Correlation-based carbon determination in steel without explicitly involving carbon-related emission lines in a LIBS spectrum.

As any spectrochemical analysis method, laser-induced breakdown spectroscopy (LIBS) usually relates characteristic spectral lines of the elements or molecules to be analyzed to their concentrations in a material. It is however not always possible for a given application scenario, to rely on such lines because of various practical limitations as well as physical perturbations in the spectrum excitation and recording process. This is actually the case for determination of carbon in steel with LIBS operated in the ambient gas, where the intense C I 193.090 nm VUV line is absorbed, while the C I 247.856 nm near UV one heavily interferes with iron lines. This work uses machine learning, especially a combination of least absolute shrinkage and selection operator (LASSO) for spectral feature selection and back-propagation neural networks (BPNN) for regression, to correlate a LIBS spectrum to the carbon concentration for its precise determination without explicitly including carbon-related emission lines in the selected spectral features.

Machine learning efficiently corrects LIBS spectrum variation due to change of laser fluence.

This work demonstrates the efficiency of machine learning in the correction of spectral intensity variations in laser-induced breakdown spectroscopy (LIBS) due to changes of the laser pulse energy, such changes can occur over a wide range, from 7.9 to 71.1 mJ in our experiment. The developed multivariate correction model led to a precise determination of the concentration of a minor element (magnesium for instance) in the samples (aluminum alloys in this work) with a precision of 6.3% (relative standard deviation, RSD) using the LIBS spectra affected by the laser pulse energy change. A comparison to the classical univariate corrections with laser pulse energy, total spectral intensity, ablation crater volume and plasma temperature, further highlights the significance of the developed method.

Expanded Potential Stem Cells from Human Embryos Have an Open Chromatin Configuration with Enhanced Trophoblast Differentiation Ability.

Human expanded potential stem cells (hEPSC) have been derived from human embryonic stem cells and induced pluripotent stem cells. Here direct derivation of hEPSC from human pre-implantation embryos is reported. Like the reported hEPSC, the embryo-derived hEPSC (hEPSC-em) exhibit a transcriptome similar to morula, comparable differentiation potency, and high genome editing efficiency. Interestingly, the hEPSC-em show a unique H3 lysine-4 trimethylation (H3K4me3) open chromatin conformation; they possess a higher proportion of H3K4me3 bound broad domain (>5 kb) than the reported hEPSC, naive, and primed embryonic stem cells. The open conformation is associated with enhanced trophoblast differentiation potency with increased trophoblast gene expression upon induction of differentiation and success in derivation of trophoblast stem cells with bona fide characteristics. Hippo signaling is specifically enriched in the H3K4me3 broad domains of the hEPSC-. Knockout of the Hippo signaling gene, YAP1 abolishes the ability of the embryo-derived EPSC to form trophoblast stem cells.

Chemical profiles and health-promoting effects of porcini mushroom (Boletus edulis): A narrative review.

Boletus edulis is an edible mushroom with nutritious, delicacy, and pharmacological properties. It is rich in carbohydrates, proteins, minerals, and taste compounds, while low in fat and calories. Diverse bioactive compounds, including polysaccharides, phenolic compounds, and phytosterols, have been found in B. edulis. In addition, bioactive compounds and chemical extracts from B. edulis have been verified to possess constipation prevention, antioxidant, antineoplastic, anti-inflammatory, hepato-protective, antibacterial, and antiviral activities. This paper provides an overview of B. edulis research in recent two decades, emphasizing the nutrition constituents, taste and flavor components, bioactive compounds, and health-promoting effects of B. edulis. Boletus edulis appeared to have health-promoting effects that may effectively prevent or treat various chronic diseases. The potential of B. edulis as a functional food and medicine needs to be further investigated and confirmed.

Discernment on assistive technology for the care and support requirements of older adults and differently-abled individuals.

Assistive technology for the differently abled and older adults has made remarkable achievements in providing rehabilitative, adaptive, and assistive devices. It provides huge assistance for people with physical impairments to lead a better self-reliant daily life, in terms of mobility, education, rehabilitation, etc. This technology ranges from simple hand-held devices to complex robotic accessories which promote the individual's independence. This study aimed at identifying the assistance required by differently-abled individuals, and the solutions proposed by different researchers, and reviewed their merits and demerits. It provides a detailed discussion on the state of art assistive technologies, their applications, challenges, types, and their usage for rehabilitation. The study also identifies different unexplored research areas related to assistive technology that can improve the daily life of individuals and advance the field. Despite their high usage, assistive technologies have some limitations which have been briefly described in the study. This review, therefore, can help understand the utilization, and pros and cons of assistive devices in rehabilitation engineering and assistive technologies.

From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration.

With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining the elemental compositions of soils, crusts, and rocks. The American Perseverance rover and the Chinese Zhurong rover respectively landed on Mars on February 18 and May 15, 2021, further increase the number of LIBS instruments on Mars. Such an unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data analysis. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock compositions leading to the chemical matrix effect, and the difference in surface physical properties between laboratory standards (in pressed powder pellet, glass or ceramic) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matrix effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standards offering a good representation of various compositions of Mars rocks. The present work more specifically deals with the physical matrix effect which is still lacking a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific application of total alkali-silica (TAS) classification of rocks (either with a polished surface or in the raw state), the results show a significant improvement in the ability to predict of pellet-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct TAS classification rate increases from 25% for polished rocks and 33.3% for raw rocks with a machine learning model, to 83.3% with a transfer learning model for both types of rock samples.

Radiomics models for diagnosing microvascular invasion in hepatocellular carcinoma: which model is the best model?

OBJECTIVES: To explore the feasibility of diagnosing microvascular invasion (MVI) with radiomics, to compare the diagnostic performance of different models established by each method, and to determine the best diagnostic model based on radiomics. METHODS: A retrospective analysis was conducted with 206 cases of hepatocellular carcinoma (HCC) confirmed through surgery and pathology in our hospital from June 2015 to September 2018. Among the samples, 88 were MVI-positive, and 118 were MVI-negative. The radiomics analysis process included tumor segmentation, feature extraction, data preprocessing, dimensionality reduction, modeling and model evaluation. RESULTS: A total of 1044 sets of texture feature parameters were extracted, and 21 methods were used for the radiomics analysis. All research methods could be used to diagnose MVI. Of all the methods, the LASSO+GBDT method had the highest accuracy, the LASSO+RF method had the highest sensitivity, the LASSO+BPNet method had the highest specificity, and the LASSO+GBDT method had the highest AUC. Through Z-tests of the AUCs, LASSO+GBDT, LASSO+K-NN, LASSO+RF, PCA + DT, and PCA + RF had Z-values greater than 1.96 (p<0.05). The DCA results showed that the LASSO + GBDT method was better than the other methods when the threshold probability was greater than 0.22. CONCLUSIONS: Radiomics can be used for the preoperative, noninvasive diagnosis of MVI, but different dimensionality reduction and modeling methods will affect the diagnostic performance of the final model. The model established with the LASSO+GBDT method had the optimal diagnostic performance and the greatest diagnostic value for MVI.