The structure and organization of acoustic signals arise through evolutionary processes and adaptive pressures on each species. During learning, natural or anthropogenic factors, such as high noise levels in urban areas, pose challenges to acoustic communication in birds. Many species adjust their acoustic signals to higher noise levels by increasing the frequency of vocalizations. The objectives of this study were to compare the dominant frequency of songs among birds dwelling in forest fragments distant from and near to urban areas, establish correlations between the dominant frequency of song and noise levels in these environments and verified the difference of response between oscines, suboscines and non-passerines. We recorded vocalizations of birds between July/2013 and November/2014 in four forest fragments, two of them near and two distant from urban areas. We used Audacity software to measure the dominant frequency. We measured the ambient noise by a calibrated sound pressure level meter in decibels (dBA) in each of the forest fragments. We analyzed 3740 vocalizations of nine tropical bird species. Forest fragments near to urban areas have higher noise levels than more distant forest fragments. Eight of nine studied species presented higher dominant frequencies of songs in forest fragments near to urban areas. Only one species, Myiothlypis flaveola, did not change the dominant frequency of song between the four analyzed forest fragments. The difference in dominant frequency between the forest fragments distant and closer to the urban areas did not vary between oscines, suboscines and non-passerines. Eight tropical birds exhibited higher dominant frequencies of song in forest fragments near urban areas with high level of ambient noise. Oscine, suboscine and non-passerine showed song variations. Bird species that have differences in the vocalization dominant frequency can be used in environmental monitoring and in ethological studies, as they are sensitive to high noise levels.Noise pollution caused by the vehicular traffic and urbanization are correlates with changes in the vocalization of tropical birds in forest fragments.

With increasing demand for recycled wastewater for irrigation purposes, there is a need to evaluate the potential for manufactured nanomaterials in waste water to impact crop production and agroecosystems. Copper oxide nanoparticles (CuO NPs) have previously been shown to negatively impact the growth of duckweed (Landoltia punctata) a model aquatic plant consumed by water fowl and widely found in agricultural runoff ditches in temperate climates. However, prior studies involving CuO NP toxicity to duckweed have focused on systems without the presence of dissolved organic matter (DOM). In the current study, duckweed growth inhibition was shown to be a function of aqueous Cu²⁺ concentration. Growth inhibition was greatest from aqueous CuCl₂ and, for particles, increased with decreasing CuO particle size. The dissolution of CuO NPs in ½ Hoagland's solution was measured to increase with decreasing particle size and in the presence of Suwannee river humic and fulvic acids (HA; FA). However, the current results suggest that HA, and to a lesser extent, FA, decrease the toxicity of both CuO NPs and free ionized Cu to duckweed, likely by inhibiting Cu availability through Cu-DOM complex formation. Such results are consistent with changes to Cu speciation as predicted by speciation modeling software and suggest that DOM changes Cu speciation and therefore toxicity in natural systems.

During the last years, declines in honey bee colonies are being registered worldwide. Cholinergic pesticides and their extensive use have been correlated to the decline of pollinators and there is evidence that pesticides act as neuroendocrine disruptors affecting the metabolism of neuropeptides. However, there is a big absence of studies with quantitative results correlating the effect of pesticide exposure with changes on neuropeptides insects, and most of them are conducted under laboratory conditions, typically with individual active ingredients. In this study, we present an analytical workflow to evaluate pesticide effects on honey bees through the analysis of (neuro)peptides. The workflow consists of a rapid extraction method and liquid chromatography with triple quadrupole for preselected neuropeptides. For non-target analysis, high resolution mass spectrometry, multivariate analysis and automatic identification of discriminated peptides using a specific software and protein sequence databases. The analytical method was applied to the analysis of target and non-target (neuro)peptides in honey bees with low and high content of a wide range of pesticides to which have been exposed in field conditions. Our findings show that the identification frequency of target neuropeptides decreases significantly in honey bees with high concentration of pesticides (pesticide concentrations ≥ 500 μg kg⁻¹) in comparison with the honey bees with low content of pesticides (pesticide concentrations ≤ 20 μg kg⁻¹). Moreover, the principal component analysis in non-target search shows a clear distinction between peptide concentration in honey bees with high level of pesticides and honey bees with low level. The use of high resolution mass spectrometry has allowed the identification of 25 non-redundant peptides responsible for discrimination between the two groups, derived from 18 precursor proteins.

Fungi are well associated with the degradation of hydrocarbons by the production of different enzymes, among which catalases (CBH), laccases (LCC) and peroxidases (LiP and MnP) are of immense importance. In this study, crude oil tolerance and enzyme secretions were demonstrated by rhizospheric fungal strains. Four most abundant strains were isolated from the rhizosphere of grasses growing in aged oil spill sites and identified through morphological characterization and molecular PCR-amplification of 5.8–28S ribosomal rRNA using ITS1 and ITS4 primers. These strains were subjected to crude oil tolerance test at 0–20% concentrations. Presence and transcriptase responses of putative genes lig (1–6), mnp, cbh (1.1, 1.1 and 11), and lcc encoding lignin peroxidase, manganese peroxidase, catalase, and laccase enzymes respectively were also studied in these strains using RT-PCR. In addition, activities of secreted enzymes by each strain were studied in aliquots. The strains were identified as Aspergillus niger asemoA (KY473958), Talaromyces purpurogenus asemoF (KY488463), Trichoderma harzianum asemoJ (KY488466), and Aspergillus flavus asemoM (KY488467) through sequencing and comparing the sequences’ data at NCBI BLAST search software. All the isolated strains showed tolerance to crude oil at 20% concentration, but the growth rate reduced with increasing in oil concentrations. All the isolated strains possess the tested genes and lig 1–6 gene was overexpressed in A. niger and T. harzianum while lcc and mnp genes were moderately expressed in all the four strains. Almost 145 U.mL⁻¹ of lignin and manganese peroxidase, 87 U.mL⁻¹ of catalase, and 180 U.mL⁻¹ of laccase enzymes were produced by these strains and it was also observed that these strain mostly produced studied enzymes in response to increasing crude oil concentrations. Considering the robust nature and diverse production of these catalytic enzymes by these strains, they can be exploited for various bioremediation technologies as well as other biotechnological applications.

Airborne systems such as lightweight and highly portable unmanned aerial vehicles (UAVs) are becoming increasingly widespread in both academia and industry - with an ever-increasing range of applications, including (but not limited to), air quality sampling, wildlife monitoring and land-use mapping.In this work, high-resolution airborne photogrammetry obtained using a multi-rotor system operating at low survey altitudes, is combined with ground-based radiation mapping data acquired at an interim storage facility for wastes removed as part of the large-scale Fukushima clean-up program. The investigation aimed to assess the extent to which the remediation program at a specific site has contained the stored contaminants, as well as present a new methodology for rapidly surveying radiological sites globally. From the three-dimensional rendering of the site of interest, it was possible to not only generate a powerful graphic confirming the elevated radiological intensity existing at the location of the waste bags, but also to also illustrate the downslope movement of contamination due to species leakage from the large 1m³ storage bags. The entire survey took less than 1 h to perform, and was subsequently post-processed using graphical information software to obtain the renderings. The conclusions within this study not only highlight the usefulness of incorporating three-dimensional renderings within radiation mapping protocols, but also conclude that current methods of monitoring these storage facilities in the long term could be improved through the integration of UAVs within the standard protocol.

In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM) and the adaptive neuro-fuzzy inference system (ANFIS) combined air pollutant concentration prediction method. Firstly, Gaussian membership function parameters are selected to fuzzify the input values and calculate the membership degree of each input variable. Secondly, corresponding fuzzy rules are activated, and the firing strength is normalized to calculate the output matrix of hidden nodes. Then, the optimal parameters (C, M), weights are assigned to weighted ELM by using locally weighted linear regression, and the regularized WELM target formula with equality constraint is optimized by the Karush–Kuhn–Tucker (KKT) conditions, the output weight matrix is calculated, and finally the prediction output matrix is calculated. Based on the air pollutant concentration data collected in Datong, Taiwan, the data on the pollutants containing carbon monoxide (CO), nitric oxide (NO), PM2.5 (particulate matter) and PM10, are selected by different historical time series lengths, using genetic algorithm-backpropagation neural network (GA-BPNN), support vector regression (SVR), extreme learning machine (ELM), WELM, ANFIS, regularized extreme learning adaptive neuro-fuzzy inference system (R-ELANFIS) and ANFIS-WELM are built for predict the concentration of each pollutant collected by single monitoring point in single-step time series. The experimental results show that the ANFIS-WELM presented in this paper has better prediction accuracy and real-time performance, realizes the prediction of multi-step time series on the basis of the ANFIS-WELM, and realizes the engineering application of the ANFIS-WELM algorithm package on the self-developed mobile source emissions online monitoring data center software system.

Seagrass meadows are complex social-ecological systems. Understanding seagrass meadows demands a fresh approach integrating “the human dimension”. Citizen science is widely acknowledged for providing significant contributions to science, education, society and policy. Although the take up of citizen science in the marine environment has been slow, the need for such methods to fill vast information gaps is arguably great. Seagrass meadows are easy to access and provide an example of where citizen science is expanding. Technological developments have been pivotal to this, providing new opportunities for citizens to engage with seagrass. The increasing use of online tools has created opportunities to collect and submit as well as help process and analyse data. Citizen science has helped researchers integrate scientific and local knowledge and engage communities to implement conservation measures. Here we use a selection of examples to demonstrate how citizen science can secure a future for seagrass.

In many developing countries, a landfill remains one of the most extensively employed solid waste disposal solutions. Although a landfill is a well-designed engineering system, the base lining of a landfill may perform poorly and allow the leachate to reach the underlying soil layers and groundwater. Leachates contain a variety of toxic and hazardous contaminants, which are attenuated in the soil by various processes that slow or transform them. Thus, the objective of this research was to study the water and leachate permeability and retention of the liner soil in a landfill experimental cell by subjecting it to geotechnical, chemical-mineralogical, and physicochemical characterizations, water and leachate permeability tests, and mercury intrusion porosimetry (MIP). In addition, the water and leachate retention curves were determined and analyzed using RETention Curve (RETC) software to obtain the unsaturated permeability curves. The leachate in the soil decreased the suction considering the moisture content of the compacted soil in the field, which consequently increased the leachate permeability of the mineral liner. For the same suction value, in the drying pathways, the soil retained a greater amount of distilled water than leachate. In the wetting pathways, the opposite occurred. Microorganisms were detected in the soil during the filter paper test. The permeability coefficients of the unsaturated soil were directly proportional to the gravimetric moisture content for the water and the leachate, which demonstrated that the soil presents lower unsaturated permeability coefficients for water than for leachate for the same water content.

Mitochondria play an essential role to supply the cell with metabolic energy in the form of adenosine triphosphate (ATP) through oxidative phosphorylation (OXPHOS). As a consequence, they are also the primary source of cellular reactive oxygen species (ROS) which can cause oxidative damage of individual respiratory chain complexes. Indeed, affected OXPHOS subunits result in decreases in ATP production and increases in ROS formation which generate oxidative phosphorylation deficiency leading to mitochondrial dysfunctions. It has been suggested that ROS play a vital role in the pathogenesis of mitochondrial diseases. To the best of our knowledge, this is the first study which aimed to investigate the genetic variant effect of the antioxidant enzymes GSTM1 and GSTT1 on mitochondrial disease among a Tunisian population. In this report, 109 patients with mitochondrial disease and 154 healthy controls were genotyped by multiplex PCR amplification, and data were analyzed by SPSS v20 software. The results showed that GSTM1 null genotype was found to be associated with mitochondrial disease with a protective effect; however, no significant association of GSTT1 polymorphism with mitochondrial disease risk was revealed. But, interestingly, our findings highlight that GSTM1 active and GSTT1 null genotype combination increased by three fold the risk of developing mitochondrial disease with p c = 0.020, notably mitochondrial myopathy with p c = 0.046 and Leigh syndrome with p c = 0.042. In conclusion, this study suggests that GSTM1 active and GSTT1 null genotype combination might be a risk factor in developing mitochondrial disease.

Today, widespread growth in the world is intertwined with multichannel monitoring systems based on remote sensing. Such systems allow for the collection of operational information that focuses on the environmental status of marine systems on different scales. The technique of identifying real-time abnormal phenomena in the marine environment requires the presence of algorithmic remote sensing measurements and appropriate software. The present study presents a new monitoring system for remotely sensed anomalies on the marine surface, equipped with a “spotting” model based on empirical data. The experimental verification of the efficiency of the system and the algorithms developed was based on data from the Cosmos-1500 satellite for several seas. Finally, it is stressed that the proposed system, in addition to water, can also be used to detect abnormal phenomena in both air and soil.