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With this in mind, pricewise, WBE is considered cost-effective when comparing to clinical nasal swabs.The physiological and behavioural activities of animals have far-reaching impacts on the characteristics and functioning of soil. This includes vertebrates, which are capable of modifying the physicochemical and biochemical properties of soil. To date, however, no species is known to be responsible for the entire process of soil formation, modification and maintenance. Large-bodied birds build nests which they then use for several years or even decades. During nest construction or renovation, birds gather and transport to the nesting site organic and mineral matter that includes tree branches of various sizes, twigs, turf, straw and hay. Over time, during subsequent breeding events, adult birds supply further loads of organic matter to the nest, such as food remains, excrement, pellets, feathers, egg shells and other materials. Taking the White Stork Ciconia ciconia as an example, we have shown that the materials deposited in the nests of large-bodied birds gradually produce ornithogenic soils over the years, with distinguishable layers having different physicochemical characteristics and biochemical activities. The tested nesting substrate met the criteria for ornithogenic material; the layers had appropriate thickness and phosphorus pentoxide (P2O5) content. Results of the study indicates that the material contained in White Stork nests have the characteristics of Histosols. Moreover, such nests harbour assemblages of fungi and arthropods that contain species typical of soil mycobiota and fauna, respectively. This study is the first to describe a soil that is formed, modified and maintained entirely by vertebrates and is physically isolated from the ground. Our results highlight the fact that the nests of large birds are unique structures in ecosystems and provide a habitat for a rich and diverse assemblage of organisms.The adverse health effects of rare earth elements (REEs) on reproductive health remain a subject of debate, and few clinical observations are available. This study investigated the association between light REEs (LREEs) exposure and the outcome of in vitro fertilization-embryo transfer (IVF-ET). We recruited a total of 305 women undergoing IVF-ET in Beijing City and Shandong Province of northern China. Their demographic information and lifestyle characteristics were collected using questionnaires at enrollment. Fasting blood samples were collected on the day before the IVF-ET treatment cycle began. Serum concentrations of the LREEs of concern were analyzed using inductively coupled plasma-mass spectrometry, and four LREEs were measured with a high detection rate, including lanthanum (La), cerium (Ce), praseodymium (Pr), and neodymium (Nd). We found that a higher serum La concentration was associated with a 30% increased likelihood of clinical pregnancy failure [relative risk (RR) = 1.30, 95% confidence interval (CI) 1.00-1.67] and a 230% increased likelihood of preclinical spontaneous abortion (RR = 3.30, 95% CI 1.57-6.94). There was a negative correlation between serum La concentration and the number of good-quality oocytes. For the other LREEs, no statistically significant associations were observed. We concluded that a high serum La concentration may have an adverse effect on IVF-ET outcomes.Studies in aerosol properties, types and sources in the Himalayas are important for atmospheric and climatic issues due to high aerosol loading in the neighboring plains. This study uses in situ measurements of aerosol optical and microphysical properties obtained during the Ganges Valley Aerosol eXperiment (GVAX) at Nainital, India over the period June 2011-March 2012, aiming to identify key aerosol types and mixing states for two particle sizes (PM1 and PM10). Using a classification matrix based on SAE vs. AAE thresholds (scattering vs. buy Motolimod absorption Ångström exponents, respectively), seven aerosol types are identified, which are highly dependent on particle size. An aerosol type named "large/BC mix" dominates in both PM1 (45.4%) and PM10 (46.9%) mass, characterized by aged BC mixed with other aerosols, indicating a wide range of particle sizes and mixing states. Small particles with low spectral dependence of the absorption (AAE less then 1) account for 31.6% and BC-dominated aerosols for 14.8% in PM1, while in PM10, a large fraction (39%) corresponds to "large/low-absorbing" aerosols and only 3.9% is characterized as "BC-dominated". The remaining types consist of mixtures of dust and local emissions from biofuel burning and display very small fractions. The main optical properties e.g. spectral scattering, absorption, single scattering albedo, activation ratio, as well as seasonality and dependence on wind speed and direction of identified types are examined, revealing a large influence of air masses originating from the Indo-Gangetic Plains. This indicates that aerosols over the central Himalayas are mostly composed by mixtures of processed and transported polluted plumes from the plains. This is the first study that identifies key aerosol populations in the central Indian Himalayas based on in situ measurements and the results are highly important for aerosol-type inventories, chemical transport models and reducing the uncertainty in aerosol radiative forcing over the third pole.Information on weed occurrence within croplands is vital but is often unavailable to support weeding practices and improve cropland productivity assessments. To date, few studies have been conducted to estimate and map weed abundances within agroecological systems from spaceborne images over wide-area landscapes, particularly for the genus Striga. Therefore, this study attempts to increase the detection capacity of Striga at subpixel size using spaceborne high-resolution imagery. In this study, a two-step classification approach was used to detect Striga (Striga hermonthica) weed occurrence within croplands in Rongo, Kenya. Firstly, multidate and multiyear Sentinel-2 (S2) data (2017 to 2018) were utilized to map cropland and non-cropland areas using the random forest algorithm within the Google Earth Engine. The non-cropland class was thereafter masked out from a single date S2 image of the 13th of December 2017. The remaining cropland area was then used in a subpixel multiple endmember spectral mixture analysis (MESMA) to detect Striga occurrence and infestation using endmembers (EMs) obtained from the in-situ hyperspectral data.
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