Effects of Salt Stress on Plant Growth, Nutrient Partitioning, Chlorophyll Content, Leaf Relative Water Content, Accumulation of Osmolytes and Antioxidant Compounds in Pepper (Capsicum annuum L.) Cultivars

The salinity of soil is among the most important abiotic stresses which limit agricultural productivity worldwide. The effects of salinity on growth, nutrient partitioning, chlorophyll, leaf relative water content, osmolytes accumulation and antioxidant compounds of pepper (Capsicum annuum L.) cultivars (‘Granada’, ‘Goliath’ and ‘Nobili’), widely used in Cameroon, were investigated. Plants were subjected to four levels of NaCl (0, 50, 100 and 200 mM) at early seedling growth stage of plant development. Application of NaCl treatment led to a significant increase in total  soluble  sugars,  proline; soluble proteins; total free amino acids content, peroxydase and superoxide dismutase activity and total phenolic content in salt-tolerant ‘Granada’ and ‘Nobili’ compared to salt-sensitive ‘Goliath’ and untreated plants, on the contrary, decreased in root dry weight, shoot dry weight, number of leaves, shoot length, stem diameter, total leaf area, chlorophyll and leaf relative water content in ‘Goliath’ at low salinity level. Flavonoid content, K, Ca and Mg concentrations were significantly reduced with increasing salinity in all cultivars. The highest Na concentrations were detected in the leaves while the lowest were recorded in the roots of ‘Goliath’ at high salinity level. The salt sensitivity of ‘Goliath’ seems to be increased osmotic adjustment through the strongly accumulation of Na in leaves while the salt tolerance of ‘Granada’ was related to its induce of antioxidative enzyme system more efficiently, resulting in higher osmolytes accumulation under salinity. ‘Granada’ was more tolerant and stable in physiological and biochemical traits suggesting that it could be grown in salt-affected soils

Kernel Spectral Clustering and applications

In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane in a high dimensional space induced by a kernel. In addition, the multi-way clustering can be obtained by combining a set of binary decision functions via an Error Correcting Output Codes (ECOC) encoding scheme. Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing. In the validation stage model selection is performed to obtain tuning parameters, like the number of clusters present in the data. This is a major advantage compared to classical spectral clustering where the determination of the clustering parameters is unclear and relies on heuristics. Once a KSC model is trained on a small subset of the entire data, it is able to generalize well to unseen test points. Beyond the basic formulation, sparse KSC algorithms based on the Incomplete Cholesky Decomposition (ICD) and $L_0$, $L_1, L_0 + L_1$, Group Lasso regularization are reviewed. In that respect, we show how it is possible to handle large scale data. Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented. Finally, real-world applications such as image segmentation, power load time-series clustering, document clustering and big data learning are considered.Comment: chapter contribution to the book "Unsupervised Learning Algorithms

New agegraphic dark energy in Horava-Lifshitz cosmology

We investigate the new agegraphic dark energy scenario in a universe governed by Horava-Lifshitz gravity. We consider both the detailed and non-detailed balanced version of the theory, we impose an arbitrary curvature, and we allow for an interaction between the matter and dark energy sectors. Extracting the differential equation for the evolution of the dark energy density parameter and performing an expansion of the dark energy equation-of-state parameter, we calculate its present and its low-redshift value as functions of the dark energy and curvature density parameters at present, of the Horava-Lifshitz running parameter $\lambda$, of the new agegraphic dark energy parameter $n$, and of the interaction coupling $b$. We find that $w_0=-0.82^{+0.08}_{-0.08}$ and $w_1=0.08^{+0.09}_{-0.07}$. Although this analysis indicates that the scenario can be compatible with observations, it does not enlighten the discussion about the possible conceptual and theoretical problems of Horava-Lifshitz gravity.Comment: 17 pages, no figures, version published at JCA

The effects of high-energy proton irradiation on the electrical characteristics of Au/Ni/4H-SiC Schottky barrier diodes

Please read abstract in the article.The University of Pretoria and the National Research Foundation (NRF) of South Africa, Grant number 98961.http://www.elsevier.com/locate/nimb2018-10-15hj2017Physic

