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General General

An artificial neural network-based model to predict chronic kidney disease in aged cats.

In Journal of veterinary internal medicine ; h5-index 37.0

BACKGROUND : Chronic kidney disease (CKD) frequently causes death in older cats; its early detection is challenging.

OBJECTIVES : To build a sensitive and specific model for early prediction of CKD in cats using artificial neural network (ANN) techniques applied to routine health screening data.

ANIMALS : Data from 218 healthy cats ≥7 years of age screened at the Royal Veterinary College (RVC) were used for model building. Performance was tested using data from 3546 cats in the Banfield Pet Hospital records and an additional 60 RCV cats-all initially without a CKD diagnosis.

METHODS : Artificial neural network (ANN) modeling used a multilayer feed-forward neural network incorporating a back-propagation algorithm. Clinical variables from single cat visits were selected using factorial discriminant analysis. Independent submodels were built for different prediction time frames. Two decision threshold strategies were investigated.

RESULTS : Input variables retained were plasma creatinine and blood urea concentrations, and urine specific gravity. For prediction of CKD within 12 months, the model had accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 88%, 87%, 70%, 53%, and 92%, respectively. An alternative decision threshold increased specificity and PPV to 98% and 87%, but decreased sensitivity and NPV to 42% and 79%, respectively.

CONCLUSIONS AND CLINICAL IMPORTANCE : A model was generated that identified cats in the general population ≥7 years of age that are at risk of developing CKD within 12 months. These individuals can be recommended for further investigation and monitoring more frequently than annually. Predictions were based on single visits using common clinical variables.

Biourge Vincent, Delmotte Sebastien, Feugier Alexandre, Bradley Richard, McAllister Molly, Elliott Jonathan


CKD modeling, artificial intelligence, prediction tool, prevention, senior health check

General General

Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System

ArXiv Preprint

Industry 4.0 will make manufacturing processes smarter but this smartness requires more environmental awareness, which in case of Industrial Internet of Things, is realized by the help of sensors. This article is about industrial pharmaceutical systems and more specifically, water purification systems. Purified water which has certain conductivity is an important ingredient in many pharmaceutical products. Almost every pharmaceutical company has a water purifying unit as a part of its interdependent systems. Early detection of faults right at the edge can significantly decrease maintenance costs and improve safety and output quality, and as a result, lead to the production of better medicines. In this paper, with the help of a few sensors and data mining approaches, an anomaly detection system is built for CHRIST Osmotron water purifier. This is a practical research with real-world data collected from SinaDarou Labs Co. Data collection was done by using six sensors over two-week intervals before and after system overhaul. This gave us normal and faulty operation samples. Given the data, we propose two anomaly detection approaches to build up our edge fault detection system. The first approach is based on supervised learning and data mining e.g. by support vector machines. However, since we cannot collect all possible faults data, an anomaly detection approach is proposed based on normal system identification which models the system components by artificial neural networks. Extensive experiments are conducted with the dataset generated in this study to show the accuracy of the data-driven and model-based anomaly detection methods.

Mohammad Sadegh Sadeghi Garmaroodi, Faezeh Farivar, Mohammad Sayad Haghighi, Mahdi Aliyari Shoorehdeli, Alireza Jolfaei


General General

DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.

In Proteins

Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction. The major improvements over the DNSS1 method include (a) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict 3-state and 8-state secondary structure, and (b) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on independent test data sets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the protein targets of 2018 CASP13 experiment and achieved the Q3 score of 81.62%, SOV score of 72.19%, and Q8 score of 73.28%. DNSS2 is freely available at:

Guo Zhiye, Hou Jie, Cheng Jianlin


CASP, deep learning, secondary structure prediction

Pathology Pathology

Convolution Neural Networks for diagnosing colon and lung cancer histopathological images

ArXiv Preprint

Lung and Colon cancer are one of the leading causes of mortality and morbidity in adults. Histopathological diagnosis is one of the key components to discern cancer type. The aim of the present research is to propose a computer aided diagnosis system for diagnosing squamous cell carcinomas and adenocarcinomas of lung as well as adenocarcinomas of colon using convolutional neural networks by evaluating the digital pathology images for these cancers. Hereby, rendering artificial intelligence as useful technology in the near future. A total of 2500 digital images were acquired from LC25000 dataset containing 5000 images for each class. A shallow neural network architecture was used classify the histopathological slides into squamous cell carcinomas, adenocarcinomas and benign for the lung. Similar model was used to classify adenocarcinomas and benign for colon. The diagnostic accuracy of more than 97% and 96% was recorded for lung and colon respectively.

Sanidhya Mangal, Aanchal Chaurasia, Ayush Khajanchi


General General

Impact of extreme weather conditions on European crop production in 2018.

In Philosophical transactions of the Royal Society of London. Series B, Biological sciences

Extreme weather increases the risk of large-scale crop failure. The mechanisms involved are complex and intertwined, hence undermining the identification of simple adaptation levers to help improve the resilience of agricultural production. Based on more than 82 000 yield data reported at the regional level in 17 European countries, we assess how climate affected the yields of nine crop species. Using machine learning models, we analyzed historical yield data since 1901 and then focus on 2018, which has experienced a multiplicity and a diversity of atypical extreme climatic conditions. Machine learning models explain up to 65% of historical yield anomalies. We find that both extremes in temperature and precipitation are associated with negative yield anomalies, but with varying impacts in different parts of Europe. In 2018, Northern and Eastern Europe experienced multiple and simultaneous crop failures-among the highest observed in recent decades. These yield losses were associated with extremely low rainfalls in combination with high temperatures between March and August 2018. However, the higher than usual yields recorded in Southern Europe-caused by favourable spring rainfall conditions-nearly offset the large decrease in Northern European crop production. Our results outline the importance of considering single and compound climate extremes to analyse the causes of yield losses in Europe. We found no clear upward or downward trend in the frequency of extreme yield losses for any of the considered crops between 1990 and 2018. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

Beillouin Damien, Schauberger Bernhard, Bastos Ana, Ciais Phillipe, Makowski David


climate extremes, crop yields, drought, random forest, wheat, yield anomalies

General General

Multivariable times series classification through an interpretable representation

ArXiv Preprint

Multivariate time series classification is a task with increasing importance due to the proliferation of new problems in various fields (economy, health, energy, transport, crops, etc.) where a large number of information sources are available. Direct extrapolation of methods that traditionally worked in univariate environments cannot frequently be applied to obtain the best results in multivariate problems. This is mainly due to the inability of these methods to capture the relationships between the different variables that conform a multivariate time series. The multivariate proposals published to date offer competitive results but are hard to interpret. In this paper we propose a time series classification method that considers an alternative representation of time series through a set of descriptive features taking into account the relationships between the different variables of a multivariate time series. We have applied traditional classification algorithms obtaining interpretable and competitive results.

Francisco J. Baldán, José M. Benítez