Regression models for analyzing radiological visual grading studies – an empirical comparison

Background: For optimizing and evaluating image quality in medical imaging, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To analyze the grading data, several regression methods are available, and this study aimed at empirically comparing such techniques, in particular when including random effects in the models, which is appropriate for observers and patients. Methods: Data were taken from a previous study where 6 observers graded or ranked in 40 patients the image quality of four imaging protocols, differing in radiation dose and image reconstruction method. The models tested included linear regression, the proportional odds model for ordinal logistic regression, the partial proportional odds model, the stereotype logistic regression model and rank-order logistic regression (for ranking data). In the first two models, random effects as well as fixed effects could be included; in the remaining three, only fixed effects. Results: In general, the goodness of fit (AIC and McFaddens Pseudo R-2) showed small differences between the models with fixed effects only. For the mixed-effects models, higher AIC and lower Pseudo R-2 was obtained, which may be related to the different number of parameters in these models. The estimated potential for dose reduction by new image reconstruction methods varied only slightly between models. Conclusions: The authors suggest that the most suitable approach may be to use ordinal logistic regression, which can handle ordinal data and random effects appropriately

Machine Learning Models that Remember Too Much

Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data. We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that "memorize" information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data

A Layered Architecture for Detecting Malicious Behaviors

We address the semantic gap problem in behavioral monitoring by using hierarchical behavior graphs to infer high-level behaviors from myriad low-level events that could be parts of many different kinds of behavior. Our experimental system traces the execution of a process, performing data-flow analysis to identify meaningful actions such as \u201cproxying\u201d, \u201ckeystroke logging\u201d, \u201cdata leaking\u201d, and \u201cdownloading and executing a program\u201d from complex combinations of rudimentary system calls. To preemptively address evasive malware behavior, our specifications are carefully crafted to detect alternate sequences of events that achieve the same high-level goal. We tested seven malicious bots and eleven benign programs and found that we were able to thoroughly identify high-level behaviors across this diverse code base. Moreover, we were able to distinguish malicious execution of high-level behaviors from benign by distinguishing remotely-initiated from locally-initiated actions

Treatment with disease modifying drugs for people with a first clinical attack suggestive of multiple sclerosis

This is the protocol for a review and there is no abstract. The objectives are as follows: To estimate the benefit and safety of all DMDs that have been evaluated in all studies (randomised and non-randomised) for early treatment. We will employ novel, high-quality methods for systematic reviews and network meta-analysis in collaboration with the Cochrane Multiple Interventions Group. To evaluate the quality of the evidence provided by existing studies. We will consider the credibility of included studies and other characteristics of the evidence base as we characterise conclusions pertaining to high, low or very low quality of evidence. We will undertake this review in accordance with the methods described by the template protocol published online and will use this template as we prepare the review

Brief International Cognitive Assessment for MS (BICAMS): International Standards for Validation

An international expert consensus committee recently recommended a brief battery of tests for cognitive evaluation in multiple sclerosis. The Brief International Cognitive Assessment for MS (BICAMS) battery includes tests of mental processing speed and memory. Recognizing that resources for validation will vary internationally, the committee identified validation priorities, to facilitate international acceptance of BICAMS. Practical matters pertaining to implementation across different languages and countries were discussed. Five steps to achieve optimal psychometric validation were proposed. In Step 1, test stimuli should be standardized for the target culture or language under consideration. In Step 2, examiner instructions must be standardized and translated, including all information from manuals necessary for administration and interpretation. In Step 3, samples of at least 65 healthy persons should be studied for normalization, matched to patients on demographics such as age, gender and education. The objective of Step 4 is test-retest reliability, which can be investigated in a small sample of MS and/or healthy volunteers over 1–3 weeks. Finally, in Step 5, criterion validity should be established by comparing MS and healthy controls. At this time, preliminary studies are underway in a number of countries as we move forward with this international assessment tool for cognition in MS

Metabolically exaggerated cardiac reactions to acute psychological stress: The effects of resting blood pressure status and possible underlying mechanisms

The study aimed to: confirm that acute stress elicits metabolically exaggerated increases in cardiac activity; test whether individuals with elevated resting blood pressure show more exaggerated cardiac reactions to stress than those who are clearly normotensive; and explore the underlying mechanisms. Cardiovascular activity and oxygen consumption were measured pre-, during, and post- mental stress, and during graded submaximal cycling exercise in 11 young men with moderately elevated resting blood pressure and 11 normotensives. Stress provoked increases in cardiac output that were much greater than would be expected from contemporary levels of oxygen consumption. Exaggerated cardiac reactions were larger in the relatively elevated blood pressure group. They also had greater reductions in total peripheral resistance, but not heart rate variability, implying that their more exaggerated cardiac reactions reflected greater β-adrenergic activation