Deep Samples Research Articles

Establishing Modern Peat Analogs to Decipher Mangal Sub-Habitats From Historical Peats

In mangroves of South Florida, plant debris accumulates and humifies to form peat. The structure and composition of mangrove peat differs among mangal sub-habitats, leading to categorically distinct peat types reflective of the taphonomically active zone (TAZ). Here, taphonomic processes degrade and shape the peat until it is sequestered in the depth of final burial (DFB). Sequestered peats provide historical archives of the mangal depositional environment and the palaeoecological context of peat formation that are used to reconstruct mangal sub-habitats. However, as peat passes through the TAZ, information about the precursor mangal sub-habitat is reduced, which may skew mangrove community reconstructions. To better understand the influence of the TAZ on peat formation, we analyzed plant organ- and taxon-based measures by characterizing surficial mangrove peats from two contrasting mangal sub-habitats in Barnes Sound, Florida: a tidally influenced, Rhizophora-dominated fringe sub-habitat; and an inundated, interior mixed forest basin sub-habitat. We found (1) peats formed in basin sites have greater amounts of leaf litter, which correlates with reduced tidal activity and restricted detritivore access to the litter layer; (2) peats formed in fringe sites have higher root percentages, or root–shoot ratios, which provide a reliable method to differentiate between peats at depth, and (3) mangal sub-habitats differ in preserved organismal signals, such as foraminifera and insect parts. Further, we compare our surficial core samples to historical, deep core samples from other South Florida mangrove peat deposits to establish modern peat analogs needed to decipher preserved mangrove peats. These comparisons suggest that few aerial plant organs survive the TAZ and sequestered peats are biased towards root-rich peats characteristic of fringe sub-habitats; however, sequestered peats with lower root-shoot ratios indicate leaf litter-rich peats formed in basin sub-habitats.

Fracture Fractal and Energy Transfer Characteristics of Deep-Mine Marble under an Impact Load

With changes in mining depth, the dynamic mechanical characteristics of the same type of rock also change, so that the blasting excavation process must be constantly adjusted and optimized to meet the requirements of safe and efficient mining. To study the energy evolution law and fractal characteristics of deep marble during the destruction process, dynamic impact tests under different strain rates were carried out on a deep marble sample using the three-axis dynamic combination Hopkinson pressure bar system. The experimental results show that the larger the incident energy is, the more energy is transmitted. The proportion of absorbed energy does not increase with the increase in the incident energy. Only 30%–38% of the incident energy is absorbed by the impacted rock. With an increase in the strain rate, the energy consumption density gradually increases, the fragmentation degree is intensified, the fractal dimension gradually increases, and the failure mode changes from compression–shear failure to crushing failure.

Anaerobic methane-oxidizing activity in a deep underground borehole dominantly colonized by Ca. Methanoperedenaceae.

The family Ca. Methanoperedenaceae archaea mediates the anaerobic oxidation of methane (AOM) in different terrestrial environments. Using a newly developed high-pressure laboratory incubation system, we investigated 214- and 249-m deep groundwater samples at Horonobe Underground Research Laboratory, Japan, where the high and low abundances of Ca. Methanoperedenaceae archaea have been shown by genome-resolved metagenomics, respectively. The groundwater samples amended with 13 C-labelled methane and amorphous Fe(III) were incubated at a pressure of 1.6MPa. After 3-7 days of incubation, the AOM rate was 45.8 ± 19.8nM/day in 214-m groundwater. However, almost no activity was detected from 249-m groundwater. Based on the results from 16S rRNA gene analysis, the abundance of Ca. Methanoperedenaceae archaea was high in the 214-m deep groundwater sample, whereas Ca. Methanoperedenaceae archaea was undetected in the 249-m deep groundwater sample. These results support the in situ AOM activity of Ca. Methanoperedenaceae archaea in the 214-m deep subsurface borehole interval. Although the presence of Fe-bearing phyllosilicates was demonstrated in the 214-m deep groundwater, it needs to be determined whether Ca. Methanoperedenaceae archaea use the Fe-bearing phyllosilicates as in situ electron acceptors by high-pressure incubation amended with the Fe-bearing phyllosilicates.

Direct Production of Bio-Recalcitrant Carboxyl-Rich Alicyclic Molecules Evidenced in a Bacterium-Induced Steroid Degradation Experiment.

