dataesr/scientific-paragraphs-categorization huggingface dataset:
In our implementation the normalization is carried out by the software. Distribution The rise of on-line predictions markets and crowd-prediction websites, like, for instance, Metaculus or PredictIt, has created the need for intuitive tools to enter predicted values. For instance, Metaculus uses an interface based on a bounded bell-shaped curve, that is controlled with Left and right handles add mass on the left (or right) of the distribution, increasing in passing its dispersion. We reached out to Metaculus to ask for their code, but did receive a limited reply that made us unable to exactly replicate their interface. We hence created a Distribution elicitation interface based on a skew normal distribution, defined by three parameters: ξ for the location, ω for the scale, and α for the shape.
Measures We collect data on each click the subjects make. This allows us to derive two main quantitative measures of the performance of each interface. Accuracy. We measure accuracy as the complement to 100% of the normalized distance of the final, submitted distribution from the target. Better interfaces have higher accuracy. Adjustment path. We measure accuracy at each moment subjects interacted with the interface. 4  This allows us to see the path followed by the subjects to get nearer to the target, and hence to assess the speed of convergence to the final, submitted distribution. Some interfaces could allow subjects to quickly get the main strokes done, to then fine-tune the resulting distribution, while others might require to advance by smaller steps. Better interfaces allow for quicker convergence to the target distribution.
Confirmatory results We pre-registered our main hypotheses on OSF, at https://osf. io/ft3s6. All data and analysis script to reproduce all the results in this paper are available at the OSF page of this project or in the dedicated github repository. We hypothesized that the Click-and-Drag interface would outperform the other three tested interface in terms of accuracy, robustness to increased number of bins, different distribution shapes, and stricter time constraint, convergence in time, and that it would be self-reported as more intuitive and less frustrating. 56.
The third competing interface, a Distribution 'shifting and stretching' interface, inspired by the interface of metaculus. com, tended to perform a little better (however overall still worse), but suffered from being found unintuitive and frustrating according to a participant questionnaire. Moreover, the Distribution interface generated the highest drop-out rate of all interfaces, being arguably harder to manipulate. Our experiment, including the detailed data on participant performance over time, gives us a good insight into how people react to the different elicitation interfaces. One element to be possibly explored for future research is how changing the starting distribution affects the performance of the Click-and-Drag interface, as we saw that the interface where we provided participants with a ready made normal distribution, performed quite well under certain settings.
Solutions procedure Two-phase fluid flow is a complex flow system with hydrodynamic and thermal coupling, where the numerical solution of governing equations is conducted individually for each phase. The velocity, temperature, viscosity, pressure gradient, and shear rate calculation of the fluid need to be iterated until all converges. A computer code is established for the computation of the governing equations and a flow chart algorithm is described in Fig. 4. The solution is explained in the following steps below:   (1) Flowline operations conditions are set such as flowlines length and diameter, inlet and surrounding temperature, heat flux, and two-phase fluid flow rates. (2) Estimate the two-phase physical properties. The liquid physical properties are calculated. T T w ¼ To ¼ T inlet vT vx ¼ 0 h ¼ ðT wall À TmÞ ¼ l vT vr p À rgh vP vx ¼ 0 P w&o;outlet ¼ 0 vP vz ¼ 0 k k w;o ¼ k w;&o;inlet vk w;o vx ¼ 0 kw;o;g ¼ k wall m t m t;cal ¼ 0 m t;cal ¼ 0 m t;kwallfunc ¼ m t;wall ε ε w;o ¼ ε inlet vεw;o vx ¼ 0 ε wallfunc ¼ ε wall (3) The velocity, kinetic energy, and dissipation rate are acquired from the momentum conservation equation and turbulence model resolved for each phase by iteration until the convergence criterion is achieved.
The purpose of this study is to map the cavitational activity of the well documented 20kHz sonoreactors by SCL, in a large range of experimental conditions, including the liquid height, the transducer immersion depth, and the input current amplitude, which is the quantity experimentally controlled by the generator level button. For each experimental configuration, a calorimetric measurement is also performed and all electrical data are monitored. Finally, each configuration is also simulated with our model and systematically compared to luminol maps (183 in total), after a careful correction of image aberrations due to the vessel curvature. Results of KI oxidation experiments under the same conditions will be presented in a future paper.
