dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, the set of three data streams can be the daily crime incident numbers for two cities indexed by l and m, and n is the daily regional temperature. Here we use past data from these three data streams to estimate , the crime incident number on day t = 0 for city l. The functional relationship between data streams can be represented using a directed graph, with data streams indicated by nodes, and directed edges connecting nodes that represent independent data streams to nodes representing dependent data stream. As both AR and VAR models require the self-dependent functional relationship for any t, the edge pointing from y l back to y l is assumed and not indicated in the presented figures. The set of all concurrent resilient models composes the network graph, G = {f i=1 , f i=2 ,…,f i=K } with i indexing the set of all K functions.
The system begins with preprocessing the data streams, described in section 3a, followed by relationship analysis for sets of streams. Relationship analysis is performed to reduce the search space for optimal resilience models, as described in section 3b. Potential resilience models are then evaluated, the optimal models are selected, and the network is identified. These steps are iterated at user-determined intervals to maintain an updated, optimal resilience network. Qualitative analysis is also used to determine correlations between the network and any pertinent data relating the data streams. Through this qualitative analysis, additional information sources can be used to reduce the search space of potential resilience models and subsequently reduce computation time and cost. 1) DATA PREPROCESSING-Data preprocessing can involve many steps including data wrangling, and feature vector normalization.
Using this method can greatly reduce the hypothesis search space for resilience models. b: QUALITATIVE ANALYSIS: Qualitative analysis can be used to determine if underlying latent parameters dictate the relationship between data streams. If such parameters are found, they can be used to reduce the hypothesis space of possible resilient models, thus reducing computation cost and required data sharing network traffic. For this work, the multi-dimensional data scaling (MDS) method was used to visualize the relationship between potential descriptive variables and resilience model performance. MDS operates by mapping points from a high dimensional Euclidian space to a lower dimensional space while attempting to preserve dissimilarity relationships between the points. For the case study, geospatial, topological and demographic parameters are investigated for their utility in predicting resilience model accuracy.
IV. CASE STUDY: CROSS COUNTY CRIME A. DATA COLLECTION AND PREPROCESSING For this work, a diverse set of data was collected including crime statistics, weather data, demographics data and geospatial data. Weather data is included as previous research has found that temperature influences crime rates  [18]. Scalar geospatial and demographic data are included to explore the relationship between these city parameters and the resilience network. The data characteristics are summarized as follows: a. Crime dataset 2 : 116375 records were collected for crime events reported throughout Montgomery County, Maryland (MD) for the 1/1/2014 to 5/26/2016 period. Each record has twenty-four attributes including date and time (start, end, police dispatch) for the incident, location of the incident (longitude, latitude, zip code, city, state, address, description of urban or residential environment city), police district name and number, agency, uniform crime reporting number and description.
b. Weather dataset 3  : Daily weather data was collected over the same period as the crime dataset, for the cities in Montgomery County, MD. Each record has the attributes: temperature, humidity, sea level pressure, visibility miles, wind speed and direction, dew point, precipitation, cloud coverage. Each attribute is described with min, mean, max features. For this work, only the daily mean temperature is used. Also, Montgomery County is covered by three weather centers (College Park Airport, (MD), Ronald Reagan Washington National Airport, (VA) and Montgomery County Airpark, (MD)). For this analysis, data from the Montgomery County Airpark was selected.
d. Geospatial topological distance: The distances between cities were collected by using Google Maps 5  service, where the distance is calculated as a driving distance from the center of the town to the center of the city. For this case study, it is assumed that the reporting city collects the event data streams, and each Montgomery County city is identified as a separate data stream and a single node in the resilience network graph. The crime dataset was programmatically preprocessed by first removing crime incidents that occurred outside Montgomery County or crime incidents with unresolvable errors in the location field. Next, the crime incident context features were extracted, and the incidents were sorted by offense type using the MD offense policy categorization, which includes among sixty different categories including assault, abuse, burglary, offense, robbery, theft.
