International Benchmark for Total Metabolic Tumor Volume Measurement in Baseline 18F-FDG PET/CT of Lymphoma Patients: A Milestone Toward Clinical Implementation.

Total metabolic tumor volume (TMTV) is prognostic in lymphoma. However, cutoff values for risk stratification vary markedly, according to the tumor delineation method used. We aimed to create a standardized TMTV benchmark dataset allowing TMTV to be tested and applied as a reproducible biomarker. Methods: Sixty baseline 18F-FDG PET/CT scans were identified with a range of disease distributions (20 follicular, 20 Hodgkin, and 20 diffuse large B-cell lymphoma). TMTV was measured by 12 nuclear medicine experts, each analyzing 20 cases split across subtypes, with each case processed by 3-4 readers. LIFEx or ACCURATE software was chosen according to reader preference. Analysis was performed stepwise: TMTV1 with automated preselection of lesions using an SUV of at least 4 and a volume of at least 3 cm3 with single-click removal of physiologic uptake; TMTV2 with additional removal of reactive bone marrow and spleen with single clicks; TMTV3 with manual editing to remove other physiologic uptake, if required; and TMTV4 with optional addition of lesions using mouse clicks with an SUV of at least 4 (no volume threshold). Results: The final TMTV (TMTV4) ranged from 8 to 2,288 cm3, showing excellent agreement among all readers in 87% of cases (52/60) with a difference of less than 10% or less than 10 cm3 In 70% of the cases, TMTV4 equaled TMTV1, requiring no additional reader interaction. Differences in the TMTV4 were exclusively related to reader interpretation of lesion inclusion or physiologic high-uptake region removal, not to the choice of software. For 5 cases, large TMTV differences (>25%) were due to disagreement about inclusion of diffuse splenic uptake. Conclusion: The proposed segmentation method enabled highly reproducible TMTV measurements, with minimal reader interaction in 70% of the patients. The inclusion or exclusion of diffuse splenic uptake requires definition of specific criteria according to lymphoma subtype. The publicly available proposed benchmark allows comparison of study results and could serve as a reference to test improvements using other segmentation approaches.

Comparison of sensitivity between blood parameters and a genotoxic biomarker at low blood Pb levels: A population-based study.

BACKGROUND: Previous studies reported that lead (Pb) exposure induced adverse health effects at high exposure concentrations, however, there have been limited data on sensitivity comparisons among different health outcomes at low blood Pb levels. OBJECTIVES: To compare sensitivity between blood parameters and a genotoxic biomarker among workers exposed to low blood Pb levels (< 20 µg/dl), and to estimate a benchmark dose (BMD). METHODS: Pb-exposed workers were recruited from a lead-acid storage battery plant. Their blood lead levels (BLLs) were measured. Blood parameters and micronuclei (MN) frequencies were determined. Multivariate linear or Poisson regression was used to analyze relationships between blood parameters or MN frequencies with BLLs. Two BMD software were used to calculate BMD and its 95 % lower confidence limit (BMDL) for BLLs. RESULTS: The median BLL for 611 workers was 10.44 µg/dl with the 25th and 75th percentile being 7.37 and 14.62 µg/dl among all participants. There were significantly negative correlations between blood parameters and BLLs. However, MN frequencies correlated positively with BLLs (all P<0.05). Results from the two BMD software revealed that the dichotomous model was superior to the continuous model, and the BMDL for BLL derived from red blood cell (RBC) was 15.11 µg/dl, from hemoglobin (HGB) was 8.50 µg/dl, from mean corpuscular hemoglobin (MCH) was 7.87 µg/dl, from mean corpuscular hemoglobin concentration (MCHC) was 3.98 µg/dl, from mean corpuscular volume (MCV) was 11.44 µg/dl, and from hematocrit (HCT) was 6.65 µg/dl. The conservative BMDL obtained from the MN data was 7.52 µg/dl. CONCLUSION: Our study shows that low dose Pb exposure caused decrease of blood parameters and increase of MN frequencies. The genotoxic biomarker was more sensitive than most blood parameters. BMDLs for BLL derived from MN frequencies and the red blood cell indicators should be considered as new occupational exposure limits. Our results suggest that MN assay can be considered as a part of occupational health examination items.

PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.

Protein language models (PLMs) play a dominant role in protein representation learning. Most existing PLMs regard proteins as sequences of 20 natural amino acids. The problem with this representation method is that it simply divides the protein sequence into sequences of individual amino acids, ignoring the fact that certain residues often occur together. Therefore, it is inappropriate to view amino acids as isolated tokens. Instead, the PLMs should recognize the frequently occurring combinations of amino acids as a single token. In this study, we use the byte-pair-encoding algorithm and unigram to construct advanced residue vocabularies for protein sequence tokenization, and we have shown that PLMs pre-trained using these advanced vocabularies exhibit superior performance on downstream tasks when compared to those trained with simple vocabularies. Furthermore, we introduce PETA, a comprehensive benchmark for systematically evaluating PLMs. We find that vocabularies comprising 50 and 200 elements achieve optimal performance. Our code, model weights, and datasets are available at https://github.com/ginnm/ProteinPretraining . SCIENTIFIC CONTRIBUTION: This study introduces advanced protein sequence tokenization analysis, leveraging the byte-pair-encoding algorithm and unigram. By recognizing frequently occurring combinations of amino acids as single tokens, our proposed method enhances the performance of PLMs on downstream tasks. Additionally, we present PETA, a new comprehensive benchmark for the systematic evaluation of PLMs, demonstrating that vocabularies of 50 and 200 elements offer optimal performance.

Unified control of diverse actions in a wastewater treatment activated sludge system using reinforcement learning for multi-objective optimization.

The operation of modern wastewater treatment facilities is a balancing act in which a multitude of variables are controlled to achieve a wide range of objectives, many of which are conflicting. This is especially true within secondary activated sludge systems, where significant research and industry effort has been devoted to advance control optimization strategies, both domain-driven and data-driven. Among data-driven control strategies, reinforcement learning (RL) stands out for its ability to achieve better than human performance in complex environments. While RL has been applied to activated sludge process optimization in existing literature, these applications are typically limited in scope, and never for the control of more than three actions. Expanding the scope of RL control has the potential to increase the optimization potential while concurrently reducing the number of control systems that must be tuned and maintained by operations staff. This study examined several facets of the implementation of multi-action, multi-objective RL agents, namely how many actions a single agent could successfully control and what extent of environment data was necessary to train such agents. This study observed improved control optimization with increasing action scope, though control of waste activated sludge remains a challenge. Furthermore, agents were able to maintain a high level of performance under decreased observation scope, up to a point. When compared to baseline control of the Benchmark Simulation Model No. 1 (BSM1), an RL agent controlling seven individual actions improved the average BSM1 performance metric by 8.3 %, equivalent to an annual cost savings of $40,200 after accounting for the cost of additional sensors.

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.

Proceedings of the EuBIC-MS developers meeting 2023.

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

Approaches to Backbone Flexibility in Protein-Protein Docking.

Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.

Assessing the genotoxicity of N-nitrosodiethylamine with three in vivo endpoints in male Big Blue® transgenic and wild-type C57BL/6N mice.

The detection of N-nitrosamines in drug products has raised global regulatory interest in recent years due to the carcinogenic potential of some nitrosamines in animals and a need to identify a testing strategy has emerged. Ideally, methods used would allow for the use of quantitative analysis of dose-response data from in vivo genotoxicity assays to determine a compound-specific acceptable intake for novel nitrosamines without sufficient carcinogenicity data. In a previous study we compared the dose-response relationships of N-nitrosodiethylamine (NDEA) in three in vivo genotoxicity endpoints in rats. Here we report a comparison of NDEA's genotoxicity profile in mice. Big Blue® mice were administered NDEA at doses of 0.001, 0.01, 0.1, 1 and 3 mg/kg/day by oral gavage for 28 days followed by 3 days of expression. Statistically significant increases in the NDEA induced mutations were detected by both the transgenic rodent mutation assay (TGR) using the cII endpoint and by duplex sequencing in the liver but not bone marrow of mice. In addition, administration of NDEA for two consecutive days in male C57BL/6N mice caused elevated DNA damage levels in the liver as measured by % tail DNA in comet assay. The benchmark dose (BMD) analysis shows a BMDL50 of 0.03, 0.04 and 0.72 mg/kg/day for TGR, duplex sequencing and comet endpoints, respectively. Overall, this study demonstrated a similar genotoxicity profile of NDEA between mice and rats and provides a reference that can be used to compare the potential potency of other novel nitrosamines for the induction of gene mutations.

Comparative analysis of integrative classification methods for multi-omics data.

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.

Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications.

Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel Many-Objective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.

Hepatotoxicity of oral exposure to 2-methyl-4-nitroaniline: toxicity prediction and in vivo evaluation.

2-Methyl-4-nitroaniline (MNA), an intermediate in the synthesis of azo dyes, is widely distributed in various environmental media and organisms. Although there is speculation regarding MNA's potential to be hepatotoxic, the underlying mechanisms of its hepatotoxicity and its definitive diagnostic process remain largely unexplored. In this research. In the present study, we initially predicted the toxicity and possible toxic effect pathways of MNA using ProTox-II, and found that MNA binds to the PPARγ receptor (binding energy －6.118 kcal/mol) with a potential PPARγ agonist effect. Subsequently, in vivo exposure evaluation was conducted on Wistar rats to assess the impact of MNA after a 90-day exposure period, by detecting serum biochemical indexes, hematological indexes, urinary indexes, inflammatory factors, liver histopathological observations and liver tissue PPARγ mRNA expression. The results showed that MNA causes liver function abnormalities, liver histopathological changes and inflammatory response, along with a pronounced increase in PPARγ mRNA levels. This study suggests that the hepatotoxic mechanism of MNA may be related to its possible upregulation of PPARγ expression, increased liver dysfunction and inflammatory responses. Based on these results, the benchmark dose lower limit (BMDL) of 1.503 mg/kg for male Wistar rats was also established, providing a vital benchmark for determining the safety threshold of MNA. Our data highlight the hepatotoxic mechanism of MNA and contribute to a better understanding of its potential etiological diagnosis.

Data Set and Benchmark (MedGPTEval) to Evaluate Responses From Large Language Models in Medicine: Evaluation Development and Validation.

Background: Large language models (LLMs) have achieved great progress in natural language processing tasks and demonstrated the potential for use in clinical applications. Despite their capabilities, LLMs in the medical domain are prone to generating hallucinations (not fully reliable responses). Hallucinations in LLMs' responses create substantial risks, potentially threatening patients' physical safety. Thus, to perceive and prevent this safety risk, it is essential to evaluate LLMs in the medical domain and build a systematic evaluation. Objective: We developed a comprehensive evaluation system, MedGPTEval, composed of criteria, medical data sets in Chinese, and publicly available benchmarks. Methods: First, a set of evaluation criteria was designed based on a comprehensive literature review. Second, existing candidate criteria were optimized by using a Delphi method with 5 experts in medicine and engineering. Third, 3 clinical experts designed medical data sets to interact with LLMs. Finally, benchmarking experiments were conducted on the data sets. The responses generated by chatbots based on LLMs were recorded for blind evaluations by 5 licensed medical experts. The evaluation criteria that were obtained covered medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with 16 detailed indicators. The medical data sets include 27 medical dialogues and 7 case reports in Chinese. Three chatbots were evaluated: ChatGPT by OpenAI; ERNIE Bot by Baidu, Inc; and Doctor PuJiang (Dr PJ) by Shanghai Artificial Intelligence Laboratory. Results: Dr PJ outperformed ChatGPT and ERNIE Bot in the multiple-turn medical dialogues and case report scenarios. Dr PJ also outperformed ChatGPT in the semantic consistency rate and complete error rate category, indicating better robustness. However, Dr PJ had slightly lower scores in medical professional capabilities compared with ChatGPT in the multiple-turn dialogue scenario. Conclusions: MedGPTEval provides comprehensive criteria to evaluate chatbots by LLMs in the medical domain, open-source data sets, and benchmarks assessing 3 LLMs. Experimental results demonstrate that Dr PJ outperforms ChatGPT and ERNIE Bot in social and professional contexts. Therefore, such an assessment system can be easily adopted by researchers in this community to augment an open-source data set.

Subchronic oral toxic effects of 2,4-dinitroaniline in wistar rats: A comprehensive toxicity evaluation.

2,4-dinitroaniline (2,4-D), a widely used dye intermediate, is one of the typical pollutants, and its potential health risks and toxicity are still largely unknown. To explore its subchronic oral toxicity, Wistar rats (equal numbers of males and females) were used as test animals, and a 90-day oral dosing experiment was conducted, divided into control group, low-dose group (0.055 mg/kg), medium-dose group (0.22 mg/kg), medium-high dose group (0.89 mg/kg), and high-dose group (3.56 mg/kg). The body weight data, clinical appearance, and drug reactions of each test rat within 90 days of dosing were recorded; morning urine samples were collected four times to test for eight urinary indicators; blood samples were collected to test for nineteen hematological indicators and sixteen biochemical indicators; tissue samples were collected for pathological analysis; moreover, the no-observed-adverse-effect level (NOAEL) was determined, and the benchmark dose method was used to support this determination and provide a statistical estimate of the dose corresponding. The results indicated that the chronic toxicity of 2,4-dinitroaniline showed certain gender differences, with the eyes, liver, and kidneys being the main potential target organs of toxicity. Moreover, the subchronic oral NOAEL for 2,4-dinitroaniline was determined to be 0.22 mg/kg body weight (0.22 mg/kg for males and 0.89 mg/kg for females), and a preliminary calculation of the safe exposure limit for human was 0.136 mg/kg. The research results greatly enriched the safety evaluation data of 2,4-dinitroaniline, contributing to a robust scientific foundation for the development of informed safety regulations and public health precautions.

Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods.

BACKGROUND: Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. RESULTS: In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. CONCLUSIONS: Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.

A gold standard dataset and evaluation of methods for lineage abundance estimation from wastewater.

During the SARS-CoV-2 pandemic, genome-based wastewater surveillance sequencing has been a powerful tool for public health to monitor circulating and emerging viral variants. As a medium, wastewater is very complex because of its mixed matrix nature, which makes the deconvolution of wastewater samples more difficult. Here we introduce a gold standard dataset constructed from synthetic viral control mixtures of known composition, spiked into a wastewater RNA matrix and sequenced on the Oxford Nanopore Technologies platform. We compare the performance of eight of the most commonly used deconvolution tools in identifying SARS-CoV-2 variants present in these mixtures. The software evaluated was primarily chosen for its relevance to the CDC wastewater surveillance reporting protocol, which until recently employed a pipeline that incorporates results from four deconvolution methods: Freyja, kallisto, Kraken 2/Bracken, and LCS. We also tested Lollipop, a deconvolution method used by the Swiss SARS-CoV-2 Sequencing Consortium, and three additional methods not used in the C-WAP pipeline: lineagespot, Alcov, and VaQuERo. We found that the commonly used software Freyja outperformed the other CDC pipeline tools in correct identification of lineages present in the control mixtures, and that the VaQuERo method was similarly accurate, with minor differences in the ability of the two methods to avoid false negatives and suppress false positives. Our results also provide insight into the effect of the tiling primer scheme and wastewater RNA extract matrix on viral sequencing and data deconvolution outcomes.

Testosterone Mediates Reproductive Toxicity in Caenorhabditis elegans by Affecting Sex Determination in Germ Cells through nhr-69/mpk-1/fog-1/3.

