-
Cho Aaen posted an update 2 months ago
To determine whether root-supplied ABA alleviates saline stress, tomato (Solanum lycopersicum L. cv. Sugar Drop) was grafted onto two independent lines (NCED OE) overexpressing the SlNCED1 gene (9-cis-epoxycarotenoid dioxygenase) and wild type rootstocks. After 200 days of saline irrigation (EC = 3.5 dS m-1 ), plants with NCED OE rootstocks had 30% higher fruit yield, but decreased root biomass and lateral root development. Although NCED OE rootstocks upregulated ABA-signalling (AREB, ATHB12), ethylene-related (ACCs, ERFs), aquaporin (PIPs) and stress-related (TAS14, KIN, LEA) genes, downregulation of PYL ABA receptors and signalling components (WRKYs), ethylene synthesis (ACOs) and auxin-responsive factors occurred. Elevated SlNCED1 expression enhanced ABA levels in reproductive tissue while ABA catabolites accumulated in leaf and xylem sap suggesting homeostatic mechanisms. NCED OE also reduced xylem cytokinin transport to the shoot and stimulated foliar 2-isopentenyl adenine (iP) accumulation and phloem transport. Moreover, increased xylem GA3 levels in growing fruit trusses were associated with enhanced reproductive growth. Improved photosynthesis without changes in stomatal conductance was consistent with reduced stress sensitivity and hormone-mediated alteration of leaf growth and mesophyll structure. Combined with increases in leaf nutrients and flavonoids, systemic changes in hormone balance could explain enhanced vigour, reproductive growth and yield under saline stress.
Breast ultrasound (BUS) image segmentation plays a crucial role in computer-aided diagnosis systems for BUS examination, which are useful for improved accuracy of breast cancer diagnosis. However, such performance remains a challenging task owing to the poor image quality and large variations in the sizes, shapes, and locations of breast lesions. In this paper, we propose a new convolutional neural network with coarse-to-fine feature fusion to address the aforementioned challenges.
The proposed fusion network consists of an encoder path, a decoder path, and a core fusion stream path (FSP). The encoder path is used to capture the context information, and the decoder path is used for localization prediction. The FSP is designed to generate beneficial aggregate feature representations (i.e., various-sized lesion features, aggregated coarse-to-fine information, and high-resolution edge characteristics) from the encoder and decoder paths, which are eventually used for accurate breast lesion segmentation. To be to handle challenging task of segmentation, while outperforming the SOTA segmentation methods. The code is publicly available at https//github.com/mniwk/CF2-NET.
The proposed fusion network could effectively segment lesions from BUS images, thereby presenting a new feature fusion strategy to handle challenging task of segmentation, while outperforming the SOTA segmentation methods. The code is publicly available at https//github.com/mniwk/CF2-NET.Plant pathogens cause disease through secreted effector proteins, which act to promote infection. Typically, the sequences of effectors provide little functional information and further targeted experimentation is required. Here, we utilized a structure/function approach to study SnTox3, an effector from the necrotrophic fungal pathogen Parastagonospora nodorum, which causes cell death in wheat-lines carrying the sensitivity gene Snn3. TKI-258 We developed a workflow for the production of SnTox3 in a heterologous host that enabled crystal structure determination and functional studies. We show this approach can be successfully applied to study effectors from other pathogenic fungi. The β-barrel fold of SnTox3 is a novel fold among fungal effectors. Structure-guided mutagenesis enabled the identification of residues required for Snn3 recognition. SnTox3 is a pre-pro-protein, and the pro-domain of SnTox3 can be cleaved in vitro by the protease Kex2. Complementing this, an in silico study uncovered the prevalence of a conserved motif (LxxR) in an expanded set of putative pro-domain-containing fungal effectors, some of which can be cleaved by Kex2 in vitro. Our in vitro and in silico study suggests that Kex2-processed pro-domain (designated here as K2PP) effectors are common in fungi and this may have broad implications for the approaches used to study their functions.
Men comprise the minority of entry-level baccalaureate nursing students and are at increased risk of experiencing gender-associated incivility.
Uncivil peer-to-peer behavior can negatively affect students’ mental and physical well-being, and learning experience. Nursing faculty must be able to identify and address gender-associated incivility among students.
The purpose of this quality improvement program was to train nursing faculty to prevent, identify, and manage gender-associated incivility in the educational environment.
A day-long interactive workshop utilizing trigger films, small group discussions, and interactive theater was developed to train nursing faculty to implement proactive and reactive techniques to address uncivil behavior which will enhance the learning environment for all students. Utilizing Kirkpatrick’s Model of Evaluation, participants were surveyed at the conclusion of the workshop and four months postworkshop to evaluate their learning and its implementation.
Participants gained greater understanding of the impact of gender-associated incivility and felt both empowered and better prepared to manage gender-associated conflict.
Similar approaches may be useful for schools of nursing that wish to empower their nursing faculty to support an equitable nursing education environment free of gender-associated incivility.
Similar approaches may be useful for schools of nursing that wish to empower their nursing faculty to support an equitable nursing education environment free of gender-associated incivility.
Deformable image registration is a fundamental task in medical imaging. Due to the large computational complexity of deformable registration of volumetric images, conventional iterative methods usually face the tradeoff between the registration accuracy and the computation time in practice. In order to boost the performance of deformable registration in both accuracy and runtime, we propose a fast unsupervised convolutional neural network for deformable image registration.
The proposed registration model FDRN possesses a compact encoder-decoder network architecture which employs a pair of fixed and moving images as input and outputs a three-dimensional displacement vector field (DVF) describing the offsets between the corresponding voxels in the fixed and moving images. In order to efficiently utilize the memory resources and enlarge the model capacity, we adopt additive forwarding instead of channel concatenation and deepen the network in each encoder and decoder stage. To facilitate the learning efficiency, we leverage skip connection within the encoder and decoder stages to enable residual learning and employ an auxiliary loss at the bottom layer with lowest resolution to involve deep supervision.
Please follow & like us :)
All content contained on CatsWannaBeCats.Com, unless otherwise acknowledged,is the property of CatsWannaBeCats.Com and subject to copyright.
Start with a huge, uncovered box, or even a kiddie pool, and fill with a thick layer (about 6 inches) of unscented clay or fine sand-like particulate clumping/scoopable litter. For Ollie, you may need to play around with the substrate types to make it more natural. Try using soil or peat.