Georgepappas7389

The COVID-19 pandemic and associated public health measures have had unanticipated effects on ecosystems and biodiversity. Conservation physiology and its mechanistic underpinnings are well positioned to generate robust data to inform the extent to which the Anthropause has benefited biodiversity through alterations in disturbance-, pollution- and climate change-related emissions. The conservation physiology toolbox includes sensitive biomarkers and tools that can be used both retroactively (e.g. to reconstruct stress in wildlife before, during and after lockdown measures) and proactively (e.g. future viral waves) to understand the physiological consequences of the pandemic. The pandemic has also created new risks to ecosystems and biodiversity through extensive use of various antimicrobial products (e.g. hand cleansers, sprays) and plastic medical waste. Conservation physiology can be used to identify regulatory thresholds for those products. Moreover, given that COVID-19 is zoonotic, there is also opportunity for conservation physiologists to work closely with experts in conservation medicine and human health on strategies that will reduce the likelihood of future pandemics (e.g. what conditions enable disease development and pathogen transfer) while embracing the One Health concept. The conservation physiology community has also been impacted directly by COVID-19 with interruptions in research, training and networking (e.g. conferences). Because this is a nascent discipline, it will be particularly important to support early career researchers and ensure that there are recruitment pathways for the next generation of conservation physiologists while creating a diverse and inclusive community. We remain hopeful for the future and in particular the ability of the conservation physiology community to deliver relevant, solutions-oriented science to guide decision makers particularly during the important post-COVID transition and economic recovery.The tall (>4 m), charismatic and threatened columnar cacti, pasacana [Echinopsis atacamensis (Vaupel) Friedrich & G.D. Rowley)], grows on the Bolivian Altiplano and provides environmental and economic value to these extremely cold, arid and high-elevation (~4000 m) ecosystems. Yet very little is known about their growth rates, ages, demography and climate sensitivity. Using radiocarbon in spine dating time series, we quantitatively estimate the growth rate (5.8 and 8.3 cm yr-1) and age of these cacti (up to 430 years). These data and our field measurements yield a survivorship curve that suggests precipitation on the Altiplano is important for this species' recruitment. Our results also reveal a relationship between nighttime temperatures on the Altiplano and the variation in oxygen isotope values in spines (δ18O). The annual δ18O minimums from 58 years of in-series spine tissue from pasacana on the Altiplano provides at least decadal proxy records of temperature (r = 0.58; P  less then  0.0001), and evidence suggests that there are longer records connecting modern Altiplano temperatures to sea-surface temperatures (SSTs) in the Atlantic Ocean. While the role of Atlantic SSTs on the South American Summer Monsoon (SASM) and precipitation on the Bolivian Altiplano is well described, the impact of SSTs on Altiplano temperatures is disputed. Understanding the modern impact of SSTs on temperature on the Altiplano is important to both understand the impact of future climate change on pasacana cactus and to understand past climate changes on the Altiplano. This is the best quantitative evidence to date of one of the oldest known cactus in the world, although there are likely many older cacti on the Altiplano, or elsewhere, that have not been sampled yet. Together with growth, isotope and age data, this information should lead to better management and conservation outcomes for this threatened species and the Altiplano ecosystem.The outbreak of Coronavirus (COVID-19) has spread between people around the world at a rapid rate so that the number of infected people and deaths is increasing quickly every day. Accordingly, it is a vital process to detect positive cases at an early stage for treatment and controlling the disease from spreading. Several medical tests had been applied for COVID-19 detection in certain injuries, but with limited efficiency. In this study, a new COVID-19 diagnosis strategy called Feature Correlated Naïve Bayes (FCNB) has been introduced. The FCNB consists of four phases, which are; Feature Selection Phase (FSP), Feature Clustering Phase (FCP), Master Feature Weighting Phase (MFWP), and Feature Correlated Naïve Bayes Phase (FCNBP). The FSP selects only the most effective features among the extracted features from laboratory tests for both COVID-19 patients and non-COVID-19 people by using the Genetic Algorithm as a wrapper method. The FCP constructs many clusters of features based on the selected features from FSP by using a novel clustering technique. These clusters of features are called Master Features (MFs) in which each MF contains a set of dependent features. The MFWP assigns a weight value to each MF by using a new weight calculation method. The FCNBP is used to classify patients based on the weighted Naïve Bayes algorithm with many modifications as the correlation between features. The proposed FCNB strategy has been compared to recent competitive techniques. Experimental results have proven the effectiveness of the FCNB strategy in which it outperforms recent competitive techniques because it achieves the maximum (99%) detection accuracy.COVID-19 has posed a significant threat to global health. Early data has revealed that IL-6, a key regulatory cytokine, plays an important role in the cytokine storm of COVID-19. Multiple trials are therefore looking at the effects of Tocilizumab, an IL-6 receptor antibody that inhibits IL-6 