Estimation of Monthly Total Dissolved Solids Using ANN and LS-SVM Techniques in the Aji Chay River, Iran

This research follows on from diverse international efforts to safeguard one of the largest natural lakes in the world, Urmia lake in North West Iran. In this research two new numerical packages based on Artificial Neural Networks (ANN) and the Least Square Support Vector Machine (LS-SVM) models were developed to estimate monthly Total Dissolved Solid (TDS) in the Aji Chay River, one the main tributaries of Urmia lake, Iran. A feed forward back propagation (FFB) model was used to obtain a set of coefficients for a linear model, and the radial basis function (RBF) kernel was employed for the LS-SVM model. The input data sets of both the ANN and LS-SVM models consists of six water quality parameters: TDS, Mg2+, Na+, Ca2+, Cl-, and SO4 2-, all collected on a monthly time scale over a period of 30 years from the Vanyar and Zarnagh stations, in the Aji Chay watershed. The research demonstrated that both models can effectively predict the variability of TDS, but for the Vanyar station with the ANN model (giving an R2 value of 0.913 and RMSE of 0.0032, a Nash-Sutcliffe Efficiency (NSE) coefficient 0.812 and as such has a more efficient and accurate estimation when compared to the LS-SVM model with R2=0.871 and RMSE =0.097 and NSE=0.86. The analysis of Zarnagh station data shows R2=0.853 and RMSE=0.0162, NSE= 0.854 for SVM and R2=0.903 and RMSE =0.0091 and NSE=0.85 for ANN

Validation of demographic equilibrium theory against tree-size distributions and biomass density in Amazonia

Predicting the response of forests to climate and land-use change depends on models that can simulate the time-varying distribution of different tree sizes within a forest – so-called forest demography models. A necessary condition for such models to be trustworthy is that they can reproduce the tree-size distributions that are observed within existing forests worldwide. In a previous study, we showed that demographic equilibrium theory (DET) is able to fit tree-diameter distributions for forests across North America, using a single site-specific fitting parameter (μ) which represents the ratio of the rate of mortality to growth for a tree of a reference size. We use a form of DET that assumes tree-size profiles are in a steady state resulting from the balance between a size-independent rate of tree mortality and tree growth rates that vary as a power law of tree size (as measured by either trunk diameter or biomass). In this study, we test DET against ForestPlots data for 124 sites across Amazonia, fitting, using maximum likelihood estimation, to both directly measured trunk diameter data and also biomass estimates derived from published allometric relationships. Again, we find that DET fits the observed tree-size distributions well, with best-fit values of the exponent relating growth rate to tree mass giving a mean of ϕ=0.71 (0.31 for trunk diameter). This finding is broadly consistent with exponents of ϕ=0.75 (ϕ=1/3 for trunk diameter) predicted by metabolic scaling theory (MST) allometry. The fitted ϕ and μ parameters also show a clear relationship that is suggestive of life-history trade-offs. When we fix to the MST value of ϕ=0.75, we find that best-fit values of μ cluster around 0.25 for trunk diameter, which is similar to the best-fit value we found for North America of 0.22. This suggests an as yet unexplained preferred ratio of mortality to growth across forests of very different types and locations

Effect of Amoxicillin in combination with Imipenem-Relebactam against Mycobacterium abscessus

Infections caused by Mycobacterium abscessus are increasing in prevalence in cystic fibrosis patients. This opportunistic pathogen′s intrinsic resistance to most antibiotics has perpetuated an urgent demand for new, more effective therapeutic interventions. Here we report a prospective advance in the treatment of M. abscessus infection; increasing the susceptibility of the organism to amoxicillin, by repurposing the β-lactamase inhibitor, relebactam, in combination with the front line M. abscessus drug imipenem. We establish by multiple in vitro methods that this combination works synergistically to inhibit M. abscessus. We also show the direct competitive inhibition of the M. abscessus β-lactamase, BlaMab, using a novel assay, which is validated kinetically using the nitrocefin reporter assay and in silico binding studies. Furthermore, we reverse the susceptibility by overexpressing BlaMab in M. abscessus, demonstrating relebactam-BlaMab target engagement. Finally, we highlight the in vitro efficacy of this combination against a panel of M. abscessus clinical isolates, revealing the therapeutic potential of the amoxicillin-imipenem-relebactam combination

Tailorable stimulated Brillouin scattering in nanoscale silicon waveguides

Nanoscale modal confinement is known to radically enhance the effect of intrinsic Kerr and Raman nonlinearities within nanophotonic silicon waveguides. By contrast, stimulated Brillouin-scattering nonlinearities, which involve coherent coupling between guided photon and phonon modes, are stifled in conventional nanophotonics, preventing the realization of a host of Brillouin-based signal-processing technologies in silicon. Here we demonstrate stimulated Brillouin scattering in silicon waveguides, for the first time, through a new class of hybrid photonic–phononic waveguides. Tailorable travelling-wave forward-stimulated Brillouin scattering is realized—with over 1,000 times larger nonlinearity than reported in previous systems—yielding strong Brillouin coupling to phonons from 1 to 18 GHz. Experiments show that radiation pressures, produced by subwavelength modal confinement, yield enhancement of Brillouin nonlinearity beyond those of material nonlinearity alone. In addition, such enhanced and wideband coherent phonon emission paves the way towards the hybridization of silicon photonics, microelectromechanical systems and CMOS signal-processing technologies on chip.United States. National Nuclear Security Administration (Contract DE-AC04-94AL85000)United States. Air Force (Contract FA8721-05-C-000)United States. Defense Advanced Research Projects Agency (MesoDynamic Architectures Program)Sandia National Laboratories (Directed Research and Development Program

Equilibrium forest demography explains the distribution of tree sizes across North America

The distribution of tree sizes within a forest strongly influences how it will respond to disturbances and environmental changes such as future climate change and increases in atmospheric CO2. This means that global vegetation models must include variation in tree size to accurately represent carbon sinks, such as that seen in North America. Here we use an analytical model of large-scale forest demography which assumes tree growth varies as a power of tree diameter whilst tree mortality is independent of size. The equilibrium solutions of this model are able to accurately reproduce the tree-size distributions, for 61 species and four plant functional types, measured across North America, using just a single species-specific fitting parameter, μ, which determines the ratio of mortality to growth. The predictions of metabolic scaling theory for tree-size distributions are also tested and found to deviate significantly from observations and that maybe explained by the assumptions made about how individual trees fill the available space. We show that equilibrium forest demography implies a single curve that relates mean tree diameter to μ, and that this can be used to make reasonable estimates of the whole dataset mean trunk diameter by fitting only to the larger trees. Our analysis suggests that analytical solutions such as those in this paper may have a role in aiding the understanding and development of next-generation Dynamic Global Vegetation Models based on ecosystem demography

Robust Ecosystem Demography (RED version 1.0): a parsimonious approach to modelling vegetation dynamics in Earth system models

A significant proportion of the uncertainty in climate projections arises from uncertainty in the representation of land carbon uptake. Dynamic global vegetation models (DGVMs) vary in their representations of regrowth and competition for resources, which results in differing responses to changes in atmospheric CO2 and climate. More advanced cohort-based patch models are now becoming established in the latest DGVMs. These models typically attempt to simulate the size distribution of trees as a function of both tree size (mass or trunk diameter) and age (time since disturbance). This approach can capture the overall impact of stochastic disturbance events on the forest structure and biomass – but at the cost of increasing the number of parameters and ambiguity when updating the probability density function (pdf) in two dimensions. Here we present the Robust Ecosystem Demography (RED), in which the pdf is collapsed onto the single dimension of tree mass. RED is designed to retain the ability of more complex cohort DGVMs to represent forest demography, while also being parameter sparse and analytically solvable for the steady state. The population of each plant functional type (PFT) is partitioned into mass classes with a fixed baseline mortality along with an assumed power-law scaling of growth rate with mass. The analytical equilibrium solutions of RED allow the model to be calibrated against observed forest cover using a single parameter – the ratio of mortality to growth for a tree of a reference mass (μ0). We show that RED can thus be calibrated to the ESA LC_CCI (European Space Agency Land Cover Climate Change Initiative) coverage dataset for nine PFTs. Using net primary productivity and litter outputs from the UK Earth System Model (UKESM), we are able to diagnose the spatially varying disturbance rates consistent with this observed vegetation map. The analytical form for RED circumnavigates the need to spin up the numerical model, making it attractive for application in Earth system models (ESMs). This is especially so given that the model is also highly parameter sparse

Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study

Objectives To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination.Design Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries.Settings Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021.Main outcome measures Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices.Results Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down’s syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson’s disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%.Conclusion This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination

Protocol for the development and evaluation of a tool for predicting risk of short-term adverse outcomes due to COVID-19 in the general UK population

Introduction Novel coronavirus 2019 (COVID-19) has propagated a global pandemic with significant health, economic and social costs. Emerging emergence has suggested that several factors may be associated with increased risk from severe outcomes or death from COVID-19. Clinical risk prediction tools have significant potential to generate individualised assessment of risk and may be useful for population stratification and other use cases. Methods and analysis We will use a prospective open cohort study of routinely collected data from 1205 general practices in England in the QResearch database. The primary outcome is COVID-19 mortality (in or out-of-hospital) defined as confirmed or suspected COVID-19 mentioned on the death certificate, or death occurring in a person with SARS-CoV-2 infection between 24 th January and 30 th April 2020. Our primary outcome in adults is COVID-19 mortality (including out of hospital and in hospital deaths). We will also examine COVID-19 hospitalisation in children. Time-to-event models will be developed in the training data to derive separate risk equations in adults (19-100 years) for males and females for evaluation of risk of each outcome within the 3-month follow-up period (24 th January to 30 th April 2020), accounting for competing risks. Predictors considered will include age, sex, ethnicity, deprivation, smoking status, alcohol intake, body mass index, pre-existing medical co-morbidities, and concurrent medication. Measures of performance (prediction errors, calibration and discrimination) will be determined in the test data for men and women separately and by ten-year age group. For children, descriptive statistics will be undertaken if there are currently too few serious events to allow development of a risk model. The final model will be externally evaluated in (a) geographically separate practices and (b) other relevant datasets as they become available. Ethics and dissemination The project has ethical approval and the results will be submitted for publication in a peer-reviewed journal. Strengths and limitations of the study The individual-level linkage of general practice, Public Health England testing, Hospital Episode Statistics and Office of National Statistics death register datasets enable a robust and accurate ascertainment of outcomes The models will be trained and evaluated in population-representative datasets of millions of individuals Shielding for clinically extremely vulnerable was advised and in place during the study period, therefore risk predictions influenced by the presence of some ‘shielding’ conditions may require careful consideratio

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.

OBJECTIVE: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. DESIGN: Population based cohort study. SETTING AND PARTICIPANTS: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. MAIN OUTCOME MEASURES: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. RESULTS: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. CONCLUSION: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves