Fast and automated biomarker detection in breath samples with machine learning

Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a machine learning-based system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. We evaluate this new approach on clinical samples and with four types of convolutional neural networks (CNNs): VGG16, VGG-like, densely connected and residual CNNs. The proposed machine learning methods showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed novel approach can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency

Characterizing driver–response relationships in marine pelagic ecosystems for improved ocean management

Scientists and resources managers often use methods and tools that assume ecosystem components respond linearly to environmental drivers and human stressor. However, a growing body of literature demonstrates that many relationships are non-linear, where small changes in a driver prompt a disproportionately large ecological response. Here we aim to provide a comprehensive assessment of the relationships between drivers and ecosystem components to identify where and when non-linearities are likely to occur. We focus our analyses on one of the best-studied marine systems, pelagic ecosystems, which allowed us to apply robust statistical techniques on a large pool of previously published studies. In this synthesis, we (1) conduct a wide literature review on single driver-response relationships in pelagic systems, (2) use statistical models to identify the degree of non-linearity in these relationships, and (3) assess whether general patterns exist in the strengths and shapes of non-linear relationships across drivers. Overall we found that non-linearities are common in pelagic ecosystems, comprising at least 52% of all driver-response relationships. This is likely an underestimate, as papers with higher quality data and analytical approaches reported non-linear relationships at a higher frequency - on average 11% more. Consequently, in the absence of evidence for a linear relationship, it is safer to assume a relationship is non-linear. Strong non-linearities can lead to greater ecological and socio-economic consequences if they are unknown (and/or unanticipated), but if known they may provide clear thresholds to inform management targets. In pelagic systems, strongly non-linear relationships are often driven by climate and trophodynamic variables, but are also associated with local stressors such as overfishing and pollution that can be more easily controlled by managers. Even when marine resource managers cannot influence ecosystem change, they can use information about threshold responses to guide how other stressors are managed and to adapt to new ocean conditions. As methods to detect and reduce uncertainty around threshold values improve, managers will be able to better understand and account for ubiquitous non-linear relationships

Automated Searching and Identification of Self-Organized Nanostructures

Currently, researchers spend significant time manually searching through large volumes of data produced during scanning probe imaging to identify specific patterns and motifs formed via self-assembly and self-organisation. Here, we use a combination of Monte Carlo simulations, general statistics and machine learning to automatically distinguish several spatially-correlated patterns in a mixed, highly varied dataset of real AFM images of self-organised nanoparticles. We do this regardless of feature-scale and without the need for manually labelled training data. Provided that the structures of interest can be simulated, the strategy and protocols we describe can be easily adapted to other self-organised systems and datasets

B-Type Natriuretic Peptide and Cardiac Troponin I Are Associated With Adverse Outcomes in Stable Kidney Transplant Recipients

Approximately 200,000 kidney transplant recipients are living in the US; they are at increased risk for cardiovascular and other adverse outcomes. Biomarkers predicting these outcomes are needed. Using specimens collected during the FAVORIT (Folic Acid for Vascular Outcome Reduction In Transplantation) trial, we determined whether plasma levels of B-type natriuretic peptide (BNP) and cardiac troponin I are associated with adverse outcomes in stable kidney transplant recipients

Impact of Achieved Blood Pressure on Cardiovascular Outcomes in the Irbesartan Diabetic Nephropathy Trial

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Kidney Function and Risk of Cardiovascular Disease and Mortality in Kidney Transplant Recipients: The FAVORIT Trial: GFR and CVD Risk in Kidney Transplant

In kidney transplant recipients, cardiovascular disease (CVD) is the leading cause of death. The relationship of kidney function with CVD outcomes in transplant recipients remains uncertain. We performed a post-hoc analysis of the Folic Acid for Vascular Outcome Reduction in Transplantation (FAVORIT) Trial to assess risk factors for CVD and mortality in kidney transplant recipients. Following adjustment for demographic, clinical and transplant characteristics, and traditional CVD risk factors, proportional hazards models were used to explore the association of estimated GFR with incident CVD and all-cause mortality. In 4016 participants, mean age was 52 years and 20% had prior CVD. Mean eGFR was 49±18 mL/min/1.73m2. In 3,676 participants with complete data, there were 527 CVD events over a median of 3.8 years. Following adjustment, each 5 mL/min/1.73m2 higher eGFR at levels below 45 mL/min/1.73m2 was associated with a 15% lower risk of both CVD [HR = 0.85 (0.80, 0.90)] and death [HR = 0.85 (0.79, 0.90)], while there was no association between eGFR and outcomes at levels above 45 mL/min/1.73m2. In conclusion, in stable kidney transplant recipients, lower eGFR is independently associated with adverse events, suggesting that reduced kidney function itself rather than pre-existing comorbidity may lead to CVD

Baseline Characteristics of Participants in the Folic Acid for Vascular Outcome Reduction in Transplantation (FAVORIT) Trial

Hyperhomocysteinemia may be a modifiable risk factor for the prevention of arteriosclerotic outcomes in chronic kidney disease (CKD). Few clinical trials of homocysteine lowering have been conducted in persons with CKD prior to reaching end-stage renal disease. Kidney transplant recipients are considered individuals with CKD

Using statolith elemental signatures to confirm ontogenetic migrations of the squid Doryteuthis gahi around the Falkland Islands (Southwest Atlantic)

This work was supported by the Falkland Islands Government. We thank Dr. Simon Chenery and the British Geological Survey for assistance with the LA-ICP-MS analysis and training and use of their facilities. We are grateful to the scientific observers from the Falkland Islands Fisheries Department for sample collection. We thank the Director of Fisheries, John Barton, and the director of SAERI, Paul Brickle, for supporting this work. We thank Dr. Elena Ieno, Dr. Andreas Winter, Dr. Haseeb Randhawa and three anonymous reviewers for their helpful comments that greatly improved the manuscript.Peer reviewedPostprin

The Elusive Third Subunit IIa of the Bacterial B-Type Oxidases: The Enzyme from the Hyperthermophile Aquifex aeolicus

The reduction of molecular oxygen to water is catalyzed by complicated membrane-bound metallo-enzymes containing variable numbers of subunits, called cytochrome c oxidases or quinol oxidases. We previously described the cytochrome c oxidase II from the hyperthermophilic bacterium Aquifex aeolicus as a ba3-type two-subunit (subunits I and II) enzyme and showed that it is included in a supercomplex involved in the sulfide-oxygen respiration pathway. It belongs to the B-family of the heme-copper oxidases, enzymes that are far less studied than the ones from family A. Here, we describe the presence in this enzyme of an additional transmembrane helix “subunit IIa”, which is composed of 41 amino acid residues with a measured molecular mass of 5105 Da. Moreover, we show that subunit II, as expected, is in fact longer than the originally annotated protein (from the genome) and contains a transmembrane domain. Using Aquifex aeolicus genomic sequence analyses, N-terminal sequencing, peptide mass fingerprinting and mass spectrometry analysis on entire subunits, we conclude that the B-type enzyme from this bacterium is a three-subunit complex. It is composed of subunit I (encoded by coxA2) of 59000 Da, subunit II (encoded by coxB2) of 16700 Da and subunit IIa which contain 12, 1 and 1 transmembrane helices respectively. A structural model indicates that the structural organization of the complex strongly resembles that of the ba3 cytochrome c oxidase from the bacterium Thermus thermophilus, the IIa helical subunit being structurally the lacking N-terminal transmembrane helix of subunit II present in the A-type oxidases. Analysis of the genomic context of genes encoding oxidases indicates that this third subunit is present in many of the bacterial oxidases from B-family, enzymes that have been described as two-subunit complexes