Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanisms of Amyloid-Beta Peptide Uptake by Neurons: The Role of Lipid Rafts and Lipid Raft-Associated Proteins

A hallmark pathological feature of Alzheimer's disease (AD) is the accumulation of extracellular plaques composed of the amyloid-beta (Aβ) peptide. Thus, classically experiments were designed to examine Aβ toxicities within the central nervous system (CNS) from the extracellular space. However, a significant amount of evidence now suggests that intraneuronal accumulation of Aβ is neurotoxic and may play an important role in the disease progression of AD. One of the means by which neurons accumulate intracellular Aβ is through uptake of extracellular Aβ peptides, and this process may be a potential link between Aβ generation, synaptic dysfunction, and AD pathology. Recent studies have found that neuronal internalization of Aβ involves lipid rafts and various lipid raft-associated receptor proteins. Uptake mechanisms independent of lipid rafts have also been implicated. The aim of this paper is to summarize these findings and discuss their significance in the pathogenesis of AD.
The Hamburg Quasar Monitoring Program (HQM) at Calar Alto III. Lightcurves of optically violent variable sources

HQM is an optical broad-band photometric monitoring program carried out since September 1988. We use a CCD camera at the MPIA 1.2 m telescope. Fully automatic photometric reduction relative to stars in the frames is done within a few minutes after each exposure, thus interesting brightness changes can be followed in detail. The typical photometric error is 1-2% for a 17.5 mag quasar. We here present lightcurves of 14 known violently variable sources and compare them with literature data. For two BL Lac objects, 1E 1229+645 and 4C 56.27 (1823+568), this paper is the first variability study. We have also carried out POSS photometry to obtain indications for variability on a longer timescale
Markov Chain Monte Carlo Algorithms for the Bayesian Analysis of Phylogenetic Trees

We further develop the Bayesian framework for analyzing aligned nucleotide sequence data to reconstruct phylogenies, assess uncertainty in the reconstructions, and perform other statistical inferences. We employ a Markov chain Monte Carlo sampler to sample trees and model parameter values from their joint posterior distribution. All statistical inferences are naturally based on this sample. The sample provides a most-probable tree with posterior probabilities for each clade, information that is qualitatively similar to that for the maximum-likelihood tree with bootstrap proportions and permits further inferences on tree topology, branch lengths, and model parameter values. On moderately large trees, the computational advantage of our method over bootstrapping a maximum-likelihood analysis can be considerable. In an example with 31 taxa, the time expended by our software is orders of magnitude less than that a widely used phylogeny package for bootstrapping maximum likelihood estimation would require to achieve comparable statistical accuracy. While there has been substantial debate over the proper interpretation of bootstrap proportions, Bayesian posterior probabilities clearly and directly quantify uncertainty in questions of biological interest, at least from a Bayesian perspective. Because our tree proposal algorithms are independent of the choice of likelihood function, they could also be used in conjunction with likelihood models more complex than those we have currently implemented.
Impaired Fast-Spiking, Suppressed Cortical Inhibition, and Increased Susceptibility to Seizures in Mice Lacking Kv3.2 K+ Channel Proteins

Voltage-gated K+ channels of the Kv3 subfamily have unusual electrophysiological properties, including activation at very depolarized voltages (positive to −10 mV) and very fast deactivation rates, suggesting special roles in neuronal excitability. In the brain, Kv3 channels are prominently expressed in select neuronal populations, which include fast-spiking (FS) GABAergic interneurons of the neocortex, hippocampus, and caudate, as well as other high-frequency firing neurons. Although evidence points to a key role in high-frequency firing, a definitive understanding of the function of these channels has been hampered by a lack of selective pharmacological tools. We therefore generated mouse lines in which one of the Kv3 genes, Kv3.2, was disrupted by gene-targeting methods. Whole-cell electrophysiological recording showed that the ability to fire spikes at high frequencies was impaired in immunocytochemically identified FS interneurons of deep cortical layers (5-6) in which Kv3.2 proteins are normally prominent. No such impairment was found for FS neurons of superficial layers (2-4) in which Kv3.2 proteins are normally only weakly expressed. These data directly support the hypothesis that Kv3 channels are necessary for high-frequency firing. Moreover, we found that Kv3.2 −/− mice showed specific alterations in their cortical EEG patterns and an increased susceptibility to epileptic seizures consistent with an impairment of cortical inhibitory mechanisms. This implies that, rather than producing hyperexcitability of the inhibitory interneurons, Kv3.2 channel elimination suppresses their activity. These data suggest that normal cortical operations depend on the ability of inhibitory interneurons to generate high-frequency firing.
Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health

