A Benchmark Study of DFT-Computed p-Block Element Lewis Pair Formation Enthalpies against Experimental Calorimetric Data.

The quantification of Lewis acidity is of fundamental and applied importance in chemistry. While the computed fluoride ion affinity (FIA) is the most widely accepted thermodynamic metric, only sparse experimental values exist. Accordingly, a benchmark of methods for computing Lewis pair formation enthalpies, also with a broader set of Lewis bases against experimental data, is missing. Herein, we evaluate different density functionals against a set of 112 experimentally determined Lewis acid/base binding enthalpies and gauge influences such as solvation correction in structure optimization. From that, we can recommend r2SCAN-3c for robust quantification of this omnipresent interaction.

CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations.

Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database. Subsequently, we encoded the distinctive features of these mutations. Utilizing feature correlation analysis, we developed a cancer driver missense mutation predictor called CDMPred employing feature selection through the ensemble learning technique XGBoost. The proposed CDMPred method, utilizing the top 10 features and XGBoost, achieved an area under the receiver operating characteristic curve (AUC) value of 0.83 and 0.80 on the training and independent test sets, respectively. Furthermore, CDMPred demonstrated superior performance compared to existing state-of-the-art methods for cancer-specific and general diseases, as measured by AUC and area under the precision-recall curve. Including high-quality passenger mutations in the training data proves advantageous for CDMPred's prediction performance. We anticipate that CDMPred will be a valuable tool for predicting cancer driver mutations, furthering our understanding of personalized therapy.

Redox Activity and Potentials of Bidentate N-Ligands Commonly Applied in Nickel-Catalyzed Cross-Coupling Reactions.

Bidentate N-ligands are paramount to recent advances in nickel-catalyzed cross-coupling reactions. Through comprehensive organometallic, spectroscopic, and computational studies on bi-oxazoline and imidazoline ligands, we reveal that a square planar geometry enables redox activity of these ligands in stabilizing nickel radical species. This finding contrasts with the prior assumption that bi-oxazoline lacks redox activity due to strong mesomeric donation. Moreover, we conducted systematic cyclic voltammetry (CV) analyses of bidentate pyridyl, oxazoline, and imidazoline nitrogen ligands, along with their corresponding nickel complexes. Complexation with nickel shifts the reduction potentials to a more positive region and narrows the differences in redox potentials among the ligands. Additionally, various ligands led to different degrees of bromide dissociation from singly reduced (L)Ni(Ar)(Br) complexes, reflecting varying reactivity in the subsequent activation of alkyl halides, a crucial step in cross-electrophile coupling. These insights highlight the significant electronic effects of ligands on the stability of metalloradical species and their redox potentials, which interplay with coordination geometry. Quantifying the electron-donating, p-accepting properties of these ligands, as well as their effect on catalyst speciation, provides crucial benchmarks for controlling catalytic activity and enhancing catalyst stability.

Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.

BACKGROUND: Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. METHODS: In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell's C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. RESULTS: Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. CONCLUSIONS: Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure.

Radical Addition Reactions: Hierarchical Ab initio Benchmark and DFT Performance Study.

We performed a hierarchical ab initio benchmark study of the gas-phase radical addition reactions of X• + C2H2 and X• + C2H4 (X•= CH3•, NH2•, OH•, SH•). The hierarchical series of ab initio methods (HF, MP2, CCSD, CCSD(T)) were paired with a hierarchal series of Dunning basis sets with and without diffuse functions ((aug)-cc-pVDZ, (aug)-cc-pVTZ, (aug)-cc-pVQZ). The HF ground-state wavefunctions were transformed into quasi-restricted orbital (QRO) reference wavefunctions to address spin contamination. Following extrapolation to the CBS limit, the energies from our highest- QRO-CCSD(T)/CBS+ level converged within 0.0-3.4 kcal mol-1 and 0.0-1.0 kcal mol-1 concerning the ab initio method and basis set, respectively. Our QRO-CCSD(T)/CBS+ reference data was used to evaluate the performance of 98 density functional theory (DFT) approximations. The MAE of the best functionals for reaction barriers and energies were: OLYP (1.9 kcal mol-1), BMK (1.0 kcal mol-1), M06-2X (0.9 kcal mol-1), MN12-SX (0.8 kcal mol-1) and CAM-B3LYP (0.8 kcal mol-1). These functionals also accurately reproduce key geometrical parameters of the stationary points within an average 2% deviation from the reference QRO-CCSD(T)/CBS+ level.

Applying cell painting in non-tumorigenic breast cells to understand impacts of common chemical exposures.

The general population is exposed to many chemicals which have putative, but incompletely understood, links to breast cancer. Cell Painting is a high-content imaging-based in vitro assay that allows for unbiased measurements of concentration-dependent effects of chemical exposures on cellular morphology. We used Cell Painting to measure effects of 16 human exposure relevant chemicals, along with 21 small molecules with known mechanisms of action, in non-tumorigenic mammary epithelial cells, the MCF10A cell line. Using CellProfiler image analysis software, we quantified 3042 morphological features across approximately 1.2 million cells. We used benchmark concentration modeling to identify features both conserved and different across chemicals. Benchmark concentrations were compared to exposure biomarker concentration measurements from the National Health and Nutrition Examination Survey to assess which chemicals induce morphological alterations at human-relevant concentrations. We found significant feature overlaps between chemicals, including similarities between the organochlorine pesticide DDT metabolite p,p'-DDE and an activator of Wnt signaling CHIR99201. We validated these findings by assaying the activation of Wnt, as reflected by translocation of ꞵ-catenin, following p'-p' DDE exposure. Consistent with Wnt signaling activation, low concentration p',p'-DDE (25 nM) significantly enhanced the nuclear translocation of ꞵ-catenin. Overall, these findings highlight the ability of Cell Painting to enhance mode-of-action studies for toxicants which are common in our environment but incompletely characterized with respect to breast cancer risk.

In-silico heart model phantom to validate cardiac strain imaging.

The quantification of cardiac strains as structural indices of cardiac function has a growing prevalence in clinical diagnosis. However, the highly heterogeneous four-dimensional (4D) cardiac motion challenges accurate "regional" strain quantification and leads to sizable differences in the estimated strains depending on the imaging modality and post-processing algorithm, limiting the translational potential of strains as incremental biomarkers of cardiac dysfunction. There remains a crucial need for a feasible benchmark that successfully replicates complex 4D cardiac kinematics to determine the reliability of strain calculation algorithms. In this study, we propose an in-silico heart phantom derived from finite element (FE) simulations to validate the quantification of 4D regional strains. First, as a proof-of-concept exercise, we created synthetic magnetic resonance (MR) images for a hollow thick-walled cylinder under pure torsion with an exact solution and demonstrated that "ground-truth" values can be recovered for the twist angle, which is also a key kinematic index in the heart. Next, we used mouse-specific FE simulations of cardiac kinematics to synthesize dynamic MR images by sampling various sectional planes of the left ventricle (LV). Strains were calculated using our recently developed non-rigid image registration (NRIR) framework in both problems. Moreover, we studied the effects of image quality on distorting regional strain calculations by conducting in-silico experiments for various LV configurations. Our studies offer a rigorous and feasible tool to standardize regional strain calculations to improve their clinical impact as incremental biomarkers.

Time-efficient consultation hours in the department of gynecological endocrinology.

BACKGROUND: The aim was to conduct a benchmark pilot study to find the best practice for consultation hours in the field of gynecological endocrinology. Suitable benchmarking participants were found in China, Germany, Greece, and Switzerland. Specifically, the study aimed to find the most time-efficient and beneficial consultation type in gynecological endocrinology focused on menopause and whether a shorter face-to-face consultation correlates with lower patient satisfaction. METHODS: This was an observational study. To analyze the processes of all benchmarking participants three tools were used: a measurement of time needed for the different consultation types, a questionnaire for patients and one for physicians. The primary endpoint was the time measurement of first consultations. Secondary endpoints were the time measurements of follow-up consultations and phone consultations and patient satisfaction. RESULTS: The mean overall duration of a first consultation differed from 20.4 min to 39.7 min (p = 0.003), mainly based on differences of the mean time to acquire the patient history, 5.6 to 21.6 min (p < 0.001). The percentage of patients who felt they had enough time to discuss questions ranged from 70% to 100% (p < 0.001). The percentage of patients who felt fully understood by their physician ranged from 62.5% to 92% (p = 0.006). The duration of a first consultation did not correlate with patients feeling well consulted (r=-0.048, p = 0.557). CONCLUSIONS: A concise patient history that concentrates on the most relevant points can reduce the total consultation time. Reducing consultation time can be made without compromising how well patients feel consulted.

International publication activity in orthopaedic surgery over a ten-year interval.

PURPOSE: International English language publication activities in orthopaedic surgery comparing the years 2008/09 to 2018/19 were analyzed. METHODS: 20 international journals listed on PubMed were examined. The impact factor (IF) for each journal was determined using the InCites Journal Citation Report. RESULTS: 9,205 publications in 2008/09 and 15,549 in 2018/19 with 21,435 cumulative IF (CIF) in 2008/09 and 50,552 in 2018/19 were registered. Most publications consisted of narrative reviews (42.0%), followed by clinical studies (22.0%), experimental investigations (16.9%), randomized controlled trials (6.0%), and meta-analyses (5.6%). The highest increase in publications was observed for narrative reviews from 33.5% in 2008/09 to 41.1% in 2018/19. The USA had the highest number of publications (2,981 and 4,796), followed by UK (806 and 879) and Germany (606 and 922) in 2008/09 and 2018/19, resp. Per 1 Mio inhabitants, Switzerland (13.6 and 28.4), Sweden (10.9 and 18.1), the Netherlands (9.6 and 15.4), and Denmark (9.0 and 21.8) were the most productive countries in 2008/09 and 2018/19, resp. CONCLUSIONS: International publishing activity in orthopaedic surgery has increased substantially over the last 10 years. The quality of the published articles has not increased in the same way, as evidenced by the disproportionate rise in narrative reviews. Over the entire period, the US were the leader with respect to number of publications and CIF. In terms of population, however, smaller countries such as Switzerland and Sweden were much more active.

Comprehensive assessment of long-read sequencing platforms and calling algorithms for detection of copy number variation.

Copy number variations (CNVs) play pivotal roles in disease susceptibility and have been intensively investigated in human disease studies. Long-read sequencing technologies offer opportunities for comprehensive structural variation (SV) detection, and numerous methodologies have been developed recently. Consequently, there is a pressing need to assess these methods and aid researchers in selecting appropriate techniques for CNV detection using long-read sequencing. Hence, we conducted an evaluation of eight CNV calling methods across 22 datasets from nine publicly available samples and 15 simulated datasets, covering multiple sequencing platforms. The overall performance of CNV callers varied substantially and was influenced by the input dataset type, sequencing depth, and CNV type, among others. Specifically, the PacBio CCS sequencing platform outperformed PacBio CLR and Nanopore platforms regarding CNV detection recall rates. A sequencing depth of 10x demonstrated the capability to identify 85% of the CNVs detected in a 50x dataset. Moreover, deletions were more generally detectable than duplications. Among the eight benchmarked methods, cuteSV, Delly, pbsv, and Sniffles2 demonstrated superior accuracy, while SVIM exhibited high recall rates.

Temporal Trends in Acute Adrenal Insufficiency Events in Children With Congenital Adrenal Hyperplasia During 2019-2022.

Background: It is unclear whether targeted monitoring of acute adrenal insufficiency (AI) related adverse events (AE) such as sick day episodes (SDEs) and hospitalization rate in congenital adrenal hyperplasia (CAH) is associated with a change in the occurrence of these events. Aim: Study temporal trends of AI related AE in the I-CAH Registry. Methods: In 2022, data on the occurrence of AI-related AE in children aged <18 years with 21-hydroxylase deficiency CAH were compared to data collected in 2019. Results: In 2022, a total of 513 children from 38 centers in 21 countries with a median of 8 children (range 1-58) per center had 2470 visits evaluated over a 3-year period (2019-2022). The median SDE per patient year in 2022 was 0 (0-2.5) compared to 0.3 (0-6) in 2019 (P = .01). Despite adjustment for age, CAH phenotype and duration of study period, a difference in SDE rate was still apparent between the 2 cohorts. Of the 38 centers in the 2022 cohort, 21 had also participated in 2019 and a reduction in SDE rate was noted in 13 (62%), an increase was noted in 3 (14%), and in 5 (24%) the rate remained the same. Of the 474 SDEs reported in the 2022 cohort, 103 (22%) led to hospitalization compared to 299 of 1099 SDEs (27%) in the 2019 cohort (P = .02). Conclusion: The I-CAH Registry can be used for targeted monitoring of important clinical benchmarks in CAH. However, changes in reported benchmarks need careful interpretation and longer-term monitoring.

