Deep Samples Research Articles

Hydrochemical Characteristics, Controlling Factors and Strontium Enrichment Sources of Groundwater in the Northwest Plain of Shandong Province, China

To elucidate the hydrochemical characteristics, controlling factors, sources and mechanisms of strontium ion enrichment in groundwater in the northwest plain of Shandong Province, China, 88 groundwater samples were collected, including 51 shallow pore groundwater samples, 29 deep pore groundwater samples and 8 karst groundwater samples. The hydrochemical characteristics of the different types of groundwater were quite different. The karst groundwater samples were all fresh water with a single hydrochemical type, either HCO3-Ca or HCO3-Ca·Mg. The deep pore groundwater samples were mainly brackish water, and the shallow pore groundwater samples were brackish water–salt water, which has complex hydrochemical types. The hydrochemical characteristics of all the types of groundwater were controlled by mineral dissolution and active positive cation exchange. In shallow pore groundwater, deep pore groundwater and karst groundwater, the dissolution of silicate, evaporite and carbonate minerals dominated the hydrogeochemical process. The strontium in groundwater was derived from the dissolution of minerals with strontium isomorphism. The average contents of strontium in shallow, deep and karst groundwater were 1.59 mg/L, 0.58 mg/L and 0.50 mg/L, respectively. The strontium in shallow pore groundwater was mainly derived from the enrichment of groundwater runoff, and its sources are abundant, with silicic rock being the main source. The deep pore groundwater mainly derived from the evaporative minerals containing strontium, and the karst water mainly derived from carbonate rock dissolution with similar characteristics.

Abstract PR014: Multiregion biopsies and corresponding neurosphere cultures reveal spatial divergence in glioblastoma evolution

Abstract Spatial organization of normal tissues is key for their function, yet the role of distribution of distinct clones of cancer cells in tumor in not usually studied as a potential vulnerability in cancer. This is of particular interest in glioblastoma (GBM), most aggressive brain tumor, in which extensive intratumor heterogeneity hampers effectiveness of treatment. Multiregion sampling has been used in multiple studies to uncover evolutionary trajectories of the tumor. To better understand tumor evolution and the complexity of cancer ecosystem in GBM, we performed profiling of biopsies collected from MRI-defined regions of the tumors, spanning the surface, tumor core and deep tumor margins, which drive local recurrence after surgery. To test how spatially distinct biopsies differ in their cancer cell composition and how heterogeneous the tumor microenvironment is in these samples, we took advantage of 5-ALA metabolite fluorescence, which accumulates in GBM cells. 5-ALA administration prior to the surgery allows the surgeon to visualize cancer under blue light. We used it to separate cancer cells (predominantly 5-ALAhigh) from tumor microenvironment (5-ALAlow) and subjected the fractions to bulk and single cell transcriptomics, respectively. We found a gradient of cellular states associated with location in the tumor, with the deep margin samples having more 5-ALAlow cancer cells and generally less defined phenotype. We have also developed neurosphere cultures from our collection of spatially distant biopsies. Despite the selection pressure of the in vitro culture conditions, cells from different regions of the tumor adopted different morphologies, suggesting retention of some of their differential features. The differences between cultures were reflected in metabolism, proliferation, and differential sensitivity to a drug panel. Whole exome sequencing and transcriptomic analysis were also performed to identify the selection bottleneck of culture conditions, establish evolutionary trajectory of the distinct regions and neurosphere cultures, and test how strongly our cultures represent both the 5-ALAhigh and 5-ALAlow subpopulations of GBM cells. Our study identified properties associated with different spatial locations within GBM tumors, which will help uncover the cellular and microenvironmental dependencies that could lead to novel therapeutic targets for this highly heterogeneous tumor. Citation Format: Roberto Salatino, Ugoma Onubogu, Benjamin Oakes, Oszkar Szentirmai, Michalina Janiszewska. Multiregion biopsies and corresponding neurosphere cultures reveal spatial divergence in glioblastoma evolution [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR014.

Neighboring Envelope Embedded Stacked Autoencoder for Deep Learning on Hierarchically Structured Samples

A stacked autoencoder (SAE) is a widely used deep network. However, existing deep SAEs focus on original samples without considering the hierarchical structural information between samples. This limits the accuracy of the SAE. In recent years, state-of-the-art SAEs have suggested improvements in network structure, cost function, parameter optimization, and thereby the accuracy has been enhanced. However, the problem mentioned above is still not solved. Therefore, this paper is concerned with how to design a SAE that can conduct deep learning on hierarchically structured samples. This proposed SAE - neighboring envelope embedded stacked autoencoder (NE_ESAE) mainly consists of two parts. The first is the neighboring sample envelope learning mechanism (NSELM) that constructs sample-pairs by combining neighboring samples. In addition, the NSELM constructs multilayer sample spaces by multilayer iterative mean clustering, which considers similar samples and generates layers of envelope samples with hierarchical structural information. The second is an embedded stacked autoencoder (ESAE) to consider the original samples during training and in network structure, thereby finding the relationship of the samples with original features and deep features in a better manner. The experimental results show that our method has significantly better performance than some representative methods. Different from existing SAEs, the proposed NE_ESAE realizes deep learning on hierarchical structured samples, and makes SAE able to conduct cooperative deep sample and feature learning. The advantage that has been gained can be applied to other deep neural networks.

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages, vital carriers of traditional culture, have faced significant alterations due to urbanization in recent years, urgently necessitating artificial intelligence data updates. This study integrates high spatial resolution remote sensing imagery with deep learning techniques, proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images. Using 0.54 m spatial resolution imagery of traditional village areas as the data source, this method analyzes the geometric and spectral image characteristics of village building rooftops. It constructs a deep learning feature sample library tailored to the target types. Employing a semantically enhanced version of the improved Mask R-CNN (Mask Region-based Convolutional Neural Network) for building recognition, the study conducts experiments on localized imagery from different regions. The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops, achieving an of 0.7520 and an of 0.7400. It improves the current problem of misidentification and missed detection caused by feature heterogeneity. This method offers a viable and effective approach for industrialized data monitoring of traditional villages, contributing to their sustainable development.

Regulation of soil nitrogen cycling by shrubs in grasslands

Globally, grasslands are experiencing shrub expansion, but the effects of such plant community shifts on soil N cycling are seldom investigated. We explored the spatial heterogeneity of total soil N content by grid sampling down to a soil depth of 1.2 m, and analyzed net ammonification and nitrification rates in the top 0.2 m, during the growing season, in response to the patchiness of grasses and four shrub species in a grazed meadow on the Tibetan Plateau. The δ15N analyses of plant, soil, and mineral N (NH4+ and NO3−) revealed differences in N absorption strategies between shrubs and grasses. Heterogeneity in the total soil N distribution was common to at least a depth of 1.2 m. The lower δ15N values of the 1.2 m soil profile under shrubs were consistent with the lower δ15N values of leaves, aboveground litter, and roots of shrubs compared to grasses, reflecting the imprint of long-term N cycling in the soil as affected by shrubs. Increases in total soil N stock in the entire 1.2 m profile by 28%–32% under non-leguminous shrubs relative to grasses were ascribed to N accumulation in the lower 0.8 m. Consequently, deep sampling is essential for accurate estimation of soil N storage in shrubby meadows. The total N stock in soil under the leguminous shrub species was similar to that under grasses, which showed that the effect of shrubification on soil N storage depends on the shrub species. Nitrification rates in the topsoil under all shrubs were greater than under grasses, producing higher 15N-depleted NO3− contents under shrubs. Therefore, the lower δ15N signatures of shrub tissues compared to grasses reflected a preferential or adaptive uptake of NO3− by shrubs. The expansion of shrubs in grasslands creates high heterogeneity in N cycling and storage down to a considerable soil depth.

Increasing SMEs Productivity Through Digital Transformation Adoption of Software as a Service and Development of Entrepreneurial Attitudes

This study aims to test the influence digital transformation adoption of Software as a Service cloud accounting services and development of entrepreneurial attitude to productivity SMEs in Lubuklinggau as measured by indicators make transactions easier, speeds up transactions, providing additional profits when completing transaction, increase efficiency in do transaction For variables digital transformation, adoption of Software as a Service cloud accounting services. Planning indicators, beliefs, orientation on task, taking risk, leadership, And honesty For variables development of entrepreneurial attitude . As well as indicator efficiency, effectiveness, and economical for variables SMEs productivity. This research uses the Purposive Sampling method. Deep sample study was 37 respondent SMEs in Lubuklinggau. Data used in this research was primary data obtained from distribution questionnaire directly. This study used a quantitative approach. Analysis data was done using Smart PLS 3. The results of this study indicate that the effect of digital transformation adoption of Software as a Service cloud accounting services and the development of entrepreneurial attitudes on productivity of SMEs in Lubuklinggau influential positive. This study was expected can provide benefits for SMEs in Lubuklinggau with the digital transformation of adopting Software as a Service cloud accounting services so that realization enhancement productivity Which Good and develop attitude entrepreneurial with Good so that productivity increase with good too.

