Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection

In this paper, we improve the single-vehicle 3D object detection models using LiDAR by extending their capacity to process point cloud sequences instead of individual point clouds. In this step, we extend our previous work on rectification of the shadow effect in the concatenation of point clouds to boost the detection accuracy of multi-frame detection models. Our extension includes incorporating HD Map and distilling an Oracle model. Next, we further increase the performance of single-vehicle perception using multi-agent collaboration via Vehicle-to-everything (V2X) communication. We devise a simple yet effective collaboration method that achieves better bandwidth-performance tradeoffs than prior arts while minimizing changes made to single-vehicle detection models and assumptions on inter-agent synchronization. Experiments on the V2X-Sim dataset show that our collaboration method achieves 98% performance of the early collaboration while consuming the equivalent amount of bandwidth usage of late collaboration which is 0.03% of early collaboration. The code will be released at https://github.com/quan-dao/practical-collab-perception.Comment: Work in progres

Pharmacology and clinical drug candidates in redox medicine

SIGNIFICANCE Oxidative stress is suggested to be a disease mechanism common to a wide range of disorders affecting human health. However, so far, the pharmacotherapeutic exploitation of this, for example, based on chemical scavenging of pro-oxidant molecules, has been unsuccessful. Recent Advances: An alternative emerging approach is to target the enzymatic sources of disease-relevant oxidative stress. Several such enzymes and isoforms have been identified and linked to different pathologies. For some targets, the respective pharmacology is quite advanced, that is, up to late-stage clinical development or even on the market; for others, drugs are already in clinical use, although not for indications based on oxidative stress, and repurposing seems to be a viable option. CRITICAL ISSUES For all other targets, reliable preclinical validation and drug ability are key factors for any translation into the clinic. In this study, specific pharmacological agents with optimal pharmacokinetic profiles are still lacking. Moreover, these enzymes also serve largely unknown physiological functions and their inhibition may lead to unwanted side effects. FUTURE DIRECTIONS The current promising data based on new targets, drugs, and drug repurposing are mainly a result of academic efforts. With the availability of optimized compounds and coordinated efforts from academia and industry scientists, unambiguous validation and translation into proof-of-principle studies seem achievable in the very near future, possibly leading towards a new era of redox medicine

Le portail g-INFO pour surveiller la grippe Influenza A

Le portail g-INFO pour surveiller la grippe Influenza

Reactive oxygen-related diseases: therapeutic targets and emerging clinical indications

SIGNIFICANCE Enhanced levels of reactive oxygen species (ROS) have been associated with different disease states. Most attempts to validate and exploit these associations by chronic antioxidant therapies have provided disappointing results. Hence, the clinical relevance of ROS is still largely unclear. RECENT ADVANCES We are now beginning to understand the reasons for these failures, which reside in the many important physiological roles of ROS in cell signaling. To exploit ROS therapeutically, it would be essential to define and treat the disease-relevant ROS at the right moment and leave physiological ROS formation intact. This breakthrough seems now within reach. CRITICAL ISSUES Rather than antioxidants, a new generation of protein targets for classical pharmacological agents includes ROS-forming or toxifying enzymes or proteins that are oxidatively damaged and can be functionally repaired. FUTURE DIRECTIONS Linking these target proteins in future to specific disease states and providing in each case proof of principle will be essential for translating the oxidative stress concept into the clinic. Antioxid. Redox Signal. 23, 1171-1185

g-INFO portal: a solution to monitor Influenza A on the Grid for non-grid users

International audienceIn this paper, we introduce a portal for monitoring Influenza A on a grid-based system. Influenza A keeps on being a major threat to public health worldwide; especially if one virus can mutate itself so that it acquires the capacity for human to human transmission of H1N1 as well as the high death rate of H5N1. The existing g-INFO (Grid-based Information Network for Flu Observation) project provides a complete system for monitoring flu virus on the Grid. We present here a portal that operates on top of the g-INFO system as a solution for non-grid users to utilize grid services for analyzing molecular biology data of Influenza A

Temporal variation in body measurements in three Taurine cattle populations of Burkina Faso supports introgression of Zebu genes into West African Taurine cattle

