dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, for phylogenetic analyses we kept only sequences which lay inside long contigs, or full-size or near full-size transcripts. We also checked once again the absence of animal-type alpha-amylase (GH13_15 or 24) outside the Bilateria using the sequence of the bivalve Corbicula fluminea (AAO17927) as a BLASTP query. The CAZy database (cazy. org (Lombard et al. 2014 )), which is devoted to glycosyl hydrolases and related enzymes was used to check assignment of some of the sequences we found to the GH13_1 subfamily. Intron-exon gene structures were recovered either from alignments between genomic sequences and their mRNA counterparts, or using annotated graphic views when available in the databases. In cases of likely erroneous annotations we reanalyzed the gene region by eye, correcting dubious frameshifts if necessary (see.
RESULTS The sequences retrieved from the databases are listed in Table  1. The metazoans investigated are listed in Tables S1 (non-bilaterians) and S2 (bilaterians) with indication of the current state of genome/ transcriptome sequencing, the database, the presence or absence of GH13_1 sequences, and the number of gene copies, where possible. A general protein alignment of the sequences found in this study along with already known GH13_1 sequences is shown in Fig. S2.
GH13_1 sequences retrieved from unicellular taxa We confirmed the presence of GH13_1 in dictyostelids, in ciliates and also in oomycetes, some representatives of which (but not all) are indicated in Table  1. In two oomycetes, Saprolegnia diclina and Achlya hypogyna, the GH13_1-like sequences were the C-terminal half of longer sequences, the N-terminal half of which was similar to unclassified GH13 sequences found in e. g. , Acanthamoeba histolytica (GenBank accession BAN39582), according to the CAZy database. n■ Table  1  GH13_1-like sequences found after BLAST searches in online databases (not comprehensive for unicellars, without the Fungi). Ã : sequences which have not been characterized as protein-coding, in sequenced genomes with long contigs; (1): from short DNA sequences (except Sequence reads archive); ÃÃ : reported as GH13_1 in CAZy.
Most of the SRA data are from transcriptome studies; see Tables  S1  and S2  Phylum In our general phylogenetic tree (Figure  1 ), these sequences were used as outgroups. In choanoflagellates, where Monosiga brevicollis was already known to harbor a GH13_1 sequence (Da Lage et al. 2007), we found a GH13_1 sequence in the genome of Salpingoeca rosetta. A partial sequence was also returned from incomplete genome data from Monosiga ovata (at Compagen, not shown).
GH13_1 sequences retrieved from nonbilaterian animals In Cnidaria, a number of GH13_1 sequences were recovered from many Anthozoa species (sea anemones, corals and allies), from genome as well as transcriptome data, at the Reefgenomics database (Table  S1 ). Interestingly, we found no alpha-amylase sequences at all in Medusozoa (jellyfishes, hydras) nor in Endocnidozoa (parasitic cnidarians). In the general tree (Figure  1 ), cnidarian sequences form a clear cluster with two main branches, grouping Actiniaria (sea anemones) and Pennatulacea (soft corals) on one branch, and Scleractinia (hard corals) and Corallimorpharia (mushroom anemones) on the other branch. In sponges (Porifera), data were less abundant. No alpha-amylase sequence was found in Sycon ciliatum (Calcarea) and Oscarella carmela (Homoscleromorpha).
GH13_1 sequences retrieved from bilaterian animals The surprising finding of this study, on which we will focus our attention, is the consistent, albeit sparse, occurrence of GH13_1 alpha-amylase sequences in several bilaterian phyla: hemichordates, which are deuterostomes, brachiopods, phoronids (Brachiozoa) and Bryozoa, and in some molluscs and annelids (Eutrochozoa), which are all protostomes. In the well annotated genomes of the brachiopod Lingula anatina and the phoronid Phoronis australis, two paralogs were found (Table  1 ). In both species, the two copies are located on different contigs. The paralog sequences are rather divergent, given their positions in the tree (Figure  1 ) and each paralog groups the two species together. This indicates that not only duplication, but also the divergence between paralogs is ancestral to these species, dating back at least to basal Cambrian, according to the TimeTree database  (Kumar et al.
