Rabbit Anti-Human FAU (a.a. 1-133) Polyclonal antibody

Faujasite-type zeolite (FAU zeolite) is a prospective adsorbent for wastewater treatment. Its large pores not only allow toxic heavy metal cations but also larger harmful organics to enter its structure. It is desirable to apply FAU zeolite in the form of fine particles with a high specific surface area to maximize its adsorption performance; however, the separation of the particles after the treatment is difficult. Therefore, numerous studies are dedicated to preparing composites of zeolites and magnetic particles (magnetic zeolites) to enable their quick and easy separation using magnetic force. Meanwhile, seed-assisted synthesis is a powerful technique for the rapid and selective synthesis of zeolites, although it has not been applied to the fabrication of magnetic zeolites. Here, we report seed-assisted synthesis of magnetic FAU zeolites for the first time, and evaluate its adsorption performance via the adsorption of methylene blue used as a model contaminant. The seed-assisted synthesis provides increased yield of the magnetic FAU zeolite. Moreover, we demonstrate that the magnetic Fe3O4 particles incorporated in the FAU zeolites enable the facile separation of the zeolites from water with the aid of a magnet, while their adsorption performance remains unaffected.

Background: Individuals with autism spectrum disorders (ASD) may be differentiated from typically developing controls (TDC) based on phenotypic features in spontaneous facial expressions. Computer vision technology can automatically track subtle facial actions to gain quantitative insights into ASD related behavioral abnormalities. Method: This study proposes a novel psychovisual human-study to elicit spontaneous facial expressions in response to a variety of social and emotional contexts. We introduce a markerless facial motion capture and computer vision methods to track spontaneous and subtle activations of facial muscles. The facial muscle activations are encoded into ten representative facial action units (FAU) to gain quantitative, granular, and contextual insights into the psychophysical development of the participating individuals. Statistical tests are performed to identify differential traits in individuals with ASD after comparing those in a cohort of age-matched TDC individuals. Results: The proposed framework has revealed significant difference (p < 0.001) in the activation of ten FAU and contrasting activations of FAU between the group with ASD and the TDC group. Unlike the TDC group, the group with ASD has shown unusual prevalence of mouth frown (FAU 15) and low correlations in temporal activations of several FAU pairs: 6-12, 10-12, and 10-20. The interpretation of different FAU activations suggests quantitative evidence of expression bluntness, lack of expression mimicry, incongruent reaction to negative emotions in the group with ASD. Conclusion: Our generalized framework may be used to quantify psychophysical traits in individuals with ASD and replicate in similar studies that require quantitative measurements of behavioral responses.