Welfare epidemiology as a tool to assess the welfare impact of inherited defects on the pedigree dog population

The effect that breed standards and selective breeding practices have on the welfare of pedigree dogs has recently come under scrutiny from both the general public and scientific community. Recent research has suggested that breeding for particular aesthetic traits, such as tightly curled tails, highly domed skulls and short muzzles predisposes dogs with these traits to certain inherited defects, such as spina bifida, syringomyelia and brachycephalic airway obstruction syndrome, respectively. Further to this, there is a very large number of inherited diseases that are not related to breed standards, which are thought to be prevalent, partly as a consequence of inbreeding and restricted breeding pools. Inherited diseases, whether linked to conformation or not, have varying impact on the individuals affected by them, and affect varying proportions of the pedigree dog population. Some diseases affect few breeds but are highly prevalent in predisposed breeds. Other diseases affect many breeds, but have low prevalence within each breed. In this paper, we discuss the use of risk analysis and severity diagrams as means of mapping the overall problem of inherited disorders in pedigree dogs and, more specifically, the welfare impact of specific diseases in particular breeds.

Risk-informed decision-making in the presence of epistemic uncertainty

An important issue in risk analysis is the distinction between epistemic and aleatory uncertainties. In this paper, the use of distinct representation formats for aleatory and epistemic uncertainties is advocated, the latter being modelled by sets of possible values. Modern uncertainty theories based on convex sets of probabilities are known to be instrumental for hybrid representations where aleatory and epistemic components of uncertainty remain distinct. Simple uncertainty representation techniques based on fuzzy intervals and p-boxes are used in practice. This paper outlines a risk analysis methodology from elicitation of knowledge about parameters to decision. It proposes an elicitation methodology where the chosen representation format depends on the nature and the amount of available information. Uncertainty propagation methods then blend Monte Carlo simulation and interval analysis techniques. Nevertheless, results provided by these techniques, often in terms of probability intervals, may be too complex to interpret for a decision-maker and we, therefore, propose to compute a unique indicator of the likelihood of risk, called confidence index. It explicitly accounts for the decisionmaker’s attitude in the face of ambiguity. This step takes place at the end of the risk analysis process, when no further collection of evidence is possible that might reduce the ambiguity due to epistemic uncertainty. This last feature stands in contrast with the Bayesian methodology, where epistemic uncertainties on input parameters are modelled by single subjective probabilities at the beginning of the risk analysis process.

Offenders' Risk-Taking Attitude Inside and Outside the Prison Walls

It has long been assumed that risk taking is closely associated with criminal behavior. One reason for placing criminals behind bars-aside from punishment and protecting the public-is to prevent them from engaging in further risky criminal activities. Limited attention has been paid to whether being inside or outside prison affects offenders' risk-taking behaviors and attitudes. We compared risk-taking behaviors and attitudes in five risk domains (ethical, financial, health/safety, recreational, social) among 75 incarcerated offenders (i.e., offenders who are currently in prison) and 45 ex-offenders (i.e., offenders who have just been released from prison). Ex-offenders reported higher likelihood of engaging in risky behavior, driven largely by a willingness to take more risks in the recreational and ethical domains. Benefits attributed to risk taking as well as risk perception did not differ between incarcerated and ex-offenders, indicating that the opportunity to take risks might underlie behavioral risk intentions. Our results also indicate that risk-taking activities are better predicted by the expected benefits rather than by risk perception, aside from the health/safety domain. These results highlight the importance of studying the person and the environment and examining risk taking in a number of content domains.

Child Maltreatment and High Risk Families: Protecting children and young people

Most child maltreatment occurs within the context of high risk families. There are ethical, economic and ecological reasons why physical abuse in such families should be a major concern. Physical abuse is a significant issue throughout the UK. Yet, while neglect and other forms of abuse are receiving focused attention, physical abuse may languish under the misconceptions that it is no longer a problem, is addressed elsewhere, or is just too overwhelming an issue.
The physical abuse of children can involve regular, violent treatment at the hands of parents or carers over a number of years. Its physical effects may last for days and may result in actual physical injury. It is not accidental. Although physical abuse can occur in any family, it is prevalent in particular sectors of society, where families may be vulnerable to a combination of complex risk factors such as domestic abuse, alcohol and drug (mis)use, and mental health issues. These factors are present in 34% of Serious Case Reviews (SCRs).
The authors provide an increased understanding of risk, analysis, impact, learning and the current landscape of service delivery in relation to the physical abuse of children living in high risk families for professional, postgraduate and policy-making audiences.

