dataesr/scientific-paragraphs-categorization huggingface dataset:
79 on 111 metabolites detected by two platforms. Absolute concentrations were provided on only 31 metabolites across the 5 platforms, and 28% of identified metabolites were not listed in public databases  [128]. All these studies highlighted a modest overlap in terms of metabolite detection from one facility to the other, and two of them reported on inconsistencies in compound identifiers that limit the integration of datasets  [127, 128]. This is especially the case with isomers that can be discriminated or not from one platform to the other. Such issues could be addressed by the development and sharing of spectral databases  [33] , by fostering the evaluation and the use of standard reference materials within metabolomics data production facilities  [127, 128] , and of course by providing absolute concentrations on identified metabolites present in the chemical libraries of data production facilities, in the frame of untargeted approaches.
To achieve this, clinical biologists will have to upgrade analytical technologies to generate data, integrate bioinformatics solutions, and develop an automated algorithm to express targeted and accurate results from complex data. Actually, clinical biochemistry is likely to undergo the same technological revolution as molecular genetics has undergone over the last 20 years with the advent of genomics and next-generation sequencing. One of the main challenges will be to obtain concentrations of key metabolites expressed in units of molarity, in order to meet the shortterm needs of clinicians, while at the same time having the possibility of building databases of metabolic profiles that will be interrogated in different medical contexts. Although there are some liquid chromatography coupled to mass spectrometry systems that are CE-IVD (i.
Furthermore, the small metabolite concentration variations that are often observed between groups in many metabolomics and lipidomics studies represent an important limitation for clinical translation, which will call for new ways of thinking in the fields of analytical chemistry and data sciences to overcome this issue. Funding Christian Moguet is the recipient of a PhD grant funded by CEA. We acknowledge funding from the MICROB-PREDICT project. The MICROB-PREDICT project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 825694. This reflects only the author's view, and the European Commission is not responsible for any use that may be made of the information it contains.
We selected the clusters with more than 5 sequences and with an average prediction score of 0. 7 or above for further investigation, resulting in 21 clusters. To further investigate the clusters we chose one representative sequence per cluster (Table  S1 ). We carried out two analyses (i) to search for overlapping known Pfam domains, using the highly sensitive iterative jackhmmer search, and (ii) to predict the structure of the sequence using Al-phaFold  [18, 11]. Using Jackhmmer can be considered a more sensitive version of our previous analysis where we used a less significant HMMER threshold for known Pfam adhesive domains. Using Jackhmmer we are able to detect even more distant similarities to known domains. To find out more about the function, particularly for sequences without overlapping Pfam domains, we searched with the predicted AlphaFold structure models against the PDB database for similar structures  [19]  (Suppl.
faecalis  [22]. Thus we propose that proteins in cluster 1 are likely to bind fibrinogen or other animal extracellular matrix proteins. Cluster 20 For cluster 20 AlphaFold predicted a structure composed of two domains (Figure  5c ). The DALI search indicates, that like cluster 1, for the N-terminal domain a Big_8 domain is the top hit (PDB:5cf3-A) and for the C-terminal domain a Collagen_bind domain is the best match (PDB:2z1p-A) (Figure  S8c ), thereby confirming the jackhmmer search results. The Big_8 domain functions together with the Collagen_bind domain as supra-domain, enabling the binding to collagen by the collagen hug binding mechanism  [23].
Cluster 18 The jackhmmer search as well as the DALI search with the AlphaFold prediction indicate cluster 18 being a class II TED (Pfam:PF08341) adhesive domain (PDB:6fx6:A) (Figure  5d , S8d), which is known to bind to fibrinogen  [24, 25]. The TED adhesive domain is categorized into a class I and class II TED domain, depending on an additional N-terminal indel forming an alpha helix or an additional C-terminal indel folding into a beta-sandwich, respectively  [24]. Cluster group 18 includes the fibrinogen binding E. faecalis Fss3 protein (UniProtKB:Q833P7) as well as an E. faecalis protein (UniProtKB:Q831Z5) encoded by the virulence associated EF2347 gene  [22].
