Radioprotective action of thiadiazole derivatives
Continuing previous investigations on the search for radioprotectors among the sulfurcontaining heterocycles [i, 2], we synthesized and studied the radioprotective properties of derivatives of 1,3,4and 1,2,4-thiadiazoles with various substituents: amino groups, alkyls, and mercapto groups. The following thiadiazole derivatives were investigated: 2alkylamino-5-alkyl-l,3,4-thiadiazoles and their methiodides (I-VII); 2-alkylamino-l,3,4thiadiazole-5-thiones (VIII-XII); 2-alkylamino-5-alkylmercapto-l,3,4-thiadiazoles (XIIIXVIII); 2,5-diamino-l,3,4-thiadiazole and its methiodide (XIX, XX), and 1,2,4-thiadiazoles (XXI-XXIII).
Evaluation of new thiadiazoles and benzothiazoles as potential radioprotectors: free radical scavenging activity in vitro and theoretical studies (QSAR, DFT).
Thiol and aminothiol compounds are among the most efficient chemical radioprotectors. To increase their efficiency, we synthesized two new classes of thiol and aminothiol compounds derived from benzothiazole (T1, T2, AM1, AM2) and thiadiazole (T3, T4, AM3) structures. We examined them for their ability to scavenge free radicals (DPPH*, ABTS(*+), *OH). Thiol derivatives with a thiadiazole structure are the most active compounds scavenging DPPH* and ABTS(*+) free radicals, with an IC(50) of 0.053+/-0.006 and 0.023+/-0.002 mM, respectively, for the derivative T3. Moreover, compounds T1, T2, and T3 at 60 microM gave 83% protection against 2-deoxyribose degradation by *OH. The ability of these compounds to protect DNA against *OH produced by a Fenton reaction and gamma-irradiation (15 Gy)-induced strand breaks was also evaluated on pBR322 plasmid DNA. In both tests thiol derivatives were the most efficient compounds. Derivatives T2 and T3 totally inhibit DNA strand breaks at the concentration of 50 microM. The protection afforded by these derivatives was comparatively higher than that of the radioprotectors WR-2721 and WR-1065. Our data indicate that these two compounds are free radical scavengers and potential antioxidant agents. Finally, DFT and QSAR studies were performed to support the experimental observations.
Benchmark database for fine-grained image classification of benthic macroinvertebrates
Abstract Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.