SkICAT: A cataloging and analysis tool for wide field imaging surveys

We describe an integrated system, SkICAT (Sky Image Cataloging and Analysis Tool), for the automated reduction and analysis of the Palomar Observatory-ST ScI Digitized Sky Survey. The Survey will consist of the complete digitization of the photographic Second Palomar Observatory Sky Survey (POSS-II) in three bands, comprising nearly three Terabytes of pixel data. SkICAT applies a combination of existing packages, including FOCAS for basic image detection and measurement and SAS for database management, as well as custom software, to the task of managing this wealth of data. One of the most novel aspects of the system is its method of object classification. Using state-of-theart machine learning classification techniques (GID3* and O-BTree), we have developed a powerful method for automatically distinguishing point sources from non-point sources and artifacts, achieving comparably accurate discrimination a full magnitude fainter than in previous Schmidt plate surveys. The learning algorithms produce decision trees for classification by examining instances of objects classified by eye on both plate and higher quality CCD data. The same techniques will be applied to perform higher-level object classification (e.g., of galaxy morphology) in the near future. Another key feature of the system is the facility to integrate the catalogs from multiple plates (and portions thereof) to construct a single catalog of uniform calibration and quality down to the faintest limits of the survey. SkICAT also provides a variety of data analysis and exploration tools for the scientific utilization of the resulting catalogs. We include initial results of applying this system to measure the counts and distribution of galaxies in two bands down to Bj is approximately 21 mag over an approximate 70 square degree multi-plate field from POSS-II. SkICAT is constructed in a modular and general fashion and should be readily adaptable to other large-scale imaging surveys

Study the Efficiency of Two Concentrations from Algae Cladophora glomerata Extract on the Giardia lamblia parasite

خلال الدراسة الحالية تم عزل طفيلي Giardia lamblia من عينات البراز لمرضى مصابين باسهال Giardiasis  حيث تم عزل الطفيلي و تنميته باستخدام وسط HSP . استعان الباحثون بفئران تجريبيه بواقع اربع مجاميع من الفئران وذلك لتقييم فعالية تركيزين (128,256)ملغم /مل من مستخلص كلوروفورم للطحالب الخضراء Cladophora glomerata ضد الطفيلي المعزول ومقارنة بالعلاج التجاري للطفيلي) (Flagyl وذلك بقياس بعض المؤشرات مثل انزيمات الكبد GPTand GOT)) , مستوى تراكيز الصوديوم والبوتاسيوم والحديد بالدم اضافة الى تعداد اكياس الطفيلي لبراز كل مجموعة من مجاميع الفئران المستخدمة خلال التجربة , اظهرت النتائج انحدار في مستويات انزيمات الكبد بعد معالجة الفئران المصابة بالطفيلي بمستخلص الطحالب. بينما اشرت قياسات مستوى الصوديوم والبوتاسيوم و الحديد زيادة بعد العلاج بمستخلص الطحلب .وبالنسبة لتعداد اكياس الطفيلي فقد قل تعدادها في براز الفئران المصابة بعد تجريعها فمويا بمستخلص الطحلب مقارنة بالعلاج التجاري .واخيرا تم الكشف عن المركبات الفعالة في مستخلص الطحالب المدروسة باجراء فحص  GC-Mass حيث اظهرت نتائج الفحص وجود العديد من المركبات ذات فعالية بايولوجية متنوعة . تعتبر هذه الدراسة الاولى على مستوى العالم لبيان امكانية استخدام المركبات الفعالة بايولوجيا الموجوده في طحلب Cladophora glomerata كعلاج مناسب وبديل عن العلاج المصنع للقضاء على اصابات الطفيلي Giardia.Giardia lamblia parasite was isolated from the diarrhea samples of patients with Giardiasis dysentery and was developed in HSP media, four mice groups have been used to find in vivo efficacy of two concentrations (128,256) mg/ml of chlorophorm extracts from Cladophora glomerata algae against Giardia lamblia parasite  as compared with (Flagyl) by measuring several biochemical markers as ( GPT and GOT) enzymes ,sodium ,potassium and iron concentration as well as counting the number of parasitic cysts in each mice groups. The results demonstrate that levels of GPTA GOT enzymes have been decreased in mice treated with algal extract. As for the concentration of the Sodium, Potassium and Iron increased in mice treated with algal extract. The number of the Giardia cyst is also reduced in orally inoculated mice with both concentrations of algal extract as compared with positive control and the Flagyl treated group. In terms of bioactive compounds, GC-Mass results indicate the presence of many phytochemicals with different biologically active properties This study represents the first attempt to use Cladophora glomerata derived from phytochemicals to treat giardiasis in vivo

