SAGES consensus recommendations on an annotation framework for surgical video

Background: The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration. Methods: Four working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes: (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a consensus survey based on suggested recommendations from each of the working groups. Results: After three Delphi rounds, consensus was reached on recommendations for annotation within each of these domains. A hierarchy for annotation of temporal events in surgery was established. Conclusions: While additional work remains to achieve accepted standards for video annotation in surgery, the consensus recommendations on a general framework for annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration

Lowering apolipoprotein CIII protects against high-fat diet-induced metabolic derangements

Increased levels of apolipoprotein CIII (apoCIII), a key regulator of lipid metabolism, result in obesity-related metabolic derangements. We investigated mechanistically whether lowering or preventing high-fat diet (HFD)-induced increase in apoCIII protects against the detrimental metabolic consequences. Mice, first fed HFD for 10 weeks and thereafter also given an antisense (ASO) to lower apoCIII, already showed reduced levels of apoCIII and metabolic improvements after 4 weeks, despite maintained obesity. Prolonged ASO treatment reversed the metabolic phenotype due to increased lipase activity and receptor-mediated hepatic uptake of lipids. Fatty acids were transferred to the ketogenic pathway, and ketones were used in brown adipose tissue (BAT). This resulted in no fat accumulation and preserved morphology and function of liver and BAT. If ASO treatment started simultaneously with the HFD, mice remained lean and metabolically healthy. Thus, lowering apoCIII protects against and reverses the HFD-induced metabolic phenotype by promoting physiological insulin sensitivity

Normalization of high dimensional genomics data where the distribution of the altered variables is skewed

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods