A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment

Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a N˙ aive Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1000 best binders are identified after docking only 14% of the chemical library, (ii). 9 or 10 best-binders are identified after docking only 19% of the chemical library, and (iii). no significant enrichment is observed after docking 70% of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening

A Guide to Measuring Advocacy and Policy

The overall purpose of this guide is twofold. To help grantmakers think about and talk about measurement of advocacy and policy, this guide puts forth a framework for naming outcomes associated with advocacy and policy work as well as directions for evaluation design. The framework is intended to provide a common way to identify and talk about outcomes, providing philanthropic and non-profit audiences an opportunity to react to, refine and adopt the outcome categories presented. In addition, grantmakers can consider some key directions for evaluation design that include a broad range of methodologies, intensities, audiences, timeframes and purposes. Included in the guide are a tool to measure improved policies, a tool to measure a strengthened base of public support, and a survey to measure community members' perceptions about the prioritization of issues

Knowledge-based design support and inductive learning

Designing and learning are closely related activities in that design as an ill-structure problem involves identifying the problem of the design as well as finding its solutions. A knowledge-based design support system should support learning by capturing and reusing design knowledge. This thesis addresses two fundamental problems in computational support to design activities: the development of an intelligent design support system architecture and the integration of inductive learning techniques in this architecture.This research is motivated by the belief that (1) the early stage of the design process can be modelled as an incremental learning process in which the structure of a design problem or the product data model of an artefact is developed using inductive learning techniques, and (2) the capability of a knowledge-based design support system can be enhanced by accumulating and storing reusable design product and process information.In order to incorporate inductive learning techniques into a knowledge-based design model and an integrated knowledge-based design support system architecture, the computational techniques for developing a knowledge-based design support system architecture and the role of inductive learning in Al-based design are investigated. This investigation gives a background to the development of an incremental learning model for design suitable for a class of design tasks whose structures are not well known initially.This incremental learning model for design is used as a basis to develop a knowledge-based design support system architecture that can be used as a kernel for knowledge-based design applications. This architecture integrates a number of computational techniques to support the representation and reasoning of design knowledge. In particular, it integrates a blackboard control system with an assumption-based truth maintenance system in an object-oriented environment to support the exploration of multiple design solutions by supporting the exploration and management of design contexts.As an integral part of this knowledge-based design support architecture, a design concept learning system utilising a number of unsupervised inductive learning techniques is developed. This design concept learning system combines concept formation techniques with design heuristics as background knowledge to build a design concept tree from raw data or past design examples. The design concept tree is used as a conceptual structure for the exploration of new designs.The effectiveness of this knowledge-based design support architecture and the design concept learning system is demonstrated through a realistic design domain, the design of small-molecule drugs one of the key tasks of which is to identify a pharmacophore description (the structure of a design problem) from known molecule examples.In this thesis, knowledge-based design and inductive learning techniques are first reviewed. Based on this review, an incremental learning model and an integrated architecture for intelligent design support are presented. The implementation of this architecture and a design concept learning system is then described. The application of the architecture and the design concept learning system in the domain of small-molecule drug design is then discussed. The evaluation of the architecture and the design concept learning system within and beyond this particular domain, and future research directions are finally discussed

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi

The "fuzzy front end" of innovation

The fast transformation of technologies into new products or processes is one of the core challenges for any technology-based enterprise. Within the innovation process, we believe, the early phases (fuzzy front end) to have the highest impact on the whole process and the result (Input-Output Process), since it will influence the design and total costs of the innovation extremely. However the Fuzzy Front End is unfortunately the least-well structured part of the innovation process, both in theory and in practice. The focus of the present chapter is on methods and tools to manage the fuzzy front end of the innovation process. Firstly, the activities, characteristics, and challenges of the front end are described. Secondly, a framework of the application fields for different methods and tools is presented: Since a product upgrade requires a different approach compared to radical innovation, where the market is unknown and a new technology is applied, we believe such a framework to be useful for practitioners. Thirdly, a selection of methods and tools that can be applied to the fuzzy front end are presented and allocated within the framework. The methods selected here address process improvements, concept generation, and concept testing. --fuzzy front end,innovation management,stage-gate process,frontloading,triz,dsm-matrix,lead user

