Sampling Period Research Articles

Spatial and temporal (annual and decadal) trends of metal(loid) concentrations and loads in an acid mine drainage-affected river.

Acid mine drainage (AMD) is a worldwide problem that degrades river systems and is difficult and expensive to remediate. To protect affected catchments, it is vital to understand the behaviour of AMD-related metal(loid) contaminants as a function of space and time. To address this, the sources, loads and transport mechanisms of arsenic (As), copper (Cu), zinc (Zn), iron (Fe) and sulfur (S) in a representative AMD-affected catchment (the Carnon River in Cornwall, UK) were determined over a 12-month sampling period and with 22years of monitoring data collected by the Environment Agency (England) (EA). The main source of metal(loid)s to the Carnon River was the County Adit which drains AMD from approximately 60km of underground historical mine workings. Maximum aqueous concentrations of Fe, Cu and Zn occurred immediately downstream of the County Adit confluence with the Carnon River, whereas maximum As and S concentrations occurred further downstream, suggesting the presence of diffuse sources. Discharge and concentration relationships suggested that discharge drove Cu and Zn release, whereas pH and Eh influenced Fe, S, and As mobility. Total loads (represented by unfiltered sample contaminant concentrations) to the coastal zone were high, ranging from 183 to 354kg/month As, 307-742kg/month Cu, 189-1960kg/month Fe, 53,400-125,000kg/monthS and 1280-3320kg/month Zn. The longevity and increasing amounts of contaminant discharge were confirmed with 22years of EA monitoring data. This study highlights the complex and multifaceted behaviour of contaminant metal(loid)s within AMD-affected riverine systems and the fact that point and diffuse sources can constitute significant long-term liabilities for such environments.

Exposure of insects to current use pesticide residues in soil and vegetation along spatial and temporal distribution in agricultural sites

Current use pesticides (CUPs) are recognised as the largest deliberate input of bioactive substances into terrestrial ecosystems and one of the main factors responsible for the current decline in insects in agricultural areas. To quantify seasonal insect exposure in the landscape at a regional scale (Rhineland-Palatine in Germany), we analysed the presence of multiple (93) active ingredients in CUPs across three different agricultural cultivation types (with each three fields: arable, vegetable, viticulture) and neighbouring meadows. We collected monthly soil and vegetation samples over a year. A total of 71 CUP residues in different mixtures was detected, with up to 28 CUPs in soil and 25 in vegetation in single samples. The concentrations and numbers of CUPs in vegetation fluctuated over the sampling period, peaking in the summer months in the vegetation but remaining almost constant in topsoil. We calculated in-field additive risks for earthworms, collembola, and soil-living wild bees using the measured soil concentrations of CUPs. Our results call for the need to assess CUP mixture risks at low concentrations, as multiple residues are chronically present in agricultural areas. Since this risk is not addressed in regulation, we emphasise the urgent need to implement global pesticide reduction targets.

Comparison of colored sticky traps for Stomoxys calcitrans (Diptera: Muscidae) on dairy cattle farms in Saraburi province, Thailand.

A sticky trap is a simple, low-cost method for the field estimation of insect population density. The present study was investigated the attraction of Stomoxys calcitrans using 5 different colors of sticky trap (blue, gray, transparent, white, and yellow). Throughout the sampling period, the sticky traps collected 1,559 S. calcitrans adults, of which 560 (36%) were females and 999 (64%) were males. The mean numbers of S. calcitrans on the 3 dairy cattle farms sampled differed significantly among the different trap colors, with both sexes being preferentially attracted to white or blue sticky traps. However, the white sticky traps were more effective at trapping S. calcitrans than the yellow and transparent sticky traps. Thus, a white-colored fly trap design was recommended for monitoring purposes.

Biogeochemical and Physical Controls on the Microbial Degradation of Dissolved Organic Matter Along a Temperate Microtidal Estuary

Dissolved organic matter (DOM) is the foundation of the microbial loop and plays an important role in estuarine water quality and ecosystem metabolism. Because estuaries are influenced by DOM with different sources and composition, changing hydrologic regimes, and diverse microbial community assemblages, the biological fate of DOM (i.e., microbial degradation) differs across spatiotemporal scales and between DOM pools. To better understand controls on DOM degradation, we characterized the biogeochemical and physical conditions of the York River Estuary (YRE), a sub-estuary of the Chesapeake Bay in southeast Virginia (USA), during October 2018 and February, April, and July 2019. We then evaluated how these conditions influenced the degradation of dissolved organic carbon (DOC) and nitrogen (DON) and chromophoric dissolved organic matter (CDOM) by conducting parallel dark incubations of surface water collected along the YRE. Compared to other sampling dates, DOC reactivity (ΔDOC (%)) was over two-fold higher in October when freshwater discharge was lower, temperatures were warmer, and autochthonous, aquatic sources of DOC dominated. ΔDOC (%) was near zero when allochthonous, terrestrial sources of DOC were more abundant and when temperatures were cooler during higher discharge periods in February when precipitation in the Chesapeake Bay region was anomalously high. DON was up to six times less reactive than DOC and was sometimes produced during the incubations whereas ΔCDOM (%) was highly variable between sampling periods. Like ΔDOC (%), spatiotemporal patterns in ΔDON (%) were controlled primarily by hydrology and DOM source and composition. Our results show that higher freshwater discharge associated with prolonged wet periods decreased estuarine flushing time and increased the delivery of allochthonous DOM derived from terrestrial sources into coastal waters, resulting in lower rates of DOM degradation especially under cool conditions. While these findings provide evidence for seasonal variation in DOM degradation, shifting environmental conditions (e.g., increasing temperatures and precipitation) due to climate change may also have interactive effects on the magnitude and composition of DOM exported to estuaries and its subsequent reactivity.

Testing for Asset Price Bubbles Using Options Data

We present a new approach to identifying asset price bubbles based on options data. We estimate asset bubbles by exploiting the differential pricing between put and call options. We apply our methodology to two stock market indexes, the S&P 500 and the Nasdaq-100, and two technology stocks, Amazon and Facebook, over the 2014–2018 sample period. We find that, while indexes do not exhibit significant bubbles, Amazon and Facebook show frequent and significant bubbles. The estimated bubbles tend to be associated with large volatility and large trading volume. Since our approach can be implemented in real time, it is useful to both policy-makers and investors.

The perils of open landfill: a study on environmental risk assessment in Dharamshala, Himachal Pradesh, India.

Improper and unscientific management of municipal solid waste (MSW) landfill sites has increasingly become a pressing environmental issue especially in the mountainous regions worldwide. In view of this, an attempt was made to assess the detrimental effects of MSW landfill on the natural water sources at Dharamshala, Himachal Pradesh. Further, the suitability of potential landfill site and dispersion of pollutant air masses were stipulated using Arc GIS and HYSPLIT model. The findings show a discernible increase in electrical conductivity (323-858 μS/cm) and total dissolved solids (1086-1144mg/kg levels) during sampling period. The results exhibited a notable increasing trend in the mean concentrations of heavy metals viz. As (0.13mg/kg and 0.10mg/kg), Hg (0.52mg/kg and 0.65mg/kg), Pb (0.10mg/kg and 0.06mg/kg), Zn (30.40mg/kg and 0.22mg/kg), Cd (0.46mg/kg and 0.04mg/kg), Cr (0.10mg/kg and 0.05mg/kg), Ni (0.28mg/kg and 0.10mg/kg), Mn (24.40mg/kg and 0.35mg/kg) and Fe (1.81mg/kg and 0.96mg/kg) during monsoon and post monsoon. High HPI values were observed at the sampling location near to landfill drain (9060), followed by spring site (7338). However, most of sampling points consistently exceeding the critical HPI value, across all seasons, indicated a severe level of heavy metal pollution, where sampling sites near to landfill drain pose significant environmental health risks of 63%. An overwhelming 93% population in vicinity of MSW site expressed their concern that the current landfill site poses substantial threat to their health and livestock. Furthermore, the obtained forward trajectories showed the downhill dispersion of polluted air arising from solid waste burning. A continuous monitoring of landfill leachate dynamics, atmospheric pollutants due to burning of waste and their potential impact on regional climate followed by appropriate adaptation strategies will be a promising step towards a sustainable future for the Indian Himalayan Region (IHR).

