Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression and distribution of vimentin and keratin filaments in heterokaryons of human fibroblasts and amnion epithelial cells

The expression of intermediate filaments of the keratin- and the vimentin-type was studied in heterokaryons of human fibroblasts and amnion epithelial cells by immunofluorescence microscopy. Fibroblasts and their homokaryons showed a fibrillar, vimentin-specific fluorescence throughout the cytoplasm but were negative when stained for keratin. Amnion epithelial cells and their homokaryons, on the other hand, showed a keratin-specific fibrillar staining, and only some of them contained also detectable vimentin. When suspended epithelial cells were fused with adherent fibroblasts, keratin fibrils spread within 3 h into the fibroblasts, intermixing with the vimentin fibrils. 1-3 d after fusion, both vimentin and keratin filaments were expressed as typical fibrillar cytoplasmic arrays, and the distribution of keratin in heterokaryons resembled closely that of vimentin. A typical cell-to-cell arrangement of keratin fibrils, seen in cultures of amnion epithelial cells, could also be found between heterokaryons. Treatment of the cultures with vinblastine sulphate induced coiling of the vimentin filaments in both homo- and heterokaryons, whereas the keratin organization was only slightly affected. Our results show that both vimentin and keratin filaments are incorporated into the cytoskeleton of heterokaryons formed between fibroblasts and epithelial cells, and that they behave in the same way as in their parental cells. Both epithelial and fibroblastic characteristics thus appear to the coexpressed in such heterokaryons.
Methods of Weighted Averaging with Application to Biomedical Signals

During the analysis of real biomedical signals it can almost always be seen noise that distorts the image. The presence of interference is associated with the specific acquisition of these signals. For example in the case of bioelectric signals, disturbances may come from the hardware retrieves those signals, the powerline or the bioelectric activity of body cells. The bioelectric signals, which are widely used in most fields of biomedicine, are generated by nerve cells or muscle cells. The electric field propagates through the tissue and can be acquired from the body surface, eliminating the potential need to invade the biosystem. However, using surface electrodes results in high amplitude of noise and the noise should be suppressed to extract a priori desired information (Bruce, 2001). There are many approaches to the noise reduction problem while preserving the variability of the desired signal morphology. One of the possible methods of noise attenuation is low-pass filtering such as arithmetic mean. The classical band-pass filtering is very simple method but also very ineffective because the frequency characteristics of signal and noise significantly overlap. Therefore there are developed other methods of noise attenuation based on transforming the input space of signal and creating a new space with the help of discrete cosine transform (Paul et al., 2000) or wavelets transform (Augustyniak, 2006), based on fuzzy nonlinear regression (Momot et al., 2005), nonlinear projective filtering (Kotas, 2009), higher-order statistics at different wavelet bands (Sharma et al., 2010) or extreme points determination by mean shift algorithm and dynamical model-based nonlinear filtering (Yan et al., 2010). In the case of repeatable biomedical signals, another possible method of noise attenuation is the synchronized averaging (Jane et al., 1991). The method assumes that the biomedical signal is quasi-cyclic and the noise is additive, independent and with zero mean. Averaging could be performed by simple arithmetic mean or its generalization, namely weighted mean where the weights are tuned by some adaptive algorithm. Recently there have been published several works concerning different approaches to the problem of determining the weights. The algorithm of adaptive estimation of the weights is described in (Bataillou et al., 1995). In (Leski, 2002) there is described method of estimation of the weights based on criterion function minimization. Application of Bayesian inference to the weights estimation problem is presented in (Momot et al., 2007a) and (Momot, 2008b). Weighted averaging method based on partition of input data set in time domain is described in (Momot et al, 2007b). The generalization of the method is presented in (Momot, 16
Suzaku Wide-Band Observations of SN 1006

We report on the wide band spectra of SN 1006 as observed by Suzaku. Thermal and nonthermal emission are successfully resolved thanks to the excellent spectral response of Suzaku's X-ray CCD XIS. The nonthermal emission cannot be reproduced by a simple power-law model but needs a roll-off at 5.7$\times 10^{16}$ Hz = 0.23 keV. The roll-off frequency is significantly higher in the northeastern rim than in the southwestern rim. We also have placed the most stringent upper limit of the flux above 10 keV using the Hard X-ray Detector.
Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures. The replicability and reproducibility issues are considered for computational models that are equally diverse, including the models for intracellular signal transduction of neurons and glial cells, in addition to single glial cells, neuron-glia interactions, and selected examples of spiking neuronal networks. We also address the comparability of the simulation results with one another to comprehend if the studied models can be used to answer similar research questions. In addition to presenting the challenges in reproducibility and replicability of published results in computational neuroscience, we highlight the need for developing recommendations and good practices for publishing simulation tools and computational models. Model validation and flexible model description must be an integral part of the tool used to simulate and develop computational models. Constant improvement on experimental techniques and recording protocols leads to increasing knowledge about the biophysical mechanisms in neural systems. This poses new challenges for computational neuroscience: extended or completely new computational methods and models may be required. Careful evaluation and categorization of the existing models and tools provide a foundation for these future needs, for constructing multiscale models or extending the models to incorporate additional or more detailed biophysical mechanisms. Improving the quality of publications in computational neuroscience, enabling progressive building of advanced computational models and tools, can be achieved only through adopting publishing standards which underline replicability and reproducibility of research results.
Totally corrective boosting algorithms that maximize the margin

We consider boosting algorithms that maintain a distribution over a set of examples. At each iteration a weak hypothesis is received and the distribution is updated. We motivate these updates as minimizing the relative entropy subject to linear constraints. For example AdaBoost constrains the edge of the last hypothesis w.r. t. the updated distribution to be at most γ = 0. In some sense, AdaBoost is "corrective" w.r. t. the last hypothesis. A cleaner boosting method is to be "totally corrective": the edges of all past hypotheses are constrained to be at most γ, where γ is suitably adapted.Using new techniques, we prove the same iteration bounds for the totally corrective algorithms as for their corrective versions. Moreover with adaptive γ, the algorithms provably maximizes the margin. Experimentally, the totally corrective versions return smaller convex combinations of weak hypotheses than the corrective ones and are competitive with LPBoost, a totally corrective boosting algorithm with no regularization, for which there is no iteration bound known.
The Morphologically Divided Redshift Distribution of Faint Galaxies

We have constructed a morphologically divided redshift distribution of faint field galaxies using a statistically unbiased sample of 196 galaxies brighter than I=21.5 for which detailed morphological information (from the Hubble Space Telescope) as well as ground-based spectroscopic redshifts are available. Galaxies are classified into three rough morphological types according to their visual appearance (E/S0s, spirals, Sdm/dE/Irr/Pec galaxies), and redshift distributions are constructed for each type. The most striking feature is the abundance of low- to moderate-redshift Sdm/dE/Irr/Pec galaxies at I<19.5. This confirms that the faint-end slope of the luminosity function (LF) is steep (α<-1.4) for these objects. We also find that Sdm/dE/Irr/Pec galaxies are fairly abundant at moderate redshifts, and this can be explained by a strong luminosity evolution. However, the normalization factor (or the number density) of the LF of Sdm/dE/Irr/Pec galaxies is not much higher than that of the local LF of Sdm/dE/Irr/Pec galaxies. Furthermore, as we go to fainter magnitudes, the abundance of moderate- to high-redshift Irr/Pec galaxies increases considerably. This cannot be explained by strong luminosity evolution of the dwarf galaxy populations alone: these Irr/Pec galaxies are probably the progenitors of present-day ellipticals and spiral galaxies that are undergoing rapid star formation or merging with their neighbors. On the other hand, the redshift distributions of E/S0s and spirals are fairly consistent with those expected from passive luminosity evolution and are only in slight disagreement with the nonevolving model.
Grief Communication and Privacy Rules: Examining the Communication of Individuals Bereaved by the Death of a Family Member

ABSTRACT Communication about one’s grief is both essential and complicated. We employ communication privacy management theory to examine how bereaved individuals manage the revelation and concealment of private information about the death of a parent or sibling. In-depth interviews (N = 21) with the bereaved indicate that (a) they conceptualize information surrounding the death and their grief as private, (b) they create rules to govern their private information, and (c) violations of those rules result in turbulence. Specifically, individuals employ selectivity, avoidance, and positivity rules that dictate what and with whom they share. We discuss the implications of our findings on grief communication scholarship, CPM theory, and practical issues for helping bereaved individuals and their loved ones navigate discussions about death and the deceased.
Complex Systems in Language Evolution: the Cultural Emergence of Compositional Structure

Language arises from the interaction of three complex adaptive systems — biological evolution, learning, and culture. We focus here on cultural evolution, and present an Iterated Learning Model of the emergence of compositionality, a fundamental structural property of language. Our main result is to show that the poverty of the stimulus available to language learners leads to a pressure for linguistic structure. When there is a bottleneck on cultural transmission, only a language which is generalizable from sparse input data is stable. Language itself evolves on a cultural time-scale, and compositionality is language's adaptation to stimulus poverty.
A log-stuctured organization for tertiary storage

We present the design of a log-structured tertiary storage (LTS) system. The advantage of this approach is that it allows the system to hide the details of jukebox robotics and media characteristics behind a uniform, random-access, block-oriented interface. It also allows the system to avoid media mount operations for writes, giving a write performance similar to that of secondary storage.
Appliance of Neuron Networks in Cryptographic Systems

This study is dedicated to the examination of a problem of postquantum encryption algorithms which are connected with a potential crisis in modern cryptography that is caused by appearance of quantum computers. General problem formulation is given as well as an example of danger from the quantum algorithms against classical cryptosystems. Existing postquantum systems are analyzed and the complication of their realization and cryptosecurity are estimated. Among the others algorithms on the basis of neural networks are chosen as a starting point. The study demonstrates neuro cryptographic protocol based on a three-level neural network of the direct propagation. There was evaluated it's cryptosecurity and analyzed three types of this algorithm attack to show the reality of the hypothesis that neuro cryptography is currently one of the most promising post quantum cryptographic systems.
Regulation of ubiquitin ligase dynamics by the nucleolus

Cellular pathways relay information through dynamic protein interactions. We have assessed the kinetic properties of the murine double minute protein (MDM2) and von Hippel-Lindau (VHL) ubiquitin ligases in living cells under physiological conditions that alter the stability of their respective p53 and hypoxia-inducible factor substrates. Photobleaching experiments reveal that MDM2 and VHL are highly mobile proteins in settings where their substrates are efficiently degraded. The nucleolar architecture converts MDM2 and VHL to a static state in response to regulatory cues that are associated with substrate stability. After signal termination, the nucleolus is able to rapidly release these proteins from static detention, thereby restoring their high mobility profiles. A protein surface region of VHL's β-sheet domain was identified as a discrete [H+]-responsive nucleolar detention signal that targets the VHL/Cullin-2 ubiquitin ligase complex to nucleoli in response to physiological fluctuations in environmental pH. Data shown here provide the first evidence that cells have evolved a mechanism to regulate molecular networks by reversibly switching proteins between a mobile and static state.
An Upper Limit on the Temporal Variations of the Solar Interior Stratification

We have analyzed changes in the acoustic oscillation eigenfrequencies measured over the past 7 years by the GONG, MDI, and LOWL instruments. The observations span the period from 1994 to 2001 that corresponds to half a solar cycle, from minimum to maximum solar activity. These data were inverted to look for a signature of the activity cycle on the solar stratification. A one-dimensional structure inversion was carried out to map the temporal variation of the radial distribution of the sound speed at the boundary between the radiative and convective zones. Such variation could indicate the presence of a toroidal magnetic field anchored in this region. We found no systematic variation with time of the stratification at the base of the convection zone. However, we can set an upper limit to any fractional change of the sound speed at the level of 3 × 10-5.
Design a Neural Network for Features Selection in Non-intrusive Monitoring of Industrial Electrical Loads

This paper proposes to compare the performance of neural network classifiers between back propagation (BP) and learning vector quantization (LVQ) for pattern analyses of features selection in a non-intrusive load monitoring (NILM) system. Load recognition for identifying loads being connected and disconnected is applied to a NILM by using a neural network, especially for industrial electrical loads, even though some loads are activated at the nearly same time. In order to accurately decompose the aggregate load into its components, a feature-based model for describing the signatures of individual appliances and load combinations is used. The model will suggest the certain signatures which can be detected for all loads in order to indicate the activities of the separate components. To verify the performance of the model for the features selection, the data sets of the electrical loads and the load recognition techniques apply an electromagnetic transient program (EMTP) and a neural network, respectively. The effectiveness and computation equipment of load recognition are analyzed and compared by using the back propagation classifier and the learning vector quantization classifier. To obtain a maximum recognition accuracy rate, the calculation of the turn-on transient energy signature employs a window of samples, At, to adaptively segment a transient representative of a class of loads. Experiments performed with a variety of model data sets which reveal the back propagation classifier is superior to the learning quantization classifier in the effectiveness and computation equipment of load recognition.
Modelling long-term changes in stream water and soil chemistry in catchments with contrasting vulnerability to acidification (Lysina and Pluhuv Bor, Czech Republic)

