dataesr/scientific-paragraphs-categorization huggingface dataset:
The 3D structure design, upgrade, and review Linear epitopes of PlpE have been reported to be potent enough to raise the immune response; however, the potential conformational epitopes of PlpE were investigated to determine whether they stimulate more potent immune response. To achieve this goal, the 3D structures were designed. For this purpose, amino acid sequences of different PlpEs were analyzed using the I-TASSER tool and LOMETS method. To upgrade the 3D structures, the energy minimization tool was used, which makes the molecules more similar to the native form. This program enhances the 3D structure of the protein and also promotes the 3D structure of the desired protein. After applying the changes to the secondary structure of the protein, the alpha helix and beta sheets of the proteins were evaluated by WebLab viewer (https://www.
Following massive parallel sequencing of both HL and LL DNA fractions, and by the use of a custom-made algorithm, we identified 23,905 replication origins in nuclei of the human cell line EJ30. We show that these detected origins exhibit an excellent overlap with origins identified by SNS-seq in the same cell line, and also with an existing database of 'core' origins identified by SNS-seq in 19 other human cell types  (27). There is also a good overlap with initiation zones determined by Ok-seq experiments performed in 9 different cell lines  (15)  (see also Material & Methods). Our combined origin localisation and efficiency information allows us to show that the most efficient origins in our set are not randomly distributed, but rather cluster at the edges of initiation zones, revealing a previously unappreciated fine structure to origin activity within these zones.
Custom ini-seq 2 origin caller We devised a custom script in R, calling subroutines in bedtools  (32)  and Awk, to call origins based on the reads in the HL and LL fractions and attribute to them an efficiency score. Briefly, we started by counting sequencing reads in 100 bp windows for both HL and LL fractions. Read counts were normalised to the total number of reads in each sequencing library. We then kept only those windows with a log2(HL/LL) read ratio ≥ 2. The remaining windows that were ≤ 500 bp from each other were merged. Only domains ≥ 200bp were retained. These domains we now call islands. We recounted the sequencing tags for all islands and normalised the counts to the total number of reads before calculating the efficiency score for each island (HL/(HL+LL)). For an island to be called an origin we set a cut off at ≥ 0.
Software and packages used All scripts and statistical analyses were executed in R  (33)  or the Unix command line. Venn diagrams were created with the bedtools closest function and plotted with the venneuler package. For comparison of replication initiation sites between ini-seq 2 and other techniques, an intersect distance of 0 kb has been set for distributions of initiation sites larger than ini-seq 2 (Ok-seq, Bubble-seq and ORM). For comparison with SNS-seq and the original ini-seq experiment  (23) , whose distribution is within the same range as ini-seq 2, a maximum intersect distance of 5 kb was applied, and finally for the narrowly defined S-jump a distance of 10 kb was chosen. Ini-domains were generated using the bedtools merge function at a maximum distance of 100kb, only domains containing at least 6 origins were kept.
Predictors of replication origin efficiency Origin sequences were recovered from the hg38 human genome and base composition statistics were performed using the Biostrings R/Bioconductor package  (37). Information about chromatin state at origins, i. e. DNaseI hypersensitivity and histone marks from H9 cells, data generated by the lab of Bing Ren (UCSD), was recovered from the ENCODE portal (https://www. encodeproject. org/, (38)). Non-B DNA-forming motifs, comprising direct repeats (DR), mirror repeats (MR), inverted repeats (IR), short tandem repeats (STR), Z-DNA (Z) and G-quadruplexes (GQ), were recovered from the non-B database (https://nonbabcc. ncifcrf. gov/apps/site/default, (39)). Enrichment or depletion of each feature at replication origins was quantified by averaging the coverage signal over the origins obtained with the computeMatrix function of deepTools (35) using bin sizes of 10 nt and scaling origin regions to 5 kb.
Principal component analysis and statistical modelling Principal component analysis (PCA) and the selection of predictive models were performed using the caret package in the R environment  (40). We first assessed the correlations (using a threshold of |correlation| ≤ 0. 85) and linear dependencies (using QR decomposition) in between the selected predictors and found that all selected predictors were independent. Predictor values were then centred and scaled, i. e. calculated as Z-scores, and the PCA was performed using the prcomp built-in command of R without consideration of efficiency values. Eigenvalues and vectors were visualised using the fviz_pca_var function of the factoextra R package. The 20 selected predictors were then used to build regression models predicting origin efficiency.
This approach identified 1018 Sjumps. Bubble-seq data were taken from Mesner et al  (41)  and ORM data from Wang et al  (17). Replication timing data were taken from Rivera-Mulia et al  (42). SNS-seq data were taken from Akerman et al  (27) , using the existing categorisation of 'core' and 'stochastic' origins proposed by the authors. Finally, Ok-seq data were retrieved from https://github. com/CL-CHEN-Lab/OK-Seq  (15, 43) , from which we used data from the nine cell lines BL79, IARC385, K562, IB118, IMR90, Raji, TLSE19, GM0699, and HeLa. For these data sets, we defined core initiation zones as being present in eight out of the nine cell lines. The remaining zones were classified as stochastic.
Importantly, this substantial local conversion of LL to HL DNA suggests that the LL fraction cannot be treated as a conventional 'input' for computational normalisation of the HL signal, and for peak calling. We therefore created a custom origin calling algorithm that exploits the concomitant increase of the HL and depletion of LL reads to compute not only the location of each origin, but also an efficiency score of origin activation for that site (Figure  1D ). Biological duplicates of this experiment exhibited both a high overlap of locations and a positive correlation in efficiency scores for these origins (Figure  S1A  & B ). Therefore, we pooled the data from both replicates to yield 175 ´ 10 6 HL reads and 200 ´ 10 6 LL reads and identified 23,905 unique origins. We then compared this dataset to the origins that were called using MACS2  (48) , a conventional peak caller designed for the analysis of ChIP-seq data.
When applied to the HL fraction using the parameters of Akerman et al  (27)  (Figure  S1C ), this analysis showed that practically all the origins identified by our custom peak caller were a subset of those called by MACS2. Further, we find that using the LL fraction in our peak-caller improves the dynamic range of the efficiency scores (Figure  S1D ). We also compared our data with the original ini-seq dataset  (23) , and observed 75% overlap (Figure  S1E ). Our custom peak caller did not call any further origins beyond a read depth of ~ 150 ´ 10 6 for both HL and LL (Figure  1E ). Notably, by fixing the number of LL reads at 200 ´ 10 6 , only ~ 50 million HL reads were needed to reach saturation (Figure  1E ), suggesting that depletion of LL reads is important in calling origins. We further explored this idea by examining read coverage as a function of origin efficiency (Figure  1F ), which highlighted a much greater correlation with LL than HL reads.
We then compared our ini-seq 2 dataset with both bubbleseq  (41)  and optical replication mapping  (17)  and observed a modest overlap of 44% and 26%, respectively (Figure  S3C  & D ). A recent study applying SNS-seq to 19 human cells lines  (27)  allowed the identification of a subset of origins, termed 'core' origins, that are present in all cell lines suggesting that they are highly conserved across tissues. In contrast, 'stochastic' origins are found in only one or a few of the cell lines. While 74% of the ini-seq 2 origins were found within the SNS-core origin group, only 19% were found in the SNS-stochastic group, leaving 7% in neither group (Figure  3C ). Finally, we compared the ini-seq 2 dataset with published Ok-seq data. Applying the same classification approach as used by Akerman et al  (27)  to the published Ok-seq data from 9 different cell types (15, 43 and see Materials & Methods), we identified 2,103 'core' Ok-Seq initiation zones of which 80% include ini-seq 2 origins.
