Miracle-dz/UniFAR-FTUs huggingface dataset:
Synapse Geometry and Receptor Dynamics Modulate Synaptic Strength

Synaptic transmission relies on several processes, such as the location of a released vesicle, the number and type of receptors, trafficking between the postsynaptic density (PSD) and extrasynaptic compartment, as well as the synapse organization. To study the impact of these parameters on excitatory synaptic transmission, we present a computational model for the fast AMPA-receptor mediated synaptic current. We show that in addition to the vesicular release probability, due to variations in their release locations and the AMPAR distribution, the postsynaptic current amplitude has a large variance, making a synapse an intrinsic unreliable device. We use our model to examine our experimental data recorded from CA1 mice hippocampal slices to study the differences between mEPSC and evoked EPSC variance. The synaptic current but not the coefficient of variation is maximal when the active zone where vesicles are released is apposed to the PSD. Moreover, we find that for certain type of synapses, receptor trafficking can affect the magnitude of synaptic depression. Finally, we demonstrate that perisynaptic microdomains located outside the PSD impacts synaptic transmission by regulating the number of desensitized receptors and their trafficking to the PSD. We conclude that geometrical modifications, reorganization of the PSD or perisynaptic microdomains modulate synaptic strength, as the mechanisms underlying long-term plasticity.
Neurite outgrowth can be modulated in vitro using a tetracycline‐repressible gene therapy vector expressing human nerve growth factor

The delivery of neurotrophic factors to the adult nervous system has potential applications for the treatment of neurodegenerative diseases and trauma. In vivo and ex vivo gene therapy offer a means of delivering growth factors and other therapeutic substances to the central nervous system (CNS) in an intraparenchymal, accurately targeted, and regionally restricted manner. Ideally, gene therapy delivery systems should also be regulatable, allowing exogenous control of amount of gene product delivery. In the present experiment, a tetracycline‐regulatable gene expression system was generated to determine whether controllable release of nerve growth factor (NGF) and green fluorescent protein (GFP) from primary rat fibroblasts could modulate biological responses (neurite outgrowth) in vitro. Using a tetracycline‐repressible construct, it was found that NGF mRNA, NGF protein, and NGF‐induced neurite outgrowth could be tightly regulated within a 24 hour period, and in a dose‐dependent fashion, by exposure to the tetracycline analog doxycycline. Similarly, levels of green fluorescence could be regulated in GFP‐transfected cells. These findings in a neurobiological system lay the framework for future studies using regulated neurotrophin delivery in in vivo models of neurodegenerative diseases and CNS injury. J. Neurosci. Res. 59:402–409, 2000 © 2000 Wiley‐Liss, Inc.
Parallel algorithms for force directed scheduling of flattened and hierarchical signal flow graphs

In this paper we present some novel algorithms for scheduling hierarchical signal flow graphs in the domain of high-level synthesis. There are several key contributions of this paper. First, we develop a novel extension of the force directed scheduling problem which naturally handles loops and conditionals by coming up with a scheme of scheduling hierarchical signal flow graphs. Second, we develop three new parallel algorithms for the scheduling problem. Third, our parallel algorithms are portable across a wide range of parallel platforms. We report results on a set of high-level synthesis benchmarks on 8-processor SGI Challenge and a network of 4 SUN SPARCstation5 work stations. Finally, while some parallel algorithms for VLSI CAD reported by earlier researchers have reported a loss of qualities of results, our parallel algorithms produce exactly the same results as the sequential algorithms on which they are based.
Global Computing for Bioinformatics

Global computing, the collaboration of idle PCs via the Internet in a SETI@home style, emerges as a new way of massive parallel multiprocessing with potentially enormous CPU power. Its relations to the broader, fast-moving field of Grid computing are discussed without attempting a review of the latter. This review (i) includes a short table of milestones in global computing history, (ii) lists opportunities global computing offers for bioinformatics, (iii) describes the structure of problems well suited for such an approach, (iv) analyses the anatomy of successful projects and (v) points to existing software frameworks. Finally, an evaluation of the various costs shows that global computing indeed has merit, if the problem to be solved is already coded appropriately and a suitable global computing framework can be found. Then, either significant amounts of computing power can be recruited from the general public, or--if employed in an enterprise-wide Intranet for security reasons--idle desktop PCs can substitute for an expensive dedicated cluster.
Robust fingerprint-based indoor positioning with multi-frequency signal analysis

