In this paper, we present a novel, ecologically friendly technology for the synthesis and modification of kombucha-derived bacterial cellulose in order to produce textiles of desired physicochemical and mechanical properties. The procedure of manufacturing cellulose in the form of a stable hydrogel bacterial cellulose (HGBC) ensures the desired properties for the application of such a material, e.g., in the textile industry. Bacterial cellulose was obtained from a yeast/bacteria kombucha culture (a symbiotic consortium also known as “tea fungus” or SCOBY) that is easy and cheap to breed. The process of bacterial cellulose manufacturing and modification was optimized in order to obtain a maximum recovery of raw materials, minimal energy consumption and ensure the use of only natural and renewable resources. The obtained materials were characterized in terms of their wettability, mechanical properties, and flame resistance. Moreover, the morphology and composition of the materials were determined by using scanning electron microscopy and infrared spectroscopy, respectively. Additionally, it was proven that the HGBC materials might be used to manufacture various articles of clothing using commonly available sewing techniques, which are not adequate for non-modified cellulose-based materials. Finally, the synthesized fabrics were used as wristbands and parts of T-shirts and tested on volunteers to determine a skin-to-skin contact behaviour of the prepared fabrics. The reported results allow for confirming that the HGBC fabric may be used as a new textile and the proposed synthesis method is in accordance with the “green chemistry.” Graphic

The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes: fast scattering/crown and low-frequency blips . Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that fast scattering/crown, linked to ground motion at the detector sites, is especially prevalent, and identify two subclasses linked to different types of ground motion. Reclassification of data based on the updated model finds that ∼27% of all transient noise at LIGO Livingston belongs to the fast scattering class, while ∼8% belongs to the low-frequency blip class, making them the most frequent and fourth most frequent sources of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets.

To date, six human coronaviruses have been known, all of which are associated with respiratory infections in humans. With the exception of the highly pathogenic SARS and MERS coronaviruses, human coronaviruses (HCoV-NL63, HCoV-OC43, HCoV-229E, and HCoV-HKU1) circulate worldwide and typically cause the common cold. In most cases, infection with these viruses does not lead to severe disease, although acute infections in infants, the elderly, and immunocompromised patients may progress to severe disease requiring hospitalization. Importantly, no drugs against human coronaviruses exist, and only supportive therapy is available. Previously, we proposed the cationically modified chitosan, N-(2-hydroxypropyl)-3-trimethylammonium chitosan chloride (HTCC), and its hydrophobically-modified derivative (HM-HTCC) as potent inhibitors of the coronavirus HCoV-NL63. Here, we show that HTCC inhibits interaction of a virus with its receptor and thus blocks the entry. Further, we demonstrate that HTCC polymers with different degrees of substitution act as effective inhibitors of all low-pathogenic human coronaviruses.