Low noise charge injection in the CCD22

The inclusion of a charge injection structure on a charge coupled device (CCD) allows for the mitigation of charge transfer loss which can be caused by radiation induced charge trapping defects. Any traps present in the pixels of the CCD are filled by the injected charge as it is swept through the device and consequently, the charge transfer efficiency is improved in subsequently acquired images. To date, a number of different types of CCD have been manufactured featuring a variety of charge injection techniques. The e2v Technologies CCD22, used in the EPIC MOS focal plane instruments of XMM-Newton, is one such device and is the subject of this paper. A detailed understanding of charge injection operation and the use of charge injection to mitigate charge transfer losses resulting from radiation damage to CCDs will benefit a number of space projects planned for the future, including the ESA GAIA and X-ray Evolving Universe Spectrometry (XEUS) missions.The charge injection structure and mode of operation of the CCD22 are presented, followed by a detailed analysis of the uniformity and repeatability of the charge injection amplitude across the columns of the device. The effects of proton irradiation on the charge injection characteristics are also presented, in particular the effect of radiation induced bright pixels on the injected charge level

Ab initio structure prediction methods for battery materials a review of recent computational efforts to predict the atomic level structure and bonding in materials for rechargeable batteries

Portable electronic devices, electric vehicles and stationary energy storage applications, which encourage carbon-neutral energy alternatives, are driving demand for batteries that have concurrently higher energy densities, faster charging rates, safer operation and lower prices. These demands can no longer be met by incrementally improving existing technologies but require the discovery of new materials with exceptional properties. Experimental materials discovery is both expensive and time consuming: before the efficacy of a new battery material can be assessed, its synthesis and stability must be well-understood. Computational materials modelling can expedite this process by predicting novel materials, both in stand-alone theoretical calculations and in tandem with experiments. In this review, we describe a materials discovery framework based on density functional theory (DFT) to predict the properties of electrode and solid-electrolyte materials and validate these predictions experimentally. First, we discuss crystal structure prediction using the Ab initio random structure searching (AIRSS) method. Next, we describe how DFT results allow us to predict which phases form during electrode cycling, as well as the electrode voltage profile and maximum theoretical capacity. We go on to explain how DFT can be used to simulate experimentally measurable properties such as nuclear magnetic resonance (NMR) spectra and ionic conductivities. We illustrate the described workflow with multiple experimentally validated examples: materials for lithium-ion and sodium-ion anodes and lithium-ion solid electrolytes. These examples highlight the power of combining computation with experiment to advance battery materials research.(1) Gates Cambridge Trust, University of Cambridge, UK (2) EPSRC Centre for Doctoral Training in Computational Methods for Materials Science, UK, Grant No. EP/L015552/1. (3) Winton Programme for the Physics of Sustainability, University of Cambridge, UK (4) Sims Fund, University of Cambridge, UK (5) EPSRC Grant No. EP/P003532/1 (6) EPSRC Collaborative Computational Projects on the Electronic Structure of Condensed Matter (CCP9), Grant No. EP/M022595/1, and NMR crystallography, Grant No. EP/M022501/1 (7) Computing resources on the Tier 1 resource ARCHER were provided through the UKCP EPSRC High-End computational consortium (EP/P022561/1) and on the Tier 2 resources HPC Midlands+ (EP/P020232/1) and CSD3 (EP/P020259/1)

Natural recreational waters and the risk that exposure to antibiotic resistant bacteria poses to human health

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordAntimicrobial resistance (AMR) is widely recognised as a considerable threat to human health, wellbeing and prosperity. Many clinically important antibiotic resistance genes are understood to have originated in the natural environment. However, the complex interactions between humans, animals and the environment makes the health implications of environmental AMR difficult to quantify. This narrative review focuses on the current state of knowledge regarding antibiotic resistant bacteria (ARB) in natural bathing waters and implications for human health. It considers the latest research focusing on the transmission of ARB from bathing waters to humans. The limitations of existing evidence are discussed, as well as research priorities. The authors are of the opinion that future studies should include faecally contaminated bathing waters and people exposed to these environments to accurately parameterise environment-to-human transmission.Natural Environment Research CouncilMedical Research CouncilNatural Environment Research CouncilNatural Environment Research CouncilEnvironmental Protection Agenc

