Attenuation potentials of royal jelly against hydroxyurea-induced infertility through inhibiting oxidation and release of pro-inflammatory cytokines in male rats

Environmental Science and Pollution Research - Hydroxyurea (HDU), a class of antineoplastic drugs, has a powerful efficacy in the treatment of several types of malignancies. However, it has...     

PM10 and surface dust source characterization in Baguio City Central Business District (CBD), Philippines

Environmental Geochemistry and Health - This study measured both PM10 and surface dust concentrations at roadside in the Central Business District of Baguio City. A total of 66 PM10 filters and 25...     

Honeybees (<Emphasis Type="Italic">Apis mellifera</Emphasis>) as a biological barrier for contamination of honey by environmental toxic metals

The aim of the study was to investigate the transfer of toxic metals from honeybee workers (Apis mellifera L.) to bee honey in relation to the ecological state of the environment. The materials of the study consisted of samples of honeybee bodies and varietal honeys taken from the same apiary located in three areas: R1—urbanized (16), R2—ecologically clean (16) and R3—industrialized (15) of south-eastern Poland. The contents of 14 elements in all tested samples, including toxic metals (Cd, Pb, Hg, Al, Ni, Tl) as well as bioelements (K, Mg, Ca, Mn, Fe, Zn, Cu, Se), were analysed by the ICP-OES method with prior microwave mineralization. The concentrations of the majority of the studied elements, excluding aluminum and lead, were significantly higher in bee bodies than in honey samples (P?<?0.05). The pollution of bee bodies by toxic metals was dependent on the environmental cleanliness, and the most pollution was observed in the industrialized area. The bee body was the most effective barrier for Cd and Tl transfer to the honey, while the level of Ni was similar in both tested materials. The Al concentration was significantly higher in honey than bee bodies (14.81?±?24.69 and 6.51?±?5.83 mg kg^?1, respectively), which suggests the possibility of secondary contamination of honey. The greatest sensitivity to heavy metal pollution was observed in honeydew honey compared to nectar honeys (P?<?0.05). It was proved for the first time that bees work as biofilters for toxic metals and prevent honey contamination.     

Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks

Soil specific surface area (SSA) is an important property of soil. Depending on the measurement techniques, determination of the SSA is costly and time consuming. Hence, a limited number of studies have been conducted to predict the SSA from the soil variables. In this study, the soil samples were taken from the literature. Fractal parameters (FP) were calculated by the model of Bird et al. (European Journal of Soil Science 51, 55–63, 2000) used as the input variables to predict the SSA. Some studies have been carried out on the prediction capability of the different parameters using the artificial neural networks (ANNs). The ANNs were further used and 20 models were developed to investigate the value of input variables to predict the SSA. The results showed that the PTF13 (RMSE?=?0.13) and PTF18 (RMSE?=?0.13) with the input variables of particle-size distribution and Atterberg limits revealed better performance than the other PTFs (in the training step). It is because of the fact that free swelling index (FSI) and Atterberg limits were closely correlated to the soil clay mineralogy as one of the important factors controlling the SSA. In general, this results demonstrated that the PTF9 with the variables of sand, clay, plastic limit (PL), liquid limit (LL), and FSI showed the best (RMSE?=?0.37) results in the estimation of the SSA. In conclusion, there was not a strong correlation between the soil mechanical properties and SSA but also ANNs were a suitable method to predict the SSA from the soil variables.     

The tree species matters: Biodiversity and ecosystem service implications of replacing Scots pine production stands with Norway spruce

Ambio - The choice of tree species used in production forests matters for biodiversity and ecosystem services. In Sweden, damage to young production forests by large browsing herbivores is helping...     

Mobility of free surface in different liquids and its influence on water striders locomotion

Dynamics of the surface layer in different liquids is examined by means of infrared thermography of the surface and simultaneous velocity fields measurements using surface and infrared Particle Image Velocimetry. This technique allows measurements and comparison of two velocity fields—at the surface and at small depth about 50–200 μm. In distilled water the velocity fields at the surface and at small depth exhibit significant dissimilarity. The flow field below the surface is essentially 3D, whereas the surface flow is characterized by vanishing 2D divergence of velocity, indicating predominantly planar motion. In contrast, in ethanol–butanol mixture two velocity fields are well correlated, both corresponding to 3D flow with continuous surface renewal. Thermal patterns, observed at the surface, and the flow field structure in different liquids are associated with different boundary conditions for velocity at the surface. Water surface is seldom renewed, which inhibits heat and mass exchange between the liquid and atmosphere. However, absence of vertical advection also enables organisms to live within the surface layer, to stand and walk on the free surface. This is illustrated by the difficulties a water strider faces on the surface of ultrapure water, which exhibits Marangoni convection.     

Evaluating knowledge integration and co-production in a 2-year collaborative learning process with smallholder dairy farmer groups

Although knowledge integration and co-production are integral to transdisciplinary approaches to foster sustainable change in social–ecological systems, this type of research is usually not evaluated based on assessments of the learning process. While participants are meant to be central in such approaches, too often, their perspectives are not central to the evaluation. Moreover, there is limited empirical information about how new knowledge is transformed into action. We respond to these knowledge gaps by analyzing (A) farmers’ perspectives on the collaborative learning process and (B) how farmers’ new knowledge can serve as the basis for changed actions. Theoretically, we are guided by second-order cybernetics and have integrated the Control Loop Model with Learning Loops to extend Kirkpatrick (Evaluating training programs: the four levels, 2nd edn. Berrett-Koehler Publisher, San Francisco, 1998) four-level evaluation scheme. We apply this to evaluate a 2-year collaborative learning process with two smallholder dairy farmer groups in Nakuru County, Kenya that aimed to co-develop local sustainable pathways to reduce milk losses. Results showed that farmers learned by (1) implementing corrective actions based on known cause–effect relations (single-loop learning); (2) discovering new cause–effect relations and testing their effect (double-loop learning); and (3) further questioning and changing their aims (triple-loop learning). Highlighting the importance of knowledge integration and co-production, this collaboration between farmers, researchers, and field assistants improved the farmers’ ability to respond, adapt, and intentionally transform their farming system in relation with complex sustainability challenges. Results demonstrate that the potential of our evaluation scheme to better reflect learning and empowerment experienced by actors involved in transdisciplinary research for sustainability.     

Assessing and managing intensification in smallholder dairy systems for food and nutrition security in Sub-Saharan Africa

Smallholder farmers play an important part in the dairy value chain in Sub-Saharan Africa. Three technological approaches have been used to improve productivity. These are through, applying agricultural ecological processes (ecological intensification), utilising modern livestock breeding (genetic intensification), and socio-economic intensification. Ecological intensification includes continuous housing of cows applying a cut-and-carry feeding system, introduction of purpose-bred forages and pastures, and the introduction of agro-forestry within the dairy systems. Genetic intensification strategies include: importation of dairy breeds such as Holstein–Friesian (HF) and cross-breeding of the indigenous breeds with HF. Training and capacity-building activities to create sustainable livelihoods have been initiated for farming and technological practices of animal husbandry, but also to enhance appropriate leadership and corporative-building skills that would create and support an enabling environment for sustainability. These improvements and initiatives in the service delivery have been championed by national governments, development partner institutions, or non-governmental organisations through different programmes. Challenges of intensification include matching management to genetic potential of imported and cross-bred improved dairy breeds, ensuring low post-harvest losses, proper utilisation, and reducing environmental impact. Using examples from Kenya, Malawi, Mozambique, Tanzania, and Zambia, this paper reviews the management and assessment approaches used in fostering smallholder dairy development strategies and dairy’s contribution to sustainable livelihoods in the face of intensification.     

Assessing relative vulnerability to sea-level rise in the western part of the Mekong River Delta in Vietnam

The Mekong River Delta in Vietnam plays a crucial role for the region in terms of food security and socioeconomic development; however, it is one of the most low-lying and densely populated areas in the world. It is vulnerable to seawater incursion, flood risk, and shoreline change, exacerbated as a consequence of sea-level rise (SLR) related to climate change. This study examined the Kien Giang coast in the western part of the delta, comprising seven coastal districts (namely Ha Tien, Kien Luong, Hon Dat, Rach Gia, Chau Thanh, An Bien, and An Minh), the economy of which is important in terms of agriculture and aquaculture. The analytical hierarchical process (AHP) method of multi-criteria decision making was integrated directly into geographic information systems (GIS) to derive a composite vulnerability index that indicated areas most likely to be vulnerable to SLR. The hierarchical structure comprised three key components: exposure (E), sensitivity (S), and adaptive capacity (A), at level 1. At the next level, 8 sub-components were mapped: seawater incursion, flood risk, shoreline change, population characteristics, land use/land cover, and socioeconomic, infrastructure, and technological capability, beyond which a further 22 variables (level 3) and 24 sub-variables (level 4) related to vulnerability were also mapped. Variables were assigned weights for incorporation into AHP pairwise comparisons after discussion with stakeholders. Maps were generated to visualise areas where the relative vulnerability was very low, low, moderate, high, and very high. Societal data were generally only available at district level; however, several regional patterns emerged. Relatively high exposure to flooding and inundation, salinity, and moderate loss of mangroves occurred along the coastal fringe of each district. This western section of the delta, which is low-lying and remote from the distributaries that carry sediment to the coast, appears to be particularly vulnerable. The most sensitive areas tended to be ethnic households engaged in rice cultivation and with moderate population density. The least adaptable areas consisted of high numbers of poor households, with low income, and moderate densities of transport, irrigation and drainage systems. Most coastal districts were determined to be moderately to relatively highly vulnerable, with scattered hotspots along the coast.