Carboxyl-rich alicyclic molecules (CRAM) are highly unsaturated compounds extensively distributed throughout aquatic environments and sediments. This molecular group is widely referred to as a major proxy of recalcitrant organic materials, but its direct biosynthesis remains unclear. Steroids are a typical anthropogenic contaminant and have been previously suggested to be precursors of CRAM; however, experimental evidence to support this hypothesis is lacking. Here, a steroid-degrading bacterium, Comamonas testosteroni ATCC 11996, was incubated in a liquid medium supplemented with testosterone (a typical steroid) as the sole carbon source for 90 days. Testosterone-induced metabolites (TIM) were extracted for molecular characterization and to examine the bioavailability during an additional 90-day incubation after inoculation with a natural coastal microbial assemblage. The results showed that 1,775 molecular formulas (MFs) were assigned to TIM using ultrahigh-resolution mass spectrometry, with 66.99% categorized as CRAM-like constituents. A large fraction of TIM was respired or transformed during the additional 90-day seawater incubation; nevertheless, 638 MFs of the TIM persisted or increased during incubation. Among the 638 MFs, 394 were commonly assigned in natural deep seawater samples (depths of 500 to 2,000 m) from the South China Sea. Compared to the catabolites of the well-established testosterone degradation pathway, we compiled a list of bio-refractory MFs and potential chemical structures, some of which shared structural homology with CRAM. These results demonstrated direct microbial production of bio-refractory CRAM from steroid hormones and indicated that some of the biogenic CRAM resisted microbial decomposition, potentially contributing to the aquatic refractory dissolved organic matter (DOM) pool. IMPORTANCE CRAM are an operationally defined DOM group comprising a complex mixture of carboxylated and fused alicyclic structures. This DOM group is majorly characterized as refractory DOM in the marine environment. However, the origins of the complex CRAM remain unclear. In this study, we demonstrated that testosterone (a typical steroid) could be transformed into bio-refractory CRAM by a single bacterial strain and observed that some of the CRAM highly resisted microbial degradation. Through molecular comparison and screening, potential chemical structures of steroid-induced CRAM were suggested. This study established the biological connection between steroids and bio-refractory CRAM, and it provides a novel perspective explaining the fate of terrestrial contaminants in aquatic environments.

Three to six tissue specimens for histopathological analysis are most accurate for diagnosing periprosthetic joint infection.

The aim of this study was to evaluate the optimal deep tissue specimen sample number for histopathological analysis in the diagnosis of periprosthetic joint infection (PJI). In this retrospective diagnostic study, patients undergoing revision surgery after total hip or knee arthroplasty (n = 119) between January 2015 and July 2018 were included. Multiple specimens of the periprosthetic membrane and pseudocapsule were obtained for histopathological analysis at revision arthroplasty. Based on the Infectious Diseases Society of America (IDSA) 2013 criteria, the International Consensus Meeting (ICM) 2018 criteria, and the European Bone and Joint Infection Society (EBJIS) 2021 criteria, PJI was defined. Using a mixed effects logistic regression model, the sensitivity and specificity of the histological diagnosis were calculated. The optimal number of periprosthetic tissue specimens for histopathological analysis was determined by applying the Youden index. Based on the EBJIS criteria (excluding histology), 46 (39%) patients were classified as infected. Four to six specimens showed the highest Youden index (four specimens: 0.631; five: 0.634; six: 0.632). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of five tissue specimens were 76.5% (95% confidence interval (CI) 67.6 to 81.4), 86.8% (95% CI 81.3 to 93.5), 66.0% (95% CI 53.2 to 78.7), and 84.3% (95% CI 79.4 to 89.3), respectively. The area under the curve (AUC) was calculated with 0.81 (as a function of the number of tissue specimens). Applying the ICM and IDSA criteria (excluding histology), 40 (34%) and 32 (27%) patients were categorized as septic. Three to five specimens had the highest Youden index (ICM 3: 0.648; 4: 0.651; 5: 0.649) (IDSA 3: 0.627; 4: 0.629; 5: 0.625). Three to six tissue specimens of the periprosthetic membrane and pseudocapsule should be collected at revision arthroplasty and analyzed by a pathologist experienced and skilled in interpreting periprosthetic tissue.Cite this article: Bone Joint J2023;105-B(2):158-165.

