Incorporating a mucosal environment in a dynamic gut model results in a more representative colonization by lactobacilli

To avoid detrimental interactions with intestinal microbes, the human epithelium is covered with a protective mucus layer that traps host defence molecules. Microbial properties such as adhesion to mucus further result in a unique mucosal microbiota with a great potential to interact with the host. As mucosal microbes are difficult to study in vivo, we incorporated mucin-covered microcosms in a dynamic in vitro gut model, the simulator of the human intestinal microbial ecosystem (SHIME). We assessed the importance of the mucosal environment in this M-SHIME (mucosal-SHIME) for the colonization of lactobacilli, a group for which the mucus binding domain was recently discovered. Whereas the two dominant resident Lactobacilli, Lactobacillus mucosae and Pediococcus acidilactici, were both present in the lumen, L. mucosae was strongly enriched in mucus. As a possible explanation, the gene encoding a mucus binding (mub) protein was detected by PCR in L. mucosae. Also the strongly adherent Lactobacillus rhamnosus GG (LGG) specifically colonized mucus upon inoculation. Short-term assays confirmed the strong mucin-binding of both L. mucosae and LGG compared with P. acidilactici. The mucosal environment also increased long-term colonization of L. mucosae and enhanced its stability upon antibiotic treatment (tetracycline, amoxicillin and ciprofloxacin). Incorporating a mucosal environment thus allowed colonization of specific microbes such as L. mucosae and LGG, in correspondence with the in vivo situation. This may lead to more in vivo-like microbial communities in such dynamic, long-term in vitro simulations and allow the study of the unique mucosal microbiota in health and disease

Mass spectrometry based metabolomics of volume-restricted in-vivo brain samples: actual status and the way forward

Brain metabolomics is gaining interest because of the aging of the population, resulting in more central nervous system disorders such as Alzheimer's and Parkinson's disease. Most often these diseases are studied in vivo, such as for example by analysing cerebrospinal fluid or brain extracellular fluid. These sample types are often considered in pre-clinical studies using animal models. However, the scarce availability of both matrices results in some challenges related to sampling, sample preparation and normalization. Much effort has been made towards the development of alternative, less invasive sampling techniques for collecting small sample volumes (pL till mid mL range) over the past years. Despite recent advances, the analysis of low volumes is still a tremendous challenge. Therefore, proper pre-concentration and sample pretreatment strategies are necessary together with sensitive analysis and detection techniques suitable for low-volume samples. In this review, an overview is given of the stateof-the-art mass spectrometry-based analytical workflows for probing (endogenous) metabolites in volume-restricted in-vivo brain samples. In this context, special attention is devoted to challenges related to sampling, sample preparation and preconcentration strategies. Finally, some general conclusions and perspectives are provided. (C) 2021 The Author(s). Published by Elsevier B.V.Analytical BioScience

CE-MS metabolic profiling of volume-restricted plasma samples from an acute mouse model for epileptic seizures to discover potentially involved metabolomic features

Currently, a high variety of analytical techniques to perform metabolomics is available. One of these techniques is capillary electrophoresis coupled to mass spectrometry (CE-MS), which has emerged as a rather strong analytical technique for profiling polar and charged compounds. This work aims to discover with CE-MS potential metabolic consequences of evoked seizures in plasma by using a 6Hz acute corneal seizure mouse model. CE-MS is an appealing technique because of its capability to handle very small sample volumes, such as the 10 mu L plasma samples obtained using capillary microsampling in this study. After liquid-liquid extraction, the samples were analyzed with CE-MS using low-pH separation conditions, followed by data analysis and biomarker identification. Both electrically induced seizures showed decreased values of methionine, lysine, glycine, phenylalanine, citrulline, 3-methyladenine and histidine in mice plasma. However, a second provoked seizure, 13 days later, showed a less pronounced decrease of the mean concentrations of these plasma metabolites, demonstrated by higher fold change ratios. Other obtained markers that can be related to seizure activities based on literature data, are isoleucine, serine, proline, tryptophan, alanine, arginine, valine and asparagine. Most amino acids showed relatively stable plasma concentrations between the basal levels (Time point 1) and after the 13-day wash-out period (Time point 3), which suggests its effectiveness. Overall, this work clearly demonstrated the possibility of profiling metabolite consequences related to seizure activities of an intrinsically low amount of body fluid using CE-MS. It would be useful to investigate and validate, in the future, the known and unknown metabolites in different animal models as well as in humans.Analytical BioScience

Determinants of the Use of a Diabetes Risk-Screening Test

A study was designed to investigate why people do or do not make use of a diabetes risk test developed to facilitate the timely diagnosis of diabetes. Data were collected using a web-based questionnaire, which was based on the Health Belief Model, the Theory of Planned Behavior, and the Threatening Medical Situations Inventory. People who had and had not used the risk test were recruited to complete the survey. The sample consisted of 205 respondents: 44% who had used the test and 56% who had not. The hypothesized relationships between the dependent variable (diabetes risk test use) and the determinants used in this study were tested using logistic regression analysis. Only two significant predictors of diabetes risk test use were found: gender and barriers. More women than men use the test. Furthermore, people who experience more barriers will be less inclined to use the test. The contribution of diabetes screening tests fully depends on people’s willingness to use them. To optimize the usage of such test, it is especially important to address the barriers as perceived by the public. Two types of barriers must be addressed: practical barriers (time to take the test, fear of complexity of the test), and consequential barriers (fear of the disease and treatment, uncertainties about where to go in the case of an increased risk of diabetes)