[Translated article] Descriptive study of spinal instrumentation-related infections in a tertiary hospital

IntroductionSpinal instrumentation-related infections (SIRI) are one of the main causes of post-surgical complication and comorbidity. Our objective was to describe the clinical and microbiological characteristics, treatment and prognosis of these infections. Material and methodsWe conducted a retrospective study in our institution (2011–2018) including adult patients undergoing spinal instrumentation who met the diagnostic criteria for confirmed infection. Superficial surgical wound and deep intraoperative samples were processed for microbiological culture. The medical and orthopaedic team was always the same. ResultsForty-one cases were diagnosed of which 39 patients (95.1%) presented early infection (<3 months after initial surgery) with symptoms in the first two weeks, mean CRP at diagnosis was 133mg/dl and 23% associated bacteremia. The remaining two patients (4.8%) were chronic infections (symptoms >3 months after surgery). The treatment of choice in early infections was the Debridement, Antibiotics and Implant Retention (DAIR) strategy without removal of the bone graft, which successfully resolved 84.2% of the infections. The main aetiology was gram-positive (Staphylococcus aureus: 31.7%), followed by gram-negative and polymicrobial flora. Antibiotics were optimised according to cultures with a mean duration of 12 weeks. ConclusionsIn early infections, early diagnosis and DAIR strategy (with bone graft retention) demonstrated a healing rate higher than 80%.

Integrating multivariate hydrogeochemical analysis with human health risk assessment: An inverse geochemical and statistical modeling approach

Chandigarh, an urban center meticulously planned in India, partially relies on groundwater for its water supply. However, rapid urbanization, geological heterogeneity, and anthropogenic actions have elevated the significance of ensuring water quality. This research employs advanced methodologies including inverse geochemical modeling through PHREEQC, multivariate statistics, and the Nemerow Pollution Index (NPI) to discern the governing factors influencing water quality. Furthermore, a human health risk assessment evaluates the potential non-carcinogenic impacts of NO3− and F− on human health. Results indicate during PRM (pre-monsoon), 90 % of shallow aquifer samples exceed an NPI value of 1.0, denoting water impurities. In POM (post-monsoon), 70 % of both shallow and deep groundwater samples surpass this threshold. Approximately 70 % of PRM deep groundwater samples and 75 % in POM exhibit “hard water” characteristics. Hydrogeochemistry is primarily influenced by rock-water interactions, corroborated by Gibb's diagram and negative values of chloro-alkaline indices (CAI-I and CAI-II) indicating cation exchange reactions. Piper's plot illustrates a prevailing Ca-Mg-HCO3 hydrochemical facies, with oversaturation of calcite, dolomite, aragonite, and fluorite at varying depths. Saturation indices (SI) affirm calcite oversaturation, implying CaCO3 precipitation and CaF2− dissolution. Additionally, 33 % of PRM and 52.4 % of POM shallow groundwater samples indicate carbonate weathering's influence on mineral content. Principal Component Analysis (PCA) and Pearson's correlation demonstrate contributions from irrigation return flows and anthropogenic activities alongside geogenic factors, enriching Na+, SO42−, NO3−, and Cl− concentrations. The combined statistical and geochemical models offer a comprehensive insight into groundwater geochemistry which will help the policy makers and municipality to manage groundwater resources effectively in the area.

Thermal evolution and sediment provenance of the Cooper–Eromanga Basin: Insights from detrital apatite

