scholarly journals Predicting the Habitat Suitability of a Potential Invasive Fern, Cyclosorus afer in Lafia, Nigeria using Species Distribution Modelling

2019 ◽  
Vol 12 ◽  
pp. 1-10
Author(s):  
GBENGA FESTUS AKOMOLAFE ◽  
ZAKARIA BIN RAHMAD
Keyword(s):  
Test Data ◽  
Habitat Suitability ◽  
Area Under The Curve ◽  
Predictor Variable ◽  
Species Distribution Modelling ◽  
Environmental Predictors ◽  
Operating Characteristics ◽  
Jackknife Test ◽  
Presence Data ◽  
Precipitation Seasonality

The vast colonisation of some wetlands by Cyclosorus afer in Lafia, Nigeria has been a serious concern to ecologists and indigenous dwellers. In this study, we used the Maximum Entropy (Maxent) modelling technique to predict the habitat suitability of this fern in Lafia, Nigeria. We obtained the presence data of this fern in three already invaded wetlands of size 500 x 500m2 each using multiple 200m transect. Bioclimatic and elevation variables which were obtained from different databases were imputed into the model as predictor variables of C. afer. After that, the Maxent model was run with 70% of the presence data as training and 30% as test data. Our model result revealed that the area under the curve for receiver operating characteristics of training is 0.847 while and test data is 0.970. The model’s sensitivity was observed to be 100%. The model was assessed based on a jackknife test of individual contributions of each predictor variable to the model. Therefore, the environmental predictors of the occurrence of C. afer in this study area include precipitation seasonality, Precipitation of driest quarter, precipitation of coldest quarter and elevation. This model could be described as accurate, and the occurrence of C. afer in Lafia, Nigeria, is influenced by limiting environmental factors

scholarly journals INFLUENCE OF TEMPERATURE AND ALTITUDE ON THE EXPANSION OF COFFEE CROPS IN MATAS DE MINAS, BRAZIL

2020 ◽  
Vol 28 ◽  
pp. 157-165
Author(s):  
Karine Rabelo Oliveira ◽  
Williams Pinto Marques Ferreira ◽  
Humberto Paiva Fonseca ◽  
Cecília Fátima Souza
Keyword(s):  
Area Under The Curve ◽  
Growth Potential ◽  
Maximum Temperature ◽  
Annual Average ◽  
Jackknife Test ◽  
Average Temperature ◽  
Annual Minimum Temperature ◽  
Precipitation Seasonality ◽  
Omission Rate ◽  
Annual Average Temperature

Coffee is among the most significant products in Brazil. Minas Gerais is the largest state producer of Arabica coffee. Coffee activity has excellent growth potential, which justifies the identification of new areas for expansion of the culture. This study aimed to determine factors that affect the spatial distribution of coffee plantations the most, as well as to identify areas with a greater aptitude for its expansion in the region of the Matas de Minas (63 municipalities). The MaxEnt software was used to elaborate a model capable of describing the area with the highest potential for estimating the probability of coffee adequacy. The elaboration of the model considered the records of occurrence, climatic and topographic variables of Matas de Minas, the second largest state producing region. The area under the curve (AUC), the omission rate and the Jackknife test were used for validation and analysis of the model. The model was accurate with an AUC of 0.816 and omission rate of 0.54% for the ‘test’. It was identified that the potential distribution of coffee in Matas de Minas is determined by changes in the annual maximum temperature, although it did not generate a significant gain when omitted, accounting for a considerable loss in the model. However, the most influential variables on the delineation of distribution were, the altitude and the annual average temperature. The most favorable areas for expansion of coffee culture in the Matas de Minas were found in the vicinity of the region of Alto Caparaó.Abbreviations used: A1 (altitude); A2 (maximum annual temperature); A3 (annual minimum temperature); BIO 1 (annual average temperature 1); BIO 4 (temperature seasonality), BIO 12 (annual precipitation); BIO 15 (precipitation seasonality); csv (comma-separated values); AUC (area under the curve).

scholarly journals Maxent modelling for habitat suitability of vulnerable tree Dalbergia latifolia in Nepal

Silva Fennica ◽  
2021 ◽  
Vol 55 (4) ◽  
Author(s):  
Dipak Mahatara ◽  
Amul Acharya ◽  
Bishnu Dhakal ◽  
Dipesh Sharma ◽  
Sunita Ulak ◽  
...  
Keyword(s):  
Model Prediction ◽  
Habitat Suitability ◽  
Habitat Management ◽  
Natural Habitat ◽  
Area Under The Curve ◽  
Forest Conversion ◽  
Suitable Habitat ◽  
Timber Species ◽  
Jackknife Test ◽  
Maxent Model

Roxb., commonly known as rosewood, is one of the highly valuable tropical timber species of Nepal. The tree species was widely distributed in the past, however, over-exploitation of natural habitat, deforestation, forest conversion for agriculture, illegal logging and the invasion of alien species resulted in the classification of this species as vulnerable by the IUCN (International Union for Conservation of Nature) category. So, the prediction of habitat suitability and potential distribution of the species is required to develop restoration mechanisms and conservation interventions. In this study, we modelled the suitable habitat of over the entire possible range of Nepal using a Maxent model. We compiled 23 environmental variables (19 bioclimatic, 3 topographic and a vegetative layer), however, only 12 least correlated variables along with 43 spatially representative presence locations were retained for model prediction. We used a receiver operating characteristic (ROC) curve to assess the model’s performance and a Jackknife procedure to evaluate the relative importance of predictor variables. The model was statistically significant with an area under the curve (AUC) value of 0.969. The internal Jackknife test indicated that elevation was the most important variable for the model prediction with 71.3% contribution followed by mean temperature of driest quarter (9.8%). The most (>0.6) suitable habitat for the was 235 484 hectares with large sections of area in two provinces whereas, the western most provinces were not suitable for as per Maxent model. The information presented here can provide a framework for nature conservation planning, monitoring and habitat management of this rare and endangered species.Dalbergia latifoliaD. latifoliaD. latifoliaD. latifolia

scholarly journals Using Sentinel-2 Multispectral Images to Map the Occurrence of the Cossid Moth (Coryphodema tristis) in Eucalyptus Nitens Plantations of Mpumalanga, South Africa

2019 ◽  
Vol 11 (3) ◽  
pp. 278 ◽  
Author(s):  
Samuel Kumbula ◽  
Paramu Mafongoya ◽  
Kabir Peerbhay ◽  
Romano Lottering ◽  
Riyad Ismail
Keyword(s):  
South Africa ◽  
Management Strategies ◽  
Vegetation Indices ◽  
Model Performance ◽  
Area Under The Curve ◽  
Mass Trapping ◽  
Eucalyptus Nitens ◽  
Operating Characteristics ◽  
Presence Data ◽  
Sentinel 2

Coryphodema tristis is a wood-boring insect, indigenous to South Africa, that has recently been identified as an emerging pest feeding on Eucalyptus nitens, resulting in extensive damage and economic loss. Eucalyptus plantations contributes over 9% to the total exported manufactured goods of South Africa which contributes significantly to the gross domestic product. Currently, the distribution extent of the Coryphodema tristis is unknown and estimated to infest Eucalyptus nitens compartments from less than 1% to nearly 80%, which is certainly a concern for the forestry sector related to the quantity and quality of yield produced. Therefore, the study sought to model the probability of occurrence of Coryphodema tristis on Eucalyptus nitens plantations in Mpumalanga, South Africa, using data from the Sentinel-2 multispectral instrument (MSI). Traditional field surveys were carried out through mass trapping in all compartments (n = 878) of Eucalyptus nitens plantations. Only 371 Eucalyptus nitens compartments were positively identified as infested and were used to generate the Coryphodema tristis presence data. Presence data and spectral features from the area were analysed using the Maxent algorithm. Model performance was evaluated using the receiver operating characteristics (ROC) curve showing the area under the curve (AUC) and True Skill Statistic (TSS) while the performance of predictors was analysed with the jack-knife. Validation of results were conducted using the test data. Using only the occurrence data and Sentinel-2 bands and derived vegetation indices, the Maxent model provided successful results, exhibiting an area under the curve (AUC) of 0.890. The Photosynthetic vigour ratio, Band 5 (Red edge 1), Band 4 (Red), Green NDVI hyper, Band 3 (Green) and Band 12 (SWIR 2) were identified as the most influential predictor variables. Results of this study suggest that remotely sensed derived vegetation indices from cost-effective platforms could play a crucial role in supporting forest pest management strategies and infestation control.

scholarly journals Habitat suitability of Proboscis monkey (Nasalis larvatus) in Berau Delta, East Kalimantan, Indonesia

2020 ◽  
Vol 21 (11) ◽  
Author(s):  
Tri Atmoko ◽  
Ani Mardiastuti ◽  
Muhammad Bismark ◽  
Lilik Budi Prasetyo ◽  
Entang Iskandar
Keyword(s):  
Habitat Suitability ◽  
Model Performance ◽  
Area Under The Curve ◽  
Suitable Habitat ◽  
Proboscis Monkey ◽  
Environmental Predictors ◽  
Swamp Forest ◽  
Nasalis Larvatus ◽  
East Kalimantan ◽  
Habitat Suitability Map

The proboscis monkey (Nasalis larvatus) is an endemic species to Borneos’ island and is largely confined to mangrove, riverine, and swamp forest. Most of their habitat is outside the conservation due to degraded and habitat converted. Habitat loss is a significant threat to a decreased in the monkey's population. Berau Delta is an unprotected habitat of proboscis monkey, lacking in attention and experiencing a lot of disturbances. This study was conducted on April – August 2019; with aims of the study is to determine Species Distribution Modeling (SDM) for identifying proboscis monkey habitat suitability in Delta Berau, East Kalimantan. The MaxEnt algorithm was used to produce a habitat suitability map based on this species’ occurrence records and environmental predictors. We built the models using 208 points of proboscis monkey presence and 12 environment variables within the study area. Model performance was assessed by examining the area under the curve. The variables most influencing the habitat suitability model were the riverine habitat (60.9%), distance from the pond (16.0%), and distance from the coastline (5.2%). The proboscis monkey suitable habitat is only 9.32% (8,726.58 ha) from 93,631.41 ha total area. The appropriate habitat areas are Sapinang Island, Bungkung Island, Sambuayan Island, Saodang Kecil Island, Besing Island, Lati River, Bebanir Lama, Batu-Batu, and Semanting Bay. We provide some suggestions for the proboscis monkey conservation, which are local protection of uninhabited islands, participatory ecotourism management, and company involvement in protection and management efforts.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals Multi-scale characterizations of colon polyps via computed tomographic colonography

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Weiguo Cao ◽  
Marc J. Pomeroy ◽  
Yongfeng Gao ◽  
Matthew A. Barish ◽  
Almas F. Abbasi ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Texture Features ◽  
Learning Model ◽  
Colon Polyps ◽  
Texture Descriptor ◽  
Operating Characteristics ◽  
Computed Tomographic ◽  
Multi Scale ◽  
Definition Of ◽  
Occurrence Matrix

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

scholarly journals A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez and Amalin (Hymenoptera: Encyrtidae) in the Philippines

Insects ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 26
Author(s):  
Billy Joel M. Almarinez ◽  
Mary Jane A. Fadri ◽  
Richard Lasina ◽  
Mary Angelique A. Tavera ◽  
Thaddeus M. Carvajal ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Habitat Suitability ◽  
Species Distribution Model ◽  
Biological Control Agent ◽  
The Philippines ◽  
Distribution Model ◽  
Field Surveys ◽  
Probability Of Occurrence ◽  
Precipitation Seasonality ◽  
Bioclimatic Variables

