The status of intranetwork magnetic field observations with THEMIS

Weak magnetic fields inside network cells are an important component of the solar magnetic field and they have been observed by a number a groups in the past few years, which yielded important clues to their behavior. However it is important to confirm these different results and to state with greater precision the properties of intranetwork magnetic features (vector magnetic field, lifetime...). In this paper we describe some of the multi-line spectro-polarimetric observations made with the THEMIS telescope in June 2000 in order to study the intranetwork magnetic fields. We highlight the data processing and some limitations we met when performing this study

MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin

Background: A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by allele-specific gene expression that varies between clonal cell lineages. MAE is highly cell-type specific and mapping it in a large number of cell and tissue types can provide insight into its biological function. Its detection, however, remains challenging. Results: We previously reported that a sequence-independent chromatin signature identifies, with high sensitivity and specificity, genes subject to MAE in multiple tissue types using readily available ChIP-seq data. Here we present an implementation of this method as a user-friendly, open-source software pipeline for monoallelic gene inference from chromatin (MaGIC). The source code for the MaGIC pipeline and the Shiny app is available at https://github.com/gimelbrantlab/magic Conclusion: The pipeline can be used by researchers to map monoallelic expression in a variety of cell types using existing models and to train new models with additional sets of chromatin marks.National Institutes of Health (U.S.) (award U54 HG007963

CTCF binding site classes exhibit distinct evolutionary, genomic, epigenomic and transcriptomic features

CTCF DNA binding sites are classified into distinct functional classes, with distinct biological properties, shedding light on the differing functional roles of CTCF binding

Quantum device fine-tuning using unsupervised embedding learning

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes a novel approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of less than 30 minutes, and sometimes as little as 1 minute. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices

Sensitive radio-frequency read-out of quantum dots using an ultra-low-noise SQUID amplifier

Fault-tolerant spin-based quantum computers will require fast and accurate qubit readout. This can be achieved using radio-frequency reflectometry given sufficient sensitivity to the change in quantum capacitance associated with the qubit states. Here, we demonstrate a 23-fold improvement in capacitance sensitivity by supplementing a cryogenic semiconductor amplifier with a SQUID preamplifier. The SQUID amplifier operates at a frequency near 200 MHz and achieves a noise temperature below 600 mK when integrated into a reflectometry circuit, which is within a factor 120 of the quantum limit. It enables a record sensitivity to capacitance of 0.07 aF/ \sqrt{Hz}. The setup is used to acquire charge stability diagrams of a gate-defined double quantum dot in a short time with a signal-to-noise ration of about 38 in 1 μs of integration time

Report of the Working Group on Commercial Catches (WGCATCH)

