dataesr/scientific-paragraphs-categorization huggingface dataset:
Meta-Data In total, 9 of 33 respondents (27%) appended metabolite meta-data for publication (Figure  6 ). Unique identifiers such as those from publicly available metabolomics databases such as Human Metabolome Database (HMDB) and PUBCHEM were most often appended to metabolomics study results (n = 5). The addition of pathway information and mass-to-charge ratio (m/z ratio) were also reported (n = 4) along with retention time (n = 3) and internal database compound tracking number in the platform's chemical library (COMP ID) (n = 1). Metabolites 2019, 9, x FOR PEER REVIEW 9 of 23 90%) as did the proportion of testing data (10% to 40%). Five respondents used bootstrapping, and 11 externally validated findings in another cohort.
Annotations Eight respondents (24%) annotated metabolites. Of those, databases that were used for annotation included HMDB (n = 6), Metlin (n = 3), KEGG pathways (n = 2), Metabolon proprietary system (n = 1), and the National Institute for Standards and Technology (NIST) library (n = 1) (Figure  7 ). Metabolites 2019, 9, x FOR PEER REVIEW 10 of 23.
Coding Language Most respondents (61%) wrote their statistical code in R (Figure  8 ). Other popular coding languages included SAS (33%) and STATA (15%). Fewer respondents used other languages (e. g. , Python, Haskell, Matlab; n = 1 each). Four respondents (19%) used a statistical coding style guide (e. g. , tidyverse, Google's R style guide) in the design of their code.
Coding Language Most respondents (61%) wrote their statistical code in R (Figure  8 ). Other popular coding languages included SAS (33%) and STATA (15%). Fewer respondents used other languages (e. g. , Python, Haskell, Matlab; n = 1 each). Four respondents (19%) used a statistical coding style guide (e. g. , tidyverse, Google's R style guide) in the design of their code. Metabolites 2019, 9, x FOR PEER REVIEW 10 of 23.
Minimum Reporting Standards There were many suggested minimum reporting standards for analysis of metabolomics data, including reporting: study aim and objectives; study hypothesis; statistical assumptions; overall analytical strategy; metabolomics data standard operating procedures; pre-analytical processing measures; analytical platform; quality control measures used; statistical packages and software used; strategy for adjustment for multiple comparisons; meta-data; effect estimates and confidence (betas, confidence intervals, P-values and Q-values for both nominal and statistically adjusted results); confounder selection; cross-validation strategy; external validation strategy; and providing statistical code for replication.
Data Pre-Processing A first step in analysis of metabolomics data includes data pre-processing. Extreme metabolite values are frequently observed even after applying any one of a variety of normalization methods to large metabolomics datasets  [10] , yet less than half of respondents evaluated them. A variety of sources drive extreme metabolite values including pre-analytical conditions (e. g. , processing delay), batch variability (due to a variety of technical factors), misalignment (between or within batches), chemical instability (typically manifesting as within-batch variation), true biological variation that may arise from rare genetic determinants or rare exposures, other random effects that are not easily classifiable, or some combination of the above  [44]. Filtering or censoring extreme values can reduce the skewness of a distribution and stabilize metabolite variance, improving reliability and interpretability of statistical analysis results.
As an emerging field, metabolomics databases are still incomplete and thus interpreting results from metabolomics datasets in a biological context is challenging. Data-driven network-based approaches support a better understanding of the biological processes driving exposure-disease associations  [60] , and can provide biological information independent of background databases as well as incorporating unknown metabolites  [61]. Examples include Gaussian graphical modeling  [62] , weighted gene co-expression network analysis  [63] , sparse network modeling  [64] , Bayesian approaches  [65] , and machine learning methods such as random forests  [66]. Automated text mining is a bioinformatics approach and queries databases to provide biological context based on a metabolite list  [61]. As metabolomics technologies continue to evolve and expand to include larger numbers of novel (i.
e. , unknown) molecules, the ability for existing databases to provide structure for network analyses becomes more limited. For chemically characterized metabolites, chemical pathway analysis  [67]  can identify biologically meaningful metabolite groups (i. e. , representing biochemical pathways) using information from biochemical databases, and may also serve to strengthen power to detect associations compared with evaluating single metabolites. To facilitate metabolomics analyses, which are considerably more complex than traditional epidemiological studies, online platforms that aid in identification of relevant biochemical pathways, such as Metaboanalyst  [68] , the Metabolomics Workbench  [69] , and others [70] were developed. Moreover, an increasing number of studies are measuring more than one 'omics data type  [71, 72]  and methods that integrate multiple 'omics datasets are also under development.
Meta-Data Metabolite metadata includes information on metabolomics platform and procedures such as software used, reliability (CV, ICC), chemical identification, mass-to-charge ratio and retention time, chemical pathway, and biological information, among others. This information was not commonly presented in publications. Metadata is crucial for linkage across metabolite databases to retrieve metabolite information, conducting between-study comparisons, metabolite annotation, and informing replication efforts. Databases such as the Human Metabolome database (HMDB)  [84]  and PubChem  [85]  assign unique identifiers and compile useful accompanying metadata from previous studies. Quality of metabolomics metadata has been reviewed previously  [86]. In an epidemiological setting, appending metabolite metadata will support future replication and meta-analysis efforts, such as those proposed within COMETS.
Coding Language Most statistical programming was conducted in R. There are many freely available software packages for metabolomics analysis, summarized by the Metabolomics Society  [91]  and elsewhere  [92]. Moreover, platforms such as Galaxy, originally designed for developing genomics research workflow pipelines, have been applied to metabolomics  [59]. The recognized advantages of using R and other open source coding languages include the open source framework, which allow investigators and programmers to fully access each line of code and edit or adapt code as needed for a given data management or analysis purpose. For this reason, flexible and open tools are likely to continue being developed in R, with more user-friendly adaptations of code being developed as ready-to-use R packages.
Recommendations for standardizing reporting of epidemiological studies with metabolomic analyses and infrastructure to support it have also been proposed  [59, 77, 97] , including: experimental design; analytical dataset format; sample handling and data acquisition parameters; post-instrument data processing; multivariate statistical procedures; data modeling; and model validation. Based on our findings, a summary of observed workflow is presented in Figure  9. In summary, there is a need to develop standardized analytical workflows, reporting standards, and tools for analysis of metabolomics data in epidemiological studies. Our survey was conducted among a small number of respondents, but they were representatives of their respective prospective cohorts and therefore represent the views of a larger population of analysts.
The source code is publicly available through GitHub (https://rdrr. io/github/CBIIT/R-cometsAnalytics/). The platform In summary, there is a need to develop standardized analytical workflows, reporting standards, and tools for analysis of metabolomics data in epidemiological studies. Our survey was conducted among a small number of respondents, but they were representatives of their respective prospective cohorts and therefore represent the views of a larger population of analysts. Nonetheless, conducting similar surveys in a larger sample would strengthen the current findings. To coordinate and streamline consortium-based data analyses, COMETS developed COMETS Analytics, a secure online statistical analysis platform for metabolomics data analysis  [98]. COMETS Analytics processes summary data generated by participating cohorts, as opposed to individual-level data.
The web-based application performs three main tasks: it harmonizes metabolite identifiers across cohorts that utilized different metabolomics platforms, conducts statistical analyses in large batches of user-defined models (including correlation analysis and multivariable regression), and produces standardized, meta-analysis ready output. The data pre-processing steps are completed by each cohort according to their workflow prior to analysis in COMETS Analytics. The source code is publicly available through GitHub (https://rdrr. io/github/CBIIT/R-cometsAnalytics/). The platform aims to accelerate data analysis and lower error rates compared with more conventional approaches  [14]. Educational tools are under development to guide analytical workflow and reporting of findings. These resources are intended to be open-source and freely available to the public to support rigorous research efforts and training in the analysis of metabolomics data.
