Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness

Surgical action triplet recognition provides a better understanding of the surgical scene. This task is of high relevance as it provides to the surgeon with context-aware support and safety. The current go-to strategy for improving performance is the development of new network mechanisms. However, the performance of current state-of-the-art techniques is substantially lower than other surgical tasks. Why is this happening? This is the question that we address in this work. We present the first study to understand the failure of existing deep learning models through the lens of robustness and explainabilty. Firstly, we study current existing models under weak and strong $\delta-$perturbations via adversarial optimisation scheme. We then provide the failure modes via feature based explanations. Our study revels that the key for improving performance and increasing reliability is in the core and spurious attributes. Our work opens the door to more trustworthiness and reliability deep learning models in surgical science

TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios

Multi-object tracking in traffic videos is a crucial research area, offering immense potential for enhancing traffic monitoring accuracy and promoting road safety measures through the utilisation of advanced machine learning algorithms. However, existing datasets for multi-object tracking in traffic videos often feature limited instances or focus on single classes, which cannot well simulate the challenges encountered in complex traffic scenarios. To address this gap, we introduce TrafficMOT, an extensive dataset designed to encompass diverse traffic situations with complex scenarios. To validate the complexity and challenges presented by TrafficMOT, we conducted comprehensive empirical studies using three different settings: fully-supervised, semi-supervised, and a recent powerful zero-shot foundation model Tracking Anything Model (TAM). The experimental results highlight the inherent complexity of this dataset, emphasising its value in driving advancements in the field of traffic monitoring and multi-object tracking.Comment: 17 pages, 7 figure

High-free Fatty Acid Treatment Induced Anti-inflammatory Changes in a Natural Killer (NK) Cell Line

Background: Natural killer (NK) cells play a role in the pathogenesis of various metabolic diseases related to obesity. While our initial findings have indicated a potential involvement of NK cells in the pathogenesis of type 2 diabetes mellitus, the precise mechanism underlying NK cell-mediated development of this form of diabetes remains inadequately comprehended.Objective: To investigate the impact and the underlying mechanism of high glucose and elevated levels of free fatty acids (FFAs) on immune and inflammatory responses and oxidative stress in NK92 cells.Methods: In this experiment, the CCK8 cytotoxicity assay was used to select the 44.4 mM and 1.5 mM concentrations of high glucose and high FFAs, respectively, to treat NK92 cells for 4 days. The concentrations of superoxide dismutase (SOD) and glutathione (GSH) were determined using a biochemical analyzer. Intracellular reactive oxygen species (ROS) levels, cytokines concentrations (TNF-α, IFN-γ, IL-6, and IL-10), and the expression levels of intracellular molecules (perforin and granzyme B) were assessed by flow cytometry.Results: The number of NK92 cell clumps was significantly reduced in the high-FFA (HF) group. In addition, the production of ROS and levels of cytokines (TNF-α, IFN-γ, IL-6, and IL-10) significantly decreased in the HF group but showed no significant change in the high-glucose (HG) group. This observation was consistent with the expression levels of perforin and granzyme B that decreased in the HF group.Conclusion: High FFAs induced morphological changes and serious damage to oxidative stress and inflammatory response in NK92 cells

Atomic-scale structure and nonlinear optical absorption of two-dimensional GeS

info:eu-repo/semantics/publishedVersio

Skywork: A More Open Bilingual Foundation Model

In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts. This bilingual foundation model is the most extensively trained and openly published LLMs of comparable size to date. We introduce a two-stage training methodology using a segmented corpus, targeting general purpose training and then domain-specific enhancement training, respectively. We show that our model not only excels on popular benchmarks, but also achieves \emph{state of the art} performance in Chinese language modeling on diverse domains. Furthermore, we propose a novel leakage detection method, demonstrating that test data contamination is a pressing issue warranting further investigation by the LLM community. To spur future research, we release Skywork-13B along with checkpoints obtained during intermediate stages of the training process. We are also releasing part of our SkyPile corpus, a collection of over 150 billion tokens of web text, which is the largest high quality open Chinese pre-training corpus to date. We hope Skywork-13B and our open corpus will serve as a valuable open-source resource to democratize access to high-quality LLMs

Structural basis of SETD6-mediated regulation of the NF-kB network via methyl-lysine signaling

SET domain containing 6 (SETD6) monomethylates the RelA subunit of nuclear factor kappa B (NF-κB). The ankyrin repeats of G9a-like protein (GLP) recognizes RelA monomethylated at Lys310. Adjacent to Lys310 is Ser311, a known phosphorylation site of RelA. Ser311 phosphorylation inhibits Lys310 methylation by SETD6 as well as binding of Lys310me1 by GLP. The structure of SETD6 in complex with RelA peptide containing the methylation site, in the presence of S-adenosyl-l-methionine, reveals a V-like protein structure and suggests a model for NF-κB binding to SETD6. In addition, structural modeling of the GLP ankyrin repeats bound to Lys310me1 peptide provides insight into the molecular basis for inhibition of Lys310me1 binding by Ser311 phosphorylation. Together, these findings provide a structural explanation for a key cellular signaling pathway centered on RelA Lys310 methylation, which is generated by SETD6 and recognized by GLP, and incorporate a methylation–phosphorylation switch of adjacent lysine and serine residues. Finally, SETD6 is structurally similar to the Rubisco large subunit methyltransferase. Given the restriction of Rubisco to plant species, this particular appearance of the protein lysine methyltransferase has been evolutionarily well conserved