dataesr/scientific-paragraphs-categorization huggingface dataset:
Effector cloning Bacterial strains from the ATCC collection were ordered from LGS Standard Standard (Wesel, Germany) or ATCC in the US (Manassas, Virginia). Bacterial strains from the DSMZ collection were obtained from the Leibniz-Institut DSMZ (Braunschweig, Germany) and strains from the BEI collection were ordered at BEI resources (Manassas, Virginia, USA) (Extended Data Table  2 ). Effectors identified from MAGs and effectors for the PRS were ordered at Twist Bioscience (San Francisco, CA, 660 USA). If no genomic DNA could be obtained strains were cultured according to the manufacturer's protocol and DNA was extracted using the NucleosSpin (MATα mating type) as DB-X ORFs as described  84.
Assembling reference sets To identify additional reliably documented interactions between bacterial effectors and human proteins for the positive control set (bhLit_BM-v1), we queried the IMEx consortium protein interaction databases  87  through the PSICQUIC webservice  88  (May 10 th , 2021) using the T3 effectors UniprotKB accession numbers and fetched all the PubMed identifiers of the articles describing additional interactions. In total, we gathered 67 interactions between 29 T3 effectors and 64 human proteins, described in 13 distinct publications that underwent the manual curation step for inclusion in the PRS (Extended Data Table  3 ).
Y2H assay sensitivity Effector ORFs from bhLit_BM-v1 and bhRRS-v1 (Extended Data Table  3 ) were transferred into pDEST-DB (DB-X) and transformed into Saccharomyces cerevisiae Y8930 (MATα). Yeast strains containing the corresponding AD-Y human ORF were picked from hORFeome9. 1  86  and ORF identity verified by end-read Sanger sequencing of PCR products. Yeast strains harboring plasmids containing ORFs from hsPRS-v2/hsRRS-v2  89  were provided by the Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA. DB-X and AD-Y were mated fourfold with each other, as well as against yeast strains containing the corresponding DB-empty or AD-empty plasmid. Growth scoring was performed as described above for the fourfold verification. Pairs scoring positive at least three out of the four repeats qualified as bona fide Y2H interactors.
Interactome validation by yN2H 200 interactions were randomly picked from HuMMI and all ORFs from the indicated datasets (Extended Data Table  3 ) were transferred by Gateway LR reactions into pDEST-N2H-N1 and pDEST-N2H-N2 plasmids containing a LEU2 or TRP1 auxotrophy marker, respectively  89. Extended Data Table  3  reports the results at log2 NLR = 0. Reported P values were calculated by Fisher's exact test.
Interface predictions We used as input a representative set of effectors identified in isolated strains (2300 sequences clustered at 90% sequence identity) and all effectors identified in MAGs (186). We ran mimicINT as described in  32  and available at [https://github. com/TAGC-NetworkBiology/mimicINT]. Briefly, mimicINT performs domain searches in effector sequences with InterProScan 90 using the domain signatures from the InterPro database  91  retaining matches with an E-value below 10 -5. For host-like motif detection, mimicINT uses the SLiMProb tool from the SLiMSuite software package  92  by exploiting the motif definitions available in the ELM database  93. Motifs are detected in disordered regions as defined by the IUPred 20 algorithm  94  using both short and long models (motif disorder propensity = 0.
2, minimum size of the disordered region = 5). The interface inference step relies on the 3did database  95  (ii) the ELM database  93. The workflow checks whether any of the effector proteins contains at least one domain or motif for which an interaction template is available. In this case, it infers the interaction between the given protein and all the host proteins containing the cognate domain (i. e. , the interacting domain in the template). To control for false positive inference using motifdomain templates, mimicINT provides two scoring strategies. First, considering binding specificity of domains belonging to the same group (as PDZ or SH3)  96  an HMM-based domain score  97  is computed used to rank or filter the inferred interactions. Second, given the degenerate nature of motifs  98  , mimicINT, using Monte-Carlo simulations, assesses the probability of a given SLiM to occur by chance in query sequences and, thus, can be used to filter false positives  99.
To avoid artifacts due to differential sampling we only considered interactions in the HuMMIMAIN, excluding those human proteins targeted by effectors of the unknown strains and targets outside HuRI. The rationale for the latter is that a substantial proportion of proteins that are not in HuRI may not be suitable for Y2H analysis. Thus, restricting the analysis to the HuRI subset increases the stringency. To estimate the significance of the convergence of effectors from different strains (interspecies convergence), we used a conditional permutation test that preserves the strain contribution. For each iteration, we generated 18 samples, where for each sample, we randomly picked the number of proteins equivalent to the observed targets of each strain (Extended Data Table  3 ). From the full list of random picks that are assigned to all strains, the frequency of selecting a protein was recorded.
Function enrichment analysis We used the "gost()" function from the gprofiler2 version 0. 2. 1 R package 101 to identify enriched functions in effector targets. This function implements a hypergeometric test to estimate the significance of the abundance of genes considering the frequency of the genes in the function annotation databases. The main input argument for this function is the gene list ("query"). The function allows the user to optionally set input arguments, including the background ("custom_bg"), evidence codes ("evcodes"), annotation databases ("sources"), methods for correcting the hypergeometric test P values ("correction_method"), and other arguments that were set to their default options. We used the target official symbol identifiers as the "query" argument. The list of HuRI proteins was the "custom_bg" argument.
The annotations inferred from electronic annotations were excluded by setting the "exclude_iea" argument to "TRUE". The hypergeometric test P values were corrected using Benjamin-Hochberg method by setting the "correction_method" argument to "fdr". The argument ("sources") was set to a vector ("GO:BP", "KEGG","REAC"), which encodes the search space across three function annotation databases: gene ontology biological process terms ("GO:BP") 102 , Kyoto encyclopedia of genes and genomes ("KEGG") pathways 103 , and Reactome pathway database ("REAC")  104. After plugging in these inputs into the "gost()" function, the output is a named list where "result" is a data frame that tabulates the enrichment analysis results. We calculated the odds ratio and the fold enrichment to estimate the effect size of each tested function.
NF-κB activation assay HEK 293 (RRID: CVCL_0045, DSMZ) were maintained in DMEM with 10% FBS and 100 U/mL penicillin and 100 U/mL streptomycin at 37°C and 5% CO2. IKKβ (in pRK5 with a Flag-tag) served as positive control whereas A20 (in pEF4 with a Flag-tag) as the negative control. In a 60 mm cell culture dish 1 x 10 GE28-9068-36) were used.
Cell culture supernatant of the above samples was used to analyze IL1beta. To this end, IL1beta ELISAs were performed using the ELISA MAX™ Deluxe Set Human IL1beta kit (437015, Biolegend) following the protocol provided by the manufacturer. Statistical significance was evaluated using Kruskal-Wallis test with uncorrected Dunn's test. Raw values and statistical analysis are in Extended Data Table  6.
Protein ecology Metagenomic assemblies from the Inflammatory Bowel Disease Multi'omics DataBases (IBDMBD)  64  and from the skin metagenome 109 were downloaded, and each samples protein repertoire predicted using Prodigal (options; -p meta)  110. Effector proteins were compared to the metagenomic protein repertoires using DIAMOND (options; >90% query length, >80% identity). For analyses in Fig. Full data for all panels in Extended Data Table  1. * P = 0. 04; *** P = 0. 0006; ns "no significant difference" (Fisher exact test; Extended Data Table  3 ). Error bars present SE of proportion. g, Left: degree distribution for the most connected effectors; right: effector-degree distribution for most targeted human proteins. Colors represent strains according to legend. h, Observed number of total effector targets in the human reference interactome (HuRI), compared to random expectation (exp.
ACKNOWLEDGEMENTS Plasmids and strains for hsPRS/RRS_v2 were kindly provided by  Marc Vidal ,  David E. Hill , and  Mike Calderwood ,  CCSB ,  Dana-Farber Cancer Institute, Boston, MA. The computational results presented have been achieved in part using the  Vienna Scientific Cluster (VSC). Centre de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its high-performance computing resources.