Records pertaining to non-crime events, such as natural death, were removed. For each city and date, the number of crime events was then tabulated. A plot of the number of crime events occurring in each city during the last month of data for the available dates of May 1st and May 26th (showing the last 26 out of 877 days) is shown in Figure  3. From this figure, it is evident that Silver Spring dominates of crime events while Chevy Chase, Potomac, and Montgomery Village typically fall near the bottom. with an average of only a few events per day. This trend is shared throughout the period investigated. The eight cities with the highest number of crime events were chosen for analysis, they are (in descending order) Silver Spring, Bethesda, Gaithersburg, Rockville, Germantown, Montgomery Village, Potomac, and Chevy Chase.
The rest of the cities were not considered for analysis as they exhibited a deficient number of events per day, e. g. , between zero and two daily events. The eight city-based daily crime rate data streams and the one daily weather data stream were normalized by subtracting the mean of each data stream and dividing each data stream by its the standard deviation. All work described in this paper was performed in the R statistical computing language. 6  The vars 7 package was used for implementing AIC, Granger test, AR models, and VAR models. Graphics were generated using the ggplot 8 package.
B. RELATIONSHIP ANALYSIS: IDENTIFYING POTENTIAL NETWORK CONNECTIONS 1) BIVARIATE AND TRIVARIATE GRANGER CAUSALITY TEST-After data normalization, each crime rate data stream, as well as the weather data stream, were programmatically confirmed to be stationary. The Granger test was then applied to each pair of data streams to quantify the bi-directionally predictive causal relationships, as described above, with the lag parameter automatically selected by the AIC method. Similarly, the multivariate Granger test was performed for each triple of data sources. For both types of models, the weather was removed from the set of target variables. The results of the Granger test analysis for two-city models are shown in Table  1. A Granger test p-value below or equal to the significance level of 0. 05 is used to identify if the forecast data stream is a good predictor for the target data stream.
C. RESILIENCE MODEL EVALUATION All possible resilience models were investigated for their prediction performance. The hypothesis space includes all possibilities of the three model types: 1) AR models, 2) twocity VAR models, and 3) three-city VAR models. In our case, we have 9 data streams, and after the data selection functionality, we have the hypothesis space of 9 AR models, 72 VAR 9 "Everything is related to everything else, but near things are more related than distant things", First Law of Geography. Tobler W (1970) Economic Geography, 46(2): 234-240. models with one supplemental data stream, and 252 VAR models with two supplemental data streams. Table  2a  shows the MSE prediction errors computed for the first two model types, over the full-time range, with forecast data streams listed as columns and target data streams listed as rows.
D. RESILIENCE NETWORK DYNAMICS Resilience network dynamics allow the model to self-adapt to changes in the data streams, so that it always provides optimal performance. Iterating network determination achieves dynamics at user-determined intervals or from a user-provided trigger signal. Figure  6  shows a dynamic implementation for Silver Spring with only models of type one and two investigated. For this implementation, at each date, the network is provided data from the previous four weeks, ensuring that trends learned by the models are local in time. The model which provides the best performance is chosen dynamically for network implementation. Here it can be seen that for the first four weeks, use of weather data provides the best prediction performance. The network graph for Silver Spring is diagramed above these dates, with a directed edge from weather to Silver Spring.
On day 28, the optimal resilience model changes, with the weather being replaced with the Rockville data stream. On day 83, the network updates again to depend on the weather data stream and day 106 to the Germantown data stream. As discussed above, in implementing this system delay between model analysis and model selection may be necessary to improve system stability. Selecting the best model with a user-specified periodicity will reduce the likelihood of rapidly alternating between models due to small variations in data. For example, the Montgomery County city-to-city network may be re-evaluated on a weekly basis.
V. DISCUSSION AND CONCLUSION This paper presents a dynamic network model for improving smart city resilience to data loss. The system utilizes the Granger causality test to identify statistically significant shared temporal trends across multivariate data streams and utilizes VAR to capitalize on those trends to ensure improved data prediction in the case of data loss. Each data stream is provided a ranking of potential resilience models with the top performing model selected for implementation in the network. If the top model can no longer be executed for a particular data stream, the next best model is selected. Iterative evaluation of the system provides a dynamic, self-adaptability to changes in data quality, loss of data streams, and the addition of new information flows. The network model is demonstrated on City-based daily crime rates reported in eight cities across Montgomery County, MD as well as a daily weather data stream.
