SOURCE SPECIFIC QUANTIFICATION AND CHARACTERISATION OF SOLID WASTE ALONG A SANDY BEACH IN CAPE COAST, GHANA

Ghana is dealing with extensive urban periphery settlements due to the massive migration of rural inhabitants to the cities, especially to the political and economic capital, Accra and other regional capitals including Cape Coast. This phenomenon has culminated in indiscriminate solid waste disposal. With no effective municipal solid waste collection system in place, heaps of refuse have become ubiquitous in Cape Coast especially along the beaches. The quantity and composition of solid waste at two locations along a sandy beach in the Cape Coast municipality was investigated in this study. Using five permanent 20 x 4 quadrats over seven weeks in each of the two locations, the amount and composition of solid waste were assessed. The results indicated that paper, bottle, wood, leather, textile, metal, plastics, organic matter and styrofoam were the main categories of solid waste found at the sandy beach. The results also indicated that the quantity of solid waste generated at Duakor and West Gate was 514 kgha-1 and 374 kgha-1 respectively. This study suggests that source specific waste quantification and characterisation of solid waste at different scales should be a vital part of planning in municipal solid waste management systems.solid waste characterisation; quantification; sandy beach; Ghana.

Impact of two rounds of praziquantel mass drug administration on Schistosoma mansoni infection prevalence and intensity: a comparison between community wide treatment and school based treatment in western Kenya

AbstractThis study compared the effectiveness of the community-wide treatment and school-based treatment approaches in the control of Schistosoma mansoni infections in villages with ⩾25% prevalence in western Kenya. Stool samples from first year students, 9–12year olds and adults (20–55years) were analyzed by the Kato–Katz technique for S. mansoni eggs. After two rounds of treatment, S. mansoni prevalence and intensity levels significantly declined in both treatment approaches. Prevalence comparisons between the two approaches did not show any significant differences following treatment. However, infection intensity levels in the 9–12year old school-attending pupils were significantly higher in the community-wide treatment arm than in the school-based treatment arm. Nevertheless, significant reductions in S. mansoni infection prevalence and intensity levels were achieved among school-age children regardless of the treatment approach used

Associations of common breast cancer susceptibility alleles with risk of breast cancer subtypes in BRCA1 and BRCA2 mutation carriers

Introduction: More than 70 common alleles are known to be involved in breast cancer (BC) susceptibility, and several exhibit significant heterogeneity in their associations with different BC subtypes. Although there are differences in the association patterns between BRCA1 and BRCA2 mutation carriers and the general population for several loci, no study has comprehensively evaluated the associations of all known BC susceptibility alleles with risk of BC subtypes in BRCA1 and BRCA2 carriers. Methods: We used data from 15,252 BRCA1 and 8,211 BRCA2 carriers to analyze the associations between approximately 200,000 genetic variants on the iCOGS array and risk of BC subtypes defined by estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and triple-negative- (TN) status; morphologic subtypes; histological grade; and nodal involvement. Results: The estimated BC hazard ratios (HRs) for the 74 known BC alleles in BRCA1 carriers exhibited moderate correlations with the corresponding odds ratios from the general population. However, their associations with ER-positive BC in BRCA1 carriers were more consistent with the ER-positive as

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost