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Language-specific effects on number computation in toddlers.

A fundamental question in developmental science is how brains with and without language compute numbers. Measuring young children's verbal reactions in France (Paris) and in England (Oxford), here we show that, although there is a general arithmetic ability for small numbers that is shared by monkeys and preverbal infants, the development of such initial knowledge in humans follows specific performance patterns, depending on what language the children speak.

Analysis of Variance↗

Preparing a business justification for going electronic.

Exponential advances in the technology sector and computer industry have benefited the science and practice of radiology. Modalities such as digital radiography, computed radiography, computed tomography, magnetic resonance imaging, ultrasound, digital angiography, and gamma cameras are all capable of producing DICOM compliant images. Text can likewise be acquired using voice recognition technology (VRT) and efficiently rendered into a digital format. All of these digital data sets can subsequently be transferred over a network between machines for display and further manipulation on workstations. Large capacity archiving units are required to store these voluminous data sets. The enterprise components of radiology departments and imaging centers--radiology information systems (RIS) and picture archiving and communications systems (PACS)--have thus undergone a transition from hardcopy to softcopy. When preparing to make transition to a digital environment, the first step is introspective. A detailed SWOT (strengths, weaknesses, opportunities and threats) analysis, with a focus on the status of "electronic preparedness," ensues. The next step in the strategic planning process is to formulate responses to the following questions: Will this technology acquisition provide sufficient value to my organization to justify the expense? Is there a true need for the new technology? What issues or problems does this technology address? What customer needs will this technology satisfy today and tomorrow? How will the organization's shareholders benefit from this technology? The answers to these questions and the questions that they in turn generate will stimulate the strategic planning process to define demands, investigate technology and investment options, identify resources and set goals. The mission of your radiology center will determine what you will demand from the electronic environment. All radiology practices must address the demand of clinical service. Additional demands based on your mission may include education and research. The investigation of options is probably the most time consuming portion of the analysis. It is in this stage where the system architecture is drafted. Important contributions must be solicited from your information technology division, radiologists and other physicians, hospital administration and any other service where the use of imaging technology information is required and beneficial. Vendors and consultants can be extremely valuable in generating workflow diagrams, which include imaging acquisition components and imaging display components. A request for proposal (RFP) may facilitate this step. A detailed inventory of imaging equipment, imaging equipment locations and use, imaging equipment DICOM compatibility, imaging equipment upgrade requirements, reading locations and user locations must be obtained and confirmed. It is a good idea to take a careful inventory of your resources during the process of investigating system architecture and financial options. An often-ignored issue is the human resource allocation that is required to implement, maintain and upgrade the system. These costs must be estimated and included in the financial analysis. Further, to predict the finances of your operation in the future, a solid understanding of your center's historical financial data is required. This will enable you to make legitimate and reasonable financial calculations using incremental volumes. The radiology center must formulate and articulate discrete clinical and business goals for the transition to a digital environment that are consistent with the institutional or enterprise mission. Once goals are set, it is possible to generate a strategic plan. It is necessary to establish individual accountability for all aspects of the planning and implementation process. A realistic timetable should be implemented. Keep in mind that this is a dynamic process; technology is rapidly changing, as are clinical service demands and regulatory initiatives. It is therefore prudent to monitor the process, make appropriate revisions when necessary and address contingencies as they arise.

Capital Expenditures↗

Distance education through the Internet: the GNA-VSNS biocomputing course.

