Association of mitochondrial polymorphism m.709G>A with Behcet's disease

info:eu-repo/semantics/publishedVersio

ВЫБОР СМЕЩЕНИЯ ПРИ ПОШАГОВОЙ МАРШРУТИЗАЦИИ В БЕСПРОВОДНОЙ СЕТИ

In order to send the frame to the address the authors suggest selecting an adjacent node, taking into account the preferred predetermined weights of those nodes and their current load rates. The adaptive algorithm of alternate path choice is based on logical neural network. Simulation, made by the authors, has shown better values of mean time to execute applications and higher probability of their execution as compared to algorithm of non-alternative choice of neighboring routers. Thus a mean time of execution of application with alternative path choice algorithm DТalt exceeds minimum possible time for the range of change 12 ≤ s ≤ 16 (applications per step) in a changing range 0,3 ≤ DТальт ≤ 1 (steps). The mean time of execution of application with the method of alternative path choice has been reduced as compared to non-alternative path choice by 8% for s = 12, and by 23% – for s = 16.Для передачи кадра по адресу выбор смежного узла предлагается производить с учётом предпочтительных весов этих узлов, определённых заранее, и текущих значений коэффициентов их загрузки. В основе адаптивного  алгоритма альтернативного  смещения лежит логическая нейронная сеть. Моделирование, проведенное авторами, показало лучшие значения среднего времени и вероятности выполнения заявок  в данном варианте по сравнению с алгоритмом безальтернативного выбора искомого узла

Neural sensing and control in a kilometer-scale gravitational-wave observatory

Suspended optics in gravitational-wave (GW) observatories are susceptible to alignment perturbations, particularly slow drifts over time, due to variations in temperature and seismic levels. Such misalignments affect the coupling of the incident laser beam into the optical cavities, degrade both the circulating power and optomechanical photon squeezing, and thus decrease the astrophysical sensitivity to merging binaries. Traditional alignment techniques involve differential wave-front sensing using multiple quadrant photodiodes but are often bandwidth restricted and limited by the sensing noise. We present a successful implementation of neural-network-based sensing and control at a GW observatory and demonstrate low-frequency control of the signal-recycling mirror at the GEO 600 detector. Alignment information for three critical optics is simultaneously extracted from the interferometric dark-port camera images via a convolutional neural net-long short-term memory network architecture and is then used for multiple-input-multiple-output control using soft actor-critic-based deep reinforcement learning. The overall sensitivity improvement achieved using our scheme demonstrates the capabilities of deep learning as a viable tool for real-time sensing and control for current and next-generation GW interferometers

First demonstration of neural sensing and control in a kilometer-scale gravitational wave observatory

Suspended optics in gravitational wave (GW) observatories are susceptible toalignment perturbations and, in particular, to slow drifts over time due tovariations in temperature and seismic levels. Such misalignments affect thecoupling of the incident laser beam into the optical cavities, degrade bothcirculating power and optomechanical photon squeezing, and thus decrease theastrophysical sensitivity to merging binaries. Traditional alignment techniquesinvolve differential wavefront sensing using multiple quadrant photodiodes, butare often restricted in bandwidth and are limited by the sensing noise. Wepresent the first-ever successful implementation of neural network-basedsensing and control at a gravitational wave observatory and demonstratelow-frequency control of the signal recycling mirror at the GEO 600 detector.Alignment information for three critical optics is simultaneously extractedfrom the interferometric dark port camera images via a CNN-LSTM networkarchitecture and is then used for MIMO control using soft actor-critic-baseddeep reinforcement learning. Overall sensitivity improvement achieved using ourscheme demonstrates deep learning's capabilities as a viable tool for real-timesensing and control for current and next-generation GW interferometers.<br

Gene Expression Profiling and Association Studies Implicate the Neuregulin Signaling Pathway in Behçet's Disease Susceptibility

Behçet's disease (BD) is a complex disease with genetic and environmental risk factors implicated in its etiology; however, its pathophysiology is poorly understood. To decipher BD's genetic underpinnings, we combined gene expression profiling with pathway analysis and association studies. We compared the gene expression profiles in peripheral blood mononuclear cells (PBMCs) of 15 patients and 14 matched controls using Affymetrix microarrays and found that the neuregulin signaling pathway was over-represented among the differentially expressed genes. The Epiregulin (EREG), Amphiregulin (AREG), and Neuregulin-1 (NRG1) genes of this pathway stand out as they are also among the top differentially expressed genes. Twelve haplotype tagging SNPs at the EREG-AREG locus and 15 SNPs in NRG1 found associated in at least one published BD genome-wide association study were tested for association with BD in a dataset of 976 Iranian patients and 839 controls. We found a novel association with BD for the rs6845297 SNP located downstream of EREG, and replicated three associations at NRG1 (rs4489285, rs383632, and rs1462891). Multifactor dimensionality reduction analysis indicated the existence of epistatic interactions between EREG and NRG1 variants. EREG-AREG and NRG1, which are members of the epidermal growth factor (EGF) family, seem to modulate BD susceptibility through main effects and gene–gene interactions. These association findings support a role for the EGF/ErbB signaling pathway inBD pathogenesis that warrants further investigation and highlight the importance of combining genetic and genomic approaches to dissect the genetic architecture of complex diseases

