The Glycosyltransferase Repertoire of the Spikemoss Selaginella moellendorffii and a Comparative Study of Its Cell Wall

Spike mosses are among the most basal vascular plants, and one species, Selaginella moellendorffii, was recently selected for full genome sequencing by the Joint Genome Institute (JGI). Glycosyltransferases (GTs) are involved in many aspects of a plant life, including cell wall biosynthesis, protein glycosylation, primary and secondary metabolism. Here, we present a comparative study of the S. moellendorffii genome across 92 GT families and an additional family (DUF266) likely to include GTs. The study encompasses the moss Physcomitrella patens, a non-vascular land plant, while rice and Arabidopsis represent commelinid and non-commelinid seed plants. Analysis of the subset of GT-families particularly relevant to cell wall polysaccharide biosynthesis was complemented by a detailed analysis of S. moellendorffii cell walls. The S. moellendorffii cell wall contains many of the same components as seed plant cell walls, but appears to differ somewhat in its detailed architecture. The S. moellendorffii genome encodes fewer GTs (287 GTs including DUF266s) than the reference genomes. In a few families, notably GT51 and GT78, S. moellendorffii GTs have no higher plant orthologs, but in most families S. moellendorffii GTs have clear orthologies with Arabidopsis and rice. A gene naming convention of GTs is proposed which takes orthologies and GT-family membership into account. The evolutionary significance of apparently modern and ancient traits in S. moellendorffii is discussed, as is its use as a reference organism for functional annotation of GTs

Myocardial tagging by Cardiovascular Magnetic Resonance: evolution of techniques--pulse sequences, analysis algorithms, and applications

Cardiovascular magnetic resonance (CMR) tagging has been established as an essential technique for measuring regional myocardial function. It allows quantification of local intramyocardial motion measures, e.g. strain and strain rate. The invention of CMR tagging came in the late eighties, where the technique allowed for the first time for visualizing transmural myocardial movement without having to implant physical markers. This new idea opened the door for a series of developments and improvements that continue up to the present time. Different tagging techniques are currently available that are more extensive, improved, and sophisticated than they were twenty years ago. Each of these techniques has different versions for improved resolution, signal-to-noise ratio (SNR), scan time, anatomical coverage, three-dimensional capability, and image quality. The tagging techniques covered in this article can be broadly divided into two main categories: 1) Basic techniques, which include magnetization saturation, spatial modulation of magnetization (SPAMM), delay alternating with nutations for tailored excitation (DANTE), and complementary SPAMM (CSPAMM); and 2) Advanced techniques, which include harmonic phase (HARP), displacement encoding with stimulated echoes (DENSE), and strain encoding (SENC). Although most of these techniques were developed by separate groups and evolved from different backgrounds, they are in fact closely related to each other, and they can be interpreted from more than one perspective. Some of these techniques even followed parallel paths of developments, as illustrated in the article. As each technique has its own advantages, some efforts have been made to combine different techniques together for improved image quality or composite information acquisition. In this review, different developments in pulse sequences and related image processing techniques are described along with the necessities that led to their invention, which makes this article easy to read and the covered techniques easy to follow. Major studies that applied CMR tagging for studying myocardial mechanics are also summarized. Finally, the current article includes a plethora of ideas and techniques with over 300 references that motivate the reader to think about the future of CMR tagging

Colchicine-Induced paracrystals in root cells of wheat (Triticum Aestivum L.)

