| 1. |
- Nordin, Maria, et al.
(författare)
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Young women's mental health in northern Sweden
- 2016
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Ingår i: International Journal of Behavioral Medicine. - : Springer. - 1070-5503 .- 1532-7558. ; 23, s. S3-S3
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 2. |
- Abdillahi, Suado M., et al.
(författare)
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Collagen VI Contains Multiple Host Defense Peptides with Potent In Vivo Activity
- 2018
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Ingår i: Journal of Immunology. - : AMER ASSOC IMMUNOLOGISTS. - 0022-1767 .- 1550-6606. ; 201:3, s. 1007-1020
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Tidskriftsartikel (refereegranskat)abstract
- Collagen VI is a ubiquitous extracellular matrix component that forms extensive microfibrillar networks in most connective tissues. In this study, we describe for the first time, to our knowledge, that the collagen VI von Willebrand factor type A like domains exhibit a broad-spectrum antimicrobial activity against Gram-positive and Gram-negative bacteria in human skin infections in vivo. In silico sequence and structural analysis of VWA domains revealed that they contain cationic and amphipathic peptide sequence motifs, which might explain the antimicrobial nature of collagen VI. In vitro and in vivo studies show that these peptides exhibited significant antibacterial activity against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa through membrane disruption. Our findings shed new light on the role of collagen VI derived peptides in innate host defense and provide templates for development of peptide-based antibacterial therapies.
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| 3. |
- Abdillahi, Suado M, et al.
(författare)
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The Pulmonary Extracellular Matrix Is a Bactericidal Barrier Against Haemophilus influenzae in Chronic Obstructive Pulmonary Disease (COPD) : Implications for an in vivo Innate Host Defense Function of Collagen VI
- 2018
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Ingår i: Frontiers in Immunology. - : Frontiers Media SA. - 1664-3224. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Non-typeable Haemophilus influenzae (NTHi) is a Gram-negative human commensal commonly residing in the nasopharynx of preschool children. It occasionally causes upper respiratory tract infection such as acute otitis media, but can also spread to the lower respiratory tract causing bronchitis and pneumonia. There is increasing recognition that NTHi has an important role in chronic lower respiratory tract inflammation, particularly in persistent infection in patients suffering from chronic obstructive pulmonary disease (COPD). Here, we set out to assess the innate protective effects of collagen VI, a ubiquitous extracellular matrix component, against NTHi infection in vivo. In vitro, collagen VI rapidly kills bacteria through pore formation and membrane rupture, followed by exudation of intracellular content. This effect is mediated by specific binding of the von Willebrand A (VWA) domains of collagen VI to the NTHi surface adhesins protein E (PE) and Haemophilus autotransporter protein (Hap). Similar observations were made in vivo specimens from murine airways and COPD patient biopsies. NTHi bacteria adhered to collagen fibrils in the airway mucosa and were rapidly killed by membrane destabilization. The significance in host-pathogen interplay of one of these molecules, PE, was highlighted by the observation that it confers partial protection from bacterial killing. Bacteria lacking PE were more prone to antimicrobial activity than NTHi expressing PE. Altogether the data shed new light on the carefully orchestrated molecular events of the host-pathogen interplay in COPD and emphasize the importance of the extracellular matrix as a novel branch of innate host defense.
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| 4. |
- Mahabal, Ashish, et al.
(författare)
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Machine Learning for the Zwicky Transient Facility
- 2019
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Ingår i: Publications of the Astronomical Society of the Pacific. - : IOP Publishing. - 0004-6280 .- 1538-3873. ; 131:997
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Tidskriftsartikel (refereegranskat)abstract
- The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.
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