Different Vancomycin Immunoassays Contribute to the Variability in Vancomycin Trough Measurements in Neonates

Substantial interassay variability (up to 20%) has been described for vancomycin immunoassays in adults, but the impact of neonatal matrix is difficult to quantify because of blood volume constraints in neonates. However, we provide circumstantial evidence for a similar extent of variability. Using the same vancomycin dosing regimens and confirming similarity in clinical characteristics, vancomycin trough concentrations measured by PETINIA (2011-2012, n = 400) were 20% lower and the mean difference was 1.93 mg/L compared to COBAS (2012-2014, n = 352) measurements. The impact of vancomycin immunoassays in neonatal matrix was hereby suggested, supporting a switch to more advanced techniques (LC-MS/MS)

Scale Sequence Joint Deep Learning (SS-JDL) for land use and land cover classification

Choosing appropriate scales for remotely sensed image classification is extremely important yet still an open question in relation to deep convolutional neural networks (CNN), due to the impact of spatial scale (i.e., input patch size) on the recognition of ground objects. Currently, the optimal scale selection processes are extremely cumbersome and time-consuming requiring repetitive experiments involving trial-and-error procedures, which significantly reduces the practical utility of the corresponding classification methods. This issue is crucial when trying to classify large-scale land use (LU) and land cover (LC) jointly (Zhang et al., 2019). In this paper, a simple and parsimonious scale sequence joint deep learning (SS-JDL) method is proposed for joint LU and LC classification, in which a sequence of scales is embedded in the iterative process of fitting the joint distribution implicit in the joint deep learning (JDL) method, thus, replacing the previous paradigm of scale selection. The sequence of scales, derived autonomously and used to define the CNN input patch sizes, provides consecutive information transmission from small-scale features to large-scale representations, and from simple LC states to complex LU characterisations. The effectiveness of the novel SS-JDL method was tested on aerial digital photography of three complex and heterogeneous landscapes, two in Southern England (Bournemouth and Southampton) and one in North West England (Manchester). Benchmark comparisons were provided in the form of a range of LU and LC methods, including the state-of-the-art joint deep learning (JDL) method. The experimental results demonstrated that the SS-JDL consistently outperformed all of the state-of-the-art baselines in terms of both LU and LC classification accuracies, as well as computational efficiency. The proposed SS-JDL method, therefore, represents a fast and effective implementation of the state-of-the-art JDL method. By creating a single, unifying joint distribution framework for classifying higher order feature representations, including LU, the SS-JDL method has the potential to transform the classification paradigm in remote sensing, and in machine learning more generally

VPRS-based regional decision fusion of CNN and MRF classifications for very fine resolution remotely sensed images

Recent advances in computer vision and pattern recognition have demonstrated the superiority of deep neural networks using spatial feature representation, such as convolutional neural networks (CNN), for image classification. However, any classifier, regardless of its model structure (deep or shallow), involves prediction uncertainty when classifying spatially and spectrally complicated very fine spatial resolution (VFSR) imagery. We propose here to characterise the uncertainty distribution of CNN classification and integrate it into a regional decision fusion to increase classification accuracy. Specifically, a variable precision rough set (VPRS) model is proposed to quantify the uncertainty within CNN classifications of VFSR imagery, and partition this uncertainty into positive regions (correct classifications) and non-positive regions (uncertain or incorrect classifications). Those “more correct” areas were trusted by the CNN, whereas the uncertain areas were rectified by a Multi-Layer Perceptron (MLP)-based Markov random field (MLP-MRF) classifier to provide crisp and accurate boundary delineation. The proposed MRF-CNN fusion decision strategy exploited the complementary characteristics of the two classifiers based on VPRS uncertainty description and classification integration. The effectiveness of the MRF-CNN method was tested in both urban and rural areas of southern England as well as Semantic Labelling datasets. The MRF-CNN consistently outperformed the benchmark MLP, SVM, MLP-MRF and CNN and the baseline methods. This research provides a regional decision fusion framework within which to gain the advantages of model-based CNN, while overcoming the problem of losing effective resolution and uncertain prediction at object boundaries, which is especially pertinent for complex VFSR image classification

