dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014)  and more sensitive  (Strain et al. , 2013)  than qPCR, but less specific at low HIV-1 template numbers  (Henrich et al. , 2012). In addition, ddPCR was found to be less sensitive than qPCR in quantitation of cytomegalovirus  (Hayden et al. , 2013)  and Chlamydia trachomatis  (Schachter, 2013)  in clinical samples, suggesting that further methodological optimisation was needed for ddPCR to match the sensitivity of qPCR. The manufacturer's software interprets the fluorescent amplitudes of each droplet to generate both a positive and a negative cluster. A proprietary method is then used to define a fluorescence threshold, thus allowing each droplet to be classified as either positive or negative for target DNA. The Quantasoft software (Bio-Rad, CA, USA) provided by the manufacturer also allows a user-defined cutoff to be entered to define the threshold between the positive and negative clusters.
The droplet amplitude files generated by the machine can be exported in a format which allows users to generate their own droplet classification schemes to analyse the output. An openaccess freely available web-based JavaScript program is presented, based on k-nearest neighbour clustering called 'definetherain'. This can be run within most web browsers to call more accurately the positive ddPCR droplets, with particular utility at low template numbers. The performance of 'definetherain' is compared with the rare event detection mode setting of the Quantasoft package, and the software is applied to control standards for analysis of HIV-1 proviral DNA and the human 'housekeeping' albumin gene.
Both qPCR amplifications were performed using the following program: one cycle of 95 • C for 10 min; 45 cycles of 95 • C for 15 s and 60 • C for 1 min. The data were analysed using proprietary Roche LightCycler software. 8E5 cells (NIH AIDS reagent Program, Germantown, USA), that contain one integrated copy of HIV-1 per cell, were used in duplicate as qPCR standards, with cell and HIV-1 copy numbers ranging in serial 10-fold dilutions from 1 × 10 5 to 1 × 10 0 DNA copies per reaction. 8E5 cells were either diluted in water or spiked into a background of 1 × 10 5 PBMC DNA equivalents.
Sealed plates were cycled using a C-1000 thermal cycler (Bio-Rad, Hercules, USA) under the following conditions: 10 min hold at 95 • C, 45 cycles of 95 • C for 15 s then 60 • C for 60 s. After amplification, the plate was transferred to a Bio-Rad droplet reader from which raw fluorescence amplitude data was extracted from the Quantasoft software for downstream analysis.
Development and application of 'definetherain' data analysis software The fluorescence amplitude of each analysed droplet was exported and analysed using 'definetherain', a bespoke software package designed to improve calling of positive droplets when the template is of low copy number. The software is written in Javascript, and is available to use at http://www. definetherain. org. uk. A simple movie is also on-line demonstrating how to use the software. Full source and the data used to develop the program are also available at github: https://github. com/jacobhurst/definetherain. The system uses a control well of known input copy number to define the amplitudes of a valid positive and negative response. The k-nearest neighbour algorithm is used with k set to 2. After defining the positive and negative clusters within this control sample, cutoffs are determined using each cluster's mean and standard deviation.
When analysing subsequent test samples the amplitude of each droplet is examined. If the value is larger than the mean of the negative cluster plus three times the negative standard deviation (SD), and smaller than the mean of the positive cluster minus three times the positive SD, the droplet is defined as 'rain', and is removed from any further calculations. The concentration is then calculated using the following formula: c = -ln N neg /N V droplet Here, N Neg is the number of negative droplets, N is the total number of valid droplets and V droplet is the volume per droplet (0. 91 l). Having defined a positive and a negative response, the software provides the facility of loading and analysing other datasets using the cut-offs defined using the control.
The ddPCR platform was applied to two assays that were optimised for the quantitation of the human albumin gene (Fig. 1a ) and HIV-1 proviral DNA (Fig. 1b ). Initial testing of the qPCR protocols in the ddPCR workflow, indicated that the fluorescent probe concentration would have to be reduced to perform optimally on the ddPCR platform. A probe titration was performed with the HIV-1 assay and applying the QuantaSoft software's (QS) 'auto' threshold showed that at both the 250 and 500 nM probe concentrations, droplets that clustered with the negative population (green dots in Fig. 1c ) were classified falsely as positive (red dots within the blue shaded regions in Fig. 1c ). Probe concentration impacted considerably on both the sensitivity and specificity of the assay, with the concentration used for the optimised qPCR (500 nM) performing poorly in both regards.
To assess the HIV-1 ddPCR's specificity at different probe concentrations, water controls were assayed in duplicate to determine if positive droplets would be detected by ddPCR. In this assay, false positives were virtually eliminated at both the 125 and 250 nM concentrations compared to the 500 nM concentration (Fig. 1d ). The sensitivity of the assay using different probe concentrations was tested by assaying a known target input of 33,000 HIV-1 copies (Fig. 1e ). Probe concentrations of 125, 250 and 500 nM were tested, and measured 25,480, 20,700 and 15,258 HIV-1 copies, respectively, with 125 nM concentration providing the closest result. At probe concentrations below 125 nM, discrimination between positive and negative droplets was impaired (data not shown) and consequently, all subsequent assays used 125 nM of probe per 20 l ddPCR.
The QuantaSoft software (QS) provided with the Bio-Rad ddPCR system, classifies droplets by first determining a fluorescence threshold and subsequently, all droplets with a fluorescence value greater than this threshold are considered positive. To calculate this threshold, every droplet is allocated to either a positive or negative cluster, and a proprietary method is applied to the data to define the fluorescence threshold. For the QS 'Rare Event Detection' setting recommended for low copy number quantitation, a different threshold is set. The result of this method of classifying droplets is a threshold that is set generally much closer to the negative population than the positive population resulting in the majority of rain droplets being classified as positive. For example in Fig. 2 , all droplets above the hashed line in the high (10 5 albumin copies) and low (10 2 albumin copies) template input assays (2a and 2b, respectively) would be classified by QuantaSoft as positive, including the red droplets which might be considered as rain.
Application of 'definetherain' to improve ddPCR droplet calling To address this problem a JavaScript program, 'definetherain', that can run within a web browser was devised. 'definetherain' defines indeterminate rain droplets using the positive population rather than the negative population, and a k-nearest neighbour algorithm. Using droplet amplitude raw data extracted from each well, the software forms two clusters, from which the extreme members are defined. A control is used to define the position of the positive and negative clusters. This control can then be used to define the extreme members of rain from other wells. By excluding the rain from the Poisson calculation more accurate copy number calls can be made in low copy number situations. Fig. 3  shows screen shots from 'definetherain' to illustrate how it is applied.
Using known copy number templates for both albumin and HIV-1 proviral DNA ranging from 10 5 to 10 0 copies, ddPCR was carried out using both 'definetherain' and QuantaSoft software. Fig. 4  shows data for the albumin (panels a and b) and HIV (panels c and d) ddPCRs. For each target, results are presented as reported by QuantaSoft (panels a and c) and 'definetherain' (panels b and d). For 'definetherain', the distribution of the data points and the associated linear regression of those points (with 95% confidence intervals shown as shaded regions) approximates much better to the 'x = y' black line in the figure, which represents unity between    10 2 ) copy number samples assayed in the albumin quantitation assay. Droplets that do not appear to be distinguishable as either positive or negative (rain) are coloured red.
Conclusion The ddPCR is an elegant adaptation of the current qPCR format and has the potential to be applied widely. However, investigators should be aware that reaction conditions are not interchangeable between the two formats and -where the target is present at low copy numbers -the implementation of 'definetherain' might decrease the number of false positive droplets. This software is available for free at http://www. definetherain. org. uk with instructions on how to use it.
g. , using different IPs, hosting providers, ASs), that consistently seed the same set of torrents, and that are thus managed by a single BitTorrent content provider. We then estimate the seeding cost incurred by BitTorrent content providers, and correlate the popularity of seedbox hosting in our dataset to criteria (e. g. , cost, storage space, Web popularity) that can bias their selection (Sec. 5). Finally, we conclude the paper outlining also future directions (Sec. 6).
Correlation in the TCP/IP Space (P) Let us then address the correlation in the TCP/IP space, and more precisely focus on the TCP port number (denoted as "P" in the following). It is typically considered that whenever multiple ports are observed for a single IP address, this is due to peers behind a NAT, so that the IP address observed is that of the NAT device  [4, 5]. Additionally, authors in  [5]  notice that the number of ports grows proportionally to the number of torrents, suggesting that this is indeed due to multiple users downloading torrents with clients configured with different random ports. We argue that while this reasoning is correct in most of the cases, some content providers purposely exploit this belief to disguise themselves. To support this argument, Fig. 1  shows the frequency of port number per peer in the typical week in our dataset.
Classification Results We apply the above heuristic in series to the whole dataset D: we first apply the singleobservation heuristic S to obtain a dataset of matching peers and torrent S; we then apply the time-correlation heuristic T , gathering a T dataset made of a subset of peers from S and torrents not in S. We next obtain P by applying the space-correlation heuristic P to D\(S ∪ T ) and finally obtain C applying C to D\(S ∪ T ∪ P).
Ground Truth To assess the quality of the classification, we perform a reverse DNS lookup of the IP addresses of the peers individuated as content originator. The most reliable way to assess whether a seed (peer) owns torrent data is to download all (some) data chunks and verify their MD5 signatures. Our university policy forbids us to engage in direct exchange of illegal content via BitTorrent, for which reason we cannot join torrents as a means of verification (besides, the use of super-seeding techniques would possibly void the usefulness of checks based on meta-data). Yet, we point out that this step is unnecessary in terms of our verification. Recall that we are more interested in detecting "seedboxes" (as opposite to detecting generic "seeds"): this is because the use of a seedbox is correlated with continuous and sustained seeding, typical of professional activities (as opposite to sporadic seeding activities).