Successful cloning was monitored by PCR-mediated evaluation of insert size, and positive clones transformed into haploid Saccharomyces cerevisiae Y8930 (MATα) and Y8800 (MATa) strains, respectively. Protein pairs from all datasets were randomly distributed across matching 96-well plates. 5 µL of each haploid culture of opposite mating type grown to saturation was mated in 160 µL YEPD medium and incubated overnight. Additionally, each position was mated with yeast stains containing empty N1 or N2 plasmids, to measure background. 10 µL mated culture was inoculated in 160 µL SC-Leu-Trp and grown overnight. 50 µL of this overnight culture was reinoculated in 1. 2 ml SC-Leu-Trp and incubated for 24 h at 1000 rpm. Cells were harvested 15 min at 3000 rpm, the supernatant discarded, and each cell pellet was fully resuspended in 100 µl NanoLuc Assay solution (Promega corp.
Madison, WI, USA, cat# 1120). Homogenized solutions were transferred to white flat-bottom 96-well plates (Greiner Bio-One, Frickenhausen, Germany, cat# 655904) and incubated in the dark for 1 h at room temperature. Luminescence for each sample was measured on a SpectraMax ID3 (Molecular Devices, San Jose, CA, USA) with 2 s integration time. The normalized luminescence ratio (NLR) was calculated by dividing the raw luminescence of each pair (N1-X N2-Y) by the maximum luminescence value of one of the two background measurements. All obtained NLR values were log2 transformed and the positive fraction for each dataset was determined at log2 NLR thresholds between -2 and 2, in 0. 01 increments. Statistical results were robust across a wide range of stringency thresholds.
Client Monitoring for Business Metrics Footfall, also known as client counting or simply traffic, is a measure of the number of visitors entering a commercial site, such as a retail shop, commercial center, museum, and so on. Footfall counts have been used for decades to help retailers and site managers gauge the attractiveness of their offers and improve the overall customer experience. Initially an affair of simple hand clickers, turnstiles, or weight-sensitive mats placed in doorways, footfall has evolved into what is today considered a key business metric that is usually obtained using more sophisticated techniques such as still and video surveillance cameras, Passive InfraRed (PIR) thermal detectors, active infrared Time of Flight (ToF) imagers, WiFi, and others. Data from the sensors is typically transferred via an internet protocol to be stored and analyzed on a server.
A Proposed Toolbox The article presents a set of nine tools designed to facilitate the preprocessing, display, interpretation, localization, and counting of outdoor WiFi network clients based on raw randomized MAC PR counts. The objective of the toolkit is to transform randomized MAC Probe Request counts into a density map calibrated to the number of clients at each position. In many instances, the mathematical machinery deployed in the tools is well established. The contribution of the proposed work lies in assembling the necessary elements into a coherent package to make sense of a complicated problem that many network operators have dismissed as intractable. The toolkit contains the following: -Preprocessing Tool for selecting client populations for study; -"Bowl" Tool for displaying localization probability densities; -Renormalization Tool for condensing distributed probabilities into decision regions; Sensors 2023,  23 , 6142 4 of 31 -Counting Tool for estimating the proportionality between PRs and clients; -Filtering Tool for removing peaks in the density that obscure smaller scale structures; -Localization Tool for device localization and calibration of the propagation model; -Fiducial Tool for delineating client density regions; -Drill-Down Tool for the study of client densities on different scales; -Re-assembly Tool to create the final map calibrated for clients rather than PR.
Though rigorous and often quite effective, a recognized drawback of this approach is that wireless device manufacturers are perpetually on the lookout for such security loopholes, which the next generation of devices is likely to close. A consensus is growing that data privacy is here to stay and that researchers would do better to concentrate on intrinsically non-device-specific privacy-preserving approaches. Indeed, this is the philosophy adopted in the present work as well as in other recent contributions  [1, [6] [7] [8]. A second workaround amounts to monitoring only connected clients or others with fixed MAC addresses. This choice, however, comes at a heavy price. In outdoor WiFi networks, only a small percentage of clients nowadays choose to subscribe to the service, preferring to rely on cellular telephone connectivity to access the internet.
