dataesr/scientific-paragraphs-categorization huggingface dataset:
Cross-patient analyses Each GO-BP mechanism had an associated FDR for each patient. The GO-BP terms were then ranked according to the total number of patients sharing a given GO term that reached significance at FDR ≤5%. The prioritized GO-BP terms were listed from the most commonly to the least observed in lung adenocarcinoma patients, yet significant in at least one patient. The N-of-1-pathways statistical analysis component is available in R and Java at http://Lussierlab. org/publications/N-of-1pathways. Theoretical results: validation using synthetic data A synthetic geneset that contains a percentage of concordant deregulated genes was generated using the exploration dataset. Each point of figure  1  represents one geneset size of a simulated pathway varying from 15 to 500 genes by increments of 5 and generated by randomly selecting 'n' genes among the 20 502 reported in the exploration study.
Proxy gold standard for the internal and external validations Since a gold standard (GS) for lung adenocarcinoma does not exist, we generated proxy GSs  15 17 18  in order to objectively Figure  1  Synthetic data: evaluation of the size and ratio of concordant deregulated genes within a pathway required to be found deregulated in the N-of-1-pathways statistical analysis component. Each point represents one size of a simulated pathway generated by randomly selecting n genes and a ratio r of the deregulated genes within the pathway. The ratio r is artificially increased by a k-fold change in a simulated pathway seeded in the exploration dataset (k∈{1. 3, 1. 5, 2}). We then applied the N-of-1-pathways statistical analysis component to verify if the simulated pathway was found deregulated with a significance threshold (type I error) chosen as an unadjusted p value of ≤0.
Heatmap The heatmap drawn in figure 4A was computed using the default clustering parameters of the HeatPlus package in R (the Euclidean metric and complete aggregation method were used). z Scores were computed for each pathway of each individual patient (see the 'N-of-1-pathways framework' section in the Methods). These individual patient lists of p values (vectors) were then annotated side-by-side as a matrix for creating a heatmap of the 55 TCGA samples.
The Kaplan-Meier survival curve The Kaplan-Meier survival curve (figure  4B ) was computed using GraphPad Prism V. 6. 02 software using the survival data associated to the exploration dataset (table  1 ). Patients who died of non-cancer causes in the first 12 months were excluded (defined as 'tumor-free' or 'unknown tumor recurrence status'; exploration dataset). Three clusters of GO-BP z scores were obtained using the Partitioning Around Medoids (PAM) clustering method (cluster package in R; see the 'N-of-1-pathways framework' section in the Methods).
Internal validation: N-of-1-pathways unveiled deregulated pathways comparable to those of conventional geneset-level methods In order to assess the relevance of the genesets uncovered by N-of-1-pathways, we compared its results against those computed with conventional enrichment methods: GSEA and DEG enrichment within the exploratory datasets comparing uninvolved to tumoral lung tissues. We also compared the results with the ssGSEA method,  21  as a single-sample alternative method (see 'ssGSEA' in the online supplementary methods). Because N-of-1-pathways and ssGSEA identify deregulated genesets for each patient individually, instead of a global estimation over a cohort, we grouped the results of the 55 patients together in order to make the comparison. We compared the union of the deregulated genesets over the 55 patients derived from N-of-1-pathways (905 for Bonferroni 1% cutoff and 2662 for FDR 5% cutoff) and ssGSEA (78 for FDR 5% cutoff) to the genesets of GSEA  30  and DEG enrichment.
External validation: biological relevance of the deregulated mechanisms found in the context of lung adenocarcinoma In order to further assess the accuracy of the pathways we found deregulated in the exploration dataset, we used the three Figure  3  Concordant deregulated pathways between exploration and external validation studies. To establish that N-of-1-pathways yielded relevant Gene Ontology (GO) terms associated pathways, we compared the deregulated pathways to two conventional enrichment methods in three independent lung adenocarcinoma studies (see Methods and table  1 ). In (A), the Venn diagram corresponds to the overlap of deregulated GO annotations of Biological Process (GO-BP) terms associated pathways between the three external validation studies I, II and III (deregulated pathways were directly retrieved from the ones published in study I, while they were enriched using DEG enrichment from the DE genes list published in studies II and III, at FDR≤5%).
