Classification of Foetal Distress and Hypoxia Using Machine Learning Approaches

© 2018, Springer International Publishing AG, part of Springer Nature. Foetal distress and hypoxia (oxygen deprivation) is considered as a serious condition and one of the main factors for caesarean section in the obstetrics and Gynecology department. It is the third most common cause of death in new-born babies. Many foetuses that experienced some sort of hypoxic effects can develop series risks including damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe the foetal well being. Foetal surveillance by monitoring the foetal heart rate with a cardiotocography is widely used. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. In this paper, machine-learning algorithms are utilized to classify foetuses which are experiencing oxygen deprivation using PH value (a measure of hydrogen ion concentration of blood used to specify the acidity or alkalinity) and Base Deficit of extra cellular fluid level (a measure of the total concentration of blood buffer base that indicates the metabolic acidosis or compensated respiratory alkalosis) as indicators of respiratory and metabolic acidosis, respectively, using open source partum clinical data obtained from Physionet. Six well know machine learning classifier models are utilised in our experiments for the evaluation; each model was presented with a set of selected features derived from the clinical data. Classifier’s evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as the confusion matrix. Our simulation results indicate that machine-learning algorithms provide viable methods that could delivery improvements over conventional analysis

Combining qualitative and quantitative understanding for exploring cross-sectoral climate change impacts, adaptation and vulnerability in Europe

Climate change will affect all sectors of society and the environment at all scales, ranging from the continental to the national and local. Decision-makers and other interested citizens need to be able to access reliable science-based information to help them respond to the risks of climate change impacts and assess opportunities for adaptation. Participatory integrated assessment (IA) tools combine knowledge from diverse scientific disciplines, take account of the value and importance of stakeholder ‘lay insight’ and facilitate a two-way iterative process of exploration of ‘what if’s’ to enable decision-makers to test ideas and improve their understanding of the complex issues surrounding adaptation to climate change. This paper describes the conceptual design of a participatory IA tool, the CLIMSAVE IA Platform, based on a professionally facilitated stakeholder engagement process. The CLIMSAVE (climate change integrated methodology for cross-sectoral adaptation and vulnerability in Europe) Platform is a user-friendly, interactive web-based tool that allows stakeholders to assess climate change impacts and vulnerabilities for a range of sectors, including agriculture, forests, biodiversity, coasts, water resources and urban development. The linking of models for the different sectors enables stakeholders to see how their interactions could affect European landscape change. The relationship between choice, uncertainty and constraints is a key cross-cutting theme in the conduct of past participatory IA. Integrating scenario development processes with an interactive modelling platform is shown to allow the exploration of future uncertainty as a structural feature of such complex problems, encouraging stakeholders to explore adaptation choices within real-world constraints of future resource availability and environmental and institutional capacities, rather than seeking the ‘right’ answers

An ALS-Linked Mutant SOD1 Produces a Locomotor Defect Associated with Aggregation and Synaptic Dysfunction When Expressed in Neurons of Caenorhabditis elegans

The nature of toxic effects exerted on neurons by misfolded proteins, occurring in a number of neurodegenerative diseases, is poorly understood. One approach to this problem is to measure effects when such proteins are expressed in heterologous neurons. We report on effects of an ALS-associated, misfolding-prone mutant human SOD1, G85R, when expressed in the neurons of Caenorhabditis elegans. Stable mutant transgenic animals, but not wild-type human SOD1 transgenics, exhibited a strong locomotor defect associated with the presence, specifically in mutant animals, of both soluble oligomers and insoluble aggregates of G85R protein. A whole-genome RNAi screen identified chaperones and other components whose deficiency increased aggregation and further diminished locomotion. The nature of the locomotor defect was investigated. Mutant animals were resistant to paralysis by the cholinesterase inhibitor aldicarb, while exhibiting normal sensitivity to the cholinergic agonist levamisole and normal muscle morphology. When fluorescently labeled presynaptic components were examined in the dorsal nerve cord, decreased numbers of puncta corresponding to neuromuscular junctions were observed in mutant animals and brightness was also diminished. At the EM level, mutant animals exhibited a reduced number of synaptic vesicles. Neurotoxicity in this system thus appears to be mediated by misfolded SOD1 and is exerted on synaptic vesicle biogenesis and/or trafficking

A review of source tracking techniques for fine sediment within a catchment

Excessive transport of fine sediment, and its associated pollutants, can cause detrimental impacts in aquatic environments. It is therefore important to perform accurate sediment source apportionment to identify hot spots of soil erosion. Various tracers have been adopted, often in combination, to identify sediment source type and its spatial origin; these include fallout radionuclides, geochemical tracers, mineral magnetic properties and bulk and compound-specific stable isotopes. In this review, the applicability of these techniques to particular settings and their advantages and limitations are reviewed. By synthesizing existing approaches, that make use of multiple tracers in combination with measured changes of channel geomorphological attributes, an integrated analysis of tracer profiles in deposited sediments in lakes and reservoirs can be made. Through a multi-scale approach for fine sediment tracking, temporal changes in soil erosion and sediment load can be reconstructed and the consequences of changing catchment practices evaluated. We recommend that long-term, as well as short-term, monitoring of riverine fine sediment and corresponding surface and subsurface sources at nested sites within a catchment are essential. Such monitoring will inform the development and validation of models for predicting dynamics of fine sediment transport as a function of hydro-climatic and geomorphological controls. We highlight that the need for monitoring is particularly important for hilly catchments with complex and changing land use. We recommend that research should be prioritized for sloping farmland-dominated catchments

A review of source tracking techniques for fine sediment within a catchment

Excessive transport of fine sediment, and its associated pollutants, can cause detrimental impacts in aquatic environments. It is therefore important to perform accurate sediment source apportionment to identify hot spots of soil erosion. Various tracers have been adopted, often in combination, to identify sediment source type and its spatial origin; these include fallout radionuclides, geochemical tracers, mineral magnetic properties and bulk and compound-specific stable isotopes. In this review, the applicability of these techniques to particular settings and their advantages and limitations are reviewed. By synthesizing existing approaches, that make use of multiple tracers in combination with measured changes of channel geomorphological attributes, an integrated analysis of tracer profiles in deposited sediments in lakes and reservoirs can be made. Through a multi-scale approach for fine sediment tracking, temporal changes in soil erosion and sediment load can be reconstructed and the consequences of changing catchment practices evaluated. We recommend that long-term, as well as short-term, monitoring of riverine fine sediment and corresponding surface and subsurface sources at nested sites within a catchment are essential. Such monitoring will inform the development and validation of models for predicting dynamics of fine sediment transport as a function of hydro-climatic and geomorphological controls. We highlight that the need for monitoring is particularly important for hilly catchments with complex and changing land use. We recommend that research should be prioritized for sloping farmland-dominated catchments

Stress granules, RNA-binding proteins and polyglutamine diseases: too much aggregation?

Stress granules (SGs) are membraneless cell compartments formed in response to different stress stimuli, wherein translation factors, mRNAs, RNA-binding proteins (RBPs) and other proteins coalesce together. SGs assembly is crucial for cell survival, since SGs are implicated in the regulation of translation, mRNA storage and stabilization and cell signalling, during stress. One defining feature of SGs is their dynamism, as they are quickly assembled upon stress and then rapidly dispersed after the stress source is no longer present. Recently, SGs dynamics, their components and their functions have begun to be studied in the context of human diseases. Interestingly, the regulated protein self-assembly that mediates SG formation contrasts with the pathological protein aggregation that is a feature of several neurodegenerative diseases. In particular, aberrant protein coalescence is a key feature of polyglutamine (PolyQ) diseases, a group of nine disorders that are caused by an abnormal expansion of PolyQ tract-bearing proteins, which increases the propensity of those proteins to aggregate. Available data concerning the abnormal properties of the mutant PolyQ disease-causing proteins and their involvement in stress response dysregulation strongly suggests an important role for SGs in the pathogenesis of PolyQ disorders. This review aims at discussing the evidence supporting the existence of a link between SGs functionality and PolyQ disorders, by focusing on the biology of SGs and on the way it can be altered in a PolyQ disease context.ALG-01-0145-FEDER-29480, SFRH/BD/133192/2017, SFRH/BD/133192/2017, SFRH/BD/148533/2019info:eu-repo/semantics/publishedVersio

Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies