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An important problem in the field of force-reflecting systems and telerobotics is poor rendering of contact, particularly of contact with stiff surfaces. There are numerous possible sources of poor performance, including poor contact models, sampling errors, and delays due to computation or data transmission. In this paper we examine effects due to sample-and-hold, which is a fundamental property of both the discrete domain and also of the sensors and power amplifiers used in a force-reflecting system. We propose sample-and-hold be generalized to sample-estimate-hold. We show why ordinary sample-and-hold generates an active contact interface, and provide ways of improving the feeling of the interface. We have developed a suite of numerical methods for improving the performance of rendering of surfaces by force reflection. We have conducted both simulations and experiments to demonstrate the efficacy of the proposed scheme. Our contributions are a new method of digitally processing force data, and a systematic method for coupling force-processing systems that run at different rates.

Summary: Background: Melanoma risk prediction models could be useful for matching preventive interventions to patients' risk. Objectives: To develop and validate a model for incident first‐primary cutaneous melanoma using clinically assessed risk factors. Methods: We used unconditional logistic regression with backward selection from the Australian Melanoma Family Study (461 cases and 329 controls) in which age, sex and city of recruitment were kept in each step, and we externally validated it using the Leeds Melanoma Case–Control Study (960 cases and 513 controls). Candidate predictors included clinically assessed whole‐body naevi and solar lentigines, and self‐assessed pigmentation phenotype, sun exposure, family history and history of keratinocyte cancer. We evaluated the predictive strength and discrimination of the model risk factors using odds per age‐ and sex‐adjusted SD (OPERA) and the area under curve (AUC), and calibration using the Hosmer–Lemeshow test. Results: The final model included the number of naevi ≥ 2 mm in diameter on the whole body, solar lentigines on the upper back (a six‐level scale), hair colour at age 18 years and personal history of keratinocyte cancer. Naevi was the strongest risk factor; the OPERA was 3·51 [95% confidence interval (CI) 2·71–4·54] in the Australian study and 2·56 (95% CI 2·23–2·95) in the Leeds study. The AUC was 0·79 (95% CI 0·76–0·83) in the Australian study and 0·73 (95% CI 0·70–0·75) in the Leeds study. The Hosmer–Lemeshow test P‐value was 0·30 in the Australian study and < 0·001 in the Leeds study. Conclusions: This model had good discrimination and could be used by clinicians to stratify patients by melanoma risk for the targeting of preventive interventions. What's already known about this topic? Melanoma risk prediction models may be useful in prevention by tailoring interventions to personalized risk levels.For reasons of feasibility, time and cost many melanoma prediction models use self‐assessed risk factors. However, individuals tend to underestimate their naevus numbers. What does this study add? We present a melanoma risk prediction model, which includes clinically‐assessed whole‐body naevi and solar lentigines, and self‐assessed risk factors including pigmentation phenotype and history of keratinocyte cancer.This model performs well on discrimination, the model's ability to distinguish between individuals with and without melanoma, and may assist clinicians to stratify patients by melanoma risk for targeted preventive interventions. Linked Comment: Toland. Br J Dermatol 2020; 182:1089–1090. Respond to this article Plain language summary available online [ABSTRACT FROM AUTHOR]