Cluster 19 The jackhmmer search indicates cluster 19 to also be distantly related to the TED adhesive domain  [25]. The DALI results with the best hit is a class II TED domain (PDB:6fwv) (Figure  5e , S8e) strengthens this hypothesis. To confirm that cluster 19 is a class II TED domain, we extended the representative sequence above the 400 residues limit by around 100 residues in order to include the characteristic C-terminal indel.
Clusters with structure models indicating role in adhesion function The cluster groups 2, 6, 7, 8, 10, 11, 15, 17 and 21 don't yield persuasive jackhmmer matches, but their predicted structures resemble structures of known adhesive domains or adhesion associated domains, suggesting that these clusters might be novel domains with potential binding functions. 6a-e ). The jackhmmer search for cluster group 6 showed an overlap with the MBG stalk domain at the C-terminus of the sequence, which was trimmed off, leaving a significant N-terminal sequence region for which the structure was predicted. The top DALI matches are related to surface adhesins with the best match being a Clostridium perfringens pilin protein (PDB:5xcb:A) with the GramPos_pilinBB domain (Pfam:PF16569) (Figure  S9b ). The majority of the DALI hits for cluster 8 are related to bacterial adhesins, with the best hit being the Big_8 domain of the functional region of the fibronectin binding FnBPA protein (PDB:4b5z:A) (Figure  6c , S9c).
A disordered region is found C-terminal to the cluster 8 domain (UniPro-tKB:J9W466). The disordered region presumably acts as a flexible linker between the adhesive domain and the stalk leading to an extended radius the adhesive domain can find the binding target as well as to a higher flexibility regarding the orientation of the adhesive domain to its binding target (Suppl. figure  S12 ). For cluster group 10 the best DALI hit was the GramPos_pilinBB domain of the RrgB pilus backbone (PDB:2x9x:A). (Figure  6d, S9d ). The top DALI hit indicated for cluster group 11 a similarity to an human integrin alpha-V (PDB: 6avu:A), followed by an A-macroglobulin receptor binding domain in cattle (PDB:1ayo:B, Pfam: PF07677) (Figure  6e , S9e), suggesting a protein binding function. Similar to cluster 8, the cluster 11 potential adhesive domain is followed by a disordered region (UniProtKB:X0SH12) potentially functioning as a flexible linker between the adhesive domain and the stalk in order to increase the binding ability (Suppl.
The SasG_G5-E stalk is also described to promote biofilm formation  [30]. Cluster group 15 resembles an ice binding domain (PDB:4nuh:A) (Figure  S10d ), where the representative protein sequence (UniProt:A0A0F7RLJ7) is identical to a Bacillus anthracis protein (UniProt:A0A384LNE7) with the gene name BA_0871 or BASH2_04951, which was described to be collagen binding and to be linked to the bacterial pathogenicity  [31].
Remaining clusters with structure models indicating potential adhesion function The best DALI hit for the cluster 7 structure model (Figure  8b ) is a N-terminal helical domain of a group B streptococcus immunogenic bacterial adhesin named BibA (PDB:6poo:A) (Figure  S11b ), which superposes with the N-terminal alpha helices of the structure model  [32]. Run- shown to be involved in the initial stage of Group B Streptococcus infection  [33] , suggesting adhesion function. The best DALI match for cluster 17 is the human integrin alpha-5 protein (PDB:7nxd:A), followed by the best bacterial match being the N-termini of the S. gordonii adhesins Sgo0707 (PDB:4igb:B) (Figure  8b, S11c ). Here, the structure model aligns to the Sgo0707_N2 domain. The cluster includes a E. faecalis protein (UniProt: Q82YW8) encoded by the EF3314 gene, which was described to contribute to the virulence properties of this pathogen  [34].
Clusters unlikely to be adhesive domains The jackhmmer and DALI search indicated cluster group 3, 4, 5, 12, 13, 14 and 16 to be unlikely to function as binding domains (Suppl. figure  S13 ) and all, except cluster 5 and 12, to be similar to structures with catalytic functions. Indeed, it is challenging to differentiate between fibrillar adhesins and as we call them 'fibrillar enzymes'. Fibrillar enzymes are also composed out of repeating domains, but have an enzymatic domain instead of an adhesive domain. Nevertheless, the enzymatic region can be able to have binding functions, for example, cluster group 13 is similar to the Streptomyces pectate lyase (PDB:4pex:B), which is suggested it may have sugar binding function  [35]. The structure predictions for cluster 5 and 12 yielded DALI matches without catalytic functions.
The best DALI hit for the cluster 12 structure model is a human integrin (PDB:6v4p:A). For cluster 5, AlphaFold predicted a structure composed of two domains. The best DALI hits for both domains are all related to bacterial surface adhesins with the top DALI match being each time the BcpA pilus subunit of Bacillus cereus (PDB:3rkp:A or 3kpt:B) (Figure  S13d )  [36]. The model superposes to the XNA domain of the major subunit of BcpA, whose isopeptide bonds within the XNA domain are critical for bundle and pili formation  [36]. The jackhmmer search yielded the DUF11 stalk domain. This is strengthened by the predicted structures, which strongly resemble stalk domains, nevertheless leaving open whether these stalk domains can be involved in the binding function  [7]. One possibility is that the function of these proteins is not adhesive, but to act as a steric regulator altering the access of other adhesive proteins to binding partners.
This suggests that the Pfam domain families can be expanded to include these sequences or novel domain families related to the overlapping domain families can be created. We have taken advantage of the recent release of the AlphaFold2 software to validate our machine learning approach as well as use it to refine predictions of adhesive domains in our predicted fibrillar adhesins. Given that the predicted structures confirm the jackhmmer results, highlights the high accuracy of the structure prediction method AlphaFold2. We see many new opportunities to use large scale structure predictions to identify and investigate the components of the bacterial cell surface that are likely to interact with the host. While further investigating the described N-terminal sequence clusters the difficulty to differentiate between fibrillar adhesins and the newly discovered class of fibrillar enzymes was shown.
The high number of homologous sequences of these domains in the metagenomic MGnify database and in known pathogenic genera in the UniProt database underline their relevance. For the potential novel adhesive domains discovered in the course of this study the predicted structure models and the DALI search results give a first understanding of their function and potential binding partners. AlphaFold2 and the recent announcement of AlphaFold-Multimer opens up further ways to predict the structures of fibrillar adhesins-target protein complexes  [40]. We believe that we are at the beginning of a new age of discovery where computational analyses will lead to fundamental improvements in our understanding of microbial host interactions.
Training data selection We selected as positive training data the FA-like proteins of Actinobacteria and Firmicutes discovered with the domain-based detection approach in our previous study  [7]. Additionally, we included 25 additional FA-like proteins, which don't have either a known adhesive and stalk domain. As negative training data we selected randomly non FA-like proteins in reference proteomes, in which FA-like proteins could be detected with the domain-based discovery approach.
Identification features calculation and random forests classification To search in the protein sequences for known adhesive, stalk and anchor domains, the collection of Pfam domain HMMs from our previous study was used  [7]. Additional, the adhesive domain GspA_SrpA_N (Pfam:PF20164) and the stalk domains aRib (Pfam:PF18938), RibLong (Pfam:PF18957), SasG_E (Pfam:PF17041), GA-like (Pfam:PF17573), YDG (Pfam:PF18657), Lipoprotein_17 (Pfam:PF04200) and IgG_binding_B (Pfam:PF01378) were used. These HMMs were run against the protein sequences using the HMMER tool (version 3. 1b2) with the gathering (GA) threshold option. Using regular expression, we searched within the C-terminal 50 residues of the protein sequences for the following sortase anchor motifs: 'LPxTG', 'LPxTA', 'LPxTN', 'LPxTD', 'LPxGA', 'LAxTG', 'IPxTG', 'NPxTG', 'NPQTM' ('x' can be any amino acid).
To identify highly similar tandem sequence repeats that may represent potential unknown stalk domains we applied the T-REKS software on the sequences using as parameters a minimum of 70% sequence identity, 50 residues as minimum length of the repeat region and 5 residues as minimum seed length  [12]. Disordered regions were predicted using IUPred (IUPred2a) with the IUPred2 type 'long' for predicting long disordered regions  [13]. Each residue with an IUPred score above 0. 5 was counted as predicted disordered. The predicted disordered fraction was calculated using the percentage of predicted disordered residues from the total protein length. The cellular localisation was predicted using Inmembrane (version 0. 95. 0) with the default settings  [14]. The proteins predicted by Inmembrane as potential surface exposed (PSE) proteins at the cellwall ('PSE-cellwall') were selected for the identification feature 'Cellwall'.