In contrast, in Nemertea, none of the 14 species investigated returned any GH13_1 sequence, including the annotated genome of Notosperma geniculatus. Similarly, we found three gene copies in the genomes of the hemichordates Saccoglossus kowalevskii and Ptychodera flava. In both species, two copies are close to each other (XP_006816581 and XP_006816582 in S. kowalevskii, and their counterparts in P. flava) as shown by the topology of the gene tree (Figure  1 ). This could suggest independent gene duplication in each species. However, we observed that the two duplicates were arranged in tandem in both species, which would rather suggest concerted evolution of two shared copies. In P. flava, this genome region is erroneously annotated as a single gene at the OIST Marine Genomics database. The third paralog is very divergent from the two other copies, so its divergence from the ancestral copy probably occurred before the species split, as well.
The three copies were therefore probably already present before the split of the two lineages, some 435 mya  (Kumar et al. 2017). Three other hemichordate species, Schizocardium californicum, Torquaratoridae antarctica and Rhabdopleura sp. harbor a GH13_1 gene, as shown by SRA search in GenBank (Table  1 ). A positive result was also retrieved from the genome of Glandiceps talaboti (Héctor Escrivà, Oceanology Observatory at Banyuls-sur-mer, personal communication). In molluscs, we found BLAST hits with significant e-values in gastropod species from two clades only, the Vetigastropoda (e. g. , the abalone Haliotis sp. ) and the Caenogastropoda (e. g. , Ampullariidae such as Pomacea canaliculata). We consistently found one copy in eight species belonging to the family Ampullariidae. In P. canaliculata, the genome of which has been well annotated, the GH13_1 sequence (XP_025109323) lies well inside a 26 Mbp long scaffold (linkage group 10, NC_037599) and is surrounded by bona fide molluscan genes (Table  S3 ).
GH13_1 sequences were found in other Caenogastropoda from SRA or transcriptome databases (Table  1  and S2 ). We also found GH13_1 sequences in several bivalve clades: Mytiloida (e. g. , the mussel Mytilus galloprovincialis), Pterioida (e. g. , the pearl oyster Pinctada imbricata), Arcoida (e. g. , Scapharca broughtoni)  and in the Unionoida Cristaria plicata. For sequences retrieved from the TSA or SRA databases (see Table  1 ), whose issues were mentioned above, we performed reciprocal BLAST in GenBank nr. Almost always Lingula anatina was recovered as the best hit. However, as an example of the necessary careful examination of results, we found a significant HSP in a transcriptome database of the sea hare Aplysia californica (TSA GBDA01069500). This sequence was not found in the well annotated A.
e. , lower-quality assembled genomes, transcriptomes or sequence read archive databases, which added some support to the presence of these amylase genes. Although transcriptome and rough genomic data should be handled with care, this lends support to our observations. Moreover, reciprocal BLAST from the transcriptome hits always returned a bilaterian (L. anatina or S. kowalevskii) best hit, not fungal, protist or other non-bilaterian GH13_1 sequence. The new data unveils an evolutionary story more complicated than previously supposed. There are two alternative explanations. The first explanation is that several HGTs occurred from non-bilaterian to both hemichordate and Lophotrochozoa ancestors. The second explanation is that the ancestral GH13_1 gene was not lost in all bilaterian lineages, but remained (given the current data) in hemichordates, Brachiozoa, Bryozoa, and in scattered lineages across Mollusca and Annelida.
Further, some of them have been assigned to this subfamily in the reference database CAZy. org (see Table  1 ). In addition, if we add sequences from the closest subfamilies, namely GH13_2 or GH13_19  (Stam et al. 2006)  in the alignment and in the phylogenetic tree, the putative GH13_1 and the ascertained GH13_1 remain well clustered together (not shown). It is possible that modifications of a few amino acid positions could bring a change in the substrate or catalytic activity. For instance, concerning the substrate affinity, when the genome of L. anatina was released, the authors hypothesized a biomineralization pathway that involves acid proteins, as found in scleractinians and molluscs  (Marin et al. 2007; Ramos-Silva et al. 2013). Given the calcium binding activity of alpha-amylases  (Boel et al.
