The aim of the current study was to investigate the impact of long-acting fibroblast growth factor 21 (FGF21) on retinal vascular leakage utilizing machine learning and to clarify the mechanism ...underlying the protection. To assess the effect on retinal vascular leakage, C57BL/6J mice were pre-treated with long-acting FGF21 analog or vehicle (Phosphate Buffered Saline; PBS) intraperitoneally (i.p.) before induction of retinal vascular leakage with intravitreal injection of mouse (m) vascular endothelial growth factor 164 (VEGF164) or PBS control. Five hours after mVEGF164 injection, we retro-orbitally injected Fluorescein isothiocyanate (FITC) -dextran and quantified fluorescence intensity as a readout of vascular leakage, using the Image Analysis Module with a machine learning algorithm. In FGF21- or vehicle-treated primary human retinal microvascular endothelial cells (HRMECs), cell permeability was induced with human (h) VEGF165 and evaluated using FITC-dextran and trans-endothelial electrical resistance (TEER). Western blots for tight junction markers were performed. Retinal vascular leakage in vivo was reduced in the FGF21 versus vehicle- treated mice. In HRMECs in vitro, FGF21 versus vehicle prevented hVEGF-induced increase in cell permeability, identified with FITC-dextran. FGF21 significantly preserved TEER compared to hVEGF. Taken together, FGF21 regulates permeability through tight junctions; in particular, FGF21 increases Claudin-1 protein levels in hVEGF-induced HRMECs. Long-acting FGF21 may help reduce retinal vascular leakage in retinal disorders and machine learning assessment can help to standardize vascular leakage quantification.
Monitoring marine fauna is essential for mitigating the effects of disturbances in the marine environment, as well as reducing the risk of negative interactions between humans and marine life. ...Drone-based aerial surveys have become popular for detecting and estimating the abundance of large marine fauna. However, sightability errors, which affect detection reliability, are still apparent. This study tested the utility of spectral filtering for improving the reliability of marine fauna detections from drone-based monitoring. A series of drone-based survey flights were conducted using three identical RGB (red-green-blue channel) cameras with treatments: (i) control (RGB), (ii) spectrally filtered with a narrow ‘green’ bandpass filter (transmission between 525 and 550 nm), and, (iii) spectrally filtered with a polarising filter. Video data from nine flights comprising dolphin groups were analysed using a machine learning approach, whereby ground-truth detections were manually created and compared to AI-generated detections. The results showed that spectral filtering decreased the reliability of detecting submerged fauna compared to standard unfiltered RGB cameras. Although the majority of visible contrast between a submerged marine animal and surrounding seawater (in our study, sites along coastal beaches in eastern Australia) is known to occur between 515–554 nm, isolating the colour input to an RGB sensor does not improve detection reliability due to a decrease in the signal to noise ratio, which affects the reliability of detections.
Japanese encephalitis virus is a leading cause of neurological infection in the Asia-Pacific region with no means of detection in more remote areas. We aimed to test the hypothesis of a Japanese ...encephalitis (JE) protein signature in human cerebrospinal fluid (CSF) that could be harnessed in a rapid diagnostic test (RDT), contribute to understanding the host response and predict outcome during infection. Liquid chromatography and tandem mass spectrometry (LC–MS/MS), using extensive offline fractionation and tandem mass tag labeling (TMT), enabled comparison of the deep CSF proteome in JE vs other confirmed neurological infections (non-JE). Verification was performed using data-independent acquisition (DIA) LC–MS/MS. 5,070 proteins were identified, including 4,805 human proteins and 265 pathogen proteins. Feature selection and predictive modeling using TMT analysis of 147 patient samples enabled the development of a nine-protein JE diagnostic signature. This was tested using DIA analysis of an independent group of 16 patient samples, demonstrating 82% accuracy. Ultimately, validation in a larger group of patients and different locations could help refine the list to 2–3 proteins for an RDT. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034789 and 10.6019/PXD034789.
