Weak Glycolipid Binding of a Microdomain-Tracer Peptide Correlates with Aggregation and Slow Diffusion on Cell Membranes

10.1371/journal.pone.0051222PLoS ONE712

The CHUVA Lightning Mapping Campaign

The primary science objective for the CHUVA lightning mapping campaign is to combine measurements of total lightning activity, lightning channel mapping, and detailed information on the locations of cloud charge regions of thunderstorms with the planned observations of the CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement) field campaign. The lightning campaign takes place during the CHUVA intensive observation period October-December 2011 in the vicinity of S o Luiz do Paraitinga with Brazilian, US, and European government, university and industry participants. Total lightning measurements that can be provided by ground-based regional 2-D and 3-D total lightning mapping networks coincident with overpasses of the Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS) and the SEVIRI (Spinning Enhanced Visible and Infrared Imager) on the Meteosat Second Generation satellite in geostationary earth orbit will be used to generate proxy data sets for the next generation US and European geostationary satellites. Proxy data, which play an important role in the pre-launch mission development and in user readiness preparation, are used to develop and validate algorithms so that they will be ready for operational use quickly following the planned launch of the GOES-R Geostationary Lightning Mapper (GLM) in 2015 and the Meteosat Third Generation Lightning Imager (LI) in 2017. To date there is no well-characterized total lightning data set coincident with the imagers. Therefore, to take the greatest advantage of this opportunity to collect detailed and comprehensive total lightning data sets, test and validate multi-sensor nowcasting applications for the monitoring, tracking, warning, and prediction of severe and high impact weather, and to advance our knowledge of thunderstorm physics, extensive measurements from lightning mapping networks will be collected in conjunction with electric field mills, field change sensors, high speed cameras and other lightning sensors, dual-polarimetric radars, and aircraft in-situ microphysics which will allow for excellent cross-network inter-comparisons, assessments, and physical understanding

Zika virus and temperature modulate Elizabethkingia anophelis in Aedes albopictus

Abstract Background Vector-borne pathogens must survive and replicate in the hostile environment of an insect’s midgut before successful dissemination. Midgut microbiota interfere with pathogen infection by activating the basal immunity of the mosquito and by synthesizing pathogen-inhibitory metabolites. Methods The goal of this study was to assess the influence of Zika virus (ZIKV) infection and increased temperature on Aedes albopictus midgut microbiota. Aedes albopictus were reared at diurnal temperatures of day 28 °C/night 24 °C (L) or day 30 °C/night 26 °C (M). The mosquitoes were given infectious blood meals with 2.0 × 108 PFU/ml ZIKV, and 16S rRNA sequencing was performed on midguts at 7 days post-infectious blood meal exposure. Results Our findings demonstrate that Elizabethkingia anophelis albopictus was associated with Ae. albopictus midguts exposed to ZIKV infectious blood meal. We observed a negative correlation between ZIKV and E. anophelis albopictus in the midguts of Ae. albopictus. Supplemental feeding of Ae. albopictus with E. anophelis aegypti and ZIKV resulted in reduced ZIKV infection rates. Reduced viral loads were detected in Vero cells that were sequentially infected with E. anophelis aegypti and ZIKV, dengue virus (DENV), or chikungunya virus (CHIKV). Conclusions Our findings demonstrate the influence of ZIKV infection and temperature on the Ae. albopictus microbiome along with a negative correlation between ZIKV and E. anophelis albopictus. Our results have important implications for controlling vector-borne pathogens. Graphical Abstract </jats:sec

Zika virus and temperature modulate Elizabethkingia anophelis in Aedes albopictus

