The diagnostic Accuracy of Visual versus automated dipstick proteinuria testing in Pregnancy: A systematic review and Meta-Analysis.

OBJECTIVE: To evaluate the diagnostic accuracy of point-of-care (POC) tests for detecting proteinuria in pregnant women. DESIGN: Systematic review and meta-analysis. DATA SOURCES: MEDLINE and EMBASE databases were searched from inception to 13 November 2020. ELIGIBILITY CRITERIA AND DATA ANALYSIS: Included studies measured the sensitivity and specificity ofPOC proteinuria testing compared to laboratory reference standards (protein-creatinine ratio (PCR), 24-hour urine collection). Bivariate meta-analyses determined pooled sensitivity and specificity. Random-effects inverse-variance model determinedheterogeneity. MAIN OUTCOME MEASURES: The primary outcome was overall sensitivity and specificity, stratified by method of POC testing and reference standard. Secondary outcomes were sensitivity and specificity within thesubgroupstest brand, reference standard, and hypertension status. RESULTS: 1078 studies were identified, 17 studies comprising 23 comparisons were included. The meta-analysis included 13 studies with 19 comparisons. Pooled sensitivity and specificity of visual dipsticks against PCR was 72 % (95 % CI: 56 % to 84 %) and 92 % (95 % CI: 76 % to 98 %), respectively. Pooled sensitivity and specificity of visual dipsticks against 24-hour collection was 69 % (55 % to 80 %) and 70 % (51 % to 84 %), respectively. Pooled sensitivity and specificity for automated readers against PCR was 73 % (53 % to 86 %) and 91 % (83 % to 95 %), respectively. Pooled sensitivity and specificity of automated readers against 24-hour collection was 65 % (42 % to 83 %) and 82 % (46 % to 96 %), respectively. CONCLUSION: Visual dipsticks have comparable accuracy to automated readers, yet are notadequate as a rule-out test for proteinuria. Proteinuria POC testing maybe beneficial inantenatal care when repeatfollow-up tests are performed. PROSPERO Registration Number: CRD42021231914.

[An intracranial haemorrhage in a febrile newborn]. / L'image du mois. Une hémorragie intracrânienne chez un nouveau-né fébrile.

A 15 days old newborn was admitted to the emergency room for a recent fever onset. In newborns, the priority is to rule out any bacterial infection and initiate broad spectrum antibiotics. The hemorrhagic appearance of the cerebrospinal fluid after lumbar puncture can sometimes be the only sign to suspect, other than obvious infectious aetiology, an intracranial haemorrhage.

: Un nouveau-né de 15 jours est admis en salle d'urgence pour de la fièvre. Au vu de l'immaturité immunitaire des nouveau-nés et selon les recommandations internationales, la priorité est d'exclure une infection d'origine bactérienne et d'administrer une antibiothérapie intraveineuse empirique. Lors de la réalisation du bilan étiologique, l'aspect hémorragique du liquide céphalo-rachidien peut être le témoin d'un syndrome méningé d'étiologie autre qu'infectieuse, et notamment d'une hémorragie intracérébrale.

Association between coffee consumption with serum lipid profile in ELSA-Brasil study: a metabolomic approach.

