Simple Modification of Karl-Fischer Titration Method for Determination of Water Content in Colored Samples

The most commonly used technique for water content determination is Karl-Fischer titration with electrometric detection, requiring specialized equipment. When appropriate equipment is not available, the method can be performed through visual detection of a titration endpoint, which does not enable an analysis of colored samples. Here, we developed a method with spectrophotometric detection of a titration endpoint, appropriate for moisture determination of colored samples. The reaction takes place in a sealed 4 ml cuvette. Detection is performed at 520 nm. Titration endpoint is determined from the graph of absorbance plotted against titration volume. The method has appropriate reproducibility (RSD = 4.3%), accuracy, and linearity (R2 = 0.997)

River Sources of Dissolved Inorganic Carbon in the Gulf of Trieste (N Adriatic): Stable Carbon Isotope Evidence

River inputs can significantly affect carbon dynamics in the costal ocean. Here, we investigate the influence of four rivers (Isonzo/Soča, Timavo/Reka, Rižana, and Dragonja) on inorganic carbon (C) in the Gulf of Trieste in the northern Adriatic Sea using stable isotope signatures of dissolved inorganic carbon (δ¹³CDIC). In 2007, rivers exported 1.03 × 10¹¹ g C in the form of dissolved inorganic carbon (DIC) to the Gulf of Trieste with the lowest export observed in the Dragonja and the highest in the Isonzo/Soča. River plumes were associated with higher total alkalinity (TA) and pCO2 values compared with Gulf of Trieste waters, but their inputs showed high spatial variability. The δ¹³CDIC values and the isotopic mass balance suggested that river input during the spring of 2007 represented about 16 % of DIC at our study site VIDA, located in the southeastern part of the Gulf of Trieste. During autumn of 2007, the riverine contribution of DIC was less pronounced (3 %) although the river export of C was higher relative to the spring season. Convective mixing with the Gulf of Trieste waters and bora wind events appear to reduce the riverine contribution to the DIC system. Our results suggest that river plumes play an important role in C cycling in the Gulf of Trieste by direct inputs of higher riverine DIC and by increased biological uptake of DIC promoted by the supply of riverine nutrients

New Noncovalent Inhibitors of Penicillin-Binding Proteins from Penicillin-Resistant Bacteria

BACKGROUND: Penicillin-binding proteins (PBPs) are well known and validated targets for antibacterial therapy. The most important clinically used inhibitors of PBPs beta-lactams inhibit transpeptidase activity of PBPs by forming a covalent penicilloyl-enzyme complex that blocks the normal transpeptidation reaction; this finally results in bacterial death. In some resistant bacteria the resistance is acquired by active-site distortion of PBPs, which lowers their acylation efficiency for beta-lactams. To address this problem we focused our attention to discovery of novel noncovalent inhibitors of PBPs. METHODOLOGY/PRINCIPAL FINDINGS: Our in-house bank of compounds was screened for inhibition of three PBPs from resistant bacteria: PBP2a from Methicillin-resistant Staphylococcus aureus (MRSA), PBP2x from Streptococcus pneumoniae strain 5204, and PBP5fm from Enterococcus faecium strain D63r. Initial hit inhibitor obtained by screening was then used as a starting point for computational similarity searching for structurally related compounds and several new noncovalent inhibitors were discovered. Two compounds had promising inhibitory activities of both PBP2a and PBP2x 5204, and good in-vitro antibacterial activities against a panel of Gram-positive bacterial strains. CONCLUSIONS: We found new noncovalent inhibitors of PBPs which represent important starting points for development of more potent inhibitors of PBPs that can target penicillin-resistant bacteria.Eur-Intafa

