Recruitment of Activation Receptors at Inhibitory NK Cell Immune Synapses

Natural killer (NK) cell activation receptors accumulate by an actin-dependent process at cytotoxic immune synapses where they provide synergistic signals that trigger NK cell effector functions. In contrast, NK cell inhibitory receptors, including members of the MHC class I-specific killer cell Ig-like receptor (KIR) family, accumulate at inhibitory immune synapses, block actin dynamics, and prevent actin-dependent phosphorylation of activation receptors. Therefore, one would predict inhibition of actin-dependent accumulation of activation receptors when inhibitory receptors are engaged. By confocal imaging of primary human NK cells in contact with target cells expressing physiological ligands of NK cell receptors, we show here that this prediction is incorrect. Target cells included a human cell line and transfected Drosophila insect cells that expressed ligands of NK cell activation receptors in combination with an MHC class I ligand of inhibitory KIR. The two NK cell activation receptors CD2 and 2B4 accumulated and co-localized with KIR at inhibitory immune synapses. In fact, KIR promoted CD2 and 2B4 clustering, as CD2 and 2B4 accumulated more efficiently at inhibitory synapses. In contrast, accumulation of KIR and of activation receptors at inhibitory synapses correlated with reduced density of the integrin LFA-1. These results imply that inhibitory KIR does not prevent CD2 and 2B4 signaling by blocking their accumulation at NK cell immune synapses, but by blocking their ability to signal within inhibitory synapses

Data-driven aerodynamic shape design with distributionally robust optimization approaches

We formulate and solve data-driven aerodynamic shape design problems with distributionally robust optimization (DRO) approaches. Building on the findings of the work \cite{gotoh2018robust}, we study the connections between a class of DRO and the Taguchi method in the context of robust design optimization. Our preliminary computational experiments on aerodynamic shape optimization in transonic turbulent flow show promising design results

Transfert isolé des signaux de commande dans le contexte de l'intégration pour les composants actifs d'électronique de puissance

Cet article présente plusieurs solutions originales afin de réaliser un transfert isolé des ordres de commutation pour les semiconducteurs de puissance. Sous la contrainte de l'intégration aussi bien monolithique qu'hétérogène, trois solutions sont présentées de façons théoriques et expérimentales. Le premier mode de réalisation intégrée de l'isolation galvanique est une solution optique intégrée au composant de puissance et/ou au sein de son circuit de commande de type CMOS. Les performances statiques et dynamiques des récepteurs optiques sont tout à fait compatibles avec l'application. Enfin, plusieurs transformateurs sans noyau magnétique sont conçus, analysés et caractérisés via une réalisation CMOS, en prenant un soin particulier aux couplages hautes fréquences et éléments parasites. Ce circuit de transmission électromagnétique des ordres est intégré avec plusieurs fonctions de pilotages et le bon fonctionnement de l'ensemble du driver est démontré aussi bien pour les composants Haute Tension High Side que Low Side

Adaptive Evolution and the Birth of CTCF Binding Sites in the <i>Drosophila</i> Genome

Changes in the physical interaction between cis-regulatory DNA sequences and proteins drive the evolution of gene expression. However, it has proven difficult to accurately quantify evolutionary rates of such binding change or to estimate the relative effects of selection and drift in shaping the binding evolution. Here we examine the genome-wide binding of CTCF in four species of Drosophila separated by between ∼2.5 and 25 million years. CTCF is a highly conserved protein known to be associated with insulator sequences in the genomes of human and Drosophila. Although the binding preference for CTCF is highly conserved, we find that CTCF binding itself is highly evolutionarily dynamic and has adaptively evolved. Between species, binding divergence increased linearly with evolutionary distance, and CTCF binding profiles are diverging rapidly at the rate of 2.22% per million years (Myr). At least 89 new CTCF binding sites have originated in the Drosophila melanogaster genome since the most recent common ancestor with Drosophila simulans. Comparing these data to genome sequence data from 37 different strains of Drosophila melanogaster, we detected signatures of selection in both newly gained and evolutionarily conserved binding sites. Newly evolved CTCF binding sites show a significantly stronger signature for positive selection than older sites. Comparative gene expression profiling revealed that expression divergence of genes adjacent to CTCF binding site is significantly associated with the gain and loss of CTCF binding. Further, the birth of new genes is associated with the birth of new CTCF binding sites. Our data indicate that binding of Drosophila CTCF protein has evolved under natural selection, and CTCF binding evolution has shaped both the evolution of gene expression and genome evolution during the birth of new genes.</p

On the use of supervised machine learning for assessing schedulability: application to Ethernet TSN

