dataesr/scientific-paragraphs-categorization huggingface dataset:
PoS(ICHEP2020)884 The CMS Trigger system for the HL-LHC Alexandre Zabi during Phase-1). The event reconstruction will be performed on data delivered by a more advanced detector with enhanced granularity and increased acceptance as described in Section 2. The average processing time per event should be kept under 500 ms 2 while this HLT processing time is expected to increase with instantaneous luminosity and pileup. Current studies are ongoing using extrapolation from 2017 data and are indicating that the processing time does not scale linearly at higher pileup. The current HLT system implements a CPU farm composed of more than 30,000 cores (see Fig. 3 ). The software event reconstruction and selection algorithms run on commercial servers. The software includes about 4000 CMS Software (CMSSW) modules and exploits multithreading (processing multiple events concurrently).
The average HLT processing time per event is 264 ms, calculated on 2018 CPU and considering an average pileup of 50. The challenges imposed by the HL-LHC running conditions require an upscaling of the HLT system by a factor 18 in terms of computing power and a factor 25 more data throughput (65 GB/s instead of 2. 5 GB/s at the start of Run-2)  [4]. Highlighted are the processes considered to be offloaded to GPUs. The main strategy used at HLT is to deploy the same framework and the same algorithms as the ones used in the offline data analyses. This allows new triggers (or physics seeds) to be rapidly developed and maintained while ensuring reproducibility and quick assessment of trigger efficiencies. The same strategy is employed for the Phase-2 upgrade of the HLT system, while the infrastructure is considering innovative solutions to optimise processing time.
As other experiments at the LHC, CMS has used CPUs to process events at the HLT 3 during Run-1 and Run-2. The ongoing R&D aims at using coprocessors as offload engines for specific online reconstruction algorithms. A heterogeneous architecture would combine both CPUs and GPUs, where 80% of the online processing could be offloaded to GPUs. A first demonstration of this original architecture has been conducted and is referred to as "patatrack"  [8]. This demonstrator performs pixel-based tracks and vertices reconstruction on a GPU platform. The workflow consists of copying the raw data from CPU to GPU, where multiple kernels run to perform the various steps of the tracking reconstruction algorithms before copying the tracks and vertices back to the host CPU. More details on the technical implementation of the patatrack demonstrator can be found in Ref.
Conclusion The CMS experiment is proposing solid solutions to the trigger and data acquisition challenge imposed by the extreme HL-LHC running conditions. The Phase-2 Level-1 trigger upgrade project is constructing a flexible and modular architecture with enhanced capabilities complying with the physics requirements. Sophisticated algorithms have been prototyped in FPGAs and exploit target hardware with demonstrated functionalities. The HLT proposes an innovative heterogenous architecture combining CPUs and GPUs along with a new programming model. Both projects intend to perform extensive demonstration of part of their system already during Run-3 of the LHC. Combination of Level-1 and HLT systems will provide CMS with event selection capabilities beyond the standard realm of triggering techniques.
In this study, we reanalyzed available datasets of gene expression changes in female Drosophila head induced by mating. Mated females present metabolic phenotypic changes and display behavioral characteristics that are not observed in virgin females, such as repulsion to male sexual aggressiveness, fidelity to food spots selected for oviposition, and restriction to the colonization of new niches. We characterize gene networks that play a role in female brain plasticity after mating using AMINE, a novel algorithm to find dysregulated modules of interacting genes. The uncovered networks of altered genes revealed a strong specificity for each successive period of life span after mating in the female head, with little conservation between them. This finding highlights a temporal order of recruitment of waves of interconnected genes which are apparently transiently modified: the first wave disappears before the emergence of the second wave in a reversible manner and ends with few consolidated gene expression changes at day 20.
At present, we know little about the molecular mechanisms that affect female brain gene expression after mating. In this study, we use a new approach to analyze the transcriptomic changes occurring in the female head after mating. This approach relies on (1) a novel method, AMINE  (Pasquier et al, 2021 Preprint) , for the identification of dysregulated gene modules, and (2) accurate RNA-Seq data from the head of both virgin and mated Drosophila females at three time periods after mating, made available by the modENCODE project. AMINE applies a greedy algorithm on a highly informative and compact representation combining general knowledge about gene interactions and measured gene variation. This strategy allows us to assess both the extent of gene variation and the topology of gene interactions, overcoming limitations of classical enrichment analysis of the most differentially expressed genes and earlier network-based approaches.
Enrichment analysis of differentially expressed genes Enrichment analysis is a classical process to obtain a synthetic view of a gene list. It consists in associating to this list the most typical annotations originating from various databases. We therefore performed enrichment analyses for the differentially expressed genes identified at each time point. We did this for all differentially expressed genes, for underexpressed genes, and then for overexpressed genes (results are listed in Table  S4 ). Annotations characterizing the whole list of differentially expressed genes at day 1 are associated with relatively modest false discovery rates (FDRs) on the order of 10 × 10 -4. Among these annotations, we find several terms related to development (in particular cuticle development with a FDR of 1 × 10 -4 and chitinbased cuticle development with a FDR of 1.
To this list, we add WMAXC  (Amgalan & Lee, 2014) , a method belonging to the category of maximum clique identification, GiGA  (Breitling et al, 2004) , which is based on a greedy algorithm and MRF  (Robinson et al, 2017) , which uses a diffusion-flow emulation model. This makes 25 methods, whose basic principles and results differ, but which also have differences from the point of view of usability; some are easy to use by a non-expert, others are more demanding, involving command line execution, complex setup, the need to properly format input data in a particular layout or requiring many steps to obtain the final result. Usability is a very important aspect for programs intended to be used by people who are not necessarily experts in computer science or data analysis. Difficulties may arise during installation, data preparation, or program execution.
(A) Mated versus virgin females at day 1. The analysis highlighted 625 differentially expressed genes identified with high confidence (most of these genes are associated with an adjusted P-value well below 0. 05). (B) Mated versus virgin females at day 4. Eighty-two genes are considered differentially expressed. Important variations were found between replicates because most of the adjusted P-values were higher than 0. 1, as seen in the panel. (C) Mated versus virgin females at day 20. At this time period, the large variations between the quantifications performed with the different replicates only allowed the identification of six differentially expressed genes. access to a program simply by accessing a web address on a browser. A special case is represented by applications that come in the form of a plugin for Cytoscape  (Shannon et al, 2003) , a popular platform dedicated to network visualization and analysis.
There is also the case of three methods, which are available for free but must run in the MATLAB environment which is a commercial product. This makes a total of 13 non-useable methods which are identified by rows with white background in Tables  1  and S5. In these tables, eight methods that are potentially useable but not in an easy way for a non-specialist are displayed with an orange background. The difficulties come, for half of the cases, from the running of the applications in a software environment that has become obsolete: two methods work with java five which was released in 2004 (Java is currently in version 11) with a graphical interface based on Swing whose development was abandoned in 2014; two methods are plugins for Cytoscape two whose latest version dates from 2012. It might be possible to run these applications by reinstalling old versions of Java or Cytoscape, but one can expect to face difficulties in running these programs on recent software environments.
In any case, these applications cannot be considered easy to use, so they have not been tested in our review. The four other programs considered as being difficult to use require the execution of several commands or transformations on the data which require a fair amount of work. At the end of this filtering, there are only four methods that an end user can use: JActiveModules  (Ideker et al, 2002) , the oldest method that comes as a plugin for Cytoscape 3, GiGA  (Breitling et al, 2004)  an application written in Perl, ClustEx2  (Gu et al, 2010) , an application written in C++ and DIAMOnD  (Ghiassian et al, 2015) , an application written in Python 2. These last three applications must be run from the command line, but their invocation is straightforward. It should be noted that DIAMOnD identifies only one set of genes involved, unlike other methods that identify several modules.
JActiveModules requires specification of an additional parameter which is the number of modules to identify. The three other methods require indicating the maximum size of the modules to be identified. The commonality between all the existing methods is that their efficiency is highly dependent on the network topology. For these methods, two genes or gene products are as likely to be part of the same functional module as they are close on the interaction network. This proximity is described by the existence of an interaction or a chain of interactions between the two molecules. Unfortunately, molecular interaction networks stored in public databases are known to be noisy and incomplete  (Wang et al, 2009). It has also been shown that PPI networks are "small-world" networks, meaning that the neighbors of a given node are likely to be neighbors of each other, and most nodes can be reached from every other node by a small number of hops.
Over the past few years, network embedding has emerged as a powerful network analysis approach by generating a highly informative and compact vector representation for each vertex in the network  (Cui et al, 2018). This approach maps nodes into a vector space in which the distances between nodes accurately reflect their proximity in the original network. We hypothesize that network embedding methods can provide the basis for a more reliable method by estimating distances between nodes that take into account the entire topology of the graph and that in addition are little affected by the proportion of missing edges. Based on this assumption, we have developed AMINE, a new efficient method for detecting active modules in the vector space generated by an embedding of the interaction network  (Pasquier et al, 2021 Preprint).
Identification of active modules Using the web server at http://amine. i3s. unice. fr to execute the method AMINE, we proceeded to identify active modules in an independent manner for each of the three time points. Our method identified seven active modules of genes at day 1, 38 active modules at day 4 and four active modules at day 20 (Figs 3-5 and Tables  S6-S8 ). Drawing the modules identified at the different time points on the same picture showed few overlapping genes (only five genes, FBgn0038252/BigH1, FBgn0260653/serp, FBgn0061492/loj, FBgn00 39805/Cpr100A, and FBgn0022770/PeriA, which belong to modules identified at days 1 and 4) but highlights the fact that many modules interact between the different time points. Fig 6 depicts a network that expands from seven to 38 modules from day 1-4 and then shrinks to the four modules that are still active at 20 d.
Among the other detected active modules, we found one module that contains overexpressed genes involved in the cell cycle and developmental processes, which are functions that have already been highlighted by the standard enrichment analysis procedure. This enrichment is also mostly related to the mitotic cell cycle and germ cell development and has been implied in metabolic processes. The third module was related to protein processing in the On the networks, the nodes correspond to genes, and the edges correspond to interactions reported in the String database with an evidence score ≥0. 7. The number associated with each module corresponds to the module ID specified in Table  S6. Each module is annotated with a representative enrichment. The complete lists of the enrichments of all the modules are shown in Table  S9.
The node colors represent the log 2 FC values of the corresponding gene on a scale varying from blue (for the most underexpressed genes) to red (for the most overexpressed genes). The diamond-shaped nodes represent genes that are considered differentially expressed based on the DESeq2 method. endoplasmic reticulum; the fourth module was involved in vitelline membrane formation; the fifth module was involved in the receptor signaling process; the sixth module was related to RNA polymerase II pretranscription events; and the seventh module was related to COPI-dependent Golgi-to-ER retrograde trafficking (Fig  3  and Table  S9 ). At day 4, we retrieved a large module (module 1 of Fig 4) containing overexpressed genes linked to rRNA processing and On the networks, the nodes correspond to genes, and the edges correspond to interactions reported in the String database with an evidence score ≥0.
Comparison between AMINE, JActiveModules, ClustEx, DIAMOnD, and GiGA The different programs were used to analyze the same data set, namely, the differential expression of genes at day 1. The execution of AMINE was performed as presented above. JActiveModules was executed using default parameters. ClustEx2 asks to specify the size of the largest module. There is a procedure to follow, described in the documentation, to determine the most appropriate size. Following the instructions, we have specified this size as 100. DIAMOnD generates a unique and ordered list of the most involved genes. The program asks for the size of this list which we set to 100. GiGA also uses the maximum module size as a parameter. The default value is 20; we have left it unchanged. On this dataset, AMINE finds seven modules ranging from 4 to 29 genes (Table  S6 ), ClustEx2 finds 86 modules, of which some are very small and not really informative (Table  S12 ).
By keeping only the modules containing more than three genes, we obtain a list of 30 modules of size 4-100 (the maximum specified size). JActiveModules finds five modules (the number of modules specified in parameter) with size ranging from 23 to 228 (Table  S13 ). DIAMOnD outputs an ordered list of 100 genes (Table  S14 ). GiGA finds 21 modules of which two contain only two genes (Table  S15 ). By keeping only the modules containing more than three genes, we obtain a list of 19 modules of size 4-19 (just below the specified maximum size). The first thing we notice about jActiveModules is that although the method is able to identify several modules, they are very overlapping. Modules 1-3 contain a very similar group of genes. The content of modules 4 and 5 is more different, but mainly because these modules are larger.
An Euler diagram displaying the five On the networks, the nodes correspond to genes, and the edges correspond to interactions reported in the String database with an evidence score ≥0. 7. The number associated with each module corresponds to the module ID specified in Table  S8. Each module is annotated with a representative enrichment. The complete lists of the enrichments of all the modules are shown in Table  S11. The node colors represent the log 2 FC values of the corresponding gene on a scale varying from blue (for the most underexpressed genes) to red (for the most overexpressed genes). The diamond-shaped nodes represent genes that are considered differentially expressed based on the DESeq2 method. S16. Not surprisingly, the first three modules identified by JActiveModules correspond to the same function which is "chitin-based cuticle development.
" The two other modules correspond to "carbohydrate metabolic process. " The 100 genes highlighted by DIAMOnD correspond to the very generic function "cell cycle. " By selecting only the first genes appearing in the list, we obtain more specific functions: "cell cycle process" for the top 50 genes and "mitotic cell cycle process" for the top 25 genes. ClustEx2 and GiGA, like AMINE, generate non-overlapping gene modules. AMINE, ClustEx2, and GiGA identify a different number of modules but it is difficult to decide which is better for the user. We can gain an idea of the relevance of the modules by looking at which ones are significantly enriched in terms from GO biological process. Among the seven modules identified by AMINE, six are enriched with GO Biological Process with a FDR of less than 0.
