Temporal coherence-based self-supervised learning for laparoscopic workflow analysis

In order to provide the right type of assistance at the right time, computer-assisted surgery systems need context awareness. To achieve this, methods for surgical workflow analysis are crucial. Currently, convolutional neural networks provide the best performance for video-based workflow analysis tasks. For training such networks, large amounts of annotated data are necessary. However, collecting a sufficient amount of data is often costly, time-consuming, and not always feasible. In this paper, we address this problem by presenting and comparing different approaches for self-supervised pretraining of neural networks on unlabeled laparoscopic videos using temporal coherence. We evaluate our pretrained networks on Cholec80, a publicly available dataset for surgical phase segmentation, on which a maximum F1 score of 84.6 was reached. Furthermore, we were able to achieve an increase of the F1 score of up to 10 points when compared to a non-pretrained neural network.Comment: Accepted at the Workshop on Context-Aware Operating Theaters (OR 2.0), a MICCAI satellite even

The Longest Baseline Record of Vegetation Dynamics in Antarctica Reveals Acute Sensitivity to Water Availability

Against a changing climate, the development of evidence-based and progressive conservation policies depends on robust and quantitative baseline studies to resolve habitat natural variability and rate of change. Despite Antarctica's significant role in global climate regulation, climate trend estimates for continental Antarctica are ambiguous due to sparse long-term in situ records. Here, we present the longest, spatially explicit survey of Antarctic vegetation by harmonizing historic vegetation mapping with modern remote sensing techniques. In 1961, E. D. Rudolph established a permanent survey plot at Cape Hallett, one of the most botanically diverse areas along the Ross Sea coastline, harboring all known types of non-vascular Antarctic vegetation. Following a survey in 2004 using ground-based photography, we conducted the third survey of Rudolph's Plot in 2018 using near-ground remote sensing and methodologies closely mirroring the two historic surveys to identify long-term changes and trends. Our results revealed that the vegetation at Cape Hallett remained stable over the past six decades with no evidence of transformation related to a changing climate. Instead, the local vegetation shows strong seasonal phenology, distribution patterns that are driven by water availability, and steady perennial growth of moss. Given that East Antarctica is at the tipping point of drastic change in the near future, with biological change having been reported at certain locations, this record represents a unique and potentially the last opportunity to establish a meaningful biological sentinel that will allow us to track subtle yet impactful environmental change in terrestrial Antarctica in the 21st century

A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data

Diagnosing rare diseases presents a common challenge in clinical practice, necessitating the expertise of specialists for accurate identification. The advent of machine learning offers a promising solution, while the development of such technologies is hindered by the scarcity of data on rare conditions and the demand for models that are both interpretable and trustworthy in a clinical context. Interpretable AI, with its capacity for human-readable outputs, can facilitate validation by clinicians and contribute to medical education. In the current work, we focus on choroid neoplasias, the most prevalent form of eye cancer in adults, albeit rare with 5.1 per million. We built the so-far largest dataset consisting of 750 patients, incorporating three distinct imaging modalities collected from 2004 to 2022. Our work introduces a concept-based interpretable model that distinguishes between three types of choroidal tumors, integrating insights from domain experts via radiological reports. Remarkably, this model not only achieves an F1 score of 0.91, rivaling that of black-box models, but also boosts the diagnostic accuracy of junior doctors by 42%. This study highlights the significant potential of interpretable machine learning in improving the diagnosis of rare diseases, laying a groundwork for future breakthroughs in medical AI that could tackle a wider array of complex health scenarios

Hepatic p53 is regulated by transcription factor FOXO1 and acutely controls glycogen homeostasis