2 , where a single IP address is responsible for seeding 39 torrents (grouped by a token of their name in the picture) using over 4000 ports, in generally restricted ranges (unless for the AXXP token). As it clearly emerges from the picture, the IP appears to be running multiple seedboxes, that are likely managed and configured by different individuals (or organizations). While the low-level details of the virtualization techniques used are not openly advertised by seedbox hosting services (see Sec. 5), available offers however range from managing custom full-blown virtual machines, to simply running pre-configured copies of popular BitTorrent software (e. g. , ruTorrent, rtorrent) possibly employing containerbased emulation techniques. Additionally, we also find that 125 peers are organized in groups of 2.
Characterization of Seedbox Hosting Service We first report a detailed characterization of the seedbox-hosting ecosystem in Tab. 3, combining information coming from three sources. The first portion addresses the popularity of the hosting service in our dataset, reporting the percentage of peers P % and torrents T % employing each service, as well as the average amount of torrents seeded by peers as an indication of their relative level of activity. The second portion pertains to the popularity in the Web, reporting the global and local Alexa rank and the number of links pointing to the Website of the hosting service. The third portion reports the service SLA (i. e. , storage space and bandwidth 3  ) and cost (in USD) for low-vs high-end services, gathered by manually browsing the websites.
Tab. 3 only reports the bulk of services accounting for 97. 5% and 90% of the torrents and peers, and exclude a relatively long tail of unpopular hosting services. Notice that only a limited subset of hosting services that are popular in our trace appears to have been previously listed (e. g. , in http://seedboxgui. de/). From the above dataset, we can extrapolate that the monthly operational expenditure (only considering the hosting service) of the 150 seedboxes observed during our study is about 33,000 USD -a rather tiny amount. While the average cost per seedbox is lower than 100 USD/mo, expenditure may be higher for providers using multiple boxes. Tab. 4 reports a correlation-based analysis of criteria for the choice of seedbox hosting service. We consider what criteria affect this choice by computing the correlation coefficient ρ(X, Y ) between pairs of vectors in Tab.
Conclusion We propose a lightweight detection method of content providers in BitTorrent that exploit correlation in time, TCP/IP space and content. Analysis on a large dataset shows that the heuristic reliably detects seeding professionals. Notably, we uncover emerging trends of (i) groups of seedboxes hosted by multiple providers, and (ii) a systematic use of large ranges of TCP ports, that were both undetected by previous methodologies. Finally, we report a preliminary study of the cost incurred by BitTorrent providers, quantifying their operational expenditure for seedbox services. Despite novel insights, this work also leaves some interesting points unanswered. First, detection algorithm could be improved (e. g. , by additional sources of correlation such as tokens in the torrent name) and fine-tuned (e.
Assignment of Genome Codes We propose to assign codes as follows (see also Figure  1 ): (A) The first organism that is submitted for code assignment will be assigned ''0'' at all positions of its code. (B) The genome of the second organism that is submitted for code assignment is then compared to the genome of the first organism and assigned its code based on its calculated percentage of DNA identity compared to the first organism. (C) The genome of the third organism submitted for code assignment is compared to the genomes of the first two organisms and the organism most similar to the third organism is identified. A code is assigned to the third organism   2 to 5 ). A second genome (G2) is then added to the database and compared to G1. A based on its similarity to the organism identified to have the most similar genome to its own.
Measurement of Genome Similarities for Genome Code Assignment To implement genome codes, a method to accurately measure the difference between two genomes as a similarity percentage is needed. Such methods have already been developed and are being used to calculate average nucleotide identity (ANI) values  [6, 15, 16]  to assign bacteria to named species, thereby replacing experimentally determined DNA-DNA hybridization (DDH) values  [4]. ANI calculation is most often based on BLAST  [17]  and an ANI value of 94% was found to approximately correspond to 70% DDH  [15]. Other algorithms that are faster than BLAST have also been used, but they are not suitable for comparing distantly related genomes (  [16]  and our own experience). Therefore, ANIb (ANI calculated with BLAST) is in our opinion the currently best method to measure the similarity of genomes over a wide range of similarity and was chosen for validating the here described code system.
