dataesr/scientific-paragraphs-categorization huggingface dataset:
Importantly, by delaying the response mapping to after the stimulus presentation in an (for the individual) unpredictable manner, neural patterns during relevant periods putatively cannot be confounded by response selection/preparation. Both experiments were programmed in Matlab using the open source Psychophysics Toolbox  (63). MEG Data Acquisition and Preprocessing. MEG was recorded at a sampling rate of 1 kHz using a 306-channel (204 first-order planar gradiometers, 102 magnetometers) VectorView MEG system for the first experiment in Trento and a Triux MEG system for the control experiment in Salzburg (Elekta-Neuromag Ltd. ) in a magnetically shielded room (AK3B; Vakuumschmelze). Before the experiments, individual head shapes were acquired for each participant including fiducials (nasion and preauricular points) and around 300 digitized points on the scalp with a Polhemus Fastrak digitizer.
Head positions of the individuals relative to the MEG sensors were continuously controlled within a run using five coils. Head movements did not exceed 1 cm within and between blocks. Data were analyzed using the Fieldtrip toolbox  (64)  and the CoSMoMVPA toolbox  (65)  in combination with MATLAB 8. 5 (MathWorks). First, a highpass filter at 0. 1 Hz (FIR filter with transition bandwidth 0. 1 Hz) was applied to the continuous data. Then the data were segmented from 1,000 ms before to 1,000 ms after target stimulation onset and down-sampled to 512 Hz. Trials containing physiological or acquisition artifacts were rejected. A semiautomatic artifact detection routine identified statistical outliers of trials and channels in the datasets using a set of different summary statistics (variance, maximum absolute amplitude, maximum z value).
These trials and channels were removed from each dataset. Finally, the data were visually inspected and any remaining trials and channels with artifacts were removed manually. Across subjects, an average of five channels (±2 SD) were rejected. Bad channels were excluded from the whole dataset. A detailed report of the remaining number of trials per condition for each participant can be found SI Appendix, Table  S1. Finally, in all further analyses and within each sensory modality for each subject, an equal number of detected and undetected trials was randomly selected to prevent any bias across conditions  (66). Source Analyses. Neural activity evoked by stimulus onset was investigated by computing ERFs. For all source-level analyses, the preprocessed data were 30-Hz lowpass filtered and projected to source level using a linearly constrained minimum variance (LCMV) beamformer analysis  (67).
For each participant, realistically shaped, single-shell head models  (68)  were computed by coregistering the participants' head shapes either with their structural MRI orwhen no individual MRI was available (three participants and two participants, for the initial experiment and the control experiment, respectively)-with a standard brain from the Montreal Neurological Institute (MNI), warped to the individual head shape. A grid with 1. 5-cm resolution based on an MNI template brain was morphed into the brain volume of each participant. A common spatial filter (for each grid point and each participant) was computed using the lead fields and the common covariance matrix, taking into account the data from both conditions (detected and undetected or catch and sham) for each sensory modality separately.
The covariance window for the beamformer filter calculation was based on 200 ms pre-to 500 ms poststimulus. Using this common filter, the spatial power distribution was then estimated for each trial separately. The resulting data were averaged relative to the stimulus onset in all conditions (detected, undetected, catch, and sham) for each sensory modality. Only for visualization purposes a baseline correction was applied to the averaged source-level data by subtracting a time window from 200 ms prestimulus to stimulus onset. Based on a significant difference between event-related fields of the two conditions over time for each sensory modality, the source localization was performed restricted to specific time windows of interest. All source images were interpolated from the original resolution onto an inflated surface of an MNI template brain available within the Caret software package  (69).
The respective MNI coordinates and labels of localized brain regions were identified with an anatomical brain atlas (AAL atlas; ref. 70 ) and a network parcellation atlas  (29). Source analysis of MEG data is an inherently underspecified problem and no unique solution exists. Furthermore, source leakage cannot be avoided, further reducing the accuracy of any analysis. Finally, we remind the reader that we do not expect more than 3 cm precision on our results because we used standard LCMV source localization with a 1. 5-cm grid. In other words, source plots should be seen as suggestive rather conclusive evidence for underlying brain regions only. MVPA Decoding. MVPA decoding was performed for the period 0 to 500 ms after stimulus onset based on normalized (z-scored) single-trial source data down-sampled to 100 Hz (i.
3B  and 5B ). Significant accuracy brain maps resulting from the searchlight analysis on previously identified time points were reported for each decoding condition. The maximum 10% of averaged accuracies were depicted for each significant decoding cluster on brain maps (Figs. 4  and 5 ). Data Availability. A down-sampled (to 100 Hz) version of the data is available at the OSF public repository (https://osf. io/E5PMY/). The original nonresampled raw data are available, upon reasonable request, from the corresponding author. Data analysis code is available at the corresponding author's GitLab repository (https://gitlab. com/gaetansanchez).
The classification is based on mapping of the duration, magnitude and direction of human-induced disturbances (Supplementary Fig. 7 ) from annual forest probability time-series data (2002-2017, 500 × 500 m 2 resolution), trained and compared with a full area coverage of 2-m GF-1 satellite imagery. The association of the different land use types with carbon sequestration is shown in Fig. 3 , and the spatial distributions are shown in Fig. 4a, b  (close-ups are shown in Supplementary Figs. 5, 6 ). Due to their coarse spatial resolution (25 × 25 km 2 ), biomass carbon change data from SMOS could not be downscaled into the different land use types, but they were used as independent check of the spatially detailed MODIS-based estimates at 500 m resolution. Similarly, SM trends were assessed at 25 km resolution.
Methods Overview. This study estimated dynamics in forested areas and C density at a scale of 500 × 500 m 2 for 2002-2017 (see flowchart in Supplementary Fig. 9 ). We produced annual forest maps using MODIS, which were compared with independent tree-cover data at scales of 30 m 28 and 5. 6 km  27. We derived a typology of forest continuity and dynamics from the annual forest maps. Since forest management in this area is a very dynamic process, the typology requires annual maps at a reasonable spatial scale, which is not provided by any publicly available product. As analyses were conducted at a spatial scale of 0. 25 km 2 , the land-surface types inevitably included cases of multiple processes and the final classification was determined by the majority of these processes within a grid cell, implying that the data likely over-or underestimated the real areal extents, particularly for forest dynamics.
For example, an area of 500 × 500 m 2 reported to belong to the afforestation type means that afforestation was the major process occurring in this grid cell, but does not imply that trees had been planted on the entire area of 0. 25 km². MODIS satellite data. Daily BRDF (bidirectional reflectance distribution function) corrected MODIS imagery (MCD43A4 collection 6)  49  from 2000 to 2017 was the basis for this study. We used all seven bands. Cloud cover is severely impacting the number of good quality observations from optical remote sensing sensors in southern China, and we chose the annual median of daily MCD43A4 images to retrieve an annual image. Due to the fact that even the annual median images were not free of noise, we additionally applied a 3 years moving median window to the annual time series, shortening the available period of data to 2002-2017.
A three-fold cross-validation of the model (excluding random subsets of the training data that were then predicted) was satisfactory (r = 0. 93). However, since the chosen points were rather homogeneous, we applied comparisons with independent forest cover maps to assess the quality of the forest probability maps (see section Intercomparison of independent data sets). We applied this model to the annual MODIS bands to generate annual probability maps of the forest/non-forest classification. The probability ranged from 0 to 1 and indicated the likeliness that an area showed characteristics similar to those of the forest training points, implying that temporally increasing forest probability represented the growth of forests  50. Likewise, a decrease in forest probability was linked with harvesting.
5). Carbon density. We used a static unpublished global benchmark map of C density of woody vegetation for 2015 to study forest carbon changes in China  54, 55. The map was constructed using three types of data: (1) more than 8 million waveforms from the Geoscience Laser Altimeter System (GLAS) onboard ICESat-1 (Ice, Cloud and land Elevation Satellite) that reflect the vertical structure of forest and woody vegetation using a semi-systematic sampling approach, (2) a large number of ground data from national inventory and research biomass plots to develop models for converting the GLAS lidar data to C density for different forest types globally and (3) satellite imagery from Landsat, ALOS Phased Array L-band Synthetic Aperture Radar data for 2015, and land elevation data from SRTM. Using GLASpredicted biomass as the dependent variable, satellite imagery as independent variables and a Bayesian maximum entropy algorithm  54, 56  , C density and its uncertainty were predicted and were globally at 1-ha spatial grid cells.
10 ). The model was applied to the data sets for each year to derive maps of annual C density. The dynamics were compared with low-frequency passive microwave satellite data (next section), which also includes an estimation of the temporal uncertainties. Intercomparison of independent data sets. Since no field data on land use types was available, we conducted a visual comparison between the classification and the full areal coverage of GF-1 satellite imagery at a resolution of 2 m (~10,000 images; see Supplementary Figs. 2 3 4 ). However, no statistics could be derived from this comparison, thereby requiring additional result comparisons to be taken based on independent data sets. The first step was a comparison with the Landsat-based treecover map  26  for 2010, available at a resolution of 30 m (Supplementary Fig.
1 ). The map was aggregated to 500 m to show the average tree cover of a 500 m pixel from our analysis. The agreement between the aggregated tree-cover map and our forest probability map was very high (r = 0. 88, n = 8,796,179), confirming that our probability maps capture well the average tree cover per grid cell. We then used the annual long-term data set from ref. 27  for 1982-2016 to provide an overview of the long-term dynamics and to compare with our classification of forest dynamics (Supplementary Fig. 1 ). The data set is based on long-term data record advanced very high-resolution radiometer (AVHRR) imagery and contains the fractions of tree cover, short vegetation and bare ground at a resolution of 0. 05°. The average tree cover (2001-2016) spatially agreed well with our forest probability map (aggregated to 0.
This data set is available at a resolution of 25 × 25 km 2 for 2010-2017, and the conversion was done using both Saatchi's and Baccini's biomass benchmark maps following  25  , and taking the average of both calibrations. At the pixel level, the spatial correlation between the MODIS C density and the L-VOD C density was satisfactory (r = 0. 7). For temporal comparisons, we averaged for each year both the MODIS C density and L-VOD maps over the region and the temporal dynamics for both data sets agreed well (r = 0. 90), providing confidence in the dynamics of C density. The uncertainty in the temporal dynamics was assessed by the RMSE between MODIS-and SMOSderived changes in C density. For an uncertainty assessment based on different benchmark calibration maps, we refer to ref. 25. Due to the high resolution required to distinguish different forest types, the temporal uncertainty could only be provided for the study area as a whole, and the uncertainty of the different forest types was limited to the spatial level.
Here we calculated the RMSE between the benchmark map used for calibration and the MODIS-derived C density maps for each forest type. The negative trend found in SMOS SM is contrasting other studies showing a positive trend in SM this period  24. However, whereas the SMOS satellite is able to retrieve SM data in densely vegetated areas  59  , traditional SM products based on high-frequency passive and active microwave data do not agree in China  60  and often fail in densely vegetated areas  59. Model simulations and field measurements instead confirm the negative trend in SM  10. CO 2 emissions. The data set from ref. 28  contains fossil CO 2 emissions for each province for 1997-2015. We used the sectoral approach and converted CO 2 to C by dividing the value by 3. 67. Forestry data. The timber production data were collected from the Chinese Forestry Statistical Yearbooks, including the timber volumes from 2002 to 2017 for each province.
We applied the accumulation method to estimate timber carbon based on the volume of timber. The main species are pine, cedar and eucalyptus, and we used the average wood basic density of these three species to calculate the biomass of the timber production, which was then converted to carbon by multiplying the biomass with 0. 5. SMOS microwave data. L-VOD was used as independent data set to assess the dynamics of C density, and SM was used to monitor the patterns of SM dynamics. Both L-VOD and SM products were retrieved simultaneously from the SMOS observations  35. The use of multi-angular and dual-polarization SMOS observation ensures that L-VOD and SM are independent of each other. The SMOS L-VOD and SM data sets used here were produced using the SMOS-IC algorithm (version 105) at a resolution of 25 × 25 km 2 for 2010-2017  [61] [62] [63].
