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Geographically-based screening policies for diabetic retinopathy (DR) can be effective in developing teleretinal imaging (TRI) guidelines while identifying patients with limited geographic access to eye care. This study conducts cost-effectiveness analysis of different screening policies for urban and rural diabetic patients in Western Pennsylvania. A Monte Carlo simulation model was used to evaluate the cost-effectiveness of 2 standardized screening policies (annual clinic-based screening (ACS) and annual TRI-based screening (ATRI)) and a personalized TRI-based screening policy (PTRI) for both urban and rural cohorts. PTRI was generated by a previously developed mathematical model that autonomously makes semi-annual screening recommendations based on each patient’s disease progression and compliance (Dorali et al. IOVS 2022; 63(7)). For each policy, hypothetical urban and rural cohorts of 50,000 patients were simulated and lifetime QALYs and costs were collected for each patient. TRI compliance rates were derived from electronic medical records. Compliance with clinic-based screening was selected from literature-based values (12-45% for rural patients and 50-65% for urban patients). For a base case urban cohort with an A1C level of 7% and entering age of 40, costs per QALY gain (CPQ) for ACS, ATRI, and PTRI were $744.93±1.57, $792.38±1.64, and $714.60±1.56, respectively; PTRI produced more cost saving than ACS with the same QALY gain (See Fig 1). For a base case rural cohort, CPQ for ACS, ATRI, and PTRI were $869.15±1.80, $819.24±1.88, and $761.51±1.42, respectively; both ATRI and PTRI dominated ACS in QALY gains and cost saving (Fig 1). PTRI recommended TRI more to rural patients (94.13±0.01%) than to urban patients (87.20±0.02%). For the rural cohort, the minimum average TRI compliance rate such that ATRI is more cost-effective than ACS was 56% (Fig 2). TRI-based screening was found more beneficial for rural patients. PTRI was found dominant in QALY gain and cost saving for both urban and rural cohorts against standardized policies. These findings suggest that TRI is best utilized when location-specific factors such as geographic access to care or TRI compliance are considered. 

The atmospheric Green's function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the “pattern effect.” To better study this method, this paper presents a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere‐only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming versus cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation.

 

Abstract Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves. Through discussions with multiple stakeholders at various institutions, we have generated a set of guidelines with 10 key principles to guide the responsible and appropriate use and sharing of clinical data for the purposes of care and discovery. Industry, universities, and healthcare institutions can build upon these guidelines toward creating a responsible, ethical, and practical response to data sharing. 

The ever increasing amount of personal data accumulated by companies offering innovative services through the cloud, Internet of Things devices and, more recently, social robots has started to alert consumers and legislative authorities. In the advent of the first modern laws trying to protect user privacy, such as the European Union General Data Protection Regulation, it is still unclear what are the tools and techniques that the industry should employ to comply with regulations in a transparent and cost effective manner. We propose an architecture for a public blockchain based ledger that can provide strong evidence of policy compliance. To address scalability concerns, we define a new type of off-chain channel that is based on general state channels and offers verification for information external to the blockchain. We also create a model of the business relationships in a smart home setup that includes a social robot and suggest a sticky policy mechanism to monitor cross-boundary policy compliance. 

As artificially intelligent humanoids become increasingly prevalent in the home, it is imperative that we develop secure designs to guard against cyberattacks. The next evolution of AI-powered home devices, such as Alexa, is to create physical effectors to enable these devices to alter their environments. Current humanoids on the market, such as the EZ-Robot JD and NAO, are examples of artificially intelligent robots that may one day become common in home environments. If these humanoids are not designed to be safe against cybersecurity vulnerabilities, they may be used to cause harm to living spaces and possibly even the humans living in these spaces. This paper examines the cybersecurity of two humanoid robots and provides recommendations for future safe designs and protections in artificial intelligent social robots. 

Abstract We present a calibration component for the Murchison Widefield Array All-Sky Virtual Observatory (MWA ASVO) utilising a newly developed PostgreSQL database of calibration solutions. Since its inauguration in 2013, the MWA has recorded over 34 petabytes of data archived at the Pawsey Supercomputing Centre. According to the MWA Data Access policy, data become publicly available 18 months after collection. Therefore, most of the archival data are now available to the public. Access to public data was provided in 2017 via the MWA ASVO interface, which allowed researchers worldwide to download MWA uncalibrated data in standard radio astronomy data formats (CASA measurement sets or UV FITS files). The addition of the MWA ASVO calibration feature opens a new, powerful avenue for researchers without a detailed knowledge of the MWA telescope and data processing to download calibrated visibility data and create images using standard radio astronomy software packages. In order to populate the database with calibration solutions from the last 6 yr we developed fully automated pipelines. A near-real-time pipeline has been used to process new calibration observations as soon as they are collected and upload calibration solutions to the database, which enables monitoring of the interferometric performance of the telescope. Based on this database, we present an analysis of the stability of the MWA calibration solutions over long time intervals.