ACKNOWLEDGMENTS We want to thank  Pedro E. Vieira  for introducing AD to molecular analyses. We are grateful to  Didier Casane  and  Emmanuelle Renard  for fruitful advise and discussion and two anonymous reviewers for critical reading of the manuscript. We thank  Héctor Escrivà  for sharing sequence data. This work was funded by the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  (process no. 141565/2017-9 ) to AD and regular funding of the CNRS to JLDL and CG. CG was also supported by a grant from  Agence Nationale de la Recherche  ( ANR-15-CE32-0011-01  TransVir).
Methods: We assessed monopolar reviews in 16 consecutive STN-DBS PD patients and retrospectively compared clinical capsular thresholds, which had been assessed according to standard clinical practice, to those predicted by volume of tissue activated (VTA) model software. All the modeling steps were performed blinded from patients' clinical evaluations. Results: At the group level, we found a significant correlation (p = 0. 0001) when performing statistical analysis on the z-scored capsular thresholds, but with a low regression coefficient (r = 0. 2445). When considering intra-patient analysis, we found significant correlations (p < 0. 05) between capsular threshold as modeled with the software and capsular threshold as determined clinically in five patients (31. 2%).
Importantly, the number of individual stimulation parameter combinations increases substantially with the use of directional electrodes, making post-operative management more challenging and time-consuming. Thus, clinical tools including VTA models could be helpful in identifying effective parameter settings for each patient more easily. The goal of this pilot study was to evaluate the usefulness of a new software designed for clinical practice in the refinement of DBS parameters. Abbreviations: STN, subthalamic nucleus; DBS, deep brain stimulation; VTA, volume of tissue activated; CTA, capsular threshold amplitude; VTA-CTA, VTA modeled capsular threshold amplitude; IC, internal capsule; CSNT, corticospinal and corticonuclear tracts.
Capsular Threshold Modeling We used the Guide TM XT software for all steps detailed in this section. MB performed manual refinement of the IC volume and AK performed capsular threshold determination on the images using the VTA. We carried out all the modeling procedures blinded from patients' clinical evaluations, with anonymization of the patient's name on the MRI images. We first performed coregistration of the preoperative 3D T1, specific STN visualization T2 MRI sequences (3D T2 SPACE in sagittal orientation, FOV 450 mm, TR/TE 2400/225 ms, flip angle 120 • ), and the post-operative CT scan. Then, an automatic anatomical segmentation of the STN and the IC was computed. For each patient, we manually refined the IC using both the T2 image and the Schaltenbrand atlas  (24) , in order to extend IC boundaries to the cerebral peduncles in the midbrain, a process that enables visualization of the CSNT in the vicinity of the STN.
Statistics For each patient, we computed statistical analysis on the data to test the correlation between the clinical thresholds and the modeled capsular threshold. We used the corrcoef function of the Matlab (Mathworks, Natick, MA) statistical toolbox. We also performed a correlation analysis on the whole group and normalized the data to compare patients by using the z-scores. We then reported the resulting p and correlation coefficients r for each patient. Lastly, we refined the analysis for the patients who did not show any significant effect by separating the analysis for the left and right hemispheres to see if the effect had been hidden by one side where the modeling predicted the capsular threshold poorly.
Vertical and Horizontal Virtual Current Steering We analyzed virtual current steering in patients for whom CTA was correctly predicted by the VTA-CTA model. We arbitrarily chose the constant parameter settings as follows: 3 mA, 60 µs, and 130 Hz. Then, we built a VTA according to the software and successively considered the vertical and horizontal virtual current steering (see Figure  3 ). Vertical virtual current steering consisted of representing the lead position and overlap between the VTA and STN automatically segmented by the software. Horizontal virtual current steering consisted of representing the lead position and overlap between VTA, STN, and IC in the best ring previously identified, in order to rank its contacts from lowest to highest capsular threshold.