The utility of Twitter data as a medium to support population-level mental health monitoring is not well understood. In an effort to better understand the predictive power of supervised machine learning classifiers and the influence of feature sets for efficiently classifying depression-related tweets on a large-scale, we conducted two feature study experiments. In the first experiment, we assessed the contribution of feature groups such as lexical information (e.g., unigrams) and emotions (e.g., strongly negative) using a feature ablation study. In the second experiment, we determined the percentile of top ranked features that produced the optimal classification performance by applying a three-step feature elimination approach. In the first experiment, we observed that lexical features are critical for identifying depressive symptoms, specifically for depressed mood (-35 points) and for disturbed sleep (-43 points). In the second experiment, we observed that the optimal F1-score performance of top ranked features in percentiles variably ranged across classes e.g., fatigue or loss of energy (5th percentile, 288 features) to depressed mood (55th percentile, 3,168 features) suggesting there is no consistent count of features for predicting depressive-related tweets. We conclude that simple lexical features and reduced feature sets can produce comparable results to larger feature sets.
Dynamic DFS in Undirected Graphs: breaking the O(m) barrier

Given an undirected graph G = (V, E) on n vertices and m edges, we address the problem of maintaining a DFS tree when the graph is undergoing updates (insertion and deletion of vertices or edges). We present the following results for this problem. 1. Fault tolerant DFS tree: There exists a data structure of size O(m) 1 such that given any set F of failed vertices or edges, a DFS tree of the graph G\F can be reported in O(n|F|) time. 2. Fully dynamic DFS tree: There exists a fully dynamic algorithm for maintaining a DFS tree that takes worst case O([EQUATION]) time per update for any arbitrary online sequence of updates. 3. Incremental DFS tree: Given any arbitrary online sequence of edge insertions, we can maintain a DFS tree in O(n) worst case time per edge insertion. These are the first o(m) worst case time results for maintaining a DFS tree in a dynamic environment. Moreover, our fully dynamic algorithm provides, in a seamless manner, the first deterministic algorithm with O(1) query time and o(m) worst case update time for the dynamic subgraph connectivity, biconnectivity, and 2-edge connectivity.
INNOVATION STRATEGY : IT , R & D AND PROFITABILITY

Studies have been performed concerning the individual success of R&D intensity, Information Technology (IT) usage or the effects of innovation over time. Each separate analysis has shown to varying degrees why each of these strategic areas is important. This paper investigates the association between IT, R&D, innovation and profitability. A sample of 36 firms in the consumer goods industry was chosen to test inter-relationships among the above variables. Paired t-tests and non-parametric equivalents were the statistical analyses used to test the hypotheses. It was found that R&D intensity was greater for smaller firms while IT innovation was stronger in larger companies. Further, statistical results reveal that the two separate strategies of deploying high R&D intensity or investing heavily in IT innovation both yield similar profitability results. An interesting observation was that even though no firm pursued both strategies at high levels, profitability was not harmed, i.e. intensive pursuit of one of these strategies was sufficient for profitability.
Power Efficient Transmission of Layered Video Through Wireless Relay Nodes