Sphingosine Kinase 1 Promotes Malignant Progression in Colon Cancer and Independently Predicts Survival of Patients With Colon Cancer by Competing Risk Approach in South Asian Population

OBJECTIVES: Sphingosine kinase 1 (SphK1) phosphorylates the membrane sphingolipid, sphingosine, to sphingosine-1-phosphate (S1P), an oncogenic mediator, which drives tumor cell growth and survival. Although SphK1 has gained increasing prominence as an oncogenic determinant in several cancers, its potential as a therapeutic target in colon cancer remains uncertain. We investigated the clinical relevance of SphK1 expression in colon cancer as well as its inhibitory effects in vitro.

METHODS: SphK1 expression in human colon tumor tissues was determined by immunohistochemistry and its clinicopathological significance was ascertained in 303 colon cancer cases. The effects of SphK1 inhibition on colon cancer cell viability and the phosphoinositide 3-kinase (PI3K)/Akt cell survival pathway were investigated using a SphK1-selective inhibitor-compound 5c (5c). The cytotoxicity of a novel combination using SphK1 inhibition with the chemotherapeutic drug, 5-fluorouracil (5-FU), was also determined.

RESULTS: High SphK1 expression correlated with advanced tumor stages (AJCC classification). Using a competing risk analysis model to take into account disease recurrence, we found that SphK1 is a significant independent predictor for mortality in colon cancer patients. In vitro, the inhibition of SphK1 induced cell death in colon cancer cell lines and attenuated the serum-dependent PI3K/Akt signaling. Inhibition of SphK1 also enhanced the sensitivity of colon cancer cells to 5-FU.

CONCLUSION: Our findings highlight the impact of SphK1 in colon cancer progression and patient survival, and provide evidence supportive of further development in combination strategies that incorporate SphK1 inhibition with current chemotherapeutic agents to improve colon cancer outcomes.

When opportunity matters: Comparing the risk taking attitudes of prisoners and recently released ex-prisoners

Risk-taking tendencies and environmental opportunities to commit crime are two key features in understanding criminal behavior. Upon release from prison, ex-prisoners have a much greater opportunity to engage in risky activity and to commit criminal acts. We hypothesized that ex-prisoners would exhibit greater risk-taking tendencies compared to prisoners who have fewer opportunities to engage in risky activity and who are monitored constantly by prison authorities. Using cumulative prospect theory to compare the risky choices of prisoners and ex-prisoners our study revealed that ex-prisoners who were within 16 weeks of their prison release made riskier choices than prisoners. Our data indicate that previous studies comparing prisoners behind bars with nonoffenders may have underestimated the risk-taking tendencies of offenders. The present findings emphasize the central role played by risk-taking attitudes in criminal offending and highlight a need to examine offenders after release from prison.

The Value of Reducing Cancer Risks at Contaminated Sites: Are More Knowledgeable People Willing to Pay More?

We use conjoint choice questions to investigate people's tastes for cancer risk reductions and income in the context of public programs that would provide for remediation at abandoned industrial contaminated sites. Our survey was self-administered using the computer by persons living in the vicinity of an important contaminated site on the Italian National Priority List. The value of a prevented case of cancer is €2.6 million, but this figure does vary with income, perceived exposure to contaminants, and respondent opinions about priorities that should be pursued by cleanup programs. © 2011 Society for Risk Analysis.

Computing probable maximum loss in catastrophe reinsurance portfolios on multi-core and many-core architectures

In the reinsurance market, the risks natural catastrophes pose to portfolios of properties must be quantified, so that they can be priced, and insurance offered. The analysis of such risks at a portfolio level requires a simulation of up to 800 000 trials with an average of 1000 catastrophic events per trial. This is sufficient to capture risk for a global multi-peril reinsurance portfolio covering a range of perils including earthquake, hurricane, tornado, hail, severe thunderstorm, wind storm, storm surge and riverine flooding, and wildfire. Such simulations are both computation and data intensive, making the application of high-performance computing techniques desirable.

In this paper, we explore the design and implementation of portfolio risk analysis on both multi-core and many-core computing platforms. Given a portfolio of property catastrophe insurance treaties, key risk measures, such as probable maximum loss, are computed by taking both primary and secondary uncertainties into account. Primary uncertainty is associated with whether or not an event occurs in a simulated year, while secondary uncertainty captures the uncertainty in the level of loss due to the use of simplified physical models and limitations in the available data. A combination of fast lookup structures, multi-threading and careful hand tuning of numerical operations is required to achieve good performance. Experimental results are reported for multi-core processors and systems using NVIDIA graphics processing unit and Intel Phi many-core accelerators.

Kernelized structural classification for 3D dogs body parts detection

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.