AbstractThe prolific hydrocarbon and geothermal potential of the Cooper–Eromanga Basin has long been recognised and studied, however, the thermal history which underpins these resources has largely remained elusive. This study presents new apatite fission track and U–Pb data for eight wells within the southwestern domain of the Cooper–Eromanga Basin, from which thermal history and detrital provenance reconstructions were conducted. Samples taken from sedimentary rocks of the upper Eromanga Basin (Winton, Mackunda and Cadna‐owie Formations) yield dominant Early‐Cretaceous and minor Late‐Permian–Triassic apatite U–Pb ages that are (within uncertainty) equivalent to corresponding fission track age populations. Furthermore, the obtained Cretaceous apatite ages correlate well with the stratigraphic ages for each analysed formation, suggesting (1) little time lag between apatite exposure in the source region and sediment deposition, and (2) that no significant (&gt;ca. 100°C) reheating affected these formations in this region following deposition. Cretaceous apatites were likely distally sourced from an eastern Australian volcanic arc, (e.g. the Whitsunday Igneous Association), and mixed with Permian–Triassic sediment sources from the New England and/or Mossman Orogens. Deeper samples (&gt;2000 m) from within the southwestern Cooper Basin yielded partially reset fission track ages, indicative of heating to temperatures exceeding ca. 100–80°C after deposition. The associated thermal history models are broadly consistent with previous studies and suggest that maximum temperatures were reached at ca. 100–70 Ma as a result of hydrothermal circulation correlating with high rates of sedimentation. Subsequent Late‐Cretaceous–Palaeogene cooling is interpreted to reflect post magmatic thermal subsidence and cessation of hydrothermal activity, as well as potential modified rock thermal conductivity as a response to fluid flow. Five of the seven modelled wells record a Neogene heating event, the geological significance of which remains tentative but may suggest possible reactivation of the Cooper Hot Spot and associated hydrothermal circulation.

Low strain hardening enables improved water droplet erosion performance through deep rolling

The present work investigates the role of deep rolling surface treatment on the water droplet erosion behavior of 17–4 PH stainless steel. Standard heat treatments were first applied to samples of 17–4 PH stainless steel to achieve three states, solution treated and two different aging conditions. The mechanical properties of these conditions were determined using hardness and tensile tests. Then, deep rolling surface treatments were applied to the various conditions of the 17–4 PH. Water droplet erosion tests were subsequently performed at impact velocities of 250 m/s and 300 m/s, and the erosion performance in terms of incubation period and maximum erosion rate before and after the deep rolling treatment is compared. It was found that deep rolled samples exhibited considerably longer incubation periods compared to only heat-treated samples. The improvement in erosion resistance of the 17–4 PH stainless steel is attributed to its lower strain hardening exponent values, which reduces embrittlement that is usually associated with the work hardening due to deep rolling. Hence, it is concluded that low strain hardening exponent is essential to the effectiveness and success of mechanical surface treatments to combat water droplet erosion.

Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling.

Objective and Impact Statement: Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained BC tissue images. Introduction: Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Methods: Our deep learning-based method analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. Results: This approach addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Conclusion: This automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might substantially impact cancer treatment planning.

Thermo-hydro-mechanical behaviour of deep Ypresian clays

Investigating the thermo-hydro-mechanical behaviour of deep Ypresian clays (300 to 400 m below ground) is crucial since they are one of Belgium’s potential host rock formations for deep geological disposal of radioactive waste. To this aim, a newly designed and fully instrumented thermal oedometer cell with lateral stress measurement was used to determine the stress state accurately. First, well-preserved core samples were loaded to bring them to the large in situ stresses and reduce the induced matric suction on deep water-undrained sampling. The remaining matric suction was then reduced by soaking with synthetic water of the formation. Next, drained loading and unloading paths were followed to attain different vertical effective stresses at varying overconsolidation ratios (OCRs) before the drained thermal paths (heating and cooling). The results indicated a systematic increase in the horizontal effective stress on heating at constant vertical effective stress. In addition, thermal-induced volume changes in heating/cooling at different OCRs were studied. Test results were finally interpreted within a thermo-mechanical elastoplastic framework.

Deep Adversarial Inconsistent Cognitive Sampling for Multiview Progressive Subspace Clustering.

Deep multiview clustering methods have achieved remarkable performance. However, all of them failed to consider the difficulty labels (uncertainty of ground truth for training samples) over multiview samples, which may result in a nonideal clustering network for getting stuck into poor local optima during the training process; worse still, the difficulty labels from the multiview samples are always inconsistent, and such a fact makes it even more challenging to handle. In this article, we propose a novel deep adversarial inconsistent cognitive sampling (DAICS) method for multiview progressive subspace clustering. A multiview binary classification (easy or difficult) loss and a feature similarity loss are proposed to jointly learn a binary classifier and a deep consistent feature embedding network, throughout an adversarial minimax game over difficulty labels of multiview consistent samples. We develop a multiview cognitive sampling strategy to select the input samples from easy to difficult for multiview clustering network training. However, the distributions of easy and difficult samples are mixed together, hence not trivial to achieve the goal. To resolve it, we define a sampling probability with a theoretical guarantee. Based on that, a golden section mechanism is further designed to generate a sample set boundary to progressively select the samples with varied difficulty labels via a gate unit, which is utilized to jointly learn a multiview common progressive subspace and clustering network for more efficient clustering. Experimental results on four real-world datasets demonstrate the superiority of DAICS over state-of-the-art methods.