Frost forecasting through machine learning algorithms

Agriculture continues to be one of the world’s main sources of income and provides great environmental, territorial and social value. However, frost is a recurring problem for farmers each year, representing a significant threat to agricultural production. In a matter of hours, temperatures below the freezing point can result in the loss of nearly the entire crop from a producer. In this article, we have analyzed and compared the application of a set of machine learning algorithms to predict the occurrence of frost events in the next 24 hours. The prediction process covers several challenges, such as data capture, processing, extracting each relevant parameter and finally building different prediction models to compared their performance. Furthermore, we have employed the Synthetic Minority Oversampling Technique (SMOTE) methodology to address the issue of imbalanced datasets, given the natural scarcity of frost events during the data sampling period. Our results show that among the machine learning algorithms we compared, the most efficient in terms of Recall score is K-Nearest Neighbor (KNN), while using the Area Under Curve (AUC) criteria, the highest score belongs to the Extra Trees algorithm, with 0.9909. Moreover, by applying the SMOTE balancing process, the AUC score of our models increased 13%, while the Recall score increased from 55% to 82%.

Rapid Sampling and Polarimetric Statistics of Lightning With a Phased Array Radar

AbstractPrevious studies of lightning detection by radar mostly consisted of observations with reflector‐antenna systems yielding slow volume scan times. Phased array radars offer much faster scan times that are likely to capture echoes from propagating lightning channels. Rapidly updated range‐height indicator scans were used to observe severe storms that occurred in central Oklahoma with the fully digital S‐band Horus PAR to examine echoes from lightning plasma. Numerous lightning echoes were observed during the sampling period in good spatial and temporal agreement with lightning mapping array detections of very high frequency radiation sources. Statistically, they result in increased horizontal reflectivity factor, deviations in radial velocity and spectrum width, highly variable differential reflectivity and differential phase, and decreases in correlation coefficient. Results presented also highlight the capability of phased array radars to better observe lightning compared to current radars, and aid in the study of storm electrification and lightning physics.

Safe havens for Bitcoin and Ethereum: evidence from high-frequency data

Investing in cryptocurrencies is progressively becoming a norm; however, these assets are excessively volatile and often decrease or increase in value instantly. Thus, rational investors holding cryptocurrencies for extended periods firmly search for assets that can diversify their risk, preferably with assets other than cryptocurrencies. In this study, we consider the two most studied cryptocurrencies with the highest capitalization and trading volume/value, namely Bitcoin and Ethereum. Specifically, we examine whether high-performing leading US tech stocks (Facebook, Amazon, Apple, Netflix, Google [FAANG]) can provide any diversification benefits to cryptocurrency investors. To do so, we employ dynamic conditional correlation (DCC), asymmetric DCC, time-varying parameter vector autoregression-based connectedness measures, dynamic correlation-based hedge and safe-haven regression analyses, portfolio optimization and hedging strategies, time- and frequency-based wavelet coherence, and high-frequency 10-min intraday data from January 1, 2018 to January 31, 2023. We find that FAANG stocks can be considered (at least weak) safe havens for Bitcoin and Ethereum during the sample period. Our subperiod analyses reveal that the safe-haven role of FAANG stocks, specifically for Bitcoin, has noticeably increased. While the safe-haven property of Facebook is the most promising, for Netflix it is blurred between a weak–safe-haven and a hedge. Our findings may help investors, policymakers, and academicians to invest in cryptocurrencies, formulate relevant investment guidelines, and extend the literature on cryptocurrencies, respectively.

Impact of Key DMD Parameters on Modal Analysis of High-Reynolds-Number Flow Around an Idealized Ground Vehicle

This study provides a detailed analysis of the convergence criteria for dynamic mode decomposition (DMD) parameters, with a focus on sampling frequency and period in high-Reynolds-number flows. The analysis is based on flow over an idealized road vehicle, the Ahmed body (Re=7.7×105), using computational fluid dynamics (CFD) data from improved delayed detached eddy simulation (IDDES). The pressure and velocity spectrum analysis validated IDDES’s ability to capture system dynamics, consistent with existing studies. For a comprehensive understanding of the contributions of different components of the circle, the Ahmed body was divided into three regions: (a) front; (b) side, lower, and upper surfaces; and (c) rear fascia. Both pressure and skin-friction drag were analyzed in terms of frequency spectra and cumulative energy. Key findings show that a 90% contribution to the pressure drag comes from modes with a frequency of less than 26 Hz (St = 0.187), while the friction drag requires 84 Hz (St = 0.604) for similar energy capture. This study highlights the significance of accounting for intermittency and non-stationary behavior in turbulent flows for DMD convergence. A minimum of 3000 snapshots is necessary for the convergence of DMD eigenvalues, and sampling frequency ratios between 5 and 10 are needed to achieve a reconstruction error of less than 1%. The sampling period’s convergence showed that T*=250 (equivalent to 20 cycles of the slowest coherent structures) stabilizes coherent mode shapes and energy levels. Beyond this, DMD may become unstable. Additionally, mean subtraction was found to improve DMD stability. These results offer critical insights into the effective application of DMD in analyzing complex vehicle flow fields.

