Impact of environmental variables on the distribution of phytoplankton communities in the Southern Yellow Sea.

To gain a comprehensive understanding of the seasonal variation in the structure of phytoplankton communities in the Southern Yellow Sea (SYS), two research expeditions were conducted from 12 to 24 in April 2019, and from 12 to 22 in October of 2019. During the spring season, the phytoplankton community within the SYS was primarily comprised of diatoms and dinoflagellates, while in autumn, diatoms and cyanobacteria dominated. Thalassiosira rotula and Paralia sulcata were the dominant species in both seasons. In spring, P. sulcata displayed no obvious correlation with any environmental parameter, while in autumn, it exhibited negative correlations with environmental factors. According to the cluster and multidimensional scaling analyses, the phytoplankton community was stratified into three distinct ecological provinces in the SYS: the Western Yellow Sea, the Yellow Sea basin, and the southern coastal region. The phytoplankton community composition was predominantly affected by seasonal fluctuations in temperature and nutrient levels. Notably, the Yellow Sea basin exhibited the lowest phytoplankton abundance, largely because of the impact of the Yellow Sea Cold Water Mass. Furthermore, the presence of cyanobacteria, particularly prevalent in the Yellow Sea basin, may have been facilitated by transport mechanisms associated with the Kuroshio current. Aggregated boosted tree (ABT) and Generalized Additive models (GAM) suggested that temperature, DIN, salinity, and DIP were significant parameters of phytoplankton abundance in SYS. Additionally, the N:P nutrient ratio was a key parameter in governing the structure of phytoplankton communities during both seasons.

Microbial response to seasonal variation in arctic biocrusts with a focus on fungi and cyanobacteria.

Biocrusts are crucial components of Arctic ecosystems, playing significant roles in carbon and nitrogen cycling, especially in regions where plant growth is limited. However, the microbial communities within Arctic biocrusts and their strategies for surviving the harsh conditions remain poorly understood. In this study, the microbial profiles of Arctic biocrusts across different seasons (summer, autumn, and winter) were investigated in order to elucidate their survival strategies in extreme conditions. Metagenomic and metatranscriptomic analyses revealed significant differences in microbial community composition among the sites located in different elevations. The bacterial communities were dominated by Actinobacteria and Proteobacteria, while the fungal communities were mainly represented by Ascomycota and Basidiomycota, with lichenized and saprotrophic traits prevailing. Cyanobacteria were primarily composed of heterocystous cyanobacteria. Furthermore, the study identified molecular mechanisms underlying cold adaptation, including the expression of heat shock proteins and cold-inducible RNA helicases in cyanobacteria and fungi. Overall, the microbial communities appear to be permanently well adapted to the extreme environment.

Study on the variation characteristics and influencing factors of stem water content of Acer truncatum during the overwintering period.

Stem water content serves as a pivotal parameter that reflects the plant vitality and maintains their internal water balance. Given the insufficient comprehension regarding the stem water content characteristics and its influencing factors during different stages of the overwintering period, the study focused on Acer truncatum Bunge and developed an Internet of Things (IoT)-based ecological information monitoring system. The system incorporated a proprietary stem water content sensor, allowing non-invasive, in-situ and real time acquisition of stem water content while monitoring diverse environmental parameters. We conducted a detailed elucidation of stem water content variation characteristics and their responses to diverse environmental factors. The results showed: (1) During the overwintering period, stem water content exhibited diurnal variations characterized by " daytime ascent and nighttime descent" across the three stages, exhibiting differences in the moment when the stem water content reaches extremal values and daily fluctuations ranges. Stem water content exhibited minimal fluctuations during deciduous and bud-breaking stages but experienced significant freezing-thawing alternations during the dormant stage, leading to an increased daily fluctuation range. (2) The Pearson correlation coefficients between environmental parameters and stem water content varied dynamically across stages. Path analysis revealed that during the deciduous stage, stem temperature and saturation vapor pressure deficit were dominant factors influencing stem water content; during dormant stage, air temperature and saturation vapor pressure deficit directly impacted stem water content; during the bud-breaking stage, the primary parameters affecting stem water content were saturation vapor pressure deficit and stem temperature. The study provides valuable insights into unveiling the water transport patterns within tree stems tissue and their environmental adaptation mechanisms during the overwintering period, aiding in the scientific development of winter management strategies to protect trees from severe cold and freezing damage, while fostering healthy growth in the subsequent year.