Numerical study The Finite Element Method (FEM) was used to implement our model in the commercial software COMSOL MULTIPHYSICS. The model used for the cavitating liquid has been described and discussed in earlier studies  [39, 40, 42, 43]. It is based on a nonlinear Helmholtz equation, derived from the Caflisch model describing nonlinear propagation of sound waves in bubbly liquids  [35]. This equation is used in those regions where the acoustic pressure exceeds the Blake threshold  [66] [67] [68]   P B = p 0 ( 1 + ( 4 27 S 3 1 + S ) 1/2 ) , S = 2σ p 0 R 0 , ( 4 ) where σ is the surface tension, R 0 the bubble ambient radius, and p 0 the static pressure. In other regions, a pure liquid is assumed. The complex amplitude of the acoustic pressure field is therefore solution of: ∇ 2 P + k 2 l P = 0 where|P| < P B (no bubbles), ∇ 2 P + k 2 NL P = 0 where|P|⩾P B (bubbles), (5) where k l = ω/c l.
Piezoelectric rings are modelled by classical equations coupling the stress tensor T, the strain tensor S, the electric field E and the electric displacement field D, available in the COMSOL Piezoelectric Effect Multiphysics, which couples the Solid Mechanics physics module to Gauss law implemented in a Electrostatics physics. The liquid pressure field is coupled to the vibrations of the solid boundaries by standard continuity conditions. The free liquid surface is assumed infinitely soft and the external boundaries of transducer and the vessel are assumed free. The reader interested in the details of the equations is referred to Refs  [43, 42]. Fig. 3  summarizes all the boundary conditions used in the model. The exact value of the operating frequency used in experiments is not exactly known, but is set automatically by the generator.
Acknowledgements The authors gratefully acknowledge  SinapTec  for financially sup-porting the research through an industrial  PhD grant  ( N • 2019/1344 ) awarded to  Igor Garcia-Vargas  by the  French Association Nationale de la Recherche et de la Technologie (ANRT). Special thanks are extended to  Jean-Pierre Escafit  and  Bruno Boyer  from  LGC  for their valuable technical support with the experimental setup. Additionally, the authors would like to acknowledge the  French Centre National de la Recherche Scientifique (CNRS)  for supporting the cavitation research group ( GDR CAVITATION) , which facilitated fruitful discussions with colleagues in the field.
1A ). While previous structural studies have characterized TG2 in two conformational states ("open" and "closed") using crystallography  36, 37  , evidence suggests that these two states do not adequately capture the protein's conformational plasticity. When TG2 is bound to GDP, GTP or other purine nucleotides, it adopts an enzymatically inactive "closed" conformation. In this state, the C-terminal β-barrel domains tightly fold over the catalytic domain, obstructing access to the active site (PDB:1KV3)  37  (Fig. 1B ). However, when the active site is covalently bound to an irreversible inhibitor Z-DON, TG2 undergoes a large conformational change (PDB:2Q3Z)  36. The β-barrel domains are prevented from interacting with the catalytic core, resulting in an "open", extended conformation (Fig 1B).
Although the PDB:2Q3Z conformation is often associated with the catalytically active TG2 due to the readily accessible active site, there are doubts regarding its true representation of the active form  38. Firstly, PDB:2Q3Z contains a Cys370-Cys371 disulfide bridge near the active site, which inhibits TG2 catalytic activity  39, 40. Secondly, the formation of this disulfide bridge induces local changes in peptide backbone conformation, disrupting the calcium binding sites  40. Consequently, the PDB:2Q3Z structure, bound to Z-DON inhibitor, does not include any calcium ions, despite their known requirement for the crosslinking function  36. Finally, no crystal structure of catalytically active TG2 bound to calcium ions has been solved to date. Therefore, it is unlikely that the extended conformation observed in the Z-DON inhibitor-bound TG2 structure accurately represents the calcium-bound catalytically active protein.