SARS-CoV-2 excretion and genetic evolution in nasopharyngeal and stool samples from primary immunodeficiency and immunocompetent pediatric patients

BackgroundPrimary Immunodeficiency disorders (PID) can increase the risk of severe COVID-19 and prolonged infection. This study investigates the duration of SARS-CoV-2 excretion and the genetic evolution of the virus in pediatric PID patients as compared to immunocompetent (IC) patients.Materials and methodsA total of 40 nasopharyngeal and 24 stool samples were obtained from five PID and ten IC children. RNA detection was performed using RT-qPCR, and whole-genome sequencing was conducted with the NexSeq 1000 platform. Data analysis used the nextflow/viralrecon pipeline. Hotspot amino acid frequencies were investigated using GraphPad Prism v10. Phylodynamic analysis was conducted with BEAST software.ResultsIn IC children, the viral excretion period lasted up to 14 days in nasopharyngeal swabs, with an average duration of 7 days, and ranged from 7 to 14 days in stool samples. In PID patients, the viral RNA was detected in nasopharyngeal for periods between 7 and 28 days, with an average duration of 15 days, and up to 28 days in stool samples. Two SARS-CoV-2 variants were detected in PID patients: Delta (AY.122) and Omicron (BA.1.1). Patients with antibody and combined deficiencies, exhibited the most prolonged shedding periods in both nasopharyngeal and stool samples and one patient presented complications and fatal outcome. Specific Hotspot amino acid changes were detected in PID: A2821V and R550H (ORF1ab).ConclusionOur findings underscore the prolonged excretion of SARS-CoV-2 RNA in patients with antibody and combined deficiencies. Thus, specialized care is essential for effectively managing PID patients.

Water quality and eutrophication status of the Zarivar Wetland (Iran)

Wetlands are critically impacted by a variety of pollutants, primarily due to their position at the terminus of both surface and subsurface water flows. This study aims to assess the eutrophication status of Zarivar Wetland, situated in Kurdistan Province, Iran. To evaluate the eutrophication levels, the research employed the Iranian Water Quality Index for Surface Water Resources-Conventional Parameters (IRWQISC) and the Carlson’s Trophic State Index (TSI) during two sampling periods in July and September 2021. The findings indicate that in July, the wetland exhibited an upper-mesotrophic condition, with an average Carlson index value of 52.2, while in September, it transitioned to both upper-mesotrophic and eutrophic conditions, reflected by an average Carlson index value of 60.17. The zoned map of the eutrophication index identified domestic sewage from Marivan city as a significant contributor to elevated eutrophication levels in the southeastern region. Additionally, runoff from irrigated agricultural lands, orchards, and domestic wastewater from surrounding villages contributed to heightened eutrophication in the northeastern and eastern areas of the wetland. A comparative analysis of water quality between July and September 2021 revealed IRWQISC values ranging from 3.57 to 9.72 in July and from 2.63 to 4.72 in September, categorizing them as relatively good and good, respectively. To safeguard Zarivar Wetland, it is recommended to implement measures that prevent livestock waste discharge into the wetland, manage urban and rural sewage systems, control agricultural runoff, and optimize fertilizer application practices.