A total of 769 adult females belonging to 3 taurine and one zebu cattle populations sampled in 3 provinces of Burkina Faso were assessed for 19 body measurements during two different years (2014 and 2018). The aim of this research was to identify temporal morphological variation in cattle bred in the humid southern zones to obtain empirical evidence supporting a possible introgression of zebu cattle genes into Gourounsi and Lobi taurine cattle breeds. Zebu cattle samples were used as out-group for both 2014 and 2018 subsets. Least square means of body measurements allowed to classify Burkina Faso taurine cattle into three subgroups according to body size (Gourounsi–Sanguié –GourS-, Gourounsi-Nahouri –GourN- and Lobi from the tallest to the smallest respectively). Principal Component Analysis suggested that in 2014, taurine populations were structured. Dispersion map constructed using the two first factors informed that the GourS population was well separated from both the Lobi and the GourN, which, in turn, overlapped. However, in 2018 a strong signal of homogenization was identified, with GourN partially overlapping the other two populations. Linear Discriminant Analysis suggested that about 20% of both GourS and GourN individuals were reciprocally misclassified. Clues for such increase have been pointed out by MANOVA analysis. Although on 2014, Lobi cattle was clearly smaller than Gourounsi and both GourS and GourN populations showed clear differences on body traits, on 2018 it could be assessed an increase in size in Lobi cattle and a strong homogenization signal within Gourounsi cattle. Zebu cattle gene flow southwards in Burkina Faso is likely to have caused these changes, suggesting a fast erosion of taurine cattle genetic background. Keywords: Body traits, quantitative traits, Gourounsi cattle, Lobi, Burkina Faso

Coordinated investigation of midlatitude upper mesospheric temperature inversion layers and the associated gravity wave forcing by Na lidar and Advanced Mesospheric Temperature Mapper in Logan, Utah

Mesospheric inversion layers (MIL) are well studied in the literature but their relationship to the dynamic feature associated with the breaking of atmospheric waves in the mesosphere/lower thermosphere (MLT) region are not well understood. Two strong MIL events (ΔT ~30 K) were observed above 90 km during a 6 day full diurnal cycle Na lidar campaign conducted from 6 August to 13 August Logan, Utah (42°N, 112°W). Colocated Advanced Mesospheric Temperature Mapper observations provided key information on concurrent gravity wave (GW) events and their characteristics during the nighttime observations. The study found both MILs were well correlated with the development and presence of an unstable region ~2 km above the MIL peak altitudes and a highly stable region below, implicating the strengthening of MIL is likely due to the increase of downward heat flux by the enhanced saturation of gravity wave, when it propagates through a highly stable layer. Each MIL event also exhibited distinct features: one showed a downward progression most likely due to tidal-GW interaction, while the peak height of the other event remained constant. During further investigation of atmospheric stability surrounding the MIL structure, lidar measurements indicate a sharp enhancement of the convective stability below the peak altitude of each MIL. We postulate that the sources of these stable layers were different; one was potentially triggered by concurrent large tidal wave activity and the other during the passage of a strong mesospheric bore

Uncovering Biosynthetic Relationships Between Antifungal Nonadrides and Octadrides

Maleidrides are a class of bioactive secondary metabolites unique to filamentous fungi, which contain one or more maleic anhydrides fused to a 7-, 8- or 9- membered carbocycle (named heptadrides, octadrides and nonadrides respectively). Herein structural and biosynthetic studies on the antifungal octadride, zopfiellin, and nonadrides scytalidin, deoxyscytalidin and castaneiolide are described. A combination of genome sequencing, bioinformatic analyses, gene disruptions, biotransformations, isotopic feeding studies, NMR and X-ray crystallography revealed that they share a common biosynthetic pathway, diverging only after the nonadride deoxyscytalidin. 5-Hydroxylation of deoxyscytalidin occurs prior to ring contraction in the zopfiellin pathway of Diffractella curvata. In Scytalidium album, 6-hydroxylation-confirmed as being catalysed by the α-ketoglutarate dependent oxidoreductase ScyL2-converts deoxyscytalidin to scytalidin, in the final step in the scytalidin pathway. Feeding scytalidin to a zopfiellin PKS knockout strain led to the production of the nonadride castaneiolide and two novel ring-open maleidrides. © The Royal Society of Chemistry

Reconstructing cell cycle and disease progression using deep learning

We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer