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"Brown, Colin"
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The unsold mindset : redefining what it means to sell
\"A how-to guide for salespeople to revitalize their sales strategy and become more effective in building relationships\"-- Provided by publisher.
Book
BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment
We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain.
•First deep convolutional neural network architecture designed for connectomes.•Novel convolutional layers for leveraging topological locality in brain networks.•Prediction of neurodevelopmental outcomes in preterm infants.•Visualization of brain connections learned to be important for prediction.
Journal Article
Evolution : the human story
\"Evolution investigates each of our ancestors in detail and in context, from the anatomy of their bones to the environment they lived in. Key fossil finds are showcased on double-page feature spreads. Detailed maps show where each species has been found and plot the gradual spread of humans around the world. The book has been fully updated to include the latest discoveries and research--including the newly discovered species Homo naledi--and presents the latest thinking on some of the most captivating questions in science, such as whether modern humans and Neanderthals interacted with each other. Written and authenticated by a team of acknowledged experts and illustrated by renowned Dutch paleoartists the Kennis brothers, Evolution presents the story of our species with unique richness, authority, and detail\"-- Provided by publisher.
BOOK
The limitations of renal epithelial cell line HK-2 as a model of drug transporter expression and function in the proximal tubule
Acquiring a mechanistic understanding of the processes underlying the renal clearance of drug molecules in man has been hampered by a lack of robust in vitro models of human proximal tubules. Several human renal epithelial cell lines derived from the renal cortex are available, but few have been characterised in detail in terms of transporter expression. This includes the HK-2 proximal tubule cell line, which has been used extensively as a model of nephrotoxicity. The aim of this study was to investigate the expression and function of drug transporters in HK-2 cells and their suitability as an in vitro model of the human proximal tubule. qPCR showed no mRNA expression of the SLC22 transporter family (OAT1, OAT3, OCT2) in HK-2 cells compared to renal cortex samples. In contrast, SLC16A1 (MCT1), which is important in the uptake of monocarboxylates, and SLCO4C1 (OATP4C1) were expressed in HK-2 cells. The functional expression of these transporters was confirmed by uptake studies using radiolabelled prototypic substrates
dl
-lactate and digoxin, respectively. The mRNA expression of apical membrane efflux transporters ABCB1 (MDR1) and several members of the ABCC family (multidrug resistance proteins, MRPs) was shown by qPCR. ABCG1 (BCRP) was not detected. The efflux of Hoechst 33342, a substrate for MDR1, was blocked by MDR1 inhibitor cyclosporin A, suggesting the functional expression of this transporter. Similarly, the efflux of the MRP-specific fluorescent dye glutathione methylfluorescein was inhibited by the MRP inhibitor MK571. Taken together, the results of this study suggest that HK-2 cells are of limited value as an in vitro model of drug transporter expression in the human proximal tubule.
Journal Article
Enhanced IoT Spectrum Utilization: Integrating Geospatial and Environmental Data for Advanced Mid-Band Spectrum Sharing
The anticipated surge of Internet of Things (IoT) devices is expected to intensify the demand for mid-band spectrum resources, posing challenges to traditional spectrum sharing methods. This paper addresses the limitations of static database-assisted spectrum management frameworks and proposes a novel approach integrating high-resolution geospatial and real-time environmental data. Leveraging these inputs, the proposed framework enhances spectrum allocation accuracy, and mitigates interference more effectively, thereby increasing the access opportunities for IoT deployments. A detailed example scenario illustrates the efficacy of the proposed approach, demonstrating significant gains in spectrum sharing efficiency. It shows gains in the number of new entrants accessing the spectrum, ranging from 77% to 140%. These gains occur when moving to less conservative interference conditions and including more complex geospatial information in the propagation environment. These findings underscore the critical role of advanced spectrum sharing techniques in optimizing spectrum utilization for future IoT networks.
Journal Article
Meta-analysis of predictive symptoms for Ebola virus disease
One of the leading challenges in the 2013-2016 West African Ebola virus disease (EVD) outbreak was how best to quickly identify patients with EVD, separating them from those without the disease, in order to maximise limited isolation bed capacity and keep health systems functioning.
We performed a systematic literature review to identify all published data on EVD clinical symptoms in adult patients. Data was dual extracted, and random effects meta-analysis performed for each symptom to identify symptoms with the greatest risk for EVD infection.
Symptoms usually presenting late in illness that were more than twice as likely to predict a diagnosis of Ebola, were confusion (pOR 3.04, 95% CI 2.18-4.23), conjunctivitis (2.90, 1.92-4.38), dysphagia (1.95, 1.13-3.35) and jaundice (1.86, 1.20-2.88). Early non-specific symptoms of diarrhoea (2.99, 2.00-4.48), fatigue (2.77, 1.59-4.81), vomiting (2.69, 1.76-4.10), fever (1.97, 1.10-4.52), muscle pain (1.65, 1.04-2.61), and cough (1.63, 1.24-2.14), were also strongly associated with EVD diagnosis.
The existing literature fails to provide a unified position on the symptoms most predictive of EVD, but highlights some early and late stage symptoms that in combination will be useful for future risk stratification. Confirmation of these findings across datasets (or ideally an aggregation of all individual patient data) will aid effective future clinical assessment, risk stratification tools and emergency epidemic response planning.
Journal Article
Protecting healthcare and patient pathways from infection and antimicrobial resistance
Innovative whole system approaches to integrate research and novel technologies within patient pathways are needed to target antibiotic use, minimise healthcare associated infections, and adapt to novel pathogens, write Derek Cocker and colleagues
Journal Article
Rapid expansion and international spread of M1UK in the post-pandemic UK upsurge of Streptococcus pyogenes
The UK observed a marked increase in scarlet fever and invasive group A streptococcal infection in 2022 with severe outcomes in children and similar trends worldwide. Here we report lineage M1
UK
to be the dominant source of invasive infections in this upsurge. Compared with ancestral M1
global
strains, invasive M1
UK
strains exhibit reduced genomic diversity and fewer mutations in two-component regulator genes
covRS
. The emergence of M1
UK
is dated to 2008. Following a bottleneck coinciding with the COVID-19 pandemic, three emergent M1
UK
clades underwent rapid nationwide expansion, despite lack of detection in previous years. All M1
UK
isolates thus-far sequenced globally have a phylogenetic origin in the UK, with dispersal of the new clades in Europe. While waning immunity may promote streptococcal epidemics, the genetic features of M1
UK
point to a fitness advantage in pathogenicity, and a striking ability to persist through population bottlenecks.
Exponential growth of toxigenic
Streptococcus pyogenes
M1
UK
lineage accounted for most of the 2022/2023 invasive infection upsurge in the UK. Authors provide evidence that M1
UK
first emerged in 2008, has genetic evidence of enhanced fitness, and has disseminated to 3 continents.
Journal Article