Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automated Method to Quantify Microglia Morphology and Application to Monitor Activation State Longitudinally In Vivo

Microglia are specialized immune cells of the brain. Upon insult, microglia initiate a cascade of cellular responses including a characteristic change in cell morphology. To study the dynamics of microglia immune response in situ, we developed an automated image analysis method that enables the quantitative assessment of microglia activation state within tissue based solely on cell morphology. Per cell morphometric analysis of fluorescently labeled microglia is achieved through local iterative threshold segmentation, which reduces errors caused by signal-to-noise variation across large volumes. We demonstrate, utilizing systemic application of lipopolysaccharide as a model of immune challenge, that several morphological parameters, including cell perimeter length, cell roundness and soma size, quantitatively distinguish resting versus activated populations of microglia within tissue comparable to traditional immunohistochemistry methods. Furthermore, we provide proof-of-concept data that monitoring soma size enables the longitudinal assessment of microglia activation in the mouse neocortex imaged via 2-photon in vivo microscopy. The ability to quantify microglia activation automatically by shape alone allows unbiased and rapid analysis of both fixed and in vivo central nervous system tissue.
A Standardized, Evidence-Based Massage Therapy Program for Decentralized Elite Paracyclists: Creating the Model

Background and Purpose Evidence suggests that para-athletes are injured more often than able-bodied athletes. The benefits of massage therapy for these disabled athletes are yet to be explored. This paper documents the process followed for creating a massage program for elite paracycling athletes with the goal to assess effects on recovery, rest, performance, and quality of life both on and off the bike. Setting Massage therapists’ private practices throughout the United States. Participants A United States Paracycling team consisting of 9 elite athletes: 2 spinal cord injury, 2 lower limb amputation, 1 upper limb amputation, 1 transverse myelitis, 1 stroke, 1 traumatic brain injury, and 1 visually impaired. Design The process used to develop a massage therapy program for para-cyclists included meetings with athletes, coaching staff, team exercise physiologist, and sports massage therapists; peer-reviewed literature was also consulted to address specific health conditions of para-athletes. Results Team leadership and athletes identified needs for quicker recovery, better rest, and improved performance in elite paracyclists. This information was used to generate a conceptual model for massage protocols, and led to creation of the intake and exit questionnaires to assess patient health status and recovery. Forms also were created for a general health intake, therapist information, and a therapist’s SOAAP notes. Discussion The conceptual model and questionnaires developed herein will help to operationalize an exploratory study investigating the feasibility of implementing a standardized massage therapy program for a decentralized elite paracycling team.
Impact of variants in CETP and apo AI genes on serum HDL cholesterol levels in men and women from the Polish population

Introduction Polymorphisms in the cholesterol ester transfer protein (CETP) gene and apolipoprotein AI (apo AI) gene are identified as the most common genetic factors influencing high-density lipoprotein cholesterol (HDL cholesterol) levels. Low HDL cholesterol is an important risk factor for cardiovascular disease. We investigated the effect of the TaqIB polymorphism of the CETP gene and the 75G/A polymorphism of the apo AI gene on the HDL cholesterol concentration in a sample of Polish adults. Material and methods A total of 621 subjects, 414 women and 207 men, were included in this study. Lipid levels were measured using standard protocols, and apolipoprotein AI was determined by immunoturbidimetric assay. CETP and apo AI genotyping was performed using a restriction fragment length polymorphism based method. Results Significantly lower HDL cholesterol concentrations were found in B1B1 homozygotes than in carriers of the B2 allele of the TaqIB polymorphism in the CETP gene among both men and women. In GG homozygotes of the 75G/A polymorphism in the apo AI gene lower HDL cholesterol levels were observed, but the difference did not reach statistical significance. A statistically significant association of low HDL cholesterol (< 25th percentile) with CETP genotypes was found in women (p < 0.0001) and in men (p = 0.0368). Conclusions These data demonstrate a significant impact of the TaqIB polymorphism in the CETP gene on HDL cholesterol levels in the studied Polish population, while the effect of the 75G/A polymorphism in the apo AI gene appears not to be significant.
Integrating SAAM in Domain-centric and Reuse-based Development Processes

Architecture analysis allows to assess an architecture with respect to various characteristics. For example, it is possible to predict non-functional properties of applications based on the architecture , and to estimate the effort for specific maintenance situations. After a short introduction to the SAAM method for scenario-based architecture analysis, we point out how this technique can be deployed in reuse-based development processes. It is then shown how the analysis technique itself can be extended to consider reusable assets and reuse activities. Subsequently, we discuss how the extended SAAM method can be integrated in development approaches focused at reuse of domain knowledge and at architectural reuse.
Unique Function of Kinesin Kif5A in Localization of Mitochondria in Axons