05 We calculated the identification features for the protein sequences of the training data. With the calculated feature data we trained a random forest classifier from sklearn ensemble methods with 50 trees with maximum 3 features per tree and random state 2  [41]. The Random Forest method takes the 30 features as input and outputs a score per protein between 0 and 1 with FA-like proteins scoring closer to 1. The reliability curve was calculated for the applied random forest model on the training data set using calibration_curve from the sklearn calibration module and a 10-fold cross-validation approach. For calculating the precision and recall of the model and generating the precision-recall curve, we generated a testing data set of 258 proteins, composed of 128 FA-like proteins and 130 non FA-like proteins.
We artificially adapt the features of the testing set to have no adhesive or stalk domains, whereby all other features are retained. For these calculations the proteins of the testing set were excluded from the training data set. The precision and recall of the model as well as the precision-recall curve was calculated using the macro-average method to determine how the random forest model performs overall across the two classes: FA-like, non FA-like proteins. The precision-recall curve was also calculated using a cross-validation approach with the training set. To use the random forest discovery approach, we provide the code in our GitHub repository (see below).
Analysing predicted FA-like proteins We further analysed the predicted FA-like proteins by differentiating the prediction scores. The number of stalk domains per protein were counted regardless whether these are stalk domains of the same or different domain families. The fraction of the protein's length between the adhesive domain and the anchor were determined using the middle point per domain or motif. Using profile HMM-search (version 3. 1b2) with the gathering (GA) threshold option the Pfam database (version 33. 1) was run against the sequences of the predicted FA-like proteins.
identified a Big_8 domain (Pfam:PF17961), which is found in a variety of bacterial adhesins such as the Staphylococcus aureus proteins FnBPA, ClfA and ClfB. A DALI search with the N-terminal domain strengthens the jackhmmer results as the best hit is the N-terminal Big_8 domain of the FnBPA binding region (PDB:4b60:B). For the C-terminal end of the C-terminal domain of cluster 1 the jackhmmer search indicates an overlap to the Collagen_bind domain (Pfam:PF05737). The second domain structure yields with the DALI search the Streptococcus gordonii adhesin Sgo0707, where the structure superposes to the Sgo0707_N2 domain (Pfam:PF20623) (PDB:4igb:B). The subsequent DALI hits are the SdrG_C_C adhesive domain (Pfam:PF10425) (PDB:4jdz:A) (Figure.
ning the DALI search for the domain in the middle of the structure model separately, results in the S. aureus SdrD adhesive protein (PDB:4jdz:A), where the structure model superposes with the SdrD_B stalk domain (Pfam:PF17210). The cluster 7 includes one Streptococcus merionis protein (UniProtKB:A0A239SMH4), which is encoded by the bca gene. The bca gene has been.
Multiple target tracking in WSN was proposed in  [1]  with the objective of monitoring and recording data regarding vehicles crossing an area possessing vandalizing environments. In this work, consideration like future mission monitoring was taken into account in addition to the energy consumption involved. Moreover with the assumption that spatial trajectories of the targets being familiar, target speed were also estimated contingent on changeability. With this, multiple targets were able to track at the same time in WSN with better communication between sensors. Moreover, a linear programming model was utilized with the result being maximization of the overall energy being left. Despite improvement observed with better communication for multiple targets, with the dynamic changing network, targets were lost, compromising the accuracy.
Work organization The rest of this paper is organized as follows. Section 1. 2 describes our methodology and contributions. In Section 3, we present a literature of work conducted for target tracking in WSN. In Section 3 the proposed method Radial Distance and Lucas Cluster-based Ridge Regression (RD-LCRR) for target detection and tracking is presented with system model in 3. 1, problem statement in 3. 2 followed by the elaborated description. Numerical results along with the experimental settings and dataset description are provided in Section 4. The paper is concluded in Section 5.
Case scenario Internet of things (IoT) comprises of diverse elements, like, data collection, data transfer analyses of data and so on. Data collection is performed by sensors via health monitoring devices, then the collected data is sent to a pivotal point for analysis and decision making and accordingly, emergency or ordinary follow up is made forward. In the current scenario, IoT is hitherto utilized in managing certain characteristics of the COVID-19. To name a few instances are, the usage of drones for public citizen inspection to make certain quarantine and the wearing of masks. IoT can also be utilized in making certain patient consent once the probably contaminated or infected persons become part of quarantine. By severe tracking of the object (i. e. , infected persons) health personnel can easily monitor the patients remaining quarantined and the patients breaching the quarantine.
ii) A second option uses empirical responses of stomata to a proxy of water status (such as leaf water potential (symplasmic) or soil water potential). These can be easily implemented when the soil or leaf symplasmic water potentials corresponding to 0. 9gStom_max (Pgs10) and 0. 1gStom_max (Pgs90) are known. In this case, gStom is computed as: 𝑔 eh3< = 𝑚𝑖𝑛 Â𝑔 eh3<_…»K ; 𝑔 eh3<_<1 ¶ × •0. 8 …4… 'UÄy … 'USy 4… 'UÄy + 0. 1¢Å [49] With P corresponding to the soil or symplasmic water potential. This also presents the advantage to be easily implemented for multiple species thanks to available databases (i. e. the SurEau database, Martin-StPaul et al. 2017). iii) A third option is called finalist, because EStom is regulated assuming that stomata close so that a target organ water potential (e. g.
Dataset description Participants were instructed to use the hashtag #MattiaTw, followed by the number of the chapter they were currently reading, in their comments posted on Twitter (e. g. , #MattiaTw/1). Tweets incorporating the hashtag #MattiaTw have been collected by employing a Python script interrogating the Twitter Streaming Advanced Programming Interfaces (APIs) iii. Data collection lasted for a period of 3 months, from November 2016 to January 2017. In the considered period, a set of 14,072 distinct tweets has been gathered (18,962 including L i b r a r y H i T e c h retweets). Accidental duplicates have been removed before starting the analysis. Due to the possible occasional instability of the Twitter APIs and of the server running the Python script, the dataset has been compared and reconciliated with a similar but simpler (in terms of metadata) dataset archived by TwLetteratura in the same period.
Participants TwLetteratura provided us with the names of the Twitter accounts of the registered classes and teachers, together with their group information. The number of classes who registered for the DSR was 211, with approximately 3,900 students in total. Group A had 57 classes and group B 54. There was also an additional group of registered classes (OtherReg 100), who were not asked to follow any specific restriction with respect to the tweeting calendar. In order to include all the students and teachers' accounts that have been used, we manually checked the usernames found in the dataset and, where possible, assigned them to the correct group. Overall, readers who participated in #MattiaTw can hence be divided into six groups: A (n = 135): instructed to carry out the DSR activity from chapter 11 to chapter 20 (January).
Analysis We analyzed the collected data, by using the R programming language (R Core Team, 2013) and the RStudio environment. We computed the number of tweets/retweets over time for each group; the number of tweets/retweets per chapter; the aggregated activity of users within each group; and the mentions that users received. Furthermore, we analyzed the interactions between groups (Social Network Analysis) and performed content analysis (text classification and words frequency). Since from a preliminary manual check of the dataset we noted that the content of many tweets was constituted by citations or paraphrases of the novel, we employed automated text reuse identification techniques  (Smith et al. , 2014)  to classify as "reuse-based" these kinds of tweets. After testing "passim"  (Smith, 2012)  and BLAST  (Vesanto et al.
, 2017)  we opted for the latter because of its better results. The tweets identified as text reuse were 7,267 (52% of the total number of tweets). We then randomly selected a sample of 100 tweets among those automatically identified as not being text reuse and, via a manual investigation, L i b r a r y H i T e c h we found that 20% of them were indeed text reuse, but we did not find any false positive. Thus, we can estimate our classification error rate to be around 10% (see Supplementary material for more details). To analyze the social reading activity per chapter, we selected a subset of the dataset, keeping only the tweets explicitly mentioning chapters (15,724 tweets and retweets, 83% of the total). Mean values have been calculated by considering only the number of authors active in each phase.