dataesr/scientific-paragraphs-categorization huggingface dataset:
This includes the installation and tuning of the Operating System and the installation of support tools that might be needed to achieve its full potential. Today, CREW compares the EEW software PRESTo  [Satriano et al. , 2011]  and VS -Virtual Seismologist  [Behr et al. , 2016] , on the real time data streaming from ISNet  [Festa et al. , 2021]. Each virtual guest has been assigned the same amount of (virtual) hardware resources, so that its final performances will only depend on the EEW software algorithms and configuration, given the availability of the same set of recorded waveform data in a timely fashion. Inputs and outputs to each EEW system are provided in a standardized and widely accessible format. Each EEW systems, receive data in an identical manner from the SeedLink server and produce real-time alarm messages in case of earthquake detection.
A new alarm is issued every time the estimate of a relevant earthquake source it/cgi-bin/isnet-events/isnet. cgi). The latter might be more complete in terms of lower magnitudes for events well located within the network. The earthquake information from the bulletins is parsed and inserted in a PostgreSQL database, which forms the authoritative source of real earthquakes and source parameters. This event database is also updated when the external agencies revise some of their published information. Finally, a GUI (http://lccepos. fisica. unina. it) has been implemented that consists of a web application that can list the alarm messages from each system as compared to reference seismic bulletins, plot them on a map and provide statistics about the quality and speed of the outputs of each system.
Scientists can access to CREW for testing new EEW software or some parts, to compare against state-of-the-art software, through the direct plugin of their modules and the control of parametrization. Also, a user can evaluate the performance of existing, state-of-the-art software on their own data, finely tune the system parameters and design tailored decision modules. Access to CREW may be granted as Transnational Access [TNA;  Wessels et al. , 2022]. Beyond scientists, additional stakeholders can be public agencies representatives, and private companies. Within testing EEW in CREW, stakeholders can design and evaluate the performances of software to be interfaced with actuators for end-to-end EEW applications: this will include target-oriented software and output combination and a direct connection of this output to appropriate decision modules, for risk mitigation actions specific to the site to protect.
These tools will evolve with the improved models of transient processes ICDP is instead currently involved in (e. g. STAR, standing for A Strainmeter Array Along the Alto Tiberina Fault System, Central Italy project in TABOO-NFO area; https://www. icdp-online. org/projects/world/europe/northernapennines-italy/). STAR is an ongoing project led by INGV in collaboration with UNAVCO US agency, finalised to the instrumentation with strain-and seismometers of six 80-160 m deep vertical boreholes surrounding the creeping portion of the ATF. Each station, named "small observatory", is also equipped with surface GPS, meteorological instruments, and fibre optic cables (see Figure  5 ). The suite of instruments will enable the collection and calibration of strain records with exquisitely high pre- For these reasons, we expect the implementation of many Near Fault infrastructures in the coming years, focused on the physics of tectonic faulting and able to generate data and scientific products interrogating a broad spectrum of fault slip behaviour  [Peng and Gomberg, 2010] ; key ingredient for the next generation of scientists finally able to embrace a real multidisciplinary investigating approach.
in/around fault zones. NFO can provide the finest context to exploit this expertise and testing. The NFOs are expected to become ideal sites for hosting state-of-the-art experiments for testing in situ both new scientific ideas/models and new geophysical instruments or arrays. A NFO would provide the proper environment in terms of reference instrumentation, data and infrastructures facilities. The accessibility of NFOs databases allows for rapidly evaluating and selecting the most appropriate site for the instrument or software testing and/or new scientific hypotheses validation. This will achieve synergies in research, monitoring and tools (e. g. , automatic analysis) by sharing technological best practice for implementation (e. g. , boreholes instrumentation for multi-sensor stations) and technological issues regarding instrumentation and software.
In the present study, we provide a retrospective genomic surveillance of the SARS-CoV-2 pandemic in Lebanon; we newly sequence the viral genomes of 200 nasopharyngeal samples collected between July 2020 and February 2021 from patients in different regions of Lebanon and from travelers crossing the Lebanese-Syrian border, and we also analyze the Lebanese genomic dataset available at GISAID. Our results show that SARS-CoV-2 infections in Lebanon during this period were shaped by the turnovers of four dominant SARS-CoV-2 lineages, with B. 1. 398 being the first to thoroughly dominate. Lebanon acted as a dispersal center of B. 1. 398 to other countries, with intercontinental transmissions being more common than within-continent. Within the country, the district of Tripoli, which was the source of 43% of the total B.
Most of the genomic surveillance of SARS-CoV-2 is conducted in a small number of countries located in North America and Europe. Developing countries originated only ~9. 7% of the sequenced genomes available in GISAID (https://www. gisaid. org/ assessed on 9 May 2022)  [6, 7]  despite being home to almost 49% of SARS-CoV-2 cases, as reported by the database on May 9th, 2022  [8]. This may be related to differences across the world in scientific funding, sequencing capacity, and the number of trained researchers  [9, 10]. The circulation of SARS-CoV-2 under the radar of researchers and public health authorities allows the unnoticed accumulation of worrisome mutations in lineages, resulting in the emergence of VoCs. It is not surprising, then, that most VoCs were first identified in developing countries.
Collecting Information and Handling Samples Phone calls were made to patients, and after they gave their consent to participate in the study, we filled out a questionnaire gathering socio-demographic information, health data, and data related to SARS-CoV-2 infection (i. e. , the source of infection, travel during or before the period of infection, symptoms experienced, whether there was hospitalization or not, and whether or not medication was taken). After obtaining the requested number of participants (200), the corresponding samples underwent a new coding process. Then, total RNAs were extracted at LMSE with the AccuPrep Viral RNA Extraction Kit (BIONEER, Daejeon, Korea) and the QIAamp Viral RNA Kit (QIAGEN, Germantown, TN, USA), according to the recommendations of the suppliers. Finally, the RNA extracts were sent to the Laboratory of Virology, Institute of Infectious Agents, which is associated with the National Reference Center of Respiratory Infection Viruses, Hospice civil de Lyon in Lyon, France, for genomic sequencing by Illumina COVIDSeq (Illumina, San Diego, CA, USA).
The amplification of extracted RNA was conducted with an automatized (SPTLabtech) COVIDSeq-Test™ protocol (Illumina) with an ARTIC nCoV-2019 Amplicon Panel v4 of primers. Libraries were sequenced with 100 base pair paired-end reads using the NovaSeq 6000 sequencing system with the SP Reagent Kit v1. 0 200 cycles (Illumina)  [15]. Reads were processed using the in-house bioinformatic pipeline seqmet, available at https://github. com/genepii/seqmet (accessed on 28 November 2021). The generation of the consensus sequence and the identification of the SNPs of each genome were performed by aligning the reads with the reference genome (NC_045512. 2) using bwa v0. 7. 17  [16]  and post-processing with samtools v1. 11, bcftools v1. 10. 2  [17] , and bedtools v2. 27. 1  [18]. Lineages were identified with the Pangolin program v.
Evolutionary and Phylogeographic Analyses In addition to the genomes sequenced in this study, we obtained the sequences of SARS-CoV-2 genomes collected in Lebanon that were available in the GISAID database as of 7 February 2022, listed at https://doi. org/10. 55876/gis8. 220630un (accessed on 30 June 2022). This dataset was filtered using the options "Complete", "Low coverage exclude", and "Collection date complete" available in GISAID. The final dataset containing the newly sequenced genomes and the GISAID sequences contained 1042 genomes. These sequences were aligned to the WH01 (EPI ISL 406798) genome from Wuhan, China using MAFFT  [20]. The 3 and 5 ends were trimmed with the seqkit package  [21]  using the WH01 sequence as a reference. The maximum likelihood tree was then inferred using IQ-TREE  [22] , using the GTR+F+I+G4 model selected by the built-in algorithm ModelFinder  [23]  and 1000 ultrafast bootstrap replicates  [24].
To evaluate how SARS-CoV-2 has spread across Lebanon, we used the dispersal of the B. 1. 398 lineage as a case study. To confirm the number of introductions of the lineage in Lebanon, we obtained all sequences of B. 1. 398 available in GISAID on February 7th of 2022, listed at https://doi. org/10. 55876/gis8. 220630un (accessed on 30 June 2022), using the same filters described previously. Sequences obtained from samples collected before June of 2020 were removed because they had higher diversity than expected based on their sampling dates (Figure  A1 ). Among the excluded sequences were genomes sampled in January 2020, as described in the GISAID database. We believe this date to be an error made during the submission of sequences, as the first confirmed COVID-19 cases in Lebanon occurred in February 2020.
1. 398 within Lebanon was reconstructed using BEAST  [26]. Because the sequences from Lebanon available in GISAID did not have information about the district or province of sampling, in this analysis, we used only the sequences generated in this study. We employed the Relaxed Random Walk model with Cauchy's distribution on coordinates randomly selected within each sample's collection district; we employed the GTR substitution model with estimated base frequencies and the "Gamma + Invariant Sites" heterogeneity model with four Gamma categories. We also used a coalescent tree prior with the GMRF Bayesian Skyride model and a uniform prior for the clock rate. The MCMC ran through a chain of 100,000,000, with sampling every 10,000th and burn-in of 10% of the trees. Dispersal routes were extracted from the consensus tree using the seraphim package  [27]  and plotted using the ggplot2 package, both in R software.
The mean age of the patients was 38. 7 years. Regarding infection status, most of the patients (94%) developed symptoms with a mean duration of 10. 2 days, and 6% of the patients went to the hospital after infection. After sequencing, the SARS-CoV-2 genome assembly failed for seven samples, resulting in 193 sequences. We were able to attribute PANGO lineages to all but three of these sequences. In total, 21 PANGO lineages were identified, with B. 1. 398 predominant (52. 5%) followed by the B. 1. 1. 7 lineage (18. 5%). With regard to nationalities, the lineages detected in non-Lebanese residents of Lebanon, including Syrians and Palestinians, were generally found in Lebanese citizens as well. Among the travelers to Syria, the lineages were mainly B. 1   Analyzing the whole Lebanese dataset in GISAID, including the new sequences, 40 different lineages were identified within Lebanon for the period ending December 2021.
Discussion In the present study, we provided a retrospective genomic surveillance of the main SARS-CoV-2 lineages circulating between July 2020 and February 2021 in Lebanon by sequencing a set of 200 samples and analyzing the Lebanese genomic dataset available at GISAID as of February 7th, 2022. We then focused our analysis on the B. 1. 398 lineage by reconstructing the transmission routes on both Lebanese and worldwide scales. The new genomes sequenced in this work more than doubled the number of sequences available for the first year of the pandemic in Lebanon. In particular, it greatly improved the sam-.
Discussion In the present study, we provided a retrospective genomic surveillance of the main SARS-CoV-2 lineages circulating between July 2020 and February 2021 in Lebanon by sequencing a set of 200 samples and analyzing the Lebanese genomic dataset available at GISAID as of 7 February 2022. We then focused our analysis on the B. 1. 398 lineage by reconstructing the transmission routes on both Lebanese and worldwide scales. The new genomes sequenced in this work more than doubled the number of sequences available for the first year of the pandemic in Lebanon. In particular, it greatly improved the sampling of the B. 1. 398 lineage in this period (Figure  A2 ), which is critical for understanding how SARS-CoV-2 first spread across Lebanon and caused the first surge of COVID-19 cases in the country.
In June 2020, although the international airport was closed, a new dominant lineage, namely B. 1. 398, was detected. This lineage was characterized by the mutation V1291I in the ORF1a gene, P314L in ORF1b, D614G and T95I in the spike gene, S194L in the nucleocapsid gene, and S84L in ORF8. The D614G mutation in the spike protein gene has been suggested as being responsible for an evolutionary advantage in comparison to the lineages that first circulated in Wuhan  [32] [33] [34]. The presence of this lineage might be explained either by unnoticeable circulation during the first few months due to the low number of sequences available from the initial period, which is a factor we discussed herein, or by recent introduction (or emergence) from an unknown source. Unfortunately, there are no sequences from April in the Lebanese GISAID dataset to support this hypothesis.
If we compare cases and deaths in the period in which B. 1. 398 was prevalent (i. e. , between June and December 2020: 189,865 cases, 1378 deaths) and the period in which the Alpha variant was prevalent (between January and May 2021: 355,015 cases, 6282 deaths), we observe the higher transmissibility and mortality of the Alpha variant  [12, 13]. To our knowledge, there are no sufficient data available in the literature regarding the B. 1. 398 lineage. According to the PANGO lineages website, this lineage appeared approximately in January 2020 and was denoted as a European lineage  [2]. Before our study, there were 377 sequences of this lineage in the GISAID dataset, derived from 24 countries; almost all sequences were from Denmark 138 (37%), Lebanon 116 (31%), the United Kingdom 24 (6%), Germany 19 (5%), and Saudi Arabia 16 (4%)  [14].