In (B-F), the unveiled pathways of the exploration set were compared to those discovered in the independent validation studies using either genesets enrichment analysis (GSEA) (C and E), DEG enrichment as a proxy gold standard (D and F), or pathways reported in study I (B). Bonf. , Bonferroni; FDR, false discovery rate; PPV, positive predictive value. independent lung adenocarcinoma studies described in table 1 to derive GSs (see the 'Proxy gold standard for the internal and external validations' section in the Methods). In figure  3 , the GO-BP genesets found deregulated across patients in the exploration set were compared to those found deregulated in each of the three independent validation studies. We used a GO-ITS >0. 7 semantic similarity threshold for these curves. We conducted GSEA and DEG enrichment methods on the exploration dataset using the GO-BP terms published in study I (figure  3B ) and GO-BP terms enriched with both DEG enrichment and GSEA in studies II and III (figure 3C-F, respectively).
When the overlap of two studies is compared, the commonality is modest to moderate. This observation concurs with the reports that lung adenocarcinoma is a very complex and heterogeneous disease. 23 Further, these results also suggest that high heterogeneity and convergence of molecular mechanisms underlie both disease development and chemoresistance. 32 These results support our initial hypothesis that patient-specific interpretations of deregulated pathways are required. However, the stronger overlap found between studies II and III suggests a confounded artifact: an increased overlap due to the enrichment method used. We confirmed this by comparing GSEA with DEG enrichment on the same datasets and across datasets (data not shown).
7), GO:0031667, response to nutrient levels; unrelated (GO-ITS=0. 25), GO:0032259, methylation. In order to evaluate whether the individual mechanisms uncovered by N-of-1-pathways were related to disease outcome, we performed a survival analysis (see 'The Kaplan-Meier Figure  5  Comparison of the Gene Ontology annotations of Biological Process (GO-BP) terms predicted by N-of-1-pathways in the exploration dataset and their similarity to those of the external gold standard (combination of three validation studies). (A) Global comparison of the 55 patients' results taken together. Overall, 92% of GO-BP terms of the external gold standard (GS) are found to be predicted by the N-of-1-pathways (0. 7<ITS<1 and ITS=1). Conversely, 61% of the predicted and related GO-information theoretic similarity (GO-ITS) overlap with the GS (0.
Given these preliminary survival results, we curated two diametric extreme survival phenotypes: 'death of disease in less than a year' and 'disease-free survival >5 years', and then performed a PCA (see the 'Principal component analysis of individual GO-BP terms' section in the Methods). The component plot shows a clear clustering of the two diametric extreme prognosis phenotypes (Wilcoxon test, p<0. 05)   5B ). The top 15 deregulated mechanisms between these groups were calculated using the N-of-1-pathways statistical analysis component in the exploration dataset. (A) Hierarchical clustering of the 15 GO terms using the GO-information theoretic similarity (GO-ITS) metric (see the Methods section). (B) Legend of the star plots, each edge corresponding to one GO-BP term. Each star reflects a single patient's deregulation of GO-BP terms (see the end of the first paragraph of the 'N-of-1-pathways framework' section in Methods).
N-of-1-pathways is a method intended for precision medicine, requiring paired samples from a single patient. Our method predicts patientspecific deregulated pathways overlooked by traditional methodologies, as they focus on identifying common genes or pathways across patients. 5] [36]  However, the latter were not designed to address two samples per patient or the overwhelming number of multiple comparisons arising from genomic datasets. In contrast, N-of-1-pathways can also be applied to different types of 'omics datasets (eg, transcriptomes, epigenomes, methylomes, etc). Specifically, the principle of generating This table lists the GO-BP terms presented in figure  6  along with their curated classes and complete GO description. GO-BP, Gene Ontology annotations of Biological Process. the statistical power from multiple genes (or molecules of life)in each geneset of a pair of samples-can be extended to methylations, or related modifications of histone or DNA binding proteins, etc.