Data Analytics for the Cryptocurrencies Behavior

The cryptocurrencies are a new paradigm of transferring money be-tween users. Their anonymous and non-centralized is a subject of debate around the globe that paired with the massive spikes and declines in value that are in-herit to an unregistered asset. These facts make difficult for the common daily use of the cryptocurrencies as an exchange currency as instead they are being used as a new way to invest. What we propose in this article is a system for the better understanding of the cryptocurrencies economical behavior against the global market. For that we are using Data Analytics techniques to build a pre-dictor that uses as inputs said external financial variable. These forecasts would help determine if a coin is safe to trade with, if those forecasts can be precise by only using this external data. The results obtained indicates us that there is a certain degree of influence of the global market to the cryptocurrencies, but that is it not enough to correctly predict the fluctuations in price of the coins and that they care more about others factors and that they have their own bubbles, like the crypto collapse in late 2017.Instituto de Investigación en Informátic

Outcome of Depression and Anxiety After War: A Prospective Epidemiologic Study of Children and Adolescents

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106884/1/jts21895.pd

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio

Designing visual analytics methods for massive collections of movement data

Exploration and analysis of large data sets cannot be carried out using purely visual means but require the involvement of database technologies, computerized data processing, and computational analysis methods. An appropriate combination of these technologies and methods with visualization may facilitate synergetic work of computer and human whereby the unique capabilities of each “partner” can be utilized. We suggest a systematic approach to defining what methods and techniques, and what ways of linking them, can appropriately support such a work. The main idea is that software tools prepare and visualize the data so that the human analyst can detect various types of patterns by looking at the visual displays. To facilitate the detection of patterns, we must understand what types of patterns may exist in the data (or, more exactly, in the underlying phenomenon). This study focuses on data describing movements of multiple discrete entities that change their positions in space while preserving their integrity and identity. We define the possible types of patterns in such movement data on the basis of an abstract model of the data as a mathematical function that maps entities and times onto spatial positions. Then, we look for data transformations, computations, and visualization techniques that can facilitate the detection of these types of patterns and are suitable for very large data sets – possibly too large for a computer's memory. Under such constraints, visualization is applied to data that have previously been aggregated and generalized by means of database operations and/or computational techniques

A conceptual framework and taxonomy of techniques for analyzing movement

Movement data link together space, time, and objects positioned in space and time. They hold valuable and multifaceted information about moving objects, properties of space and time as well as events and processes occurring in space and time. We present a conceptual framework that describes in a systematic and comprehensive way the possible types of information that can be extracted from movement data and on this basis defines the respective types of analytical tasks. Tasks are distinguished according to the type of information they target and according to the level of analysis, which may be elementary (i.e. addressing specific elements of a set) or synoptic (i.e. addressing a set or subsets). We also present a taxonomy of generic analytic techniques, in which the types of tasks are linked to the corresponding classes of techniques that can support fulfilling them. We include techniques from several research fields: visualization and visual analytics, geographic information science, database technology, and data mining. We expect the taxonomy to be valuable for analysts and researchers. Analysts will receive guidance in choosing suitable analytic techniques for their data and tasks. Researchers will learn what approaches exist in different fields and compare or relate them to the approaches they are going to undertake