dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Foremost, we deeply appreciate the permission to work on the Povegliano Veronese skeletal collection of Soprintendenza Archeologia belle arti e paesaggio per le province di Verona,  Rovigo e Vicenza. We want to thank  Dr. Rossella Bedini  and  Ing. Raffaella Pecci  of the  Istituto Superiore di Sanità in Rome (IT ) for acquiring the Micro CT scans of the vertebra. We thank  Dr. Tania Ruspandini  of the  Department of Scienze della Terra, Sapienza, University of Rome (IT ) and  Dr. Marco Albano  at  CNR in Rome (IT ) for SEM investigation. We thank  Ryan Mills  at the  University of North Carolina  for help with the Sr ratio analysis. Ultimately, we would like to thank  Dr. Matteo Mussoni  of the  Centro Veterinaio Valmarecchia, Rimini , for the X-ray acquisition. This work is part of the doctoral thesis in Environmental and Evolutionary Biology that the first author is carrying out at the  Sapienza University of Rome (Italy).
42%  [39]. It states that the most reliable performance for emotion recognition is obtained using the three frequency bands which are delta, alpha, and gamma. To classify human emotions using EEG signals, researchers often use musical videos or short clips to elicit different emotions in participants. Participants may report their emotions in various ways, and the duration of each clip can range from a few seconds to six minutes. Despite the increasing interest in studying the effects of emotions on brain signals, only a limited number of databases are available. Most of the data sets aim to detect emotions on three main scales: Valence, Arousal, and Dominance. The Arousal axis ranges from inactive or uninterested to active and excited, while the Valence axis ranges from sad or stressed to happy.
For academics and practitioners looking for quick and accurate assessments of emotional responses, its simplicity and use make it an appealing option. However, the main issue with the currently available databases that use SAM is that participants are asked to express their feelings with a single value for the entire duration of the video or trial. In reality, it is more likely that a participant's feelings may change throughout the experiment period, and a single value may not capture the full range of emotions experienced. In this study, we have utilized the widely used DEAP database  [44] , which includes recordings of 32 participants aged 19 to 37 years engaging in various emotional processing tasks. Males and females are split equally among the participants. Each participant was shown 40 stimulus videos, each with a duration of 60 s.
Physiological signals including EEG were recorded from 32 electrodes positioned using the international 10-20 method with the 512 Hz sampling frequency rate, which was later down-sampled to 128 Hz. In addition, the database includes supplementary data such as facial expressions, eye movements, mouth shape, galvanic skin response (GSR), and body temperature obtained from the left pinky finger  [45]. However, it is important to note that none of the aforementioned supplementary data were taken into consideration in the current research. Using SAM methodology, each participant is evaluating each watched video based on his feelings by one single value between 1 and 9. Numerous studies on emotion recognition using physiological signals have made use of the DEAP database. The database has been utilized by researchers to create and assess algorithms for automatic emotion recognition as well as to explore the connections between physiological markers and emotional reactions.
Researchers in the disciplines of affective computing, human-computer interaction, and psychology can benefit from the database because it is freely accessible and can be downloaded from the official website. The optimization of the EEG channel selection for emotion recognition can be classified into two broad categories  [46] : (i) prior knowledge-based approach with repeated experimentation, and (ii) data-driven approaches. Only a few studies exist using the latter approach while adopting the DEAP data set, which ensures repeatability and crossverification. One approach proposed by Liu et al. [47]  calculates the mutual information between each channel and the target emotion and then uses a genetic algorithm to select the optimal subset of channels. This approach achieved a higher classification accuracy than using all channels or a random subset of channels.
Patel and Patel  [48]  used a combination of mutual information, correlation coefficient, and principal component analysis to rank the channels, and then used a genetic algorithm to select the best subset of channels. They also achieved higher accuracy than using all channels or a random subset of channels. Zhou et al. [49]  demonstrated the use of a feature selection algorithm based on binary particle swarm optimization to optimize EEG channel selection for emotion recognition with the DEAP dataset. Their approach achieved higher accuracy than using all channels or a random subset of channels when evaluated with three different classifiers. Wang et al. [50]  proposed a multi-objective genetic algorithm to optimize EEG channel selection for emotion recognition with the DEAP dataset. Their approach successfully balanced the number of selected channels with classification accuracy using their method.
Guo et al. [51]  presented an enhanced binary particle swarm optimization algorithm to optimize the EEG channel selection for emotion recognition with the DEAP dataset, which achieved higher accuracy and selected fewer channels than other feature selection algorithms. To the best of the authors' knowledge, the work presented in this article is the first demonstration of optimum EEG channel selection for emotion recognition (with the DEAP data set), using frequency-dependent EEG topographic maps to construct an objective function, which is maximized using the simplicial homology global optimization method. The primary contribution of this work is the derivation and subsequent implementation of an objective function, which captures the difference in the distribution of the EEG brain activity of participants, who are exposed to different types of emotional stimulus.
