dataesr/scientific-paragraphs-categorization huggingface dataset:
Over 200 G4 structures are currently available in the Protein Database (PDB) structure database. Unfortunately, only a few of them correspond to viral G-quadruplexes. Two solution NMR structures of DNA G4 motifs found in the HIV-1 long terminal repeat (LTR) are known, and one example is presented (Fig. 4 )  (De Nicola et al. , 2016; Butovskaya et al. , 2018). This HIV G-quadruplex offers specific epitopes for recognition and can be considered as "druggable. " The other solved structure by NMR belongs to the DNA motif of human papillomavirus type 52 (Maru si c and Plavec, 2019). G-quadruplex stability depends on a number of factors. Intracellular ionic conditions are favorable for G4 formation, and molecular crowding further stabilizes G4s  (Matsumoto et al. , 2020). Long G-runs allow the formation of more quartets, and this is often correlated with high stability.
The relative paucity of medicinal chemistry studies on G4 ligands against viruses makes it difficult to provide a general core or scaffold for G-quadruplex ligands [for a review on G-quadruplex ligand structure, please refer to  Sun et al. (2019) ]. Hit optimization also uses in silico toxicological profile software and databases to ensure the relative safety of the compounds and their lack of mutagenic (and possibly carcinogenic) potential. Unfortunately, in the G-quadruplex field, 1) the target is not always known, 2) some ligands may bind to multiple targets, and 3) either the three-dimensional structure of the target is poorly known, especially in a physiologic environment (topology may be affected by (macro)molecular crowding (Amjadi Oskouie and Abiri, 2021), for example), or no data are available on ligandbinding mode: although there are over 250 G4 structures in the Protein Database (PDB), only a few highresolution structures of drug-G4 complexes are known.
In this context, alterations to species composition and decreasing species diversity are expected to have further cascading effects on biological interactions and ecosystem functioning, further increasing the probability of species' extinction  37. In this context, we show that on average, vascular plants are expected to be the most vulnerable (Extended Data figure  3 ), but this might partially be the result of the small number of species modelled for ferns, gymnosperms, and lycophytes. Nonetheless, flowering plants are by far the most abundant group in our dataset -15,162 species -and apart from the three aforementioned groups, it is the most vulnerable group in terms of complete loss of species geographic ranges. The fact that flowering plants are projected to suffer substantial negative impacts increases the concerns about the vulnerability of biodiversity in general, due to possible future alterations to ecological interactions and ecosystem functioning  8, 39, 40.
Materials and Methods Species occurrence records. We compiled data on the distribution of species of four tetrapod and four vascular plant groups (amphibians, reptiles, birds, mammals, lycophytes, ferns, gymnosperms, and flowering plants; Supplementary Methods). Species occurrence records were obtained from the Global Biodiversity Information Facility (GBIF)  44  , the International Union of Conservation of Nature (IUCN)  45  , and BirdLife  46, 47. For vascular plants, we used geographic occurrence data obtained from the Global Biodiversity Information Facility by querying all records under 'Tracheophyta' (we only considered 'Preserved Specimens' in our search). We taxonomically homogenized and cleaned the occurrence records following the procedures described in Ramírez-Barahona et al. 48 using Kew's Plants of the World database  49  as the source of taxonomic information.
For countries in which data for vascular plants were scarce or absent (e. g. , India), we complemented occurrence information with IUCN polygons from the IUCN (although IUCN data for plants remains limited). We explored the possibility of using occurrence records from GBIF for tetrapods, but data for megadiverse countries were scarce. Consequently, we decided to use the distribution polygons provided by the IUCN for amphibians, reptiles and mammals (terrestrial and freshwater species)  45  , and the distribution polygons provided by BirdLife  46. This decision was based on the fact that ecological niche modeling using IUCN polygons has been proven to give robust results  15. For the IUCN polygons, we retained species that have been categorized as "extant", "possibly extinct", "probably extant", "possibly extant", and "presence uncertain", discarding species considered to be "extinct".