In this work, we ask if Machine Learning (ML) can provide a viable alternative to conventional schedulability analysis to determine whether a real-time Ethernet network meets a set of timing constraints. Otherwise said, can an algorithm learn what makes it difficult for a system to be feasible and predict whether a configuration will be feasible without executing a schedulability analysis? To get insights into this question, we apply a standard supervised ML technique, k-nearest neighbors (k-NN), and compare its accuracy and running times against precise and approximate schedulability analyses developed in Network-Calculus. The experiments consider different TSN scheduling solutions based on priority levels combined for one of them with traffic shaping. The results obtained on an automotive network topology suggest that k-NN is efficient at predicting the feasibility of realistic TSN networks, with an accuracy ranging from 91.8% to 95.9% depending on the exact TSN scheduling mechanism and a speedup of 190 over schedulability analysis for 10^6 configurations. Unlike schedulability analysis, ML leads however to a certain rate "false positives'' (i.e., configurations deemed feasible while they are not). Nonetheless ML-based feasibility assessment techniques offer new trade-offs between accuracy and computation time that are especially interesting in contexts such as design-space exploration where false positives can be tolerated during the exploration process

A Second Actin-Like MamK Protein in Magnetospirillum magneticum AMB-1 Encoded Outside the Genomic Magnetosome Island

Magnetotactic bacteria are able to swim navigating along geomagnetic field lines. They synthesize ferromagnetic nanocrystals that are embedded in cytoplasmic membrane invaginations forming magnetosomes. Regularly aligned in the cytoplasm along cytoskeleton filaments, the magnetosome chain effectively forms a compass needle bestowing on bacteria their magnetotactic behaviour. A large genomic island, conserved among magnetotactic bacteria, contains the genes potentially involved in magnetosome formation. One of the genes, mamK has been described as encoding a prokaryotic actin-like protein which when it polymerizes forms in the cytoplasm filamentous structures that provide the scaffold for magnetosome alignment. Here, we have identified a series of genes highly similar to the mam genes in the genome of Magnetospirillum magneticum AMB-1. The newly annotated genes are clustered in a genomic islet distinct and distant from the known magnetosome genomic island and most probably acquired by lateral gene transfer rather than duplication. We focused on a mamK-like gene whose product shares 54.5% identity with the actin-like MamK. Filament bundles of polymerized MamK-like protein were observed in vitro with electron microscopy and in vivo in E. coli cells expressing MamK-like-Venus fusions by fluorescence microscopy. In addition, we demonstrate that mamK-like is transcribed in AMB-1 wild-type and ΔmamK mutant cells and that the actin-like filamentous structures observed in the ΔmamK strain are probably MamK-like polymers. Thus MamK-like is a new member of the prokaryotic actin-like family. This is the first evidence of a functional mam gene encoded outside the magnetosome genomic island

Using Machine Learning to Speed Up the Design Space Exploration of Ethernet TSN networks

In this work, we ask if Machine Learning (ML) can provide a viable alternative to conventional schedulability analysis to determine whether a real-time Ethernet network meets a set of timing constraints. Otherwise said, can an algorithm learn what makes it difficult for a system to be feasible and predict whether a configuration will be feasible without executing a schedulability analysis? In this study, we apply standard supervised and unsupervised ML techniques and compare them, in terms of their accuracy and running times, with precise and approximate schedulability analyses in Network-Calculus. We show that ML techniques are efficient at predicting the feasibility of realistic TSN networks and offer new trade-offs between accuracy and computation time especially interesting for design-space exploration algorithms

Groundwater Use Habits and Environmental Awareness in Ca Mau Province, Vietnam: Implications for Sustainable Water Resource Management

The Vietnamese Mekong Delta including Ca Mau province (CMP) is seriously affected by land subsidence. Groundwater over-extraction is considered to be a major driver for this process. To address the reduction of groundwater (GW) extraction as a potential counter measure for further subsidence, this study focuses on understanding the importance of GW in people\u27s life and water using habits as well as their awareness with current environmental problems in Ca Mau. Therefore, GW sampling campaigns and surveys were conducted in all 9 districts of Ca Mau province in 2019 and 2020. The analyzed water samples showed a connection with information from questionnaires and created a general picture of water using habits. GW plays an important role in people\u27s lives, it is used for washing, cooking, drinking and other activities. People use GW for different purposes depending on their perception of water quality. For important and direct health related purposes, such as cooking or drinking, people prepare to treat water more carefully or choose another alternative water resource. The analytical approach to evaluation results based on viewpoints from general to detail helped to dig deeper into people\u27s stories to explain research results with their behavior in each situation. When people are dependent on GW and have no option to use alternative water resources, the importance level of GW in their life increases and their awareness of GW over-extraction becomes less. If people have another water source to use such as tap water (TW), habits of using GW change. This opens up the idea that a potential alternative water will reduce the dependence of people on GW and protect GW from over-exploitation. Besides, people in Ca Mau do not have much awareness of land subsidence or the reason leading to environmental problems. Therefore, raising the awareness of people by well-design education campaigns should be strongly considered