Real‐time monitoring by proton relaxometry of radical polymerization reactions of acrylamide in aqueous solution

The potential of time‐domain nuclear magnetic resonance (TD‐NMR) for the real‐time monitoring of solution radical polymerizations is demonstrated. A model system composed of a redox‐pair initiator system, acrylamide as monomer and water as solvent was investigated. A second‐generation continuous wave free precession technique was employed to measure the longitudinal relaxation time constant (T₁) of the samples throughout the polymerization reactions. This parameter was shown to be sensitive to the reactant feed free‐radical enhancement of the water molecule relaxation time, making it a good probe to monitor monomer conversion in real time in an automated, non‐destructive fashion. It was found that the T₁ value was better than the transverse relaxation time constant (T₂) for describing the evolution of the polymerization reactions, due to its greater sensitivity to paramagnetic effects. The TD‐NMR signal variation observed was linked to the formation, propagation and termination steps of the radical polymerization kinetics scheme. These first results may contribute to the application of real‐time monitoring of radical polymerization reactions employing low‐cost and robust TD‐NMR spectrometers. © 2018 Society of Chemical Industry     

Lecture capture with real‐time rearrangement of visual elements: impact on student performance

The primary goal of this study is to create and test a lecture‐capture system that can rearrange visual elements while recording is still taking place, in such a way that student performance can be positively influenced. The system we have devised is capable of integrating and rearranging multimedia sources, including learning content, the instructor and students' images, into lecture videos that are embedded in a website for students to review after school. The present study employed a two‐group experimental design, with 153 participants (145 females and 8 males) making up an experimental group in which lecture courses were recorded using the new lecture‐capture system, and 149 participants (140 females and 9 males) forming a control group whose lectures were recorded by traditional means. All participants were in the freshman college and studying Introduction to Computer and Information Science in one of six classes, and were randomly assigned to one of the two groups. The participants' midterm examination and final examination scores were collected as indicators of their academic performance, with their mathematics entrance scores used as a pre‐test. The findings obtained from analysis of covariance (ANCOVA) suggest that appropriate rearrangement of visual elements in lecture videos can significantly impact students' learning performance.     

Occurrence and distribution of dibenzofurans and benzo[b]naphthofurans in the crude oils from the Northern and offshore Niger Delta basin,Nigeria

The occurrence and distributions of dibenzofurans (DBFs) and benzo[b]naphthofurans were investigated in crude oils from Niger Delta, Nigeria, by gas chromatography-mass spectrometry-mass spectrometry. The distribution of DBFs was characterized by the predominance of C₂-dibenzofurans. 4-Methyldibenzofuran was the most abundant among the methyldibenzofurans isomers while dimethyldibenzofuran-2 (DMDBF-2), ethyldibenzofuran-1, DMDBF-3, and DMDBF-6 occurred in higher amounts when compared with other DMDBFs. Among the benzonaphthofurans, the abundance of benzo[b]naphtho[2,1-d]furan was higher than other isomers. The DBFs distributions in the oils were not affected by source facies and depositional environments. However, the DBFs concentrations increased with increasing maturity in oils from ADL and MJO oilfields.     

Identification of Host Cellular Protein Substrates of SARS-COV-2 Main Protease

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-19 (COVID-19) being associated with severe pneumonia. Like with other viruses, the interaction of SARS-CoV-2 with host cell proteins is necessary for successful replication, and cleavage of cellular targets by the viral protease also may contribute to the pathogenesis, but knowledge about the human proteins that are processed by the main protease (3CLpro) of SARS-CoV-2 is still limited. We tested the prediction potentials of two different in silico methods for the identification of SARS-CoV-2 3CLpro cleavage sites in human proteins. Short stretches of homologous host-pathogen protein sequences (SSHHPS) that are present in SARS-CoV-2 polyprotein and human proteins were identified using BLAST analysis, and the NetCorona 1.0 webserver was used to successfully predict cleavage sites, although this method was primarily developed for SARS-CoV. Human C-terminal-binding protein 1 (CTBP1) was found to be cleaved in vitro by SARS-CoV-2 3CLpro, the existence of the cleavage site was proved experimentally by using a His₆-MBP-mEYFP recombinant substrate containing the predicted target sequence. Our results highlight both potentials and limitations of the tested algorithms. The identification of candidate host substrates of 3CLpro may help better develop an understanding of the molecular mechanisms behind the replication and pathogenesis of SARS-CoV-2.     

Towards Diastereomeric Pure Salts with Antisolvent Methods

Chiral molecules, especially enantiomers and diastereomers of purity > 99 %, present a significant market share within the chemical, pharmaceutical, and flavor industries. Antisolvent precipitations, both batch and semicontinuous operations to serve the current trends in flow chemistry were demonstrated to be environmentally benign and efficient tools in achieving high optical purities. Although salts are known to be insoluble in supercritical CO₂, instabilities of the nascent salts were detected and applied for increasing efficiency. Diastereomeric excess values of the crystalline products exceeded 99 % in maximum of three consecutive steps both by repeated resolution with half molar equivalent of the amine to the acid and by direct recrystallization of the salts.     

Milk from cows fed a diet with a high forage:concentrate ratio improves inflammatory state,oxidative stress,and mitochondrial function in rats

Excessive energy intake may evoke complex biochemical processes characterized by inflammation, oxidative stress, and impairment of mitochondrial function that represent the main factors underlying noncommunicable diseases. Because cow milk is widely used for human nutrition and in food industry processing, the nutritional quality of milk is of special interest with respect to human health. In our study, we analyzed milk produced by dairy cows fed a diet characterized by a high forage:concentrate ratio (high forage milk, HFM). In view of the low n-6:n-3 ratio and high content of conjugated linoleic acid of HFM, we studied the effects of this milk on lipid metabolism, inflammation, mitochondrial function, and oxidative stress in a rat model. To this end, we supplemented for 4 wk the diet of male Wistar rats with HFM and with an isocaloric amount (82 kJ, 22 mL/d) of milk obtained from cows fed a diet with low forage:concentrate ratio, and analyzed the metabolic parameters of the animals. Our results indicate that HFM may positively affect lipid metabolism, leptin:adiponectin ratio, inflammation, mitochondrial function, and oxidative stress, providing the first evidence of the beneficial effects of HFM on rat metabolism.     

Carbon supported bimetallic Ru‐Co catalysts for H2 production through NaBH4 and NH3BH3 hydrolysis

This work investigates the effect of the addition of small amounts of Ru (0.5‐1 wt%) to carbon supported Co (10 wt%) catalysts towards both NaBH₄ and NH₃BH₃ hydrolysis for H₂ production. In the sodium borohydride hydrolysis, the activity of Ru‐Co/carbon catalysts was sensibly higher than the sum of the activities of corresponding monometallic samples, whereas for the ammonia borane hydrolysis, the positive effect of Ru‐Co systems with regard to catalytic activity was less evident. The performances of Ru‐Co bimetallic catalysts correlated with the occurrence of an interaction between Ru and Co species resulting in the formation of smaller ruthenium and cobalt oxide particles with a more homogeneous dispersion on the carbon support. It was proposed that Ru^°, formed during the reduction step of the Ru‐Co catalysts, favors the H₂ activation, thus enhancing the reduction degree of the cobalt precursor and the number of Co nucleation centers. A subsequent reduction of cobalt and ruthenium species also occurs in the hydride reaction medium, and therefore the state of the catalyst before the catalytic experiment determines the state of the active phase formed in situ. The different relative reactivity of the Ru and Co active species towards the two investigated reactions accounted for the different behavior towards NaBH₄ and NH₃BH₃ hydrolysis.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.