PURPOSE: This study evaluated the association between coffee consumption and serum lipid profile in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). METHODS: This is a cross-sectional study on baseline data from participants of the cohort ELSA-Brasil. Only participants of São Paulo Research Center who underwent a nuclear magnetic resonance (NMR) spectroscopy examination of lipid profile were included (N = 4736). Coffee intake was categorized into four categories (cups/day, in reference cup size of 50 mL, which is the household measure adopted in Brazil): never/almost never, ≤ 1, 1-3, and > 3. Serum lipid profile [i.e., Total Cholesterol (TC), Total Triglycerides (TG), Very Low-Density Lipoprotein-cholesterol (VLDL-c), Low-Density Lipoprotein-cholesterol (LDL-c), High-Density Lipoprotein-cholesterol (HDL-c), Triglyceride-rich Lipoprotein Particles (TRLP) and subfractions particles] was analyzed. To estimate the effect of coffee consumption on serum lipid profile, multivariate Generalized Linear Models were performed. RESULTS: Compared to participants who never or almost never drink coffee, individuals who consumed more than 3 cups/day showed an increase in concentrations of TC (ß: 4.13; 95% CI 0.81, 7.45), TG (ß: 9.53; 95% CI 1.65, 17.42), VLDL-c (ß: 1.90; 95% CI 0.38, 3.42), TRLP (ß: 8.42; 95% CI 1.24, 15.60), and Very Small-TRLP and Medium-TRLP subfractions (ß: 7.36; 95% CI 0.21, 14.51; ß: 2.53; 95% CI 0.89, 4.16, respectively), but not with HDL-c and LDL-c. Among individuals with low (≤ 1 cup/day) and moderate (1-3 cups/day) coffee consumption, no significant associations with lipids was observed. CONCLUSION: High coffee consumption (more than 3 cups per day) was associated with an increase in serum lipids, namely TC, TG, VLDL-c, and TRL particles, highlighting the importance of a moderate consumption of this beverage.

Changes to management of hypertension in pregnancy, and attitudes to self-management: An online survey of obstetricians, before and following the first wave of the COVID-19 pandemic.

OBJECTIVE: This study aimed to understand the views and practice of obstetricians regarding self-monitoring for hypertensive disorders of pregnancy (blood pressure (BP) and proteinuria), the potential for self-management (including actions taken on self-monitored parameters) and to understand the impact of the COVID-19 pandemic on such views. DESIGN: Cross-sectional online survey pre- and post- the first wave of the COVID-19 pandemic. SETTING AND SAMPLE: UK obstetricians recruited via an online portal. METHODS: A survey undertaken in two rounds: December 2019-January 2020 (pre-pandemic), and September-November 2020 (during pandemic) RESULTS: 251 responses were received across rounds one (150) and two (101). Most obstetricians considered that self-monitoring of BP and home urinalysis had a role in guiding clinical decisions and this increased significantly following the first wave of the COVID-19 pandemic (88%, (132/150) 95%CI: 83-93% first round vs 96% (95%CI: 92-94%), (97/101), second round; p = 0.039). Following the pandemic, nearly half were agreeable to women self-managing their hypertension by using their own readings to make a pre-agreed medication change themselves (47%, 47/101 (95%CI: 37-57%)). CONCLUSIONS: A substantial majority of UK obstetricians considered that self-monitoring had a role in the management of pregnancy hypertension and this increased following the pandemic. Around half are now supportive of women having a wider role in self-management of hypertensive treatment. Maximising the potential of such changes in pregnancy hypertension management requires further work to understand how to fully integrate women's own measurements into clinical care.

Calibration of an in-situ fluorescence-based sensor platform for reliable BOD5 measurement in wastewater.

Reliance on biochemical oxygen demand (BOD5) as an indicator of wastewater quality has hindered the development of efficient process control due to the associated uncertainty and lag-times. Surrogate measurements have been proposed, with fluorescence spectroscopy a promising technique. Yet, assessment of in-situ fluorescence sensors across multiple wastewater treatment plants (WwTPs), and at different treatment stages, is limited. In this study a multi-parameter sonde (two fluorescence peaks, turbidity, temperature and electrical conductivity) was used to provide a BOD5 surrogate measurement. The sonde was deployed at three WwTPs, on post primary settlement tanks (PST) and final effluent (FE). Triplicate laboratory measurements of BOD5, from independent laboratories were used to calibrate the sensor, with high variability apparent for FE samples. Site and process specific sensor calibrations yielded the best results (R2cv = 0.76-0.86; 10-fold cross-validation) and mean BOD5 of the three laboratory measurements improved FE calibration. When combining PST sites a reasonable calibration was still achieved (R2cv = 0.67) suggesting transfer of sensors between WwTPs may be possible. This study highlights the potential to use online optical sensors as robust BOD5 surrogates in WwTPs. However, careful calibration (i.e. replicated BOD5 measurements) is required for FE as laboratory measurements can be associated with high uncertainty.

Predicting Antimicrobial Resistance Using Partial Genome Alignments.

Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions. IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.

Deep medical image analysis with representation learning and neuromorphic computing.

Deep learning is increasingly used in medical imaging, improving many steps of the processing chain, from acquisition to segmentation and anomaly detection to outcome prediction. Yet significant challenges remain: (i) image-based diagnosis depends on the spatial relationships between local patterns, something convolution and pooling often do not capture adequately; (ii) data augmentation, the de facto method for learning three-dimensional pose invariance, requires exponentially many points to achieve robust improvement; (iii) labelled medical images are much less abundant than unlabelled ones, especially for heterogeneous pathological cases; and (iv) scanning technologies such as magnetic resonance imaging can be slow and costly, generally without online learning abilities to focus on regions of clinical interest. To address these challenges, novel algorithmic and hardware approaches are needed for deep learning to reach its full potential in medical imaging.

Cone-beam CT for imaging of the head/brain: Development and assessment of scanner prototype and reconstruction algorithms.

PURPOSE: Our aim was to develop a high-quality, mobile cone-beam computed tomography (CBCT) scanner for point-of-care detection and monitoring of low-contrast, soft-tissue abnormalities in the head/brain, such as acute intracranial hemorrhage (ICH). This work presents an integrated framework of hardware and algorithmic advances for improving soft-tissue contrast resolution and evaluation of its technical performance with human subjects. METHODS: Four configurations of a CBCT scanner prototype were designed and implemented to investigate key aspects of hardware (including system geometry, antiscatter grid, bowtie filter) and technique protocols. An integrated software pipeline (c.f., a serial cascade of algorithms) was developed for artifact correction (image lag, glare, beam hardening and x-ray scatter), motion compensation, and three-dimensional image (3D) reconstruction [penalized weighted least squares (PWLS), with a hardware-specific statistical noise model]. The PWLS method was extended in this work to accommodate multiple, independently moving regions with different resolution (to address both motion compensation and image truncation). Imaging performance was evaluated quantitatively and qualitatively with 41 human subjects in the neurosciences critical care unit (NCCU) at our institution. RESULTS: The progression of four scanner configurations exhibited systematic improvement in the quality of raw data by variations in system geometry (source-detector distance), antiscatter grid, and bowtie filter. Quantitative assessment of CBCT images in 41 subjects demonstrated: ~70% reduction in image nonuniformity with artifact correction methods (lag, glare, beam hardening, and scatter); ~40% reduction in motion-induced streak artifacts via the multi-motion compensation method; and ~15% improvement in soft-tissue contrast-to-noise ratio (CNR) for PWLS compared to filtered backprojection (FBP) at matched resolution. Each of these components was important to improve contrast resolution for point-of-care cranial imaging. CONCLUSIONS: This work presents the first application of a high-quality, point-of-care CBCT system for imaging of the head/ brain in a neurological critical care setting. Hardware configuration iterations and an integrated software pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to achieve image quality that could be valuable for point-of-care detection and monitoring of a variety of intracranial abnormalities, including ICH and hydrocephalus.

The Future of Neurocritical Care Research: Proceedings and Recommendations from the Fifth Neurocritical Care Research Network Conference.

The Fifth Neurocritical Care Research Network (NCRN) Conference held in Boca Raton, Florida, in September of 2018 was devoted to challenging the current status quo and examining the role of the Neurocritical Care Society (NCS) in driving the science and research of neurocritical care. The aim of this in-person meeting was to set the agenda for the NCS's Neurocritical Care Research Central, which is the overall research arm of the society. Prior to the meeting, all 103 participants received educational content (book and seminar) on the 'Blue Ocean Strategy®,' a concept from the business world which aims to identify undiscovered and uncontested market space, and to brainstorm innovative ideas and methods with which to address current challenges in neurocritical care research. Three five-member working groups met at least four times by teleconference prior to the in-person meeting to prepare answers to a set of questions using the Blue Ocean Strategy concept as a platform. At the Fifth NCRN Conference, these groups presented to a five-member jury and all attendees for open discussion. The jury then developed a set of recommendations for NCS to consider in order to move neurocritical care research forward. We have summarized the topics discussed at the conference and put forward recommendations for the future direction of the NCRN and neurocritical care research in general.

Self-monitoring of Blood Pressure in Patients With Hypertension-Related Multi-morbidity: Systematic Review and Individual Patient Data Meta-analysis.

BACKGROUND: Studies have shown that self-monitoring of blood pressure (BP) is effective when combined with co-interventions, but its efficacy varies in the presence of some co-morbidities. This study examined whether self-monitoring can reduce clinic BP in patients with hypertension-related co-morbidity. METHODS: A systematic review was conducted of articles published in Medline, Embase, and the Cochrane Library up to January 2018. Randomized controlled trials of self-monitoring of BP were selected and individual patient data (IPD) were requested. Contributing studies were prospectively categorized by whether they examined a low/high-intensity co-intervention. Change in BP and likelihood of uncontrolled BP at 12 months were examined according to number and type of hypertension-related co-morbidity in a one-stage IPD meta-analysis. RESULTS: A total of 22 trials were eligible, 16 of which were able to provide IPD for the primary outcome, including 6,522 (89%) participants with follow-up data. Self-monitoring was associated with reduced clinic systolic BP compared to usual care at 12-month follow-up, regardless of the number of hypertension-related co-morbidities (-3.12 mm Hg, [95% confidence intervals -4.78, -1.46 mm Hg]; P value for interaction with number of morbidities = 0.260). Intense interventions were more effective than low-intensity interventions in patients with obesity (P < 0.001 for all outcomes), and possibly stroke (P < 0.004 for BP control outcome only), but this effect was not observed in patients with coronary heart disease, diabetes, or chronic kidney disease. CONCLUSIONS: Self-monitoring lowers BP regardless of the number of hypertension-related co-morbidities, but may only be effective in conditions such obesity or stroke when combined with high-intensity co-interventions.

Variability in body temperature in healthy adults and in patients receiving chemotherapy: prospective observational cohort study.

Between-individual variability of body temperature has been little investigated, but is of clinical importance: for example, in detection of neutropenic sepsis during chemotherapy. We studied within-person and between-person variability in temperature in healthy adults and those receiving chemotherapy using a prospective observational design involving 29 healthy participants and 23 patients undergoing chemotherapy. Primary outcome was oral temperature. We calculated each patient's mean temperature, standard deviation within each patient (within-person variability), and between patients (between-person variability). Secondary analysis explored temperature changes in the three days before admission for neutropenic sepsis. 1,755 temperature readings were returned by healthy participants and 1,765 by chemotherapy patients. Mean participant temperature was 36.16 C (95% CI 36.07-36.26) in healthy participants and 36.32 C (95% CI 36.18-36.46) in chemotherapy patients. Healthy participant within-person variability was 0.40 C (95% CI 0.36-0.44) and between-person variability was 0.26 C (95% CI 0.16-0.35). Chemotherapy patient within-person variability was 0.39 C (95% CI 0.34-0.44) and between-person variability was 0.34 C (95% CI 0.26-0.48). Thus, use of a population mean rather than personalised baselines is probably sufficient for most clinical purposes as between-person variability is not large compared to within-person variability. Standardised guidance and provision of thermometers to patients might help to improve recording and guide management.

Community-based active case-finding to reach the most vulnerable: tuberculosis in tribal areas of India.

