dataesr/scientific-paragraphs-categorization huggingface dataset:
2015). All these models put together result in the prior distributions of the variables of interest (i. e. , IM, DS, S). • 'Live' data, which represents the observations (strong-motion recordings and damage identification of bridges) that can be entered as evidence in the variables IM and DS. The objective is to constrain the prior estimates by generating posterior distributions of the variables of interest, given the evidence. The road network is subjected to a pre-processing step, with the objective of simplifying the initial graph topology into an abstract Super-Network that is specifically suited to connectivity analysis. This step has the effect of reducing the network's size and complexity, thus facilitating the identification of MLSs. All the data is then entered in a BN structure, which is solved with OpenBUGS: the sampling inference algorithm generates Monte-Carlo Markov Chains (MCMCs), which contain numerous samples of all variables, given the evidence.
It is proposed here to build a BN with the OpenBUGS tool  (Lunn et al. 2009 ), which enables the modelling of continuous and discrete variables in the same BN. The OpenBUGS library is freely available and integrated in the R (www. r-proje ct. org) environment (see demonstration script in the Electronic Supplement of the paper). This tool has been used in various seismic risk applications, such as the derivation of empirical fragility functions for residential buildings  (Ioannou et al. 2020) , the updating of damage models for water pipelines  (Gehl et al. 2021) , or the integration of various types of observations for the identification of structural damage  (Tubaldi et al. 2021 ). The trade-off is the use of approximate inference, via MCMC sampling, instead of exact inference such as the junction-tree algorithm  (Huang and Darwiche 1996).
Results The comparison between the Gaussian BN and the OpenBUGS outcomes is detailed in Table  1 , by considering two evidence scenarios: scenario 1 only considers that log im 3 = -0. 1; while scenario 2 assumes that an additional type of observation is available, i. e. that component #2 has survived (i. e. , ds 2 = 1). The R script that builds the corresponding OpenBUGS BN is provided as an Electronic Supplement of the paper, as a demonstrator of the updating procedure. An example of OpenBUGS model is shown in Fig. 6. Due to the sampling approximation present in both methods, the posterior values cannot be perfectly matched; however, they are in very close agreement, and most discrepancies appear around the 3rd or 4th decimals. Such a level of precision is sufficient for seismic risk applications since it appears that the mismatch is negligible in light of the much greater uncertainties that usually accompany loss assessment models.
Description of the case-study area The proposed BN approach for rapid response is applied to a road network composed of 118 bridges (i. e. , vulnerable components), which connects 53 municipalities (i. e. , built areas) located in a valley around Bagnères-de-Luchon (Pyrenees, France). The case-study area is detailed in Fig. 8. The typologies of the 118 bridges are identified based on photographs and aerial pictures, and their conditional probability of failure is defined by fragility functions, some of which are taken from the SYNER-G database  (Crowley et al. 2011). The most common bridge type consists of short single-span bridges (length < 50 m), followed by masonry arch bridges. In total, 18 different fragility curves have been assigned (3 models for the 83 single-span bridges, 3 for the 7 continuous multi-span bridges, and 12 for the 28 arch bridges).
Finally, the results of such a BN application can constitute the starting point of rapid repair strategies for bridges, in order to improve the seismic resilience of transportation systems. While the present study has focused on methodological developments and on the demonstration of a simple yet realistic case-study, further work should be devoted to the verification of this framework on a real earthquake case, where collected post-earthquake data could be used to replay the sequence of events and evaluate the ability of the BN to reduce uncertainties on loss estimates. Although the theory behind the BN model has been checked (e. g. , validation of the ground-motion updating part in Sect. 5. 1), an application to a real earthquake case would have the merit of confronting the method to actual conditions (e.
Similarly, we found that increasing drying spatial extent decreased average community recovery, while drying duration had a smaller effect, echoing some recent empirical findings  (Crabot et al. 2021). Furthermore, we showed that local species richness in non-drying patches also decreases with connectivity loss in a nonlinear way. This result is corroborated by the empirical findings of  Horváth et al. (2019) , who used a dataset on invertebrate species in ponds spanning six decades of habitat loss to demonstrate that habitat loss also translates into species loss within the remaining habitats. Using patch connectivity loss as a common currency is promising to gain general insights on the ecological impact of disturbances, which are expected to be more intense and more frequent in the future  (Coumou and Rahmstorf 2012, Harris et al.
The stability of microtearing (MT) in the JET H-mode pedestal is investigated by means of both linear gyrokinetic simulations using the GENE code and a theoretical calculation. In order to determined the role played by MT in tokamak pedestal and to evaluate the role played by physical parameters, on MT destabilization a reduced linear model has been presented and compared with gyrokinetic simulations. The analytical model allows a good prediction of the impact of the different physical parameters, like the collisionality in the pedestal.