Testosterone (T), an environmental androgen, significantly disrupts endocrine systems in wildlife and ecosystems. Despite growing concern over its high levels in aquatic environments, the reproductive toxicity of testosterone and its mechanisms are not well understood. In this study, we investigated the reproductive toxicity and mechanisms of testosterone using Caenorhabditis elegans (C. elegans) and assessed its ecological toxicity through the benchmark dose (BMD) method. Our results indicate that T concentrations exceeding 0.01 µg/L significantly reduce the brood size, decrease germ cell counts, and prolong the generation time in C. elegans as T concentrations increase. Furthermore, to elucidate the specific mechanisms, we analyzed the expression of nhr-69, mpk-1, and other genes involved in sex determination. These findings suggest that the nhr-69-mediated reproductive toxicity of T primarily affects sperm formation and the offspring number by influencing its downstream targets, mpk-1 and fog-1/3, which are critical in the germ cell sex-determining pathway. Additionally, this study determined that the 10% lower boundary of the baseline dose (BMDL10) is 1.160 ng/L, offering a more protective reference dose for the ecological risk assessment of T. The present study suggests that nhr-69 mediates the reproductive toxicity of T by influencing mpk-1 and fog-1/3, critical genes at the end of the germ cell sex-determining pathway, thereby providing a basis for establishing reproductive toxicity thresholds for T.

Using Zebrafish to Screen Developmental Toxicity of Per- and Polyfluoroalkyl Substances (PFAS).

Per- and polyfluoroalkyl substances (PFAS) are found in many consumer and industrial products. While some PFAS, notably perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), are developmentally toxic in mammals, the vast majority of PFAS have not been evaluated for developmental toxicity potential. A concentration-response study of 182 unique PFAS chemicals using the zebrafish medium-throughput, developmental vertebrate toxicity assay was conducted to investigate chemical structural identifiers for toxicity. Embryos were exposed to each PFAS compound (≤100 µM) beginning on the day of fertilization. At 6 days post-fertilization (dpf), two independent observers graded developmental landmarks for each larva (e.g., mortality, hatching, swim bladder inflation, edema, abnormal spine/tail, or craniofacial structure). Thirty percent of the PFAS were developmentally toxic, but there was no enrichment of any OECD structural category. PFOS was developmentally toxic (benchmark concentration [BMC] = 7.48 µM); however, other chemicals were more potent: perfluorooctanesulfonamide (PFOSA), N-methylperfluorooctane sulfonamide (N-MeFOSA), ((perfluorooctyl)ethyl)phosphonic acid, perfluoro-3,6,9-trioxatridecanoic acid, and perfluorohexane sulfonamide. The developmental toxicity profile for these more potent PFAS is largely unexplored in mammals and other species. Based on these zebrafish developmental toxicity results, additional screening may be warranted to understand the toxicity profile of these chemicals in other species.

Optimizing the Steering of Driverless Personal Mobility Pods with a Novel Differential Harris Hawks Optimization Algorithm (DHHO) and Encoder Modeling.

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods.

N-Nitrosamine Impurity Risk Assessment in Pharmaceuticals: Utilizing In Vivo Mutation Relative Potency Comparison to Establish an Acceptable Intake for NTTP.

The finding of N-nitrosodiethylamine (NDEA) and N-nitrosodimethylamine (NDMA) in marketed drugs has led to implementation of risk assessment processes intended to limit exposures to the entire class of N-nitrosamines. A critical component of the risk assessment process is establishing exposure limits that are protective of human health. One approach to establishing exposure limits for novel N-nitrosamines is to conduct an in vivo transgenic rodent (TGR) mutation study. Existing regulatory guidance on N-nitrosamines provides decision making criteria based on interpreting in vivo TGR mutation studies as an overall positive or negative. However, point of departure metrics, such as benchmark dose (BMD), can be used to define potency and provide an opportunity to establish relevant exposure limits. This can be achieved through relative potency comparison of novel N-nitrosamines with model N-nitrosamines possessing robust in vivo mutagenicity and carcinogenicity data. The current work adds to the dataset of model N-nitrosamines by providing in vivo TGR mutation data for N-nitrosopiperidine (NPIP). In vivo TGR mutation data was also generated for a novel N-nitrosamine impurity identified in sitagliptin-containing products, 7-nitroso-3-(trifluoromethyl)-5,6,7,8-tetrahydro-[1,2,4]triazolo-[4,3-a]pyrazine (NTTP). Using the relative potency comparison approach, we have demonstrated the safety of NTTP exposures at or above levels of 1500 ng/day.