Recently, high-precision indoor positioning has become an important technology in the O2O (online-to-offline) services. In indoor positioning method, fingerprinting is better than trilateration in that it is possible to avoid problems that are not effectively applied to indoor wireless attenuation model. Fingerprinting compares the radio propagation condition that is measured at the time of positioning with the set of radio propagation conditions that are measured in initial training, and estimates the position according to its similarity. However, since radio waves are superimposed by the reflection of the walls and floors in the indoor environment, positioning accuracy decreases when the layout of the environment is changed after creation of the initial training data. To solve this problem, we propose a method to infer whether some obstacle exists between each pair of transmitter and receiver by principal component analysis of RSSI (Received Signal Strength Indicator) of multi-frequency, to detect LOS (Line-of-Sight)-shielded transmitters and to eliminate them from calculation for positioning. Comparing with existing method on typical fingerprinting, our method improved up to 31% on robustness.
Partial Deletion of the cAMP Response Element-Binding Protein Gene Promotes Alcohol-Drinking Behaviors

The cAMP response element-binding protein (CREB) gene transcription factor has been shown to play a role in the synaptic plasticity associated with drug addictive behaviors; however, the causal role of the CREB gene in alcohol-drinking behaviors is unknown. The present investigation evaluated alcohol-drinking behaviors in mice that are haplodeficient in CREB as a result of targeted CREB (α and Δ) gene disruption. It was found that CREB-haplodeficient (+/-) mice have higher preference for ethanol but not for sucrose solution than wild-type (+/+) littermates. The functional aspects of the CREB gene transcription factor were also investigated by measuring the protein levels of phosphorylated CREB (p-CREB) and the expression of cAMP-inducible genes such as neuropeptide Y (NPY) and brain-derived neurotrophic factor (BDNF). Deletion of the CREB (α and Δ) gene significantly decreases total CREB, p-CREB levels and the expression of NPY and BDNF in the brain structures of CREB-deficient (+/-) mice. It was also found that CREB-deficient (+/-) mice displayed more anxiety-like behaviors and that acute ethanol exposure produced anxiolytic effects and significantly increased protein levels of p-CREB and NPY in the central and medial but not in the basolateral amygdala of wild-type mice, but these effects are attenuated in CREB-deficient mice compared with wild-type mice. These results provide the first direct evidence that a haplodeficiency of the CREB gene is associated with increased alcohol-drinking behaviors. Furthermore, alcohol drinking and anxiety-like behaviors in CREB-haplodeficient mice may possibly be related to decreased expression of NPY and BDNF in the brains of these mice.
Sustainability Research Project: Research of Suitable Locations, Design and Operation of Microalgae Production Plants for Biofuel’s

One of the most damaging of these environmental issues is the use of fossil fuels resulting global warming due to the release of carbon dioxide (CO2) into the atmosphere. The world’s dependency on fossil fuels has also led to depleting supplies creating a need to develop alternative energy sources. Fossil fuels are used to supply energy for many applications such as industry and farming but the use of fossil fuels for transport is by far the most extensive.
Learning of probabilistic grasping strategies using Programming by Demonstration

The planning of grasping motions is demanding due to the complexity of modern robot systems. In Programming by Demonstration, the observation of a human teacher allows to draw additional information about grasping strategies. Rosell showed, that the motion planning problem can be simplified by globally restricting the set of valid configurations to a learned subspace. In this work, the transformation of a humanoid grasping strategy to an anthropomorphic robot system is described by a probabilistic model, called variation model, in order to account for modeling and transformation errors. The variation model resembles a soft preference for grasping motions similar to the demonstration and therefore induces a non-uniform sampling distribution on the configuration space. The sampling distribution is used in a standard probabilistic motion planner to plan grasping motions efficiently for new objects in new environments.
Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents

Sensory receptors determine the type and the quantity of information available for perception. Here, we quantified and characterized the information transferred by primary afferents in the rat whisker system using neural system identification. Quantification of “how much” information is conveyed by primary afferents, using the direct method (DM), a classical information theoretic tool, revealed that primary afferents transfer huge amounts of information (up to 529 bits/s). Information theoretic analysis of instantaneous spike-triggered kinematic stimulus features was used to gain functional insight on “what” is coded by primary afferents. Amongst the kinematic variables tested—position, velocity, and acceleration—primary afferent spikes encoded velocity best. The other two variables contributed to information transfer, but only if combined with velocity. We further revealed three additional characteristics that play a role in information transfer by primary afferents. Firstly, primary afferent spikes show preference for well separated multiple stimuli (i.e., well separated sets of combinations of the three instantaneous kinematic variables). Secondly, neurons are sensitive to short strips of the stimulus trajectory (up to 10 ms pre-spike time), and thirdly, they show spike patterns (precise doublet and triplet spiking). In order to deal with these complexities, we used a flexible probabilistic neuron model fitting mixtures of Gaussians to the spike triggered stimulus distributions, which quantitatively captured the contribution of the mentioned features and allowed us to achieve a full functional analysis of the total information rate indicated by the DM. We found that instantaneous position, velocity, and acceleration explained about 50% of the total information rate. Adding a 10 ms pre-spike interval of stimulus trajectory achieved 80–90%. The final 10–20% were found to be due to non-linear coding by spike bursts.
Remote sensing and GIS based geothermal exploration in southwest Tengchong, China

This work focuses on using remote sensing and geographic information systems (GIS) to identify promising geothermal areas in southwest Tengchong, China. Thematic information, including surface temperature, urban area, and surface slope are derived from Enhanced Thematic Mapper Plus (ETM+) data and digital elevation models (DEM). GIS is applied as a decision-support tool to integrate the thematic information for suitability analysis. The results indicate that combining remote sensing with GIS is an overall effective and accurate method for geothermal exploration. Three developed geothermal fields are successfully extracted in Tengchong, and promising areas are found to the north of study area and warrant further exploration.
Color digital halftoning taking colorimetric color reproduction into account

Taking colorimetric color reproduction into account, the conventional error diffusion method is modified for color digital half-toning. Assuming that the input to a bilevel color printer is given in CIE-XYZ tristimulus values or CIE-LAB values instead of the more conventional RGB or YMC values, two modified versions based on vector operation in (1) the XYZ color space and (2) the LAB color space were tested. Experimental results show that the modified methods, especially the method using the LAB color space, resulted in better color reproduction performance than the conventional methods. Spatial artifacts that appear in the modified methods are presented and analyzed. It is also shown that the modified method (2) with a thresholding technique achieves a good spatial image quality.
Attribution: a computational approach

Our society is overwhelmed with an ever growing amount of information. Effective management of this information requires novel ways to filter and select the most relevant pieces of information. Some of this information can be associated with the source or sources expressing it. Sources and their relation to what they express affect information and whether we perceive it as relevant, biased or truthful. In news texts in particular, it is common practice to report third-party statements and opinions. Recognizing relations of attribution is therefore a necessary step toward detecting statements and opinions of specific sources and selecting and evaluating information on the basis of its source. The automatic identification of Attribution Relations has applications in numerous research areas. Quotation and opinion extraction, discourse and factuality have all partly addressed the annotation and identification of Attribution Relations. However, disjoint efforts have provided a partial and partly inaccurate picture of attribution. Moreover, these research efforts have generated small or incomplete resources, thus limiting the applicability of machine learning approaches. Existing approaches to extract Attribution Relations have focused on rule-based models, which are limited both in coverage and precision. This thesis presents a computational approach to attribution that recasts attribution extraction as the identification of the attributed text, its source and the lexical cue linking them in a relation. Drawing on preliminary data-driven investigation, I present a comprehensive lexicalised approach to attribution and further refine and test a previously defined annotation scheme. The scheme has been used to create a corpus annotated with Attribution Relations, with the goal of contributing a large and complete resource than can lay the foundations for future attribution studies. Based on this resource, I developed a system for the automatic extraction of attribution relations that surpasses traditional syntactic pattern-based approaches. The system is a pipeline of classification and sequence labelling models that identify and link each of the components of an attribution relation. The results show concrete opportunities for attribution-based applications.
Learning Two-Person Interaction Models for Responsive Synthetic Humanoids

JVRB, 11(2014), no. 1. - Imitation learning is a promising approach for generating life-like behaviors of virtual humans and humanoid robots. So far, however, imitation learning has been mostly restricted to single agent settings where observed motions are adapted to new environment conditions but not to the dynamic behavior of interaction partners. In this paper, we introduce a new imitation learning approach that is based on the simultaneous motion capture of two human interaction partners. From the observed interactions, low-dimensional motion models are extracted and a mapping between these motion models is learned. This interaction model allows the real-time generation of agent behaviors that are responsive to the body movements of an interaction partner. The interaction model can be applied both to the animation of virtual characters as well as to the behavior generation for humanoid robots.
Algorithm 488: A Gaussian pseudo-random number generator

