An approach to automatic display layout using combinatorial optimization algorithms

The introduction of automatic display layout (ADL), i.e. the automatic placing and sizing of windows in a window-oriented graphical user interface, is a major contribution towards an improved user interface. Our approach to ADL is to treat this problem as a combinatorial optimization problem. In this article we describe the concepts we used for implementing an experimental system which controls the computer screen contents and its layout. We give two examples of different standard applications into which we included ADL successfully, namely hypertext for a window layout problem and graph-browser for a hierarchical graph layout problem within a particular window. The results show that automatic (and tool independent) display layout will be possible in the near future even in an interactive environment.     

Context relevance assessment and exploitation in mobile recommender systems

In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings. A large amount of data and complex context-aware predictive models must be exploited to understand these relationships. In this paper, we take a new approach for assessing and modeling the relationship between contextual factors and item ratings. Rather than using the traditional approach to data collection, where recommendations are rated with respect to real situations as participants go about their lives as normal, we simulate contextual situations to more easily capture data regarding how the context influences user ratings. To this end, we have designed a methodology whereby users are asked to judge whether a contextual factor (e.g., season) influences the rating given a certain contextual condition (e.g., season is summer). Based on the analyses of these data, we built a context-aware mobile recommender system that utilizes the contextual factors shown to be important. In a subsequent user evaluation, this system was preferred to a similar variant that did not exploit contextual information.     

Improving sub-pixel accuracy for long range stereo

Dense stereo algorithms are able to estimate disparities at all pixels including untextured regions. Typically these disparities are evaluated at integer disparity steps. A subsequent sub-pixel interpolation often fails to propagate smoothness constraints on a sub-pixel level.We propose to increase the sub-pixel accuracy in low-textured regions in four possible ways: First, we present an analysis that shows the benefit of evaluating the disparity space at fractional disparities. Second, we introduce a new disparity smoothing algorithm that preserves depth discontinuities and enforces smoothness on a sub-pixel level. Third, we present a novel stereo constraint (gravitational constraint) that assumes sorted disparity values in vertical direction and guides global algorithms to reduce false matches, especially in low-textured regions. Finally, we show how image sequence analysis improves stereo accuracy without explicitly performing tracking. Our goal in this work is to obtain an accurate 3D reconstruction. Large-scale 3D reconstruction will benefit heavily from these sub-pixel refinements.Results based on semi-global matching, obtained with the above mentioned algorithmic extensions are shown for the Middlebury stereo ground truth data sets. The presented improvements, called ImproveSubPix, turn out to be one of the top-performing algorithms when evaluating the set on a sub-pixel level while being computationally efficient. Additional results are presented for urban scenes. The four improvements are independent of the underlying type of stereo algorithm.     

Refining Genotypes and Phenotypes in KCNA2-Related Neurological Disorders

Pathogenic variants in KCNA2, encoding for the voltage-gated potassium channel K_v1.2, have been identified as the cause for an evolving spectrum of neurological disorders. Affected individuals show early-onset developmental and epileptic encephalopathy, intellectual disability, and movement disorders resulting from cerebellar dysfunction. In addition, individuals with a milder course of epilepsy, complicated hereditary spastic paraplegia, and episodic ataxia have been reported. By analyzing phenotypic, functional, and genetic data from published reports and novel cases, we refine and further delineate phenotypic as well as functional subgroups of KCNA2-associated disorders. Carriers of variants, leading to complex and mixed channel dysfunction that are associated with a gain- and loss-of-potassium conductance, more often show early developmental abnormalities and an earlier onset of epilepsy compared to individuals with variants resulting in loss- or gain-of-function. We describe seven additional individuals harboring three known and the novel KCNA2 variants p.(Pro407Ala) and p.(Tyr417Cys). The location of variants reported here highlights the importance of the proline(405)–valine(406)–proline(407) (PVP) motif in transmembrane domain S6 as a mutational hotspot. A novel case of self-limited infantile seizures suggests a continuous clinical spectrum of KCNA2-related disorders. Our study provides further insights into the clinical spectrum, genotype–phenotype correlation, variability, and predicted functional impact of KCNA2 variants.     

Structure-Activity Relationships of Benzamides and Isoindolines Designed as SARS-CoV Protease Inhibitors Effective against SARS-CoV-2

Inhibition of coronavirus (CoV)-encoded papain-like cysteine proteases (PL^pro) represents an attractive strategy to treat infections by these important human pathogens. Herein we report on structure-activity relationships (SAR) of the noncovalent active-site directed inhibitor (R)-5-amino-2-methyl-N-(1-(naphthalen-1-yl)ethyl) benzamide ( 2 b ), which is known to bind into the S3 and S4 pockets of the SARS-CoV PL^pro. Moreover, we report the discovery of isoindolines as a new class of potent PL^pro inhibitors. The studies also provide a deeper understanding of the binding modes of this inhibitor class. Importantly, the inhibitors were also confirmed to inhibit SARS-CoV-2 replication in cell culture suggesting that, due to the high structural similarities of the target proteases, inhibitors identified against SARS-CoV PL^pro are valuable starting points for the development of new pan-coronaviral inhibitors.     

Continuous size fractionation of magnetic nanoparticles by using simulated moving bed chromatography

The size fractionation of magnetic nanoparticles is a technical problem, which until today can only be solved with great effort. Nevertheless, there is an important demand for nanoparticles with sharp size distributions, for example for medical technology or sensor technology. Using magnetic chromatography, we show a promising method for fractionation of magnetic nanoparticles with respect to their size and/or magnetic properties. This was achieved by passing magnetic nanoparticles through a packed bed of fine steel spheres with which they interact magnetically because single domain ferro-/ferrimagnetic nanoparticles show a spontaneous magnetization. Since the strength of this interaction is related to particle size, the principle is suitable for size fractionation. This concept was transferred into a continuous process in this work using a so-called simulated moving bed chromatography. Applying a suspension of magnetic nanoparticles within a size range from 20 to 120 nm, the process showed a separation sharpness of up to 0.52 with recovery rates of 100%. The continuous feed stream of magnetic nanoparticles could be fractionated with a space-time-yield of up to 5 mg/(L∙min). Due to the easy scalability of continuous chromatography, the process is a promising approach for the efficient fractionation of industrially relevant amounts of magnetic nanoparticles.     

(S)-Reutericyclin: Susceptibility Testing and In Vivo Effect on Murine Fecal Microbiome and Volatile Organic Compounds

We aimed to assess the in vitro antimicrobial activity and the in vivo effect on the murine fecal microbiome and volatile organic compound (VOC) profile of (S)-reutericyclin. The antimicrobial activity of (S)-reutericyclin was tested against Clostridium difficile, Listeria monocytogenes, Escherichia coli, Enterococcus faecium, Staphylococcus aureus, Staphylococcus (S.) epidermidis, Streptococcus agalactiae, Pseudomonas aeruginosa and Propionibacterium acnes. Reutericyclin or water were gavage fed to male BALBc mice for 7 weeks. Thereafter stool samples underwent 16S based microbiome analysis and VOC analysis by gas chromatography mass spectrometry (GC-MS). (S)-reutericyclin inhibited growth of S. epidermidis only. Oral (S)-reutericyclin treatment caused a trend towards reduced alpha diversity. Beta diversity was significantly influenced by reutericyclin. Linear discriminant analysis Effect Size (LEfSe) analysis showed an increase of Streptococcus and Muribaculum as well as a decrease of butyrate producing Ruminoclostridium, Roseburia and Eubacterium in the reutericyclin group. VOC analysis revealed significant increases of pentane and heptane and decreases of 2,3-butanedione and 2-heptanone in reutericyclin animals. The antimicrobial activity of (S)-reutericyclin differs from reports of (R)-reutericyclin with inhibitory effects on a multitude of Gram-positive bacteria reported in the literature. In vivo (S)-reutericyclin treatment led to a microbiome shift towards dysbiosis and distinct alterations of the fecal VOC profile.     

A Uniform Approach for Utilizing Synergy between Inspection Technologies and Computational Methods

Emerging scanning technologies currently capture diverse data that includes geometrical and physical properties (i.e. normals, material and color). Yet, existing inspection computational methods do not satisfy industry demands. This paper proposes a new approach for inherently handling diverse data and utilizing the synergy between inspection technologies and digital processing methods. Two methods were developed: a Hierarchical Space Decomposition Model (HSDM), which sorts the sampled data, and a 3D Geometrical Bilateral Filter (3D GBF) that utilizes the diverse data. These methods provide efficient data reduction while preserving sharp features. The feasibility of this approach is demonstrated on the diverse data.     

Optimization of thermodynamic properties of the K2O-SiO2 system at high temperatures

An optimized set of thermodynamic functions for the K₂O-SiO₂ system at 10⁵ Pa pressure was obtained by considering available phase diagram and thermodynamic data. KSi_0.25O and SiO₂ were selected as components for the liquid phase. With respect to these components, the highly non-ideal interactions of potassium oxide and silica could be described by using only three temperature-independent Redlich-Kister coefficients for the excess G of the liquid phase. The result is in good agreement with experimental data.