Modeling dominant height growth using permanent plot data for Pinus brutia stands in the Eastern Mediterranean region

Aim of the study: At current, forest management in the Eastern Mediterranean region is largely based on experience rather than on management plans. To support the development of such plans, this study develops and compares site index equations for pure even-aged Pinus brutia stands in Syria using base-age invariant techniques that realistically describe dominant height growth.Materials and methods: Data on top height and stand age were obtained in 2008 and 2016 from 80 permanent plots capturing the whole range of variation in site conditions, stand age and stand density. Both the Algebraic Difference Approach (ADA) and the Generalized Algebraic Difference Approach (GADA) were used to fit eight generalized algebraic difference equations in order to identify the one which describes the data best. For this, 61 permanent plots were used for model calibration and 19 plots for validation.Main results: According to both biological plausibility and model accuracy, the so-called Sloboda equation based on the GADA approach showed the best performance.Research highlights: The study provides a solid classification and comparison of Pinus brutia stands growing in the Eastern Mediterranean region and can thus be used to support sustainable forest management planning.Keywords: site index; Generalized Algebraic Difference Approach (GADA); Sloboda equation

Estimated glomerular filtration rate slope and risk of primary and secondary major adverse cardiovascular events and heart failure hospitalization in people with type 2 diabetes: An analysis of the EXSCEL trial

Aim: The decline in estimated glomerular filtration rate (eGFR), a significant predictor of cardiovascular disease (CVD), occurs heterogeneously in people with diabetes because of various risk factors. We investigated the role of eGFR decline in predicting CVD events in people with type 2 diabetes in both primary and secondary CVD prevention settings. Materials and Methods: Bayesian joint modelling of repeated measures of eGFR and time to CVD event was applied to the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial to examine the association between the eGFR slope and the incidence of major adverse CV event/hospitalization for heart failure (MACE/hHF) (non‐fatal myocardial infarction, non‐fatal stroke, CV death, or hospitalization for heart failure). The analysis was adjusted for age, sex, smoking, systolic blood pressure, baseline eGFR, antihypertensive and lipid‐lowering medication, diabetes duration, atrial fibrillation, high‐density cholesterol, total cholesterol, HbA1c and treatment allocation (once‐weekly exenatide or placebo). Results: Data from 11 101 trial participants with (n = 7942) and without (n = 3159) previous history of CVD were analysed. The mean ± SD eGFR slope per year in participants without and with previous CVD was −0.68 ± 1.67 and −1.03 ± 2.13 mL/min/1.73 m2, respectively. The 5‐year MACE/hHF incidences were 7.5% (95% CI 6.2, 8.8) and 20% (95% CI 19, 22), respectively. The 1‐SD decrease in the eGFR slope was associated with increased MACE/hHF risks of 48% (HR 1.48, 95% CI 1.12, 1.98, p = 0.007) and 33% (HR 1.33, 95% CI 1.18,1.51, p < 0.001) in participants without and with previous CVD, respectively. Conclusions: eGFR trajectories over time significantly predict incident MACE/hHF events in people with type 2 diabetes with and without existing CVD, with a higher hazard ratio for MACE/hHF in the latter group

Transcriptomics-based characterization of the toxicity of ZnO nanoparticles against chronic myeloid leukemia cells

Introduction: Zinc oxide nanoparticles (ZnO NPs) have recently attracted attention as potential anti-cancer agents. To the best of our knowledge, the toxicity of ZnO NPs against human chronic myeloid leukemia cells (K562 cell line) has not been studied using transcriptomics approach. Objective: The goals of this study were to evaluate the capability of ZnO NPs to induce apoptosis in human chronic myeloid leukemia cells (K562 cells) and to investigate the putative mechanisms of action. Methods: We used viability assay and flowcytometry coupled with Annexin V-FITC and propidium iodide to investigate the toxicity of ZnO NPs on K562 cells and normal peripheral blood mononuclear cells. Next we utilized a DNA microarray-based transcriptomics approach to characterize the ZnO NPs-induced changes in the transcriptome of K562 cells. Results: ZnO NPs exerted a selective toxicity (mainly by apoptosis) on the leukemic cells (p≤ 0.005) and altered their transcriptome; 429 differentially expressed genes (DEGs) with fold change (FC)≥ 4 and p≤ 0.008 with corrected p≤ 0.05 were identified in K562 cells post treatment with ZnO NPs. The over-expressed genes were implicated in “response to zinc”, “response to toxic substance” and “negative regulation of growth” (corrected p≤ 0.05). In contrast, the repressed genes positively regulated “cell proliferation”, “cell migration”, “cell adhesion”, “receptor signaling pathway via JAK-STAT” and “phosphatidylinositol 3-kinase signaling” (corrected p≤ 0.05). Lowering the FC to ≥ 1.5 with p≤ 0.05 and corrected p≤ 0.1 showed that ZnO NPs over-expressed the anti-oxidant defense system, drove K562 cells to undergo mitochondrial-dependent apoptosis, and targeted NF-κB pathway. Conclusion: Taken together, our findings support the earlier studies that reported anti-cancer activity of ZnO NPs and revealed possible molecular mechanisms employed by ZnO NPs to induce apoptosis in K562 cells

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10&nbsp;years; 78.2% included were male with a median age of 37&nbsp;years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Deep Learning Approach for SDN-Enabled Intrusion Detection System in IoT Networks

Owing to the prevalence of the Internet of things (IoT) devices connected to the Internet, the number of IoT-based attacks has been growing yearly. The existing solutions may not effectively mitigate IoT attacks. In particular, the advanced network-based attack detection solutions using traditional Intrusion detection systems are challenging when the network environment supports traditional as well as IoT protocols and uses a centralized network architecture such as a software defined network (SDN). In this paper, we propose a long short-term memory (LSTM) based approach to detect network attacks using SDN supported intrusion detection system in IoT networks. We present an extensive performance evaluation of the machine learning (ML) and deep learning (DL) model in two SDNIoT-focused datasets. We also propose an LSTM-based architecture for the effective multiclass classification of network attacks in IoT networks. Our evaluation of the proposed model shows that our model effectively identifies the attacks and classifies the attack types with an accuracy of 0.971. In addition, various visualization methods are shown to understand the dataset’s characteristics and visualize the embedding features

Agreement between cardiovascular disease risk assessment tools: An application to the United Arab Emirates population.

