Reference Dataset Research Articles

Detecting drought events over a region in Central Europe using a regional and two satellite-based precipitation datasets

In this study, the accuracy of two satellite-based datasets is evaluated. The evaluation includes monthly precipitation estimates, spatial detection of precipitation, and drought monitoring against a regional gridded dataset spanning 2007–2019. A study area covering Poland and parts of the neighboring countries in Central Europe was selected for this evaluation. The Standardized Precipitation Index (SPI) at multi-time scales was employed to monitor meteorological (SPI-3), agricultural (SPI-6, SPI-9), and hydrological (SPI-12) droughts over the study region. This study selected PERSIANNCDR as a top-down precipitation dataset and SM2RAIN-ASCAT as a bottom-up dataset. According to the results, both datasets exhibit good accuracy for precipitation estimations, but PERSIANNCDR shows higher accuracy based on the R (coefficient of correlation) and KGE (Kling-Gupta Efficiency) performance indicators. However, SM2RAIN-ASCAT has a lower bias according to PBIAS(%) (percent bias). The reference dataset indicates that the study area experienced dry conditions over 50% of the months. Specifically, based on the reference dataset, 12 (SPI-6) and 16 (SPI-9) severe agricultural droughts were detected. Twenty-four severe agricultural drought events were identified via SPI-6, while the longer SPI window (SPI-9) demonstrated that PERSIANNCDR assessed 20 severe droughts over the study area. SM2RAIN-ASCAT detected 11 severe agricultural droughts via SPI-6 and SPI-9. Furthermore, based on SPI-12, the reference dataset identified 75 hydrological droughts, while the top-down dataset indicated a lower number of hydrological droughts (67 events) than the reference dataset over the studied period. In contrast, the bottom-up dataset detected 84 hydrological droughts. The spatial distribution of severe meteorological droughts showed a clear pattern with predominant occurrence in eastern parts (Vistula River Basin), as shown by the reference dataset, while this pattern changed for agricultural and hydrological droughts (Odra River Basin). Additionally, the results reveal that meteorological drought does not have a similar spatial distribution to agricultural and hydrological droughts.

Improved myelin water fraction mapping with deep neural networks using synthetically generated 3D data

We introduce a generative model for synthesis of large scale 3D datasets for quantitative parameter mapping of myelin water fraction (MWF). Our model combines a MR physics signal decay model with an accurate probabilistic multi-component parametric T2 model. We synthetically generate a wide variety of high quality signals and corresponding parameters from a wide range of naturally occurring prior parameter values. To capture spatial variation, the generative signal decay model is combined with a generative spatial model conditioned on generic tissue segmentations. Synthesized 3D datasets can be used to train any convolutional neural network (CNN) based architecture for MWF estimation. Our source code is available at: https://github.com/quin-med-harvard-edu/synthmapReduction of acquisition time at the expense of lower SNR, as well as accuracy and repeatability of MWF estimation techniques, are key factors that affect the adoption of MWF mapping in clinical practice. We demonstrate that the synthetically trained CNN provides superior accuracy over the competing methods under the constraints of naturally occurring noise levels as well as on the synthetically generated images at low SNR levels. Normalized root mean squared error (nRMSE) is less than 7% on synthetic data, which is significantly lower than competing methods. Additionally, the proposed method yields a coefficient of variation (CoV) that is at least 4x better than the competing method on intra-session test-retest reference dataset.

Personalized laboratory medicine in the digital health era: recent developments and future challenges.

Interpretation of laboratory data is a comparative procedure and requires reliable reference data, which are mostly derived from population data but used for individuals in conventional laboratory medicine. Using population data as a "reference" for individuals has generated several problems related to diagnosing, monitoring, and treating single individuals. This issue can be resolved by using data from individuals' repeated samples, as their personal reference, thus needing that laboratory data be personalized. The modern laboratory information system (LIS) can store the results of repeated measurements from millions of individuals. These data can then be analyzed to generate a variety of personalized reference data sets for numerous comparisons. In this manuscript, we redefine the term "personalized laboratory medicine" as the practices based on individual-specific samples and data. These reflect their unique biological characteristics, encompassing omics data, clinical chemistry, endocrinology, hematology, coagulation, and within-person biological variation of all laboratory data. It also includes information about individuals' health behavior, chronotypes, and all statistical algorithms used to make precise decisions. This approach facilitates more accurate diagnosis, monitoring, and treatment of diseases for each individual. Furthermore, we explore recent advancements and future challenges of personalized laboratory medicine in the context of the digital health era.

