Data compression for the microgravity experiments

Researchers present the environment and conditions under which data compression is to be performed for the microgravity experiment. Also presented are some coding techniques that would be useful for coding in this environment. It should be emphasized that researchers are currently at the beginning of this program and the toolkit mentioned is far from complete

Human helminth therapy to treat inflammatory disorders - where do we stand?

Parasitic helminths have evolved together with the mammalian immune system over many millennia and as such they have become remarkably efficient modulators in order to promote their own survival. Their ability to alter and/or suppress immune responses could be beneficial to the host by helping control excessive inflammatory responses and animal models and pre-clinical trials have all suggested a beneficial effect of helminth infections on inflammatory bowel conditions, MS, asthma and atopy. Thus, helminth therapy has been suggested as a possible treatment method for autoimmune and other inflammatory disorders in humans

Opportunities for topical antimicrobial therapy: permeation of canine skin by fusidic acid

BACKGROUND: Staphylococcal infection of the canine epidermis and hair follicle is amongst the commonest reasons for antimicrobial prescribing in small animal veterinary practice. Topical therapy with fusidic acid (FA) is an attractive alternative to systemic therapy based on low minimum inhibitory concentrations (MICs, commonly <0.03 mg/l) documented in canine pathogenic staphylococci, including strains of MRSA and MRSP (methicillin-resistant Staphylococcus aureus and S. pseudintermedius). However, permeation of canine skin by FA has not been evaluated in detail. This study aimed to define the degree and extent of FA permeation in canine skin in vitro from two sites with different hair follicle density following application of a licensed ophthalmic formulation that shares the same vehicle as an FA-betamethasone combination product approved for dermal application in dogs. Topical FA application was modelled using skin held in Franz-type diffusion cells. Concentrations of FA in surface swabs, receptor fluid, and transverse skin sections of defined anatomical depth were determined using high-performance liquid chromatography and ultraviolet (HPLC-UV) analysis. RESULTS: The majority of FA was recovered by surface swabs after 24 h, as expected (mean ± SEM: 76.0 ± 17.0%). FA was detected within 424/470 (90%) groups of serial sections of transversely cryotomed skin containing follicular infundibula, but never in 48/48 (100%) groups of sections containing only deeper follicular structures, nor in receptor fluid, suggesting that FA does not permeate beyond the infundibulum. The FA concentration (mean ± SEM) in the most superficial 240 μm of skin was 2000 ± 815 μg/g. CONCLUSIONS: Topically applied FA can greatly exceed MICs for canine pathogenic staphylococci at the most common sites of infection. Topical FA therapy should now be evaluated using available formulations in vivo as an alternative to systemic therapy for canine superficial bacterial folliculitis.Peer reviewedFinal Published versio

Despite the suspension of conditionality, benefit claimants are already looking for work

The unique challenges that COVID-19 presents have meant a ‘pause’ in overt work-related requirements. Despite this, and the dire job prospects facing many, the majority of the new COVID-19 cohort of benefit claimants who do not have a job are actively looking for work, find Daniel Edmiston, Ben Baumberg Geiger, Lisa Scullion, Jo Ingold and Kate Summers. This questions many of the assumptions that underpin our increasingly conditional social security system and should encourage policymakers to rethink what income and employment support might look like as we move beyond this pandemic

Σ\Sigma-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction

We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps. The networks in $\Sigma$-net are trained in a supervised way, including content and GAN losses, and with various ways of data consistency, i.e., proximal mappings, gradient descent and variable splitting. A semi-supervised finetuning scheme allows us to adapt to the k-space data at test time, which, however, decreases the quantitative metrics, although generating the visually most textured and sharp images. For this challenge, we focused on robust and high SSIM scores, which we achieved by ensembling all models to a $\Sigma$-net.Comment: fastMRI challenge submission (team: holykspace

Search for the standard model Higgs boson in the H to ZZ to 2l 2nu channel in pp collisions at sqrt(s) = 7 TeV

A search for the standard model Higgs boson in the H to ZZ to 2l 2nu decay channel, where l = e or mu, in pp collisions at a center-of-mass energy of 7 TeV is presented. The data were collected at the LHC, with the CMS detector, and correspond to an integrated luminosity of 4.6 inverse femtobarns. No significant excess is observed above the background expectation, and upper limits are set on the Higgs boson production cross section. The presence of the standard model Higgs boson with a mass in the 270-440 GeV range is excluded at 95% confidence level.Comment: Submitted to JHE

Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction

We present a deep network interpolation strategy for accelerated parallel MR image reconstruction. In particular, we examine the network interpolation in parameter space between a source model that is formulated in an unrolled scheme with L1 and SSIM losses and its counterpart that is trained with an adversarial loss. We show that by interpolating between the two different models of the same network structure, the new interpolated network can model a trade-off between perceptual quality and fidelity.Comment: Presented at 2020 ISMRM Conference & Exhibition (Abstract #4958

Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination

PurposeTo systematically investigate the influence of various data consistency layers and regularization networks with respect to variations in the training and test data domain, for sensitivity-encoded accelerated parallel MR image reconstruction.Theory and MethodsMagnetic resonance (MR) image reconstruction is formulated as a learned unrolled optimization scheme with a down-up network as regularization and varying data consistency layers. The proposed networks are compared to other state-of-the-art approaches on the publicly available fastMRI knee and neuro dataset and tested for stability across different training configurations regarding anatomy and number of training samples.ResultsData consistency layers and expressive regularization networks, such as the proposed down-up networks, form the cornerstone for robust MR image reconstruction. Physics-based reconstruction networks outperform post-processing methods substantially for R = 4 in all cases and for R = 8 when the training and test data are aligned. At R = 8, aligning training and test data is more important than architectural choices.ConclusionIn this work, we study how dataset sizes affect single-anatomy and cross-anatomy training of neural networks for MRI reconstruction. The study provides insights into the robustness, properties, and acceleration limits of state-of-the-art networks, and our proposed down-up networks. These key insights provide essential aspects to successfully translate learning-based MRI reconstruction to clinical practice, where we are confronted with limited datasets and various imaged anatomies.</p