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Using nonadiabatic and adiabatic classical trajectory methods, we investigate nonadiabatic effects in collision of H + with H 2 for the collision energy in the range of 0.01 and 3.0 eV. Comparison with adiabatic calculations are used to give insight on the nonadiabatic charge transfer process. [ABSTRACT FROM AUTHOR]

The thermally induced phase separation (TIPS) process was employed to prepare poly (vinylidene fluoride-co-hexafluoropropylene; PVDF-HFP) microporous membranes using sulfolane as the diluent. The phase diagram of the PVDF-HFP/sulfolane system was drawn and analyzed. The effects of polymer content in casting solution and cooling rate on the cross-sectional morphology, crystallinity, crystal structure, and porous structure of the resulting membranes were investigated using scanning electron microscopy (SEM), differential scanning calorimetry (DSC), X-ray diffraction (XRD), and a mercury porosimeter, respectively. The mechanical properties of the membranes were evaluated by tensile tests. It was found that a solid-liquid phase separation occurred in the PVDF-HFP/sulfolane system. Spherulites and “net-shaped” structures coexisted in the obtained membranes. Polymer content and cooling rates had some influences on the crystallinity, porous structure, and mechanical properties of the membranes, but no influence on the polymer crystal structure of the membranes. [ABSTRACT FROM AUTHOR]

Background: The present study investigated the prevalence of osteoporosis (OP) among patients with essential hypertension (EH) in the Changchun community and analysed the correlation between EH and OP. Methods: The study included 425 subjects with EH and 425 age- and sex-matched healthy controls. Bone mineral density (BMD) and serum creatinine (CR) levels were measured, and the subjects' current EH and OP statuses were surveyed to analyse the correlation between EH and OP. Results: The EH group exhibited lower BMD and a higher rate of having OP than the control group, and this difference was statistically significant (p < 0.05). A significant sex difference in the BMD T-score was observed among the subjects (male: − 1.19 ± 1.55, female: − 1.70 ± 1.34). In both the EH group and the control group, the rate of having OP in females was greater than that in males. However, the OP prevalence among subjects with EH varied significantly by age, body weight, fracture history, nocturnal urination frequency, depression and anxiety status, duration of hypertension, and antihypertensive medication use (p < 0.05). Two-way analysis of variance suggested an effect of the interaction between different EH statuses and bone mass conditions on the serum CR values (F = 3.584, p = 0.028, bias η2 = 0.008). Conclusions: The prevalence of OP and low BMD were significantly higher among subjects with EH than among healthy controls. Additionally, the findings indicate that age, weight, fracture history, nocturnal urination frequency, depression and anxiety, duration of hypertension and antihypertensive drug use may be correlated to having OP in EH subjects, requiring further studies. Moreover, serum CR levels in subjects with different bone mass profiles were strongly influenced by the presence or absence of EH, and the serum CR levels differed significantly with the interaction of these two factors. [ABSTRACT FROM AUTHOR]

The high mortality rate of lung cancer is largely attributed to metastasis. Lung cancer stem cells (CSC) are conducive to cancer heterogeneity. Long noncoding RNAs are known to participate in various biological processes regulating the development of lung cancer. However, characterization of the role and mechanisms of lncRNA in lung cancer metastasis remains a challenge. We demonstrate that ROLLCSC, a highly expressed lncRNA in LLC-SDs, promotes the metastasis of the low metastatic LLCs both in vitro and in vivo. ROLLCSC can be transferred from LLC-SD to LLC through encapsulation in extracellular vesicles (EVs), ultimately leading to the enhancement of the metastatic phenotype of LLCs. Mechanistically, we demonstrate that the pro-metastatic activity of ROLLCSC is achieved through its function as a competing endogenous RNA (ceRNA) of miR-5623-3p and miR-217-5p to stimulate lipid metabolism. In this study, we have characterized ROLLCSC, a novel lncRNA, as a pivotal regulator in the metastasis of lung cancer, highlighting its potential as a therapeutic target. Specifically, we show that ROLLCSC is encapsulated by the EVs of LLC-SDs and transmitted to the LLCs, where it acts as a ceRNA of miR-5623-3p and miR-217-5p to stimulate lipid metabolism and ultimately augments metastatic colonization of LLCs. [ABSTRACT FROM AUTHOR]

As an in-situ and stand-off detection technique, laser-induced breakdown spectroscopy (LIBS) can perform efficient geochemical sample identification and classification with chemometrics, and therefore LIBS has played a shining role in planetary exploration missions. Unlike in laboratory experiments, the LIBS sampling distance in field detection for planetary exploration naturally varies. The considerable spectral differences caused by the varying distance can be a critical challenge for chemometrics model training and testing. In this research, we address this issue by focusing on the construction of a chemometrics model with powerful learning ability rather than the conventional spectral data processing for distance correction. Specifically, we have investigated the performance of a designed deep convolutional neural network (CNN) on datasets consisting of multi-distance spectra. More than 18,000 LIBS spectra were collected by a duplicate model of the MarSCoDe instrument for China's Tianwen-1 Mars mission, at eight different distances ranging from 2.0 m to 5.0 m. These spectra were acquired from 39 geochemical standard samples, which were classified by the deep CNN. The competence of the CNN is compared with that of four alternative chemometrics, i. e. back-propagation neural network, support vector machine, linear discriminant analysis, and logistic regression. The CNN can surpass the other four algorithms in terms of overall prediction accuracy. In addition, we have inspected the dependence of the CNN performance on the distance number involved in the training set and the data properties of the testing set. Furthermore, it has been revealed that the CNN model can behave even better if an extremely simple distance correction procedure is supplemented. Our results show that CNN is an extraordinary chemometrics for material classification on multi-distance spectra datasets, implying that CNN-LIBS is a promising methodology for geochemical sample identification/classification in Tianwen-1 mission and other future planetary exploration missions, and in even more field detection scenarios with varying sampling distance. [Display omitted] • A chemometrics-focused solution to the LIBS distance effect issue has been proposed. • A deep CNN was designed to classify geochemical samples on multi-distance spectra. • The CNN surpasses four other methods (BPNN, SVM, LDA and LR) in overall accuracy. • The CNN can behave even better if a very simple distance correction is supplemented. [ABSTRACT FROM AUTHOR]

Abstract: We analyze the orbital angular momentum (OAM) crosstalk of single photons propagation through low-order atmospheric turbulence. The probability models of the orbital angular momentum crosstalk for single photons propagation in the channel with the non-Kolmogorov turbulence tilt, coma, and astigmatism and defocus aberration have been established. It is found, for α=11/3, that the turbulent tilt is the dominant aberration which causes the orbital angular momentum crosstalk, the coma is second and the astigmatism is third, but the defocus aberration has no impact on OAM. The results also indicate that the regularities of orbital angular momentum crosstalk caused by the tilt, the coma and the astigmatism are almost the same, respectively. The crosstalk probability of the orbital angular momentum increases as the azimuth mode index p of Laguerre–Gaussian (LG) beam increases, the turbulent strength C n 2 enhances, the orbital angular momentum quantum number rises, the diameter of circular sampling aperture D and the channel zenith angle θ increase. [ABSTRACT FROM AUTHOR]

In the past decades various categories of chemometrics for laser-induced breakdown spectroscopy (LIBS) analysis have been developed, among which an important category is that based on artificial neural network (ANN). The most common ANN scheme employed in LIBS researches so far is back-propagation neural network (BPNN), while there are also several other kinds of neural networks appreciated by the LIBS community, including radial basis function neural network (RBFNN), convolutional neural network (CNN), self-organizing map (SOM), etc. In this paper, we introduce the principles of some representative ANN methods, and offer criticism on their features along with comparison between them. Then we afford an overview of ANN-based chemometrics applied in LIBS analysis, involving material identification/classification, component concentration quantification, and some unconventional applications as well. Furthermore, a comprehensive discussion on ANN-LIBS methodologies is provided from four aspects. First, a few general progressing trends are displayed. Next we expound some specific implementation techniques, including variable selection, network construction, data set utilization, network training, model evaluation, and chemometrics selection. In addition, the limitations of ANN approaches are remarked, mainly concerning overfitting and interpretability. Finally a prospect of future development of ANN-LIBS chemometrics is presented. Throughout the discussion quite a few good practices have been highlighted. This review is expected to shed light on the further upgrade of ANN-based LIBS chemometrics in the future. [Display omitted] • A review on artificial neural network (ANN) based LIBS chemometrics is provided. • Representative ANN algorithms are expounded and their applications in LIBS studies are presented. • The ANN-LIBS schemes are comprehensively discussed and good practices are extracted. [ABSTRACT FROM AUTHOR]