Macroalgae and Eelgrass Mapping in Great Bay Estuary Using AISA Hyperspectral Imagery.

Results Increases in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending eutrophication for New Hampshire’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the Piscataqua Region Estuaries Partnership adopted the assumption that eelgrass survival can be used as the target for establishing numeric water quality criteria for nutrients in NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that an eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To determine the extent of this effect, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral image was made by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was then used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. Here we outline the procedure for mapping the macroalgae and eelgrass beds. Hyperspectral imagery was effective where known spectral signatures could be easily identified. Comprehensive eelgrass and macroalgae maps of the estuary could only be produced by combining hyperspectral imagery with ground-truth information and expert opinion. Macroalgae was predominantly located in areas where eelgrass formerly existed. Macroalgae mats have now replaced nearly 9% of the area formerly occupied by eelgrass in Great Bay

Artificial intelligence assisted detection of large vessel occlusion on CT angiography in acute stroke patients: a multi-reader multi-case study

Objectives: We assessed the impact of artificial intelligence (AI) software (e-CTA, Brainomix) on clinical decision-making in patients with suspected acute ischemic stroke. Methods: A retrospective, multi-reader-multi-case crossover design compared readers’ performance with vs without software support. Twenty cases were included, 10 with large vessel occlusion (LVO) and 10 without LVO. Twenty-one NHS clinicians, representing intended software users ranging in experience, conducted 2 sessions (washout period > 2 weeks). In session 1, software support was provided for 10 randomly selected cases. In session 2, support allocation was reversed. Outcome measures included LVO detection, collateral scoring, diagnosis, treatment decision, time taken and confidence. Results: Sensitivity, specificity, and accuracy of LVO detection improved with imaging software for LVO detection, with increased confidence and reduced time taken. There was no significant difference in collateral scoring or diagnoses. Conclusion: e-CTA can improve performance of NHS clinicians when interpreting acute stroke imaging. Advances in knowledge: This paper provides new evidence that AI decision support software has the capacity to improve the performance of representative users in the NHS when interpreting imaging to identify patients for acute stroke treatments

Quantification of Serial Cerebral Blood Flow in Acute Stroke Using Arterial Spin Labeling

Background and Purpose— Perfusion-weighted imaging is used to select patients with acute ischemic stroke for intervention, but knowledge of cerebral perfusion can also inform the understanding of ischemic injury. Arterial spin labeling allows repeated measurement of absolute cerebral blood flow (CBF) without the need for exogenous contrast. The aim of this study was to explore the relationship between dynamic CBF and tissue outcome in the month after stroke onset. Methods— Patients with nonlacunar ischemic stroke underwent ≤5 repeated magnetic resonance imaging scans at presentation, 2 hours, 1 day, 1 week, and 1 month. Imaging included vessel-encoded pseudocontinuous arterial spin labeling using multiple postlabeling delays to quantify CBF in gray matter regions of interest. Receiver–operator characteristic curves were used to predict tissue outcome using CBF. Repeatability was assessed in 6 healthy volunteers and compared with contralateral regions of patients. Diffusion-weighted and T2-weighted fluid attenuated inversion recovery imaging were used to define tissue outcome. Results— Forty patients were included. In contralateral regions of patients, there was significant variation of CBF between individuals, but not between scan times (mean±SD: 53±42 mL/100 g/min). Within ischemic regions, mean CBF was lowest in ischemic core (17±23 mL/100 g/min), followed by regions of early (21±26 mL/100 g/min) and late infarct growth (25±35 mL/100 g/min; ANOVA P &lt;0.0001). Between patients, there was marked overlap in presenting and serial CBF values. Conclusions— Knowledge of perfusion dynamics partially explained tissue fate. Factors such as metabolism and tissue susceptibility are also likely to influence tissue outcome. </jats:sec

Bi-allelic loss-of-function CACNA1B mutations in progressive epilepsy-dyskinesia

The occurrence of non-epileptic hyperkinetic movements in the context of developmental epileptic encephalopathies is an increasingly recognized phenomenon. Identification of causative mutations provides an important insight into common pathogenic mechanisms that cause both seizures and abnormal motor control. We report bi-allelic loss-of-function CACNA1B variants in six children from three unrelated families whose affected members present with a complex and progressive neurological syndrome. All affected individuals presented with epileptic encephalopathy, severe neurodevelopmental delay (often with regression), and a hyperkinetic movement disorder. Additional neurological features included postnatal microcephaly and hypotonia. Five children died in childhood or adolescence (mean age of death: 9 years), mainly as a result of secondary respiratory complications. CACNA1B encodes the pore-forming subunit of the pre-synaptic neuronal voltage-gated calcium channel Cav2.2/N-type, crucial for SNARE-mediated neurotransmission, particularly in the early postnatal period. Bi-allelic loss-of-function variants in CACNA1B are predicted to cause disruption of Ca2+ influx, leading to impaired synaptic neurotransmission. The resultant effect on neuronal function is likely to be important in the development of involuntary movements and epilepsy. Overall, our findings provide further evidence for the key role of Cav2.2 in normal human neurodevelopment

Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel

Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants.Peer reviewe

The UK10K project identifies rare variants in health and disease.

The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7×) or exomes (high read depth, 80×) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p

Macroalgae and Eelgrass Mapping in Great Bay Estuary Using AISA Hyperspectral Imagery

Increases in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending eutrophication for New Hampshire’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the Piscataqua Region Estuaries Partnership adopted the assumption that eelgrass survival can be used as the target for establishing numeric water quality criteria for nutrients in NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that an eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To determine the extent of this effect, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral image was made by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was then used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. Here we outline the procedure for mapping the macroalgae and eelgrass beds. Hyperspectral imagery was effective where known spectral signatures could be easily identified. Comprehensive eelgrass and macroalgae maps of the estuary could only be produced by combining hyperspectral imagery with groundtruth information and expert opinion. Macroalgae was predominantly located in areas where eelgrass formerly existed. Macroalgae mats have now replaced nearly 9% of the area formerly occupied by eelgrass in Great Bay

Tourism development and behavioural changes: evidences from Ratanakiri province, Kingdom of Cambodia

International audienc