lasing. The key insight relies on tailoring the crystallite size to the other important length scales-wavelength of light and interatomic dopant distances and show that fine crystallite sizes result in sufficiently low optical losses and over-equilibrium levels of optically active RE-ions, the combination of which results in gain. The emission bandwidth is broad, ~13THz, a new record for Nd3+ transitions, enabling tuning from ~1050nm-1100nm and/or ultra-short pulses in a host with superior thermal-mechanical figure of merit. Laser grade Nd:Al2O3 opens a pathway for lasers with revolutionary performance.
Mathematical Modeling of Reactive Fluid Flow in Solid Rocket Motor Combustion Chamber with/without Nozzle

In the present work, a complete simulation of reactive flow in the combustion chamber of a rocket motor equipped with convergent-divergent nozzle has been introduced. The model describes the combustion process inside the combustion chamber considering a steady premixed reactant gas injected through side porous walls of the combustion chamber. The products flow through a convergent-divergent nozzle with adiabatic impermeable walls. The reactants are treated as two-dimensional, multi-components, turbulent compressible flow. The local properties of the mixture are calculated and updated during the solution process. At the boundary of the combustion chamber, a constant mass flux and predefined properties are considered. The proposed model employs the basic conservation equations of continuity, momentum and energy as well as the finite rate of reaction and species transport equations. Finite volume method is used to solve the basic nonlinear partial differential equations numerically. The details of the numerical scheme, structure of the used grids, numerical accuracy and stability are introduced. The effect of grid resolution as well as the validity of the results is included. The results showed fair agreement with other models in the literature; specially the reaction zone depth, temperature contours and species concentration along the entire space of the combustion chamber.
Automatic calibration of sensor-phones using gaussian processes

In the context of environmental sensors embedded into location-aware cell phones, this paper explores how to automatically calibrate the bias of each sensor based on readings from nearby phones, thus exploiting the mobility of the sensors. Gaussian process regression is used interpolate values and also infer the bias of the sensors. The impact of dense vs sparse sampling is explored, and future strategies for efficiency, gain calibration, and cyclical covariance are suggested.
Influence of polarization on two unequal semi-permeable cracks in a piezoelectric media

In this paper, the influence of arbitrary poling direction on a cracked piezoelectric medium is studied. For this purpose, an infinite piezoelectric plate cut along to equal semipermeable collinear cracks is considered. Stroh’s formalism and complex variable technique are used to formulate the problem. The closed-form analytical expressions are derived for the various fracture parameters viz. crack opening displacement, crack opening potential drop, intensity factors, and energy release rate. Moreover, the quadratic equation is derived to find the electric displacement inside the crack gap media. For a poled PZT-5H ceramic plate, an illustrative numerical investigation is implemented to demonstrate the effect of arbitrary polarization on the various fracture parameters.
Retrograde Wiring of Collateral Channels of the Heart in Chronic Total Occlusions

Aim: To conduct a systematic review and meta-analysis on retrograde wiring in chronic total occlusions (CTOs) with focus on its safety and feasibility. Methods and Results: We searched publications from 1990 to December 2013 in PubMed, Ovid, EMBASE, and the Cochrane database inserting a number of terms relating to the collateral circulation of the heart in CTOs. A total of 18 case series (n range17-462) with a total of 2280 CTO revascularization attempts fulfilled criteria for a study of retrograde wiring of collateral channels in CTOs. There were no randomized studies comparing a primary antegrade with a primary retrograde approach. Procedural CTO revascularization rates ranged from 67% to 90.6% with a large proportion having previously failed an “antegrade” approach. The septal perforator collaterals and epicardial channels were used in 73.2% (n = 1670) and 21.7% (n = 495) of cases. Although collateral/coronary perforation was not infrequent (n = 90, 5%), serious acute complications were uncommon; in the combined population 18 cases of cardiac tamponade (0.8%) and 3 deaths (0.1%). Septal perforating wiring (79.3%) was significantly more likely to be successful compared to epicardial coronary artery wiring (72.5%) when chosen by the operator as a route of retrograde access to the CTO body (relative risk 1.11 [95% confidence interval: 1.02-1.20; P = .013]). Conclusion: Successful retrograde wiring of collateral channels in selected patients undertaken by “CTO dedicated” operators can significantly enhance the chances of revascularization of complex CTOs with a low risk of acute serious complications. Septal perforator channels are significantly more likely to be successfully retrogradely wired compared to epicardial vessels when either is selected, by reference to their anatomical suitability by the operator, as a route of access.
Premorbid adjustment in first-episode non-affective psychosis: Distinct patterns of pre-onset course

Background Knowledge about premorbid development in psychosis can shed light upon theories about aetiology and schizophrenic heterogeneity, and form a basis for early detection initiatives. Aims To identify and validate patterns of premorbid functioning in first-episode psychosis. Method The Premorbid Adjustment Scale was used to examine 335 patients. Results Social and academic function constituted fairly independent dimensions. Cluster analysis identified groups varying both in level and course. Patients with a stable social course compared with a deteriorating one had a shorter duration of untreated psychosis, were older, had more friends and less negative symptoms. Good childhood academic function correlated with more education, more meaningful activities and better working memory. Patients with a stable academic course were older at admission. Conclusions Patterns of premorbid development suggest both neuro-developmental and neuroregressive pathways to illness.
High Precision Information Retrieval with Multi-Term Relation

Enterprise search differs from Internet search in many ways. The notion of a “good” answer to a query is quite different. On an intranet, the notion of a “good” answer is often defi ned as the “right” answer. Finding the right answer is often more diffi cult than fi nding the best answer. On the other hand, nowadays when searching on Internet, a large amount of information returned for a query, the most important is how to improve the precision of a query. In order to improve the search relevance and precision, a search approach integrated multi-term relations into search process is presented in this paper. In this approach, two level mixed indices are established including term index and term relations index. The Maximum Match algorithm in Chinese segmentation is used to fi nd multi-term relations from text data. With this approach, we can obtain the “right” answer quickly and effi ciently.
Constitutive Localization of the Gonadotropin-releasing Hormone (GnRH) Receptor to Low Density Membrane Microdomains Is Necessary for GnRH Signaling to ERK*

Specialized membrane microdomains known as lipid rafts are thought to contribute to G-protein coupled receptor (GPCR) signaling by organizing receptors and their cognate signaling molecules into discrete membrane domains. To determine if the GnRHR, an unusual member of the GPCR superfamily, partitions into lipid rafts, homogenates of αT3-1 cells expressing endogenous GnRHR or Chinese hamster ovary cells expressing an epitope-tagged GnRHR were fractionated through a sucrose gradient. We found the GnRHR and c-raf kinase constitutively localized to low density fractions independent of hormone treatment. Partitioning of c-raf kinase into lipid rafts was also observed in whole mouse pituitary glands. Consistent with GnRH induced phosphorylation and activation of c-raf kinase, GnRH treatment led to a decrease in the apparent electrophoretic mobility of c-raf kinase that partitioned into lipid rafts compared with unstimulated cells. Cholesterol depletion of αT3-1 cells using methyl-β-cyclodextrin disrupted GnRHR but not c-raf kinase association with rafts and shifted the receptor into higher density fractions. Cholesterol depletion also significantly attenuated GnRH but not phorbol ester-mediated activation of extracellular signal-related kinase (ERK) and c-fos gene induction. Raft localization and GnRHR signaling to ERK and c-Fos were rescued upon repletion of membrane cholesterol. Thus, the organization of the GnRHR into low density membrane microdomains appears critical in mediating GnRH induced intracellular signaling.
A Comparison of Different Soft Computing Models for Credit Scoring

— It has become crucial over the years for nations to improve their credit scoring methods and techniques in light of the increasing volatility of the global economy. Statistical methods or tools have been the favoured means for this; however artificial intelligence or soft computing based techniques are becoming increasingly preferred due to their proficient and precise nature and relative simplicity. This work presents a comparison between Support Vector Machines and Artificial Neural Networks two popular soft computing models when applied to credit scoring. Amidst the different criteria’s that can be used for comparisons; accuracy, computational complexity and processing times are the selected criteria used to evaluate both models. Furthermore the German credit scoring dataset which is a real world dataset is used to train and test both developed models. Experimental results obtained from our study suggest that although both soft computing models could be used with a high degree of accuracy, Artificial Neural Networks deliver better results than Support Vector Machines.
Discovering Structured Variations Via Template Matching

Understanding patterns of variation from raw measurement data remains a central goal of shape analysis. Such an understanding reveals which elements are repeated, or how elements can be derived as structured variations from a common base element. We investigate this problem in the context of 3D acquisitions of buildings. Utilizing a set of template models, we discover geometric similarities across a set of building elements. Each template is equipped with a deformation model that defines variations of a base geometry. Central to our algorithm is a simultaneous template matching and deformation analysis that detects patterns across building elements by extracting similarities in the deformation modes of their matching templates. We demonstrate that such an analysis can successfully detect structured variations even for noisy and incomplete data.
Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Unfortunately, microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.
Bone Morphogenetic Protein 4 Promotes Pulmonary Vascular Remodeling in Hypoxic Pulmonary Hypertension