Comperiella calauanica is a host-specific endoparasitoid and effective biological control agent of the diaspidid Aspidiotus rigidus, whose outbreak from 2010 to 2015 severely threatened the coconut industry in the Philippines. Using the maximum entropy (Maxent) algorithm, we developed a species distribution model (SDM) for C. calauanica based on 19 bioclimatic variables, using occurrence data obtained mostly from field surveys conducted in A. rigidus-infested areas in Luzon Island from 2014 to 2016. The calculated the area under the ROC curve (AUC) values for the model were very high (0.966, standard deviation = 0.005), indicating the model’s high predictive power. Precipitation seasonality was found to have the highest relative contribution to model development. Response curves produced by Maxent suggested the positive influence of mean temperature of the driest quarter, and negative influence of precipitation of the driest and coldest quarters on habitat suitability. Given that C. calauanica has been found to always occur with A. rigidus in Luzon Island due to high host-specificity, the SDM for the parasitoid may also be considered and used as a predictive model for its host. This was confirmed through field surveys conducted between late 2016 and early 2018, which found and confirmed the occurrence of A. rigidus in three areas predicted by the SDM to have moderate to high habitat suitability or probability of occurrence of C. calauanica: Zamboanga City in Mindanao; Isabela City in Basilan Island; and Tablas Island in Romblon. This validation in the field demonstrated the utility of the bioclimate-based SDM for C. calauanica in predicting habitat suitability or probability of occurrence of A. rigidus in the Philippines.

scholarly journals Diagnostic work up of anemic patients: role of iron deficiency

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Daniela Meiser ◽  
Lale Kayikci ◽  
Matthias Orth
Keyword(s):  
Iron Deficiency ◽  
Complete Blood Count ◽  
Iron Status ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Diagnostic Tools ◽  
Operating Characteristics ◽  
Inflammatory Conditions ◽  
Reliable Parameter ◽  
Work Up

AbstractObjectivesDiagnosing disturbances in iron metabolism can be challenging when accompanied by inflammation. New diagnostic tools such as the “Thomas-plot” (TP) (relation of soluble transferrin receptor [sTfR]/log ferritin to reticulocyte hemoglobin content [RET-He]) were established to improve classification of anemias. Aim of this retrospective study was to assess the added diagnostic value of the TP in anemia work up.MethodsPatients from December 2016 to September 2018 with a complete blood count, iron status, RET-He and sTfR were manually classified into the four quadrants of the TP on basis of conventional iron markers. Manual and algorithm-based classifications were compared using cross tabulations, Box–Whisker-Plots as well as Receiver-Operating-Characteristics (ROC) to calculate the diagnostic accuracy using Area under the Curve (AUC) analysis.ResultsA total of 3,745 patients with a conventional iron status, including 1,721 TPs, could be evaluated. In 70% of the cases the manual classification was identical to the TP, in 10% it was deviant. 20% could not clearly be classified, mostly due to inflammatory conditions. In the absence of an inflammatory condition, ferritin was a reliable parameter to define iron deficiency (ID) (AUC 0.958). In the presence of inflammation, the significance of the ferritin index (AUC 0.917) and of the RET-He (AUC 0.957) increased.ConclusionsThe TP can be useful for narrowing down the causes of anemia in complex cases. Further studies with focus on special patient groups, e.g., oncological or rheumatic patients, are desirable.

scholarly journals New screening approach to detecting congenital syphilis in China: a retrospective cohort study

