University of Bordeaux

About: University of Bordeaux is a education organization based out in Bordeaux, France. It is known for research contribution in the topics: Population & Laser. The organization has 28811 authors who have published 55536 publications receiving 1619635 citations. The organization is also known as: UB.

Genome sequences of the human body louse and its primary endosymbiont provide insights into the permanent parasitic lifestyle

Abstract: As an obligatory parasite of humans, the body louse (Pediculus humanus humanus) is an important vector for human diseases, including epidemic typhus, relapsing fever, and trench fever. Here, we present genome sequences of the body louse and its primary bacterial endosymbiont Candidatus Riesia pediculicola. The body louse has the smallest known insect genome, spanning 108 Mb. Despite its status as an obligate parasite, it retains a remarkably complete basal insect repertoire of 10,773 protein-coding genes and 57 microRNAs. Representing hemimetabolous insects, the genome of the body louse thus provides a reference for studies of holometabolous insects. Compared with other insect genomes, the body louse genome contains significantly fewer genes associated with environmental sensing and response, including odorant and gustatory receptors and detoxifying enzymes. The unique architecture of the 18 minicircular mitochondrial chromosomes of the body louse may be linked to the loss of the gene encoding the mitochondrial single-stranded DNA binding protein. The genome of the obligatory louse endosymbiont Candidatus Riesia pediculicola encodes less than 600 genes on a short, linear chromosome and a circular plasmid. The plasmid harbors a unique arrangement of genes required for the synthesis of pantothenate, an essential vitamin deficient in the louse diet. The human body louse, its primary endosymbiont, and the bacterial pathogens that it vectors all possess genomes reduced in size compared with their free-living close relatives. Thus, the body louse genome project offers unique information and tools to use in advancing understanding of coevolution among vectors, symbionts, and pathogens.

Ploidy and Large-Scale Genomic Instability Consistently Identify Basal-like Breast Carcinomas with BRCA1/2 Inactivation

Abstract: BRCA1 inactivation is a frequent event in basal-like breast carcinomas (BLC). However, BRCA1 can be inactivated by multiple mechanisms and determining its status is not a trivial issue. As an alternate approach, we profiled 65 BLC cases using single-nucleotide polymorphism arrays to define a signature of BRCA1-associated genomic instability. Large-scale state transitions (LST), defined as chromosomal break between adjacent regions of at least 10 Mb, were found to be a robust indicator of BRCA1 status in this setting. Two major ploidy-specific cutoffs in LST distributions were sufficient to distinguish highly rearranged BLCs with 85% of proven BRCA1-inactivated cases from less rearranged BLCs devoid of proven BRCA1-inactivated cases. The genomic signature we defined was validated in a second independent series of 55 primary BLC cases and 17 BLC-derived tumor cell lines. High numbers of LSTs resembling BRCA1-inactivated BLC were observed in 4 primary BLC cases and 2 BLC cell lines that harbored BRCA2 mutations. Overall, the genomic signature we defined predicted BRCA1/2 inactivation in BLCs with 100% sensitivity and 90% specificity (97% accuracy). This assay may ease the challenge of selecting patients for genetic testing or recruitment to clinical trials of novel emerging therapies that target DNA repair deficiencies in cancer.

Glial cells in (patho)physiology.

Abstract: Neuroglial cells define brain homeostasis and mount defense against pathological insults. Astroglia regulate neurogenesis and development of brain circuits. In the adult brain, astrocytes enter into intimate dynamic relationship with neurons, especially at synaptic sites where they functionally form the tripartite synapse. At these sites, astrocytes regulate ion and neurotransmitter homeostasis, metabolically support neurons and monitor synaptic activity; one of the readouts of the latter manifests in astrocytic intracellular Ca(2+) signals. This form of astrocytic excitability can lead to release of chemical transmitters via Ca(2+) -dependent exocytosis. Once in the extracellular space, gliotransmitters can modulate synaptic plasticity and cause changes in behavior. Besides these physiological tasks, astrocytes are fundamental for progression and outcome of neurological diseases. In Alzheimer's disease, for example, astrocytes may contribute to the etiology of this disorder. Highly lethal glial-derived tumors use signaling trickery to coerce normal brain cells to assist tumor invasiveness. This review not only sheds new light on the brain operation in health and disease, but also points to many unknowns.

Network Intrusion Detection for IoT Security Based on Learning Techniques

Abstract: Pervasive growth of Internet of Things (IoT) is visible across the globe. The 2016 Dyn cyberattack exposed the critical fault-lines among smart networks. Security of IoT has become a critical concern. The danger exposed by infested Internet-connected Things not only affects the security of IoT but also threatens the complete Internet eco-system which can possibly exploit the vulnerable Things (smart devices) deployed as botnets. Mirai malware compromised the video surveillance devices and paralyzed Internet via distributed denial of service attacks. In the recent past, security attack vectors have evolved bothways, in terms of complexity and diversity. Hence, to identify and prevent or detect novel attacks, it is important to analyze techniques in IoT context. This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques. Our main focus is on network intrusion detection systems (NIDSs); hence, this paper reviews existing NIDS implementation tools and datasets as well as free and open-source network sniffing software. Then, it surveys, analyzes, and compares state-of-the-art NIDS proposals in the IoT context in terms of architecture, detection methodologies, validation strategies, treated threats, and algorithm deployments. The review deals with both traditional and machine learning (ML) NIDS techniques and discusses future directions. In this survey, our focus is on IoT NIDS deployed via ML since learning algorithms have a good success rate in security and privacy. The survey provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems. We believe that, this paper will be useful for academia and industry research, first, to identify IoT threats and challenges, second, to implement their own NIDS and finally to propose new smart techniques in IoT context considering IoT limitations. Moreover, the survey will enable security individuals differentiate IoT NIDS from traditional ones.