Mutations in Kinesin proteins (Kifs) are linked to various neurological diseases, but the specific and redundant functions of the vertebrate Kifs are incompletely understood. For example, Kif5A, but not other Kinesin-1 heavy-chain family members, is implicated in Charcot-Marie-Tooth disease (CMT) and Hereditary Spastic Paraplegia (HSP), but the mechanism of its involvement in the progressive axonal degeneration characteristic of these diseases is not well understood. We report that zebrafish kif5Aa mutants exhibit hyperexcitability, peripheral polyneuropathy, and axonal degeneration reminiscent of CMT and HSP. Strikingly, although kif5 genes are thought to act largely redundantly in other contexts, and zebrafish peripheral neurons express five kif5 genes, kif5Aa mutant peripheral sensory axons lack mitochondria and degenerate. We show that this Kif5Aa-specific function is cell autonomous and is mediated by its C-terminal tail, as only Kif5Aa and chimeric motors containing the Kif5Aa C-tail can rescue deficits. Finally, concurrent loss of the kinesin-3, kif1b, or its adaptor kbp, exacerbates axonal degeneration via a nonmitochondrial cargo common to Kif5Aa. Our results shed light on Kinesin complexity and reveal determinants of specific Kif5A functions in mitochondrial transport, adaptor binding, and axonal maintenance.
EMOTIONAL CONTAGION IN INTERACTIVE ART

The study reported in this paper is part of a larger project, Chameleon, that through a multi-disciplinary collaboration investigates the scientific foundations of emotional contagion, transforming it into an art experience. The aim is to create an emotional bond with the audience and to trigger reflection on emotions. The Chameleon art installation uses facial expression recognition technology to detect the emotional state of the audience and responds using a simple emotional contagion response mechanism and a video portrait selected in real time from a database of emotional video portraits built by the artist. In this paper we evaluate the ability of Chameleon to induce a need for emotional introspection and reflection in the audience. For this aim, an art exhibition was installed with emotional video portraits projected on different walls. The audience was told that the artwork was able to read their emotions and to respond to them while they were walking around the space. Visitors of the exhibition were invited at the exit to participate in the project by being interviewed. Semistructured interviews were carried out and grounded theory was used to formally analyze the recorded interviews. The results showed that the audience was able to emotionally engage with the expressive portraits often by feeling that an emotional communication loop had emerged. The results also shed light on how this type of technology and the environment in which it is demonstrated could be improved to facilitate the communication loop to take place. The paper provides a detailed discussion of these results.
Service-Oriented Smart-Home Architecture Based on OSGi and Mobile-Agent Technology

The architecture of a conventional smart home is usually server-centric and thus causes many problems. Mobile devices and dynamic services affect a dynamically changing environment, which can result in very difficult interaction. In addition, how to provide services efficiently and appropriately is always an important issue for a smart home. To solve the problems caused by traditional architectures, to deal with the dynamic environment, and to provide appropriate services, we propose a service-oriented architecture (SOA) for smart-home environments, based on Open Services Gateway Initiative (OSGi) and mobile-agent (MA) technology. This architecture is a peer-to-peer (P2P) model based on multiple OSGi platforms, in which service-oriented mechanisms are used for system components to interact with one another, and MA technology is applied to augment the interaction mechanisms
Evaluation of the Add-On Effect of Chinese Patent Medicine for Patients with Stable or Unstable Angina: A Systematic Review and Meta-Analysis

Chinese herbal medicine (CHM) has been widely used as an adjunct to western medicine in treating angina in China. We carried out this systematic review to evaluate the effectiveness of CHM on top of western medicine for angina. This meta-analysis included 46 randomized control trials with 4212 patients. For trials that included stable angina patients, the CHM group had significant lower incidence of total heart events (relative risk (RR) = 0.50, 95% confidence interval (CI) 0.33–0.78), myocardial infarction (RR = 0.32, 95% CI 0.14–0.72), heart failure (RR = 0.37, 95% CI 0.15–0.91), and angina (RR = 0.46, 95% CI 0.30–0.71) than that of control group. For trials that included unstable angina patients, CHM led to significantly lower occurrence of total heart events (RR = 0.46, 95% CI 0.32–0.66), myocardial infarction (RR = 0.37, 95% CI 0.26–0.54), and angina (RR = 0.36, 95%CI 0.26–0.51). Likewise, for trials that included stable or unstable angina patients, the rates of myocardial infarction (RR = 0.34, 95% CI 0.17–0.68) and angina (RR = 0.46, 95% CI 0.30–0.70) in CHM group were significantly lower than that in control group. In conclusion, CHM is very likely to be able to improve the survival of angina patients who are already receiving western medicine.
Signal Processing over Graphs