In contrast, of the 19,576 identified 'stochastic' Ok-Seq zones, only 25% include an ini-seq 2. 0 origin (Figure  3D ). Taken together these observations show that origins defined by ini-seq represent a subset of narrowly defined origins that overlap with core origins and cover core replication zones as identified by independent methods and across multiple cell types. Using previously reported replication timing data average from nine cell lines (51 and see Materials & Methods) we found that both the ini-seq 2 and SNS-core, and, to a lesser extent the SNS-stochastic origins, are enriched in early replicating regions (Figure  3E ). They are additionally characterised by low replication timing heterogeneity (i. e. conservation of replication timing between cell types (51 and see Materials & Methods)), in contrast to the stochastic origins (Figure  3F ).
We found an efficiencydependent enrichment of G4s, inverted repeats, mirror repeats and short tandem repeats at origins, suggesting secondary structure formation may promote origin activity, possibly by creating a more accessible chromatin environment. Taking datasets from the ENCODE project (38 and see Materials & Methods), we find that, consistent with this idea, the most efficient origins have the greatest DNaseI accessibility, the signal for which is very sharply defined at our called origins and correlated with origin efficiency (Figure  4C ). Further, histone modifications associated with accessible chromatin, such as H3K9ac, are also enriched, while those associated with heterochromatin, such as H3K9me3, are excluded (Figure  4C  & S4C). We next examined the more general correlation of local genetic and epigenetic features at and around DNA replication origins and the efficiency with which they fire.
We then asked whether we could define origin-rich domains ab initio using our ini-seq 2 data by simply merging all origins within 100 kb of each other, regardless of their efficiency class (see Material & Methods). This exercise generated 699 domains, which have a median size of 205 kb (Figure  S5 ) and included 15,942 of the total 23,905 ini-seq 2 origins. Then we asked how the origins are organised by efficiency class within those domains and found that the high efficiency origins are also preferentially enriched, compared to the medium and low efficiency origins, at their borders (Figure  5C  & D). We also observed a reduction of high efficiency origins within the domains (Figure  5E ). This implied that the highly efficient origins tend to be isolated by at least 100kb on either their 5' or 3' side.
With the caveat that very small quantities of unreplicated DNA may have escaped detection by sequencing, the observation of unreplicated DNA depletion at efficient origins suggests that (almost) all nuclei must have initiated DNA replication at these locations. When analysed by immunofluorescence microscopy, only about 40-70% of template late G1 phase nuclei usually have established replication foci under these experimental conditions in vitro  (24, 25, 44). The higher per-nucleus efficiency of initiation at the subset of highly efficient origins found here by ini-seq 2 is not inconsistent with these published microscopy data because the very short segments of replicated DNA at some origins monitored by ini-seq would fall short of the detection threshold of effective replication foci by immunofluorescence microscopy.
e. , fast at the beginning and slow at later stages). This study offers a more comprehensive perspective on similarities between an informal settlement and a medieval neighbourhood. However, outcomes are hardly generalisable as they are valid only for the two case studies considered. Furthermore, since some metrics requires data at a very fine level of spatial granularity, which is generally hard to obtain for informal settlements, the methodology is hardly replicable to further case studies. Finally, similarities were only assessed through visual inspections of graphs, thus lacking of statistical robustness. In this paper, we present a quantitative methodology for the comparison of informal and medieval settlements that is based on a set of metrics of urban form, that can be easily extracted from open data, robust statistical comparison and correlation analysis.
Methodology The methodology proposed in this paper mainly consists of four parts: (i. ) selection of case studies; (ii. ) extraction of metrics of urban form from open data; (iii. ) application of the two-sample Kolmogorov-Smirnov test to ascertain whether the distributions of these metrics are similar across the case studies; (iv. ) correlation analysis to understand whether the spatial distribution of urban amenities is associated with measures of street network centrality. Before presenting each step in detail, we introduce the dataset from which to extract the proposed metrics of urban form.
g. , Google Earth). The aim of this comparison is to find discrepancies between the two sources of spatial data and, in particular, to identify and integrate missing buildings and streets. The former can be added by drawing roof edges from the satellite imagery used for comparison. To detect the latter, neat and continuous cuts in the urban fabric should be considered as potential signs of street presence. If the OSM data does not cover these cuts, new street segments should be added to reflect the real situation in the settlement. Examples of these additions can be found in the section Application and, more specifically, in Figs. 1  and 2. We argue that the implementation of this technique can grant enough data coverage for the scale at which the proposed metrics of urban form are computed.
The rectification of a street, for example, does not alter the topology of the street network as the number of streets connected to intersections stays the same, thus the values of the configurational metrics do not change. Akweteman, Kanyama, and Kibera were selected because they developed in a dense manner in areas relatively devoid of strong spatial constraints (e. g. , rivers, rail tracks, highways). Their growths seemed to have taken place through the settling and inserting modes proposed by  Dovey and King (2011)  and align with the district typology as they are mixed-use and relatively large in size. In terms of presence and validity of OSM data, the six settlements presented different levels of data coverage. By inspecting satellite pictures and data extracted from OSM, we observed that buildings were mapped at a sufficient level of detail across all the settlements under examination.
However, this was not the case for main street networks. In fact, while the three medieval Italian towns and Kanyama showed accurate spatial information, Akweteman and Kibera did not. We thus used the technique presented in the section above to increase data coverage in these two settlements. We present in Figs. 1  and 2  examples of additions to the main street networks of Akweteman and Kibera, respectively. In terms of site topography, the six settlements were comparable and characterised by the absence of steep topographic conditions. Finally, the selected settlements were similar in size, with total areas going from 127 ha (Kibera) to 334 ha (Kanyama) and total number of main street segments ranging between 309 (Kibera) and 837 (Kanyama). OSM data for carrying out this analysis was obtained in March 2019.
Metrics A further limitation regards the source of spatial information (i. e. , OSM). As we explained above, although previous studies found OSM data to be accurate and various humanitarian initiatives mapped several informal settlements around the globe, it is still likely that inaccuracies are present or that crucial spatial information is missing completely. To avoid this, we proposed a technique, based on the inspection of satellite imagery, to manually add missing streets and buildings. We acknowledge, though, that this might not be sufficient and that some streets might still be missing. To a certain extent, this absence can bias the values of the metrics proposed in this paper. For example, the distribution function of SL might be biased towards greater values, if smaller streets were to be absent from the dataset.
OSM POIs, representing commerce and services, are further elements that might be missing or placed inaccurately. However, the one metric based on this data (i. e. , NKA) is computed through a smoothing technique that outputs a density measure at the street level, thus an eventual partial absence of POIs might not constitute a severe bias. Due to the use of open data and satellite imagery for improving coverage, the methodology presented in this paper focuses, among other aspects of urban form, on the configuration of the main street network, which includes main streets and lanes. However, we acknowledge that informal and medieval settlements also feature smaller public laneways, such as tunnelled or cantilevered passageways, which are hardly detectable through the method for increasing data coverage presented in this paper.
This future work should involve the identification and analysis of other case studies, in both diatopic and diachronic manners, not only to strengthen the evidence on the similarity between informal and medieval settlements, but also shed more light on the consolidation process reported by  Hillier et al. (2000)  and  Iovene (2018) , and seemingly found in this analysis in Kibera. Future research endeavours might also focus on the creation of a Python tool or package that automatically carries out comparison across settlements through the extraction of OSM data, computation of metrics at street and block levels, and statistical analysis. This can eventually improve an existing Python package, i. e. OSMnx  (Boeing, 2017) , that, at the moment, only computes metrics of the street network and outputs summary statistics at settlement level.