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Vector-borne pathogens must survive and replicate in the hostile environment of an insect’s midgut before successful dissemination. Midgut microbiota interfere with pathogen infection by activating the basal immunity of the mosquito and by synthesizing pathogen-inhibitory metabolites. Methods: The goal of this study was to assess the influence of Zika virus (ZIKV) infection and increased temperature on Aedes albopictus midgut microbiota. Aedes albopictus were reared at diurnal temperatures of day 28 °C/night 24 °C (L) or day 30 °C/night 26 °C (M). The mosquitoes were given infectious blood meals with 2.0 × 108 PFU/ml ZIKV, and 16S rRNA sequencing was performed on midguts at 7 days post-infectious blood meal exposure. Results: Our findings demonstrate that Elizabethkingia anophelis albopictus was associated with Ae. albopictus midguts exposed to ZIKV infectious blood meal. We observed a negative correlation between ZIKV and E. anophelis albopictus in the midguts of Ae. albopictus. Supplemental feeding of Ae. albopictus with E. anophelis aegypti and ZIKV resulted in reduced ZIKV infection rates. Reduced viral loads were detected in Vero cells that were sequentially infected with E. anophelis aegypti and ZIKV, dengue virus (DENV), or chikungunya virus (CHIKV). Conclusions: Our findings demonstrate the influence of ZIKV infection and temperature on the Ae. albopictus microbiome along with a negative correlation between ZIKV and E. anophelis albopictus. Our results have important implications for controlling vector-borne pathogens

Artificial Intelligence-based automated CT brain interpretation to accelerate treatment for acute stroke in rural India: An interrupted time series study.

In resource-limited settings, timely treatment of acute stroke is dependent upon accurate diagnosis that draws on non-contrast computed tomography (NCCT) scans of the head. Artificial Intelligence (AI) based devices may be able to assist non-specialist physicians in NCCT interpretation, thereby enabling faster interventions for acute stroke patients in these settings. We evaluated the impact of an AI device by comparing the time to intervention (TTI) from NCCT imaging to significant intervention before (baseline) and after the deployment of AI, in patients diagnosed with stroke (ischemic or hemorrhagic) through a retrospective interrupted time series analysis at a rural hospital managed by non-specialists in India. Significant intervention was defined as thrombolysis or antiplatelet therapy in ischemic strokes, and mannitol for hemorrhagic strokes or mass effect. We also evaluated the diagnostic accuracy of the software using the teleradiologists' reports as ground truth. Impact analysis in a total of 174 stroke patients (72 in baseline and 102 after deployment) demonstrated a significant reduction of median TTI from 80 minutes (IQR: 56·8-139·5) during baseline to 58·50 (IQR: 30·3-118.2) minutes after AI deployment (Wilcoxon rank sum test-location shift: -21·0, 95% CI: -38·0, -7·0). Diagnostic accuracy evaluation in a total of 864 NCCT scans demonstrated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) in detecting intracranial hemorrhage to be 0·89 (95% CI: 0·83-0·93), 0·99 (0·98-1·00), 0·96 (0·91-0·98) and 0·97 (0·96-0·98) respectively, and for infarct these were 0·84 (0·79-0·89), 0·81 (0·77-0·84), 0·58 (0·52-0·63), and 0·94 (0·92-0·96), respectively. AI-based NCCT interpretation supported faster abnormality detection with high accuracy, resulting in persons with acute stroke receiving significantly earlier treatment. Our results suggest that AI-based NCCT interpretation can potentially improve uptake of lifesaving interventions for acute stroke in resource-limited settings

Additional file 13 of Zika virus and temperature modulate Elizabethkingia anophelis in Aedes albopictus

Additional file 13: Table S9. An excel sheet of ZIKV titer of sequential inoculation of C636 cells with ZIKV and E. anophelis aegypti or E. coli

Additional file 6 of Zika virus and temperature modulate Elizabethkingia anophelis in Aedes albopictus

Additional file 6. Excel file showing the transmission of ZIKV at 7 dpi by Ae. albopictus reared in the L and M temperature regimens

Additional file 3 of Zika virus and temperature modulate Elizabethkingia anophelis in Aedes albopictus

Additional file 3: Figure S3. A biomarker analysis cluster file bearing patterns of relative abundance of taxa species in response to temperature variations

Additional file 9 of Zika virus and temperature modulate Elizabethkingia anophelis in Aedes albopictus

Additional file 9. Excel file of the Ae. albopictus ZIKV/E. anophelis aegypti correlation analysis

Additional file 5 of Zika virus and temperature modulate Elizabethkingia anophelis in Aedes albopictus

Additional file 5. Excel file showing the dissemination of ZIKV at 7 dpi by Ae. albopictus reared in the L and M temperature regimens