Greenland ice core “signal” characteristics: An expanded view of climate change

The last millenium of Earth history is of particular interest because it documents the environmental complexities of both natural variability and anthropogenic activity. We have analyzed the major ions contained in the Greenland Ice Sheet Project 2 (GISP 2) ice core from the present to ∼674 A.D. to yield an environmental reconstruction for this period that includes a description of nitrogen and sulfur cycling, volcanic emissions, sea salt and terrestrial influences. We have adapted and extended mathematical procedures for extracting sporadic (e.g., volcanic) events, secular trends, and periodicities found in the data sets. Finally, by not assuming that periodic components (signals) were “stationary” and by utilizing evolutionary spectral analysis, we were able to reveal periodic processes in the climate system which change in frequency, “turn on,” and “turn off” with other climate transitions such as\u27that between the little ice age and the medieval warm period

Greenland Ice Core Greenland Ice Core Signal Characteristics: An Expanded View of Climate Change

The last millenium of Earth history is of particular interest because it documents the environmental complexities of both natural variability and anthropogenic activity. We have analyzed the major ions contained in the Greenland Ice Sheet Project 2 (GISP 2) ice core from the present to ∼674 A.D. to yield an environmental reconstruction for this period that includes a description of nitrogen and sulfur cycling, volcanic emissions, sea salt and terrestrial influences. We have adapted and extended mathematical procedures for extracting sporadic (e.g., volcanic) events, secular trends, and periodicities found in the data sets. Finally, by not assuming that periodic components (signals) were “stationary” and by utilizing evolutionary spectral analysis, we were able to reveal periodic processes in the climate system which change in frequency, “turn on,” and “turn off” with other climate transitions such as that between the little ice age and the medieval warm period

Psalms for Organ, Volume 11: Ulster

Pages 23-27 in Psalms for Organ, Volume 11

Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model

Several firing patterns experimentally observed in neural populations have been successfully correlated to animal behavior. Population bursting, hereby regarded as a period of high firing rate followed by a period of quiescence, is typically observed in groups of neurons during behavior. Biophysical membrane-potential models of single cell bursting involve at least three equations. Extending such models to study the collective behavior of neural populations involves thousands of equations and can be very expensive computationally. For this reason, low dimensional population models that capture biophysical aspects of networks are needed. \noindent The present paper uses a firing-rate model to study mechanisms that trigger and stop transitions between tonic and phasic population firing. These mechanisms are captured through a two-dimensional system, which can potentially be extended to include interactions between different areas of the nervous system with a small number of equations. The typical behavior of midbrain dopaminergic neurons in the rodent is used as an example to illustrate and interpret our results. \noindent The model presented here can be used as a building block to study interactions between networks of neurons. This theoretical approach may help contextualize and understand the factors involved in regulating burst firing in populations and how it may modulate distinct aspects of behavior.Comment: 25 pages (including references and appendices); 12 figures uploaded as separate file

Epidemiology and Genetic Epidemiology of the Liver Function Test Proteins

The liver function test (LFT) is among the most commonly used clinical investigations to assess hepatic function, severity of liver diseases and the effect of therapies, as well as to detect drug-induced liver injury (DILI)

Impaired LXRa phosphorylation attenuates progression of fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a very common indication for liver transplantation. How fat-rich diets promote progression from fatty liver to more damaging inflammatory and fibrotic stages is poorly understood. Here, we show that disrupting phosphorylation at Ser196 (S196A) in the liver X receptor alpha (LXRα, NR1H3) retards NAFLD progression in mice on a high-fat-high-cholesterol diet. Mechanistically, this is explained by key histone acetylation (H3K27) and transcriptional changes in pro-fibrotic and pro-inflammatory genes. Furthermore, S196A-LXRα expression reveals the regulation of novel diet-specific LXRα-responsive genes, including the induction of Ces1f, implicated in the breakdown of hepatic lipids. This involves induced H3K27 acetylation and altered LXR and TBLR1 cofactor occupancy at the Ces1f gene in S196A fatty livers. Overall, impaired Ser196-LXRα phosphorylation acts as a novel nutritional molecular sensor that profoundly alters the hepatic H3K27 acetylome and transcriptome during NAFLD progression placing LXRα phosphorylation as an alternative anti-inflammatory or anti-fibrotic therapeutic target

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non-small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image-based deep-learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep-learning image-based classifier accurately identifies cancer, even when a limited number of samples are available.publishedVersio

Mannose-Binding Lectin 2 Polymorphisms Do Not Influence Frequency or Type of Infection in Adults with Chemotherapy Induced Neutropaenia

BACKGROUND: Mannose-binding Lectin protein (MBL) has been suggested to be relevant in the defence against infections in immunosuppressed individuals. In a Swedish adult cohort immunosuppressed from both the underlying disease and from iatrogenic treatments for their underlying disease we investigated the role of MBL in susceptibility to infection. METHODS: In this cross sectional, prospective study, blood samples obtained from 96 neutropaenic febrile episodes, representing 82 individuals were analysed for single nucleotide polymorphism (SNP) in the MBL2 gene. Concurrent measurement of plasma MBL protein concentrations was also performed for observation of acute response during febrile episodes. FINDINGS: No association was observed between MBL2 genotype or plasma MBL concentrations, and the type or frequency of infection. Adding to the literature, we found no evidence that viral infections or co-infections with virus and bacteria would be predisposed by MBL deficiency. We further saw no correlation between MBL2 genotype and the risk of fever. However, fever duration in febrile neutropaenic episodes was negatively associated with MBL2 SNP mutations (p<0.05). Patients with MBL2 SNP mutations presented a median febrile duration of 1.8 days compared with 3 days amongst patients with wildtype MBL2 genotype. INTERPRETATION: We found no clear association between infection, or infection type to MBL2 genotypes or plasma MBL concentration, and add to the reports casting doubts on the benefit of recombinant MBL replacement therapy use during iatrogenic neutropaenia

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

International audienceIncreasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system