Maassanalytische Bestimmung des Quecksilbers

Ohne Zusammenfassung     

Classification of doubly-periodic minimal surfaces of genus zero

We prove that if the quotient surface of a properly embedded doubly–periodic minimal surface in ℝ³ has genus zero, then the surface is one of the classical Scherk examples. Oblatum 30-IX-1998 & 18-II-2000?Published online: 30 October 2000     

Eine Gehaltsbestimmung des k?uflichen Jodkaliums

Ohne Zusammenfassung     

Retrieval of complex refractive index and size distribution of spherical particles from Dual-Polarization Polar Nephelometer data

An algorithm to retrieve the complex refractive index and size distribution of spherical particles from Dual-Polarization Polar Nephelometer data are presented. The assessment of the refractive index is based on the lookup-table approach, which is safe against converging to a local minimum, i.e. false solutions. The results of tests of the algorithm against real experimental data are outlined. The retrieved characteristics are in the expected ranges and in good agreement with the nature of particles and the specifications of the aerosol generators. It is demonstrated that, when a population of small particles, e.g., atmospheric aerosols, is dealt with and a gradient algorithm is employed to estimate the complex refractive index, either the algorithm must be able to overcome the problem of local minima or one should have some a priori information about credible ranges of the real and imaginary parts.     

Ueber die Abscheidung des Chinins aus dem Urin

Ohne Zusammenfassung     

A xanthone and a polyketide derivative from the leaves of Cassia obtusifolia (Leguminosae)

A new xanthone, 1,8-dihydroxy-3-methoxy-6-methylxantone and a new polyketide derivative, (4R^∗,5S^∗,6E,8Z)-ethyl-4-((E)-but-1-enyl)-5-hydroxypentdeca-6,8-dienoate, together with 20 known secondary metabolites, including 2 steroids, 4 xanthones, 10 anthraquinones, 2 triterpenoids, 1 fatty ester, and (E)-eicos-14-enoic acid, were isolated from the leaves of Cassia obtusifolia. To the best of our knowledge, the last compound was isolated from a natural source for the first time. The structures of all the compounds were elucidated on the basis of 1D and 2D NMR experiments. Some of the compounds were tested against Salmonella typhi, Staphylococcus aureus, Candida albicans ATCC 9002, and Candida tropicalis, they did not show any activity.     

Prenylated anthraquinones and other constituents from the seeds of Vismia laurentii

Two new prenylated anthraquinones, laurenquinone A (1) and B (2) were isolated from the seeds of Vismia laurentii together with four known compounds; xanthone V(1) (3), physcion (4), 3-geranyloxyemodin anthrone (5) and friedelin (6). The structures of the new metabolites were determined with the help of spectroscopic data including extensive 2D-NMR spectroscopy. The known compounds were identified by comparison of their physical and spectroscopic data with those reported in the literature. Compounds 1, 4 and 5 exhibited moderate algicidal activity against Chlorella fusca and 3 showed moderate activity against the gram-positive bacterium Bacillus megaterium.     

Machine learning designs non-hemolytic antimicrobial peptides

Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure–activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug-resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however mostly without addressing the associated toxicity to human red blood cells. Here we trained recurrent neural networks (RNN) with data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) to design short non-hemolytic AMPs. Synthesis and testing of 28 generated peptides, each at least 5 mutations away from training data, allowed us to identify eight new non-hemolytic AMPs against Pseudomonas aeruginosa, Acinetobacter baumannii, and methicillin-resistant Staphylococcus aureus (MRSA). These results show that machine learning (ML) can be used to design new non-hemolytic AMPs.

Machine learning models trained with experimental data for antimicrobial activity and hemolysis are shown to produce new non-hemolytic antimicrobial peptides active against multidrug-resistant bacteria.