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[Objective] Refining the accuracy and intelligence of passion fruit quality assessment. [Methods] The study used the capabilities of OpenCV along with a compact neural network architecture, MobileNetV3_large_ssld, to accurately determine the fruit's diameter, ripeness, and the degree of its wrinkling. The diameter measurement was achieved by analyzing the short side of the fruit's minimum bounding rectangle. The ripeness assessment was based on the pixel ratio of H component values within specific ranges （H∈[0,10]∪[156,180], [11,25], [26,34], [125,155]） in the HSV color space. Furthermore, the study developed a MobileNetV3_large_ssld model to evaluate the wrinkling of the fruit's skin. Leveraging these three key indicators, a comprehensive fruit quality evaluation model was established using a rating scale approach, and an online detection and sorting system was subsequently developed. This system employed KNN background subtraction to extract the fruits target, excludes stems, and used interval frame sampling method to capture single image for each fruit from the video. The comprehensive evaluation model was utilized to assess the quality of passion fruits, which were then sorted into their appropriate grade channels through a sorting mechanism. [Results] The test results indicated a high degree of consistency between the system's sorting and manual sorting, with an overall accuracy of 97.02%. The consistency rates for top-grade fruits, first-grade, and second-grade fruits were 95.51%, 97.84%, and 100%, respectively. [Conclusion] This system could be used for online detection and sorting of passion in different quality grades.

BackgroundDengue is one of the most common diseases in the tropics and subtropics. Whilst mortality is a rare event when adequate supportive care can be provided, a large number of patients get hospitalised with dengue every year that places a heavy burden on local health systems. A better understanding of the support required at the time of hospitalisation is therefore of critical importance for healthcare planning, especially when resources are limited during major outbreaks.MethodsHere we performed a retrospective analysis of clinical data from over 1500 individuals hospitalised with dengue in Vietnam between 2017 and 2019. Using a broad panel of potential biomarkers, we sought to evaluate robust predictors of prolonged hospitalisation periods.ResultsOur analyses revealed a lead-time bias, whereby early admission to hospital correlates with longer hospital stays - irrespective of disease severity. Importantly, taking into account the symptom duration prior to hospitalisation significantly affects observed associations between hospitalisation length and previously reported risk markers of prolonged stays, which themselves showed marked inter-annual variations. Once corrected for symptom duration, age, temperature at admission and elevated neutrophil-to-lymphocyte ratio were found predictive of longer hospitalisation periods.ConclusionThis study demonstrates that the time since dengue symptom onset is one of the most significant predictors for the length of hospital stays, independent of the assigned severity score. Pre-hospital symptom durations need to be accounted for to evaluate clinically relevant biomarkers of dengue hospitalisation trajectories.

ObjectiveTo investigate the significance of plasma metabolites for gastric cancer diagnosis based on metabolomics and machine learning algorithms. MethodsPlasma samples were collected from 20 gastric cancer patients and 20 gender- and age-matched healthy volunteers (controls). After extracted with methanol, the metabolites in the plasma samples were analyzed with chromatography-mass spectrometry and annotated with mzCloud, mzVault and Masslist databases. The differential metabolites between the cases and controls were screened with the value of variable importance of projection (VIP: > 1, P < 0.05, log2|fold change| > 1); the identified differential metabolites were subjected to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database enrichment using hypergeometric tests to detect differential metabolic pathways between the two groups. Specific metabolite-based diagnostic model and receiver operating characteristic (ROC) curve were established using Boruta algorithm to identify significant differential metabolites for gastric cancer diagnosis. Relative contents of the differential metabolites were also calculated. ResultsA total of 230 differential metabolites were screened out from the plasma extracts of the two groups and 5 differential metabolic pathways were identified, including phenylalanine metabolism, biosynthesis of phenylalanine/tyrosine/tryptophan, arginine biosynthesis, histidine metabolism, and pantothenic acid/coenzyme A biosynthesis. The identified 9 significant differential metabolites for gastric cancer diagnosis were L-aspartic acid, 2-phenylethylamine, ornithine, hippuric acid, citrulline, pantetheine, 1-methylhistamine, L-tyrosine, and L-histidine and the relative levels of all the 9 differential metabolites were significantly lower in the plasma from the cases than those from the controls (P < 0.05 for all). ConclusionThe enriched five metabolic differential pathways may be involved in the development of gastric cancer, and the nine differential metabolites can be used as metabolic biomarkers for gastric cancer diagnosis. The combination of metabolomics and machine learning algorithms could help identify markers for gastric cancer diagnosis.

