dataesr/scientific-paragraphs-categorization huggingface dataset:
2 Shoreline change analysis The ArcGIS 10. 4 and ENVI 5. 2 software were used to create shoreline database. The high waterline was considered as a proxy for the shoreline extraction. The shoreline of four interval periods  was traced manually using visual image interpretation techniques. All tracing procedures were attempted independently to avoid subjective biases. Using the DSAS tool, developed by the United States Geological Survey (USGS), a vector-based transect study of coastline changes was attempted to assess displacement rates  (Oyedotun, 2014). This tool was added as an ArcGIS extension, which quickly calculates the shoreline change rate statistics (Abou Samra and Ali, 2020). Initially, the shoreline of 2018 was used to offset (2000 m) towards the sea (offshore) and considered as a baseline for shoreline change measurements  (Nor et al.
Discussion The intensified anthropogenic activities and sea-level rise pose significant challenges and risks to residents of low-lying coastal regions  (Parthasarathy and Deka, 2019). On a global picture, coastal parts of South Asia are the most hazard-prone areas, with an increased intensity of cyclones, seasonal inundation of low-lying coastal regions, tsunami events, accelerated coastline erosions and unprecedented wetland losses. All these processes are observed in the coastal areas in Tamil Nadu. Since erosion of shorelines is highly associated with disaster risk, monitoring shoreline changes is essential for developing micro-level plans and policy options to combat disaster risks. Qualitatively, physical (in-situ) observation of shoreline dynamics yields a good understanding, but remote sensing datasets offer a better quantitative analysis  (Sridhar et al.
, 2009). The availability of satellite data and access to sophisticated tools enabled researchers to conduct a hassle-free multi-temporal shoreline change analysis with a more comprehensive statistical report  (Parthasarathy and Deka, 2019). In addition to quantitative metrics generated through satellite images, the field survey and opinions of the coastal community provide a better picture of shoreline dynamics and associated impacts. The combination of remotely sensed datasets and field-based observations of shoreline changes in Tamil Nadu's coasts shows that the southern tip region of Tamil Nadu, i. e. Kanyakumari district, is prone to severe erosion. The EPR and NSM metrics reveal severe shoreline erosions in all the watersheds of Kanyakumari district. The impacts of shoreline erosion are very high in these regions, as observed through the public perception survey.
Conclusion The study is carried out throughout the coastal stretch of Tamil Nadu and categorises the coastal plains into different levels based on natural watershed boundaries, long-term satellite-based datasets, quantitative metrics and field surveys. The mean annual rate of shoreline changes is À0. 26 m/year, suggesting erosion dominated the state's shorelines. About 55 percent of coastal residents felt that they were impacted by coastal erosion in the recent past. The watershed-wise shoreline changes indicate that southern watersheds facing very severe coastal erosion (À1. 7 m/year), the northern watersheds facing moderate coastal erosion (À0. 2 m/year), and central watersheds facing moderate coastal accretion (0. 7 m/year). Among the coastal towns, Marakkanam, Cuddalore, Karaikal, Nagapattinam, and Kanyakumari are higher risk of coastal erosion.
Methods Dataset, sample description, and relevant prior processing All genomes and metagenomes analyzed here were previously published  [9] , apart from a supplemental dataset of 12 metagenomes collected from chickpea root nodules in Pakistan, using the same methodology. A total of 648 Illumina-sequenced paired-end metagenomes, 514 metagenome-assembled genomes (MAGs), 266 Illumina-sequenced isolate draft genomes, and 14 PacBio-sequenced isolate genomes was downloaded from NCBI (12/18/2021). A brief summary of methods for generating those datasets appears below, and see Supplementary Table  S1  for a list of sample accession numbers, countries of origin, and sample type (nodule metagenome or isolate). All 660 short-read metagenomes (648 from  [9]  under Bioproject number PRJNA453501 and 12 from Pakistan that will be made available upon publication) are publicly available on the NCBI short read archive.