Efficacy of Soft-Rot Disease Biocontrol Agents in the Inhibition of Production Field Pathogen Isolates

The Dickeya and Pectobacterium bacterial species cause blackleg and soft-rot diseases on potato plants and tubers. Prophylactic actions are essential to conserve a high quality of seed potato tubers. Biocontrol approaches are emerging, but we need to know how efficient biocontrol agents are when facing the natural diversity of pathogens. In this work, we sampled 16 production fields, which were excluded from the seed tuber certification scheme, as well as seven experimental parcels, which were planted with seed tubers from those production fields. We collected and characterized 669 Dickeya and Pectobacterium isolates, all characterized using nucleotide sequence of the gapA gene. This deep sampling effort highlighted eleven Dickeya and Pectobacterium species, including four dominant species namely D. solani, D. dianthicola, P. atrosepticum and P. parmentieri. Variations in the relative abundance of pathogens revealed different diversity patterns at a field or parcel level. The Dickeya-enriched patterns were maintained in parcels planted with rejected seed tubers, suggesting a vertical transmission of the pathogen consortium. Then, we retained 41 isolates representing the observed species diversity of pathogens and we tested each of them against six biocontrol agents. From this work, we confirmed the importance of prophylactic actions to discard contaminated seed tubers. We also identified a couple of biocontrol agents of the Pseudomonas genus that were efficient against a wide range of pathogen species.

Defining cell-specific physical basis of gene expression and signatures of functional chromatin 3D interactions: integrated analysis of single-cell 3D chromatin models, RNA/ATAC-seq, and epigenetic data.

Three-dimensional structures of chromatin play important roles in gene regulation. Hi-C measurement provided a wealth of information on chromatin interactions. Emerging evidence suggests functional chromatin interactions are related to gene expression. However, gene promoters and their regulatory elements can be separated by 103-106 bp apart. Therefore, examining 3D chromatin conformations and identifying functionally long-range interactions are important for deciphering the structure-function relationship of 3D genome. Our recent work of CHROMATIX deep sampling algorithms can identify non-random chromatin interactions (5-7% of total Hi-C). These can be used to generate large ensembles of 3D single-cell chromatin conformations, accurately reproducing both single-cell capture and imaging studies, as well as population Hi-C measurements. In this study, we examine predicted ensembles of single-cell 3D chromatin conformations of loci among leukemia K562 cells, lymphoblastoid GM12878 cells, and lung fibroblast IMR90 cells. We identified small subsets of non-random chromatin interactions from Hi-C data based on large random ensembles of 3D chromatin chains folded in confined nuclear volume. These non-random interactions were used to generate ensembles of 5.0×104 single-cell 3D conformations for individual loci. We further quantified the heterogeneity of 3D chromatin conformations by clustering, with each spatial cluster and its proportion defined. In addition, we carried out an integrated analysis of RNA-seq, ATAC-seq, and epigenetic data of these cell lines. Results show that cell-specific functional chromatin interactions provide 3D basis for differential gene expression. There are well-defined signatures of 3D subpopulations of chromatin conformations involving distinctive patterns of epigenetic marks, and these signatures are strongly associated with gene differential expression.

A Novel Gas Recognition Algorithm for Gas Sensor Array Combining Savitzky–Golay Smooth and Image Conversion Route

In recent years, the application of Deep Neural Networks to gas recognition has been developing. The classification performance of the Deep Neural Network depends on the efficient representation of the input data samples. Therefore, a variety of filtering methods are firstly adopted to smooth filter the gas sensing response data, which can remove redundant information and greatly improve the performance of the classifier. Additionally, the optimization experiment of the Savitzky–Golay filtering algorithm is carried out. After that, we used the Gramian Angular Summation Field (GASF) method to encode the gas sensing response data into two-dimensional sensing images. In addition, data augmentation technology is used to reduce the impact of small sample numbers on the classifier and improve the robustness and generalization ability of the model. Then, combined with fine-tuning of the GoogLeNet neural network, which owns the ability to automatically learn the characteristics of deep samples, the classification of four gases has finally been realized: methane, ethanol, ethylene, and carbon monoxide. Through setting a variety of different comparison experiments, it is known that the Savitzky–Golay smooth filtering pretreatment method effectively improves the recognition accuracy of the classifier, and the gas recognition network adopted is superior to the fine-tuned ResNet50, Alex-Net, and ResNet34 networks in both accuracy and sample processing times. Finally, the highest recognition accuracy of the classification results of our proposed route is 99.9%, which is better than other similar work.

