Spatial Statistical Models: an overview under the Bayesian Approach

Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior knowledge and data likelihood. Nevertheless, this modeling class is not well explored as the classification and regression machine learning models given their simplicity and often weak (data) independence supposition. In this manner, this systematic review aimed to unravel the main models presented in the literature in the past 20 years, identify gaps, and research opportunities. Elements such as random fields, spatial domains, prior specification, covariance function, and numerical approximations were discussed. This work explored the two subclasses of spatial smoothing global and local.Comment: 33 pages, 6 figure

Brainwave nets: Are sparse dynamic models susceptible to brain manipulation experimentation?

© Copyright © 2020 Nascimento, Pinto-Orellana, Leite, Edwards, Louzada and Santos. Sparse time series models have shown promise in estimating contemporaneous and ongoing brain connectivity. This paper was motivated by a neuroscience experiment using EEG signals as the outcome of our established interventional protocol, a new method in neurorehabilitation toward developing a treatment for visual verticality disorder in post-stroke patients. To analyze the [complex outcome measure (EEG)] that reflects neural-network functioning and processing in more specific ways regarding traditional analyses, we make a comparison among sparse time series models (classic VAR, GLASSO, TSCGM, and TSCGM-modified with non-linear and iterative optimizations) combined with a graphical approach, such as a Dynamic Chain Graph Model (DCGM). These dynamic graphical models were useful in assessing the role of estimating the brain network structure and describing its causal relationship. In addition, the class of DCGM was able to visualize and compare experimental conditions and brain frequency domains [using finite impulse response (FIR) filter]. Moreover, using multilayer networks, the results corroborate with the susceptibility of sparse dynamic models, bypassing the false positives problem in estimation algorithms. We conclude that applying sparse dynamic models to EEG data may be useful for describing intervention-relocated changes in brain connectivity

Entropy analysis of high-definition transcranial electric stimulation effects on EEG dynamics

A foundation of medical research is time series analysis—the behavior of variables of interest with respect to time. Time series data are often analyzed using the mean, with statistical tests applied to mean differences, and has the assumption that data are stationary. Although widely practiced, this method has limitations. Here we present an alternative statistical approach with sample analysis that provides a summary statistic accounting for the non-stationary nature of time series data. This work discusses the use of entropy as a measurement of the complexity of time series, in the context of Neuroscience, due to the non-stationary characteristic of the data. To elucidate our argument, we conducted entropy analysis on a sample of electroencephalographic (EEG) data from an interventional study using non-invasive electrical brain stimulation. We demonstrated that entropy analysis could identify intervention-related change in EEG data, supporting that entropy can be a useful “summary” statistic in non-linear dynamical systems

A análise de dois filmes quanto ao potencial para a promoção da reflexão de questões de cunho epistemológico sobre ciência na formação inicial de professores de Química

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Case and Activity Identification for Mining Process Models from Middleware

Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider

Manipulation of human verticality using high-definition transcranial direct current stimulation

Background: Using conventional tDCS over the temporo-parietal junction (TPJ) we previously reported that it is possible to manipulate subjective visual vertical (SVV) and postural control. We also demonstrated that high-definition tDCS (HD-tDCS) can achieve substantially greater cortical stimulation focality than conventional tDCS. However, it is critical to establish dose-response effects using well-defined protocols with relevance to clinically meaningful applications. Objective: To conduct three pilot studies investigating polarity and intensity-dependent effects of HD-tDCS over the right TPJ on behavioral and physiological outcome measures in healthy subjects. We additionally aimed to establish the feasibility, safety, and tolerability of this stimulation protocol. Methods: We designed three separate randomized, double-blind, crossover phase I clinical trials in different cohorts of healthy adults using the same stimulation protocol. The primary outcome measure for trial 1 was SVV; trial 2, weight-bearing asymmetry (WBA); and trial 3, electroencephalography power spectral density (EEG-PSD). The HD-tDCS montage comprised a single central, and 3 surround electrodes (HD-tDCS3x1) over the right TPJ. For each study, we tested 3x2 min HD-tDCS3x1 at 1, 2 and 3 mA; with anode center, cathode center, or sham stimulation, in random order across days. Results: We found significant SVV deviation relative to baseline, specific to the cathode center condition, with consistent direction and increasing with stimulation intensity. We further showed significant WBA with direction governed by stimulation polarity (cathode center, left asymmetry; anode center, right asymmetry). EEG-PSD in the gamma band was significantly increased at 3 mA under the cathode. Conclusions: The present series of studies provide converging evidence for focal neuromodulation that can modify physiology and have behavioral consequences with clinical potential

The Combination of Gefitinib With ATRA and ATO Induces Myeloid Differentiation in Acute Promyelocytic Leukemia Resistant Cells

In approximately 15% of patients with acute myeloid leukemia (AML), total and phosphorylated EGFR proteins have been reported to be increased compared to healthy CD34(+) samples. However, it is unclear if this subset of patients would benefit from EGFR signaling pharmacological inhibition. Pre-clinical studies on AML cells provided evidence on the pro-differentiation benefits of EGFR inhibitors when combined with ATRA or ATO in vitro. Despite the success of ATRA and ATO in the treatment of patients with acute promyelocytic leukemia (APL), therapy-associated resistance is observed in 5-10% of the cases, pointing to a clear need for new therapeutic strategies for those patients. In this context, the functional role of EGFR tyrosine-kinase inhibitors has never been evaluated in APL. Here, we investigated the EGFR pathway in primary samples along with functional in vitro and in vivo studies using several APL models. We observed that total and phosphorylated EGFR (Tyr992) was expressed in 28% and 19% of blast cells from APL patients, respectively, but not in healthy CD34(+) samples. Interestingly, the expression of the EGF was lower in APL plasma samples than in healthy controls. The EGFR ligand AREG was detected in 29% of APL patients at diagnosis, but not in control samples. In vitro, treatment with the EGFR inhibitor gefitinib (ZD1839) reduced cell proliferation and survival of NB4 (ATRA-sensitive) and NB4-R2 (ATRA-resistant) cells. Moreover, the combination of gefitinib with ATRA and ATO promoted myeloid cell differentiation in ATRA- and ATO-resistant APL cells. In vivo, the combination of gefitinib and ATRA prolonged survival compared to gefitinib- or vehicle-treated leukemic mice in a syngeneic transplantation model, while the gain in survival did not reach statistical difference compared to treatment with ATRA alone. Our results suggest that gefitinib is a potential adjuvant agent that can mitigate ATRA and ATO resistance in APL cells. Therefore, our data indicate that repurposing FDA-approved tyrosine-kinase inhibitors could provide new perspectives into combination therapy to overcome drug resistance in APL patients