Isolate draft genomes and MAGs are available on the NCBI Assembly database (Bioproject: PRJNA453501), and long read assembled genomes are available on RefSeq (accession numbers can be found in supplementary table S7 of  [9] ). Brie y, root nodules of both wild and domesticated chickpea plants (speci cally the species of Cicer arietinum, C. echinospermum, and C. reticulatum) were collected from ve continents (Europe, Africa, Asia, Australia, and North America) and eight countries (Turkey, Ethiopia, Morocco, India, Australia, Canada, USA, and Pakistan). DNA was extracted either directly from root nodules (nodule metagenomes) or from pure cultures of Mesorhizobium isolated from the nodules (isolate draft genomes). Library preparation for metagenomic DNA from root nodules and genomic DNA from Mesorhizobium cultures was performed with the Illumina Nextera XT kit.
Nodule microbiome analysis To determine whether Mesorhizobium was the dominant organism in nodules, we analyzed the 648 nodule metagenomes in this study (see sup. table  S1 ) using the SingleM pipeline with default parameters (https://wwood. github. io/singlem). The resulting coverage table les were sorted by coverage and the organism with the highest coverage was retained. We then calculated the fraction of coverage by organisms which were assigned the taxonomy "Mesorhizobium".
Calculating vOTU presence in isolate genomes and nodule metagenomes All reads from the 914 sequenced Illumina libraries were quality trimmed and PhiX removed, using trimmomatic (v0. 39) [18] in paired-end mode with parameters set to sliding window 4:30 and minlen 50. The trimmed reads were mapped back to vOTUs using BBmap (v38-72)  [19] , requiring a minimum identity of 90%. The resulting BAM les were then sorted using Samtools (v1. 15. 1, (20)), followed by generation of a coverage table with CoverM (v. 0. 6. 1) (https://github. com/wwood/CoverM), requiring a minimum covered-fraction (breadth) of 50% to detect a vOTU in a dataset (nodule metagenome or cultured Mesorhizobium isolate). From the coverage table, all vOTUs that were below these detection limits in all samples were removed from further analysis.
Statistical analysis For calculations of beta-diversity, only datasets that contained at least three vOTUs were included to increase statistical power, as dissimilarity between samples was extremely high. This step removed all samples from Australia, retaining 158 metagenomes from India, Pakistan, Ethiopia, Morocco, Turkey, Canada and the USA. A Jaccard dissimilarity matrix was generated, based on the vOTU RPKM table (function vegdist), using the vegan package (v2. 6. 2,6) and tidyverse (v1. 3. 1,7)  [20, 21]. A Jaccard matrix (presence/absence) was preferred due to the low overlap of vOTUs between dataset, which can bias metrics that take abundance into account. A principal coordinates analysis was performed on the dissimilarity matrix with the function pcoa in the R library ape (v5. 6. 2)  [22]  and plotted with ggplot2  [23].
To determine overlap in phage assemblage composition between countries, an upset plot was generated, using the R package ComplexUpset  [24]. For these beta-diversity measurements, our targeted approach was only meant to consider vOTUs that likely infect Mesorhizobium, so these are 'assemblages' of vOTUs with the dominant strain of Mesorhizobium as likely host, rather than 'communities' of vOTUs infecting the full nodule microbiome membership. Correlations between viral beta-diversity and continuous environmental factors detailed in supplementary table S1 from  [9]  (latitude, longitude, altitude, mean annual precipitation, mean annual temperature, and soil pH) were calculated using Mantel tests (R package vegan, function mantel). The extent to which viral community beta-diversity was structured by categorical variables (dominant Mesorhizobium strain, country of origin, plant species, and soil genus) was evaluated using an ANOSIM test within vegan  [20]  with 999 permutations and Euclidean distances.
Acknowledgments The authors would like to thank everyone involved in collecting the original samples, particularly  Atsede Muleta ,  Sripada Udupa , and  Zehara Mohamed. We would also like to thank the IT team and in particular  Parwana Osmani  of the high-performance computing center at the  University of California, Davis  for resources and computational support. This study was primarily supported by  USDA NIFA AFRI  award #  2021-144 67013-34815-0  (grant to JBE). Additional support was provided by  USDA NIFA Hatch  #  CA-D145 PPA-2464-H  and by the  2020 Tri-Institutional Partnership in Microbiome Research Initiative , with funding from the  University of California, Davis, University of California, San Francisco , and the  National Microbiome Data Collaborative (NMDC)  (grants to JBE).
