Automating selective area electron diffraction phase identification using machine learning

Astrocytes undergo activation during brain injury through an ill‐defined process known as gliosis. Recent work focused on elucidating the molecular underpinnings of glial response to injury. However, astrocytes exist in a unique three‐dimensional space within the brain, and this location is highly relevant to the astrocyte’s function. Therefore, generating neuroanatomical assays, which maintain CNS positional information of astrocytes relative to their neural networks, represent optimal modalities to assess astrocyte function and reactivity. The majority of astrocytic neuroanatomical assays in the scientific literature measure mean fluorescence intensity of GFAP or ALDH1L1. Modern advances in computer vision and machine learning algorithms have now given us the tools with which we can extract a multitude of features from microscopic imaging data. By segmenting astrocytes from an image into numerous GFAP or ALDH1L1 positive “objects”, extracted numerical values indicate object location, object intensities, object shapes, as well as object textures from the original image. We tested the hypothesis that morphological changes in astrocyte reaction to injury represents a heterogeneous response in different brain regions. To test this hypothesis, we developed an automated segmentation algorithm for both GFAP and ALDH1L1 immunostained mouse brain sections treated with vehicle or systemic lipopolysaccharide to induce neuroinflammation. These images were then captured on a laser scanning confocal microscope and features characterizing shape, fluorescent intensity, and fluorescent texture. We then utilized a covariance matrix, random forest‐based boruta algorithm, cluster analysis and principal component analysis to evaluate the differences between reactive astrocytes and baseline astrocytes. By implementing a support vector machine algorithm, we show classification identification methods of reactive versus baseline astrocytes. Machine learning models developed to classify neuroanatomical images provides a highly insightful methodology to discover new processes by which astrocytes react to injury. Support or Funding Information 3R01HL132355

It is a known fact that companies strive to cut their production costs. One of the most effective ways to reduce costs is to automate processes, for example, replacing employees with various robots or information systems. Often, at a construction site or a large-scale production, it is necessary to monitor employees' work. In its simplest form, monitoring can be carried out with the help of video cameras and people who view recordings from these cameras. However, this entails labor costs, taxes and other expenses. So, what one can do instead is introduce a system that tracks movements of workers, which will likely reduce the costs described above. To this day, machine learning and computer vision algorithms have developed quite strongly and allow the implementation of such tracking systems. There are already ready-made versions of tracking systems based on computer vision algorithms, but they are expensive, which makes them suitable only for large-scale industries. Another option is to develop your own tracking system, taking into account the nature of the business in which you plan to implement it. Aim. The authors aim at analyzing technologies that can be used to develop a system for tracking workers’ movements and proposing an implementation of such a system using machine learning algorithms and neural networks. Materials and methods. The authors give an overview of current solutions of monitoring systems based on RFID techno-logies, computer vision, GPS, and motion sensors, highlighting their pros and cons. The authors consider existing methods for motion detection based on computer vision and review known algorithms for detecting workers in special clothing. The YOLOv5 algorithm, consisting of backbone, neck and head, is considered in detail. Results. In the article, the authors provide a description of algorithms used for machine learning, computer vision, and neural networks. They propose an implementation of a system monitoring workers’ movements. They describe the modules that make up the tracking system and a diagram of the interaction of the modules, as well as the development of a motion detection algorithm and an algorithm for determining workers in special clothing.

BackgroundDespite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.MethodsPUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).ResultsOf 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).ConclusionsThe ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms

The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5,048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single-institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than 0.7, which indicates clinical utility. ML algorithms have the potential to predict postoperative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared with other ML algorithms. Biochemical and preoperative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model–agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.

