Novel measurement system for respiratory aerosols and droplets in indoor environments

The SARS-CoV-2 pandemic has created a great demand for a better understanding of the spread of viruses in indoor environments. A novel measurement system consisting of one portable aerosol-emitting mannequin (emitter) and a number of portable aerosol-absorbing mannequins (recipients) was developed that can measure the spread of aerosols and droplets that potentially contain infectious viruses. The emission of the virus from a human is simulated by using tracer particles solved in water. The recipients inhale the aerosols and droplets and quantify the level of solved tracer particles in their artificial lungs simultaneously over time. The mobile system can be arranged in a large variety of spreading scenarios in indoor environments and allows for quantification of the infection probability due to airborne virus spreading. This study shows the accuracy of the new measurement system and its ability to compare aerosol reduction measures such as regular ventilation or the use of a room air purifier.     

Entity linking systems for literature reviews

Scientometrics - Computer-assisted methods and tools can help researchers automate the coding process of literature reviews and accelerate the literature review process. However, existing...     

A Potent Mimetic of the Siglec-8 Ligand 6’-Sulfo-Sialyl Lewisx

Siglecs are members of the immunoglobulin gene family containing sialic acid binding N-terminal domains. Among them, Siglec-8 is expressed on various cell types of the immune system such as eosinophils, mast cells and weakly on basophils. Cross-linking of Siglec-8 with monoclonal antibodies triggers apoptosis in eosinophils and inhibits degranulation of mast cells, making Siglec-8 a promising target for the treatment of eosinophil- and mast cell-associated diseases such as asthma. The tetrasaccharide 6’-sulfo-sialyl Lewis^x has been identified as a specific Siglec-8 ligand in glycan array screening. Here, we describe an extended study enlightening the pharmacophores of 6’-sulfo-sialyl Lewis^x and the successful development of a high-affinity mimetic. Retaining the neuraminic acid core, the introduction of a carbocyclic mimetic of the Gal moiety and a sulfonamide substituent in the 9-position gave a 20-fold improved binding affinity. Finally, the residence time, which usually is the Achilles tendon of carbohydrate/lectin interactions, could be improved.     

Role of Perceived Importance in Intergroup Contact.

Furthering G. W. Allport's (1954) contentions for optimal contact, the authors introduce a new construct: the perceived importance of contact. They propose that perceived importance is the best proximal predictor of contact's reduction of prejudice. If individuals have opportunities for contact at work or in the neighborhood, their chances to have intergroup acquaintances and friends increase. Intergroup contact among acquaintances and friends can be perceived as more or less important, which in turn determines intergroup evaluations. A 1st study shows that the new measure of perceived importance is indeed distinct from established quantity and quality indicators. The results are cross-validated in a 2nd study that also sheds light on the meaning of importance. In 3rd and 4th studies, structural equation analyses and a meta-analysis support the hypotheses. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Modeling tool wear in end-milling using enhanced GMDH learning networks

This paper presents an enhanced approach to predictive modeling for determining tool-wear in end-milling operations based on enhanced-group method of data handling (e-GMDH). Using milling input parameters (speed, feed, and depth-of-cut) and response (tool wear), the data for the model is partitioned into training and testing datasets, and the training dataset is used to realize a predictive model that is a function of the input parameters and the coefficients determined. In our approach, we first present a methodology for modeling, and then develop predictive model(s) of the problem being solved in the form of second-order equations based on the input data and coefficients realized. This approach leads to some generalization because it becomes possible to predict not only the test data obtained during experimentation, but other test data outside the experimental results can also be used. Moreover, this approach makes it easy to present the realized solution in a form that can be further optimized for the input parameters using some optimization techniques. The results realized using our e-GMDH method are promising, and the comparative study presented shows that the e-GMDH outperforms polynomial neural network (PNN); moreover, it is more flexible than the conventional GMDH, which tends to produce nonlinear solutions even for simple problems. In the investigation, the extended particle swarm optimization (PSO) technique was applied to obtain the optimal parameters. Consequently, the modeling approach is extremely useful in realizing a computer-aided process-planning system in an advanced manufacturing environment.