Efficacy of using a wireless relay node (WRN) to minimize total power dissipation in transmission of a layered video between two devices is presented. It is analytically shown that there is a feasible relay region that would have lower power consumption than direct transmission depending on both the encoding rate of each layer and received signal power level for provisioning required bit error rate.
Photosynthesis and photoprotection in symbiotic corals

In zooxanthellate corals, excess excitation energy can be dissipated as heat (nonphotochemical quenching), thereby providing protection against oxidative damage by supraoptimal light in shallow reefs. To identify and quantify the photoprotective mechanisms, we studied the diel variability of chlorophyll fluorescence yields and photosynthetic parameters in situ in corals, using moored and SCUBA‐based fast‐repetition‐rate fluorometers. The results reveal that nonphotochemical quenching is triggered prior to saturation of photosynthetic electron transport by down‐regulation of the reaction centers of Photosystem II (PSII). This process dissipates up to 80% of the excitation energy. On a sunny day in shallow waters, the daily integrated flux of photons absorbed, and subsequently dissipated as heat, is ~4 times that used for photosynthesis. Fluorescence quenching is further accompanied by a slight reduction in the functional absorption cross section for PSII that results from thermal dissipation of excitation energy in the light‐harvesting antennae. These two processes are highly dynamic and adjust to irradiance changes on timescales consistent with the passage of clouds across the sky. Under supraoptimal irradiance, however, up to 30% of PSII reaction centers become photoinhibited, and these are repaired only after several hours of low irradiance. In shallow corals, between 10% and 20% of the reactions centers are chronically photoinhibited and appear to remain permanently nonfunctional throughout the year. Our results establish, for the first time, the suite of biophysical mechanisms that optimize photosynthesis while simultaneously providing photoprotection in symbiotic corals in situ.
Motion Control of Parallel Manipulators Using Acceleration Feedback

Acceleration feedback (AF) is applied to restrain the trajectory disturbances in the motion control of parallel manipulators to increase the trajectory tracking accuracy. A dynamic model is formulated in the task space, and the optimal exciting trajectory is calculated for dynamic identification. The acceleration signals can be estimated without acceleration sensors on the basis of the closed-loop constraint equations. Furthermore, the dynamic acceleration feedback (DAF) controller is developed by introducing the AF into the robust dynamics compensation and trajectory tracking, and the stability of the closed-loop system is proven. The DAF controller is implemented on a planar parallel manipulator platform, and the corresponding experimental results are compared with an existing, robust controller.
Blind Spectrum Sensing with low rank and sparse model

Spectrum Sensing is a cornerstone in cognitive radio which can detect the spectrum holes in order to raise spectrum utilization ratio. Traditional spectrum sensing detectors depend on some prior information or are restricted by low signal-to-noise ratio and computation complexity in practical application. A GoDec based spectrum sensing detector is proposed by combining covariance based method with low rank and sparse model theory. The proposed detector divides the received signal into two segments of equal length, and then decomposes the covariance matrix respectively by GoDec decomposition. The primary user exists if the difference between the low rank matrices is lower than a predefined threshold. Simulation results show that the proposed detector has high detection probability to detect primary signals with SNR as low as -14dB.
A Geometric-based Sketch Recognition Approach for Handwritten Mandarin Phonetic Symbols I

Abstract Inputting written Chinese, unlike written English, is a non-trivial operation using a standard keyboard. To accommodate this operation, numerous existing phonetic systems using the Roman alphabet were adopted as a means of input while still making use of a Western keyboard. With the growing prevalence of computing devices capable of pen-based input, naturally sketching written Chinese using a phonetic system becomes possible, and is also generally faster and simpler than sketching entire Chinese characters. One method for sketching Chinese characters for computing devices capable of pen-based input involves using an existing non-alphabetic phonetic system called the Mandarin Phonetic Symbols I (MPS1). The benefits of inputting Chinese characters by its corresponding MPS1 symbols – unlike letters from its alphabetic-based counterpart – is that it retains the phonemic components of the corresponding Chinese characters. The work in the paper describes our geometric-based MPS1 recognition system, a system designed particularly for novice users of MPS1 symbols that gives reasonable vision-based recognition rates and provides useful feedback for symbols drawn with incorrect sketching technique such as stroke order.
Text classification using artificial neural networks