Impact of Indices on Stock Price Volatility of BRICS Countries During Crises: Comparative Study

This study aims to identify the common indices having an impact on the SPV of BRICS countries during crises. To address this, the monthly data retrieved from the database of the Global Economic Monitor (GEM), World Bank, IMF International Financial Statistics data, and OECD in the period of January 2000 to December 2023 are analyzed in two phases. In the first phase, DM classification techniques are applied to the data to identify the best common classification technique in order to use this technique in the second phase to compare the results with Multiple Linear Regression (MLR) results. In the second phase, to account for the global financial crisis and COVID-19 crisis, the sample period is divided into two sub-periods. For those sub-periods, MLR and the best classification technique that was found in the first phase are utilized to find the common indices that have an impact on the stock price volatility during individual and both crises. The findings indicate that the Random Tree method commonly classified the data among the seven classification techniques. Regarding MLR results, no common indices were identified during the global financial crisis or the COVID-19 crisis. However, based on Random Tree classifications, the CPI price percent, National Currency, and CPI index for all items were common during the global financial crisis, whereas only the CPI price percent was common during the COVID-19 crisis. While some common indices were observed in individual crises for specific countries, no indices were consistently found across both crises. This variation is attributed to the unique nature of each crisis and the diverse economic and socio-political structures of different countries. These findings provide valuable insights for financial institutions and investors to refine financial and policy decisions based on the specific characteristics of each crisis and the indices affecting each country.

Seasonal Variation of Epipelic Diatom Assemblages and Their Relationships with Abiotic Variables in the Utility and Drinking Water Source of İstanbul: Büyükçekmece Reservoir

In this study, the distribution and composition of epipelic diatoms in Büyükçekmece Reservoir, which is one of the most important and main freshwater resources for İstanbul, Türkiye were investigated seasonally. Epipelic diatom samples were collected from five different stations each season (February, May, August, and November) in 2019. A total number of eighty-seven taxa belonging to thirty-seven genera were identified, six of which were centric and eighty-one were pennate; five of the epipelic species were new record diatoms for the Büyükçekmece Reservoir. The highest number of epipelic diatom species were encountered in February while the lowest was in November. Considering the frustule counts, it was observed that the species that increased their relative abundance changed in each sampling period. Consequently, it was observed that Gomphonella olivacea was the most abundant in February, Navicula reichardtiana in May, Achnanthidium sp. in August, and Cocconeis placentula in November. Shannon-Weaver Diversity Index showed that the highest diversity of diatom species was determined in February (station 4), and the lowest was observed in August (station 3). According to the results of redundancy analysis, it was observed that silicate-Si, conductivity, and temperature were the most efficient variables in the distribution of the epipelic diatom species.

Nuclear Waste Tank Emission Contributions to Particle Size Distribution.

Pollutants from anthropogenic activities including industrial processes are ubiquitous to the environment. To understand the impact from industrial aerosol on climate and human health, industrial aerosol needs to be better characterized. In this study, particle number concentrations were used as a proxy for atmospheric pollutants, which include both particles and gases. Particle concentration and size distribution were measured using a scanning mobility particle sizer (SMPS) approximately 4.5 km from primary industrial areas at the Savannah River Site in Aiken, SC. Industrial areas include numerous nuclear waste storage and processing tanks. The SMPS data were divided into two groups depending on the wind direction measured onsite to categorize transport from the industrial area or from elsewhere. Industrial contributions were found to have a higher concentration of particles with sizes less than 200 nm, 859 ± 564 cm-3, in comparison to non-industrial attributed particles, 733 ± 495 cm-3 on average from March-July 2021. For sizes larger than 200 nm, industrial and non-industrial particles have a similar concentration, 89 ± 59 cm-3 and 99 ± 61 cm-3, with non-industrial concentrations being slightly larger. To confirm that industrial particles could travel to the sampling location, air dispersion modeling was completed for specific case studies during the sampling period. The atmospheric dispersion modeling results confirmed that particles released at the industrial areas reached the sampling location when the wind direction was favorable for transport from the industrial areas. The greater concentration of smaller-sized particles in industrial emissions has implications for typical particulate measurements (PM2.5), heath impacts, and climatological influences.

Caenorhabditis nematodes influence microbiome and metabolome characteristics of their natural apple substrates over time.

