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Electroconvulsive therapy (ECT) is a medical treatment that is most effective for mood disorders.It has also been shown to be an effective form of treatment for schizophrenia. However, many unanswered questions remain regarding its role in the management of people with schizophrenia.
Evaluate the main indications of ECT in schizophrenia patients.
To investigate the efficacy of ECT in the treatment of schizophrenic patients, evaluating its effects in the short-term and the long-term, comparing ECT with pharmacotherapy, and assessing the effects of treatment and the main indications for use in patients with schizophrenia.
A systematic review of the literature was conducted for ECT and schizophrenia. Forty-nine articles from peerreviewed journals were identified.
The most common indication for using ECT for schizophrenia patients was to augment pharmacotherapy, while the most common accompanying symptoms were, in order, catatonia, aggression and suicide. Catatonic patients responded significantly better to ECT than patients with any other subtype of schizophrenia. The combination of ECT with pharmacotherapy can be useful for drug-resistant patients. The use of an ECT-risperidone combination or ECT-clozapine combination in patients non-responsive to prior pharmacotherapy was found to be most effective.
ECT, combined with pharmacotherapy, may be a viable option for a selected group of people with schizophrenia. In particular, the use of ECT is recommended for drug-resistant patients, for schizophrenic patients with catatonia, aggression or suicidal behavior, and when rapid global improvement and reduction of acute symptomatology is desired.
Patients with sleep disorders have a significant increase in suicidal ideation and suicide attempts, at the assessment and lifetime (Goodwin et al, 2008; Chellappa et al, 2007; Wojnar et al, 2009; Li et al, 2010).
To evaluate the relationship between sleep disorders and suicidal behavior.
To study factors associated with a diagnosis of insomnia in patients admitted to the Emergency Department.
Participants were 843 patients consecutively admitted to the Emergency Department of the Sant’Andrea University Hospital in Rome, Italy, between January and December 2010. All patients admitted were referred to a psychiatrist. A clinical interview based on the MINI and a semi structured interview were performed. Patients were asked about “ongoing” suicidal ideation or plans for suicide. Clinical diagnoses were assigned according to ICD-10 criteria.
48% received a diagnosis of a mood disorders (BD and MDD) or anxiety disorders, 17.1% Schizophrenia or other non-affective psychosis. Patients with insomnia had more frequently a diagnosis of BD (23.9% vs. 12.4%) or MDD (13.3% vs. 9.5%; P< 0.001). Patients with insomnia less frequently had attempted suicide in the past 24 hours (5.3% vs. 9.5%; P< 0.05) than other patients, but suicide attempters with insomnia more frequently used violent methods (64.3% vs. 23.6%; P< 0.01) than suicide attempters without insomnia.
Our results support a relationship between sleep disorders and suicidal behavior. Clinicians should pay attention to sleep disorders when assessing suicide attempters; in fact, such conditions may have important clinical implications.
The use of Performance and Image-Enhancing Drugs (PIEDs) is on the increase and appears to be associated with several psychopathological disorders, whose prevalence in unclear.
We aimed to evaluate the differences–if any–in the prevalence of body image disorders (BIDs) and eating disorders (EDs) in PIEDs users athletes vs. PIEDs nonusers ones.
We enrolled 84 consecutive professional and amateur athletes (35.8% females; age range = 18–50), training in several sports centers in Italy. They underwent structured interviews (SCID I/SCID II) and completed the Body Image Concern Inventory (BICI) and the Sick, Control, One, Fat, Food Eating Disorder Screening Test (SCOFF). Mann-Whitney U test and Fisher's exact test were used for comparisons.
Of the 84 athletes, 18 (21.4%) used PIEDs. The most common PIEDs were anabolic androgenic steroids, amphetamine-like substances, cathinones, ephedrine, and caffeine derivatives (e.g. guarana). The two groups did not differ in socio-demographic characteristics, but differed in anamnestic and psychopathological ones, with PIEDs users athletes being characterized by significantly (P-values < 0.05) higher physical activity levels, consuming more coffee, cigarettes, and psychotropic medications (e.g. benzodiazepines) per day, presenting more SCID diagnoses of psychiatric disorders, especially Substance Use Disorders, Eating Disorders, Body Dysmorphic Disorder (BDD), and General Anxiety Disorders, showing higher BICI scores, which indicate a higher risk of BDD, and higher SCOFF scores, which suggest a higher risk of BIDs and EDs.