However, despite these precious advance the physical mechanisms at play in the MT destabilization in the pedestal region is not well understood. The purpose of this paper is, starting with the model developed in Ref. [37]  to compare the theoretical linear growth rate calculated by an eigenvalue code, called Solve AP, with gyrokinetic simulations using the GENE code  [5, 39]  and the JET 82585 experimental data (see Ref. [7] ) and to understand the effect of the physical parameters on MT destabilization. The remainder of this paper is structured as follows. In Section II, brief presentation of the linear kinetic reduced model leading to a prediction of the linear growth rate and a better understanding the impact of the physical parameters, such the collisionality on MT destabilization is described.
Brief microtearing kinetic model description The fundamental aspect of the physics behind the MT instability is the creation of a parallel current at a resonant flux surface (q = m/n, where q is the safety factor). Current sheets are strongly localized around the resonant surface. Linear gyrokinetic simulations  [34]  show that the mode structure is peaked at the low field side mid-plane and rotates in the electron diamagnetic direction. An analytical linear calculation has been developed and compared with numerical simulations using the gyrokinetic code GKW and non realistic physical parameters  [37]. Here, the same model has been compared with gyrokinetic simulations using the GENE code and JET H-mode pedestal experimental data. A detailed description of the kinetic model can be found in  [37].
Impact of the collisionality on the pedestal stability The figure  5  shows a comparison between the linear growth rate of MTs obtained from the eigenvalue code that solves KRMHD equations (see eqs. 8) with the numerical simulations using the GENE code. A reasonable agreement between Solve AP and GENE simulations is found in particular for ν ei = 1. 8 × 10 -2 to ν ei = 3. 5 × 10 -2. At low collisionality, ν ei = 0. 143 × 10 -2 , the difference observed between the calculation and the gyrokinetic code can be explained by the Legendre polynomials restriction to the two first components to express the collision operator (see Ref. [37] ) and also by the use of an effective magnetic drift. Whereas, at high collisionality, the current sheet width becomes wider, and the "constant-ψ" approximation  [40]  is debatable.
ACKNOWLEDGMENTS This work has been carried out within the framework of the  EUROfusion  Consortium, funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No  101052200 -EUROfusion ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the  European Commission. Neither the  European Union  nor the European Commission can be held responsible for them. This work was performed by using computational resources provided by the  MARCONI-FUSION HPC.
Google trends tool Google Trends is a publicly available tool to provide data on the temporal trends of search queries the general population have submitted via the Google search engine, using weekly relative search volume (RSV). Weekly RSV is calculated by Google Trends based on how often search terms were entered in Google relative to the total search volume in a specific country or region. The search volume for a specific search term is normalized to range from 0 to 100, with 100 corresponding to the peak of RSV obtained for the search term during the period of interest (in this case, one year). This can be used as a proxy for changes in public interest across a year.
Selection of countries and time periods for assessment We examined Google Trends weekly RSVs over one year (2019 or 2020) for searches specifically related to COVID-19 symptoms, and for searches related to general sleep, health and wellbeing, across five countries -the UK, the USA, France, Italy and Japan. The rationale for selection of these five countries was to gain a global perspective based on high-income countries in which Google is a commonly used search engine. Data were then selected for assessment during the 'early pandemic period' (EPP), which was defined as a six-week period (01 March 2020 -11 April 2020), or an equivalent timeframe to the EPP (based on the weeks shown in the Google Trends output) in 2019 to generate a comparison with online search trends prior to the COVID-19 pandemic.
Potential 'masking relationship' related to the EPP Whilst it is unsurprising that searches related specifically to COVID-19 symptoms increased in March and April 2020 (the EPP) due to the increased media attention surrounding the pandemic globally  [ 40 , 41 ] , it is notable that some searches regarding sleep, health and wellbeing did not increase and in some cases even decreased relative to the equivalent period in 2019. From these findings, it could be conceived that a potential 'masking relationship' exists during such health crises as the   pandemic (and more noticeably during the EPP), which is linked with shifts in the general public's online health searches. This 'masking relationship' may be supported by a similar study, which used Google Trends data to evaluate the impact of lockdown in Europe and the USA.
It is of note that the term 'coronavirus disease 2019 ′ did not produce any search results in the 2019 EPP equivalent period in France, Italy or Japan, but did generate results in the UK and the USA. The small number of searches for this term in the UK and USA prior to the COVID-19 pandemic may have been related to coronavirus variants other than SARS-CoV-2, for example SARS-CoV or MERS-CoV  [53]. The reason as to why this term produced no search results in Japan in the EPP of 2020 remains unclear but may be due to differences in terminology used when searching in the Japanese language that were not detected by the Google Trends algorithm.
Study limitations Our study includes several limitations regarding the Google Trends tool and selection criteria. Google Trends analyzes online searches at a general population level, and does not account for age, gender, underlying health conditions, or ongoing mental health or sleep-related issues. We are also unable to make complete interpretations as some internet users may use alternative search engines to Google. However, Google is a dominant internet search provider and accounts for 91% of desktop search traffic in Italy, 86% in the UK, 84% in France, 83% in the USA and 76% in Japan  [56]. Google Trends calculates its data via a proprietary algorithm, which may call into question its accuracy and reproducibility as a resource for scientific research, but is nonetheless a useful tool  [20].
With our optical system and a simple data analysis (thresholding and 3D viewer software), we were able to visualize the tortuosity and the size of the capillaries. As previously explained, with our setup, the same sample contained inside a microfluidic chip can be repositioned for daily observation. Figure  6  illustrates this property by reporting 3D rendering of the same area of the same vascular network after respectively 1 day, 4 days, 6 days and 8 days of culture. After 1 day, the cells occupy a sphere as during the trapping of the organoid; there is no evidence of network development. From the 4th day, we clearly see that the cells self-organize into a three-dimensional microvascular network. After 6 and 8 days, vessels become distinguishable, and the spatial extent of the network and its complexity increase.
Using this immense dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate, and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.
New high-throughput experiments have the potential to transform our understanding of stability by quantifying the effects of mutations across a vast number of protein contexts, revealing new biophysical insights and empowering modern machine learning methods. Here, we present a powerful new high-throughput stability assay along with a uniquely massive dataset of 851,552 folding stability measurements. Our new method -cDNA display proteolysis -combines the strengths of cell-free molecular biology and next-generation sequencing and requires no on-site equipment larger than a qPCR machine. Assaying one library (up to 900,000 sequences in our experiments) requires one week and only ~$2,000 in reagents, excluding the cost of DNA synthesis and sequencing. Compared to mass spectrometry-based high-throughput stability assays  (25) (26) (27) (28) , cDNA display proteolysis achieves a 100-fold larger scale and can easily be applied to study mutational libraries that pose difficulties for proteomics.
Compared to our earlier yeast display proteolysis method  (29) , cDNA display proteolysis resolves a wider dynamic range of stability and is more reproducible even at a 50-fold larger experimental scale. Large-scale proteolysis data have already played a key role in the development of machine learning methods for protein design and protein biophysics  (30) (31) (32) (33) (34) (35) (36). The cDNA display proteolysis method massively expands this capability and has the potential to expand our knowledge of stability to the scale of all known small domains. Our new dataset of 851,552 absolute folding stabilities is unique in size and character. Current thermodynamic databases contain a skewed assortment of mutations measured under many varied conditions  (14). In contrast, our new dataset comprehensively measures all single mutants for 354 natural domains and 188 designed proteins -including single deletions and two insertions at each position -all under identical conditions.
Our dataset also includes comprehensive double mutations at 595 site pairs spread across 208 domains (a total of 222,265 double mutants). By maintaining uniform experimental conditions, our data can be used to examine the determinants of absolute folding stability in addition to the effects of mutations. Using our unique dataset, we investigated how individual amino acids and pairs of amino acids contribute to folding stability (Figs. 3  and 4 ) as well as how selection for stability interacts with other selective pressures in natural protein domains (Figs. 5  and 6 ). We also explored how our unique scale of data can be applied in protein design (Fig. 7 ).
Comprehensive mutational analysis across designed and natural protein domains To systematically examine how individual residues influence folding stability, we used cDNA display proteolysis to measure stability for all single substitutions, deletions, and Gly and Ala insertions in 983 natural and designed domains. We chose our natural domains to cover almost all of the small monomeric domains in the Protein Data Bank (30-72 amino acids in length). Our designed domains included (1) previous Rosetta designs with ααα, αββα, βαββ, and ββαββ topologies (40-43 a. a. )  (29, 66) , (2) new ββαα proteins designed using Rosetta (47 a. a. ), and (3) new domains designed by trRosetta hallucination (46 to 69 a. a. )  (42, 67). We collected these data using four giant synthetic DNA oligonucleotide libraries and obtained K 50 values for 2,520,337 sequences; 1,844,548 of these measurements are included here.
S6 ). In four domains, mutational scanning revealed trypsin-sensitive loops that could be cleaved in the folded state, leading to inconsistent stabilities between trypsin and chymotrypsin (Fig. S7 ). In these cases, we introduced one to two substitutions into the wild-type sequences to remove trypsin-sensitive sites, then performed new mutational scanning experiments in these alternative backgrounds. This led to consistent results between the two proteases. In total, we performed deep mutational scanning for 983 domain sequences, including both original and revised wild-type backgrounds. Our overall categorization of all domains is shown in Fig. 2B  (see Fig. S8  for inclusion criteria). Based on these categories, we assembled three curated datasets for machine learning (Fig. 2A ). Our ∆∆G dataset (Dataset #1) includes 586,938 sequences (single and double mutants) from 251 natural domains and 145 designs.