CHIME Discovery of a Binary Pulsar with a Massive Non-Degenerate Companion

Of the more than 3000 radio pulsars currently known, only ∼300 are in binary systems, and only five of these consist of young pulsars with massive nondegenerate companions. We present the discovery and initial timing, accomplished using the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope, of the sixth such binary pulsar, PSR J2108+4516, a 0.577 s radio pulsar in a 269 day orbit of eccentricity 0.09 with a companion of minimum mass 11 M⊙. Notably, the pulsar undergoes periods of substantial eclipse, disappearing from the CHIME 400–800 MHz observing band for a large fraction of its orbit, and displays significant dispersion measure and scattering variations throughout its orbit, pointing to the possibility of a circumstellar disk or very dense stellar wind associated with the companion star. Subarcsecond resolution imaging with the Karl G. Jansky Very Large Array unambiguously demonstrates that the companion is a bright, V ≃ 11 OBe star, EM* UHA 138, located at a distance of 3.26(14) kpc. Archival optical observations of EM* UHA 138 approximately suggest a companion mass ranging from 17.5 M⊙ < Mc < 23 M⊙, in turn constraining the orbital inclination angle to 50fdg3 ≲ i ≲ 58fdg3. With further multiwavelength follow-up, PSR J2108+4516 promises to serve as another rare laboratory for the exploration of companion winds, circumstellar disks, and short-term evolution through extended-body orbital dynamics

Positrons and antiprotons from inert doublet model dark matter

In the framework of the Inert Doublet Model, a very simple extension of the Standard Model, we study the production and propagation of antimatter in cosmic rays coming from annihilation of a scalar dark matter particle. We consider three benchmark candidates, all consistent with the WMAP cosmic abundance and existing direct detection experiments, and confront the predictions of the model with the recent PAMELA, ATIC and HESS data. For a light candidate, M_{DM} = 10 GeV, we argue that the positron and anti-proton fluxes may be large, but still consistent with expected backgrounds, unless there is an enhancement (boost factor) in the local density of dark matter. There is also a substantial anti-deuteron flux which might be observable by future experiments. For a candidate with M_{DM} = 70 GeV, the contribution to positron and anti-proton fluxes is much smaller than the expected backgrounds. Even if a boost factor is invoked to enhance the signals, the candidate is unable to explain the observed positron and anti-proton excesses. Finally, for a heavy candidate, M_{DM} = 10 TeV, it is possible to fit the PAMELA excess (but, unfortunately, not the ATIC one) provided there is a large enhancement, either in the local density of dark matter or through the Sommerfeld effect.Comment: 17 pages ; v2: matches JCAP published versio

Quality indicators for patients with traumatic brain injury in European intensive care units

Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur

Changing care pathways and between-center practice variations in intensive care for traumatic brain injury across Europe

Purpose: To describe ICU stay, selected management aspects, and outcome of Intensive Care Unit (ICU) patients with traumatic brain injury (TBI) in Europe, and to quantify variation across centers. Methods: This is a prospective observational multicenter study conducted across 18 countries in Europe and Israel. Admission characteristics, clinical data, and outcome were described at patient- and center levels. Between-center variation in the total ICU population was quantified with the median odds ratio (MOR), with correction for case-mix and random variation between centers. Results: A total of 2138 patients were admitted to the ICU, with median age of 49 years; 36% of which were mild TBI (Glasgow Coma Scale; GCS 13–15). Within, 72 h 636 (30%) were discharged and 128 (6%) died. Early deaths and long-stay patients (> 72 h) had more severe injuries based on the GCS and neuroimaging characteristics, compared with short-stay patients. Long-stay patients received more monitoring and were treated at higher intensity, and experienced worse 6-month outcome compared to short-stay patients. Between-center variations were prominent in the proportion of short-stay patients (MOR = 2.3, p < 0.001), use of intracranial pressure (ICP) monitoring (MOR = 2.5, p < 0.001) and aggressive treatme

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches