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General General

Third-order nanocircuit elements for neuromorphic engineering.

In Nature ; h5-index 368.0

Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that naturally express neuromorphic nonlinear dynamics1-4. Generating neuromorphic action potentials in a circuit element theoretically requires a minimum of third-order complexity (for example, three dynamical electrophysical processes)5, but there have been few examples of second-order neuromorphic elements, and no previous demonstration of any isolated third-order element6-8. Using both experiments and modelling, here we show how multiple electrophysical processes-including Mott transition dynamics-form a nanoscale third-order circuit element. We demonstrate simple transistorless networks of third-order elements that perform Boolean operations and find analogue solutions to a computationally hard graph-partitioning problem. This work paves a way towards very compact and densely functional neuromorphic computing primitives, and energy-efficient validation of neuroscientific models.

Kumar Suhas, Williams R Stanley, Wang Ziwen


Radiology Radiology

Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients.

In Scientific reports ; h5-index 158.0

Intensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs before any quantitative analysis because signal intensities (SI) do not have standardized units. Radiomics combine quantification of tumors' radiological phenotype with machine-learning to improve predictive models, such as metastastic-relapse-free survival (MFS) for sarcoma patients. We post-processed the initial T2-weighted-imaging of 70 sarcoma patients by using 5 IHTs and extracting 45 radiomics features (RFs), namely: classical standardization (IHTstd), standardization per adipose tissue SIs (IHTfat), histogram-matching with a patient histogram (IHTHM.1), with the average histogram of the population (IHTHM.All) and plus ComBat method (IHTHM.All.C), which provided 5 radiomics datasets in addition to the original radiomics dataset without IHT (No-IHT). We found that using IHTs significantly influenced all RFs values (p-values: < 0.0001-0.02). Unsupervised clustering performed on each radiomics dataset showed that only clusters from the No-IHT, IHTstd, IHTHM.All, and IHTHM.All.C datasets significantly correlated with MFS in multivariate Cox models (p = 0.02, 0.007, 0.004 and 0.02, respectively). We built radiomics-based supervised models to predict metastatic relapse at 2-years with a training set of 50 patients. The models performances varied markedly depending on the IHT in the validation set (range of AUROC from 0.688 with IHTstd to 0.823 with IHTHM.1). Hence, the use of intensity harmonization and the related technique should be carefully detailed in radiomics post-processing pipelines as it can profoundly affect the reproducibility of analyses.

Crombé Amandine, Kind Michèle, Fadli David, Le Loarer François, Italiano Antoine, Buy Xavier, Saut Olivier


Pathology Pathology

Automated thermal imaging for the detection of fatty liver disease.

In Scientific reports ; h5-index 158.0

Non-alcoholic fatty liver disease (NAFLD) comprises a spectrum of progressive liver pathologies, ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis and cirrhosis. A liver biopsy is currently required to stratify high-risk patients, and predicting the degree of liver inflammation and fibrosis using non-invasive tests remains challenging. Here, we sought to develop a novel, cost-effective screening tool for NAFLD based on thermal imaging. We used a commercially available and non-invasive thermal camera and developed a new image processing algorithm to automatically predict disease status in a small animal model of fatty liver disease. To induce liver steatosis and inflammation, we fed C57/black female mice (8 weeks old) a methionine-choline deficient diet (MCD diet) for 6 weeks. We evaluated structural and functional liver changes by serial ultrasound studies, histopathological analysis, blood tests for liver enzymes and lipids, and measured liver inflammatory cell infiltration by flow cytometry. We developed an image processing algorithm that measures relative spatial thermal variation across the skin covering the liver. Thermal parameters including temperature variance, homogeneity levels and other textural features were fed as input to a t-SNE dimensionality reduction algorithm followed by k-means clustering. During weeks 3,4, and 5 of the experiment, our algorithm demonstrated a 100% detection rate and classified all mice correctly according to their disease status. Direct thermal imaging of the liver confirmed the presence of changes in surface thermography in diseased livers. We conclude that non-invasive thermal imaging combined with advanced image processing and machine learning-based analysis successfully correlates surface thermography with liver steatosis and inflammation in mice. Future development of this screening tool may improve our ability to study, diagnose and treat liver disease.

Brzezinski Rafael Y, Levin-Kotler Lapaz, Rabin Neta, Ovadia-Blechman Zehava, Zimmer Yair, Sternfeld Adi, Finchelman Joanna Molad, Unis Razan, Lewis Nir, Tepper-Shaihov Olga, Naftali-Shani Nili, Balint-Lahat Nora, Safran Michal, Ben-Ari Ziv, Grossman Ehud, Leor Jonathan, Hoffer Oshrit


General General

Machine learning-driven electronic identifications of single pathogenic bacteria.

In Scientific reports ; h5-index 158.0

A rapid method for screening pathogens can revolutionize health care by enabling infection control through medication before symptom. Here we report on label-free single-cell identifications of clinically-important pathogenic bacteria by using a polymer-integrated low thickness-to-diameter aspect ratio pore and machine learning-driven resistive pulse analyses. A high-spatiotemporal resolution of this electrical sensor enabled to observe galvanotactic response intrinsic to the microbes during their translocation. We demonstrated discrimination of the cellular motility via signal pattern classifications in a high-dimensional feature space. As the detection-to-decision can be completed within milliseconds, the present technique may be used for real-time screening of pathogenic bacteria for environmental and medical applications.

Hattori Shota, Sekido Rintaro, Leong Iat Wai, Tsutsui Makusu, Arima Akihide, Tanaka Masayoshi, Yokota Kazumichi, Washio Takashi, Kawai Tomoji, Okochi Mina


Public Health Public Health

The Effects of Obesity-Related Anthropometric Factors on Cardiovascular Risks of Homeless Adults in Taiwan.

In International journal of environmental research and public health ; h5-index 73.0

Homelessness is a pre-existing phenomenon in society and an important public health issue that national policy strives to solve. Cardiovascular disease (CVD) is an important health problem of the homeless. This cross-sectional study explored the effects of four obesity-related anthropometric factors-body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR)-on cardiovascular disease risks (expressed by three CVD markers: hypertension, hyperglycemia, and hyperlipidemia) among homeless adults in Taipei and compared the relevant results with ordinary adults in Taiwan. The research team sampled homeless adults over the age of 20 in Taipei City in 2018 and collected 297 participants. Through anthropometric measurements, blood pressure measurements, and blood tests, we calculated the obesity-related indicators of the participants and found those at risks of cardiovascular disease. The results showed that the prevalence of hypertension, hyperglycemia, and hyperlipidemia in homeless adults was significantly higher than that of ordinary adults in Taiwan. Among the four obesity-related indicators, WHtR showed the strongest association with the prevalence of hypertension and hyperlipidemia, followed by WHR, both of which showed stronger association than traditional WC and BMI indicators. It can be inferred that abdominal obesity characterized by WHtR is a key risk factor for hypertension and hyperlipidemia in homeless adults in Taiwan. We hope that the results will provide medical clinical references and effectively warn of cardiovascular disease risks for the homeless in Taiwan.

Chen Ching-Lin, Chen Mingchih, Liu Chih-Kuang


BMI, WC, WHR, WHtR, cardiovascular risk, homeless adults

General General

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

In Animals : an open access journal from MDPI

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.

Cockburn Marianne


big data, cluster, data analysis, data integration, sensor, smart farming