Leaders, leadership and future primary care clinical research

Background: A strong and self confident primary care workforce can deliver the highest quality care and outcomes equitably and cost effectively. To meet the increasing demands being made of it, primary care needs its own thriving research culture and knowledge base. Methods: Review of recent developments supporting primary care clinical research. Results: Primary care research has benefited from a small group of passionate leaders and significant investment in recent decades in some countries. Emerging from this has been innovation in research design and focus, although less is known of the effect on research output. Conclusion: Primary care research is now well placed to lead a broad re-vitalisation of academic medicine, answering questions of relevance to practitioners, patients, communities and Government. Key areas for future primary care research leaders to focus on include exposing undergraduates early to primary care research, integrating this early exposure with doctoral and postdoctoral research career support, further expanding cross disciplinary approaches, and developing useful measures of output for future primary care research investment

Replication and Recombination Factors Contributing to Recombination-Dependent Bypass of DNA Lesions by Template Switch

Damage tolerance mechanisms mediating damage-bypass and gap-filling are crucial for genome integrity. A major damage tolerance pathway involves recombination and is referred to as template switch. Template switch intermediates were visualized by 2D gel electrophoresis in the proximity of replication forks as X-shaped structures involving sister chromatid junctions. The homologous recombination factor Rad51 is required for the formation/stabilization of these intermediates, but its mode of action remains to be investigated. By using a combination of genetic and physical approaches, we show that the homologous recombination factors Rad55 and Rad57, but not Rad59, are required for the formation of template switch intermediates. The replication-proficient but recombination-defective rfa1-t11 mutant is normal in triggering a checkpoint response following DNA damage but is impaired in X-structure formation. The Exo1 nuclease also has stimulatory roles in this process. The checkpoint kinase, Rad53, is required for X-molecule formation and phosphorylates Rad55 robustly in response to DNA damage. Although Rad55 phosphorylation is thought to activate recombinational repair under conditions of genotoxic stress, we find that Rad55 phosphomutants do not affect the efficiency of X-molecule formation. We also examined the DNA polymerase implicated in the DNA synthesis step of template switch. Deficiencies in translesion synthesis polymerases do not affect X-molecule formation, whereas DNA polymerase δ, required also for bulk DNA synthesis, plays an important role. Our data indicate that a subset of homologous recombination factors, together with DNA polymerase δ, promote the formation of template switch intermediates that are then preferentially dissolved by the action of the Sgs1 helicase in association with the Top3 topoisomerase rather than resolved by Holliday Junction nucleases. Our results allow us to propose the choreography through which different players contribute to template switch in response to DNA damage and to distinguish this process from other recombination-mediated processes promoting DNA repair

A Pre-Landing Assessment of Regolith Properties at the InSight Landing Site

This article discusses relevant physical properties of the regolith at the Mars InSight landing site as understood prior to landing of the spacecraft. InSight will land in the northern lowland plains of Mars, close to the equator, where the regolith is estimated to be ≥3--5 m thick. These investigations of physical properties have relied on data collected from Mars orbital measurements, previously collected lander and rover data, results of studies of data and samples from Apollo lunar missions, laboratory measurements on regolith simulants, and theoretical studies. The investigations include changes in properties with depth and temperature. Mechanical properties investigated include density, grain-size distribution, cohesion, and angle of internal friction. Thermophysical properties include thermal inertia, surface emissivity and albedo, thermal conductivity and diffusivity, and specific heat. Regolith elastic properties not only include parameters that control seismic wave velocities in the immediate vicinity of the Insight lander but also coupling of the lander and other potential noise sources to the InSight broadband seismometer. The related properties include Poisson’s ratio, P- and S-wave velocities, Young’s modulus, and seismic attenuation. Finally, mass diffusivity was investigated to estimate gas movements in the regolith driven by atmospheric pressure changes. Physical properties presented here are all to some degree speculative. However, they form a basis for interpretation of the early data to be returned from the InSight mission.Additional co-authors: Nick Teanby and Sharon Keda

Does the evidence referenced in NICE guidelines reflect a primary care population?

BACKGROUND: Guidelines are a common and important tool in providing high-quality health care. The National Institute for Health and Clinical Excellence (NICE) guidelines are now being used to set standards for assessing the quality of care in UK general practice, and so the evidence behind them needs to be relevant to primary care. AIM: To assess the extent to which guideline recommendations aimed at primary care are based on research conducted in a primary care setting. DESIGN OF STUDY: Purposeful selection of a sample of NICE guidelines for conditions commonly seen in general practice, with identification of the evidence underpinning recommendations that are relevant to primary care. METHOD: Three recent NICE guidelines were selected: chronic obstructive pulmonary disease (COPD), hypertension, and respiratory tract infection in adults and children. Publications referenced as evidence for each individual primary care relevant recommendation were classified as to whether or not they were based in primary care relevant settings. RESULTS: In the three guidelines assessed, 160 studies were used to derive the 115 recommendations that were relevant to, or aimed at primary care. A wide variation was found in the proportion of studies that recruited patients from a setting relevant to primary care (range 26% to 80%). CONCLUSION: In this sample of three NICE guidelines, a significant proportion of studies underlying the primary care relevant recommendations were derived from studies that were not conducted in that setting. In producing guidelines for a primary care audience, the guideline development groups should include explicit information about the setting of studies underpinning the recommendations

