Miracle-dz/UniFAR-FTUs huggingface dataset:
Induction of the Plasminogen Activator System Accompanies Peripheral Nerve Regeneration after Sciatic Nerve Crush

Peripheral nerve regeneration is dependent on the ability of regenerating neurites to migrate through cellular debris and altered extracellular matrix at the injury site, grow along the residual distal nerve sheath conduit, and reinnervate synaptic targets. In cell culture, growth cones of regenerating axons secrete proteases, specifically plasminogen activators (PAs), which are believed to facilitate growth cone movement by digesting extracellular matrices and cell adhesions. In this study, the PA system was shown to be specifically activated in sensory neurons after sciatic nerve crush in adult mice. The number of sensory neurons expressing urokinase PA receptor (uPAR) mRNA levels increased above sham levels by 8 hr after crush, whereas the number of sensory neurons expressing uPA and tissue PA (tPA) mRNAs was significantly increased by 3 d after crush. PA mRNA levels were also increased at the crush site, with uPA mRNA elevated by 8 hr after crush and tPA and uPAR mRNA levels markedly increased by 7 d. PA-dependent enzymatic activity was significantly increased from 1 to 7 d after crush in nerves that had been crushed compared with uncrushed nerves. Immunohistochemistry showed that tPA was localized within regenerating axons of the sciatic nerve. There were no significant changes in plasminogen activator inhibitor 1 activity between crush and sham after the injury. These results clearly demonstrated that after injury the PA system was rapidly induced in sensory neurons, where it may play an important role in nerve regeneration in vivo.
The role of ECL 2 in CGRP receptor activation : a combined modelling and experimental approach

The calcitonin gene-related peptide (CGRP) receptor is a complex of a calcitonin receptor-like receptor (CLR), which is a family B G-protein-coupled receptor (GPCR) and receptor activity modifying protein 1. The role of the second extracellular loop (ECL2) of CLR in binding CGRP and coupling to Gs was investigated using a combination of mutagenesis and modelling. An alanine scan of residues 271–294 of CLR showed that the ability of CGRP to produce cAMP was impaired by point mutations at 13 residues; most of these also impaired the response to adrenomedullin (AM). These data were used to select probable ECL2-modelled conformations that are involved in agonist binding, allowing the identification of the likely contacts between the peptide and receptor. The implications of the most likely structures for receptor activation are discussed.
A successive interference cancellation algorithm in MIMO systems via breadth-first search

A successive interference cancellation (SIC) algorithm based on breadth-first search (BFS) is developed to achieve a soft-input soft-output detector via the tree structure of the MIMO system model in this paper. Instead of visiting all nodes of the tree, the proposed BFS-SIC algorithm only browses and extends those paths with large metrics. If paths are enough, the performance of BFS-SIC algorithm can approach that of sphere decoding but is much more flexible due to its providing a good tradeoff between complexity and performance. Moreover, the BFS-SIC algorithm possesses path metrics including only scalar operations rather than matrix operations. Simulation results demonstrate the effectiveness of the proposed algorithm.
The Anatomy of Web Search Result Clustering and Search Engines

World Wide Web is a very large distributed digital information space. The ability to search and retrieve information from the Web efficiently and effectively is an enabling technology for realizing its full potential. Current search tools retrieve too many documents, of which only a small fraction are relevant to the user query. Furthermore, the most relevant documents do not necessarily appear at the top of the query output order. Clustering Techniques are now being used to give a meaningful search result on web. Text document clustering has been traditionally investigated as a means of improving the performance of search engines. We present a thorough comparison of the algorithms based on the various facets of their features and functionality. Furthermore, we highlight the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance.
DNN Filter Bank Cepstral Coefficients for Spoofing Detection

With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of speaker verification systems, we develop a new filter bank-based cepstral feature, deep neural network (DNN) filter bank cepstral coefficients, to distinguish between natural and spoofed speech. The DNN filter bank is automatically generated by training a filter bank neural network (FBNN) using natural and synthetic speech. By adding restrictions on the training rules, the learned weight matrix of FBNN is band limited and sorted by frequency, similar to the normal filter bank. Unlike the manually designed filter bank, the learned filter bank has different filter shapes in different channels, which can capture the differences between natural and synthetic speech more effectively. The experimental results on the ASVspoof 2015 database show that the Gaussian mixture model maximum-likelihood classifier trained by the new feature performs better than the state-of-the-art linear frequency triangle filter bank cepstral coefficients-based classifier, especially on detecting unknown attacks.
Nonlinear Modelbased Predictive Control for cruise control on combine harvesters

In recent years control technology is becoming more and more present on heavy duty machinery. Some controllers are used to alleviate operator tasks, others are used to conform to the more and more stringent environmental legislation. In this paper the propulsion system of a combine harvester is modeled. This model is then used to develop a cruise control system.
A Numerical Study on the Size Dependent Strength in Nano-Sized Crystalline Materials

Macro-sized crystalline materials are defined by constant properties that are not variant with size. However, when samples of such materials reach dimensions below a few tens of micrometers, properties such as yield strength are no longer constant and can be 200 times that of the bulk material strength. The physics that causes this strength increase in nano-sized crystals is examined through computer simulation of crystalline material specimens by employing discrete dislocation dynamic methods. This numerical analysis leads to the conclusion that the behavior of these smaller materials is controlled by a dislocation starvation mechanism. Dislocation source size scales with specimen dimensions and. The smaller dislocation sources present in smaller materials are harder to activate, which in turn increases the yield strength.
Predictive modeling for default risk using a multilayered feedforward neural network with Bayesian regularization

In this study we propose a multilayered feedforward neural network (MFNN) with Bayesian Regularization, and apply it to the credit risk evaluation problem domain using a real world data set from a financial services company in England. We choose the MFNN because of its broad applicability to many problem domains of relevance to business: principally prediction, classification, and modelling. We employ two different methods to determine their prowess in identifying the true positives, that is, defaulters. We analyzed the effect of making the number of observed bad equal the number of observed good in the data by over sampling of the minority class (bad obligors) by resampling without replacement, and compare this to the dimensionality reduction of the input vector space using Principal Component Analysis. Overall results indicate that using the Receiver Operating Characteristic as a measure of discriminatory power, over sampling of the minority class has been found to be effective in identifying the true positives.
Equalizer technology – Equal rights for disparate beads

One major limitation in proteomics is the detection and analysis of low‐abundant proteins, i.e. in plasma. Several years ago, a technique to selectively enrich the relative concentration of low‐abundant proteins was introduced by Boschetti and co‐workers. It is based on a specific and saturable interaction of proteins to a high diversity of binding sites, realized by a hexapeptide library coupled to beads. This technology was commercialized as Equalizer beads or ProteoMiner. However, during application of ProteoMiner beads to plasma samples unexpected results questioned the proposed mode of action. Therefore, ProteoMiner beads were compared with chromatographic beads exhibiting completely different surface chemistry. Sepabeads FP‐OD400 octadecyl, FP‐DA400 diethylamine, FP‐BU400 butyl, FP‐HG400 hydroxyl and EXE056 epoxy were used. The results show that ProteoMiner or the different Sepabeads behave surprisingly similarly in the separation of complex protein mixtures. ProteoMiner beads interact with protein mixtures according to a general hydrophobic binding mechanism, where diversity in surface ligands plays only a negligible role.
Quantitative structure-activity relationship of multidrug resistance reversal agents.

