dataesr/scientific-paragraphs-categorization huggingface dataset:
This WG will organise integration of DDSS elements into both community and EPOS Integrated Core Services (ICS) data portals. In particular: -assess candidate products and services for their feasibility to be integrated into the EPOS DDSS granularity Besides the integration into the main EPOS ICS (Integrated Core Service) cross-community data portal, tsunami community resources will be also presented in a harmonised way through the new Community Portal (Figure  8 ) (http://www. tsunamidata. org). Due to its natural affinity to the rapidly developing HPC-technologies, cTCS Tsunami will contribute to the Last but not least, at a broader scope, the Tsunami Community considers participation in EPOS also as an integration point for community development. The existence of a TCS: -may become a community "meeting point", a permanent alternative to large (but intermittent) international projects; -motivates and provides practical support in bringing research outcomes (data, software) to high technology readiness level and, as a consequence, "closer" to less-specialised end-users; -may push researchers to create synergies and to make their products interoperable (e.
database; -develop a roadmap for integration of individual services; -provide technical support along the roadmap (e. g. , by following blueprint solutions); -provide support for the TCS Tsunami entry into the EPOS Cost-Book. The two pilot community services --(i) Global sea-level monitoring facility presented by Flanders Marine Institute (Oostende, Belgium) and (ii) European probabilistic tsunami hazard model NEAMTHM18 presented by INGVTowards the new EPOS TCS Tsunami(Rome, Italy) -have already undergone EPOS ICS data portal integration efforts during 2021 TCS-ICS development pitches, and, after the final validation and performance testing activities, will be soon available through the Service Delivery portal. These two pilot services will open the door, as blueprints, for the further integration of community data and products (see Section 4) under the support of WG-Services.
Using classical statistical methods may affect the accuracy and the reliability of the developed QSAR models. This work aims to use a machine learning approach to establish a QSAR model for phenols cytotoxicity prediction. This issue concern many chemists and biologists. In this investigation, the dataset is diverse, and the cytotoxicity data are sparse. Multi-component description of the compounds has then been considered. A set of molecular descriptors fed the deep neural network (DNN) and served to train the DNN. The established DNN model was able to predict the cytotoxicity of the phenols at high precision. The correlation coefficient at the fitting stage was higher than other statistical methods reported in the literature or developed in the present work, specifically multiple linear regression (MLR) and shallow artificial neural networks (ANN), and was equal to 0.
943. The predictive capability of the model, as estimated by the coefficient of determination on an external predictive dataset, was significantly high and was about 0. 739. This finding could help implement many molecular descriptors relevant to describing the compounds, representing the effects governing the phenols cytotoxicity toward Tetrahymena pyriformis, avoiding overfitting and outlier exclusion. processes or models predicting the toxicity accurately. Quantitative Structure-Activity Relationships (QSAR) are relevant techniques that develop theoretical models and help to understand such mechanisms  [18] ,  [19]. QSAR models are of big interest to biologists and chemists in many research domains for modeling the relationships between key parameters and endpoints  [20]. Endpoints may be a biological activity, toxicity, or physicochemical property.
They proved to be more efficient than other statistical methods and ML models. However, although the advantages ANN present, they suffer from serious problems: Actually, molecular descriptors constitute the inputs of the networks, and in many instances, there is a need to use many of them since they contribute to a better description of the molecules and their effects on the biological activity or property. Often, correlations between them added to the dataset size limit their implementation in the model and reduce the model interpretability and reliability  [24]. In previous work, we were brought to determine a ratio ρ to avoid this problem  [25]. Very often, the application of ANN leads to the overfitting problem that significantly affects the model's prediction ability. Much attention was given to these problems, and the typical solutions proposed to deal with these issues were to reduce the number of inputs, which affects the chemical information provided to the model, to use few neurons in the hidden layers, or limit the number of samples used to train the network by removing the samples that the modeling technique cannot appropriately handle.
