dataesr/scientific-paragraphs-categorization huggingface dataset:
Most of the Antarctic notothenioid fishes, show constitutive expression of HSP70  (Place et al. , 2004; Clark et al. , 2008). However, some Antarctic zoarcid such as Lycodichthys dearborni, reveal an ability to upregulate the Hsp70 gene in response to a short exposure to 4 • C  (Place and Hofmann, 2005). In crustaceans the expressions of Hsp70 were measured as thermal stress markers within three species (Paraceradocus miersi, krill Euphausia superba and E. crystallorophias). The expression of Hsp70 transcript in P. miersi was up-regulated, while E. superba and E. crystallorophias in experiments using to moderate thermal shock of 3 • C and 6 • C, was possible identified 5 types of Hsps, constitutive and inducible forms in both species. Overall, these data confirm that juveniles of G. antarticus showed a poor response, which was similar to the Antarctic crustacean P.
The ability to activate the production of Luciferase by aldosterone was calculated by the ratio of normalized luminescence measured in wells treated with aldosterone over normalized luminescence in control samples. Last, the ability of spironolactone to inhibit the formation of the aldosterone/MR complex was evaluated. For this purpose, the ratio of normalized luminescence measured in wells treated with aldosterone + spironolactone over normalized luminescence in control samples was estimated. Cells treated with fresh spironolactone and aldosterone were used as positive control. Data Analysis: Statistical analysis and graphs were processed with Graphpad Prism and Origin. All data were expressed as mean ± standard deviation and analyzed with one-way ANOVA test followed by Tukey's post hoc analysis unless otherwise indicated.
Acknowledgements This work received financial support from the  National Research Agency of France (ANR , No. 14-CE16-0010 ) and the  Science and Technology Development Fund of Macao  ( FDCT 0009/2019/A, 0083/2019/A2 ). The authors would like to thank  Agathe Ribas ,  Claudia Almeida ,  Charles Marcelin , and  Julien Bachelet  for their precious help in this project.
Moreover, there is also an indication that an asymmetry in the occurrence rate between preceding and succeeding TWABs (depending on its sequence to the observation of BBFs) exists. However, it is yet to be investigated if they have common origins. To better understand the evolution of tailward flows in the plasma sheet, we will conduct a statistical survey on TWABs using data from the Cluster mission. This study can be divided into three main parts. First, we will examine the occurrence and the characteristics of TWABs (Section 4. 1). Second, we will compare the plasma properties between TWABs and the general tailward flows in the plasma sheet (Section 4. 2). Third, we will compare the plasma properties of TWABs depending on whether they are observed to precede or succeed the observation of BBFs (Section 4.
Database of All Tailward Flows (GTWs) First, we compile a database of all tailward flows in the magnetotail's plasma sheet (i. e. , B z > 0 nT and plasma β > 0. 5  (Baumjohann, Paschmann, & Cattell, 1989; Baumjohann, Paschmann, Sckopke, et al. , 1988; Boakes et al. , 2014) ). The database is limited to X GSM < -7 R E and |Y GSM | < 10 R E. Plasma data with V x < 0 km/s are considered tailward flows. This results in a total of 141,256 tailward data points. This database of tailward flows in the magnetotail's plasma sheet with positive B z will be referred to GTWs (general tailward flows, in short).
Database of Tailward Flows That are Observed Around BBFs In this work, BBFs are identified in an approach similar to  Angelopoulos, Kennel, Coroniti, Pellat, Kivelson, et al. (1994)  and  Cao et al. (2006) : BBFs are intervals of plasma velocity perpendicular to the instantaneous magnetic field along X-axis (V ⊥Bx ) greater than 100 km/s with at least one data point exceeding 250 km/s in the plasma sheet (β > 0. 5). To minimize the effect of the fluctuating instrumental noise of CODIF, a minimum duration of 24 s (∼3 × the sampling period of CODIF) is required for all BBFs. We only select intervals where the average magnitude of perpendicular velocity component V ⊥B is greater than the fieldaligned velocity component V ‖B. This is to distinguish the BBFs from the magnetic field-aligned flows which are common in the plasma sheet boundary layer (PSBL)  (Baumjohann, Paschmann, Sckopke, et al.
, 1988; Decoster & Frank, 1979; Takahashi & Hones, 1988). We merge BBFs to become a single BBF if the beginning and the end of the consecutive BBFs are observed within 5 min from each other. For each BBF, the instance when V ⊥Bx first exceeds (drops below) 100 km/s is considered the begin (end) of the BBF. In total, 217 BBFs, which amounts to a total of 8,551 data points, are identified. To identify TWABs, we scan the plasma data 5 min before the beginnings and 5 min after the ends of the BBFs. The criteria of plasma β > 0. 5 and B z > 0 nT are applied to rule out (1) the tailward flows that are ejected tailward of the reconnection sites, and (2) tailward field-aligned flows in the PSBL. Any continuous interval of V x < 0 km/s for at least 24 s (∼3 × the sampling period of CODIF) is considered a TWAB in this study.
TWABs begin and end when V x first drops below and exceed 0 km/s, respectively. Note that there are cases where more than one interval of tailward flows are separated by intervals of earthward flows during the 5 min window scan. Multiple flow reversals could be related to sloshing-like phenomena (e. g. , De  Spiegeleer et al. , 2017). In such cases, only the interval that is closest to the BBF is considered to be a TWAB. We further categorize TWABs according to their sequence with the BBFs: TWABs that precede the observation of BBFs and TWABs that succeed the observation of BBFs. They will be referred to as "Pre. TWABs" and "Suc. TWABs" respectively hereon. In total, 219 TWABs, which amounts to a total of 2,479 data points, are identified. Note that these 2,479 data points are a subset of database GTW.
Occurrence of TWABs In this subsection, we analyze the occurrence of TWABs and its relation to the dynamics of BBFs. A summary of the results on the identification of TWABs is shown in Figures  1a  and 1b. In total, 217 BBFs are identified; 162 (∼75%) of the BBFs are associated with TWABs and 55 (∼25%) of them are not associated with any TWABs. Of the 162 BBFs that are associated with TWABs; 28 (∼13%) are associated with only Pre. TWABs, 77 (∼35%) are associated with only Suc. TWABs, and 57 (∼27%) are associated with both Pre. and Suc. TWABs. In total, our database consists of 219 TWABs where 85 (∼39%) are Pre. TWABs and 134 (∼61%) are Suc. TWABs. The typical duration of TWABs is ∼1 min (average duration is 66 ± 5 s). Further analysis shows that there are only seven (out of 219) TWABs that have durations longer than the 5 min window scan (all are <7 min).
10, 11 mbrolizumab, a programmed death receptor 1 (PD-1)-blocking antibody, demonstrated antitumor activity and a favorable safety profile in patients with advanced HCC who were previously treated with sorafenib in the KEYNOTE-224 study [ClinicalTrials. gov identifier NCT02702414]. 10 ased on the results of KEYNOTE-224, pembrolizumab received accelerated approval for the treatment of patients with HCC who have previously received sorafenib. 12 In a subsequent study that examined pembrolizumab plus best supportive care (BSC) compared with placebo plus BSC in patients with advanced HCC who were previously treated with sorafenib-KEYNOTE-240 [ClinicalTrials. gov identifier NCT02702401]similar clinically significant but not statistically significant findings were observed. 11 Although the findings of KEYNOTE-240 did not meet prespecified statistical criteria, the study demonstrated a favorable benefit-to-risk profile for pembrolizumab.
Acknowledgements Medical writing and editorial assistance were provided by  Lauren D'Angelo, PhD , of  ApotheCom (Yardley, PA, USA ), and was funded by  Merck Sharp & Dohme Corp. , a subsidiary of  Merck & Co. , Inc. , Kenilworth, NJ,  USA. Prior presentation: presented in part as an e-poster presentation at the  European Society for Medical Oncology (ESMO) Virtual Congress 2020 , Madrid, Spain, 18-22 September 2020.
Data availability and materials Merck Sharp & Dohme Corp. , a subsidiary of Merck & Co. , Inc. , Kenilworth, NJ, USA (MSD) is committed to providing qualified scientific researchers access to anonymized data and clinical study reports from the company's clinical trials for the purpose of conducting legitimate scientific research. MSD is also obligated to protect the rights and privacy of trial participants and, as such, has a procedure in place for evaluating and fulfilling requests for sharing company clinical trial data with qualified external scientific researchers. The MSD data sharing website (available at: http://engagezone. msd. com/ds_doc umentation. php) outlines the process and requirements for submitting a data request. Applications will be promptly assessed for completeness and policy compliance.
Data analysis For all statistical analyses, R 3. 6. 3 (R Core Team, 2020) was used. We assessed normality, homoscedasticity, and model fit using residual plots, Shapiro-Wilk test, Levene test, and outlier tests. We log transformed the data if we observed a poor fit. Correlation and multiple regression analyses were conducted to test the relation between MBC, PLFA, brGDGT, and carbon concentrations. To test how warming affected microbial abundance and community, mixed effect models were built using the gls and lme functions in the nlme package. We used restricted maximum likelihood with the random effect block (n = 3) and the fixed effects treatment, depth, and their interaction. We used Akaike's information criterion (AIC) to test whether variance structures improved model fit. If we found significant treatment-depth interaction or, upon visual inspection of the plots, the model was run on a subset of depth increments.
Acknowledgements. We thank  Cristina Castanha  (LBL), for the field assistance, and  Tatjana Kraut  ( UZH ), for the laboratory assistance. Sylvie Derenne  ( Sorbonne Université ) and  Marijn van de Broek (University of Zurich ) contributed to the advancement of our work with valuable comments and discussions. The  University Research Priority Program Global Change and Biodiversity (URPP-GCB) at the University of Zurich  offered opportunities for helpful discussions and exchange. Financial support. This research has been supported by the  Swiss National Science Foundation  (SNF; project no. 200021_172744 ), the  U. S. Department of Energy Office of Science , the  Office of Biological and Environmental Research Terrestrial Ecosystem Science Program  (grant no. DE-SC-0001234 ), and the  SATW Germaine de Staël  (project no.
Overview of Changes Several changes have been made to the manuscript [1] in response to the comments provided by both reviewers. These include additional information about personality disorder (PD) being included in the Introduction to further contextualize the population of interest in this paper. Information about the risk needs responsivity (RNR) model and readiness for treatment has been removed from the Introduction. Further details have been provided in the Methods section, and a visualization of the data analysis plan has been included as a second figure. Within the Discussion section, a new subsection focusing on the clinical and practical implications has been added. General sentence restructuring and grammatical errors have been amended throughout.
Since galliform birds are known to be highly susceptible to viral integration  (Holmes, 2011; Kaleta, 1990; McGeoch et al. , 2000; Morissette & Flamand, 2010; Nair, 2005) , we hypothesized that the cNRE might have a viral origin. To test this hypothesis, we systematically screened for cNRE sequences in the avian viral database (see materials and methods). We found statistically significant cNRE matches in the genomes of papillomaviruses, paramyxoviruses, retroviruses and herpesviruses (Figure  5B ). This suggests that the cNRE might have originally been associated with the SMyHC III gene through viral infection of an ancestral galliform host and that integration into the genome occurred early during galliform radiation in the Cretaceous period, about 63 million years ago. Taken together, our study demonstrates that the cNRE is a tripartite cis-regulatory element that confers cardiac chamber specificity and arose during early diversification of galliform birds by genomic integration of a virus-derived sequence.
Bioinformatics profiling of nuclear receptor binding sites at the SMyHC III promoter We devised a simulation approach to identify nuclear receptor binding sites (Hexads) in the SMyHC III promoter. The principle of the approach is based on the Poisson-Boltzmann theory and aims at calculating interaction energies between nuclear receptors and DNA as an approximation of their respective binding affinities. Protein/DNA complexes were assembled for molecular dynamics profiling by positioning three-dimensional (3D) structures of nuclear receptor DNA-binding domains on the 3D structure of the cNRE DNA. RXR, RAR, and VDR crystal structures are available at the Protein Data Bank (http://www. rcsb. org) with the codes 1DSZ, 1KB4 and 1BY4, respectively  (Rastinejad et al. , 2000; Shaffer & Gewirth, 2002; Zhao et al.
, 2000). The free binding energy was calculated for protein/DNA complexes using the trajectories obtained from the molecular dynamics profiling with the software MM-PBSA in the AMBER package  (Sekijima et al. , 2003). Each complex (AB) was split into two parts: the nuclear receptor structure (A) and the cNRE structure (B), and the energy was calculated for the whole complex AB as well as for each part, A and B. Binding energy differences were obtained according to: ΔΔG = ΔGAB -(ΔGA + ΔGB). Binding free energies for all cNRE Hexads were plotted with reference to its first nucleotide. Data were pooled and analyzed as a box plot to identify values below the 10th percentile. These values were used to identify all potential Hexads within the cNRE. To quantify the potential for nuclear receptor binding within the cNRE we scored the number of times a given cNRE nucleotide was part of a Hexad.