The microbiomes of host organisms and their direct source environments are closely linked and key for shaping microbial community dynamics. The relationship between these linked dynamics is largely unexplored because source substrates are usually unavailable. To address this current knowledge gap, we employed bacteriovorous Caenorhabditis nematodes as a unique model system, for which source substrates like rotting apples can be easily collected. We compared single host microbiomes with their corresponding apple source substrates, as well as nematode-free substrates, over a 2-year sampling period in the botanical garden in Kiel, Germany. We found that single worms have unique microbiomes, which overlap most strongly with nematodes from the same source apple. A comparison to previous, related work revealed that variation in microbiome composition of natural Caenorhabditis isolates is significantly influenced by the substrate type, from which worms were obtained (e.g., fruits or compost). Our current sampling further showed that microbiome assembly is mostly driven by dispersal limitation. Importantly, two independent analysis approaches consistently suggest that worm microbiomes significantly influence characteristics of the apple microbiomes, possibly indicating niche construction by nematodes. Moreover, combining apple microbiome and metabolome data, we identified individual microbes and specific compounds indicative of fruit ripening that are significantly associated with nematode presence. In conclusion, our study elucidates the complex relationship between host microbiomes and their directly connected substrate microbiomes. Our analyses underscore the significant influence of nematode microbiomes on shaping the apple microbiome and, consequently, the fruit's metabolic capacity, thereby enhancing our general understanding of host-microbiome interactions in their natural habitat.IMPORTANCEAlmost all complex organisms are host to a microbial community, the microbiome. This microbiome can influence diverse host functions, such as food processing, protection against parasites, or development. The relationship between host and microbiome critically depends on the assembly of the microbial community, which may be shaped by microbes in the directly linked environment, the source microbiome. This assembly process is often not well understood because of the unavailability of source substrates. Here, we used Caenorhabditis nematodes as a model system that facilitates a direct comparison of host and source microbiomes. Based on a 2-year sampling period, we identified (i) a clear link between assembly dynamics of host and source microbiomes, (ii) a significant influence of nematode microbiomes on apple microbiomes, and (iii) specific microbes and compounds that are associated with the presence of nematodes in the sampled substrates. Overall, our study enhances our understanding of microbiome assembly dynamics and resulting functions.

Asymmetric risk contagion effect of the interaction between the real economy and the financial sector—an analysis based on the domestic commodity price index

As the uncertainty of the global economy intensifies, domestic real economy risks and financial risks may interact and worsen under international risk shocks. Commodity prices are an important channel for transmitting international economic and financial risks. Taking January 2007 to July 2021 as the sample period and using data from nine commodity price indices and banking, diversified finance, and insurance industry indices, this article uses rolling regression method to construct different commodity price risk and financial sector risk indicators. Combined with a vector autoregressive model, the risk contagion effect is calculated. The analysis indicates that there exist significant asymmetric risk contagion effects between various commodities (with energy and steel having the largest risk spillover effects) and there are significant net risk spillover effects on financial sectors (with insurance having the largest risk spillover effect). There are asymmetric risk contagion effects among different financial sectors, with varying degrees of risk spillover effects on commodity prices (with the banking sector having the greatest risk spillover effects on commodity prices and other financial sectors, and diversified finance having the greatest risk spillover effect on commodities). During major global financial events, the risk spillover effects of commodities and input effects increase significantly due to the intensification of the risk spillover effects of energy, steel, and nonferrous metals. The risk spillover effect between commodities and the overall risk spillover effect have significantly increased. The financial sector’s overall net risk input effect has increased, while the net risk input effect of the insurance sector has also increased. These findings are significant for improving systemic financial risk monitoring indicators and adopting accurate prevention and control measures.

Different Times for Different Metrics: Predicting 90 Days of Intermittently Scanned Continuous Glucose Monitoring Data in Subjects With Type 1 Diabetes on Multiple Daily Injection Therapy. Findings From a Multicentric Real-World Study.