Abstract. In two Czech catchments covered by Norway spruce forests, the MAGIC model was used to simulate annual stream water and soil chemistry for the period 1851–2030. These two sites represent geochemical end-members of ecosystem sensitivity to acidification (acid-sensitive granitic Lysina catchment vs. acid-resistant serpentinitic Pluhuv Bor catchment). Although the total deposition of sulphur to the catchments declined by 75% between 1990 and 2002, the recovery of stream water pH was relatively small over this period. At Lysina, the annual discharge-weighted mean pH of stream water increased only from 3.92 to 4.01, although SO 4 concentration declined very sharply from 570 μeq L –1 in 1990 to 150 μeq L –1 in 2002. Stream water buffering was caused mainly by dissociation of organic acids. At Pluhuv Bor, the annual mean pH varied inversely with the annual discharge. Stream water concentrations of SO 4 declined dramatically at Pluhuv Bor, from 1040 μeq L –1 in 1992 to 220 μeq L –1 in 2002. Using atmospheric deposition as specified in the Gothenburg Protocol, the model predicts that, at Lysina, stream water pH will increase to 4.3 and soil base saturation will increase to 6.0% by 2030 (from 5.6% in 2002); corresponding pre-industrial stream water pH was simulated to be 5.5 and soil base saturation to be 25%. At Pluhuv Bor, the pre-industrial pH was estimated to be 7.2 and the corresponding base saturation was 94%. Large anthropogenic acidification in the 20th century caused only a small decline in pH (to 6.9) and base saturation (to 88%). Simulations in accordance with the Gothenburg Protocol predict that the pH should increase by 0.2 pH units and the base saturation by 1% by 2030. Under this protocol, critical loads of atmospheric deposition for SO 4 and NO 3 will not be exceeded at Pluhuv Bor but will be exceeded at Lysina. Keywords: MAGIC model, catchment, critical loads, Gothenburg Protocol, soil and water acidification, granite, serpentinite, Czech Republic
Natural throw and tilt interaction between mobile phones and distant displays

To provide intuitive ways of interacting with media data, this research work addresses the seamless combination of sensor-enabled phones with large displays. An intuitive basic set of tilt gestures is introduced for a stepwise or continuous interaction with both mobile applications and distant user interfaces by utilizing the handheld as a remote control. In addition, we introduce throwing gestures to transfer media documents and even running interfaces to a large display. To improve usability, data and interfaces can be thrown from a mobile phone to a distant screen and also fetched back to achieve mobility. We demonstrate the feasibility of the interaction methods with several advanced application prototypes facilitating a natural flow of interaction.
Gray Image Recognition Using Hopfield Neural Network With Multi-Bitplane and Multi-Connect Architecture

In this work, a method for applying Hopfield neural network (HNN) with gray images is presented. Hopfield networks are iterative auto-associative networks consisting of a single layer of fully connected processing elements thus categorizes as an associative memory. Associative memories provide one approach to the computer-engineering problem of storing and retrieving data which is based on content rather than storage address. HNN deals with the bipolar system (i.e. -1 and +1) for direct input data, however it is useful for binary images, but unuseful for gray-level or color images unless we suppose another way for input data of such images. To overcome this obstacle, one can suppose for 8-bit gray-level image that consists of 8-layers (bitplanes) of binaries can be represented as bipolar data. In this way it is possible to express each bitplane as single binary image for HNN. The experimental results showed the usefulness of using HNN in gray-level images recognition with good results. Furthermore, there are no limitations to the number of 8-bit gray level images that can be stored in the net memory with the same efficient results
FOXP2 drives neuronal differentiation by interacting with retinoic acid signaling pathways

FOXP2 was the first gene shown to cause a Mendelian form of speech and language disorder. Although developmentally expressed in many organs, loss of a single copy of FOXP2 leads to a phenotype that is largely restricted to orofacial impairment during articulation and linguistic processing deficits. Why perturbed FOXP2 function affects specific aspects of the developing brain remains elusive. We investigated the role of FOXP2 in neuronal differentiation and found that FOXP2 drives molecular changes consistent with neuronal differentiation in a human model system. We identified a network of FOXP2 regulated genes related to retinoic acid signaling and neuronal differentiation. FOXP2 also produced phenotypic changes associated with neuronal differentiation including increased neurite outgrowth and reduced migration. Crucially, cells expressing FOXP2 displayed increased sensitivity to retinoic acid exposure. This suggests a mechanism by which FOXP2 may be able to increase the cellular differentiation response to environmental retinoic acid cues for specific subsets of neurons in the brain. These data demonstrate that FOXP2 promotes neuronal differentiation by interacting with the retinoic acid signaling pathway and regulates key processes required for normal circuit formation such as neuronal migration and neurite outgrowth. In this way, FOXP2, which is found only in specific subpopulations of neurons in the brain, may drive precise neuronal differentiation patterns and/or control localization and connectivity of these FOXP2 positive cells.
Regularized Gibbs Sampling for User Profiling with Soft Constraints

In this paper we extend the formulation of the User Rating Profile model, providing a Gibbs Sampling derivation for parameter estimation. Validation tests on Movielens data show that the proposed approach outperforms significantly the variational version in terms of both prediction accuracy and learning time. Gibbs Sampling provides a simple and flexible learning procedure which can be extended to include external evidence, in the form of soft constraints. More specifically, given a-priori information about user-neighbors, we propose an effective regularization technique that drives the first sampling iterations pushing the model towards a state which better represents the user-neighborhoods specified in input.