Outdoor WiFi Network Data Sets Studied The datasets studied include detailed, time-stamped PR records from public outdoor WiFi networks at an outdoor tourist site, Site 1, and a campground, Site 2, both in France. They are part of a larger data set, including the French city data introduced in  [1]. Note that for client counting purposes, there are some important differences between the present data and the city data of  [1]  (to be discussed in Sections 3. 2 and 6. 1). As such, the two studies are complementary. The data presented here were accumulated from the last week of March to the end of June in 2021, giving total PR counts of 7. 0 million for Site 1 and 5. 3 million for Site 2. A separate file stores connection detail records for clients who have at some point formally signed into the WiFi service.
A Caveat: OUI and IoT Despite randomization of the MAC, its first 3 bytes, called the Organizationally Unique Identifier, or OUI, are retained and become part of a PR entry in the data record. The OUI is intended to provide information on the manufacturer of the WiFi network card producing the PR, available by interrogating a public OUI web repository. In practice, OUI responses include a baffling array of manufacturers that are sometimes difficult to clearly identify. A detailed investigation  [1]  suggests up to 80% are manufacturers of IoT devices such as lighting, heating, cameras, etc. , whose network behavior is quite distinct from that of true client devices. These observations, aside from the issue of GDPR, provide another strong motivation for basing crowd estimation solutions exclusively on CR-class clients, as these almost certainly correspond to bona fide clients.
Filtering Tool Before advancing to the applications in the next section, it is important to discuss a simple, yet troublesome additional configuration frequently encountered in real outdoor WiFi datasets. In some networks, certain APs may be located inside buildings or in other areas favoring very close proximity to the AP-a few meters, for instance. Because of the enclosed geometry and/or accompanying increased distance from the other APs, triangulation is usually not possible. In these cases, the bowl associated with the AP will be very sharply peaked, because of its small radius. As long as the radius is not too large, the consequences for localization are in fact minimal, since one may simply tabulate the total number of PR received-almost exclusively single AP-and associate them with the narrow bowl surrounding the AP location (see Figure  4 ).
This relative independence of Apollonius client positions on propagation parameters allows us to envisage using them to calibrate A and n, which is a very interesting proposition in the not uncommon real-world situations where propagation ground truth is not available. Figure  6  shows a plot of RSS versus log-distance for the Site 1 dataset, using as distances Apollonius solutions deemed to be of good quality. The resulting distribution is well described by the Friis formula, with A = 36. 2 dBm and n = 2. 24. These values are then used to calibrate the Friis formula for the site in question. sense, which is often rather good. The added redundancy of using all three circles inst of just two can also be helpful in such cases. In practice, nonetheless, depending on site under study, the rate of localizations with poor or no Apollonius solutions can several tens of percent.
The importance of the Apollonius localization solution, apart from being indepe ent of the parameter A, is that the position found also depends only weakly upon fading exponent n for the range 2 < n < 3. A simple calculation shows that for n in range, the radius of an Apollonius circle varies by only about 16% of the distance Dij tween two APs, which in turn is of the same order as the intrinsic resolution obtainabl outdoor WiFi networks. This relative independence of Apollonius client positions propagation parameters allows us to envisage using them to calibrate A and n, which very interesting proposition in the not uncommon real-world situations where propa tion ground truth is not available. Figure  6  shows a plot of RSS versus log-distance for Site 1 dataset, using as distances Apollonius solutions deemed to be of good quality.
Application to Real Datasets In this section, the tools introduced are applied to examples drawn from real WiFi datasets. Additionally, presented are the Fiducial and Drill-Down tools that enable a more detailed and quantitative interpretation of the data reduction process than presented thus far. For each site, a density plot of PR counts over a three-hour period is selected, sufficient to assure a large enough number of accumulated PRs while still allowing time-dependent analysis of client activity. The examples chosen are typical, yet they also illustrate certain particular configurations.