The prevalence of massive datasets is transforming the field of statistics as we attempt to simultaneously address the statistical efficiency and computational complexity of statistical methods. The modern explosion of data, viz. internet data, sensor networks, genomic microarrays, and financial data, requires that statistical methodology adapts to the assumptions and structure unique to these problems, while ensuring statistical and computational soundness. Much of the recent developments in high-dimensional statistical theory addresses the estimation of mean parameters under sparsity assumptions. My research departs from this framework by considering complex heterogeneous structural assumptions, that appear in modern statistical applications such as sensor networks. Throughout my work, I focus on the development of algorithms with provably low statistical risk and low computational complexity to new statistical problems with complex structural and modeling assumptions.
Coordination of size and number of excitatory and inhibitory synapses results in a balanced structural plasticity along mature hippocampal CA1 dendrites during LTP

Enlargement of dendritic spines and synapses correlates with enhanced synaptic strength during long‐term potentiation (LTP), especially in immature hippocampal neurons. Less clear is the nature of this structural synaptic plasticity on mature hippocampal neurons, and nothing is known about the structural plasticity of inhibitory synapses during LTP. Here the timing and extent of structural synaptic plasticity and changes in local protein synthesis evidenced by polyribosomes were systematically evaluated at both excitatory and inhibitory synapses on CA1 dendrites from mature rats following induction of LTP with theta‐burst stimulation (TBS). Recent work suggests dendritic segments can act as functional units of plasticity. To test whether structural synaptic plasticity is similarly coordinated, we reconstructed from serial section transmission electron microscopy all of the spines and synapses along representative dendritic segments receiving control stimulation or TBS‐LTP. At 5 min after TBS, polyribosomes were elevated in large spines suggesting an initial burst of local protein synthesis, and by 2 h only those spines with further enlarged synapses contained polyribosomes. Rapid induction of synaptogenesis was evidenced by an elevation in asymmetric shaft synapses and stubby spines at 5 min and more nonsynaptic filopodia at 30 min. By 2 h, the smallest synaptic spines were markedly reduced in number. This synapse loss was perfectly counterbalanced by enlargement of the remaining excitatory synapses such that the summed synaptic surface area per length of dendritic segment was constant across time and conditions. Remarkably, the inhibitory synapses showed a parallel synaptic plasticity, also demonstrating a decrease in number perfectly counterbalanced by an increase in synaptic surface area. Thus, TBS‐LTP triggered spinogenesis followed by loss of small excitatory and inhibitory synapses and a subsequent enlargement of the remaining synapses by 2 h. These data suggest that dendritic segments coordinate structural plasticity across multiple synapses and maintain a homeostatic balance of excitatory and inhibitory inputs through local protein‐synthesis and selective capture or redistribution of dendritic resources. ©2010 Wiley‐Liss, Inc.
Co-learning nouns and adjectives

In cognitive robotics community, categories belonging to adjectives and nouns have been learned separately and independently. In this article, we propose a prototype-based framework that conceptualize adjectives and nouns as separate categories that are, however, linked to and interact with each other. We demonstrate how this co-learned concepts might be useful for a cognitive robot, especially using a game called “What object is it? ” that involves finding an object based on a set of adjectives.
Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method

The task of site selection for electric vehicle charging stations (EVCS) is hugely important from the perspective of harmonious and sustainable development. However, flaws and inadequacies in the currently used multi-criteria decision making methods could result in inaccurate and irrational decision results. First of all, the uncertainty of the information cannot be described integrally in the evaluation of the EVCS site selection. Secondly, rigorous consideration of the mutual influence between the various criteria is lacking, which is mainly evidenced in two aspects: one is ignoring the correlation, and the other is the unconscionable measurements. Last but not least, the ranking method adopted in previous studies is not very appropriate for evaluating the EVCS site selection problem. As a result of the above analysis, a Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) method-based decision system combined with the cloud model is proposed in this paper for EVCS site selection. Firstly, the use of the PROMETHEE method can bolster the confidence and visibility for decision makers. Secondly, the cloud model is recommended to describe the fuzziness and randomness of linguistic terms integrally and accurately. Finally, the Analytical Network Process (ANP) method is adopted to measure the correlation of the indicators with a greatly simplified calculation of the parameters and the steps required.
A Parallel Skeleton Library for Multi-core Clusters

A parallel skeleton library is a collection of parallel computations that abstract generic and recurring patterns within parallel programs and conceal parallel behaviors as skeletons. It enables users to develop parallel programs as if they were sequential ones by composing suitable skeletons. However, many existing parallel skeleton libraries for distributed environments do not take into account the potential performance of multi-core CPUs, because they operate under the premise that each node (computer) has a single-core CPU. To resolve this problem, this paper proposes the design and implementation of a parallel skeleton library for multi-core clusters. The proposed library adopts a two-stage dynamic task scheduling strategy; the first is among nodes and the second is among cores. This scheduling strategy enables the library to appropriately balance the load both between nodes and cores. The library also dynamically fuses successive skeleton calls to reduce the cost of control flows and increase the locality of data. The proposed skeletons are implemented from scratch for matrices within a parallel skeleton library called SkeTo by using the template techniques in C++ language. We confirmed that our implementation was efficient through various benchmarks.
A New Algorithm of Blind Source Separation Based on ICA

