Anatomo-functional correspondence in the superior temporal sulcus

The superior temporal sulcus (STS) is an intriguing region both for its complex anatomy and for the multiple functions that it hosts. Unfortunately, most studies explored either the functional organization or the anatomy of the STS only. Here, we link these two aspects by investigating anatomo-functional correspondences between the voice-sensitive cortex (Temporal Voice Areas) and the STS depth. To do so, anatomical and functional scans of 116 subjects were processed such as to generate individual surface maps on which both depth and functional voice activity can be analyzed. Individual depth profiles of manually drawn STS and functional profiles from a voice localizer (voice > non-voice) maps were extracted and compared to assess anatomo-functional correspondences. Three major results were obtained: first, the STS exhibits a highly significant rightward depth asymmetry in its middle part. Second, there is an anatomo-functional correspondence between the location of the voice-sensitive peak and the deepest point inside this asymmetrical region bilaterally. Finally, we showed that this correspondence was independent of the gender and, using a machine learning approach, that it existed at the individual level. These findings offer new perspectives for the understanding of anatomo-functional correspondences in this complex cortical region

Ascorbic acid is a regulator of the intracellular cAMP concentration: Old molecule, new functions?

AbstractRecently, using an animal model of Charcot-Marie-Tooth human disorder, we showed that ascorbic acid (AA) represses PMP22 gene expression by acting on intracellular cAMP concentrations. In this work, we present kinetics data on the inhibitory effect of AA upon adenylate cyclase activity. The data show that this molecule acts as a competitive inhibitor of the enzyme, a finding that opens new pharmacological avenues

Low vocal pitch preference drives first impressions irrespective of context in male voices but not in female voices

Vocal pitch has been found to influence judgments of perceived trustworthiness and dominance from a novel voice. However, the majority of findings arise from using only male voices and in context-specific scenarios. In two experiments, we first explore the influence of average vocal pitch on first-impression judgments of perceived trustworthiness and dominance, before establishing the existence of an overall preference for high or low pitch across genders. In Experiment 1, pairs of high- and low-pitched temporally reversed recordings of male and female vocal utterances were presented in a two-alternative forced-choice task. Results revealed a tendency to select the low-pitched voice over the high-pitched voice as more trustworthy, for both genders, and more dominant, for male voices only. Experiment 2 tested an overall preference for low-pitched voices, and whether judgments were modulated by speech content, using forward and reversed speech to manipulate context. Results revealed an overall preference for low pitch, irrespective of direction of speech, in male voices only. No such overall preference was found for female voices. We propose that an overall preference for low pitch is a default prior in male voices irrespective of context, whereas pitch preferences in female voices are more context- and situation-dependent. The present study confirms the important role of vocal pitch in the formation of first-impression personality judgments and advances understanding of the impact of context on pitch preferences across genders

Structural effects of anomalous current densities on manganese hexacyanoferrate for Li-ion batteries

A battery management system (BMS) plays a pivotal role in providing optimal performance of lithium-ion batteries (LIBs). However, the eventual malfunction of the BMS may lead to safety hazards or reduce the remaining useful life of LIBs. Manganese hexacyanoferrate (MnHCF) was employed as the positive electrode material in a Li-ion half-cell and subjected to five cycles at high current densities (10 A g$_{MnHCF}$$^{−1}$) and to discharge at 0.1 A g$_{MnHCF}$$^{−1}$, instead of classical charge/discharge cycling with initial positive polarization at 0.01 A g$_{MnHCF}$$^{−1}$, to simulate a current sensor malfunctioning and to evaluate the electrochemical and structural effects on MnHCF. The operando set of spectra at the Mn and Fe K-edges was further analyzed through multivariate curve resolution analysis with an alternating least squares algorithm (MCR–ALS) and extended X-ray absorption fine structure (EXAFS) spectroscopy to investigate the structural modifications arising during cycling after the applied electrochemical protocol. The coulombic efficiency in the first cycle was dramatically affected; however, the local structural environment around each photo absorber recovered during charging. The identification of an additional spectral contribution in the electrochemical process was achieved through MCR-ALS analysis, and the Mn-local asymmetry was thoroughly explored via EXAFS analysis

Low-cost image annotation for supervised machine learning. Application to the detection of weeds in dense culture

An open problem in robotized agriculture is to detect weeds in dense culture. This problem can be addressed with computer vision and machine learning. The bottleneck of supervised approaches lay in the manual annotation of training images. We propose two different approaches for detecting weeds position to speed up this process. The first approach is using synthetic images and eye-tracking to annotated images [4] which is at least 30 times faster than manual annotation by an expert, the second approach is based on real RGB and depth images collected via Kinect v2 sensor. We generated a data set of 150 synthetic images which weeds were randomly positioned on it. Images were gazed by two observers. Eye tracker sampled eye position during the execution of this task [5, 6]. Area of interest was recorded as rectangular patches. A patch is considered as including weeds if the average fixation time in this patch exceeds 1.04 seconds. The quality of visual annotation by eye-tracking is assessed by two ways. First, direct comparison of visual annotation with ground-truth which is shown an average 94.7% of all fixations on an image which fell within ground-truth bounding-boxes. Second, as shown in fig.1 eye-tracked annotated data is used as a training data set in four machine learning approaches and compare the recognition rate with the ground-truth. These four machine learning methods are tested in order to assess the quality of the visual annotation. These methods correspond to handcrafted features adapted to texture characterization. They are followed by a linear support vector machine binary classifier. The table 1 gives the average accuracy and standard deviation. Experimental results prove that visual eye-tracked annotated data are almost the same as in-silico ground-truth and performances of supervised machine learning on eye-tracked annotated data are very close to the one obtained with ground-truth

Intermittency and the Slow Approach to Kolmogorov Scaling

From a simple path integral involving a variable volatility in the velocity differences, we obtain velocity probability density functions with exponential tails, resembling those observed in fully developed turbulence. The model yields realistic scaling exponents and structure functions satisfying extended self-similarity. But there is an additional small scale dependence for quantities in the inertial range, which is linked to a slow approach to Kolmogorov (1941) scaling occurring in the large distance limit.Comment: 10 pages, 5 figures, minor changes to mirror version to appear in PR