A Critical Review of the Propagation Models Employed in LoRa Systems.

LoRa systems are emerging as a promising technology for wireless sensor networks due to their exceptional range and low power consumption. The successful deployment of LoRa networks relies on accurate propagation models to facilitate effective network planning. Therefore, this review explores the landscape of propagation models supporting LoRa networks. Specifically, we examine empirical propagation models commonly employed in communication systems, assessing their applicability across various environments such as outdoor, indoor, and within vegetation. Our investigation underscores the prevalence of logarithmic decay in most empirical models. In addition, we survey the relationship between model parameters and environmental factors, clearing their nuanced interplay. Analyzing published measurement results, we extract the log-distance model parameters to decipher environmental influences comprehensively. Drawing insights from published measurement results for LoRa, we compare them with the model's outcomes, highlighting successes and limitations. We additionally explore the application of multi-slope models to LoRa measurements to evaluate its effectiveness in enhancing the accuracy of path loss prediction. Finally, we propose new lines for future research in propagation modelling to improve empirical models.

Contribution of sulfur-containing precursors to release of hydrogen sulfide in sludge composting.

Hydrogen sulfide (H2S) production during composting can impact the environment and human health. Especially during the thermophilic phase, H2S is discharged in large quantities. However, in sludge composting, the contributions of different sulfur-containing precursors to H2S fluxes, key functional microorganisms, and key environmental parameters for reducing H2S flux remain unclear. Analysis of cysteine (Cys), methionine (Met), and sulfate (SO42-) concentrations, multiple stepwise regression analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation analysis of metagenomes showed that Cys was the main contributor to the production of H2S and that Met was among the main sources during the first three days of composting, while the SO42- contribution to H2S was negligible. Fifteen functional genera involved in the conversion of precursors to H2S were identified by co-occurrence network analysis. Only Bacillus showed high temperature resistance (>50 °C) and the ability to reduce H2S. Redundancy analysis showed that total carbon (64.0 %) and pH (23.3 %) had significant effects on functional bacteria. H2S had a quadratic relationship with sulfur-containing precursors. All microbial network sulfur-containing precursors metabolism modules showed a highly significant relationship with Cys.

Irrigation intelligence-enabling a cloud-based Internet of Things approach for enhanced water management in agriculture.

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.

Shotgun metagenomics reveals a diverse mycobiome in the seawater from a High Arctic fjord (Kongsfjorden, Svalbard).

In the Arctic fjords, the marine mycobiome experiences significant changes under environmental conditions driven by climate change. However, research on the ecological roles and the adaptive mechanisms of marine mycobiome in the Arctic fjord remains insufficiently explored. The present study employed shotgun metagenomics to comprehensively characterize the mycobiome in 24 seawater samples from Kongsfjorden, a High Arctic fjord situated in Svalbard. It revealed the presence of a diverse mycobiome with eight phyla, 34 classes, 71 orders, 152 families, 214 genera, and 293 species. The taxonomic and functional composition of the mycobiome differed significantly among the three layers, i.e., upper layer (depth of 0 m), middle layer (depths of 30-100 m), and lower layer (depths of 150-200 m). Several taxonomic groups (e.g., phylum Ascomycota, class Eurotiomycetes, order Eurotiales, family Aspergillaceae, and genus Aspergillus) and KOs (e.g., K03236/EIF1A, K03306/TC.PIT, K08852/ERN1, and K03119/tauD) were significantly distinct among the three layers. Among the measured environmental parameters, depth, NO2-, and PO43- were identified as the key factors influencing the mycobiome composition. Conclusively, our findings revealed that the mycobiome was diverse in the Arctic seawater and significantly impacted by the variability of environmental conditions in the High Arctic fjord. These results will assist future studies in exploring the ecological and adaptive responses towards the changes within the Arctic ecosystems.

Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis.