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification

An object-based convolutional neural network (OCNN) for urban land use classification

Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images

Yoghurt and curd cheese addition to wheat bread dough: Impact on in vitro starch digestibility and estimated glycemic index

The effect of yoghurt and curd cheese additions on pasting properties, starch digestibility and estimated glycemic index of wheat bread were studied. Yoghurt and curd cheese incorporations (6% up to 25% w/w) promoted considerable changes on starch performance based on gelatinization and final dough consistency properties. These changes led to a significant impact on starch digestibility, reducing significantly the rapidly digestible starch while increasing the resistant starch. The estimated glycemic index reflected the changes promoted on starch performance from both dairy products addition, at higher level tested (25%): a significant reduction of around 30% for yoghurt bread and 38% for curd cheese bread, was obtained, resulting in medium to low (55–69) glycemic index breads. Correlations were found between pasting properties, starch digestibility and glycemic index, revealing that the effects observed are proportional to the levels of dairy products added. Microstructure images of the starch granules supported these findingsinfo:eu-repo/semantics/publishedVersio

Ascaris suum informs extrasynaptic volume transmission in nematodes

Neural circuit synaptic connectivities (the connectome) provide the anatomical foundation for our understanding of nematode nervous system function. However, other nonsynaptic routes of communication are known in invertebrates including extrasynaptic volume transmission (EVT), which enables short- and/or long-range communication in the absence of synaptic connections. Although EVT has been highlighted as a facet o

The Threat of Capital Drain: A Rationale for Public Banks?

This paper yields a rationale for why subsidized public banks may be desirable from a regional perspective in a financially integrated economy. We present a model with credit rationing and heterogeneous regions in which public banks prevent a capital drain from poorer to richer regions by subsidizing local depositors, for example, through a public guarantee. Under some conditions, cooperative banks can perform the same function without any subsidization; however, they may be crowded out by public banks. We also discuss the impact of the political structure on the emergence of public banks in a political-economy setting and the role of interregional mobility

Opportunities for machine learning and artificial intelligence in national mapping agencies:enhancing ordnance survey workflow

National Mapping agencies (NMA) are frequently tasked with providing highly accurate geospatial data for a range of customers. Traditionally, this challenge has been met by combining the collection of remote sensing data with extensive field work, and the manual interpretation and processing of the combined data. Consequently, this task is a significant logistical undertaking which benefits the production of high quality output, but which is extremely expensive to deliver. Therefore, novel approaches that can automate feature extraction and classification from remotely sensed data, are of great potential interest to NMAs across the entire sector. Using research undertaken at Great Britain’s NMA; Ordnance Survey (OS) as an example, this paper provides an overview of the recent advances at an NMA in the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) based applications. Examples of these approaches are in automating the process of feature extraction and classification from remotely sensed aerial imagery. In addition, recent OS research in applying deep (convolutional) neural network architectures to image classification are also described. This overview is intended to be useful to other NMAs who may be considering the adoption of similar approaches within their workflows

Krill Excretion Boosts Microbial Activity in the Southern Ocean

Antarctic krill are known to release large amounts of inorganic and organic nutrients to the water column. Here we test the role of krill excretion of dissolved products in stimulating heterotrophic bacteria on the basis of three experiments where ammonium and organic excretory products released by krill were added to bacterial assemblages, free of grazers. Our results demonstrate that the addition of krill excretion products (but not of ammonium alone), at levels expected in krill swarms, greatly stimulates bacteria resulting in an order-of-magnitude increase in growth and production. Furthermore, they suggest that bacterial growth rate in the Southern Ocean is suppressed well below their potential by resource limitation. Enhanced bacterial activity in the presence of krill, which are major sources of DOC in the Southern Ocean, would further increase recycling processes associated with krill activity, resulting in highly efficient krill-bacterial recycling that should be conducive to stimulating periods of high primary productivity in the Southern Ocean.This research is a contribution to projects ICEPOS (REN2002-04165-CO3-O2) and ATOS (POL2006-00550/CTM), funded by the Spanish Ministry of Science and Innovation