This supports the ability of N-of-1-pathways to recapitulate common signatures of conventional geneset methods although designed for paired data. Moreover, N-of-1-pathways predictions can be scaled down to a single geneset, which provides opportunities to target limited number of genes of a given deregulated pathway for a qPCR analysis, while ssGSEA requires the whole background of genes for the same evaluation. Although N-of-1-pathways performed similarly to GSEA and DEG enrichment (figure  3 ), we are planning to conduct a number of studies to quantify the gain of accuracy of this new method. We are considering a resampling by decreasing subsets of patients to estimate the threshold for which N-of-1-pathways becomes no more accurate than alternate methods when conducted over the full dataset of 55 patients.
Finally, with the abundance of microarray datasets, an analysis of the accuracy might be valuable for obtaining a signal in microarrays by re-analyzing well-understood legacy datasets with the intention of unveiling individual differences overlooked by crosspatient studies. TCGA also contains paired DNA-sequencing of uninvolved and tumoral lung tissues in the context of lung adenocarcinoma disease for the same patients than the RNA-Seq exploration dataset, from which somatic mutations can readily be extracted. We plan to improve analytical techniques for N-of-1 genomic studies through the use of additional sources of external knowledge (eg, eQTL) and paired samples from other genomic scales for predicting individualized response to therapy. Indeed, the new pathologic classification of lung cancer identifies key mutations relevant to chemoresistance and disease progression such as EGFR, ALK, TTF-1, p53, and KRAS.
Figure2Concordant deregulated pathways (genesets) uncovered by N-of-1-pathways, ssGSEA, differentially expressed gene (DEG) enrichment, and genesets enrichment analysis (GSEA) methods within the exploration dataset (internal validation). To evaluate the Gene Ontology annotations of Biological Process (GO-BP) associated terms yielded by the N-of-1-pathways method, we compared these pathways to those found by a single sample method: ssGSEA, and two well-established cohort-based methods: differentially expressed gene (DEG) enrichment and GSEA. We then generated precision-recall curves based on the perfect GO overlap (A), and GO semantic similarity overlap (B; GO-Information Theoretic Similarity (GO-ITS) ≥0. 7; see the 'Information Theory Similarity' section in the Methods). When GSEA is chosen as the proxy gold standard (see the 'Proxy gold standard for the internal and external validations' section in the Methods), the N-of-1-pathways method uncovered deregulated pathways comparable to, or better than, those of DEG enrichment analysis, with or without GO-ITS analysis, respectively.
Acknowledgements We thank  Dr Liqiang Xi  for providing the datasets of validation study II. We thank  Ms Colleen Kenost  for help with proofreading. We thank  Drs Terry Speed ,  Walter W Piegorsch , and  Dean Billheimer  for their statistical insights. Contributors YAL, VG: conceived the experiments; VG, JL, GP, MMC: conducted the computational biology and high-throughput experiments; VG, IA: provided the figures and tables; IA: provided the knowledge bases; VG, IA, LP, RW, JGNG, YAL: analyzed the results; VG, IA, MMC, HL, JGNG, RW, IF, NB, YAL: wrote and revised the manuscript. Funding The study was supported in part by  NIH  grants  UL1TR000050  ( University of Illinois CTSA ),  1S10RR029030-01  ( BEAGLE Cray Supercomputer ), and  K22LM008308 , and the  University of Illinois Cancer Center.