Complete Data Reduction Scenario Applied to Site 1 5. 1. 1. Applying the Filtering Tool The raw PR density over the three-hour afternoon period from 12:00 to 15:00 is shown in Figure  7 , both in 2D and 3D. From the 3D plot, it is clear that sharp peaks caused by clients within a few meters of an AP obscure the finer-scale data. So pronounced is the effect that the 2D plot appears almost empty. To alleviate this problem, the integrated PR count from indoor APs, as well as all PRs with RSS > -60 dBm, is recorded, and then these PRs are removed from the plot (Filtering step). The density after filtering appears in Figure  7b ), where the underlying structure of the density over the full site begins to become apparent. to assure a large enough number of accumulated PRs while still allowing time-dependen analysis of client activity.
Column 4: mean value, of P 3 /P in the CR data of the two sites. Column 5: correction to the value in column 2 using the formula derived in the text from P 3 and P. Column 6: predicted X12 values directly comparable to X values from the city data in  [1]. After the correction, the mean values in column 6 compare well with the city data in  [1]. The large variance of the Site 2 value is discussed in the text. The first column of the table gives the percentage of CC retained after the ES-cut. For Site 1, very few clients had abnormally large PR emission rates, while for Site 2, the percentage was much higher. It is supposed that this is due to the different characters of the two sites. At a historical tourist site such as Site 1, most guests will stay only a short time and spend most of it shuttling from one point of interest to the next, and as such, they will have little incentive to connect to the local WiFi to set up special situations.
As a caveat, the constant value X 12 in the above equations must be adjusted to correspond to the three-hour time window chosen for the client density plots in the present work, i. e. , X 12 = 524 × (8/24) = 65. In creating the strawman, some simplifying approximations have been made. Clearly, a more rigorous treatment is possible-for example, by explicitly including a term for P 2 or P 4 , etc. -but given the statistical uncertainty in X 12 to begin with and the systematic uncertainties in applying it to new data sets, this may not be the major priority at this stage. The strawman provides an informed, first order estimation of the results that may be expected in estimating numerical client densities from raw probe requests in outdoor WiFi networks for which ground truth is not available.
Discussion and Conclusions With the arrival of client privacy concerns as well as the rapid growth of IoT widespread practice of using client MAC addresses contained in PRs for monitorin mapping client activity has become untenable, creating a need today for new tools. a set of nine tools is presented to transform raw randomized MAC PR counts from world outdoor WiFi networks directly into density maps calibrated for clients, all wi the use of any ground truth. The technique has been applied to data from two actua work sites in France, with interesting results. At present, the density predictions of the technique make use of a baseline PR sion probability, X, adopted from a similar study, augmented with a few reasonabl approximate, corrections. A high-priority perspective is thus to discover more preci timation methods and use them to evaluate our technique on current data.
Discussion and Conclusions With the arrival of client privacy concerns as well as the rapid growth of IoT, the widespread practice of using client MAC addresses contained in PRs for monitoring and mapping client activity has become untenable, creating a need today for new tools. Here, a set of nine tools is presented to transform raw randomized MAC PR counts from real-world outdoor WiFi networks directly into density maps calibrated for clients, all without the use of any ground truth. The technique has been applied to data from two actual network sites in France, with interesting results. At present, the density predictions of the technique make use of a baseline PR emission probability, X, adopted from a similar study, augmented with a few reasonable, yet approximate, corrections. A high-priority perspective is thus to discover more precise estimation methods and use them to evaluate our technique on current data.
An important route towards this goal is to encourage interactions with outdoor WiFi service providers and site managers so as to evaluate and calibrate the tools proposed here. Indeed, personnel having day-to-day familiarity with the details of monitored sites are likely to possess precious expert information not easily available to the data scientist, armed with an Excel file and a Google Maps satellite view of the site, charged with extracting the necessary metrics. Several examples are given as follows: • Local network managers will probably retain records of the site IoT devices, such as cameras, lighting, loudspeakers, etc. , in regular use, as well as of user equipment assigned to site personnel and potentially participating in P2P or similar services. Such information will be invaluable for separating the corresponding PR counts of these devices from those derived from bona fide clients.