Abstract Knowledge of the clinical presentation of central nervous system (CNS) infections and the causative pathogens is crucial for appropriate diagnosis and rapid initiation of appropriate treatment to prevent severe neurological sequelae. The aim of this study is to understand the aetiology of CNS infections based on the clinical presentation of Vietnamese patients. A prospective hospital-based cohort study was conducted between May 2014 and May 2017. We screened 137 patients with clinically suspected CNS infection for fungal, bacterial and viral pathogens using their cerebrospinal fluid (CSF) and blood cultures. In addition, DNA or RNA extracted from CSF samples were subjected to nucleic acid testing (NAT) with a selective panel of bacterial, viral and fungal pathogens. At least one pathogen could be detected in 41% (n = 56) of the patients. The main pathogens causing CNS infections were Streptococcus suis (n = 16; 12%) and Neisseria meningitidis (n = 9; 7%), followed by Herpes simplex virus 1/2 (n = 4; 3%) and Klebsiella pneumoniae (n = 4; 3%). Other pathogens were only identified in a few cases. Patients with bacterial CNS infections were significantly older, had a worse outcome, a lower Glasgow Coma Scale (GCS), a higher rate of speech impairment and neck stiffness than patients with viral or tuberculous CNS infections. In northern Vietnam, adults are mostly affected by bacterial CNS infections, which have a severe clinical course and worse outcomes compared to viral or tuberculous CNS infections. Clinicians should be aware of the regional occurrence of pathogens to initiate rapid and appropriate diagnosis and treatment.

This study included 468 patients and aimed to use multimodal ultrasound radiomic technology to predict the malignancy of TI-RADS 4-5 thyroid nodules. First, radiomic features are extracted from conventional two-dimensional ultrasound (transverse ultrasound and longitudinal ultrasound), strain elastography (SE), and shear-wave-imaging (SWE) images. Next, the least absolute shrinkage and selection operator (LASSO) is used to screen out features related to malignant tumors. Finally, a support vector machine (SVM) is used to predict the malignancy of thyroid nodules. The Shapley additive explanation (SHAP) method was used to intuitively analyze the specific contributions of radiomic features to the model's prediction. Our proposed model has AUCs of 0.971 and 0.856 in the training and testing sets, respectively. Our proposed model has a higher prediction accuracy compared to those of models with other modal combinations. In the external validation set, the AUC of the model is 0.779, which proves that the model has good generalization ability. Moreover, SHAP analysis was used to examine the overall impacts of various radiomic features on model predictions and local explanations for individual patient evaluations. Our proposed multimodal ultrasound radiomic model can effectively integrate different data collected using multiple ultrasound sensors and has good diagnostic performance for TI-RADS 4-5 thyroid nodules. [ABSTRACT FROM AUTHOR]

To investigate the seismic behavior of composite columns with high-strength concrete-filled steel tube flanges and honeycomb steel webs (STHHC) after being subjected to freeze-thaw cycles, 36 full-scale STHHCs were designed with the following main parameters: the shear span ratio (λs), the axial compression ratio (n0), the number of freeze-thaw cycles (Nc), the concrete cubic compression strength (fcu), and the steel ratio of the section (αs). Compared with existing experimental data, the validity of the finite element modeling method was verified. Parameter analysis was conducted on 36 full-scale STHHCs to obtain the hysteresis curve of the composite columns and to clarify the impact of the different parameters on the skeleton curve, the energy dissipation capacity, the stiffness degradation, and the ductility of the composite columns. The results showed that the hysteresis curves of all specimens after freeze-thaw cycles exhibited an ideal shuttle shape, reflecting that this kind of composite column has good energy dissipation ability and freeze-thaw resistance. The specimens' maximum bulging deformation and maximum stress both occurred at the column base. Finally, the restoring force model of this kind of composite column is therefore established, and design recommendations based on these results are proposed. [ABSTRACT FROM AUTHOR]

