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

Ant-Based Phylogenetic Reconstruction (ABPR): A new distance algorithm for phylogenetic estimation based on ant colony optimization

We propose a new distance algorithm for phylogenetic estimation based on Ant Colony Optimization (ACO), named Ant-Based Phylogenetic Reconstruction (ABPR). ABPR joins two taxa iteratively based on evolutionary distance among sequences, while also accounting for the quality of the phylogenetic tree built according to the total length of the tree. Similar to optimization algorithms for phylogenetic estimation, the algorithm allows exploration of a larger set of nearly optimal solutions. We applied the algorithm to four empirical data sets of mitochondrial DNA ranging from 12 to 186 sequences, and from 898 to 16,608 base pairs, and covering taxonomic levels from populations to orders. We show that ABPR performs better than the commonly used Neighbor-Joining algorithm, except when sequences are too closely related (e.g., population-level sequences). The phylogenetic relationships recovered at and above species level by ABPR agree with conventional views. However, like other algorithms of phylogenetic estimation, the proposed algorithm failed to recover expected relationships when distances are too similar or when rates of evolution are very variable, leading to the problem of long-branch attraction. ABPR, as well as other ACO-based algorithms, is emerging as a fast and accurate alternative method of phylogenetic estimation for large data sets

Understanding and Predicting the Legislative Process in the Chamber of Deputies of Brazil = Compreendendo e prevendo o processo legislativo na câmara dos deputados do Brasil

In this article, based on of open legislative data mining, we propose a methodology to create a model capable of indicating which characteristics have a positive or negative impact on the approval of a bill by the Chamber of Deputies. Added to the explanatory capacity, the model can also predict whether a bill will be approved or not. The model was submitted to experiments and analysis that measured and validated its explanatory and predictive capacity. In order to identify the most relevant characteristics we use an impact formula that calculates the relevance of the characteristics of the model in its final approval or archiving decision. In the end, the generated model contributed by clarifying characteristics relevant to the approval or not of the bill and achieved a good performance in its predictive capacity