We show that 1 of the type II bone morphogenetic protein (BMP) receptor ligands, BMP4, is widely expressed in the adult mouse lung and is upregulated in hypoxia-induced pulmonary hypertension (PH). Furthermore, heterozygous null Bmp4lacZ/+ mice are protected from the development of hypoxia-induced PH, vascular smooth muscle cell proliferation, and vascular remodeling. This is associated with a reduction in hypoxia-induced Smad1/5/8 phosphorylation and Id1 expression in the pulmonary vasculature. In addition, pulmonary microvascular endothelial cells secrete BMP4 in response to hypoxia and promote proliferation and migration of vascular smooth muscle cells in a BMP4-dependent fashion. These findings indicate that BMP4 plays a dominant role in regulating BMP signaling in the hypoxic pulmonary vasculature and suggest that endothelium-derived BMP4 plays a direct, paracrine role in promoting smooth muscle proliferation and remodeling in hypoxic PH.
Probabilistic Diffusion in Random Network Graphs

In this paper, we consider a random network such that there could be a link between any two nodes in the network with a certain probability (p link ). Diffusion is the phenomenon of spreading information throughout the network, starting from one or more initial set of nodes (called the early adopters). Information spreads along the links with a certain probability (p diff ). Diffusion happens in rounds with the first round involving the early adopters. The nodes that receive the information for the first time are said to be covered and become candidates for diffusion in the subsequent round. Diffusion continues until all the nodes in the network have received the information (successful diffusion) or there are no more candidate nodes to spread the information but one or more nodes are yet to receive the information (diffusion failure). On the basis of exhaustive simulations conducted in this paper, we observe that for a given p link and p diff values, the fraction of successful diffusion attempts does not appreciably change with increase in the number of early adopters; whereas, the average number of rounds per successful diffusion attempt decreases with increase in the number of early adopters. The invariant nature of the fraction of successful diffusion attempts with increase in the number of early adopters for a random network (for fixed p link and p diff values) is an interesting and noteworthy observation (for further research) and it has not been hitherto reported in the literature.
Network-based multimodal Human-Robot Interactions in ubiquitous computing environment

This paper presents the autonomous mobile robot interactive behavior in ubiquitous computing environment. We have constructed multimodal network-based interfaces for Human-Robot Interactions (HRI) and present examples in providing services via the direct or indirect interaction in responding to the user's demands. The mobile robot consists of seven systems including vision, speech, remote supervisory and sensory systems, locomotion, robotic arms and power management systems. Various combination schemes have been developed towards network-based interfaces in ubiquitous computing environments. The goal of this paper is to provide a service scheme of an autonomous mobile robot in the loop to be accepted by people as natural assistant. In this paper we have demonstrated three kinds of scenario and successfully tested on Security Warrior, an autonomous mobile robot developed in our lab.
Strain field measurements in polycrystalline tantalum

The unterstanding and the local quantification of surface strain distribution is a key element of crack initiation analysis. Experiments following up the local strain distribution have been carried out, based on the construction of several hundred micrometers wide strain maps ; and using gratings of regularly spaced micrometric markers deposited by lithography. The strain maps are computed using a specific routine, allowing to quantify the local strain distribution at the grain scale for different interrupted tensile loads.
Learning to rank social update streams

As online social media further integrates deeper into our lives, we spend more time consuming social update streams that come from our online connections. Although social update streams provide a tremendous opportunity for us to access information on-the-fly, we often complain about its relevance. Some of us are flooded with a steady stream of information and simply cannot process it in full. Ranking the incoming content becomes the only solution for the overwhelmed users. For some others, in contrast, the incoming information stream is pretty weak, and they have to actively search for relevant information which is quite tedious. For these users, augmenting their incoming content flow with relevant information from outside their first-degree network would be a viable solution. In that case, the problem of relevance becomes even more prominent. In this paper, we start an open discussion on how to build effective systems for ranking social updates from a unique perspective of LinkedIn -- the largest professional network in the world. More specifically, we address this problem as an intersection of learning to rank, collaborative filtering, and clickthrough modeling, while leveraging ideas from information retrieval and recommender systems. We propose a novel probabilistic latent factor model with regressions on explicit features and compare it with a number of non-trivial baselines. In addition to demonstrating superior performance of our model, we shed some light on the nature of social updates on LinkedIn and how users interact with them, which might be applicable to social update streams in general.
An Integrated Architecture for Software Defined and Virtualized Radio Access Networks with Fog Computing

Today, billions of communication devices connecting to wireless networks impose serious challenges to network deployment, management, and data processing. Among all emerging technologies tackling these challenges, SDNs decouple the control plane from the data plane to provide network programmability, and virtualization can share network and radio resources among various applications. On the other hand, fog computing offloads computing services from the cloud to the edge of networks, offering real-time data services to nearby data terminals. In this article, we present an integrated architecture for software defined and virtualized radio access networks with fog computing. We propose a design of software as a service called OpenPipe, which enables network-level virtualization. To integrate SDNs and network virtualization with fog computing, we adopt a hybrid control model with two hierarchical control levels, where an SDN controller forms the higher level and local controllers comprise the lower level. Typical use cases of the proposed network architecture are validated through laboratory demonstrations.