Miracle-dz/UniFAR-FTUs huggingface dataset:
Axelrod Symposium 2019: Phosphoproteomic Analysis of G-Protein–Coupled Pathways

By limiting unrestricted activation of intracellular effectors, compartmentalized signaling of cyclic nucleotides confers specificity to extracellular stimuli and is critical for the development and health of cells and organisms. Dissecting the molecular mechanisms that allow local control of cyclic nucleotide signaling is essential for our understanding of physiology and pathophysiology, but mapping the dynamics and regulation of compartmentalized signaling is a challenge. In this minireview we summarize advanced imaging and proteomics techniques that have been successfully used to probe compartmentalized cAMP signaling in eukaryotic cells. Subcellularly targeted fluorescence resonance energy transfer sensors can precisely locate and measure compartmentalized cAMP, and this allows us to estimate the range of effector activation. Because cAMP effector proteins often cluster together with their targets and cAMP regulatory proteins to form discrete cAMP signalosomes, proteomics and phosphoproteomics analysis have more recently been used to identify additional players in the cAMP-signaling cascade. We propose that the synergistic use of the techniques discussed could prove fruitful in generating a detailed map of cAMP signalosomes and reveal new details of compartmentalized signaling. Compiling a dynamic map of cAMP nanodomains in defined cell types would establish a blueprint for better understanding the alteration of signaling compartments associated with disease and would provide a molecular basis for targeted therapeutic strategies. SIGNIFICANCE STATEMENT cAMP signaling is compartmentalized. Some functionally important cellular signaling compartments operate on a nanometer scale, and their integrity is essential to maintain cellular function and appropriate responses to extracellular stimuli. Compartmentalized signaling provides an opportunity for precision medicine interventions. Our detailed understanding of the composition, function, and regulation of cAMP-signaling nanodomains in health and disease is essential and will benefit from harnessing the right combination of advanced biochemical and imaging techniques.
Capacitance of Rough Surfaces

Miniature electrodes are in growing demand for applications such as medical sensing and alternative energy technologies. The potential for increasing the capacitance of such electrodes by roughening the surface was investigated, which may allow for further miniaturization. Although roughening the surface did increase the capacitance, nanometer-scale roughness seems to reduce the capacitor ideality. This indicates that the roughening techniques should increase micron-scale roughness while minimizing nanometer-scale roughness. Electrodeposition of gold nanoparticles was found to achieve this goal.
[Nebivolol, a beta blocker of the 3rd generation: modern therapy of arterial hypertension. Results of a multicenter observation study].

BACKGROUND: Nebivolol represents a therapeutic class of beta blockers with high beta 1 selectivity and modulatory effect on vascular reactions by releasing nitric oxide (NO) from endothelial cells. Its antihypertensive effect by once a day application is established. The aim of the study was to investigate the acceptability and the antihypertensive efficacy of Nebivolol in hypertensives with and without concomitant diseases. METHODS AND RESULTS: An observational study was carried out in 6376 patients with arterial hypertension in 1529 centres in a period of time of six weeks. The initial dosage was 5 mg daily resp. 2.5 mg daily in patients over 65 years. The systolic blood pressure (BP) decreased during treatment from initial values of 173 +/- 18 mm Hg (mean +/- standard deviation) by 29 mm Hg to 144 +/- 14 mm Hg at the end of the observational period. The diastolic BP decreased from 101 +/- 9 mm Hg initially by 16 mm Hg to 85 +/- 8 mm Hg at the last examination of the patients. The normalization of the diastolic BP (< 90 mm Hg) was achieved in 62.2% of the patients. The mean heart rate (HR) was 84 +/- 12 beats/minute at the beginning of the study and decreased by 11 to 73 +/- 8 beats/minute. During the observational period cholesterol, triglycerides and blood glucose showed a significant decrease (p < 0.001). Triglycerides were diminished by 13%, cholesterol by 8%. In diabetic patients the most favourable effect was observed (decrease of triglycerides by 18% and cholesterol by 9%); glucose decreased in diabetics by 16%. CONCLUSIONS: In this multicentre observational study Nebivolol was proved as a safe and well-tolerated antihypertensive drug. The results of the analysis of metabolic parameters during Nebivolol treatment are of interest as a contribution to the preventive effect of this beta blocker on coronary heart disease.
Learning Analytics to Understand the Students’ Sentiments