The algorithm calculates the exact cumulative distribution of the two-sided Kolmogorov-Smirnov statistic for samples with few observations. The general problem for which the formula is needed is to assess the probability that a particular sample comes from a proposed distribution. The problem arises specifically in data sampling and in discrete system simulation. Typically, some finite number of observations are available, and some underlying distribution is being considered as characterizing the source of the observations.
The cyclin-dependent kinase inhibitor p21 (WAF1) is required for survival of differentiating neuroblastoma cells

We are employing recent advances in the understanding of the cell cycle to study the inverse relationship between proliferation and neuronal differentiation. Nerve growth factor and aphidicolin, an inhibitor of DNA polymerases, synergistically induce neuronal differentiation of SH-SY5Y neuroblastoma cells and the expression of p21WAF1, an inhibitor of cyclin-dependent kinases. The differentiated cells continue to express p21WAF1, even after removal of aphidicolin from the culture medium. The p21WAF1 protein coimmunoprecipitates with cyclin E and inhibits cyclin E-associated protein kinase activity. Each of three antisense oligonucleotides complementary to p21WAF1 mRNA partially blocks expression of p21WAF1 and promotes programmed cell death. These data indicate that p21WAF1 expression is required for survival of these differentiating neuroblastoma cells. Thus, the problem of neuronal differentiation can now be understood in the context of negative regulators of the cell cycle.
Dynamics of on-line learning in neural networks

One of the most important features of natural as well as artificial neural networks is their ability to adjust to their environment by “learning”. This results in the network’s ability to “generalize”, i.e. to generate with high probability the appropriate response to an unknown input. The theoretical description of generalization in artificial neural networks by means of statistical physics is the subject of this thesis. The focus is on on-line learning, where the presentation of examples used in the learning process occurs in a sequential manner. Hence, the systems investigated are dynamical in nature. They typically consist of a large number of degrees of freedom, requiring a description in terms of order parameters. In the first part of this work the most fundamental network, the perceptron, is investigated. Following a recent proposal by Kinouchi and Caticha it will be shown how one can derive a learning dynamics starting from first principles that results in an optimal generalization ability. Results will be presented for learning processes where the training examples are corrupted by different types of noise. The resulting generalization ability will be shown to be comparable to the noiseless case. Furthermore the results obtained reveal striking similarities to those obtained for batch learning. The optimal algorithms derived will be shown to depend on the characteristics of the particular learning task including the type and strength of the corrupting noise. In general this requires an additional estimation of such characteristic quantities. For the strength of the noise this estimation leads to interesting dynamical phase transitions. The second part deals with the dynamical properties of two-layer neural networks. This is of particular importance since these networks are known to represent universal approximators. Understanding the dynamical features will help to construct fast training algorithms that lead to best generalization. Specifically, an exact analysis of learning a rule by on-line gradient descent (backpropagation of error) in a two-layered neural network will be presented. Hereby, the emphasis is on adjustable hidden-to-output weights which have been left out of the analysis in the literature so far. Results are compared with the training of networks having the same architecture but fixed weights in the second layer. It will be shown, that certain features of learning in a two-layered neural network are independent of the state of the second layer. Motivated by this result it will be argued that putting the dynamics of the hidden-to-output weights on a faster time scale will speed up the learning process. For all systems investigated, simulations confirm the results.
Program using rjMCMC for exploring allopolyploid network priors

Suppose there are d diploids and m tetraploids. In the language of [2], there are m models h = 1, h = 2, . h = m, where h is the number of hybridizations. Each model has a different number of parameters. The main difficulty with the network prior is that the normalization constants are needed for the different models. Let W be the network topology and node times. The conditional priors π(W |h) must be comparable for different values of h. To do this analytically, it is necessary to integrate out the parameters in W , ie calculate for each h the value of
Framework for Real-Time Event Detection using Multiple Social Media Sources

Information about events happening in the real world are generated online on social media in real-time. There is substantial research done to detect these events using information posted on websites like Twitter, Tumblr, and Instagram. The information posted depends on the type of platform the website relies upon, such as short messages, pictures, and long form articles. In this paper, we extend an existing real-time event detection at onset approach to include multiple websites. We present three different approaches to merging information from two different social media sources. We also analyze the strengths and weaknesses of these approaches. We validate the detected events using newswire data that is collected during the same time period. Our results show that including multiple sources increases the number of detected events and also increase the quality of detected