MarrowQuant Across Aging and Aplasia: A Digital Pathology Workflow for Quantification of Bone Marrow Compartments in Histological Sections.

The bone marrow (BM) exists heterogeneously as hematopoietic/red or adipocytic/yellow marrow depending on skeletal location, age, and physiological condition. Mouse models and patients undergoing radio/chemotherapy or suffering acute BM failure endure rapid adipocytic conversion of the marrow microenvironment, the so-called "red-to-yellow" transition. Following hematopoietic recovery, such as upon BM transplantation, a "yellow-to-red" transition occurs and functional hematopoiesis is restored. Gold Standards to estimate BM cellular composition are pathologists' assessment of hematopoietic cellularity in hematoxylin and eosin (H&E) stained histological sections as well as volumetric measurements of marrow adiposity with contrast-enhanced micro-computerized tomography (CE-μCT) upon osmium-tetroxide lipid staining. Due to user-dependent variables, reproducibility in longitudinal studies is a challenge for both methods. Here we report the development of a semi-automated image analysis plug-in, MarrowQuant, which employs the open-source software QuPath, to systematically quantify multiple bone components in H&E sections in an unbiased manner. MarrowQuant discerns and quantifies the areas occupied by bone, adipocyte ghosts, hematopoietic cells, and the interstitial/microvascular compartment. A separate feature, AdipoQuant, fragments adipocyte ghosts in H&E-stained sections of extramedullary adipose tissue to render adipocyte area and size distribution. Quantification of BM hematopoietic cellularity with MarrowQuant lies within the range of scoring by four independent pathologists, while quantification of the total adipocyte area in whole bone sections compares with volumetric measurements. Employing our tool, we were able to develop a standardized map of BM hematopoietic cellularity and adiposity in mid-sections of murine C57BL/6 bones in homeostatic conditions, including quantification of the highly predictable red-to-yellow transitions in the proximal section of the caudal tail and in the proximal-to-distal tibia. Additionally, we present a comparative skeletal map induced by lethal irradiation, with longitudinal quantification of the "red-to-yellow-to-red" transition over 2 months in C57BL/6 femurs and tibiae. We find that, following BM transplantation, BM adiposity inversely correlates with kinetics of hematopoietic recovery and that a proximal to distal gradient is conserved. Analysis of in vivo recovery through magnetic resonance imaging (MRI) reveals comparable kinetics. On human trephine biopsies MarrowQuant successfully recognizes the BM compartments, opening avenues for its application in experimental, or clinical contexts that require standardized human BM evaluation

Mitochondrial and endoplasmic reticulum stress pathways cooperate in zearalenone-induced apoptosis of human leukemic cells

<p>Abstract</p> <p>Background</p> <p>Zearalenone (ZEA) is a phytoestrogen from <it>Fusarium </it>species. The aims of the study was to identify mode of human leukemic cell death induced by ZEA and the mechanisms involved.</p> <p>Methods</p> <p>Cell cytotoxicity of ZEA on human leukemic HL-60, U937 and peripheral blood mononuclear cells (PBMCs) was performed by using 3-(4,5-dimethyl)-2,5-diphenyl tetrazolium bromide (MTT) assay. Reactive oxygen species production, cell cycle analysis and mitochondrial transmembrane potential reduction was determined by employing 2',7'-dichlorofluorescein diacetate, propidium iodide and 3,3'-dihexyloxacarbocyanine iodide and flow cytometry, respectively. Caspase-3 and -8 activities were detected by using fluorogenic Asp-Glu-Val-Asp-7-amino-4-methylcoumarin (DEVD-AMC) and Ile-Glu-Thr-Asp-7-amino-4-methylcoumarin (IETD-AMC) substrates, respectively. Protein expression of cytochrome c, Bax, Bcl-2 and Bcl-xL was performed by Western blot. The expression of proteins was assessed by two-dimensional polyacrylamide gel-electrophoresis (PAGE) coupled with LC-MS2 analysis and real-time reverse transcription polymerase chain reaction (RT-PCR) approach.