BACKGROUND: The viability and virulence of COVID-19 are complex in nature. Although the relationship between environmental parameters and COVID-19 is well studied across the globe, in India, such studies are limited. This research aims to explore long-term exposure to weather conditions and the role of air pollution on the infection spread and mortality due to COVID-19 in India. METHOD: District-level COVID-19 data from April 26, 2020 to July 10, 2021 was used for the study. Environmental determinants such as land surface temperature, relative humidity (RH), Sulphur dioxide (SO2), Nitrogen dioxide (NO2), Ozone (O3), and Aerosol Optical Depth (AOD) were considered for analysis. The bivariate spatial association was used to explore the spatial relationship between Case Fatality Rate (CFR) and these environmental factors. Further, the Bayesian multivariate linear regression model was applied to observe the association between environmental factors and the CFR of COVID-19. RESULTS: Spatial shifting of COVID-19 cases from Western to Southern and then Eastern parts of India were well observed. The infection rate was highly concentrated in most of the Western and Southern regions of India, while the CFR shows more concentration in Northern India along with Maharashtra. Four main spatial clusters of infection were recognized during the study period. The time-series analysis indicates significantly more CFR with higher AOD, O3, and NO2 in India. CONCLUSIONS: COVID-19 is highly associated with environmental parameters and air pollution in India. The study provides evidence to warrant consideration of environmental parameters in health models to mediate potential solutions. Cleaner air is a must to mitigate COVID-19.

Role of environmental ownership and associated parameters to assess green patents in technologies with environmental scanning system as a controlling factor.

There is a general conception that environmental firms are more adapted to green solutions, and environmental patents are just lagging. The existing literature has paid particular attention to identifying obstacles and situational factors associated with established firms going green and has concentrated on how and why established businesses are becoming more financially viable and ecologically sustainable. In changing environment, manufacturing companies are direct contributors to environmental impacts. Increased awareness of consumers about the environment puts a handful amount of pressure on manufacturing companies to care about the environment. It also asserts unseen pressure on the financial performance of the companies. Therefore, it is time to go for green patenting of such firms while satisfying the eco-innovation and environmental scanning process. Moreover, Environmental ownership and its associated parameters keenly monitor this aspect. This paper evaluates the performance of the support vector machine (SVM/SVR) approach for estimating patents in environment-related technologies (PERT) in China from 1995 to 2021. For this work, six independent variables related to environmental ownership and environment-related technologies were selected, which include medium and high-tech exports (MHTE), green patents applicants (GPA), listed domestic companies (LDC), human capital index (HCI), self-employment (SE), and manufacturing value added in GDP (MVA). Data for dependent and independent variables were gathered from the World Bank (WB) official data bank portal. To make an initial understanding of the data basic statistical summary was computed in R programming to see the mean, minimum and maximum values in the data set. A correlation matrix plot showed the association between dependent and independent variables. SVM/SVR with radial basis function (RBF) regression was applied to see the impact of contributing parameters that influence PERT. For PERT, the model generated 0.95 R2 (RMSE = 92.43). The results of the SVR showed that the association among environmental parameters is strong. With a value of 4.82, the strongest coefficient in the SVR model is PAR. This work is novel and will benefit the manufacturing sector, analysts, policymakers, environmentalists as how green patenting can boost the eco innovation and environmental ownership and scanning system with advance technologies and practices.

The Influence of External Parameters on the Ripeness of Pumpkins.

Growing pumpkins in controlled environments, such as greenhouses, has become increasingly important due to the potential to optimise yield and quality. However, achieving optimal environmental conditions for pumpkin cultivation requires precise monitoring and control, which can be facilitated by modern sensor technologies. The objective of this study was to determine the optimal placement of sensors to determine the influence of external parameters on the maturity of pumpkins. The greenhouse used in the study consisted of a plastic film for growing pumpkins. Five different sensors labeled from A1 to A5 measured the air temperature, humidity, soil temperature, soil humidity, and illumination at five different locations. We used two methods, error-based sensor placement and entropy-based sensor placement, to evaluate optimisation. We selected A3 sensor locations where the monitored data were close to the reference value, i.e., the average data of all measurement locations and parameters. Using this method, we selected sensor positions to monitor the influence of external parameters on the maturity of pumpkins. These methods enable the determination of optimal sensor locations to represent the entire facility environment and detect areas with significant environmental disparities. Our study provides an accurate measurement of the internal environment of a greenhouse and properly selects the base installation locations of sensors in the pumpkin greenhouse.

Meteorological parameters and hospitalizations of patients with sickle cell anemia: a 20-year retrospective study in Campinas, São Paulo, Brazil.

To investigate the influence of climate on hospitalizations of sickle cell anemia (SCA) adults and children, we analyzed the health and meteorological parameters from a metropolis (1999-2018). 1462 hospitalizations were coded for SCA patients in crisis (M:F = 715:747) and 1354 hospitalizations for SCA patients without crisis (M:F = 698:656) [age = 22.9 vs 15.2 years and duration of hospitalization (DoH) = 5.7 vs 4.4 days, respectively,]. More hospitalizations were for adults than children in crisis, and for children than adults without crisis. More children and adults were hospitalized in winter andspring than in summer and autumn Hospitalizations correlated positively with humidity (lag -5), maximum pressure (lag -2), mean pressure (lag -2), and thermal amplitude (lag -2), and negatively with maximum temperature (lag -3). DoH positively correlated with minimum temperature (lag -4). Understanding these complex associations would induce attitudinal/behavioral modifications among patients and their caregivers.

Dynamics of fungal and bacterial communities in different types of soil ageing with different dosages of cadmium.

This study investigated the structure of fungal and bacterial communities in different types of Cd-contaminated soils. The results showed that obvious variations in microbial structure between contaminated alkaline soils and acidic soils. Proteobacteria, Gemmatimonadetes, Bacteroidetes and Basidiomycota dominated the studied communities in the alkaline soils, whereas Actinobacteria, Chloroflexi, Firmicutes, Acidobacteria, Saccharibacteria and Ascomycota were more abundant in the acidic soils. Additionally, Cd tolerant (Proteobacteria, Bacteroidetes, Ascomycota) and sensitive (Actinobacteria, Acidobacteria, Basidiomycota) in alkaline soils and JL-soils, Cd tolerant (Actinobacteria, Acidobacteria, Basidiomycota) and sensitive (Saccharibacteria, Proteobacteria, Bacteroidetes, Ascomycota, Mucoromycota) in the acidic soils were identified. Redundancy analysis and correlation analysis demonstrated that it was significantly affected by different environment parameters in alkaline soils and acidic soils. Varied bacterial community structures in all soils were dominantly influenced by pH and SOM. The similarities among different groups indicated the effect of soil type on microbial community structure was greater than that of Cd level. The above conclusions may provide a new perspective for the bio-remediation of Cd in different types of soils.

Quantification of adaptive forces on SNP rs1010211 due to viral zoonotic pathogens.

Widespread genotyping of human populations in environmental homeostasis has created opportunities to quantify how environmental parameters affect the genomic distribution of variants in healthy populations. This represents an ongoing natural experiment upon the human species that can only be understood through developing models of adaptation. By examining the information dynamics of optimal SNP distributions within such populations, "adaptive forces" on genomic variants can be quantified through comparisons between different populations. To this end, we are performing double-blind scans of SNPs in order to ascertain any potential smooth functional relationships between the frequencies of the variants and changes in quantified environmental parameters. At present, we have sequentially examined more than twenty thousand SNPs (on chromosome 3) of nine homeostatic native populations for potential adaptive flagging of the variants as functions of 15 environmental parameters. Our first significant flag has related rs1010211 to viral pathogens in mammalian hosts. Such pathogens present a significant risk for the emergence of new infectious diseases in humans. This genomic variant is within the gene TNIK, which is a germinal center kinase (GCK). GCKs are participants in both adaptive and innate immune regulation. However, the function of TNIK is not fully understood. We quantify the adaptive force on the C allele due to the pathogens as 0.04 GEU's/viral species.

Various quantification methods for estimating ammonia volatilization from wheat-maize cropping system.

Ammonia volatilization (AV) dominates the pathway of nitrogen (N) fertilizer losses in crops throughout the world. However, different methods are highly responsible for the different measurements of AV. The existing techniques were separated into static chamber methods (SCM), dynamic chamber methods (DCM), calibrated Dräger-tube method (DTM) and micrometeorological methods (MMM), which were analyzed by a meta-study of 595 observations from 33 published studies. An exponential relationship (P < 0.01) was found between AV and the N fertilizer applied to wheat and maize using all the methods. The amount of AV using SCM was the lowest. The AV monitored by DCM was 24.5%-55.0% (wheat) and 46.9%-65.0% (maize) lower than that for the DTM. Additionally, the AV measured by DTM did not differ significantly in the wheat season but was 58.9% lower (P < 0.05) in the maize season than that in the MMM. To reveal the influencing factors responsible that were for DCM and DTM, a field experiment was conducted during the period of Oct. 2016 to Oct. 2017. The study indicated that the AV was 15.8%-28.3% (wheat, P < 0.05) and 36.7%-44.2% (maize, P < 0.05) lower when monitored by the DCM than when estimated by DTM. The concentration of soil NH4+-N, air temperature, and wind speed positively correlated with the NH3 fluxes. In addition, there was a significant linear correlation (P < 0.01) between the AV measured by DCM and DTM when the wind speed was <1.5 m s-1. This study highlighted the fact that wind speed was the main factor that caused the large difference between DCM and DTM. Herein, DTM or MMM was first recommended, and DCM was accepted when wind speed was <1.5 m s-1 for quantitative estimates of AV. However, only a straight comparison between DCM and DTM under the same field experiment was done, the other comparisons only being based on similar fertilization and environmental conditions. Consequently, the differences between methods have to be treated carefully.

Use of geographical information systems (GIS) in assessing ecological profile, fish community structure and production of a large reservoir of Himachal Pradesh.

The present study demonstrates the spatial analysis and mapping of fish and different measures of environmental parameters and fish diversity of Pong reservoir, Himachal Pradesh, using Kriging spatial interpolation methods for geographical information system mapping. Seasonal data on environmental parameters, potential fish habitat and fish diversity was collected from lentic (dam), lentic (reservoir), transitional and lotic zone of the reservoir.. Important environmental parameters like water temperature, dissolved oxygen, electrical conductivity, water depth and transparency showed variations across the different zones of the reservoir. The sediment of the reservoir was sandy clay loam in nature as per texture analysis. Fish species richness, Shannon index and evenness index showed a similarity of the lotic and lentic (reservoir) zones of the reservoir. Six potential fish breeding grounds were identified in the reservoir indicating high conservation significance. The analysis of data showed a declining trend in fish production from 456.9 tonnes during the decade 1976-1987 to 347.91 tonnes during 2009-2020. The factors like anthropogenic climate change, predation of a stocked fish juvenile by water birds, undersized fish stocking and unscientific management are the probable reasons for the decreasing fish production. The spatial variation pattern of the water spread area, environmental parameters, fish catch and potential fish breeding grounds depicted in the GIS platform can be used as an important information base by the policy makers for fisheries management. The stocking of large size fish as a stocking material and adequate protection of the potential fish breeding grounds are the key advisories for the sustainable enhancement of fisheries as well as conservation.

Molecular Dynamics and Light Absorption Properties of Atmospheric Dissolved Organic Matter.

The light-absorbing organic aerosol referred to as brown carbon (BrC) affects the global radiative balance. The linkages between its molecular composition and light absorption properties and how environmental factors influence BrC composition are not well understood. In this study, atmospheric dissolved organic matter (ADOM) in 55 aerosol samples from Guangzhou was characterized using Fourier transform ion cyclotron resonance mass spectrometry and light absorption measurements. The abundant components in ADOM were aliphatics and peptide-likes (in structure), or nitrogen- and sulfur-containing compounds (in elemental composition). The light absorption properties of ADOM were positively correlated with the levels of unsaturated and aromatic structures. Particularly, 17 nitrogen-containing species, which are identified by a random forest, characterized the variation of BrC absorption well. Aggregated boosted tree model and nonmetric multidimensional scaling analysis show that the BrC composition was largely driven by meteorological conditions and anthropogenic activities, among which biomass burning (BB) and OH radical were the two important factors. BrC compounds often accumulate with elevated BB emissions and related secondary processes, whereas the photolysis/photooxidation of BrC usually occurs under high solar radiance/•OH concentration. This study first illuminated how environmental factors influence BrC at the molecular level and provided clues for the molecular-level research of BrC in the future.

A model of algal growth depending on nutrients and inorganic carbon in a poorly mixed water column.

