dataesr/scientific-paragraphs-categorization huggingface dataset:
[34]r sequences from our SARS-CoV-2 sequence database are indicated by a blue font when classified as of the B. 1. 160variant,and by a yellow font when classified as of the Alpha variant. Sequences are labeled with their GISAID (https://www. gisaid. org/;accessed on 18 May 2022)[34]identifiers. Trees are rooted with the genome of the Wuhan-Hu-1 isolate GenBank accession no. NC_045512. 2. 2. 3. Genome of the initial virus. a. b. NC 045512. 2 0,00050 NC 045512. 2 99 0. 00050 EPI ISL 3934683 [34]0 variant, and by a yellow font when classified as of the Alpha variant. Sequences are labeled with their GISAID (https://www. gisaid. org/;accessed on 18 May 2022)[34]identifiers. Trees are rooted with the genome of the Wuhan-Hu-1 isolate Gen-Bank accession no. NC_045512. 2. 2. 3. Genome of the initial virus.
GISAID Identifier Sampling Date of the Nasopharyngeal Sample Time to Cytopathic Effect (Days) Next-Generation Sequencing Technology, Instrument EPI_ISL_10816730 5 May 2021 8 Illumina, NovaSeq EPI_ISL_10816732 14 June 2021 4 Illumina, NovaSeq EPI_ISL_10816734 12 August 2021 4 Illumina, NovaSeq EPI_ISL_10816735 24 November 2021 5 Illumina, NovaSeq EPI_ISL_10816738 2 December 2021 7 Illumina, NovaSeq See also Supplementary Table S3b.
Hi-C data We used publicly available Hi-C data of lymphoblastoid GM12878 and lung IMR90 cells from Gene Expression Omnibus (GEO) accession GSE63525  [9]. We also used publicly available Hi-C data of mouse embryonic stem (ES) and cortical neuron (CN) cells from GEO accession GSE96107  [29]. Hi-C data were binned at 25 and 50 kb resolutions and normalized by matrix balancing  [30].
ChIP-seq data We used publicly available binding peaks of 73 chromatin proteins (Rad21, CTCF, YY1, ZBTB33, MAZ, JUND, ZNF143, EZH2, ATF2, ATF3, BATF, BCL11A, BCL3, BCLAF1, BHLHE40, BRCA1, CEBPB, CFOS, CHD1, CHD2, CMYC, COREST, E2F4, EBF1, EGR1, ELF1, ELK1, FOXM1, GABP, IKZF1, IRF4, MAX, MEF2C, MTA3, MXI1, NFATC1, NFE2, NFIC, NFKB, NFYA, NFYB, NRF1, NRSF, P300, PAX5, PBX3, PML, POL2, POL3, POU2F2, RFX5, RUNX3, RXRA, SIN3A, SIX5, SMC3, SP1, SPI1, SRF, STAT1, STAT3, STAT5, TBLR1, TBP, TCF12, TCF3, TR4, USF1, USF2, WHIP, ZEB1, ZNF274, ZZZ3) of GM12878 cells from ENCODE  [31]. We downloaded peaks that were uniformly processed (Uniform Peaks). We also used publicly available CTCF ChIP-seq data of mouse embryonic stem (ES) and cortical neuron (CN) cells from GEO accession GSE96107  [29].
Best subset selection was used to select the best insulation variables when estimating the β X parameters by adding an L0 penalty: as done using the L0Learn R package (https://cran. r-project. org/web/packages/ L0Learn). Parameter was obtained by 10 fold cross-validation of the mean square error (L0Learn. cvfit function with default parameters). Often the number of insulation variables was too big for L0Learn R package (>5000) and we had to prefilter the variables. For this purpose, we used lasso regression (glmnet R package, https://cran. r-project. org/web/packages/glmnet/) and kept variables with | βx i | > 0. 2. This allowed to reduce the number of variables to few thousands for L0Learn (1) IS i = log 2 M i 1 p p i=1 M i , ( 2 ) log E y|d = β 0 + f (d) (3) E z|X = β 0 + Xβ X (4) min β 0 ,β X 1 N N j=1 l(z j , β 0 + X j β X ) + ||β X || 0 to work, while still keeping most relevant variables.
Identification of TAD borders and facilitators We proposed the sparse insulation model (SIM) to estimate the insulating/facilitating effects of genomic loci on long-range interactions (Fig. 1A ). SIM required only one parameter, the maximal distance between two bins from the Hi-C matrix, which we set here to bin size ×10 in order to reduce computational burden. We illustrated the model with high-depth Hi-C data at 25 kb resolution from human IMR90 lung cells, whose TADs could be easily visualized. We plotted the example of a 10-Mb-long genomic region of chromosome 1 (Fig. 1B ). We first computed the insulation score (IS) to identify loci of high insulation. The insulation score is a standard measure reflecting the aggregate of interactions occurring across each interval. It is often used by experimentalists because of its simple and quantitative interpretation: the lower, the higher the insulation effect of the loci on overlapping contacts  [8].
2C ). At 25 kb, 76. 5% of borders detected by SIM were also Fig. 2  Comparison with existing TAD calling methods using GM12878 Hi-C dataset. A Number of TAD borders called by each method (50 kb resolution). B TAD border prediction concordance with manual annotation from  [32]  (50 kb). C TAD border prediction concordance between methods (50 kb). D TAD border prediction concordance between methods  (25 kb)  identified by the other tools, and 21. 4% of other tools' borders were called by SIM, which was similar to HiCSeg, TopDom and DomainCaller (Fig. 2D ). Thus, SIM ranked among the best tools to predict TAD border. Meanwhile, SIM was relatively fast and memory efficient. For chromosome 1 with 25 kb resolution and considering a maximal distance of 250 kb, SIM ran in only 151 seconds for one core and around 6.
Conclusion In this article, we propose a versatile regression framework for Hi-C data analyses. Our framework was designed for TAD identification (SIM model), but also differential analysis (DIM model) and Hi-C data predictions after chromosomal rearrangement (PIM model). First, SIM accurately detected TAD borders in a quantitative manner, and was ranked among the top TAD callers when comparing with state-of-the-art methods on an unbiased dataset. Moreover, SIM also identified a novel class of elements we called facilitators which facilitated long-range contacts as opposed to borders, and were shown to be associated with specific transcription factors. Second, DIM identified novel borders during neuronal differentiation. Such novel borders were particularly enriched for other factors than CTCF, in particular, numerous transcriptional factors specific to neurons including Foxd3, NFATC3, NEUROD2, HiC1, Dmbx1, Hmx2 and NFATC1.
Leptoquarks (LQs) have attracted increasing attention within recent years, mainly since they can explain the flavor anomalies found in R(D ( * ) ), b → s + -transitions and the anomalous magnetic moment of the muon. In this article, we lay the groundwork for further automated analyses by presenting the complete Lagrangian and the corresponding set of Feynman rules for scalar leptoquarks. This means we consider the five representations Φ 1 , Φ 1, Φ 2 , Φ 2 and Φ 3 and include the triple and quartic self-interactions, as well as couplings to the Standard Model (SM) fermions, gauge bosons and the Higgs. The calculations are performed using FeynRules and all model files are publicly available online at https://gitlab. com/lucschnell/SLQrules.
In this article, we lay the groundwork for future automated LQ analyses by presenting the complete set of Feynman rules for all five scalar LQs. We include the LQ-LQ-Higgs(-Higgs), LQ-LQ-LQ(-Higgs) and LQ-LQ-LQ-LQ couplings, as well as all couplings to the SM fermions and gauge bosons. The Feyn-Rules  [166]  model files as well as the corresponding MOD and UFO files are publicly available online at https://gitlab. com/lucschnell/SLQrules and can be used for automatized LQ phenomenology using FeynArts  [167]  or MadGraph  [168]. In Sections 2 and 3, we present our conventions by defining the complete Lagrangian for the SM extended with all five scalar LQ representations. Sec. 4  gives an overview on the computational tools and in Sec. 5, the Feynman rules for the SM interactions are listed, while the ones for the scalar LQs are given in Sec.
Methods The determination of the Feynman rules to be presented in Chapters 5 and 6 is carried out using the Mathematica package FeynRules 2. 3. 36  [166]. All model files and the Mathematica notebook needed to display and export the Feynman rules can be found online at GitLab. In the following, the structure of the code files will be discussed. For more information about FeynRules, the interested reader is referred to Ref. [166].
GitLab Project This GitLab project can be accessed under https://gitlab. com/lucschnell/SLQrules. SLQrules. nb contains a Mathematica notebook that is used to determine the Feynman rules and to export them to MadGraph or FeynArts. The FeynRules model files can be found in the folder SLQrules. In order to use them, download the entire folder and add it to the FeynRules directory FeynRules/Models on your computer. We also provide prefabricated MOD and UFO model files that can be used in FeynArts  [167]  and MadGraph  [168].
Similarly, the export for FeynArts is carried out using We provide prefabricated FeynArts files under SLQrules/FeynArts. Additionally, the export to Sherpa, CalcHep or Whizard is possible. The corresponding commands can be found in SLQrules. nb, but we opt not to include prefabricated files for these applications. The package NLOCT 1. 0  [175]  even allows for the automatic determination of UV and R 2 counterterms, making the export of NLO models possible. We include corresponding commands in SLQrules. nb, but note that these determinations become highly time-consuming for complex models (i. e. involving LQ mixing). In this work we only consider models at tree-level.
An example for (ii), the definition of a LQ field, is given in Listing 2. The original fields Φ a before the diagonalization of the mass terms are defined as unphysical fields, whereas the fields Φa represent the physical fields in the FeynRules code. In this way, Φ a can be used when defining LQ Lagrangians (see below) and the FeynRules code automatically replaces them by the corresponding mass eigenstates Φa and the rotation matrices W q Φ q a = W q * n b na Φq b ( 50 ) Such a replacement is defined for Φ 1 on the lines 25-30 of Listing 2. (* physical fields *) S [100] == { ClassName -> S1m13hat , Mass -> { m1m13hat , Internal } , Width -> { W1m13hat , Internal } , SelfConjugate -> False , PropagatorLabel -> " S1m13hat " , PropagatorType -> ScalarDash , PropagatorArrow -> None , QuantumNumbers -> { Q -> -1/3} , Indices -> { Index [ Colour ]} , ParticleName -> " S1m13hat " , AntiParticleName -> " S1m13hat ~" , FullName -> " S1m13hat " } , (* unphysical fields *) S [101] == { ClassName -> S1m13 , Unphysical -> True , SelfConjugate -> False , QuantumNumbers -> { Y -> -1/3} , Indices -> { Index [ Colour ]} , Definitions -> { S1m13 [ cc_ ] : > HC [ W13mat [1 ,1]] S1m13hat [ cc ] + HC [ W13mat [2 ,1]] R2tm13hat [ cc ] + HC [ W13mat [3 ,1]] S3m13hat [ cc ]} } , Listing 2: Leptoquark field definition (singlet field Φ 1 ) Finally, to exemplify the definition of (iii), the Lagrangian for the LQ-LQ-LQ-Higgs interaction The assignment of FlavorExpand on line 5 makes sure that both Φ 2 and Φ 3 are expanded into their SU (2) L components.
Acknowledgements We thank  Dario Müller  and  Francesco Saturnino  for useful discussions. The work of A. C. is supported by a  Professorship Grant  ( PP00P2 176884 ) of the  Swiss National Science Foundation. L. S. thanks the Restaurant  Veranda Bern  for the use of their meeting room during the Covid-19 outbreak. The work of L. S. is supported by the " Excellence Scholarship & Opportunity Programme " of the  ETH Zürich Foundation.
Background In the era of data explosion, DNA emerged as a promising data storage medium due to its high information density and longevity  [1]. The process of DNA storage is complicated and each stage produces noises and errors, which might lead to data corruption. Faced with this challenge, a series of error-correcting codes were introduced to recover the files from erroneous data by adding redundancy while encoding  [2] [3] [4] [5] [6]. To find the optimal encoding method and redundancy level, the redundancy must be introduced in a proper way to combat the particular noise structure  [7]. A deep understanding of the noise structure for a given DNA data storage channel is thus.
required for optimizing encoding methods accordingly. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation pipeline is needed to reveal the particular noise structure of the DNA data storage channel in silico and optimize the redundancy design systematically. To build a systematic error simulation pipeline for the DNA data storage channel, some important factors must be considered: 1. A systematic end-to-end model is desired which covers all the key stages of the storage process to reveal how errors are generated and propagated to form final sequencing results. 2. Both sequence-lost errors and errors within a sequence should be simulated. 3. When simulating within-sequence errors, the unique character of the DNA data storage process should be addressed: a sequence is stored in the pool with several copies instead of one and retrieved by combing several sequencing readouts, so the proportion of errors in the population instead of the raw error rate of a single sequencing read should be analyzed.
In the previous literature, extensive experiments were performed to characterize each stage from synthesis to Polymerase Chain Reaction (PCR) to sequencing  [8] [9] [10] [11]. These single-stage characterization studies build up the foundations for building a model for DNA data storage but can't be directly used when stages are combined. In the DNA data storage area, Hamoum put forward a channel model for the Nanopore Sequencing platform  [12]. The model experts in simulating one stage accurately, but can't capture the evolution of errors across stages in the end-to-end process systematically. For the end-to-end simulation, the sequence lost errors have been studied by Heckel and Chen  [13, 14] , but within-sequence errors were not well addressed. Schwarz simulated within-sequence errors in a more general context  [15]  but only performed simulations on a single sequence, making it not suitable for DNA data storage based on the oligo pool which consists of multiple sequences copies.
By now, we still lack a systematic model that can simulate both the sequence lost and the within-sequence errors across all stages in the particular context of the DNA data storage channel. Here, we present DeSP, a systematic DNA storage error Simulation Pipeline. Addressing the requirements mentioned above, we established a model for the endto-end simulation of the DNA data storage process, to reveal noise structures for a given channel quantitively. The model is flexible, as it can adapt to diverse experiment conditions, such as different sequencing platform and PCR conditions, by changing parameters accordingly and modeling different stages separately or as a whole. Based on the model's simulation functions, we put forward a method for optimizing the redundancy level systematically, finding the suitable trade-off point between information density and success probability (Fig.
Results To validate the model and demonstrate its usage, we compared the simulation results against real experiment data, analyzed noise structures of DNA data storage channel using the error simulation system, and demonstrated the process of choosing proper redundancy with the encoding optimization methods. We implemented the simulation pipeline in Python, and the package can be downloaded from https:// github. com/ WangL abTHU/ DeSP. Users can easily construct a model fitting their needs by linking the building modules and customizing the parameters following the instructions on GitHub. The model receives DNA sequences prepared to be synthesized as input and provides raw DNA readouts as output, and we also provide functions to inspect the simulation results. A demonstrative web application is available to show how the model works (Additional file 1: Fig.
Data output and generation of voting errors: The data is retrieved from the DNA pool by sequencing, and one sequence will have multiple sequencing readouts. The simulation model will provide the original sequencing readouts as output, following the representation similar to that in the pool (several error types with corresponding readout numbers). The raw sequencing readouts can be further combined with different methods to obtain the final consensus sequences ("Methods"). After this step, two types of errors might be produced: the whole sequence might be lost if no reads are found, and a voting error is formed if a wrong base has a stronger signal than the real base in the population.
Combining basic modules to build a complete pipeline To reveal the noise structures, the model needs to simulate how each stage in the data storage process influences the composition of each sequence to form the final voting errors. We abstracted two basic modules to model two kinds of influence (Fig. 2b ): The Error Generation Module: Errors might be introduced within a sequence when synthesis, decay, and sequencing, generating new error types: {e i1 , e i2. e ik } → {e i1 , e i2. e ik ,. e ik+j }. We simulated the error generation process with a stochas- tic mutation process. The base substitution events were first simulated with possibilities controlled by substitution rate T sub (the transformation matrix, as shown in Fig. 2b. Deletion and insertion events were then simulated according to deletion rate P del and insertion rate P ins.
Comparison between simulation results and real data To test the validity of the model, we compared the simulation data against the real data obtained from an in vitro experiment. In the experiment, we encoded a compressed file of five baseball images into 1891 DNA sequences with DNA Fountain code and synthesized them into DNA sequences by the iGeneTech oligonucleotides pool. Each DNA sequence contains 126 bases, including 64 bp (base pairs) data payload, 16 bp RS code, 16 bp seed, and 15 bp primer on both sides. The synthesized DNA sequences were then sampled, PCR amplified, and finally readout using the Illumina NextSeq500 platform. Corresponding in silico modules were linked to build a simulation model of this process; most parameters of the simulation model were set identical to the real experiment, and some unclear parameters (such as PCR bias) were determined by parameter fitting (Additional file 1: Fig.
We hope the model can deepen the understanding of the DNA data storage channel's noise structures and facilitate designing encoding methods for this particular noise channel. In the future, the simulation model can be further improved in terms of efficiency and fidelity with optimized simulation algorithms and results from real experiments. With the simulation model, methods for optimizing encoding designs for other types of codes can be added under the general framework, and the optimizing targets can be extended from the encoding system to both the encoding system and the data channel, optimizing encoding success probability, information density and experiment cost.
