RIP kinases: key decision makers in cell death and innate immunity

Innate immunity represents the first line of defence against invading pathogens. It consists of an initial inflammatory response that recruits white blood cells to the site of infection in an effort to destroy and eliminate the pathogen. Some pathogens replicate within host cells, and cell death by apoptosis is an important effector mechanism to remove the replication niche for such microbes. However, some microbes have evolved evasive strategies to block apoptosis, and in these cases host cells may employ further countermeasures, including an inflammatory form of cell death know as necroptosis. This review aims to highlight the importance of the RIP kinase family in controlling these various defence strategies. RIP1 is initially discussed as a key component of death receptor signalling and in the context of dictating whether a cell triggers a pathway of pro-inflammatory gene expression or cell death by apoptosis. The molecular and functional interplay of RIP1 and RIP3 is described, especially with respect to mediating necroptosis and as key mediators of inflammation. The function of RIP2, with particular emphasis on its role in NOD signalling, is also explored. Special attention is given to emphasizing the physiological and pathophysiological contexts for these various functions of RIP kinases.

Planning Bill (4): Implementation, Performance and Decision making: Issues of Capacity, Delivery and Quality

This paper examines issues of capacity, delivery and quality in relation to the Planning Bill. It is one of four papers and follows a common format highlighting the key issues arising in the Bill; summarising the findings of the public consultation and the Government’s response; reviewing comparable arrangements in comparable jurisdictions and highlighting potential contentious issues.

Recovery of Sleep or Recovery of Self? A Grounded Theory Study of Residents’ Decision Making Regarding How to Spend Their Nonclinical Postcall Time

Purpose: As resident work hours policies evolve, residents’ off-duty time remains poorly understood. Despite assumptions about how residents should be using their postcall, off-duty time, there is little research on how residents actually use this time and the reasoning underpinning their activities. This study sought to understand residents’ nonclinical postcall activities when they leave the hospital, their decision-making processes, and their perspectives on the relationship between these activities and their well-being or recovery.

Method: The study took place at a Liaison Committee on Medical Education–accredited Canadian medical school from 2012 to 2014. The authors recruited a purposive and convenience sample of postgraduate year 1–5 residents from six surgical and nonsurgical specialties at three hospitals affiliated with the medical school. Using a constructivist grounded theory approach, semistructured interviews were conducted, audio-taped, transcribed, anonymized, and combined with field notes. The authors analyzed interview transcripts using constant comparative analysis and performed post hoc member checking.

Results: Twenty-four residents participated. Residents characterized their predominant approach to postcall decision making as one of making trade-offs between multiple, competing, seemingly incompatible, but equally valuable, activities. Participants exhibited two different trade-off orientations: being oriented toward maintaining a normal life or toward mitigating fatigue.

Conclusions: The authors’ findings on residents’ trade-off orientations suggest a dual recovery model with postcall trade-offs motivated by the recovery of sleep or of self. This model challenges the dominant viewpoint in the current duty hours literature and suggests that the duty hours discussion must be broadened to include other recovery processes.

Genomic markers for decision making:What is preventing us from using markers?

The advent of novel genomic technologies that enable the evaluation of genomic alterations on a genome-wide scale has significantly altered the field of genomic marker research in solid tumors. Researchers have moved away from the traditional model of identifying a particular genomic alteration and evaluating the association between this finding and a clinical outcome measure to a new approach involving the identification and measurement of multiple genomic markers simultaneously within clinical studies. This in turn has presented additional challenges in considering the use of genomic markers in oncology, such as clinical study design, reproducibility and interpretation and reporting of results. This Review will explore these challenges, focusing on microarray-based gene-expression profiling, and highlights some common failings in study design that have impacted on the use of putative genomic markers in the clinic. Despite these rapid technological advances there is still a paucity of genomic markers in routine clinical use at present. A rational and focused approach to the evaluation and validation of genomic markers is needed, whereby analytically validated markers are investigated in clinical studies that are adequately powered and have pre-defined patient populations and study endpoints. Furthermore, novel adaptive clinical trial designs, incorporating putative genomic markers into prospective clinical trials, will enable the evaluation of these markers in a rigorous and timely fashion. Such approaches have the potential to facilitate the implementation of such markers into routine clinical practice and consequently enable the rational and tailored use of cancer therapies for individual patients. © 2010 Macmillan Publishers Limited. All rights reserved.

A decision support system for managing irrigation in agriculture

In this paper, an automatic Smart Irrigation Decision Support System, SIDSS, is proposed to manage irrigation in agriculture. Our system estimates the weekly irrigations needs of a plantation, on the basis of both soil measurements and climatic variables gathered by several autonomous nodes deployed in field. This enables a closed loop control scheme to adapt the decision support system to local perturbations and estimation errors. Two machine learning techniques, PLSR and ANFIS, are proposed as reasoning engine of our SIDSS. Our approach is validated on three commercial plantations of citrus trees located in the South-East of Spain. Performance is tested against decisions taken by a human expert.

An analysis of International NGOs design decision making for housing reconstruction in developing country contexts

The purpose of this paper is to explore the current design decision making process of selected foreign international non governmental organisations (INGO’s) operating in the field of housing and post disaster housing design and delivery in developing countries. The study forms part of a wider on-going study relation to a decision making in relation to affordable and sustainable housing in developing
countries. The paper highlights the main challenges and opportunities in relation to the design and delivery of low cost sustainable housing in developing countries as identified in current literature on the subject. Interviews and case studies with INGO’s highlight any specific challenges faced by foreign INGO’s operating in a developing country. The preliminary results of this research study provide a concise insight into the design decision making process of leading foreign INGO’s operating in developing countries and will be beneficial to policy makers, NGOs, government bodies and community organisations in practice as it offers unique evidence based insights into international bodies housing design decision making process.

Multilinear and Integer Programming for Markov Decision Processes with Imprecise Probabilities

Markov Decision Processes (MDPs) are extensively used to encode sequences of decisions with probabilistic effects. Markov Decision Processes with Imprecise Probabilities (MDPIPs) encode sequences of decisions whose effects are modeled using sets of probability distributions. In this paper we examine the computation of Γ-maximin policies for MDPIPs using multilinear and integer programming. We discuss the application of our algorithms to “factored” models and to a recent proposal, Markov Decision Processes with Set-valued Transitions (MDPSTs), that unifies the fields of probabilistic and “nondeterministic” planning in artificial intelligence research.