We present a code for automated detection, classification, and tracking of solar filaments in full-disk Hα images that can contribute to Living With a Star science investigations and space weather forecasting. The program can reliably identify filaments; determine their chirality and other relevant parameters like filament area, length, and average orientation with respect to the equator. It is also capable of tracking the day-by-day evolution of filaments while they travel across the visible disk. The code was tested by analyzing daily Hα images taken at the Big Bear Solar Observatory from mid-2000 until beginning of 2005. It identified and established the chirality of thousands of filaments without human intervention. We compared the results with a list of filament proprieties manually compiled by Pevtsov, Balasubramaniam and Rogers (2003) over the same period of time. The computer list matches Pevtsov's list with a 72% accuracy. The code results confirm the hemispheric chirality rule stating that dextral filaments predominate in the north and sinistral ones predominate in the south. The main difference between the two lists is that the code finds significantly more filaments without an identifiable chirality. This may be due to a tendency of human operators to be biased, thereby assigning a chirality in less clear cases, while the code is totally unbiased. We also have found evidence that filaments obeying the chirality rule tend to be larger and last longer than the ones that do not follow the hemispherical rule. Filaments adhering to the hemispheric rule also tend to be more tilted toward the equator between latitudes 10 • and 30 • , than the ones that do not.
Background: The gut microbiome is altered in several neurologic disorders including Parkinson's disease (PD).Objectives: Profile the fecal gut metagenome in PD for alterations in microbial composition, taxon abundance, metabolic pathways, and microbial gene products, and their relationship with disease progression.Methods: Shotgun metagenomic sequencing was conducted on 244 stool donors from two independent cohorts in the United States, including individuals with PD (n=48, n=47, respectively), environmental Household Controls (HC, n=29, n=30), and community Population Controls (PC, n=41, n=49). Microbial features consistently altered in PD compared to HC and PC subjects were identified. Data were cross-referenced to public metagenomic datasets from two previous studies in Germany and China to determine generalizable microbiome features.
Results:The gut microbiome in PD shows significant alterations in community composition. Robust taxonomic alterations include depletion of putative "beneficial" gut commensals Faecalibacterium prausnitzii and Eubacterium and Roseburia species, and increased abundance of Akkermansia muciniphila and Bifidobacterium species. Pathway enrichment analysis and metabolic potential, constructed from microbial gene abundance, revealed disruptions in microbial carbohydrate and lipid metabolism and increased amino acid and nucleotide metabolism. These global gene-level signatures indicate an increased response to oxidative stress, decreased cellular growth and microbial motility, and disrupted inter-community signaling.
Conclusions:A metagenomic meta-analysis of PD shows consistent and novel alterations in taxonomic representation, functional metabolic potential, and microbial gene abundance across four independent studies from three continents. These data reveal stereotypic changes in the gut microbiome are a consistent feature of PD, highlighting potential diagnostic and therapeutic avenues for future research.
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