Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging

Surface molecules, distributed in diverse patterns and clusters on cell membranes, influence vital functions of living cells. It is therefore important to understand their molecular surface organisation under different physiological and pathological conditions. Here, we present a model-free, quantitative method to determine the distribution of cell surface molecules based on TIRF illumination and super-resolution optical fluctuation imaging (SOFI). This SOFI-based approach is robust towards single emitter multiple-blinking events, high labelling densities and high blinking rates. In SOFI, the molecular density is not based on counting events, but results as an intrinsic property due to the correlation of the intensity fluctuations. The effectiveness and robustness of the method was validated using simulated data, as well as experimental data investigating the impact of palmitoylation on CD4 protein nanoscale distribution in the plasma membrane of resting T cells.Comment: 9 pages, 3 figures plus Supplementary Informatio

In-situ defect detection systems for R2R flexible PV films

The atomic layer deposition technique (ALD) is used to apply a thin (40-100 nm thick) barrier coating of Al2O3 on polymer substrates for flexible PV cells, to minimise and control the degradation caused by water vapour ingress. However, defects appearing on the film surfaces during the Al2O3 ALD growth have been seen to be highly significant in deterioration of the PV module efficiency and lifespan [1]. In order to improve the process yield and product efficiency, it is desirable to develop an inspection system that can detect transparent barrier film defects in the production line during film processing. Off-line detection of defects in transparent PV barrier films is difficult and time consuming. Consequently, implementing an accurate in-situ defects inspection system in the production environment is even more challenging, since the requirements on positioning, fast measurement, long term stability and robustness against environmental disturbance are demanding. For in-situ R2R defects inspection systems the following conditions need to be satisfied by the inspection tools. Firstly the measurement must be fast and have no physical contact with the inspected film surface. Secondly the measurement system must be robust against the environmental disturbance inspection. Finally the system should have sub-micrometre lateral resolution and nanometre vertical resolution in order to be able to distinguish defects on the film surface. Optical interferometry techniques have the potentially to be used as a solution for such application. However they are extremely sensitive to environmental noise such as mechanical vibration, air turbulence and temperature drift. George [2] reported that a single shot interferometry system “FlexCam” developed by 4D Technology being used currently to detect defects for PV barrier films manufactured by R2R technology. It is robust against environmental disturbances; but it has a limited vertical range, which is restricted by the phase ambiguity of the phase shift interferometry. This vertical measurement range (a few hundreds nanometres) is far less than the normal vertical range of defects (a few micrometres up to a few tens micrometres). It is not possible to detect the majority of defects in the R2R flexible PV barrier films

Supernova neutrino physics with a nuclear emulsion detector

The existence of the coherent neutrino-nucleus scattering reaction requires to evaluate, for any detector devoted to WIMP searches, the irreducible background due to conventional neutrino sources and at same time, it gives a unique chance to reveal supernova neutrinos. We report here a detailed study concerning a new directional detector, based on the nuclear emulsion technology. A Likelihood Ratio test shows that, in the first years of operations and with a detector mass of several tens of tons, the observation of the supernova signal can be achieved. The determination of the distance of the supernova from the neutrinos and the observation of $^8$B neutrinos are also discussed.Comment: 22 pages, 12 figure

Load management strategy for Particle-In-Cell simulations in high energy particle acceleration

In the wake of the intense effort made for the experimental CILEX project, numerical simulation cam- paigns have been carried out in order to finalize the design of the facility and to identify optimal laser and plasma parameters. These simulations bring, of course, important insight into the fundamental physics at play. As a by-product, they also characterize the quality of our theoretical and numerical models. In this paper, we compare the results given by different codes and point out algorithmic lim- itations both in terms of physical accuracy and computational performances. These limitations are illu- strated in the context of electron laser wakefield acceleration (LWFA). The main limitation we identify in state-of-the-art Particle-In-Cell (PIC) codes is computational load imbalance. We propose an innovative algorithm to deal with this specific issue as well as milestones towards a modern, accurate high-per- formance PIC code for high energy particle acceleration

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

An analysis of schema change intervention

Successful organizational transformation relies on being able to achieve paradigm or collective schema change, and more particularly, the ability to manage the interplay between pre-existing schemas and alternative schemas required for new environments. This conceptual paper presents an analysis and critique of collective schema change dynamics. Two schema change pathways are reflected in the literature: frame-juxtapose-transition and frame-disengage-learning. Research findings in each pathway are limited and/or contradictory. Moreover, research on schema change focuses primarily on social dynamics and less on the relationship between social schema change dynamics and individual schema change dynamics. One implication of this lack of focus on individual schema change dynamics is the masking of the high level of cognitive processing and cognitive effort required by individuals to effect schema change. The capacity to achieve organizational transformation requires that more attention is given to managing these dynamics, which, in turn, requires significant investment in developing the change leadership capabilities of managers and the organizations they manage