Merck KGAA v. Integra Lifesciences I, Ltd.: Greater Research Protection for Drug Manufacturers

Merck sought protection under a statutory exemption from claims of patent infringement brought by Integra Lifesciences. The Court held unanimously that the safe harbor contained in 35 U.S.C. § 271(e)(1) protected the use of patented inventions used in preclinical research where the results were not submitted to the FDA. The Court\u27s interpretation of the safe harbor provision broadened protection for those engaged in drug research at a substantial cost to patent-holders

Making friends on the fly : advances in ad hoc teamwork

textGiven the continuing improvements in design and manufacturing processes in addition to improvements in artificial intelligence, robots are being deployed in an increasing variety of environments for longer periods of time. As the number of robots grows, it is expected that they will encounter and interact with other robots. Additionally, the number of companies and research laboratories producing these robots is increasing, leading to the situation where these robots may not share a common communication or coordination protocol. While standards for coordination and communication may be created, we expect that any standards will lag behind the state-of-the-art protocols and robots will need to additionally reason intelligently about their teammates with limited information. This problem motivates the area of ad hoc teamwork in which an agent may potentially cooperate with a variety of teammates in order to achieve a shared goal. We argue that agents that effectively reason about ad hoc teamwork need to exhibit three capabilities: 1) robustness to teammate variety, 2) robustness to diverse tasks, and 3) fast adaptation. This thesis focuses on addressing all three of these challenges. In particular, this thesis introduces algorithms for quickly adapting to unknown teammates that enable agents to react to new teammates without extensive observations. The majority of existing multiagent algorithms focus on scenarios where all agents share coordination and communication protocols. While previous research on ad hoc teamwork considers some of these three challenges, this thesis introduces a new algorithm, PLASTIC, that is the first to address all three challenges in a single algorithm. PLASTIC adapts quickly to unknown teammates by reusing knowledge it learns about previous teammates and exploiting any expert knowledge available. Given this knowledge, PLASTIC selects which previous teammates are most similar to the current ones online and uses this information to adapt to their behaviors. This thesis introduces two instantiations of PLASTIC. The first is a model-based approach, PLASTIC-Model, that builds models of previous teammates' behaviors and plans online to determine the best course of action. The second uses a policy-based approach, PLASTIC-Policy, in which it learns policies for cooperating with past teammates and selects from among these policies online. Furthermore, we introduce a new transfer learning algorithm, TwoStageTransfer, that allows transferring knowledge from many past teammates while considering how similar each teammate is to the current ones. We theoretically analyze the computational tractability of PLASTIC-Model in a number of scenarios with unknown teammates. Additionally, we empirically evaluate PLASTIC in three domains that cover a spread of possible settings. Our evaluations show that PLASTIC can learn to communicate with unknown teammates using a limited set of messages, coordinate with externally-created teammates that do not reason about ad hoc teams, and act intelligently in domains with continuous states and actions. Furthermore, these evaluations show that TwoStageTransfer outperforms existing transfer learning algorithms and enables PLASTIC to adapt even better to new teammates. We also identify three dimensions that we argue best describe ad hoc teamwork scenarios. We hypothesize that these dimensions are useful for analyzing similarities among domains and determining which can be tackled by similar algorithms in addition to identifying avenues for future research. The work presented in this thesis represents an important step towards enabling agents to adapt to unknown teammates in the real world. PLASTIC significantly broadens the robustness of robots to their teammates and allows them to quickly adapt to new teammates by reusing previously learned knowledge.Computer Science

Pharmacoeconomic impact of patient-centric oncology service model

Kasey Rubin PharmD. Samuel Jacobson PharmD. BCOP, Shannon Buxell PharmD. Providence Health & Services, Portland Oregon Pharmacoeconomic impact of patient-centric oncology service model Providence Health & Services recently embedded ambulatory oncology pharmacists into the ambulatory oncology clinic setting. The overall purpose of this retrospective study is to quantify the financial benefits of ambulatory oncology pharmacists as well as quantify patient-centric factors through utilization of an onsite specialty pharmacy. This study was submitted and approved by Institutional Review Board. Electronic health records of patients who visited the ambulatory oncology clinic will be retrospectively reviewed over two time periods: 12 months preceding integration of a pharmacist and 12 months post integration of a pharmacist on the ambulatory oncology team at Providence Portland Medical Center (PPMC). Each group will consist of 250 patients. The following patient data will be collected: Patient MRN, Patient visit encounter (if multiple visits), sex, ICD10 code for diagnosis, number of prescriptions/dispense report, cost of medication, financial revenue, nausea scores, pain scores, and number of Interventions completed by pharmacists. These endpoints will be assessed and an estimated dollar amount will be attached to each non-monetary service provided by pharmacists. Epic projects an equivalent dollar amount per each intervention completed by a pharmacists. This projected dollar amount will be utilized to quantify the benefits of a pharmacist and will be considered as cost-savings. Number of prescriptions/dispense report and cost data will be provided by Providence specialty pharmacy, Credena. Credena utilizes Epic and CPR+. The primary investigator will not access to CPR+ and will defer to secondary investigators to collect prescription data. Preliminary pharmacoeconomic data will be presented. Clinical results from the addition of the pharmacists show an initial review of the oral antineoplastic in 70% and 86% of patients in sites #1 and #2, respectively. Follow-up calls were made to 55% and 85% of patients in sites #1 and #2, respectively. Patients getting their prescriptions through Credena are continually counseled by Credena specialty pharmacists. Pharmacists reviewing medications reviewed drug-drug interactions, with the true number of interventions not able to be calculated. In conclusion, pharmacists have added both financial and patient centered benefits after integration with the care team.https://digitalcommons.psjhealth.org/pharmacy_PGY1/1008/thumbnail.jp

Energy Efficiency, Business Competitiveness, and Untapped Potential in Maine

This report discusses the importance of energy efficiency to Maine businesses, describes the current energy situation in Maine, and examines both the potential benefits of energy efficiency and barriers and constraints