India has the world's largest indigenous/tribal population. Many areas with large tribal populations suffer from weak infrastructure and services. Surveys have shown a high prevalence of TB among Saharia communities, who rarely access public services. We evaluated a community-based active TB case-finding intervention. Community health workers screened people for TB symptoms in Saharia communities, made referrals, collected sputum for transport to laboratories, and initiated and supported anti-tuberculosis treatment. Microscopy testing was performed at government laboratories. The intervention tracked the people screened, referrals, the people tested, laboratory results, treatment initiation and outcomes. Community health workers verbally screened 65 230 people, 8723 (13%) of whom had symptoms. Of these, 5600 were tested, 964 (17%) of whom were smear-positive. During the intervention, we observed a +52% increase in people tested at laboratories and an +84% increase in TB case notifications. Pre-treatment loss to follow-up decreased and treatment success increased slightly. In India, particularly among tribal populations, many people with TB are missed by current approaches due to poor access. Community-based active case-finding can help identify more people with TB in tribal and remote rural areas by addressing barriers to health seeking as well as help reach ambitious country and global notification targets. .

Optimization of Time- and Code-Division-Multiplexed Readout for Athena X-IFU.

Readout of a large, spacecraft-based array of superconducting transition-edge sensors (TESs) requires careful management of the layout area and power dissipation of the cryogenic-circuit components. We present three optimizations of our time- (TDM) and code-division-multiplexing (CDM) systems for the X-ray Integral Field Unit (X-IFU), a several-thousand-pixel-TES array for the planned Athena-satellite mission. The first optimization is a new readout scheme that is a hybrid of CDM and TDM. This C/TDM architecture balances CDM's noise advantage with TDM's layout compactness. The second is a redesign of a component: the shunt resistor that provides a dc-voltage bias to the TESs. A new layout and a thicker Pd-Au resistive layer combine to reduce this resistor's area by more than a factor of 5. Third, we have studied the power dissipated by the first-stage SQUIDs (superconducting quantum-interference devices) and the readout noise versus the critical current of the first-stage SqUIDs. As a result, the X-IFU TDM and C/TDM SQUIDs will have a specified junction critical current of 5 µA. Based on these design optimizations and TDM experiments described by Durkin, et al. (these proceedings), TDM meets all requirements to be X-IFU's backup-readout option. Hybrid C/TDM is another viable option that could save spacecraft resources.

Demonstration of Athena X-IFU Compatible 40-Row Time-Division-Multiplexed Readout.

Time-division multiplexing (TDM) is the backup readout technology for the X-ray Integral Field Unit (X-IFU), a 3,168-pixel X-ray transition-edge sensor (TES) array that will provide imaging spectroscopy for ESA's Athena satellite mission. X-0IFU design studies are considering readout with a multiplexing factor of up to 40. We present data showing 40-row TDM readout (32 TES rows + 8 repeats of the last row) of TESs that are of the same type as those being planned for X-IFU, using measurement and analysis parameters within the ranges specified for X-IFU. Singlecolumn TDM measurements have best-fit energy resolution of (1.91 ± 0.01) eV for the Al Kα complex (1.5 keV), (2.10 ± 0.02) eV for Ti Kα (4.5 keV), (2.23 ± 0.02) eV for Mn Kα (5.9 keV), (2.40 ± 0.02) eV for Co Kα (6.9 keV), and (3.44 ± 0.04) eV for Br Kα (11.9 keV). Three-column measurements have best-fit resolution of (2.03 ± 0.01) eV for Ti Kα and (2.40 ± 0.01) eV for Co Kα. The degradation due to the multiplexed readout ranges from 0.1 eV at the lower end of the energy range to 0.5 eV at the higher end. The demonstrated performance meets X-IFU's energy-resolution and energy-range requirements. True 40-row TDM readout, without repeated rows, of kilopixel scale arrays of X-IFU-like TESs is now under development.

Timings for HbA1c testing in people with diabetes are associated with incentive payments: an analysis of UK primary care data.

AIMS: Guidelines recommend testing HbA1c every 3-6 months in people with diabetes. In the United Kingdom (UK), primary care clinics are financially incentivized to monitor HbA1c at least annually and report proportions of patients meeting targets on 31 March. We explored the hypothesis that this reporting deadline may be associated with over-frequent or delayed HbA1c testing. METHODS: This analysis used HbA1c results from 100 000 people with diabetes during 2005-2014 in the Clinical Practice Research Datalink UK primary care database. Logistic regression was used to explore whether the four months prior to the deadline for quality reporting (December to March) or individual's previous HbA1c were aligned with retesting HbA1c within 60 days or > 1 year from the previous test, and identify other factors associated with the timing of HbA1c testing. RESULTS: Retesting HbA1c within 60 days or > 1 year was more common in December to March compared with other months of the year (odds ratio 1.06, 95% confidence interval 1.04-1.08 for retesting within 60 days). Those with higher HbA1c were more likely to have a repeat test within 60 days and less likely to have a repeat test > 1 year from the previous test. CONCLUSIONS: We have found that retesting HbA1c within 60 days and > 1 year from the previous test was more common in December to March compared with the other months of the year. This work suggests that both practice-centred administrative factors and patient-centred considerations may be influencing diabetes care in the UK.

Statistical weights for model-based reconstruction in cone-beam CT with electronic noise and dual-gain detector readout.

Cone-beam CT (CBCT) systems commonly incorporate a flat-panel detector (FPD) with multiple-gain readout capability to reduce electronic noise and extend dynamic range. In this work, we report a penalized weighted least-squares (PWLS) method for CBCT image reconstruction with a system model that includes the electronic noise characteristics of FPDs, including systems with dynamic-gain or dual-gain (DG) readout in which the electronic noise is spatially varying. Statistical weights in PWLS were modified to account for the contribution of the electronic noise (algorithm denoted [Formula: see text]), and the method was combined with a certainty-based approach that improves the homogeneity of spatial resolution (algorithm denoted [Formula: see text]). The methods were tested in phantom studies designed to stress DG readout characteristics and translated to a clinical study for CBCT of patients with head traumas. The [Formula: see text] method demonstrated superior noise-resolution tradeoffs compared to filtered back-projection (FBP) and conventional PWLS. For example, with spatial resolution (edge-spread function width) matched at 0.65 mm, [Formula: see text] reduced variance by 28%-39% and 15%-25% compared to FBP and PWLS, respectively. The [Formula: see text] method achieved more homogeneous spatial resolution than [Formula: see text] while maintaining similar variance reduction. These findings were confirmed in clinical studies, which showed ~20% variance reduction in peripheral regions of the brain, potentially improving visual image quality in detection of epidural and/or subdural intracranial hemorrhage. The results are consistent with the general notion that incorporating a more accurate system model improves performance in optimization-based statistical CBCT reconstruction-in this case, a more accurate model for (spatially varying) electronic noise to improve detectability of low-contrast lesions.

Negative relationships between species richness and temporal variability are common but weak in natural systems.

Effects of species diversity on population and community stability (or more precisely, the effects of species richness on temporal variability) have been studied for several decades, but there have been no large-scale tests in natural communities of predictions from theory. We used 91 data sets including plants, fish, small mammals, zooplankton, birds, and insects, to examine the relationship between species richness and temporal variability in populations and communities. Seventy-eight of 91 data sets showed a negative relationship between species richness and population variability; 46 of these relationships were statistically significant. Only five of the 13 positive richness-population variability relationships were statistically significant. Similarly, 51 of 91 data sets showed a negative relationship between species richness and community variability; of these, 26 were statistically significant. Seven of the 40 positive richness-community-variability relationships were statistically significant. We were able to test transferability (i.e., the predictive ability of models for sites that are spatially distinct from sites that were used to build the models) for 69 of 91 data sets; 35 and 31 data sets were transferable at the population and community levels, respectively. Only four were positive at the population level, and two at the community level. We conclude that there is compelling evidence of a negative relationship between species richness and temporal variability for about one-half of the ecological communities we examined. However, species richness explained relatively little of the variability in population or community abundances and resulted in small improvements in predictive ability.

