Development of an Optimized Process for Functional Recombinant SARS-CoV-2 Spike S1 Receptor-Binding Domain Protein Produced in the Baculovirus Expression Vector System.

To map the spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and evaluate immune response variations against this virus, it is essential to set up efficient serological tests locally. The SARS-CoV-2 immunogenic proteins were very expensive and not affordable for lower- middle-income countries (LMICs). For this purpose, the commonly used antigen, receptor-binding domain (RBD) of spike S1 protein (S1RBD), was produced using the baculovirus expression vector system (BEVS). In the current study, the expression of S1RBD was monitored using Western blot under different culture conditions. Different parameters were studied: the multiplicity of infection (MOI), cell density at infection, and harvest time. Hence, optimal conditions for efficient S1RBD production were identified: MOI 3; cell density at infection 2-3 × 106 cells/mL; and time post-infection (tPI or harvest time) of 72 h and 72-96 h, successively, for expression in shake flasks and a 7L bioreactor. A high production yield of S1RBD varying between 4 mg and 70 mg per liter of crude cell culture supernatant was achieved, respectively, in the shake flasks and 7L bioreactor. Moreover, the produced S1RBD showed an excellent antigenicity potential against COVID-19 (Wuhan strain) patient sera evaluated by Western blot. Thus, additional serological assays, such as in-house ELISA and seroprevalence studies based on the purified S1RDB, were developed.

Immunohistochemical Characterization Improves the Reproducibility of the Histological Diagnosis of Ovarian Carcinoma

Background: Ovarian cancer is the leading cause of gynecologic cancer-related death. Histological assessment remains the standard clue for the diagnosis of ovarian carcinoma. Misinterpretation and inconsistent application of histological criteria may lead to significant interobserver variability and poor reproducibility of the diagnosis. In this study, we investigated the discrepancy in histological diagnosis and the significance of a designed panel of immunohistochemical markers for the improvement of the diagnostic reproducibility of ovarian carcinomas. Methods: We performed a retrospective study on 74 ovarian carcinomas. All tumor slides were independently reviewed by two pathologists. The results for seven available immunomarkers as p53, WT-1, p16INK4A, CK7, CK20, and estrogen and progesterone receptors were determined for all cases by immunohistochemistry. Results: The histological diagnosis review performed using standard histology showed a concordance of diagnoses in 86% of cases with Cohen's kappa of 0.80. Immunohistochemical results increased significantly the diagnosis reproducibility with a concordance of 91% and a Cohen's kappa of 0.86 (P = 0.001). Conclusion: Although the histological diagnosis remains reliable, the use of a designed panel of immunohistochemical markers improves significantly the interobserver concordance and the classification accuracy of ovarian carcinomas.

Measuring the environmental sustainability performance of global supply chains: A multi-regional input-output analysis for carbon, sulphur oxide and water footprints.

Measuring the performance of environmentally sustainable supply chains instead of chain constitute has become a challenge despite the convergence of the underlining principles of sustainable supply chain management. This challenge is exacerbated by the fact that supply chains are inherently dynamic and complex and also because multiple measures can be used to characterize performances. By identifying some of the critical issues in the literature regarding performance measurements, this paper contributes to the existing body of literature by adopting an environmental performance measurement approach for economic sectors. It uses economic sectors and evaluates them on a sectoral level in specific countries as well as part of the Global Value Chain based on the established multi-regional input-output (MRIO) modeling framework. The MRIO model has been used to calculate direct and indirect (that is supply chain or upstream) environmental effects such as CO2, SO2, biodiversity, water consumption and pollution to name just a few of the applications. In this paper we use MRIO analysis to calculate emissions and resource consumption intensities and footprints, direct and indirect impacts, and net emission flows between countries. These are exemplified by using carbon emissions, sulphur oxide emissions and water use in two highly polluting industries; Electricity production and Chemical industry in 33 countries, including the EU-27, Brazil, India and China, the USA, Canada and Japan from 1995 to 2009. Some of the highlights include: On average, direct carbon emissions in the electricity sector across all 27 member states of the EU was estimated to be 1368 million tons and indirect carbon emissions to be 470.7 million tons per year representing 25.6% of the EU-27 total carbon emissions related to this sector. It was also observed that from 2004, sulphur oxide emissions intensities in electricity production in India and China have remained relatively constant at about 62.8 gSOx/, respectively, $ and 84.4 gSOx/$ although being higher than in other countries. In terms of water use, the high water use intensity in China (1040.27 L/$) and India (961.63 L/$), which are among the highest in the sector in the electricity sector is exacerbated by both countries being ranked as High Water Stress Risk countries. The paper also highlights many advantages of the MRIO approach including: a 15-year time series study (which provides a measurement of environmental performance of key industries and an opportunity to assess technical and technological change during the investigated time period), a supply chain approach that provides a consistent methodological framework and accounts for all upstream supply chain environmental impacts throughout entire global supply chains. The paper also discusses the implications of the study to environmental sustainability performance measurement in terms of the level of analysis from a value chain hierarchy perspective, methodological issues, performance indicators, environmental exchanges and policy relevance.

Strategies for increasing the efficiency of a genetic algorithm for the structural optimization of nanoalloy clusters.

An improved genetic algorithm (GA) is described that has been developed to increase the efficiency of finding the global minimum energy isomers for nanoalloy clusters. The GA is optimized for the example Pt12Pd12, with specific investigation of: the effect of biasing the initial population by seeding; the effect of removing specified clusters from the population ("predation"); and the effect of varying the type of mutation operator applied. These changes are found to significantly enhance the efficiency of the GA, which is subsequently demonstrated by the application of the best strategy to a new cluster, namely Pt19Pd19.

Adaptive hybrid learning for neural networks.

A robust locally adaptive learning algorithm is developed via two enhancements of the Resilient Propagation (RPROP) method. Remaining drawbacks of the gradient-based approach are addressed by hybridization with gradient-independent Local Search. Finally, a global optimization method based on recursion of the hybrid is constructed, making use of tabu neighborhoods to accelerate the search for minima through diversification. Enhanced RPROP is shown to be faster and more accurate than the standard RPROP in solving classification tasks based on natural data sets taken from the UCI repository of machine learning databases. Furthermore, the use of Local Search is shown to improve Enhanced RPROP by solving the same classification tasks as part of the global optimization method.