Geopiracy: Patent law, climate change, and geoengineering

Patent law is a regime of intellectual property, which provides exclusive rights regarding scientific inventions, which are novel, inventive, and useful. There has been much debate over the limits of patentable subject matter relating to emerging technologies. The Supreme Court of the US has sought to rein in the expansive interpretation of patentability by lower courts in a series of cases dealing with medical information (Prometheus), finance (Bilski), and gene patents (Myriad). This has led to a reinvigoration of the debate over the boundaries of patentable subject matter. There has been controversy about the rise in patenting of geoengineering - particularly by firms such as Intellectual Ventures.

Teledermatology: Its use in the detection and management of actinic keratosis

Teledermatology can profoundly improve access to medical services for those who may have limited access to dermatology due to workforce shortages, distance to providers, or limitations in their mobility. Two common ways of teledermatology are differentiated: life synchronous, where patient and doctor communicate directly, or store and forward asynchronous methods, where the patient and doctor provide and assess the medical information independently. Teledermatology has been tested for its safety, feasibility and accuracy for a number of dermatological conditions, including the early detection of skin cancer and is usually safe, feasible and accurate. Studies reported somewhat better results for synchronous than asynchronous methods, possibly because of loss of information if no direct patient doctor contact is feasible. However asynchronous methods are easier to organize, require less sophisticated technology and are more widely accessible, and are more convenient for both patients and doctors. No study to date focused solely on teledermatology of actinic keratosis, but such lesions are typically found during teledermatology examinations for other main target lesions. In studies where such results were reported, actinic keratoses seemed to be readily identifiable for teledermatologists and adequate management and treatment can be suggested within remote consultations.

The clinical reasoning characteristics of diagnostic experts

The aim of this study was to identify and describe the clinical reasoning characteristics of diagnostic experts. A group of 21 experienced general practitioners were asked to complete the Diagnostic Thinking Inventory (DTI) and a set of 10 clinical reasoning problems (CRPs) to evaluate their clinical reasoning. Both the DTI and the CRPs were scored, and the CRP response patterns of each GP examined in terms of the number and type of errors contained in them. Analysis of these data showed that six GPs were able to reach the correct diagnosis using significantly less clinical information than their colleagues. These GPs also made significantly fewer interpretation errors but scored lower on both the DTI and the CRPs. Additionally, this analysis showed that more than 20% of misdiagnoses occurred despite no errors being made in the identification and interpretation of relevant clinical information. These results indicate that these six GPs diagnose efficiently, effectively and accurately using relatively few clinical data and can therefore be classified as diagnostic experts. They also indicate that a major cause of misdiagnoses is failure to properly integrate clinical data. We suggest that increased emphasis on this step in the reasoning process should prove beneficial to the development of clinical reasoning skill in undergraduate medical students.

Assessing clinical reasoning: A method to monitor its development in a PBL curriculum

The aim of this study was to develop and trial a method to monitor the evolution of clinical reasoning in a PBL curriculum that is suitable for use in a large medical school. Termed Clinical Reasoning Problems (CRPs), it is based on the notion that clinical reasoning is dependent on the identification and correct interpretation of certain critical clinical features. Each problem consists of a clinical scenario comprising presentation, history and physical examination. Based on this information, subjects are asked to nominate the two most likely diagnoses and to list the clinical features that they considered in formulating their diagnoses, indicating whether these features supported or opposed the nominated diagnoses. Students at different levels of medical training completed a set of 10 CRPs as well as the Diagnostic Thinking Inventory, a self-reporting questionnaire designed to assess reasoning style. Responses were scored against those of a reference group of general practitioners. Results indicate that the CRPs are an easily administered, reliable and valid assessment of clinical reasoning, able to successfully monitor its development throughout medical training. Consequently, they can be employed to assess clinical reasoning skill in individual students and to evaluate the success of undergraduate medical schools in providing effective tuition in clinical reasoning.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.

Forming Networks of Strategic Agents with Desired Topologies

Many networks such as social networks and organizational networks in global companies consist of self-interested agents. The topology of these networks often plays a crucial role in important tasks such as information diffusion and information extraction. Consequently, growing a stable network having a certain topology is of interest. Motivated by this, we study the following important problem: given a certain desired network topology, under what conditions would best response (link addition/deletion) strategies played by self-interested agents lead to formation of a stable network having that topology. We study this interesting reverse engineering problem by proposing a natural model of recursive network formation and a utility model that captures many key features. Based on this model, we analyze relevant network topologies and derive a set of sufficient conditions under which these topologies emerge as pairwise stable networks, wherein no node wants to delete any of its links and no two nodes would want to create a link between them.

Design, construction and evaluation of systems to predict risk in obstetrics.

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

Protocol for the "Implementation, adoption, and utility of family history in diverse care settings" study.

BACKGROUND: Risk assessment with a thorough family health history is recommended by numerous organizations and is now a required component of the annual physical for Medicare beneficiaries under the Affordable Care Act. However, there are several barriers to incorporating robust risk assessments into routine care. MeTree, a web-based patient-facing health risk assessment tool, was developed with the aim of overcoming these barriers. In order to better understand what factors will be instrumental for broader adoption of risk assessment programs like MeTree in clinical settings, we obtained funding to perform a type III hybrid implementation-effectiveness study in primary care clinics at five diverse healthcare systems. Here, we describe the study's protocol. METHODS/DESIGN: MeTree collects personal medical information and a three-generation family health history from patients on 98 conditions. Using algorithms built entirely from current clinical guidelines, it provides clinical decision support to providers and patients on 30 conditions. All adult patients with an upcoming well-visit appointment at one of the 20 intervention clinics are eligible to participate. Patient-oriented risk reports are provided in real time. Provider-oriented risk reports are uploaded to the electronic medical record for review at the time of the appointment. Implementation outcomes are enrollment rate of clinics, providers, and patients (enrolled vs approached) and their representativeness compared to the underlying population. Primary effectiveness outcomes are the percent of participants newly identified as being at increased risk for one of the clinical decision support conditions and the percent with appropriate risk-based screening. Secondary outcomes include percent change in those meeting goals for a healthy lifestyle (diet, exercise, and smoking). Outcomes are measured through electronic medical record data abstraction, patient surveys, and surveys/qualitative interviews of clinical staff. DISCUSSION: This study evaluates factors that are critical to successful implementation of a web-based risk assessment tool into routine clinical care in a variety of healthcare settings. The result will identify resource needs and potential barriers and solutions to implementation in each setting as well as an understanding potential effectiveness. TRIAL REGISTRATION: NCT01956773.

Ascertainment of cause-specific mortality in COPD - operations of the TORCH clinical endpoint Committee

Background: TORCH (Towards a Revolution in COPD Health) is an international multicentre, randomised, placebo-controlled clinical trial of inhaled fluticasone propionate/salmeterol combination treatment and its monotherapy components for maintenance treatment of moderately to severely impaired patients with chronic obstructive pulmonary disease (COPD). The primary outcome is all-cause mortality. Cause-specific mortality and deaths related to COPD are additional outcome measures, but systematic methods for ascertainment of these outcomes have not previously been described. Methods: A Clinical Endpoint Committee (CEC) was tasked with categorising the cause of death and the relationship of deaths to COPD in a systematic, unbiased and independent manner. The key elements of the operation of the committee were the use of predefined principles of operation and definitions of cause of death and COPD-relatedness; the independent review of cases by all members with development of a consensus opinion; and a substantial infrastructure to collect medical information. Results: 911 deaths were reviewed and consensus was reached in all. Cause-specific mortality was: cardiovascular 27%, respiratory 35%, cancer 21%, other 10% and unknown 8%. 40% of deaths were definitely or probably related to COPD. Adjudications were identical in 83% of blindly re-adjudicated cases ( = 0.80). COPD-relatedness was reproduced 84% of the time ( = 0.73). The CEC adjudication was equivalent to the primary cause of death recorded by the site investigator in 52% of cases. Conclusion: A CEC can provide standardised, reliable and informative adjudication of COPD mortality that provides information which frequently differs from data collected from assessment by site investigators.

