Mapping Water Levels across a Region of the Cuvette Centrale Peatland Complex

Inundation dynamics are the primary control on greenhouse gas emissions from peatlands. Situated in the central Congo Basin, the Cuvette Centrale is the largest tropical peatland complex. However, our knowledge of the spatial and temporal variations in its water levels is limited. By addressing this gap, we can quantify the relationship between the Cuvette Centrale’s water levels and greenhouse gas emissions, and further provide a baseline from which deviations caused by climate or land-use change can be observed, and their impacts understood. We present here a novel approach that combines satellite-derived rainfall, evapotranspiration and L-band Synthetic Aperture Radar (SAR) data to estimate spatial and temporal changes in water level across a sub-region of the Cuvette Centrale. Our key outputs are a map showing the spatial distribution of rainfed and flood-prone locations and a daily, 100 m resolution map of peatland water levels. This map is validated using satellite altimetry data and in situ water table data from water loggers. We determine that 50% of peatlands within our study area are largely rainfed, and a further 22.5% are somewhat rainfed, receiving hydrological input mostly from rainfall (directly and via surface/sub-surface inputs in sloped areas). The remaining 27.5% of peatlands are mainly situated in riverine floodplain areas to the east of the Congo River and between the Ubangui and Congo rivers. The mean amplitude of the water level across our study area and over a 20-month period is 22.8 ± 10.1 cm to 1 standard deviation. Maximum temporal variations in water levels occur in the riverine floodplain areas and in the inter-fluvial region between the Ubangui and Congo rivers. Our results show that spatial and temporal changes in water levels can be successfully mapped over tropical peatlands using the pattern of net water input (rainfall minus evapotranspiration, not accounting for run-off) and L-band SAR data

Intramembranous Bone Healing Process Subsequent to Tooth Extraction in Mice: Micro-Computed Tomography, Histomorphometric and Molecular Characterization

<div><p>Bone tissue has a significant potential for healing, which involves a significant the interplay between bone and immune cells. While fracture healing represents a useful model to investigate endochondral bone healing, intramembranous bone healing models are yet to be developed and characterized. In this study, a micro-computed tomography, histomorphometric and molecular (RealTimePCRarray) characterization of post tooth-extraction alveolar bone healing was performed on C57Bl/6 WT mice. After the initial clot dominance (0h), the development of a provisional immature granulation tissue is evident (7d), characterized by marked cell proliferation, angiogenesis and inflammatory cells infiltration; associated with peaks of growth factors (BMP-2-4-7,TGFβ1,VEGFa), cytokines (TNFα, IL-10), chemokines & receptors (CXCL12, CCL25, CCR5, CXCR4), matrix (Col1a1-2, ITGA4, VTN, MMP1a) and MSCs (CD105, CD106, OCT4, NANOG, CD34, CD146) markers expression. Granulation tissue is sequentially replaced by more mature connective tissue (14d), characterized by inflammatory infiltrate reduction along the increased bone formation, marked expression of matrix remodeling enzymes (MMP-2-9), bone formation/maturation (RUNX2, ALP, DMP1, PHEX, SOST) markers, and chemokines & receptors associated with healing (CCL2, CCL17, CCR2). No evidences of cartilage cells or tissue were observed, strengthening the intramembranous nature of bone healing. Bone microarchitecture analysis supports the evolving healing, with total tissue and bone volumes as trabecular number and thickness showing a progressive increase over time. The extraction socket healing process is considered complete (21d) when the dental socket is filled by trabeculae bone with well-defined medullary canals; it being the expression of mature bone markers prevalent at this period. Our data confirms the intramembranous bone healing nature of the model used, revealing parallels between the gene expression profile and the histomorphometric events and the potential participation of MCSs and immune cells in the healing process, supporting the forthcoming application of the model for the better understanding of the bone healing process.</p></div