A Pharmacology-Based Enrichment Program for Undergraduates Promotes Interest in Science

There is a strong need to increase the number of undergraduate students who pursue careers in science to provide the “fuel” that will power a science and technology–driven U.S. economy. Prior research suggests that both evidence-based teaching methods and early undergraduate research experiences may help to increase retention rates in the sciences. In this study, we examined the effect of a program that included 1) a Summer enrichment 2-wk minicourse and 2) an authentic Fall research course, both of which were designed specifically to support students\u27 science motivation. Undergraduates who participated in the pharmacology-based enrichment program significantly improved their knowledge of basic biology and chemistry concepts; reported high levels of science motivation; and were likely to major in a biological, chemical, or biomedical field. Additionally, program participants who decided to major in biology or chemistry were significantly more likely to choose a pharmacology concentration than those majoring in biology or chemistry who did not participate in the enrichment program. Thus, by supporting students\u27 science motivation, we can increase the number of students who are interested in science and science careers

State Capacity and Non-state Service Provision in Fragile and Conflict-affected States

How can governments effectively engage with non-state providers (NSPs) of basic services where capacity is weak? This paper examines whether and how fragile and conflict affected states can co-ordinate, finance, and set and apply standards for the provision of basic services by NSPs. It explores ways of incrementally engaging the state, beginning with activities that are least likely to do harm to non-state provision. Through the ‘indirect’ roles of setting the policy environment and engaging in policy dialogue, regulating and facilitating, contracting, and entering into mutual and informal agreements with NSPs, the state can in principle assume responsibility for the provision of basic services without necessarily being involved in direct provision. But government capacity to perform these roles is constrained by the state’s weak legitimacy, coverage and competence, lack of basic information about the non-state sector, and lack of basic organisational capacity to form and maintain relationships with NSPs. The experience of the exercise of the indirect roles in fragile settings suggests: * Governments may be more willing to engage with NSPs where there is recognition that government cannot alone deliver all services, where public and private services are not in competition, and where there is evidence that successful collaboration is possible (demonstrated through small-scale pilots). * The extent to which engagements are ‘pro-service’may be influenced by government motives for engagement and the extent to which the providers that are most important to poor people are engaged. * Formal policy dialogue between government and NSPs may be imperfect, unrepresentative and at times unhelpful in fragile settings. Informal dialogue - at the operational level - could more likely be where synergies can be found. * Regulation is more likely to be ‘pro-service’ where it offers incentives for compliance, and where it focuses on standards in terms of outputs and outcomes rather than inputs and entry controls. * Wide scale, performance-based contracting has been successful in delivering services in some cases, but the sustainability of this approach is often questioned. Some successful contractual agreements have a strong informal, relational element and grow out of earlier informal connections. * Informal and mutual agreements can avoid the capacity problems and tensions implicit in formal contracting but may present problems of non-transparency and exclusion of competition. Paradoxically, the need for large-scale approaches and quick co-ordination of services in fragile and conflict-affected settings may require ‘prematurely high’ levels of state-NSP engagement, before the development of the underlying institutional structures that would support them. When considering strategies to support the capacity of government to engagement with NSPs, donors should: * Recognise non-state service provision and adopt the ‘do no harm’ principle: It would be wrong to set the ambition of 'managing ‘ non-state provision in its entirety, and it can be very harmful for low-capacity states to seek to regulate all NSP or to draw it into clumsy contracts. * Beware of generalisation: Non-state provision takes many forms in response to different histories and to political and economic change. The possibilities and case for state engagement have to be assessed not assumed. The particular identities of NGOs and enterprises should be considered. * Recognise that state building can occur through any of the types of engagement with NSPs: Types of engagement should therefore be selected on the basis of their likely effectiveness in improving service delivery. * Begin with less risky/small scale forms of engagement where possible: State interventions that imply a direct controlling role for the state and which impose obligations on NSPs (i.e. contracting and regulation) require greater capacity (on both sides) and present greater risk of harm if performed badly than the roles of policy dialogue and entering into mutual agreements. * Adopt mixed approaches: The choice between forms of engagement does not have to be absolute. Rather than adopting a uniform plan of engagement in a particular country, it may be better to try different approaches in different regions or sectors