The immense progress provided by the DNN helps to circumvent these issues  [26] -  [29]. In fact, by effectively using a multi-level learning strategy, the DNN can process data more accurately. We go hierarchically through the different layers from low-level pattern extraction to a higher level. These different levels correspond to different levels of data abstraction. Processing these extracted patterns leads to high accuracy in detecting meaningful features and making accurate predictions. They are rapidly optimized, and they avoid the overfitting issue from which they suffer the application of shallow neural networks  [19] ,  [30] ,  [31]. Using numerous descriptors, large datasets, and multiple hidden layers becomes possible. They now gain significant interest in almost all problems that need information extraction without human intervention and QSAR model development.
It is noteworthy that DNNs are intensively used in classification and transcription problems. Few works use DNN in building regression models  [32] ,  [33]. They achieve high performance in predicting biological and pharmaceutical properties  [32] -  [35]. Many QSAR models studying different datasets of phenols were established  [17] ,  [36] -  [41]. Most of them have used multiple linear regression (MLR) and led to many linear models. Other models were also developed, namely by MLR and ANN. In this study, we use DNN to develop a regression-based QSAR model to predict the cytotoxicity of phenols to Tetrahymena pyriformis. For this purpose, we generated several molecular descriptors to serve as network inputs. Vol. 8.
Method 2. 1. Dataset Phenols are chemical compounds with a structure containing a hydroxy group connected to an aromatic ring. In the present work, we investigate a dataset containing 250 phenolic compounds with different types of substituents on the aromatic ring. Substituents in different positions (ortho, meta, and para) were considered. These compounds were studied earlier by Cronin et al. [42] , and are available via the QDB Databank repository  [42] ,  [43]. The data contain mono-, bi-, and tri-substituted phenols. In previous work, Selassi et al. [39] ,  [44] ,  [45]  studied different datasets of phenols. Datasets with electronreleasing and electron-withdrawing substituents were considered separately, and in the present work, both types of substituents were implemented in the same dataset.
The structures of phenol and some studied phenolic compounds are represented in Fig. 1. IGC50 expresses the cytotoxicity of the 250 phenols to the Tetrahymena pyriformis, the 50% growth inhibitory concentration (mmol/L) of a compound to Tetrahymena pyriformis, regardless of their mode of action (MOA). For calculation conformity, the log values (1/IGC50) were considered as endpoints. It is noteworthy that the dataset contains very diversified endpoints. The log values (1/IGC50) range from -1. 5 to 2. 71, with a mean value of 0. 739 and a standard deviation of 0. 828. The variability of the cytotoxicity in our data is shown in Fig. 2. To build the model, the dataset was divided into two subsets. We used randomly selected 80% of phenols to perform the fitting stage, and the remaining 20 % served as an external predictive dataset.
Generation of Molecular Descriptors From our experience, a successful QSAR study relies largely on the set of molecular descriptors used and their ability to depict the biological effect meticulously  [25] ,  [46]. There are two levels of molecular description; the first level consists of describing only the substituents, and the second consists of describing the whole molecule. For this investigation, the endpoint values were sparse, and the substituents were significantly diversified, and there was a need to generate unswerving molecular descriptors that represent the features of the whole dataset and characterize its diversity. We choose then to use molecular descriptors that describe the entire molecule. DNN accepts as inputs a large amount of information as precise as possible to extract features and achieve good results.
In fact, a chemical compound is constituted of a connection of atoms of different nature. Each atom has its inherent characteristics, and it is connected to other atoms and so affects the behavior of the whole compound. A small change in this structure induces, in many instances, meaningful changes in the biological behavior of the compound. The molecular connectivity approach based on chemical graph theory  [47]  provides such precision and information. Many structural descriptors that give details on the molecular connectivity were implemented. They were represented by Kier  [47] ,  [48]  indices a structural fingerprint  [49]. They were generated by the QSARIN software  [50]. Actually, fingerprints are numerical values that encode fragments or subgroups in a molecule. Molecular reactivity depends not only on the graphical representation of a molecule but also on the atoms' intrinsic quantum and physicochemical properties.
