Genotyping and multivariate regression trees reveal ecological diversification within the Microcystis aeruginosa complex along a wide environmental gradient

2021 ◽  
Author(s):  
Gabriela Martínez de la Escalera ◽  
Angel M. Segura ◽  
Carla Kruk ◽  
Badih Ghattas ◽  
Frederick M. Cohan ◽  
...  
Keyword(s):  
Microcystis Aeruginosa ◽  
Environmental Conditions ◽  
Algal Blooms ◽  
Environmental Gradient ◽  
Prediction Models ◽  
Complex Form ◽  
Regression Trees ◽  
Local Environment ◽  
Temperature Conductivity ◽  
Wide Range

Addressing the ecological and evolutionary processes underlying biodiversity patterns is essential to identify the mechanisms shaping community structure and function. In bacteria, the formation of new ecologically distinct populations (ecotypes) is proposed as one of the main drivers of diversification. New ecotypes arise when mutations in key functional genes or acquisition of new metabolic pathways by horizontal gene transfer allow the population to exploit new resources, permitting their coexistence with the parental population. We previously reported the presence of microcystin-producing organisms of the Microcystis aeruginosa complex (toxic MAC) through an 800 km environmental gradient ranging from freshwater to estuarine-marine waters in South America. We hypothesize that the success of toxic MAC in such a gradient is due to the existence of very closely related populations that are ecologically distinct (ecotypes), each specialized to a specific arrangement of environmental variables. Here, we analyzed toxic MAC genetic diversity through qPCR and high-resolution melting analysis (HRMA) of a functional gene ( mcyJ , microcystin synthetase cluster). We explored the variability of the mcyJ gene along the environmental gradient by multivariate classification and regression trees ( m CART). Six groups of mcyJ genotypes were distinguished and associated with different combinations of water temperature, conductivity and turbidity. We propose that each mcyJ variant associated to a defined environmental condition is an ecotype (or species) whose relative abundances vary according to their fitness in the local environment. This mechanism would explain the success of toxic MAC in such a wide array of environmental conditions. Importance Organisms of the Microcystis aeruginosa Complex form harmful algal blooms (HABs) in nutrient-rich water bodies worldwide. MAC HABs are difficult to manage owing to the production of potent toxins (microcystins) that resist water treatment. Besides, the role of microcystins in the ecology of MAC organisms is still elusive, meaning that the environmental conditions driving the toxicity of the bloom are not clear. Furthermore, the lack of coherence between morphology-based and genomic-based species classification makes it difficult to draw sound conclusions about when and where each member species of the MAC will dominate the bloom. Here, we propose that the diversification process and success of toxic MAC in a wide range of waterbodies involves the generation of ecotypes, each specialized in a particular niche, whose relative abundance varies according to its fitness in the local environment. This knowledge can improve the generation of accurate prediction models of MAC growth and toxicity, helping to prevent human and animal intoxication.

scholarly journals Genotyping and functional regression trees reveals environmental preferences of toxic cyanobacteria (Microcystis aeruginosa complex) along a wide spatial gradient

2019 ◽  
Author(s):  
Gabriela Martínez de la Escalera ◽  
Angel M. Segura ◽  
Carla Kruk ◽  
Badih Ghattas ◽  
Claudia Piccini
Keyword(s):  
Microcystis Aeruginosa ◽  
Environmental Conditions ◽  
Regression Trees ◽  
Spatial Gradient ◽  
Functional Regression ◽  
Environmental Preferences ◽  
Temperature Conductivity ◽  
Toxic Cyanobacteria ◽  
Melting Analysis

AbstractAddressing ecological and evolutionary processes explaining biodiversity patterns is essential to identify the mechanisms driving community assembly. In the case of bacteria, the formation of new ecologically distinct populations or ecotypes is proposed as one of the main drivers of diversification. New ecotypes arise when mutation in key functional genes or acquisition of new metabolic pathways by horizontal gene transfer allow the population to exploit new resources, making possible their coexistence with parental population. Recently, we have reported the presence of toxic, microcystin-producing organisms from the Microcystis aeruginosa complex (MAC) through a wide environmental gradient (800 km) in South America, ranging from freshwater to estuarine-marine waters. In order to explain this finding, we hypothesize that the success of toxic organisms of MAC in such array of environmental conditions is due to the existence of ecotypes having different environmental preferences. So, we analysed the genetic diversity of microcystin-producing populations of Microcystis aeruginosa complex (MAC) by qPCR and high resolution melting analysis (HRMA) of a functional gene (mcyJ, involved in microcystin synthesis) and explored its relationship with the environmental conditions through the gradient by functional classification and regression trees (fCART). Six groups of mcyJ genotypes were distinguished and selected by different combinations of water temperature, conductivity and turbidity, determining the environmental preferences of each group. Since these groups were based on the basis of similar sequence and ecological characteristics they were defined as ecotypes of toxic MAC. Taking into account that the role of microcystins in MAC biology and ecology has not yet been elucidated, we propose that the toxin might have a role in MAC fitness that would be mainly controlled by the physical environment in a way such that the ecotypes that thrive in the riverine zone of the gradient would be more stable and less influenced by salinity fluctuations than those living at the marine limit of the estuary. These would periodically disappear or being eliminated by salinity increases, depending on the estuary dynamics. Thus, ecotypes generation would be an important mechanism allowing toxic MAC adapting to and succeed in a wide array of environmental conditions.

scholarly journals Expanded prediction equations of human sweat loss and water needs

2009 ◽  
Vol 107 (2) ◽  
pp. 379-388 ◽  
Author(s):  
R. R. Gonzalez ◽  
S. N. Cheuvront ◽  
S. J. Montain ◽  
D. A. Goodman ◽  
L. A. Blanchard ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Prediction Models ◽  
Water Vapor Pressure ◽  
Prediction Equation ◽  
Whole Body ◽  
Evaporative Heat Loss ◽  
Independent Data ◽  
Metabolic Rates ◽  
Sweating Rate ◽  
Wide Range

The Institute of Medicine expressed a need for improved sweating rate (ṁsw) prediction models that calculate hourly and daily water needs based on metabolic rate, clothing, and environment. More than 25 years ago, the original Shapiro prediction equation (OSE) was formulated as ṁsw (g·m−2·h−1) = 27.9· Ereq·( Emax)−0.455, where Ereq is required evaporative heat loss and Emax is maximum evaporative power of the environment; OSE was developed for a limited set of environments, exposures times, and clothing systems. Recent evidence shows that OSE often overpredicts fluid needs. Our study developed a corrected OSE and a new ṁsw prediction equation by using independent data sets from a wide range of environmental conditions, metabolic rates (rest to ≤450 W/m2), and variable exercise durations. Whole body sweat losses were carefully measured in 101 volunteers (80 males and 21 females; >500 observations) by using a variety of metabolic rates over a range of environmental conditions (ambient temperature, 15–46°C; water vapor pressure, 0.27–4.45 kPa; wind speed, 0.4–2.5 m/s), clothing, and equipment combinations and durations (2–8 h). Data are expressed as grams per square meter per hour and were analyzed using fuzzy piecewise regression. OSE overpredicted sweating rates ( P < 0.003) compared with observed ṁsw. Both the correction equation (OSEC), ṁsw = 147·exp (0.0012·OSE), and a new piecewise (PW) equation, ṁsw = 147 + 1.527· Ereq − 0.87· Emax were derived, compared with OSE, and then cross-validated against independent data (21 males and 9 females; >200 observations). OSEC and PW were more accurate predictors of sweating rate (58 and 65% more accurate, P < 0.01) and produced minimal error (standard error estimate < 100 g·m−2·h−1) for conditions both within and outside the original OSE domain of validity. The new equations provide for more accurate sweat predictions over a broader range of conditions with applications to public health, military, occupational, and sports medicine settings.

scholarly journals Genomic insight for Algicidal activity in Rhizobium sp. (AQ_MP)

2021 ◽  
Author(s):  
Mili Pal ◽  
Asifa Qureshi ◽  
Hemant Purohit
Keyword(s):  
Microcystis Aeruginosa ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Nutrient Release ◽  
Glycoside Hydrolases ◽  
Polysaccharide Lyases ◽  
Responsible Gene ◽  
Wide Range ◽  
Biological Detoxification ◽  
Rhizobium Sp

Abstract Occurrence of Harmful Algal Blooms (HABs) creates a threat to aquatic ecosystem affecting the existing flora and fauna. Hence, the mitigation of HABs through an eco-friendly approach remains a challenge for environmentalists. The present study provides the genomic insights of Rhizobium sp. (AQ_MP), an environmental isolate that showed the capability of degrading Microcystis aeruginosa (Cyanobacteria) at laboratory scale. Genome sequence analysis of Rhizobium sp. (AQ_MP) was performed to determine the algal lysis properties and toxin degradative pathway. It is envisaged that Rhizobium sp. (AQ_MP) secreted CAZymes like Glycosyltransferases (GT), Glycoside Hydrolases (GH), polysaccharide lyases (PL), which allowed algal polysaccharide degradation (lysis) and enabled nutrient release for the subsequent growth of Rhizobium sp. (AQ_MP) Genome analysis also showed the presence of the glutathione metabolic pathway, which is the biological detoxification pathway responsible for microcystin degradation. The conserved region mlrC, a microcystin toxin degrading responsible gene, was also annotated in Rhizobium sp. (AQ_MP). This study confirmed that Rhizobium sp. (AQ_MP) harbours a wide range of crucial enzymes released for lysis of Microcystis aeruginosa (M. aeruginosa) cells and also for degradation of microcystin toxin. This study thus find promiscuity for scaling the lab based analysis to field level in future.

Microalgal blooms in a eutrophic estuary (Golden Horn, Sea of Marmara) following a remediation effort

2019 ◽  
Vol 62 (6) ◽  
pp. 537-547
Author(s):  
Seyfettin Tas
Keyword(s):  
Environmental Conditions ◽  
Algal Blooms ◽  
Secchi Depth ◽  
Sea Of Marmara ◽  
Annual Average ◽  
Golden Horn ◽  
Bloom Formation ◽  
Wide Range ◽  
Remediation Effort ◽  
Microalgal Species

Abstract The present work describes microalgal blooms that occurred in a eutrophic estuary (Golden Horn, Sea of Marmara, Turkey) between October 2013 and September 2014 following a remediation effort. The relationships between bloom-forming microalgal species and environmental factors were investigated during the study period. The changing environmental conditions (e.g. increasing water transparency and salinity) after seawater transfer to the Golden Horn Estuary stimulated phytoplankton growth with dense algal blooms. Annual average values of Secchi depth, salinity and dissolved oxygen increased in comparison with those in an earlier study in 2009–2010. Nine microalgal species, which consisted of four diatoms, two dinoflagellates, one cryptophycean, one raphidophycean and one euglenophycean, formed the blooms with water discolorations during spring and summer. The species that reached the highest bloom density were Plagioselmis prolonga (62.4 × 106 cells l−1) among crytophyceans, Heterocapsa triquetra (21.8 × 106 cells l−1) among dinoflagellates and Skeletonema marinoi (39 × 106 cells l−1) among diatoms. The abundance of dinoflagellates and phytoflagellates increased particularly in the upper estuary when compared to diatoms and their rapid growth and bloom formation revealed that they have a wide range of tolerance to changing environmental conditions and a strong ability to compete with other species in this study area.

scholarly journals Dynamic Electro-Thermal PV Temperature and Power Output Prediction Model for Any PV Geometries in Free-Standing and BIPV Systems Operating under Any Environmental Conditions

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4743
Author(s):  
Eleni Kaplani ◽  
Socrates Kaplanis
Keyword(s):  
Prediction Model ◽  
Environmental Conditions ◽  
Power Output ◽  
Prediction Models ◽  
Tracking System ◽  
The Sun ◽  
Free Standing ◽  
Temperature Prediction ◽  
Fixed Angle ◽  
Wide Range

