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.     

Thermoplastic Starch Nanocomposites Reinforced with Cellulose Nanocrystal Suspensions Containing Residual Salt from Neutralization

Sulfuric acid-catalyzed hydrolysis of cellulose commonly isolates cellulose nanocrystals (CNCs). Neutralizing the reactant solution with sodium hydroxide facilitates efficient downstream processing, but residual salt remains in the product. This study examines the reinforcing effects of CNCs from suspensions that contain residual salt on the mechanical properties of thermoplastic starch nanocomposites. By reinforcing starch films with up to 5 wt% CNCs, stiffness and strength are improved by 118% and 79%, respectively, indicating a good dispersion of CNCs in the starch matrix. Compared to nanocomposites incorporating salt-free CNCs, the remaining salt has no significant impact on the material's mechanical performance. The results indicate great potential of CNCs containing residual salt as biobased, low-cost nanofiller in hydrophilic polymer matrices.     

(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.     

酚醛树脂-CarboresP结合镁碳材料的性能与应用

研究了环境友好型含碳材料CarboresP在镁碳砖中的应用。采用MT14A镁碳砖生产工艺,改变CarboresP的加入量,对试样180℃处理后的体积密度、高温抗折强度(1400℃),以及1000℃埋炭处理后的显气孔率、耐压强度等指标进行了研究和对比,确定镁碳砖中合适的CarboresP添加量为1%,并且可适量降低金属防氧化剂的加入量;分析了酚醛树脂-CarboresP结合炭的显微结构,表明其为均质炭和非均质石墨化炭的混合结构,这将有助于改善炭化结构的抗氧化性能;采用尖劈试验绘制镁碳砖试样的应力-应变曲线,表明添加CarboresP的镁碳砖的特征长度LCH值得到明显提高,说明它具有很好的韧性和吸收应力的能力;与传统工艺生产的MT14A镁碳砖相比较,采用酚醛树脂-CarboresP“软性结合”工艺生产的MT14A镁碳砖,成型性能及热处理后各项性能指标均得到改善,在国内某40tLF-VD精炼钢包渣线上进行现场使用证明,平均使用寿命比现用渣线镁碳砖提高18.4%。     

Energieinformatik

Due to the increasing importance of producing and consuming energy more sustainably, Energy Informatics (EI) has evolved into a thriving research area within the CS/IS community. The article attempts to characterize this young and highly dynamic field of research by describing current EI research topics and methods and provides an outlook of how the field might evolve in the future. It is shown that two general research questions have received the most attention so far and are likely to dominate the EI research agenda in the coming years: How to leverage information and communication technology (ICT) to (1) improve energy efficiency, and (2) to integrate decentralized renewable energy sources into the power grid. Selected EI streams are reviewed, highlighting how the respective research questions are broken down into specific research projects and how EI researchers have made contributions based on their individual academic background.     

Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition

Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes.     

Ubicon and its applications for ubiquitous social computing

The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques, and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.     

Airwriting: a wearable handwriting recognition system

We present a wearable input system which enables interaction through 3D handwriting recognition. Users can write text in the air as if they were using an imaginary blackboard. The handwriting gestures are captured wirelessly by motion sensors applying accelerometers and gyroscopes which are attached to the back of the hand. We propose a two-stage approach for spotting and recognition of handwriting gestures. The spotting stage uses a support vector machine to identify those data segments which contain handwriting. The recognition stage uses hidden Markov models (HMMs) to generate a text representation from the motion sensor data. Individual characters are modeled by HMMs and concatenated to word models. Our system can continuously recognize arbitrary sentences, based on a freely definable vocabulary. A statistical language model is used to enhance recognition performance and to restrict the search space. We show that continuous gesture recognition with inertial sensors is feasible for gesture vocabularies that are several orders of magnitude larger than traditional vocabularies for known systems. In a first experiment, we evaluate the spotting algorithm on a realistic data set including everyday activities. In a second experiment, we report the results from a nine-user experiment on handwritten sentence recognition. Finally, we evaluate the end-to-end system on a small but realistic data set.     

Metallo-Supramolecular Polymerization: A Route to Easy-To-Process Organic/Inorganic Hybrid Materials

The self-assembly polymerization of ditopic macromolecules via metal–ligand binding is a facile route for the preparation of metallo-supramolecular polymers (MSPs). We herein review our recent work focused on the synthesis and investigation of metallo-supramolecular polymers based on 2,6-bis(1′-methylbenzimidazolyl)pyridine endcapped poly(p-phenylene ethynylene) and poly(p-xylene) macromonomers. These materials are readily solution-processable and display appreciable mechanical properties as well as other attractive properties such as specific opto/electrical functions or high thermal stability. Our work illustrates that metallosupramolecular polymerization offers an attractive approach to assemble high-molecular-weight macromolecules from well-defined, easy to process precursors. Variation of the ditopic ligands and metal ions allows one to easily tailor the desired properties. This paper is dedicated to Professor Ian Manners and his scientific accomplishments.     

Temporal evolution of contacts and communities in networks of face-to-face human interactions

Temporal dynamics of social interaction networks as well as the analysis of communities are key aspects to gain a better understanding of the involved processes, important influence factors, their effects, and their structural implications. In this article, we analyze temporal dynamics of contacts and the evolution of communities in networks of face-to-face proximity. As our application context, we consider four scientific coil- ferences. On a structural level, we focus on static and dynamic properties of the contact graphs. Also, we analyze the resulting community structure using state-of-the-art automatic community detection algorithms. Specifically, we analyze tile evolution of contacts and communities over time to consider the stability of the respective conmmnities. Furthermore, we assess different factors which have an influence on the quality of com- munity prediction. Overall, we provide first important insights into the evolution of contacts and communities in face-to-face contact networks.