2020 ◽  
pp. archdischild-2020-320549
Author(s):  
Fang Hu ◽  
Shuai-Jun Guo ◽  
Jian-Jun Lu ◽  
Ning-Xuan Hua ◽  
Yan-Yan Song ◽  
...  
Keyword(s):  
Predictive Value ◽  
Congenital Syphilis ◽  
Area Under The Curve ◽  
Diagnostic Tools ◽  
Operating Characteristics ◽  
Loss To Follow Up ◽  
Sensitivity Specificity ◽  
Mother To Child ◽  
Rule Out

BackgroundDiagnosis of congenital syphilis (CS) is not straightforward and can be challenging. This study aimed to evaluate the validity of an algorithm using timing of maternal antisyphilis treatment and titres of non-treponemal antibody as predictors of CS.MethodsConfirmed CS cases and those where CS was excluded were obtained from the Guangzhou Prevention of Mother-to-Child Transmission of syphilis programme between 2011 and 2019. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using receiver operating characteristics (ROC) in two situations: (1) receiving antisyphilis treatment or no-treatment during pregnancy and (2) initiating treatment before 28 gestational weeks (GWs), initiating after 28 GWs or receiving no treatment for syphilis seropositive women.ResultsAmong 1558 syphilis-exposed children, 39 had confirmed CS. Area under the curve, sensitivity and specificity of maternal non-treponemal titres before treatment and treatment during pregnancy were 0.80, 76.9%, 78.7% and 0.79, 69.2%, 88.7%, respectively, for children with CS. For the algorithm, ROC results showed that PPV and NPV for predicting CS were 37.3% and 96.4% (non-treponemal titres cut-off value 1:8 and no antisyphilis treatment), 9.4% and 100% (non-treponemal titres cut-off value 1:16 and treatment after 28 GWs), 4.2% and 99.5% (non-treponemal titres cut-off value 1:32 and treatment before 28 GWs), respectively.ConclusionsAn algorithm using maternal non-treponemal titres and timing of treatment during pregnancy could be an effective strategy to diagnose or rule out CS, especially when the rate of loss to follow-up is high or there are no straightforward diagnostic tools.

First trimester prediction of maternal glycemic status

2015 ◽  
Vol 43 (3) ◽  
Author(s):  
Rinat Gabbay-Benziv ◽  
Lauren E. Doyle ◽  
Miriam Blitzer ◽  
Ahmet A. Baschat
Keyword(s):  
Risk Score ◽  
Predictive Value ◽  
Challenge Test ◽  
Area Under The Curve ◽  
First Trimester ◽  
Tolerance Test ◽  
Oral Glucose ◽  
Operating Characteristics ◽  
Maternal Characteristics ◽  
Predictive Algorithm

AbstractTo predict gestational diabetes mellitus (GDM) or normoglycemic status using first trimester maternal characteristics.We used data from a prospective cohort study. First trimester maternal characteristics were compared between women with and without GDM. Association of these variables with sugar values at glucose challenge test (GCT) and subsequent GDM was tested to identify key parameters. A predictive algorithm for GDM was developed and receiver operating characteristics (ROC) statistics was used to derive the optimal risk score. We defined normoglycemic state, when GCT and all four sugar values at oral glucose tolerance test, whenever obtained, were normal. Using same statistical approach, we developed an algorithm to predict the normoglycemic state.Maternal age, race, prior GDM, first trimester BMI, and systolic blood pressure (SBP) were all significantly associated with GDM. Age, BMI, and SBP were also associated with GCT values. The logistic regression analysis constructed equation and the calculated risk score yielded sensitivity, specificity, positive predictive value, and negative predictive value of 85%, 62%, 13.8%, and 98.3% for a cut-off value of 0.042, respectively (ROC-AUC – area under the curve 0.819, CI – confidence interval 0.769–0.868). The model constructed for normoglycemia prediction demonstrated lower performance (ROC-AUC 0.707, CI 0.668–0.746).GDM prediction can be achieved during the first trimester encounter by integration of maternal characteristics and basic measurements while normoglycemic status prediction is less effective.

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