A prototype course on biocomputing was delivered via international computer networks in early summer 1995. The course lasted 11 weeks, and was offered free of charge. It was organized by the BioComputing Division of the Virtual School of Natural Sciences, which is a member school of the Globewide Network Academy. It brought together 34 students and 7 instructors from all over the world, and covered the basics of sequence analysis. Five authors from Germany and USA prepared a hypertext book which was discussed in weekly study sessions that took place in a virtual classroom at the BioMOO electronic conferencing system. The course aimed at students with backgrounds in molecular biology, biomedicine or computer science, complementing and extending their skills with an interdisciplinary curriculum. Special emphasis was placed on the use of Internet resources, and the development of new teaching tools. The hypertext book includes direct links to sequence analysis and databank search services on the Internet. A tool for the interactive visualization of unit-cost pairwise sequence alignment was developed for the course. All course material will stay accessible at the World Wide Web address (Uniform Resource Locator) http://+www.techfak.uni-bielefeld.de/bcd/welcome .html. This paper describes the aims and organization of the course, and gives a preliminary account of this novel experience in distance education.

Computational Biology↗

Biological data becomes computer literate: new advances in bioinformatics.

Bioinformatics is an art and science concerned with the use of computing in biological research areas such as genomics, transcriptomics, proteomics, genetics, and evolution. This review paints a broad picture of bioinformatics, drawing examples from genomic sequencing and microarray analysis. I highlight the role of bioinformatics at multiple points along the path from high-tech data generation to biological discovery.

Computational Biology↗

Recent translational research: computational studies of breast cancer.

The combination of mathematics--queen of sciences--and the general utility of computers has been used to make important inroads into insight-providing breast cancer research and clinical aids. These developments are in two broad areas. First, they provide useful prognostic guidelines for individual patients based on historic evidence. Second, by suggesting numeric tumor growth laws that are correlated to clinical parameters, they permit development of biologically relevant theories and comparison with patient data to help us understand complex biologic processes. These latter studies have produced many new ideas that are testable in clinical trials. In this review we discuss these developments from a clinical perspective, and ask whether and how they translate into useful tools for patient treatment.

Breast Neoplasms↗

The computer synoptic operative report--a leap forward in the science of surgery.

BACKGROUND: Quality of surgery is a proven prognostic factor in many tumors. It is critical to ensure that an effective method is in place to evaluate surgery accurately. MATERIAL AND METHODS: A provincial Cancer Surgery Working Group designed and piloted a computerized synoptic operative report template (WebSMR) in rectal cancer surgery, to replace the standard narrative operative record (NR). This included a precise description of the procedure, data on demographics, diagnostic evaluation, staging, and functional measures. A total of 70 items for anterior resection (AR) and 63 items for abdominoperinal excision (APR) were included. The WebSMR was assessed for comparison with 40 NR randomly selected from seven hospitals in Southern Alberta from 2001 to 2003. RESULTS: The NR contained 45.9% of the specified data elements and the WebSMR captured 99%. The most complete NR data (68.8% to 97%) concerned hospital and patient data, anesthetist and surgeon information, approach, and closure details. The important details of laparotomy and tumor resection were the next most complete data (33.5% to 47.5%) and the least complete (0 to 25%) concerned preoperative treatment, comorbidity, and metastatic and local assessment. All differences among these groups were statistically different (P < .001). No statistically significant differences were seen in the completeness of the NR according to the type of surgery (AR vs. APR; P = .1) or the dictating surgeon (colorectal vs. general vs. resident; P = .175). The time needed to complete the WebSMR test was only 6 minutes. CONCLUSION: The science of surgical technique can be better measured by this unique instrument and will create accountability in surgery.

Data Collection↗

[NIHS information and computing infrastructure (NICI)].

We describe the information and computing infrastructure in National Institute of Health Sciences, which were constructed until May, 1996. The in house computer network and computing facilities for common usage in NIHS have been developed under the initiative of Division of Chem-Bio Informatics since 1989. The present LAN (Local Area Network) consists of coaxial cables and optic fibers which are connected by a LAN Switch. The LAN is connected to the Internet via IMnet, the inter ministry network back bone of the Science and Technology Agency. Various types of workstations and personal computers such as SUN WS, Silicon Graphics WS, IBM WS & PC, Macintosh, and NEC PC are connected to the LAN. This computing network environment which we named NICI (NIHS Information and Computing Infrastructure) not only provides network communications but also facilitates advanced computating systems for chemical safety research at NIHS as a COE.

Biological Science Disciplines↗

Collaborative development of a uniform graphical interface.

A uniform graphical user interface to informational databases is evolving at the University of Washington through a collaborative development process. The interface, called WILLOW, has grown from model analysis and preliminary design to working prototype. The design replicates a natural flow of search retrieval. Development continues in a spiral of test and linear improvements based on user analysis. WILLOW's internal structure is built on a Unix client-server model communicating over the campus TCP/IP backbone network. Its external structure is an X-Windows/Motif visual presentation emphasizing a simple, consistent, graphical face to disparate information databases. The WILLOW collaborators have grown from an initial group composed of the Health Sciences Library & Information Center and Computing & Communications' Information Systems to the University Libraries, Computing & Communications divisions, Medical Center Information Systems, and departments throughout the health sciences.

Information Systems↗

The use of artificial neural networks in biomedical technologies: an introduction.

Artificial neural networks (NN) are systems than can learn. In the most common situation, an operator trains the system on a set of input and output data belonging to a particular category. If new data of the same category, but not in the training set, are presented to the system, the NN can use the learned data to predict outcomes without any specific programming relating to the category of events involved. The fields of application of NN have increased dramatically in the past few years. Originally, the NN technique was mainly in the hands of computer programming specialists and the applications concentrated on tasks such as decision systems and signal processing. However, this picture has changed due to the emergence of user-friendly NN software for personal computers. A large variety of possible NN applications now exist for non-computer specialists. Thus, with only a very modest knowledge of the theory behind neural networks, it is possible to attack complicated problems in a researcher's own area of specialty with the NN technique. This is especially true in the field of medical technology, the topic of this review. The review is divided into three sections: 1) an elementary introduction to useful NN methods; 2) a review of the most important applications of the NN technique to this point in time; 3) a summary of available computer details that would be needed for a beginner in this field.

Humans↗

Parasitology tutoring system: a hypermedia computer-based application.

The teaching of parasitology is a basic course in all life sciences curricula, and up to now no computer-assisted tutoring system has been developed for this purpose. By using Knowledge Pro, an object-oriented software development tool, a hypermedia tutoring system for teaching parasitology to college students was developed. Generally, a tutoring system contains a domain expert, a student model, a pedagogical expert and the user interface. In this project, particular emphasis was given to the user interface design and the expert knowledge representation. The system allows access to the educational material through hypermedia and indexing at the pace of the student. The hypermedia access is facilitated through key words defined as hypertext and objects in pictures defined as hyper-areas. The indexing access is based on a list of parameters that refers to various characteristics of the parasites, e.g. taxonomy, host, organ, etc. In addition, this indexing access can be used for testing the student's level of understanding. The advantages of this system are its user-friendliness, graphical interface and ability to incorporate new educational material in the area of parasitology.

Artificial Intelligence↗

Computational physics: a perspective.

Computing comprises three distinct strands: hardware, software and the ways they are used in real or imagined worlds. Its use in research is more than writing or running code. Having something significant to compute and deploying judgement in what is attempted and achieved are especially challenging. In science or engineering, one must define a central problem in computable form, run such software as is appropriate and, last but by no means least, convince others that the results are both valid and useful. These several strands are highly interdependent. A major scientific development can transform disparate aspects of information and computer technologies. Computers affect the way we do science, as well as changing our personal worlds. Access to information is being transformed, with consequences beyond research or even science. Creativity in research is usually considered uniquely human, with inspiration a central factor. Scientific and technological needs are major forces in innovation, and these include hardware and software opportunities. One can try to define the scientific needs for established technologies (atomic energy, the early semiconductor industry), for rapidly developing technologies (advanced materials, microelectronics) and for emerging technologies (nanotechnology, novel information technologies). Did these needs define new computing, or was science diverted into applications of then-available codes? Regarding credibility, why is it that engineers accept computer realizations when designing engineered structures, whereas predictive modelling of materials has yet to achieve industrial confidence outside very special cases? The tensions between computing and traditional science are complex, unpredictable and potentially powerful.