System Architecture Our OCR error detection and correction system uses a hybrid methodology with a symbolic module for linguistic preprocessing, a POS tagger, followed by statistical decoding correction modules. The SxPipe toolchain  (Sagot and Boullier, 2008)  is used for shallow processing tasks (tokenisation, sentence segmentation, named entity recognition). The NE-tagged text is input to POS tagging with MElt-h, a hybrid version of the MElt tagger  (Denis and Sagot, 2010; Denis and Sagot, 2012). MELT-h can take both NE tagged texts and raw text as input. The decoding phase is based on the noisy channel model  (Shannon, 1948)  adapted to spell checking  (Kernighan et al. , 1990). In a noisy channel model, given an input string s, we want to find the word w which maximizes P (w|s). Using Bayes theorem, this can be written as: argmax(w)P (s|w) * P (w) (1) where P(w) is given by the language model obtained from clean corpora.
NE recognition methodology As a first step in error detection, the OCR output is analysed in search of "irregular" character sequences such as named entities. This process is implemented with SxPipe  (Sagot and Boullier, 2008) , a freely available 1  , robust and modular multilingual processing chain for unrestricted text. SxPipe contains modules for named entity recognition, tokenization, sentence segmentation, non-deterministic multi-word expression detection, spelling correction and lexicon-based patterns detection. The SxPipe chain is fully customizable with respect to input language, domain, text type and the modules to be used. Users are also free to add their own modules to the chain. In accordance with our purposes, we defined named entities as sequences of characters which cannot be analysed morphologically or syntactically, yet follow productive patterns.
Autonomic M2M Manager The autonomic M2M manager is able to monitor the system in information level, analyze the situation, plan the required actions, and execute the control events towards the system automatically or at least semi-automatically, Figure  3. It is a generic and extensible control loop for the self-management of M2M systems  [65]  based on the IBM MAPE-K control loop model for enabling self-management capabilities such as self-configuring, self-optimizing, self-healing and self-protecting  [66]. The control loop operates as an expert system to emulate the decision-making ability of humans and is designed to solve complex problems by reasoning about knowledge. The control loop consists of modules for monitoring, analyzing, planning and executing, Figure  4. Monitoring module answers to the question "what is happening?" It collects events from sensors from managed resources, updates a model describing the sensors environment and topology located on the knowledge base with relevant information from events, and infers new knowledge about symptom occurrences, then extracts relevant information and sends them to the analyzer.
The communication interfaces provided by the ETSI platform allow the following methods: register, deregister, invoke, subscribe and data publish/subscribe. Registered M2M applications/devices can provide their services in two different ways:  Synchronized: Whenever a new value is available, the M2M application shall publish it on the corresponding data resource. This is ideal for a service that only publishes data (a temperature for example);  Retargeted: All the operation on the given resource will be retargeted to the corresponding device/application. This is ideal for invocation services such as actuators. The platform also provides a publish/subscribe mechanism, which can be used to subscribe to data from other applications or to discover other applications (when they register for example).
Then the Network Application Enablement capability checks if the issuer is authorized to be registered and treats the request (002). The registration information is then stored by the Network Reachability, Addressing and Repository Capability. The Network Application receives a positive answer (003). The Network Application subscribes to the data produced by the desired sensor (Device application) (004). This data can also follow a certain criteria specified by the issue. If the issuer is authorized to subscribe to the given Device Application, the Gateway Reachability, Addressing and Reachability capability treat the request (005). The Network application receives a positive response (006). Device application sends information to the Gateway (007). The Gateway Reachability, Addressing and Repository Capability identify an event that needs to be reported to a subscriber (008).
The M2M REST resources can be accessed through URIs (see  [5]  for more information). M2M REST resources of the tree are mapped to HTTP REST resources and Extensible markup language (XML) data structures. The data types can be mapped to equivalent JavaScript Object Notation (JSON) data structures as well. Each normal HTTP REST resource shall have a representation in XML. One M2M resource corresponds to one XML or JSON data structure. The resource representation shall be carried in create requests (POST), update requests (PUT), successful retrieve (GET) responses and notify (POST) requests. When transported in a HTTP request or HTTP response, the resource representation shall be carried in the body. ETSI M2M SCL has been specified to enable generic horizontal service capability layer to be applicable for multiple applications.
