Radiative impact of mineral dust on monsoon precipitation variability over West Africa

The radiative forcing of dust and its impact on precipitation over the West Africa monsoon (WAM) region is simulated using a coupled meteorology and aerosol/chemistry model (WRF-Chem). During the monsoon season, dust is a dominant contributor to aerosol optical depth (AOD) over West Africa. In the control simulation, on 24-h domain average, dust has a cooling effect (−6.11 W m⁻²) at the surface, a warming effect (6.94 W m⁻²) in the atmosphere, and a relatively small TOA forcing (0.83 W m⁻²). Dust modifies the surface energy budget and atmospheric diabatic heating. As a result, atmospheric stability is increased in the daytime and reduced in the nighttime, leading to a reduction of late afternoon precipitation by up to 0.14 mm/h (25%) and an increase of nocturnal and early morning precipitation by up to 0.04 mm/h (45%) over the WAM region. Dust-induced reduction of diurnal precipitation variation improves the simulated diurnal cycle of precipitation when compared to measurements. However, daily precipitation is only changed by a relatively small amount (−0.17 mm/day or −4%). The dust-induced change of WAM precipitation is not sensitive to interannual monsoon variability. On the other hand, sensitivity simulations with weaker to stronger absorbing dust (in order to represent the uncertainty in dust solar absorptivity) show that, at the lower atmosphere, dust longwave warming effect in the nighttime surpasses its shortwave cooling effect in the daytime; this leads to a less stable atmosphere associated with more convective precipitation in the nighttime. As a result, the dust-induced change of daily WAM precipitation varies from a significant reduction of −0.52 mm/day (−12%, weaker absorbing dust) to a small increase of 0.03 mm/day (1%, stronger absorbing dust). This variation originates from the competition between dust impact on daytime and nighttime precipitation, which depends on dust shortwave absorption. Dust reduces the diurnal variation of precipitation regardless of its absorptivity, but more reduction is associated with stronger absorbing dust

Water lifting technologies for smallholder farmers provide opportunities for sustainable intensification

United States Agency for International Developmen

The Elephants of Gash-Barka, Eritrea: Nuclear and Mitochondrial Genetic Patterns

Eritrea has one of the northernmost populations of African elephants. Only about 100 elephants persist in the Gash-Barka administrative zone. Elephants in Eritrea have become completely isolated, with no gene flow from other elephant populations. The conservation of Eritrean elephants would benefit from an understanding of their genetic affinities to elephants elsewhere on the continent and the degree to which genetic variation persists in the population. Using dung samples from Eritrean elephants, we examined 18 species-diagnostic single nucleotide polymorphisms in 3 nuclear genes, sequences of mitochondrial HVR1 and ND5, and genotyped 11 microsatellite loci. The sampled Eritrean elephants carried nuclear and mitochondrial DNA markers establishing them as savanna elephants, with closer genetic affinity to Eastern than to North Central savanna elephant populations, and contrary to speculation by some scholars that forest elephants were found in Eritrea. Mitochondrial DNA diversity was relatively low, with 2 haplotypes unique to Eritrea predominating. Microsatellite genotypes could only be determined for a small number of elephants but suggested that the population suffers from low genetic diversity. Conservation efforts should aim to protect Eritrean elephants and their habitat in the short run, with restoration of habitat connectivity and genetic diversity as long-term goals.https://digitalcommons.snc.edu/faculty_staff_works/1030/thumbnail.jp

Cell abundance aware deep learning for cell detection on highly imbalanced pathological data

Automated analysis of tissue sections allows a better understanding of disease biology, and may reveal biomarkers that could guide prognosis or treatment selection. In digital pathology, less abundant cell types can be of biological significance, but their scarcity can result in biased and sub-optimal cell detection model. To minimize the effect of cell imbalance on cell detection, we proposed a deep learning pipeline that considers the abundance of cell types during model training. Cell weight images were generated, which assign larger weights to less abundant cells and used the weights to regularize Dice overlap loss function. The model was trained and evaluated on myeloma bone marrow trephine samples. Our model obtained cell detection F1-score of 0.78, a 2% increase compared to baseline models, and it outperformed baseline models at detecting rare cell types. We found that scaling deep learning loss function by the abundance of cells improves cell detection performance. Our results demonstrate the importance of incorporating domain knowledge on deep learning methods for pathological data with class imbalance

Deep learning enables spatial mapping of the mosaic microenvironment of myeloma bone marrow trephine biopsies

Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphological, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an area under the curve of >0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and post-treatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1+ tumor cells to CD8+ cells in MGUS compared with NDMM samples. Following treatment of multiple myeloma patients, there was a reduction in the density of BLIMP1+ tumor cells, effector CD8+ T cells, and T regulatory cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of MM patients. In summary, deep-learning based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques

Self-supervised deep learning for highly efficient spatial immunophenotyping

Background: Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets. / Methods: This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model. / Findings: With 1% annotations (18–114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828–11,459 annotated cells (−0.002 to −0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression. / Interpretation: By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data. / Funding: This study was funded by the Royal Marsden/ ICR National Institute of Health Research Biomedical Research Centre

Assessment of animal African trypanosomiasis (AAT) vulnerability in cattle-owning communities of sub-Saharan Africa

Background: Animal African trypanosomiasis (AAT) is one of the biggest constraints to livestock production and a threat to food security in sub-Saharan Africa. In order to optimise the allocation of resources for AAT control, decision makers need to target geographic areas where control programmes are most likely to be successful and sustainable and select control methods that will maximise the benefits obtained from resources invested. Methods: The overall approach to classifying cattle-owning communities in terms of AAT vulnerability was based on the selection of key variables collected through field surveys in five sub-Saharan Africa countries followed by a formal Multiple Correspondence Analysis (MCA) to identify factors explaining the variations between areas. To categorise the communities in terms of AAT vulnerability profiles, Hierarchical Cluster Analysis (HCA) was performed. Results: Three clusters of community vulnerability profiles were identified based on farmers’ beliefs with respect to trypanosomiasis control within the five countries studied. Cluster 1 communities, mainly identified in Cameroon, reported constant AAT burden, had large trypanosensitive (average herd size = 57) communal grazing cattle herds. Livestock (cattle and small ruminants) were reportedly the primary source of income in the majority of these cattle-owning households (87.0 %). Cluster 2 communities identified mainly in Burkina Faso and Zambia, with some Ethiopian communities had moderate herd sizes (average = 16) and some trypanotolerant breeds (31.7 %) practicing communal grazing. In these communities there were some concerns regarding the development of trypanocide resistance. Crops were the primary income source while communities in this cluster incurred some financial losses due to diminished draft power. The third cluster contained mainly Ugandan and Ethiopian communities which were mixed farmers with smaller herd sizes (average = 8). The costs spent diagnosing and treating AAT were moderate here. Conclusions: Understanding how cattle-owners are affected by AAT and their efforts to manage the disease is critical to the design of suitable locally-adapted control programmes. It is expected that the results could inform priority setting and the development of tailored recommendations for AAT control strategies

Structure and dynamics of surface uplift induced by incremental sill emplacement

Shallow-level sill emplacement can uplift Earth’s surface via forced folding, providing insight into the location and size of potential volcanic eruptions. Linking the structure and dynamics of ground deformation to sill intrusion is thus critical in volcanic hazard assessment. This is challenging, however, because (1) active intrusions cannot be directly observed, meaning that we rely on transient host-rock deformation patterns to model their structure; and (2) where ancient sill-fold structure can be observed, magmatism and deformation has long since ceased. To address this problem, we combine structural and dynamic analyses of the Alu dome, Ethiopia, a 3.5-km-long, 346-m-high, elliptical dome of outward-dipping, tilted lava flows cross-cut by a series of normal faults. Vents distributed around Alu feed lava flows of different ages that radiate out from or deflect around its periphery. These observations, coupled with the absence of bounding faults or a central vent, imply that Alu is not a horst or a volcano, as previously thought, but is instead a forced fold. Interferometric synthetic aperture radar data captured a dynamic growth phase of Alu during a nearby eruption in A.D. 2008, with periods of uplift and subsidence previously attributed to intrusion of a tabular sill at 1 km depth. To localize volcanism beyond its periphery, we contend that Alu is the first forced fold to be recognized to be developing above an incrementally emplaced saucer-shaped sill, as opposed to a tabular sill or laccolith

Enhanced El Niño‐Southern Oscillation variability in recent decades

The El Nino-Southern Oscillation (ENSO) represents the largest source of year-to-year global climate variability. While Earth system models suggest a range of possible shifts in ENSO properties under continued greenhouse gas forcing, many centuries of preindustrial climate data are required to detect a potential shift in the properties of recent ENSO extremes. Here we reconstruct the strength of ENSO variations over the last 7,000 years with a new ensemble of fossil coral oxygen isotope records from the Line Islands, located in the central equatorial Pacific. The corals document a significant decrease in ENSO variance of similar to 20% from 3,000 to 5,000 years ago, coinciding with changes in spring/fall precessional insolation. We find that ENSO variability over the last five decades is similar to 25% stronger than during the preindustrial. Our results provide empirical support for recent climate model projections showing an intensification of ENSO extremes under greenhouse forcing.Plain Language Summary Recent modeling studies suggest that El Nino will intensify due to greenhouse warming. Here new coral reconstructions of the El Nino-Southern Oscillation (ENSO) record sustained, significant changes in ENSO variability over the last 7,000 years and imply that ENSO extremes of the last 50 years are significantly stronger than those of the preindustrial era in the central tropical Pacific. These records suggest that El Nino events already may be intensifying due to anthropogenic climate change

The West African Monsoon Onset: a concise comparison of definitions

The onset of the West African Monsoon (WAM) marks a vital time for local and regional stakeholders. Whilst the seasonal progression of monsoon winds and the related migration of precipitation from the Guinea Coast towards the Soudan/Sahel is apparent, there exist contrasting man-made definitions of what the WAM onset means. Broadly speaking, onset can be analyzed regionally, locally or over a designated intermediate scale. There are at least eighteen distinct definitions of the WAM onset in publication with little work done on comparing observed onset from different definitions or comparing onset realizations across different datasets and resolutions. Here, nine definitions have been calculated using multiple datasets of different metrics at different resolution. It is found that mean regional onset dates are consistent across multiple datasets and different definitions. There is low inter-annual variability in regional onset suggesting that regional seasonal forecasting of the onset provides few benefits over climatology. In contrast, local onsets show high spatial, inter-annual and inter-definition variability. Furthermore it is found that there is little correlation between local onset dates and regional onset dates across West Africa implying a disharmony between regional measures of onset and the experience on a local scale. The results of this study show that evaluation of seasonal monsoon onset forecasts is far from straightforward. Given a seasonal forecasting model, it is possible to simultaneously have a good and bad prediction of monsoon onset simply through selection of onset definition and observational dataset used for comparison