</p> <p>Results</p> <p>ZEA was cytotoxic to U937 > HL-60 > PBMCs and caused subdiploid peaks and G1 arrest in both cell lines. Apoptosis of human leukemic HL-60 and U937 cell apoptosis induced by ZEA was via an activation of mitochondrial release of cytochrome c through mitochondrial transmembrane potential reduction, activation of caspase-3 and -8, production of reactive oxygen species and induction of endoplasmic reticulum stress. Bax was up regulated in a time-dependent manner and there was down regulation of Bcl-xL expression. Two-dimensional PAGE coupled with LC-MS2 analysis showed that ZEA treatment of HL-60 cells produced differences in the levels of 22 membrane proteins such as apoptosis inducing factor and the ER stress proteins including endoplasmic reticulum protein 29 (ERp29), 78 kDa glucose-regulated protein, heat shock protein 90 and calreticulin, whereas only <it>ERp29 </it>mRNA transcript increased.</p> <p>Conclusion</p> <p>ZEA induced human leukemic cell apoptosis via endoplasmic stress and mitochondrial pathway.</p

Impact of Climate Change on the Spatiotemporal Variability of A Coastal Ecosystem in the Tunisian Sahel

Wetlands are some of the most important ecosystems on Earth. Despite their importance for water and carbon cycle regulation, wildlife survival and economic value. Furthermore, wetlands are experiencing rapid degradation due to severe transformations.&nbsp; They have been polluted and declined dramatically as land cover has changed in many regions. Whereas, human activities along with severe climate changes have led to critical loss and degradation of these ecosystems. This study evaluates changes of Halq El Mingel wetland, Tunisia, between 2006 and 2017. Spatial and temporal dynamics of wetland changes were quantified using Landsat and Google Earth images and three radiometric indexes have been calculated; Normalized Difference Vegetation Index, Normalized Difference Water Index and Salinity Index. Results revealed that important spatial and temporal variations are detected for each index.Also, the area of wetland in Hergla city decreased significantly over the last 10 years from 1146.7 ha to 806.6 ha respectively. A notable change is the shrinkage of the wetland area during 2006-2017 period which is linked to the decline of rainfall over the years. This study proposes a methodology to monitor changes in wetland using geospatial technology&nbsp; and thus to support decision-making for sustainable management. Cite as: Bel Fekih Bousemma S, Chaabane B, Khebour Allouchea F, Bel Haj Salah R. Impact of climate change on the spatiotemporal variability of a coastal ecosystem in the Tunisian Sahel. Alger. J. Eng. Technol. 2021, 5:72-79. http://dx.doi.org/10.5281/zenodo.5780189 References Boussema S., Allouche F., Chaabane B. Tools andIndicators for Integrated Wetland Monitoring&nbsp;: Case of Hergla Wetland –Tunisia. European Academic Research. 2020, 8 (6)&nbsp;: 3230-3245. http://www.euacademic.org/UploadArticle/4516.pdf Sader, S.A., Ahl, D., Liou, W.S. Accuracy of landsat-TM and GIS rule-based methods for forest wetland classification in Maine. Remote Sensing of Environment. 1995, 53: 133–144. Lyon, J.G., Lopez, R.D., Lyon, L.K., Lopez, D.K. Wetland landscape characterization: GIS, remote sensing and image analysis. CRC Press, Boca Raton, FL. 2001. Weiers, S., Bock, M., Wissen, M., &amp;Rossner, G. Mapping and indicator approaches for the assessment of habitats at different scales using remote sensing and GIS methods. Landsc Urban Plan. 2004, 67: 43– 65. Rebelo, L.M., Finlayson, C.M., Nagabhatla, N. Remote sensing and GIS for wetland inventory, mapping and change analysis. Journal of Environmental Management. 2009, 90&nbsp;: 2144–2153. Mahmud M.S, Masrur A., Ishtiaque A., Haider F., Habib U. Remote Sensing &amp; GIS Based Spatio-Temporal Change Analysis of Wetland in Dhaka City, Bangladesh. Journal of Water Resource and Protection, 2011, 3: 781-787. https://doi.org/10.4236/jwarp.2011.311088 Anees, M.T., Javed, A. and Khanday, M.Y. Spatio-Temporal Land Cover Analysis in Makhawan Watershed (M.P.), India through Remote Sensing and GIS Techniques.Journal of Geographic Information System. 2014, 6: 298- 306. http://dx.doi.org/10.4236/jgis.2014.64027 Hassan, M.A., Ratna, S.J., Hassan, M. and Tamanna, S. Remote Sensing and GIS for the Spatio-Temporal Change Analysis of the East and the West River Bank Erosion and Accretion of Jamuna River (1995-2015), Journal of Geoscience and Environment Protection. 2017, 5: 79-92. https://doi.org/10.4236/gep.2017.59006 Bedoui C.. Study of Vulnerable and Water Erosion Risk Areas in Sareg Catchment (Central Tunisia) Using Remote Sensing, GIS And P.A.P/R.A.C. Qualitative Approach, International Journal of Environment and Geoinformatics. 2020, 7(1): 33-44 https://doi.org/10.30897/ijegeo.623877 Convention on Wetlands (previously The Ramsar Convention Manual). Ramsar Convention Secretariat, Gland, Switzerland. 2016. Barducci, A.; Guzzi, D.; Marcoionni, P.; Pippi, I. Aerospace wetland monitoring by hyperspectral imaging sensors: A case study inthecoastal zone of San Rossore Natural Park. J.Environ. Manag. 2009, 90: 2278–2286. Costanza, R., R. de Groot, P. Sutton, S. Van der Ploeg, S. J. Anderson, I. Kubiszewski, S. Farber, and R. Kerry Turner. Changes in the Global Value of Ecosystem Services. Global Environmental Change. 2014, 26: 152–158. https://doi.org/10.1016/j.gloenvcha.2014.04.002. Mitsch, W. J., and J. G. Gosselink. Wetlands. Hoboken. 4th ed. Hoboken, New Jersey : John Wiley &amp; Sons. 2007. Sabater, S., A. Butturini, J.-C. Clement, T. Burt, D. Dowrick, M. Hefting, V. Matre, G. Pinay, C. Postolache, and M. Rzepecki. Nitrogen Removal by Riparian Buffers along a European Climatic Gradient: Patterns and Factors of Variation. Ecosystems. 2003, 6 (1): 20–30. https://doi.org/10.1007/ s10021-002-0183-8. Verhoeven, J. T. A., B. Arheimer, C. Yin, and M. M. Hefting. Regional and Global Concerns over Wetlands and Water Quality.” Trends in Ecology &amp; Evolution. 2006, 21 (2): 96–103. https://doi.org/10.1016/j.tree.2005.11.015. De Groot, R., M. Stuip, M. Finlayson, and N. Davidson. Valuing Wetlands: Guidance for Valuing the Benefits Derived from Wetland Ecosystem Services. Ramsar Technical Report No 3, CBD Technical Series No 27. 2006. https://www.cbd.int/doc/ publications/cbd-ts-27.pdf. Finlayson, C. M., J. A. Davis, P. A. Gell, R. T. Kingsford, and K. A. Parton. The Status of Wetlands and the Predicted Effects of Global Climate Change: The Situation in Australia. Aquatic Sciences. 2003, 75 (1): 73–93. https://doi.org/10.1007/ s00027-011-0232-5. Gardner, R. C., S. Barchiesi, C. Beltrame, C. M. Finlayson, T. Galewski, I. Harrison, M. Paganini, C. Perennou, D. Pritchard, and A. Rosenqvist. State of the World's Wetlands and Their Services to People: A Compilation of Recent Analyses. 2015. https://www.ramsar.org/sites/default/files/ documents/library/bn7e_0.pdf Ávila D.M, Soto Barrera V.C and Lara Z.M..Spatio-temporal modelling of wetland ecosystems using Landsat time series: case of the BajoSinú Wetlands Complex (BSWC)– Córdoba– Colombia, Annals of GIS. 2019, 25 (1): 231-245, https://doi.org/10.1080/19475683.2019.1617347 Fisher, B., and Turner R.K. Ecosystem Services: Classification for Valuation. Biological Conservation. 2008, 141 (5): 1167–1169. doi:10.1016/j.biocon.2008.02.019. Mitsch, W. J., B. Bernal, A. M. Nahlik, Ü. Mander, L. Zhang, C. J. Anderson, S. E. Jørgensen, and H. Brix. Wetlands, Carbon, and Climate Change. Landscape Ecology. 