Multidrug resistance (MDR) is one of the major obstacles to long term successful cancer chemotherapy. The use of MDR reversal (MDRR) agents is a promising approach to overcome the undesired MDR phenotype. To design more effective MDRR agents that are urgently needed for clinical use, a data set of 609 diverse compounds tested for MDRR activity against P388/ADR-resistant cell lines was submitted to the MULTICASE computer program for structure-activity analysis. Some substructural features related to MDRR activity were identified. For example, the CH2-CH2-N-CH2-CH2 group was found in most of the active compounds, and the activity was further enhanced by the presence of (di)methoxylphenyl groups, whereas the presence of a stable quaternary ammonium salt, a carboxylic, a phenol, or an aniline group was found to be detrimental to activity. Possible explanations for these observations are proposed. Some physicochemical properties, e.g., the partition coefficient (log P) and the graph index (which in some sense measures the "complexity" of a molecule) were also found to be relevant to activity. Their role in MDRR was also rationalized. Based on our quantitative structure-activity relationship study of MDRR agents, some compounds with desired substructural features and activity were identified from the MACCS-II and National Cancer Institute DIS databases and tested experimentally. Our study may also help the rational design of anti-cancer drugs. Based on this study and on observations by other researchers, we postulate that P-glycoprotein-mediated resistance to paclitaxel could probably be eliminated by proper substitution of its benzamido and phenyl groups. Several novel compounds with the paclitaxel skeleton are proposed, which may lead to a new generation of paclitaxel anti-cancer drugs with less MDR potential.
A Magic Set Implementation for Disjunctive Logic Programming with Function Symbols

The Magic Sets rewriting technique (MS) consists in rewriting a logic program P with respect to a query Q in such a way that, the bottom-up evaluation of the rewritten program simulates the top-down evaluation of Q in the original program. In this way, only a restricted part of the ground program of P is evaluated, thus obtaining valuable eﬃciency improvements. Many extensions and modiﬁcations of the base technique have been proposed in literature, but most of them are conﬁned to the Datalog realm. In this paper we present an implementation of the Magic Set rewriting technique that is applicable to positive disjunctive logic programs with function symbols under the answer set semantics. We show how the base technique has to be modiﬁed when both disjunction in the rule heads and function symbols occur contemporarily in the input program. Finally, we describe our implementation of the technique, which is able to produce magic programs compatible with DLV syntax.
Effects of land use changes on streamflow generation in the Rhine basin

The hydrological regime of the Rhine basin is expected to shift from a combined snowmelt‐rainfall regime to a more rainfall‐dominated regime because of climate change, leading to more extreme flood peaks and low flows. Land use changes may reinforce the effects of this shift through urbanization or may counteract them through, for example, afforestation. In this study, we investigate the effect of projected land use change scenarios on river discharge. Sensitivity of mean and extreme discharge in the Rhine basin to land use changes is investigated at various spatial scales. The variable infiltration capacity (VIC) (version 4.0.5) model is used for hydrological modeling forced by a high‐resolution atmospheric data set spanning the period 1993–2003. The model is modified to allow for bare soil evaporation and canopy evapotranspiration simultaneously in sparsely vegetated areas, as this is more appropriate for simulating seasonal effects. All projected land use change scenarios lead to an increase in streamflow. The magnitude of the increase, however, varies among subbasins of different scales from about 2% in the upstream part of the Rhine (about 60,000 km2) to about 30% in the Lahn basin (about 7000 km2). Streamflow at the basin outlet proved rather insensitive to land use changes because over the entire basin affected areas are relatively small. Moreover, projected land use changes (urbanization and conversion of cropland into (semi)natural land or forest) have opposite effects. At smaller scales, however, the effects can be considerable.
A Video Descriptor Using Orientation Tensors and Shape-Based Trajectory Clustering

Dense trajectories have been shown as a very promising method in the human action recognition field. In this paper, we propose a new kind of video descriptor, generated from the relationship between the trajectory’s optical flow with the gradient field in its neighborhood. Orientation tensors are used to accumulate relevant information over the video, representing the tendency of direction in the descriptor space for that kind of movement. Furthermore, a method to cluster trajectories using their shape is proposed. This method allows us to accumulate different motion patterns in different tensors and easier distinguish trajectories that are created by real movements from the trajectories created by the camera’s movement. The proposed method is capable to achieve the best known recognition rates for methods based on the self-descriptor constraint in popular datasets — Hollywood2 (up to 46%) and KTH (up to 94%).
When Is Operation Ordering Required in Replicated Transactional Storage?