Exposure to oral bisphosphonates and risk of esophageal cancer

Context: Use of oral bisphosphonates has increased dramatically in the United States and elsewhere. Esophagitis is a known adverse effect of bisphosphonate use, and recent reports suggest a link between bisphosphonate use and esophageal cancer, but this has not been robustly investigated.
Objective: To investigate the association between bisphosphonate use and esophageal cancer.
Design, Setting, and Participants: Data were extracted from the UK General Practice Research Database to compare the incidence of esophageal and gastric cancer in a cohort of patients treated with oral bisphosphonates between January 1996 and December 2006 with incidence in a control cohort. Cancers were identified from relevant Read/Oxford Medical Information System codes in the patient's clinical files. Cox proportional hazards modeling was used to calculate hazard ratios and 95% confidence intervals for risk of esophageal and gastric cancer in bisphosphonate users compared with nonusers, with adjustment for potential confounders.
Main Outcome Measure: Hazard ratio for the risk of esophageal and gastric cancer in the bisphosphonate users compared with the bisphosphonate nonusers. Results: Mean follow-up time was 4.5 and 4.4 years in the bisphosphonate and control cohorts, respectively. Excluding patients with less than 6 months' follow-up, there were 41 826 members in each cohort (81% women; mean age, 70.0 (SD, 11.4) years). One hundred sixteen esophageal or gastric cancers (79 esophageal) occurred in the bisphosphonate cohort and 115 (72 esophageal) in the control cohort. The incidence of esophageal and gastric cancer combined was 0.7 per 1000 person-years of risk in both the bisphosphonate and control cohorts; the incidence of esophageal cancer alone in the bisphosphonate and control cohorts was 0.48 and 0.44 per 1000 person-years of risk, respectively. There was no difference in risk of esophageal and gastric cancer combined between the cohorts for any bisphosphonate use (adjusted hazard ratio, 0.96 [95% confidence interval, 0.74-1.25]) or risk of esophageal cancer only (adjusted hazard ratio, 1.07 [95% confidence interval, 0.77-1.49]). There also was no difference in risk of esophageal or gastric cancer by duration of bisphosphonate intake.
Conclusion: Among patients in the UK General Practice Research Database, the use of oral bisphosphonates was not significantly associated with incident esophageal or gastric cancer.

USE OF RETRODIRECTIVE ARRAY PHASE CONJUGATION CIRCUITRY FOR NEAR- AND FAR-FIELD POSITION ANGLE OF ARRIVAL DETECTION

In this article, we present position indication functionality as obtained by using a retrodirective array, thereby allowing location information extraction of the position of the remote transmitter with which the retrodirective array is cooperating. This is carried out using straightforward circuitry with no requirement for complex angle of arrival algorithms, thereby giving a result in real time enabling tracking of fast moving transmitters. We show using a 10 x element retrodirective array, operating at 2.4 GHz that accuracies of far-field angle of arrival of within +/- 1 degrees over the arrays +/- 30 degrees azimuth field of view are possible. While in the near-field for angles of arrival of +/- 10 degrees it is possible to extract the position of a dipole source down to a resolution of 032 lambda. (C) 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1031-1034, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.25097

Integrating textual analysis and evidential reasoning for decision making in Engineering design

Decision making is an important element throughout the life-cycle of large-scale projects. Decisions are critical as they have a direct impact upon the success/outcome of a project and are affected by many factors including the certainty and precision of information. In this paper we present an evidential reasoning framework which applies Dempster-Shafer Theory and its variant Dezert-Smarandache Theory to aid decision makers in making decisions where the knowledge available may be imprecise, conflicting and uncertain. This conceptual framework is novel as natural language based information extraction techniques are utilized in the extraction and estimation of beliefs from diverse textual information sources, rather than assuming these estimations as already given. Furthermore we describe an algorithm to define a set of maximal consistent subsets before fusion occurs in the reasoning framework. This is important as inconsistencies between subsets may produce results which are incorrect/adverse in the decision making process. The proposed framework can be applied to problems involving material selection and a Use Case based in the Engineering domain is presented to illustrate the approach. © 2013 Elsevier B.V. All rights reserved.

Preferences for a third-trimester ultrasound scan in a low-risk obstetric population: a discrete choice experiment

Objective: Establish maternal preferences for a third-trimester ultrasound scan in a healthy, low-risk pregnant population.

Design: Cross-sectional study incorporating a discrete choice experiment.

Setting: A large, urban maternity hospital in Northern Ireland.

Participants: One hundred and forty-six women in their second trimester of pregnancy.

Methods: A discrete choice experiment was designed to elicit preferences for four attributes of a third-trimester ultrasound scan: health-care professional conducting the scan, detection rate for abnormal foetal growth, provision of non-medical information, cost. Additional data collected included age, marital status, socio-economic status, obstetric history, pregnancy-specific stress levels, perceived health and whether pregnancy was planned. Analysis was undertaken using a mixed logit model with interaction effects.

Main outcome measures: Women's preferences for, and trade-offs between, the attributes of a hypothetical scan and indirect willingness-to-pay estimates.

Results: Women had significant positive preference for higher rate of detection, lower cost and provision of non-medical information, with no significant value placed on scan operator. Interaction effects revealed subgroups that valued the scan most: women experiencing their first pregnancy, women reporting higher levels of stress, an adverse obstetric history and older women.

Conclusions: Women were able to trade on aspects of care and place relative importance on clinical, non-clinical outcomes and processes of service delivery, thus highlighting the potential of using health utilities in the development of services from a clinical, economic and social perspective. Specifically, maternal preferences exhibited provide valuable information for designing a randomized trial of effectiveness and insight for clinical and policy decision makers to inform woman-centred care.

Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.