Computer Vision and Machine Learning are two branches of Artificial Intelligence which in combination have granted computational systems the ability of artificial vision and perception. Nowadays, human-machine interaction and the analysis of human behavior can be achieved using cameras. The subject of the current thesis is the implementation of a system for automatic recognition of static hand gestures in real-time videos with cameras, using modern techniques and algorithms of Machine Learning and Deep Learning. To begin with, we make an introduction into Computer Vision and its applications and we describe the problem of hand gesture recognition. Next, we refer to the field of Machine Learning and the main categories of the problems it includes. We describe the methodology of training a model and ways to measure its performance, focusing on Classification problems. Also, we introduce Deep Learning and Artificial Neural Networks. We present the most common network architectures used nowadays and the procedure of training a Neural Network and methods for optimization. Also, we mention the use of Convolutional Neural Networks for solving Computer Vision problems and we present some of the most known algorithms used for this task. Finally, we present our implementation of the hand gesture recognition tool, using Python programming language. We apply innovative algorithms of Convolutional Neural Networks for the detection of several key points of the hand and we train a Support Vector Machine and a Feedforward Neural Network model for the classification of those points into several predetermined static gestures. The implementation is targeted for practical real-time applications, runs using the CPU of the computer and requires a common RGB camera.

Most computer techniques used to index Selected Area electron Diffraction (SAD) patterns deal with simple patterns in the sense of there being present a single linear array of two reciprocal lattice vectors. If more than one crystal orientation contributes to the pattern, the investigator must judiciously pick and choose pairs of reflections (or groups of reflections if measuring errors are to be minimized) in an effort to sort out which reflections belong to what orientation. Even further complicating the issue is the usual desire to establish the orientational relationship between the different diffracting materials. These problems have been addressed at our laboratories by creating a computer code to isolate and identify overlapping SAD patterns.The basis of our analysis rests upon the fact that each crystal orientation giving rise to an SAD pattern is but a twodimensional sectioning of a much larger three-dimensional reciprocal lattice. At 100 keV operation, the reciprocal lattice vectors associated with each reflection are, to a good approximation, parallel to the vector from the diffraction pattern origin to the reflection in question.

Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN), Naive Bayes Classifier (NBC), and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.

This paper reviews objective methods for prognostic modelling of cancer tumours located within radiology images, a process known as radiomics. Radiomics is a novel feature transformation method for detecting clinically relevant features from radiological imaging data that are difficult for the human eye to perceive. To facilitate the detection machine learning and deep learning methods are increasingly investigated with the aim of improving patient diagnosis, treatment options and outcomes. A review of the relevant works in the expanding field of radiomics for survival prediction from cancer is provided. Research works outside the field of radiomics which define techniques that may be of future use to improve feature extraction and analysis are also reviewed. Radiomics is a rapidly advancing field of clinical image analysis with a vast potential for supporting decision making involved in the diagnosis and treatment of cancer. The realisation of this goal of more effective decision making requires significant individual and integrated expertise from domain experts in medicine, biology and computer science to allow advances in computer vision and machine learning techniques to be applied effectively. Deep learning combined with machine learning has the potential to advance the field of radiomics significantly in the years to come, provided that mechanisms for data sharing or distributed learning are established to increase the availability of data across all patient and tumour types.

Adapting intelligent context-aware systems (CAS) to future operating rooms (OR) aims to improve situational awareness and provide surgical decision support systems to medical teams. CAS analyzes data streams from available devices during surgery and communicates real-time knowledge to clinicians. Indeed, recent advances in computer vision and machine learning, particularly deep learning, paved the way for extensive research to develop CAS. In this work, a deep learning approach for analyzing laparoscopic videos for surgical phase recognition, tool classification, and weakly-supervised tool localization in laparoscopic videos was proposed. The ResNet-50 convolutional neural network (CNN) architecture was adapted by adding attention modules and fusing features from multiple stages to generate better-focused, generalized, and well-representative features. Then, a multi-map convolutional layer followed by tool-wise and spatial pooling operations was utilized to perform tool localization and generate tool presence confidences. Finally, the long short-term memory (LSTM) network was employed to model temporal information and perform tool classification and phase recognition. The proposed approach was evaluated on the Cholec80 dataset. The experimental results (i.e., 88.5% and 89.0% mean precision and recall for phase recognition, respectively, 95.6% mean average precision for tool presence detection, and a 70.1% F1-score for tool localization) demonstrated the ability of the model to learn discriminative features for all tasks. The performances revealed the importance of integrating attention modules and multi-stage feature fusion for more robust and precise detection of surgical phases and tools.

To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models—a subcategory of macro-element models—are a common choice because of their low computational cost. However, an existing bottleneck in modeling pipelines is generating the geometry of the model, which is currently a slow and laborious process that is done manually using computer-aided design tools. In this paper, we address this by automating the modelling process using recent advancements in computer vision and machine learning. We present an image-based end-to-end pipeline that automatically generates finite element meshes for solid-element and equivalent-frame models of the outer walls of free-standing historical masonry buildings. As the input, our framework requires RGB images of the buildings that are processed using structure-from-motion algorithms, which create 3D geometries, and convolutional neural networks, which segment the openings and their corners. These layers are then combined to generate level of detail models. We tested our pipeline on structures with irregular surface geometries and opening layouts. While generating the solid element mesh from the level of detail model is straightforward, generating equivalent frame models required algorithms for segmenting the façade and the meshing. Experts in the field analyzed the generated equivalent frame models and determined them to be useful for numerical modeling. These finite element geometries will be invaluable for future predictions of the seismic response of damaged and undamaged buildings. The codes and dataset are publicly available for future studies and benchmarking (https://github.com/eesd-epfl/FEM_buildings and https://doi.org/10.5281/zenodo.8094306).

Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors. Furthermore, thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. However, Deep Neural Networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed on-board. In this paper, we propose a DNN distribution methodology within UAVs to enable data classification in resource-constrained devices and avoid extra delays introduced by the server-based solutions due to data communication over air-to-ground links. The proposed method is formulated as an optimization problem that aims to minimize the latency between data collection and decision-making while considering the mobility model and the resource constraints of the UAVs as part of the air-to-air communication. We also introduce the mobility prediction to adapt our system to the dynamics of UAVs and the network variation. The simulation conducted to evaluate the performance and benchmark the proposed methods, namely Optimal UAV-based Layer Distribution (OULD) and OULD with Mobility Prediction (OULD-MP), were run in an HPC cluster. The obtained results show that our optimization solution outperforms the existing and heuristic-based approaches.

Selected area electron diffraction and energy dispersive X-ray analysis for the identification of asbestos fibres, a comparison

Weed diversity plays an important role in the functioning of agroecosystems. Moreover, a number of endangered/threatened plant species occur as weeds in arable fields and/or field boundaries. Agricultural intensification has imposed negative consequences on weed diversity in general, and the survival of the endangered/threatened plant species in particular. The objective of this review is to provide a theoretical framework for promoting cropland weed diversity through precision agriculture. A systematic review was conducted based on literature analysis, existing knowledge gaps, and current needs to identify a suitable approach for promoting cropland biodiversity while protecting crop yields. While nonchemical weed management methods and economic threshold–based approaches are touted to improve weed diversity, they are either ineffective or insufficient for this purpose; long-term economic consequences and the risk of weed adaptation are major concerns. A plant functional trait-based approach to promoting weed diversity, one that considers a plant’s ecosystem service potential and competitiveness with the crop, among other factors, has been proposed by researchers. This approach has tremendous potential for weed diversity conservation in commercial production systems, but field implementation has been limited thus far due to our inability to selectively control weeds at the individual-plant level. However, recent advancements in computer vision, machine learning, and site-specific weed management technologies may allow for the accurate elimination of unwanted plants while retaining the important ones. Here, we present a novel framework for the utilization of precision agriculture for the conservation of cropland weed diversity, including the protection of endangered/threatened plant species, while protecting crop yields. This approach is the first of its kind in which the control priority is ranked on an individual-plant basis, by integrating intrinsic weed trait values with field infestation characteristics, while management thresholds are tailored to specific goals and priorities.

Queuing theory guided intelligent traffic scheduling through video analysis using Dirichlet process mixture model