The Working Group on Commercial Catches (WGCATCH), chaired by Hans Gerritsen (Ireland) and Nuno Prista (Sweden), met in Lisbon, Portugal, 9–13 November 2015. WGCATCH is responsible for documenting national fishery sampling schemes, establishing best practice and guidelines on sampling and estimation procedures, and providing advice on other uses of fishery data. The meeting was attended by 30 participants from 15 countries. The group addressed a large number of terms of reference and the meeting was con-ducted through presentations, discussions and analysis of questionnaires. The main terms of reference were addressed in subgroups. The report is structured directly along the terms of reference and the main outcomes are listed below. Data collection schemes for small-scale fisheries WGCATCH provided descriptions of national small-scale fisheries through question-naires. An overview was obtained on the current data collection methods. Two major approaches were identified - census (e.g., sales, logbooks) and sampling methods (e.g., catch surveys) - and their main pros and cons were discussed. In most cases, specific sampling approaches are needed for these fisheries. The group developed a work plan to establish good-practice guidelines. Analysis of case studies of commercial fishery sampling designs and estimation Case studies of sampling designs and estimation involving megrim in divisions 7-8 were presented. A common theme is that issues with practical implementation of prob-ability-based sampling remain. WGCATCH summarized the main issues and provided a set of possible solutions. The group also provided guidance on dealing with previous data collected under métier-based sampling designs. Simulation models to investigate survey designs Several simulation studies were presented, most of them outlining the work of fishPi project (funded under MARE/2014/19) in evaluating regional sampling designs. A crit-ical review was carried out and WGCATCH produced general considerations and guidelines. WGCATCH recommends that these are taken into account when analysing the results of simulations of regional sampling design at RCM level. The affect of the landing obligation on catch sampling opportunities The affects on sampling and data quality of the current implementation of the landing obligation in the Baltic were reviewed. The group found that refusal rates for observer trips have increased to nearly 100% in at least one country, while in many other coun-tries on-board observer programmes did not suffer noticeable changes. WGCATCH established that the catches below the minimum size cannot be accurately estimated by sampling the landings below the minimum size because an unknown proportion of the catches may be discarded. The group also reiterated that it is important that the logbooks distinguish landings below and above the minimum size. Publication on statistically sound sampling schemes WGCATCH drafted detailed plans to produce a peer-reviewed paper in 2016. The pa-per will provide a synthesis of the evolution of sampling design towards best practice, illustrated with a number of concise case studies. Estimation procedures in the Regional Database (RDB) The work of WKRDB 2015 presented alongside existing and planned estimation pro-cedures in the RDB. Current work by Norway on a software package that will allow design-based estimation and optimization for stock assessment purposes was also pre-sented. The advantages of ensuring compatibility of this new software with the devel-opments currently planned for RDB-FishFrame are underscored. Repository of resources relevant to catch sampling WGCATCH initiated a repository with key resources; putting them into context with brief descriptions or review of each report, paper, book, website, software package etc. The intention is for this repository to be made available online by ICES. Sampling of incidental bycatches WGCATCH agreed to start routine documentation of sampling practices for bycatches of protected, endangered and threatened species (PETS) and rare fish species as well as routine evaluation of the limitations of current methods for collection and analysis. Training course on Design and Analysis of Statistical Sound catch sampling pro-grammes WGCATCH considered continuous training and expertise on sampling design, estima-tion and simulation to be the basis for successful implementation of statistical sound catch sampling programs. A new ICES Training Course in Design and Analysis of Sta-tistical Sound will take place at ICES HQ in Copenhagen, 12–16 September 2016. WGCATCH recommends that RCMs promote the attendance of these meetings among all MS involved

A double blind randomized trial of wound infiltration with ropivacaine after breast cancer surgery with axillary nodes dissection

<p>Abstract</p> <p>Background</p> <p>The effect of local infiltration after breast surgery is controversial. This prospective double blind randomized study sought to document the analgesic effect of local anaesthetic infiltration after breast cancer surgery.</p> <p>Methods</p> <p>Patients scheduled for mastectomy or tumorectomy and axillary nodes dissection had immediate postoperative infiltration of the surgical wound with 20 ml of ropivacaine 7.5 mg.ml^-1or isotonic saline. Pain was assessed on a visual analogue scale at H2, H4, H6, H12, H24, H72, and at 2 month, at rest and on mobilization of the arm. Patient'comfort was evaluated with numerical 0-3 scales for fatigue, quality of sleep, state of mood, social function and activity.</p> <p>Results</p> <p>Twenty-two and 24 patients were included in the ropivacaine and saline groups respectively. Postoperative pain was lower at rest and on mobilization at 2, 4 and 6 hour after surgery in the ropivacaine group. No other difference in pain intensity and patient 'comfort scoring was documented during the first 3 postoperative days. Patients did not differ at 2 month for pain and comfort scores.</p> <p>Conclusion</p> <p>Single shot infiltration with ropivacaine transiently improves postoperative pain control after breast cancer surgery.</p> <p>Trial registration number</p> <p><a href="http://www.clinicaltrials.gov/ct2/show/NCT01404377">NCT01404377</a></p