dataesr/scientific-paragraphs-categorization huggingface dataset:
All the regression models were compared based on error measurement metrics. The performance of the KRR model outperformed the benchmark regression methods. The transportation index estimated from the KRR model is compared with actual test data (from real cities). KRR has comparatively less MAE of 1. 39 and a higher R 2 value of 0. 71. Thus, the proposed hybrid model can be used to predict the transportation index in terms of road network density for CityGAN-generated towns and cities. Our proposal can be treated as a versatile decision-support system for sustainable planning and development of new small and medium-sized towns and cities in terms of transportation infrastructure. However, the effectiveness of the proposed model may vary depending on factors such as the complexity of the urban landscape, the diversity of the training dataset, and its availability.
Larger cities tend to have more diverse and intricate urban patterns, which might pose challenges in building the CityGAN model. Some potential limitations include having substantial variations in the urban landscape between large cities and small and medium cities. The model might not perform as well when applied to areas with more diverse characteristics. Hence, generating realistic large Indian urban landscapes using CityGAN requires large and diverse datasets. The availability of the number of data points will be limited for large cities as compared to small and mediumsized cities of India. The proposed method is useful for establishing a relationship between coverage measures (network density) of transportation indices with HSIs, but the current work doesn't consider any connectivity measure.
Our study fills this gap in the literature with the two-fold objectives. Such research is of considerable significance in the realm of urban planning particularly in rapidly developing nations like India and also for a better understanding of urban transport systems and human settlements. Therefore, the primary goal of our research consists of the following two main objectives: (1) generate realistic urban patterns to capture the great diversity of urban forms across small and medium-sized Indian cities by using CityGAN; (2) disentangling urban human settlement indices in the population dynamics and establish a nonlinear association between road network density and human settlement indices. We utilize a uniform and standardized two-step approach to preprocess open datasets from diverse built environment domains (WSF 2019 and OSM) and to implement the integrated model.
A Python implementation of CCC can be done using the "TripleCpy" package in Python. We can define CCC mathematically as follows: given a pair of random variables (X, Y) and suppose the realizations X i 's and Y i 's have no tie. A rearrangement of the pairs as X (1) , Y (1) ,. , X (n) , Y (n) is done such that X (1) ≤ • • • ≤ X (n). Let r i be the rank of Y (i) then CCC is defined using the formula: where n is the number of observations. Once the relationships are assessed between TI and HSIs, selecting the "best" regression model is easier so that the model explains the variability in the response variables with possibly lower prediction error. However, the ridge regression (RR) method can directly handle multicollinearity structures in the data along with the instability of least square estimators  63.
02; it controls the trade-off between fitting the training data and prevents overfitting. The input features are a wide range of human settlement indices related to network density considered. We selected six settlement indices such as total class area (CA), number of patches (NP), largest patch index (LPI), clumpiness index (CLUMPY), aggregation index (AI), and normalized landscape shape index (NLSI) to represent the characteristics of human settlements. The target variable is network density. We use KRR to establish the relationship between HSIs and TI as shown in Fig. 3d.
In many such countries like India, socioeconomic, demographic, and economic data are limited and unreliable. Together, the limitations of existing traditional urban models (discussed above) that need detailed GIS data layers and do not scale from country to global levels along with the urgent need for data-driven decisions for expanding cities in developing countries, motivated us to develop and build a hybrid predictive model (RidgeGAN) that were calibrated using a globally consistent data. Our proposal is not only able to capture the urban morphology of a large number of Indian smaller and medium-sized cities but also gives insight into the network density prediction (i. e. , transportation index) through a set of spatial metrics (i. e. , settlement indices). For this, we have utilized a uniform and standardized two-step approach to pre-processing open source datasets from diverse built environment domains (WSF2019 and OSM Data) to implement the integrated hybrid model.
Drawbacks, limitations, and future scope of the study As our proposed hybrid approach uses two key models: KRR and CityGAN, there are a few limitations of the proposal. In contrast to conventional deep learning architectures that employ convergence-based optimization of the objective function, generative models like GANs utilize a minimax loss function, trained iteratively to establish equilibrium between the generator and discriminator networks  73. The absence of an objective loss function for GAN training restricts the ability of loss measurements to assess training progress or model performance. To address this challenge, a mix of qualitative and quantitative GAN evaluation approaches has been developed  74. These evaluation measures particularly vary based on the quality and diversity of the generated synthetic data, as well as the potential applications of the generated data  75.
Genomic datasets We retrieved all the complete genomes of the NCBI non-redundant RefSeq database (ftp://ftp. ncbi. nlm. nih. gov/genomes/refseq/, last accessed in March 2021), including 21 084 bacteria, 21 520 plasmids and 3725 phages. The distribution of the bacterial genomes in phylum, class and order are shown in File S1. Additional phage nucleotide sequences, corresponding to all complete or draft genomes, were obtained from the NCBI database (accessed in May 2021). We made a quality filter to retain only those sequences with 10 predicted proteins, resulting in a dataset of 15 558 phages. We also retrieved and annotated both complete and draft genomes of Vibrio spp. (n = 11627 in total), using PanACoTA (version 1. 3. 1  (20) ), ran with the Singularity module. We used the methods 'prepares < species>' (where < species > was iteratively replaced with the most representative species of Vibrionacea: Vibrio, Aliivibrio, Enterovibrio, Photobacterium, Grimontia, Salinivibrio and Thaumasiovibrio, databases accessed in March 2022), with the options '-min 0 -max 0.
For PICI, we also used the portal, headtail connector and head-tail adaptor of cf-PICI as forbidden components, to avoid the detection of cfPICI as PICI. This iterative procedure resulted in a MacSyFinder model for each family of satellites. These models were used to systematically search for putative phage satellites in bacterial genomes. The MacSyFinder models for each satellite family are available within the Docker package of SatelliteFinder (https://hub. docker. com/r/gempasteur/ satellite finder).
Identification of markers and construction of HMM profiles The first step in identifying novel satellites is to characterize the known ones and identify the genes that are most frequently associated with them (markers). We collected the satellites experimentally verified or proposed as homologs in previous publications and clustered their proteins by 40% protein sequence similarity (or 20% similarity for some PLE components PLEs, since the limited number of genomes resulted in some more diverse clusters not reaching the higher threshold). This revealed families of proteins that were the most frequent in a given family of satellite. The majority of these frequent components was adequately identified by Nucleic Acids Research, 2023 5 existing HMM profiles of the PFAM database. For this, we used HMMER and collected hits with maximum e-value of 10 -3 and a minimum of 40% coverage as thresholds.
wGRR is the fraction of bi-directional best hits between two genomes weighted by the sequence identity of the homologs. It varies between zero (no bi-directional best hits) and one (all genes of the smallest genome have an identical homolog in the largest genome). wGRR integrates information on the frequency of homologs and sequence identity. For example, when the smallest genome has 10 proteins, a wGRR of 0. 2 can result from two identical homologs or five homologs each with a lower sequence similarity (40%). The hierarchical clustering of the wGRR matrix, and the corresponding heatmap, were computed with the clustermap function from the seaborn package (version 0. 11. 1, developed for Python 3. 9), using the Ward clustering algorithm. The latter algorithm was also used to cluster the presence/absence of satellite-like components across all phage and satellite genomes.
Calculation of diversity of bacterial hosts We assessed the diversity of the phage satellites' bacterial hosts using two measures. The species diversity (S) is the number of bacterial species where at least one phage satellite was identified. Since the genome datasets are very unbalanced, with some species accounting for a large fraction of genomes, the Species Diversity measure can misrepresent the true diversity of the hosts of satellites. Hence, we compute the Shannon's diversity index (H )  (29)  for each phage satellite family. The index is calculated according to the formula: H = - R i =1 p i ln ( p i ) where R is the total number of bacterial species with at least one satellite, and p i is the proportion of satellites found in the ith bacterial host species, in relation to the total number of satellites for the corresponding family (e.
coli genomes, computed with PanACoTA  (20). Briefly, the genomes of E. coli in our dataset were retrieved, filtered to remove very closely related genomes (MASH distance less than 0. 0001), and then clustered using mmseqs2 with a minimal threshold of 80% identity in protein sequences. Within the pangenome of these 657 genomes there were 2107 families present in >90% of the genomes, which make the core genome. The core genome was aligned (for families with not more than a single gene per genome) using mafftlinsi, rendering one multiple alignment with 2 069 583 positions that was used to build a phylogenetic tree by FastTree 2. 1 with model JC  (35). Afterwards, we edited (i. e. rounded) the branch distance of very close nodes (<0. 0001) to 0. 0001 (1145 nodes out of 2630 in total), since Coinfinder detects these as branches with 0 distance.
Availability We provide the MacSyFinder models and the customized python scripts to perform the abovementioned post-processing as a tool we call Satel-liteFinder, that is available as a Docker package (https://hub. docker. com/r/gempasteur/satellite finder) and also as a Galaxy server interface (36) (https: //galaxy. pasteur. fr/root?tool id = toolshed. pasteur. fr/ repos/fmareuil/satellitefinder/SatelliteFinder/0. 9). A brief summary on how to use both these approaches, as well as how to interpret the output of SatelliteFinder, is shown in File S3. The genomes (proteomes) of the satellite-like elements extracted from bacterial genomes and analysed in this manuscript are available in supplementary material (File S4).
The P4 MacSyFinder model identified a similar amount (1054 vs 1037) of P4-like elements relative to our previous search in the same database  (5). More than 93% of the most Nucleic Acids Research, 2023 7 complete elements (927) are strictly identical in both approaches (Jaccard similarity = 1). The remainder elements are in the same locations and only differ in terms of gene content. This is due to the ambiguity in the process of delimitation of P4-like satellites when they are flanked by two integrases (Figure  S2 ). We used this model to search for P4like elements in a much larger dataset of 21084 bacterial genomes, where the most abundant phyla are Proteobacteria (57%), Firmicutes (22%) and Actinobacteria (10%) (Figure  2 , File S1). We more than doubled the number of putative P4-like satellites previously identified (2160).
The majority (1621) of these elements encode all the seven core components (henceforth called Type A, Figure  2A  and B ), whilst 350 elements lacked one of them (Type B). The missing component may be absent, be non-identifiable by the protein profile, or may be replaced by a functional analog (see Materials and Methods). The most abundant of these variants lacks Psu (Type B#var06). Since Sid and Psu are structural homologs  (39)  it is possible that some variants of the former may compensate for the absence of the latter. There are 189 elements that lack two core components (Type C), most often ε and ␣ (Type C#var01) or AlpA and ␣ (Type C#var02). The vast majority of the putative P4-like phage satellites (93%) were detected in Enterobacteriaceae, a family that comprises ca 20% of all the bacterial genomes in the dataset.
The gene repertoires of cfPICI are more similar than those of PICI, with a median wGRR of 0. 2 and almost 10% of pairs with a wGRR higher than 0. 8. Clustering the cfPICI by their wGRR reveals several subfamilies, usually associated with either monoderms (e. g. Enterococcus and Lactobacillus) or Proteobacteria (mostly Escherichia, Klebsiella, Salmonella and Citrobacter). This fits the previously obtained phylogeny of these elements  (8). Within these subfamilies, cfPICI tend to be more similar between closely related hosts. The cfPICIs added by the post-processing script (e. g. with neighboring prophage genes) integrate the existing clusters, suggesting that they are valid elements (Figure  S8 ). Although there is a strong association between cfPICI subfamilies and particular bacterial hosts, ca.
Seven other highly frequent markers (M1 to M7) were uncharacterized, and we were unable to annotate them using PFAM. Given the substantial variation in terms of presence/absence of these markers in the known PLE genomes (see Table  S1 ), we used all the 15 markers to study the natural variation of this satellite family. PLE are specific of a few Vibrio and are rare in our original dataset. To increase the sample size, we retrieved from Genbank all complete and draft genomes of Vibrionacea (11 627 genomes, see Methods). We detected 410 elements of Types A to I, i. e. with between all (Type A) and 7 (Type I) PLE markers (Figure  5A ). Most genomes have a single PLE. Some of these types correspond to known variants of previously identified PLEs. Elements of Type A (n = 238) correspond to PLE1 and elements of Type B (n = 38), for which we only find a single variant, correspond to PLE5.
PLE2 and PLE4 correspond to two different variants of Type D and PLE 3 corresponds to a variant of Type F (Supplementary Table  S1 ). The more incomplete elements (Types G, H and I) tend to either lack or have highly distinct first or final half of the PLE markers we assembled, and it is likely that they result from assembling artifacts inherent to draft genomes. All the putative PLE-like satellites detected in the Vibrionaceae dataset were found in V. cholerae (in 12% out of 3446 total genomes for this species). The core components present across the PLE variants are very diverse (Figure  5B ), but the order of the components is extremely well conserved (Figure  5C ). We extracted the region between the integrase and the furthest component (typically M4) in Type A elements. These regions have a median size of 17.
These results show that the different families of satellites are clearly distinct, but they also suggest gene flow of core genes between satellite families. A concern about the analysis is that it could mistakenly annotate phages as satellites, because they have homologous components. To test if this is frequently the case, we retrieved complete genomes of 3725 RefSeq phages and 15 558 complete or draft phage nucleotide sequences obtained from the NCBI database (see Materials and Methods). Importantly, these sequences were not used when modelling the satellites. We used this larger phage dataset as an input to MacSyFinder and found only 31 identified as satellites (14 as PICI and 17 as cfPICI). We then analyzed the presence of satellite-like markers in phages, in the same way that we did above for the satellites.
We thus assessed the evolutionary relationships in this pair in particular, as the two families of satellites share several core components. We tested if SatelliteFinder is able to accurately distinguish them. First, no elements were simultaneously identified by the cfPICI and PICI models. Second, no PICI satellites were identified when searching for them directly in the dataset of cfPICI genomes. Finally, no cfPICI was identified when searching for them directly in the PICI genomes. This suggests we can discriminate them accurately. To confirm this, we quantified the genome-wide similarities between PICI and cfPICI, by computing the wGRR between them. The clustering of the wGRR values revealed little to no mixing between the major clusters of these two types of satellites (Supplementary Figure  S9 ).
Phage-satellites of different types co-occur in bacterial genomes Finally, since the families of satellites can be separated accurately, we assessed if multiple phage satellites, each belonging to a different family, could co-integrate the same bacterial genome, or if they mostly exclude each other. PLE never co-occur with other satellites, which is justified by the narrow bacterial host range of its helper phage. We analysed the associations of the other three families of satellites in E. coli genomes using Coinfinder (34) (see Methods). This software tool examines the presence/absence of pairs of genes across a phylogeny to determine if their relationship is coincidental, meaning that these genes are observed together or apart more often that would be expected by chance. We find that satellites of the P4-like, PICI and cfPICI families co-occur less than expected within E.
For instance, in our previous analysis, we inferred that a large number of Type B P4-like satellites had a pseudogene of alpA. However, this variant is very rare in the current analysis, because the most recent RefSeq database annotates alpA correctly. Finally, the PLE analysis was done with draft genomes where the phage satellites might be split across different contigs, erroneously suggesting that some elements are either missing or have alternative core components. This seems to be the case for the less complete PLE variants (Types H and I) that had homology to either the first or the last 'half' of the PLE model. This is not an issue when using complete genomes, as was the case for the analysis of P4-like, PICI and cfPICI. Both the commonalities and uniqueness of the different families of phage satellites provide insights on their function and evolution.
ACKNOWLEDGEMENTS We thank  Kim Seed  for comments and suggestions on earlier versions of the manuscript,  Graham Hatfull  for helpful discussion regarding the PhiRv1 and PhiRv2 elements in M. tuberculosis, and  Bertrand Néron  and  Fabien Mareuil  from the  Institut Pasteur's Bioinformatics and Biostatistics Hub  for help in the development of the Docker and Galaxy version of SatelliteFinder.
