Loss of angiotensin II receptor expression in dopamine neurons in Parkinson’s disease correlates with pathological progression and is accompanied by increases in Nox4- and 8-OH guanosine-related nucleic acid oxidation and caspase-3 activation

In rodent models of Parkinsons disease (PD), dopamine neuron loss is accompanied by increased expression of angiotensin II (AngII), its type 1 receptor (AT1), and NADPH oxidase (Nox) in the nigral dopamine neurons and microglia. AT1 blockers (ARBs) stymie such oxidative damage and neuron loss. Whether changes in the AngII/AT1/Nox4 axis contribute to Parkinson neuropathogenesis is unknown. Here, we studied the distribution of AT1 and Nox4 in dopamine neurons in two nigral subregions: the less affected calbindin-rich matrix and the first-affected calbindin-poor nigrosome 1 of three patients, who were clinically asymptomatic, but had nigral dopamine cell loss and Braak stages consistent with a neuropathological diagnosis of PD (prePD). For comparison, five clinically- and neuropathologically-confirmed PD patients and seven age-matched control patients (AMC) were examined.AT1 and Nox4 immunoreactivity was noted in dopamine neurons in both the matrix and the nigrosome 1. The total cellular levels of AT1 in surviving dopamine neurons in the matrix and nigrosome 1 declined from AMC>prePD>PD, suggesting that an AngII/AT1/Nox4 axis orders neurodegenerative progression. In this vein, the loss of dopamine neurons was paralleled by a decline in total AT1 per surviving dopamine neuron. Similarly, AT1 in the nuclei of surviving neurons in the nigral matrix declined with disease progression, i.e., AMC>prePD>PD. In contrast, in nigrosome 1, the expression of nuclear AT1 was unaffected and similar in all groups. The ratio of nuclear AT1 to total AT1 (nuclear + cytoplasmic + membrane) in dopamine neurons increased stepwise from AMC to prePD to PD. The proportional increase in nuclear AT1 in dopamine neurons in nigrosome 1 of prePD and PD patients was accompanied by elevated nuclear expression of Nox4, oxidative damage to DNA, and caspase-3-mediated cell loss.Our observations are consistent with the idea that AngII/AT1/Nox4 axis-mediated oxidative stress gives rise to the dopamine neuron dysfunction and loss characteristic of the neuropathological and clinical manifestations of PD and suggest that the chance for a neuron to survive increases in association with lower total as well as nuclear AT1 expression. Our results support the need for further evaluation of ARBs as disease-modifying agents in PD

Ocean carbon sequestration: Particle fragmentation by copepods as a significant unrecognised factor?

Ocean biology helps regulate global climate by fixing atmospheric CO2 and exporting it to deep waters as sinking detrital particles. New observations demonstrate that particle fragmentation is the principal factor controlling the depth to which these particles penetrate the ocean's interior, and hence how long the constituent carbon is sequestered from the atmosphere. The underlying cause is, however, poorly understood. We speculate that small, particle‐associated copepods, which intercept and inadvertently break up sinking particles as they search for attached protistan prey, are the principle agents of fragmentation in the ocean. We explore this idea using a new marine ecosystem model. Results indicate that explicitly representing particle fragmentation by copepods in biogeochemical models offers a step change in our ability to understand the future evolution of biologically‐mediated ocean carbon storage. Our findings highlight the need for improved understanding of the distribution, abundance, ecology and physiology of particle‐associated copepods