Characterization of algal community composition and structure from the nearshore environment, Lake Tahoe (United States).

Periphyton assemblages from the nearshore environment of the west (California) side of Lake Tahoe, were analyzed to determine their taxonomic composition and community structure across habitats and seasons. Lake Tahoe is the second deepest lake in the US and an iconic oligotrophic subalpine lake with remarkable transparency. It has experienced offshore cultural eutrophication since the 1960s with observations of nuisance nearshore algal growth since the mid 2000s attributed to anthropogenic stressors. Samplings from November 2019-September 2020 provide useful snapshots against which older monitoring may be contextualized. A voucher flora, complete with descriptions, photo-documentation and referencing to species concepts employed, was created as a method of providing reproducible identification and enumeration of algal species, and more seamless reconciliation of detailed taxonomic data with future monitoring projects. The eulittoral zone (0-2 m) is seasonally dominated by elongate araphid (Synedra, Ulnaria) and stalked or entubed diatoms (Gomphonema, Cymbella, Encyonema). The sublittoral zone (>2 m) is dominated by a nitrogen-fixing Epithemia-cyanobacteria assemblage with less seasonal changes in dominance and composition that expanded to impinge on the 2 m depths of the eulittoral zone in the Fall. Sublittoral epipsammic samples, despite their proximity to rocks, had a very distinct diatom composition and high species dominance, similar to what was seen in the Fall eulittoral samples, with high numbers of Staurosirella chains and small biraphid diatoms. The deeper samples at 30 and 50 m contained high numbers of live Epithemia, and indicate a thriving sublittoral assemblage at these greater depths, but with less biomass. The 2019-20 data show many of the same diatom taxa observed in the 1970's and 1980's but with changes in species dominance. Notably, there was less of the green alga Mougeotia, when compared to the 1970's data, and a higher dominance by nitrogen fixing Epithemia in the sublittoral zone, persisting year-round. These new data show roughly double the algal species biodiversity that had been documented previously in the Lake Tahoe nearshore, and is largely attributed to the methods employed. Adopting these new methods in future monitoring efforts should improve harmonization of taxonomic data and help advance our knowledge of the contributions to nearshore cultural eutrophication.

Single-cell RNA-seq reveals cellular heterogeneity from deep fascia in patients with acute compartment syndrome.

High stress in the compartment surrounded by the deep fascia can cause acute compartment syndrome (ACS) that may result in necrosis of the limbs. The study aims to investigate the cellular heterogeneity of the deep fascia in ACS patients by single-cell RNA sequencing (scRNA-seq). We collected deep fascia samples from patients with ACS (high-stress group, HG, n=3) and patients receiving thigh amputation due to osteosarcoma (normal-stress group, NG, n=3). We utilized ultrasound and scanning electron microscopy to observe the morphologic change of the deep fascia, used multiplex staining and multispectral imaging to explore immune cell infiltration, and applied scRNA-seq to investigate the cellular heterogeneity of the deep fascia and to identify differentially expressed genes. Notably, we identified GZMK+interferon-act CD4 central memory T cells as a specific high-stress compartment subcluster expressing interferon-related genes. Additionally, the changes in the proportions of inflammation-related subclusters, such as the increased proportion of M2 macrophages and decreased proportion of M1 macrophages, may play crucial roles in the balance of pro-inflammatory and anti-inflammatory in the development of ACS. Furthermore, we found that heat shock protein genes were highly expressed but metal ion-related genes (S100 family and metallothionein family) were down-regulated in various subpopulations under high stress. We identified a high stress-specific subcluster and variations in immune cells and fibroblast subclusters, as well as their differentially expressed genes, in ACS patients. Our findings reveal the functions of the deep fascia in the pathophysiology of ACS, providing new approaches for its treatment and prevention.