In PIEDs users athletes body image and eating disorders, and more in general psychopathological disorders, are more common than in PIEDs nonusers athletes.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
Adding Au to Pd nanoparticles (NPs) can impart high catalytic activity with respect to hydrogenation of a wide range of substances. These materials are often synthesized by reducing metallic precursors; hence, sonochemical and solvothermal processes are commonly used to anchor these bimetals onto thin supports, including graphene. Although similar NPs have been studied reasonably well, a clear understanding of structural characteristics relative to their synthesis parameters is lacking, due to limitations in characterization techniques, which may prevent optimization of this very promising catalyst. In this report, a strategic approach has been used to identify this structural and material synthesis correlation, starting with controlled sample preparation and followed by detailed characterization. This includes advanced scanning transmission electron microscopy and electron energy loss spectroscopy; the latter using a state-of-the-art instrumentation to map the distribution of Pd and Au, and to identify chemical state of the Pd NPs, which has not been previously reported. Results show that catalytic bimetal NP clusters were made of small zero-valent Pd NPs aggregating to form a shell around an Au core. Not only can the described characterization approach be applied to similar material systems, but the results can guide the optimization of the synthesis procedures.
In this paper, anhydrous calcium sulphate CaSO4 (anhydrite) is considered as a carrier material for organic matter delivery from Space to Earth. Its capability of incorporating important fractions of water, leading to different species like bassanite and gypsum, as well as organic molecules; its discovery on Mars surface and in meteorites; the capability to dissipate much energy by its chemical decomposition into solid (CaO) and gaseous (SO3) oxide, make anhydrite a very promising material in an astrobiological perspective. Since chemical cooling has been recently considered by some of the present authors for the case of Ca/Mg carbonates, CaSO4 can be placed into a class of ‘white soft minerals’ (WSM) of astrobiological interest. In this context, CaSO4 is evaluated here by using the atmospheric entry model previously developed for carbonates. The model includes grain dynamics, thermochemistry, stoichiometry, radiation and evaporation heat losses. Results are discussed in comparison with MgCO3 and CaCO3 and show that sub-mm anhydrite grains are potentially effective organic matter carriers. A Monte Carlo simulation is used to provide distributions of the sulphate fraction as a function of altitude. Two-zone model results are presented to support the isothermal grain hypothesis.
In this paper, a first study of the atmospheric entry of carbonate micrometeoroids, in an astrobiological perspective, is performed. Therefore an entry model, which includes two-dimensional dynamics, non-isothermal atmosphere, ablation and radiation losses, is build and benchmarked to literature data for silicate micrometeoroids. A thermal decomposition model of initially pure magnesium carbonate is proposed, and it includes thermal energy, mass loss and the effect of changing composition as the carbonate grain is gradually converted into oxide. Several scenarios are obtained by changing the initial speed, entry angle and grain diameter, producing a systematic comparison of silicate and carbonate grain. The results of the composite model show that the thermal behaviour of magnesium carbonate is markedly different from that of the corresponding silicate, much lower equilibration temperatures being reached in the first stages of the entry. At the same time, the model shows that the limit of a thermal protection scenario, based on magnesium carbonate, is the very high decomposition speed even at moderate temperatures, which results in the total loss of carbon already at about 100 km altitude. The present results show that, although decomposition and associated cooling are important effects in the entry process of carbonate grains, the specific scenario of pure MgCO3 micrograin does not allow complex organic matter delivery to the lower atmosphere. This suggests us to consider less volatile carbonates for further studies.
Nowadays there is no field research which is not flooded with data. Among the sciences, astrophysics has always been driven by the analysis of massive amounts of data. The development of new and more sophisticated observation facilities, both ground-based and spaceborne, has led data more and more complex (Variety), an exponential growth of both data Volume (i.e., in the order of petabytes), and Velocity in terms of production and transmission. Therefore, new and advanced processing solutions will be needed to process this huge amount of data. We investigate some of these solutions, based on machine learning models as well as tools and architectures for Big Data analysis that can be exploited in the astrophysical context.
Radio survey datasets comprise an increasing number of individual observations stored as sets of multidimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large-scale comparative visual analytics framework. encube can utilise advanced visualization environments such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer and 84 million pixels) for collaborative analysis of large subsets of data from radio surveys. It can also run on standard desktops, providing a capable visual analytics experience across the display ecology. encube is composed of four primary units enabling compute-intensive processing, advanced visualisation, dynamic interaction, parallel data query, along with data management. Its modularity will make it simple to incorporate astronomical analysis packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between high-end display systems (such as CAVE2) and the classical desktop, preserving all traces of the work completed on either platform – allowing the research process to continue wherever you are.
Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.