Flexibility and expansibility of the model Modularity brings flexibility for using the model. Using the simulation model, users may pass the data through separate stages to explore what happens in different stages to understand the DNA data storage channel (Fig. 3a , Additional file 1: Fig. S5 ). They can also use a composed model to examine how different parameters influence the final error rate. The model also enjoys high expansibility. It can be configured to model various experiment setups, serves as a general simulation framework for the DNA data storage area. Users can choose individual modules and link them properly (Fig. 2c ), assign parameters to selected modules, and even create a new module without changing other modules. With a minor modification of the code, users can establish a whole model to suit their needs.
Choose redundancy level for DNA Fountain code DNA Fountain code is one of the most popular encoding methods used in the DNA data storage area. It uses fountain code to deal with the sequence lost error and RS (Reed-Solomon) code to combat the within-sequence errors. The redundancy level of DNA Fountain code is related to the parameter of the fountain code, α , and the length of the RS code L RS. The parameter α determines how many extra sequences are generated when encoding with the fountain code. When encoding, data are divided into M chunks and encoded into (1 + α)M droplets, which are then synthesized into DNA. To recover the data, only (1 + β)M droplets are needed, which permits (αβ)M sequences to be lost. Increasing α will provide higher tolerance to the sequence loss, but will also lead to a lower infor- mation density.
Using eBI to Characterize Cross-Section Channel Diversity/Heterogeneity To demonstrate the ability of eBI to quantify channel diversity, we analyzed a braided section of the Indus River at mean annual discharge. We used a 30 m spatial resolution mask from the Global River Widths from Landsat database  (Allen & Pavelsky, 2018)  with the Python package RivGraph  (Schwenk et al. , 2020; Schwenk & Hariharan, 2021)  to automatically obtain channel counts and widths, generate transects, and directly compute BI and eBI across each transect. Figure  2a  shows the locations of several cross sections whose corresponding measurements of braiding intensity are shown in Figures  2b-2d. This comparison showcases the key properties of eBI with regard to the traditional BI: (a) eBI is more informative and robust-eBI provides information about the changes in the planform geometry of the multi-thread system in terms of distribution of water discharge among the active channels, while BI is insensitive to these properties.
Acknowledgments E. F. G. L. V. , and A. T. received partial support from  NSF  Grants  EAR-1811909 ,  DMS-1839336 , and the  UK Research and Innovation Global Challenges Research Fund Living Deltas Hub  Grant  NES0089261. Moreover,  L. Vulis  was supported under the  NASA Earth  and  Space Science Fellowship Program Grant   0NSSC18K1409  and the  UC-National Lab In-Residence Graduate Fellowship  Grant  L21GF3569. JS was supported by the  Laboratory Directed Research and Development program of Los Alamos National Laboratory  under project number  20170668PRD1. The authors want to thank the editor,  T. Y. Dong ,  E. R. Bamber  and a third anonymous reviewer for their valuable feedback on improving the manuscript.
Data Availability Statement The Global River Widths from Landsat database  (Allen & Pavelsky, 2018)  is publicly available https:// doi. org/10. 5281/zenodo. 1297434. The Indus water mask used in our analysis can be found in https://esurf. copernicus. org/articles/8/87/2020/esurf-8-87-2020-supplement. zip. The model data used is available at: https://doi. org/10. 5281/zenodo. 6983538. The software used for the analysis, RivGraph  (Schwenk & Hariharan, 2021) , is available at https://zenodo. org/record/4591559#. Yo1LIajMKUk.
Single-Cell RNA Sequencing Data Analysis Previously published human single-cell data from two ABMR biopsies and five healthy references corresponding to transplant surveillance biopsies were used. The associated raw counts or matrices were downloaded from the Gene Expression Omnibus (GEO, GSE145927, https://www. ncbi. nlm. nih. gov/geo) (36)  and Kidney Precision Medicine Project (https://atlas. kpmp. org/ repository). Details on the gene expression analyses are described in the Supplementary Appendix. The CellChat R package  (37)  was used to analyze the cell-cell communication between these cells.
Patients were censored at the time of death with a functioning graft or at the time of the last follow-up date. Time to failure was counted starting at the day of sample. We assessed the differences in protein levels in the in vitro experiments using paired T-tests on the log10-transformed protein levels. We used SAS (version 9. 4; SAS Institute, Cary, NC), R (version 3. 6. 3, R core team, 2014, packages corrplot, FactoMineR, heatmap3), and GraphPad Prism (version 8; GraphPad Software, San Diego, CA) for statistical analysis and data presentation. Figures were created with BioRender. com.