The Effect of Job Training and Work Environment on Employee Performance Through Job Satisfaction at BPR Alwashliyah

Employee performance is very determining progress something company Because performance is something very important thing in effort organization For reach objective . If employee own good performance _ naturally will give impact profitable for company vice versa _ if performance employee low so company will experience difficulties and losses in achievement goals that have been determined by the Company. Study This aim For test and analyze influence training and environment Work to performance employee through satisfaction Work in a way direct nor in a way No direct . Approach used _ in study This is approach causal . Population in study This is all over BPR Alwashliyah employees . Deep sample study This use sample fed up totaling 50 people from BPR Alwashliyah . Deep data collection techniques study This use technique documentation , observation , and questionnaires . Deep data analysis techniques study This use approach quantitative use analysis statistics with using the Analysis test Outer Model , Inner Model Analysis , and Hypothesis Testing . Deep data processing study This using the PLS ( Partial Least Square ) software program . Research result This show that in a way direct training , environment work and satisfaction Work influential significant to performance employees , training and environment Work influential significant to satisfaction Work as well as in a way No direct training and environment Work influential significant to performance employee through satisfaction works at BPR Alwashliyah

The Influence of Profitability, Liquidity, and Company Growth on Capital Structure and Company Value: A Study of Companies Listed on the LQ45 Index for the 2016-2020 Period

This research was conducted to test and analyze the effect of profitability on capital structure, liquidity on capital structure, company growth on capital structure, profitability on company value, liquidity on company value, company growth on company value, and capital structure on company value. The type of research is associative quantitative research. This research uses secondary data from the company's annual financial report data published at www.idx.co.id. Deep sample study This numbered 21 companies and sample selection was carried out randomly through purposive sampling. This research uses multiple linear regression analysis with SPSS tools (statistical package for social sciences) version 23. The research results show that liquidity has an effect on capital structure, while profitability and business risk have no effect on capital structure. Capital structure has an effect on company value, while profitability, liquidity, and company growth have no effect on company value.

Leveraging museum specimens, genomics and legacy datasets to unravel the phylogeny and biogeography of cryptin wasps (Hymenoptera, Ichneumonidae, Cryptini)

AbstractModern genomic techniques have enabled the generation of phylogenetic datasets of unprecedented scale. However, there are also troves of molecular data accumulated from past studies using Sanger sequencing, often at fine taxonomic scales. Combining both sources of data is an obviously appealing possibility, but it can also lead to inconsistency due to high levels of missing data, disparities in the scale of Sanger versus genomic datasets, and little overlap in sequences across terminals. To provide an empirical investigation of the potential of such ‘hybrid’ datasets, we combined data from ultraconserved elements (UCEs) for 183 species of Cryptini (Ichneumonidae, Hymenoptera) with a previously existing dataset of 7 loci and morphological data including 308 species plus outgroup taxa. Bioinformatics pipelines allowed recovery of ‘legacy’ markers from the bycatch of UCE sequencing, reducing the problem of limited character overlap. The resulting tree combining Sanger and UCE data is highly supported and includes dense taxon sampling of the group, allowing for a better understanding of the global radiation of Cryptini. The Neotropical region had the highest phylogenetic diversity but the lowest level of phylogenetic dispersion when corrected for standardized effect size, while the Oriental fauna showed the highest level of phylogenetic dispersion. Our results highlight the potential of hybrid datasets to produce a more complete picture of the Tree of Life combining affordability, robust support and deep taxonomic sampling.

The landscape of genomic structural variation in Indigenous Australians

Indigenous Australians harbour rich and unique genomic diversity. However, Aboriginal and Torres Strait Islander ancestries are historically under-represented in genomics research and almost completely missing from reference datasets1–3. Addressing this representation gap is critical, both to advance our understanding of global human genomic diversity and as a prerequisite for ensuring equitable outcomes in genomic medicine. Here we apply population-scale whole-genome long-read sequencing4 to profile genomic structural variation across four remote Indigenous communities. We uncover an abundance of large insertion–deletion variants (20–49 bp; n = 136,797), structural variants (50 b–50 kb; n = 159,912) and regions of variable copy number (>50 kb; n = 156). The majority of variants are composed of tandem repeat or interspersed mobile element sequences (up to 90%) and have not been previously annotated (up to 62%). A large fraction of structural variants appear to be exclusive to Indigenous Australians (12% lower-bound estimate) and most of these are found in only a single community, underscoring the need for broad and deep sampling to achieve a comprehensive catalogue of genomic structural variation across the Australian continent. Finally, we explore short tandem repeats throughout the genome to characterize allelic diversity at 50 known disease loci5, uncover hundreds of novel repeat expansion sites within protein-coding genes, and identify unique patterns of diversity and constraint among short tandem repeat sequences. Our study sheds new light on the dimensions and dynamics of genomic structural variation within and beyond Australia.

