MPCNN: A Novel Matrix Profile Approach for CNN-based Single Lead Sleep Apnea In Classification Problem.

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Deep Learning (DL) has emerged as an efficient tool for the classification problem in electrocardiogram (ECG)-based SA diagnoses. Despite these advancements, most common conventional feature extractions derived from ECG signals in DL, such as R-peaks and RR intervals, may fail to capture crucial information encompassed within the complete ECG segments. In this study, we propose an innovative approach to address this diagnostic gap by delving deeper into the comprehensive segments of the ECG signal. The proposed methodology draws inspiration from Matrix Profile algorithms, which generate an Euclidean distance profile from fixed-length signal subsequences. From this, we derived the Min Distance Profile (MinDP), Max Distance Profile (MaxDP), and Mean Distance Profile (MeanDP) based on the minimum, maximum, and mean of the profile distances, respectively. To validate the effectiveness of our approach, we use the modified LeNet-5 architecture as the primary CNN model, along with two existing lightweight models, BAFNet and SE-MSCNN. Our experiment results on the PhysioNet Apnea-ECG dataset (70 overnight recordings), and the UCDDB dataset (25 overnight recordings) revealed that our new feature extraction method achieved per-segment accuracies of up to 92.11% and 81.25%, respectively. Moreover, using the PhysioNet data, we achieved a per-recording accuracy of 100% and yielded the highest correlation of 0.989 compared to state-of-the-art methods. By introducing a new feature extraction method based on distance relationships, we enhanced the performance of certain lightweight models in DL, showing potential for home sleep apnea test (HSAT) and SA detection in IoT devices. The source code for this work is made publicly available in GitHub: https://github.com/vinuni-vishc/MPCNN-Sleep-Apnea.

The Chicken Egg: An Advanced Material for Tissue Engineering.

The chicken egg, an excellent natural source of proteins, has been an overlooked native biomaterial with remarkable physicochemical, structural, and biological properties. Recently, with significant advances in biomedical engineering, particularly in the development of 3D in vitro platforms, chicken egg materials have increasingly been investigated as biomaterials due to their distinct advantages such as their low cost, availability, easy handling, gelling ability, bioactivity, and provision of a developmentally stimulating environment for cells. In addition, the chicken egg and its by-products can improve tissue engraftment and stimulate angiogenesis, making it particularly attractive for wound healing and tissue engineering applications. Evidence suggests that the egg white (EW), egg yolk (EY), and eggshell membrane (ESM) are great biomaterial candidates for tissue engineering, as their protein composition resembles mammalian extracellular matrix proteins, ideal for cellular attachment, cellular differentiation, proliferation, and survivability. Moreover, eggshell (ES) is considered an excellent calcium resource for generating hydroxyapatite (HA), making it a promising biomaterial for bone regeneration. This review will provide researchers with a concise yet comprehensive understanding of the chicken egg structure, composition, and associated bioactive molecules in each component and introduce up-to-date tissue engineering applications of chicken eggs as biomaterials.

Differences in Oligomerization of the SARS-CoV-2 Envelope Protein, Poliovirus VP4, and HIV Vpu.

Viroporins constitute a class of viral membrane proteins with diverse roles in the viral life cycle. They can self-assemble and form pores within the bilayer that transport substrates, such as ions and genetic material, that are critical to the viral infection cycle. However, there is little known about the oligomeric state of most viroporins. Here, we use native mass spectrometry in detergent micelles to uncover the patterns of oligomerization of the full-length SARS-CoV-2 envelope (E) protein, poliovirus VP4, and HIV Vpu. Our data suggest that the E protein is a specific dimer, VP4 is exclusively monomeric, and Vpu assembles into a polydisperse mixture of oligomers under these conditions. Overall, these results revealed the diversity in the oligomerization of viroporins, which has implications for the mechanisms of their biological functions as well as their potential as therapeutic targets.

Ensemble learning of myocardial displacements for myocardial infarction detection in echocardiography.