Questionnaire Participants were invited to complete an online questionnaire designed to collect information on the workflow and analytical strategies used for past and current metabolomics analyses of epidemiological data within their cohort. The questionnaire was conducted using Survey Monkey (https: //www. surveymonkey. com) between 8 June and 5 October 2018. Questions were informed by common reporting of metabolomics analysis methods in the literature and were either multiple choice or multiple choice with the option of open-ended responses. Topics included: (1) Study information (purpose of the analysis, study design, type of analysis [targeted, untargeted, or both]); (2) Exploratory analyses and pre-processing (power calculations, data normalization, dealing with missing data, assessing technical platform reliability); (3) statistical analysis (analytic strategies, visualizations, cross-validation); (4) metabolite annotations; (5) other (statistical software, biostatistician input, open-source packages used, statistical coding language, minimal reporting standards).
Moreover, there is a clear need to establish benchmarks in relation to other data pre-processing steps, use of cross and external validation, and minimum reporting standards including reporting metabolite reliability estimates and appending meta-data to study results. Although there was a wide range of analytic approaches applied to metabolomics data, it is likely that analytical choices will continue to depend on the study question and the nature of the data (i. e. , targeted or untargeted). Altogether, our results indicate the need for standardized analytical workflows, reporting standards, and openly shared tools for analysis of metabolomics data in large-scale epidemiological studies-an approach that has catalyzed scientific progress in other similarly expansive fields  [99]. Accordingly, the open-source COMETS Analytics initiative  [98]  is currently developing a set of educational modules and analytic code using common statistical coding language to support the analysis and interpretation of large-scale metabolomics data derived from epidemiological studies.
Immuno-reactants were detected using horseradish peroxidase-conjugated anti-rabbit or anti-mouse immunoglobulin G antibody, ECL reagents (Abcam, Cambridge, UK), through a ChemiDoc camera (Bio Rad, Hercules, CA, USA). Image J software was used to estimate protein quantity. Results were expressed as protein amount relative to GAPDH and to uninfected or transfected against siCTL MDM.
Data Collection and Statistical Analysis Study data were collected and managed using REDCap electronic data capture tools hosted at the University Hospital of Clermont-Ferrand. REDCap (Research Electronic Data Capture) is a secure web-based application designed to support data capture for research studies, providing: 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources. The statistical analyses were performed on SAS (SAS v9. 4, Cary, NC, USA). All statistical tests were conducted with a two-sided type I error set at 0. 05. Continuous variables were described as mean and standard deviation (SD) or median and interquartile range when appropriate, according to statistical distribution (assumption of normality studied using Shapiro-Wilk test).
, 2013)  and AMIRA  (Diab, 2009). Indeed, MADAMIRA provides applications for tokenization/segmentation, part-ofspeech tagging, morphological disambiguation, lemmatization, diacritization, named entity recognition, and base phrase chunking. Recently, another fast and accurate Arabic text processing toolkit, called Farasa 2  , has been proposed. This toolkit consists of modules for tokenization/segmentation, part-of-speech tagging, Arabic text diacritization, and dependency parsing. Farasa segmenter is based on SVM-rank using linear kernels  (Abdelali et al. , 2016; Darwish and Mubarak, 2016). This segmenter relies on several features and lexicons to rank the possible segmentations for a given word. Feature vectors were built for each possible segmentation and mark a valid one for each word.
Farasa showed comparable segmentation accuracy to the state-of-the-art MADAMIRA word segmenter, while being significantly faster. Farasa is an order of magnitude faster than Stanford and two orders of magnitude faster than MADAMIRA. A successful integration of Farasa toolkit modules, from word segmentation to constituency parsing, into community Question Answering (cQA) architecture using the UIMA-based framework for Arabic is introduced in  (Romeo et al. , 2017). The proposed UIMA pipeline is used to extract mainly lexical and syntactic features from Arabic texts. These features are used to train their machine learning models for cQA. Ranking documents using term dependencies has been of a central interest in the IR community  (Croft et al. , 1991; Metzler and Croft, 2005; Sordoni et al. , 2013).
Test Collections and Evaluation In order to assess the different models presented above, we performed experiments using the TREC- The dataset is described in Table  3. The experiments are accomplished by extending Terrier 4  IR Platform v3. 5. Whenever possible, we used the existing Terrier implementations of the aforementioned models; we nevertheless had to implement the PLM, BM25_CT and QLM models, as well as the MWT extensions for all models. The extensions are also compared with several non bag-of-words models using the Mean Average Precision (MAP) and the precision at 10 documents (P10). The best performances on the MAP and the P10 values are shown by bold and bold-italic respectively. Moreover, we performed a significance paired t-test and attached ↑ to the performance number in the tables when the test passes at 90%.
The overall comparison results show that incorporating MWT into the IR models, except the language model, yields to a slightly better improvement over the proximity-based models. For Farasa stemmer, LGD_MWT and BM25_MWT achieve the best MAP performance. For the light stemmer, the best MAP values for all test collections are obtained by the SPL_MWT and the BM25_MWT models. Moreover, both SPL extensions (SPL_CT and SPL_MWT) yield to a better P10 than the other models. Even though the SPL_MWT and BM25_MWT improve significantly the CRTER and SPL_CT on the TREC-2001, the overall comparison results show that there is no significant difference between models that are based on CTs or explicit MWTs. Unlike the LM model, integrating MWT to the BM25, SPL, PL2 and LGD models shows a small enhancement over the CT models for light stemming approach.
Conclusion In this paper, we have investigated the impact of term proximity and explicit MWTs for Arabic IR based on term dependencies, using and comparing three different stemming approaches (Farasa, light and heavy stemming). Our analysis led us to conclude that: 1. The Farasa stemmer is in general preferable to the classical light and the heavy stemming approaches; 2. The use of cross-terms and MWT extensions for all the standard models (LM, BM25, LGD, and SPL) led to significant improvements; this has to be contrasted with the absence of significant improvements obtained with the positional, Markov random field and quantum (models PLM, MRF and QLM) extensions of the language model; 3. The best overall results are obtained by integrating MWTs to the SPL and BM25 models. The model CRTER is particularly interesting on the Arabic collections used in this study.
II. ENERGY EFFICIENCY MONITORING AND EVALUATION In this section, we address the EE quantification and measurement methodology extended to ICT services. It is fair to say that a significant set of KPIs have been developed during this last decade addressing a large variety of ICT sectors from the access to core networks including data centers. Those KPIs are mainly focused on hardware and infrastructure, and they helped all the green community to enforce the efforts towards lower consumption and higher efficiency. However, the major traffic transported by networks (≈ 80%) is mainly generated by service providers like over-the-top (OTT) companies or GAFA (Google, Apple, Facebook and Amazon). We observe a clear shift of operator's role from simply connecting people to providing a service allowing customers to access a specific content.
A. KPI FOR SERVICE ENERGY CONSUMPTION ETSI defined a KPI, called ''RUN'', as the ratio of carried traffic to the corresponding consumed energy without a given boundary  [33]. This indicator allows to evaluate the performance of the BSs but does not have any efficiency classification. In order to have a workable and useful operation classification, we propose to study two data sets from two different countries corresponding to 140 sites (2G, 3G and 4G sites) under operation. The traffic volume and the corresponding energy consumption are harvested using embedded probes directly connected to BSs in operating networks. The data volume (in bits) and the energy consumption (in joules) are hourly probed at each BS, for each technology. The studied data sets contain 1) the data category, e. g. , streaming, download, voice, etc, 2) traffic load and, 3) energy consumption.
These data sets are obtained from an online monitoring tool in a real European network. Figure  3  shows an example of data and corresponding energy consumption in the network. Figure  4  depicts the traffic share of each service category in the network obtained from the operating network. It is worth mentioning that the data is hourly collected from monitoring real BSs in 140 sites in two countries over the period of two years. Following our data, we propose the classification shown in Figure  1  for this indicator. This classification is still under work and should be harmonized with other major operators. The classification can be enhanced and updated following new services to be delivered, traffic increase, new measurements and novel network site management. Figure  2  shows the daily variation of the given indicator for different technologies (2G, 3G and 4G) from our case study.
• Central Cloud (CC), which consists of multi-server data centers including Central Servers (CS), Switching Equipment (SE) and (optional) other Telco Equipment (TE). The Energy Efficiency (EE CRAN ) of the system is given by the ratio of Data Volume (DV CRAN ) over Energy Consumption (EC CRAN ). DV CRAN is the sum of uplink (DV-UL) and downlink (DV-DL) data volumes, flowing through each of the Radio Access Points. EC CRAN is obtained by summing the contributions of the three segments: Radio Access, Edge Cloud and Central Cloud, each weighted by its specific Site Energy Efficiency (SEE). SEE, defined as the inverse of the Power Usage Effectiveness (PUE), which accounts for the specific energy characteristic of the equipment used in the segment. Typical values are 90% for the Radio Access Points (SEE RAP ), 75% for the Edge Cloud (SEE EC ) and 65% for the Central Cloud (SEE CC ), due to the necessity of active cooling.
Concerning the energy monitoring aspects, the undergoing network architecture evolution offers a unique possibility to integrate them from the beginning, enabling a native view of the energy consumption dynamics. Through data analytics, it allows deriving useful insights and correlations between telecom events (e. g. traffic load variations, new user connection), IT events (CPU, memory, net usage) and energy consumption. Moreover, an integrated monitoring solution allows service orchestrators to take decisions in line with minimal energy consumption targets. SooGREEN defined and experimentally validated a novel cloud-native energy monitoring solution, capable to work at micro-service level. The proposed solution exploits lightweight software power meters, based on the Power-API tool  [34] , that can be deployed on the different servers.
Unlike current state-of-the-art technology, these probes do not require any external device to estimate the energy consumption of the different micro-services executing on the machine  [35]. The estimation is part of a two-step process: 1) The probe performs raw metrics acquisition on a monitored server (eg. hardware counters on central processing unit (CPU) and memory) and stores them in a database. 2) The backend accesses the raw information to compute per-micro-service (or per VM) real-time energy consumption estimation. The software power meters do not require any complex and cumbersome calibration phase: after an offline configuration phase, the probes can be deployed on the server, ready to work. This is a fundamental aspect for an industrial utilization, where the servers are already deployed and cannot undergo a calibration phase which disrupts their operation.
The overall energy-monitoring solution proposed and experimented in SooGREEN integrates both server-level monitoring (based on physical power meters) and microservice level monitoring (based on software probes) into a complete cloud-native approach (Figure  8 ): while the physical power meter is statically installed, the software probes (and related backend) can be deployed as micro-services at system start-time or at runtime, on demand. This allows the necessary flexibility to respond to different needs, such as an end-to-end always-on monitoring set-up, or the ability to.
This break-before-make procedure incurs a delay between 10-12. 5 msec. Another series of signaling messages take place in the completion step for switching the path and bearer of data flow from control nodes in the evolved packet core, causing additional interruption and delay. Clearly, the adverse effect of this interruption becomes much more significant when 5G cell densification is considered, and with 5G low-latency services in mind. To support the ongoing densification while keeping a low number of BS handovers for mobile users, a virtualized CRAN (V-CRAN) architecture was proposed in SooGREEN in  [13] ,  [14]. In this architecture, CRAN is proposed to be deployed side-by-side with a virtualized timewavelength division multiplexing (TWDM)-passive optical network (PON) fronthaul. Specifically, the centralized node, which accommodates the BBU pool, is connected to the RUs through a software-defined reconfigurable high-speed TDWM-PON fronthaul.