SmartPIV: flow velocity estimates by smartphones for education and field studies

In this paper, a smartphone application is presented that was developed to lower the barrier to introduce particle image velocimetry (PIV) in lab courses. The first benefit is that a PIV system using smartphones and a continuous wave (cw-) laser is much cheaper than a conventional system and thus much more affordable for universities. The second benefit is that the design of the menus follows that of modern camera apps, which are intuitively used. Thus, the system is much less complex and costly than typical systems, and our experience showed that students have much less reservations to work with the system and to try different parameters. Last but not least the app can be applied in the field. The relative uncertainty was shown to be less than 8%, which is reasonable for quick velocity estimates. An analysis of the computational time necessary for the data evaluation showed that with the current implementation the app is capable of providing smooth live display vector fields of the flow. This might further increase the use of modern measurement techniques in industry and education

Automation of tree‐ring detection and measurements using deep learning

Abstract Core samples from trees are a critical reservoir of ecological information, informing our understanding of past climates, as well as contemporary ecosystem responses to global change. Manual measurements of annual growth rings in trees are slow, labour‐intensive and subject to human bias, hindering the generation of big datasets. We present an alternative, neural network‐based implementation that automates detection and measurement of tree‐ring boundaries from coniferous species. We trained our Mask R‐CNN extensively on over 8000 manually annotated ring boundaries from microscope‐imaged Norway Spruce Picea abies increment cores. We assessed the performance of the trained model after post‐processing on real‐world data generated from our core processing pipeline. The CNN after post‐processing performed well, with recognition of over 98% of ring boundaries (recall) with a precision in detection of 96% when tested on real‐world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were 98% correlated with human measurements of the same samples. Tests on other three conifer species demonstrate that the CNN generalizes well to other species with similar structure. We demonstrate the efficacy of automating the measurement of growth increment in tree core samples. Our CNN‐based system provides high predictive performance in terms of both tree‐ring detection and growth rate determination. Our application is readily deployable as a Docker container and requires only basic command line skills. Additionally, an easy re‐training option allows users to expand capabilities to other wood types. Application outputs include both editable annotations of predictions as well as ring‐width measurements in a commonly used .pos format, facilitating the efficient generation of large ring‐width measurement datasets from increment core samples, an important source of environmental data

Repression of chromomethylase 3 prevents epigenetic collateral damage in arabidopsis

DNA methylation has evolved to silence mutagenic transposable elements (TEs) while typically avoiding the targeting of endogenous genes. Mechanisms that prevent DNA methyltransferases from ectopically methylating genes are expected to be of prime importance during periods of dynamic cell cycle activities including plant embryogenesis. However, virtually nothing is known regarding how DNA methyltransferase activities are precisely regulated during embryogenesis to prevent the induction of potentially deleterious and mitotically stable genic epimutations. Here, we report that microRNA-mediated repression of CHROMOMETHYLASE 3 (CMT3) and the chromatin features that CMT3 prefers help prevent ectopic methylation of thousands of genes during embryogenesis that can persist for weeks afterwards. Our results are also consistent with CMT3-induced ectopic methylation of promoters or bodies of genes undergoing transcriptional activation reducing their expression. Therefore, the repression of CMT3 prevents epigenetic collateral damage on endogenous genes. We also provide a model that may help reconcile conflicting viewpoints regarding the functions of gene-body methylation that occurs in nearly all flowering plants

Arabidopsis histone deacetylase HD2A and HD2B regulate seed dormancy by repressing DELAY OF GERMINATION 1

Seed dormancy is a crucial developmental transition that affects the adaption and survival of plants. Arabidopsis DELAY OF GERMINATION 1 (DOG1) is known as a master regulator of seed dormancy. However, although several upstream factors of DOG1 have been reported, the exact regulation of DOG1 is not fully understood. Histone acetylation is an important regulatory layer, controlled by histone acetyltransferases and histone deacetylases. Histone acetylation strongly correlates with transcriptionally active chromatin, whereas heterochromatin is generally characterized by hypoacetylated histones. Here we describe that loss of function of two plant-specific histone deacetylases, HD2A and HD2B, resulted in enhanced seed dormancy in Arabidopsis. Interestingly, the silencing of HD2A and HD2B caused hyperacetylation of the DOG1 locus and promoted the expression of DOG1 during seed maturation and imbibition. Knockout of DOG1 could rescue the seed dormancy and partly rescue the disturbed development phenotype of hd2ahd2b. Transcriptomic analysis of the hd2ahd2b line shows that many genes involved in seed development were impaired. Moreover, we demonstrated that HSI2 and HSL1 interact with HD2A and HD2B. In sum, these results suggest that HSI2 and HSL1 might recruit HD2A and HD2B to DOG1 to negatively regulate DOG1 expression and to reduce seed dormancy, consequently, affecting seed development during seed maturation and promoting seed germination during imbibition

nf-core/eager: 2.5.0 - Bopfingen -2023-11-07

<h3><code>Added</code></h3> <ul> <li><a href="https://github.com/nf-core/eager/issues/1020">#1020</a> Added mapDamage2 as an alternative for damage calculation.</li> </ul> <h3><code>Fixed</code></h3> <ul> <li><a href="https://github.com/nf-core/eager/issues/1017">#1017</a> Fixed file name collision in niche cases with multiple libraries of multiple UDG treatments.</li> <li><a href="https://github.com/nf-core/eager/issues/1024">#1024</a> <code>multiqc_general_stats.txt</code> is now generated even if the table is a beeswarm plot in the report.</li> <li><a href="https://github.com/nf-core/eager/issues/655">#655</a> Updated RG tags for all mappers. RG-id now includes Sample as well as Library ID. Added <code>LB:</code> tag with the library ID.</li> <li><a href="https://github.com/nf-core/eager/issues/1031">#1031</a> Always index fasta regardless of mapper. This ensures that DamageProfiler and genotyping processes get submitted when using bowtie2 and not providing a fasta index.</li> </ul> <h3><code>Dependencies</code></h3> <ul> <li><code>multiqc</code>: 1.14 -> 1.16</li> </ul> <h3><code>Deprecated</code></h3&gt