dataesr/scientific-paragraphs-categorization huggingface dataset:
Benchmark Scenario 5: Benchmarking influence of long-read sequencing coverage On primary assemblies of lower contig N50 length, CSA can play its strength in gap closure. As this was already observed in Scenario 3, we now asked the question how long-read sequencing coverage influences the results of CSA assembly. We randomly subsampled reads from the H. sapiens 60× SMRT sequencing dataset, to obtain subsets of 15×, 20×, 30×, and 40× sequencing coverage. We observed only slight changes of the final results for 60×, 40×, and 30× sequencing coverage. Although contig N50 of Figure Dot plots CSA results against reference genome for assembly of Siniperca chuatsi supported by contigs of a diverged draft genome assembly in comparison to the best-case CSA assembly. For explanation of the dot plot properties, see Figure  2.
the primary assembly started to drop below 30×, the CSA gap closures in Steps 3 and 4 still enabled a final contig N50, similar to what was obtained from the 40× and 60× datasets (Supplementary Table  S4 ). The 20× and 15× data had significantly lower contig N50; here the improvement by the CSA gap closure was clearly the highest (3. 8-fold for 20× and 4. 5-fold for 15×), but assembly errors (especially fusion errors) started to increase (Fig. 5 ). Similar results were observed when the diverged P. abelii genome was used as reference. It seems worthwhile to mention that contig N50 length of the primary assembly (CSA Step 1) is an important factor and should be at least in the megabase range because low contiguity of the contigs increases the chance of wrongly resolving rearrangements between query and reference genomes.
Benchmark Scenario 6: Ultra long-read assembly Ultra-long reads (ULR, N50 read length >50 kb) are currently gaining importance in the sequencing community and will possibly be available to many researchers soon. CSA default parameters have been optimized for current long-read data (N50 read length <30 kb). It has been reported recently that WTDBG2 performs relatively poorly on ULR data compared with the SHASTA assembler, which was designed for ULR assembly  [67]. We found that optimization of some parameters of WTDBG2 overcame these issues (increasing minimum read length cut-off to roughly N50 read length, while maintaining sequence coverage >25× and increasing minimum overlap cut-off to ∼30% of the N50 read length). We found that CSA was running more slowly due to its several read re-mapping steps, which are computationally less efficient when using ULR data.
CSA GitHub project CSA2. 6 and future updates can be downloaded from  [68]. All tools needed to run the pipeline will be installed by a script in the folder "CSA2. 6/INSTALL". Simply run "bash INSTALL. bash" and follow the instructions. Some system-specific installation issues are mentioned on GitHub. We have tested CSA2. 6 on fresh server installations of Red Hat 8 and Ubuntu 18. 04/19. 04, OpenSuse LEAP 15. 1, and CentOS 7 as well as older Red Hat and Ubuntu versions. Owing to ongoing development of the CSA pipeline we provide the code that has been used to benchmark Scenarios 1-5 and 6 (see Additional Files 3 and 4 in the Supplementary Material). CSA default parameters are currently tweaked for PacBio RSII and ONT reads (30-60×, N50 read length 10-30 kb). We have found that some SEQUEL datasets behave quite differently; here adding custom parameters for WTDBG2 will help: -l "-p 0 -k 15 -L5000 -S 2 -A.
Benchmark Scenario 1-Data and CSA parameters For the best-case scenario we downloaded reference genomes for H. sapiens (GRCh38. p12; RefSeq assembly accession: GCF 000001405. 38, here we kept only the chromosomes and removed alternative loci) and T. guttata (bTaeGut1 v1. p; RefSeq assembly accession: GCF 003957565. 1). For S. chuatsi we used our new reference genome sinChu7 (BioProject accession: PRJNA513951). SMRT long-read data for H. sapiens were downloaded from the SRA accession SRP044331. SMRT long-read data for T. guttata were downloaded from SRA using the accessions: SRR5224495-SRR5224503. The SMRT data for S. chuatsi is available through the BioProject accession PRJNA513951. All SMRT data were selected for longest subreads and converted to gzip compressed fasta files. CSA assemblies were run by the following commands: CSA2.
Benchmark Scenario 3-Data and CSA parameters To assemble the Perca fluviatilis genome by CSA, we used the P. fluviatilis reference genome for the best-case scenario (Bio-Project Accession: PRJNA549142, the assembly has been updated recently, note that the reference used here was an earlier version called PFLU1. 1 and is available from the supporting data  [71] ). A 10X Genomics Supernova assembly (Accession: GCA 003412525.
Benchmark Scenario 5-Data and CSA parameters To get subsets of the H. sapiens SMRT data we used SEQTK to randomly subsample reads from the full dataset in a way that we obtained ∼40×, 30×, 20×, and 15× sequencing coverage. We ran CSA with these read sets using either the GRCh38 genome (bestcase) or the P. abelii genome (diverged reference) as reference. Best-case: Benchmark Scenario 6-CSA using ultra-long reads ULRs (N50 read length >50 kb) from ONT sequencing will be available to many researchers soon. CSA default parameters are currently tweaked for common SMRT or ONT long-read data (N50 < 30 kb). Nevertheless, 2 CSA parameters may be set to improve ULR assembly: A) Set "-p 2" to circumvent issues with the wtdbg-cns tool (WTDBG, RRID:SCR 017225) that might otherwise crash on ULRs. CSA uses the "-p" option to set the WTDBG2 consensus caller (0 = wtdbg-cons (default); 1 = wtpoa-cons; consensus calculation in step 1; 2 = wtdbg-cons -S 0; wtpoa-cns is slower but a bit more accurate; wtdbg-cns with option -S 0 is more stable on very long reads).
B) Set "-l "-L 70000 -aln-min-length 25000 -keep-multiplealignment-parts 1 -A" to vastly improve contig N50 on ULR datasets. Make sure you still have enough coverage (e. g. , ∼30×) left when skipping reads with length <70,000 bp, otherwise try -L 60000 or -L 50000 and so on. CSA uses the -I ". " parameter to pass on detailed parameters to the wtdbg2 assembler. Parameters provided by -I ". " may overrule other wtdbg2 parameters set by CSA (e. g. , -k, -s, -e, or -m). We downloaded ULR data (CHM13 cell line) for benchmarking from the Telomere-to-Telomere (T2T) consortium  [69].
Acknowledgements We thank  Yann Guiguen  for granting early access to P. fluviatilis long-read data and  Werner Kloas  for support at the IGB. We also thank the authors of various tools that make up the CSA pipeline, especially  Jue Ruan  ( WTDBG2 ),  Heng Li  ( MINIMAP2 ),  Mikhail Kolmogorov  (RAGOUT), and  Martin C. Frith  (LAST). We thank the  Vertebrate Genomes Project (VGP)  and the  Telomereto-Telomere  ( T2T ) consortium for making reference genome assemblies and read data publicly available for benchmarking purposes, and last but not least 2 reviewers for comments on earlier versions of the manuscript. The publication of this article was funded by the  Open Access Fund of the Leibniz Association.
Availability of Source Code and Requirements Project name: CSA-Chromosome-Scale Assembler Project home page: https://github. com/HMPNK/CSA2. 6  and Reference 68 Operating system(s): Linux Programming language: PERL, AWK, and BASH scripting Other requirements: CSA was tested on Ubuntu 18. 04/19. 04, Red Hat 8, OpenSuse Leap 15. 1, CentOS 7 License: MIT RRID:SCR 017960 biotoolsID: biotools: CSA2. 6 (https://bio. tools/CSA2. 6).
Basic Compiler Correctness. The gold standard for compiler correctness is semantic preservation, which intuitively says that the semantics of a compiled program (in the target language) is compatible with the semantics of the original program (in the source language). For practical verified compilers, such as CompCert  [34]  and CakeML  [64] , semantic preservation is stated extrinsically, by referring to traces. In these two settings, a trace is an ordered sequence of events-such as inputs from and outputs to an external environment-that are produced by the execution of a program. A basic definition of compiler correctness can be given by the set inclusion of the traces of the compiled program into the traces of the original program. Formally  [34] : Definition 1. 1 (Basic Compiler Correctness (CC)).
These assumptions are often too strong, and hence in practice verified compiler efforts use different formulations of compiler correctness: CompCert  [34]  The original compiler correctness theorem of CompCert  [34]  can be seen as an instance of basic compiler correctness, but it does not provide any guarantees for programs that can exhibit undefined behavior  [57]. As allowed by the C standard, such unsafe programs are not even considered to be in the source language, so are not quantified over. This has important practical implications, since undefined behavior often leads to exploitable security vulnerabilities  [13, 24, 25]  and serious confusion even among experienced C and C++ developers  [33, 57, 65, 66]. As such, since 2010, CompCert provides an additional top-level correctness theorem 3 that better accounts for the presence of unsafe programs by providing guarantees for them up to the point when they encounter undefined behavior  [57].