The lcpB gene was successfully deleted from NZ9000, in contrast to lcpA, suggesting an essential role for LcpA in the growth and/or survival of L. lactis NZ9000. A conditional mutant of NZ9000 was obtained by cloning lcpA under the control of the nisin-inducible promoter P nisA. Also, a conditional mutant was obtained in the NZ9000ΔlcpB background. Intact cells of the lcp mutants were examined by HR-MAS NMR. Identical spectra were obtained for the ΔlcpB mutant and wild-type NZ9000, indicating that the ΔlcpB mutant displays the PSP pellicle on its surface like NZ9000 (Fig. 5B  and D ). When the NZ9000 lcpA conditional mutant grown in the absence of nisin was examined, the NMR spectrum obtained contained PSP signals but also signals corresponding to rhamnan (Fig. 5E ). An identical spectrum was obtained for the lcpA lcpB double mutant (data not shown).
NMR spectroscopy analysis. NMR experiments were carried out with a Varian INOVA 500-MHz ( 1 H) spectrometer with a 3-mm gradient probe at 25°C with an acetone internal reference (2. 225 ppm for 1 H and 31. 45 ppm for 13 C) by using standard pulse sequences for double-quantum COSY, total correlated spectroscopy (mixing time, 120 ms), rotating-frame nuclear Overhauser effect spectroscopy (mixing time, 500 ms), HSQC, and HMBC (100-ms long-range transfer delay). The acquisition time was kept at 0. 8 to 1 s for H-H correlations and 0. 25 s for HSQC, and 256 increments were acquired for t1. Assignment of spectra was performed with the Topspin 2 (Bruker Biospin) program for spectrum visualization and overlap. HR-MAS NMR experiments were performed with an 18. 8-T Bruker Avance III spectrometer. Experimental data were acquired with a 1 H/ 13 C/ 31 P/ 2 H probe with uniaxial gradients.
ACKNOWLEDGMENTS We thank  Christian Cambillau  for his support and helpful discussions. This work was supported by the  Institut National de la Recherche Agronomique (INRA)  and the  Région Île-de-France  (P. C. , M. M. , E. G. , S. P. , S. F. , S. K. , and M. -P. C. -C. ) and the  French ANR  project " Lactophages"  ( ANR-11-BSV8-004-01 ) (I. S. , P. C. , S. K. , Y. G. , and M. -P. C. -C. ). J. A. was the recipient of a fellowship from the  Région Île-de-France. J. M. is supported by a  technology innovation development award  (reference no. 14/TIDA/ 2287 ) and a starting investigator research grant (reference no. 15/SIRG/3430 ), both funded by  Science Foundation Ireland (SFI). D. V. S. is supported by a principal investigator award (reference no. 13/IA/1953 ) through  SFI. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
The obtained tool, called IpiRId, shows prediction results that attain more than 90% of accuracy on different tested species (human, mouse and fly), outperforming all existing tools. Besides, our method makes it possible to study the validity of each given feature in a given species. Finally, the developed tool is modular and easily extensible, and can be adapted for predicting other types of ncRNAs. The IpiRId software and the user-friendly web-based server of our tool are now freely.
Feature Species References First Uridine Fly, Mouse, Human, Rat, Nematode (C. elegans), Zebrafish and Silkworm (Bombyx mori)  [17] [18] [19]  Tenth Adenine Human, Fly, Mouse, Zebrafish and Silkworm (Bombyx mori)  [17, 18]  Occurrence in clusters Mammals and Insects  [18]  Binding with transposons Mammals and Insects  [19]  CpG islands Mammals  [22, 27]  G-Quadruplex Human, Mouse, Rat and Macaque  [26]  Transposable elements presence Mouse and Marmoset  [24, 25]  Promoter A-Myb Mouse  [23]  Inverted repeats Mouse  [24]  Distance to centromeres/ telomeres Fly  [7]  Histone methylation Fly  [21]  https://doi. org/10. 1371/journal. pone. 0179787. t001 the optimal combination of kernels. In this work, we choose a Gaussian kernel which is a universal kernel for feature representation. It consists of a square similarity matrix of size N Ã N, N being the size of the training dataset (positive and negative samples).
