dataesr/scientific-paragraphs-categorization huggingface dataset:
However, BitterIntense, despite being a classification algorithm, discriminates between "very bitter" and "non very bitter" compounds, thus accessing the level of bitterness of query molecules. Despite all prediction tools employ a different methodology, a common structural features can be noticed among all of them, which is typical of most ML workflows: (i) the definition of a compound database also including the respective taste, preferably experimentally validated; (ii) the compound featurization, i. e. , the derivation of effective molecular descriptors; (iii) the dataset splitting into training and test sets (and in some cases also a validation set); (iv) the choice of the ML method for the classification/regression; (v) performance evaluation and validation. It is worth mentioning that most of the discussed algorithms followed the guidelines defined by the Organization for Economic Cooperation and Development (OECD), which indicates the strategies for correct development and validation of robust QSAR models: (i) a defined endpoint; (ii) an unambiguous algorithm; (iii) a defined domain of applicability; (iv) appropriate measures of goodness-of-fit, robustness and predictivity; (v) a mechanistic interpretation, if possible  [87].
On the other hand, 3D descriptors can also account for specific molecule conformations, such as different conformers/isomers, and spatial properties. Therefore, the possibility to obtain very good results also using 2D descriptors allows designing faster tools suitable for screening very large databases. Several algorithms have been applied for taste prediction, including RF, SVM, SVR, QSTR, GFA, ANN, KNN, GBM, DNN, AB, SCM, and XGBoost. Multiple Linear Regression (MLR) and Support Vector Machine (SVM) are among the first models for binary classification. These models were exceeded by tree-based models, i. e. , Random Forest (RF) or AdaBoost (AB), and Neural Network (NN), which support multiclass classification and work very well in the non-linear range if they have a sufficiently large number of database elements.
However, none of these approaches consider the multi-objective nature of the dimensionality reduction techniques and thus fail to balance between the objectives of optimizing prediction performance measured in multiple classifications and regression metrics, minimizing the number of selected features and maximizing the overall interpretability/explainability of the derived prediction models. Moreover, not only feature selection but also normalization/standardization can further improve model performance, such as in the case of the flatkinson standardization method  [50] , as well as modern techniques to handle the class imbalances in the data such as SMOTE  [89]. Finally, all existing methods lacked a strict definition of negative datasets with most of them using random compounds as negative datasets and thus jeopardizing the prediction performance and generalization properties of the models.
As also defined by the OECD guidelines, another relevant aspect in the development of the prediction tool is the definition of the applicability domain (AD), which indicates the reliability of the prediction evaluating if investigated compounds are within the chemical space of the training data. In this context, PrediSweet, along with an applicability domain, developed a reliability domain, which considers the density of information around the compound, and a decidability domain, which evaluates the confidence of the prediction. The PrediSweet chemical space of the dataset used (Sweet-enersDB) was compared with the most comprehensive sweet database (SuperSweet): more than 99. 5% of the compounds in SuperSweet are structurally similar to a representative structure in the SweetenersDB, suggesting the large sweeteners spectrum covered by the SweetenersDB.
e-Bitter and e-Sweet used the ECFP-based Tanimoto similarity between the query and the five closest neighboring molecules in the training set. Similarly, the Rojas Sweet Predictor developed an AD using a threshold on the Jaccard-Tanimoto average distance between the query molecule and the compounds in the dataset. BitterPredict AD, known as Bitter Domain, includes molecules with molecular weight MW ≤ 700 and hydrophobicity -3 ≤ AlogP ≤ 7: all used datasets were previously filtered using this domain to ensure the reliability of the prediction. In BitterSweet, a query molecule is considered inside the applicability domain, if its median Euclidean distance from similar compounds in the training set is below a selected threshold. Interestingly, BitterSweet covered a remarkably wider applicability domain than the Rojas Sweet Predictor while achieving similar performance.
