Extraordinary linear dynamic range in laser-defined functionalized graphene photodetectors

Graphene-based photodetectors have demonstrated mechanical flexibility, large operating bandwidth, and broadband spectral response. However, their linear dynamic range (LDR) is limited by graphene's intrinsichot-carrier dynamics, which causes deviation from a linear photoresponse at low incident powers. At the same time, multiplication of hot carriers causes the photoactive region to be smeared over distances of a few micro-meters, limiting the use of graphene in high-resolution applications. We present a novel method for engineer-ing photoactive junctions in FeCl3-intercalated graphene using laser irradiation. Photocurrent measured at these planar junctions shows an extraordinary linear response with an LDR value at least 4500 times larger than that of other graphene devices (44 dB) while maintaining high stability against environmental contamination without the need for encapsulation. The observed photoresponse is purely photovoltaic, demonstrating complete quenching of hot-carrier effects. These results pave the way toward the design of ultrathin photode-tectors with unprecedented LDR for high-definition imaging and sensing.Comment: 44 pages, includes supplementar

Random subwindows and extremely randomized trees for image classification in cell biology

Background: With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large amount of images at different imaging modalities/scales. It stresses the need for computer vision methods that automate image classification tasks. Results: We illustrate the potential of our image classification method in cell biology by evaluating it on four datasets of images related to protein distributions or subcellular localizations, and red-blood cell shapes. Accuracy results are quite good without any specific pre-processing neither domain knowledge incorporation. The method is implemented in Java and available upon request for evaluation and research purpose. Conclusion: Our method is directly applicable to any image classification problems. We foresee the use of this automatic approach as a baseline method and first try on various biological image classification problems

ADAM17-dependent proteolysis of L-selectin promotes early clonal expansion of cytotoxic T cells

L-selectin on T-cells is best known as an adhesion molecule that supports recruitment of blood-borne naïve and central memory cells into lymph nodes. Proteolytic shedding of the ectodomain is thought to redirect activated T-cells from lymph nodes to sites of infection. However, we have shown that activated T-cells re-express L-selectin before lymph node egress and use L-selectin to locate to virus-infected tissues. Therefore, we considered other roles for L-selectin proteolysis during T cell activation. In this study, we used T cells expressing cleavable or non-cleavable L-selectin and determined the impact of L-selectin proteolysis on T cell activation in virus-infected mice. We confirm an essential and non-redundant role for ADAM17 in TCR-induced proteolysis of L-selectin in mouse and human T cells and show that L-selectin cleavage does not regulate T cell activation measured by CD69 or TCR internalisation. Following virus infection of mice, L-selectin proteolysis promoted early clonal expansion of cytotoxic T cells resulting in an 8-fold increase over T cells unable to cleave L-selectin. T cells unable to cleave L-selectin showed delayed proliferation in vitro which correlated with lower CD25 expression. Based on these results, we propose that ADAM17-dependent proteolysis of L-selectin should be considered a regulator of T-cell activation at sites of immune activity

Population genomics and antimicrobial resistance in Corynebacterium diphtheriae

Background: Corynebacterium diphtheriae, the agent of diphtheria, is a genetically diverse bacterial species. Although antimicrobial resistance has emerged against several drugs including first-line penicillin, the genomic determinants and population dynamics of resistance are largely unknown for this neglected human pathogen. Methods: Here, we analyzed the associations of antimicrobial susceptibility phenotypes, diphtheria toxin production, and genomic features in C. diphtheriae. We used 247 strains collected over several decades in multiple world regions, including the 163 clinical isolates collected prospectively from 2008 to 2017 in France mainland and overseas territories. Results: Phylogenetic analysis revealed multiple deep-branching sublineages, grouped into a Mitis lineage strongly associated with diphtheria toxin production and a largely toxin gene-negative Gravis lineage with few toxin-producing isolates including the 1990s ex-Soviet Union outbreak strain. The distribution of susceptibility phenotypes allowed proposing ecological cutoffs for most of the 19 agents tested, thereby defining acquired antimicrobial resistance. Penicillin resistance was found in 17.2% of prospective isolates. Seventeen (10.4%) prospective isolates were multidrug-resistant (≥ 3 antimicrobial categories), including four isolates resistant to penicillin and macrolides. Homologous recombination was frequent (r/m = 5), and horizontal gene transfer contributed to the emergence of antimicrobial resistance in multiple sublineages. Genome-wide association mapping uncovered genetic factors of resistance, including an accessory penicillin-binding protein (PBP2m) located in diverse genomic contexts. Gene pbp2m is widespread in other Corynebacterium species, and its expression in C. glutamicum demonstrated its effect against several beta-lactams. A novel 73-kb C. diphtheriae multiresistance plasmid was discovered. Conclusions: This work uncovers the dynamics of antimicrobial resistance in C. diphtheriae in the context of phylogenetic structure, biovar, and diphtheria toxin production and provides a blueprint to analyze re-emerging diphtheria

Scenario trees and policy selection for multistage stochastic programming using machine learning

We propose a hybrid algorithmic strategy for complex stochastic optimization problems, which combines the use of scenario trees from multistage stochastic programming with machine learning techniques for learning a policy in the form of a statistical model, in the context of constrained vector-valued decisions. Such a policy allows one to run out-of-sample simulations over a large number of independent scenarios, and obtain a signal on the quality of the approximation scheme used to solve the multistage stochastic program. We propose to apply this fast simulation technique to choose the best tree from a set of scenario trees. A solution scheme is introduced, where several scenario trees with random branching structure are solved in parallel, and where the tree from which the best policy for the true problem could be learned is ultimately retained. Numerical tests show that excellent trade-offs can be achieved between run times and solution quality