DISCUSSION In this pilot study, we tested the hypothesis that VTA model software could help clinicians with capsular threshold determination in STN-DBS PD patients during post-operative management. To do this, we retrospectively compared predicted capsular thresholds modeled using the software with clinical capsular thresholds clinically assessed during the monopolar review in 16 STN-DBS PD patients. At the group level, we found a significant correlation (p = 0. 0001), between image-defined and clinically defined capsular thresholds, but the regression coefficient was relatively low (r = 0. 2445 points). Intra-patient analyses showed significant (p < 0. 05) correlations in only 5/16 patients (31. 2%), meaning that for each of these patients, contacts were accurately ranked by the software from the lowest to the highest capsular threshold.
The low regression coefficient in analysis at the group level can therefore be explained by some variability of the tool as confirmed in intra-patient analyses, with an excellent matching in some, but not all, of the patients. Although the small size of this pilot study, a lack of statistical power, and a learning curve effect for both clinical and VTAmodeled capsular threshold measurements must be mentioned first, factors related to the different steps needed for capsular threshold modeling may be considered one by one to explain the discrepancy observed between global and intra-patient analyses. Coregistration between preoperative 3D T1, specific STN visualization T2 MRI sequences, and the post-operative CT scan was performed automatically by the software. Visual inspection of coregistration quality was systematically performed before anatomical segmentation.
Coregistration was correctly done in all cases and does not appear to be a critical factor contributing to the variability observed between patients. Anatomical segmentation in the software does not include the CSNT per se but the IC, which does not integrate the diencephalic-mesencephalic junction. Thus, in order to determine the VTA-modeled capsular threshold, we manually drew the CSNT in the vicinity of the STN, by extending the automatically segmented IC from the diencephalon to the mesencephalon. Nevertheless, this step, the longest of the capsular threshold modeling, was not highly reproducible, which might broadly explain the variability observed from one patient to another. One would expect that capsular threshold as modeled with the software depends on the distance between the internal border of the CSNT drawn manually and the lead where the VTA is built.
The VTA model used by the software for capsular threshold modeling is a critical point to discuss. The model makes several assumptions that influence its threshold estimates. The first is a simplified electrical medium that represents the brain as a homogenous and isotropic volume conductor. This means that every point in the model has the same electrical conductivity value (homogeneous) and that those conductivity values are the same in every direction (isotropic). Heterogenous and anisotropic estimates of tissue conductivity are an area of ongoing research  (30). Furthermore, we do not know the amplitude range for which the model is valid (power dispersion in biological anisotropic tissue). Additionally, all axons in the model are assumed to be equal in diameter, perfectly straight, and running perpendicular to the trajectory of the lead.
The diameter of an axon is known to strongly influence its excitability (31), and the IC exhibits axons with a range of diameters  (32). Furthermore, the trajectory and orientation of the axons also influence the excitability of the model. These model assumptions were instituted both to simplify the model and to tend toward the side of excitability. Despite the multiple sources of error and current limitations discussed above, the software could offer the opportunity to obtain a more comprehensive anatomy-based approach for directional DBS and potentially less time-consuming bedside management for a given patient. Although clinical capsular threshold determination remains the gold standard, VTA model software could be a useful tool for the refinement of parameter settings in patients for whom the clinical threshold and VTA modeled threshold are congruent (see Figure  3 ).
CONCLUSION In this pilot study, software that superimposes VTA and anatomy was of limited assistance in the identification of capsular thresholds for the whole cohort due to large inter-patient variability. So far, this new tool allows visualization of the IC, but it has not been designed to identify corticonuclear and corticobulbar tracts inside this very large structure. Integration of fiber tracking tools that can visualize these fiber systems might lead to a better match between visual modeling and clinical testing, which would be an important first step to a more automatized post-operative management of DBS. Currently, clinical testing remains the gold standard in selecting stimulation parameters for STN-DBS in PD. Further studies with larger sample sizes remain mandatory to assess the usefulness of VTA model software for practical management of STN-DBS PD patients.
In some cases, in-network processing may take place within the sensor networks. The processed data can be utilized for several objectives, such as leak localization. For water supply network management applications, continuous measurement and monitoring of multiple parameters within the pipe is required. For example, in the water quality applications, parameters such as pH, dissolved oxygen, flow rate, turbidity, and conductivity, amongst others, must be measured continuously. Sensors, as well as actuators, are important elements in a CPS. Sensors are used to acquire data concerning the state of the physical environment. Howbeit, only examining the state of the physical environment is not enough in many applications. It is necessary also to act on the sensed data by performing specific actions.
LoRa and LoRaWAN LoRa is a promising technology offering low-cost, low-power, and long-range communications. It has a communication range of 1 km in urban settlements and 10 km in rural regions  [58]. LoRaWAN is based on LoRa and are both standardized by LoRa Alliance. LoRaWAN can be viewed as an upgrade to LoRa with improved data rate and communication distance. LoRa has a low data rate of 0. 3-50 kbps, while in LoRaWAN, an improved data rate of up to 100 kbps can be achieved. Both wireless communication technologies use a chirp spread spectrum (CSS) modulation, which employs wideband linear frequency modulated chirp pulses to encode information  [58, 59]. They use an ISM band with a different operating frequency, which differs from one region to the other. For example, in Europe, LoRa and LoRaWAN operate in 868 MHz in Asia and North America, frequencies of around 433 and 915 MHz are reported  [58].
SigFox SigFox is a low-power wide area network similar to LoRa and LoRaWAN but with an improved communication coverage. It utilizes unlicensed ISM band, which varies between regions, with 868 MHz in Europe, 915 MHz in North America, and 433 MHz in Asia. SigFox wireless communication technologies use a binary phase shift keying (BPSK) modulation in an ultra-narrow band sub-GHz ISM band carrier. Thus, by utilizing the ultra-narrow band, SigFox offers very low power consumption, and low-cost antenna design  [58]. In comparison to LoRa and LoRaWAN, SigFox has a very low data rate (<10 kbps), though with very good communication coverage with 10 km in urban environments and 15 km in rural areas  [58]. A summary and comparison of these wireless communication technologies are presented in Table  1.
As shown in Table  1 , most of these communication technologies are low-power with varying coverage area and data rates. For instance, Bluetooth and Wi-Fi cover short distances with high data rates. Data rate refers to the amount of data transmitted per unit time. Amongst the low-power wireless communications, SigFox offers long-range capability especially in the rural settings (up to 50 km) and it consumes less power. However, its data transmission rate is relatively low. Likewise, LoRaWAN can cover long distances up to 20 km in rural areas but with a low data rate. Moreover, 3G/LTE and LTE-A cover long distances up to 30 km and have a relatively high data rate, however, their power usage is very high when compared to some other wireless communication technology. Therefore, for water supply system infrastructure monitoring, long-range communications with a relatively good data rate is a requirement.
Connection Level (C1) This level entails providing a seamless connection to the acquired sensor data from the physical environment. To provide a monitoring platform to critical infrastructure such as water supply systems, the data related to this infrastructure must be acquired in a secure way. Thus, in the first level of a CPS, error-free data must be collected. These data are acquired by the installed sensors. The sensors are arranged in sensor nodes to provide seamless connectivity and communications among the sensing elements. Communication protocols, therefore, play a vital role in this level to allow tether-free communication between sensor networks and for data transfer to the central server  [10]. Just like the IoT-based technology, low-power communication protocols are requirements for most applications and used for this purpose.
During leak occurrence, AE sensors positioned on the pipe can trace the AE signal as it travels along the pipe  [4]. Unfortunately, signal characteristics and the variations in the environmental parameters surrounding the pipe make AE signal classification challenging  [78]. Nonetheless, computational tools, such as support vector machines  [74, 76] , artificial neural networks  [79] , wavelet transform, and independent component analysis  [80, 81] , may be applied for AE signal feature extraction to detect patterns in the dataset and classify the AE signals. These tools are mostly utilized at stage C3 of the CPS functional attributes.