What emotional experience can students live in digital mediated learning processes? In this paper we connect Learning analytics and Grounded theory to analyse the emotional presence of students in 11 courses within EduOpen (www.eduopen.org) MOOCs’ platform. We analysed through a bottom up process and Nvivo 11 Plus software the forum dedicated to the students’ self-presentation from all of the courses. By going ahead with the analysis, we defined a set of categories composed by a three-level system. At a more general level we have the macrodimensions “Sentiment about EduOpen” and “Emotions toward topics”. Each of these dimensions is composed by some “child” categories and subcategories (nodes to Nvivo’s language). After defining the entire set of categories and categorizing all the texts (circular process), we run some graphs on Nvivo showing the hierarchical structure of dimensions, the relations among dimensions and sources, and the clusters of dimensions by coding similarity. Results show how some courses are more composed by negative or positive sentiments and how the motivations dimension heavily characterizes the broad emotional dimension of students. In an evidence based action-research perspective, these results give interesting suggestions to personalize the learning activities proposed to students by EduOpen. Abstract in Italian
Critical areas and intervention strategies on emotional and behavioral disabilities amongst primary school going children in Kakamega County Kenya

Tel. :+254722453620 Emotional and behavioral disabilities is a condition in which behavior or emotional responses of an individual are so different from generally accepted norms that they adversely affect the child’s performance. The term EBD is broad, often used to group range of difficulties such as behavior which interfere with child’s own learning or learning of their peers (Clavina and Radionova 2016). According to ACSM (2014), it is a specific diagnosis where a child displays persistent and severe behaviors and at times referred to as” Social, Emotional and Behavioral Difficulties” (SEBD). In general Children with Emotional and Behavioral Difficulties (EBD) are characterized by: Disruptive, anti-social and aggressive behavior, Poor peer and family relationships, Hyperactivity, attention and concentration problems. This paper reviews on status, causes and critical areas for intervention. This paper used desktop research methodology to gather up to date information on descriptions, causes characteristics and impact of emotional and behavioral disability (EBD) on learning abilities of the children. The paper highlight intervention strategies which may be used to mitigate against this condition in an inclusive setting within the area of disability studies.
ON-THE-FLY X-RAY TOMOGRAPHY OF THE POROUS HYDROGEL SUBJECTED TO COMPRESSIVE LOADING

In bone tissue engineering proper description of its deformation behaviour is one of the most important characteristics for assessment of biocompatibility and bone-integration characteristics of proposed structure intended to be used as a bone scaffold [1]. Newly synthesized hydrogel based bone scaffold in detail presented in [2] was subjected to compressive loading to obtain its deformation characteristic. The investigated material was synthesized as the porous spongy-like structure improved by the bioactive glass (BAG) nano-particles to comply the requirements for diffusion of nutrients and oxygen [3]. Low specific stiffness, which makes the sample susceptible to damage, and simultaneous low attenuation to X-rays makes both mechanical and imaging part of the deformation experiments challenging.
MONITORING AND ANALYSES OF SEISMIC EVENTS AT THE VELENJE COAL MINE

Complaints about ground shaking and tremors were regularly addressed to the management of the Velenje Coal Mine. A micro-seismic monitoring system was set up on the surface in nearby urban areas and also directly in the vicinity of the mining activities. The results of these measurements were carefully analysed and presented to the public together with various safe-vibration-limit standards (in this case national standards). A system for automatically publishing measurements immediately after the event is recorded was also set up. This resulted in a dramatic reduction in the number of complaints. Routine micro-seismic monitoring became part of the regular monitoring of mining activities as some patterns of seismic response to mass mining were revealed.
Comparative analysis of the cryptocurrency and the stock markets using the Random Matrix Theory

This article demonstrates the comparative possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency and developed stock market. Then, combining the empirical cross-correlation matrix with the Random Matrix Theory, we mainly examine the statistical properties of cross-correlation coefficients, the evolution of the distribution of eigenvalues and corresponding eigenvectors in both markets using the daily returns of price time series. The result has indicated that the largest eigenvalue reflects a collective effect of the whole market, and is very sensitive to the crash phenomena. It has been shown that introduced the largest eigenvalue of the matrix of correlations can act like indicators-predictors of falls in both markets.
Numerical analysis of composite structures: creep, aging and failure

The present work describes some results of a research program on the numerical modeling of structures in composite material, developed along the last years by a team at CEMACOM-UFRGS. Formulation for anisotropic viscoelastic behavior (including the effect of aging) and anisotropic damage are combined and implemented into a FE program that allows the analysis of shell structures in the context of finite displacements with small strains. Four numerical examples used for validation and verification are included.
Guidance for the experimental tank testing of wave energy converters

