2DPHOT: A Multi-purpose Environment for the Two-dimensional Analysis of Wide-field Images

We describe 2DPHOT, a general purpose analysis environment for source detection and analysis in deep wide-field images. 2DPHOT is an automated tool to obtain both integrated and surface photometry of galaxies in an image, to perform reliable star-galaxy separation with accurate estimates of contamination at faint flux levels, and to estimate completeness of the image catalog. We describe the analysis strategy on which 2DPHOT is based, and provide a detailed description of the different algorithms implemented in the package. This new environment is intended as a dedicated tool to process the wealth of data from wide-field imaging surveys. To this end, the package is complemented by 2DGUI, an environment that allows multiple processing of data using a range of computing architectures.Comment: Accepted to PAS

Hygienisation, gentrification, and urban displacement in Brazil

This article engages recent debates over gentrification and urban displacement in the global South. While researchers increasingly suggest that gentrification is becoming widespread in “Southern” cities, others argue that such analyses overlook important differences in empirical context and privilege EuroAmerican theoretical frameworks. To respond to this debate, in this article, we outline the concept of higienização (hygienisation), arguing that it captures important contextual factors missed by gentrification. Hygienisation is a Brazilian term that describes a particular form of urban displacement, and is directly informed by legacies of colonialism, racial and class stigma, informality, and state violence. Our objective is to show how “Southern” concepts like hygienisation help urban researchers gain better insight into processes of urban displacement, while also responding to recent calls to decentre and provincialise urban theory

Laboratory Microprobe X-Ray Fluorescence in Plant Science: Emerging Applications and Case Studies

In vivo and micro chemical analytical methods have the potential to improve our understanding of plant metabolism and development. Benchtop microprobe X-ray fluorescence spectroscopy (μ-XRF) presents a huge potential for facing this challenge. Excitation beams of 30 μm and 1 mm in diameter were employed to address questions in seed technology, phytopathology, plant physiology, and bioremediation. Different elements were analyzed in several situations of agronomic interest: (i) Examples of μ-XRF yielding quantitative maps that reveal the spatial distribution of zinc in common beans (Phaseolus vulgaris) primed seeds. (ii) Chemical images daily recorded at a soybean leaf (Glycine max) infected by anthracnose showed that phosphorus, sulfur, and calcium trended to concentrate in the disease spot. (iii) In vivo measurements at the stem of P. vulgaris showed that under root exposure, manganese is absorbed and transported nearly 10-fold faster than iron. (iv) Quantitative maps showed that the lead distribution in a leaf of Eucalyptus hybrid was not homogenous, this element accumulated mainly in the leaf border and midrib, the lead hotspots reached up to 13,400 mg lead kg-1 fresh tissue weight. These case studies highlight the ability of μ-XRF in performing qualitative and quantitative elemental analysis of fresh and living plant tissues. Thus, it can probe dynamic biological phenomena non-destructively and in real time

Affective recognition from EEG signals: an integrated data-mining approach

Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity

Polarimetric SAR Image Segmentation with B-Splines and a New Statistical Model

We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GHP distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or manually, and the proposed algorithm uses a deformable contours technique to find the boundary. In doing this, the parameters of the polarimetric GHP model for the data are estimated, in order to find the transition points between the region being segmented and the surrounding area. This is a local algorithm since it works only on the region to be segmented. Results of its performance are presented

Staging Bipolar Disorder.

The purpose of this study was to analyze the evidence supporting a staging model for bipolar disorder. The authors conducted an extensive Medline and Pubmed search of the published literature using a variety of search terms (staging, bipolar disorder, early intervention) to find relevant articles, which were reviewed in detail. Only recently specific proposals have been made to apply clinical staging to bipolar disorder. The staging model in bipolar disorder suggests a progression from prodromal (at-risk) to more severe and refractory presentations (Stage IV). A staging model implies a longitudinal appraisal of different aspects: clinical variables, such as number of episodes and subsyndromal symptoms, functional and cognitive impairment, comorbidity, biomarkers, and neuroanatomical changes. Staging models are based on the fact that response to treatment is generally better when it is introduced early in the course of the illness. It assumes that earlier stages have better prognosis and require simpler therapeutic regimens. Staging may assist in bipolar disorder treatment planning and prognosis, and emphasize the importance of early intervention. Further research is required in this exciting and novel area