PV temperature significantly affects the module’s power output and final system yield, and its accurate prediction can serve the forecasting of PV power output, smart grid operations, online PV diagnostics and dynamic predictive management of Building Integrated Photovoltaic (BIPV) systems. This paper presents a dynamic PV temperature prediction model based on transient Energy Balance Equations, incorporating theoretical expressions for all heat transfer processes, natural convection, forced convection, conduction and radiation exchanges between both module sides and the environment. The algorithmic approach predicts PV temperature at the centre of the cell, the back and the front glass cover with fast convergence and serves the PV power output prediction. The simulation model is robust, predicting PV temperature with high accuracy at any environmental conditions, PV inclination, orientation, wind speed and direction, and mounting configurations, free-standing and BIPV. These, alongside its theoretical basis, ensure the model’s wide applicability and clear advantage over existing PV temperature prediction models. The model is validated for a wide range of environmental conditions, PV geometries and mounting configurations with experimental data from a sun-tracking, a fixed angle PV and a BIPV system. The deviation between predicted and measured power output for the fixed-angle and the sun-tracking PV systems was estimated at −1.4% and 1.9%, respectively. The median of the temperature difference between predicted and measured values was as low as 0.5 °C for the sun-tracking system, and for all cases, the predicted temperature profiles were closely matching the measured profiles. The PV temperature and power output predicted by this model are compared to the results produced by other well-known PV temperature models, illustrating its high predictive capacity, accuracy and robustness.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Intelligent Techniques Analysis for Glycosylation Site Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Cell Interaction ◽  
Glycosylation Site ◽  
Machine Learning Classification ◽  
Site Prediction ◽  
Glycosylation Sites ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

scholarly journals Portable near Infrared Spectroscopy as a Tool for Fresh Tomato Quality Control Analysis in the Field

2021 ◽  
Vol 11 (7) ◽  
pp. 3209
Author(s):  
Karla R. Borba ◽  
Didem P. Aykas ◽  
Maria I. Milani ◽  
Luiz A. Colnago ◽  
Marcos D. Ferreira ◽  
...  
Keyword(s):  
Citric Acid ◽  
Near Infrared ◽  
Prediction Models ◽  
Quality Attributes ◽  
Internal Quality ◽  
Control Analysis ◽  
Tomato Quality ◽  
Wide Range ◽  
Different Types ◽  
Fresh Tomato

Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes non-destructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way.

scholarly journals Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

2021 ◽  
Vol 13 (7) ◽  
pp. 3870
Author(s):  
Mehrbakhsh Nilashi ◽  
Shahla Asadi ◽  
Rabab Ali Abumalloh ◽  
Sarminah Samad ◽  
Fahad Ghabban ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Sustainability Assessment ◽  
Regression Trees ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Sustainability Performance ◽  
Learning Techniques ◽  
Self Organizing

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

scholarly journals Risk Factors and Prediction Models for Venous Thromboembolism in Ambulatory Patients with Lung Cancer

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 778
Author(s):  
Ann-Rong Yan ◽  
Indira Samarawickrema ◽  
Mark Naunton ◽  
Gregory M. Peterson ◽  
Desmond Yip ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Prediction Models ◽  
Poor Performance ◽  
Risk Models ◽  
Related Risk ◽  
Wide Range ◽  
Ambulatory Patients

Venous thromboembolism (VTE) is a significant cause of mortality in patients with lung cancer. Despite the availability of a wide range of anticoagulants to help prevent thrombosis, thromboprophylaxis in ambulatory patients is a challenge due to its associated risk of haemorrhage. As a result, anticoagulation is only recommended in patients with a relatively high risk of VTE. Efforts have been made to develop predictive models for VTE risk assessment in cancer patients, but the availability of a reliable predictive model for ambulate patients with lung cancer is unclear. We have analysed the latest information on this topic, with a focus on the lung cancer-related risk factors for VTE, and risk prediction models developed and validated in this group of patients. The existing risk models, such as the Khorana score, the PROTECHT score and the CONKO score, have shown poor performance in external validations, failing to identify many high-risk individuals. Some of the newly developed and updated models may be promising, but their further validation is needed.

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