Potential Mechanisms of Photodynamic Inactivation of Virus by Methylene BlueI. RNA–Protein Crosslinks and Other Oxidative Lesions in Qβ Bacteriophage

A spectrum of oxidative lesions was observed in a bacteriophage-based model system that is very sensitive to the photodynamic activity of selected dyes. When suspensions of the intact bacteriophage Qβ were exposed to methylene blue plus light (MB+L), inactivating events, or "hits" occurred that were oxygen-dependent and that were associated with the formation of several specific lesions: (1) carbonyl moieties on proteins, (2) 8-oxo-7,8-dihydroguanine (8-oxoGua), and (3) single-strand breaks (ssb) in the RNA genome and (4) RNA-protein crosslinks. Formation of carbonyl groups associated with protein in the Qβ phage preparation correlated positively with photoinactivation of the phage with increasing doses of either of the sensitizers MB or rose bengal. Strand breaks in the Qβ genomic RNA were observable at high MB concentrations but appeared not to be significant at the lower concentrations of MB, as full-length Qβ RNA was observable well beyond the 99% inactivation point in MB dosage. It was shown that the number of 8-oxoGua lesions were unlikely to be sufficient to account for the number of lethal events. Following exposure to MB+L, crosslink formation between Qβ RNA and protein was observed by virtue of the location of RNA at the interface of phenol-aqueous extractions of phage suspensions. A significant increase over background of RNA-protein complexes (including full-length Qβ RNA) was observed at the lowest concentration of MB tested (0.5 μ M ), which corresponded roughly to an average of 2 lethal hits per phage or approximately 13% survival compared to the zero MB control (100% survival). Due to its close correlation with Qβ inactivation and its expected lethality, RNA-protein crosslink formation may be important as an inactivating lesion in bacteriophage Qβ following MB+L exposure.     

Amino acid analysis by using comprehensive two-dimensional gas chromatography

The separation characteristics of alkylchloroformate-derivatised amino acids (AAs) by using comprehensive two-dimensional gas chromatography (GC×GC) is reported. The use of a low-polarity/polar column set did not provide as good a separation performance as that achieved with a polar/non-polar column set, where the latter appeared to provide less correlation over the separation space. The degree of component correlation in each column set was estimated by using the correlation coefficient (r²; for ¹t_R and ²t_R data) with the low-polarity/polar and polar/low-polarity sets returning correlation coefficients of 0.86, and 0.00 respectively, under the respective conditions employed for the experiments. The 1.5-m non-polar ²D column (0.1-mm ID; 0.1-m film thickness) gave peak halfwidths of the order of 50–80 ms. Linearity of detection was good, over a three order of magnitude concentration range, with typical lower detection limit of ca. 0.01 mg L^–1, compared with 0.5 mg L^–1 for normal GC operation with splitless injection. The method was demonstrated for analysis of AAs in a range of food and beverage products, including wine, beer and honey. The major AA in these samples was proline. The Heineken beer sample had a relatively more complex and more abundant AA content compared with the other beer sample. The wine and honey samples also gave a range of AA compounds. Repetition of the sample preparation/analysis procedure for the honey sample gave acceptable reproducibility for individual AAs.     

Synthesis of 20-deoxoluteone

20-Deoxoluteone has been synthesized from sclareol. Sclareol was brominated with phosphorus tribromide to form a mixture of primary allyl bromides, from which, by the malonic synthesis, a mixture of bicyclogeranylgeranylacetic acids was obtained which was cyclized with fluorosulfonic acid to form a mixture of two diastereomeric -lactones. The predominating lactone was converted by successive reduction with lithium tetrahydroaluminate, oxidation with oxalyl chloride in dimethyl sulfoxide, reaction with methylmagnesium iodide, and oxidation by the chromium trioxide/pyridine complex into 20-deoxyluteone.Institute of Chemistry, Moldavian SSR Academy of Science, Kishinev. Translated from Khimiya Prirodnykh Soedinenii, No. 3, pp. 346–353, May–June, 1990.     