We start from the 5' of the sequence and search upstream for all possible motifs and stop at the first one found. We maintain three types of information on the discovered motif: the motif length (L), the distance (D) between the motif and the sequence, and a probability calculated as 4 L /D, which allows selecting the position with the closest motif to the sequence as well as the longest one. Transposons. To study the presence of TEs around piRNAs, we used the RepeatMasker software (http://www. repeatmasker. org). We expect to find TEs around and in piRNA clusters. For that purpose, we look until D kb upstream and D kb downstream of the given sequence. Based on the ReapetMasker outputs, we calculate two kinds of information: the cumulated identity and the cumulated length for each TE. The identity is calculated as: Identity ¼ 1 À RM À RD À RS ð5Þ where RM, RD and RS are, respectively, the ratio of mismatch, deletion and substitution.
Dataset construction In order to build high-quality training and test datasets, we create three datasets with both positive and negative piRNA sequences, each of which refers to one of three species considered in this study: human (Homo sapiens), mouse (Mus musculus) and fly (Drosophila melanogaster). Positive non-redundant piRNA sequences were collected from both piRNAbank  [38]  (http:// pirnabank. ibab. ac. in/) and piRBase  [39]  (www. regulatoryrna. org/database/piRNA/) databases, from where we downloaded 32 208, 39 986 and 18 508 human, mouse and fly piRNA sequences, respectively. For negative sequences, we considered: • 449, 244 and 93 human, mouse and fly tRNA sequences, respectively, downloaded from the genomic tRNA database (http://lowelab. ucsc. edu/GtRNAdb/). • 1 747, 712 and 288 human, mouse and fly mature miRNA high-confidence sequences  [40] , respectively, downloaded from miRBase (http://www.
In order to build the TE binding kernel, transposons were gathered from "rmsk" table of the UCSC Genome Browser  [41] , excluding those with rich annotation, repeated nucleotides and redundant transposons. For computational reasons, we considered only transposons with length between 35 and 100 nt, and finally selected randomly 1 000 from the whole set. This allows us to look at the same number of transposons of similar length for each species. This length is a parameter that could be fixed by the user. Finally, epigenetics ChIP-Seq data represented by the positions of histones H3K9me3 and H3K27me3 were taken from the NCBI epigenomic repository. The considered tissues/cells in our study are: T cells for human (Downloaded from: http://dir. nhlbi. nih. gov/papers/lmi/ epigenomes/hgtcell. aspx), embryonic stem cells for mouse (Downloaded from: http://www.
Then, it uses RNAplex to fold each sequence with transposons with a maximum of three mismatches, in order to perform prediction using SVM classifier. In our team, we recently proposed a tool for piRNA prediction called piRPred based on the MKL approach  [16] , composed of three kernels implementing respectively the following features: (i) presence of uridine ('U') at the first position of the sequence and k-mer motif frequency (k-mers considered by  [10]  as the most discriminant ones), (ii) occurrence into clusters, and (iii) distance to centromeric and telomeric regions. To undertake a comparison between our tool IpiRId and the other existing tools, we have retrained these tools on our datasets using 5-cross validation technique. We should note that we had many problems to retrain piRNApredictor, Piano and Pibomd tools, since they are functional only in prediction mode, and information to retrain them are not mentioned in their manuals or publications.
As described above, three species were considered in our study: human, mouse and fly. We built a dataset containing 5000 piRNA sequences and 5000 pseudo piRNA sequences for both human and mouse species and 1100 piRNA sequences and 1100 pseudo piRNA sequences for fly. These sequences were obtained by random selection from the original downloaded set of data for each species. Table  4  reports the 5-fold cross validation results of our tool IpiRId and the other existing tools (piRNApredictor, Piano, Pibomd and piRPred) on the three species. The results are given according to five measures, usually used in supervised classification tasks: Sensitivity (Se), Specificity (Sp), Precision (Pre), Accuracy (Acc) and F1 score (F1). They are described below using the following abbreviations: TP: True Positive, FP: False Positive, TN: True Negative and FN: False Negative.
