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General General

WASCO: A Wasserstein-based statistical tool to compare conformational ensembles of intrinsically disordered proteins.

In Journal of molecular biology ; h5-index 65.0

The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, one ensemble being characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the interest of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations, and discuss other potential applications such as in machine-learning-based approaches. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO.

González-Delgado Javier, Sagar Amin, Zanon Christophe, Lindorff-Larsen Kresten, Bernadó Pau, Neuvial Pierre, Cortés Juan

2023-Mar-17

General General

Fully nondestructive analysis of capsaicinoids electrochemistry data with deep neural network enables portable system.

In Food chemistry

Electrochemical methods have been extensively applied for the detection of chemical information from food or other analytes. However, existing electrochemical methods are limited to focusing solely on the absorption peaks and disregard much of the hidden chemical fingerprint information. Consequently, electrochemical sensors are constrained by their ability to detect samples containing multiple source-material mixtures with overlapping constituents. We hypothesized that the target substances can be effectively identified and detected using differential sensor data combined with artificial intelligence (AI). In this study, we developed a novel signal array composed of five metal electrodes and used a convolutional neural network (CNN) model for feature extraction to detect capsaicinoids in stews. Our results indicate that the proposed method achieved satisfactory predictions with a root mean square error (RMSE) of 5.407 in independent brine samples. This provides a promising strategy and practical approach for the nondestructive analysis of multidimensional electrochemical data of mixed analytes.

Xing Zheng, Jiang Ying, Zogona Daniel, Wu Ting, Xu Xiaoyun

2023-Mar-07

Capsaicinoids, Deep learning, Portable platform, Quantitative, Sensor arrays

General General

Global synchronization of complex-valued neural networks with unbounded time-varying delays.

In Neural networks : the official journal of the International Neural Network Society

This paper investigates global synchronization of complex-valued neural networks (CVNNs) with unbounded time-varying delays. By applying analytical method and inequality techniques, an algebraic criterion is established to ensure global synchronization of the CVNNs via a devised feedback controller, which generalizes some existing outcomes. Finally, two numerical simulations and one application in image encryption are provided to verify the effectiveness of the theoretical results.

Sheng Yin, Gong Haoyu, Zeng Zhigang

2023-Mar-06

Complex-valued neural networks, Global synchronization, Unbounded time-varying delays

Public Health Public Health

Artificial intelligence for secondary prevention of myocardial infarction: A qualitative study of patient and health professional perspectives.

In International journal of medical informatics ; h5-index 49.0

BACKGROUND : Artificial intelligence (AI) has potential to improve self-management of several chronic conditions. However, the perspective of patients and healthcare professionals regarding AI-enabled health management programs, which are key to successful implementation, remains poorly understood.

PURPOSE : To explore the opinions of people with a history of myocardial infarction (PHMI) and health professionals on the use of AI for secondary prevention of MI.

PROCEDURE : Three rounds of focus groups were conducted via videoconferencing with 38 participants: 22 PHMI and 16 health professionals.

FINDINGS : We identified 21 concepts stemming from participants' views, which we classified into five categories: Trust; Expected Functions; Adoption; Concerns; and Perceived Benefits. Trust covered the credibility of information and safety to believe health advice. Expected Functions covered tailored feedback and personalised advice. Adoption included usability features and overall interest in AI. Concerns originated from previous negative experience with AI. Perceived Benefits included the usefulness of AI to provide advice when regular contact with healthcare services is not feasible. Health professionals were more optimistic than PHMI about the usefulness of AI for improving health behaviour.

CONCLUSIONS : Altogether, our findings provide key insights from end-users to improve the likelihood of successful implementation and adoption of AI-enabled systems in the context of MI, as an exemplar of broader applications in chronic disease management.

Pelly Melissa, Fatehi Farhad, Liew Danny, Verdejo-Garcia Antonio

2023-Mar-14

Artificial intelligence, Co-design, Myocardial infarction, Perspectives, Qualitative study, Secondary prevention

General General

Deep learning pose estimation for multi-cattle lameness detection.

In Scientific reports ; h5-index 158.0

The objective of this study was to develop a fully automated multiple-cow real-time lameness detection system using a deep learning approach for cattle detection and pose estimation that could be deployed across dairy farms. Utilising computer vision and deep learning, the system can analyse simultaneously both the posture and gait of each cow within a camera field of view to a very high degree of accuracy (94-100%). Twenty-five video sequences containing 250 cows in varying degrees of lameness were recorded and independently scored by three accredited Agriculture and Horticulture Development Board (AHDB) mobility scorers using the AHDB dairy mobility scoring system to provide ground truth lameness data. These observers showed significant inter-observer reliability. Video sequences were broken down into their constituent frames and with a further 500 images downloaded from google, annotated with 15 anatomical points for each animal. A modified Mask-RCNN estimated the pose of each cow to output 5 key-points to determine back arching and 2 key-points to determine head position. Using the SORT (simple, online, and real-time tracking) algorithm, cows were tracked as they move through frames of the video sequence (i.e., in moving animals). All the features were combined using the CatBoost gradient boosting algorithm with accuracy being determined using threefold cross-validation including recursive feature elimination. Precision was assessed using Cohen's kappa coefficient and assessments of precision and recall. This methodology was applied to cows with varying degrees of lameness (according to accredited scoring, n = 3) and demonstrated that some characteristics directly associated with lameness could be monitored simultaneously. By combining the algorithm results over time, more robust evaluation of individual cow lameness was obtained. The model showed high performance for predicting and matching the ground truth lameness data with the outputs of the algorithm. Overall, threefold lameness detection accuracy of 100% and a lameness severity classification accuracy of 94% respectively was achieved with a high degree of precision (Cohen's kappa = 0.8782, precision = 0.8650 and recall = 0.9209).

Barney Shaun, Dlay Satnam, Crowe Andrew, Kyriazakis Ilias, Leach Matthew

2023-Mar-18

Radiology Radiology

Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis

ArXiv Preprint

While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail. In this paper, we progress into the realms of large-scale modeling in medical synthesis by proposing Cheff - a foundational cascaded latent diffusion model, which generates highly-realistic chest radiographs providing state-of-the-art quality on a 1-megapixel scale. We further propose MaCheX, which is a unified interface for public chest datasets and forms the largest open collection of chest X-rays up to date. With Cheff conditioned on radiological reports, we further guide the synthesis process over text prompts and unveil the research area of report-to-chest-X-ray generation.

Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

2023-03-20