Key inflammatory pathway activations in the MCI stage of Alzheimer's disease

OBJECTIVE: To determine the key inflammatory pathways that are activated in the peripheral and CNS compartments at the mild cognitive impairment (MCI) stage of Alzheimer's disease (AD). METHODS: A cross-sectional study of patients with clinical and biomarker characteristics consistent with MCI-AD in a discovery cohort, with replication in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Inflammatory analytes were measured in the CSF and plasma with the same validated multiplex analyte platform in both cohorts and correlated with AD biomarkers (CSF Aβ42, total tau (t-tau), phosphorylated tau (p-tau) to identify key inflammatory pathway activations. The pathways were additionally validated by evaluating genes related to all analytes in coexpression networks of brain tissue transcriptome from an autopsy confirmed AD cohort to interrogate if the same pathway activations were conserved in the brain tissue gene modules. RESULTS: Analytes of the tumor necrosis factor (TNF) signaling pathway (KEGG ID:4668) in the CSF and plasma best correlated with CSF t-tau and p-tau levels, and analytes of the complement and coagulation pathway (KEGG ID:4610) best correlated with CSF Aβ42 levels. The top inflammatory signaling pathways of significance were conserved in the peripheral and the CNS compartments. They were also confirmed to be enriched in AD brain transcriptome gene clusters. INTERPRETATION: A cell-protective rather than a proinflammatory analyte profile predominates in the CSF in relation to neurodegeneration markers among MCI-AD patients. Analytes from the TNF signaling and the complement and coagulation pathways are relevant in evaluating disease severity at the MCI stage of AD

An evaluation of planktonic foraminiferal zonation of the Oligocene

42 p., 8 pl., 7 fig.http://paleo.ku.edu/contributions.htm

On the zero set of G-equivariant maps

Let $G$ be a finite group acting on vector spaces $V$ and $W$ and consider a smooth $G$-equivariant mapping $f:V\to W$. This paper addresses the question of the zero set near a zero $x$ of $f$ with isotropy subgroup $G$. It is known from results of Bierstone and Field on $G$-transversality theory that the zero set in a neighborhood of $x$ is a stratified set. The purpose of this paper is to partially determine the structure of the stratified set near $x$ using only information from the representations $V$ and $W$. We define an index $s(\Sigma)$ for isotropy subgroups $\Sigma$ of $G$ which is the difference of the dimension of the fixed point subspace of $\Sigma$ in $V$ and $W$. Our main result states that if $V$ contains a subspace $G$-isomorphic to $W$, then for every maximal isotropy subgroup $\Sigma$ satisfying $s(\Sigma)>s(G)$, the zero set of $f$ near $x$ contains a smooth manifold of zeros with isotropy subgroup $\Sigma$ of dimension $s(\Sigma)$. We also present a systematic method to study the zero sets for group representations $V$ and $W$ which do not satisfy the conditions of our main theorem. The paper contains many examples and raises several questions concerning the computation of zero sets of equivariant maps. These results have application to the bifurcation theory of $G$-reversible equivariant vector fields

Morphological asymmetry and interspecific hybridization: A case study using hylid frogs

The limited studies addressing developmental stability of interspecific hybrids suggest a positive association between the level of fluctuating asymmetry and 1) the degree of divergence between parental species, and 2) the recency of the contact zone. To evaluate these associations, we examined asymmetry in a recentlyestablished hybrid population of treefrogs (Hyla cinerea and H. gratiosa) that show marked structural gene divergence. Fluctuating asymmetry (FA), directional asymmetry, and antisymmetry were assessed for eight paired osteometric traits in allozymically-defined parental and hybrid categories. FA levels varied considerably among traits. Nonetheless, for any given trait, the hybrid categories did not demonstrate elevated levels of FA compared to the parental categories, or compared to frogs from a non-hybridizing parental population. The only trait that differed statistically among categories (pterygoid length) involved a significantly lower FA value for the Fl hybrids. Thus, observed FA values do not support expectations that the hybrid categories should experience decreased developmental stability

Morphological asymmetry and interspecific hybridization: A case study using hylid frogs

The limited studies addressing developmental stability of interspecific hybrids suggest a positive association between the level of fluctuating asymmetry and 1) the degree of divergence between parental species, and 2) the recency of the contact zone. To evaluate these associations, we examined asymmetry in a recentlyestablished hybrid population of treefrogs (Hyla cinerea and H. gratiosa) that show marked structural gene divergence. Fluctuating asymmetry (FA), directional asymmetry, and antisymmetry were assessed for eight paired osteometric traits in allozymically-defined parental and hybrid categories. FA levels varied considerably among traits. Nonetheless, for any given trait, the hybrid categories did not demonstrate elevated levels of FA compared to the parental categories, or compared to frogs from a non-hybridizing parental population. The only trait that differed statistically among categories (pterygoid length) involved a significantly lower FA value for the Fl hybrids. Thus, observed FA values do not support expectations that the hybrid categories should experience decreased developmental stability

Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017

The impact of decadal-scale Indian Ocean Sea Surface Temperature Anomalies on Sahelian rainfall and the North Atlantic Oscillation

The sea surface temperatures (SSTs) of the tropical Indian Ocean show a pronounced warming since the 1950s. We have analyzed the impact of this warming on Sahelian rainfall and on the North Atlantic Oscillation (NAO) by conducting ensemble experiments with an atmospheric general circulation model. Additionally, we investigate the impact of the other two tropical oceans on these two climate parameters. Our results suggest that the warming trend in the Indian Ocean played a crucial role for the drying trend over the West Sahel from the 1950s to 1990s and may also have contributed to the strengthening of the NAO during the most recent decades

Spontaneous Initiation of Detonations in White Dwarf Environments: Determination of Critical Sizes

Some explosion models for Type Ia supernovae (SN Ia), such as the gravitationally confined detonation (GCD) or the double detonation sub-Chandrasekhar (DDSC) models, rely on the spontaneous initiation of a detonation in the degenerate C/O material of a white dwarf. The length scales pertinent to the initiation of the detonation are notoriously unresolved in multi-dimensional stellar simulations, prompting the use of results of 1D simulations at higher resolution, such as the ones performed for this work, as guidelines for deciding whether or not conditions reached in the higher dimensional full star simulations successfully would lead to the onset of a detonation. Spontaneous initiation relies on the existence of a suitable gradient in self-ignition (induction) times of the fuel, which we set up with a spatially localized non-uniformity of temperature -- a hot spot. We determine the critical (smallest) sizes of such hot spots that still marginally result in a detonation in white dwarf matter by integrating the reactive Euler equations with the hydrodynamics code FLASH. We quantify the dependences of the critical sizes of such hot spots on composition, background temperature, peak temperature, geometry, and functional form of the temperature disturbance, many of which were hitherto largely unexplored in the literature. We discuss the implications of our results in the context of modeling of SNe Ia.Comment: 43 pages, 12 figures, 12 table