The Colombian conflict: a description of a mental health program in the Department of Tolima.

Colombia has been seriously affected by an internal armed conflict for more than 40 years affecting mainly the civilian population, who is forced to displace, suffers kidnapping, extortion, threats and assassinations. Between 2005 and 2008, Médecins Sans Frontières-France provided psychological care and treatment in the region of Tolima, a strategic place in the armed conflict. The mental health program was based on a short-term multi-faceted treatment developed according to the psychological and psychosomatic needs of the population. Here we describe the population attending during 2005-2008, in both urban and rural settings, as well as the psychological treatment provided during this period and its outcomes.We observed differences between the urban and rural settings in the traumatic events reported, the clinical expression of the disorders, the disorders diagnosed, and their severity. Although the duration of the treatment was limited due to security reasons and access difficulties, patient condition at last visit improved in most of the patients. These descriptive results suggest that further studies should be conducted to examine the role of short-term psychotherapy, adapted specifically to the context, can be a useful tool to provide psychological care to population affected by an armed conflict

Cyber-physical system based on image recognition to improve traffic flow: A case study

Vehicular traffic in metropolitan areas turns congested along either paths or periods. As a case study, we have considered a mass transport system with a bus fleet that rides over exclusive lanes across streets and avenues in an urban area that does not allow the circulation of lightweight vehicles, cargo, and motorcycles. This traffic flow becomes congested due to the absence of restriction policies based on criteria. Moreover, the exclusive lanes are at ground level, decreasing lanes for other vehicles. The main objective of this proposal consists of controlling the access to the exclusive lanes by a cyber-physical system following authorization conditions, verifying the permission status of a vehicle by the accurate recognition of license plates to reduce traffic congestion. Therefore, in the case of invading an exclusive lane without permission, the vehicle owner gets a notification of the fine with the respective evidence

Immature rats show ovulatory defects similar to those in adult rats lacking prostaglandin and progesterone actions

Gonadotropin-primed immature rats (GPIR) constitute a widely used model for the study of ovulation. Although the equivalence between the ovulatory process in immature and adult rats is generally assumed, the morphological and functional characteristics of ovulation in immature rats have been scarcely considered. We describe herein the morphological aspects of the ovulatory process in GPIR and their response to classical ovulation inhibitors, such as the inhibitor of prostaglandin (PG) synthesis indomethacin (INDO) and a progesterone (P) receptor (PR) antagonist (RU486). Immature Wistar rats were primed with equine chorionic gonadotropin (eCG) at 21, 23 or 25 days of age, injected with human chorionic gonadotropin (hCG) 48 h later, and sacrificed 16 h after hCG treatment, to assess follicle rupture and ovulation. Surprisingly, GPIR showed age-related ovulatory defects close similar to those in adult rats lacking P and PG actions. Rats primed with eCG at 21 or 23 days of age showed abnormally ruptured corpora lutea in which the cumulus-oocyte complex (COC) was trapped or had been released to the ovarian interstitum, invading the ovarian stroma and blood and lymphatic vessels. Supplementation of immature rats with exogenous P and/or PG of the E series did not significantly inhibit abnormal follicle rupture. Otherwise, ovulatory defects were practically absent in rats primed with eCG at 25 days of age. GPIR treated with INDO showed the same ovulatory alterations than vehicle-treated ones, although affecting to a higher proportion of follicles. Blocking P actions with RU486 increased the number of COC trapped inside corpora lutea and decreased ovulation. The presence of ovulatory defects in GPIR, suggests that the capacity of the immature ovary to undergo the coordinate changes leading to effective ovulation is not fully established in Wistar rats primed with eCG before 25 days of age

Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation

Artificial olfaction systems, which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a potentially low-cost tool in many areas of industry such as perfumery, food and drink production, clinical diagnosis, health and safety, environmental monitoring and process control. However, successful applications of these systems are still largely limited to specialized laboratories. Sensor drift, i.e., the lack of a sensor's stability over time, still limits real in dustrial setups. This paper presents and discusses an evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems. The proposed approach exploits a cutting-edge evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can transparently correct raw sensors' measures thus mitigating the negative effects of the drift. The method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the physical chemical sensors