Purpose – This study aims to conduct a thorough examination and bibliometric analysis of the scholarly articles on PjBL (Project-Based Learning) and 21st-century skills published from 2020 to 2023. ...The analysis will specifically concentrate on the global distribution of these articles and identifying any prevailing research patterns or trends. Methodology – Utilizing “PjBL” and “21st-century skills” as search terms, the Scopus database was employed for the analysis encompassing annual publications, nations, institutions, authors, journals, references, and keywords in the field. This analysis was facilitated by VOSviewer and Microsoft Excel 2019 software. Findings – There has been a significant increase in publication volume between 2020 and 2023. Among the 2,472 articles within the Scopus database by the end of October 2023, Indonesia emerged as the primary contributor of publications among all nations. The VOSviewer indicates that the key issues revolve around, project-based learning (PjBL), pedagogy, critical thinking, education, sustainability, higher education, and 21st-century skills. Significance – A comprehensive analysis of the global landscape concerning project-based learning (PjBL) and, between 2020 and 2023, aims to uncover pivotal trends, emerging issues, influential authors, and seminal works in educational research. The anticipated outcomes hold the potential to offer multifaceted insights into PjBL and 21st-century skills, contributing to the exploration of innovative educational methodologies. Additionally, this study’s investigation of specific research inquiries aims to provide a holistic overview, further enriching the understanding of these crucial educational paradigms.
Abstract
Modern people pay more and more attention to individualized learning. The traditional teaching method is to explain all the learning contents in a unified way. The setting of teaching ...contents and courseware are relatively fixed, which can not provide individualized choices for different learners. The core of the adaptive learning system based on feature extraction studied in this paper is that the system recommends personalized learning content for learners according to the learner model. To establish and personalize the self-adaptive learning engine mechanism, a personalized self-adaptive learning content presentation based on clustering is proposed. This study can analyze the data of students’ learning behavior and knowledge mastery, recommend reasonable learning path and learning resources with appropriate difficulty, give timely and accurate feedback to students’ learning effect, provide personalized service intervention, and promote teaching and learning.
Concrete-filled double skin steel tubular (CFDST) column is a hollow composite structure component, which shows better performance than traditional reinforced concrete and steel columns due to the ...favorable composite action between steel and concrete. In the current study, a machine learning based interaction model combine with the extended Rankine method is developed to predict fire resistance of eccentrically loaded CFDST cylinder columns. The prediction of the reliable shear bond parameter was conducted by back propagation artificial neural network (BP-ANN) and Extreme Gradient Boosting Tree (XGBoost). To perform a reliable production, the architecture and the parametric setting of both models were constructed. Furthermore, the results of the prediction were verified by experimental results and finite element analysis. The results show that the proposed method can predict the behavior of the eccentrically load CFDST columns under fire attack with reasonable accuracy.
Synthetic cathinones are some of the most prevalent new psychoactive substances (NPSs) globally, with alpha-pyrrolidinoisohexanophenone (α-PiHP) being particularly noted for its widespread use in the ...United States, Europe, and Taiwan. However, the analysis of isomeric NPSs such as α-PiHP and alpha-pyrrolidinohexiophenone (α-PHP) is challenging owing to similarities in their retention times and mass spectra. This study proposes a dual strategy based on in vitro metabolic experiments and machine learning-based classification modelling for differentiating α-PHP and α-PiHP in urine samples: (1) in vitro metabolic experiments using pooled human liver microsomes and liquid chromatography tandem quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) were conducted to identify the key metabolites of α-PHP and α-PiHP from the high-resolution MS/MS spectra. After 5 h incubation, 71.4 % of α-PHP and 64.7 % of α-PiHP remained unmetabolised. Nine phase I metabolites were identified for each compound, including primary β-ketone reduction (M1) metabolites. Comparing the metabolites and retention times confirmed the efficacy of in vitro metabolic experiments for differentiating NPS isomers. Subsequently, analysis of seven real urine samples revealed the presence for various metabolites, including M1, that could be used as suitable detection markers at low concentrations. The aliphatic hydroxylation (M2) metabolite peak counts and metabolite retention times were used to determine α-PiHP use. (2) Classification models for the parent compounds and M1 metabolites were developed using principal component analysis for feature extraction and logistic regression for classification. The training and test sets were devised from the spectra of standard samples or supernatants from in vitro metabolism experiments with different incubation times. Both models had classification accuracies of 100 % and accurately identified α-PiHP and its M1 metabolite in seven real urine samples. The proposed methodology effectively distinguished between such isomers and confirmed their presence at low concentrations. Overall, this study introduces a novel concept that addresses the complexities in analysing isomeric NPSs and suggests a path towards enhancing the accuracy and reliability of NPS detection.
Display omitted
•Challenges in analyzing isomeric NPS samples.•Duel strategies are proposed for identifying α-PHP and α-PiHP in urine samples.•Key metabolites were identified using LC-QTOF-MS spectral fragmentation.•Innovative classification models based on PCA and LR were developed.•Accurate identification of α-PiHP and its M1 metabolite in 7 urine samples.
