Seasonal Variations in Vegetation Indices derived from in situ Type Vegetation Monitoring System at typical landcovers in Japan : From the Observation Results in PGLIERC and Lake Biwa Project

研究概要:本研究では光学センサー搭載衛星データの検証及び地表面フラックスとの対応関係を調べるために簡易式の地上設置型植生モニタリングシステムを日本を代表する土地被覆上(草地,水田,アカマツ林,落葉広葉樹)に設置し,それぞれの土地被覆から得られる植生指標の季節変化について示した.その結果,以下の知見が得られた;1.草原系(草地・水田)では各植生の季節変化特性を良好にモニターすることが可能である,2.森林系(アカマツ林・落葉広葉樹)ではセンサーとキャノピーの距離が近すぎるため,思うような結果を得ることが出来なかった.3.ただし全般としては各土地被覆特性を示す連続したデータを取得することができ,システムの妥当性を示すことができた

Differentiation of participants´ microbiomes.

<p><b>RDA biplot at OTU level with Hellinger-transformed data.</b> Redundancy analysis is a constrained method based on multiple linear regression which enables correlation of explanatory variables with the RDA axes. The black dots represent individuals without ADHD; the green dots, individuals with ADHD diagnosis. Species and factors correlated with the RDA axes were determined by the envfit function of the R package vegan, the cut-off for plotted results was p = 0.01.</p

Reduced microbiome alpha diversity in young patients with ADHD

<div><p>ADHD is a psychiatric disorder which is characterized by hyperactivity, impulsivity and attention problems. Due to recent findings of microbial involvement in other psychiatric disorders like autism and depression, a role of the gut microbiota in ADHD pathogenesis is assumed but has not yet been investigated. In this study, the gut microbiota of 14 male ADHD patients (mean age: 11.9 yrs.) and 17 male controls (mean age: 13.1 yrs.) was examined via next generation sequencing of 16S rDNA and analyzed for diversity and biomarkers. We found that the microbial diversity (alpha diversity) was significantly decreased in ADHD patients compared to controls (p_Shannon = 0.036) and that the composition (beta diversity) differed significantly between patients and controls (p_ANOSIM = 0.033, p_ADONIS = 0.006, p_betadisper = 0.002). In detail, the bacterial family <i>Prevotellacae</i> was associated with controls, while patients with ADHD showed elevated levels of <i>Bacteroidaceae</i>, and both <i>Neisseriaceae</i> and <i>Neisseria spec</i>. were found as possible biomarkers for juvenile ADHD. Our results point to a possible link of certain microbiota with ADHD, with <i>Neisseria spec</i>. being a very promising ADHD-associated candidate. This finding provides the basis for a systematic, longitudinal assessment of the role of the gut microbiome in ADHD, yielding promising potential for both prevention and therapeutic intervention.</p></div

Sample-specific features of predicted CNVs.

<p>Given are the median and, in parentheses, the inter-quartile range per sample. <b>DDR:</b> Ratio of deletions to duplications.</p><p>Sample-specific features of predicted CNVs.</p

Sample-specific features of validated CNVs.

<p>Given are the median and, in parentheses, the inter-quartile range per sample. <b>DDR:</b> Ratio of deletions to duplications,</p><p>Sample-specific features of validated CNVs.</p

CNV prediction and family-based validation.

<p><b>A:</b> Number of CNVs predicted per sample. <b>B:</b> Proportion of CNVs per sample validated by parental information. <b>Light grey:</b> HMM-based algorithms; <b>dark grey:</b> segmentation algorithms.</p

Median sample-specific CNV length.

<p>Kernel-smoothed histogram of the median CNV length per sample. Outliers were excluded. <b>Solid line:</b> HMM-based algorithms; <b>dotted lines:</b> segmentation algorithms.</p

Sample-specific CNV validation rate.

<p>Given are the median and, in parentheses, the inter-quartile range per sample. <b>DDR:</b> Ratio of deletions to duplications.</p><p>Sample-specific CNV validation rate.</p

Family-based CNV validation by chance alone?

<p>Given are the median and, in parentheses, the inter-quartile range.</p><p>Family-based CNV validation by chance alone?</p

Median sample-specific number of validated CNVs.

<p><b>Light grey:</b> duplications; <b>dark grey:</b> deletions.</p