XAI-TRIS: non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance.

The field of 'explainable' artificial intelligence (XAI) has produced highly acclaimed methods that seek to make the decisions of complex machine learning (ML) methods 'understandable' to humans, for example by attributing 'importance' scores to input features. Yet, a lack of formal underpinning leaves it unclear as to what conclusions can safely be drawn from the results of a given XAI method and has also so far hindered the theoretical verification and empirical validation of XAI methods. This means that challenging non-linear problems, typically solved by deep neural networks, presently lack appropriate remedies. Here, we craft benchmark datasets for one linear and three different non-linear classification scenarios, in which the important class-conditional features are known by design, serving as ground truth explanations. Using novel quantitative metrics, we benchmark the explanation performance of a wide set of XAI methods across three deep learning model architectures. We show that popular XAI methods are often unable to significantly outperform random performance baselines and edge detection methods, attributing false-positive importance to features with no statistical relationship to the prediction target rather than truly important features. Moreover, we demonstrate that explanations derived from different model architectures can be vastly different; thus, prone to misinterpretation even under controlled conditions.

Extended benchmark set for lattice parameters of inorganic solids.

The development of novel methods in solid-state quantum chemistry necessitates reliable reference data sets for their assessment. The most fundamental solid-state property of interest is the crystal structure, quantified by the lattice parameters. In the last decade, several studies were conducted to assess theoretical approaches based on the agreement of calculated lattice parameters with respect to experiment as a measure. However, most of these studies used a limited number of reference systems with high symmetry. The present work offers a more comprehensive reference benchmark denoted as Sol337LC, which consists of 337 inorganic compounds with 553 symmetry-inequivalent lattice parameters, representing every element of the periodic table for atomic numbers between 1 and 86, except noble gases, the radioactive elements and lanthanoids. The reference values were taken from earlier benchmarks and from measurements at very low temperature or extrapolation to 0 K. The experimental low-temperature lattice parameters were then corrected for zero-point energy effects via the quasi-harmonic approximation for direct comparison with quantum-chemical optimized structures. A selection of standard density functional approximations was assessed for their deviations from the experimental reference data. The calculations were performed with the crystal orbital program CRYSTAL23, applying optimized atom-centered basis sets of triple-zeta plus polarization quality. The SCAN functional family and the global hybrid functional PW1PW, augmented with the D3 dispersion correction, were found to provide closest agreement with the Sol337LC reference data.

Evaluating MODIS cloud-free snow cover datasets using massive spatial benchmark data in the Tibetan Plateau.

Accurate snow cover data is crucial for understanding climate change, managing water resources, and calibrating models. The MODIS (Moderate-resolution Imaging Spectroradiometer) and its cloud-free snow cover datasets are widely used, but they have not been systematically evaluated due to different benchmark data and evaluation parameters. Conventional methods using station observations as a ground truth suffer from underrepresentation and mismatches in temporal and spatial scales. This study established a scale-matched spatial benchmark dataset, compiling from 18,433 Landsat series and 11,172 Sentinel-2 images over two decades, totaling ∼1.86 billion samples and ∼320 million snow samples. We evaluated seven MODIS cloud-free snow cover datasets for seasons, elevation zones, land covers and subregions using this benchmark data. For the clear-sky part, NIEER_MODIS_SCE (MODIS snow cover extent product over China) performs best due to its use of optimal NDSI thresholds suitable for each land use type. This highlights the importance of regional customization in snow mapping algorithms, and it can be further improved in spring, forests and zone 1 by combining it with M*D10A1GL06. For the cloud removed part, one-step integrated spatiotemporal cloud removal datasets perform better than any other approach does. The second-best dataset is obtained from a simple but effective single temporal cloud removal method using nearby time information. For the whole dataset, the best NIEER_MODIS_SCE has an overall accuracy of 0.82 and snow retrieval accuracy of 84.56 %. It performs excellently in most settings but weakest in forests thus requiring more efficient strategies. This research provides new perspectives and methods for objectively assessing MODIS snow cover products and other relevant datasets. These methods can be readily extended to other regions and adapted to future satellite missions. And such findings may guide the selection of more valid snow cover data and the developing of even better snow detecting strategies.