Funding sources This work was funded by the  French government , through the  UCA JEDI Investments  in the  Future  project managed by the  French National Research Agency (ANR) , with the reference number  ANR-15-IDEX-01  CRediT authorship contribution statement Alessandro Venerandi: Conceptualization, Methodology, Software, Formal analysis, Visualization, Data Curation, Writing -Original draft, Writing -Review & editing; Maddalena Iovene: Resources, Validation, Writing -Review & editing;  Giovanni Fusco: Methodology , Writing -Review & editing.
To deal with the complex design issues of Dynamically Reconfigurable Systems-on-Chip (DRSoCs), it is extremely relevant to raise the abstraction level in which models are expressed. A high abstraction level allows great flexibility and reusability while bypassing low-level implementation details. In this context, model-driven engineering (MDE) provides support to build and transform precise and structured models for a particular purpose at different levels of abstraction. Indeed, high-level models are successively refined to low-level models until reaching the executable ones. us, this paper presents an MDE-based framework for DRSoCs design enabling the transformation of UML/MARTE specifications to SystemC/TLM implementation. To achieve a high degree of expressiveness for modeling dynamic reconfiguration, we use a suitable software engineering approach based on service-oriented component architecture.
Of all system-level description languages, SystemC  [2]  seems to be the most appropriate to meet the TLM requirements. SystemC-based transaction-level modeling involves communication between SystemC processes using function calls, while respecting the principle of separating communication from computation. By replacing all pinlevel events with a single function call, it is possible to reach speedup factors up to 10. 000 x  [3]. Dynamically Reconfigurable Systems (DRSs) can be defined differently depending on the research community. In electronics, DRSs are able to dynamically modify their functions at runtime allowing the activation (addition) and deactivation (removal) of hardware components  [4]. A DRS is considered in  [5]  as a system whose subsystems can be modified or have their settings modified during operation to achieve a specific goal.
In software engineering, adaptive systems assimilated to DRS are subject to behavior or structure adaptation at runtime in response to unplanned or planned execution changes (user requirements, resources limitations, and environmental conditions)  [6]. Most reconfigurable systems are based on FPGA (field-programmable gate array) technology; their dynamic partial reconfiguration (DPR) capability enables reconfiguring parts of them during runtime without the need to stop the device. Such systems, capable of dynamically loading and unloading IP Cores (Intellectual Property Cores) while the rest of the system is still running, are called Dynamically Reconfigurable Systems-on-Chip (DRSoCs). For modeling DRSoC features at a high abstraction level, traditional codesign techniques become obsolete; indeed, vendor design flows such as ISE or Vivado design tools from Xilinx work at a relatively low abstraction level  [7].
is problem would be wrongly tackled by suspending the simulation and making changes to the elaboration phase. e elaboration aims to create internal data structures within the kernel as required to support the semantics of simulation; thus, the dynamic reconfiguration process is still not modeled  [18]. Without altering the SystemC kernel while complying with the standard, two different strategies for DPR modeling may be used to represent reconfigurable areas: treating them as C++ objects or as SystemC modules  [19]. Modeling reconfigurable areas as C++ objects makes it impossible to use SystemC dynamic processes, given that objects are passive. For a hardware designer, it is more intuitive to use modules than to use objects. In order to get a high expressiveness degree for DPR modeling, we explored software engineering approaches for dynamic adaptation.
Secondly, interchangeable modules communicate through well-defined interfaces without too many implementation details, thereby improving the scalability and substitutability in DRSoCs beyond simply reusing IP  [23]. To cope with the lack of partial and dynamic reconfigurable FPGA modeling tools at ESL, we propose a MARTE-based service-oriented component modeling framework targeting the automatic generation of SystemC code at transaction level. Key contributions are as follows: (i) A high-level design flow is provided to enable SystemC/TLM code generation from an extended MARTE front end according to the model-based development tenets. A fully integrated Eclipse toolchain is built to materialize the proposal. (ii) e MARTE profile is extended with DPR and SCA domain-specific semantics for systems modeling, since no specified support is provided by the MARTE standard for this purpose.