Artificial Neural Networks on FPGAs for Real-Time Energy Reconstruction of the ATLAS LAr Calorimeters

International audienceThe ATLAS experiment at the Large Hadron Collider (LHC) is operated at CERN and measures proton–proton collisions at multi-TeV energies with a repetition frequency of 40 MHz. Within the phase-II upgrade of the LHC, the readout electronics of the liquid-argon (LAr) calorimeters of ATLAS are being prepared for high luminosity operation expecting a pileup of up to 200 simultaneous proton–proton interactions. Moreover, the calorimeter signals of up to 25 subsequent collisions are overlapping, which increases the difficulty of energy reconstruction by the calorimeter detector. Real-time processing of digitized pulses sampled at 40 MHz is performed using field-programmable gate arrays (FPGAs). To cope with the signal pileup, new machine learning approaches are explored: convolutional and recurrent neural networks outperform the optimal signal filter currently used, both in assignment of the reconstructed energy to the correct proton bunch crossing and in energy resolution. The improvements concern in particular energies derived from overlapping pulses. Since the implementation of the neural networks targets an FPGA, the number of parameters and the mathematical operations need to be well controlled. The trained neural network structures are converted into FPGA firmware using automated implementations in hardware description language and high-level synthesis tools. Very good agreement between neural network implementations in FPGA and software based calculations is observed. The prototype implementations on an Intel Stratix-10 FPGA reach maximum operation frequencies of 344–640 MHz. Applying time-division multiplexing allows the processing of 390–576 calorimeter channels by one FPGA for the most resource-efficient networks. Moreover, the latency achieved is about 200 ns. These performance parameters show that a neural-network based energy reconstruction can be considered for the processing of the ATLAS LAr calorimeter signals during the high-luminosity phase of the LHC

Machine Learning for Real-Time Processing of ATLAS Liquid Argon Calorimeter Signals with FPGAs

The Phase-II upgrade of the LHC will increase its instantaneous luminosity by a factor of 7 leading to the High Luminosity LHC (HL-LHC). At the HL-LHC, the number of proton-proton collisions in one bunch crossing (called pileup) increases significantly, putting more stringent requirements on the LHC detectors electronics and real-time data processing capabilities. The ATLAS Liquid Argon (LAr) calorimeter measures the energy of particles produced in LHC collisions. This calorimeter has also trigger capabilities to identify interesting events. In order to enhance the ATLAS detector physics discovery potential, in the blurred environment created by the pileup, an excellent resolution of the deposited energy and an accurate detection of the deposited time is crucial. The computation of the deposited energy is performed in real-time using dedicated data acquisition electronic boards based on FPGAs. FPGAs are chosen for their capacity to treat large amount of data with very low latency. The computation of the deposited energy is currently done using optimal filtering algorithms that assume a nominal pulse shape of the electronic signal. These filter algorithms are adapted to the ideal situation with very limited pileup and no timing overlap of the electronic pulses in the detector. However, with the increased luminosity and pileup, the performance of the filter algorithms decreases significantly and no further extension nor tuning of these algorithms could recover the lost performance. The back-end electronic boards for the Phase-II upgrade of the LAr calorimeter will use the next high-end generation of INTEL FPGAs with increased processing power and memory. This is a unique opportunity to develop the necessary tools, enabling the use of more complex algorithms on these boards. We developed several neural networks (NNs) with significant performance improvements with respect to the optimal filtering algorithms. The main challenge is to efficiently implement these NNs into the dedicated data acquisition electronics. Special effort was dedicated to minimising the needed computational power while optimising the NNs architectures. Five NN algorithms based on CNN, RNN, and LSTM architectures will be presented. The improvement of the energy resolution and the accuracy on the deposited time compared to the legacy filter algorithms, especially for overlapping pulses, will be discussed. The implementation of these networks in firmware will be shown. Two implementation categories in VHDL and Quartus HLS code are considered. The implementation results on Stratix 10 INTEL FPGAs, including the resource usage, the latency, and operation frequency will be reported. Approximations for the firmware implementations, including the use of fixed-point precision arithmetic and lookup tables for activation functions, will be discussed. Implementations including time multiplexing to reduce resource usage will be presented. We will show that two of these NNs implementations are viable solutions that fit the stringent data processing requirements on the latency (O(100ns)) and bandwidth (O(1Tb/s) per FPGA) needed for the ATLAS detector operation

Managing multimorbidity in primary care

Approximately eight in ten of all GP consultations involve a patient with multimorbidity. An average consultation covers 2.5 problems, but those involving a patient with multimorbidity will often be more complex, usually without additional time being available to address the extra problems. As the population ages the prevalence of multimorbidity and the pressures it creates will increase. Although there is little evidence to suggest the best way to deliver care for these patients, it is apparent that the use of single-disease-oriented guidelines without due regard for the individual is often inappropriate. GPs need the confidence and ability to interpret evidence-based recommendations within the context of individual patients. This article discusses the growing phenomenon of multimorbidity, its impact on patients, GPs and the health service, and outlines the skills required of GPs to provide optimal management