Status of the SOLEIL femtosecond X-ray source

http://accelconf.web.cern.ch/AccelConf/FEL2012/papers/wepd04.pdfInternational audienceAn electron bunch slicing setup is presently under construction on the SOLEIL storage ring for delivering 100 fs (rms) long photon pulses to two undulator-based beamlines providing soft (TEMPO) and hard X-rays (CRISTAL). Thanks to the non-zero dispersion function present in all straight sections of the storage ring, the sliced bunches can be easily separated from the core bunches. The modulator is a wiggler composed of 20 periods of 164.4 mm. It produces a magnetic field of 1.8 T at a minimum gap of 14.5 mm. To modulate the kinetic energy of the electrons in the wiggler, a Ti:Sa laser will be used, which produces 50 fs pulses at 800 nm with a repetition rate of 2.5 kHz. The laser beam is splitted into two branches in order to provide 2 mJ to the modulator and 0.5 mJ as pump pulse for the CRISTAL and TEMPO end stations. Focusing optics and beam path, from the laser hutch to the inside of the storage ring tunnel are presently under finalization. In this paper, we will report on the specificities of the SOLEIL setup, the status of its installation and the expected performances

High quality RNA isolation from Aedes aegypti midguts using laser microdissection microscopy

Background: Laser microdissection microscopy (LMM) has potential as a research tool because it allows precise excision of target tissues or cells from a complex biological specimen, and facilitates tissue-specific sample preparation. However, this method has not been used in mosquito vectors to date. To this end, we have developed an LMM method to isolate midgut RNA using Aedes aegypti

Revisiting the technical validation of tumour biomarker assays: how to open a Pandora's box

A tumour biomarker is a characteristic that is objectively measured and evaluated in tumour samples as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. The development of a biomarker contemplates distinct phases, including discovery by hypothesis-generating preclinical or exploratory studies, development and qualification of the assay for the identification of the biomarker in clinical samples, and validation of its clinical significance. Although guidelines for the development and validation of biomarkers are available, their implementation is challenging, owing to the diversity of biomarkers being developed. The term 'validation' undoubtedly has several meanings; however, in the context of biomarker research, a test may be considered valid if it is 'fit for purpose'. In the process of validation of a biomarker assay, a key point is the validation of the methodology. Here we discuss the challenges for the technical validation of immunohistochemical and gene expression assays to detect tumour biomarkers and provide suggestions of pragmatic solutions to address these challenges

LUNEX5: A French FEL Test Facility Light Source Proposal

http://accelconf.web.cern.ch/AccelConf/IPAC2012/papers/tuppp005.pdfInternational audienceLUNEX5 is a new Free Electron Laser (FEL) source project aimed at delivering short and coherent X-ray pulses to probe ultrafast phenomena at the femto-second scale, to investigate extremely low density samples as well as to image individual nm scale objects

Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: application to measurement of oestrogen and progesterone receptor levels in breast cancer

INTRODUCTION: Manual interpretation of immunohistochemistry (IHC) is a subjective, time-consuming and variable process, with an inherent intra-observer and inter-observer variability. Automated image analysis approaches offer the possibility of developing rapid, uniform indicators of IHC staining. In the present article we describe the development of a novel approach for automatically quantifying oestrogen receptor (ER) and progesterone receptor (PR) protein expression assessed by IHC in primary breast cancer. METHODS: Two cohorts of breast cancer patients (n = 743) were used in the study. Digital images of breast cancer tissue microarrays were captured using the Aperio ScanScope XT slide scanner (Aperio Technologies, Vista, CA, USA). Image analysis algorithms were developed using MatLab 7 (MathWorks, Apple Hill Drive, MA, USA). A fully automated nuclear algorithm was developed to discriminate tumour from normal tissue and to quantify ER and PR expression in both cohorts. Random forest clustering was employed to identify optimum thresholds for survival analysis. RESULTS: The accuracy of the nuclear algorithm was initially confirmed by a histopathologist, who validated the output in 18 representative images. In these 18 samples, an excellent correlation was evident between the results obtained by manual and automated analysis (Spearman\u27s rho = 0.9, P \u3c 0.001). Optimum thresholds for survival analysis were identified using random forest clustering. This revealed 7% positive tumour cells as the optimum threshold for the ER and 5% positive tumour cells for the PR. Moreover, a 7% cutoff level for the ER predicted a better response to tamoxifen than the currently used 10% threshold. Finally, linear regression was employed to demonstrate a more homogeneous pattern of expression for the ER (R = 0.860) than for the PR (R = 0.681). CONCLUSIONS: In summary, we present data on the automated quantification of the ER and the PR in 743 primary breast tumours using a novel unsupervised image analysis algorithm. This novel approach provides a useful tool for the quantification of biomarkers on tissue specimens, as well as for objective identification of appropriate cutoff thresholds for biomarker positivity. It also offers the potential to identify proteins with a homogeneous pattern of expression