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"Krause, Stefan"
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Abundance, Distribution, and Drivers of Microplastic Contamination in Urban River Environments
Given the persistence of microplastics in the environment and their potential toxicity to ecosystems, understanding of likely microplastic accumulation ‘hotspots’ in rivers is urgently needed. To contribute to this challenge, this paper reports results of a microplastic survey from a heavily urbanised catchment, the River Tame and four of its tributaries, which flows through the city of Birmingham, UK. All sediment sampled was found to contain microplastics with an average abundance of 165 particles kg−1. While urban areas generally have a greater abundance of microplastics as compared with rural, there is no simple relationship between microplastic numbers and population density or proximity to wastewater treatment sites. The greatest change in microplastic abundance was due to the presence of a lake along the course of the River Tame—i.e., flow velocities are reduced on entering the lake, which promotes the deposition of fine sediment and potentially microplastics. This suggests that the greatest concentrations of microplastics will not be found in-channel but rather on the floodplain and other low velocity environments such as meander cutoffs. We also identified a new mechanism of microplastic fixation in freshwater environments through ecological engineers, specifically caddisflies, that incorporated microplastics into their casing. These results highlight the need to explore further hydrodynamic and ecological impacts on microplastics fate and transport in rivers.
Journal Article
ASXL1 mutations predict inferior molecular response to nilotinib treatment in chronic myeloid leukemia
Gene mutations independent of BCR::ABL1 have been identified in newly diagnosed patients with chronic myeloid leukemia (CML) in chronic phase, whereby mutations in epigenetic modifier genes were most common. These findings prompted the systematic analysis of prevalence, dynamics, and prognostic significance of such mutations, in a clinically well-characterized patient population of 222 CML patients from the TIGER study (CML-V) by targeted next-generation sequencing covering 54 myeloid leukemia-associated genes. In total, 53/222 CML patients (24%) carried 60 mutations at diagnosis with ASXL1 being most commonly affected (n = 20). To study mutation dynamics, longitudinal deep sequencing analysis of serial samples was performed in 100 patients after 12, 24, and 36 months of therapy. Typical patterns of clonal evolution included eradication, persistence, and emergence of mutated clones. Patients carrying an ASXL1 mutation at diagnosis showed a less favorable molecular response to nilotinib treatment, as a major molecular response (MMR) was achieved less frequently at month 12, 18, and 24 compared to all other patients. Patients with ASXL1 mutations were also younger and more frequently found in the high risk category, suggesting a central role of clonal evolution associated with ASXL1 mutations in CML pathogenesis.
Journal Article
Groundwater–Surface Water Interactions: Recent Advances and Interdisciplinary Challenges
The interactions of groundwater with surface waters such as streams, lakes, wetlands, or oceans are relevant for a wide range of reasons—for example, drinking water resources may rely on hydrologic fluxes between groundwater and surface water. However, nutrients and pollutants can also be transported across the interface and experience transformation, enrichment, or retention along the flow paths and cause impacts on the interconnected receptor systems. To maintain drinking water resources and ecosystem health, a mechanistic understanding of the underlying processes controlling the spatial patterns and temporal dynamics of groundwater–surface water interactions is crucial. This Special Issue provides an overview of current research advances and innovative approaches in the broad field of groundwater–surface water interactions. The 20 research articles and 1 communication of this Special Issue cover a wide range of thematic scopes, scales, and experimental and modelling methods across different disciplines (hydrology, aquatic ecology, biogeochemistry, environmental pollution) collaborating in research on groundwater–surface water interactions. The collection of research papers in this Special Issue also allows the identification of current knowledge gaps and reveals the challenges in establishing standardized measurement, observation, and assessment approaches. With regards to its relevance for environmental and water management and protection, the impact of groundwater–surface water interactions is still not fully understood and is often underestimated, which is not only due to a lack of awareness but also a lack of knowledge and experience regarding appropriate measurement and analysis approaches. This lack of knowledge exchange from research into management practice suggests that more efforts are needed to disseminate scientific results and methods to practitioners and policy makers.
Journal Article
Human domination of the global water cycle absent from depictions and perceptions
Human water use, climate change and land conversion have created a water crisis for billions of individuals and many ecosystems worldwide. Global water stocks and fluxes are estimated empirically and with computer models, but this information is conveyed to policymakers and researchers through water cycle diagrams. Here we compiled a synthesis of the global water cycle, which we compared with 464 water cycle diagrams from around the world. Although human freshwater appropriation now equals half of global river discharge, only 15% of the water cycle diagrams depicted human interaction with water. Only 2% of the diagrams showed climate change or water pollution—two of the central causes of the global water crisis—which effectively conveys a false sense of water security. A single catchment was depicted in 95% of the diagrams, which precludes the representation of teleconnections such as ocean–land interactions and continental moisture recycling. These inaccuracies correspond with specific dimensions of water mismanagement, which suggest that flaws in water diagrams reflect and reinforce the misunderstanding of global hydrology by policymakers, researchers and the public. Correct depictions of the water cycle will not solve the global water crisis, but reconceiving this symbol is an important step towards equitable water governance, sustainable development and planetary thinking in the Anthropocene.Only about 15% of water cycle diagrams include human interaction with water, although human freshwater appropriation amounts to about half of global river discharge, according to an analysis of 464 water cycle diagrams and a synthesis of the global water cycle.