The NMDC is supported by the  Genomic Science Program  in the  U. S. Department of Energy, O ce of Science, O ce of Biological and Environmental Research  under contract numbers  DE-AC02-05CH11231 ,  89233218CNA000001 ,  DE-AC05-00OR22725 , and  DE-AC05-76RL01830. ETS was partially funded by  Marie Sklodowska-Curie postdoctoral fellowship DIVOBIS.
Test results Vibration test results were the sum of forces recorded by the four load cells and displacements measured by the LVDT device. The total number of records was 120 forces and 120 displacements (6 measurements × 4 activities × 5 crowd sizes per parameter). Force and displacement signals were processed using MATLAB. For each activity and crowd size, the relevant window during which participants performed rhythmic activities was determined, starting when a force amplitude reached 0. 5 % of the mean force and lasting 45 s. All  mean values (corresponding to static parameters) were also removed from test records in order to keep only the dynamical component of forces and displacements useful for further analysis. Furthermore, the signals were filtered to meet the usual frequency range of human activities (ranging between 0 and 10 Hz  [12] ) using Fast Fourier Transform.
Hence, the optimal vector θ opt was obtained by minimizing an objective function as follows  [27] : θ opt = argmin θ [ ⃦ ⃦ S p,th (f , θ) -S p,exp (f ) ⃦ ⃦ 2 2 ] (8) A practical procedure was established to identify load model parameters collected in the vector θ ={f p , α, δ} T  [25]. First of all, an initial set of parameters θ 0 was selected, and the analytical PSD load model vector {S p,th (θ)} was calculated using Eq. (  5 ) for all analysed frequency values. The experimental PSD load vector {S p,exp } was obtained from the time domain force signal using the periodogram method implemented in MATLAB. The optimal parameter vector θ opt was then calculated by the least-squares method (see Eq. (  8 )) using lsqnonlin solver (with Levenberg-Marquardt algorithm) available in the Optimization toolbox of MATLAB.
The statistical variability of the resulting S RMS forces was then evaluated by their mean values and standard deviations. The variation of the mean RMS force Frms (N) against crowd size N would exhibit a growth with a decreasing rate due to "inter-subject variability". This variation was fitted by selecting an appropriate function that best describes this tendency, noted F rms (N, λ) (λ being the vector of function parameters). Corresponding optimal parameters λ opt were then determined by means of least-squares curve fitting using lsqcurvefit function available in the Optimization toolbox of MATLAB. This lead to an equivalent RMS force describing a single person  F rms,eq (N) = F rms ( N, λ opt ) N ( 10 ) where F rms (N, λ opt ) is the best-fit function for measured crowd RMS forces. The coordination factor for a group of N individuals C(N) was then deduced by: C(N) = F rms,eq (N) F rms (1) (11) where F rms (1) is the RMS force corresponding to the case of a single person on the floor and F rms,eq (N) the equivalent RMS force for a single person within the group of N individuals.
Despite a five-order magnitude range in size, the mammalian brain exhibits many shared anatomical and functional characteristics that should translate into cortical network commonalities. Here we develop a framework employing machine learning to quantify the degree of predictability of the weighted interareal cortical matrix. Data were obtained with retrograde tract-tracing experiments supplemented by projection length measurements. Using this framework with consistent and edge-complete empirical datasets in the macaque and mouse cortex, we show that there is significant amount of predictability embedded in the interareal cortical networks of both species. At the binary level, links are predictable with an Area Under the ROC curve of at least 0. 8 for the macaque. At the weighted level, strengths of the medium and strong links are predictable with at least 85-90% accuracy in mouse and 70-80% in macaque, whereas weak links are not predictable in either species.