This phenomenon may embody the butterfly effect, where a lineage that emerged in a small area can propagate around the world. Moreover, Lebanon has witnessed many scourges that converged with and exacerbated the COVID-19 pandemic. Most important was the catastrophic economic and financial crisis that ranked among the world's three worst crises since the mid-1800s; it caused more than 80% of the Lebanese population to sink into poverty and created shortages of essential medicines, fuel, and electricity. Therefore, the government was not able to apply anti-COVID-19 measures with a tight rein, and for some Lebanese, COVID-19 was the least of their concerns  [45, 46]. It is worth mentioning that our conclusions may be limited by the absence of historical data both in Lebanon and in neighboring countries.
In Lebanon, there is a substantial lack of sequences on both the temporal and spatial scales as many months and districts have no (or a limited number of) sequences. In Syria, there is a lack of genomic surveillance, and only 89 Syrian sequences obtained in Turkey from a short period (December 2021 and January/February 2022) are available in the GISAID database (6 April 2022); these samples correspond to 10 lineages, with BA. 1. 1 and AY. 122 being the most prevalent. We demonstrated here a diverse pool of lineages harbored by the travelers crossing the Lebanese-Syrian border, of which three lineages, B. 1. 1. 274, B. 1. 177, and B. 1. 22, were not detected in the Lebanese population, hinting at the possible existence of a specific Syrian pool. With regard to the identified lineage B. 1. 398, our analysis showed that Lebanon spread this lineage to Syria.
Additionally, we acknowledge the effort of the personnel and laboratories who generated and submitted the genomics sequences used in this work to the GISAID dataset, listed at https://doi. org/10. 55876/gis8. 220630un (accessed on 30 June 2022). The authors would also like to thank Nada Ghosn, the head of the Epidemiological Surveillance Program in the Ministry of Public Health, for her cooperation and support. They also.
To be able to account for happiness stability, we only retained individuals who provided at least five observations, leaving for the analysis 1687 observations (98% of all) over 347 days and 74 individuals (mean number of observations per day = 4. 56, mean number of observations per participants = 22. 8). Sixty-three participants were women (85. 1%). The mean age was 42. 8 years old (SD=14. 0). After the end of the data collection, the personal well-being curves of participants were provided upon their demand.
Data analysis We used R (R Core Team, 2020) for all our analyses. Five multilevel models served to test our hypotheses, with happiness scores (Model 1), the short-term instability (Model 2 and 3), and the long-term instability (Model 4 and 5) as dependent variables. To predicts happiness levels (H1 & H2), model 1 was fitted using linear mixed models with the lmer function of the lme4 package  (Bates et al. , 2015). The classical operation in multilevel analyses used to examine both between-and within-individual effects is to use the cluster means and the individual scores centered within cluster as predictors in the model  (Enders & Tofighi, 2007). Here, observations were nested within individuals. Therefore, individual selflessness means served as our cluster mean predictor, which we call "person-mean selflessness.
Thus, insertion of a gene into the 42AB cluster leads to generation of abundant piRNAs that trigger its repression. To understand why 20A reporters do not produce piRNAs, we analyzed expression of 20A cluster transcripts by RT-qPCR using primer sets designed to detect cluster transcripts upstream and downstream of the reporter insertion site. Surprisingly, we found that the abundance of 20A cluster transcripts was strongly (> 20-fold) reduced in flies with reporter insertions compared to both wild-type flies and the original MIMIC flies used for RMCE (Fig. 1G ). In agreement with the decreased cluster transcript level, 20A piRNA levels dropped 84-and 321-fold in 20A[-] and 20A  [+]  flies, respectively, throughout the whole cluster as far as 38 kb from the insertion site (Fig. 1B, G ). In contrast, the insertion in the 42AB cluster did not affect piRNA level from this cluster (data not shown).
Bioinformatic analysis ChIP-seq processing and mapping. Trimmomatic (version 0. 33)  (Bolger et al. , 2014)  and cutadapt (version 1. 15)  (Martin, 2011)  were used to trim off adaptors and filter out those shorter than 50 nt after trimming. The first 50 nt from each read were mapped to dm3 genome and vector sequence respectively, using Bowtie  (Langmead et al. , 2009)  (version 1. 0. 1, parameters: -v 2 -k 1 -m 1 -t --best -y --strata). After mitochondria reads were removed, aligned reads were then used to generate piled-up RPM signals and enrichment profiles by our customized scripts and deepTools  (Ramirez et al. , 2016). Regions blacklisted by ENCODE  (Amemiya et al. , 2019)  were excluded from enrichment analysis. Read counts over equal-sized bins were calculated using deepTools2 and BEDOPS  (Neph et al.
, 2012) , and figures were made using Matlab. All the scripts we used can be found on GitHub (https://github. com/brianpenghe/Luo_2021_piRNA/blob/main/ChIP-seq. md). To map fusion read, the first 20 nt and full length of the reads were mapped to vector sequences using the aforementioned bowtie settings. Reads where the first 20nt mapped to vector sequences but the full length did not were selected. The last 20 nt of such reads was mapped to the reference genome with the same settings. Mappable reads among these were considered fusion reads between the vector and genome which were used to identify insertion location. The scripts are available on GitHub (https://github. com/brianpenghe/Luo_2021_piRNA/blob/main/FusionReads. md). For small RNA-seq analysis, Trimmomatic and cutadapt were used to trim off adaptors and filter out reads shorter than 20 nt after trimming.
Acknowledgements We thank members of the  Aravin and Fejes Toth labs  for discussion and comments. We thank  Julius Brennecke  for providing the Rhino antibody. We thank  Igor Antoshechkin  ( Caltech ) for help with sequencing. This work was supported by grants from the  National Institutes of Health  ( R01 GM097363 ) and by the  HHMI   Faculty Scholar Award  to AAA.
Data availability Libraries generated from this study are deposited in GEO under accession codes GSE193091. The scripts are available on GitHub: https://github. com/brianpenghe/Luo_2021_piRNA. Pol ll ChIP-seq data analyzed in this study were from GSE43829 (Le  Thomas et al. , 2013)  and GSE97719  (Andersen et al. , 2017).
Reporter coverage was calculated based on 10 nt bin size, and figures were made using Wang, J. , Schultz, N. , Zhang, F. , Parhad, S. S. , Tu, S. , Vreven, T. , Zamore, P. D. , Weng, Z. , and Theurkauf, W. E. (2014). The HP1 homolog rhino anchors a nuclear complex that suppresses piRNA precursor splicing. Cell 157, 1353Cell 157,  -1363. Zhang, Z. , Matlab. The mapping scripts are available on GitHub (https://github. com/brianpenghe/Luo_2021_piRNA/blob/main/piRNA-seq. md).
Participants The participants were 549 unselected French-speaking adults aged from 18 to 64 years. Advertisements, university intranets, and the waiting rooms of healthcare centers were used to recruit participants. Data analyses were based on 520 adults (338 females, 182 males) with a mean age of 30. 99 years  (SD = 11. 89;. Five participants were excluded because of a history of psychotic mental disorders, two were excluded because of a history of substance abuse or dependence including alcohol (less than 3 years of abstinence-except nicotine or caffeine), 11 for a history of neurological disorder, and two for the use of anxiolytics or other drugs on the day of the assessment. In addition, two participants were excluded because of anti-psychotic medication, two because of recent changes in antidepressant medication (less than 4 weeks).
Disengagement From Sad Cues and Attention to Happy Cues The exogenous cueing task (ECT) is a reaction-time-based attention task, which was programmed using OpenSesame software and was run on a computer with a 60 Hz, 15-inch color monitor. The original exogenous cueing task asked participants to detect a visual target presented in the left or right peripheral location of the screen  (Posner, 1980). In affective science, the paradigm has been modified by using emotional and neutral cues to allow a comparison of their attentional processing. The task was created with faces (14 happy, 14 sad and 14 neutral) selected from the Karolinska Directed Emotional Face (KDEF) database  (Lundqvist et al. , 1998; Goeleven et al. , 2008). Faces were sized 280 pixels high X 280 pixels wide with visual angles of 5.