Enhanced power grid performance through Gorilla Troops Algorithm-guided thyristor controlled series capacitors allocation.

This article introduces an innovative application of the Enhanced Gorilla Troops Algorithm (EGTA) in addressing engineering challenges related to the allocation of Thyristor Controlled Series Capacitors (TCSC) in power grids. Drawing inspiration from gorilla group behaviors, EGTA incorporates various methods, such as relocation to new areas, movement towards other gorillas, migration to specific locations, following the silverback, and engaging in competitive interactions for adult females. Enhancements to EGTA involve support for the exploitation and the exploration, respectively, through two additional strategies of periodic Tangent Flight Operator (TFO), and Fitness-based Crossover Strategy (FCS). The paper initially evaluates the effectiveness of EGTA by comparing it to the original GTA using numerical CEC 2017 single-objective benchmarks. Additionally, various recent optimizers are scrutinized. Subsequently, the suitability of the proposed EGTA for the allocation of TCSC apparatuses in transmission power systems is assessed through simulations on two IEEE power grids of 30 and 57 buses, employing various TCSC apparatus quantities. A comprehensive comparison is conducted between EGTA, GTA, and several other prevalent techniques in the literature for all applications. According to the average attained losses, the presented EGTA displays notable reductions in power losses for both the first and second systems when compared to the original GTA. Specifically, for the first system, the proposed EGTA achieves reductions of 1.659 %, 2.545 %, and 4.6 % when optimizing one, two, and three TCSC apparatuses, respectively. Similarly, in the second system, the suggested EGTA achieves reductions of 6.096 %, 7.107 %, and 4.62 %, respectively, when compared to the original GTA's findings considering one, two, and three TCSC apparatuses. The findings underscore the superior effectiveness and efficiency of the proposed EGTA over both the original GTA and several other contemporary systems.

A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset.

Cell-type annotation is a critical step in single-cell data analysis. With the development of numerous cell annotation methods, it is necessary to evaluate these methods to help researchers use them effectively. Reference datasets are essential for evaluation, but currently, the cell labels of reference datasets mainly come from computational methods, which may have computational biases and may not reflect the actual cell-type outcomes. This study first constructed an experimentally labeled immune cell-subtype single-cell dataset of the same batch and systematically evaluated 18 cell annotation methods. We assessed those methods under five scenarios, including intra-dataset validation, immune cell-subtype validation, unsupervised clustering, inter-dataset annotation, and unknown cell-type prediction. Accuracy and ARI were evaluation metrics. The results showed that SVM, scBERT, and scDeepSort were the best-performing supervised methods. Seurat was the best-performing unsupervised clustering method, but it couldn't fully fit the actual cell-type distribution. Our results indicated that experimentally labeled immune cell-subtype datasets revealed the deficiencies of unsupervised clustering methods and provided new dataset support for supervised methods.

Impact of reference design on estimating SARS-CoV-2 lineage abundances from wastewater sequencing data.

BACKGROUND: Sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA from wastewater samples has emerged as a valuable tool for detecting the presence and relative abundances of SARS-CoV-2 variants in a community. By analyzing the viral genetic material present in wastewater, researchers and public health authorities can gain early insights into the spread of virus lineages and emerging mutations. Constructing reference datasets from known SARS-CoV-2 lineages and their mutation profiles has become state-of-the-art for assigning viral lineages and their relative abundances from wastewater sequencing data. However, selecting reference sequences or mutations directly affects the predictive power. RESULTS: Here, we show the impact of a mutation- and sequence-based reference reconstruction for SARS-CoV-2 abundance estimation. We benchmark 3 datasets: (i) synthetic "spike-in"' mixtures; (ii) German wastewater samples from early 2021, mainly comprising Alpha; and (iii) samples obtained from wastewater at an international airport in Germany from the end of 2021, including first signals of Omicron. The 2 approaches differ in sublineage detection, with the marker mutation-based method, in particular, being challenged by the increasing number of mutations and lineages. However, the estimations of both approaches depend on selecting representative references and optimized parameter settings. By performing parameter escalation experiments, we demonstrate the effects of reference size and alternative allele frequency cutoffs for abundance estimation. We show how different parameter settings can lead to different results for our test datasets and illustrate the effects of virus lineage composition of wastewater samples and references. CONCLUSIONS: Our study highlights current computational challenges, focusing on the general reference design, which directly impacts abundance allocations. We illustrate advantages and disadvantages that may be relevant for further developments in the wastewater community and in the context of defining robust quality metrics.