Today’s replicated transactional storage systems typically have a layered architecture, combining protocols for transaction coordination, consistent replication, and concurrency control. These systems generally require costly strongly-consistent replication protocols like Paxos, which assign a total order to all operations. To avoid this cost, we ask whether all replicated operations in these systems need to be strictly ordered. Recent research has yielded replication protocols that can avoid unnecessary ordering, e.g., by exploiting commutative operations, but it is not clear how to apply these to replicated transaction processing systems. We answer this question by analyzing existing transaction processing designs in terms of which replicated operations require ordering and which simply require fault tolerance. We describe how this analysis leads to our recent work on TAPIR, a transaction protocol that e ﬃ ciently provides strict serializability by using a new replication protocol that provides fault tolerance but not ordering for most operations.
Downregulation of Nuclear Factor Erythroid 2–Related Factor and Associated Antioxidant Genes Contributes to Redox-Sensitive Vascular Dysfunction in Hypertension

Oxidative stress is implicated in vascular dysfunction in hypertension. Although mechanisms regulating vascular pro-oxidants are emerging, there is a paucity of information on antioxidant systems, particularly nuclear factor erythroid 2–related factor (Nrf2), a master regulator of antioxidants enzymes. We evaluated the vascular regulatory role of Nrf2 in hypertension and examined molecular mechanisms, whereby Nrf2 influences redox signaling in small arteries and vascular smooth muscle cells from Wistar Kyoto (WKY) and stroke-prone spontaneously hypertensive rats (SHRSP). Cells were stimulated with angiotensin II in the absence/presence of Nrf2 activators (bardoxolone/L-sulforaphane). Increased vascular reactive oxygen species production (chemiluminescence and amplex red) was associated with reduced Nrf2 activity in arteries (18%) and vascular smooth muscle cells (48%) in SHRSP (P<0.05 versus WKY). Expression of antioxidant enzymes, including superoxide dismutase-1 (64%), catalase (60%), peroxiredoxin 1 (75%), and glutathione peroxidase (54%), was reduced in SHRSP. L-sulforaphane reversed these effects. Angiotensin II increased nuclear accumulation of Nrf2 in vascular smooth muscle cells from WKY (197% versus vehicle), with blunted effects in SHRSP (44% versus vehicle). These responses were associated with increased antioxidant expression (superoxide dismutase-1, 32%; catalase, 42%; thioredoxin, 71%; peroxiredoxin, 1%–90%; quinone oxidoreductase, 84%; P<0.05 versus vehicle) and increased activity of superoxide dismutase-1, catalase, and thioredoxin in WKY but not in SHRSP, which exhibited increased Bach1 expression. Nrf2 activators blocked angiotensin II–induced reactive oxygen species generation. Vascular function demonstrated increased contractility (Emax WKY 113.4±5.6 versus SHRSP 159.0±8.3) and decreased endothelial-dependent relaxation (Emax WKY 88.6±3.1 versus SHRSP 74.6±3.2, P<0.05) in SHRSP, effects corrected by L-sulforaphane. Our findings suggest that Nrf2 downregulation contributes to redox-sensitive vascular dysfunction in hypertension.
Histone Acetylation-independent Effect of Histone Deacetylase Inhibitors on Akt through the Reshuffling of Protein Phosphatase 1 Complexes*