This algorithm was selected here, as it does not rely on ancillary data such as modelled SM and vegetation optical indices (such as LAI or NDVI), making the SMOS-IC product more robust for applications monitoring both biomass carbon and SM dynamics  62. SMOS-IC data were processed as follows: first, scene flags were applied and both the L-VOD and SM data for ascending (ASC) and descending (DESC) orbits were filtered according to flags for strong topography, frozen soils (soil temperature <273. 5 K), urban areas and water bodies. We then applied a filter for the effects of radio frequency interference (RFI) by excluding data with a TB-RMSE index higher than 10 K. We further applied specific filtering to the L-VOD data: a moving average smoothing (window size = 30 days) was applied on the filtered ASC and DESC L-VOD time series.
Acknowledgements X. T. and Y. Y. were funded by the  National Key Research and Development Program of China  (no. 2016YFC0502400 ,  2018YFD1100103 ), the  National Natural Science Foundation of China  ( 41930652 ), and the  Strategic Priority Research Program of Chinese Academy of Sciences  ( XDA23060100 ). X. T. was funded by a  Marie Curie fellowship , grant number  795970. M. B. received funding from the  AXA  research grant. R. F. acknowledges the funding from the  Danish Council for Independent Research (DFF)  Grant ID:  DFF-6111-00258. X. X. was funded by the  NASA Land Cover and Land Use Change Program  ( NNX14AD78G ). P. C. and J. P. were supported by the  European Research Council Synergy  grant  ERC-2013-726 SyG-610028 IMBALANCE-P.
Data availability MODIS MCD43 is available from Google Earth Engine. Tree-cover data from Landsat can be downloaded at https://earthenginepartners. appspot. com/science-2013-globalforest/download_v1. 2. html. Tree cover from AVHRR (1982-2016) can be downloaded at https://search. earthdata. nasa. gov. CO 2 emission data can be downloaded from https:// www. nature. com/articles/sdata2017201. SMOS data are available from https://www. catds. fr/. The C density map is available from S. S. The forest management classification and carbon density maps will be made available upon acceptance of the manuscript.
biochem. vt. edu) database, which contains the TE libraries for several species of mosquitoes, was used to annotate the seven genomes using RepeatMasker version open-4. 0. 9  (Smit et al. 2015). Families with more than three matches longer than 90% in any genome were selected and their hit with the highest identity from each genome was extracted. These sequences were added to the REPET library and all the consensuses were clustered using CD-HIT version 4. 8. 1 (140) with the -c and -s parameters set to 0. 8. These filters ensured that all TEs with an identity greater than 80% throughout more than 80% of their sequence were grouped in the same family. 85 clusters contained sequences only identified by TEfam. The sequences belonging to the same cluster were used to perform a multiple sequence alignment and the consensuses were obtained.
The consensuses were classified using PASTEC (141) with default parameters. Next their bidirectional best-hits were calculated using BLAST (142) against the TEfam (tefam. biochem. vt. edu), AnoTExcel (143) and Repbase (144) databases. When more than 80% of a consensus matched to a feature from the databases with an identity higher than 80%, the classification was transferred to the consensus. While not an order per se, MITEs were grouped together for subsequent analysis. Additionally, we classified the families based on the conservation of features characteristic of their orders into putative autonomous, putative autonomous lacking terminal inverted repeats (TIRs) or long terminal repeats (LTRs), putative non-autonomous, such as MITEs and TRIMs, and degenerated (Additional file 1: Table  S4 )  (Fonseca et al 2019).
Given that we observed few significant differences, we continued using the landscape data obtained using the BLAST procedure. We identified the families where the majority of the bases of their insertions were on the peak of identical sequences in the TE landscape (>50% of the bases with >99% base identity) in more than five of the seven genomes we analyzed. Finally, we assessed the ability to actively transpose of strong candidates by identifying their intact ORFs, LTRs (in the case of LTR retrotransposons) and target site duplication (TSD), and estimated the % identity between the two LTR of each TE copy.
new anopheline TE families discovered in An. coluzzii To identify the TE families present in each of the genomes, we used the TEdenovo pipeline from the REPET package (51). After several rounds of manual curation, we identified between 172 and 294 TE families for each genome (Table  1 ; Additional file 1: Table  S2 ). Remarkably, while using a single reference would have only allowed the identification of a median of 244 TE families, clustering the TE libraries from an increasing number of genomes allowed the identification of a total of 435 well supported TE families (Figure  1B ; see Material and Methods). Interestingly, 64 of these families (32 DNA, 9 LINEs and 23 LTRs) are described here for the first time. The majority of the new families (43/64) had partial matches to other known TEs, thus allowing us to classify them at the superfamily level (Additional file 1: Table  S3 ).
The use of multiple references was especially relevant for identifying these previously undescribed families given that using a single genome would have only allowed to identify a median of 37 (25-48) novel TE families (Figure  1B ). To further characterize these novel families, we estimated the average number of insertions in the seven An. coluzzii genomes, and their distribution and abundance in other species from the Anopheles genus (Figure  2 ; Additional file 3: Figure  S2 ; Additional file 1: Table  S3 ). To do this, we first annotated individual TE insertions in the seven An. coluzzii genomes using the TEannot pipeline from the REPET package (52). To ensure that our annotation was as complete as possible, in addition to the 435 families identified using REPET de novo approach, we also added to our library 85 TE families from the TEfam database that we found to also be present in the An.
Data accessibility All the genome sequencing data obtained in this work, as well as the genome assemblies are available in NCBI SRA and NCBI Genbank respectively, under the BioProject accession number PRJNA676011. TE annotations in each of the seven genomes in gff files, transferred annotations across the seven genomes in txt format, and the TE library in fasta format are also available at https://digital. csic. es/handle/10261/224416.
Acknowledgements We thank members of the  González Lab  for comments on the manuscript. We thank the  Ecology of Vectorial Systems team at the CIMRF (Franceville, Gabon)  for their support in field collections. We thank  Jean Pierre Agbor  and  Serge Donfanck  for their commitment in larvae collections in Douala (Cameroon). Version 3 of this preprint has been peer-reviewed and recommended by  Peer Community In Genomics  (https://doi. org/10. 24072/pci. genomics. 100006).
Known important viral CNS infections in SE Asia include tropmedres. ac/data-sharing Demographic and geographic data are available from Lao DECIDE: http://www. decide. la/en/ Road network data are available from OpenStreetMaps Laos: http://www. openstreetmap. la/ Remote sensing (MODIS) data are available from: https://modis. gsfc. nasa. gov/ data/. Japanese encephalitis, dengue, and rabies viruses  [1]. Important bacterial CNS infections include Streptococcus pneumoniae, Haemophilus influenzae, S. suis, Mycobacterium tuberculosis and Neisseria meningitidis. Orientia tsutsugamushi, Rickettsia typhi and Leptospira spp. are increasingly recognized as important causes  [9]. Detailed analyses that confirm the cause of CNS related infections or assess their spatial and temporal distribution in the region are rare  [10] [11] [12] [13].
Data sources, processing and merging Data used in this research were compiled from four main sources (S1 Fig). The epidemiological data come from an 8-year research project on CNS infections in Lao PDR from all patients who received diagnostic lumbar puncture (LP) at Mahosot Hospital in Vientiane, Lao PDR between January 2003 and August 2011 and consenting to participate  [17]. All patients were admitted to the hospital because of suspected CNS infection and Mahosot Hospital is the only medical facility in Lao PDR capable of performing diagnostic LP and cerebral spinal fluid (CSF) analysis. LP was performed on all patients with suspected CNS infection, who presented with altered consciousness or neurological findings, and who were not contraindicated for LP. Tests for a large panel of pathogens were then performed at the Microbiology Laboratory following international standards (details provided in S1 Text and  [17] ).