A reaction-diffusion-advection model is proposed to describe the growth of algae depending on both nutrients and inorganic carbon in a poorly mixed water column. Nutrients from the water bottom and inorganic carbon from the water surface form an asymmetric resource supply mechanism for the algal growth. The existence and stability of semi-trivial steady state and positive steady state of the model are proved, and a threshold condition for the regime shift from extinction to survival of algae is established. The influence of environmental parameters on the vertical distribution of algae is investigated in the water column. It is shown that the vertical distribution of algae can exhibit many different profiles under the combined limitation of nutrients and inorganic carbon.

Water masses influence bacterioplankton community structure in summer Kongsfjorden.

To ascertain the saying "Everything is everywhere, but the environment selects", it was imperative to find out the main factor influencing bacterioplankton composition at genus level of Kongsfjorden where was influenced both by glacier melting water and Atlantic water. Thus, bacterioplankton diversity was investigated using pyrosequencing. In addition, nutrients, chlorophyll a, in situ temperature and salinity were measured. There were seventeen of 33 identified genera with relative abundance > 0.1%. Redundancy analysis showed that 73.02% of bacterioplankton community variance could be explained by environmental parameters. Furthermore, most of the abundant genera demonstrated significant correlation with environment parameters revealed by correlation analysis. Moreover, phosphate, nitrate and Chl a concentration, and the abundance of top nine identified genera varied with water mass significantly as shown by analysis of variance. Our results supported the notion that environmental factors, especially water mass had significant effect on bacterioplankton distribution at genus level. Considering the high sensitivity to environmental change and low error rate in identification, bacterioplankton at genus level could be potential bio-markers for monitoring environmental changes.

Snow and Glacial Algae: A Review1.

Snow or glacial algae are found on all continents, and most species are in the Chlamydomonadales (Chlorophyta) and Zygnematales (Streptophyta). Other algal groups include euglenoids, cryptomonads, chrysophytes, dinoflagellates, and cyanobacteria. They may live under extreme conditions of temperatures near 0°C, high irradiance levels in open exposures, low irradiance levels under tree canopies or deep in snow, acidic pH, low conductivity, and desiccation after snow melt. These primary producers may color snow green, golden-brown, red, pink, orange, or purple-grey, and they are part of communities that include other eukaryotes, bacteria, archaea, viruses, and fungi. They are an important component of the global biosphere and carbon and water cycles. Life cycles in the Chlamydomonas-Chloromonas-Chlainomonas complex include migration of flagellates in liquid water and formation of resistant cysts, many of which were identified previously as other algae. Species differentiation has been updated through the use of metagenomics, lipidomics, high-throughput sequencing (HTS), multi-gene analysis, and ITS. Secondary metabolites (astaxanthin in snow algae and purpurogallin in glacial algae) protect chloroplasts and nuclei from damaging PAR and UV, and ice binding proteins (IBPs) and polyunsaturated fatty acids (PUFAs) reduce cell damage in subfreezing temperatures. Molecular phylogenies reveal that snow algae in the Chlamydomonas-Chloromonas complex have invaded the snow habitat at least twice, and some species are polyphyletic. Snow and glacial algae reduce albedo, accelerate the melt of snowpacks and glaciers, and are used to monitor climate change. Selected strains of these algae have potential for producing food or fuel products.

ELF Magnetic Field Exposure System for In Vitro Studies Based on Lee-Whiting Coils.

In order to run a series of in vitro studies on the effect of extremely low-frequency magnetic fields on cell cultures, developing and characterizing an appropriate exposure system is required. The present design is based on a two-shielded Lee-Whiting coils system. The circular design was chosen because its axial symmetry allowed for both reducing simulation unknowns and measurement points during the characterization, and additionally made the machining of the parts easier. The system can generate magnetic flux densities (B fields) up to 1 mT root-mean-square amplitude (rms) with no active cooling system in the incubator, and up to 3 mTrms with it. The double-wrapped windings with twisted pairs allow for the use of each set of coils either as exposure or control with no detectable parasitic B field in the control. The artifacts have also been analyzed; the B field in the center of the sham control chamber is about 1 µTrms for a maximum of 3 mTrms in the exposure chamber, the parasitic incident electric fields are less than 1 V/m, the temperature difference between sham and exposure chamber is less than or equal to 0.2 °C, and the typical vibration difference between sham and exposure is less than 0.1 m/s2 . © 2020 Bioelectromagnetics Society.