Generation of Molecular Descriptors To establish the QSAR model, we developed our own program using the Keras library package  [54]  with the Tensorflow framework  [28] ,  [55]. DNNs are based on ANN concepts with many deep nodes  [26]. Many hyperparameters had to be adjusted with attention to the calculation time optimization and the network accuracy. The inputs of the networks were carefully chosen. Although many molecular descriptors were generated, only the relevant ones were considered and served as inputs. A total number of 24 molecular descriptors were then implemented. The robustness of DNN lies in its non-linear units that process data features at one level into feature data at a higher level. The hyperbolic tangent function was used as the activation function of the hidden nodes.
Results and Discussion The dataset was randomly split into a training dataset (200 samples) and a predictive dataset (50 samples). The distributions of the cytotoxicity for both datasets (training and predictive) were quasi similar. The training stage afforded an opportunity to fine-tune the internal networks hyperparameters To assess the accuracy of the established models, two metrics were adopted; the statistical root-meandeviation (RMSD) and the correlation coefficient R². The analytical formulae of these parameters are given in (  1 ) and (  2 ) below. 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = � ∑ (𝑇𝑇 𝑡𝑡 ^-𝑇𝑇 𝑡𝑡 ) 𝑡𝑡=𝑁𝑁 𝑡𝑡=1 𝑁𝑁 (1) 𝑅𝑅² = ∑ (𝑇𝑇 𝑡𝑡 -𝑇𝑇 ^) 𝑡𝑡 ∑ (𝑇𝑇 𝑡𝑡 -𝑇𝑇 𝑡𝑡 ¯) 𝑡𝑡 (2) where 𝑇𝑇 ^ is the calculated cytotoxicity, 𝑇𝑇 is the mean of the observed cytotoxicity, and N is the number of studied compounds. A correlation coefficient and an RMSD as close as possible to 1 and 0, respectively, are anticipated.
The previous works reported in the literature had to remove any outliers. However, no outliers were detected in the present study. Much information can be deduced from these compounds, and removing them from the model will lead to poor performance. After the fulfilling results of the fitting stage, we had to validate the model and ensure that the network built could process new data and predict the cytotoxicity of new compounds. A testing stage was then performed using a Leave-One-Out procedure. The promising results led to a leave-one-out cross-validated R² (q²) of 0. 941 and an RMSD of 0. 203. These results led to adopting the constructed model to predict new activities. In this investigation, our main focus was the model's predictive accuracy. Fifty phenolic compounds were used as an external prediction set.
We ensured that this dataset comprised new phenolic compounds structurally close to the compounds in the fitting stage (Fig. 2 ), and the network had never been seen. To assess the DNN prediction capability, we used the coefficient of determination and the root-meansquare error of prediction (RMSEP) metrics. This stage resulted in a high R² that equals 0. 739, and an RMSEP equals 0. 434. The predicted values versus the valid cytotoxicity values are reported in Fig. 4. It shows that the established DNN model can predict the cytotoxicity of new phenols with high accuracy. Moreover, a residual analysis has been carried out to assess our model. It led to a closer examination of the established regression-model quality and its capability to predict the cytotoxicity of new phenols to Tetrahymena pyriformis.
This analysis offers the opportunity to investigate each error made on the target outputs. The results are depicted in Fig. 5. A gaussian-like distribution of the prediction residuals was obtained. As shown in the histogram of Fig. 5 , most of the errors made on the predicted values of cytotoxicity were between -0. 5 and +0. 5. This proves the accuracy and the validity of the model and that the choice of the model is appropriate. Outliers, the samples from the dataset that a developed model cannot fit correctly, represent a big challenge for QSAR modeling, yet the success of the QSAR model relies on its ability to predict the endpoints of new compounds. Removal of these prototypes is assumed to improve the accuracy of the model. Interestingly, for almost all the established QSAR models for phenols cytotoxicity reported in the literature  [39] ,  [42]  using other statistical methods (MLR, partial least squares (PLS)), there was a necessity to exclude large numbers of outliers to obtain good fitting models.
Conclusion The research objective in this QSAR investigation is to build an accurate predictive model. Thus, an external dataset of 50 phenols, as sparse as the training dataset and with structural features close to the fitting dataset (containing electron-donor, electron-attracting, and mono, bi-, tree-substituents) served as a predictive dataset. In contrast to the models reported in the literature, the present DNN predicted the cytotoxicity of new compounds at about 74% of precision. All the proposed compounds were well.
0, 0. 5 M NaCl. Szl-SeMet was eluted using the peptide TETSQVAPA (Genscript), at 400 µg/mL. For elution, 3 mL of the peptide solution was added to the resin with bound Szl-SeMet then shaken at 4 °C for 2 h. Three fractions of 1. 5 mL each were collected in 20 mM MES pH 6. 0, 0. 5 M NaCl, then Szl-SeMet eluates were pooled and loaded onto a Superdex 200 10/300 Increase column (GE Healthcare) pre-equilibrated and eluted using the same buffer. Szl-SeMet eluates from peak fractions were then pooled and concentrated using Vivaspin concentrators (Sartorius) for setting up crystallization screens. The final yield of Szl-SeMet was 450 µg from 12 L of conditioned medium. Small angle X-ray scattering. SAXS data were collected at B21 at the Diamond Light Source UK using samples in 20 mM MES pH 6. 0, 0.
Crystallization of native Szl and Szl-SeMet. Prior to crystallization, proteins were concentrated to 12 mg/mL using Vivaspin concentrators. Initial crystals of native Szl were grown employing the sitting-drop vapour-diffusion method using 96-well plates (Greiner) at 20 °C. A Mosquito Nanolitre Robot (STP Labtech) was used to set up crystallization screens. Crystals of Szl were grown in 0. 1 M NH 4 OAc and 18% PEG 3350 after incubation for 2 weeks. Diffraction quality crystals were then obtained in optimized conditions using 0. 2 M NH 4 OAc, 18% PEG 3350 as precipitant. These latter were flash frozen in liquid nitrogen with 17% glycerol added to the crystallization buffer as cryo-protectant. Crystals of native Szl were reproduced in hanging drops and used for X-ray data collection. For Szl-SeMet crystallization, screening was carried out as for Szl.
Data collection, data reduction, phasing and structure determination. Diffraction data for native and Szl-SeMet, were collected at 100 K on beamlines I02 and I03 of the Diamond Light Source (Oxfordshire, UK). Data for Szl-SeMet crystals were collected by inverse beam SAD in two sweeps starting at 0-45° and 180° apart. Native Szl crystals diffracted X-rays to 1. 9 Å while for Szl-SeMet the best crystal diffracted X-rays to 3. 3 Å resolution. Data were processed using the Xia2 pipeline  31  at Diamond combining XDS  32  and POINTLESS from the CCP4 suite  33. For phasing, four independent datasets from 4 different crystals were merged to enhance the anomalous signal. Heavy-atom coordinates of merged datasets for Szl-SeMet were located using Autosol from the PHENIX suite 34 though with poor phasing power.
Szl reflections were finally phased by combining a partial molecular replacement model obtained using the PDB coordinates of the cysteine rich domain (CRD) of mouse SFRP-3 (PDB ID 1IJX)  35  and also the "search for model in map" module of the programme MOLREP 36 employing the 1. 95 Å native Szl dataset. Manual iterative rebuilding and fitting were done using COOT  37  and model refinement was done with phenix. refine  38 and Refmac 5. 6 39 using a combination of TLS and restrained refinement. Model geometry and stereochemistry was examined using Molprobity 40.
Molecular docking. Prior to docking, the protein preparation wizard PrepWizard (Schrödinger release 2018-3, Bioluminate, Schrödinger (suites2018-3/psp-v5. 3), LLC, New York, NY, 2018) was used to optimize the crystal structures of Szl (determined in this study) and of the BMP-1 catalytic domain (PDB ID 3EDH)  24. This included addition of hydrogen atoms and missing side-chains, filling missing loops, optimization of bond orders and hydrogen bonds and generation of ionized states. Water and other hetero-atoms were also removed. Restrained minimization of structures was carried out within an overall root mean square deviation (RMSD) of 0. 3 from the initial energy minimized state. The PRIME structure prediction panel of the Schrödinger-Bioluminate suite was used for building the missing loop/linker ( 138 DTLSKEYQYSYKE 150 ) region connecting the CRD and NTR domains of Szl.
Loops were further refined in PRIME to avoid any clashes. The crystal structure of Szl has two independent monomers (chains A and B) in the asymmetric unit, with the chain B structure being the most complete. Chain B was therefore further subjected to energy minimization and used for docking (with and without inter-domain linker loops) into the BMP-1 catalytic pocket. Previously acquired data on key residues in Szl (Glu44, Asp92)  5  and BMP-1 (Arg182)  24  required for inhibition of BMP-1 activity were exploited to generate docking poses of the proteinase-inhibitor complex using the PIPER module of the Schrödinger-Bioluminate suite  41  , followed by refinement and energy minimization in PRIME. PIPER uses efficient Fast Fourier Transformation (FFT) in combination with pairwise interaction potentials using a docking code  42  which greatly reduces the number of initial poses.
Here we show that key residues in Szl occupy one of these subsites (S1′) as well as other subsites S2′, S1, S2 that span the catalytic site cleft (Fig. 1 , Supplementary Figs. S7  and S8 ). In this regard, it has recently been shown  22  that the binding to BMP-1 of small molecule hydroxamate (PDB IDs 6BTP, 6BTQ) and reverse hydroxamate (PDB IDs 6BSL, 6BSM) inhibitors can be associated with important movements in key residues of BMP-1, particularly its vicinal disulphidebridge (Cys65-Cys66), as demonstrated most clearly with reverse hydroxamate compound 22 (PDB ID 6BSM, Supplementary Fig. S12b ). These observations confirm the previously suspected flexibility of this region  24, 43. Regarding the role of the vicinal disulphide bridge in the BMP-1cat/Szl complex, Supplementary Fig. S12a  shows that movements in this region similar to those observed with small molecule inhibitors may lead to changes in the relative orientations of BMP-1cat and Szl, particularly near subsite S2.
Acknowledgements This research was funded by the  French National Research Agency  (project 'TOLLREG' awarded to CM and NA),  FP7 PCUBE  (ID:  227764 ),  FP7  BioStruct-X  FP7  (ID:  283570 ),  CNRS  and the  Université Claude Bernard Lyon 1. US is currently supported by a  Ramalingaswami Re -entry fellowship,  Department of Biotechnology, Government of India. The  Wellcome Centre for Human Genetics  is supported by the  Wellcome Trust  (Grant.
Cognitive test batteries are widely used in diverse research fields, such as cognitive training, cognitive disorder assessment, or brain mechanism understanding. Although they need flexibility according to their usage objectives, most test batteries are not available as open-source software and are not be tuned by researchers in detail. The present study introduces an open-source cognitive test battery to assess attention and memory, using a javascript library, p5. js. Because of the ubiquitous nature of dynamic attention in our daily lives, it is crucial to have tools for its assessment or training. For that purpose, our test battery includes seven cognitive tasks (multiple-objects tracking, enumeration, go/no-go, load-induced blindness, task-switching, working memory, and memorability), common in cognitive science literature.
While this constraint allows researchers to share a common standard framework, it does not easily allow the work to be extended to new goals. Since the trend of experimental environments quickly changes depending on the technology development, flexibility and openness of the software are essential to ensure that the test battery is used over a long period. For instance, there has recently been a great demand to investigate online experiments. Some recent cognitive training studies also utilize online training. To evaluate the effect of such training, one needs to evaluate the cognitive ability using an online test battery. Since our test battery uses a browserbased platform, using a javascript library, p5. js, experimenters can flexibly launch the experiment under various environments referring to its source code.
For the cognitive mechanism understanding, our investigation can clarify what kind of underlying cognitive abilities mediate memorability performance by comparing other cognitive task performances. Moreover, for the test battery development, including a cognitive task using natural scene images is needed to assess the ecological validity of cognitive training because most tests use artificial stimuli. The difficulty of using natural scene images is how to control the task difficulty as it has to be controlled constantly using different natural images. An advantage of the memorability datasets is that the previous works of the memorability share the experimental data  (Isola et al. , 2011; Bylinskii et al. , 2015 Bylinskii et al. , , 2021;; Khosla et al. , 2015) , and we can extract constant difficulty images from the datasets.