dataesr/scientific-paragraphs-categorization huggingface dataset:
Preoperative and operative characteristics The preoperative data of all included patients are summarized in Table  1. By the time passing, the number of patients who underwent minimally invasive cardiac surgery has increased especially during the last months of the study due to encouraging early outcomes and the progressive mastering of the technique. Two patients (one mitral and one aortic patient) who were initially included for minimally invasive approach finally underwent sternotomy due to lung adherences to thoracic wall. All the cases were electively performed by one senior surgeon. Patients included for minithoracotomy AVR were associated with more cardiovascular risk factors. There was no approach conversion and operative mortality was nil. Table  2  shows the core of intraoperative parameters Aortic EOA (cm 2 ) -0.
All data was acquired using informationdependent acquisition (IDA) mode with Analyst TF 1. 6 software (ABSCIEX, USA). For IDA parameters, 0. 25 s MS survey scan in the mass range of 400-1250 were followed by 20 MS/MS scans of 100 ms in the mass range of 100-1600 (total cycle time: 2. 3 s). Switching criteria were set to ions greater than mass to charge ratio (m/z) 400 and smaller than m/z 1250 with a charge state of 2-5 and an abundance threshold of more than 150 counts. Former target ions were excluded for 5 s. A sweeping collision energy setting of 37 ± 15 eV was applied to all precursor ions for collision-induced dissociation. The MS/MS spectra analysis was performed using X-tandem and the GPM. All proteins had ≥ 2 peptides identified and a log 10 expectation of less than -4. Serine hydrolases are reported based on their activities as either a metabolic or serine protease.
Taken together, further exploration of these serine hydrolase candidates and their differential enzyme activities may provide fresh insight into the analysis histologically identical but functionally divergent graft phenotypes associated with subclinical and clinical rejection. The present studies specifically focused on the differences between clinical and subclinical transplant rejection patients. Thus the data does not necessarily indicate that any of the enzyme activities detected are unique to the rejection process. Pooled urine samples were used for the ABPP isolation because of the limitations on the amount of clinical material available. This introduced the potential for a few samples to skew the results and limits the ability to unequivocally assign the presence of a given enzyme as a general feature of a patient group.
1: Number of models with finite group of length 8, we are able to conclude that no word of length 9 can be formed which does not contain at least one of the found relators as a subword. Thus the group is finite. Its order is 24, the number of words that do not contain any of the relators as a subword. From a set of relations that completely characterizes a finite group, we can recognize the group using the SmallGroups package in GAP (GAP). Only 243 of the 10,847,434 models surviving the filters described in the previous section have a group with ≤ 400 elements. This is the fourth filter. The generating function is 8u 4 + 15u 6 + 12u 7 + 21u 8 + 12u 9 + 50u 10 + 24u 11 + 15u 12 + 36u 13 + 20u 14 + 6u 15 + 18u 16 + 6u 17. The groups turn out to be D 12 , S 4 , and Z 2 ×S 4. We believe that the groups with > 400 elements are in fact infinite and expect that the corresponding generating functions are not D-finite.
Computation of Terms To compute terms of the generating functions, we used the straightforward algorithm: we maintain a 3dimensional array containing all terms q i,j,k,n for a given value of n. Given the step set, we can compute from that the numbers q i,j,k,n+1 and iterate over the desired values of n. Unfortunately, this is extremely costly in both time and memory. We used the improvements described below to make the computation more tractable. The C language code for computing the coefficients was automatically generated from the step set by a Sage (The Sage Developers, 2015) script. Reduction modulo a prime. Instead of computing the whole coefficients, we compute only the residues modulo a prime p. We chose primes satisfying p ≤ 2 15 , so that the residues fit in a 16-bit integer. Eliminate known zeros.
Guessed Asymptotic Behavior For all 170 models in question, we calculated the first 2001 terms of the series Q(0, 0, 0, t) counting excursions and of the series Q(1, 1, 1, t) counting walks with arbitrary endpoint. We want to get an idea about the asymptotic behaviour of these sequences as n → ∞. We assume that the asymptotic behaviour of these sequences is given by a linear combination of terms φ n n α , and our goal is to determine accurate estimates for the constants φ and α. For estimating these constants, it is not enough to know the terms of the counting sequences modulo a prime as computed by the code described above. However, we can apply the code for several primes and reconstruct the integer values from the various modular images using Chinese remaindering. The number of primes needed depends on the size of the integer to be reconstructed.
An Intermittent Flow Profile Promotes Differentiation and Enhances Mineralized Matrix Deposition In order to examine whether either the intermittent or continuous flow profile has any effect on osteogenic differentiation and mineralized matrix production, bone microfluidic chips were maintained in OIM for up to 21 days with either the static, intermittent (0. 8-3. 2 mL/min) or continuous (3. 2 mL/min) flow profile. ALP activity and total DNA were quantified on day 14 and calcium deposition and collagen production on day 21. Total DNA agreed with the metabolic activity data that cell number is indeed lower when cells were exposed to flow (Figure  4A ). When an intermittent unidirectional flow profile was applied, ALP activity, calcium deposition and collagen synthesis were 2. 3, 1. 8, and 2.
Applying TPP to viral pathogens can provide a comprehensive and orthogonal view on various protein state changes during infection and highlight key proteins and cellular processes required for viral replication  (Hashimoto et al, 2020). However, no study to date has applied TPP to the infection context under biosafety level 3 (BSL3) conditions. Here, we present the first dataset on the time-resolved interrogation of host protein thermostability changes during infection of Caco-2 cells with SARS-CoV-2. Hundreds of proteins changed in abundance and thermal stability, starting from as early as 1 h postinfection (hpi), indicating rapid remodelling of the host cell proteomic state. Integration of our data with SARS-CoV-2 induced phosphorylation events and viral-host PPI maps revealed common cellular processes, such as RNA splicing, that are affected within the first 1-2 h postinfection, followed by disturbances in cell cycle and the cell cytoskeleton towards later stages of infection.
We detected 7,414 proteins with at least two unique peptides. This included three viral proteins: the spike glycoprotein (S), the nucleoprotein (N) and Orf9b. Abundance changes of these three proteins over time largely recapitulated the microscopy observations for viral replication  (Fig 1D). SARS-CoV-2 proliferation kinetics and the viral protein coverage were comparable to those reported previously in Caco-2 cells  (Bojkova et al, 2020). We then calculated abundance changes relative to the non-infected control for 5,564 proteins (detected at the two lowest temperatures in at least two replicates), and thermal stability changes for 4,076 proteins (detected at the two lowest temperatures in at least two replicates and overall detected in at least ten temperatures; Dataset EV1; see Materials and Methods for details).
Of these, 350 proteins showed significant changes in abundance (|z-score| > 1. 96 and q-value < 0. 05; see Materials and Methods for details) and 278 proteins showed changes in thermal stability in at least one time point  (Dataset EV1, Appendix Fig S2). Consistent with previous observations, the majority of protein abundance changes were the result of down-regulation (62. 9% of overall abundance changes; 220 proteins in total)  (Bouhaddou et al, 2020) , whereas stability changes were dominated by protein destabilization (61. 9% of overall thermal stability changes: 172 proteins in total)  (Fig 2A and Appendix Fig S2). For instance, we observed an early increase in the abundance of host transcription and translation-related proteins (including viral-associated processes), specifically within the first hour of infection, whereas thermostability changes for these processes were mostly sustained throughout the infection time course  (Fig 2B and Dataset EV2).
Proteins associated with mitochondrial translation changed only in abundance within the first 2 h of infection, and proteins related to cytoskeletal remodelling and protein folding changed only in thermal stability mostly towards the later stages of infection  (Fig 2B and Dataset EV2). Hits related to protein folding were mostly the result of thermal destabilization and cell-cell adhesion proteins were mostly thermally stabilized  (Appendix Figs S3 and S4, and Dataset EV2). In summary, we reveal a rich and dynamic map of host protein abundance and thermal stability changes upon SARS-CoV-2 infection that may represent novel therapeutically relevant targets.
SARS-CoV-2-induced thermal stability changes converge on viralhost PPIs and phosphosite regulation Next, we asked whether proteins that change in abundance or thermal stability during SARS-CoV-2 infection also participate in viralhost PPIs and/or possess phosphosites that are differentially regulated upon SARS-CoV-2 infection  (Bouhaddou et al, 2020; preprint: Samavarchi-Tehrani et al, 2020; Gordon et al, 2020b). To do this, we compared this list of proteins to previously acquired proteome-wide datasets describing SARS-CoV-2-host PPIs and phosphosite regulation upon infection  (Bouhaddou et al, 2020; Gordon et al, 2020b). We found an overlap of 30 proteins that had altered abundance or thermal stability and were previously shown to physically interact with viral baits in affinity purification mass spectrometry (AP-MS) experiments  (Gordon et al, 2020b; Appendix Fig S5A)   Bouhaddou et al, 2020).
Proteins displaying significantly altered changes in thermal stability were used as seeds for building the network. SARS-CoV-2 infection regulated phosphosites and their predicted upstream kinases as well as protein-protein interactions between viral baits and host prey proteins were used to propagate the network. After network propagation, clustering was performed to split the network into modules (see Materials and Methods). GO term enrichments for each module are shown bottom right. Protein thermal stability data is from this study, whereas phosphorylation/ kinase regulation  (Bouhaddou et al, 2020)  and viral-human protein-protein interaction  (Gordon et al, 2020b)  data were acquired from previous work and used to propagate the TPP hits that seed this network. All TPP thermal stability hits per module are displayed.
Importantly, the functional requirement for spliceosome function in SARS-CoV-2 infection has been previously demonstrated using the spliceosome inhibitor, pladienolide B, which suppressed SARS-CoV-2 replication  (Bojkova et al, 2020). Interestingly, the SARS-CoV-2 protein nsp16 disrupts mRNA splicing events through binding of the mRNA recognition domains U1/U2 snRNAs, thus preventing translation of antiviral gene products  (Boudreault et al, 2019; Maranon et al, 2020; Banerjee et al, 2020). Furthermore, we observed pronounced changes in the thermal stability of structural components of desmosomes which are involved in cell-cell adhesion; desmoplakin (DSP) was thermally destabilized between 7 and 24 h of infection while desmoglein-2 (DSG2) and plakophilin-2 (PKP2) showed thermal stabilization towards later time points  (Fig 3A, and Dataset EV1).
Abundance and thermal stability score calculation We calculated abundance and thermal stability scores for every protein at every infection time point by combining the data from the three replicates similarly to previously described  (Mateus et al, 2018). Briefly, the overall distribution of signal sum intensities was normalized with vsn to compensate for slight differences in protein amounts from each TMT channel. Then, for every protein, we calculated the ratio of the signal sum intensity of each time point to the signal sum of the uninfected sample at the same temperature. The abundance score of each protein at each time point was calculated as the average log 2 fold change at the two lowest temperatures weighted for the number of temperatures in which the protein was identified for each replicate (requiring that there was data for two biological replicates).
The thermal stability score of each protein at each time point was then calculated by subtracting the abundance score from the log2 fold changes of all temperatures, and summing the resulting fold changes weighted for the number of temperatures in which the protein was identified for each replicate (requiring that there were at least ten data points to calculate this score). To assess the significance of abundance and thermal stability scores, we used a limma analysis, followed by an FDR analysis, using the fdrtool package (see analysis script at: https://github. com/andrenmateus/TPP-SARSCoV2/). Abundance and thermal stability scores for all time points were separately transformed to z-scores. Proteins with calculated |z-score| > 1. 96 (corresponding to a global P < 0. 05 for the effect size) and with q-value < 0.
Acknowledgements We acknowledge funding from  EMBL  for this research. AM, KM, CGPV and CMP were supported by a fellowship from the  EMBL Interdisciplinary Postdoc  ( EI3POD )  Programme under Marie Skłodowska-Curie Actions  COFUND (grant number  664726 ). MS and SB were supported by research grants from the  Deutsche Forschungsgemeinschaft (DFG) : (Project number  240245660 ,  278001972 ,  415089553  and  272983813  to SB and  416072091  to MS), the state of  Baden-Wuerttemberg  (AZ:  33. 7533. -6-21/5/1 )and the  Bundesministerium f€ ur Bildung und Forschung (BMBF , grant number  01KI20198A ); RB by the  German Center for Infection Research (DZIF) , project numbers  8029801806  and  8029705705 ; and AT by an  ERC  consolidator grant, uCARE (ID 819454). We thank the  EMBL Genomics Core Facility  for providing a thermal cycler for use in the  BSL3 lab.
Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD022115 (https://www. ebi. ac. uk/pride/arc hive/projects/PXD022115). All code has been deposited at https://github. com/andrenmate us/TPP-SARSCoV2/. Expanded View for this article is available online.
EZH2 inhibition with Tazemetostat did not have an effect on the cell surface expression of HLA Class I, while we observed a slight increase in HLA-A mRNA in OMM2. 5 and HLA-B mRNA in cell line MP38. The combination with Quisinostat led to results that varied between cell lines and HLA allele. A Phase 1 trial is planned to use the HDAC 1, 2, and 3 inhibitor Domatinostat together with immune checkpoint inhibitors as the neo-adjuvant treatment of cutaneous melanoma patients with lymph node metastases (ClinicalTrials. gov NCT-4133048). This trial is set up under the assumption that the HDAC inhibitor will stimulate the interferon pathway, making the tumour "hot". Such tumours may contribute to a good induction of cytotoxic T cells. As already mentioned earlier, high-risk UMs already have a high HLA expression, and it is unclear whether the same approach will help to treat the metastases of UMs.
Flow Cytometry Cells were incubated with an optimal dilution of mouse monoclonal anti-HLA Class I antibody (W6/32, 311414, Alexa Fluor 647; Bio Legend, Amsterdam, The Netherlands) and the data were collected (10,000-15,000 cells per live gate) using the BD LSR II system (BD Biosciences, San Diego, CA, USA); results were analysed using FACSDiva software (BD Biosciences). Three independent experiments have been performed. RNA isolation and quantitative real-time polymerase chain reaction (qPCR) have been described previously  [51]. Briefly, total RNA was extracted from cell cultures using an RNeasy Mini Kit (Qiagen Benelux B. V. , Venlo, The Netherlands). Using the iScript cDNA synthesis kit (Bio-Rad Laboratories B. V. , Veenendaal, The Netherlands), complementary cDNA was synthesised. Quantitative real-time polymerase chain reaction (qPCR) was performed in three independent experiments using the CFX-384 machine, with triplicates of one experiment used to make the graphs.
Statistical Analysis Data were analysed with SPSS software version 22. 0 (SPSS, nc. Chicago, IL, USA). Spearman correlation was performed in order to test correlations between non-parametric data. Bonferroni correction was applied for multiple testing, where appropriate. The Mann-Whitney U test was used to compare non-normal groups. Graphs were obtained using GraphPad Prism version 5. 0 for Windows (GraphPad Software, La Jolla, CA, USA). An independent t-test was used to compare the difference between the means of the in vitro studies.
EEG surgery, recording, and analysis Average ages and weights of mice at the time of surgery were 23. 8 ± 1. 2 weeks and 31. 9 ± 0. 8 g, and 14. 7 ± 1. 5 weeks and 31. 7 ± 1. 5 g for Vamp2 rlss and WT mice, respectively. Age differences were permitted so that animals were matched by weight. Previous data indicate that this age difference does not contribute to the differences in sleep observed  (31, 32). Vamp2 rlss mice were fed a high-nutrient diet in addition to regular pellets. Surgical methods, including drug administration and aseptic techniques, were as described  (32). EEG screws (Fine Science Tools Inc. ) were placed in the frontal (anteroposterior, +2 mm; mediolateral, 2 mm) and occipital (anteroposterior, -3. 5 to -4 mm; mediolateral, 2. 5 mm) cortical regions; reference and ground screws were implanted above the cerebellum and contralaterally to the occipital screw, respectively.
Two stainless steel wires were inserted in the neck muscle for EMG. Mice were singly housed postsurgery. After 2 weeks of recovery, mice were placed in cages (20. 3 × 32 × 35 cm 3 ) in ventilated, sound-attenuated Faraday chambers (Campden Instruments, UK) under a 12:12-h light-dark cycle with food and water available ad libitum. EEG and EMG data were filtered (0. 1 to 100 Hz), amplified (PZ5 NeuroDigitizer preamplifier, Tucker-Davis Technologies), and stored on a local computer (sampling rate, 256. 9 Hz). Signals were extracted and converted using custom-written MATLAB (The MathWorks Inc. , USA) scripts and the open-source Neurotraces software. Vigilance states-wake, NREM, REM, or brief awakenings (short arousals ≤16 s during sleep)-were scored manually using the SleepSign software (Kissei Comtec Co.
Open-field behavior Mice were placed into one corner of a walled arena (45 cm by 45 cm) and allowed to explore on two consecutive days for 30 min  (34). Animal movements and position were tracked using EthoVision XT analysis software (Noldus). Light/dark box Individual mice were placed into one corner of an enclosed arena separated into light and dark compartments  (35). Over 20 min, animal movements and positions were monitored by EthoVision XT.
Auditory brainstem response Auditory brainstem response (ABR) tests were performed using a click stimulus in addition to frequency-specific tone-burst stimuli as described  (38). ABRs were collected, amplified, and averaged using TDT System 3 (Tucker Davies Technology) driven by BioSig RZ (v5. 7. 1) software. All stimuli were presented free field to the right ear of the anesthetized mouse, starting at 70-dB sound pressure level (SPL) and decreasing in 5-dB steps. Auditory thresholds were defined as the lowest dB SPL that produced a reproducible ABR trace pattern and were determined visually.
PMT gain was optimized individually, using 10% area oversaturation on the shortest wavelength as a reference for all channels (n > 12 for each genotype). Then, Z-stacks acquired were compressed into single-layer images by maximum projection. To quantify synaptic protein distribution further, we investigated VAMP2 distribution pattern by analyzing VAMP2-containing synaptic projections and associated neuritic branching using a MATLAB-based SynD program  (42). Multichannel fluorescence images were acquired using ZEN software and converted into monochromatic and color-inverted pictures by ImageJ for further analyses. Statistical comparisons were performed using a Mann-Whitney test with genotype and intensity as independent factors.
The elicited EPSCs were recorded from pyramidal neurons of CA1 that were whole-cell patch clamped using 4-to 6-megohm resistance recording pipettes and held at -70 mV. The pipette solution contained 125 mM Csgluconate, 10 mM Hepes, 10 mM Na-phosphocreatine, 8 mM NaCl, 4 mM Mg-ATP, 0. 3 mM Na 3 -GTP, 0. 2 mM EGTA, 5 mM TEA-Cl, 0. 5 mM biocytin. Data were acquired using a PCI-6221 interface (National Instruments) and custom software (LabVIEW). Currents were low-pass filtered (4 to 5 kHz) and digitized at 10 to 20 kHz before analysis.
Data analysis and statistics Unless otherwise stated, statistical differences were established using a Student's t test. Behavioral phenotyping, synaptic spine analysis, electron microscopy, Western blot quantification, and immunohistochemical quantification were analyzed using GraphPad Prism 7 (GraphPad Software). EEG sleep analysis was performed using MATLAB (MathWorks) and SPSS (IBM Corp). SypHy fluorescence imaging experiments were analyzed using GraphPad Prism 7 and SigmaPlot (Systat Software Inc. ). The electrophysiological recordings were analyzed using LabVIEW (National Instruments), PClamp 10 (Molecular Devices), and MATLAB (MathWorks) software. Significance level for all analysis was set at P < 0. 05.
Acknowledgments: We thank the biomedical and research support staff at the  Mammalian Genetics Unit and Mary Lyon Centre, MRC Harwell Institute. We thank  S. D. M. Brown  for the helpful discussion and comments on the manuscript. Funding: P. M. N. and M. R. B. were supported by the  MRC  (grant codes  MC_U142684173  and  MC_UP_1503/2 ). M. C. C. G. was supported by a  BBSRC   DTP grant  ( BB/J014427/1 ) and by a  Clarendon Scholarship  ( University of Oxford ). V. V. V. was funded by a  John Fell OUP Research Fund  Grant ( 131/032 ) and a  Wellcome Trust   Strategic Award  ( 098461/Z/12/Z ). K. V. was supported by the  Wellcome Trust  ( 104033/z/14/z ) and  ERUK  project grant ( P1806 ). S. N. P. was funded by the  BBSRC  ( BB/ I021086/1 ) and a  Wellcome Trust   Strategic Award  ( 098461/Z/12/Z ).
This data suggests that PPARβ/δ was specifically required for disordered blood vessel formation in the mouse retina. of at least 4 veins/arteries were measured per retina. (c) Representative confocal images for NG2 (red) and CD31 (green) staining of the retinal vasculature in adult Pparβ/δ -/-and wild type retinas. Scale bar: 50 µ m. Corresponding quantification of the vessel density and pericyte coverage in wild type (n = 3) and Pparβ/δ -/-(n = 5) retinas. Relative gene expression of (d) Vegf, (e) Tgfβ1, (f) Tie2, and (g) Pdgfrβ mRNA of adult Pparβ/δ -/-and wild type retinas (n ≥ 3), as determined by RT-qPCR analysis. Data are expressed as mean ± SEM. Unpaired, two-tailed t-test was used for statistical analysis; * p < 0. 05.