Biochemical and molecular characterization of Klebsiella sp. isolated from environment polluted with perfluoroalkyl substances

Poster presented at: 13th Symposium “Nоvel Technologies and economic development”, October 18-19, 2019, Leskovac, SerbiaRazvoj hemijske industrije u prošlom veku doprineo je povećanoj proizvodnji hrane, efikasnijoj kontroli bolesti i poboljšanju životnog standarda. Ipak, usled industrijskog razvoja došlo je do nagomilavanja velikih količina toksičnih supstanci u životnoj sredini. Dugotrajni organski zagađivači su hemikalije koje se zadržavaju, akumuliraju u čitavom lancu ishrane i imaju štetan uticaj na ljudsko zdravlje i životnu sredinu. Perfluorovana jedinjenja, kao što su perfluoroktan sulfonska kiselina, njene soli i perfluoroktan sulfonil fluorid, nalaze se na listi dugotrajnih organskih zagađivača. Nekoliko skorašnjih publikacija je pokazalo da mikroorganizmi izolovani iz životne sredine zagađene perfluorovanim jedinjenjima mogu smanjiti nivo istih. U ovom radu je bakterijski soj izolovan iz takve životne sredine fiziološki, biohemijski i molekularno okarakterisan. U preliminarnim laboratorijskim testovima soj je pokazao sposobnost smanjenja količine perfluorovanih jedinjenja. Mikroorganizam je Gram-negativan, nepokretan, oksidaza negativan i pozitivan na katalazu, proizvodi različite hidrolazne enzime. Mikroorganizmu je određen i masno-kiselinski profil. Molekularna karakterizacija je potvrdila da izolovani soj pripada rodu Klebsiella. Mikroorganizam je uspešno okarakterisan različitim metodama i ubuduće će biti korišćen u detaljnoj laboratorijskoj studiji analize mehanizama smanjenja koncentracije perfluorovanih jedinjenja.Conference paper: [https://cer.ihtm.bg.ac.rs/handle/123456789/4994

Participatory Ecological Monitoring (PEM) : participatory research methods for sustainability ‐ toolkit #4

Participatory Ecological Monitoring (PEM) is a conservation methodology aiming to include local communities in the collection and analysis of biodiversity and threats data in a managed conservation zone. Often implemented annually, PEM optimises local knowledge to help understand ecological change which is an essential step towards assessing the success or failure of conservation activity and improving conservation effectiveness

Additional measures needed to ensure clove industry does not contribute to tree cover loss in Madagascar

This paper explores the relationship between clove essential oil processing and tree cover loss, with a comparison to the incidence and effect of wildfires in Analanjirofo in eastern Madagascar between 2012 and 2021. We used Generalised Additive Mixed Models with the proportion of tree cover left around chef-lieu municipalities as response variables. The number of fires detected, the number of traditional and modern clove processing facilities in the municipality, and overlap with Protected Areas, and the number of villages in the municipality were set as fixed factors. Tree cover loss was associated with increased number of traditional and modern facilities. Clove operators show a motivation to keep using traditional facilities since they are more feasible, produce higher quality of clove oil, and reinforce social cohesion. The number of the traditional facilities per municipality remains 2.9 times higher than modern facilities despite their promotion since 2011. The use of the modern facilities is motivated by the lower wood consumption and shorter distillation time. Wildfires, often related to slash-and-burn agriculture, remain a major environmental threat to forest, especially in remote areas and more fires were detected in areas with higher tree cover. The overlap of municipality with Protected Areas has no effect on tree cover loss. Expanding the Agroforestry Systems (AFS) around municipalities and ensuring that they can produce enough fuelwood will improve the clove sector and thrive local economy. Controlling wildfires, developing a long-term clove industry management plan, and improving commercialisation policies could be immediate priorities for achieving sustainable development in the region

Nonlinear analysis of biomagnetic signals recorded from uterine myomas

OBJECTIVE: To determine if there is any non-linearity in the biomagnetic recordings of uterine myomas and to find any differences that may be present in the mechanisms underlying their signal dynamics. METHODS: Twenty-four women were included in the study. Sixteen of them were characterised with large myomas and 8 with small ones. Uterine artery waveform measurements were evaluated by use of Pulsatility Index (PI) (normal value PI<1.45). RESULTS: Applying nonlinear analysis to the biomagnetic signals of the uterine myomas, we observed a clear saturation value for the group of large ones (mean = 11.35 ± 1.49) and no saturation for the small ones. CONCLUSION: The comparison of the saturation values in the biomagnetic recordings of large and small myomas may be a valuable tool in the evaluation of functional changes in their dynamic behavior

A decade of GOSAT Proxy satellite CH4_{4} observations

This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4/XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of -0.84 ppb

Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size

Abstract Background Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions, are generally uncharacterized and unknown. Methods We proposed a novel network inference methods based on Relevance Low order Partial Correlation (RLowPC). RLowPC method uses a two-step approach to select on the high-confidence edges first by reducing the search space by only picking the top ranked genes from an intial partial correlation analysis and, then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association. Results We selected six co-expression-based methods with good performance in evaluation studies from the literature: Partial correlation, PCIT, ARACNE, MRNET, MRNETB and CLR. The evaluation of these methods was carried out on simulated time-series data with various network sizes ranging from 100 to 3000 nodes. Simulation results show low precision and recall for all of the above methods for large networks with a small number of expression profiles. We improved the inference significantly by refinement of the top weighted edges in the pre-inferred partial correlation networks using RLowPC. We found improved performance by partitioning large networks into smaller co-expressed modules when assessing the method performance within these modules. Conclusions The evaluation results show that current methods suffer from low precision and recall for large co-expression networks where only a small number of profiles are available. The proposed RLowPC method effectively reduces the indirect edges predicted as regulatory relationships and increases the precision of top ranked predictions. Partitioning large networks into smaller highly co-expressed modules also helps to improve the performance of network inference methods. The RLowPC R package for network construction, refinement and evaluation is available at GitHub: https://github.com/wyguo/RLowPC

Episodic Memory in Detoxified Alcoholics: Contribution of Grey Matter Microstructure Alteration

Even though uncomplicated alcoholics may likely have episodic memory deficits, discrepancies exist regarding to the integrity of brain regions that underlie this function in healthy subjects. Possible relationships between episodic memory and 1) brain microstructure assessed by magnetic resonance diffusion tensor imaging (DTI), 2) brain volumes assessed by voxel-based morphometry (VBM) were investigated in uncomplicated, detoxified alcoholics

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe

Nrf2-interacting nutrients and COVID-19 : time for research to develop adaptation strategies

There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPAR gamma:Peroxisome proliferator-activated receptor, NF kappa B: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2 alpha:Elongation initiation factor 2 alpha). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT(1)R axis (AT(1)R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity