dataesr/scientific-paragraphs-categorization huggingface dataset:
The identity of the callers has been removed and the exact location of the communication towers has been slightly modified for confidentiality reasons. In addition, a time series containing the number of calls per hour for the month of January is also available as part of the supplementary material (S3 File). To obtain the dataset over the entire year, one would need to contact Sonatel directly and present the research project that would require the data (contact: Mr El Hadji Birahim Gueye, Direction des Systèmes d'information Sonatel, ebgueye@orange-sonatel. com or post mail: Orange-Sonatel, 46 Boulevard de la Republique, BP 69 Dakar, Senegal). The analysis was performed using R. This research uses data from Senegal. It was approved by the Senegalese Commission de Protection des Données Personnelles (private data protection commission) on the 13th of July 2015 as part of the "Data for Development (D4D)" project.
This technique could complement for example the approach by Lai et. al. which shares the same aim, but uses direct regressions from average values only  [19]. Note that contrary to previous methods, the clustering relies solely on mobile phone data, without requiring a population training dataset. In addition, it can accommodate external information known a priori. It should be possible for example to use satellite data to subset potential reference locations into obviously low or obviously high density areas. This is a step in the direction proposed by the director of UN Global Pulse, Robert Kirkpatrick, who asserted that "the next phase in call-records research should be cost-benefit analyses that look at the investment needed to conduct a study, roll out an intervention and appraise the advantages for communities.
" [20]. We appreciate that our method requires access to a sufficiently large mobile phone dataset and that Senegal is a leader in sub-Saharan Africa in terms of electrification rate, mobile phone penetration and census data collection. However, since we are not using the full mobile phone penetration rate, but only the 65% market share of Sonatel, we believe that there should be enough underlying data in many other developing countries to apply the method there. Beware, however, that in some cases, using a single operator with an inhomogeneous market share might introduce some important biases (cheaper providers may be chosen more by people who consume less electricity for example).
The CTA (CUA) Leu11 codon is the least frequent (7%) of the six leucine codons in H. sapiens genes, according to the Codon Usage Database  (31). Relative codon usage positively correlates with translation efficiency and the abundance of the corresponding anticodon tRNAs (reviewed in ref. (32) ). In allele rs179008 T, our analysis of the adaptation of the 5ʹ end of the TLR7 coding sequence to the human codon usage bias (Supplemental Figure  5 ) predicted the Leu11 CUA codon to be the first low-adaptiveness codon  (33)  met by the elongationcompetent ribosome under the 5ʹ-to-3ʹ scanning model for translation  (34). As such, this codon could throttle translation efficiency through a longer ribosome residence time  (35)  relative to the higher-adaptiveness Gln11 CAA codon of rs179008 A. To explore the hypothesis of a codon effect on translation, we introduced single-nucleotide mutations (CTA to CTC, CTT, or CTG) in the Leu11 TLR7-SEAP reporter plasmid to generate a set of four expression vectors differentiated by their synonymous CTN Leu11 codon.
Chemiluminescent detection was carried out with Amersham ECL Select or ECL Prime reagents (GE Healthcare) as necessary, and densitometric analysis performed with the Image Lab 5. 0 software (Bio-Rad). TLR7 protein quantification in healthy subjects of the different rs179008 genotypes was performed on cryopreserved PBMCs. PBMCs were thawed and cultured at 37°C for 2 hours before counting, cell lysate preparation and western blotting as above. The densitometric signals of the TLR7 145-kDa and 75-kDa forms were normalized to b-actin and then to an internal standard PBMC lysate that was loaded in each gel for inter-gel data normalization as described in Supplemental Figure  6.
TLR7-SEAP reporter vectors. The signal peptide reporter vectors encoded a secreted alkaline phosphatase (SEAP) where the autologous signal peptide was replaced by an allelic form of the human TLR7 signal peptide (Fig. 3A ). The TLR7 and SEAP moieties connected at a proline residue analogous to Pro31 in TLR7 and to Pro20 in SEAP (GenBank U89937. 1). Preparatory analyses with the SignalP 4. 1 software (60) predicted in each case a signal peptide with a near-identical profile to that of the parental TLR7 form (not shown). The SEAP gene under the control of the human EF-1a promoter was released from pYSEAP (61) (Addgene plasmid #37326) as a 3. 5-kb SpeI-SalI fragment and inserted into a modified pBC KS+ vector (Stratagene) to generate pYSEAP-JM562. Next, a 360-bp fragment of pYSEAP-JM562 overlapping the 5ʹ end of the SEAP coding sequence was excised with EcoRI and NdeI, and in-frame replaced by one of a series of synthetic TLR7-SEAP DNA fragments.
Acknowledgments We gratefully acknowledge support from  F. L'Faqihi ,  A. L. Iscache  and  V. Duplan  at the flow cytometry facility (CPTP);  E. Lhuillier ,  C. Naylies ,  A. Emile  and  Y. Lippi  for DNA sequencing and RNA analyses at the  GeT-Purpan  and  GeT-TRiX  facilities; and the technical assistance of  M. Requena ,  M. Cazabat  and  R. Carcénac. We also thank  J. Plumas  for GEN2. 2 cells;  Anaïs Bellin-Robert  and  Guilhem Vazzoler  for assistance during plasmid cloning;  G. von Heijne  and  H. Nielsen  for comments on SignalP;  H. Blanché-Koch  for curating the CEPH sample panel;  S. Lupold  for the Metridia luciferase vector; and  W. Chowdhury  and  S. Chavanas  for advice on luciferase assays. This work was supported by grants from the  French National Agency for Research on AIDS and Viral Hepatitis (ANRS ,  EP-53  study),  Fondation pour la Recherche Médicale  ( DEQ20180339187 ), and  Conseil Régional Occitanie-Midi-Pyrénées.
SignalP 4. 1 software (67) at http://www. cbs. dtu. dk/services/SignalP/, with the setting "Input sequences do not include TM regions" and default D-cutoff values. For a more sensitive prediction of the cleavage site (67), complementary analyses were performed with SignalP version 3. 0 (68) at http://www. cbs. dtu. dk/services/SignalP-3. 0/. Codon adaptation analysis of the 5ʹ-terminal region of the TLR7 coding sequence was performed with the gcua software at http://gcua. schoedl. de/seqoverall_v2. html. Study approval. The PRIMO cohort was previously registered with ClinicalTrials. gov under identifier NCT03148964. The X-LIBRIS cohort was previously registered with ClinicalTrials. gov under identifier NCT01952587. This study was conducted in adherence to the ethical principles of the Declaration of Helsinki, and approved by the relevant medical ethics board, Comité de Protection des Personnes Sud-Ouest et Outre-Mer I, Toulouse, France.
sources. Recombination rate data from the Phase II HapMap project (66) the data. JEM and JCG prepared the manuscript with input from their co-authors. All authors read and approved the final version of the manuscript. PA, JEM and CC contributed equally to this work. PA initiated the study, JEM and CC performed the mechanistic and genetic association studies, CC analyzed TLR7 expression in human peptide the analyzed and interpreted cleavage site using were downloaded from ftp://ftp. ncbi. nlm. nih. gov/hapmap/recombination/2011- 01_phaseII_B37/. Lists of TLR7 polymorphisms, allelic frequencies, and individual genotype data were downloaded from the Ensembl database at http://www. ensembl. org/. Human codon usage data was from the Codon Usage Database (31) at http://www. kazusa. or. jp/codon/.
In the case of large databases, mining techniques are an appropriate and efficient way to determine meaningful association rules between variables of interest under the form of sets of conditions on their values, along with measures of confidence and frequency  (Agrawal, Imielinski, & Swami, 1993; Fournier-Viger, Wu, & Tseng, 2012; Han, Pei, Yin, & Mao, 2004). Significant rules are typically frequent patterns encountered in the data set at hand  (Han et al. , 2004) , but methods are also developed for mining rare patterns  (Piri, Delen, Liu, & Paiva, 2018).
| Sampling design Stations were located at eighteen sites representative of NC coral reef areas: remote sites in the CSMP (Entrecasteaux, Petrie, Astrolabe, Chesterfield, Bellona, Matthew, Hunter, Walpole), and others around the main island (Nouméa, Koné, Pouebo, Hienghène, Bourail, Borendy, Merlet, Corne Sud and Ouano) and in the Loyalty Is. (Lifou) (Figure  1 ). Data were collected between 2007 and 2015, between March and September, outside of the summer season. The sampling design at each site was stratified using geomorphological maps  (Andréfouët & Torres-Pullizza, 2004 ) and included main reef areas and associated soft-bottom habitats. Within each stratum, stations were distributed to cover the entire site area and account for management status (marine protected area (MPA), WH property, unprotected areas).
| Data validation and image analysis After fieldwork, video footage was validated when (a) underwater visibility (estimated from reference images; see below) was at least 5 m, and (b) the field of view was not obstructed by any sea floor or benthos relief that would prevent image analysis within a 5-m radius around the system. For each valid video, habitat attributes (Table  1 ) were evaluated from a single rotation for an estimated 5-m radius around the video system, corresponding to an observed surface area Fish and other marine animals were identified at the most precise taxonomic level based on a reference species list, and counted on each frame and for each of three undisturbed rotations within a 5-m radius around the system. The reference list included 42 families (Appendix 1). For each species at each station, abundance was calculated as the mean count over three rotations, which averaged out the variability between rotations.
The algorithm was implemented using the SPMF software  (Fournier-Viger et al. , 2012). Top1000 rules were searched for three min_conf values, 80%, 90%, and 95%, producing three sets of 1,000 rules. The Top1000 rulesets were then selected and reorganized based on expert knowledge, to achieve a compromise between representativeness (i. e. , a large proportion of the stations in each habitat were described by the rules with a high confidence level) and parsimony (not having too many rules). Each rule had to (a) include a condition on the archetypical attribute of each habitat; (b) comprise up to four conditions on habitat attributes; and (c) not overlap with another rule. Expert knowledge was also useful to identify specific habitat attributes that were relevant to describe within-habitat heterogeneity.
| Habitat typology and proxy Five clusters (i. e. , habitat types) were retained, each clearly characterized by an archetypical attribute and named accordingly. Three habitats pertained to soft sand-dominated bottoms (Macroalgae, Seagrass, Sandy), while two habitats corresponded to dominant hard substrates (Live Coral and Debris). In each cluster, the archetypical attribute was larger than 15%, but for the Live Coral habitat, 113 stations displaying a lower live coral cover were assigned because they also had a substantial dead coral cover. They were set aside from the coastal station data set, which was then used to train a RF classification model (based on 1,000 trees, out-of-bag (OOB) error of 3. 9%). From this model, habitat was predicted for the 113 stations: Respectively 77 and 35 stations were classified in the Debris and Sandy habitats, and one in the Live Coral habitat (live coral cover, 14.
| Classification rules for habitat description and prediction Supervised classification rules constitute a novel approach to habitat description previously achieved through clustering  (Pelletier et al. , 2012) , nonparametric multidimensional scaling  (Davis et al. , 2016; Giménez-Casalduero, Gomariz-Castillo, & Calvín, 2011) , or other statistical modeling. In cluster analysis, within-habitat heterogeneity is measured through the variance of habitat attributes in each cluster. In contrast, classification rules tackle heterogeneities through distinct sets of conditions on threshold values for habitat attributes. In this respect, rules capture both local heterogeneities and nonlinearities, representing an original and complementary approach for pattern analysis in large multivariate data sets.
| Implications for conservation and management The habitat proxy was derived from a comprehensive baseline data set comprising areas subject to a range of anthropogenic pressures, and it is consistent at the scale of New Caledonia's EEZ, including the 1. 3 million km 2 CSMP and the 15,743 km 2 WH property. It has been successfully used in a number of assessments of the ecological status of fish communities, biotic covers, and other marine animals such as turtles (e. g. ,  Pelletier, Bockel, Roman, Carpentier, & Laugier, 2016; Pelletier et al. , 2014; Schohn, Bockel, Carpentier, & Pelletier, 2017; Schohn, Pelletier, & Carpentier, 2017) , where it better explained habitat-related variations of biodiversity than geomorphological maps. The abundance, diversity, and community composition observed in the five habitats showed that an ecosystem-based monitoring strategy must encompass not only reef areas (hard-bottom areas) TA B L E 5 Proportion of stations classified and corresponding confidence for the (a) Top1000 rules with three conditions (columns 2-4), and (b) rules from Tables  3  and 4  (columns 6-7) but also soft-bottom areas, such as in this case Sandy, Seagrass, and Macroalgae habitats.
Analyses were performed with R 3. 5. 1 (R Core Team, 2018) using the FactomineR package (V 1. 41,  Lê, Josse, & Husson, 2008)  and the randomForest package  (Liaw & Wiener, 2002). A Association rules are used to describe multivariate data sets, particularly for mining large data sets of categorical variables  (Agrawal et al. , 1993 ). An association rule r is an implication of the form r: R →Q, with R the antecedent of the rule and Q the consequent of the rule. Classification rules are association rules that conclude to a particular attribute being a label, for example, a class index. The label of the consequent Q was here the habitat proxy from the typology, while the antecedent R comprised the conditions on the habitat descriptors.
In each set, a given station may satisfy several rules, and conversely, some stations may not satisfy any rule, for example, if they correspond to rare patterns. For the TopK algorithm, quantitative habitat descriptors were recoded as categorical variables based on bins of equal width, except for extreme values, which may be meaningful for characterization. Cover categories were <5%,  [5%, 20%[, [20%, 40%[, [40%, 60%[, [60%, 80%[, [80%, 95%[, and ≥95%. Depth categories were [0. 8 m, 5 m[, [5 m, 10 m[, [10 m, 15 m[, [15 m, 20 m[, [20 m, 25 m[, and ≥25 m. Topography and complexity were recoded in four categories:  [1] [2] [2] [3] [3] [4] [4] [5]  TopKRules algorithm was implemented using the SPMF software  (Fournier-Viger et al. , 2014).
Here, we present a framework to infer ad hoc (i. e. timely but preliminary) estimates of global-scale glacier contributions to sea-level rise from annual updates of glaciological observations. For this purpose, we combine the annual anomaly provided by the glaciological sample (relative to a decadal mean) with the (mean) absolute masschange rate of a reference dataset (i. e. Zemp et al. , 2019)  over a common calibration period (from 2006/07 to 2015/16). As a result, we here provide preliminary estimates of regional and global glacier mass changes and related uncertainties for the hydrological years 2016/17 and 2017/18. We also discuss the regional biases of the glaciological sample and conclude with a brief outlook on possible applications and remaining limitations of the glaciological observation network of the World Glacier Monitoring Service (WGMS).
Regional glacier areas and related change rates The global distribution of glaciers is taken from the Randolph Glacier Inventory (RGI) version 6. 0  (RGI, 2017). This dataset lists 215 547 glaciers covering a total area of 705 739 km 2 , mainly for survey years between 2000 and 2010. The glaciers in the RGI are grouped into 19 firstorder regions, which seems to be appropriate with respect to the spatial correlation distance of glacier mass-balance variability (i. e. several hundred kilometres;  Cogley and Adams, 1998; Letréguilly and Reynaud, 1990). We consider changes in glacier area over time by using annual change rates for all first-order regions from  Zemp et al. (2019, and references therein) , based on a data collection from the literature. The regional glacier surface area S for a given year t 1 was calculated as S t 1 = S t 0 + (t 1 -t 0 ) • δS δt = S t 0 + n • δS δt , (1) where S t 0 is the regional glacier area in the (regionally averaged) survey year of the RGI, δS/δt the annual area-change rate, and n the number of years between t 0 and t 1.
Regional glacier mass changes We use the regional and global glacier mass changes (based on spatial interpolation) from 1961/62 to 2015/16 from  Zemp et al. (2019)  as a reference dataset, including corrected values for Iceland (cf. Zemp et al. , 2020). For each region, this data set combines the temporal variability from the glaciological sample, obtained using a spatio-temporal variance decomposition model, with the glacier-specific change rates of the geodetic sample. These calibrated annual time series in the metre water equivalent unit (1 m w. e. = 1000 kg m -2 ) were extrapolated from the observational to the full glacier sample within the region and multiplied by regional surface areas, resulting in regional mass changes in the unit gigatonnes (1 Gt = 10 12 kg). Full details are found in  Zemp et al.
Glaciological observations The glaciological method provides glacier-wide mass changes by using point measurements from seasonal or annual in situ campaigns, extrapolated to the overall glacier surface (cf. Cogley et al. , 2011). We used the latest release of the Fluctuations of Glaciers (FoG) database as available from the WGMS (2019   Cogley et al. , 2011). As a consequence, the results refer to different time periods when comparing regions from the Northern to the Southern Hemisphere, or to the low latitudes.
For 2017/18, data reported thus far from 103 glaciers (from 14 out of 19 regions) comprise about two-thirds of the currently observed glaciers (Table  S1 ). This is related to the 1-year retention period that is granted to allow investigators time to properly analyse, document, and publish their Table  1. Ad hoc estimates of glacier mass changes in 2016/17 and 2017/18. For both years, the table shows glacier areas (S) based on RGI 6. 0 (2017) and corrected for annual area change rates from  Zemp et al. (2019) , specific mass changes calculated as arithmetic mean of the glaciological sample (B glac ) and based on the ad hoc estimation (B adhoc , Eq. 4), as well as anomaly-corrected mass change ( M, Eq. 6) for all regions and global totals. Global specific mass changes and related biases are calculated as area-weighted regional means.
Uncertainties correspond to 95 % confidence intervals. The annual global mass changes in 2016/17 and 2017/18 correspond to 0. 9 ± 0. 6 and 1. 4 ± 0. 5 mm SLE, respectively. Glaciological input data are from WGMS (2019). data before submission to the WGMS. Based on this preliminary data, the global average specific mass change was -0. 8 ± 0. 3 m w. e. (Table  1 ). The ad hoc estimation results in a global mass change of -502 ± 197 Gt, corresponding to 1. 4 ± 0. 5 mm SLE. Reported mass balances were negative in all regions. Ad hoc estimates indicate that specific mass balances were more negative than -1 m w. e. in six regions, with New Zealand and Alaska even exceeding -2 m w. e. (Table  1 , Fig. 1 ). With respect to sea-level rise, Alaska  and the Canadian Arctic (North and South combined: -129 Gt) were the largest contributors in this year.
Comparison to global reference datasets We calculated annual ad hoc estimates for all years back to 1961/62 using the period from 2006/07 to 2015/16 for anomaly calculation and bias correction (Fig. 2a ). These can be compared to the global reference data over the 45 years before 2006/07. We note that this is not a validation against independent data but an approach to test the ability of the glaciological sample of a given year to estimate the global glacier contribution to sea-level rise. Overall, the ad hoc estimates are in good agreement with the reference data but feature a slightly larger variability. The latter can be partially explained by the smaller sample size available for the ad hoc estimates. The best agreement is found over the more recent period back to 1990, followed by a strong variability and corresponding over and underestimations during the 1980s, and relative good agreement again in the 1970s and 1960s.
The uncertainty range of the ad hoc estimates is larger than that of the reference data in the most recent  validation period, since it combines the error bars of the reference data with those from the bias estimates. For the earlier validation periods, the uncertainty range of the reference data becomes larger, whereas the one from the ad hoc estimates is still based on reference data from the calibration period. This is arguably an artefact from the optimization of our approach to the estimation of mass changes for the most recent years 2016/17 and 2017/18. The use of  Zemp et al. (2019)  as the reference dataset has the advantage of analysing the performance of the ad hoc estimation at annual time resolution back to the 1960s, and it allows for adjusting the reference period. We show that the ad hoc estimate can be sensitive to the choice of the refer-ence period (i.
e. 2006/07-2016/17 in Fig. 1  and 2003/04-2008/09 in Fig. S3a-e ), especially when it results in major changes in the glaciological sample such as the use of glaciers from neighbouring regions. As an example, the annual ad hoc estimates for Arctic Canada North change from +8 and -93 Gt (Fig. 1c ) to +6 and -69 Gt (Fig. S3a ) for 2016/17 and 2017/18, respectively. However, our approach can be applied to other reference datasets that provide regional mass changes for a multi-year period but without annual resolution. In Fig. S3 , we demonstrate this with ad hoc estimations for selected regions (with large glacierization but limited data coverage in  Zemp et al. , 2019)  and different reference datasets  (Bolch et al. , 2013; Gardner et al. , 2013 ;   Wouters et al. , 2019). The relative difference between the anomalies (derived from the glaciological sample) of the two ad hoc years are consistent, but the absolute values vary between the different reference datasets (due to different mass change rates over the calibration periods).
Lessons learned for the glaciological observation network In the field of glacier monitoring, one outstanding question is how representative the local glaciological observations for regional to global mass changes are  (Fountain et al. , 2009; Kaser et al. , 2006). With the availability of the global reference dataset by  Zemp et al. (2019) , the present approach allows us to assess the bias in the glaciological observations for all regions (cf. Eq. 5, basically the difference between B glac and B adhoc in Table  1 ). At a regional level, the annual bias ranges between -0. 6 and +0. 5 m w. e. This confirms that the glaciological observations are well suited to represent the temporal variability but not necessarily the absolute value of regional glacier mass changes. At global level, the bias averaged out for the area-weighted mean.