Experimental tank testing is a key aspect of wave energy conversion research. The performance of designs can be assessed in an accessible and controlled environment and at a fraction of the cost of sea trials. There are many different ways to conduct experiments and it is beyond the scope of this document to cover them all. Instead, the author would like to raise readers’ awareness on several key aspects of wave tank testing for wave energy conversion. These have been divided into three categories: • Model making: construction techniques, scaling considerations instrumentations. • Measurements: wave measurement, measurement of model motions, data processing (mainly focussed on Fourier analysis). • Wave generation: deterministic and non-deterministic methods, wave tank calibration and phase locking. This document is based on tank testing practices that have been developed over the years at the University of Edinburgh. As such it is focused on the use of equipment that has been developed by the University of Edinburgh and Edinburgh Designs Ltd.
Trade Margins, Price, and Product Quality Adjustments to Non-Tariff Measures: Evidence from French Firms*

The paper tests whether the “quality”-focused non-tariff measures, such as technical barriers to trade (TBT) measure or sanitary and phytosanitary (SPS) measures, are trade-distorting and quality-improving. We first develop a firm-based trade model with information asymmetry on product quality. We show that the enforcement of a minimum quality standard intended to solve the asymmetric information problems induces the exit of low-quality and low-productivity firms. However, it also causes the exit of some high-quality firms because of a reallocation of demand from low-productivity to high-productivity low-quality firms. Thus, the effect of stricter quality standards on average quality of products is ambiguous. Then, using French data at the firm-product level, we study the impact of SPS and TBT measures on export decisions of firms (participation, export values and price) and average quality of products. We find that both SPS and TBT measures force low-productivity firms to exit. We also show that more SPS measures (resp. TBT measures) raise the market share of high-productivity firms at the expense of low-productivity incumbents and average quality of products in the food industry (resp., in the manufacturing industries). JEL Codes: D21, D22, F12, F14, Q17
The Impact of the European Carbon Market on Firm Productivity: Evidence from Italian Manufacturing Firms

In the Kyoto Protocol framework, the EU committed itself to reducing its greenhouse gas (GHG) emissions by 20 percent until 2020 compared with 1990 levels. One of the main policies adopted to fulfill this goal is the European Union Emission Trading System (EU ETS): a cap-and-trade scheme for GHG emission allowances. In this project, we exploit the introduction of the ETS and its institutional changes to provide evidence of the causal impact of this European policy on firms’ outcomes, disentangling the effects on profitability and productivity. This study is based on an original and comprehensive database of Italian manufacturing plants gathering data on EU ETS obligations and exchanged allowances, revenues, labor, polluting emissions and financial data. The empirical analysis combines robust and recent techniques for public policy evaluation with structural estimation of firms’ production function. Preliminary results show a positive effect of the policy on productivity and heterogeneous effects among sectors.
Normalization-based neighborhood model for cold start problem in recommendation system

Existing approaches for Recommendation Systems (RS) are mainly based on users’ past knowledge and the more popular techniques such as the neighborhood models focus on finding similar users in making recommendations. The cold start problem is due to inaccurate recommendations given to new users because of lack of past data related to those users. To deal with such cases where prior information on the new user is not available, this paper proposes a normalization technique to model user involvement for cold start problem or user likings based on the details of items used in the neighborhood models. The proposed normalization technique was evaluated using two datasets namely MovieLens and GroupLens. The results showed that the proposed technique is able to improve the accuracy of the neighborhood model, which in turn increases the accuracy of an RS.
EVERYBODY NEEDS GOOD NEIGHBOURS: AT LEAST FOR SOCIAL SUPPORT

We estimate the effect of neighborhood peer effects on individual welfare participation in France using a reverse engineering approach. We exploit variation that arise purely through residential migration in the number of individuals participating to a social program in a neighborhood on the take-up rate of individuals that stay in this same geographical area during this period. Furthermore we investigate heterogeneity and non-linearity in the effect of neighborhood participation regarding the kind of welfare program as well as individual and neighborhood characteristics. Our results show that the rate of welfare participation in the neighborhood has a significant effect on individual welfare participation. We also show that this effect is particularly strong in small cities and rural areas as well as in neighborhoods with an initially level of participation below the median take-up rate of our sample.
crystalAligner: a computer program to align crystal directions in a scanning electron microscope by global optimization

Alignment of a crystal with the coordinate system of a scanning electron microscope (SEM) is essential for obtaining controlled diffraction conditions or investigating anisotropic material properties. The computer programcrystalAlignerwas developed to enable the weighted constrained alignment of one or two crystal directions in crystals of arbitrary orientation and structure with the coordinate system of a SEM. For a given set of crystal directions, alignment objectives and rotational constraints, the algorithm simulates the rotation of the crystal on any constrained SEM stage and minimizes the misalignment of the crystal directions with prescribed coordinate axes of the SEM. The possibility of applying crystal symmetry and the use of a genetic algorithm for optimization ensure the determination of the global optimal alignment even in severely constrained conditions. In cases where ideal alignment is not obtainable, weighting factors can be applied to conflicting alignment objectives. Two application examples, the alignment of two crystal directions for a lamella lift out for nano-mechanical testing and the rotation of an orientation-related dual-phase microstructure into a standard projection, demonstrate the functionality and workflow of the freely available open-source programcrystalAligner.
Chimie et lumière Vivant Lumières sur le vivant Protéines fluorescentes et senseurs optogénétiques

