1-(1-phenethylpiperidin-4-yl)-1-phenylethanols as potent and highly selective 5-HT2A antagonists

The discovery of a novel class of highly potent and selective 5-HT2A antagonists is reported herein. Selectivity for the serotonin 5-HT2A receptor was optimized, decreasing the affinity of these antagonists toward the adrenergic alpha1 and dopaminergic D2 receptors, and especially to the 5-HT2C receptor. A series of corresponding 7-substituted indoles is described for the first time as serotonergic ligands. The enantiomer R-(+)-1-(4-fluorophenyl)-1-{1-[2-(4-fluorophenyl)ethyl]piperidin-4-yl} ethanol (R-(+)-74) was identified to have superior affinity for the serotonergic 5-HT2A receptor [IC50=0.37 nM] and selectivity toward the dopaminergic D2- [IC50=2300 nM], adrenergic alpha1- [IC50=1000 nM] and 5-HT2C receptors [IC50=490 nM].     

Rapid electrochemiluminoimmunoassay of human C-reactive protein at planar disposable oxide-coated silicon electrodes

Electrochemiluminescence (ECL) of aromatic Tb(III) chelates at thin insulating film-coated electrodes provides a means for extremely sensitive detection of Tb(III) chelates and also of biologically interesting compounds if these chelates are used as labels in bioaffinity assays. The suitability of silicon electrodes coated with thermally grown silicon dioxide film as disposable working electrodes in sensitive time-resolved ECL measurements is demonstrated, and a rapid electrochemiluminoimmunoassay (ECLIA) of human C-reactive protein (hCRP) is described. Tb(III) chelate labels can be detected almost down to picomolar level, and the calibration curve of these labels covers more than 6 orders of magnitude of chelate concentration. The calibration curve of the present immunometric hCRP assay was found to be linear over a wide range, approximately 4 orders of magnitude of hCRP concentration, the detection limit of the protein being 0.3 ng mL(-1) (mean background + 2SD) on CV values of about 10-30%, depending on the immunoassay incubation time. In the ECLIA measurements, different incubation times were tested from 15 min (giving above-mentioned performance) to as short as only 2 min, which still gave successful results with approximately 20,000 times better detection limit levels than traditional commercial assay methods. During the ECLIA process, also the Si electrode surface morphology was also investigated by atomic force microscope monitoring.     

Fully Enzymatic N→C‐Directed Peptide Synthesis Using C‐Terminal Peptide α‐Carboxamide to Ester Interconversion

Chemoenzymatic peptide synthesis is potentially the most cost‐efficient technology for the synthesis of short and medium‐sized peptides with some important advantages. For instance, stoichiometric amounts of expensive coupling reagents are not required and racemisation does not occur rendering purification easier compared to chemical peptide synthesis. In this paper, a novel interconversion reaction of peptide C‐terminal α‐carboxamides into primary alkyl esters with alcalase was used to develop a fully enzymatic peptide synthesis strategy. For each elongation step a cost‐efficient amino acid carboxamide building block was used followed by the interconversion of the elongated peptide carboxamide to the corresponding primary alkyl ester. These peptide esters are the starting materials for the next enzymatic peptide elongation step.     

Self-Learning of Multivariate Time Series Using Perceptually Important Points

In machine learning, positive-unlabelled (PU) learning is a special case within semi-supervised learning. In positive-unlabelled learning, the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes. Positive-unlabelled learning has gained attention in many domains, especially in time-series data, in which the obtainment of labelled data is challenging. Examples which originate from the negative class are especially difficult to acquire. Self-learning is a semi-supervised method capable of PU learning in time-series data. In the self-learning approach, observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached. The model is retrained after each addition with the existent labels. The main problem in self-learning is to know when to stop the learning. There are multiple, different stopping criteria in the literature, but they tend to be inaccurate or challenging to apply. This publication proposes a novel stopping criterion, which is called Peak evaluation using perceptually important points, to address this problem for time-series data. Peak evaluation using perceptually important points is exceptional, as it does not have tunable hyperparameters, which makes it easily applicable to an unsupervised setting. Simultaneously, it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.      

An open-source population balance modeling framework for the simulation of polydisperse multiphase flows

Polydispersity is a challenging feature of many industrial and environmental multiphase flows, influencing all related transfer and transport processes. Besides their size, the fluid or solid particles may be distributed with respect to other properties such as their velocity or shape. Here, a population balance model based on the method of classes is combined with a multifluid solver within the open source computational fluid dynamics library OpenFOAM. The model allows for tracking the evolution of one or more size-conditioned secondary properties. It is applied to two different problems, the first being bubbly flow of air and water in a vertical pipe, where considering the velocity as a secondary property allows to resolve the size-dependent radial segregation. The second application is the gas phase synthesis of titania powder, where non-spherical particle aggregates appear whose shape is modeled through a collision diameter, leading to an improved prediction of the size distribution.     

Immobilization of Chromium in Stainless Steel Slag Using Low Zinc Electric Arc Furnace Dusts

Metallurgical and Materials Transactions B - A new synergistic treatment of stainless steel slag and low zinc content electric arc furnace (EAF) dusts is proposed to immobilize harmful chromium in...     

Compact modular exponentiation accelerator for modern FPGA devices

We present a compact FPGA implementation of a modular exponentiation accelerator suited for cryptographic applications. The implementation efficiently exploits the properties of modern FPGAs. The accelerator consumes 434 logic elements, four 9-bit DSP elements, and 13604 memory bits in Altera Stratix EP1S40. It performs modular exponentiations with up to 2250-bit integers and scales easily to larger exponentiations. Excluding pre- and post-processing time, 1024-bit and 2048-bit exponentiations are performed in 26.39 ms and 199.11 ms, respectively. Due to its compactness, standard interface, and support for different clock domains, the accelerator can effortlessly be integrated into a larger system in the same FPGA. The accelerator and its performance are demonstrated in practice with a fully functional prototype implementation consisting of software and hardware components.