The tumor suppressor p53 is involved in the adaptation of hepatic metabolism to nutrient availability. Acute deletion of p53 in the mouse liver affects hepatic glucose and triglyceride metabolism. However, long-term adaptations upon the loss of hepatic p53 and its transcriptional regulators are unknown. Here we show that short-term, but not chronic, liver-specific deletion of p53 in mice reduces liver glycogen levels, and we implicate the transcription factor forkhead box O1 protein (FOXO1) in the regulation of p53 and its target genes. We demonstrate that acute p53 deletion prevents glycogen accumulation upon refeeding, whereas a chronic loss of p53 associates with a compensational activation of the glycogen synthesis pathway. Moreover, we identify fasting-activated FOXO1 as a repressor of p53 transcription in hepatocytes. We show that this repression is relieved by inactivation of FOXO1 by insulin, which likely mediates the upregulation of p53 expression upon refeeding. Strikingly, we find that high-fat diet-induced insulin resistance with persistent FOXO1 activation not only blunted the regulation of p53 but also the induction of p53 target genes like p21 during fasting, indicating overlapping effects of both FOXO1 and p53 on target gene expression in a context-dependent manner. Thus, we conclude that p53 acutely controls glycogen storage in the liver and is linked to insulin signaling via FOXO1, which has important implications for our understanding of the hepatic adaptation to nutrient availability

Screening and identification of seed-specific genes using digital differential display tools combined with microarray data from common wheat

<p>Abstract</p> <p>Background</p> <p>Wheat is one of the most important cereal crops for human beings, with seeds being the tissue of highly economic value. Various morphogenetic and metabolic processes are exclusively associated with seed maturation. The goal of this study was to screen and identify genes specifically expressed in the developing seed of wheat with an integrative utilization of digital differential display (DDD) and available online microarray databases.</p> <p>Results</p> <p>A total of 201 unigenes were identified as the results of DDD screening and microarray database searching. The expressions of 6 of these were shown to be seed-specific by qRT-PCR analysis. Further GO enrichment analysis indicated that seed-specific genes were mainly associated with defense response, response to stress, multi-organism process, pathogenesis, extracellular region, nutrient reservoir activity, enzyme inhibitor activity, antioxidant activity and oxidoreductase activity. A comparison of this set of genes with the rice (<it>Oryza sativa</it>) genome was also performed and approximately three-fifths of them have rice counterparts. Between the counterparts, around 63% showed similar expression patterns according to the microarray data.</p> <p>Conclusions</p> <p>In conclusion, the DDD screening combined with microarray data analysis is an effective strategy for the identification of seed-specific expressed genes in wheat. These seed-specific genes screened during this study will provide valuable information for further studies about the functions of these genes in wheat.</p

Mutation discovery in mice by whole exome sequencing

We report the development and optimization of reagents for in-solution, hybridization-based capture of the mouse exome. By validating this approach in a multiple inbred strains and in novel mutant strains, we show that whole exome sequencing is a robust approach for discovery of putative mutations, irrespective of strain background. We found strong candidate mutations for the majority of mutant exomes sequenced, including new models of orofacial clefting, urogenital dysmorphology, kyphosis and autoimmune hepatitis

Messenger RNA Oxidation Occurs Early in Disease Pathogenesis and Promotes Motor Neuron Degeneration in ALS

BACKGROUND: Accumulating evidence indicates that RNA oxidation is involved in a wide variety of neurological diseases and may be associated with neuronal deterioration during the process of neurodegeneration. However, previous studies were done in postmortem tissues or cultured neurons. Here, we used transgenic mice to demonstrate the role of RNA oxidation in the process of neurodegeneration. METHODOLOGY/PRINCIPAL FINDINGS: We demonstrated that messenger RNA (mRNA) oxidation is a common feature in amyotrophic lateral sclerosis (ALS) patients as well as in many different transgenic mice expressing familial ALS-linked mutant copper-zinc superoxide dismutase (SOD1). In mutant SOD1 mice, increased mRNA oxidation primarily occurs in the motor neurons and oligodendrocytes of the spinal cord at an early, pre-symptomatic stage. Identification of oxidized mRNA species revealed that some species are more vulnerable to oxidative damage, and importantly, many oxidized mRNA species have been implicated in the pathogenesis of ALS. Oxidative modification of mRNA causes reduced protein expression. Reduced mRNA oxidation by vitamin E restores protein expression and partially protects motor neurons. CONCLUSION/SIGNIFICANCE: These findings suggest that mRNA oxidation is an early event associated with motor neuron deterioration in ALS, and may be also a common early event preceding neuron degeneration in other neurological diseases