05 (which accounts for 85% of the total). It is also the case for 15 of the 30 modules identified by ClustEx2 (50% of the total) and 9 of the 19 modules identified by GiGA (47%). These ratios are not proof in themselves, but the fact that the identified modules correspond to known processes is rather a sign that coherent and functional modules have been identified. If we adopt a more global view by looking at the high-level GO terms, we obtain, Visualization of the intermodule interactions in the form of a network in which each node is a module and each link represents an interaction between one or more genes of the corresponding modules. The modules are labeled with the time point followed by the number of modules. Thus, the module name "d4_m1" refers to module number 1 identified at day 4.
for AMINE, modules corresponding to "cuticle development," "cell cycle," "transport," "reproduction," and "signaling. " For clustex2, 8 of the 15 modules found are associated with these same annotations except for "cell cycle," for JActiveModules, three modules are associated with "cuticle development. " The set of genes highlighted by DIAMOnD are associated with "cell cycle" and GiGA reports modules associated with three annotations found by AMINE, 1 by ClustEx2, and 1 by DIAMOnD. In addition to the six main biological processes highlighted by AMINE, ClustEx2 identifies modules corresponding to "cell organisation," "cellular metabolic process," and "cell differentiation. " The two large modules identified by JActiveModules correspond to "carbohydrate metabolic process" and GiGA finds modules associated with: "cellular metabolic process," "small molecule metabolic process," "response to radiation," and "system development.
However, this proportion of genes only identified by one method differs; it is 32% for amine, 36% for GiGA, 62% for JActiveModules, 69% for ClustEx2, and 86% for DIAMOnD. Overall, we note that the AMINE method identifies the fewest genes but has the most genes found in common with each of the other methods. If we compare with the genes identified by differential expression analysis using a Pvalue less than 0. 05 and a log 2 FC greater than 0. 32 or less than -0. 32, we find that most of the genes with significant variation are not highlighted by either of the module detection methods. Conversely, 443 genes are only identified by module detection methods  (Fig S3B). This means that all the reviewed module detection methods have the ability to pinpoint genes that may have an influence on the studied process but whose expression variations are very small (a log 2 FC of 0.
32 corresponds to a variation of ±25%). The Biological Process "chitin-based cuticle development" is an important process occurring at day 1. This annotation has been highlighted both by differential gene analysis and by four out of five module detection methods. AMINE found a cluster of 22 genes implied in this process, ClustEx2 highlighted a cluster of 100 genes, JActiveModules detected three overlapping modules representing a total of 37 genes, and GiGA found a cluster of 19 genes. Fig  S4A  shows the overlap between the modules associated with "chitinbased cuticle development" for the four different methods. Differential expression analysis reveals 625 genes with a log 2 FC greater than 0. 32 or less than -0. 32. Of these 625 genes, 63 (representing 10%) were also identified by one or more of the module detection methods.
However, even when using a log 2 FC threshold as low as 0. 32, 45 genes were only revealed using module detection methods. This again emphasizes that searching for groups of genes working together can pick out important genes that move very little during the experiment. The overlap between differentially expressed genes and modules associated with "chitin-based cuticle development" for AMINE, ClustEx2, JActiveModules, and GiGA is shown in Fig S4B. This illustrates that only using methods that combine both the differential expression of genes and their interactions can relate the process under study to particular genes that vary only slightly. This comparative analysis on a real-world dataset highlights the advantages of AMINE over the few other easy-to-use methods. First of all, it does not require installation because it can be run via a web interface.
It does not require any special formatting of the input data because it is sufficient to provide tabulated or Excel files generated by differential analysis pipelines. The use of AMINE is particularly simple because no parameters are required, unlike the other four tested methods which requires either the number of modules to search or the maximum size of the modules. In terms of results, they are rather more concise than the other methods because the modules are less numerous or contain fewer genes. This conciseness allows biologists to focus on a smaller number of genes that appear to be highly relevant to the process being studied because a large proportion of them are also identified by other methods.
Differential expression analysis Data of gene expression in the head of both virgin and mated Drosophila females made available by the modENCODE project were downloaded as FASTQ files from the Sequence Read Archive with the accession ID: PRJNA75285. For the RNA isolation, Illumina RNA-Seq library construction and sequencing, Read mapping and filtering, Differential Gene Expression Analysis, and Statistical methods are developed in supplemental information of the two related canonical articles  (Graveley et al, 2011; Brown et al, 2014). The data contain the results from RNA-Seq experiments performed at three different time points: 1, 4, and 20 d. Details of these datasets are presented in Table  S17. The transcript abundance was quantified with Salmon  (Patro et al, 2017) , and the differential gene expression at each time point was calculated using DESeq2.