dataesr/scientific-paragraphs-categorization huggingface dataset:
In that way, from tens of thousands of peptides consistently being detected in urine, only 1183 and 1206 were considered for further analysis in the binary and multiclass classifications, respectively. In that context, using UMAP to further reduce the feature space (and thus, the correspondent dataset information) to only 3 spatial coordinate features resulted, not entirely unexpectedly, in a model of poorer predictive performance on the independent test set (~70% accuracy) (as well as in terms of train set CV mean and sd) in comparison to the SVM-alone approach (~85% accuracy); but still appeared to be able to retain a substantial part of the dataset's "information". Consequently, in different scientific designs in which an efficient feature selection/removal method is not available, the inclusion of UMAP in the pipeline could potentially contribute to noise removal and thus, to higher model performance.
Other differences in performance can also be, among others, attributed to the train sets not being identical, different pipelines (e. g. selecting features based on differential expression(3) instead of 30% frequency threshold, alternative hyperparameter optimization etc. ) as well as the initial number of features (sequenced peptides) during model development, as per availability in each case. The presented study has limitations, some of which may be improved in future work. First, class balance was not the case for the multiclass classification. Class imbalance is hardly avoidable when working with such retrospective datasets, among others due to the inherent difference in disease prevalence. Therefore, as noted, an oversampling approach was used in an attempt to enhance the pathophysiology-centered nature of the classifier by compensating for inappropriate effects (against the minority classes) as a potential result of the class imbalance.
However, no repolarization of the Mn-H bond and proton transfer was observed for hydride complexes 2 and 3 with various bases (pyridine, HMPA, DBU, TBD  (1,5,7-triazabicyclo[4. 4. 0]dec-5-ene, pKa = 26. 03 in MeCN  [28] )) even in polar solvents (MeCN, THF). Fortunately, treatment of complex 2 at ambient temperature in THF with KHMDS excess (2 equiv. , pKa = 26 in THF  [29] ) led to partial proton transfer indicated by the appearance of two new low-frequency νCO bands at 1770 and 1741 cm -1 in IR spectrum that correspond to anionic complex 2 -(Figure  S5 )  [30]. From these spectral data the pKa value 27. 3 for manganese hydride 2 in THF was estimated. The reaction of hydride complex 3 with KHMDS (5 equiv. ) in THF leads to full proton transfer and the appearance of three new low-frequency-shifted CO bands (1957, 1871, 1876 cm -1 ) in the IR spectra (Figure  1 ).
Optimizations were done in toluene, THF and CH3CN introduced by a SMD solvent model  [38]. Cationic complexes mer,trans-[(PPh3)2Mn(CO)3(MeCN)][BF4] 2 MeCN and fac-[(P-NHC)Mn(CO)3(MeCN)][BF4] 4 MeCN were crystalized from the CH2Cl2/hexane system. Xray diffraction data were collected at 100 K with a Bruker Quest D8 CMOS diffractometer (Bruker, MA, USA), using graphite monochromated Mo-Kα radiation ( = 0. 71073 Å). Using Olex2  [39] , the structures were solved with the ShelXT  [40]  structure solution program using Intrinsic Phasing and refined with the XL  [41]  refinement package using Least-Squares minimization against F 2 in anisotropic approximation for non-hydrogen atoms. Positions of hydrogen atoms were calculated, and they were refined in the isotropic approximation within the riding model.
, 2015). It combines a high mass resolution with a high scan speed, allowing the identification of unknown compounds by nontarget screening and by suspect screening when database information is available. LC-QTOF-MS has recently successfully been applied in the analysis of emerging pollutants and TPs generated by AOPs  (Black et al. , 2021; Campos-Mañas et al. , 2020; Chen et al. , 2021a; Law et al. , 2021) , as well as in effect-directed analysis  (Houtman et al. , 2020; Zwart et al. , 2020). In this work, a BDD anode was used for the electrochemical degradation of EE2. Different parameters such as electrolyte type, electrolyte concentration and electric current were first optimized to obtain the most efficient degradation of EE2. To separate, identify and quantify EE2 and its TPs, a LC-QTOF-MS method was developed and validated.