Background: People at high risk of developing melanoma are usually identified by pigmentary and naevus phenotypes. Objective: We examined whether associations of these phenotypes with melanoma risk differed by ambient sun exposure or participant characteristics in two population‐based, case–control studies with comparable ancestry but different ambient sun exposure. Methods: Data were analysed from 616 cases and 496 controls from the Australian Melanoma Family Study and 2012 cases and 504 controls from the Leeds (UK) case–control study. Questionnaire, interview and dermatological skin examination data were collected using the same measurement protocols. Relative risks were estimated as odds ratios using unconditional logistic regression, adjusted for potential confounders. Results: Hair and skin colour were the strongest pigmentary phenotype risk factors. All associations of pigmentary phenotype with melanoma risk were similar across countries. The median number of clinically assessed naevi was approximately three times higher in Australia than Leeds, but the relative risks for melanoma associated with each additional common or dysplastic naevus were higher for Leeds than Australia, especially for naevi on the upper and lower limbs. Higher naevus counts on the head and neck were associated with a stronger relative risk for melanoma for women than men. The two countries had similar relative risks for melanoma based on self‐reported naevus density categories, but personal perceptions of naevus number differed by country. There was no consistent evidence of interactions between phenotypes on risk. Conclusions: Classifying people at high risk of melanoma based on their number of naevi should ideally take into account their country of residence, type of counts (clinical or self‐reported), body site on which the naevus counts are measured and sex. The presence of naevi may be a stronger indicator of a genetic predisposition in the UK than in Australia based on less opportunity for sun exposure to influence naevus development. [ABSTRACT FROM AUTHOR]

Abstract: In this study we test the feasibility of using a coupled atmosphere–fire model for real time simulations of massive fires. A physics-based coupled atmosphere–fire model is used to resolve the large-scale and local weather as well as the atmosphere–fire interactions, while combustion is represented simply using an existing operational surface fire behavior model. This model combination strikes a balance between fidelity and speed of execution. The feasibility of this approach is examined based on an analysis of a numerical simulation of two very large Santa Ana fires using WRF–Sfire, a coupled atmosphere–fire model developed by the Open Wild Fire Modeling Community (OpenWFM.org). The study demonstrates that a wind and fire spread forecast of reasonable accuracy was obtained at an execution speed that would have made real-time wildfire forecasting of this event possible. [Copyright &y& Elsevier]

The ATM gene variants segregating in ataxia-telangiectasia families are associated with increased breast cancer risk, but the contribution of specific variants has been difficult to estimate. Previous small studies suggested two functional variants, c.7271T>G and c.1066-6T>G (IVS10-6T>G), are associated with increased risk. Using population-based blood samples we found that 7 out of 3,743 breast cancer cases (0.2%) and 0 out of 1,268 controls were heterozygous for the c.7271T>G allele (P=0.1). In cases, this allele was more prevalent in women with an affected mother (odds ratio [OR]=5.5, 95% confidence interval [CI]=1.2-25.5; P=0.04) and delayed child-bearing (OR=5.1; 95% CI=1.0-25.6; P=0.05). The estimated cumulative breast cancer risk to age 70 years (penetrance) was 52% (95% CI=28-80%; hazard ratio [HR]=8.6; 95% CI=3.9-18.9; P<0.0001). In contrast, 13 of 3,757 breast cancer cases (0.3%) and 10 of 1,268 controls (0.8%) were heterozygous for the c.1066-6T>G allele (OR=0.4; 95% CI=0.2-1.0; P=0.05), and the penetrance was not increased (P=0.5). These findings suggest that although the more common c.1066-6T>G variant is not associated with breast cancer, the rare ATM c.7271T>G variant is associated with a substantially elevated risk. Since c.7271T>G is only one of many rare ATM variants predicted to have deleterious consequences on protein function, an effective means of identifying and grouping these variants is essential to assess the contribution of ATM variants to individual risk and to the incidence of breast cancer in the population. Hum Mutat 27(11), 1122-1128, 2006. Published 2006 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Summary: 黑色素瘤是一种在黑素细胞(皮肤的色素细胞)中发生的癌症。黑色素瘤发生率(表示其常见程度)在皮肤白皙人群中增加,其中澳大利亚、新西兰、北美和欧洲的发病率最高。 在澳大利亚,14 名男性中有 1 名和 24 名女性中有 1 名将在其一生中被诊断患有黑色素瘤。黑色素瘤的危险因素包括日晒、日光浴、皮肤上痣的数量、皮肤对阳光的敏感度、雀斑数量、肤色、眼睛颜色、头发颜色、家族史和许多易感基因(使您面临更高风险的基因)。 黑色素瘤风险预测模型(将许多个体黑色素瘤危险因素结合到总体风险中)可能有助于通过匹配预防策略(干预)与患者的黑色素瘤风险水平来预防黑色素瘤。 许多黑色素瘤风险预测模型使用自我评估的危险因素,因为与临床(医学)危险因素评估相比,这样更快且成本更低,但很多人低估了他们的痣数。本研究旨在推导一个黑色素瘤风险预测模型,其中包括身上痣的数量和日光性雀斑痣(阳光引起的深色皮肤斑)作为临床评估的危险因素,以及色素(肤色)特征、日晒、家族史和皮肤癌病史作为自我评估的危险因素。 痣的数量是模型中最强的危险因素。该模型在识别能力(模型区分患有和未患黑色素瘤的个体的能力)方面表现出色。在临床环境中,该模型可能有助于临床医生根据患者的黑色素瘤风险确定适合针对性预防策略的患者。 本摘要涉及研究:一项关于纳入临床评估的痣和日光性雀斑痣的黑色素瘤风险预测模型的开发和外部验证研究 Linked Article: Vuong et al. Br J Dermatol 2020; 182:1262–1268 [ABSTRACT FROM AUTHOR]