Blind source separation has been one of the hottest areas in the signal processing fields,and it has application in the Telecommunication System, speech enhancement, remote sensing and medical imaging. To improve the fast fixed-point algorithm based on independent component analysis (ICA) with only one activated function,we propose a new algorithm which includes three activated functions. In this paper, the nonlinear functions are hyperbolic cosine function, Beta distribution function and Pearson system function. Results from experiments show that it will not only maintain the characteristics of the original algorithm, but also can separate some signals which can't be separated by the original algorithm.
Reduced complexity window decoding schedules for coupled LDPC codes

Window decoding schedules are very attractive for message passing decoding of spatially coupled LDPC codes. They take advantage of the inherent convolutional code structure and allow continuous transmission with low decoding latency and complexity. In this paper we show that the decoding complexity can be further reduced if suitable message passing schedules are applied within the decoding window. An improvement based schedule is presented that easily adapts to different ensemble structures, window sizes, and channel parameters. Its combination with a serial (on-demand) schedule is also considered. Results from a computer search based schedule are shown for comparison.
The Mystery of Zero-Leverage Firms

We present the puzzling evidence that, from 1962 to 2009, an average 10.2% of large public nonfinancial US firms have zero debt and almost 22% have less than 5% book leverage ratio. Zero-leverage behavior is a persistent phenomenon. Dividend-paying zero-leverage firms pay substantially higher dividends, are more profitable, pay higher taxes, issue less equity, and have higher cash balances than control firms chosen by industry and size. Firms with higher Chief Executive Officer (CEO) ownership and longer CEO tenure are more likely to have zero debt, especially if boards are smaller and less independent. Family firms are also more likely to be zero-levered.
Natural Illumination from Multiple Materials Using Deep Learning

Recovering natural illumination from a single Low-Dynamic Range (LDR) image is a challenging task. To remedy this situation we exploit two properties often found in everyday images. First, images rarely show a single material, but rather multiple ones that all reflect the same illumination. However, the appearance of each material is observed only for some surface orientations, not all. Second, parts of the illumination are often directly observed in the background, without being affected by reflection. Typically, this directly observed part of the illumination is even smaller. We propose a deep Convolutional Neural Network (CNN) that combines prior knowledge about the statistics of illumination and reflectance with an input that makes explicit use of these two observations. Our approach maps multiple partial LDR material observations represented as reflectance maps and a background image to a spherical High-Dynamic Range (HDR) illumination map. For training and testing we propose a new data set comprising of synthetic and real images with multiple materials observed under the same illumination. Qualitative and quantitative evidence shows how both multi-material and using a background are essential to improve illumination estimations.
Molecular Association of CaV 1 . 3 L-Type Calcium Channels with Shank

Neurons express multiple types of voltage-gated calcium (Ca 2 ) channels. Two subtypes of neuronal L-type Ca 2 channels are encoded by CaV1.2 and CaV1.3 pore-forming subunits. Both CaV1.2 and CaV1.3 subunits contain class I PDZ (postsynaptic density-95/Discs large/zona occludens-1) domain-binding consensus at their C termini. In yeast two-hybrid screen of rat brain cDNA library with the C-terminal bait of CaV1.3a (long C-terminal splice variant) L-type Ca 2 channel subunit, we isolated multiple clones of postsynaptic adaptor protein Shank. We demonstrated a specific association of CaV1.3a C termini, but not of CaV1.2 C termini, with Shank PDZ domain in vitro. We further demonstrated that the proline-rich region present in C termini of CaV1.3a subunit binds to Shank Src homology 3 domain. We established that CaV1.3a and Shank localized to postsynaptic locations in cultured rat hippocampal neurons. By expressing epitope-tagged recombinant CaV1.3 subunits in rat hippocampal neuronal cultures, we demonstrated that the presence of Shank-binding motifs in CaV1.3a sequence is both necessary and sufficient for synaptic clustering of CaV1.3 L-type Ca 2 channels. In experiments with dominant-negative peptides and dihydropyridine-resistant CaV1.3a mutants, we demonstrated an importance of Shank-binding motif in CaV1.3a sequence for phosphorylated cAMP response element-binding protein (pCREB) signaling in cultured hippocampal neurons. Our results directly link CaV1.3 neuronal L-type Ca 2 channels to macromolecular signaling complex formed by Shank and other modular adaptor proteins at postsynaptic density and provide novel information about the role played by CaV1.3 L-type Ca 2 channels in pCREB signaling.