Background: Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. Existing attempts typically formulate this task as classification and rely on a single segmentation model to estimate myocardial segment displacements. However, there has been no examination of how segmentation accuracy affects MI classification performance or the potential benefits of using ensemble learning approaches. Our study investigates this relationship and introduces a robust method that combines features from multiple segmentation models to improve MI classification performance by leveraging ensemble learning. Materials and Methods: Our method combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI. We validated the proposed approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for training and validation, and an E-Hospital dataset (60 echocardiograms) from a local clinical site in Vietnam for independent testing. Model performance was evaluated based on accuracy, sensitivity, and specificity. Results: The proposed approach demonstrated excellent performance in detecting MI. It achieved an F1 score of 0.942, corresponding to an accuracy of 91.4%, a sensitivity of 94.1%, and a specificity of 88.3%. The results showed that the proposed approach outperformed the state-of-the-art feature-based method, which had a precision of 85.2%, a specificity of 70.1%, a sensitivity of 85.9%, an accuracy of 85.5%, and an accuracy of 80.2% on the HMC-QU dataset. On the external validation set, the proposed model still performed well, with an F1 score of 0.8, an accuracy of 76.7%, a sensitivity of 77.8%, and a specificity of 75.0%. Conclusions: Our study demonstrated the ability to accurately predict MI in echocardiograms by combining information from several segmentation models. Further research is necessary to determine its potential use in clinical settings as a tool to assist cardiologists and technicians with objective assessments and reduce dependence on operator subjectivity. Our research codes are available on GitHub at https://github.com/vinuni-vishc/mi-detection-echo.

Differences in Oligomerization of the SARS-CoV-2 Envelope Protein, Poliovirus VP4, and HIV Vpu.

Viroporins constitute a class of viral membrane proteins with diverse roles in the viral life cycle. They can self-assemble and form pores within the bilayer that transport substrates, such as ions and genetic material, that are critical to the viral infection cycle. However, there is little known about the oligomeric state of most viroporins. Here, we use native mass spectrometry (MS) in detergent micelles to uncover the patterns of oligomerization of the full-length SARS-CoV-2 envelope (E) protein, poliovirus VP4, and HIV Vpu. Our data suggest that the E protein is a specific dimer, VP4 is exclusively monomeric, and Vpu assembles into a polydisperse mixture of oligomers under these conditions. Overall, these results revealed the diversity in the oligomerization of viroporins, which has implications for mechanisms of their biological functions as well as their potential as therapeutic targets.

VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography.

Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to support physicians and improve the accuracy of interpreting mammography. A number of large-scale mammography datasets from different populations with various associated annotations and clinical data have been introduced to study the potential of learning-based methods in the field of breast radiology. With the aim to develop more robust and more interpretable support systems in breast imaging, we introduce VinDr-Mammo, a Vietnamese dataset of digital mammography with breast-level assessment and extensive lesion-level annotations, enhancing the diversity of the publicly available mammography data. The dataset consists of 5,000 mammography exams, each of which has four standard views and is double read with disagreement (if any) being resolved by arbitration. The purpose of this dataset is to assess Breast Imaging Reporting and Data System (BI-RADS) and breast density at the individual breast level. In addition, the dataset also provides the category, location, and BI-RADS assessment of non-benign findings. We make VinDr-Mammo publicly available as a new imaging resource to promote advances in developing CADe/x tools for mammography interpretation.

An electrolyte additive for the improved high voltage performance of LiNi0.5Mn1.5O4 (LNMO) cathodes in Li-ion batteries.

High-voltage cathode materials are important for the implementation of high-energy-density Li-ion batteries. However, with increasing cut-off voltages, interfacial instabilities between electrodes and the electrolyte limit their commercial development. This study addresses this issue by proposing a new electrolyte additive, (3-aminopropyl)triethoxysilane (APTS). APTS stabilises the interface between the LiNi0.5Mn1.5O4 (LNMO) cathode and the electrolyte in LNMO‖Li half-cells due to its multifunctional character. The amino groups in APTS facilitate the formation of a robust protective cathode layer. Its silane groups improve layer stability by neutralising the electrolyte's detrimental hydrogen fluoride and water. Electrochemical measurements reveal that the addition of 0.5 wt% APTS significantly improves the long-term cycling stability of LNMO‖Li half-cells at room temperature and 55 °C. APTS-addition to the electrolyte delivers excellent capacity retention of 92% after 350 cycles at room temperature and 71% after 300 cycles at 55 °C (1C) contrasting with the much lower performances of the additive-free electrolyte. The addition of a 0.5 wt% (3-glycidyloxypropyl)trimethoxysilane (GLYMO) additive, which contains only the siloxane group, but lacks the amine group, displayed a capacity retention of 73% after 350 cycles at room temperature but degraded significantly upon cycling at 55 °C.

PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children.

Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and the advent of high-performance supervised learning algorithms. However, the development of diagnostic models for detecting and diagnosing pediatric diseases in CXR scans is undertaken due to the lack of high-quality physician-annotated datasets. To overcome this challenge, we introduce and release PediCXR, a new pediatric CXR dataset of 9,125 studies retrospectively collected from a major pediatric hospital in Vietnam between 2020 and 2021. Each scan was manually annotated by a pediatric radiologist with more than ten years of experience. The dataset was labeled for the presence of 36 critical findings and 15 diseases. In particular, each abnormal finding was identified via a rectangle bounding box on the image. To the best of our knowledge, this is the first and largest pediatric CXR dataset containing lesion-level annotations and image-level labels for the detection of multiple findings and diseases. For algorithm development, the dataset was divided into a training set of 7,728 and a test set of 1,397. To encourage new advances in pediatric CXR interpretation using data-driven approaches, we provide a detailed description of the PediCXR data sample and make the dataset publicly available on https://physionet.org/content/vindr-pcxr/1.0.0/ .

Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks.

The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. The 1D-CNN model (SEResNet18), which is trained on the digitized ECG signals, allows to identify between individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42% and 98.50% for classifying COVID-19 vs. Normal and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19.

Crop phenotype prediction using biclustering to explain genotype-by-environment interactions.

Phenotypic variation in plants is attributed to genotype (G), environment (E), and genotype-by-environment interaction (GEI). Although the main effects of G and E are typically larger and easier to model, the GEI interaction effects are important and a critical factor when considering such issues as to why some genotypes perform consistently well across a range of environments. In plant breeding, a major challenge is limited information, including a single genotype is tested in only a small subset of all possible test environments. The two-way table of phenotype responses will therefore commonly contain missing data. In this paper, we propose a new model of GEI effects that only requires an input of a two-way table of phenotype observations, with genotypes as rows and environments as columns that do not assume the completeness of data. Our analysis can deal with this scenario as it utilizes a novel biclustering algorithm that can handle missing values, resulting in an output of homogeneous cells with no interactions between G and E. In other words, we identify subsets of genotypes and environments where phenotype can be modeled simply. Based on this, we fit no-interaction models to predict phenotypes of a given crop and draw insights into how a particular cultivar will perform in the unused test environments. Our new methodology is validated on data from different plant species and phenotypes and shows superior performance compared to well-studied statistical approaches.

Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese.

Deep learning, in recent times, has made remarkable strides when it comes to impressive performance for many tasks, including medical image processing. One of the contributing factors to these advancements is the emergence of large medical image datasets. However, it is exceedingly expensive and time-consuming to construct a large and trustworthy medical dataset; hence, there has been multiple research leveraging medical reports to automatically extract labels for data. The majority of this labor, however, is performed in English. In this work, we propose a data collecting and annotation pipeline that extracts information from Vietnamese radiology reports to provide accurate labels for chest X-ray (CXR) images. This can benefit Vietnamese radiologists and clinicians by annotating data that closely match their endemic diagnosis categories which may vary from country to country. To assess the efficacy of the proposed labeling technique, we built a CXR dataset containing 9,752 studies and evaluated our pipeline using a subset of this dataset. With an F1-score of at least 0.9923, the evaluation demonstrates that our labeling tool performs precisely and consistently across all classes. After building the dataset, we train deep learning models that leverage knowledge transferred from large public CXR datasets. We employ a variety of loss functions to overcome the curse of imbalanced multi-label datasets and conduct experiments with various model architectures to select the one that delivers the best performance. Our best model (CheXpert-pretrained EfficientNet-B2) yields an F1-score of 0.6989 (95% CI 0.6740, 0.7240), AUC of 0.7912, sensitivity of 0.7064 and specificity of 0.8760 for the abnormal diagnosis in general. Finally, we demonstrate that our coarse classification (based on five specific locations of abnormalities) yields comparable results to fine classification (twelve pathologies) on the benchmark CheXpert dataset for general anomaly detection while delivering better performance in terms of the average performance of all classes.

A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms.

Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that the combination of multi-view analysis improved the overall breast exam classification. In this paper, we propose a novel multi-view DL approach for BI-RADS and density assessment of mammograms. The proposed approach first deploys deep convolutional networks for feature extraction on each view separately. The extracted features are then stacked and fed into a Light Gradient Boosting Machine (LightGBM) classifier to predict BI-RADS and density scores. We conduct extensive experiments on both the internal mammography dataset and the public dataset Digital Database for Screening Mammogra-phy (DDSM). The experimental results demonstrate that the proposed approach outperforms the single-view classification approach on two benchmark datasets by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM dataset). These results highlight the vital role of combining multi-view information to improve the performance of breast cancer risk prediction.

Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings.

Background: The purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to investigate the performance of the system in real-world clinical settings and compare its effectiveness to the in-lab performance. Method: The AI system was directly integrated into the Hospital's Picture Archiving and Communication System (PACS) after being trained on a fixed annotated dataset from other sources. The system's performance was prospectively measured by matching and comparing the AI results with the radiology reports of 6,285 chest X-ray examinations extracted from the Hospital Information System (HIS) over the last 2 months of 2020. The normal/abnormal status of a radiology report was determined by a set of rules and served as the ground truth. Results: Our system achieves an F1 score-the harmonic average of the recall and the precision-of 0.653 (95% CI 0.635, 0.671) for detecting any abnormalities on chest X-rays. This corresponds to an accuracy of 79.6%, a sensitivity of 68.6%, and a specificity of 83.9%. Conclusions: Computer-Aided Diagnosis (CAD) systems for chest radiographs using artificial intelligence (AI) have recently shown great potential as a second opinion for radiologists. However, the performances of such systems were mostly evaluated on a fixed dataset in a retrospective manner and, thus, far from the real performances in clinical practice. Despite a significant drop from the in-lab performance, our result establishes a reasonable level of confidence in applying such a system in real-life situations.

VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations.

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set.

High-throughput image-based plant stand count estimation using convolutional neural networks.

The landscape of farming and plant breeding is rapidly transforming due to the complex requirements of our world. The explosion of collectible data has started a revolution in agriculture to the point where innovation must occur. To a commercial organization, the accurate and efficient collection of information is necessary to ensure that optimal decisions are made at key points of the breeding cycle. In particular, recent technology has enabled organizations to capture in-field images of crops to record color, shape, chemical properties, and disease susceptibility. However, this new challenge necessitates the need for advanced algorithms to accurately identify phenotypic traits. This work, advanced the current literature by developing an innovative deep learning algorithm, named DeepStand, for image-based counting of corn stands at early phenological stages. The proposed method adopts a truncated VGG-16 network to act as a feature extractor backbone. We then combine multiple feature maps with different dimensions to ensure the network is robust against size variation. Our extensive computational experiments demonstrate that our DeepStand framework accurately identifies corn stands and out-performs other cutting-edge methods.

Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling.

PURPOSE: A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires an accurate classification of the phases. Current approaches to classify the CT phases are commonly based on three-dimensional (3D) convolutional neural network (CNN) approaches with high computational complexity and high latency. This work aims at developing and validating a precise, fast multiphase classifier to recognize three main types of contrast phases in abdominal CT scans. METHODS: We propose in this study a novel method that uses a random sampling mechanism on top of deep CNNs for the phase recognition of abdominal CT scans of four different phases: noncontrast, arterial, venous, and others. The CNNs work as a slicewise phase prediction, while random sampling selects input slices for the CNN models. Afterward, majority voting synthesizes the slicewise results of the CNNs to provide the final prediction at the scan level. RESULTS: Our classifier was trained on 271 426 slices from 830 phase-annotated CT scans, and when combined with majority voting on 30% of slices randomly chosen from each scan, achieved a mean F1 score of 92.09% on our internal test set of 358 scans. The proposed method was also evaluated on two external test sets: CTPAC-CCRCC (N = 242) and LiTS (N = 131), which were annotated by our experts. Although a drop in performance was observed, the model performance remained at a high level of accuracy with a mean F1 scores of 76.79% and 86.94% on CTPAC-CCRCC and LiTS datasets, respectively. Our experimental results also showed that the proposed method significantly outperformed the state-of-the-art 3D approaches while requiring less computation time for inference. CONCLUSIONS: In comparison to state-of-the-art classification methods, the proposed approach shows better accuracy with significantly reduced latency. Our study demonstrates the potential of a precise, fast multiphase classifier based on a two-dimensional deep learning approach combined with a random sampling method for contrast phase recognition, providing a valuable tool for extracting multiphase abdomen studies from low veracity, real-world data.

Experimental immune challenges reduce the quality of male antennae and female pheromone output.

Sexual signalling is a key feature of reproductive investment, yet the effects of immune system activation on investment into chemical signalling, and especially signal receiver traits such as antennae, are poorly understood. We explore how upregulation of juvenile immunity affects male antennal functional morphology and female pheromone attractiveness in the gumleaf skeletonizer moth, Uraba lugens. We injected final-instar larvae with a high or low dose of an immune elicitor or a control solution and measured male antennal morphological traits, gonad investment and female pheromone attractiveness. Immune activation affected male and female signalling investment: immune challenged males had a lower density of antennal sensilla, and the pheromone of immune-challenged females was less attractive to males than their unchallenged counterparts. Immune challenge affected female investment into ovary development but not in a linear, dose-dependent manner. While there was no effect of immune challenge on testes size, there was a trade-off between male pre- and post-copulatory investment: male antennal length was negatively correlated with testes size. Our study highlights the costs of elaborate antennae and pheromone production and demonstrates the capacity for honest signalling in species where the costs of pheromone production were presumed to be trivial.

Dynamic Programming for Resource Allocation in Multi-Allelic Trait Introgression.

Trait introgression is a complex process that plant breeders use to introduce desirable alleles from one variety or species to another. Two of the major types of decisions that must be made during this sophisticated and uncertain workflow are: parental selection and resource allocation. We formulated the trait introgression problem as an engineering process and proposed a Markov Decision Processes (MDP) model to optimize the resource allocation procedure. The efficiency of the MDP model was compared with static resource allocation strategies and their trade-offs among budget, deadline, and probability of success are demonstrated. Simulation results suggest that dynamic resource allocation strategies from the MDP model significantly improve the efficiency of the trait introgression by allocating the right amount of resources according to the genetic outcome of previous generations.

Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning.

Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields. We propose a new convolutional neural network model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. We conduct our experiment using data from 1132 counties for corn and 1076 counties for soybean across the United States. Numerical results demonstrate that our proposed method accurately predicts corn and soybean yield from one to four months before the harvest with an MAE being 8.74% and 8.70% of the average yield, respectively, and is competitive to other state-of-the-art approaches.