Computer Simulation↗

Selective use of online literature searching by a drug information service.

The use of online searching in a drug information center on a regular but selective basis is described. Of 90-100 information requests received monthly in a university-affiliated drug information center located within the health sciences library, five to eight computer searches are performed. All other questions are answered using a manual search. The computer searches are conducted by a medical librarian who works closely with the pharmacist. For each search, the library charges the drug information center for at least 12 minutes of connect time; charges cover the library's direct costs only. The drug information center is staffed by a director and assistant director; in addition, Pharm.D. students and clinical pharmacy residents work there. Factors influencing the decision to do an online rather than a manual search include budgetary constraints, how quickly an answer is needed, the success of a preliminary manual search, and the complexity of the request. Considerations for conducting online searches through a library rather than by the staff of the drug information center include requisite search skills, costs, and accessibility to computer search services. The selective use of online searches through a health sciences library is a viable means of accessing online information in a drug information center that cannot support its own online literature-retrieval system.

Budgets↗

Practicing Data Science in Interactive Notebooks.

The Jupyter Notebook is a platform for interactive computing that displays code and results in the same browser, making it valuable for teaching, prototyping, data analysis, and collaboration. Its explicit&#xa0;and transparent structure greatly reproducibility&#xa0;while&#xa0;its backend server supports flexible deployment. In the past few years, Jupyter notebooks and similar tools have become increasingly popular. In this chapter, we will review key aspects of data analysis in a cloud environment and demonstrate common tasks for analyzing metabolomics data using template notebooks. This is an accompaniment to the basic bioinformatics tools and essential data science toolkit introduced in the first edition.

Software↗

Transcriptome-wide analysis reveals sequence selection to avoid mRNA aggregation in E. coli.

The stability of RNA base pairing and its limited four-letter code create an intrinsic potential for promiscuous RNA-RNA interactions. In vitro, such interactions drive RNA to self-assemble into aggregates. This raises a fundamental unanswered question: within a confined cellular volume at physiological mRNA abundances, how much aggregation would arise from sequence-encoded chemistry alone? Here, we establish this baseline with large-scale kinetic simulations of the E. coli transcriptome. Our simulations reveal that sequence-encoded base-pairing energetics is sufficient to generate a dynamic network of large aggregates, organized by long, multivalent mRNA hubs. Strikingly, evolutionary analysis shows that native E. coli sequences exhibit clear signatures of selection to counteract this propensity: they fold more stably, minimize unstructured regions, and form weaker intermolecular contacts than dinucleotide-preserving controls. These findings demonstrate that maintaining transcriptome solubility has been a significant, previously unrecognized constraint shaping genome evolution, and provide a new lens to interpret cellular RNA management.

Biological Sciences (Biophysics and Computational ↗

How do connectionist networks compute?

Although connectionism is advocated by its proponents as an alternative to the classical computational theory of mind, doubts persist about its computational credentials. Our aim is to dispel these doubts by explaining how connectionist networks compute. We first develop a generic account of computation-no easy task, because computation, like almost every other foundational concept in cognitive science, has resisted canonical definition. We opt for a characterisation that does justice to the explanatory role of computation in cognitive science. Next we examine what might be regarded as the "conventional" account of connectionist computation. We show why this account is inadequate and hence fosters the suspicion that connectionist networks are not genuinely computational. Lastly, we turn to the principal task of the paper: the development of a more robust portrait of connectionist computation. The basis of this portrait is an explanation of the representational capacities of connection weights, supported by an analysis of the weight configurations of a series of simulated neural networks.

Cognitive Science↗