Sci Rep. 2024 Dec 3;14(1):30052. doi: 10.1038/s41598-024-81456-1.

ABSTRACT

Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. This research presents a new method for improving CRC detection using a deep learning ensemble with the Computer Aided Diagnosis (CADx). The method involves combining pre-trained convolutional neural network (CNN) models, such as ADaRDEV2I-22, DaRD-22, and ADaDR-22, using Vision Transformers (ViT) and XGBoost. The study addresses the challenges associated with imbalanced datasets and the necessity of sophisticated feature extraction in medical image analysis. Initially, the CKHK-22 dataset comprised 24 classes. However, we refined it to 14 classes, which led to an improvement in data balance and quality. This improvement enabled more precise feature extraction and improved classification results. We created two ensemble models: the first model used Vision Transformers to capture long-range spatial relationships in the images, while the second model combined CNNs with XGBoost to facilitate structured data classification. We implemented DCGAN-based augmentation to enhance the dataset's diversity. The tests showed big improvements in performance, with the ADaDR-22 + Vision Transformer group getting the best results, with a testing accuracy of 93.4% and an AUC of 98.8%. In contrast, the ADaDR-22 + XGBoost model had an AUC of 97.8% and an accuracy of 92.2%. These findings highlight the efficacy of the proposed ensemble models in detecting CRC and highlight the importance of using well-balanced, high-quality datasets. The proposed method significantly enhances the clinical diagnostic accuracy and the capabilities of medical image analysis or early CRC detection.

PMID:39627293 | DOI:10.1038/s41598-024-81456-1

Phys Med. 2024 Dec 2;129:104862. doi: 10.1016/j.ejmp.2024.104862. Online ahead of print.

ABSTRACT

BACKGROUND/INTRODUCTION: To evaluate the performance of pre-trained deep learning schemes (DLS) in hepatic steatosis (HS) grading of Non-Alcoholic Fatty Liver Disease (NAFLD) patients, using as input B-mode US images containing right kidney (RK) cortex and liver parenchyma (LP) areas indicated by an expert radiologist.

METHODS: A total of 112 consecutively enrolled, biopsy-validated NAFLD patients underwent a regular abdominal B-mode US examination. For each patient, a radiologist obtained a B-mode US image containing RK cortex and LP and marked a point between the RK and LP, around which a window was automatically cropped. The cropped image dataset was augmented using up-sampling, and the augmented and non-augmented datasets were sorted by HS grade. Each dataset was split into training (70%) and testing (30%), and fed separately as input to InceptionV3, MobileNetV2, ResNet50, DenseNet201, and NASNetMobile pre-trained DLS. A receiver operating characteristic (ROC) analysis of hepatorenal index (HRI) measurements by the radiologist from the same cropped images was used for comparison with the performance of the DLS.

RESULTS: With the test data, the DLS reached 89.15 %-93.75 % accuracy when comparing HS grades S0-S1 vs. S2-S3 and 79.69 %-91.21 % accuracy for S0 vs. S1 vs. S2 vs. S3 with augmentation, and 80.45-82.73 % accuracy when comparing S0-S1 vs. S2-S3 and 59.54 %-63.64 % accuracy for S0 vs. S1 vs. S2 vs. S3 without augmentation. The performance of radiologists' HRI measurement after ROC analysis was 82 %, 91.56 %, and 96.19 % for thresholds of S ≥ S1, S ≥ S2, and S = S3, respectively.

CONCLUSION: All networks achieved high performance in HS assessment. DenseNet201 with the use of augmented data seems to be the most efficient supplementary tool for NAFLD diagnosis and grading.

PMID:39626614 | DOI:10.1016/j.ejmp.2024.104862

Int J Med Inform. 2024 Nov 29;195:105737. doi: 10.1016/j.ijmedinf.2024.105737. Online ahead of print.

ABSTRACT

BACKGROUND: Type 2 diabetes (T2D) has become a serious health threat globally. However, the existing approaches for diabetes prediction mainly had difficulty in addressing multiple time-series features. This study aims to provide an adjunctive tool for the clinical identification of patients in physician-pharmacist collaborative clinics at high risk of poor prognosis.

METHODS: This study proposes a novel interpretable time-series Neural Turing Machine (ITS-NTM) to form patient characteristics into feature matrixes to simulate one's disease and treatment process, predicting the prognosis of patients with T2D and alerting early interventions. Model robustness was verified by 10-fold cross-validation, external validation and multi-model comparisons. We also conducted dynamic prediction and feature importance analysis to explore its interpretability.

RESULTS: The study population included patients with T2D attending physician-pharmacist collaborative clinics over 12 months in primary healthcare centers, while clinical features and behavioral indicators at baseline, 3rd, 6th, 9th and 12th months were used to reflect the fluctuation of disease control over time. Compared with five state-of-the-art prediction models, the ITS-NTM obtains 92.0 % in accuracy and 91.8 % F1-score, demonstrating the superiority performance. Feature importance demonstrated that the top 5 features were glycosylated hemoglobin, fasting blood glucose, medication adherence scores, 2-hour postprandial blood glucose and waist-to-hip ratio, which had the greatest impact on the performance of the predictive model.

CONCLUSIONS: Proposed ITS-NTM could be used to promote the implementation of physician-pharmacist collaborative clinics, and further prompt the application of artificial intelligence to optimize the allocation of medical resources and improve the quality of care in under-resourced areas.

PMID:39626597 | DOI:10.1016/j.ijmedinf.2024.105737

Int J Med Inform. 2024 Nov 23;195:105724. doi: 10.1016/j.ijmedinf.2024.105724. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to design and systematically evaluate an architecture, proposed as the Explainable Mandibular Third Molar Convolutional Neural Network (E-mTMCNN), for detecting the presence of mandibular third molars (m-M3) in panoramic radiography (PR). The proposed architecture seeks to enhance the accuracy of early detection and improve clinical decision-making and treatment planning in dentistry.

METHODS: A new dataset, named the Mandibular Third Molar (m-TM) dataset, was developed through expert labeling of raw PR images from the UESB dataset. This dataset was subsequently made publicly accessible to support further research. Several advanced image preprocessing techniques, including Gaussian filtering, gamma correction, and data augmentation, were applied to improve image quality. Various Deep learning (DL) based Convolutional Neural Network (CNN) architectures were trained and validated using Transfer Learning (TL) methodologies. Among these, the E-mTMCNN, leveraging the GoogLeNet architecture, achieved the highest performance metrics. To ensure transparency in the model's decision-making process, Local Interpretable Model-Agnostic Explanations (LIME) were integrated as an eXplainable Artificial Intelligence (XAI) approach. Clinical reliability and applicability were assessed through an expert survey conducted among specialized dentists using a decision support system based on the E-mTMCNN.

RESULTS: The E-mTMCNN architecture demonstrated a classification accuracy of 87.02%, with a sensitivity of 75%, specificity of 94.73%, precision of 77.68%, an F1 score of 75.51%, and an area under the curve (AUC) of 87.01%. The integration of LIME provided visual explanations of the model's decision-making rationale, reinforcing the robustness of the proposed architecture. Results from the expert survey indicated high clinical acceptance and confidence in the reliability of the system.

CONCLUSION: The findings demonstrate that the E-mTMCNN architecture effectively detects the presence of m-M3 in PRs, outperforming current state-of-the-art methodologies. The proposed architecture shows considerable potential for integration into computer-aided diagnostic systems, advancing early detection capabilities and enhancing the precision of treatment planning in dental practice.

PMID:39626596 | DOI:10.1016/j.ijmedinf.2024.105724

Neural Netw. 2024 Nov 28;183:106980. doi: 10.1016/j.neunet.2024.106980. Online ahead of print.

ABSTRACT

The weight-sharing mechanism of convolutional kernels ensures the translation equivariance of convolutional neural networks (CNNs) but not scale and rotation equivariance. This study proposes a SIM(2) Lie group-CNN, which can simultaneously keep scale, rotation, and translation equivariance for image classification tasks. The SIM(2) Lie group-CNN includes a lifting module, a series of group convolution modules, a global pooling layer, and a classification layer. The lifting module transfers the input image from Euclidean space to Lie group space, and the group convolution is parameterized through a fully connected network using the Lie Algebra coefficients of Lie group elements as inputs to achieve scale and rotation equivariance. It is worth noting that the mapping relationship between SIM(2) and its Lie Algebra and the distance measure of SIM(2) are defined explicitly in this paper, thus solving the problem of the metric of features on the space of SIM(2) Lie group, which contrasts with other Lie groups characterized by a single element, such as SO(2). The scale-rotation equivariance of Lie group-CNN is verified, and the best recognition accuracy is achieved on three categories of image datasets. Consequently, the SIM(2) Lie group-CNN can successfully extract geometric features and perform equivariant recognition on images with rotation and scale transformations.

PMID:39626532 | DOI:10.1016/j.neunet.2024.106980

J Transl Med. 2024 Dec 3;22(1):1097. doi: 10.1186/s12967-024-05893-2.

ABSTRACT

Despite the proven inhibitory effects of drugs targeting vascular endothelial growth factor receptor 2 (VEGFR2) on solid tumors, including non-small cell lung cancer (NSCLC), the development of anti-NSCLC drugs solely targeting VEGFR2 still faces risks such as off-target effects and limited efficacy. This study aims to develop a novel fingerprint-enhanced graph attention convolutional network (FnGATGCN) model for predicting the activity of anti-NSCLC drugs. Employing a multimodal fusion strategy, the model integrates a feature extraction layer that comprises molecular graph feature extraction and molecular fingerprint feature extraction. The performance evaluation results indicate that the model exhibits high accuracy and stability in predicting activity. Moreover, we explored the relationship between molecular features and biological activity through visualization analysis, thus improving the interpretability of the approach. Utilizing this model, we screened the ZINC database and conducted high-precision molecular docking, leading to the identification of 11 potential active molecules. Subsequently, molecular dynamics simulations and free energy calculations were performed. The results demonstrate that all 11 aforementioned molecules can stably bind to VEGFR2 under dynamic conditions. Among the short-listed compounds, the top six exhibited satisfactory inhibitory activity against VEGFR2 and A549 cells. Especially, compound Z-3 displayed VEGFR2 inhibitory with IC50 values of 0.88 μM, and anti-proliferative activity against A549 cells with IC50 values of 4.23 ± 0.45 μM. This approach combines the advantages of target-based and phenotype-based screening, facilitating the rapid and efficient identification of candidate compounds with dual activity against VEGFR2 and A549 cell lines. It provides new insights and methods for the development of anti-NSCLC drugs. Furthermore, further biological activity tests revealed that Z1-Z3 and Z6 manifested relatively strong antiproliferative activities against NCI-H23 and NCI-H460, and relatively low toxicity towards GES-1. The hit compounds were promising candidates for the further development of novel VEGFR2 inhibitors against NSCLC.

PMID:39627783 | DOI:10.1186/s12967-024-05893-2

Sci Rep. 2024 Dec 3;14(1):30136. doi: 10.1038/s41598-024-81718-y.

ABSTRACT

Cranial sex estimation often relies on visual assessments made by a forensic anthropologist following published standards. However, these methods are prone to human bias and may be less accurate when applied to populations other than those for which they were originally developed with. This study explores an automatic deep learning (DL) framework to enhance sex estimation accuracy and reduce bias. Utilising 200 cranial CT scans of Indonesian individuals, various DL network configurations were evaluated against a human observer. The most accurate DL network, which learned to estimate sex and cranial traits as an auxiliary task, achieved a classification accuracy of 97%, outperforming the human observer at 82%. Grad-CAM visualisations indicated that the DL model appears to focus on certain cranial traits, while also considering overall size and shape. This study demonstrates the potential of using DL to assist forensic anthropologists in providing more accurate and less biased estimations of skeletal sex.

PMID:39627517 | DOI:10.1038/s41598-024-81718-y

Sci Rep. 2024 Dec 3;14(1):30131. doi: 10.1038/s41598-024-81824-x.

ABSTRACT

Deep learning (DL) techniques are becoming more popular for diagnosing Parkinson's disease (PD) because they offer non-invasive and easily accessible tools. By using advanced data analysis, these methods improve early detection and diagnosis, which is crucial for managing the disease effectively. This study explores end-to-end DL architectures, such as convolutional neural networks and transformers, for diagnosing PD using self-reported voice data collected via smartphones in everyday settings. Transfer learning was applied by starting with models pre-trained on large datasets from the image and the audio domains and then fine-tuning them on the mPower voice data. The Transformer model pre-trained on the voice data performed the best, achieving an average AUC of [Formula: see text] and an average AUPRC of [Formula: see text], outperforming models trained from scratch. To the best of our knowledge, this is the first use of a Transformer model for audio data in PD diagnosis, using this dataset. We achieved better results than previous studies, whether they focused solely on the voice or incorporated multiple modalities, by relying only on the voice as a biomarker. These results show that using self-reported voice data with state-of-the-art DL architectures can significantly improve PD prediction and diagnosis, potentially leading to better patient outcomes.

PMID:39627487 | DOI:10.1038/s41598-024-81824-x

Sci Rep. 2024 Dec 3;14(1):30144. doi: 10.1038/s41598-024-81241-0.

ABSTRACT

Mitochondrial morphology is an important parameter of cellular fitness. Although many approaches are available for assessing mitochondrial morphology in mammalian cells, only a few technically demanding and laborious methods are available for yeast cells. A robust, fully automated and user-friendly approach that would allow (1) segmentation of tubular and spherical mitochondria in the yeast Saccharomyces cerevisiae from conventional wide-field fluorescence images and (2) quantitative assessment of mitochondrial morphology is lacking. To address this, we compared Global thresholding segmentation with deep learning MitoSegNet segmentation, which we retrained on yeast cells. The deep learning model outperformed the Global thresholding segmentation. We applied it to segment mitochondria in strain lacking the MMI1/TMA19 gene encoding an ortholog of the human TCTP protein. Next, we performed a quantitative evaluation of segmented mitochondria by analyses available in ImageJ/Fiji and by MitoA analysis available in the MitoSegNet toolbox. By monitoring a wide range of morphological parameters, we described a novel mitochondrial phenotype of the mmi1Δ strain after its exposure to oxidative stress compared to that of the wild-type strain. The retrained deep learning model, all macros applied to run the analyses, as well as the detailed procedure are now available at https://github.com/LMCF-IMG/Morphology_Yeast_Mitochondria .

PMID:39627480 | DOI:10.1038/s41598-024-81241-0

Sci Rep. 2024 Dec 3;14(1):30093. doi: 10.1038/s41598-024-81475-y.

NO ABSTRACT

PMID:39627431 | DOI:10.1038/s41598-024-81475-y

Eur Radiol. 2024 Dec 3. doi: 10.1007/s00330-024-11232-2. Online ahead of print.

ABSTRACT

OBJECTIVES: This study validates the use of CycleGAN-generated wrist radiographs with digitally removed splints, specifically assessing their impact on fracture visualisation.

MATERIALS AND METHODS: We retrospectively collected wrist radiographs from 1748 patients who had imaging before and after splint application at a single institution. The dataset was divided into training (1696 patients, 5353 images) and testing sets (52 patients, 965 images). A CycleGAN-based model was trained to generate splint-free wrist radiographs (generated "splint-less") from the original "splint" images. A pre-trained fracture detection model (YOLO8s) was used to assess fracture detection performance on three image groups: original "splint-less" radiographs, original "splint" radiographs, and generated "splint-less" radiographs. Two radiologists scored the generated images. Subtraction images quantified overall image alterations. Precision, recall, and F1 scores were used to compare fracture detection performance.

RESULTS: CycleGAN effectively generated splint-suppressed radiographs with minimal remaining splint density (< 10% remaining in 97.99%), hardware distortion (< 10% change in 100%), anatomical distortion (< 10% in 99.63%), and fracture lesion changes (< 10% change in 100%). New artefacts were rare (absent in 97.54%). Notably, the fracture detection model achieved higher precision (0.94 vs. 0.92), recall (0.63 vs. 0.5), and F1 score (0.75 vs. 0.65) on the generated "splint-less" radiographs compared to the original "splint" radiographs, approaching the performance on original "splint-less" radiographs (F1 0.71). Furthermore, greater image alterations by CycleGAN correlated with larger improvements in fracture detection.

CONCLUSION: CycleGAN successfully removed splint densities from wrist radiographs with splints.

KEY POINTS: Question Can CycleGAN (Generative Adversarial Networks), designed for image-to-image translation, generate synthetic "splint-less" radiographs to improve fracture visualisation in follow-up radiographs? Findings Removal of splint densities from wrist radiographs using Generative Adversarial Networks preserved anatomical structures and improved the performance of a fracture detection model. Clinical relevance Generated splint-less radiographs can enhance the performance of wrist fracture detection in wrist radiographs, benefiting both human clinicians and AI-powered diagnostic tools.

PMID:39627425 | DOI:10.1007/s00330-024-11232-2

Sci Rep. 2024 Dec 3;14(1):30149. doi: 10.1038/s41598-024-81047-0.

ABSTRACT

This study introduces a novel method for predicting the Big Five personality traits through the analysis of speech samples, advancing the field of computational personality assessment. We collected data from 2045 participants who completed a self-reported Big Five personality questionnaire and provided free-form speech samples by introducing themselves without constraints on content. Using pre-trained convolutional neural networks and transformer-based models, we extracted embeddings representing both acoustic features (e.g., tone, pitch, rhythm) and linguistic content from the speech samples. These embeddings were combined and input into gradient boosted tree models to predict personality traits. Our results indicate that personality traits can be effectively predicted from speech, with correlation coefficients between predicted scores and self-reported scores ranging from 0.26 (extraversion) to 0.39 (neuroticism), and from 0.39 to 0.60 for disattenuated correlations. Intraclass correlations show moderate to high consistency in our model's predictions. This approach captures the subtle ways in which personality traits are expressed through both how people speak and what they say. Our findings underscore the potential of voice-based assessments as a complementary tool in psychological research, providing new insights into the connection between speech and personality.

PMID:39627367 | DOI:10.1038/s41598-024-81047-0

Sci Rep. 2024 Dec 3;14(1):30116. doi: 10.1038/s41598-024-80268-7.

ABSTRACT

The array of complex and evolving patient data has limited clinical decision making in the emergency department (ED). This study introduces an advanced deep learning algorithm designed to enhance real-time prediction accuracy for integration into a novel Clinical Decision Support System (CDSS). A retrospective study was conducted using data from a level 1 tertiary hospital. The algorithm's predictive performance was evaluated based on in-hospital cardiac arrest, inotropic circulatory support, advanced airway, and intensive care unit admission. We developed an artificial intelligence (AI) algorithm for CDSS that integrates multiple data modalities, including vitals, laboratory, and imaging results from electronic health records. The AI model was trained and tested on a dataset of 237,059 ED visits. The algorithm's predictions, based solely on triage information, significantly outperformed traditional logistic regression models, with notable improvements in the area under the precision-recall curve (AUPRC). Additionally, predictive accuracy improved with the inclusion of continuous data input at shorter intervals. This study suggests the feasibility of using AI algorithms in diverse clinical scenarios, particularly for earlier detection of clinical deterioration. Future work should focus on expanding the dataset and enhancing real-time data integration across multiple centers to further optimize its application within the novel CDSS.

PMID:39627310 | DOI:10.1038/s41598-024-80268-7

Sci Rep. 2024 Dec 3;14(1):30071. doi: 10.1038/s41598-024-79266-6.

ABSTRACT

Landslides are frequent all over the world, posing serious threats to human life, infrastructure, and economic operations, making them chronic disasters. This study proposes a novel landslide detection methodology that is automated and based on a hybrid deep learning approach. Currently, Deep Learning is constrained by the lack of applicability, lack of data, and low efficiency in landslide detection but with recent advancement in deep learning-based solutions for landslide detection has sparked considerable advantages over traditional techniques. In order to prevent and mitigate disaster, we introduced a hybrid model based on remote sensing technologies such as satellite images. Specifically, the proposed approach consists hybrid U-Net model integrated with a pyramid pooling layer for landslide detection, which uses high-resolution landslide images from the Landslide4Sense dataset. The UNet-Pyramid model has the following modifications: To improve feature acquisition and advancements to strengthen the model's attention U-Net architecture is integrated with the pyramid pooling layers and OBIA technique. The UNet-Pyramid model was trained and validated using labeled images taken from the Landslide4Sense dataset and the validated set using OBIA to improve its efficacy. The overall Precision, Recall, and F1 Score of the UNet-pyramid model for landslide detection are 91%, 84%, and 87%, respectively.

PMID:39627305 | DOI:10.1038/s41598-024-79266-6

NPJ Precis Oncol. 2024 Dec 3;8(1):277. doi: 10.1038/s41698-024-00770-z.

ABSTRACT

Immunohistochemistry (IHC) is the common companion diagnostics in targeted therapies. However, quantifying protein expressions in IHC images present a significant challenge, due to variability in manual scoring and inherent subjective interpretation. Deep learning (DL) offers a promising approach to address these issues, though current models require extensive training for each cancer and IHC type, limiting the practical application. We developed a Universal IHC (UIHC) analyzer, a DL-based tool that quantifies protein expression across different cancers and IHC types. This multi-cohort trained model outperformed conventional single-cohort models in analyzing unseen IHC images (Kappa score 0.578 vs. up to 0.509) and demonstrated consistent performance across varying positive staining cutoff values. In a discovery application, the UIHC model assigned higher tumor proportion scores to MET amplification cases, but not MET exon 14 splicing or other non-small cell lung cancer cases. This UIHC model represents a novel role for DL that further advances quantitative analysis of IHC.

PMID:39627299 | DOI:10.1038/s41698-024-00770-z

J Med Internet Res. 2024 Dec 3;26:e69042. doi: 10.2196/69042.

ABSTRACT

[This corrects the article DOI: 10.2196/65994.].

PMID:39626223 | DOI:10.2196/69042

Hepatology. 2024 Dec 2. doi: 10.1097/HEP.0000000000001180. Online ahead of print.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) recurrence frequently occurs after curative surgery. Histological microvascular-invasion (MVI) predicts recurrence but cannot provide pre-operative prognostication, whereas clinical prediction scores have variable performances.

METHODS: Recurr-NET, a multimodal multiphasic residual-network random survival forest deep-learning model incorporating pre-operative CT and clinical parameters, was developed to predict HCC recurrence. Pre-operative triphasic CT scans were retrieved from patients with resected histology-confirmed HCC from four centers in Hong Kong (Internal-cohort). The internal-cohort was randomly divided in an 8:2 ratio into training and internal-validation. External-testing was performed in an independent cohort from Taiwan.

RESULTS: Among 1231 patients (Age 62.4, 83.1% male, 86.8% viral hepatitis, median follow-up 65.1 months), cumulative HCC recurrence at years 2 and 5 were 41.8% and 56.4% respectively. Recurr-NET achieved excellent accuracy in predicting recurrence from years 1-5 (Internal cohort AUROC 0.770-0.857; External AUROC 0.758-0.798), significantly out-performing MVI (Internal AUROC 0.518-0.590; External AUROC 0.557-0.615) and multiple clinical risk scores (ERASL-PRE, ERASL-POST, DFT, and Shim scores) (Internal AUROC 0.523-0.587, External AUROC: 0.524-0.620) respectively (all p<0.001). Recurr-NET was superior to MVI in stratifying recurrence risks at year 2 (Internal: 72.5% vs. 50.0% in MVI; External: 65.3% vs. 46.6% in MVI) and year 5 (Internal: 86.4% vs. 62.5% in MVI; External: 81.4% vs. 63.8% in MVI) (all p<0.001). Recurr-NET was also superior to MVI in stratifying liver-related and all-cause mortality (all p<0.001). The performance of Recurr-NET remained robust in subgroup analyses.

CONCLUSION: Recurr-NET accurately predicted HCC recurrence, out-performing MVI and clinical prediction scores respectively, highlighting its potential in pre-operative prognostication.

PMID:39626212 | DOI:10.1097/HEP.0000000000001180

FEBS Open Bio. 2024 Dec 3. doi: 10.1002/2211-5463.13938. Online ahead of print.

ABSTRACT

The development of scientific literacy (SL) skills is critical in the life sciences. A flipped classroom reverses traditional learning spaces such that foundational knowledge is acquired by students independently through recorded lectures and/or readings in advance of the lecture period and knowledge is consolidated through active learning activities in the classroom. A flipped classroom learning environment can promote critical skill development and knowledge application, and therefore, could enhance SL skill development. The objectives here were to (a) determine the effect of a flipped classroom learning environment on SL skill development in second-year kinesiology students enrolled in a research methods course and (b) reassess SL skills 4 months later. SL skills were assessed using the validated test of scientific literacy skills (TOSLS) questionnaire at the start and end of the semester (n = 57) and reassessed 4 months later after the summer semester break (n = 46). During the flipped classroom semester, practical SL skills (TOSLS scores) were increased by 16.3% and TOSLS scores were positively correlated with the students' final grade (r = 0.526, P < 0.001). Four months later, average TOSLS scores significantly decreased compared to the levels at the end of the flipped classroom learning experience. Importantly, retention of SL skills (i.e., 4 months later TOSLS scores) were related to learning approach scores and were positively correlated with deep learning approach scores (r = 0.298, P = 0.044) and negatively correlated with surface learning approach scores (r = -0.314, P = 0.034). Therefore, SL skill retention was higher in students utilizing a deep learning approach (e.g., engaged, self-regulation in learning, and seeking a deeper understanding of concepts) and lower in students utilizing a surface learning approach (e.g., limited engagement, rote memorization of concepts). Collectively, the results demonstrate the value of a flipped classroom in promoting SL skills while highlighting the role of students' learning approach in critical skill retention.

PMID:39625998 | DOI:10.1002/2211-5463.13938

PLoS One. 2024 Dec 3;19(12):e0311089. doi: 10.1371/journal.pone.0311089. eCollection 2024.

ABSTRACT

The popularity of cloud computing (CC) has increased significantly in recent years due to its cost-effectiveness and simplified resource allocation. Owing to the exponential rise of cloud computing in the past decade, many corporations and businesses have moved to the cloud to ensure accessibility, scalability, and transparency. The proposed research involves comparing the accuracy and fault prediction of five machine learning algorithms: AdaBoostM1, Bagging, Decision Tree (J48), Deep Learning (Dl4jMLP), and Naive Bayes Tree (NB Tree). The results from secondary data analysis indicate that the Central Processing Unit CPU-Mem Multi classifier has the highest accuracy percentage and the least amount of fault prediction. This holds for the Decision Tree (J48) classifier with an accuracy rate of 89.71% for 80/20, 90.28% for 70/30, and 92.82% for 10-fold cross-validation. Additionally, the Hard Disk Drive HDD-Mono classifier has an accuracy rate of 90.35% for 80/20, 92.35% for 70/30, and 90.49% for 10-fold cross-validation. The AdaBoostM1 classifier was found to have the highest accuracy percentage and the least amount of fault prediction for the HDD Multi classifier with an accuracy rate of 93.63% for 80/20, 90.09% for 70/30, and 88.92% for 10-fold cross-validation. Finally, the CPU-Mem Mono classifier has an accuracy rate of 77.87% for 80/20, 77.01% for 70/30, and 77.06% for 10-fold cross-validation. Based on the primary data results, the Naive Bayes Tree (NB Tree) classifier is found to have the highest accuracy rate with less fault prediction of 97.05% for 80/20, 96.09% for 70/30, and 96.78% for 10 folds cross-validation. However, the algorithm complexity is not good, taking 1.01 seconds. On the other hand, the Decision Tree (J48) has the second-highest accuracy rate of 96.78%, 95.95%, and 96.78% for 80/20, 70/30, and 10-fold cross-validation, respectively. J48 also has less fault prediction but with a good algorithm complexity of 0.11 seconds. The difference in accuracy and less fault prediction between NB Tree and J48 is only 0.9%, but the difference in time complexity is 9 seconds. Based on the results, we have decided to make modifications to the Decision Tree (J48) algorithm. This method has been proposed as it offers the highest accuracy and less fault prediction errors, with 97.05% accuracy for the 80/20 split, 96.42% for the 70/30 split, and 97.07% for the 10-fold cross-validation.

PMID:39625991 | DOI:10.1371/journal.pone.0311089

PLoS Comput Biol. 2024 Dec 3;20(12):e1012650. doi: 10.1371/journal.pcbi.1012650. Online ahead of print.

ABSTRACT

Protein-RNA interactions play a critical role in many cellular processes and pathologies. However, experimental determination of protein-RNA structures is still challenging, therefore computational tools are needed for the prediction of protein-RNA interfaces. Although evolutionary pressures can be exploited for structural prediction of protein-protein interfaces, and recent deep learning methods using protein multiple sequence alignments have radically improved the performance of protein-protein interface structural prediction, protein-RNA structural prediction is lagging behind, due to the scarcity of structural data and the flexibility involved in these complexes. To study the evolution of protein-RNA interface structures, we first identified a large and diverse dataset of 2,022 pairs of structurally homologous interfaces (termed structural interologs). We leveraged this unique dataset to analyze the conservation of interface contacts among structural interologs based on the properties of involved amino acids and nucleotides. We uncovered that 73% of distance-based contacts and 68% of apolar contacts are conserved on average, and the strong conservation of these contacts occurs even in distant homologs with sequence identity below 20%. Distance-based contacts are also much more conserved compared to what we had found in a previous study of homologous protein-protein interfaces. In contrast, hydrogen bonds, salt bridges, and π-stacking interactions are very versatile in pairs of protein-RNA interologs, even for close homologs with high interface sequence identity. We found that almost half of the non-conserved distance-based contacts are linked to a small proportion of interface residues that no longer make interface contacts in the interolog, a phenomenon we term "interface switching out". We also examined possible recovery mechanisms for non-conserved hydrogen bonds and salt bridges, uncovering diverse scenarios of switching out, change in amino acid chemical nature, intermolecular and intramolecular compensations. Our findings provide insights for integrating evolutionary signals into predictive protein-RNA structural modeling methods.

PMID:39625988 | DOI:10.1371/journal.pcbi.1012650