First surveillance of pesticides in soils of the perimeter of Tadla, a Moroccan sugar beet intensive area.

With the long-term application of pesticides on sugar beet farms in the irrigated perimeter of Tadla in Morocco for over 50years, pesticide monitoring is necessary to assess soil health. The objective of our study was to monitor multiple pesticide residues in topsoil samples collected from post-harvest sugar beet fields and verify their migration to deep soil layers. Topsoil and deep soil samples were collected from arbitrarily selected sugar beet fields in the IPT. In this study, a target-screening method was applied. All target pesticides were detected in soil samples, with tefluthrin being the most frequently detected pesticide. The residue with the highest concentration in soil samples was DDE. All the soil samples contained a mixture of pesticide residues, with a maximum of 13 residues per sample. The total pesticide content decreased toward more profound layers of soil, except in one field where it reached a concentration of 348µg/kg at the deeper soil layer. For pesticides detected at the three soil depths, only tefluthrin concentration increased in the deep soil layer. The results provide comprehensive and precise information on the pesticide residue status in sugar beet soils warning against the multiple risks that this contamination can cause. This study indicates the need of regular monitoring of pesticides over a large area of the perimeter to enable decision-makers to pronounce the impacts of the extension and intensification of sugar beet cultivation at the irrigated perimeter of Tadla.

Proton Binding Characteristics of Dissolved Organic Matter Extracted from the North Atlantic.

Marine dissolved organic matter (DOM) presents key thermodynamic properties that are not yet fully constrained. Here, we report the distribution of binding sites occupied by protons (i.e., proton affinity spectra) and parametrize the median intrinsic proton binding affinities (log K̅H) and heterogeneities (m), for DOM samples extracted from the North Atlantic. We estimate that 11.4 ± 0.6% of C atoms in the extracted marine DOM have a functional group with a binding site for ionic species. The log K̅H of the most acidic groups was larger (4.01-4.02 ± 0.02) than that observed in DOM from coastal waters (3.82 ± 0.02), while the chemical binding heterogeneity parameter increased with depth to values (m1= 0.666 ± 0.009) ca. 10% higher than those observed in surface open ocean or coastal samples. On the contrary, the log K̅H for the less acidic groups shows a difference between the surface (10.01 ± 0.08) and deep (9.22 ± 0.35) samples. The latter chemical groups were more heterogeneous for marine than for terrestrial DOM, and m2 decreased with depth to values of 0.28 ± 0.03. Binding heterogeneity reflects aromatic carbon compounds' persistence and accumulation in diverse, low-abundance chemical forms, while easily degradable low-affinity groups accumulate more uniformly in the deep ocean.

Effects of dissolved organic carbon on potentially toxic element desorption in stormwater bioretention systems

Stormwater runoff contains dissolved organic carbon (DOC) and potentially toxic elements (PTEs). Interactions between DOC and PTEs can impact PTE speciation and mobility, but are not fully understood. Soil samples were collected from a vegetated bioretention bed to investigate the effects of DOC (0, 15, and 50 mg-C/L) on the desorption of 10 PTEs captured by the soil media: Mn, Fe, Co, Cu, Zn, As, Cd, Sn, Sb, and Pb. In the absence of DOC, the desorbed PTE concentration from bioretention media into the aqueous phase ranking was as follows: Fe > Mn ∼ Zn > Cu > Pb > Sb > As > Co > Sn ∼ Cd. Increased DOC concentrations resulted in a reduction of the soil-water distribution coefficient (Kd) values. The greatest shift in Kd was observed for Cu and lowest for Sb. The PTE sorption capacities were lower for surficial soil samples (lower Kd) compared to the deep soil samples. Overall, the desorbed PTE (average midchannel 55.7 μg/g) fraction accounted for <1.1 % of the total extracted PTEs (5364 μg/g), and while this is a small percentage of the total, this is the fraction that is mobile. The extracted PTE fractions revealed that DOC reduced the organic matter-bound and carbonate-bound fractions. The PTE desorption trends suggest that reducing DOC in stormwater runoff could be an effective measure to mitigate the release of PTEs into the environment.