9 Nevertheless, the application of genome analysis to vaccine development, emerged a concept termed as Reverse Vaccinology, which reduces the time and cost spent on the search of new vaccine targets. 10, 11 eptide-based subunit vaccines contain a specific and welldefined fragment of a pathogen and are able to induce very specific cell-mediated and humoral immunity and to diminish the stimulation of an autoimmune response. 12 The development of a successful subunit vaccine requires the identification of antigenic and/or immunogenic regions in protein antigens. Consequently, the first step in designing synthetic peptides for vaccine application is the immunoinformatics analysis of the candidate protein. 9 Up to this direction, in silico tools and databases have been developed and are used in order to identify and characterize promising epitopes, the minimal essential protein units that are recognized by T-and B-lymphocytes and consequently stimulate cellular and humoral immune responses.
Protein selection The antigens selected for this study included LeIF proteins of the strains L. infantum, L. major and L. braziliensis. The full amino acid sequences of the proteins were extracted from Uniprot/Swiss-Prot protein database (http://www. ebi. ac. uk/uniprot). The accession numbers for each protein sequence are presented in Table  1. The SignaIP (http://www. cbs. dtu. dk/services/SignaIP) analysis confirmed that LeIF proteins are not secretory and their full amino acid sequences are presented in Fig. 1.
Protein structure and localization of epitopes The model of LieIF crystal structure was based on template structure of human eukaryotic initiation factor 4A-I (EIF4A-I, PDB: 2zu6) that shares 60. 65% homology with LieIF. The model was built using Promod-II version 3. 70. The localizations of peptides are depicted using surface-electrostatic potential meshes. The blast analysis of human PDCD4 protein with Leishmania proteins was performed using the following online available tools: www. genedb. org/ and http://tritrypdb. org/tritrypdb/.
The reverse transcription step was performed at 40 °C for 60 min and 90 °C for 5 min. Semi-quantitative PCR was performed with Taq polymerase (Invitrogen, Life Technologies, USA) and the specific primer pairs for TNF-a and bactin were derived from a published work. 40 Analysis of PCR products obtained from three independent experiments was carried out by electrophoresis in a 2% agarose gel stained with ethidium bromide and visualized by UV-illumination. The levels of PCR products were determined by scanning densinometry using the Image Quant IQ 5. 2 software and data are presented upon normalization to the products of b-actin.
LieIF structure and localization of synthetic peptides The model of LieIF structure was based on template structure of human eukaryotic initiation factor 4A-I (EIF4A-I, PDB: 2zu6). The localizations of the LieIF_1, LieIF_2, LieIF_3, LieIF_4 and LieIF_5 peptides are represented as surface meshes with different colors (LieIF_1 in cyan, LieIF_2 in yellow, LieIF_3 in purple, LieIF_4 in orange and LieIF_5 in blue) (Fig. 2 ). The LieIF_1 and LieIF_2 peptides are overlapped and their common region is represented in light green. The LieIF_2 peptide is located away from EIF4A-I's interaction sites with either Eukaryotic Initiation Factor 4G (EIF4G) or Programmed Cell Death Protein 4 (PDCD4). On the contrary, the LieIF_5 peptide is found to be localized in the interaction site with the Programmed Cell Death Protein 4 (PDCD4).
021) and IFN-c (40% vs 20%, p = 0. 021), compared to the negative control group (Fig. 5 ). However, the LieIF_2 peptide resulted in enhanced production of IL-4 and IL-10 (64% vs 48% and 43% vs 27%, p < 0. 05) (Fig. 5 ). Interestingly, the LieIF_2 peptide caused the same pattern of expression of IL-12 and IL-10 cytokines in BM-DCs derived from resistant C57BL/6 mice (data not shown). Moreover, the LieIF_2 peptide which was revealed as the most promising peptide among the five synthetic peptides, also induced significant up-regulation of TNF-a mRNA transcripts in BM-DCs, compared to the negative control group, as was revealed by semi-quantitative PCR analysis (Fig. 6 ). The cut-off score was adjusted to !20 for SYFPEITHI and <500 nM for NetMHC.