Text Categorization is the process of classifying the text or documents into its corresponding categories which are defined previously. As the text or data is increasing enormously now-a-days it’s not possible to classify all the text documents manually hence its necessary to use some techniques or methods to classify the text automatically. In this paper we are using the ANN technique to classify the text, the purpose of choosing it, its advantages and its process is described in the further sections
MFGI, a Linguistically Motivated Quantitative Model of German Prosody

The intellegibility and perceived naturalness of synthetic speech strongly depends on the prosodic quality of a TTS system. Although some recent systems avoid this problem by concatenating larger chunks of speech from a database (see, for instance, Stöber et al., 1999), an approach which preserves the natural prosodic structure at least throughout the chunks chosen, the question of optimal unitselection still calls for the development of improved prosodic models. Furthermore, the lack of prosodic naturalness of conventional TTS systems indicates that the production process of prosody and the interrelation between the prosodic features of speech is still far from being fully understood.
A Novel Method for Ship Detection Based on NSCT and ACO

Ship detection is an important section of Synthetic Aperture Radar's marine applications. Through analyzing the distribution characteristics of ship targets and speckle, a novel ship detection method based on NSCT and ACO is proposed for SAR image processing. First, apply NSCT adaptive threshold method to de-noise the SAR images, then detect the edges by ACO, last ship targets can be precisely detected. Simulating results indicate that this method can not only pick- up contour information of ship objects in complex speckle efficiently, but also keep ships structure well, show ideal anti- jamming competence, guarantee the detection accuracy.
PRODUCTION RULES GENERATION AND REFINEMENT IN BP NETWORKS 1

The problem of extracting symbolic knowledge out of backpropagation (BP) networks is examined. A solution is proposed, based on a mapping from a class of decision rules onto a class of multilayer feedforward networks, and on learning algorithms which allow this mapping to be reversed. This approach can be applied to refine an existing rule base, or to generate classification rules from a set of positive and negative examples.
Consumption , Savings and Medical Prevention in the Long Run-Impact of Health Savings Accounts

testThis paper investigates the impact of health savings accounts (HSAs) on savings and prevention e ort accounting not only for the short but also for the long term e ects of medical prevention. In the short run, medical prevention decreases health care costs. In the long run, prevention increases the likelihood of living up to a very high age and causing excessive end-of-life treatment costs. Adding the long term perspective shows that individuals increase prevention but decrease savings when savings and prevention are substitutes and the short term impact of prevention is not too high. However, if these two are complements and the short term e ect is substantial, individuals may actually save and prevent less than without HSAs. Accordingly, adding the long term perspective to the investigation shows that HSAs may not only fail at one of the originally aimed purposes but at both.
Flexible and robust robotic arm design and skill learning by using recurrent neural networks

It is undeniable that the ability to grasp and handle an object is vital for service robots. From object recognition to object grasping motion, the motion execution should be as fast as possible. Due to the possible position variation of the target object to be grasped, online planning of grasping motion should be done. In order to achieve flexible grasping motion, recurrent neural network could be implemented as an alternative to conventional manipulation method which is based on kinematic and dynamic analysis. However, the application of recurrent neural network model requires good and easily obtainable training data. Hence, a novel robotic arm design with high flexibility is proposed to facilitate the training and implementation of the recurrent neural network model. The feasibility of the proposed robotic arm design is evaluated via the training, learning and testing of stochastic continuous time recurrent neural network (S-CTRNN) model with grasping a box motion.
A 3D Visualization of Multiple Time Series on Maps

