A Study on the Correlation between Change in the Geometrical Dimension of a Free-Falling Molten Glass Gob and Its Viscosity

To produce flawless glass containers, continuous monitoring of the glass gob is required. It is essential to ensure production of molten glass gobs with the right shape, temperature, viscosity and weight. At present, manual monitoring is common practice in the glass container industry, which heavily depends on previous experience, operator knowledge and trial and error. This results in inconsistent measurements and consequently loss of production. In this article, a multi-camera based setup is used as a non-invasive real-time monitoring system. We have shown that under certain conditions, such as keeping the glass composition constant, it is possible to do in-line measurement of viscosity using sensor fusion to correlate the rate of geometrical change in the gob and its temperature. The correlation models presented in this article show that there is a strong correlation, i.e., 0.65, between our measurements and the projected viscosity.

In situ quantification of poly(3-hydroxybutyrate) and biomass in Cupriavidus necator by a fluorescence spectroscopic assay

Fluorescence spectroscopy offers a cheap, simple, and fast approach to monitor poly(3-hydroxybutyrate) (PHB) formation, a biodegradable polymer belonging to the biodegradable polyester class polyhydroxyalkanoates. In the present study, a fluorescence and side scatter-based spectroscopic setup was developed to monitor in situ biomass, and PHB formation of biotechnological applied Cupriavidus necator strain. To establish PHB quantification of C. necator, the dyes 2,2-difluoro-4,6,8,10,12-pentamethyl-3-aza-1-azonia-2-boranuidatricyclo[7.3.0.03,7]dodeca-1(12),4,6,8,10-pentaene (BODIPY493/503), ethyl 5-methoxy-1,2-bis(3-methylbut-2-enyl)-3-oxoindole-2-carboxylate (LipidGreen2), and 9-(diethylamino)benzo[a]phenoxazin-5-one (Nile red) were compared with each other. Fluorescence staining efficacy was obtained through 3D-excitation-emission matrix and design of experiments. The coefficients of determination were ≥ 0.98 for all three dyes and linear to the high-pressure liquid chromatography obtained PHB content, and the side scatter to the biomass concentration. The fluorescence correlation models were further improved by the incorporation of the biomass-related side scatter. Afterward, the resulting regression fluorescence models were successfully applied to nitrogen-deficit, phosphor-deficit, and NaCl-stressed C. necator cultures. The highest transferability of the regression models was shown by using LipidGreen2. The novel approach opens a tailor-made way for a fast and simultaneous detection of the crucial biotechnological parameters biomass and PHB content during fermentation. Key points • Intracellular quantification of PHB and biomass using fluorescence spectroscopy. • Optimizing fluorescence staining conditions and 3D-excitation-emission matrix. • PHB was best obtained by LipidGreen2, followed by BODIPDY493/503 and Nile red. Graphical abstract

Bioinformatics and Computer Simulation approaches to the discovery and analysis of Bioactive Peptides

The traditional process of separating and purifying bioactive peptides is laborious and time-consuming. Using a traditional process to identify is difficult, and there is a lack of fast and accurate activity evaluation methods. How to extract bioactive peptides quickly and efficiently is still the focus of bioactive peptides research. In order to improve the present situation of the research, bioinformatics techniques and peptidome methods are widely used in this field. At the same time, bioactive peptides have their own specific pharmacokinetic characteristics, so computer simulation methods have incomparable advantages in studying the pharmacokinetics and pharmacokinetic-pharmacodynamic correlation models of bioactive peptides. The purpose of this review is to summarize the combined applications of bioinformatics and computer simulation methods in the study of bioactive peptides, with focuses on the role of bioinformatics in simulating the selection of enzymatic hydrolysis and precursor proteins, activity prediction, molecular docking, physicochemical properties, and molecular dynamics. Our review shows that new bioactive peptide molecular sequences with high activity can be obtained by computer-aided design. The significance of the pharmacokinetic-pharmacodynamic correlation model in the study of bioactive peptides is emphasized. Finally, some problems and future development potential of bioactive peptides binding new technologies are prospected.

Modeling the pressure strain correlation in turbulent flows using deep neural networks

The pressure strain correlation plays a critical role in the Reynolds stress transport modeling. Accurate modeling of the pressure strain correlation leads to the proper prediction of turbulence stresses and subsequently the other terms of engineering interest. However, classical pressure strain correlation models are often unreliable for complex turbulent flows. Machine learning–based models have shown promise in turbulence modeling, but their application has been largely restricted to eddy viscosity–based models. In this article, we outline a rationale for the preferential application of machine learning and turbulence data to develop models at the level of Reynolds stress modeling. As an illustration, we develop data-driven models for the pressure strain correlation for turbulent channel flow using neural networks. The input features of the neural networks are chosen using physics-based rationale. The networks are trained with the high-resolution DNS data of turbulent channel flow at different friction Reynolds numbers (Reλ). The testing of the models is performed for unknown flow statistics at other Reλ and also for turbulent plane Couette flows. Based on the results presented in this article, the proposed machine learning framework exhibits considerable promise and may be utilized for the development of accurate Reynolds stress models for flow prediction.

Association between BrainAgeR scores and Alzhemier’s disease brain and plasma biomarkers

Background: The goal of this project was to study the association between the age scores assigned by the BrainAgeR computer algorithm and AD biomarker levels in the blood and brain. Methods: 123 individuals from the Indiana Memory and Aging Study underwent amyloid ([18F]florbetapir or [18F]florbetaben) and Tau [18F]flortaucipir PET. Another set of 156 individuals from the Indiana Memory and Aging Study underwent plasma testing for Amyloid B 40/42, Tau and neurofilament light chain (NFL). Additionally, all participants underwent structural MRI and were processed using BrainAgeR to receive a “brain age” score. Partial Person correlation models were used to evaluate the relationship between BrainAge difference scores (Chronological age-BrainAge) and levels of Amyloid and Tau in the brain and plasma. Age and diagnosis were evaluated as covariates but did not change the observed pattern of results. Results: Significant negative association between BrainAge difference scores and Tau uptake was observed across the neuroimaging group. This correlation persisted when analysis was limited to MCI/AD subjects but was lost when analysis was only limited to CN/SCD subjects. Across all participants in the neuroimaging group, significant negative associations were found between BrainAge differences and the levels of Amyloid deposition in the global cortex. Significant positive association was found between AB42/40 ratio and BrainAge difference scores across the entire plasma group. Significant negative relationships found between NFL and AB40 and BrainAge difference scores when analyzed in the CN/SCD group, but no statistically significant relationship was found when only the MCI/AD group was analyzed. Conclusion: BrainAge difference scores had a statistically significant association with various biomarkers of AD depending on the level of diagnosis, with cognitively normal and less impaired subjects showing an association with plasma amyloid and more impaired subjects showing an association with Tau deposition in the brain. Future studies in larger samples are warranted.

North Atlantic Oscillation and Rainfall Variability in Southeastern Nigeria: A Statistical Analysis of 30 Year Period

This study analyzed rainfall variability in Southeast region of Nigeria using graphical models, as well as using statistical approach to investigate any significant relationship between the global North Atlantic Oscillation (NAO) Index and the regional rainfall variability in region. The study was conducted in three States of Southeastern Nigeria namely, Abia, Ebonyi and Imo States that lie between Latitudes 40 40’ and 80 50’N and Longitudes 60 20’ and 80 50’E. Data for the study included 30 years (1988 - 2017) archival time-series monthly rainfall values for the three study States, acquired from Nigerian Meteorological Agency (NIMET), offices in the states, and Standardized values of NAOI (North Atlantic Oscillation Index) for the same period, which were collected from a website, on the NOAA Data Center, USA. In the data analyses, the first method was adopted by using graphs to illustrate mean annual rainfall values for thirty years. Coefficient of variability was employed in evaluating the degree of variability of values from the mean rate. The second analysis was accomplished using correlation models to ascertain any relationship between NAOI and rainfall in Southeast Nigeria. The results showed a significant variability of rainfall in the region from January to December (mean monthly) within the study period. A negative correlation value of 0.7525 was obtained from the correlation analysis, showing that the global NAO index and rainfall variability deviate in the opposite direction. Coefficient of multiple determinations (CMD) subsequently showed value of 0.031%, being the variation in rainfall as influenced by the global teleconnectivity, and this means that the NAO index has zero or no influence on rainfall variability in Southeast region of Nigeria.

Investigating the spatial correlations in univariate random fields of peak ground velocity and peak ground displacement considering anisotropy

The results of seismic risk assessment of spatially distributed infrastructure systems are significantly influenced by spatial correlation of earthquake intensity measures (IM). The assumption of isotropy is a basis for most of the existing correlation models of earthquake IMs. In this study, the isotropy assumption of intra-event residuals of peak ground velocity (PGV) and peak ground displacement (PGD) is investigated by implementing a nonparametric statistical test. Using recorded IMs of 9 earthquakes, it is concluded that there is not sufficient evidence to support the assumption of isotropy in general, and the set of intra-event residuals of PGV and PGD should be considered as the realization of anisotropic random fields. Investigations show that the anisotropy properties of intra-event residuals of PGV and PGD are related to anisotropy properties of local soil characteristics indicated by average shear wave velocity of soil profile from the 30 m depth to the surface (Vs30). Finally, predictive models are proposed based on obtained results in order to simulate the correlated univariate random fields of PGV and PGD considering anisotropy.

The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

This study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.