Focusing the pivotal role of nanotechnology in Huntington’s disease: an insight into the recent advancements

Environmental Science and Pollution Research - Neurodegeneration is the loss of neuronal capacity and structure over time which causes neurodegenerative disorders like Alzheimer, amyotrophic...     

Reproductive isolation between populations from Northern and Central Europe of the leaf beetle Chrysomela lapponica L.

Summary. Allopatric populations of the leaf beetle Chrysomela lapponica are known to feed upon either willow (Salicaceae) or birch (Betulaceae). This study aimed to elucidate the differentiation process of these allopatric populations. We investigated whether these allopatric populations specialized on different host plants are still able to produce fertile offspring when interbreeding. Individuals from a population in Finland (willow specialists) and one in the Czech Republic (birch specialists) were crossed in laboratory. Hybrid formation succeeded only between females from the Czech, birch specialized population and males from the Finnish, willow specialized population, while no eggs were produced by females of the willow specialists having mated males of the birch specialists. Behavioral, morphological, physiological, and chemical features of the F₁ hybrids were studied. The chemical composition of larval defensive secretion and feeding preferences of the resulting F₁ hybrids mainly showed similarities with the paternal phenotype, while the area of black coloring on the offspring’s elytra was intermediate between those of the parental elytra. F₁ hybrids did not accept the host plant (birch) of their mothers for feeding and only survived on willow. Thus, since mothers only lay eggs on birch, we found evidence for a postzygotic isolation mechanism between the individuals of the two investigated populations: when having been mated with willow specialized Finnish males, the birch specialized Czech mothers place the hybrid eggs on a plant species (birch), on which the hatching larvae cannot survive.     

Appraisal of salinity and fluoride in a semi-arid region of India using statistical and multivariate techniques

Various physico-chemical parameters, including fluoride (F⁻), were analyzed to understand the hydro-geochemistry of an aquifer in a semi-arid region of India. Furthermore, the quality of the shallow and deep aquifer (using tube well and hand pumps) was also investigated for their best ecological use including drinking, domestic, agricultural and other activities. Different multivariate techniques were applied to understand the groundwater chemistry of the aquifer. Findings of the correlation matrix were strengthened by the factor analysis, and this shows that salinity is mainly caused by magnesium salts as compared to calcium salts in the aquifer. The problem of salinization seems mainly compounded by the contamination of the shallow aquifers by the recharging water. High factor loading of total alkalinity and bicarbonates indicates that total alkalinity was mainly due to carbonates and bicarbonates of sodium. The concentration of F⁻ was found more in the deep aquifer than the shallow aquifer. Further, only a few groundwater samples lie below the permissible limit of F⁻, and this indicates a risk of dental caries in the populace of the study area. The present study indicates that regular monitoring of groundwater is an important step to avoid human health risks and to assess its quality for various ecological purposes.     

Identifying the possibilities and pitfalls of conducting IUCN Red List assessments from remotely sensed habitat information based on insights from poorly known Cuban mammals

The International Union for Conservation of Nature's Red List of Threatened Species (RLS) is the key global tool for objective, repeatable assessment of species’ extinction risk status, and plays an essential role in tracking biodiversity loss and guiding conservation action. Satellite remote sensing (SRS) data sets on global ecosystem distributions and functioning show exciting potential for informing range-based RLS assessment, but their incorporation has been restricted by low temporal resolution and coverage of data sets, lack of incorporation of degradation-driven habitat loss, and noninclusion of assumptions related to identification of changing habitat distributions for taxa with varying habitat dependency and ecologies. For poorly known mangrove-associated Cuban hutias (Mesocapromys spp.), we tested the impact of possible assumptions regarding these issues on range-based RLS assessment outcomes. Specifically, we used annual (1985–2018) Landsat data and land-cover classification and habitat degradation analyses across different internal time series slices to simulate range-based RLS assessments for our case study taxa to explore potential assessment uncertainty arising from temporal SRS data set coverage, incorporating proxies of (change in) habitat quality, and assumptions on spatial scaling of habitat extent for RLS parameter generation. We found extensive variation in simulated species-specific range-based RLS assessments, and this variation was mostly associated with the time series over which parameters were estimated. However, results of some species-specific assessments differed by up to 3 categories (near threatened to critically endangered) within the same time series, due to the effects of incorporating habitat quality and the spatial scaling used in RLS parameter estimation. Our results showed that a one-size-fits-all approach to incorporating SRS information in RLS assessment is inappropriate, and we urge caution in conducting range-based assessments with SRS for species for which habitat dependence on specific ecosystem types is incompletely understood. We propose novel revisions to parameter spatial scaling guidelines to improve integration of existing time series data on ecosystem change into the RLS assessment process.     

Land cover composition determinants in the Uckermark (NE Germany) over a 220-year period

There is broad consensus that land use/cover has changed in central Europe over millennia. However, few studies have addressed the roles of the anthropogenic (socio-economic) and natural (biophysical) factors driving these changes over shorter periods (e.g., 100 years). In this study, we analyse the determinants of land cover composition at three discrete time periods (c. 1780, 1890, and 2000). We hypothesise that different anthropogenic and natural factors are the determinants of the main land cover types (arable fields, grasslands, and forests) and that the effects of natural and anthropogenic factors on the main land cover types differ among landscapes with highly dynamic and more stable forest-open land distributions. The study was carried out in the Uckermark region located in northeastern Germany. We compiled data on natural and anthropogenic factors (e.g. forest cover and number of inhabitants) of 65 municipalities in four landscape sections of equal size (10 × 10 km). Landscape sections were selected to reflect different dynamics (high/low) in conversion from forest to arable fields or grasslands and vice versa from 1780 to 2000. Averaged linear mixed-effect models explained between 7.5 and 81.2 % of the variance. The unique effect of anthropogenic factors varied from 2.1 to 18.7 % and that of natural factors varied from 0.2 to 43.4 %. In four of six models that included both types of factors, the natural factors were more influential than the anthropogenic factors. Except in three cases, anthropogenic and natural factors showed opposite effects on land cover types in more dynamic and more stable windows. Though the Uckermark region has been influenced by human activity for thousands of years, natural factors were a major determinant of land cover composition during all time periods, whereas anthropogenic factors became more important only during the latest time period under investigation.     

Modeling non-point source pollution from rice farming in the Thachin River Basin

With the worldwide intensification of agriculture, non-point source pollution of surface waters has become a pressing issue. Conventional river water quality models consider non-point sources as accumulated entries into the rivers and do not investigate into the processes generating the pollution at its source, thus preventing the determination of effective mitigation measures. The models require extensive data inputs, which is a deficiency in many developing and emerging countries with limited data availability. The current study applies a Material Flow Model as a complementary approach to quantify non-point source pollution from agricultural areas. Rice farming in the Thachin River Basin is presented as a case study, with a focus on nutrients. The total nitrogen and phosphorus flows from rice farming to the river system are quantified, the key parameters influencing these flows are determined and potential mitigation measures are discussed. The results show that rice contributes considerable nutrient loads to the Thachin River Basin. Scenario simulations demonstrate that a significant nutrient load reduction could be achieved by following the official recommendations for fertilizer application, thus confirming the local efforts to introduce best management practice. Our results underline the importance of non-point source pollution control in intensive agricultural areas, particularly of tropical lowland delta areas such as the Central Plains of Thailand. The specific benefit of applying a Material Flow Model in this context is that with limited data availability, one can reach an understanding of the system and gain a first overview over its key pollution problems. This can serve as supportive basis for determining consecutive in-depth research requirements.     

The CPCAT as a novel tool to overcome the shortcomings of NOEC/LOEC statistics in ecotoxicology: a simulation study to evaluate the statistical power

Species reproduction is an important determinant of population dynamics. As such, this is an important parameter in environmental risk assessment. The closure principle computational approach test (CPCAT) was recently proposed as a method to derive a NOEC/LOEC for reproduction count data such as the number of juvenile Daphnia. The Poisson distribution used by CPCAT can be too restrictive as a model of the data-generating process. In practice, the generalized Poisson distribution could be more appropriate, as it allows for inequality of the population mean \(\mu\) and the population variance \(\sigma ^2\). It is of fundamental interest to explore the statistical power of CPCAT and the probability of determining a regulatory relevant effect correctly. Using a simulation, we varied between Poisson distribution (\(\mu =\sigma ^2\)) and generalized Poisson distribution allowing for over-dispersion (\(\mu <\sigma ^2\)) and under-dispersion (\(\mu >\sigma ^2\)). The results indicated that the probability of detecting the LOEC/NOEC correctly was \(\ge 0.8\) provided the effect was at least 20% above or below the mean level of the control group and mean reproduction of the control was at least 50 individuals while over-dispersion was missing. Specifically, under-dispersion increased, whereas over-dispersion reduced the statistical power of the CPCAT. Using the well-known Hampel identifier, we propose a simple and straight forward method to assess whether the data-generating process of real data could be over- or under-dispersed.