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

Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination R2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5 and 87.8%, respectively, and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns, and a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of <1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence–based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front line to enhance management strategies to save lives

Adhesion mechanisms of the contact interface of TiO2 nanoparticles in films and aggregates

Fundamental knowledge about the mechanisms of adhesion between oxide particles with diameters of few nanometers is impeded by the difficulties associated with direct measurements of contact forces at such a small size scale. Here we develop a strategy based on AFM force spectroscopy combined with all-atom molecular dynamics simulations to quantify and explain the nature of the contact forces between 10 nm small TiO2 nanoparticles. The method is based on the statistical analysis of the force peaks measured in repeated approaching/retracting loops of an AFM cantilever into a film of nanoparticle agglomerates and relies on the in-situ imaging of the film stretching behavior in an AFM/TEM setup. Sliding and rolling events first lead to local rearrangements in the film structure when subjected to tensile load, prior to its final rupture caused by the reversible detaching of individual nanoparticles. The associated contact force of about 2.5 nN is in quantitative agreement with the results of molecular dynamics simulations of the particle–particle detachment. We reveal that the contact forces are dominated by the structure of water layers adsorbed on the particles’ surfaces at ambient conditions. This leads to nonmonotonous force–displacement curves that can be explained only in part by classical capillary effects and highlights the importance of considering explicitly the molecular nature of the adsorbates

Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods

Recent and future trends in synthetic greenhouse gas radiative forcing

Atmospheric measurements show that emissions of hydrofluorocarbons (HFCs) and hydrochlorofluorocarbons are now the primary drivers of the positive growth in synthetic greenhouse gas (SGHG) radiative forcing. We infer recent SGHG emissions and examine the impact of future emissions scenarios, with a particular focus on proposals to reduce HFC use under the Montreal Protocol. If these proposals are implemented, overall SGHG radiative forcing could peak at around 355 mW m[superscript −2] in 2020, before declining by approximately 26% by 2050, despite continued growth of fully fluorinated greenhouse gas emissions. Compared to “no HFC policy” projections, this amounts to a reduction in radiative forcing of between 50 and 240 mW m[superscript −2] by 2050 or a cumulative emissions saving equivalent to 0.5 to 2.8 years of CO2 emissions at current levels. However, more complete reporting of global HFC emissions is required, as less than half of global emissions are currently accounted for.Natural Environment Research Council (Great Britain) (Advanced Research Fellowship NE/I021365/1)United States. National Aeronautics and Space Administration (Upper Atmospheric Research Program Grant NNX11AF17G)United States. National Oceanic and Atmospheric Administratio

Confounding patient factors affecting the proper interpretation of the periostin level as a biomarker in asthma development

Introduction: The proper use of serum periostin (POSTN) as a biomarker for asthma is hindered by inconsistent performance in different clinical settings. Objective: To explore patient’s factors that may affect POSTN expression locally and systematically and its utility as a biomarker for asthma development. Materials and Methods: Here we used bioinformatics analysis of publicly available transcriptomics data to confirm that POSTN is an asthma specific gene involved in core signaling pathways enriched in the bronchial epithelium during asthma. We then explored a large number of datasets to identify possible confounders that may affect the POSTN gene expression and consequently, its interpretation as a reliable biomarker for asthma. Plasma and saliva levels of POSTN were determined in locally recruited asthmatic patients (mild, moderate and severe) compared to healthy controls to confirm the bioinformatics findings. Results: Our bioinformatics results confirmed that POSTN was consistently upregulated in the bronchial epithelium in asthma, chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) bronchial epithelium. In asthma, its mRNA expression was affected by gender, sample anatomical site and type, steroid therapy, and smoking. In our cohort, plasma POSTN was upregulated in severe and non-severe asthmatic patients. Saliva POSTN was significantly higher in non-severe asthmatic patients compared to healthy and severe asthmatic patients (specifically those who are not on Xolair (omalizumab)). Patients’ BMI, inhaled steroid use and Xolair treatment affected POSTN plasma levels. Conclusion: Up to our knowledge, this is the first study examining the level of POSTN in the saliva of asthmatic patients. Both plasma and saliva POSTN levels can aid in early diagnosis of asthma. Saliva POSTN level was more sensitive than plasma POSTN in differentiating between severe and non-severe asthmatics. Patients’ characteristics like BMI, the use of inhaled steroids, or Xolair treatment should be carefully reviewed before any meaningful interpretation of POSTN level in clinical practice

Pharmacy education and practice in 13 middle eastern countries

The Arab world has influenced the art and science of pharmacy for centuries. Pharmacy education and practice is continuing to evolve in the Arabic-speaking traditional Middle East countries, although relatively little information has been published in the English press. Our goal was to providea high-level synopsis of conditions in this region. We selected 13 countries for review. Information was obtained by reviewing the available published literature and individual university and program web sites, as well as contacting with program or country representatives. Seventy-eight active pharmacy schools in 12 countries were identified. At least 14,000 students (over 75% from Egypt) are admitted into baccalaureate degree programs every year. The 5-year baccalaureate degree remains the first professional degree to practice. While changes in pharmacy education have been relatively rapid over the past decade, the advancement of pharmacy practice, particularly in the private sector, appears to be slower. Hospital pharmacists often possess an advanced degree and tend to have a higher level of practice compared to that of community pharmacists. Despite the adversities that face academics and practitioners alike, there is a strong desire to advance the science and practice of pharmacy in the Middle East

HACK: Hierarchical ACKs for Efficient Wireless Medium Utilization

WiFi’s physical layer has increased in speed from 802.11b’s 11 Mbps to the Gbps rates of emerging 802.11ac. Despite these gains, WiFi’s inefficient MAC layer limits achievable end-to-end throughput. The culprit is 802.11’s mandatory idle period before each medium acquisition, which has come to dwarf the duration of a packet’s transmission. This overhead is especially punishing for TCP traffic, whose every two data packets elicit a short TCP ACK. Even frame aggregation and block link-layer ACKs (introduced in 802.11n) leave signifi- cant medium acquisition overhead for TCP ACKs. In this paper, we propose TCP/HACK (Hierarchical ACKnowledgment), a system that applies cross-layer optimization to TCP traffic on WiFi networks by carrying TCP ACKs within WiFi’s link-layer acknowledgments. By eliminating all medium acquisitions for TCP ACKs in unidirectional TCP flows, TCP/HACK significantly improves medium utilization, and thus significantly increases achievable capacity for TCP workloads. Our measurements of a real-time, line-speed implementation for 802.11a on the SoRa software-defined radio platform and simulations of 802.11n networks at scale demonstrate that TCP/HACK significantly improves TCP throughput on WiFi networks