Computational study of infrared spectra of silica polymorphs via classical mechanics

Added to MOspace: April 21, 2020.A potential energy model that correctly reflects zeolite framework interactions is the premise for computational studies of the physical and chemical processes occurring inside zeolites, such as catalytic chemical reactions and adsorption. Infrared spectroscopy is a widely-used technique that is sensitive to the accuracy of the potential energy model. This work aims to develop such a potential that reproduces the infrared spectra of zeolites. In the first part of this thesis, the performance of two published potentials is tested in terms of predicting structural and dynamical properties for five silica polymorphs (three siliceous zeolites: siliceous faujasite, sodalite and silicalite; quartz; and cristobalite). Comparison between the silica polymorphs' model-predicted equilibrium angle distributions and infrared spectra shows that the core-shell model [Schroeder and Sauer, J. Phys. Chem. 1996, 100, 11043] predicts a broader Si-O-Si angle distribution and shifts angle-bending infrared modes to lower wavenumbers. The MZHB potential [Sahoo and Nair, J. Comput. Chem. 2015, 36, 1562], on the other hand, predicts angle-bending infrared modes that are consistently shifted to higher wavenumbers. The second part of this thesis presents a new potential via reparameterizing and extending the MZHB potential based on a sensitivity analysis, which investigates the relationships between model parameters and the structural properties of silica polymorphs. Better infrared predictions are achieved by the new potential. The results of the sensitivity analysis indicate that the lattice parameter might be a possible target for the parameterization of atomic partial charges for crystalline materials

Advantages of multi-dimensional biasing in accelerated dynamics: application to the calculation of the acid pKapK_a for acetic acid

The use of accelerated sampling methods such as metadynamics has shown significant advantage in calculations that involve infrequent events, which would otherwise require sampling a prohibitive number of configurations to determine, e.g., difference in free energies between two or more chemically distinct states such as in the calculation of acid dissociation constants $K_a$. In this case, the most common method is to bias the system via a single collective variable (CV) representing the $coordination~number$ of the proton donor group, which yields results in reasonable agreement with experiments. Here we study the deprotonation of acetic acid using the reactive force field ReaxFF and observe a significant dependence of $K_a$ on the simulation box size when biasing only the coordination number CV, which is due to incomplete sampling of the deprotonated state for small simulation systems, and inefficient sampling for larger ones. Incorporating a second CV representing the distance between the H$_3$O$^+$ cation and the acetate anion results in a substantially more efficient sampling both accelerating the dynamics and virtually eliminating the computational box size dependence.Comment: 22 pages, 9 figure

The BMP inhibitor follistatin-like 1 (FSTL1) suppresses cervical carcinogenesis

Follistatin-like 1 (FSTL1) is a cancer-related matricellular secretory protein with contradictory organ-specific roles. Its contribution to the pathogenesis of cervical carcinoma is still not clear. Meanwhile, it is necessary to identify novel candidate genes to understand cervical carcinoma’s pathogenesis further and find potential therapeutic targets. We collected cervical carcinoma samples and matched adjacent tissues from patients with the locally-advanced disease and used cervical carcinoma cell lines HeLa and C33A to evaluate the effects of FSTL1 on CC cells. The mRNA transcription and protein expression of FSTL1 in cervical carcinoma tumor biopsy tissues were lower than those of matched adjacent tissues. Patients with a lower ratio of FSTL1 mRNA between the tumor and its matched adjacent tissues showed a correlation with the advanced cervical carcinoma FIGO stages. High expression of FSTL1 markedly inhibited the proliferation, motility, and invasion of HeLa and C33A. Regarding mechanism, FSTL1 plays its role by negatively regulating the BMP4/Smad1/5/9 signaling. Our study has demonstrated the tumor suppressor effect of FSTL1, and these findings suggested a potential therapeutic target and biomarker for cervical carcinoma

A Secretory Protein of Necrotrophic Fungus Sclerotinia sclerotiorum That Suppresses Host Resistance

SSITL (SS1G_14133) of Sclerotinia sclerotiorum encodes a protein with 302 amino acid residues including a signal peptide, its secretion property was confirmed with immunolocalization and immunofluorescence techniques. SSITL was classified in the integrin alpha N-terminal domain superfamily, and its 3D structure is similar to those of human integrin α4-subunit and a fungal integrin-like protein. When S. sclerotiorum was inoculated to its host, high expression of SSITL was detected during the initial stages of infection (1.5-3.0 hpi). Targeted silencing of SSITL resulted in a significant reduction in virulence; on the other hand, inoculation of SSITL silenced transformant A10 initiated strong and rapid defense response in Arabidopsis, the highest expressions of defense genes PDF1.2 and PR-1 appeared at 3 hpi which was 9 hr earlier than that time when plants were inoculated with the wild-type strain of S. sclerotiorum. Systemic resistance induced by A10 was detected by analysis of the expression of PDF1.2 and PR-1, and confirmed following inoculation with Botrytis cinerea. A10 induced much larger lesions on Arabidopsis mutant ein2 and jar1, and slightly larger lesions on mutant pad4 and NahG in comparison with the wild-type plants. Furthermore, both transient and constitutive expression of SSITL in Arabidopsis suppressed the expression of PDF1.2 and led to be more susceptible to A10 and the wild-type strain of S. sclerotiorum and B. cinerea. Our results suggested that SSITL is an effector possibly and plays significant role in the suppression of jasmonic/ethylene (JA/ET) signal pathway mediated resistance at the early stage of infection

A New Regularization for Deep Learning-Based Segmentation of Images with Fine Structures and Low Contrast

Deep learning methods have achieved outstanding results in many image processing and computer vision tasks, such as image segmentation. However, they usually do not consider spatial dependencies among pixels/voxels in the image. To obtain better results, some methods have been proposed to apply classic spatial regularization, such as total variation, into deep learning models. However, for some challenging images, especially those with fine structures and low contrast, classical regularizations are not suitable. We derived a new regularization to improve the connectivity of segmentation results and make it applicable to deep learning. Our experimental results show that for both deep learning methods and unsupervised methods, the proposed method can improve performance by increasing connectivity and dealing with low contrast and, therefore, enhance segmentation results

China's power supply chain sustainability: an analysis of performance and technology gap

The power industry is a major source of carbon emissions in China and it is vital, therefore, to address the industry to promote carbon emission reduction. This study takes the power supply chain (PSC) in China, composed of coal-fired thermal power plants and downstream power grid enterprises as its primary research object. From the perspective of sustainable development, the study explores and analyzes the sustainable performance and technology heterogeneity of China’s provinces’ PSCs, proposing the two-system model to evaluate the sustainable performance, generation performance and sale performance of PSCs. In addition, to understand the technology level of PSC, this study applies the meta-frontier technique to analyze the technology heterogeneity of all PSCs across different regions. The proposed models are then applied to analyze the sustainable performance of China’s provincial PSCs. The empirical results demonstrate the market-oriented reform of the power industry in China played a role in promoting the development of power generation enterprises in China’s PSCs but had a limited effect on the power grid enterprises in the PSC. The study also shows that there are significant regional differences in the sustainable performance and technology of China’s PSC. Generally, PSCs in Eastern China have a high level of sustainable performance and technology, while the sustainable performance and technology of the PSCs in Central and Northeast China are relatively poor. Based on these empirical results, specific policy recommendations are presented to improve PSC’s sustainable performance and technology levels at government and enterprise levels

Comparison of Siliceous Zeolite Potentials from the Perspective of Infrared Spectroscopy

Zeolitesmicroporous crystalline aluminosilicate materialsare the basis of many physical and chemical processes. Computational modeling of these processes requires an accurate description of the zeolite structure and the potential energy surface. In this work, two published force fields, the modified Zimmerman, Head-Gordon, and Bell (MZHB) potential [Sahoo and Nair, <i>J. Comput. Chem.</i> <b>2015</b>, <i>36</i>, 1562–1567] and the core–shell model [Schröder and Sauer, <i>J. Phys. Chem.</i> <b>1996</b>, <i>100</i>, 11043–11049], are tested in terms of their abilities to predict the structural and dynamical properties, including infrared (IR) spectra, of five silica polymorphs (three siliceous zeolites: zeolite Y, sodalite, and silicalite-1, as well as α-quartz and α-cristobalite) via classical molecular dynamics simulations. Normal mode analysis at the Γ point and quantum mechanical cluster calculations are carried out on periodic crystals and a finite-size representative cluster model, respectively, to assist in the assignment of IR bands. We observe that the core–shell model predicts a broader distribution of bond angles because of the lack of three-body interactions defined for the Si–O–Si angles. The MZHB potential, in contrast, consistently shifts angle-bending modes to higher wavenumbers relative to experiments

A dynamic nomogram for predicting pathologic complete response to neoadjuvant chemotherapy in locally advanced rectal cancer

Abstract Aim To explore the clinical factors associated with pathologic complete response (pCR) for locally advanced rectal cancer (LARC) patients treated with neoadjuvant chemoradiotherapy (nCRT) and develop a web‐based dynamic nomogram. Methods Retrospective analysis of patients with examination confirmed LARC from 2011 to 2022. Patients from the Union Hospital of Fujian Medical University were included as the training cohort (n = 1579) and Zhangzhou Hospital of Fujian Medical University as the external validation cohort (n = 246). Results In the training cohort, after nCRT, 350 (22.2%) patients achieved pCR. More stomas were avoided in pCR patients (73.9% vs. 69.7%, p = 0.043). After a median follow‐up time of 47.7 months (IQR 2–145) shown OS (5‐year: 93.7% vs. 81.0%, HR = 0.310, 95%CI: 0.189–0.510, p < 0.001) and DFS (5‐year: 91.2% vs. 75.0%, HR = 0.204, 95%CI: 0.216–0.484, p < 0.001) were significantly better among patients with pCR than non‐pCR. Multivariable Logistic analysis shown pCR was significantly associated with Pre‐CRT CEA (HR = 0.944, 95%CI: 0.921–0.968; p < 0.001), histopathology (HR = 4.608, 95%CI: 2.625–8.089; p < 0.001), Pre‐CRT T stage (HR = 0.793, 95%CI: 0.634–0.993; p = 0.043), Pre‐CRT N stage (HR = 0.727, 95%CI: 0.606–0.873; p = 0.001), Pre‐CRT MRI EMVI (HR = 0.352, 95%CI: 0.262–0.473; p < 0.001), total neoadjuvant therapy (HR = 2.264, 95%CI: 1.280–4.004; p = 0.005). Meanwhile, the online version of the nomogram established in this study was publicized on an open‐access website (URL: https://pcrpredict.shinyapps.io/LARC2/). The model predicted accuracy with a C‐index of 0.73 (95% CI: 0.70–0.75), with an average C‐index of 0.73 for the internal cross validation and 0.78 (95% CI: 0.72–0.83) for the external validation cohort, showing excellent model accuracy. Delong test results showed the model has an important gain value for clinical characteristics to predict pCR in rectal cancer. Conclusions Patients with pCR had a better prognosis, including OS and DFS, and were independently associated with Pre‐CRT CEA, histopathology, Pre‐CRT T/N stage, Pre‐CRT MRI EMVI, and TNT. A web‐based dynamic nomogram was successfully established for clinical use at any time