In the analysis of spatially-referenced time-dependent data, gaining an understanding of the spatio-temporal distributions and relationships among the attributes in the data can be quite difficult. We present a visualization technique that addresses some of the challenges involved in visually exploring and analyzing the distributions of geo-spatial time-varying data. We have developed a pictorial representation that is based on the standard space-time cube metaphor and provides in a single display the overview and details of a large number of time-varying quantities. Our approach involves three-dimensional graphical widgets that intuitively represent profiles of the time-varying quantities and can be plotted on a geographic map to expose interesting spatio-temporal distributions of the data. We show how combining our visualization technique with standard data exploration features can assist in the exploration of salient patterns in a data set. The visualization approach described here supports expeditious exploration of multiple data sets; this in turn assists the process of building initial hypotheses about the attributes in a data set and enhances the user's ability to pose and explore interesting questions about the data.
Multi-step attack modelling and simulation (MsAMS) framework based on mobile ambients

Attackers take advantage of any security breach to penetrate an organisation perimeter and exploit hosts as stepping stones to reach valuable assets, deeper in the network. The exploitation of hosts is possible not only when vulnerabilities in commercial off-the-shelf (COTS) software components are present, but also, for example, when an attacker acquires a credential on one host which allows exploiting further hosts on the network. Finding attacks involving the latter case requires the ability to represent dynamic models. In fact, more dynamic aspects are present in the network domain such as attackers accumulate resources (i.e. credentials) along an attack, and users and assets may move from one environment to another, although always constrained by the ruling of the network. In this paper we address these dynamic issues by presenting MsAMS (Multi-step Attack Modelling and Simulation), an implemented framework, based on Mobile Ambients, to discover attacks in networks. The idea of ambients fits naturally into this domain and has the advantage of providing flexibility for modelling. Additionally, the concept of mobility allows the simulation of attackers exploiting opportunities derived either from the exploitation of vulnerable and non-vulnerable hosts, through the acquisition of credentials. It also allows expressing security policies embedded in the rules of the ambients.
Mining Databases with Different Schemas: Integrating Incompatible Classifiers

Distributed data mining systems aim to discover (and combine) usefull information that is distributed across multiple databases. The JAM system, for example, applies machine learning algorithms to compute models over distributed data sets and employs meta-learning techniques to combine the multiple models. Occasionally, however, these models (or classifiers) are induced from databases that have (moderately) different schemas and hence are incompatible. In this paper, we investigate the problem of combining multiple models computed over distributed data sets with different schemas. Through experiments performed on actual credit card data provided by two different financial institutions, we evaluate the effectiveness of the proposed approaches and demonstrate their potential utility.
Activation of the GABAB Receptor Prevents Nicotine-Induced Locomotor Stimulation in Mice

Recent studies demonstrated that activation of the GABAB receptor, either by means of orthosteric agonists or positive allosteric modulators (PAMs), inhibited different nicotine-related behaviors, including intravenous self-administration and conditioned place preference, in rodents. The present study investigated whether the anti-nicotine effects of the GABAB receptor agonist, baclofen, and GABAB PAMs, CGP7930, and GS39783, extend to nicotine stimulant effects. To this end, CD1 mice were initially treated with baclofen (0, 1.25, and 2.5 mg/kg, i.p.), CGP7930 (0, 25, and 50 mg/kg, i.g.), or GS39783 (0, 25, and 50 mg/kg, i.g.), then treated with nicotine (0 and 0.05 mg/kg, s.c.), and finally exposed to an automated apparatus for recording of locomotor activity. Pretreatment with doses of baclofen, CGP7930, or GS39783 that did not alter locomotor activity when given with nicotine vehicle fully prevented hyperlocomotion induced by 0.05 mg/kg nicotine. These data extend to nicotine stimulant effects the capacity of baclofen and GABAB PAMs to block the reinforcing, motivational, and rewarding properties of nicotine. These data strengthen the hypothesis that activation of the GABAB receptor may represent a potentially useful, anti-smoking therapeutic strategy.