Each XMPP client has an account hosted by a XMPP server, and the client can be addressed by unique Jabber ID (JID). XMPP JID contains three parts: user, domain and resource as shown in Table  1 , RFC 6122. In XMPP, network domain-part of JID must be a fully qualified domain name or IP address. Each domain presents one logical groups with one user account database. Each domain may present own user account policies. The device owner is usually considered to be also a user of the M2M domain and an owner of at least one XMPP user id. All devices share same user id (local-part and domain-part) with their owner. Separation of devices is done by examining the resource part of JID. A client connects to the server to send and receive messages. The procedure for M2M client establishing connection with the XMPP server is shown in Figure  10.
Server to server connection is an XML stream over a TCP connection, similar as to client to server connections. Most important difference is that server to server connections are not authenticated because they happen in between different domains that do not share a common user database. This is similar to how email systems work. XMPP servers may use XMPP Dial back, as defined in XEP-0220, to verify the domain of the connecting server. The domain administrator may require stronger identification verification by using TLS certificates and Simple Authentication and Security Layer (SASL). When M2M clients located in different domains would like to exchange messages, routing of messages will be done by the domain specific servers, Figure  11. Then communication link between servers of the domains are negotiated, to enable messaging between the referred M2M clients.
The proxy component is needed to handle heterogeneity of M2M devices and local M2M asset networks, data delivery over constrained wireless links and interworking functionalities for application messages between local M2M asset network and Internet with ETSI M2M SCL. The proxy can be applied to optimize M2M communications, and it is needed also to enable interworking with ETSI M2M SCL via gateway interworking proxy (GIP), Figure  7. By serving fresh information from a cache instead of querying the endpoint itself, one could experience a shorter delay or a better QoS on a particular request. Also, caching may help reducing the overall consumption of energy-constrained network by reducing the number of wireless transmissions required for collecting data. In the example case, the CoAP, IPv6 and 6LowPAN network stacks on Contiki platform has been applied  [70].
Bluetooth SIG defined service data is characterized by 16 bit universally unique identifiers (uuids) while proprietary extensions use randomized 128 uuids. The Bluetooth protocol multiplexor (L2CAP) can be used to add new transport protocols to the Bluetooth SMART stack. L2CAP is a symmetric protocol which implies that any device can create a bidirectional channel to receive or transmit data. A significant constraint on the multiplexor in Bluetooth 4. 0 was the limited MTU. As part of Bluetooth 4. 1 the multiplexor has added support for connection oriented channels with credit based flow control that allows for exchanging large data packets with MTU up to 64 kBytes. Bluetooth Smart is intended for very memory constrained devices and flow control was considered essential. To receive data on device has to give the other device credits that indicate the amount of data that can be received.
M2M Security The M2M service platform may be privately owned for operation in a closed ecosystem, but it may also be operated by a third party business entity: the "M2M service provider". Security in such a platform is quite an important issue and typically encompasses two phases: In the first initial secrets are exchanged during an enrolment (or security bootstrap) operation between parties needing to communicate privately. In the second phase, those secrets are actually used to control resource access and secure data communications. In the case of the ETSI M2M platform those two aspects are being considered and the ETSI M2M security framework addresses the definition of M2M service bootstrap and M2M service connection procedures. It supports mutual authentication, integrity protection, and confidentiality on M2M Device or M2M Gateway-to-Network Interface.
Several security bootstrapping methods are supported by the specification, including PKI based methods and techniques relying on the presence of 3GPP network elements typically deployed by Mobile network operators (MNO) such as GBA and SIM/AKA. However little support is provided for security bootstrap when using small and constrained M2M objects such as sensors. The security model currently proposed by ETSI M2M architecture is a piecewise security model: The data sent between a source device and a recipient application via the M2M service platform are protected from the device to the M2M platform and from the M2M service platform to the destination using different credentials controlled by the M2M service provider as shown on Figure  15. As a consequence, the data are always available in clear at the level of the M2M service platform.