The Digitised First Byurakan Survey (DFBS) provides low dispersion optical spectra for about 24 million sources. A two-step machine learning algorithm based on similarities to predefined templates is applied to select different classes of rare objects in the dataset automatically, for example late type stars, quasars and white dwarves. Identifying outliers from the groups of common astrophysical objects may lead to discovery of rare objects, such as gamma-ray burst afterglows.
The Wide Field Infrared Survey Telescope (WFIRST) is a 2.4 m telescope with a large field of view ( ~ 0.3 deg2) and fine angular resolution (0.11”). WFIRST’s Wide Field Instrument (WFI) will obtain images in the Z, Y, J, H, F184, W149 (wide) filter bands, and grism spectra of the same large field of view. The data volume of the WFIRST Science Archive is expected to reach a few Petabytes. We describe plans to enable users to find the data of interest and, if needed, to analyze the data in situ using sophisticated software tools provided by the archive. As preparation, we are building a mini-archive that will help us to define realistic science requirements and to design the full WFIRST Science Archive.
This paper discusses an autoregressive model for the analysis of irregularly observed time series. The properties of this model are studied and a maximum likelihood estimation procedure is proposed. The finite sample performance of this estimator is assessed by Monte Carlo simulations, showing accurate estimators. We implement this model to the residuals after fitting an harmonic model to light-curves from periodic variable stars from the Optical Gravitational Lensing Experiment (OGLE) and Hipparcos surveys, showing that the model can identify time dependency structure that remains in the residuals when, for example, the period of the light-curves was not properly estimated.
The Hubble Catalog of Variables (HCV) is a 3 year ESA funded project that aims to develop a set of algorithms to identify variables among the sources included in the Hubble Source Catalog (HSC) and produce the HCV. We will process all HSC sources with more than a predefined number of measurements in a single filter/instrument combination and compute a range of lightcurve features to determine the variability status of each source. At the end of the project, the first release of the Hubble Catalog of Variables will be made available at the Mikulski Archive for Space Telescopes (MAST) and the ESA Science Archives. The variability detection pipeline will be implemented at the Space Telescope Science Institute (STScI) so that updated versions of the HCV may be created following the future releases of the HSC.
The FP-7 (Framework Programme 7 of the European Union) PERICLES project addresses the life-cycle of large and complex data sets to cater for the evolution of context of data sets and user communities, including groups unanticipated when the data was created. Semantics of data sets are thus also expected to evolve and the project includes elements which could address the reuse of data sets at periods where the data providers and even their institutions are not available any more. This paper presents the PERICLES science case with the example of the SOLAR (SOLAR monitoring observatory) payload on International Space Station-Columbus.
After its first implementation in 2003 the Astro-WISE technology has been rolled out in several European countries and is used for the production of the KiDS survey data. In the multi-disciplinary Target initiative this technology, nicknamed WISE technology, has been further applied to a large number of projects. Here, we highlight the data handling of other astronomical applications, such as VLT-MUSE and LOFAR, together with some non-astronomical applications such as the medical projects Lifelines and GLIMPS; the MONK handwritten text recognition system; and business applications, by amongst others, the Target Holding.
We describe some of the most important lessons learned and describe the application of the data-centric WISE type of approach to the Science Ground Segment of the Euclid satellite.
In order to understand how galaxies form and evolve, the measurement of the parameters related to their morphologies and also to the way they interact is one of the most relevant requirements. Due to the huge amount of data that is generated by surveys, the morphological and interaction analysis of galaxies can no longer rely on visual inspection. For dealing with such issue, new approaches based on machine learning techniques have been proposed in the last years with the aim of automating the classification process. We tested Deep Learning using images of galaxies obtained from CANDELS to study the accuracy achieved by this tool considering two different frameworks. In the first, galaxies were classified in terms of their shapes considering five morphological categories, while in the second, the way in which galaxies interact was employed for defining other five categories. The results achieved in both cases are compared and discussed.
Large Synoptic Survey Telescope will make great contributions to many scientific fields. One of the modules will be time-domain astronomy and detection of transient events. In this paper, some considerations about transient events and alerts are presented.
We describe here the parallels in astronomy and earth science datasets, their analyses, and the opportunities for methodology transfer from astroinformatics to geoinformatics. Using example of hydrology, we emphasize how meta-data and ontologies are crucial in such an undertaking. Using the infrastructure being designed for EarthCube - the Virtual Observatory for the earth sciences - we discuss essential steps for better transfer of tools and techniques in the future e.g. domain adaptation. Finally we point out that it is never a one-way process and there is enough for astroinformatics to learn from geoinformatics as well.