A ROC analysis-based classification method for landslide susceptibility maps

[EN] A landslide susceptibility map is a crucial tool for landuse spatial planning and management in mountainous areas. An essential issue in such maps is the determination of susceptibility thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of susceptibility levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most susceptible zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method.Cantarino-Martí, I.; Carrión Carmona, MÁ.; Goerlich-Gisbert, F.; Martínez Ibáñez, V. (2018). A ROC analysis-based classification method for landslide susceptibility maps. Landslides. 1-18. doi:10.1007/s10346-018-1063-4S118Armstrong MP, Xiao N, Bennett DA (2003) Using genetic algorithms to create multicriteria class intervals for choropleth maps. Ann Assoc Am Geogr 93(3):595–623. https://doi.org/10.1111/1467-8306.9303005Atkinson P, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385. https://doi.org/10.1016/S0098-3004(97)00117-9Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31. https://doi.org/10.1016/j.geomorph.2004.06.010Baeza C, Lantada N, Amorim S (2016) Statistical and spatial analysis of landslide susceptibility maps with different classification systems. Environ Earth Science 75:1318. https://doi.org/10.1007/s12665-016-6124-1Basofi A, Fariza A, Ahsan AS, Kamal IM (2015) A comparison between natural and head/tail breaks in LSI (landslide susceptibility index) classification for landslide susceptibility mapping: a case study in Ponorogo, East Java, Indonesia. 2015 International Conference on Science in Information Technology, pp 337–342Cantarino I (2013) Elaboración y validación de un modelo jerárquico derivado de SIOSE. Revista de Teledetección 39:5–21Carrara A, Crosta GB, Frattini P (2008) Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94(3–4):353–378. https://doi.org/10.1016/j.geomorph.2006.10.033Chacón J, Irigaray C, Fernández T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65(4):341–411Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472COPUT (1998) Lithology, exploitation of industrial rocks and landslide risk in the Valencian Community. Thematic Mapping Series. Department of Public Works of the Valencian Regional GovernmentDrummond C, Holte RC (2006) Cost curves: an improved method for visualizing classifier performance. Mach Learn 65(1):95–130Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51(2):241–256. https://doi.org/10.1007/s00254-006-0322-1Evans IS (1977) The selection of class intervals. Transactions of the Institute of British Geographers. Contemp Cartograph 2(1):98–124. https://doi.org/10.2307/622195Fleiss JL, Levin B, Paik MC (2003) Statistical methods for rates and proportions, Book Series: Wiley Series in Probability and Statistics. John Wiley & Sons. Print ISBN: 9780471526292. doi: https://doi.org/10.1002/0471445428Foody GM (2004) Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogramm Eng Remote Sens 70(5):627–633Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative geography: perspectives on spatial data analysis. SAGE Publications, Thousand Oaks 270 ppFrattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111(1–4):62–72. https://doi.org/10.1016/j.enggeo.2009.12.004Geisser S (1998) Comparing two tests used for diagnostic or screening processes. Stat Probability Lett 40:113–119Greiner M, Pfeiffer D, Smith RD (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 45:23–41Günther A, Reichenbach P, Malet JP, van den Eeckhaut M, Hervás J, Dashwood C, Guzzetti F (2013) Tier-based approaches for landslide susceptibility assessment in Europe. Landslides 10:529–546. https://doi.org/10.1007/s10346-012-0349-1Günther A, Van Den Eeckhaut M, Malet J-P, Reichenbach P, Hervás J (2014) Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224:69–85Gupta RP, Kanungo DP, Arora MK, Sarkar S (2008) Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps. Int J Appl Earth Obs Geoinf 10(3):330–341. https://doi.org/10.1016/j.jag.2008.01.003Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184. https://doi.org/10.1016/j.geomorph.2006.04.007Hervás J (2017) El inventario de movimientos de ladera de España ALISSA: Metodología y análisis preliminar. In: Alonso E, Corominas J, Hürlimann M (Eds.), Taludes 2017. Proc. IX Simposio Nacional sobre Taludes y Laderas Inestables, Santander, 27–30 June 2017. CIMNE, Barcelona, pp. 629–639Jaedicke C, Van Den Eeckhaut M, Nadim F et al (2014) Identification of landslide hazard and risk ‘hotspots’ in Europe. Bull Eng Geol Environ 73:325. https://doi.org/10.1007/s10064-013-0541-0Jenks GF (1967) The data model concept in statistical mapping. Int Yearbook Cartograph 7:186–190Jiang B (2013) Head/tail breaks: a new classification scheme for data with a heavy-tailed distribution. Prof Geogr 65(3):482–494. https://doi.org/10.1080/00330124.2012.700499Kiang MY (2003) A comparative assessment of classification methods. Decis Support Syst 35(4):441–454. https://doi.org/10.1016/S0167-9236(02)00110-0Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174Langping L, Hengxing L, Changbao G, Yongshuang Z, Quanwen L, Yuming W (2017) A modified frequency ratio method for landslide susceptibility assessment. Landslides 14:727–741. https://doi.org/10.1007/s10346-016-0771-xLee S (2007) Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf Process Landforms 32:2133–2148. https://doi.org/10.1002/esp.1517Liu C, Frazier P, Kumar L (2007) Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ 107(4):606–616. https://doi.org/10.1016/j.rse.2006.10.010López-Ratón M, Rodríguez-Álvarez MX, Cadarso-Suárez C, Gude-Sampedro F (2014) Optimal cutpoints: an R package for selecting optimal cutpoints in diagnostic tests. J Stat Softw 61(8):4Malet JP, Puissant A, Mathieu A, Van Den Eeckhaut M, Fressard M (2013) Integrating spatial multi-criteria evaluation and expert knowledge for country-scale landslide susceptibility analysis: application to France. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice. Springer, Berlin. https://doi.org/10.1007/978-3-642-31325-7_40McGee S (2002) Simplifying likelihood ratios. J Gen Intern Med 17:647–650Metz C (1978) Basic principles of ROC analysis. Semin Nucl Med VIII(4):183–198Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3:159–173. https://doi.org/10.1007/s10346-006-0036-1Ohlmacher G, Davis J (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(3–4):331–343. https://doi.org/10.1016/S0013-7952(03)00069-3Powell RL, Matzke N, de Souza C Jr, Clark M, Numata I, Hess LL, Roberts DA (2004) Sources of error accuracy assessment of thematic land-cover maps in the Brazilian Amazon. Remote Sens Environ 90(2):221–234. https://doi.org/10.1016/j.rse.2003.12.007Saaty T (1980) The analytic hierarchy process. McGraw Hill, New YorkSmits PC, Dellepiane SG, Schowengerdt RA (1999) Quality assessment of image classification algorithms for land-cover mapping: a review and proposal for a cost-based approach. Int J Remote Sens 20:1461–1486Stehman SV, Czaplewski RL (1998) Design and analysis of thematic map accuracy assessment: fundamental principles. Remote Sens Environ 64:331–344Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293Van Den Eeckhaut M, Hervás J, Jaedicke C, Malet J-P, Montanarella L, Nadim F (2012) Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data. Landslides 8:357–369Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. Natural hazards. UNESCO, ParisZhu X (2016) GIS for environmental applications. Routledge, Abingdon, p 490Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561–57