Reconstruction-of-difference (RoD) imaging for cone-beam CT neuro-angiography.

Timely evaluation of neurovasculature via CT angiography (CTA) is critical to the detection of pathology such as ischemic stroke. Cone-beam CTA (CBCT-A) systems provide potential advantages in the timely use at the point-of-care, although challenges of a relatively slow gantry rotation speed introduce tradeoffs among image quality, data consistency and data sparsity. This work describes and evaluates a new reconstruction-of-difference (RoD) approach that is robust to such challenges. A fast digital simulation framework was developed to test the performance of the RoD over standard reference reconstruction methods such as filtered back-projection (FBP) and penalized likelihood (PL) over a broad range of imaging conditions, grouped into three scenarios to test the trade-off between data consistency, data sparsity and peak contrast. Two experiments were also conducted using a CBCT prototype and an anthropomorphic neurovascular phantom to test the simulation findings in real data. Performance was evaluated primarily in terms of normalized root mean square error (NRMSE) in comparison to truth, with reconstruction parameters chosen to optimize performance in each case to ensure fair comparison. The RoD approach reduced NRMSE in reconstructed images by up to 50%-53% compared to FBP and up to 29%-31% compared to PL for each scenario. Scan protocols well suited to the RoD approach were identified that balance tradeoffs among data consistency, sparsity and peak contrast-for example, a CBCT-A scan with 128 projections acquired in 8.5 s over a 180° + fan angle half-scan for a time attenuation curve with ~8.5 s time-to-peak and 600 HU peak contrast. With imaging conditions such as the simulation scenarios of fixed data sparsity (i.e. varying levels of data consistency and peak contrast), the experiments confirmed the reduction of NRMSE by 34% and 17% compared to FBP and PL, respectively. The RoD approach demonstrated superior performance in 3D angiography compared to FBP and PL in all simulation and physical experiments, suggesting the possibility of CBCT-A on low-cost, mobile imaging platforms suitable to the point-of-care. The algorithm demonstrated accurate reconstruction with a high degree of robustness against data sparsity and inconsistency.

Smoking cessation and survival in lung, upper aero-digestive tract and bladder cancer: cohort study.

BACKGROUND: The aim was to examine the association between smoking cessation and prognosis in smoking-related cancer as it is unclear that cessation reduces mortality. METHODS: In this retrospective cohort study from 1999 to 2013, we assessed the association between cessation during the first year after diagnosis and all-cause and cancer-specific mortality. RESULTS: Of 2882 lung, 757 upper aero-digestive tract (UAT) and 1733 bladder cancer patients 27%, 29% and 21% of lung, UAT and bladder cancer patients quit smoking. In lung cancer patients that quit, all-cause mortality was significantly lower (HR: 0.82 (0.74-0.92), while cancer-specific mortality (HR: 0.89 (0.76-1.04) and death due to index cancer (HR: 0.90 (0.77-1.05) were non-significantly lower. In UAT cancer, all-cause mortality (HR: 0.81 (0.58-1.14), cancer-specific mortality (HR: 0.84 (0.48-1.45), and death due to index cancer (HR: 0.75 (0.42-1.34) were non-significantly lower. There was no evidence of an association between quitting and mortality in bladder cancer. The HRs were 1.02 (0.81-1.30) for all-cause, 1.23 (0.81-1.86) for cancer specific, and 1.25 (0.71-2.20) for death due to index cancer. These showed a non-significantly lower risk in sensitivity analyses. CONCLUSIONS: People with lung and possibly UAT cancer who quit smoking have a lower risk of mortality than people who continue smoking.