Emergence of Raoultella ornithinolytica Producing Beta Lactamase Enzyme in Different Clinical Specimens in Erbil City

Background: Raoultella ornithinolytica should never be regarded as merely a saprophytic bacterium that occasionally contaminates bronchial lavage or other deep respiratory samples or surgical sites is an underreported, emerging hospital-acquired infection and is particularly related with invasive operations.R. ornithinolytica isolates have significant antimicrobial resistance rates, and physicians should be aware of this so that, before accurate microbiological data are received, an immediate broad-spectrum antibiotic treatment can be instituted..
 Objectives: The goal of our study was to retrospectively examine R. ornithinolytica, which was isolated from a variety of clinical samples in Erbil, and to examine its epidemiology, antibiotic sensitivity patterns, and ability to produce the enzyme extend spectrum beta lactamase.
 Methods: A total of 2350 samples were collected from three different sources(Urine ,High vaginal swab and wound), collected from female and male from patient attending Rizgary hospital and Central lab in Erbil city during the July 2017 to Februry 2018 . Only 10 cases had been identified as R. ornithinolytica and all isolates isolated and identified by using microscopical, morphological , biochemical tests and Vitek 2 compact system . Also antibiotics susceptibility testing was performed by using Vitec 2 compact system according to the standard protocol against 13 antibiotics which are (Ampicillin /Sulbactam , Piperacillin/ Tazobactam, Cefazolin, Ceftazidim , Ceftriaxon , Cefepime, Levofloxacin , Trimethoprim/ Sulfamethoxazol, Gentamycin, Tobramycin, Ertapenem, Imipenem, Ciprofloxacin).
 Results: Only ten R. ornithinolytica isolates isolated from 2350 distribution according to their source of isolation High vaginal swab 6(0.23%) appeared to be the most dominant specimen than other specimens followed by urine sample 3(0.13%) then wound 1(0.04%).The highest percentage of R. ornithinolytica. isolated from female .Most isolates from high vaginal swab sample 6(0.26%) followed by urine 3 (0.13%) then wound 1(0.04), when performing of antibiotic susceptibility the highest resistances rate were to Cefazolin and Trimethoprim / Sulfamethoxazol 10 (100%) followed by Ampicillin / Sulbactam , Piperacillin / Tazobactam, Ceftriaxon , Gentamycin , Tobramycin and Ciprofloxacin 7(70%) for each, 6(60%)isolates ESBL producer, all isolated R. ornithinolytica were resistance to more than 8 antibiotics and two isolates completely resistance to all thirteen antibiotics.
 Conclusion: The overall prevalence of R. ornithinolytica isolates was low in Erbil city and we can be observed and isolated from varies clinical samples and causes serious infections and susceptibility to some antibiotics are low and also most isolates multiresistance and ESBL producers and These findings offer a reliable measure of the prevalence of Raoultella ornithinolytica in our region and provide a baseline for future studies that will enable the monitoring of trends over time. If current resistance trends continue, high societal and economic costs can be anticipated; better management of infections caused by resistanceR.ornithinolytica is becoming essential.

Occurrence of parasites in the soil of public squares in Veranópolis, Rio Grande do Sul, Brazil

Human health is threatened by diseases transmitted between animals and humans, denominated zoonoses. Public parks and squares are environments used by the population and easily accessed by domestic and stray animals. The contact of dogs and cats with the soil from those places may favor the transmission of zoonoses to humans. This study aimed to determine the occurrence of parasites in the soil of public squares in the city of Veranópolis in Rio Grande do Sul State. Forty sand samples were collected in four squares throughout the months of December 2021, January, February and March 2022. The samples were processed by the Hoffman, Pons and Janer (HPJ) and Rugai methods. The results showed that 32.5% (13/40) of samples were positive for the presence of parasitic structures. Nematode larvae, including hookworm, were the most prevalent parasitic structures in the samples. In addition, Capillaria sp. eggs, Strongyloides sp. larvae, free-living larvae and Entamoeba coli cysts were also found. Two collections conducted after rainy days had a higher rate of contamination. The presence of parasites was observed in both superficial and deep samples. Therefore, the presence of parasites in the sand of Veranópolis’ public squares demonstrates that there is a risk of parasitic infection for the local population. KEY WORDS: Animals; parasites; soil; squares; zoonoses.

A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models

Real-world generalization, e.g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences. Such a seemingly easy behavioral choice requires the interplay of multiple neural mechanisms, from integrative encoding to category-based inference, weighted differently according to the circumstances. Here, we argue that a comprehensive theory of the neuro-cognitive substrates of real-world generalization will greatly benefit from empirical research with three key elements. First, the ecological validity provided by multimodal, naturalistic paradigms. Second, the model stability afforded by deep sampling. Finally, the statistical rigor granted by predictive modeling and computational controls.

