In vitro Targeting of Transcription Factors to Control the Cytokine Release Syndrome in COVID-19

Scene-dependent, feedforward eye gaze metrics can differentiate technical skill levels of trainees in laparoscopic surgery

INTRODUCTION

In laparoscopic surgery, looking in the target areas is an indicator of proficiency. However, gaze behaviors revealing feedforward control (i.e., looking ahead) and their importance have been under-investigated in surgery. This study aims to establish the sensitivity and relative importance of different scene-dependent gaze and motion metrics for estimating trainee proficiency levels in surgical skills.

METHODS

Medical students performed the Fundamentals of Laparoscopic Surgery peg transfer task while recording their gaze on the monitor and tool activities inside the trainer box. Using computer vision and fixation algorithms, five scene-dependent gaze metrics and one tool speed metric were computed for 499 practice trials. Cluster analysis on the six metrics was used to group the trials into different clusters/proficiency levels, and ANOVAs were conducted to test differences between proficiency levels. A Random Forest model was trained to study metric importance at predicting proficiency levels.

RESULTS

Three clusters were identified, corresponding to three proficiency levels. The correspondence between the clusters and proficiency levels was confirmed by differences between completion times (F2,488 = 38.94, p < .001). Further, ANOVAs revealed significant differences between the three levels for all six metrics. The Random Forest model predicted proficiency level with 99% out-of-bag accuracy and revealed that scene-dependent gaze metrics reflecting feedforward behaviors were more important for prediction than the ones reflecting feedback behaviors.

CONCLUSION

Scene-dependent gaze metrics revealed skill levels of trainees more precisely than between experts and novices as suggested in the literature. Further, feedforward gaze metrics appeared to be more important than feedback ones at predicting proficiency.

Methodological notes on pandemic virus SARS-CoV-2 research

In the fight against the COVID-19 pandemic, many brilliant results have been achieved, but the thermodynamics of the novel SARS-CoV-2 coronavirus has been completely neglected. This is a serious systematic error, which can compromise the results of the entire pandemic virus SARS-CoV-2 research. In the present work, we therefore study the thermodynamics of SARS-CoV-2 in its environment, from air to endosome and endosome-independent cell entry pathways. In the study of the thermodynamics of the new coronavirus SARS-CoV-2 in air, the presence of pollen, bacteria, other viruses, spores, dust, but more particularly, that of nanoparticles of health interest at the same scale threshold as the spike proteins of the pandemic virus, such as particulate matter, cannot be neglected. This work therefore starts from a comparative study of the air environments in China and Italy, the first countries affected by the infection. Currently, a correlation between the spread of infection and pollution is still very controversial. But our paper is not concerned with this. We propose some methodological notes which lead us to the formulation of a general mathematical apparatus (an energy landscape theory), suitable to explain at the molecular level the energetic configurations of the quasi-species of the pandemic virus SARS-CoV-2 in its environment. We focus on complexes between the viral particle and other objects in its environment at the scale threshold of the spikes of the viral particle. Then, we wondered if such complexes can lead to the generation of more aggressive viral variants and how to predict their populations and energy configurations, in order to plan an adequate prophylaxis.

i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites

DNA N6-methyladenine (6 mA), as an essential component of epigenetic modification, cannot be neglected in genetic regulation mechanism. The efficient and accurate prediction of 6 mA sites is beneficial to the development of biological genetics. Biochemical experimental methods are considered to be time-consuming and laborious. Most of the established machine learning methods have a single dataset. Although some of them have achieved cross-species prediction, their results are not satisfactory. Therefore, we designed a novel statistical model called i6mA-VC to improve the accuracy for 6 mA sites. On the one hand, kmer and binary encoding are applied to extract features, and then gradient boosting decision tree (GBDT) embedded method is applied as the feature selection strategy. On the other hand, DNA sequences are represented by vectors through the feature extraction method of ring-function-hydrogen-chemical properties (RFHCP) and the feature selection strategy of ExtraTree. After fusing the two optimal features, a voting classifier based on gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM) and multilayer perceptron classifier (MLPC) is constructed for final classification and prediction. The accuracy of Rice dataset and M.musculus dataset with five-fold cross-validation are 0.888 and 0.967, respectively. The cross-species dataset is selected as independent testing dataset, and the accuracy reaches 0.848. Through rigorous experiments, it is demonstrated that the proposed predictor is convincing and applicable. The development of i6mA-VC predictor will become an effective way for the recognition of N6-methyladenine sites, and it will also be beneficial for biological geneticists to further study gene expression and DNA modification. In addition, an accessible web-server for i6mA-VC is available from http://www.zhanglab.site/ .