Plant cell walls tackling climate change : insights into plant cell wall remodeling, its regulation, and biotechnological strategies to improve crop adaptations and photosynthesis in response to global warming

Plant cell wall (CW) is a complex and intricate structure that performs several functions throughout the plant life cycle. The CW of plants is critical to the maintenance of cells\u2019 structural integrity by resisting internal hydrostatic pressures, providing flexibility to support cell division and expansion during tissue differentiation, and acting as an environmental barrier that protects the cells in response to abiotic stress. Plant CW, comprised primarily of polysaccharides, represents the largest sink for photosynthetically fixed carbon, both in plants and in the biosphere. The CW structure is highly varied, not only between plant species but also among different organs, tissues, and cell types in the same organism. During the developmental processes, the main CW components, i.e., cellulose, pectins, hemicelluloses, and different types of CW-glycoproteins, interact constantly with each other and with the environment to maintain cell homeostasis. Differentiation processes are altered by positional effect and are also tightly linked to environmental changes, affecting CW both at the molecular and biochemical levels. The negative effect of climate change on the environment is multifaceted, from high temperatures, altered concentrations of greenhouse gases such as increasing CO2 in the atmosphere, soil salinity, and drought, to increasing frequency of extreme weather events taking place concomitantly, therefore, climate change affects crop productivity in multiple ways. Rising CO2 concentration in the atmosphere is expected to increase photosynthetic rates, especially at high temperatures and under water-limited conditions. This review aims to synthesize current knowledge regarding the effects of climate change on CW biogenesis and modification. We discuss specific cases in crops of interest carrying cell wall modifications that enhance tolerance to climate change-related stresses; from cereals such as rice, wheat, barley, or maize to dicots of interest such as brassica oilseed, cotton, soybean, tomato, or potato. This information could be used for the rational design of genetic engineering traits that aim to increase the stress tolerance in key crops. Future growing conditions expose plants to variable and extreme climate change factors, which negatively impact global agriculture, and therefore further research in this area is critical

Small-bowel necrosis complicating a cytomegalovirus-induced superior mesenteric vein thrombosis in an immunocompetent patient: a case report

<p>Abstract</p> <p>Introduction</p> <p>Superior mesenteric venous thrombosis as a result of acute cytomegalovirus infection is rare, with only a few cases reported in the literature.</p> <p>Case presentation</p> <p>We present the case of a 40-year-old Caucasian man who was admitted to our hospital with a 5-day history of fever. His serological test and pp65 antigen detection of cytomegalovirus were positive, suggesting acute infection. On the sixth day after his admission, the patient complained of acute, progressive abdominal pain. Abdominal computed tomography revealed acute superior mesenteric venous thrombosis. An emergency laparotomy showed diffuse edema and ischemic lesions of the small bowel and its associated mesentery with a 50-cm-long segmental infarction of the proximal jejunum. An extensive enterectomy of about 100 cm of jejunum that included the necrotic segment was performed, followed by an end-to-end anastomosis. Anti-coagulation therapy was administered pre-operatively in the form of small-fractionated heparin and continued postoperatively. The patient had an uneventful recovery and was discharged on the 11th postoperative day.</p> <p>Conclusion</p> <p>Acute cytomegalovirus infection can contribute to the occurrence of mesenteric venous thrombosis in immunocompetent patients. It is important for physicians and internists to be aware of the possible thrombotic complications of cytomegalovirus infection. A high level of clinical suspicion is essential to successfully treat a potentially lethal condition such as superior mesenteric venous thrombosis.</p

Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram

Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses

Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on the solar dynamo and space weather events, have benefited from improvements in resolution and measurement frequency of new instruments. However, in order to fully understand the solar cycle, high-quality data across time-scales longer than the typical lifespan of a solar instrument are required. At the moment, discrepancies between measurement surveys prevent the combined use of all available data. In this work, we show that machine learning can help bridge the gap between measurement surveys by learning to \textbf{super-resolve} low-resolution magnetic field images and \textbf{translate} between characteristics of contemporary instruments in orbit. We also introduce the notion of physics-based metrics and losses for super-resolution to preserve underlying physics and constrain the solution space of possible super-resolution outputs

Transcriptional activation of a 37 kDa ethylene responsive cysteine protease gene, RbCP1, is associated with protein degradation during petal abscission in rose

Cysteine proteases play an important role in several developmental processes in plants, particularly those related to senescence and cell death. A cysteine protease gene, RbCP1, has been identified that encodes a putative protein of 357 amino acids and is expressed in the abscission zone (AZ) of petals in rose. The gene was responsive to ethylene in petals, petal abscission zones, leaves, and thalamus. The expression of RbCP1 increased during both ethylene-induced as well as natural abscission and was inhibited by 1-MCP. Transcript accumulation of RbCP1 was accompanied by the appearance of a 37 kDa cysteine protease, a concomitant increase in protease activity and a substantial decrease in total protein content in the AZ of petals. Agro-injection of rose petals with a 2.0 kb region upstream of the RbCP1 gene could drive GUS expression in an abscission zone-specific manner and was blocked by 1-MCP. It is concluded that petal abscission is associated with a decrease in total protein content resulting from rapid transcription of RbCP1 and the expression of a 37 kDa protease

A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression.

BACKGROUND: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed. Often these analysis stages are applied disregarding the fact that the data is drawn from a time series. In this paper we propose a simple model for accounting for the underlying temporal nature of the data based on a Gaussian process. RESULTS: We review Gaussian process (GP) regression for estimating the continuous trajectories underlying in gene expression time-series. We present a simple approach which can be used to filter quiet genes, or for the case of time series in the form of expression ratios, quantify differential expression. We assess via ROC curves the rankings produced by our regression framework and compare them to a recently proposed hierarchical Bayesian model for the analysis of gene expression time-series (BATS). We compare on both simulated and experimental data showing that the proposed approach considerably outperforms the current state of the art. CONCLUSIONS: Gaussian processes offer an attractive trade-off between efficiency and usability for the analysis of microarray time series. The Gaussian process framework offers a natural way of handling biological replicates and missing values and provides confidence intervals along the estimated curves of gene expression. Therefore, we believe Gaussian processes should be a standard tool in the analysis of gene expression time series

Dynamic Facial Landmarking Selection for Emotion Recognition using Gaussian Processes

Facial features are the basis for the emotion recognition process and are widely used in affective computing systems. This emotional process is produced by a dynamic change in the physiological signals and the visual answers related to the facial expressions. An important factor in this process, relies on the shape information of a facial expression, represented as dynamically changing facial landmarks. In this paper we present a framework for dynamic facial landmarking selection based on facial expression analysis using Gaussian Processes. We perform facial features tracking, based on Active Appearance Models for facial landmarking detection, and then use Gaussian process ranking over the dynamic emotional sequences with the aim to establish which landmarks are more relevant for emotional multivariate time-series recognition. The experimental results show that Gaussian Processes can effectively fit to an emotional time-series and the ranking process with log-likelihoods finds the best landmarks (mouth and eyebrows regions) that represent a given facial expression sequence. Finally, we use the best ranked landmarks in emotion recognition tasks obtaining accurate performances for acted and spontaneous scenarios of emotional datasets