Mesoscopic simulation of diffusive contaminant spreading in gas flows at low pressure

Many modern production and measurement facilities incorporate multiphase systems at low pressures. In this region of flows at small, non-zero Knudsen- and low Mach numbers the classical mesoscopic Monte Carlo methods become increasingly numerically costly. To increase the numerical efficiency of simulations hybrid models are promising. In this contribution, we propose a novel efficient simulation approach for the simulation of two phase flows with a large concentration imbalance in a low pressure environment in the low intermediate Knudsen regime. Our hybrid model comprises a lattice-Boltzmann method corrected for the lower intermediate Kn regime proposed by Zhang et al. for the simulation of an ambient flow field. A coupled event-driven Monte-Carlo-style Boltzmann solver is employed to describe particles of a second species of low concentration. In order to evaluate the model, standard diffusivity and diffusion advection systems are considered.Comment: 9 pages, 8 figure

Kamerakalibrierung und Tiefenschätzung: Ein Vergleich von klassischer Bündelblockausgleichung und statistischen Lernalgorithmen

Die Arbeit verleicht zwei Herangehensweisen an das Problem der Schätzung der räumliche Position eines Punktes aus den Bildkoordinaten in zwei verschiedenen Kameras. Die klassische Methode der Bündelblockausgleichung modelliert zwei Einzelkameras und schätzt deren äußere und innere Orientierung mit einer iterativen Kalibrationsmethode, deren Konvergenz sehr stark von guten Startwerten abhängt. Die Tiefenschätzung eines Punkts geschieht durch die Invertierung von drei der insgesamt vier Projektionsgleichungen der Einzalkameramodelle. Die zweite Methode benutzt Kernel Ridge Regression und Support Vector Regression, um direkt eine Abbildung von den Bild- auf die Raumkoordinaten zu lernen. Die Resultate zeigen, daß der Ansatz mit maschinellem Lernen, neben einer erheblichen Vereinfachung des Kalibrationsprozesses, zu höheren Positionsgenaugikeiten führen kann

HARD: Hard Augmentations for Robust Distillation

Knowledge distillation (KD) is a simple and successful method to transfer knowledge from a teacher to a student model solely based on functional activity. However, current KD has a few shortcomings: it has recently been shown that this method is unsuitable to transfer simple inductive biases like shift equivariance, struggles to transfer out of domain generalization, and optimization time is magnitudes longer compared to default non-KD model training. To improve these aspects of KD, we propose Hard Augmentations for Robust Distillation (HARD), a generally applicable data augmentation framework, that generates synthetic data points for which the teacher and the student disagree. We show in a simple toy example that our augmentation framework solves the problem of transferring simple equivariances with KD. We then apply our framework in real-world tasks for a variety of augmentation models, ranging from simple spatial transformations to unconstrained image manipulations with a pretrained variational autoencoder. We find that our learned augmentations significantly improve KD performance on in-domain and out-of-domain evaluation. Moreover, our method outperforms even state-of-the-art data augmentations and since the augmented training inputs can be visualized, they offer a qualitative insight into the properties that are transferred from the teacher to the student. Thus HARD represents a generally applicable, dynamically optimized data augmentation technique tailored to improve the generalization and convergence speed of models trained with KD

Efficient Certified Resolution Proof Checking

We present a novel propositional proof tracing format that eliminates complex processing, thus enabling efficient (formal) proof checking. The benefits of this format are demonstrated by implementing a proof checker in C, which outperforms a state-of-the-art checker by two orders of magnitude. We then formalize the theory underlying propositional proof checking in Coq, and extract a correct-by-construction proof checker for our format from the formalization. An empirical evaluation using 280 unsatisfiable instances from the 2015 and 2016 SAT competitions shows that this certified checker usually performs comparably to a state-of-the-art non-certified proof checker. Using this format, we formally verify the recent 200 TB proof of the Boolean Pythagorean Triples conjecture

IN VITRO AND IN VIVO DISPOSITION OF 2,2-DIMETHYL-N-(2,4,6- TRIMETHOXYPHENYL)DODECANAMIDE (CI-976) Identification of a Novel Five-Carbon Cleavage Metabolite in Rats

ABSTRACT: The metabolism of CI-976, a potent inhibitor of liver and intestinal acyl coenzyme A:cholesterol acyltransferase, was investigated in isolated rat hepatocytes and Wistar rats after oral administration. The major metabolite observed both in vitro and in vivo was identified as the 6-carbon, chain-shortened 5,5-dimethyl-6-oxo-[(2,4,6-trimethoxyphenyl)amino]hexanoic acid (M-4). M-4 was determined to be formed from the -carboxylic acid 11,11-dimethyl-12-oxo ACAT 2 , (E.C. 2.3.1.1.26) is a key enzyme involved in cholesterol absorption from the gastrointestinal tract and cholesterol deposition in the body (1). The therapeutic potential of ACAT inhibitors as lipid lowering and antiatherosclerotic agents has been postulated for the treatment of hypercholesterolemia (2). The fatty acid anilide, CI-976 ( In vivo pharmacokinetic studies in male rats found CI-976 to have moderate absorption and bioavailability (29%), with an intravenous elimination half-life of 8 hr (6). After intravenous or oral administration to male rats, CI-976 was extensively metabolized to a single major urinary metabolite identified as M-4 ( To understand further the metabolism of CI-976, studies to determine the disposition and metabolism in rats were performed. The metabolism of CI-976 was examined both in hepatocyte suspensions and after oral administration to both male and female rats. In these studies, the metabolic pathways leading to the formation of M-4 were explored using metabolic intermediates as substrates, and by examining the effects of various inhibitors and inducers on the metabolism of CI-976 in hepatocyte incubations. Metabolites found in postreaction hepatocyte incubations and rat urine were characterized by HPLC, LC/MS, and GC/MS. Similar types of experiments were conducted with a new metabolite observed both in vitro and in vivo, which arises from an unusual mechanism (i.e. removal of 5-carbon units from the CI-976 fatty acid side chain). Materials and Methods CI-976 and [ 14 C]CI-976 (20.72 Ci/mg ring-labeled, 99.5% chemical and radiochemical purity); methyl-5,5-dimethyl-6-oxo-6-[(2,4,6-trimethoxyphe

CLPM: A Cross-Linked Peptide Mapping Algorithm for Mass Spectrometric Analysis

BACKGROUND: Protein-protein, protein-DNA and protein-RNA interactions are of central importance in biological systems. Quadrapole Time-of-flight (Q-TOF) mass spectrometry is a sensitive, promising tool for studying these interactions. Combining this technique with chemical crosslinking, it is possible to identify the sites of interactions within these complexes. Due to the complexities of the mass spectrometric data of crosslinked proteins, new software is required to analyze the resulting products of these studies. RESULT: We designed a Cross-Linked Peptide Mapping (CLPM) algorithm which takes advantage of all of the information available in the experiment including the amino acid sequence from each protein, the identity of the crosslinker, the identity of the digesting enzyme, the level of missed cleavage, and possible chemical modifications. The algorithm does in silico digestion and crosslinking, calculates all possible mass values and matches the theoretical data to the actual experimental data provided by the mass spectrometry analysis to identify the crosslinked peptides. CONCLUSION: Identifying peptides by their masses can be an efficient starting point for direct sequence confirmation. The CLPM algorithm provides a powerful tool in identifying these potential interaction sites in combination with chemical crosslinking and mass spectrometry. Through this cost-effective approach, subsequent efforts can quickly focus attention on investigating these specific interaction sites

Natural Image Coding in V1: How Much Use is Orientation Selectivity?

Orientation selectivity is the most striking feature of simple cell coding in V1 which has been shown to emerge from the reduction of higher-order correlations in natural images in a large variety of statistical image models. The most parsimonious one among these models is linear Independent Component Analysis (ICA), whereas second-order decorrelation transformations such as Principal Component Analysis (PCA) do not yield oriented filters. Because of this finding it has been suggested that the emergence of orientation selectivity may be explained by higher-order redundancy reduction. In order to assess the tenability of this hypothesis, it is an important empirical question how much more redundancies can be removed with ICA in comparison to PCA, or other second-order decorrelation methods. This question has not yet been settled, as over the last ten years contradicting results have been reported ranging from less than five to more than hundred percent extra gain for ICA. Here, we aim at resolving this conflict by presenting a very careful and comprehensive analysis using three evaluation criteria related to redundancy reduction: In addition to the multi-information and the average log-loss we compute, for the first time, complete rate-distortion curves for ICA in comparison with PCA. Without exception, we find that the advantage of the ICA filters is surprisingly small. Furthermore, we show that a simple spherically symmetric distribution with only two parameters can fit the data even better than the probabilistic model underlying ICA. Since spherically symmetric models are agnostic with respect to the specific filter shapes, we conlude that orientation selectivity is unlikely to play a critical role for redundancy reduction

Carbene footprinting accurately maps binding sites in protein–ligand and protein–protein interactions

Specific interactions between proteins and their binding partners are fundamental to life processes. The ability to detect protein complexes, and map their sites of binding, is crucial to understanding basic biology at the molecular level. Methods that employ sensitive analytical techniques such as mass spectrometry have the potential to provide valuable insights with very little material and on short time scales. Here we present a differential protein footprinting technique employing an efficient photo-activated probe for use with mass spectrometry. Using this methodology the location of a carbohydrate substrate was accurately mapped to the binding cleft of lysozyme, and in a more complex example, the interactions between a 100 kDa, multi-domain deubiquitinating enzyme, USP5 and a diubiquitin substrate were located to different functional domains. The much improved properties of this probe make carbene footprinting a viable method for rapid and accurate identification of protein binding sites utilizing benign, near-UV photoactivation

Detection of Crosslinks within and between Proteins by LC-MALDI-TOFTOF and the Software FINDX to Reduce the MSMS-Data to Acquire for Validation

Lysine-specific chemical crosslinking in combination with mass spectrometry is emerging as a tool for the structural characterization of protein complexes and protein-protein interactions. After tryptic digestion of crosslinked proteins there are thousands of peptides amenable to MSMS, of which only very few are crosslinked peptides of interest. Here we describe how the advantage offered by off-line LC-MALDI-TOF/TOF mass spectrometry is exploited in a two-step workflow to focus the MSMS-acquisition on crosslinks mainly. In a first step, MS-data are acquired and all the peak list files from the LC-separated fractions are merged by the FINDX software and screened for presence of crosslinks which are recognized as isotope-labeled doublet peaks. Information on the isotope doublet peak mass and intensity can be used as search constraints to reduce the number of false positives that match randomly to the observed peak masses. Based on the MS-data a precursor ion inclusion list is generated and used in a second step, where a restricted number of MSMS-spectra are acquired for crosslink validation. The decoupling of MS and MSMS and the peptide sorting with FINDX based on MS-data has the advantage that MSMS can be restricted to and focused on crosslinks of Type 2, which are of highest biological interest but often lowest in abundance. The LC-MALDI TOF/TOF workflow here described is applicable to protein multisubunit complexes and using 14N/15N mixed isotope strategy for the detection of inter-protein crosslinks within protein oligomers

Current Industrial Practices in Assessing CYP450 Enzyme Induction: Preclinical and Clinical

Induction of drug metabolizing enzymes, such as the cytochromes P450 (CYP) is known to cause drug-drug interactions due to increased elimination of co-administered drugs. This increased elimination may lead to significant reduction or complete loss of efficacy of the co-administered drug. Due to the significance of such drug interactions, many pharmaceutical companies employ screening and characterization models which predict CYP enzyme induction to avoid or attenuate the potential for drug interactions with new drug candidates. The most common mechanism of CYP induction is transcriptional gene activation. Activation is mediated by nuclear receptors, such as AhR, CAR, and PXR that function as transcription factors. Early high throughput screening models utilize these nuclear hormone receptors in ligand binding or cell-based transactivation/reporter assays. In addition, immortalized hepatocyte cell lines can be used to assess enzyme induction of specific drug metabolizing enzymes. Cultured primary human hepatocytes, the best established in vitro model for predicting enzyme induction and most accepted by regulatory agencies, is the predominant assay used to evaluate induction of a wide variety of drug metabolizing enzymes. These in vitro models are able to appropriately predict enzyme induction in patients when compared to clinical drug-drug interactions. Finally, transgenic animal models and the cynomolgus monkey have also been shown to recapitulate human enzyme induction and may be appropriate in vivo animal models for predicting human drug interactions