(B) The genome of a third organism (G3) is compared to G1 and G2. Since G3 is more similar to G1 than G2, G3 is assigned its code based on its ANI with G1. (C) Every new genome that is added to the database will be compared to all genomes already in the database and codes will always be assigned based on the ANI with the most similar genome. (D) Since every organism in the database was assigned a code based on genome similarity with the most similar organism already in the database at the time of its addition, all codes reflect the similarity of organisms with each other (as long as their genomes aligned) and thus are an approximation of their phylogenetic relationships (represented by the tree in the figure). doi:10. 1371/journal. pone. 0089142. g001 Table  1. Thresholds of Average Nucleotide Identity (ANI) used for assignment of provisional codes in Tables  2 through 5.
tomato DC3000 22 A 0 B 5 C 0 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R Shewanellaceae Shewanella amazonensis SB2B 29 A 0 B 0 C 0 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R Shewanella baltica OS155 29 A 0 B 1 C 0 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R Shewanella putrefaciens CN 32 29 A 0 B 1 C 1 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R Shewanella frigidimarina NCIMB 400 29 A 0 B 2 C 0 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R Shewanella loihica PV 4 29 A 0 B 3 C 0 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R Shewanella oneidensis MR 1 29 A 0 B 4 C 0 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R Shewanella pealeana ATCC 700345 29 A 0 B 5 C 0 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R Shewanella woodyi ATCC 51908 29 A 0 B 6 C 0 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R Xanthomonadales Stenotrophomonas maltophilia R551 3 31 A 0 B 0 C 0 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R Xanthomonas axonopodis citrumelo F1 31 A 0 B 1 C 0 D 0 E 0 F 0 G 0 H 0 K 0 L 0 M 0 P 0 Q 0 R genomes of all organisms would be submitted to the same database and assigned the next available code independently of their current classification.
Secondly, current biological taxonomy and nomenclature, in particular phylogenetic nomenclature  [9] , is based on phylogeny. However, phylogenetic relationships between individual organisms belonging to the same species are ambiguous and heavily depend on the organisms that are sampled and the algorithms and genetic markers that are employed. Also, recombination makes it sometimes impossible to decide which phylogeny represents the true evolutionary history of closely related taxa  [25]. Also, codes based on phylogeny would need to be revised when new related genomes are added and would need to be assigned based on many genomes instead of only the most similar genome requiring much higher computing power. In contrast, genome codes would not require calculation of ANI compared to all genomes in a database.
Distantly Related Organisms have Completely Different Codes Because animals are much more closely related to each other than bacteria, mitochondrial genomes of all members of the chordata can be aligned with each other using BLAST and thus all chordata mitochondria share the same code at position A. On the other hand, genomes of bacteria belonging to different families within the c proteobacteria are only distantly related, cannot be significantly aligned, and thus do not share any code positions. However, future improvements to the measurement of genome similarity may make it possible to assign codes at additional positions with lower similarity thresholds to label, for example, all members of the c proteobacteria with a shared code at the leftmost position. This could, for example, be done employing average amino acid identity (AAI)  [26]  for the left-most positions in the code.
Genome Codes as Unique Identifiers to Communicate about Emerging Pathogens and any other Newly Discovered Organisms Since genome codes could be assigned automatically to any genome without having to make a decision about species assignment and/or without describing and naming new species, codes could be used to name organisms as soon as they are isolated for the first time and their genomes have been sequenced. This is particularly important when a new pathogen emerges. It may take time to describe a new pathogen and decide if it is a new species or if it is simply a new epidemic clone of an already named species. Also, different scientists or health officials in different countries may give the same pathogen strain different names. However, if genome sequences of all isolates were submitted to the same database for code assignment, everyone could refer to the new pathogen with the code positions that are shared among all isolates.
''All Against All'' Genome Similarity Calculations The first step performed by JSpecies  [16]  is to divide a genome into 1020bp-long consecutive fragments. For any two genomes, the fragments of these genomes are compared to each other using BLASTn and their DNA similarity is reported. JSpecies then selects those fragments of the query genome that align with the subject genome over 70% of their length and with 30% overall sequence identity. The number of fragments that satisfy these two criteria divided by the total number of fragments of the query genome is called the ''percentage of aligned fragments'' from here on. Percentage DNA identity values of the selected fragments are then averaged to calculate the Average Nucleotide Identity (ANI) between the corresponding genomes. For the first step of our pipeline, we wrote a script that ran JSpecies  [16]  in sequence using as input all pairwise combinations of genomes in a selected group, for example the c proteobacteria.
Sequential Determination of the Most Similar Genome The ANI and ''percentage of aligned fragments'' values from the obtained file were then used as input for sequential identification of the most similar genome for each genome in the group using a custom script. For example, for code assignment in alphabetical order, the first genome of a group was compared to itself, the second genome was compared to the first genome, and the third genome was compared to the first and the second genome, etc. If 20% or more of the query genome fragments aligned with one or more of the subject genomes, the genome with the highest ANI was selected among these genomes as the most similar genome. We chose 20% as the cut-off because we found that ANIb based on less than 20% of the aligned fragments had no correlation with phylogeny.