Subsequently, the SHGO method utilizes this objective function in determining which of the 14 electrodes offer the most influence on recognizing an emotion. The optimization process was carried out using Python language by using the "shgo" function from the optimization module of the SciPy library, which was used to implement the technique in Python. The SHGO was utilized for optimization to select the optimal combination from 14 electrodes for emotion recognition and the optimal frequency band for the same objective. The convolutional neural network (CNN) was employed for classification using the topographies generated by the optimal electrodes in the optimal frequency band as selected in the optimization stage. The CNN classification was assessed on both 14 and 6 electrodes. Importantly, the emotion classification employed the generated frequency-dependant topographical data but did not utilize the SHGO method.
Interpolation is used to estimate brain activity at locations without electrodes, creating a more complete approximation of the estimated activity map, and the projection techniques provide partial visual representations of brainwave activity of the brain areas more involved and affected by human emotions. This technique has provided valuable insights into the neural mechanisms underlying behavior and cognition, making it an essential tool for researchers in the field of neuroscience. The EEG 2-dimensional topography contains spatial information and is thus quite useful for the task of optimizing electrode placement on the scalp. Consequently, they are the input to an objective function that maximizes the ability to discriminate between different emotional states. EEG signal data collected from the scalp electrodes is processed by computer algorithms to create EEG topographic maps.
Objective Function The objective function, which stands for the desired outcome or aims in optimization issues, is essential. It specifies the optimization criteria, such as maximizing or minimizing a particular value, and directs the optimization procedure. It is hard to judge whether the optimization process has been successful or not without a clearly defined objective function. The particular issue being treated and the desired result should be taken into consideration while choosing the objective function. In order to be effectively optimized using the existing techniques, the objective function should also be computationally effective and well-behaved. The measured topographies obtained from the DEAP data set are stored as images with the RGB (red, green, and blue) system representing the colors used on a digital display screen.
Simplicial Homology Global Optimization The SHGO algorithm is a powerful and versatile optimization technique that has demonstrated its effectiveness in a wide range of optimization problems  [63] [64] [65] [66]. One of the key benefits of SHGO is its adaptability to changing search spaces, making it ideal for dynamic optimization problems  [65]. Additionally, its level of self-learning enables it to improve its performance over time, while maintaining remarkable scalability in terms of both the number of dimensions and the number of candidate solutions explored. For optimization problems in nonlinear systems implemented using the Scipy Library in Python, the SHGO algorithm has been selected as the primary optimization method for the channel and frequency range optimization problems  [64].
CNNs are a deep learning technique that has recently been used for emotion recognition and is designed to process high-dimensional data such as images, audio, and video. By effectively recognizing local patterns, CNNs provide the best-suited methodology to analyze the RGB-encoded topographies in our study. CNNs take advantage of 2-D or 3-D convolutional layers to capture local patterns and use pooling layers to reduce the resolution or to learn features across regions. Since the main input here is the topography of the brain heat map, CNN is chosen as the type of network to be used with the input image representing the brain activity. The data available in the DEAP database were classified according to the threshold. Videos reported with levels three and below are considered low, and the other videos that reported levels seven and above are classified as high.
The first four blocks consist of a convolutional layer followed by a max pooling and activation layer. Finally, the output of the four blocks is flattened and passed through two dense layers to classify the result in the output layer, whether high or low. The full CNN structure is shown in Table  2. In the current study, a neural network model was developed using a dataset comprising 9374 topographic EEG images between high and low arousal levels of SAM reported values. To ensure a robust evaluation of the model's performance, the dataset was divided into training and validation sets. The training set accounted for 80% of the data, consisting of 7499 images, while the validation set comprised the remaining 20%, containing 1875 images. This partitioning allowed the model to learn from a substantial amount of data while also providing an independent subset for evaluating its generalization capabilities and tuning the hyper-parameters.
Both detections are based on fixed climatology baselines over the same period. The purpose of detrending is to separate the effect of continuous warming from abrupt variations. The analyses are carried out by using the Copernicus Marine Service Mediterranean multi-year SST dataset covering the period 1982-2021 and extended to 2022 with the near real-time dataset. The impact of the trend is evidenced in MHWs by analyzing their evolution in terms of frequency, intensity, and duration over the whole 41-year period. We also document a catalogue of the major MHW events that occurred in the Mediterranean Sea during 1982-2022. The catalogue presents 20 MHW events that occurred during the last four decades and highlights the main differences found with the original and detrended detection. In particular, this catalogue identifies a very recent and exceptionally long-lasting MHW event that started in May 2022 and persisted, at least, until December 2022.
The Copernicus Mediterranean multi-year SST product This product provides a stable and consistent long-term, namely, multi-year (MY), SST dataset over the Mediterranean Sea specifically designed for climate applications and distributed within the Copernicus Marine Service (see, e. g. ,  Le Traon et al. , 2019). It consists of daily optimally interpolated (level 4, L4) satellite-based estimates of the foundation SST (namely, the temperature free, or nearly free, of any diurnal cycle) at a 0. 05°resolution grid covering the period from 1 January 1982 to the present (currently up to 6 months before real time). The product (hereafter, Copernicus MY product) is built from a consistent reprocessing of the collated level 3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives  (Merchant et al.
3). In practice, the average of each calendar day is calculated across all years and the climatology then coincides with the annual cycle. The trend component has been computed using Singular Spectrum Analysis (SSA), a non-parametric multivariate statistical method  (Golyandina and Nekrutkin, 2001; Hassani, 2007; Golyandina and Zhigljavsky, 2013). SSA is a consolidated technique used to identify the main patterns of variability in a given set of observations and has been successfully applied in many contexts including climate series  (Ghil and Vautard, 1991; Schoellhamer, 2001; Macias et al. , 2014; Besȩl and Tanir Kayikci, 2020; Biriukova et al. , 2021; Yi and Sneeuw, 2021). SSA extracts the minimum independent information by which the entire original dataset can be reconstructed. The trend component is identified through the first singular value.
Changes in anomalies distribution: impact of the trend on identifying extremes We begin by quantifying the impact of a trend on identifying extreme events. To do this, we computed the probability density function (PDF) of daily SST anomalies derived from both original and detrended data, split into two equally populated datasets over the whole Mediterranean Sea and for the different sub-basins (Figure  2 ). The first dataset corresponds to the period from the beginning of 1982 to half of 2002 while the last one covers the remaining period until the end of 2022. PDFs of original anomalies clearly evidence a shift in mean SST between the two periods. This shift is well evident either considering the Mediterranean Sea as a whole or its sub-basins. The mean anomaly increases from the first to the second period, and this holds for both the entire area and the sub-basins.
However, detrending should not substitute or exclude the analysis based on the total heat stress (a combination of the warming trend and marine heatwave), which is necessary to assess the impacts on marine organisms and ecosystems. project, funded by the European Space Agency (ESA, grant agreement no. 4000137121/21/I-DT). Furthermore, it has received support from the following projects: MEDOSMOSIS, SHAREMED and MPA-Engage funded by the INTERREG-MED program (grant agreements 6119, 6184 and 1MED15_3. 2_M2_337 respectively), Futuremares (Climate Change and Future Marine Ecosystem Services and Biodiversity) funded by the EU H2020 program (grant SEP-210597628) and CORFUN ("Assessing the effectiveness of nature-based solutions for maintaining the integrity and functioning of high-diverse Mediterranean coralligenous assemblages in a climate changed world") funded by the Spanish Ministry Science and Innovation (grant TED2021-.
Acknowledgments The authors are grateful to the managers and staff of the  Columbretes Islands Marine Reserve  for research authorizations and support, and would like to thank  Jacopo Chiaggiato ,  Salvatore Marullo ,  Chunxue Yang ,  Bruno Buongiorno Nardelli , and  Rosalia Santoleri  for the fruitful initial discussions to this work. The authors wish to thank the reviewers for their valuable and constructive comments.
While synthetic phenols include BPA, triclosan, and parabens, they will be discussed as these sub-classes of phenols rather than as a group because they are used for different applications by industries and are commonly recognized as their sub-classes in the media, on consumer products, and by researchers. These compound families were chosen given their widespread exposure in general population and potential deleterious effects on human health  [1] [2] [3] [4] [5]. The search was conducted by a research librarian using Healthcare Databases Advanced Search (HDAS) and the Medline, EMBASE, and CINAHL electronic databases and was conducted for personal care products and dietary intake separately (search terms are provided in Additional file 1). The searches were conducted in March 2021 and updated in October 2021 and March 2022 to include more recent publications.
Personal Care Products A total of 1788 articles were retrieved by the initial database search conducted in March 2021 and imported into the Rayyan Review platform (https:// rayyan. qcri. org/) with 1 article identified from a web search (Fig. 1 ). A total of 1155 articles remained after de-duplication and titles and abstracts of the retained articles were screened and assessed for eligibility using two independent reviewers (TCY, FC); any disagreements were discussed and resolved between the two reviewers. N = 1146 articles were eliminated as not relevant because they did not focus on one of the EDC of interest or were not interventions, and full-text screening was conducted on the remaining nine articles. Four articles were excluded as the intervention was not on an EDC of interest and five articles were identified from the reference lists of the included studies and through an internet search.