5 ). From experiments with the TG2 and GBD and chymotrypsin as a protease, 8 additional unique inter-protein crosslinks were detected (Supplementary Fig. 11 ). Notably, we identified a crosslink between Lys30 (TG2) and Lys486 (GBD, numbering for full length FN). TG2 residue Lys30 is known to be one of the three residues (Lys30, Arg116, His134) that comprise the main FN-binding site on the N-terminal β-sandwich of TG2  45. Interestingly, the crosslink involving Lys486 was not found when TG2 was in complex with the full-length FN (Supplementary Note 2 and Supplementary Fig. 10 ). Instead, Lys30 on TG2 was crosslinked to Lys1837 and Lys1862 on FNIII14. This difference could arise from the variations in reactivity between these lysine residues and structural differences. These MS data clearly demonstrate that the C-terminal domains of TG2 interact with regions of FN outside of the canonical GBD when FN is in a compact quaternary conformation in solution, and when the C-terminal β-barrels are in spatial proximity to the N-terminal domain.
Crosslink-guided structural modelling of TG2 and FN XL-MS not only identifies which regions of a protein complex are in close contact, but also provides valuable spatial information through physical distance restraints imposed by each crosslinker. The spatial information can be used for modelling and docking to determine the position and orientation of within a complex  80. To determine the binding interface of the TG2-FN complex using low-resolution restraint data more accurately, we integrated the experimental crosslinks into a modelling pipeline  81, 82  (Supplementary Fig. 13 ). We selected a few regions of FN for modelling based on criteria such as the availability of templates in PDB, coverage by crosslinks, and existing knowledge in the literature regarding binding sites. The regions that met these criteria and were suitable for structural modelling were FNI2-3, FNI7-9, FNI6FNII1-2 FNI7-9 (GBD), and FNIII14-15 (Supplementary Note 1).
To build crosslinkguided models, we used the I-TASSER  83  and submitted experimental intra-protein crosslinks as distance restraints to structurally refine the available crystal structure templates, as was previously done  81. To evaluate the compatibility of refined models with experimental restrains, we calculated Euclidean distances (ED) between β-carbons (CB-CB) of crosslinked residues using Xwalk  84. We classify crosslinks into compatible and non-compatible based on the distance cut-off values  78, 81  : ED for DSS < 35 Å, ED for DMTMM < 25 Å, ED for PDH < 35Å. We identified 4 intra-and 3 inter-protein crosslinks for FNI2-3. A high-resolution crystal structure of FNI2-3 was available (PDB:2cg7), which we submitted as a template to I-TASSER for structural refinement with experimental crosslinks.
After structural refinement, a high scoring model was selected, that satisfied all crosslinks within the distance cut-off (Supplementary Note 1). For FN's GBD, we detected 3 intra-and 8 inter-protein crosslinks. Templates covering FNI6FNII1-2FN7 and FNI8-9 domains, except for the short linker (513-516aa) between them, were available as PDB:3mql and PDB:3ejh, respectively. However, our initial attempt to model the entire GBD with I-TASSER was not satisfactory. Thus we used ROBETTA  85  and AlphaFold  86  to determine the orientation of FNI6FNII1-2FNI7 and FNI8-9 with respect to each other. To evaluate the quality of predicted models of GBD, we submitted them to QMEAN  87  , which evaluates how the model performs in comparison to the experimental PDB structures. The AlphaFold predicted that the GBD model outperformed the ROBETTA model (QMEAN Z-score=1.
64), whereas the ROBETTA predicted model deviated by two standard deviations (QMEAN Z-score=2. 02). Therefore, we selected the AlphaFold predicted model of FNI7-9 for further docking steps, given its overall better performance (Supplementary Note 1). We identified 11 intra-and 7 inter-protein crosslinks for FNIII14-15 and submitted the high-resolution crystal structure of FNIII14-15 (PDB:1fnh) along with crosslinks for structural refinement using I-TASSER. A high scoring model that satisfied 10/11 intra-protein crosslinks was selected. The noncompatible crosslink (Lys1862-Lys1936) connects two interdomain residues located on FNIII14 and FNIII15. As these two domains are connected by a flexible linker, it is expected that structural rearrangements favouring a more closed conformation might occur.
For TG2, we detected 46 intra-protein crosslinks, 8 of which violated distance cut-off when we mapped them on the model. All the non-compatible crosslinks were between the interdomain residues. Specifically, 5 non-compatible crosslinks were between residues on the catalytic core and C-terminal domain, 2 between residues on C-terminal domains, and 1 between N-terminal domain and the catalytic core (Supplementary Note 1). As these domains are connected by flexible linkers, non-compatible residues suggest that TG2 undergoes some structural rearrangement upon binding to FN in solution. After performing structural refinement, one notable improvement was residue Lys30, which on the available TG2 template (PDB:4pyg) did not comply with our solvent accessibility criteria (Supplementary Note 1). The Lys30 residue was previously shown by mutagenesis studies to be crucial for FN-binding  45.
Crosslink-guided HADDOCK docking of TG2 with FN7-9 revealed a parallel alignment of FN modules with TG2's C-terminal β-barrels Protein complexes in solution are typically dynamic, and crosslinks can reflect an averaged ensemble of conformations. However, non-compatible crosslinks that may belong to an alternative complex conformation can negatively impact the accuracy of docking results. To address this issue, we utilized the DisVis webserver, which filters out non-compatible crosslinks and predicts key residues involved in binding at the interaction interface using experimental crosslinks  65. Our analysis using DisVis highlighted three putative incompatible crosslinks, namely the Lys319(TG2)-Glu116(FN) crosslink between TG2 and FNI2-3, the Lys550(TG2)-Lys397(FN) crosslink between TG2 and GBD, and the Lys464(TG2)-Lys1862(FN) crosslink between TG2 and FNIII14-15 (Supplementary Note 2).
Images were analysed with custom-written Python script. Briefly, Gaussian filter (sigma=1) was applied to both FN and TG2 channels, FN channel was thresholded using Otsu algorithm and binary mask created, the mask was applied to both the FN and TG2 channel, replacing all background pixels with NaN, which were excluded from analysis. Then, TG2 signal was normalized pixel-by-pixel to the FN signal (TG2-647/FN-488), and the mean normalized TG2 intensity was calculated over the maximum intensity projection of the normalized TG2 signal.
Ratio-metric FRET image analysis and a correlation of TG2-647 binding with FN-FRET ratio. Ratio-metric FRET signal calculation and a correlation of TG2-647 signal to the FN-FRET ratios was performed pixel-by-pixel using custom written Matlab script, which was validated and described in detail before  62. Briefly, images from different channels were aligned using "imregister" Matlab function, thresholded, mean background signal subtracted, and a mask generated. To remove the pixels at the edges of fibers, where light scattering can create false FRET values, edges of the mask were eroded, then a local 3x3 averaging filter and a one-deviation Gaussian averaging filer were applied. FRET ratios were calculated by dividing pixel-by-pixel the corrected intensity from the acceptor emission due to FRET (channel 1 in Supplementary Fig.
Identification of cross-linked peptides. Preliminary experiments found unequivocal evidence for UniProt proteoforms P02751-1 and -9, with isoform 1 having the most proteotypic peptides. This isoform was therefore included in the sequence database and all numbering refers to it. In addition, the database consisted of the sequence of transglutaminase and the sequence of three contaminants identified from E. coli: CH60, SLYD, and ARNA. Target/decoy searches were performed using a decoy database containing the reversed sequences of all database entries. MS/MS spectra were searched against these databases using xQuest, version 2. 1. 5 (available from https://gitlab. ethz. ch/leitner_lab) 111. The cross-linking specificities were set as follows: For DSS, K and the protein N terminus; for PDH, D and E; for DMTMM, K with D and K with E.
Acknowledgements The authors thank  Chantel Spencer  for isolating plasma fibronectin,  Dr. Mario Benn  for discussions and general support,  Zhe Lin  for involvement in early preliminary experiment,  Dr. Szymon Stoma  ( ScopeM ) for checking the Python script which outputs normalized pixel-by-pixel FN-488/TG2-647 mean intensity values,  Scientific Center for Optical and Electron Microscopy (ScopeM) of ETH Zurich ,  Joachim Hehl  and  Tobias Schwartz  from  ScopeM  for support with Nikon Spinning Disk imaging. AL acknowledges  Paola Picotti  for access to instrumentation and laboratory infrastructure. The authors are grateful to  Prof. Ludvig Sollid  and  Dr. Jorunn Stamnaes  at the  University of Oslo  for discussions on their previous TG2-FN studies. FLB was supported by the  Swiss-European Mobility Program  ( SEMP ).
Considering the critical importance for the correct CRISPR/Cas9 systems performance, our scope will emphasize in the latter approach. Hence, we present an overview of the past and the most recent guide RNA web-based design tools, highlighting their computational architecture and gRNA characteristics evolution through the years. Our study concisely explains the computational approaches that use machine learning techniques, deep neural networks, and large datasets of gRNA/target interactions to make possible both predictions and classifications directed to design, optimize, and create promising gRNAs suitable for future gene therapies.
Computational areas have been used in the design of these gRNAs with powerful tools such as deep learning  [46] , which lead to the creation of several models for prediction. The main difference between off-target and on-target CRISPR prediction is the principle: on-target prediction models focus on predicting the effectiveness of the gRNA in cutting a specific target gene. These models identify gRNAs that effectively target a desired gene and are often used in gene editing applications. On the other hand, off-target prediction models, focus on identifying potential unintended effects of a gRNA in cutting other genes in the genome that are not premeditated targets. Both types of prediction models use machine learning techniques, such as neural networks and deep learning, to analyze large datasets of gRNA-target/genome pairs and make predictions about the activity of new gRNAs.
Neural Networks in gRNA Design Neural Networks (NN) are a branch of machine learning and artificial intelligence that teaches computers to process data in a way inspired by the human brain's structure and function. It consists of layered networks of neurons that process information and make predictions. Each neuron passes information to its linked neurons by multiplying the input with the link weights and transforming the data with an activation function  [47] , allowing the network to process complex information. The final layer produces the network's output, which could be a prediction or a classification of the input data. NN are trained using a dataset of input-output pairs, where the network learns to map inputs to corresponding outputs by adjusting the weights and biases of its neurons.
This process, called backpropagation, involves repeatedly feeding inputs through the network and comparing its output to the desired output, then updating the network's parameters to minimize the difference between them. This process is repeated for many iterations until the network converges to weights that minimize the loss function. In this way, the NN "learns" to make predictions or classify inputs based on the patterns in the training data. NN can be designed in various architectures to suit data types and learning tasks. The architecture of a NN is determined by the number and types of layers, the activation functions used, and the connections between the neurons. Different architectures may be more suitable for certain types of data or learning tasks and require different "hyperparameters" to be set to optimize their performance.
The most used architectures in the area of CRSPR prediction are the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) (see Figure  5 ). CNN are designed to process two-dimensional images and typically includes convolutional and pooling layers. The main advantage of this architecture is that it combines the process of feature extraction and training in only one process. On the other hand, a RNN are designed to process data sequences and includes recurrent layers such as LSTM or GRU. The main advantage of this architecture is that it can remember previous inputs and use that information for their task. Recent advances in the field of CRISPR prediction using NN include the development of deep learning-based models that can predict on and off-target effects. These models are trained on large datasets of CRISPR-induced genomic changes and use this information to learn patterns in the data that can be used to predict off-target effects.
Reaching Efficiency Since the beginning of the gRNA-design algorithms, scientists have widely used these programs to find the gRNA of interest or gRNAs whose use must be avoided. The concerned efficiency of these programs is of significant importance for research, and it can be measured according to the evaluation metric previously presented. In 2013, Hsu et al. [35]  published their web-based off-target sites predictor, CRISPRtool, also known as the MIT CRISPR Design Tool. They designed their experimental data from which they obtained hand-crafted features and implemented a score based on correct matches and mismatches. In 2014, the CRISPRtool was used to design the best gRNAs, targeting two tumor suppressor genes and one oncogene and then mutating them  [70]  directly for mouse lung cancer; their transient transfection reached a maximum of 44 % of indels.