One of the main advantages of the reported prediction tools is their ability to fast screening huge databases of compounds and to estimate the number of compounds associated with a specific taste. A granular and detailed screening was performed by BitterPredict on DrugBank approved (1375 compounds), FooDB  (13,588 compounds) , Natural Products Dataset from ZINC15  (27,474 compounds) , and ChEBI (27,015 compounds) datasets, showing that the percentages of bitter molecules within the Bitter Domain found in these databases are 65. 94%, 38. 36%, 77. 21%, and 43. 71%, respectively. Moreover, since bitter taste is associated with toxic compounds or compliance problems, the same authors used their subsequent tool, namely BitterIntense, to screen toxic databases, i. e. , FocTox, CombiTox datasets, and DILIrank, experimental compounds from DrugBank database (10,170 compounds), natural compounds from NPatlas (24,805 compounds) and 34 potential drug candidates against COVID-19 retrieved from "Coronavirus Information-IUPHAR/BPS Guide to Pharmacology".
Interestingly, only a small portion of toxic compounds are intensively bitter, but 41. 2% of COVID-19 candidate drugs were predicted as very bitter (VB). Moreover, BitterSweet was applied to several specialized chemical databases (SuperSweet, FlavorDB, FooDB, DSSTox, SuperNatural II, and DrugBank), revealing that most natural, toxic, and drug-like chemicals are bitter, whereas the same amount of bitter and sweet molecules are present in foods. BitterSweet Forest was applied on Super-Natural II, DrugBank approved drug molecules and ProTox, and showed that toxic substances are typically bitter. In line with the previous results, VirtualSweet and VirtualBitter models were applied on approved drugs from DrugBank and natural compounds from SuperNatural II: notably, most of the approved drugs and most of the natural compounds were predicted as bitter, and only a small portion of these databases was classified as sweet.
Conclusions The present review aims at summarizing the main scientific advances in the field of taste prediction supported by MLbased algorithms. We discussed the main available database containing food-related compounds and molecules with known taste, the main tools employed to predict the taste. From the analysis of the databases, we pointed out two specific databases for the sweet taste, i. e. , SuperSweet, which is the most comprehensive DB for sweeteners, and SweetenersDB, which collects 316 sweeteners with a relative value of sweetness. For the bitter taste, BitterDB represents the most granular and complete database, with a very intuitive and user-friendly web server that allows the download of more than a thousand bitter compounds. BitterDB, as well as BitterPredict and BitterIntense, was developed by the Niv Lab (https:// bioch em-food-nutri tion.
agri. huji. ac. il/ masha niv), which has provided incredible advances in the comprehension of the bitter taste in recent years. A lot of effort has been made to develop methods specifically for the prediction of bitter peptides and another research group has continuously improved its tools publishing three consecutive works, namely iBitter-SCM, BERT4Bitter, and iBitter-Fuse, in the last few years. These tools are paramount for the fast and reliable classification of huge databases of bitter peptides and for their rational de novo design, especially considering the emerging role of this class of compounds in the drug and nutritional research field. Furthermore, the UMP442 database developed during the implementation of iUmami-SCM is probably the most complete and ready-touse database of umami and non-umami molecules, since it is available from GitHub.
In this context, the Umami Database seems a very promising source of information, but the availability of data is limited, it is impossible to obtain data from the webserver and no umami prediction tool which uses this source has been found in the previous literature. It would be incredibly valuable to have access to the resources of such a database in the future. Finally, as far as the authors know, no publicly available databases for sour and salty tastes are available and the only attempt to generate a sour dataset was made in the development of VirtualTaste. However, the used sour dataset has not been made public. To date, the multiplicity and diversity of sources make it very complex to obtain a unified DB collecting a huge amount of compounds for each taste sensation. The authors insist also on the need for developing complete databases that include all the relevant information for each entry (SMILES, InChI, IUPAC nomenclature, etc.