This definition requires that, for any target trace t produced by the compiled program W↓, there exist a source trace s that can be produced by the original program W and is related to t according to ∼ (i. e. , s ∼ t). By choosing the trace relation appropriately, one can recover the different notions of compiler correctness presented above: Basic CC Take s ∼ t to be s = t. Trivially, the basic CC of Definition 1. 1 is CC =. CompCert Undefined behavior is modeled in CompCert as a trace-terminating event Goes_wrong that can occur in any of its languages (source, target, and all intermediate languages), so for a given phase (or composition thereof), we have Trace S = Trace T. Nevertheless, the relation between source and target traces with which to instantiate CC ∼ to obtain CompCert's current theorem is: s ∼ t ≡ s = t ∨ (∃m ≤ t.
Undefined Behavior We start by expanding upon the discussion of undefined behavior in §1. We first study the model of CompCert, where source and target alphabets are the same, including the event for undefined behavior. The trace relation weakens equality by allowing undefined behavior to be replaced with an arbitrary sequence of events. Example 3. 1 (CompCert-like Undefined Behavior Relation). Source and target traces are sequences of events drawn from Σ, where Goes_wrong ∈ Σ is a terminal event that represents an undefined behavior. We then use the trace relation from the introduction: s ∼ t ≡ s = t ∨ ∃m ≤ t. s = m • Goes_wrong. Each trace of a target program produced by a CC ∼ compiler is either also a trace of the original source program or it has a finite prefix that the source program also produces, immediately before encountering undefined behavior.
As explained in §1, one of the correctness theorems in CompCert can be rephrased as this variant of CC ∼. We proved that the property mappings induced by the relation can be written as ( ): σ(πT) = {s | s∈πT ∧ s = m•Goes_wrong} ∪ {m•Goes_wrong | ∀t. m≤t =⇒ t∈πT} ; τ (π S ) = {t | t∈π S } ∪ {t | ∃m ≤ t. m•Goes_wrong ∈ π S }. These two mappings explain what a CC ∼ compiler ensures for the ∼ relation above. The target-to-source mapping σ states that to prove that a compiled program has a property π T using source-level reasoning, one has to prove that any trace produced by the source program must either be a target trace satisfying π T or have undefined behavior, but only provided that any continuation of the trace substituted for the undefined behavior satisfies π T. The source-to-target mapping τ states that by compiling a program satisfying a property π S we obtain a program that produces traces that satisfy the same property or that extend a source trace that ends in undefined behavior.
In the latter case we believe that the compilation from IMP to ASM proposed by Xia et al. [68]  can be seen as an instance of HC ∼ , for the relation they call "trace equivalence. " Compilers Where Our Work Could Be Useful. Our work should be broadly applicable to understanding the guarantees provided by many verified compilers. For instance, Wang et al. [67]  recently proposed a CompCert variant that compiles all the way down to machine code, and it would be interesting to see if the model at the end of §3. 1 applies there too. This and many other verified compilers  [12, 30, 43, 61]  beyond CakeML  [64]  deal with resource exhaustion and it would be interesting to also apply the ideas of §3. 2 to them. Hur and Dreyer  [27]  devised a correct compiler from an ML language to assembly using a cross-language logical relation to state their CC theorem.
Data Availability Data sets that were generated for this report have been deposited in Gene Expression Omnibus (GSE135310). The additional data of atherosclerotic aorta scRNA-seq have been deposited in Gene Expression Omnibus together with data from reference by Cochain et al  13 (GSE97310). Other data and analytical methods are available from the corresponding authors on request. Single-cell gene expression data sets can be browsed in a web-accessible interface: To analyze neutrophil transcriptional heterogeneity during the acute post-MI phase, we performed a multiplexed time series of scRNA-seq analyses combining cell surface epitope labeling and transcriptomics (cell hashing  16  and CITE-Seq,  15  Methods). MI was induced by permanent ligation of the left anterior descendant coronary artery in male C57BL6/J mice.
NicheNet Analysis of Neutrophil to Macrophage Communication Pathways It has been previously proposed that neutrophils may impact postischemic cardiac remodeling by inducing a pro-repair phenotype in macrophages. 6 To computationally investigate putative communication pathways between neutrophils and macrophages, we employed NicheNet, a recently developed software that predicts ligand/receptor intercellular communication and its effect on gene expression in target cells. 41 In the total CD11b + cells data set (including not only Ly6G + neutrophils but also Ly6C low CD64 hi macrophages; see Figure  1A  and 1B ), we defined neutrophils as sender cells (expressing ligands) and macrophages as receiver cells (expressing receptors; Figure  6A ). NicheNet identified several experimentally validated ligand-receptor pairs (eg, Csf1/Csf1r; Vegfa/Nrp1 and Nrp2; Tnf and various receptors; Figure  6B ).
Integration of scRNA-seq data of neutrophils obtained (1) specifically from the heart and (2) simultaneously from the heart and blood indicated that the SiglecF low state represents young blood neutrophils (CD62L hi , Slpi, Wfdc21, Retnlg). We observed an additional subset of neutrophils characterized by a typical type I interferon response (Isg15, Irf7). Although these neutrophils were abundant in the blood, only low levels were observed in the ischemic heart. Importantly, these neutrophils were observed already in the steady state, consistent with a report by Xie et al. 33  Type I IFN response neutrophils did not seem to be induced by MI, in contrast to a recently proposed emergence of type I IFN response neutrophils induced by cardiac ischemia. 37 Using SCENIC inference analysis,  30  we identified putative activity of specific transcriptional regulators in the neutrophil subsets such as Cebpb, Hif1a, or Srebf2.
Neutrophil temporal heterogeneity in the heart was also associated with increased expression of transcripts that may orchestrate the inflammatory response. In particular, we observed increased expression of Tnf, which may act on numerous immune and nonimmune cells (fibroblasts, cardiomyocytes, endothelial cells) in the ischemic heart, as well as genes that may mediate crosstalk of neutrophils with other immune cell types such as IL23R-expressing T cells (Il23a). 51 Using the NicheNet software  41  to predict ligand/receptor interaction and their impact on target cells, we identified pathways by which neutrophils may affect macrophage phenotype, such as C3/C3ar1, Sema4d/Plxnd2, and Csf1/Csf1r. Of note, ligand/receptor analysis in scRNA-seq data is inherently limited by the efficiency of transcript detection, and may not detect all potential interactions.
For instance, neutrophil-derived Lcn2 (NGAL [neutrophil gelatinase-associated lipocalin]) has previously been proposed to drive macrophage reparative functions in the heart but transcripts for its putative receptor (encoded by Slc22a17) were poorly detected in macrophages (not shown). A recent scRNA-seq study in human and mouse lung cancer has evidenced conserved neutrophil transcriptomic signatures across species in disease. 21 Notably, neutrophils with a strong type I interferon response were found across species. 21 Although the cancerous lung and the ischemic myocardium represent 2 distinct environments, the study by Zilionis et al  21  provides proof of concept that disease-associated neutrophil gene expression signatures can be conserved from mice to humans and raises the possibility that neutrophils similar to the ones we observed in our study may also populate the human ischemic heart.
This issue is exacerbated in recent devices. As we discuss below, the typical combination of 120-125 Hz input sensing and 60 Hz output refresh rate found in most mobile devices causes little to no extra jitter in theory. However, new mobile devices have started to stray from that trend, especially in terms of output frequency: 80 Hz (Oculus), 90 Hz (Google Pixel 4, HTC Vive), 120 Hz (iPad Pro), or 144 Hz (Valve Index) refresh rates can be observed in mobile or entertainment devices. We will focus on the case of the Google Pixel 4 (120 Hz input, 90 Hz output) released recently. The jitter resulting from this combination of frequencies can be directly compared to its previous version (Pixel 3), and compelled its designers to implement additional software processes to smooth out the phenomenon.
SIMULATOR STUDY WITH GOOGLE PIXEL DEVICES To evaluate the effects of re-sampling methods on input/output asynchronicity-induced jitter, we developed a program that can calculate the visual output of a simple scrolling task on any display frame rate, based on existing input data, available at ns. inria. fr/loki/async. We first assess the validity of our model using a real-case example of similar devices with equal input frequency and different frame rates, for which we could collect both input and output event timestamps. Then, using existing re-sampling techniques, we varied the delay d pre between the expected buffer swap time t swap and the targeted input estimation, and observed the resulting trade-offs between reduced spatial jitter and added latency. We selected the Google Pixel 3 and Pixel 4 as use cases, as the latter adopts a 90-Hz display compared to the more common 60 Hz used in the Pixel 3, while keeping the same touch sensing frequency.
Data collection In a controlled experiment, we collected a set of strokes from participants in a series of scrolling tasks, using the touchscreen of two mobile devices: a Google Pixel 3 XL and a Google Pixel 4, which share the same input frequency but differ in their display frame rate. These strokes were later used to simulate the amount of instantaneous, asynchronicity-induced lag using real input data, and estimate the amount and amplitude of the resulting spatial jitter under different conditions.
Method and Apparatus Both devices have an input frequency of 120Hz (one event every 8. 33 ms). The Pixel 3 XL has a 6. 3" OLED display with a definition of 2960 × 1440 pixels (523 dots per inch), an output refresh rate of 60 Hz (one frame every 16. 67 ms), a Qualcomm Snapdragon 845 processor with 8 cores at 2. 7 Ghz and was running Android 9 Pie. Pixel 4 has a 5. 7" OLED display, with a definition of 1920 × 1080 pixels (444 dpi), an output refresh rate going up to 90 Hz (one frame every 11. 11 ms), a Qualcomm Snapdragon 855 processor with 8 cores at 2. 84 Ghz and was running Android 10. Scrolling events were recorded locally on both devices, using a web application running in Android Chromium. Each recorded event conveyed enough information to reproduce the interaction sequence as it happened during data collection: coordinates, touch-up and touch-down events, and input timestamp provided by the driver/OS.
Impact of the Display Rate on Spatial Jitter We measured the spatial jitter (Equations (18, 20)) for the Pixel 3 XL and Pixel 4, using the recorded input and t swap information. As shown in Figure  7 , average absolute lag differential increases from 9 pixels at 60 Hz, to 26 pixels at 90 Hz. This difference is in line with direct observations and with our model. We then used all recorded input from the two devices to simulate different output frequencies. The resulting simulated spatial jitter displays the same trends as our temporal model, with a horizontal shift (intercept) of about 10 pixels likely due to variable input speed, which our theoretical model does not account for. The simulated average spatial jitter at 90 Hz is very similar to the one measured in our data collection (26 px), and about 4 px off for 60 Hz.
User perception of jitter We focused this work on scrolling tasks, for simplicity and as a frequent example of jitter issues in direct touch. Previous work showed that there exist perceptive thresholds for jitter, for instance in terms of amplitude  [24]  and input speed  [10]. Jitter can also be perceived differently, and generate different degrees of frustration, depending on the task at hand  [24]. The fact that Android implemented a specific software solution to tackle this phenomenon may indicate that it was reported as a significant issue; however, our work so far is limited to geometric characterizations of asynchronicity-induced jitter, and would become more applicable with clear perceptive thresholds depending on interaction context. As of now, our model (see Fig. 3 ) indicates that increasing input frequency (F i ) is an efficient 'brute force' method to decrease the maximum amplitude of asynchronicity-induced jitter.
The fitness scores were evaluated at 100 lg/ml rifampicin. The structure with PDB code 5UAC was used for representation  (Molodtsov et al, 2017). D Correlation of fitness estimated for the same mutant at 10 lg/ml rifampicin (x-axis) and 100 lg/ml rifampicin (y-axis). The orange and blue regions represent the window within 2. 96 fitness standard deviations (on each side) around the mean fitness of synonymous mutations at 10 and 100 lg/ml, respectively. E A comparison of the distribution of growth rates for 8 variants with a lower minimum inhibitory concentration (MIC) (blue) and 8 variants with a higher minimum inhibitory concentration (MIC) (orange) to rifampicin upon growth in LB without rifampicin. Within the box, the central line represents the median, the bottom and top edges of the box represent the 25 and 75% quartiles, the bottom and top whiskers represent 9 and 91% percentiles, and the dots represent outliers.
B Mutant combination at residues I572 and S574, that are individually non-resistant to rifampicin at all concentrations, demonstrates reciprocal sign epistasis in combination. The structure with PDB code 5UAC was used for representation  (Molodtsov et al, 2017). mutation bias due to MMR is not observed. MutS and MutL block branch migration during RecA recombination  (Worth et al, 1994; Tham et al, 2013). The inhibition of RecA recombination by MutL is stronger for recombination templates with a higher number of mismatches  (Tham et al, 2013). This could explain the drop in recombination efficiency with an increase in the number of mutations per sequences (Fig  2 ). Additionally, this may also explain the increase in recombination efficiency with more diverse sequences when we express MutL-E32K to inhibit MutL (Fig  3 ).