BRIDGE methodology–based quality standards to assess aquifer chemical status in the southwest Bengal Basin, Bangladesh

Assessment of natural background levels (NBLs) of compositional groundwater parameters helps to identify the potential threats to groundwater resources. This study is the first attempt to apply the pre-selection-based BRIDGE (Background cRiteria for the IDentification of Groundwater thrEshold) methodology to calculate the NBLs and threshold values (TVs) of major groundwater constituents in the southwest Bengal Basin, Bangladesh. A database consisting of 78 groundwater samples was used to assess the NBLs and associated TVs of the major groundwater parameters (EC, Ca2+, Mg2+, Na+, K+, Cl-, NO3-, SO42-, PO43-, Mn2+, and Fe2+). NBLs were derived based on 90th and 97.7th percentiles. The status of regional groundwater resources was assessed by applying 90th percentile NBL on a regional dataset (n = 196). Results revealed the "poor" chemical status of shallow aquifers denoting heavy deterioration of the groundwater quality due to anthropogenic interventions. Nitrate contamination and salinization were identified as the major threats to the deep groundwater of the southwest Bengal Basin. Finally, to verify the chemical status of groundwater in aheavily urbanized area, derived TVs were applied throughout the experimental site Khulna. Twenty-five deep groundwater samples were collected for this purpose. Though most of the parameters exhibited "good" chemical status, nitrate demonstrated anthropogenic groundwater contamination in Khulna City. Thus, the developed TVs would provide an early warning system of pollution. On a national scale, it is expected to facilitate the sustainable groundwater management of the country and contribute to achieving the Sustainable Development Goals (SDG) of the United Nations (UN) in Bangladesh.

Comparison of Microbiological Results of Superficial Swab versus Deep Tissue Biopsy in Diabetic Foot Infections: A Prospective Observational Study from North India

Introduction: Diabetes mellitus is the leading cause of nontraumatic lower extremity amputation worldwide. Risk of developing a foot ulcer in diabetic patients has increased to 12-25% during their lifetime. Inappropriate sampling technique leading to contaminated samples is a well-known threat among patients of diabetic foot. In this study we compared two microbiological sampling techniques, superficial swabbing and deep tissue biopsy for identification of pathogens. Material and Methods: It was a prospective observational cross-sectional study. Diabetic foot was defined as per standard guidelines. Careful and meticulous examination of diabetic foot ulcer was done. Two specimens were collected from each wound. The first specimen was a wound swab collected by using Levine technique; another was taken via punch biopsy from the ulcer base. It was a deep tissue sample of 4 mm size. Results: A total of 120 diabetic patients with diabetic foot infection were included in this study. In swab culture 80.83% patients had mono microbial growth, 15% had polymicrobial and 4.10% had no growth. In deep tissue culture 87.50% had mono microbial growth, 2.50% had poly microbial and 10% were sterile. Staphylococcus aureus was most common isolate followed by E. coli. It was observed that though monomicrobial growth was most common in both the types of samples but was higher in deep tissue biopsy than in superficial swab samples. Conclusion: The isolation of microorganism via deep tissue sampling in diabetic foot ulcer patients is more reliable compared to superficial swab samples and a better guide to antibiotic therapy.

Dispersive liquid-liquid microextraction utilizing menthol-based deep eutectic solvent for simultaneous determination of sulfonamides residues in powdered milk-based infant formulas

An eco-friendly, fast, sensitive dispersive liquid–liquid microextraction (DLLME) method followed by UPLC-MS/MS was developed and validated for multi-residue determination of sulfonamides in milk-based infant and young children formulas. In this study, a natural deep eutectic solvent was synthesized using menthol and octanoic acid at a molar ratio of 1:2 for the extraction of seven sulfonamides in infant formulas. Different extraction parameters including volume of deep eutectic solvent, vortex time, centrifugation time, sample volume and pH were investigated and optimized. Under the optimum conditions, determination coefficients of ≥ 0.9993 were obtained for all studied analytes. The limits of detection ranged from 0.5 to 1.5 µg Kg−1. The extraction recoveries were in the range of 82.6–100.1% with RSDs %≤ 7.5. To investigate the applicability of the proposed method, it was applied to determination of the studied sulfonamides in thirty milk-based infant formulas of different brands. The environmental impact of the presented method was evaluated using three assessment tools namely: Eco-Scale Assessment (ESA), Green Analytical Procedure Index (GAPI) and Analytical GREEnness metric (AGREE). The characteristic performance of the developed DLLME-UPLC-MS/MS method was also compared with other reported methods.