DATA AVAILABILITY The bacterial and phage genomes, as well as most profiles used to detect the core components of phage satellites, are publicly available. For the core components with-out public HMM profiles, we include the custom profiles as Files S5-S22. The models used for MacSyFinder are also available MacSyModels in the public repository. The additional custom Python scripts to post-process the output of MacSyFinder are included in the Docker image at (https://hub. docker. com/r/gempasteur/satellite finder).
Acknowledgements We thank  A. N. Andreyev ,  A. E. Barzakh , and  I. D. Moore  for useful discussions. This work was partially supported by the  STFC  Grant Nos. ST/P003885/1  and  ST/V001035/1 , by the  Polish National Science Centre  under Contract No. 2018/31/B/ST2/02220 , by a  Leverhulme Trust   Research Project Grant , and by the  Academy of Finland  under the  Academy  Project No. 339243. We acknowledge the  CSC-IT Center for Science Ltd. , Finland , for the allocation of computational resources. This project was partly undertaken on the Viking Cluster, which is a high performance compute facility provided by the  University of York. We are grateful for computational support.
The Caribbean basin is a geographical area with a high prevalence of asthma due to mineral dust. As such, it is crucial to analyze the dynamic behavior of particulate pollutants in this region. The aim of this study was to investigate the relationships between particulate matter with aerodynamic diameters less than or equal to 2. 5 and 10 µm (PM2. 5 and PM10) using Hilbert-Huang transform (HHT)-based approaches, including the time-dependent intrinsic correlation (TDIC) and time-dependent intrinsic cross-correlation (TDICC) frames. The study utilized datasets from Puerto Rico from between 2007 and 2010 to demonstrate the relationships between two primary particulate matter concentration datasets of air pollution across multiple time scales. The method first decomposes both time series using improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to obtain the periodic scales.
Li et al. [17]  used a seasonal-trend decomposition procedure based on LOESS (STL) coupled with a wavelet analysis to conduct a spatial-temporal analysis of the Air Pollution Index (API) and its relationship with meteorological factors in Guangzhou, China, from 2001 to 2011. They found a negative relationship between API, temperature, relative humidity, precipitation, and wind speed, and a positive relationship between API, the diurnal temperature range, and atmospheric pressure in the annual cycle. Fu et al. [18]  analyzed the association between PM2. 5 and other pollutants, such as O 3 , CO, SO 2 , NO 2 , in China, in addition to different meteorological parameters, considering the 2014-2018 datasets of daily and monthly scales, using the ensemble empirical mode decomposition (EEMD). In general, most of the past studies independently examined the association between PM2.
Experimental Data Puerto Rico (PR) is located at a latitude of 18. 23 degrees north of the equator and longitude 66. 50 degrees west of the prime meridian. This United States territory has a population of 3. 194 million inhabitants and is situated at the top of the Lesser Antilles in the Caribbean area, covering an area of 9104 km 2 (see Figure  1 ). According to the Köppen-Geiger classification  [57] , this island experiences a tropical rainforest climate ("Af"). PM2. 5 and PM10 data were collected at Cataño (18. 431 • N 66. 142 • W, sub-urban area) by the United States Air Quality Network and released by Air Now agencies. Both measurements were made using the Thermo Scientific tapered element oscillating microbalance (TEOM) (models 1400 ab and 1400-FDMS). The data were continuously sampled and stored as 15 min averages, which were used to compute the daily average concentrations analyzed in this study.
Concurrently, the potential benefits of agricultural digitalisation to support sustainable agriculture transformations are gaining attention among scientists, policy-makers, and other relevant actors. Indeed, technological innovations, such as the use of in situ and remote sensing technologies for precision agriculture applications, may contribute to significant resource use efficiency improvements (e. g. , for fertilizer, pesticide, or water application) and reduced greenhouse gas (GHG) emissions  [8, 9]. Nevertheless, at present, an opportunity gap remains in regards to (i) the systemsoriented integration of large available datasets documenting interconnected fieldand landscape-scale processes and (ii) integrative analysis and translation of such data into actionable crop management options  [8, 10].