SCENT\u27S INFLUENCE ON SALES STRUCTURE AND BEHAVIOUR OF INVOLVED SUBJECTS IN SALES PROCESS

V diplomskem delu opisujemo moč vonja za namene sodobnega marketinga. Čeprav je področje aroma marketinga znano in uporabljano že precej časa, pa velja dejstvo, da je takšen način oglaševanja v glavnem še vedno nerazširjena in nekonvencionalna praksa. To je presenetljivo predvsem skozi finančno sliko tovrstnega marketinga, ki je po navadi ugodna in znotraj obvladljivih okvirjev ter jo lahko opišemo po načelu »value for money« – torej visoka učinkovitost za nizko ceno. Ker je takšno pomanjkanje prisotno predvsem v Sloveniji, smo se dodatno odločili tudi za izvedbo eksperimenta, ki je pokazal vpliv vonja na manjšem primeru, kot je to v navadi. Iniciator želje po tovrstni raziskavi so bile med drugim tudi nezadostne domače raziskave in študije oziroma pomanjkanje teh. V prvem delu predstavimo senzorični marketing in ga primerjamo s tradicionalnim, razložimo pojme, kot sta senzorični in aroma marketing, ter navedemo veliko primerov uporabe vonja za namene marketinga – tako na tujih kot domačih trgih. V nadaljevanju diplomskega dela, s pomočjo podatkov pridobljenih skozi eksperiment, opisujemo vpliv vonja na dve področji. Prvo je vpliv vonja na strukturo prodaje, drugo pa vpliv vonja na vpletene v prodajni proces, kjer se ne fokusiramo zgolj na uporabnike, ampak tudi na druge, ki prisostvujejo v tem procesu. Vedenje zadnjih lahko namreč bistveno vpliva na uporabnikovo razpoloženje in vedenje. Z natančno izvedbo eksperimenta smo dokazali in razložili vpliv vonja tako na strukturo prodaje kakor na vedenje vpletenih v prodajni proces. Ob analizi rezultatov navajamo tudi odprta področja za nadaljnje raziskovanje in možnosti uporabe senzoričnega marketinga.Throughout diploma paper we are describing the power of scent for the purposes of modern marketing. Despite the fact that the field of scent marketing is known and used for some considerable time, the fact is that this method of advertising is still largely non-dispersive and non-conventional practice. This is surprising especially when looking at the financial picture, which is usually satisfactory, within manageable frame and can be described through the principle "value for money", meaning to get something of high efficiency or effectivenes for low cost. As such lack of practice is present primarily in Slovenia, we have additionally decided for the realization of experiment, which showed the influence of scent on a smaller case, as is customary. Initiator for this kind of research have also been insufficient domestic research and studies, or lack thereof. The first part of the paper describes the sensory marketing and comparison with the traditional one, explains concepts such as sensory and scent marketing and describes large amount of examples that involve the use of scent for marketing purposes, both in foreign and domestic markets. Mentioned is followed by the results of experiment, describing the scent\u27s influence onto two areas. The first being the influence on sales structure and the other the influence on behaviour of involved subjects in sales process, regarding not only the users, but also other involved subjects. Behaviour of the latter may in fact significantly affect the user\u27s mood and behaviour. With precise execution of the experiment we were able to demonstrate and explain the scent\u27s influence on both, the sales structure and the behaviour of those involved in the sales process. Furthermore we also mention the possibilities and open areas for further research and potential uses of sensory marketing

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

Inspired by natural language processing techniques, we here introduce Mol2vec, which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Like the Word2vec models, where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that point in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing the vectors of the individual substructures and, for instance, be fed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pretrained once, yields dense vector representations, and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as a reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment-independent and thus can also be easily used for proteins with low sequence similarities

From Cancer to Pain Target by Automated Selectivity Inversion of a Clinical Candidate

Elimination of inadvertent binding is crucial for inhibitor design targeting conserved protein classes like kinases. Compounds in clinical trials provide a rich source for initiating drug design efforts by exploiting such secondary binding events. Considering both aspects, we shifted the selectivity of tozasertib, originally developed against AurA as cancer target, toward the pain target TrkA. First, selectivity-determining features in binding pockets were identified by fusing interaction grids of several key and off-target conformations. A focused library was subsequently created and prioritized using a multiobjective selection scheme that filters for selective and highly active compounds based on orthogonal methods grounded in computational chemistry and machine learning. Eighteen high-ranking compounds were synthesized and experimentally tested. The top-ranked compound has 10000-fold improved selectivity versus AurA, nanomolar cellular activity, and is highly selective in a kinase panel. This was achieved in a single round of automated in silico optimization, highlighting the power of recent advances in computer-aided drug design to automate design and selection processes

From Cancer to Pain Target by Automated Selectivity Inversion of a Clinical Candidate

Elimination of inadvertent binding is crucial for inhibitor design targeting conserved protein classes like kinases. Compounds in clinical trials provide a rich source for initiating drug design efforts by exploiting such secondary binding events. Considering both aspects, we shifted the selectivity of tozasertib, originally developed against AurA as cancer target, toward the pain target TrkA. First, selectivity-determining features in binding pockets were identified by fusing interaction grids of several key and off-target conformations. A focused library was subsequently created and prioritized using a multiobjective selection scheme that filters for selective and highly active compounds based on orthogonal methods grounded in computational chemistry and machine learning. Eighteen high-ranking compounds were synthesized and experimentally tested. The top-ranked compound has 10000-fold improved selectivity versus AurA, nanomolar cellular activity, and is highly selective in a kinase panel. This was achieved in a single round of automated in silico optimization, highlighting the power of recent advances in computer-aided drug design to automate design and selection processes