Dietary Intake and Packaging A total of 1385 articles were retrieved by the initial database search performed in March 2021 and imported into Microsoft Excel (Fig. 1 ). A total of 757 articles remained after de-duplication and titles and abstracts of the retained articles were screened and assessed for eligibility using two independent reviewers (NJ, CP); any disagreements were discussed and resolved between the two reviewers. N = 743 articles were eliminated as not relevant (i. e. , not on EDC of interest or were not interventions) and full-text screening was conducted on the remaining 14 articles. Two articles were excluded. One had a small sample size (N = 5) and did not have a baseline sample since the first spot urine sample was collected 4 days after the removal of products made of plastics from the household  [42]  and the second was a weight loss intervention with no specific focus on sources of exposure to phenols, phthalates, or glycol ethers  [43].
Interventions Aiming to Reduce Exposures Three studies removed or replaced PCPs to understand their impact on urinary concentrations of the EDC of interest  [59, 60, 69]  (Table  1 ). In a US study among 100 Latina girls aged 14-18 years, researchers provided a selection of replacement PCPs and cosmetics (including shampoo, conditioner, body wash, hand soap, deodorant, moisturizing lotion, and a choice of cosmetics including foundation, sunscreen, eye and lip make-up) which were chosen by using label information and online consumer databases to identify products free from triclosan, phthalates, parabens, and the UV absorber benzophenone-3  [60]. Participants had their usual PCPs substituted with these replacement products during the 3-day intervention and were allowed to choose four replacement cosmetics items to increase compliance to the change in beauty products during the intervention phase.
Even among compliant participants,  Harley et al. (2016)   [60]  found that, while measured levels of the EDC of interest among participants decreased on average during the PCPs intervention, some participants also saw increases in their exposure to butylparaben, ethylparaben, and MBP, MiBP, which indicate how difficult it can be to reduce exposures even when willingly. While the identification of replacement PCPs -based on ingredient list information and consumer databases available to consumers -reflects real-world conditions and sources of information, complete elimination is difficult, since such sources of information only consider ingredients with no consideration for the packaging. These examples illustrate not only that it takes personal initiative to search for information and resources but also that personal behavior change may not have the desired outcome if labelling is inadequate or contamination exists.
Acknowledgements We thank  Federica Bianchini ,  Alison Forde , and  Matthew Gill  at the  Braford Royal Infirmary medical library  for carrying out the search strategy. Funding This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  874583 -the  Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project and LIFECYCLE , grant agreement number  733206. This publication reflects only the authors' view and the  European Commission  is not responsible for any use that may be made of the information it contains. Drs. McEachan  and Yang receive funding from the  National Institute for Health Research  under its Applied Research Collaboration Yorkshire and Humber [NIHR200166]. ISGlobal acknowledges support from the  Spanish Ministry of Science, Innovation and Universities  through the " Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000 , 806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Program.
coli O157:H7 wt. However, pre-incubation of E. coli O157:H7 with antibodies against OmpA resulted in a statistically significant decrease in bacterial adhesion to HT-29 cells (Figure  9 ). Thus, the adhesion inhibition assay confirmed the efficacy of OmpA antibodies in preventing bacterial adhesion to these representative intestinal epithelial cell lines. Figure  9. Adhesion inhibition assay. Effect of anti-OmpA and anti-OmpX on E. coli O157:H7 adhesion to HT-29 cells. The effect of 5%, 2. 5% and 1. 75% concentrations of anti-OmpA or anti-OmpX antibodies on bacteria-cell adhesion were evaluated and compared with E. coli O157:H7. Anti-E. coli antibodies were used as a positive control. Data were statistically analysed following ANOVA with differences considered very significant (p < 0. 01, **), or highly significant for (p < 0.
Discussion Although the adhesion factors implicated in EHEC binding to intestinal epithelial cells are extensively studied, a comprehensive view of the host receptors, or the co-receptors and adhesion mechanisms remain partial. Typically, ECM proteins are localised to Figure  9. Adhesion inhibition assay. Effect of anti-OmpA and anti-OmpX on E. coli O157:H7 adhesion to HT-29 cells. The effect of 5%, 2. 5% and 1. 75% concentrations of anti-OmpA or anti-OmpX antibodies on bacteria-cell adhesion were evaluated and compared with E. coli O157:H7. Anti-E. coli antibodies were used as a positive control. Data were statistically analysed following ANOVA with differences considered very significant (p < 0. 01, **), or highly significant for (p < 0. 001, ***).