The PAU Survey: Photometric redshifts using transfer learning from simulations

In this paper we introduce the \textsc{Deepz} deep learning photometric redshift (photo-$z$) code. As a test case, we apply the code to the PAU survey (PAUS) data in the COSMOS field. \textsc{Deepz} reduces the $\sigma_{68}$ scatter statistic by 50\% at $i_{\rm AB}=22.5$ compared to existing algorithms. This improvement is achieved through various methods, including transfer learning from simulations where the training set consists of simulations as well as observations, which reduces the need for training data. The redshift probability distribution is estimated with a mixture density network (MDN), which produces accurate redshift distributions. Our code includes an autoencoder to reduce noise and extract features from the galaxy SEDs. It also benefits from combining multiple networks, which lowers the photo-$z$ scatter by 10 percent. Furthermore, training with randomly constructed coadded fluxes adds information about individual exposures, reducing the impact of photometric outliers. In addition to opening up the route for higher redshift precision with narrow bands, these machine learning techniques can also be valuable for broad-band surveys.Comment: Accepted versio

Integrating the STOP-BANG Score and Clinical Data to Predict Cardiovascular Events After Infarction A Machine Learning Study

BACKGROUND: OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores that are obtained during the management of patients with myocardial infarction (MI). Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks. RESEARCH QUESTION: Can the integration of STOP-BANG score with clinical data and scores through ML better identify patients who experienced an in-hospital cardiovascular event after acute MI? STUDY DESIGN AND METHOD: This is a prospective observational cohort study of 124 patients with acute MI of whom the STOP-BANG score classified 34 as low (27.4%), 30 as intermediate (24.2%), and 60 as high (48.4%) OSA-risk patients who were followed during hospitalization. ML implemented feature selection and integration across 47 variables (including STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction) to identify those patients who experienced an in-hospital cardiovascular event (ie, death, ventricular arrhythmias, atrial fibrillation, recurrent angina, reinfarction, stroke, worsening heart failure, or cardiogenic shock) after definitive MI treatment. Receiver operating characteristic curves were used to compare ML performance against STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction, independently. RESULTS: There were an increasing proportion of cardiovascular events across the low, intermediate, and high OSA risk groups (P = .005). ML selected 7 accessible variables (ie, Killip class, leukocytes, GRACE score, c reactive protein, oxygen saturation, STOP-BANG score, and N-terminal prohormone of B-type natriuretic peptide); their integration outperformed all comparators (area under the curve, 0.83 [95% CI, 0.74-0.90]; P <.01). INTERPRETATION: The integration of the STOP-BANG score into clinical evaluation (considering Killip class, GRACE score, and simple laboratory values) of subjects who were admitted for an acute MI because of ML can significantly optimize the identification of patients who will experience an in-hospital cardiovascular event