Journal Article
Stakeholder alliances are essential to reduce the scourge of plastic pollution
Progress to reduce plastic pollution has been painfully slow and the consequent damage to the natural environment and to human health is likely to increase further. This has been because the views and ways of working of four distinct stakeholder communities are not sufficiently well integrated. (1) Scientists, (2) industry, (3) society at large and (4) those making policy and legislation must in future find ways to work together.
Journal Article
Comparison of Automated Differential Blood Cell Counts From Abbott Sapphire, Siemens Advia 120, Beckman Coulter DxH 800, and Sysmex XE-2100 in Normal and Pathologic Samples
Reliable automated blood cell characterization and quantification remain challenging in pathologic samples, whereas slide reviews due to unnecessary flagging should be avoided. We compared 4 modern hematology analyzers—Abbott Sapphire, Siemens Advia 120, Sysmex XE-2100, and Beckman Coulter DxH 800—regarding complete blood cell count (CBC), leukocyte differential count, and flagging efficacy in a total of 202 samples from hematology patients and normal controls. Manual differential count was used as reference. The analyzers exhibited very good correlation for CBC parameters. Neutrophils and eosinophils also showed very good correlations, whereas lymphocytes and monocytes correlated fairly. The Advia 120 displayed notably lower measurements for both parameters, which is attributable to classification of some events as large unstained cells. Basophil counts were unreliable with all analyzers. Flagging for blasts and immature granulocytes showed moderate sensitivity and specificity. Operators must not rely on blast flagging alone to detect leukemic samples with any analyzer.
Journal Article
Importance of Monitoring Frequency for Representation of Dissolved Organic Matter Dynamics in Urban Rivers
In‐situ dissolved organic matter (DOM) monitoring frequencies have often been chosen for convenience or based on perceived wisdom, without fully assessing their impact on representation of DOM dynamics. To address this gap, we collected 5‐min fluorescence data in an urban headwater and resampled it at coarser intervals to investigate the impact of monitoring frequencies on the detectability of DOM dynamics during storms. Expecting hydrometeorological conditions to modify the impact of monitoring frequency, we categorized 85 storm events into groups: Group A (low intensity, short duration), Group B (high intensity, short duration), and Group C (low intensity, long duration). Surprisingly, our analysis indicated that monitoring frequency has minimal influence on commonly used biogeochemical indexes (e.g., maximum, hysteresis and flushing index), which are employed to characterize solute behavior, regardless of storm type. To facilitate a direct comparison between monitoring frequencies, we back‐interpolated coarser data into 5‐min intervals and calculated mean squared errors by comparing them with original high‐resolution data. Our findings indicated that in colder periods with predominately Type A and C storms, a coarser monitoring frequency (>30 min) can capture DOM dynamics. Conversely, in warmer periods when Type B storms dominate, a finer frequency (≤15 min) is necessary to capture key solute chemograph processes (e.g., first flush and dilution). Generally, we suggest a 15‐min monitoring frequency as optimal for similar urban headwater systems, and advocate an adaptive approach based on seasonal variations to improve efficiency, especially when power, data transfer, and storage are constraints. Key Points High frequency monitoring (5 min) of fluorescence peaks were undertaken in an urban headwater stream and resampled at coarser intervals Commonly used biogeochemical indices (e.g., maximum, hysteresis and flushing index) were insensitive to variations in monitoring frequency Chemograph shape was sensitive to changes in monitoring frequency and hydrometeorological conditions with implications for event monitoring
Journal Article
Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study
Multiparameter flow cytometry is a cornerstone of B cell non-Hodgkin lymphoma (B-NHL) diagnostics, but interpretation requires substantial expertise and is complicated by high-dimensional data, variable sample quality, limited data for rare entities, and evolving clinical classification systems. Current artificial intelligence approaches often require large training datasets and provide limited insight into the rationale behind individual diagnostic decisions.
We developed FlowXAI, a self-explaining artificial intelligence system designed to support B-NHL classification while explicitly reporting case-level diagnostic trustworthiness. FlowXAI combines unsupervised structural analysis with a clinically motivated, multi-level diagnostic framework reflecting routine diagnostic priorities. An unsupervised Tile Mining (TM) procedure performs pre-diagnostic sample-quality assessment by identifying structurally atypical samples. TM is applied to filter training data, enabling substantial reduction of training requirements while preserving unbiased evaluation on independent test samples. FlowXAI was evaluated using repeated cross-validation on 19,493 peripheral blood samples and further assessed on an independent external benchmark dataset generated at a separate diagnostic center using a different antibody panel. Across diagnostic levels, FlowXAI achieved performance comparable to a deep learning-based system despite requiring approximately two orders of magnitude fewer training samples. When predictions were classified as confident by the system's internal self-assessment, diagnostic performance exceeded that of the neural network baseline. Unsupervised structural analysis demonstrated clear separation between normal controls and selected lymphoma entities such as chronic lymphocytic leukemia-like lymphomas and hairy cell leukemia, while other entities were not clearly separable using the antibody panels studied.
FlowXAI provides accurate, data-efficient, and transparent support for B-NHL immunophenotyping from nonstandardized flow cytometry data. By combining interpretable decision logic with explicit self-assessment, FlowXAI offers a clinically meaningful framework for diagnostic support and training, particularly in settings with limited expert availability or rare lymphoma subtypes. The main limitation is the retrospective evaluation using specific antibody panels, and FlowXAI requires prospective validation as a decision-support tool within integrated diagnostic workflows.
Journal Article