Nanozymes based on manganese oxide (MnO2) are demonstrated to be promising probes in colorimetric sensing applications. In this study, the r-MnO2/β-MnO2 heterophase nanostructure was simply prepared by a calcination process with controllable temperature. The characterization of the nanostructured material was confirmed by SEM, UV–vis spectroscopy, Raman, TGA–DSC, and XRD analysis. The r-MnO2/β-MnO2 exhibits a remarkably good catalytic activity in the oxidation process of 3,3′,5,5′-tetramethylbenzidine (TMB) compared with the r-MnO2 or Mn2O3 nanostructure owing to its heterophase junctions. The enhanced performance of the colorimetric sensor for ascorbic acid (AA) detection was investigated using the r-MnO2/β-MnO2 heterophase nanostructure as probe. The r-MnO2/β-MnO2 material enhanced the monitoring of AA in the wide linear range from 1 µM to 50 μM with a limit of detection of 0.84 µM. This work presents a promising and straightforward approach for the construction of MnO2-based colorimetric sensor and their practical application in plant growth monitoring. [ABSTRACT FROM AUTHOR]

As a metal-free photocatalyst with high catalytic performance, carbon nitride is non-toxic, harmless, and stable in the natural environment. Owing to its facile synthesis, stable physical and chemical properties, tunable structure, and suitable band gap, graphite-like carbon nitride (g-C3N4) plays an increasing role in the field of photocatalysis. It has attracted extensive attention in the fields of evolution of hydrogen and oxygen via water-splitting hydrolysis and in the degradation of organic pollutants. In particular, g-C3N4 is identified to have a high specific surface area (SSA) because of its special lamellar structure. Meanwhile, the abundant pores intrinsic in it are able to provide both transporting channels for photogenic carriers or reactive species and a large number of active sites for redox reactions. These merits endow it with high photoelectrical properties. The preparation methods of the pore structures of such catalyst include hard templates, soft templates, and non-template ones. The hard template method enables the preparation of regular pore structures but requires additional removal treatment. However, the soft templates can be decomposed during the high-temperature preparation of g-C3N4, which avoids the use of toxic reagents and consequently is harmless to the environment, and the template-free method does not involve any templates, which will simplify the experimental process from the aspect of sample preparation with reduced cost. In this paper, the advantages and disadvantages of various preparation methods were elaborated and compared based on the literature review in recent years. The developments and applications in the environmental and energy aspects were summarized by combining the commonly used modification methods, which provided the perceptions with respect to the development of metal-free g-C3N4-based photocatalysts in the future. Further, the photocatalytic mechanism was explained, and the four different precursors of g-C3N4 were compared. Finally, the ongoing outlook and perspectives will be covered in this review.

To investigate the axial compression behavior of composite columns with high-strength concrete-filled steel tube flanges and honeycombed steel web (STHHC) under load during freeze–thaw cycles, 48 full-scale composite column specimens were designed with different parameters: the restraint effect coefficient (ξ), concrete strength (fcu), number of freeze–thaw cycles (nd), slenderness ratio (λ), space–height ratio (s/hw), and hole–height ratio (d/hw). The finite element models of STHHC composite columns were simulated using ABAQUS finite element software (Version: 2021). The modeling method's rationality was verified by comparing simulation results with experimental outcomes. Based on the finite element model, a parametric analysis of the composite columns under freeze–thaw cycles was conducted, analyzing their failure modes and load-bearing processes. The results indicate that the bearing capacity of the STHHC increased with increases in ξ and fcu, and decreased with a rise in λ. In contrast, the influence of s/hw and d/hw on the ultimate bearing capacity of the composite columns was relatively minor. An equation for calculating the axial bearing capacity of the STHHC composite columns under freeze–thaw cycles was derived using statistical regression methods and considering the impact of different parameters on the axial compressive performance of the composite columns, laying the foundation for the promotion and application of this type of composite column in practical engineering projects. [ABSTRACT FROM AUTHOR]