Sepsis is an inflammatory response caused by infection with pathogenic microorganisms. The body shock caused by it is called septic shock. In view of this, we aimed to identify potential diagnostic ...gene biomarkers of the disease.
Firstly, mRNAs expression data sets of septic shock were retrieved and downloaded from the GEO (Gene Expression Omnibus) database for differential expression analysis. Functional enrichment analysis was then used to identify the biological function of DEmRNAs (differentially expressed mRNAs). Machine learning analysis was used to determine the diagnostic gene biomarkers for septic shock. Thirdly, RT-PCR (real-time polymerase chain reaction) verification was performed. Lastly, GSE65682 data set was utilized to further perform diagnostic and prognostic analysis of identified superlative diagnostic gene biomarkers.
A total of 843 DEmRNAs, including 458 up-regulated and 385 down-regulated DEmRNAs were obtained in septic shock. 15 superlative diagnostic gene biomarkers (such as RAB13, KIF1B, CLEC5A, FCER1A, CACNA2D3, DUSP3, HMGN3, MGST1 and ARHGEF18) for septic shock were identified by machine learning analysis. RF (random forests), SVM (support vector machine) and DT (decision tree) models were used to construct classification models. The accuracy of the DT, SVM and RF models were very high. Interestingly, the RF model had the highest accuracy. It is worth mentioning that ARHGEF18 and FCER1A were related to survival. CACNA2D3 and DUSP3 participated in MAPK signaling pathway to regulate septic shock.
Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
One of the greatest environmental risks in the cement industry is particulate matter emission (i.e., PM
2.5
and PM
10
). This paper aims to develop descriptive-analytical solutions for increasing the ...accuracy of predicting particulate matter emissions using resample data of Kerman cement plant. Photometer instruments DUST TRAK and BS-EN-12341 method were used to determine concentration of PM
2.5
and PM
10
. Sampling was performed on 4 environmental stations of Kerman cement plant in the four seasons. In order to accurate assessment of particulate matter concentration, a new model was proposed to resample cement plant time series data using Pandas in Python. The effect of meteorological parameters including wind speed, relative humidity, air temperature and rainfall on the particulate matter concentration was investigated through statistical analysis. The results indicated that the maximum annual average of 24-h of PM
2.5
belonged to the east side (opposite the clinker depot) in 2019 (31.50 μg m
−3
) and west side (in front of the mine) in 2020 (31.00 μg m
−3
). Also, maximum annual average of 24-h of PM
10
belonged to the west side (in front of the mine) in 2020 (121.00 μg m
−3
) and east side (opposite the clinker depot) in 2020 (120.75 μg m
−3
). The PM
2.5
and PM
10
concentrations are more than the allowable limit. The results demonstrate that particulate matter concentration increases with increasing relative humidity and rainfall. Finally, the SARIMA model was used to predict the particulate matter concentration.
Introduction and Hypothesis
As interstitial cystitis/bladder pain syndrome (IC/BPS) likely represents multiple pathophysiologies, we sought to validate three clinical phenotypes of IC/BPS patients in ...a large, multi-center cohort using unsupervised machine learning (ML) analysis.
Methods
Using the female Genitourinary Pain Index and O’Leary-Sant Indices,
k
-means unsupervised clustering was utilized to define symptomatic phenotypes in 130 premenopausal IC/BPS participants recruited through the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) research network. Patient-reported symptoms were directly compared between MAPP ML-derived phenotypic clusters to previously defined phenotypes from a single center (SC) cohort.
Results
Unsupervised ML categorized IC/BPS participants into three phenotypes with distinct pain and urinary symptom patterns: myofascial pain, non-urologic pelvic pain, and bladder-specific pain. Defining characteristics included presence of myofascial pain or trigger points on examination for myofascial pain patients (
p
= 0.003) and bladder pain/burning for bladder-specific pain patients (
p
< 0.001). The three phenotypes were derived using only 11 features (fGUPI subscales and ICSI/ICPI items), in contrast to 49 items required previously. Despite substantial reduction in classification features, unsupervised ML independently generated similar symptomatic clusters in the MAPP cohort with equivalent symptomatic patterns and physical examination findings as the SC cohort.
Conclusions
The reproducible identification of IC/BPS phenotypes, distinguishing bladder-specific pain from myofascial and genital pain, using independent ML analysis of a multicenter database suggests these phenotypes reflect true pathophysiologic differences in IC/BPS patients.
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