Images were captured with an Olympus DP74 colour camera with a ms exposure time at 2x magnification and quantified with Image J Software (U. S. National Institute of Health). In order to remove background fluorescence resulting from endogenous pigments present in the larvae, a threshold intensity of 60-255 was applied to the resulting images. Three experimental groups were considered for each estrogen test. The negative control group consisted of eleutheroembryos incubated in test medium and was used to determine the baseline fluorescence level of non-treated larvae. The positive control group consisted of larvae incubated in test medium containing 33. 8, 50. 6, 76. 0, 114. 0, 170. 9 and 488 ng/L of EE2 to determine the level of fluorescence induced by EE2 in a concentration-dependent manner.
Data analysis and statistics Fluorescence levels measured were processed according to the OECD guidelines for the statistical analysis of eco-toxicology experiments  (OECD, 2006). GraphPad Prism version 7. 00 (GraphPad Software, La Jolla, CA, USA) was used for statistical analysis and graph plotting. A D'Agostino and Pearson omnibus normality test was used to determine the distribution of values in each of the exposure groups. A one-way ANOVA and Dunnett multi-comparison test were performed in cases wherein all groups showed a normal distribution. A Kruskal-Wallis test with Dunn's multi-comparison test was used in cases where one or more of the compared groups did not pass the normality test. All groups were compared to the appropriate control group. Statistical significance was set at p< 0.
05. Data analysis for the quantification of EE2 and for the structural elucidation of TPs was performed using MassHunter and Profinder software (version 10. 0, Agilent Technologies). An algorithm was used to extract and identify peaks as potential compounds, also known as "features". The parameters used for the molecular feature extraction (MFE) are given in Table  S1  in the Supporting Information (SI). After extraction from the obtained chromatograms, the molecular features were only considered if they met the following criteria: (i) molecular features were absent in the initial sample (t= 0 min) and in blank degraded samples, (ii) a single predicted molecular formula was obtained based on high mass accuracy (< 5 ppm), isotopic distribution and molecular formula in resemblance with EE2 (C 20 H 24 O 2 ) (iii) a change in intensity was observed across the length of the experiment, with respect to the IS (EE2-13 C 2 ).
The obtained k app values were 0. 0117 min -1 and 0. 0434 min -1 for current densities of 6. 25 mA/cm 2 and 12. 5 mA/cm 2 , respectively. A current of 12. 5 mA/cm 2 was, therefore, chosen as the optimal current density. Other studies have also shown the positive effect of an increased current density on the degradation of organic molecules  (Dhawle et al. , 2022; Frontistis et al. , 2018; Pueyo et al. , 2020). Fig. 3  shows the effect of the electrolyte concentration and current density on the removal of EE2 using NaCl as the supporting electrolyte. Using 0. 05 M NaCl and a current density of 12. 5 mA/cm 2 , EE2 was removed in<30 sec (data not shown). Therefore, lower electrolyte concentrations and current densities were evaluated to ensure sufficient intermediate sampling points for the subsequent chemical and biological analysis.
Due to its larger removal efficiency, the total degradation time was set to 30 min for samples to which NaCl was added, as opposed to 120 min for samples to which Na 2 SO 4 was added. Figs. S2  and S3  show the residual concentration of EE2 and the removal rate at each time point of the electrochemical process. Likewise, EE2 was no longer detected by LC-MS after 120 min of electrochemical oxidation with Na 2 SO 4 and after 2 min with NaCl. To quantify the ChgH-GFP responsiveness to estrogenic compounds in these experiments, as demonstrated previously in other studies  (Kurauchi et al. , 2005; Spirhanzlova et al. , 2016) , the larvae were exposed to a concentration range of EE2 from 0 to 488 ng/L. Fig. 4  shows the concentration response curve constructed using the mean fluorescence obtained when the data from the negative and positive control groups from all experiments were combined.
8. By comparing the identified TPs formed in the presence of Na 2 SO and NaCl, it was found that only four TPs were commonly generated in both supporting electrolytes (TP294, TP312, TP326a-b), revealing the different oxidizing behaviors of the involved reactive species. Despite the identification of several TPs, the structural elucidation of these compounds remained challenging due to a lack of reference standards and an exhaustive database. Moreover, the low intensity of the fragmentation ions and the occurrence of rearrangements after fragmentation, further complicated the structure elucidation  (Guan et al. , 2006; Ma and Yates, 2018). Nevertheless, the experimental data clearly showed the formation of hydroxylated and/or halogenated TPs, as well as A-ring opening products during the eAOP.
Acknowledgments: The authors thank the Agence Nationale de la Recherche (ANR) for the research project grant (ANR PASSCATA-ANR-16-CE07-0029), https://anr. fr/Project-ANR-16-CE07-0029 (accessed on 26 February 2023). The authors warmly thank Virgile Rouchon and Veronique Lefebvre for the microscopy observations, Saloua Sahal, Laurent Lemaitre and Christèle Legens for XPS characterizations and Denis Barrallon for the 4-MDBT catalytic tests.
2013; Salama & Ehrenfreund 2013). However, due to advances in laboratory methods, experimental measurement of the gas-phase electronic spectra of PAH cations containing up to 42 carbon atoms using action spectroscopy techniques are reported  (Zhen et al. 2016; Hardy et al. 2017; Campbell & Maier 2017b). None of the detected laboratory absorptions are associated with DIBs, and the same conclusion applies to several protonated PAHs  (Hardy et al. 2013; Patzer et al. 2013; Rice et al. 2014; Noble et al. 2015). In this contribution, low temperature gas-phase electronic spectra of 1-CNN + are reported. These data were obtained following buffer gas cooling in a cryogenic ion trapping apparatus. Section 2. 1 describes the experimental methods, and computational procedures are outlined in Sect. 2.
Under these conditions a proportion of the primary 1-CNN + ions formed weakly bound complexes with helium atoms, as illustrated in Fig. 1. This enabled spectroscopic observation of electronic transitions by monitoring the depletion of 1-CNN + -He (m/z 157) following exposure to resonant laser excitation. To record these data, the helium buffer gas was pumped out of the trap for several hundred milliseconds before the ion cloud was irradiated with pulsed, tuneable radiation from either an OPO/OPA (Ekspla, linewidth ∼5 cm -1 ) or Nd:YAG pumped Dye Laser (Sirah, linewidth ∼0. 05 cm -1 ). The trapping sequence was repeated at a rate of 1 Hz. Through the use of a mechanical shutter, data were monitored on alternate cycles with (N i ) and without (N 0 ) exposure to radiation to account for fluctuations in the number of stored ions.
Computational Calculations were performed using the Gaussian 16 software suite  (Frisch et al. 2016). Optimised geometries, harmonic vibrational frequencies, and energies were calculated using densityfunctional theory and its time-dependant version for excited states, at the ωB97X-D/cc-pVDZ level of theory  (Chai & Head-Gordon 2008; Dunning 1989 ). The Franck-Condon (FC) calculations presented in this article were performed with ezFCF (Gozem & Krylov 2022) using the Gaussian output files. The vibrational temperature was set to 6 K.
Acknowledgements. The authors acknowledge financial assistance from the  Royal Society  [grant numbers  RGF/EA/181035 ,  EP/W03753X/1 ,  URF/R1/180162 ] and  Engineering and Physical Sciences Research Council  [grant number  EP/W03753X/1 ]. U. J. has used the HPC resources from the " Mésocentre" computing center of CentraleSupélec and École Normale Supérieure Paris-Saclay  supported by  CNRS  and  Région Île-de-France  (http://mesocentre. centralesupelec. fr/). This article is based upon work from COST Action CA21126 -Carbon molecular  nanostructures in space (NanoSpace ), supported by  COST (European Cooperation in Science and Technology).
Results and discussion The SilE sequence (Fig. S1 , ESI †) used in this work was defined according to the wild-type sequence of the Salmonella Typhimurium SilE (UniProt accession number Q9Z4N3). The first twenty residues of the signal peptide that routes SilE to the periplasm have been removed. Therefore, the SilE 21-143 construct has been successfully expressed and isotopically labeled with E. coli cells. As indicated in blue in the sequence, SilE is composed of 6 HxxM, 2 MxxH, and one HxM motifs. As previously described by Chabert et al. ,  10, 13  silver ions can bind His and Met residues and association binding constants have been calculated for each motif.