B-123 Utilization of Five Data Mining Algorithms Combined with Simplified Preprocessing to Establish Reference Intervals of Thyroid Related Hormones for Nonelderly Adults

Abstract Background Despite the extensive research on data mining algorithms, there is still a lack of a standard protocol to evaluate the performance of the existing algorithms. Therefore, the study aims to provide a novel procedure that combines data mining algorithms and simplified preprocessing to establish reference intervals (RIs), with the performance of five algorithms assessed objectively as well. Methods The Test data set and the Reference data set are the two data sets derived from the population undergoing a physical examination. After the thyroid-related hormone including thyroid stimulating hormone (TSH), free triiodo-thyronine (FT3), total triiodo-thyronine (TT3), free thyroxine(FT4), and total thyroxine (TT4) were measured by an ADVIA Centaur XP chemiluminescence immunoassay analyzer, five data algorithms were used to calculated RIs. Hoffmann, Bhattacharya, Expectation Maximum (EM), kosmic, and refineR algorithms combined with two-step data preprocessing respectively were implemented in the Test data set to establish RIs for thyroid-related hormones. The first step is to conduct a random sampling strategy to balance the ratio of sex and age, and the second step is to identify the outliers of variables in each subgroup by the Tukey method. Algorithm-calculated RIs were compared with the standard RIs calculated by transformed parametric method from the Reference data set in which reference individuals were selected following strict inclusion and exclusion criteria. RIs partition were comprehensively determined by the multiple linear regression and variance component analysis. Objective assessment of the methods is implemented by the bias ratio (BR) matrix, of which the BR threshold was set to 0.375. Results The levels of the all five thyroid-related hormones are significantly different in sex, with the male having lower TSH and higher FT3, FT4, TT3, and TT4 compared to the female. Further analysis indicates the establishment of sex-specific RIs for FT3 and FT4. Standard RIs derived from the Reference data set by transformed parametric method are 0.801–4.221 μIU/L for TSH, 2.58–3.82 pg/mL for FT3, 0.98–1.53 ng/dL for FT4, 0.80–1.38 ng/mL for TT3, 5.46–10.05 g/dL for TT4, respectively. There is a high consistency between TSH RIs established by the EM algorithm and the standard TSH RIs (BR = 0.063), although EM algorithms seems to perform poor on other hormones with the BR higher than 0.375. RIs calculated by Hoffmann, Bhattacharya, and refineR methods for free and total triiodo-thyronine, free and total thyroxine respectively are close and matched the standard RIs. Conclusion An effective approach for objectively evaluating the performance of the algorithm based on the BR matrix is established. EM algorithm combined with simplified preprocessing can handle data with significant skewness, but its performance is limited in other scenarios. The other four algorithms perform well for data with Gaussian or near-Gaussian distribution. Using the appropriate algorithm based on the data distribution characteristics is recommended.

How to train a neural network potential.

The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm change in the development of potential energy surfaces for atomistic simulations. By providing efficient access to energies and forces, they allow us to perform large-scale simulations of extended systems, which are not directly accessible by demanding first-principles methods. In these simulations, MLPs can reach the accuracy of electronic structure calculations, provided that they have been properly trained and validated using a suitable set of reference data. Due to their highly flexible functional form, the construction of MLPs has to be done with great care. In this Tutorial, we describe the necessary key steps for training reliable MLPs, from data generation via training to final validation. The procedure, which is illustrated for the example of a high-dimensional neural network potential, is general and applicable to many types of MLPs.

Novel intelligent Bayesian computing networks for predictive solutions of nonlinear multi-delayed tumor oncolytic virotherapy systems

Oncolytic viral immunotherapy is gaining considerable prominence in the realm of chronic diseases treatment and rehabilitation. Oncolytic viral therapy is an intriguing therapeutic approach due to its low toxicity and dual function of immune stimulations. This work aims to design a soft computing approach using stupendous knacks of neural networks (NNs) optimized with Bayesian regularization (BR), i.e. NNs-BR, procedure. The constructed NNs-BR technique is exploited in order to determine the approximate numerical treatment of the nonlinear multi-delayed tumor virotherapy (TVT) models in terms of the dynamic interactions between the tumor cells free of viruses, tumor cells infected by viruses, viruses, and cytotoxic T-lymphocytes (CTLs). The strength of state-of-the-art numerical approach is incorporated to develop the reference dataset for the variation in the infection rate for tumor cells, virus-free tumor cell clearance rate by CTLs, CTLs clearance rate for infectious tumor cells, the natural lifecycle of infectious tumor cells, the natural lifecycle of viral cell, the natural lifecycle of CTLs cells, tumor cells free of viruses’ maximum proliferation rate, production of tumor cells with an infection, CTLs simulated ratio for infectious tumor cells, CTLs simulated ratios for virus-free cells and delay in time. The dataset is randomly chosen/segmented for training-testing-validation samples to construct the NNs models optimized with backpropagated BR representing the approximate numerical solutions of the dynamic interactions in the TVT model. The performance of the designed NNs-BR technique is accessed/evaluated and outcomes are found in good agreement with the reference solutions having the range of accuracy from 10[Formula: see text] to 10[Formula: see text]. The efficacy of NNs-BR paradigm is further substantiated after rigorous analysis on regression metrics, learning curves on MSE, and error histograms for the dynamics of TVT model.

Simulating structurally variable Nuclear Pore Complexes for Microscopy.

The Nuclear Pore Complex (NPC) is the only passageway for macromolecules between nucleus and cytoplasm, and an important reference standard in microscopy: it is massive and stereotypically arranged. The average architecture of NPC proteins has been resolved with pseudo-atomic precision, however observed NPC heterogeneities evidence a high degree of divergence from this average. Single Molecule Localization Microscopy (SMLM) images NPCs at protein-level resolution, whereupon image analysis software studies NPC variability. However the true picture of this variability is unknown. In quantitative image analysis experiments, it is thus difficult to distinguish intrinsically high SMLM noise from variability of the underlying structure. We introduce CIR4MICS ("ceramics", Configurable, Irregular Rings FOR MICroscopy Simulations), a pipeline that synthesizes ground truth datasets of structurally variable NPCs based on architectural models of the true NPC. Users can select one or more N- or C-terminally tagged NPC proteins, and simulate a wide range of geometric variations. We also represent the NPC as a spring-model such that arbitrary deforming forces, of user-defined magnitudes, simulate irregularly shaped variations. Further, we provide annotated reference datasets of simulated human NPCs, which facilitate a side-by-side comparison with real data. To demonstrate, we synthetically replicate a geometric analysis of real NPC radii and reveal that a range of simulated variability parameters can lead to observed results. Our simulator is therefore valuable to test the capabilities of image analysis methods, as well as to inform experimentalists about the requirements of hypothesis-driven imaging studies. Code: https://github.com/uhlmanngroup/cir4mics. Simulated data: BioStudies S-BSST1058. Supplementary data are available at.

Handling missing values in the analysis of between-hospital differences in ordinal and dichotomous outcomes: a simulation study

Missing data are frequently encountered in registries that are used to compare performance across hospitals. The most appropriate method for handling missing data when analysing differences in outcomes between hospitals...

Surface Enhanced Raman Spectroscopy Pb2+ Ion Detection Based on a Gradient Boosting Decision Tree Algorithm

Lead pollution poses a serious threat to the natural environment, and a fast and high-sensitivity method is urgently needed. SERS can be used for the detection of Pb2+ ions, which is urgently needed. Based on the SERS spectral reference data set of lead nitride (Pb(NO3)2), a model for detecting Pb2+ was established by using a traditional machine learning algorithm and the GBDT algorithm. Principal component analysis was used to compare the batch effect reduction in different pretreatment methods in order to find the optimal combination of such methods and machine learning models. The combination of LightGBM algorithms successfully identified Pb2+ from cross-batch data, exceeding the 84.6% balanced accuracy of the baseline correction+ radial basis function kernel support vector machine (BC+RBFSVM) model and showing satisfactory results, with a 91.4% balanced accuracy and a 0.9313 area under the ROC curve.

DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection

BackgroundSingle-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification.ResultsHere, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We show that DISCERN is robust against differences between batches and is able to keep biological differences between batches, which is a common problem for imputation and batch correction algorithms. We use DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+ and CD8+ Tc2 T helper cells, with a potential role in adverse disease outcome. We utilize T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 80% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single-cell sequencing workflow.ConclusionsThus, DISCERN is a flexible tool for reconstructing missing single-cell gene expression using a reference dataset and can easily be applied to a variety of data sets yielding novel insights, e.g., into disease mechanisms.

SILVR: Guided Diffusion for Molecule Generation.

Computationally generating new synthetically accessible compounds with high affinity and low toxicity is a great challenge in drug design. Machine learning models beyond conventional pharmacophoric methods have shown promise in the generation of novel small-molecule compounds but require significant tuning for a specific protein target. Here, we introduce a method called selective iterative latent variable refinement (SILVR) for conditioning an existing diffusion-based equivariant generative model without retraining. The model allows the generation of new molecules that fit into a binding site of a protein based on fragment hits. We use the SARS-CoV-2 main protease fragments from Diamond XChem that form part of the COVID Moonshot project as a reference dataset for conditioning the molecule generation. The SILVR rate controls the extent of conditioning, and we show that moderate SILVR rates make it possible to generate new molecules of similar shape to the original fragments, meaning that the new molecules fit the binding site without knowledge of the protein. We can also merge up to 3 fragments into a new molecule without affecting the quality of molecules generated by the underlying generative model. Our method is generalizable to any protein target with known fragments and any diffusion-based model for molecule generation.

A graphical derivation and statistical evaluation of simplified polynomials to determine vapour pressure deficit for use in ultra-low power microcontroller applications

The aim of this work has been to derive and statistically evaluate the accuracy of second-order and third-order polynomials to determine vapour pressure deficit (VPD). These polynomials take air temperature and relative humidity measurements to determine VPD without the use of an exponential function, as proposed by F W Murray in 1967. Replacing the exponential function with a 2nd or 3rd order polynomial may be beneficial in ultra-low power microcontroller-based measurement applications where; code size, memory usage and power requirements are critical design drivers. However, oversimplification may impact precision. This work presents alternative 2nd order and 3rd order equations that have been derived from a Murray equation dataset where VPD isothermal datasets were plotted against relative humidity. These linear relationships allow y = mx + c analysis where, (i) ‘c’ can be set to zero with a offset in the relative humidity data, and, (ii) ‘m’ can be derived from a 2nd of 3rd order polynomial where ‘m’ = f(T) and is derived using Excel-based fitting of the gradients from the isothermal datasets. The resulting ‘m’ = f(T) 2nd and 3rd order polynomials presented R 2 values of 0.998 04 and 0.999 98 respectively. A Bland-Altman statistical assessment was performed, where the Murray equation (reference) dataset is plotted against the difference between the reference and polynomial datasets using the same air temperature and relative humidity inputs. The difference datasets presented 2-sigma (95% confidence interval) variances for the 2nd and 3rd order polynomials as <±0.1 kPa and <±0.01 kPa respectively. The 2nd and 3rd order polynomials also resulted in a bias values of <0.0037 kPa and <0.0013 kPa respectively. These results suggest that a 3rd order polynomial equation could be used to determine VPD in ultra-low-power microcontroller measurement applications, with minimal impact on VPD measurement precision.

Simulating a reference medium for determining bacterial growth in hospital wastewater for Raman spectroscopic investigation

Wastewater is a very complex and diverse medium, which despite low nutrient density still harbors bacteria. Especially the wastewater from hospitals contains a high germ load. However, wastewater is also very variable and differs not only from day to day, but also from house to house. Since wastewater is always changing and medium has an impact on Raman spectra of bacteria, it is necessary to find a surrogate material in which bacteria can be cultured to mimic a real hospital wastewater sample. In this study, we investigate two different artificial wastewaters for their abilities as a good alternative to real wastewater from the Jena University Hospital and to serve as a reference material for bacterial cultivation with subsequent Raman measurement. Each of the artificial wastewater on its own was not suitable to be used as a reference medium. Only the combination of the two simulated wastewaters achieved satisfactory results in the Raman spectroscopic identification of bacteria from real wastewater. These results could be used later in new experiments as a reference dataset to identify bacteria from real hospital wastewater samples.

In vivo mapping of the deep and superficial white matter connectivity in the chimpanzee brain

Mapping the chimpanzee brain connectome and comparing it to that of humans is key to our understanding of similarities and differences in primate evolution that occurred after the split from their common ancestor around 6 million years ago. In contrast to studies on macaque species’ brains, fewer studies have specifically addressed the structural connectivity of the chimpanzee brain and its comparison with the human brain. Most comparative studies in the literature focus on the anatomy of the cortex and deep nuclei to evaluate how their morphology and asymmetry differ from that of the human brain, and some studies have emerged concerning the study of brain connectivity among humans, monkeys, and apes. In this work, we established a new white matter atlas of the deep and superficial white matter structural connectivity in chimpanzees. In vivo anatomical and diffusion-weighted magnetic resonance imaging (MRI) data were collected on a 3-Tesla MRI system from 39 chimpanzees. These datasets were subsequently processed using a novel fiber clustering pipeline adapted to the chimpanzee brain, enabling us to create two novel deep and superficial white matter connectivity atlases representative of the chimpanzee brain. These atlases provide the scientific community with an important and novel set of reference data for understanding the commonalities and differences in structural connectivity between the human and chimpanzee brains. We believe this study to be innovative both in its novel approach and in mapping the superficial white matter bundles in the chimpanzee brain, which will contribute to a better understanding of hominin brain evolution.

BULK RNA-SEQ DECONVOLUTION OF IMAGE-LOCALIZED HIGGRADE GLIOMA BIOPSIES REVEALS MEANINGFUL CELLULAR STATES

Abstract AIMS High-grade glioma continues to have dismal survival with current standard-of-care treatment, owing in part to its intra- and inter-patient heterogeneity. Typical diagnostic biopsies are taken from the dense tumor core to determine the presence of abnormal cells and the status of a few key genes (e.g. IDH1, MGMT). However, the tumor core is typically resected, leaving behind possibly genetically, transcriptomically and/or phenotypically distinct invasive margins that repopulate the disease. As these remaining populations are the ones ultimately being treated, it is important to know their compositional differences from the tumor core. We aim to identify the phenotypic niches defined by the relative composition of key cellular populations and understand their variation amongst patients. METHOD We have established an image-localized research biopsy study, that samples from both the invasive margin and tumor core. From this protocol, we currently have 202 samples from 58 patients with available bulk RNA-Seq, collected between Mayo Clinic and Barrow Neurological Institute. Using a single-cell reference dataset from our collaborators at Columbia University, we used CIBERSORTx, a deconvolution method, to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell states for each sample. RESULTS We find that these cell state abundances connect to patient survival and show regional differences. For example, proneural glioma states were higher in invasive regions, whereas proliferative and mesenchymal states were higher in the tumor core. CONCLUSIONS Our analysis demonstrates a need to characterize the residual tissue following glioma resection to better understand the recurrent disease.

Single-cell analysis of graft-infiltrating host cells identifies caspase-1 as a potential therapeutic target for heart transplant rejection.

