An Introduction to Programming for Bioscientists: A Python-based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog

WSAmacd handbook 2012-13 PDF edition

The story, syllabus and course information handbook for the MA in Communication Design at Winchester School of Art. www.facebook.com/WSAmac

On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods

Owing to the recent development of sensor resolutions onboard different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made land covers. Of particular importance is the increasing amounts of available hyperspectral data originating from airborne and satellite sensors such as AVIRIS, HyMap, and Hyperion with very high spectral resolution (i.e., high number of spectral channels) containing rich information for a wide range of applications. A relevant example is the separation of different types of land-cover classes using the data in order to understand, e.g., impacts of natural disasters or changing of city buildings over time. More recently, such increases in the data volume, velocity, and variety of data contributed to the term big data that stand for challenges shared with many other scientific disciplines. On one hand, the amount of available data is increasing in a way that raises the demand for automatic data analysis elements since many of the available data collections are massively underutilized lacking experts for manual investigation. On the other hand, proven statistical methods (e.g., dimensionality reduction) driven by manual approaches have a significant impact in reducing the amount of big data toward smaller smart data contributing to the more recently used terms data value and veracity (i.e., less noise, lower dimensions that capture the most important information). This paper aims to take stock of which proven statistical data mining methods in remote sensing are used to contribute to smart data analysis processes in the light of possible automation as well as scalable and parallel processing techniques. We focus on parallel support vector machines (SVMs) as one of the best out-of-the-box classification methods.Sponsored by: IEEE Geoscience & Remote Sensing SocietyRitrýnt tímaritPeer reviewedPre prin

OFMTutor: An operator function model intelligent tutoring system

The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described

Prediction Analysis of Esophageal Variceal Degrees using Data Mining: Is Validated in Clinical Medicine?

The objective of this study is to assess the feasibility of a data mining association analysis technique in early prediction of esophageal varices in cirrhotic patients and prediction of risky groups candidates for urgent interventional procedure. A manuscript titled 201C;Detection of Risky Esophageal varices using 2D U/S: when to perform Endoscopy201D;, published in The American Journal of The Medical Science on 21Th of December 2012, to our knowledge it was the first prospective study to assess the degree of esophageal varices by 2D ultrasound using the data mining statistical computed analysis in 673 patients. A descriptive model was generated using a decision tree algorithm (Rapid Miner, version 4.6, Berlin, Germany), the over all accuracy was 95%. Following another 59 patients using statistical analysis to determine the association between esophageal variceal degrees detected by Ultrasound in comparable to Upper Endoscopy, was done. Categorical data were compared using the x2 test, where as continuous variables were compared using Student2019;s t test. The comparative results accuracy of both two studies was 97.9%

What About Inclusive Education and ICT in Italy: a Scoping Study

Strategies and approaches to inclusion in the classroom are important in developing a high quality, inclusive experience for students with Special Education Needs. Generally, strategies are not geared towards specific exceptionalities, but are instead designed to be implemented across exceptionality categories. Pavone (2014) and de Anna, Gaspari, Mura (2015) determined through their systematic literature review and research results that co-operation among staff, commitment and accountability to the teaching of all students, differentiation of instruction, and recognizing “that social interaction is the means through which student knowledge is developed” are key to successful inclusion of students with SEN. This paper looks at the issue of school inclusion by referring to the most recent laws about the inclusive education of students with special educational needs in Italy. Inclusive education means that all students attend and are welcomed by their neighbourhood schools in age-appropriate, regular classes and are supported to learn, contribute and participate in all aspects of the life of the school. Inclusive education is about how we develop and design our schools, classrooms, programs and activities so that all students learn and participate together. So ICT should be considered as a key tool for promoting equity in educational opportunities, that is using ICT to support the learning of learners with disabilities and special educational needs in inclusive settings within compulsory education. The paper also argues how the Italian teachers can realized good practices for inclusion through the use of ICT

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided

Protecting Geolocation Privacy of Photo Collections

People increasingly share personal information, including their photos and photo collections, on social media. This information, however, can compromise individual privacy, particularly as social media platforms use it to infer detailed models of user behavior, including tracking their location. We consider the specific issue of location privacy as potentially revealed by posting photo collections, which facilitate accurate geolocation with the help of deep learning methods even in the absence of geotags. One means to limit associated inadvertent geolocation privacy disclosure is by carefully pruning select photos from photo collections before these are posted publicly. We study this problem formally as a combinatorial optimization problem in the context of geolocation prediction facilitated by deep learning. We first demonstrate the complexity both by showing that a natural greedy algorithm can be arbitrarily bad and by proving that the problem is NP-Hard. We then exhibit an important tractable special case, as well as a more general approach based on mixed-integer linear programming. Through extensive experiments on real photo collections, we demonstrate that our approaches are indeed highly effective at preserving geolocation privacy.Comment: AAAI 2