Summary: Melanoma is a cancer that develops in the melanocytes, the pigment cells of the skin. Melanoma incidence rates, which show how common it is, have been increasing in people with fair skin, with the highest rates in Australia, New Zealand, North America and Europe. In Australia, one in 14 men and one in 24 women will be diagnosed with melanoma during their lifetime. Risk factors for melanoma include sun exposure, sunbed use, the number of moles on the skin, the skin's sensitivity to the sun, the number of freckles, skin colour, eye colour, hair colour, family history and a number of susceptibility genes (genes that place you at higher risk). Melanoma risk prediction models, which combine many individual melanoma risk factors into an overall risk, may be useful in the prevention of melanoma by matching prevention strategies (interventions) to the patient's melanoma risk levels. Many melanoma risk prediction models use self‐assessed risk factors because it is quicker and less costly compared to the clinical (medical) assessment of risk factors, but many people underestimate the number of moles they have. This study aims to derive a melanoma risk prediction model, which includes the number of moles on the body and solar lentigines (darker skin patches caused by the sun) as clinically‐assessed risk factors, and pigmentation (skin colour) characteristics, sun exposure, family history and history of skin cancer as self‐assessed risk factors. The number of moles is the strongest risk factor in the model. The model performs well on discrimination, the model's ability to distinguish between individuals with and without melanoma. In a clinical setting, this model may help clinicians identify patients for targeted prevention strategies based on the patient's melanoma risk. This is a summary of the study: Development and external validation study of a melanoma risk prediction model incorporating clinically assessed naevi and solar lentigines Linked Article: Vuong et al. Br J Dermatol 2020; 182:1262–1268 [ABSTRACT FROM AUTHOR]

In light of the "false or misleading" standard resulting from the recent legal ruling, it can be concluded that a true claim is one that is both factually and analytically true. Factual truth could be based on the accuracy of the information and the sufficiency of the information. Analytical truth could be based on the scientific foundation for the claim and whether the information within the claim is presented in a balanced way. Regarding the assessment of whether a truthful claim is misleading, the evaluator could consider the relevance, consistency, and context of the information. Standards are important in medication use and medication regulation. Health care professionals who must decide whether a claim is truthful and not misleading will rely on guidance from FDA in determining how to evaluate promotional claims. As the court suggested in the case reviewed here, FDA could take the lead and provide guidance "in differentiating between misleading and false promotion, exaggerations and embellishments, and truthful or non-misleading information." Existing FDA regulations provide a foundation for such guidance. The next step for the agency would be to expand existing guidance to specifically describe how an off-label claim can be identified as either false or misleading.