Understanding the cellular mechanisms underlying early allograft rejection is crucial for the development of effective immunosuppressant strategies. This study aims to investigate the cellular composition of graft-infiltrating cells during the early rejection stage at a single-cell level and identify potential therapeutic targets. A heterotopic heart transplant model was established using enhanced green fluorescent protein (eGFP)-expressing mice as recipients of allogeneic or syngeneic grafts. At 3 days post-transplant, eGFP-positive cells infiltrating the grafts were sorted and subjected to single-cell RNA-seq analysis. Potential molecular targets were evaluated by assessing graft survival and functions following administration of various pharmacological inhibitors. A total of 27,053 cells recovered from syngrafts and allografts were classified into 20 clusters based on expression profiles and annotated with a reference dataset. Innate immune cells, including monocytes, macrophages, neutrophils, and dendritic cells, constituted the major infiltrating cell types (>90%) in the grafts. Lymphocytes, fibroblasts, and endothelial cells represented a smaller population. Allografts exhibited significantly increased proportions of monocyte-derived cells involved in antigen processing and presentation, as well as activated lymphocytes, as compared to syngrafts. Differential expression analysis revealed upregulation of interferon activation-related genes in the innate immune cells infiltrating allografts. Pro-inflammatory polarization gene signatures were also enriched in these infiltrating cells of allografts. Gene profiling and intercellular communication analysis identified natural killer cells as the primary source of interferon-γ signaling, activating inflammatory monocytes that displayed strong signals of major histocompatibility complexes and co-stimulatory molecules. The inflammatory response was also associated with promoted T cell proliferation and activation in allografts during the early transplant stages. Notably, caspase-1 exhibited specific upregulation in inflammatory monocytes in response to interferon signaling. The regulon analysis also revealed a significant enrichment of interferon-related motifs within the transcriptional regulatory network of downstream inflammatory genes including caspase-1. Remarkably, pharmacological inhibition of caspase-1 was shown to reduce immune infiltration, prevent acute graft rejection, and improve cardiac contractile function. The single-cell transcriptional profile highlighted the crucial role of caspase-1 in interferon-mediated inflammatory monocytes infiltrating heart transplants, suggesting its potential as a therapeutic target for attenuating rejection.

Synergizing Digital, Biological, and Participatory Sciences for Global Plant Species Identification: Enabling access to a worldwide identification service

Human activities have a growing impact on global biodiversity. While our understanding of biodiversity worldwide is not yet comprehensive, it is crucial to explore effective means of characterizing it in order to mitigate these impacts. The advancements in data storage, exchange capabilities, and the increasing availability of extensive taxonomic, ecological, and environmental databases offer possibilities for implementing new approaches that can address knowledge gaps regarding species and habitats. This enhanced knowledge will, in turn, facilitate improved management practices and enable better local governance of territories. Meeting these requirements necessitates the development of innovative tools and methods to respond to these needs. Citizen science platforms have emerged as valuable resources for generating large amounts of biodiversity data, thanks to their visibility and attractiveness to individuals involved in territorial management and education. These platforms present new opportunities to train deep learning models for automated species recognition, leveraging the substantial volumes of multimedia data they accumulate. However, effectively managing, curating, and disseminating the data and services generated by these platforms remains a significant challenge that hinders the achievement of their objectives. In line with this, the GUARDEN and MAMBO European projects aim to utilize the Pl@ntNet participatory science platform (Affouard et al. 2021) to develop and implement novel computational services to enable the widespread creation of floristic inventories. In the pursuit of this project, various standards and reference datasets have been employed, such as the POWO (Plants of the World Online) world checklist and the WGSRPD (World Geographical Scheme for Recording Plant Distributions) standard, to establish a foundation for creating a global service that aids in plant identification through visual analysis. This service relies on a NoSQL (Not Only Structured Query Language) data management system ArangoDB (Arango Database), utilizes state-of-the-art automated visual classification models (vision transformers), and operates on a distributed IT (Information Technology) infrastructure that leverages the capabilities of collaborative stakeholders interested in supporting this initiative. Global-scale automated workflows have been established specifically for the collection, analysis, and dissemination of illustrated occurrences of plant species. These workflows now enable the development of new IT tools that facilitate the description and monitoring of species and habitat conservation statuses. A comprehensive presentation highlighting the significant advancements achieved will be provided to share the lessons learned during its development and ensure the widespread adoption of this service within the scientific community.