Parameter stringency Gapopen Gapextend Reward Penalty Lenient 2 1 1 -1 Moderate 1 1 1 -3 Stringent 5 2 1 -3 While a gapopen/gapextend ratio of 1/-3 (in the moderate and stringent parameter sets) yielded a sequence conservation of 99%, a ratio of 1/-1 (in the lenient parameter set) resulted in a sequence conservation of 75%. Hits matching at least 30 bp of the 32-bp cNRE were retained. Hit sequences against animal genomes were individually extracted from the databases, since BLAST+ is a local alignment algorithm that only yields parts of hit regions within a given genome. Each animal genome was thus loaded into R to obtain the exact position of the cNRE hit from the BLAST+ alignment. If the best hit for the cNRE was on the minus strand, the genome sequence was reverse complemented. Each sequence was subsequently extracted from the genome based on the genomic location of the initial BLAST+ hit and manually refined to allow a full alignment of the cNRE with a given genome sequence.
Acknowledgments We thank  Dr. Koichi Kawakami  for providing the plasmid used to produce transgenic zebrafish. We thank the members of the  Ramialison group  and  Akriti Varshney ,  Gulrez Chahal , and  Julian Stolper  for their feedback and support and  Jeannette Hallab ,  Jeanette Rientjes ,  Ekaterina Salimova  for their assistance with experimental troubleshooting and design. We thank the members of the  Monash Bioinformatics Platform  for invaluable advice, especially  Stuart Archer ,  Adele Barugahare ,  Paul Harrison ,  David Powell ,  Michael See  and  Nick Wong. We thank the  ARMI FishCore  staff and  Melbourne University Fish Facility. We also thank  Lucas Buscaratti  for his drawing created with BioRender. comthat helped us to illustrate the different mutations on the SMyHC III promoter comparing mice and zebrafish.
• Funding This work was supported in part by  FAPESP  grants  00/04082-1, 03/06555-2 ,  15/12549-2 , and  18/09839-7  and by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code   001 , by  CNPq  grant  481983/2013-9  and by the  Ceara State Scientist-in-chief program  of  Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico  ( FUNCAP 08908197/2019 ) to  José Xavier Neto. MR is supported by Grants from the  Australian Research Council  and the  NHMRC. The Australian Regenerative Medicine Institute is supported by grants from the  State Government of Victoria  and the  Australian Government. MS is funded by the  CNRS. • Authors' contributions LNS generated transgenic zebrafish, contributed to discussions, and wrote this manuscript.
The N-terminal peptide We first investigated the disordered properties of the N-terminal peptide in solution. Starting from the α-helical N-terminus (M1-G25) directly extracted from the crystal structure of mVDAC1  [9]  (PDB ID: 3EMN), we performed μs-long MD simulations using, on one side, the Amber ff14SB force field which is considered one of the standard and latest benchmarks for protein simulations  [17]  and, on the other side, the ff14IDPSFF force field that was specifically designed to sample disordered protein domains  [18]. Importantly, ff14IDPSFF was reported to produce results similar to ff14SB in the case of well-folded systems  [18]. Representative conformers of the N-terminal peptide obtained after running standard MD with the ff14SB and ff14IDPSFF force fields are shown in  Fig 2 (top).
Finally, we investigated the correctness of our results by estimating the NMR chemical shifts of alpha and beta carbons from each simulation and by comparing those values with experimental ones reported elsewhere  [16]. Results are provided in Fig 2 (bottom) for both ff14SB and ff14IDPSFF force fields where the former is given in cases of standard MD and aMD simulations. Notably, Cα and Cβ chemical shifts predicted using ff14SB were found to be very far from experimental data (chemical shifts obtained from MD and aMD simulations were similar, which is consistent with the similar landscapes observed). Discrepancies were   S1  in Ref. [16] ). Shifts computed from MD simulations were obtained by simple average of the shifts predicted for each generated structure using the SPARTA+ software  [20].
Such residues were originally identified from analysis of various X-ray, NMR and CD data, as being more prevalent in disordered segments  [31]. In VDAC, disordered-promoting residues specifically include A8, G11, K12, S13, A14 and R15, six amino acids involved in the α-helix domain of the N-terminus in the open state; (iii) application of disorder-prediction software like s2D  [32]  or ESpritz  [33]  predicted that the N-terminus of VDAC1 was essentially disordered although the s2D method also revealed a propensity of residues K12 to T19 to form helices (S9 Fig). In the case of ESpritz, all the N-terminal residues were predicted to be disordered (i. e. , they were all classified as "D") irrespective of the choice of the training set (X-ray or DisProt); (iv) a feature of many disordered systems is that they can fold into different structures when binding to other proteins or protein domains  [34].