Emploi des thiocarbohydrazones en analyse organique fonctionelle

Résumé Le thiocarbohydrazide est un réactif d'accès facile précipitant quantitativement les aldéhydes et quelques cétones. Les points de fusion sont nets. Les précipités formés se prêtent à la gravimétrie. Nous avons étudié avec la thermobalance leur domaine de stabilité et fourni la température à ne pas dépasser pour le séchage à poids constant.

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Summary Thiocarbohydrazide is a readily available reagent which quantitatively precipitates aldehydes and some ketones. The melting points are sharp. The resulting precipitates are suitable for gravimetry. Their regions of stability have been investigated with the thermobalance and the temperatures which should not be exceeded in drying to a constant weight have been found.

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Zusammenfassung Thiocarbohydrazid ist ein leicht zugängliches Reagens zur quantitativen Fällung der Aldehyde und mancher Ketone. Die Schmelzpunkte der Niederschläge sind scharf. Diese eignen sich zur gravimetrischen Auswertung. Ihre Stabilität wurde mit Hilfe der Thermowaage untersucht und die Höchsttemperaturen für ihre Trocknung bis zur Gewichtskonstanz bestimmt.

     

Low frequency limits of third-order harmonic coefficients for atomic hydrogen in any level of discrete energy spectrum

In this paper the analytical expression is derived for the third-order optical harmonic coefficients x _abcd ^v (,,) of the atomic hydrogen at very low frequencies 0 in many level of the discrete energy spectrum.The author expresses his thank to Prof. Nguyen Van Hieu for suggesting the problem and fruitful discussions.     

An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms

Despite the increasing applications, demands, and capabilities of drones, in practice they have only limited autonomy for accomplishing complex missions, resulting in slow and vulnerable operations and difficulty adapting to dynamic environments. To lessen these weaknesses, we present a computational framework for deducing the original intent of drone swarms by monitoring their movements. We focus on interference, a phenomenon that is not initially anticipated by drones but results in complicated operations due to its significant impact on performance and its challenging nature. We infer interference from predictability by first applying various machine learning methods, including deep learning, and then computing entropy to compare against interference. Our computational framework begins by building a set of computational models called double transition models from the drone movements and revealing reward distributions using inverse reinforcement learning. These reward distributions are then used to compute the entropy and interference across a variety of drone scenarios specified by combining multiple combat strategies and command styles. Our analysis confirmed that drone scenarios experienced more interference, higher performance, and higher entropy as they became more heterogeneous. However, the direction of interference (positive vs. negative) was more dependent on combinations of combat strategies and command styles than homogeneity.     

Coulomb forces impacts on nanomaterial transportation within porous tank with lid walls

Journal of Thermal Analysis and Calorimetry - Electrical sensor has been considered in the current attempt. Combination of buoyancy, electric and radiative forces has been included in the governing...     

Efficacy of incorporating PCMs into the commercial wall on the energy-saving annual thermal analysis

Journal of Thermal Analysis and Calorimetry - In this study, the efficacy of incorporating phase change material (PCM) into the building walls on the annual heat transfer reduction is examined....     

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.     

Zymography for Picogram Detection of Lipase and Esterase Activities

Lipases and esterases are important catalysts with wide varieties of industrial applications. Although many methods have been established for detecting their activities, a simple and sensitive approach for picogram detection of lipolytic enzyme quantity is still highly desirable. Here we report a lipase detection assay which is 1000-fold more sensitive than previously reported methods. Our assay enables the detection of as low as 5 pg and 180 pg of lipolytic activity by direct spotting and zymography, respectively. Furthermore, we demonstrated that the detection sensitivity was adjustable by varying the buffering capacity, which allows for screening of both high and low abundance lipolytic enzymes. Coupled with liquid chromatography-mass spectrometry, our method provides a useful tool for sensitive detection and identification of lipolytic enzymes.