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.

Predicting micropollutant fate during wastewater treatment using refined classical kinetic model based on quantitative monitoring in multi-metropolitan regions of South Korea

This study aimed to implement an extensive prediction model for the fate of micropollutants (MPs) in wastewater treatment plants (WWTPs). Five WWTPs equipped with seven different biological treatment processes were monitored from 2020 to 2022 with three to four sampling events in each year, and 27 datasets for 20 MPs were collected. Among these datasets, 12 were used to investigate the behavior and fate of MPs in WWTPs in South Korea. Metformin, acetaminophen, caffeine, naproxen, and ibuprofen were the MPs with the highest influent concentrations (ranging from 3,933.3–187,637.0 ng L−1) at all WWTPs. More than 90% of MPs were removed by biological treatment processes in all WWTPs. The Kruskal-Wallis test verified that their efficacy did not differ statistically (p-value > 0.05). Meanwhile, to refine the performance of the prediction model, this study optimized the biodegradation rate constants (kbio) of each MP according to the variation of seasonal water temperature. As a result, compared to the original prediction model, the mean difference between the actual data and predicted results (MEAN) decreased by 6.77%, while the Nash-Sutcliffe efficiency (NSE) increased by 0.226. The final MEAN and NSE for the refined prediction model were calculated to be 5.09% and 0.964, respectively. The prediction model made accurate predictions, even for MPs exhibiting behaviors different from other cases, such as estriol and atrazine. Consequently, the optimization strategy proposed in this study was determined to be effective because the overall removal efficiencies of MPs were successfully predicted even with limited reference datasets.

Extending the Experimentally Accessible Range of Spin Dipole-Dipole Spectral Densities for Protein-Cosolute Interactions by Temperature-Dependent Solvent Paramagnetic Relaxation Enhancement Measurements.

Longitudinal (Γ1) and transverse (Γ2) solvent paramagnetic relaxation enhancement (sPRE) yields field-dependent information in the form of spectral densities that provides unique information related to cosolute-protein interactions and electrostatics. A typical protein sPRE data set can only sample a few points on the spectral density curve, J(ω), within a narrow frequency window (500 MHz to ∼1 GHz). However, complex interactions and dynamics of paramagnetic cosolutes around a protein make it difficult to directly interpret the few experimentally accessible points of J(ω). In this paper, we show that it is possible to significantly extend the experimentally accessible frequency range (corresponding to a range from ∼270 MHz to 1.8 GHz) by acquiring a series of sPRE experiments at different temperatures. This approach is based on the scaling property of J(ω) originally proposed by Melchior and Fries for small molecules. Here, we demonstrate that the same scaling property also holds for geometrically far more complex systems such as proteins. Using the extended spectral densities derived from the scaling property as the reference dataset, we demonstrate that our previous approach that makes use of a non-Lorentzian Ansatz spectral density function to fit only J(0) and one to two J(ω) points allows one to obtain accurate values for the concentration-normalized equilibrium average of the electron-proton interspin separation ⟨r-6⟩norm and the correlation time τC, which provide quantitative information on the energetics and timescale, respectively, of local cosolute-protein interactions. We also show that effective near-surface potentials, ϕENS, obtained from ⟨r-6⟩norm provide a reliable and quantitative measure of intermolecular interactions including electrostatics, while ϕENS values obtained from only Γ1 or Γ2 sPRE rates can have significant artifacts as a consequence of potential variations and changes in the diffusive properties of the cosolute around the protein surface. Finally, we discuss the experimental feasibility and limitations of extracting the high-frequency limit of J(ω) that is related to ⟨r-8⟩norm and report on the extremely local intermolecular potential.