Structure and Functional Potential of Arctic Sea Sediment Microbiota.

Arctic ecosystems are affected by negative influence of climate change, pollution, and overexploitation of resources. Microorganisms playing a key role in preserving extreme econiches are poorly studied and require the use of modern methods for studying both their biodiversity and physiological activity. We applied Illumina MiSeq to the high-throughput 16S rRNA sequencing study of four Laptev Sea sediments from 64 - 185 m depth, using next generation sequencing enables rapid analysis of composition and diversity of prokaryotic communities. Although the dominant phylum in all samples was Proteobacteria, only the deepest sample contained a high number of archaeal organisms (19%) with the predominance of Methanosarcinaceace family in comparison with less 1% in the other three samples. This deepest sample had the lowest biodiversity and richness indices. Comparison of functional profiles of communities using Global Mapper tool revealed similar average abundance of infectiousness, drug resistance and environmental adaptation determinants in all samples, and high functional abundance for xenobiotic degradation in two samples. Among cultivated bacteria which could be promising producers of secreted RNase the representatives of Bacillus and Lysinibacillus genera were found. Our results contribute to improve our understanding of richness and ecological role of Laptev Sea microbiota.

Layer-wise physicochemical and elemental distribution in an urban river water, Bangladesh: potential pollution, sources, and human health risk assessment

Surface and deep water samples of the Buriganga River were measured to assess the water quality, pollution level, elemental sources, and their potential ecological and human health risks.

Seismic Stratigraphic Interpretation Based on Deep Active Learning

Seismic stratigraphic interpretation plays an important role in geophysics and geosciences. Recently, deep learning has been explored for seismic stratigraphic interpretation. However, deep learning-based interpretation methods usually require sufficient labeled samples. This is often too hard to be satisfied in field seismic interpretation. In this paper, we propose a deep active learning-based method to address this issue. Active learning typically exploits prediction uncertainty to reduce labeling effort. We found that uncertainty of prediction is easily obtained in the field of seismic interpretation. Since adjacent seismic images are very similar, they should have similar predictions. When the model performs poorly, the predictions of adjacent images will differ significantly. Thus, the uncertainty can be easily obtained by measuring the similarity of the predictions of adjacent seismic images. Then, data with the highest uncertainty is annotated by geological expert and used for the next round of training. For few-shot active learning, initial models obtained by different initial training sets are quite different. We combine deep clustering and uncertainty sampling to select initial training datasets, with which a good initial model can be obtained. To improve generalization, we introduce a random thin plate spine transformation to simulate changes of terrain. We apply the proposed method to the F3 field seismic data. The results demonstrated that the proposed method can effectively improve performance of learned seismic interpretation network with very limited labeled samples.

High-Confidence Sample Generation Technology and Application for Global Land-Cover Classification

Deep learning technology has become one of the most important technologies in remote sensing land classification applications. Its powerful sample-learning and information-mining abilities promote the continuous improvement of classification accuracy. A large volume of high-quality and representative sample data is the premise for the successful application of deep learning technology. Conventional methods of obtaining samples through manual delineation or surface surveys require a great deal of manpower and material resources. Therefore, the inability to obtain sufficient and widely representative high-quality samples is one of the key factors limiting the application of deep learning technology. In this study, the method of generating sample data obtains high-confidence classification results from a variety of existing high-quality classification products as deep learning samples, which are then used to support the application of deep learning technology for land-cover classification. When the three global land-cover classification products, FROM-GLC-2015, GLC_FCS30-2015, and GlobeLand30, have the same type of discrimination, the sample is considered a high-confidence sample. Based on this, a large volume of sample data widely distributed around the world was obtained. Using the extracted samples, a random forest classifier was trained using multiple types of information from the Landsat data, and land-cover classification was achieved. Application experiments were conducted in several typical regions, and the classification results were verified. The results showed that the classification accuracy of random forests under the support of samples generated using the sample extraction method proposed in this article was considerably higher than that of the three land-cover classification products.