According to the 2023 International Consensus, glucose metrics derived from two-week-long continuous glucose monitoring (CGM) can be extrapolated up to 90 days before. However, no studies have focused on adults with type 1 diabetes (T1D) on multiple daily injections (MDIs) and with second-generation intermittently scanned CGM (isCGM) sensors in a real-world setting. This real-world, retrospective study included 539 90-day isCGM data from 367 adults with T1D on MDI therapy. For each sensor metric, the coefficients of determination (R2) were computed for sampling periods from 2 to 12 weeks versus the whole 90-day interval. Correlations were considered strong for R2 ≥0.88. The two-week sampling period displayed strong correlations for time in range (TIR, 70-180 mg/dl; R2 = 0.89) and above range (TAR, >180 mg/dl; R2 = 0.88). The four-week sampling period showed additional strong correlations for time in tight range (TITR, 70-140 mg/dl; R2 = 0.92), for the coefficient of variation (CV; R2 = 0.88), and for the glycemia risk index (GRI; R2 = 0.92). The six-week sampling period displayed an additional strong correlation for time below range (TBR, <70 mg/dl; R2 = 0.90). After stratification by clinical variables, lower R2 values were found for older age quartiles (>40 years), higher CV (>36%), lower sensor use (≤94%), and higher HbA1c (>7.5%). In patients with T1D on MDI, two- to six-week intervals of isCGM use can provide clinically useful estimates of TIR, TAR, TITR, TBR, CV, and GRI, which can be extrapolated to longer (up to 90 days) time intervals. Longer intervals might be needed in case of older age, higher glucose variability, lower sensor use, and higher HbA1c.

Environmental Dissemination of SARS-CoV-2: An Analysis Employing Crassphage and Next-Generation Sequencing Protocols.

This study aimed to investigate the dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in water samples obtained during the coronavirus disease 2019 pandemic period, employing cross-assembly phage (crAssphage) as a fecal contamination biomarker and next-generation sequencing protocols to characterize SARS-CoV-2 variants. Raw wastewater and surface water (stream and sea) samples were collected for over a month in Rio de Janeiro, Brazil.Ultracentrifugation and negatively charged membrane filtration were employed for viral concentration of the wastewater and surface water samples, respectively. Viruses were detected and quantified by (RT-)qPCR applying TaqMan® system protocols. SARS-CoV-2 RNA signals were detected in 92.5% (37/40) of the wastewater samples and in 31.25% (10/32) of the stream water samples, but not in seawater samples. CrAssphage was detected in 100% of the wastewater samples, 93.75% (30/32) of the stream samples, and in 2/4 of the seawater samples. CrAssphage detection and high concentrations in stream surface waters (median 8.95 log10 gc/L) revealed diffuse contamination by domestic wastewater in a region with high sanitary coverage. The correlations detected between SARS-CoV-2 data and the moving averages of clinical cases per capita over the sampling period were moderate to strong when applying a 13-day offset, regardless of normalization by crAssphage data or not. Sequencing of the receptor-binding domain of the spike protein confirmed the detection of SARS-CoV-2, but did not characterize the circulating variant. On the other hand, the whole genome sequencing protocol identified circulation of the Gamma variant, corroborating the sampling period clinical data.

Optimization of Wetland Environmental DNA Metabarcoding Protocols for Great Lakes Region Herpetofauna

ABSTRACTMany species of reptiles and amphibians (herpetofauna) rely on wetlands that are being degraded and lost at a high rate. Characterization of herpetofauna diversity in different wetland types may help guide conservation strategies. However, traditional survey methods often involve sampling within small temporal windows, and the gear deployed may be taxonomically biased, thus, they may fail to accurately characterize species presence/absence and diversity. In contrast, environmental (e)DNA metabarcoding has been shown to effectively survey entire aquatic communities and can provide a useful complement to traditional surveys. The objective of this study was to design and optimize eDNA sampling and laboratory protocols for wetland herpetofauna. Protocols evaluated included different water sampling approaches (point versus transect sampling), seasonality of sampling, and choice of metabarcoding marker (mitochondrial 12S versus 16S rDNA). Samples collected from 10 sites across southern Michigan detected 17 amphibian and five reptile species, including four species of conservation concern (Ambystoma texanum, Clemmys guttata, Rana palustris, and Sternotherus odoratus). We observed no difference in the number of species detected between point and transect samples (p = 0.70), but point sampling required less time (p = 0.03) and allowed significantly larger volumes of water to be filtered (p = 1.13e‐5). No difference in species richness was observed between the 12S and 16S mitochondrial DNA markers (p = 0.96). However, a greater number of taxa were identifiable at the species level when using the 16S locus. There was also a significant difference in the number of species detected between early and late summer sampling periods (more species detected in the earlier period; p = 6.31e‐6), and some species were only found in the early or late sampling period. Sampling during multiple periods to fully characterize species composition, the use of point sampling, and the 16S mtDNA marker for herpetofauna eDNA metabarcoding studies may increase efficiency and reliability of results.