Quenched phosphorescence as alternative detection mode in the chiral separation of methotrexate by electrokinetic chromatography

Quenched phosphorescence was used, for the first time, as detection mode in the chiral separation of methotrexate (MTX) enantiomers by electrokinetic chromatography. The detection is based on dynamic quenching of the strong emission of the phosphorophore 1-bromo-4-naphthalene sulfonic acid (BrNS) by MTX under deoxygenated conditions. The use of a background electrolyte with 3 mg/mL 2-hydroxypropyl-β-cyclodextrin and 20% MeOH in 25 mM phosphate buffer (pH 7.0) and an applied voltage of 30 kV allowed the separation of l-MTX and its enantiomeric impurity d-MTX with sufficient resolution. In the presence of 1 mM BrNS, a detection limit of 3.2 × 10−7 M was achieved, about an order of magnitude better than published techniques based on UV absorption. The potential of the method was demonstrated with a degradation study and an enantiomeric purity assessment of l-MTX. Furthermore, l-MTX was determined in a cell culture extract as a proof-of-principle experiment to show the applicability of the method to biological samples

Guidelines for the selection of appropriate remote sensing technologies for landslide detection, monitoring and rapid mapping: the experience of the SafeLand European Project.

New earth observation satellites, innovative airborne platforms and sensors, high precision laser scanners, and enhanced ground-based geophysical investigation tools are a few examples of the increasing diversity of remote sensing technologies used in landslide analysis. The use of advanced sensors and analysis methods can help to significantly increase our understanding of potentially hazardous areas and helps to reduce associated risk. However, the choice of the optimal technology, analysis method and observation strategy requires careful considerations of the landslide process in the local and regional context, and the advantages and limitations of each technique. Guidelines for the selection of the most suitable remote sensing technologies according to different landslide types, displacement velocities, observational scales and risk management strategies have been proposed. The guidelines are meant to aid operational decision making, and include information such as spatial resolution and coverage, data and processing costs, and maturity of the method. The guidelines target scientists and end-users in charge of risk management, from the detection to the monitoring and the rapid mapping of landslides. They are illustrated by recent innovative methodologies developed for the creation and updating of landslide inventory maps, for the construction of landslide deformation maps and for the quantification of hazard. The guidelines were compiled with contributions from experts on landslide remote sensing from 13 European institutions coming from 8 different countries. This work is presented within the framework of the SafeLand project funded by the European Commission’s FP7 Programme.JRC.H.7-Climate Risk Managemen