Tubulin conformations other than microtubules in the meristematic cells of wheat roots grown in the presence of 2 mM colchicine solution were investigated by immunofluorescence and electron microscopy. In the affected cells microtubules disappeared and were replaced by tubulin fluorescent strands that occurred in the cortical cytoplasm. With increasing time of exposure to colchicine the tubulin strands became better organized and occurred also in the subcortical cytoplasm and finally they were restricted to the area around the nucleus. In prophase and preprophase cells thick strands occupied the cortical cytoplasmic zone where in normal cells a preprophase microtubule band (PMB) was expected to be assembled. In the colchicine-treated cells electron microscopy revealed an accumulation of paracrystalline aggregates, which initially occurred along the cell wall and later deeper in the cytoplasm, in the perinuclear regions and the cytoplasmic invaginations of the nucleus. In transverse planes the paracrystalline strands appear to consist of hexagonal subunits in a ’honeycomb’ arrangement, while in longitudinal and oblique sections they exhibit variable images. Since their distribution coincides with that of the tubulin strands visualized by immunofluorescence, they are considered to be the same structure. Therefore, the paracrystals consist of, or at least contain, tubulin. They are most likely to be polymers of tubulin-colchicine complexes. © 1995 Annals of Botany Company

Magnetic Hammer Actuation for Tissue Penetration using a Millirobot

Untethered magnetic navigation of millirobots within a human body using a magnetic resonance imaging (MRI) scanner is a promising technology for minimally invasive surgery or drug delivery. Because MRI scanners have a large static magnetic field, they cannot generate torque on magnetic millirobots and must instead use gradient-based pulling. However, gradient values are too small to produce forces large enough to penetrate tissue. This letter presents a method to produce large pulsed forces on millirobots. A ferromagnetic sphere is placed inside a hollow robot body and can move back and forth. This movement is created by alternating the magnetic gradient direction. On the posterior side, a spring allows the sphere to change direction smoothly. On the anterior side, a hard rod creates a surface for the sphere to impact. This impact results in a large pulsed force. The purpose of this study was to understand the functioning of magnetic hammer actuation and control, as well as demonstrate the viability of this mechanism for tissue penetration. This letter begins with modeling and simulating this system. Next, different control strategies are presented and tested. The system successfully penetrated lamb brain samples. Finally, preliminary tests inside a clinical MRI scanner demonstrate the potential of this actuation system

Automated Segmentation and 4D Reconstruction of the Heart Left Ventricle from CINE MRI

Heart disease is highly prevalent in developed countries, causing 1 in 4 deaths. In this work we propose a method for a fully automated 4D reconstruction of the left ventricle of the heart. This can provide accurate information regarding the heart wall motion and in particular the hemodynamics of the ventricles. Such metrics are crucial for detecting heart function anomalies that can be an indication of heart disease. Our approach is fast, modular and extensible. In our testing, we found that generating the 4D reconstruction from a set of 250 MRI images takes less than a minute. The amount of time saved as a result of our work could greatly benefit physicians and cardiologist as they diagnose and treat patients

BNU-Net: A Novel Deep Learning Approach for LV MRI Analysis in Short-Axis MRI

This work presents a novel deep learning architecture called BNU-Net for the purpose of cardiac segmentation based on short-axis MRI images. Its name is derived from the Batch Normalized (BN) U-Net architecture for medical image segmentation. New generations of deep neural networks (NN) are called convolutional NN (CNN). CNNs like U-Net have been widely used for image classification tasks. CNNs are supervised training models which are trained to learn hierarchies of features automatically and robustly perform classification. Our architecture consists of an encoding path for feature extraction and a decoding path that enables precise localization. We compare this approach with a parallel approach named U-Net. Both BNU-Net and U-Net are cardiac segmentation approaches: while BNU-Net employs batch normalization to the results of each convolutional layer and applies an exponential linear unit (ELU) approach that operates as activation function, U-Net does not apply batch normalization and is based on Rectified Linear Units (ReLU). The presented work (i) facilitates various image preprocessing techniques, which includes affine transformations and elastic deformations, and (ii) segments the preprocessed images using the new deep learning architecture. We evaluate our approach on a dataset containing 805 MRI images from 45 patients. The experimental results reveal that our approach accomplishes comparable or better performance than other state-of-the-art approaches in terms of the Dice coefficient and the average perpendicular distance