Lighting up life: fluorescent proteins and optogenetic sensors Fluorescent proteins of the green fluorescent protein (GFP) family have given birth to a vast array of targetable optical sensors that play a major role in the deciphering of live cell chemistry. These genetically encoded reporters benefit from unequaled specificity and integration into the cellular machinery, while they gather withing less than 100 Å the three essential functions of a nanosensor, namely functionalization, chemical detection, and optical conversion. Their numerous applications and their performances directly stem from the photodynamics and photochemistry of the chromophore carried by GFPs. Owing to mutagenesis, spectroscopy and modeling tools, the way in which the GFP structural and conformationnal characteristics modulate their optical properties is better understood today. This mechanistic view will become ever more essential in the rational engineering of new probes for bioimaging.
Understanding, Detecting and Localizing Partial Failures in Large System Software

Partial failures occur frequently in cloud systems and can cause serious damage including inconsistency and data loss. Unfortunately, these failures are not well understood. Nor can they be effectively detected. In this paper, we ﬁrst study 100 real-world partial failures from ﬁve mature systems to understand their characteristics. We ﬁnd that these failures are caused by a variety of defects that require the unique conditions of the production environment to be triggered. Manually writing effective detectors to systematically detect such failures is both time-consuming and error-prone. We thus propose OmegaGen, a static analysis tool that automatically generates customized watchdogs for a given program by using a novel program reduction technique. We have successfully applied OmegaGen to six large distributed systems. In evaluating 22 real-world partial failure cases in these systems, the generated watchdogs can detect 20 cases with a median detection time of 4.2 seconds, and pinpoint the failure scope for 18 cases. The generated watchdogs also expose an unknown, conﬁrmed partial failure bug in the latest version of ZooKeeper.
Evaluation of the lattice Boltzmann method for wind modelling in complex terrain

Abstract. The worldwide expansion of wind energy is making the choice of potential wind farm locations more and more difficult. This results in an increased number of wind farms being located in complex terrain, which is characterised by flow separation, turbulence and high shear. Accurate modelling of these flow features is key for wind resource assessment in the planning phase, as the exact positioning of the wind turbines has a large effect on their energy production and lifetime. Wind modelling for wind resource assessments is usually carried out with the linear model Wind Atlas Analysis and Application Program (WAsP), unless the terrain is complex, in which case Reynolds-averaged Navier–Stokes (RANS) solvers such as WindSim and Ansys Fluent are usually applied. Recent research has shown the potential advantages of large-eddy simulation (LES) for modelling the atmospheric boundary layer and thermal effects; however, LES is far too computationally expensive to be applied outside the research environment. Another promising approach is the lattice Boltzmann method (LBM), a computational fluid technique based on the Boltzmann transport equation. It is generally used to study complex phenomena such as turbulence, because it describes motion at the mesoscopic level in contrast to the macroscopic level of conventional computational fluid dynamics (CFD) approaches, which solve the Navier–Stokes (N–S) equations. Other advantages of the LBM include its efficiency; near-ideal scalability on high-performance computers (HPCs); and ability to easily automate the geometry, the mesh generation and the post-processing. However, the LBM has been applied very little to wind modelling in complex terrain for wind energy applications, mainly due to the lack of availability of easy-to-use tools as well as the lack of experience with this technique. In this paper, the capabilities of the LBM to model wind flow around complex terrain are investigated using the Palabos framework and data from a measurement campaign from the Bolund Hill experiment in Denmark. Detached-eddy simulation (DES) and LES in Ansys Fluent are used as a numerical comparison. The results show that there is in general a good agreement between simulation and experimental data, and the LBM performs better than RANS and DES. Some deviations can be observed near the ground, close to the top of the cliff and on the lee side of the hill. The computational costs of the three techniques are compared, and it has been shown that the LBM can perform up to 5 times faster than DES, even though the set-up was not optimised in this initial study. It can be summarised that the LBM has a very high potential for modelling wind flow over complex terrain accurately and at relatively low costs, compared to solving N–S equations conventionally. Further studies on other sites are ongoing.
Pre-miRNA Features for Automated Classiﬁcation

—We present a system for precursory microRNA classiﬁcation that implements many types of features found in the literature: structural, thermodynamical, information-theoretical, and comparative. A total of 1485 features are computed and various tests are performed. We used Random Forests ﬁrst as the classiﬁer of choice and secondly in conjunction with various feature selection strategies in order to determine the most salient features and to increase the classiﬁcation performance.