INTRODUCTION:Evidence regarding the performance of cardiovascular disease (CVD) risk assessment tools is limited in the United Arab Emirates (UAE). Therefore, we assessed the agreement between various externally validated CVD risk assessment tools in the UAE. METHODS:A secondary analysis of the Abu Dhabi Screening Program for Cardiovascular Risk Markers (AD-SALAMA) data, a large population-based cross-sectional survey conducted in Abu Dhabi, UAE during the period 2009 until 2015, was performed in July 2019. The analysis included 2,621 participants without type 2 Diabetes and without history of cardiovascular diseases. The CVD risk assessment tools included in the analysis were the World Health Organization for Middle East and North Africa Region (WHO-MENA), the systematic coronary risk evaluation for high risk countries (SCORE-H), the pooled cohort risk equations for white (PCRE-W) and African Americans (PCRE-AA), the national cholesterol education program Framingham risk score (FRAM-ATP), and the laboratory Framingham risk score (FRAM-LAB). RESULTS:The overall concordance coefficient was 0.50. The agreement between SCORE-H and PCRE-W, PCRE-AA, FRAM-LAB, FRAM-ATP and WHO-MENA, were 0.47, 0.39, 0.0.25, 0.42 and 0.18, respectively. PCRE-AA classified the highest proportion of participants into high-risk category of CVD (16.4%), followed by PCRE-W (13.6%), FRAM-LAB (6.9%), SCORE-H (4.5%), FRAM-ATP (2.7%), and WHO-MENA (0.4%). CONCLUSIONS:We found a poor agreement between various externally validated CVD risk assessment tools when applied to a large data collected in the UAE. This poses a challenge to choose any of these tools for clinical decision-making regarding the primary prevention of CVD in the country

Modeling dominant height growth using permanent plot data for Pinus brutia stands in the Eastern Mediterranean region

Aim of the study: At current, forest management in the Eastern Mediterranean region is largely based on experience rather than on management plans. To support the development of such plans, this study develops and compares site index equations for pure even-aged Pinus brutia stands in Syria using base-age invariant techniques that realistically describe dominant height growth. Materials and methods: Data on top height and stand age were obtained in 2008 and 2016 from 80 permanent plots capturing the whole range of variation in site conditions, stand age and stand density. Both the Algebraic Difference Approach (ADA) and the Generalized Algebraic Difference Approach (GADA) were used to fit eight generalized algebraic difference equations in order to identify the one which describes the data best. For this, 61 permanent plots were used for model calibration and 19 plots for validation. Main results: According to both biological plausibility and model accuracy, the so-called Sloboda equation based on the GADA approach showed the best performance. Research highlights: The study provides a solid classification and comparison of Pinus brutia stands growing in the Eastern Mediterranean region and can thus be used to support sustainable forest management planning

Distribution of biocide resistant genes and biocides susceptibility in multidrug-resistant Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii — A first report from the Kingdom of Saudi Arabia

Purposes: The aim of this study was to determine the frequency of biocide resistant genes, qacA, qacE and cepA in multidrug resistant (MDR) bacteria: Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii and to correlate the presence or absence of resistant genes with biocides susceptibility. Materials and methods: The study included 44 MDR K. pneumoniae, P. aeruginosa and A. baumannii microorganisms. The bacteria were screened for the presence of biocide resistant genes by the polymerase chain reaction (PCR) method. The test organisms were isolated from various clinical specimens in the Qassim region, Saudi Arabia. The in vitro susceptibility tests of the three biocides (benzalkonium chloride, cetrimide and chlorhexidine gluconate) were studied against the test isolates by broth microdilution method following Clinical and Laboratory Standards Institute guidelines. Results: With the distribution of biocide resistant genes in K. pneumoniae, all 9 isolates (100%) possessed cepA; 4 (44.4%) and 1 (11.1%) isolate contained qacA and qacE genes respectively. Among 24 isolates of A. baumannii tested, cepA, qacA and qacE genes were found in 54.2%, 16.7% and 33.3% of isolates respectively. Among 11 P. aeruginosa isolates, 63.6% contained cepA gene, 18.2% contained qacE genes, and none of the isolates harboured qacA gene. There was no significant correlation between presence or absence of biocide resistant genes and high MIC values of the test isolates (p ≥ 0.2). Conclusion: Our observations imply that there was no significant correlation between presence or absence of biocide resistant genes and MICs observed in MDR K. pneumoniae, P. aeruginosa and A. baumannii. Further studies are required to find to confirm the trend of reduced susceptibility to biocides of problematic nosocomial pathogens. Keywords: Chlorhexidine, Quaternary ammonium compounds, Disinfectant, Biocide resistant genes, qacA, qacE, cep

Identification of memory reactivation during sleep by EEG classification

Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Neural reactivation was classified above chance in all participants when TMR was applied in SWS, and in 5 of the 14 participants to whom TMR was applied in N2. Classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be valuable for future investigations of memory consolidation