Identification of a Grotthuss proton hopping mechanism at protonated polyhedral oligomeric silsesquioxane (POSS) - water interface

The attachment and dissociation of a proton from a water molecule and the proton transfers at solid-liquid interfaces play vital roles in numerous biological, chemical processes and for the development of sustainable functional materials for energy harvesting and conversion applications. Using first-principles computational methodologies, we investigated the protonated forms of polyhedral oligomeric silsesquioxane (POSS-H⁺) interacting with water clusters (W_n, where n = 1-6) as a model to quantify the proton conducting and localization ability at solid-liquid interfaces. Successive addition of explicit water molecules to POSS-H⁺ shows that the assistance of at least three water molecules is required to dissociate the proton from POSS with the formation of an Eigen cation (H₉O₄⁺), whereas the presence of a fourth water molecule highly favors the formation of a Zundel ion (H₅O₂⁺). Reaction pathway and energy barrier analysis reveal that the formation of the Eigen cation requires significantly higher energy than the Zundel features. This confirms that the Zundel ion is destabilized and promptly converts in to Eigen ion at this interface. Moreover, we identified a Grotthuss-type mechanism for the proton transfer through a water chain close to the interface, where symmetrical and unsymmetrical arrangements of water molecules around H⁺ of protonated POSS-H⁺ are involved in the conduction of proton through water wires where successive Eigen-to-Zundel and Zundel-to-Eigen transformations are observed in quick succession.

Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil

COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities.

Anti-corrosion and microstructural properties of Ni-W alloy coatings: effect of 3,4-Dihydroxybenzaldehyde

In the present work impact of 3,4-Dihydroxybenzaldehyde on the microstructural and corrosion behavior of nanocrystalline Ni-W alloy coatings has been elucidated. A systematic investigation on the protection ability of Ni-W alloy coatings in 0.2 M H₂SO₄ solution was done with the aid of tafel polarization curves and electrochemical impedance spectroscopy (EIS) studies. Corrosion performance of the alloy films obtained in the absence and in the presence of different concentrations of 3,4-Dihydroxybenzaldehyde (0–500 ppm) in the bath was explained in the light of additive concentration. Compared to the blank and other concentrations of additive, 250 ppm of additive containing bath was predicted as the most promising one for the introduced citrate based Ni-W alloy electrodeposition. Low corrosion rate (0.06 mm/year) and high charge transfer resistance (2505.3 Ω cm²), for the electrodeposits, obtained from the bath containing 250 ppm of 3,4-Dihydroxybenzaldehyde supports for its high anticorrosion performance. The marked difference in the corrosion resistance property is ascribed to the formation of fine-grained deposits, smooth surface, and inclusion or adsorption of additive within the deposits in the presence of the additive (250 ppm) in the bath. Further, the adsorption of additive molecules on the metal surface was explored with the help of quantum chemical calculations based on DFT.

Colorectal specialization and survival in colorectal cancer

AIM

It is recognized that higher surgeon volume is associated with improved survival in colorectal cancer. However, there is a paucity of national studies that have evaluated the relationship between surgical specialization and survival.

METHOD

We used the Surveillance, Epidemiology, and End Results Medicare cancer registry to examine the association between colorectal specialization (CRS) and disease-specific survival (DSS) between 2001 and 2009. A total of 21,432 colon cancer and 5893 rectal cancer patients who underwent elective surgical resection between 2001 and 2009 were evaluated. Univariate and multivariate Cox survival analysis was used to identify the association between surgical specialization and cancer-specific survival.

RESULTS

Colorectal specialists performed 16.3% of the colon and 27% of the rectal resections. On univariate analysis, specialization was associated with improved survival in Stage II and Stage III colon cancer and Stage II rectal cancer. In multivariate analysis, however, CRS was associated with significantly improved DSS only in Stage II rectal cancer [hazard ratio (HR) 0.70, P = 0.03]. CRS was not significantly associated with DSS in either Stage I (colon HR 1.14, P = 0.39; rectal HR 0.1.26, P = 0.23) or Stage III (colon HR 1.06, P = 0.52; rectal HR 1.08, P = 0.55) disease. When analysis was limited to high volume surgeons only, the relationship between CRS and DSS was unchanged.

CONCLUSIONS

CRS is associated with improved DSS following resection of Stage II rectal cancer. A combination of factors may contribute to long-term survival in these patients, including appropriate surgical technique, multidisciplinary treatment decisions and guideline-adherent surveillance. CRS probably contributes positively to these factors resulting in improved survival.

Predicting pathologic response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer using FDG PET/CT

505

Background: Pathologic complete response (pCR) after neoadjuvant chemoradiation has been observed in 15% to 30% of patients with locally advanced rectal cancer. The utility of FDG PET/CT scans in the management of patients with stage II or III rectal cancer is not well defined. The objective of this study is to determine if FDG PET/CT can be used to predict pCR and disease-free survival in patients receiving neoadjuvant chemoradiation with locally advanced rectal cancer.

Methods: A retrospective chart review was conducted in patients with endorectal ultrasound-staged T3 to T4 rectal tumors who underwent preoperative and postoperative FGD PET/CT imaging. All patients were treated with neoadjuvant chemoradiotherapy (CRT). Maximum standardized uptake value (SUV) of each tumor was recorded. Logistic regression was used to analyze the association of pre-CRT SUV, post-CRT SUV, % SUV change, and time between therapy and surgery in comparison to pathological complete response. Kaplan-Meier estimation was used to look for significant predictors of survival.

Results: Seventy patients (mean age 62; 42M:28F) with preoperative stage T3Nx (n = 60) and T4Nx (n = 10) underwent pre-CRT and post-CRT FDG PET/CT scans between November 2002 and March 2009. All patients underwent definitive surgery after therapy with standard pathologic evaluation.The pCR rate was 26%. Median pre-CRT SUV was 10.5 while the median post-CRT SUV was 4.05. Patients with pCR had a lower mean post-CRT SUV compared to those without pCR (2.7 vs. 4.5, p = 0.02). Median SUV decrease was 61% (range 6% to 95%) and was significant in predicting pCR (p = 0.004). Patients with a pCR had a greater time interval between neoadjuvant therapy and surgery (median 57 days vs. 50 days) than those without (p = 0.05). Furthermore, patients with post-CRT SUV < 4 had a lower local recurrence rate compared to those with post-CRT SUV > 4 (p = 0.03). Patients with SUV decrease > 61% had improved overall survival at mean follow-up of 39 months than those without (p = 0.01).

Conclusions: PET/CT can predict response to neoadjuvant chemoradiation in patients with locally advanced rectal cancer. Pre-CRT SUV was the only predictor of disease-free survival.

No significant financial relationships to disclose.