Importantly, the tendency of popular force fields to overstabilize secondary structures in disordered systems is not new and is due to force field parameters, especially dihedral correction terms, which are normally calibrated from databases of well-folded proteins  [36] [37] [38]. Such force fields include ff14SB, CHARMM27 and ff99SB which have been extensively used in previous in silico studies on VDAC  [16, [39] [40] [41]. Conversely, our simulation with the IDR-specific force field showed almost no α-helical content in the free peptide aside from a small α-turn made of D9, L10 and G11. The complete absence of an α-helical segment similar to the one observed in the crystal structures of VDAC indicates that the α-helix pattern specific of the open state is formed via induced folding through interaction with the β-barrel and not via conformational selection as previously presumed  [16].
MD software and force fields All our MD simulations, including standard and aMD simulations, were performed using the Amber16 package  [43]. The ff14SB force field  [17]. which is known as the latest Amber force field benchmark for proteins simulations is directly available from the Amber package while the ff14IDPSFF force field  [18]  was obtained from direct request to Prof. Ray Luo's group. ff14IDPSFF can also be downloaded from the Precise Force Field and Bioinformatics Laboratory website: http://cbb. sjtu. edu. cn/~hfchen/index_en. php.
Structure preparation (peptide) The N-terminal peptide was obtained by extracting the N-terminal region (M1-G25) from the mVDAC1 structure in the open state (PDB ID: 3EMN  [9] ). Protonation of the peptide was done using the PropKa algorithm from the MOE 2018 software at a neutral pH  [44]. Using the Amber's LEaP module, the system was set into an octahedral box filled with TIP3P waters requesting at least a 15 Å distance between any atom of the peptide and the box edges. Overall, 8826 water molecules were incorporated into the simulation box. Sodium and chloride ions were added to neutralize the system as well as to approximate a 0. 01M concentration consistent with experimental conditions used for NMR chemical shifts measurements  [16]. Both ff14SB and ff14IDPSFF simulations of the N-terminal peptide were based on the above preparation protocol.
However, setting up ff14IDPSFF simulations further required to generate a new topology file containing the proper force field parameters (dihedral correction terms). This was done by running the Trans_FF_IDPs. pl script available with the ff14IDPSFF force field using, as an input, the ff14SB topology file generated from the LEaP module  [43]. For each force field, equilibration of the solvated structure was performed with the Amber's pmemd utility by first minimizing the whole system (peptide and solvent) during 5000 steps using the steepest descent method followed by 5000 steps of conjugate gradient. Next, simulations in the NVT ensemble were carried out for 500 ps by slowly heating the system from 0K to 298K while restraining heavy atoms of the peptide. Lastly, NPT simulations at P = 1 bar and T = 298K were run without restraints during 500 ps to finalize the equilibration process.
Chemical shifts Cα and Cβ chemical shifts were calculated using the SPARTA+ software  [20]  which relies on a neural network algorithm to predict the shifts from an input protein structure. The high execution speed of the program enabled to compute the chemical shifts for each structure saved throughout our MD or aMD simulations (one structure saved every 2 ns). Standard averages of each chemical shift -or weighted averages in the case of aMD data -were computed from those predictions and directly compared with experimental data (Table  S1  in Ref. [16] ).
Structure preparation (channel) Structures of mVDAC1 and mVDAC1-Cys were prepared from the 3EMN crystal structure by aligning the protein to its corresponding Orientation of Protein in Membranes (OPM) structure  [48]. This enabled to translate the system so that its center of mass was positioned at the origin and the pore was oriented along the z axis. In the case of mVDAC1-Cys, the MOE program  [44]  was used to mutate residues L10 and A170 into cysteines and to create the disulfide bond. The channel was then embedded into a DOPC/DOPE membrane using the CHARMM-GUI membrane builder webserver  [49]. A rectangular simulation box with TIP3P water was considered. The membrane was built following a 2:1 PC:PE ratio as it was shown to be representative of the lipid composition of the MOM  [50]. Lipids were positioned in such a way as to roughly cover a 90 x 90 Å 2 surface in x and y directions which resulted in 186 lipid molecules.