In this dataset, the wild-type sequence is 1. 25-4. 5 kcal/mol in stability so that most ∆∆G values (including for stabilizing mutants) are correctly resolved. Our ∆G dataset (Dataset #2) includes all 851,552 single and double mutants from 354 natural domains and 188 designs. In this dataset, the large majority of mutant ∆Gs are accurately resolved, but the wild-type ∆G may lie outside the dynamic range, preventing accurate ∆∆G calculations. Finally, Dataset #3 includes all ~1. 8 million confidently estimated K 50 values, even when trypsin and chymotrypsin measurements produced inconsistent ∆G estimates. The main domain classes in Dataset #1 are shown in Fig. 2C ; all natural domains included in Dataset #1 are listed by category in Fig. S9  (see Supplementary Materials for all wild-type sequences).
Mutational scanning results for nine domains are shown in Fig  2D  and E. Like all mutational scans in Datasets 1 and 2, these examples show a strong consistency between independent ∆G measurements with trypsin and chymotrypsin (Pearson correlation 0. 94 ± 0. 04 for 542 domains in Dataset 2, median ± std. ). In each structure, sites are colored according to the average effect of an amino acid substitution, with the most critical sites (where mutations are very destabilizing) colored dark blue. Most of these critical sites are in the hydrophobic core. However, our data also reveal numerous other critical interactions, such as a side chain hydrogen bond between S23 and D42 in the U-box domain of human E4B Ubiquitin ligase and a cation-π interaction between R10 and W32 in the chromodomain of human chromobox protein homolog 7 (residues have been re-numbered based on the exact sequence included in our experiments).
Trends in amino acid fitness at different sites and across domains We first sought to define the major sources of variation between protein sites that determine the relative stabilities of all 20 amino acids at that site (i. e. the site's stability landscape). To this end, we performed principal component (PC) analysis using 293,697 ∆G measurements at 15,440 sites in 337 domains from Dataset #1 after centering our data to set the average ∆G at each site to zero (Fig. 3A, B ). Each principal component expresses specific properties of a site that determine which amino acids are stabilizing or destabilizing. Based on the loadings of the different amino acids onto each principal component (Fig. 3C ), we interpreted the first four components to reflect amino acid hydrophobicity (PC1; 31% of the total variance explained by this PC), helical probability (PC2; 15%), aliphatic vs.
These unusual patterns are unlikely to be experimental artifacts because the patterns are consistent between independent experiments with trypsin and chymotrypsin and the same patterns are seen in both our K 50 and ∆G analysis (Fig. 3H ). Our massive dataset enabled us to identify the global trends in stability landscapes as well as specific cases that depart from these trends. These unusual cases with large reconstruction errors may provide the opportunity to study how protein flexibility and/or rare side chain interactions contribute to folding stability. These unusual sites will also serve as stringent test cases for models of protein stability.
Quantifying thermodynamic coupling for hundreds of amino acid pairs Next, we examined how side chain interaction between amino acid pairs affects folding stability. We constructed comprehensive substitutions (20 x 20 amino acids) of 595 amino acid pairs from 208 natural domains and designs in our ∆G dataset (Dataset #2) and measured stability for all sequences by cDNA display proteolysis. We selected pairs that were suggested to form energetically important hydrogen bonds in our mutational scanning data as well as other pairs forming close contacts (Fig. 4A ; Methods). To quantify the interactions between side chains, we constructed an additive model for each amino acid pair with 40 coefficients that capture the independent stability contributions of each amino acid in each position. The deviations from these models quantify the "thermodynamic coupling" between specific amino acids.
We also identified an unexpected thermodynamic coupling between an amino acid pair lacking a direct side chain interaction. In the SH3 domain of Myo3, mutations at K24 are destabilizing even though the side chain makes no clear interactions. To investigate interactions of K24, we quantified thermodynamic couplings to nearby Y9 (0. 0±0. 1 kcal/mol) and D10 (1. 0±0. 2 kcal/mol) (Fig. 4F  and Fig. S13C ). The surprising K24-D10 coupling -between two side chains that appear not to interact -highlights the difficulty of inferring energetic interactions from structural data alone, and suggests a possible longer-ranged ionic interaction. We also investigated thermodynamic couplings within 36 different three-residue networks. For each triplet, we comprehensively measured stability for all possible single and double substitutions in both the wild-type background and in the background where the third amino acid was replaced by alanine (400 mutants x 3 pairs x 2 backgrounds = ~2,400 mutants in total for each triplet).