Benchmarking Giant Panda Welfare in Tourism: A Co-Design Approach for Animals, Tourists, Managers, and Researchers.

This study introduces a co-design benchmarking framework to understand tourists' perceptions of animal welfare, integrating diverse perspectives from tourists, researchers, and animals. By leveraging scientific theories to establish benchmark dimensions, the framework is refined through visitor input, ensuring a robust and adaptable methodological tool for assessing tourists' perceptions and animal informed consent in wildlife tourism. Using the Chengdu Research Base of Giant Panda Breeding as an example, we analyzed 4839 visitor comments collected from March to August 2023 to benchmark perceptions of giant panda welfare. This approach underscores the importance of effective communication in educational initiatives, aiming to enhance public literacy and knowledge about animal welfare. By addressing the complexity and variability in tourists' perceptions, the proposed framework contributes to more impactful conservation education efforts. The study demonstrates that a collaborative effort results in a benchmarking framework that is firmly grounded in theoretical foundations yet flexible enough to adapt based on visitors' insights and animal participation. Ultimately, this comprehensive approach ensures that educational initiatives resonate with tourists' diverse backgrounds, fostering a deeper understanding and commitment to animal welfare and conservation, which, we argue, should be key components of sustainable tourism.

Benchmarking computational methods for single-cell chromatin data analysis.

BACKGROUND: Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices. RESULTS: We benchmark 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluate the performance of each method at different data processing stages. This comprehensive approach allows us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection. CONCLUSIONS: Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.

Mono-n-hexyl phthalate: exposure estimation and assessment of health risks based on levels found in human urine samples.

Mono-n-hexyl phthalate (MnHexP) is a primary metabolite of di-n-hexyl phthalate (DnHexP) and other mixed side-chain phthalates that was recently detected in urine samples from adults and children in Germany. DnHexP is classified as toxic for reproduction category 1B in Annex VI of Regulation (EC) 1272/2008 and listed in Annex XIV of the European chemical legislation REACH; thereby, its use requires an authorisation. Health-based guidance values for DnHexP are lacking and a full-scale risk assessment has not been carried out under REACH. The detection of MnHexP in urine samples raises questions about the sources of exposure and concerns of consumer safety. Here, we propose the calculation of a provisional oral tolerable daily intake value (TDI) of 63 µg/kg body weight/day for DnHexP and compare it to intake levels corresponding to levels of MnHexP found in urine. The resulting mean intake levels correspond to less than 0.2% of the TDI, and maximum levels to less than 5%. The TDI was derived by means of an approximate probabilistic analysis using the credible interval from benchmark dose modelling of published ex vivo data on reduced foetal testosterone production in rats. Thus, for the dose associated to a 20% reduction in testosterone production, a lower and upper credible interval of 14.9 and 30.0 mg/kg bw/day, respectively, was used. This is considered a conservative approach, since apical developmental endpoints (e.g. changed anogenital distance) were only observed at higher doses. In addition, we modelled various scenarios of the exposure to the precursor substance DnHexP from different consumer products, taking measured contamination levels into account, and estimated systemic exposure doses. Of the modelled scenarios including the application of sunscreen (as a lotion or pump spray), the use of lip balm, and the wearing of plastic sandals, and considering conservative assumptions, the use of DnHexP-contaminated sunscreen was highlighted as a major contributing factor. A hypothetical calculation using conservative assumptions for the latter resulted in a margin of safety in relation to the lower credible interval of 3267 and 1007 for adults and young children, respectively. Most importantly, it was found that only a fraction of the TDI is reached in all studied exposure scenarios. Thus, with regard to the reported DnHexP exposure, a health risk can be considered very unlikely.