Despite advances in understanding the role of histone deacetylases (HDACs) in tumorigenesis, the mechanism by which HDAC inhibitors mediate antineoplastic effects remains elusive. Modifications of the histone code alone are not sufficient to account for the antitumor effect of HDAC inhibitors. The present study demonstrates a novel histone acetylation-independent mechanism by which HDAC inhibitors cause Akt dephosphorylation in U87MG glioblastoma and PC-3 prostate cancer cells by disrupting HDAC-protein phosphatase 1 (PP1) complexes. Of four HDAC inhibitors examined, trichostatin A (TSA) and HDAC42 exhibit the highest activity in down-regulating phospho-Akt, followed by suberoylanilide hydroxamic acid, whereas MS-275 shows only a marginal effect at 5 μm. This differential potency parallels the respective activities in inducing tubulin acetylation, a non-histone substrate for HDAC6. Evidence indicates that this Akt dephosphorylation is not mediated through deactivation of upstream kinases or activation of downstream phosphatases. However, the effect of TSA on phospho-Akt can be rescued by PP1 inhibition but not that of protein phosphatase 2A. Immunochemical analyses reveal that TSA blocks specific interactions of PP1 with HDACs 1 and 6, resulting in increased PP1-Akt association. Moreover, we used isozyme-specific small interfering RNAs to confirm the role of HDACs 1 and 6 as key mediators in facilitating Akt dephosphorylation. The selective action of HDAC inhibitors on HDAC-PP1 complexes represents the first example of modulating specific PP1 interactions by small molecule agents. From a clinical perspective, identification of this PP1-facilitated dephosphorylation mechanism underscores the potential use of HDAC inhibitors in lowering the apoptosis threshold for other therapeutic agents through Akt down-regulation.
Information-based exploration strategy for mobile robot in dynamic environment

To meet the necessity of handling environmental uncertainties of mobile robots, we proposed an efficient exploration strategy to gather information, called Entropy Sweeper. To do so, we utilized the entropy distribution and the utility function to determine which positions have more uncertainties. Proposed strategy is divided into two phases: the learning phase and the action phase. In general, uncertainties increase unevenly and never disappear in dynamic environments. So in the learning phase, robots move wall to wall to learn which positions are likely to increase uncertainties actively. In the action phase, robots explore the environment efficiently and continue lifelong learning to handle environmental uncertainties. This strategy is an optimization not only for paths but also for sequences of exploration points using information about uncertainties of dynamic environments. We demonstrated its effectiveness with several simulations.
Conception, fabrication and characterization of a MEMS microphone

Electret microphones dedicated to consumer electronics and medical applications (hearing aids) have reached the miniaturization limits. Since the release of the first microphone based on Silicon micromachining, electret microphones are constantly replaced by MEMS microphones. MEMS (Micro-Electro-Mechanical Systems) microphones use Silicon that provides exceptional mechanical characteristics along with good electric properties and mature fabrication technology. Regardless of the transduction principle (capacitive, piezoresistive, piezoelectric, optical), all of the MEMS microphones reported in the state of the art literature are based on a membrane deflecting out of the plane of the base wafer. Most of the reported microphones and all of the commercially available MEMS use capacitive transduction. Downscaling of capacitive microphones is problematic, since the sensitivity depends on capacitance value. Moreover capacitive sensors suffer of high sensitivity to parasitic capacitance and nonlinearity. The drawbacks of capacitive detection may be overcome with use of piezoresistive properties of Silicon nanowires. Unlike the classical piezoresistors integrated into silicon membrane, suspended nanowires do not suffer of leakage current. Further improvement of piezoresistive detection is possible since the longitudinal piezoresistive coefficient rises inversely proportional to nanowire section. This thesis presents the considerations of novel MEMS microphone architecture that uses microbeams which deflect in the plane of the base wafer. Signal transduction is achieved by piezoresistive nanogauges integrated in the microsystem and attached to the microbeams. Acoustic pressure fluctuations lead to the deflection of the microbeams which produces a stress concentration in the nanogauges. Accurate simulations of the discussed transducer couple acoustic, mechanical and electric behavior of the system. Due to micrometric dimensions of the MEMS acoustic system, thermal and viscous dissipative effects have to be taken into account. To reliably predict the sensor behavior two acoustic models are prepared: the complete Finite Element Model based on the full set of linearized Navier-Stokes equations and the approximative model based on the Lumped Elements (Equivalent Cirtuit Representation). Both models are complementary in the design process to finally retrieve the frequency response and the noise budget of the sensor. The work is completed by the description of the technological process and the challenges related to the prototype microfabrication. Then the approach to the MEMS microphone characterization in pressure-field and free-field is presented.