Improving tree mortality models by accounting for environmental influences

Tree-ring chronologies have been widely used in studies of tree mortality where variables of recent growth act as an indicator of tree physiological vigour. Comparing recent radial growth of live and dead trees thus allows estimating probabilities of tree mortality. Sampling of mature dead trees usually provides death-year distributions that may span over years or decades. Recent growth of dead trees (prior to death) is then computed during a number of periods, whereas recent growth (prior to sampling) for live trees is computed for identical periods. Because recent growth of live and dead trees is then computed for different periods, external factors such as disturbance or climate may influence growth rates and, thus, mortality probability estimations. To counteract this problem, we propose the truncating of live-growth series to obtain similar frequency distributions of the "last year of growth" for the populations of live and dead trees. In this paper, we use different growth scenarios from several tree species, from several geographic sources, and from trees with different growth patterns to evaluate the impact of truncating on predictor variables and their selection in logistic regression analysis. Also, we assess the ability of the resulting models to accurately predict the status of trees through internal and external validation. Our results suggest that the truncating of live-growth series helps decrease the influence of external factors on growth comparisons. By doing so, it reinforces the growth-vigour link of the mortality model and enhances the model's accuracy as well as its general applicability. Hence, if model parameters are to be integrated in simulation models of greater geographical extent, truncating may be used to increase model robustness

Delineation of soil fertility management zones for site-specific nutrient management in the maize belt region of Nigeria

Open Access Journal; Published online: 29 Oct 2020Site-specific nutrient management can reduce soil degradation and crop production risks related to undesirable timing, amount, and type of fertilizer application. This study was conducted to understand the spatial variability of soil properties and delineate spatially homogenous nutrient management zones (MZs) in the maize belt region of Nigeria. Soil samples (n = 3387) were collected across the area using multistage and random sampling techniques, and samples were analyzed for pH, soil organic carbon (SOC), macronutrients (N, P, K, S, Ca and Mg), micronutrients (S, B, Zn, Mn and Fe) content, and effective cation exchange capacity (ECEC). Spatial distribution and variability of these parameters were assessed using geostatistics and ordinary kriging, while principal component analysis (PCA) and multivariate K-means cluster analysis were used to delineate nutrient management zones. Results show that spatial variation of macronutrients (total N, available P, and K) was largely influenced by intrinsic factors, while that of S, Ca, ECEC, and most micronutrients was influenced by both intrinsic and extrinsic factors with moderate to high spatial variability. Four distinct management zones, namely, MZ1, MZ2, MZ3, and MZ4, were identified and delineated in the area. MZ1 and MZ4 have the highest contents of most soil fertility indicators. MZ4 has a higher content of available P, Zn, and pH than MZ1. MZ2 and MZ3, which constitute the larger part of the area, have smaller contents of the soil fertility indicators. The delineated MZs offer a more feasible option for developing and implementing site-specific nutrient management in the maize belt region of Nigeria

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

European 1 : a globally important clonal complex of Mycobacterium bovis

We have identified a globally important clonal complex of Mycobacterium bovis by deletion analysis of over one thousand strains from over 30 countries. We initially show that over 99% of the strains of M. bovis, the cause of bovine tuberculosis, isolated from cattle in the Republic of Ireland and the UK are closely related and are members of a single clonal complex marked by the deletion of chromosomal region RDEu1 and we named this clonal complex European 1 (Eu1). Eu1 strains were present at less than 14% of French, Portuguese and Spanish isolates of M. bovis but are rare in other mainland European countries and Iran. However, strains of the Eu1 clonal complex were found at high frequency in former trading partners of the UK (USA, South Africa, New Zealand, Australia and Canada). The Americas, with the exception of Brazil, are dominated by the Eu1 clonal complex which was at high frequency in Argentina, Chile, Ecuador and Mexico as well as North America. Eu1 was rare or absent in the African countries surveyed except South Africa. A small sample of strains from Taiwan were non-Eu1 but, surprisingly, isolates from Korea and Kazakhstan were members of the Eu1 clonal complex. The simplest explanation for much of the current distribution of the Eu1 clonal complex is that it was spread in infected cattle, such as Herefords, from the UK to former trading partners, although there is evidence of secondary dispersion since. This is the first identification of a globally dispersed clonal complex M. bovis and indicates that much of the current global distribution of this important veterinary pathogen has resulted from relatively recent International trade in cattle.This work was funded by: TBadapt project (LSHp-CT-2007-037919); B.M. received financial support from the Swiss National Science Foundation; Swedish Research Council, Swedish Heart-Lung foundation, Swedish International Development Agency; Department of Agriculture and Rural Development Northern Ireland (project DARD0407); EU project TB-STEP (KBBE-2007-1-3-04, no. 212414); Swiss National Science Foundation (Grant No. 107559); Damien Foundation, Belgium; Commission Universitaire pour le Développement (CUD), University of Liege (Project PIC); The Wellcome Trust Livestock for Life and Animal Health in the Developing World initiatives (075833/A/04/Z); Chilean National Livestock Service -FONDOSAGC5-100-10-23 and CONICYT-FIC-R-EQU18 and by the Department of Environment, Food and Rural Affairs, UK (project SB4020).http://www.elsevier.com/locate/meegidab2012ab2013 (Author correction

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

SARS-CoV-2-specific nasal IgA wanes 9 months after hospitalisation with COVID-19 and is not induced by subsequent vaccination

BACKGROUND: Most studies of immunity to SARS-CoV-2 focus on circulating antibody, giving limited insights into mucosal defences that prevent viral replication and onward transmission. We studied nasal and plasma antibody responses one year after hospitalisation for COVID-19, including a period when SARS-CoV-2 vaccination was introduced. METHODS: In this follow up study, plasma and nasosorption samples were prospectively collected from 446 adults hospitalised for COVID-19 between February 2020 and March 2021 via the ISARIC4C and PHOSP-COVID consortia. IgA and IgG responses to NP and S of ancestral SARS-CoV-2, Delta and Omicron (BA.1) variants were measured by electrochemiluminescence and compared with plasma neutralisation data. FINDINGS: Strong and consistent nasal anti-NP and anti-S IgA responses were demonstrated, which remained elevated for nine months (p < 0.0001). Nasal and plasma anti-S IgG remained elevated for at least 12 months (p < 0.0001) with plasma neutralising titres that were raised against all variants compared to controls (p < 0.0001). Of 323 with complete data, 307 were vaccinated between 6 and 12 months; coinciding with rises in nasal and plasma IgA and IgG anti-S titres for all SARS-CoV-2 variants, although the change in nasal IgA was minimal (1.46-fold change after 10 months, p = 0.011) and the median remained below the positive threshold determined by pre-pandemic controls. Samples 12 months after admission showed no association between nasal IgA and plasma IgG anti-S responses (R = 0.05, p = 0.18), indicating that nasal IgA responses are distinct from those in plasma and minimally boosted by vaccination. INTERPRETATION: The decline in nasal IgA responses 9 months after infection and minimal impact of subsequent vaccination may explain the lack of long-lasting nasal defence against reinfection and the limited effects of vaccination on transmission. These findings highlight the need to develop vaccines that enhance nasal immunity. FUNDING: This study has been supported by ISARIC4C and PHOSP-COVID consortia. ISARIC4C is supported by grants from the National Institute for Health and Care Research and the Medical Research Council. Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research. The PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research. The funders were not involved in the study design, interpretation of data or the writing of this manuscript

Inhibition of protein kinase C by tamoxifen

The antiestrogen drug tamoxifen inhibits rat brain protein kinase C in vitro, whether the enzyme is activated by Ca2+ and phospholipid (50% inhibitory dose, 100 microM), 12-O-tetradecanoylphorbol-13-acetate and phospholipid (50% inhibitory dose, 40 microM), or teleocidin and phospholipid. Tamoxifen does not inhibit the Ca2+- and phospholipid-independent phosphorylation of protamine sulfate by protein kinase C, indicating that the drug does not interact with the active site of the enzyme. The binding of [3H]phorbol dibutyrate to high-affinity membrane receptors of cultured mouse fibroblast cells is inhibited by tamoxifen (50% inhibitory dose, 5 microM). Our findings suggest that the growth-inhibitory and cytotoxic effects of tamoxifen, which have been observed at microM concentrations of the drug, may be in part due to its effects on protein kinase C

Triphenylethylenes: a new class of protein kinase C inhibitors

The Ca2+- and phospholipid-dependent phosphotransferase activity of protein kinase C was inhibited by the triphenylethylene compounds clomiphene [drug concentration causing 50% inhibition (IC50) = 25 microM], 4-hydroxytamoxifen (IC50 = 25 microM), and N-desmethyltamoxifen (IC50 = 8 microM). The Ca2+- and phospholipid-independent phosphorylation of protamine sulfate, which is catalyzed by protein kinase C, was not inhibited by the triphenylethylenes, suggesting that they do not interact directly with the active site of protein kinase C. The inhibitory potency of each triphenylethylene was reduced when the phospholipid concentration was increased, providing evidence that these drugs inhibited protein kinase C by interacting with phospholipids. The potencies of the effects of the triphenylethylenes on protein kinase C in the lipid environment of intact cells were evaluated by determining their efficacies in the inhibition of [3H]phorbol 12,13-dibutyrate (PDBu) binding to mouse embryo C3H/10T1/2 cells. Micromolar concentrations of each drug inhibited [3H]PDBu binding in these cells. N-Desmethyltamoxifen, 4-hydroxytamoxifen, and tamoxifen inhibited protein kinase C with the same order of potency as that which has been reported for their inhibition of MCF-7 cell growth by Reddel et al. (1983). N-Desmethyltamoxifen and 4-hydroxytamoxifen were also more potent than tamoxifen in the inhibition of the growth of mouse embryo fibroblast C3H/10T1/2 cells. These correlations suggest that the mechanism of growth inhibition by tamoxifen and its metabolites includes interactions with protein kinase C