For the point occurrence data for vascular plants, we identified endemic species as those with all occurrence records restricted to any given megadiverse country. We only modeled species with at least 25 unique records at a 5 arc-minute resolution (approximate 10 km at the equator). In many cases, the processing of the IUCN polygons for tetrapods resulted in species with thousands of occurrence records. In these cases, we randomly chose a maximum of 500 hundred records per species. Climatic data. We used the bioclimatic variables available at WorldClim v. 2  49  as the baseline (present-day) climatic conditions (1970-2000). For the future scenarios, we used bioclimatic variables derived the IPSL-CM5-LR ocean-atmospheric model under five scenarios: i) the highemissions RCP 8. 5 W/m 2 scenario (2006-2100); and ii) Melting scenarios consisting of four different experiments of freshwater discharge into the North Atlantic from Greenland's meltwater (see DeFrance et al.
23  for a details). The four melting scenarios are equivalent to a sea level rise of 0. 5, 1, 1. 5, and 3 meters above current levels; these are named accordingly: Melting 0. 5, Melting 1, Melting 1. 5. , and Melting 3. We obtained debiased bioclimatic variables  23   Ecological niche modeling. At their most basic, the algorithms used to construct species distribution models relate species occurrence records with climatic variables in order to create a climatic profile that can be projected onto other time periods and geographic regions  50. The resulting models have proven useful in evaluating the impacts of climate change on biodiversity, and to identify varying levels of vulnerability among species  35, 36, 38. Here, we employed a multialgorithm (ensemble) approach to construct species distribution models as implemented in the "biomod2" package 51 in R  52.
The underlying philosophy of ensemble modeling is that each individual model carries a true "signal" about the relationships the model is aiming to capture, and some "noise" created by errors and uncertainties in the data and the model structure  35, 36. By combining models created with different algorithms, ensemble models aim at capturing the true 'signal' while controlling for algorithm-derived model differences; therefore, model uncertainty is accounted for during model construction. Prior to modelling, we reduced the number of bioclimatic variables per species by estimating collinearity among present-day bioclimatic variables. We employed the 'corrSelect' function of the package fuzzySim 53 in R  52  , using a Pearson correlation threshold of 0. 8 and variance inflation factors as criteria to select variables.
We used seven algorithms with a good predictive performance (evaluated with the TSS and ROC test): Maxent (MAXENT. Phillips), Generalized Additive Models (GAM), Classification Trees Analysis (CTA), Artificial Neural Networks (ANN), Surface Range Envelope (SRE), Flexible Discriminant Analysis (FDA), and Random Forest (RF). As occurrence datasets consisted of presence only data, for each model, we randomly generated 10,000 pseudo-absences within the model calibration area; we set the prevalence to 0. 5 in order to give presences and absences the same importance during the calibration process. For each species we selected a calibration area (or M)  50  using a spatial intersection between a 4º buffer around species occurrences and terrestrial ecoregions  54  (Supplementary figure  S14 ). By doing this, we incorporated information about dispersal and ecological limitations of each species into the modelling  50.
Acknowledgements We are grateful to  Francisco Estrada-Porrúa ,  Julián A Velasco  and the members of group " Clima y Sociedad " for the ideas that prompted the development of the project and comments on earlier drafts. Part of the analyses in this paper were carried out on CONABIO's ( Comisión Nacional para el Conocimiento y Uso de la Biodiversidad ) computing cluster, supported by their system administrator and the  Subcoordinación de soporte informático ; and the other part of the analyses in this paper were carried out on the computing cluster  Tláloc (Centro de Ciencias de la Atmósfera)  supported by their system administrator.
Data availability Data for species distribution models are available at Zenodo with the identifier doi. org/10. 5281/zenodo. 4917258. The geographic occurrence data for vascular plants is available from the Global Biodiversity Information Facility with the identifier https://doi. org/10. 15468/dl. bdxzkw. The distribution polygons for tetrapods and vascular plants are available at https://www. iucnredlist. org/ and http://www. birdlife. org/.
Other works use energy consumption as a metric to study scalability or reliability. In  [29] , the authors develop an experiment-based model to build a simulator. In  [30] , the authors develop a LoRa simulator in OMNET++ to evaluate the performance of a network using an Adaptive Data Rate (ADR) mechanism. They propose solutions that significantly increase the energy efficiency in dense networks. In  [31] , the authors introduce a cross layer simulation framework to evaluate and optimize the energy consumption of a device under realistic conditions. These works are based on simulations that do not really take into account all the recommendations of LoRaWAN specifications. Moreover, these studies consider the whole network's energy consumption, whereas our analytical model focuses on the energy consumption of a LoRaWAN node in its environment.
LoRa/LoRaWAN Architecture The architecture and operations performed by an IoT network using LoRa/LoRaWAN technology have been described in detail  [1, 2, 23, 24, 32, 33]. The topology is star of stars, as shown in Figure  1. IoT nodes or End Devices (EDs) form the sensor network and are positioned as closely as possible to the physical environment. They interact with the medium and transmit data to one or more GWs using a wireless link with LoRa modulation. GWs perform a protocol transition in order to facilitate the communication from the wireless sensor network to the network server. Application servers store data and offer new services to the users. The network server administrates and secures the data transmission from the EDs. The LoRa/LoRaWAN solution fulfills the functions of the PHY and MAC layers of the OSI model.
The LoRa physical layer is in charge of the modulation and the adaptation of the signal to the media. The LoRaWAN link layer provides an Aloha-type media access method. Figure  2  shows the deployment of LoRa technology in each network element. EDs encapsulate the data to be transmitted to application servers using both LoRa/LoRaWAN layers. The GW forwards the packet by re-encapsulating the LoRaWAN layer and data into an IP stack more suitable for communication with servers. The network server uses LoRaWAN and frame layers to acknowledge and manage the data transmissions of the wireless sensor network. Nodes can communicate with one or more GWs. Packets from the various GWs are filtered by the network server before sending the data to application servers.
Data Rate The data rate is directly derived from the previous analysis and is given by: R b = SF BW 2 SF CR, (1) where R b , SF, BW and CR denote the data rate (in bits per second), the spreading factor, the bandwidth of the channel and the error code correction (from 4/5 to 4/8), respectively. The LoRa node adapts this coding rate according to the distance to the GW, but also when it resumes transmission after a failure. Table  2  presents the operating modes defined and implemented in LoRaWAN equipment  [39]. Each mode, called Data Rate (DR5 to DR0), is a combination of modulation parameters chosen in order to reduce the data rate. The chosen spreading factor SF i depends on the mode DRi. The LoRa/LoRaWAN specifications do not enforce a particular DR management strategy but recommend the following usage.
LoRaWAN Media Access Method LoRaWAN is a solution based on an Aloha-type media access method that uses LoRa modulation on the physical layer. In order to share access to the media, European regulations impose the use of a duty cycle limitation or the so called "Listen Before Talk" transmission management. The LoRaWAN specification only implements the first solution with a duty cycle that must be less than 1%. Three classes of operation-A, B and C-are defined. In this article, we focus only on class A, which is the most energy-efficient and the only one mandatorily implemented in an equipment  [37, 39]. This operation mode is represented in Figure  5. When a LoRa node has data to send, it randomly selects a channel and opens a transmission window (tx) followed by two reception windows (rx1 and rx2) used by the network server to acknowledge messages and administrate the communication.
Measurement of Consumed Powers 3. 2. 1. Current Measurement Protocol The current consumed and the time spent by a LoRa circuit in the different operating states were measured. An end-to-end LoRaWAN communication between one sensor node and a network server was implemented. Figures  8  and 9  show the testbed. The current analyzer was a Keysight CX3300 with a CX1102A dual channel current probe. The sensor node was made of a Nucleo F070RB board equipped with an sx1272mb2das LoRa shield. The gateway was a Dragino LPS-8 and the network server was built around a Chirptack solution installed on a Debian OS. All the communications respected the LoRaWAN specifications. Authentication of the node to the network server was done with the ABP method for its simplicity. The end device transmitted random data of 50 bytes at regular intervals, respecting the duty cycle, by decreasing the DR at each transmission attempt.
Then, the average energy consumption by useful bit can be defined by E bit = E tot 8•FPayload. Table  7  shows the probability that the considered node uses a specific SF. This probability is used to obtain the number of sensors using each DR. Then, the error and collision probabilities are obtained from the EDs' distribution in the network. Thus, if the number of nodes in the network increases, the probabilities in Table  7  involve an increase of the number of nodes using the same DR, and then an increase of the collision probability. Subsequently,  (10) ,  (12)  and (  13 ) are used to determine the success and failure probabilities based on this distribution and, finally, (  23 ) is used by varying different input parameters: • the transmission power of the node; • the load carried in the data frames; • the number of allowed retransmissions; • the distance between the sensor and the gateway; and • the duty cycle.
Conclusions In this paper, a refined energy consumption model based on in-situ measurements is provided for a LoRaWAN node. This improved model takes into account the number of nodes in the network, the collision probability that depends on the density of sensors and the number of retransmissions. In-situ measurements are made through a complete LoRaWAN testbed that includes the network server. The results allow us to characterize the node energy consumption and the time spent on each LoRaWAN communication step. The simulation results show the influence of the number of nodes in a LoRaWAN network on the energy consumption of a node. The energy consumption also depends on classical parameters such as the transmission power, the load of the data frame, the number of retransmissions, the distance and the duty cycle.
After the initial deployment, area specific location protocol establishment and much more precise location of the seismicity, the OBS network geometry remained stable and a number of positions were re-occupied during subsequent deployments (Figs  1c  and d ). Data acquisition protocol differs depending on the instrumentsland or ocean-bottom based. Data from land stations are acquired and transferred in real-time, except the regional QM. GGLO station from which only triggered data can be transmitted via a lowbandwidth satellite link. The onshore data are centralized at the IPGP data centre and then made available through the same protocol on its public SeedLink server. The three stations installed on Mayotte in March 2019 were not equipped with internet connection during the first months of recording but were brought online in May 2019.
Their data were manually collected from the internal storage just before the departure of the RV Marion Dufresne for the MAYOBS1 oceanographic campaign in May 2019  (Feuillet 2019). The OBSs are regularly serviced for maintenance and data recovery (every 3-4 months for INSU-IPGP, every month for micrOBSs). As of May 2020, nine different OBSs deployments have been conducted. When the OBSs are recovered, their data are downloaded and time-corrected for internal clock drift and converted to miniSEED format using L-Cheapo tools  (Orcutt & Constable 1996). These data are then integrated in the main waveform database at the IPGP data centre, along with the local and regional land station data.
They took over the role of manually picking all automatically detected events for the newly created Réseau de surveillance Volcanologique et Sismologique de Mayotte (REVOSIMA) working group. The RéNaSS also used LocSAT but with the original IASPEI91 model. In April 2020, the daily manual picking and location duty was transferred to OVPF-IPGP in La Réunion, which uses the NonLinLoc (NLL) location software  (Lomax et al. 2014)  and a new hybrid velocity model described in Section 3. The NLL configuration and 1-D local velocity model used by OVPF-IPGP in La Réunion were developed during MAYOBS1 campaign in 2019  (Feuillet et al. 2019; Saurel et al. 2019; Feuillet 2019). While a daily manual screening of continuous waveforms with WebObs  (Beauducel et al. 2020 ) and identification of every event has been performed since early 2019, the composite earthquake catalogue is based on the automatic detection of events from land stations.
OBS integration After each OBSs data recovery, we manually pick phases on the OBS data and offline land stations to improve the locations in the existing earthquake catalogue during dedicated pickathons that continue today. Adding new phase picks significantly improved the location of the earthquakes already detected and first located only by the land network. Since there are already a great number of events to relocate in the existing catalogue, we do not search for new events from the OBS data set. Searching for new events in the OBS data set is a work in progress and will be reported in future studies. During each pickathon, the time span of the OBSs data we need to process is divided across three teams using the same software setup and each team manually locates earthquakes in descending order of magnitude  (Saurel et al.