On top, the [SilE:nAg] complex secondary structure deduced from Alphafold is sketched. The different flexible segments used in the MultiFoXS calculation are coloured in blue. The SAXS data (blue circle), back-calculated SAXS data (red line), and residuals are represented in the left panel. On the right panel, the representation of the top-scoring N-ensemble of structures and their respective weight that best fit the SAXS data are presented close to each structure and are determined using the MultiFoXS server. Residuals per ith SAXS data point have been calculated according to the following expression: (I(q i ) exp À I(q i ) calc )/s i. I(q i ) exp is the measured scattering intensity, where I(q i ) calc is the intensity calculated using the MultiFoXS for scattering vector q i and s i is the experimental error.
Acknowledgements Dr R. Verel ,  Dr C. Gordon , and  L. Lätsch  are acknowledged for the help with NMR measurements. A. N. , and D. T. are grateful to the  Swiss National Foundation (SNF)  for nancial support of this work (grant no. , 200020B_192050 and 200021_169134 respectively). Z. J. B. gratefully acknowledges ETH+  Project SynMatLab  for nancial support as well as a  SNF Spark Award  (grant no. CRSK-2_190322 ). P. -A. P. is grateful to the  ICBMS, Université Lyon 1 , and the  Region Auvergne Rhone Alpes  for nancial support. The authors would like to thank  Dr L. Perrin  for fruitful discussions. The authors are grateful to the  CCIR of ICBMS, PSMN , and  GENCI-TGCC  (grants  A0100812501  and  A0120813435 ) for providing computational resources and technical support.
clearly defined while the idea of negative sample is not wellestablished  [38]. As a consequence, only a small portion of a so-constituted training set is labelled. In such scenarios, the available training data is constituted by a set of positive samples together with a set of unlabelled ones spanning both positive and negative concepts. Such a setting is known as learning from positive and unlabelled (PU) data  [2]. More precisely, this setting differs from standard supervised or semi-supervised classification by the absence of labelled negative samples in the training set. The concept of learning from PU data fits within the increasing interest in developing weakly supervised learning frameworks  [39] , such as learning from positive-only or one-class data  [16]  and semi-supervised learning  [14] , that relax the strict requirement related to the access of fully annotated datasets.
Focusing on time series data, PU learning can model real world problems coming from application domains like multimedia, medicine, aerospace, finance, manufacturing, entertainment and remote sensing  [27]. Reference  [27]  introduces a learning approach to deal with time series information under the lens of learning from PU data setting. The proposed method is based on a clustering-based solution to identify reliable negative samples. The clustering procedure is coupled with euclidean distance. Finally, once positive and reliable negative samples are available, the classification is performed by means of one nearest neighbours classifier (1NN). Another framework based on 1NN classifier is presented in  [6]. Here, the authors propose a framework in which the positive set is incrementally expanded by means of a similarity measure combining both euclidean distance and dynamic time warping  [25].
A. RELIABLE NEGATIVE SAMPLES SELECTION The first step of DMTS-PUL copes with the identification of a set of reliable negative (RN) samples. To this end, we first model the positive set of multivariate time series via an RNN autoencoder then, we use the learnt neural network to rank the samples from the unlabelled set and select reliable negative multi-variate time series data. To rank unlabelled samples, the reconstruction error is used as measure. Samples associated with a low reconstruction error probably belong to the positive class while, samples related to high reconstruction error are likely to belong to the negative class. Finally, multivariate time series with the highest reconstruction errors, coming from the unlabelled set, are identified as reliable negative samples. The use of Recurrent Neural Network naturally fits our setting since this model is especially adapted to manage sequential data  [23]  and, in particular, compressing sequential information to extract useful multi-variate time series representations  [13] ,  [17].
B. ITERATIVE PSEUDO LABELLING PROCEDURE Once both P and RN sets are available, we train a classification model to deal with the underlying binary classification task. More precisely, the classification model is learnt via an iterative pseudo labelling (IPL) procedure, reported in Algorithm 1. The objective is to iteratively enrich the set of positive P and reliable negative RN samples exploiting the set of unlabelled multi-variate time series data U , via self-training. The procedure depicted in Algorithm 1 takes as input the set of positive samples (P), the set of reliable negative samples (RN ), the set of unlabelled multi-variate time series (U ), the number of iterations (K ) and the amount of samples to be added to the positive (resp. reliable negative) set at each iteration (l). The output of the algorithm is a binary multivariate time series classification model that is trained over the enriched set of positive and reliable negative samples.
At the beginning, the classification model (Classifier) is initialized and then trained on the set of positive P and reliable negative RN samples (Line 1-2). Then, the incremental process starts (Line 4-13). At each iteration, the classification model is applied on the set of unlabelled multi-variate time series U and, for each TS ∈ U , a binary class distribution is obtained (Line 5). Subsequently, the class distribution jointly with the unlabelled set U , the amount of new samples to enrich the current training dataset l and the information about the current iteration i are taken as inputs from the SampleSel procedure. This procedure (Line 6) selects a bunch of new positive multi-variate time series (newP) and the new set of reliable negative samples (RN ). Then, the positive set of training data as well as the set of unlabelled samples is updated (Line 7-8) and a new binary classification model is trained over the current set of positive and reliable negative multi-variate time series samples from scratch (Line We underline that, at each iteration, the set of reliable negative sample RN is completely updated (Line 6).
B. DATA AND EXPERIMENTAL SETTINGS The evaluation has been carried out on seven benchmarks  [9]  coming from disparate application domains and characterized by contrasted features in terms of number of samples, number of attributes (dimensions) and time series lengths: ArabDigits, 1  Dordogne, ECG5000 1 , HAR, 2  PenDigits 1 , seqMNIST  3 and SpeechCom. 4 All datasets, except Dordogne -which was obtained contacting the authors of  [11] , are available online. To have a fair comparison among the different methods and with the aim to provide a controlled scenario for the positive unlabelled task evaluation, for each benchmark, we only consider the two majority classes (the two classes with the highest number of samples) to set up an underlying binary classification task. The majority class is considered as positive while the second more represented class is considered as negative class.
C. QUANTITATIVE RESULTS Figure  3  reports the results obtained on the different benchmarks by the competing methods varying the amount of positive labelled data. We can observe that DMTS-PUL always outperforms all the competing approaches over all the seven involved datasets. Considering the competitors, they almost share a similar behaviour over all the datasets. The least effective approaches are the ones based on the one nearest neighboors classifier and the one based on the non-negative risk estimation principle with selection bias, nnPUSB. Regarding the former group, the ones based on the 1NN classifier, despite the high level performances of such approach for univariate time series classification task  [5] ,  [6] ,  [22] , they have serious issues when multi-variate time series are considered.
The nnPU method, differently from all the other approaches, exhibits a varying behaviour depending to the dataset. While it shows interesting performances on ArabDigits and SpeechCom, it achieves poor results over all the other benchamrks. The OCSVM method, that only leverages positive labelled samples, generally exhibits a good average performance in terms of F-Measure. Finally, on six over seven cases, the best competing approach is represented by the Random Forest classification method coupled with the positive and unlabelled learning framework proposed in  [8]. This approach also shown competitive performances on the ArabDigits dataset while it is largely far from the performances obtained by DMTS-PUL on all the remaining multi-variate time series datasets. Regarding the direct comparison between DMTS-PUL and its ablation (DMTS-PUL NoIPL ), we can note that DMTS-PUL achieves general better performances than its ablation for small amount of positive labelled data (between 30 and 90).
This is particularly evident on the ArabDigits, Dordogne and SpeechCommand benchmarks. When the size of the available positive multi-variate time series set is above 90, the performances of the two approaches are comparable but DMTS-PUL behaves slightly better than its ablation on the majority of the benchmarks. In addition, we can also observe that DMTS-PUL achieves quite stable performances no matter the amount of positive labelled data it can access. This is not the case for the majority of the competing approaches. The comparison between DMTS-PUL and DMTS-PUL NoIPL highlights the quality of the IPL procedure involved in our framework. The iterative pseudo labelling procedure makes DMTS-PUL well suited for low-data regime when only a small amount of labelled data is available. Moreover, the obtained findings also suggested that our framework does not require enormous volume of data to obtain competitive results thus positively impacting the time-consuming and human-effort labelling task prior to any classification scenario.