Molecular biology of breast cancer metastasis: Clinical implications of experimental studies on metastatic inefficiency

Recent technological advances have led to an increasing ability to detect isolated tumour cells and groups of tumour cells in patients' blood, lymph nodes or bone marrow. However, the clinical significance of these cells is unclear. Should they be considered as evidence of metastasis, necessitating aggressive treatment, or are they in some cases unrelated to clinical outcome? Quantitative experimental studies on the basic biology of metastatic inefficiency are providing clues that may help in understanding the significance of these cells. This understanding will be of use in guiding clinical studies to assess the significance of isolated tumour cells and micrometastases in cancer patients

Ageing well with diabetes: A workshop to co‐design research recommendations for improving the diabetes care of older people

Aims:To identify key research questions where answers could improve care for older people living with diabetes (PLWD), and provide detailed recommendations for researchers and research funders on how best to address them.Methods:A series of online research workshops were conducted, bringing together a range of PLWD and an acknowledged group of academic and clinical experts in their diabetes care to identify areas for future research. Throughout the pre-workshop phase, during each workshop, and in manuscript preparation and editing, PLWD played an active and dynamic role in discussions as part of both an iterative and narrative process.Results:The following key questions in this field were identified, and research recommendations for each were developed:How can we improve our understanding of the characteristics of older people living with diabetes (PLWD) and their outcomes, and can this deliver better person-centred care?How are services to care for older PLWD currently delivered, both for their diabetes and other conditions? How can we optimise and streamline the process and ensure everyone gets the best care, tailored to their individual needs?What tools might be used to evaluate the level of understanding of diabetes in the older population amongst non-specialist Healthcare Professionals (HCPs)?How can virtual experts or centres most effectively provide access to specialist multi-disciplinary team (MDT) expertise for older PLWD and the HCPs caring for them?Is a combination of exercise and a nutrition-dense, high protein diet effective in the prevention of the adverse effects of type 2 diabetes and deterioration in frailty, and how might this be delivered in a way which is acceptable to people with type 2 diabetes?How might we best use continuous glucose monitoring (CGM) in older people and, for those who require support, how should the data be shared?How can older PLWD be better empowered to manage their diabetes in their own home, particularly when living with additional long-term conditions?What are the benefits of models of peer support for older PLWD, both when living independently and when in care?Conclusions:This paper outlines recommendations supported by PLWD through which new research could improve their diabetes care and calls on the research community and funders to address them in future research programmes and strategies

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Informant-reported cognitive symptoms that predict amnestic mild cognitive impairment

<p>Abstract</p> <p>Background</p> <p>Differentiating amnestic mild cognitive impairment (aMCI) from normal cognition is difficult in clinical settings. Self-reported and informant-reported memory complaints occur often in both clinical groups, which then necessitates the use of a comprehensive neuropsychological examination to make a differential diagnosis. However, the ability to identify cognitive symptoms that are predictive of aMCI through informant-based information may provide some clinical utility in accurately identifying individuals who are at risk for developing Alzheimer's disease (AD).</p> <p>Methods</p> <p>The current study utilized a case-control design using data from an ongoing validation study of the Alzheimer's Questionnaire (AQ), an informant-based dementia assessment. Data from 51 cognitively normal (CN) individuals participating in a brain donation program and 47 aMCI individuals seen in a neurology practice at the same institute were analyzed to determine which AQ items differentiated aMCI from CN individuals.</p> <p>Results</p> <p>Forward stepwise multiple logistic regression analysis which controlled for age and education showed that 4 AQ items were strong indicators of aMCI which included: repetition of statements and/or questions [OR 13.20 (3.02, 57.66)]; trouble knowing the day, date, month, year, and time [OR 17.97 (2.63, 122.77)]; difficulty managing finances [OR 11.60 (2.10, 63.99)]; and decreased sense of direction [OR 5.84 (1.09, 31.30)].</p> <p>Conclusions</p> <p>Overall, these data indicate that certain informant-reported cognitive symptoms may help clinicians differentiate individuals with aMCI from those with normal cognition. Items pertaining to repetition of statements, orientation, ability to manage finances, and visuospatial disorientation had high discriminatory power.</p

Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis

BACKGROUND. Tuberculosis (TB) kills more people than any other infection, and new diagnostic tests to identify active cases are required. We aimed to discover and verify novel markers for TB in nondepleted plasma. / METHODS. We applied an optimized quantitative proteomics discovery methodology based on multidimensional and orthogonal liquid chromatographic separation combined with high-resolution mass spectrometry to study nondepleted plasma of 11 patients with active TB compared with 10 healthy controls. Prioritized candidates were verified in independent UK (n = 118) and South African cohorts (n = 203). / RESULTS. We generated the most comprehensive TB plasma proteome to date, profiling 5022 proteins spanning 11 orders-of-magnitude concentration range with diverse biochemical and molecular properties. We analyzed the predominantly low–molecular weight subproteome, identifying 46 proteins with significantly increased and 90 with decreased abundance (peptide FDR ≤ 1%, q ≤ 0.05). Verification was performed for novel candidate biomarkers (CFHR5, ILF2) in 2 independent cohorts. Receiver operating characteristics analyses using a 5-protein panel (CFHR5, LRG1, CRP, LBP, and SAA1) exhibited discriminatory power in distinguishing TB from other respiratory diseases (AUC = 0.81). / CONCLUSION. We report the most comprehensive TB plasma proteome to date, identifying novel markers with verification in 2 independent cohorts, leading to a 5-protein biosignature with potential to improve TB diagnosis. With further development, these biomarkers have potential as a diagnostic triage test. / FUNDING. Colciencias, Medical Research Council, Innovate UK, NIHR, Academy of Medical Sciences, Program for Advanced Research Capacities for AIDS, Wellcome Centre for Infectious Diseases Research

Modelling a two-stage adult population screen for autosomal dominant familial hypercholesterolaemia: cross-sectional analysis within the UK Biobank

Background Most people with autosomal dominant familial hypercholesterolaemia (FH) remain undetected, which represents a missed opportunity for coronary heart disease prevention. Objective To evaluate the performance of two-stage adult population screening for FH. Design Using data from UK Biobank, we estimated the screening performance of different low-density lipoprotein cholesterol (LDL-C) cut-offs (stage 1) to select adults for DNA sequencing (stage 2) to identify individuals with FH-causing variants in LDLR, APOB, PCSK9 and APOE. We estimated the number of additional FH cases detected by cascade testing of first-degree relatives of index cases and compared the overall approach with screening in childhood. Setting UK Biobank. Participants 140 439 unrelated participants of European ancestry from UK Biobank with information on circulating LDL-C concentration and exome sequence. Main outcome measures For different LDL-C cut-offs, we estimated the detection and false-positive rate, the proportion of individuals who would be referred for DNA sequencing (stage 1 screen positive rate), and the number of FH cases identified by population screening followed by cascade testing. Results We identified 488 individuals with an FH-causing variant and 139 951 without (prevalence 1 in 288). An LDL-C cut-off of >4.8 mmol/L had a stage 1 detection rate (sensitivity) of 40% (95% CI 36 to 44%) for a false-positive rate of 10% (95% CI 10 to 11%). Detection rate increased at lower LDL-C cut-offs but at the expense of higher false-positive and screen positive rates, and vice versa. Two-stage screening of 100 000 adults using an LDL-C cut-off of 4.8 mmol/L would generate 10 398 stage 1 screen positives for sequencing, detect 138 FH cases and miss 209. Up to 207 additional cases could be detected through two-generation cascade testing of first-degree relatives. By comparison, based on previously published data, childhood screening followed by cascade testing was estimated to detect nearly three times as many affected individuals for around half the sequencing burden. Conclusions Two-stage adult population screening for FH could help achieve the 25% FH case detection target set in the National Health Service Long Term Plan, but less efficiently than childhood screening and with a greater sequencing requirement