Quantifying Root Colonization in Arbuscular Mycorrhizas by Image Segmentation and Machine Learning

Abstract MotivationArbuscular mycorrhizas are the most widespread plant symbioses and involve the majority of crop plants. The beneficial interaction between plant roots and a group of soil fungi (Glomeromycotina) grants the green host a preferential access to soil mineral nutrients and water, supporting plant health, biomass production and resistance to both abiotic and biotic stresses. The nutritional exchanges at the core of this symbiosis take place inside the living root cells, which are diffusely colonized by specialized fungal structures called arbuscules. For this reason, the vast majority of studies investigating arbuscular mycorrhizas and their applications in agriculture require a precise quantification of the intensity of root colonization. To this aim, several manual methods have been used for decades to estimate the extension of intraradical fungal structures, mostly based on optical microscopy observations and individual assessment of fungal abundance in the root tissues. ResultsHere we propose a novel semi-automated approach to quantify AM colonization based on digital image analysis and compare two methods based on image thresholding and machine learning. Our results indicate in machine learning a very promising tool for accelerating, simplifying and standardizing this critical type of analysis, with a direct potential interest for applicative and basic [email protected]; [email protected]

Download Full-text

Combining Virtual Reality and Organizational Neuroscience for Leadership Assessment

Applied Sciences ◽

10.3390/app11135956 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5956

Author(s):

Elena Parra ◽

Irene Alice Chicchi Giglioli ◽

Jestine Philip ◽

Lucia Amalia Carrasco-Ribelles ◽

Javier Marín-Morales ◽

...

Keyword(s):

Machine Learning ◽

Eye Tracking ◽

Ecological Validity ◽

Objective Assessment ◽

Leadership Skills ◽

Three Dimensional ◽

Machine Learning Techniques ◽

Future Research ◽

Promising Tool ◽

Future Research Agenda

In this article, we introduce three-dimensional Serious Games (3DSGs) under an evidence-centered design (ECD) framework and use an organizational neuroscience-based eye-tracking measure to capture implicit behavioral signals associated with leadership skills. While ECD is a well-established framework used in the design and development of assessments, it has rarely been utilized in organizational research. The study proposes a novel 3DSG combined with organizational neuroscience methods as a promising tool to assess and recognize leadership-related behavioral patterns that manifest during complex and realistic social situations. We offer a research protocol for assessing task- and relationship-oriented leadership skills that uses ECD, eye-tracking measures, and machine learning. Seamlessly embedding biological measures into 3DSGs enables objective assessment methods that are based on machine learning techniques to achieve high ecological validity. We conclude by describing a future research agenda for the combined use of 3DSGs and organizational neuroscience methods for leadership and human resources.

Download Full-text

CNV-P: a machine-learning framework for predicting high confident copy number variations

PeerJ ◽

10.7717/peerj.12564 ◽

2021 ◽

Vol 9 ◽

pp. e12564

Author(s):

Taifu Wang ◽

Jinghua Sun ◽

Xiuqing Zhang ◽

Wen-Jing Wang ◽

Qing Zhou

Keyword(s):

Machine Learning ◽

False Positive ◽

Copy Number ◽

Genetic Disorders ◽

Genetic Diseases ◽

Basic Research ◽

Read Depth ◽

Copy Number Variations ◽

Sequencing Data ◽

Learning Framework

Background Copy-number variants (CNVs) have been recognized as one of the major causes of genetic disorders. Reliable detection of CNVs from genome sequencing data has been a strong demand for disease research. However, current software for detecting CNVs has high false-positive rates, which needs further improvement. Methods Here, we proposed a novel and post-processing approach for CNVs prediction (CNV-P), a machine-learning framework that could efficiently remove false-positive fragments from results of CNVs detecting tools. A series of CNVs signals such as read depth (RD), split reads (SR) and read pair (RP) around the putative CNV fragments were defined as features to train a classifier. Results The prediction results on several real biological datasets showed that our models could accurately classify the CNVs at over 90% precision rate and 85% recall rate, which greatly improves the performance of state-of-the-art algorithms. Furthermore, our results indicate that CNV-P is robust to different sizes of CNVs and the platforms of sequencing. Conclusions Our framework for classifying high-confident CNVs could improve both basic research and clinical diagnosis of genetic diseases.

Download Full-text

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Animals ◽

10.3390/ani10091690 ◽

2020 ◽

Vol 10 (9) ◽

pp. 1690 ◽

Cited By ~ 1

Author(s):

Marianne Cockburn

Keyword(s):

Machine Learning ◽

Dairy Farm ◽

Poor Performance ◽

Dairy Farming ◽

Training Data ◽

Data Sources ◽

Full Potential ◽

Promising Tool ◽

Public Data ◽

Dairy Research

Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.

Download Full-text

A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections

Cancers ◽

10.3390/cancers12051344 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1344 ◽

Cited By ~ 4

Author(s):

Francesco Martino ◽

Silvia Varricchio ◽

Daniela Russo ◽

Francesco Merolla ◽

Gennaro Ilardi ◽

...

Keyword(s):

Machine Learning ◽

Tumor Cells ◽

Squamous Cell ◽

Tissue Sample ◽

Case Series ◽

Tissue Microarrays ◽

Fast Method ◽

Tissue Samples ◽

Promising Tool ◽

Nuclear Features

We introduce a machine learning-based analysis to predict the immunohistochemical (IHC) labeling index for the cell proliferation marker Ki67/MIB1 on cancer tissues based on morphometrical features extracted from hematoxylin and eosin (H&E)-stained formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples. We provided a proof-of-concept prediction of the Ki67/MIB1 IHC positivity of cancer cells through the definition and quantitation of single nuclear features. In the first instance, we set our digital framework on Ki67/MIB1-stained OSCC (oral squamous cell carcinoma) tissue sample whole slide images, using QuPath as a working platform and its integrated algorithms, and we built a classifier in order to distinguish tumor and stroma classes and, within them, Ki67-positive and Ki67-negative cells; then, we sorted the morphometric features of tumor cells related to their Ki67 IHC status. Among the evaluated features, nuclear hematoxylin mean optical density (NHMOD) presented as the best one to distinguish Ki67/MIB1 positive from negative cells. We confirmed our findings in a single-cell level analysis of H&E staining on Ki67-immunostained/H&E-decolored tissue samples. Finally, we tested our digital framework on a case series of oral squamous cell carcinomas (OSCC), arranged in tissue microarrays; we selected two consecutive sections of each OSCC FFPE TMA (tissue microarray) block, respectively stained with H&E and immuno-stained for Ki67/MIB1. We automatically detected tumor cells in H&E slides and generated a “false color map” (FCM) based on NHMOD through the QuPath measurements map tool. FCM nearly coincided with the actual immunohistochemical result, allowing the prediction of Ki67/MIB1 positive cells in a direct visual fashion. Our proposed approach provides the pathologist with a fast method of identifying the proliferating compartment of the tumor through a quantitative assessment of the nuclear features on H&E slides, readily appreciable by visual inspection. Although this technique needs to be fine-tuned and tested on larger series of tumors, the digital analysis approach appears to be a promising tool to quickly forecast the tumor’s proliferation fraction directly on routinely H&E-stained digital sections.

Download Full-text

Can Gut Microbiota Be a Good Predictor for Parkinson’s Disease? A Machine Learning Approach

Brain Sciences ◽

10.3390/brainsci10040242 ◽

2020 ◽

Vol 10 (4) ◽

pp. 242 ◽

Cited By ~ 3

Author(s):

Daniele Pietrucci ◽

Adelaide Teofani ◽

Valeria Unida ◽

Rocco Cerroni ◽

Silvia Biocca ◽

...

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Random Forest ◽

Gut Microbiota ◽

Biological Data ◽

Machine Learning Algorithms ◽

Support Vector ◽

Published Data ◽

Promising Tool

The involvement of the gut microbiota in Parkinson’s disease (PD), investigated in several studies, identified some common alterations of the microbial community, such as a decrease in Lachnospiraceae and an increase in Verrucomicrobiaceae families in PD patients. However, the results of other bacterial families are often contradictory. Machine learning is a promising tool for building predictive models for the classification of biological data, such as those produced in metagenomic studies. We tested three different machine learning algorithms (random forest, neural networks and support vector machines), analyzing 846 metagenomic samples (472 from PD patients and 374 from healthy controls), including our published data and those downloaded from public databases. Prediction performance was evaluated by the area under curve, accuracy, precision, recall and F-score metrics. The random forest algorithm provided the best results. Bacterial families were sorted according to their importance in the classification, and a subset of 22 families has been identified for the prediction of patient status. Although the results are promising, it is necessary to train the algorithm with a larger number of samples in order to increase the accuracy of the procedure.

Download Full-text

Soil pH-induced changes in root colonization, diversity, and reproduction of symbiotic arbuscular mycorrhizal fungi from healthy and declining maple forests

Canadian Journal of Forest Research ◽

10.1139/x00-090 ◽

2000 ◽

Vol 30 (10) ◽

pp. 1543-1554 ◽

Cited By ~ 64

Author(s):

Andrew P Coughlan ◽

Yolande Dalpé ◽

Line Lapointe ◽

Yves Piché

Keyword(s):

Arbuscular Mycorrhizal Fungi ◽

Mycorrhizal Fungi ◽

Acer Saccharum ◽

Sugar Maple ◽

Root Colonization ◽

Arbuscular Mycorrhizal ◽

Taxonomic Diversity ◽

Arbuscular Mycorrhizas ◽

Fungal Community Composition ◽

Spore Population

Acer saccharum Marsh. (sugar maple) is one of only few arbuscular mycorrhizal trees to form extensive stands in northern temperate biomes. Recent maple decline could result from altered intensity and quality of root colonization by associated mycobionts or possible shifts in symbiotic fungal community composition following environmental stresses. In this study the effects on arbuscular mycorrhizal fungi of soil acidification, one of several proposed causal stresses underlying forest decline, and remedial liming were investigated under glasshouse conditions. Acer saccharum seedlings were grown in unsterilized, pH altered, forest soils from healthy and declining maple stands. Over a range of treatment pHs normally tolerated by A. saccharum, fungal populations and responses to pH changes differed between the two soils. The declining site with more acidic soil had an initially larger spore population but lower taxonomic diversity than the healthy site. However, liming stimulated sporulation of several taxa initially apparently absent from the declining site spore population. The quantity of colonization generally increased with pH for both sites. Five Glomus taxa and Scutellospora calospora (Nicol. & Gerd.) Walker & Sanders are added to the list of fungi known to form arbuscular mycorrhizas with A. saccharum, and the known range of Acaulospora cavernata Blaszkowski is extended from Poland to eastern North America.

Download Full-text

Cortical colonisation is not an absolute requirement for phosphorus transfer to plants in arbuscular mycorrhizas formed by Scutellospora calospora in a tomato mutant: evidence from physiology and gene expression

Functional Plant Biology ◽

10.1071/fp09248 ◽

2010 ◽

Vol 37 (12) ◽

pp. 1132 ◽

Cited By ~ 10

Author(s):

Maria Manjarrez ◽

Helle M. Christophersen ◽

Sally E. Smith ◽

F. Andrew Smith

Keyword(s):

Gene Expression ◽

Arbuscular Mycorrhizas ◽

Physiological Data ◽

Cortical Cells ◽

Root Cells ◽

Absolute Requirement ◽

Cell Layers ◽

Mycorrhizal Colonisation ◽

Hyphal Coils ◽

Scutellospora Calospora

Arbuscules in Arum-type arbuscular mycorrhizas (AM), formed intracellularly in root cortical cells, are generally believed to be the most important and defining characteristics of the symbiosis as sites for phosphorus (P) and carbon (C) exchange. We used a Pen + Coi– phenotype (penetration of epidermal and exodermal root cells but not arbuscule formation) formed in rmc (reduced mycorrhizal colonisation) mutant tomato (Lycopersicon esculentum Mill.) by Scutellospora calospora (Nicol. & Gerd.) Walker & Sanders to determine whether the fungus is capable of transferring P from soil to plant and whether there is concurrent upregulation of AM-inducible orthophosphate (Pi) transporter gene expression in the roots. Our physiological data showed that colonisation of outer root cell layers is sufficient for P transfer from S. calospora to tomato. This transfer of P was supported by increased expression of the Pi transporter genes, LePT3 and LePT5, known to be upregulated in AM interactions. We conclude that cortical colonisation and formation of arbuscules or arbusculate hyphal coils is not an absolute prerequisite for P transfer in this symbiosis.

Download Full-text

Phase Recognition of Lung Cancer via Steerable Riesz Wavelets with Rf Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.h6520.069820 ◽

2020 ◽

Vol 9 (8) ◽

pp. 491-496

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Feature Extraction ◽

Mortality Rate ◽

Learning Algorithm ◽

Classification Model ◽

Good Prediction ◽

Texture Model ◽

Promising Tool ◽

Effectiveness Of Training

Lung cancer is one of the diseases which has a high mortality. If the condition is detected earlier, then it is easier to reduce the mortality rate. This lung cancer has caused more deaths in the world than any other cancer. The main objective is to predict lung cancer using a machine learning algorithm. Several computer-aided systems have been designed to reduce the mortality rate due to lung cancer. Machine learning is a promising tool to predict lung cancer in its early phase or stage, where the features of images are trained using a classification model. Generally, machine learning is used to have a good prediction, but in some models, due to lack of efficient feature extraction value, the training has not been done more effectively; hence the predictions are poor. In order to overcome this limitation, the proposed covariant texture model utilizing the steerable Riesz wavelets feature extraction technique to increase the effectiveness of training via the Random Forest algorithm. In this proposed model, the RF algorithm is employed to predict whether the nodule in the image is benign or malignant ii) to find the level of severity (1 to 5), if it is a malignant nodule. Our experiment result can be used as a tool to support the diagnosis and to analyze at an earlier stage of cancer to cure it.

Download Full-text

Towards Autonomous Machine Learning in Chemistry via Evolutionary Algorithms

10.26434/chemrxiv.9782387 ◽

2019 ◽

Author(s):

Gaurav Vishwakarma ◽

Mojtaba Haghighatlari ◽

Johannes Hachmann

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Model Selection ◽

Data Science ◽

Multi Objective Optimization ◽

Rational Model ◽

Hyperparameter Optimization ◽

Promising Tool ◽

Science Community ◽

Autonomous Machine

Machine learning has been emerging as a promising tool in the chemical and materials domain. In this paper, we introduce a framework to automatically perform rational model selection and hyperparameter optimization that are important concerns for the efficient and successful use of machine learning, but have so far largely remained unexplored by this community. The framework features four variations of genetic algorithm and is implemented in the chemml program package. Its performance is benchmarked against popularly used algorithms and packages in the data science community and the results show that our implementation outperforms these methods both in terms of time and accuracy. The effectiveness of our implementation is further demonstrated via a scenario involving multi-objective optimization for model selection.

Download Full-text

Forecasting Monthly Discharge Using Machine Learning Techniques

International Research Journal of Multidisciplinary Technovation ◽

10.34256/irjmtcon1 ◽

2019 ◽

Vol 1 (6) ◽

pp. 1-6

Author(s):

Bharthavarapu Srikanth ◽

Geetha Selvarani A. ◽

Bibhuti Bhusan Sahoo

Keyword(s):

Machine Learning ◽

Research Area ◽

Machine Learning Techniques ◽

Support Vector ◽

Promising Tool ◽

Learning Techniques ◽

Different Types ◽

Monthly Discharge ◽

Property Destruction ◽

Different Characteristics

Discharge prediction methods play crucial role in providing early warnings and helping local people and government agencies to prepare well before flood or managing available water for various purposes. The ability to predict future river flows helps people anticipate and plan for upcoming flooding, preventing deaths and decreasing property destruction. Different hydrological models supporting these predictions have different characteristics, driven by available data and the research area. This study applied two different types of Machine learning techniques to the Tikarpara station present in the lower end of the Mahanadi river basin India. The two Machine learning techniques include Multi-layer perception (MLP) and support vector regression (SVR) MLP has shown great deal of accuracy as compared to SVR across the cases used in the study; based on available data and the study area, MLP showed the best applicability, compared to SVR techniques. MLP out performed SVR model with r2 = 0.75 and lowest RMSE = 0.58.MLP can be used as a promising tool for forecasting monthly discharge at the selected station.

Download Full-text