Levesquebateman6291
inical investigation.Leaf senescence can be triggered by multiple abiotic stresses including darkness, nutrient limitation, salinity, and drought. Recently, heatwaves have been occurring more frequently, and they dramatically affect plant growth and development. However, the underlying molecular networks of heat stress-induced leaf senescence remain largely uncharacterized. Here we showed that PHYTOCHROME INTERACTING FACTOR 4 (PIF4) and PIF5 proteins could efficiently promote heat stress-induced leaf senescence in Arabidopsis. Transcriptomic profiling analysis revealed that PIF4 and PIF5 are likely to function through multiple biological processes including hormone signaling pathways. Further, we characterized NAC019, SAG113, and IAA29 as direct transcriptional targets of PIF4 and PIF5. The transcription of NAC019, SAG113, and IAA29 changes significantly in daytime after heat treatment. In addition, we demonstrated that PIF4 and PIF5 proteins were accumulated during the recovery after heat treatment. Moreover, we showed that heat stress-induced leaf senescence is gated by the circadian clock, and plants might be more actively responsive to heat stress-induced senescence during the day. Taken together, our findings proposed important roles for PIF4 and PIF5 in mediating heat stress-induced leaf senescence, which may help to fully illustrate the molecular network of heat stress-induced leaf senescence in higher plants and facilitate the generation of heat stress-tolerant crops.
Fourier ptychographic microscopy (FPM) is a computational microscopy technique that enables large field of view and high-resolution microscopic imaging of biological samples. However, the FPM does not yet have an adequately capable open-source software. In order to fill this gap we are presenting novel, simple, universal, semi-automatic and highly intuitive graphical user interface (GUI) open-source application called the FPM app enabling wide-scale robust FPM reconstruction. Orlistat cost Apart from implementing the FPM in accessible GUI app, we also made several improvements in the FPM image reconstruction process itself, making the FPM more automatic, noise-robust and faster.
FPM app was implemented in MATLAB and all MATLAB codes along with standalone executable version of the FPM app and the online documentation are freely accessible at https//github.com/MRogalski96/FPM-app. Our exemplary FPM datasets may be downloaded at https//bit.ly/2MxNpGb.
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Genome Architecture Mapping (GAM) was recently introduced as a digestion- and ligation-free method to detect chromatin conformation. Orthogonal to existing approaches based on chromatin conformation capture (3C), GAM's ability to capture both inter- and intra-chromosomal contacts from low amounts of input data makes it particularly well suited for allele-specific analyses in a clinical setting. Allele-specific analyses are powerful tools to investigate the effects of genetic variants on many cellular phenotypes including chromatin conformation, but require the haplotypes of the individuals under study to be known a-priori. So far however, no algorithm exists for haplotype reconstruction and phasing of genetic variants from GAM data, hindering the allele-specific analysis of chromatin contact points in non-model organisms or individuals with unknown haplotypes.
We present GAMIBHEAR, a tool for accurate haplotype reconstruction from GAM data. GAMIBHEAR aggregates allelic co-observation frequencies from GAM data and employs a GAM-specific probabilistic model of haplotype capture to optimise phasing accuracy. Using a hybrid mouse embryonic stem cell line with known haplotype structure as a benchmark dataset, we assess correctness and completeness of the reconstructed haplotypes, and demonstrate the power of GAMIBHEAR to infer accurate genome-wide haplotypes from GAM data.
GAMIBHEAR is available as an R package under the open source GPL-2 license at https//bitbucket.org/schwarzlab/gamibhear.
julia.markowski@mdc-berlin.de.
Supplementary information is available at Bioinformatics online.
Supplementary information is available at Bioinformatics online.
In recent years, there have been several meaningful advances in the understanding of the cognitive effects of chronic rhinosinusitis. However, an investigation exploring the potential link between the underlying inflammatory disease and higher-order neural processing has not yet been performed.
To describe the association of sinonasal inflammation with functional brain connectivity (Fc), which may underlie chronic rhinosinusitis-related cognitive changes.
This is a case-control study using the Human Connectome Project (Washington University-University of Minnesota Consortium of the Human Connectome Project 1200 release), an open-access and publicly available data set that includes demographic, imaging, and behavioral data for 1206 healthy adults aged 22 to 35 years. Twenty-two participants demonstrated sinonasal inflammation (Lund-Mackay score [LMS] ≥ 10) and were compared with age-matched and sex-matched healthy controls (LMS = 0). These participants were further stratified into moderate (LMS < 14, unctional hub with a central role in modulating cognition. This region also shows increased connectivity to areas that are activated during introspective and self-referential processing and decreased connectivity to areas involved in detection and response to stimuli. Future prospective studies are warranted to determine the applicability of these findings to a clinical chronic rhinosinusitis population.
Identifying rare subpopulations of cells is a critical step in order to extract knowledge from single-cell expression data, especially when the available data is limited and rare subpopulations only contain a few cells. In this paper, we present a data mining method to identify small subpopulations of cells that present highly specific expression profiles. This objective is formalized as a constrained optimization problem that jointly identifies a small group of cells and a corresponding subset of specific genes. The proposed method extends the max-sum submatrix problem to yield genes that are, for instance, highly expressed inside a small number of cells, but have a low expression in the remaining ones.
We show through controlled experiments on scRNA-seq data that the MicroCellClust method achieves a high F1 score to identify rare sub-populations of artificially planted human T cells. The effectiveness of MicroCellClust is confirmed as it reveals a subpopulation of CD4 T cells with a specific phenotype from breast cancer samples, and a subpopulation linked to a specific stage in the cell cycle from breast cancer samples as well.