activity, on treatment of COVID-19, with promising findings. As part of a clinical trial looking at the effects of Tocilizumab treatment on kidney transplant recipients with subclinical rejection, we performed single-cell RNA sequencing of comparing stimulated PBMCs before and after Tocilizumab treatment. We leveraged this data to create an in vitro cytokine storm model, to better understand the effects of Tocilizumab in the presence of inflammation. Tocilizumab-treated cells had reduced expression of inflammatory-mediated genes and biologic pathways, particularly amongst monocytes. These results support the hypothesis that Tocilizumab may hinder the cytokine storm of COVID-19, through a demonstration of biologic impact at the single-cell level.Plant lignocellulosic biomass, mostly composed of polysaccharide-rich secondary cell walls (SCWs), provides fermentable sugars that may be used to produce biofuels and biomaterials. However, the complex chemical composition and physical structure of SCWs hinder efficient processing of plant biomass. Understanding the molecular mechanisms underlying SCW deposition is, thus, essential to optimize bioenergy feedstocks. Here, we establish a xylogenic culture as a model system to study SCW deposition in sugarcane; the first of its kind in a C4 grass species. We used auxin and brassinolide to differentiate sugarcane suspension cells into tracheary elements, which showed metaxylem-like reticulate or pitted SCW patterning. The differentiation led to increased lignin levels, mainly caused by S-lignin units, and a rise in p-coumarate, leading to increased p-coumarateferulate ratios. RNAseq analysis revealed massive transcriptional reprogramming during differentiation, with upregulation of genes associated with cell wall biogenesis and phenylpropanoid metabolism and downregulation of genes related to cell division and primary metabolism. To better understand the differentiation process, we constructed regulatory networks of transcription factors and SCW-related genes based on co-expression analyses. Accordingly, we found multiple regulatory modules that may underpin SCW deposition in sugarcane. PKC-theta inhibitor mouse Our results provide important insights and resources to identify biotechnological strategies for sugarcane biomass optimization.Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. Deep learning (DL) is a branch of machine learning (ML) which focuses on densely connected networks using artificial neural networks for training the models. The objective of this research was to evaluate the potential of DL models in the Washington State University spring wheat breeding program. We compared the performance of two DL algorithms, namely multilayer perceptron (MLP) and convolutional neural network (CNN), with ridge regression best linear unbiased predictor (rrBLUP), a commonly used GS model. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat nested association mapping (NAM) population planted from 2014-2016 growing seasons. We predicted five different quantitative traits with varying genetic architecture using cross-validations (CVs), independent validations, and different sets of SNP markers. Hyperparameters were optimized for DL models by lowering the root mean square in the training set, avoiding model overfitting using dropout and regularization. DL models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Furthermore, MLP produces 5% higher prediction accuracy than CNN for grain yield and grain protein content. Altogether, DL approaches obtained better prediction accuracy for each trait, and should be incorporated into a plant breeder's toolkit for use in large scale breeding programs.Ultraviolet B (UV-B) (280-315 nm) and ultraviolet A (UV-A) (315-400 nm) radiation comprise small portions of the solar radiation but regulate many aspects of plant development, physiology and metabolism. Until now, how plants respond to UV-B in the presence of different light qualities is poorly understood. This study aimed to assess the effects of a low UV-B dose (0.912 ± 0.074 kJ m-2 day-1, at a 6 h daily UV exposure) in combination with four light treatments (blue, green, red and broadband white at 210 μmol m-2 s-1 Photosynthetically active radiation [PAR]) on morphological and physiological responses of cucumber (Cucumis sativus cv. "Lausanna RZ F1"). We explored the effects of light quality backgrounds on plant morphology, leaf gas exchange, chlorophyll fluorescence, epidermal pigment accumulation, and on acclimation ability to saturating light intensity. Our results showed that supplementary UV-B significantly decreased biomass accumulation in the presence of broad band white, blue and green light, but plants respond to UV-B radiation in the presence of different light spectra.The intensive use of groundwater in agriculture under the current climate conditions leads to acceleration of soil salinization. Given that almond is a salt-sensitive crop, selection of salt-tolerant rootstocks can help maintain productivity under salinity stress. Selection for tolerant rootstocks at an early growth stage can reduce the investment of time and resources. However, salinity-sensitive markers and salinity tolerance mechanisms of almond species to assist this selection process are largely unknown. We established a microscopy-based approach to investigate mechanisms of stress tolerance in and identified cellular, root anatomical, and molecular traits associated with rootstocks exhibiting salt tolerance. We characterized three almond rootstocks Empyrean-1 (E1), Controller-5 (C5), and Krymsk-86 (K86). Based on cellular and molecular evidence, our results show that E1 has a higher capacity for salt exclusion by a combination of upregulating ion transporter expression and enhanced deposition of suberin and lignin in the root apoplastic barriers, exodermis, and endodermis, in response to salt stress.