Areas external to the target area housing labeled neurons are referred to as source areas. The weight of an interareal connection from area  to area , defined via the counts of labeled neurons, is recorded as the Fraction of Labeled Neurons  !" found in area  ( ≠ ), when injecting into area. Existing retrograde tracing datasets do not have full network connectivity information; they do provide edge-complete subgraphs, i. e. , networks formed by a subset of vertices whose connectivity within this subset is fully known. These studies show that interareal cortical networks  (Majka et al. , 2020)  are not random graphs, but complex networks with characteristic structural features  (Ercsey-Ravasz et al. , 2013; Gămănuţ et al. , 2018; Horvát et al. , 2016; Theodoni et al. , 2020). Moreover, interareal networks appear to be in a class of their own when compared to other real-world complex networks, including technological information networks  (Milo et al.
, 2004). One of the most distinguishing feature of interareal networks is their high density of binary connectivity (connections existing or not), i. e. , containing a large fraction of the maximum number of possible connections: 0. 66 for the macaque  (Markov et al. , 2011)  and 0. 97 for the mouse (Gămănuţ achieved by the profiles of connection weights  (Gămănuţ et al. , 2018). The connectivity profile of a cortical area is the set of connections and their weights, which has been hypothesized to constrain its functional properties thereby reflecting its specialization  (Bressler, 2004; Bressler and Menon, 2010; Markov et al. , 2011). Studies of existing, self-consistent tract-tracing datasets  (Kennedy et al. , 2013)  reveal the action of a simple rule in both mouse and monkey, the so-called Exponential Distance Rule (EDR), which significantly constrains the structure of the interareal networks  (Ercsey-Ravasz et al.
Results First, we introduce the datasets in our study then give a brief summary of the prediction methods used and how they are adapted to work with retrograde tract-tracing datasets. We then present our main results on network predictability using cross-validation both at the binary and weighted levels, along with a comparison of predictability between rodent and primate cortical networks. Data description. We rely on two retrograde tract-tracing datasets, one for the macaque (mac) and the other for the mouse (mus). Both are cortico-cortical connectivity databases created with consistent methodology, having most of the data published in  (Gămănuț et al. , 2018)  in the mouse and a more limited dataset based on 29 injection areas in a 91area atlas in  (Markov et al. , 2014)   and  /,×/, )*+ , respectively.
Additionally, our datasets contain all pairwise distances along estimated shortest paths avoiding anatomical obstacles, between the area barycenters, recorded in the matrices  12×12 )-. and  /,×/, )*+ , respectively, also provided in the SI. Repeat injections across individuals allow an assessment of the consistency of the set of areas and their FLN values  (Gămănuţ et al. , 2018; Markov et al. , 2011). Due to the high sensitivity of the tracers, every injection reveals all the areas that project into the injected target area and thus, the FLN matrix  4×5 is a row submatrix of the FIN  5×5. That is, we either know the full row (corresponding to a target area) or not at all. This is illustrated in Figure  1A  where, for simplicity, we order the rows such that the first  rows represent the targets, in the full  5×5 matrix.
However, for both CL and ML, the information on which the prediction is based (scores and feature vectors) has to be computable for all pairs in an identical fashion, which limits the types of predictors that can be used for retrograde tracing datasets. In particular, for CL, path-based predictor models such as PageRank, Katz score, and Shortest Path score are effective when random links exist adjacent to the link (or non-link) to be predicted. However, in retrograde tracing datasets we are forced to select injected areas as the basis for predictions, but there are no paths into some of the vertices of the links to be predicted (i. e. , the remaining areas that were not injected), thus excluding path-based predictors. For both CL and ML, we can only use information on out-going links, being the only type of information commonly available to all node pairs, see Figure  1B.
This analysis cannot be applied to the mouse dataset, (see the ROC curves in the SI Figure  S9 ), due to its ultra-high density of 97%. This density causes a strong bias and prevents the calculation of a meaningful ROC curve, because the classifiers have only 3% true negatives to learn from, meaning that only weighted predictions can be made in the mouse brain, presented in the next section. Figure  3  shows individual link prediction errors in the macaque data for all the links with a corresponding ground truth value (lighter colors correspond to smaller errors). A prediction (link existing/1 or not/0) was obtained for every -fold run in all area-pairs , averaged over 100 randomized -fold run predictions, generating a prediction 〈 (7&8 ()〉. The error is calculated via () = S 97-& () -〈 (7&8 ()〉S, where  97-& () ∈ {0,1} is the true binary link value.
Globally, the mouse network appears more predictable than the macaque (overall lighter matrices for the mouse). This is further demonstrated in Figure  6  where we plot RMAE values as function of link weight as well as a function of link projection lengths (distance). While in both species, weaker links are harder to predict, comparing (A) to (C) we see that the medium-to-strong links are much more predictable in the mouse than in the macaque, but the situation is reversed for the weakest links. Similarly, longrange links are harder to predict in both species than shorter ones. Overall, weighted links are more predictable in the mouse than in macaque. We quantify global link predictability, and by link weight classes in Table  1 , for both species. Predictions (3-fold cross-validation) were made on the full dataset (including links with non-zero weight and also non-links) using the GB algorithm and errors computed and averaged within the corresponding groups.
The RMAE values in Table  1  show that weak links are not predicted in either species, whereas the stronger links are better predicted in both species. The stronger links are in general two-fold more predictable in the mouse than in the macaque. The non-links, however, are better predicted in the macaque, likely due to the fact that there are only 3% non-links in the mouse dataset. Since the larger errors are associated with the non-links, we performed the predictability analysis also on a reduced dataset, with only actual links included (non-links excluded). That is, we trained the ML algorithms only on the portion of the matrix with non-zero link-weights. The predictability results are shown in SI Table  S3. Except for the weak links, predictability improved in general, with mouse links being overall 1.
Discussion Using machine learning methods, we demonstrated that the network of the mammalian cortex contains significant structural predictability, suggesting that the formation and evolution of the cortex is to a good extent rule based, at the mesoscale level. This further motivates the search for universal mechanisms of brain network formation and evolution, within the mammalian order. The literature on link prediction in the brain is fairly limited. To the best of our knowledge, the earliest link prediction effort in the context of brain neuronal networks goes back to a 1998 paper by Jouve et al. (Jouve et al. , 1998) , which uses the frequency of directed transitive triples to predict missing links at the binary level (existing or not), in an early dataset on the macaque visual system  (Felleman and Van Essen, 1991).
The next brain link prediction papers appear almost a decade later, which incorporate additional topological and spatial features  (Costa et al. , 2007; Nepusz et al. , 2008) , both based on the CoCoMac database  (Kötter, 2004) , with the latter using a stochastic graph fitting method to handle the uncertainties in the data. Several other publications followed these papers  (Hoff, 2009; Cannistraci et al. , 2013; Hinne et al. , 2017; Røge et al. , 2017; Chen et al. , 2020; Shen et al. , 2019) , but all (including the earliest three) are based on preconceived network models whose parameters are fitted to the data, and then used to make predictions (usually at a binary level) on missing links. These network models quantify the belief that the existence or absence of a link is largely determined by some summary network statistics on the existing data.
One problem with this approach is that it imposes specific relationships that the modeler believes to be relevant. Another is that the summary statistics are obtained on an incomplete dataset, which inherently biases these statistics, a bias which is then built into the prediction. A further bias present in almost all the previous link prediction papers is that they are based on network models from the field of social networks. However, brain neuronal networks are quite different from social networks in many aspects and thus social networks-based models would have limited practical applicability in the brain. Here we compared the performance of the social science inspired, model-imposed link predictors (CL) with machine learning based methods (ML) that learn the structure from the data, without imposing specific models or assumptions.
Another significant issue affecting the reliability of predictions is the quality of the dataset. While the CoCoMac database is one of the largest connectomics databases, it is also a collation of results from independent studies using different approaches under different conditions, with significant inconsistencies  (Bezgin et al. , 2012). Moreover, the CoCoMac database does not distinguish absent links from links of unknown status, which is a major source of errors in network modeling  (Kennedy et al. , 2013). The results generated from (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. the two retrograde tract-tracing datasets in non-human primates (macaque) and rodent (mouse), both obtained with consistent empirical methodology, allow for interspecies comparisons  (Horvát et al.