The PAU survey: classifying low-z SEDs using Machine Learning clustering

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The version of record Monthly Notices of the Royal Astronomical Society 524.3 (2023): 3569-3581 is available online at: https://academic.oup.com/mnras/article-abstract/524/3/3569/7225529?redirectedFrom=fulltextWe present an application of unsupervised Machine Learning clustering to the PAU survey of galaxy spectral energy distribution (SED) within the COSMOS field. The clustering algorithm is implemented and optimized to get the relevant groups in the data SEDs. We find 12 groups from a total number of 5234 targets in the survey at 0.01 < z < 0.28. Among the groups, 3545 galaxies (68 per cent) show emission lines in the SEDs. These groups also include 1689 old galaxies with no active star formation. We have fitted the SED to every single galaxy in each group with CIGALE. The mass, age, and specific star formation rates (sSFR) of the galaxies range from 0.15 < age/Gyr <11; 6 < log (M/M⊙) <11.26, and -14.67 < log (sSFR/yr-1) <-8. The groups are well-defined in their properties with galaxies having clear emission lines also having lower mass, are younger and have higher sSFR than those with elliptical like patterns. The characteristic values of galaxies showing clear emission lines are in agreement with the literature for starburst galaxies in COSMOS and GOODS-N fields at low redshift. The star-forming main sequence, sSFR versus stellar mass and UVJ diagram show clearly that different groups fall into different regions with some overlap among groups. Our main result is that the joint of low- resolution (R ∼50) photometric spectra provided by the PAU survey together with the unsupervised classification provides an excellent way to classify galaxies. Moreover, it helps to find and extend the analysis of extreme ELGs to lower masses and lower SFRs in the local UniverseThis work has been supported by the Ministry of Science and Innovation of Spain, project PID2019-107408GB-C43 (ESTALLIDOS), and the Government of the Canary Islands through EU FEDER funding, projects PID2020010050 and PID2021010077. This article is based on observations made in the Observatorios de Canarias of the Instituto de Astrofísica de Canarias (IAC) with the WHT operated on the island of La Palma by the Isaac Newton Group of Telescopes (ING) in the Observatorio del Roque de los Muchachos. The PAU Survey is partially supported by MINECO under grants CSD2007-00060, AYA2015-71825, ESP2017-89838, PGC2018-094773, PGC2018-102021, PID2019-111317GB, SEV-2016-0588, SEV-2016-0597, MDM-2015-0509 and Juan de la Cierva fellowship and LACEGAL and EWC Marie Sklodowska-Curie grant No 734374 and no.776247 with ERDF funds from the EU Horizon 2020 Programme, some of which include ERDF funds from the European Union. IEEC and IFAE are partially funded by the CERCA and Beatriu de Pinos program of the Generalitat de Catalunya. Funding for PAUS has also been provided by Durham Univer sity (via the ERC StG DEGAS-259586), ETH Zurich, Leiden University (via ERC StG ADULT-279396 and Netherlands Organisation for Scientific Research (NWO) Vici grant 639.043.512), University College London and from the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 776247 EWC. The PAU data center is hosted by the Port d’Información Científica (PIC), maintained through a collaboration of CIEMAT and IFAE, with additional support from Universitat Autónoma de Barcelona and ERDF. We acknowledge the PIC services department team for their support and fruitful discussion

Black Holes in Ho\v{r}ava Gravity with Higher Derivative Magnetic Terms

We consider Horava gravity coupled to Maxwell and higher derivative magnetic terms. We construct static spherically symmetric black hole solutions in the low-energy approximation. We calculate the horizon locations and temperatures in the near-extremal limit, for asymptotically flat and (anti-)de Sitter spaces. We also construct a detailed balanced version of the theory, for which we find projectable and non-projectable, non-perturbative solutions.Comment: 17 pages. v2: Up to date with published version; some minor remarks and more reference

The PAU Survey: Narrow-band image photometry

PAUCam is an innovative optical narrow-band imager mounted at the William Herschel Telescope built for the Physics of the Accelerating Universe Survey (PAUS). Its set of 40 filters results in images that are complex to calibrate, with specific instrumental signatures that cannot be processed with traditional data reduction techniques. In this paper we present two pipelines developed by the PAUS data management team with the objective of producing science-ready catalogues from the uncalibrated raw images. The Nightly pipeline takes care of all image processing, with bespoke algorithms for photometric calibration and scatter-light correction. The Multi-Epoch and Multi-Band Analysis (MEMBA) pipeline performs forced photometry over a reference catalogue to optimize the photometric redshift performance. We verify against spectroscopic observations that the current approach delivers an inter-band photometric calibration of 0.8% across the 40 narrow-band set. The large volume of data produced every night and the rapid survey strategy feedback constraints require operating both pipelines in the Port d'Informaci\'o Cientifica data centre with intense parallelization. While alternative algorithms for further improvements in photo-z performance are under investigation, the image calibration and photometry presented in this work already enable state-of-the-art photometric redshifts down to iAB=23.0.Comment: 32 pages, 26 figures, MNRAS in pres