2019 ; Fig. 2 ). When the OBSs are recovered from an oceanographic research vessel, such as RV Marion Dufresne, large enough to board a team of around 10 analysts, we divided the work in three 4-hr-shifts (i. e. 2 or 3 analysts by shift) we can manually pick earthquakes 24/7. Otherwise, the MAYOBS/REVOSIMA seismology group meet-either virtually or at one of the group institutes-for 2 d to process the recently recovered data, produce graphics demonstrating the evolution of the crisis, and interpret the results together. Whether the phase picking, location, and interpretation are done onboard or on land, we use the same setup/configuration, starting database, and software to analyse the recently recovered data. We assigned uncertainty to each phase, from a common predefined list of values. Time uncertainties assigned to the S-phase were always equal to or larger than the uncertainty assigned to the P-phase.
After each OBS recovery, the deployment time-span is divided in three groups, that is Group 1, Group 2 and Group 3. Each group processes the same number of events, by decreasing magnitude order, using the same software setup and the same picking guidelines and recommendations. The daily catalogue of events, automatically detected and manually confirmed on land-based network data (BRGM then RéNaSS and OVPF-IPGP), is synchronized in the pickathon database. It also feeds in real-time the REVOSIMA/MAYOBS catalogue. When OBS recovery is performed by a research vessel, each group alternates a 4-hr day and night shift. At the end of the pickathon, the relocated events, with additional manual picks and polarities on OBSs data, is merged within the REVOSIMA/MAYOBS database for further use for monitoring and research.
The same procedure is applied to on land pickathons, only the work is completed during 2. 5 normal work days instead of 24-hr shifts. To enable this collaborative work, we use techniques and configurations developed during the last 10 yr for the daily routine processing of earthquakes in IPGP volcanic and seismologic observatories. Waveform data and event databases are held by a SeisComP3 instance  (Weber et al. 2007). Each earthquake analyst uses their own laptop and, regardless of their laptop's Operating System, they run a VirtualBox pre-configured Linux machine with the SeisComP3 Origin Locator GUI client (scolv,  Weber et al. 2007 ). We use NLL software and the new local 1-D velocity model described in the following section to locate the events. Magnitudes are computed with the embedded local magnitude (M L ) formula in SeisComP3  (Richter 1958).
I M P ROV E D 1 -D L O C A L V E L O C I T Y M O D E L A local velocity model is essential to provide precise location of this dense swarm seismicity. One of the first challenges in improving earthquake locations was then to build a reasonable 1-D local velocity model, because only global models were available so far. This was done onboard RV Marion Dufresne during the MAYOBS1 campaign  (Feuillet 2019). We used the data of the first OBSs recovery and of three local land stations (see Section 2). We first produced modified Wadati diagrams  (Chatelain 1978 ) and considered two different existing velocity profiles from the area. The first profile, named 'Coffin449', is based on a P-wave velocity (V P ) profile derived from a 1980 active-seismic sonobuoy. That experiment was located 100 km southeast of Mayotte  (Coffin et al.
72 (Figs  3a  and b ). We then tested different combinations of velocity model parameters (2 velocity profiles and 3 V P /V S ratios) on a more complete data set of 800 events with OBS phases, using NLL software  (Lomax et al. 2014). Contrary to Hypo71, NLL allows the use of depth variations of the V P /V S ratios and different velocity models depending on the station. Its probabilistic approach  (Lomax et al. 2014 ) also makes the reported ellipsoidal errors more meaningful and easier to interpret than estimated horizontal and vertical errors given by Hypo71. We first used only local stations (Mayotte land stations and OBSs) arrivals to assess the best local 1-D velocity model. We compared the distributions of maximum ellipsoidal error for the 2 velocity models and 3 V P /V S ratios for the 800-earthquakes MAYOBS1 data set.