2013, 28 (4): 583–597. https://doi.org/10.1007/s10980-012-9758-8. Millennium Ecosystem Assessment (MEA). Ecosystems And Human Well-Being: Wetlands And Water Synthesis.&nbsp; World Resources Institute, Washington, DC. 2005: 68. Chaggar M., and Boubaker M. Identification Of New Iconic Objects For The Sustainable Development Of Hergla (Tunisia). International Journal of Engineering Technologies and Management Research. 2018, 5(12): 101-113. https://doi.org/10.29121/ijetmr.v5.i12.2018.332. Coastal Protection and Development Agency (CPDA). Protection of sensitive sites by land management: Hergla. 2005&nbsp;: 24. Teillet, P. M. Image correction for radiometric eVects in remote sensing. International Journal of Remote Sensing. 1986, 7: 1637–1651. Richards, J. A. Remote sensing digital image processing: An introduction, 2nd edn (New York: Springer-Verlag). 1993. Olsson, H. Reflectance calibration of Thematic Mapper data for forest change detection. International Journal of Remote Sensing. 1995, 16: 81–96. Chavez, P. S. Jr, Image-based atmospheric corrections – revisited and improved. Photogrammetric Engineering and Remote Sensing. 1996, 62: 1025–1036. Richter, R. Correction of atmospheric and topographic eVects for high spatial resolution satellite imagery. International Journal of Remote Sensing. 1997, 18: 1099-1111. Campbell, J. B., &amp; Wynne, R.H. Introduction to remote sensing (5 ed.). New York, É-U.: Guilford Press. 2011. Rouse, J.W., &amp; Haas, R.H. Monitoring vegetation systems in the great plain with ERTS.Third ERTS Symposium. 1973, vo.1: 309-317. Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment. 1979, vo.8, 127-150. Wu, Q. GIS and Remote Sensing Applications in Wetland Mapping and Monitoring. Elsevier. 2018, 141-157. McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing. 1996, 17:1425-1432. Gao, B.C. A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment. 1996, 58: 257- 266. Congalton G. &amp; Green&nbsp; K. Assessing the accuracy of remotely sensed data.Principes and Practices.2"ed.CRC Press. 2009: 200. Dehni, A., &amp;Lounis, M. Remote Sensing Techniques for Salt Affected Soil Mapping: Application to the Oran Region of Algeria. Procedia Engineering. 2012, 33: 188-198. Chris, L. L., William, H. B., &amp; Jerry, L. W.Testing a GIS model of habitat suitability for a declining grassland bird. Environ Manag. 2002, 30 (1): 88–97. Adam, E., Mutanga, O., Rugege, D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecology and Management. 2010, 18&nbsp;: 281–296. Dong, Z., Wang, Z., Liu, D., Song, K., Li, L., Jia, M., Ding, Z. Mapping Wetland Areas Using Landsat-Derived NDVI and LSWI: A Case Study of West Songnen Plain, Northeast China. Indian Soc. Remote Sens. 2014, 42: 569–576. Lang, M.W., Bourgeau-Chavez, L.L., Tiner, R.W., Klemas, V.V. Advances in remotely sensed data and techniques for wetland mapping and monitoring. In: Tiner, R.W., Lang, M.W., Klemas, V.V. (Eds.), Remote sensing of wetlands: applications and advances. CRC Press, Boca Raton, FL. 2015: 79–116. A., and Budagov I.V. Environmental Change And Landuse Scenarios Under Changing Climates: The Experience In The Upper Cross River Region, Nigeria.International Journal of Engineering Technologies and Management Research. 2019, 6(11): 32-42. https://doi.org/10.5281/zenodo.3563056. Erwin, K.L. Wetlands and global climate change: the role of wetland restoration in a changing world. Wetlands Ecology and Management. 2009, 17: 71. Jiang, F.; Qi, S.; Liao, F.; Ding, M.; Wang, Y. Vulnerability of Siberian crane habitat to water level in Poyang Lake wetland, China. Gisci. Remote Sens. 2014, 51: 662–676. Karim A. Wetlands in Tunisia&nbsp;: A brief prelimenary overview. Official Publication of the Tunisian Association of Environmental Law. 2021. https://atdddenvironnement.wixsite.com/atde/post/wetlands-in-Tunisia-a-brief-preliminary-overvie