) to avoid any possible error in compound processing. Moreover, the definition of exhaustive databases would be essential for a correct definition of the molecular descriptors to be employed, due to the great number and variety of both open-source and proprietary descriptors. Similarly to the taste databases, prediction tools for sweet and bitter have been more developed during the last years. Among several examples of sweet and bitter classification tools, some proposed methods can even predict the level of sweetness (Chéron Sweet Regressor, Goel Sweet Regressor, and PrediSweet) and BitterIntense can discriminate between "very bitter" and "non very bitter compounds". Only a few reported tools were able to discriminate more than one taste sensation. BitterSweet and BitterSweet Forest are interesting examples of tools able to consider the dichotomy of sweet and bitter tastes  [59].
These tools can be pivotal for the detection of natural and synthetic compounds with a pleasant taste and without adverse effects. Furthermore, VirtualTaste is the only available tool able to predict three taste sensations (sweet, bitter, and sour) and the only one able to predict the sour taste (VirtualSour). Among the 16 reported taste prediction tools, BitterX, BitterSweet, PrediSweet, iBitter-SCM, BERT4Bitter, iBitter-Fuse, iUmami-SCM, and VirtualTaste provide web server applications, which allow the taste prediction using the SMILE/Fasta format, directly drawing the molecule or by uploading a file. On the other hand, e-Sweet, e-Bitter, and BitterPredict only provide freely accessible code available from Dropbox or GitHub. From the authors' point of view, the development of a web interface represents a considerable strength, since it allows the tool to be used even by people who are not experts in the use of these applications.
Finally, BitterSweetForest, Rojas Sweet Predictor, Goel Sweet Regressor, Chéron Sweet Regressor, and BitterIntense do not have any web server or code publicly available. It is worth mentioning that in addition to the five basic tastes, other taste qualities may be important and related to specific food ingredients. In this context, some recent publications suggested the fat taste as another basic taste quality  [3, 4, 90]. Interestingly, fatty acid detection seems to decrease as a consequence of a fat-rich diet and with a great impact on obesity disease  [91]. Therefore, the prediction of fat taste would represent a groundbreaking objective for future tools, considering the impact of fat intake on human health status. Furthermore, the use of methods capable of predicting the molecular interactions of tastants and relative taste receptors could lead to significant improvements in the predictive capabilities of these tools and to great strides in understanding the physicochemical characteristics and mechanisms underlying taste perception.
After training on a database of synthetic waveforms augmented with empirical noise, we show that the algorithm can instantaneously track an earthquake source time function on real data. Our model unlocks 'true real-time' access to the rupture evolution of large earthquakes using a portion of seismograms that is routinely treated as noise, and can be immediately transformative for tsunami early warning. The sudden displacement of rock mass induced by an earthquake generates density variations that, in turn, modify the Earth's gravity field. The signal associated with these transient gravity perturbations propagates at the speed of light, much faster than the fastest elastic waves (P-waves)  [8] [9] [10]. Recent theoretical studies have shown the potential for earthquake early warning systems (EEWS) that are based on the gravity signals that would be measured by a future generation of gravity-gradient sensors  11, 12  , yet to be developed.
The training database We focus on the Japanese subduction zone because: (1) it has experienced one of the largest and most destructive subduction events (the 2011 M w = 9. 0 Tohoku-Oki earthquake), and (2) we can rely on a large and dense network of good-quality broadband seismic stations (Fig. 1a ). Because of the paucity of PEGS observations  16  , we train PEGSNet on a database of synthetic PEGS waveforms generated with a normal-mode summation code  14  and add real noise recorded at each station in the seismic network. The training database is made of 500,000 synthetic earthquake sources distributed along the Japanese megathrust, with location, strike and dip angles following the Slab2. 0 model  38. We draw random magnitude and rake angles following uniform (from 5. 5 to 10) and Gaussian (μ = 90°, σ = 10°) distributions, respectively.
In this STF model the smaller and larger earthquakes start in the same way. We then add empirical noise recorded at each station (Fig. 1b ) to the generated waveforms. Both synthetics and noise seismograms are decimated to 1 Hz and bandpass-filtered between 2 mHz (high-pass Butterworth, two poles, causal) and 30 mHz (low-pass Butterworth, six poles, causal), to enhance long-period PEGS amplitudes by suppressing higher-frequency noise  7. Traces are clipped to a threshold value of ±10 nm s -2 (the maximum expected amplitude for PEGS in our database) and scaled by the same value to facilitate convergence of the optimizer, and at the same time to preserve information about PEGS radiation patterns across the seismic network. Finally, we store each example as 700-s-long traces centred at the origin time of the earthquake (Fig.
Building PEGSNet, labelling and training PEGSNet is a deep CNN that combines convolutional layers and fully connected layers in sequence (Extended Data Fig. 2 ; details in Methods). There exists a wealth of literature on deep learning architectures, but our goal here is to show the feasibility of PEGS-based EEWS using deep learning, and we therefore use a simple and classic Le-NET-like CNN architecture  39. Similar architectures have been used for magnitude and location estimation using P and S-waves with a single-station approach to increase the number of training examples  33, 34. Here we take advantage of our large synthetic database and adopt a multi-station approach. CNNs work best with image-like inputs, and we arrange our data as an image, sorting stations by their longitude (Fig. 1c ).
5 nm s -2 ), it can be reduced to about 7. 9 or 8. 0 (Extended Data Fig. 5 ). To examine the time-dependent performance of PEGSNet more closely, Fig. 2c  shows all the predictions (on the test set) associated with events with a final M w of 9. 0 ± 0. 05. In the first 30-40 s after origin, predictions are strongly underestimated. This arises from a combination of two effects: the lack of sensitivity below 8. 3 (in the first 30-40 s after origin the current magnitude is below 8. 3 in our STF database) and the fact that, at stations located close to the source, P-waves can mask PEGS, owing to their small time separation. A test performed on noise-free synthetics shows that at least 15 s after origin are needed by the model to reliably predict M w (Extended Data Fig. 6 ). Starting at about 40 s (when the true M w is generally above 8.
2 in our STF database), PEGSNet can track the evolution of the moment release instantaneously, that is, without time shift between estimated and true M w (t), as indicated by the mode of the distribution of the predictions. Our results show that PEGSNet can exploit the key features of PEGS: (1) PEGS sensitivity to magnitude for large earthquakes allows the model to distinguish, for example, between an M w = 8 and an M w = 9 earthquake and (2) the information about M w is effectively propagated at the speed of light, which results in instantaneous tracking of the moment release, from about 40 s into the rupture until its end. Although P-wave triggering is needed at each station, the instantaneous information about the source carried by PEGS is readily available in the data that precede P-wave arrivals.
Playback of the Tohoku-Oki earthquake We tested PEGSNet on real data from the 2011 M w 9. 0 Tohoku-Oki earthquake. The raw seismograms are processed as described in Methods and fed to PEGSNet. We simulated a data playback scenario and made an M w prediction at each second starting at the earthquake origin time and using only the previous 315 s of data (details in Methods). Figure  3  shows the results of this retrospective analysis compared with a 'true' STF calibrated against various different data types  40  , the historical JMA EEWS performance  20  , and the best-performing finite-fault EEWS based on seismic (FinDer2)  41  and GNSS data (BEFORES)  24. Further comparisons with additional algorithms are shown in Extended Data Fig. 7. After around 55 s, PEGSNet is always closer to the 'true' STF than the other algorithms (Fig.
8 ). Notably, PEGSNet does not suffer from the magnitude saturation issue that instead affects the seismic-based EEWS. Both in terms of latency and accuracy, the performance of the GNSS-based algorithm BEFORES is the closest to PEGSNet. However, the response of PEGSNet is faster: for an arbitrary threshold at M w = 8. 0, PEGSNet would issue an alert 53 s after origin, BEFORES about 8 s later. PEGSNet is more accurate: the difference with the 'true' STF is always equal to or less than 0. 3 M w units starting at 55 s, whereas BEFORES reaches that level of precision only after 100 s from origin (Fig. 3b ). More importantly, in contrast to GNSS-based EEWS, PEGSNet does not require prior assumptions on the data, because it is trained to learn features from the entire network of stations regardless of their data quality.
Implications for early warning We have demonstrated instantaneous tracking of moment release for large earthquakes (Fig. 3 ). Our results promote PEGS as a new class of observables, easily accessible from the recordings of currently deployed broadband seismometers worldwide, for practical application in early warning systems that are currently limited by the speed of P-waves. In the context of EEWS, PEGSNet can complement any existing algorithm (either seismic-or GNSS-based), to improve M w latency estimation and accuracy for M w > 8. 3 megathrust earthquakes. For example, PEGSNet could be combined with a recent deep learning model based on GNSS data  29  to eliminate intrinsic latency due to P-wave speed. At the same time, PEGSNet can immediately prove critical for tsunami early warning for which M w estimation within a few minutes is vital.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-022-04672-7. Source parameters. The event source location is randomly picked from 1,400 possible locations extracted from the Slab2. 0 model of subduction geometry  38  at two different depths (20 and 30 km). Given latitude, longitude and depth, strike and dip angles are determined by the subduction geometry and rake is randomly extracted from a normal distribution with mean = 90° and standard deviation of 10°. The event final M w is drawn from a uniform distribution with min = 5.
Computing synthetic waveforms. With the selected source parameters and STFs, we use the normal-mode approach described in a previous work  14  to compute three-component synthetic waveforms in a spatial domain of about 20° around the source epicentre. The resulting seismometer responses are convolved with the STF of the corresponding synthetic event and multiplied by the scalar moment to obtain synthetic traces of acceleration sampled at 1 Hz at each station location. Finally, traces are bandpass-filtered between 2. 0 mHz (Butterworth, two poles, causal) and 30. 0 mHz (Butterworth, six poles, causal). The final seismograms are 700 s long centred at the event origin time. Noise database. The noise database consists of 259 days of threecomponent waveforms for two non-continuous time intervals: between January 2011 and October 2011 (excluding March 2011) and between January 2014 and April 2014.
These intervals have been chosen to sample variable (seasonal) noise conditions. We note that the temporal range spanned by the noise database does not overlap with any of the earthquakes used for real data cases (Extended Data Fig. 10a ). We first divide the daily recordings into 1-h-long traces and then apply limited preprocessing, removing the instrument response, the mean and the linear trend, converting to acceleration and decimating the original traces from 20 to 1 Hz. Finally, each trace is filtered using the same bandpass filter applied to the synthetic seismograms (see previous step) and stored. Note that no a priori assumptions on levels and characteristics of the selected noise are made. On the contrary, we include all real noise conditions found in continuous seismic recordings in the specified period range.
This is because, in principle, we want the model to be able to generalize well under a broad range of noise conditions. Adding empirical noise to synthetics. From the noise database described in the previous step, a realization of noise (700 s long) is extracted by randomly selecting a starting time point. In this process, we make sure to use different noise data in training, validation and test sets. To preserve spatial coherence of noise across the seismic network, the same time interval is used for all stations for a given event. The selected noise traces are then added to the corresponding acceleration seismograms to produce the final input data for PEGSNet. If noise data are not available for one or more stations in the selected time window for a given event, we discard those stations by setting the corresponding final trace amplitudes (noise and PEGS) to zero in the input data.
This threshold is chosen according to the maximum PEGS amplitude for an M w = 10 earthquake at 315 s as found in the calculated database of noise-free synthetics. Amplitudes are finally scaled by 10 nm s -2 to facilitate convergence of the optimizer, and at the same time, to preserve information about the relative amplitudes of the PEGS radiation pattern across the seismic network. Finally, to simulate missing data and/or problematic sensors, we randomly mute 5% of the stations for each event by setting to zero the amplitudes of the corresponding traces.