Moments of spectral functions: Monte Carlo evaluation and verification

The subject of the present study is the Monte Carlo path-integral evaluation of the moments of spectral functions. Such moments can be computed by formal differentiation of certain estimating functionals that are infinitely-differentiable against time whenever the potential function is arbitrarily smooth. Here, I demonstrate that the numerical differentiation of the estimating functionals can be more successfully implemented by means of pseudospectral methods (e.g., exact differentiation of a Chebyshev polynomial interpolant), which utilize information from the entire interval $(-\beta \hbar / 2, \beta \hbar/2)$. The algorithmic detail that leads to robust numerical approximations is the fact that the path integral action and not the actual estimating functional are interpolated. Although the resulting approximation to the estimating functional is non-linear, the derivatives can be computed from it in a fast and stable way by contour integration in the complex plane, with the help of the Cauchy integral formula (e.g., by Lyness' method). An interesting aspect of the present development is that Hamburger's conditions for a finite sequence of numbers to be a moment sequence provide the necessary and sufficient criteria for the computed data to be compatible with the existence of an inversion algorithm. Finally, the issue of appearance of the sign problem in the computation of moments, albeit in a milder form than for other quantities, is addressed.Comment: 13 pages, 2 figure

Flow cytometry data standards

<p>Abstract</p> <p>Background</p> <p>Flow cytometry is a widely used analytical technique for examining microscopic particles, such as cells. The Flow Cytometry Standard (FCS) was developed in 1984 for storing flow data and it is supported by all instrument and third party software vendors. However, FCS does not capture the full scope of flow cytometry (FCM)-related data and metadata, and data standards have recently been developed to address this shortcoming.</p> <p>Findings</p> <p>The Data Standards Task Force (DSTF) of the International Society for the Advancement of Cytometry (ISAC) has developed several data standards to complement the raw data encoded in FCS files. Efforts started with the Minimum Information about a Flow Cytometry Experiment, a minimal data reporting standard of details necessary to include when publishing FCM experiments to facilitate third party understanding. MIFlowCyt is now being recommended to authors by publishers as part of manuscript submission, and manuscripts are being checked by reviewers and editors for compliance. Gating-ML was then introduced to capture gating descriptions - an essential part of FCM data analysis describing the selection of cell populations of interest. The Classification Results File Format was developed to accommodate results of the gating process, mostly within the context of automated clustering. Additionally, the Archival Cytometry Standard bundles data with all the other components describing experiments. Here, we introduce these recent standards and provide the very first example of how they can be used to report FCM data including analysis and results in a standardized, computationally exchangeable form.</p> <p>Conclusions</p> <p>Reporting standards and open file formats are essential for scientific collaboration and independent validation. The recently developed FCM data standards are now being incorporated into third party software tools and data repositories, which will ultimately facilitate understanding and data reuse.</p

Maximum Likelihood for Interval Censored Data: Consistency and Computation

1 online resource (PDF, 9 pages

Properties of continuous Fourier extension of the discrete cosine transform and its multidimensional generalization

A versatile method is described for the practical computation of the discrete Fourier transforms (DFT) of a continuous function $g(t)$ given by its values $g_{j}$ at the points of a uniform grid $F_{N}$ generated by conjugacy classes of elements of finite adjoint order $N$ in the fundamental region $F$ of compact semisimple Lie groups. The present implementation of the method is for the groups SU(2), when $F$ is reduced to a one-dimensional segment, and for $SU(2)\times ... \times SU(2)$ in multidimensional cases. This simplest case turns out to result in a transform known as discrete cosine transform (DCT), which is often considered to be simply a specific type of the standard DFT. Here we show that the DCT is very different from the standard DFT when the properties of the continuous extensions of these two discrete transforms from the discrete grid points $t_j; j=0,1, ... N$ to all points $t \in F$ are considered. (A) Unlike the continuous extension of the DFT, the continuous extension of (the inverse) DCT, called CEDCT, closely approximates $g(t)$ between the grid points $t_j$. (B) For increasing $N$, the derivative of CEDCT converges to the derivative of $g(t)$. And (C), for CEDCT the principle of locality is valid. Finally, we use the continuous extension of 2-dimensional DCT to illustrate its potential for interpolation, as well as for the data compression of 2D images.Comment: submitted to JMP on April 3, 2003; still waiting for the referee's Repor

Multiperiodicity, modulations and flip-flops in variable star light curves I. Carrier fit method

The light curves of variable stars are commonly described using simple trigonometric models, that make use of the assumption that the model parameters are constant in time. This assumption, however, is often violated, and consequently, time series models with components that vary slowly in time are of great interest. In this paper we introduce a class of data analysis and visualization methods which can be applied in many different contexts of variable star research, for example spotted stars, variables showing the Blazhko effect, and the spin-down of rapid rotators. The methods proposed are of explorative type, and can be of significant aid when performing a more thorough data analysis and interpretation with a more conventional method.Our methods are based on a straightforward decomposition of the input time series into a fast "clocking" periodicity and smooth modulating curves. The fast frequency, referred to as the carrier frequency, can be obtained from earlier observations (for instance in the case of photometric data the period can be obtained from independently measured radial velocities), postulated using some simple physical principles (Keplerian rotation laws in accretion disks), or estimated from the data as a certain mean frequency. The smooth modulating curves are described by trigonometric polynomials or splines. The data approximation procedures are based on standard computational packages implementing simple or constrained least-squares fit-type algorithms.Comment: 14 pages, 23 figures, submitted to Astronomy and Astrophysic

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data

Neuroinflammation is independently associated with brain network dysfunction in Alzheimer’s disease

Brain network dysfunction is increasingly recognised in Alzheimer’s disease (AD). However, the causes of brain connectivity disruption are still poorly understood. Recently, neuroinflammation has been identified as an important factor in AD pathogenesis. Microglia participate in the construction and maintenance of healthy neuronal networks, but pro-inflammatory microglia can also damage these circuits. We hypothesised that microglial activation is independently associated with brain connectivity disruption in AD. We performed a cross-sectional multimodal imaging study and interrogated the relationship between imaging biomarkers of neuroinflammation, Aβ deposition, brain connectivity and cognition. 42 participants (12 Aβ-positive MCI, 14 Aβ-positive AD and 16 Aβ-negative healthy controls) were recruited. Participants had 11C-PBR28 and 18F-flutemetamol PET to quantify Aβ deposition and microglial activation, T1-weighted, diffusion tensor and resting-state functional MRI to assess structural network and functional network. 11C-PBR28 uptake, structural network integrity and functional network orgnisation were compared across diagnostic groups and the relationship between neuroinflammation and brain network was tested in 26 Aβ-positive patients. Increased 11C-PBR28 uptake, decreased FA, network small-worldness and local efficiency were observed in AD patients. Cortical 11C-PBR28 uptake correlated negatively with structural integrity (standardised β = −0.375, p = 0.037) and network local efficiency (standardised β = −0.468, p < 0.001), independent of cortical thickness and Aβ deposition, while Aβ was not. Network structural integrity, small-worldness and local efficiency, and cortical thickness were positively associated with cognition. Our findings suggest cortical neuroinflammation coincide with structural and functional network disruption independent of Aβ and cortical atrophy. These findings link the brain connectivity change and pathological process in Alzheimer’s disease, and suggest a pathway from neuroinflammation to systemic brain dysfunction

Plantmetabolomics.org: mass spectrometry-based Arabidopsis metabolomics—database and tools update

The PlantMetabolomics (PM) database (http://www.plantmetabolomics.org) contains comprehensive targeted and untargeted mass spectrum metabolomics data for Arabidopsis mutants across a variety of metabolomics platforms. The database allows users to generate hypotheses about the changes in metabolism for mutants with genes of unknown function. Version 2.0 of PlantMetabolomics.org currently contains data for 140 mutant lines along with the morphological data. A web-based data analysis wizard allows researchers to select preprocessing and data-mining procedures to discover differences between mutants. This community resource enables researchers to formulate models of the metabolic network of Arabidopsis and enhances the research community's ability to formulate testable hypotheses concerning gene functions. PM features new web-based tools for data-mining analysis, visualization tools and enhanced cross links to other databases. The database is publicly available. PM aims to provide a hypothesis building platform for the researchers interested in any of the mutant lines or metabolites