Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the faults of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the fault diagnosis of on-spot low-speed heavy-duty gears and could be a significant supplement to vibration testing diagnosis. Its operational state directly affects the function of the whole equipment. Faults and failures of gears can cause great damage to the whole production. Therefore, the diagnosis of gear faults is of significant importance.The background noise in the fault signals of low-speed heavy-duty gears is complicated and of low energy, so conventional vibration testing methods are not effective. The acoustic emission is the high-frequency stress-wave signal emitted due to structural imperfections. Compared with vibration signals, the frequency spectra of acoustic emission signals are broader, and their high frequencies can inhibit the noise interferences effectively and improve the diagnosis accuracy. Besides, any dynamic imperfections can be detected through acoustic emission, and it is unnecessary to force the detected piece to approach the detection device. Therefore, there are great advantages in predicting and diagnosing the faults of low-speed heavy-duty gears with acoustic emission. It is worth noting that most previous studies have been focused on the analysis of laboratory data. On the one hand, they make the necessary preparations for the on-spot application of acoustic emission testing; on the other hand, noises interfere with the on-spot testing of acoustic emission. The application of acoustic emission to the fault diagnosis of low-speed heavy-duty gears is presently a research hot spot and of great practical significance to the industrial production management.2.Acoustic emission testing refers to a technique of testing, recording and analyzing acoustic emission signals using apparatus as well as speculating on the status of an acoustic emission source as normal or not based on acoustic emission signals. The principle of acoustic emission testing is shown in et al. distinguished the types of bearing faults using the acoustic emission technique and auto-regressive coefficients. They obtained substantial results, but did not validate them in practice [As for the methods of processing the acoustic emission signals of rotating machinery, presently there are parameter analysis methods and waveform analysis methods. The former ones are dominated by methods based on basic parameters such as the ring, energy and amplitudes ; comparepractice –8.et al. employed the morphological opening operation and the multi-scale Top-Hat transform as the analysis operators for the approximation signals and the detail signals in wavelet decomposition, respectively. Through tests, they verified the feasibility and validity of the method [et al. used wavelet scalograms to analyze in detail the time-frequency, propagation and dispersion characteristics of rubbing acoustic emission [et al. extracted the high-frequency components containing the fault signals of spindles for envelopment analysis and detected successfully fault frequencies [et al. analyzed the drawbacks of the promotion algorithm and the redundant promotion algorithm. Aiming at the cause for the generation of error transfer in the redundant promotion algorithm, they put forward an improved redundant promotion algorithm based on normalizing factors and extracted successfully the characteristics of faint fault signals using the shock pulse method [In recent years, wavelet technology has been widely applied to the testing and diagnosis of heavy-duty equipment in China and other countries. Based on the general framework of morphological undecimated wavelets, Zhang e method . He et aemission . Deng etquencies . Classicquencies . With pre method .Zhao eliminated the background noise of acoustic emission signals through wavelet analysis and reconstruction, and then identified the faults of rolling bearings using the wavelet envelopment spectrum analysis method. Test results proved that the faults of rolling bearings could be detected effectively with the wavelet envelopment spectrum analysis . Using tetc. of water, oil, gas and other types of liquid being taken into account, there are abundant signals and one of the acknowledged puzzles has been how to identify early faults effectively in the presence of strong background noise.The above research achievements show that, although acoustic emission signals are one of a few effective carriers that can be acquired from the testing diagnosis information of low-speed heavy-duty equipment, there are still great difficulties in identifying the early faults in such equipment by using acoustic emission signals. So far, the research has basically been limited to the laboratory stage, and inadequate studies have made about the applicability of on-spot engineering. The low-speed heavy-duty equipment in real operation bears enormous alternating load and various kinds of shock. With the motions and mechanical frictions, In this study, a redundant second generation wavelet transform method was constructed and it was validated that the redundant second generation wavelet transform was effective in the processing and denoising of acoustic emission signals through the analysis of the acoustic emission data from the faults of on-spot low-speed heavy-duty gears.3.3.1.P and U in the algorithm represent the predictor and the updater, respectively.The redundant second generation wavelet transform includes two processes: decomposition and reconstruction. The decomposition includes two parts: prediction and renovation. The reconstruction includes the reconstruction recovery and the renovation recovery. During the decomposition and reconstruction, the length of a signal sequence remains fixed. Symbols s(n), with the data length as L. The course of the redundant second generation wavelet decomposition is expressed as follows:lN samples through predictor P, and prediction error {dl+1 (n), n ∈ Z} is defined as detail signals:Prediction. Each sample in the signal sequence is predicted with adjacent 2dl+1 (n), updater U is used to renovate each sample in the signal sequence using 2lN detail signals. The signal sequence sl+1 (n) obtained after the renovation is defined as approximation signals:Renovation. Based on detail signal The decomposition is as follows: the original signal sequence is denoted by sl+1 and detail signal dl+1:Renovation recovery. Sample sequence Prediction recovery. Sample sequence ls:Sample sequence The reconstruction course is as follows:3.2.Ñ = 8. In order to calculate the updater coefficient at Ñ = 8, the predictor coefficient at N = 8 needs to be calculated first, then a half of the predictor coefficient is the result. The updater length can be different from the predictor length, e.g., 3.3.c was set to 2.9, the denoising results as shown in We construct a high-frequency attenuation oscillator signal with the frequency of 500 Hz, as shown in The energy ratio and standard deviation of signals were calculated using the two wavelets denoising. The energy ratio and standard deviation of signal using DB10 wavelet denoising are 0.5012, 16.2809; the energy ratio and standard deviation of signal using redundant second generation wavelet denoising are 0.3755, 18.2142. From the denoising results, it can be seen that the redundant second generation wavelet has de-noised more noise signals than traditional wavelet; from the comparison between energy ratio and standard deviation, it can be seen that the redundant second generation wavelet is more ideal.4.The acoustic emission testing was conducted on seven rough bar mills in a particular steel plant on 23 June 2010; the employed testing apparatus was a SAEU2S acoustic emission scanner produced by the Beijing Shenghua Xingye Technological Co. Ltd.Before beginning the signal acquisition, we needed to conduct sensitivity calibration for the sensors. The signals generated by the pencil lead-breaking is very similar to the signals produced by the metal crack propagation, therefore, we can use the pencil lead as a source of simulated AE signals to conduct detection. The collected signal is as shown in The installation site for sensors was shown in Step 1: Determine the number of signal decomposition layers of redundant second generation wavelet; in this paper, the AE signals were decomposed with redundant second generation wavelet into four-layer and the lengths of the predictor and the updater were set at 20;c was set to 2.9.Step 2: Determine the threshold; in this paper, soft threshold denoising and self-adaptive denoising were employed. The empirical value Step 3: Reconstruct the details signal processed with the threshold and approximation signal, and then the denoised signal is obtained.The procedure for redundant second generation wavelet denoising being performed on signals is the following:i.e., fundamental frequency) increases, the ultra-harmonics  amplitude of mesh frequency also significantly increases. The above analysis indicates that the gear wheel of the epicyclic gearbox may have suffered abrasion.From the perspective of mechanism analysis, we can see that if there is no abrasion, each gear has an involuted shape and the signal generated in the operation process is the single-frequency harmonic curve whose frequency is the gear mesh frequency; after the gear abrasion, the shape of gear changes and the signal generated in the operation process is the approximate periodic signal curve whose fundamental frequency is the gear mesh frequency; the more serious the abrasion is, the nearer time-domain curve gets to a square wave. So as the amplitude of mesh frequency (In the internal check on June 24, 2010, tooth-face abrasion and peel-off was detected on the gear wheel of the epicyclic gearbox see . The resThe vibration sensors mounted on identical sites. 5.The acoustic emission testing diagnosis could be applied to the fault diagnosis of on-spot low-speed heavy-duty gears and was a crucial supplement to the vibration testing diagnosis.The length of the approximation signals and detail signals obtained after the decomposition of redundant second generation wavelet was the same as that of original ones, so the amount of information was twice that of the original. Such a characteristic of redundant second generation wavelet guaranteed that it could achieve an effect better than conventional wavelet and second generation one. With the denoising technique based on redundant second generation wavelet, all fault shock information could be preserved, which was unmatchable for other denoising techniques.The validity of this algorithm of the redundant second generation wavelet transform during the processing and denoising of acoustic emission signals was verified.The application of redundant second generation wavelet and acoustic emission testing in gear fault diagnosis was investigated in this study. Through the analysis of on-spot cases, the following conclusions were obtained:


Although many RNA molecules contain pseudoknots, computational prediction of pseudoknotted RNA structure is still in its infancy due to high running time and space consumption implied by the dynamic programming formulations of the problem.In this paper, we introduce sparsification to significantly speedup the dynamic programming approaches for pseudoknotted RNA structure prediction, which also lower the space requirements. Although sparsification has been applied to a number of RNA-related structure prediction problems in the past few years, we provide the first application of sparsification to pseudoknotted RNA structure prediction specifically and to handling gapped fragments more generally - which has a much more complex recursive structure than other problems to which sparsification has been applied. We analyse how to sparsify four pseudoknot structure prediction algorithms, among those the most general method available  and the fastest one . In all algorithms the number of "candidate" substructures to be considered is reduced.Our experimental results on the sparsified Reeder-Giegerich algorithm suggest a linear speedup over the unsparsified implementation. Recently discovered catalytic and regulatory RNAs ,2 exhibiet al.  in both original and sparsified variants of the algorithm. Note that the number of  pairs is in order of O((j - i)2) in the worst case. Figure i', j') pairs on fragments of equal length which are checked by the two variants of the algorithm. As expected, this amount is significantly smaller for the sparsified algorithm compared to the original one. Moreover, we observe that as the fragments get longer, the difference between the average number of  pairs in the sparsified and the original algorithm increases. We define the work load per each fragment  as the number of candidate  pairs. Figure n, all fragments of smaller length are processed by the algorithm.For a better understanding of the effect of sparsification on the R&G algorithm, we measured the number of (The presented work gives four examples for sparsification in the context of gap fragments and a complex recursion structure. We successfully sparsified the fastest and the most complex pseudo-knot structure prediction algorithm for RNA, as well as two algorithms with intermediate complexity. Since sparsification is similar in all these algorithms, the paper motivates further generalization of sparsification for systematic application to complex DP-algorithms as RNA structure prediction algorithms. Even more, by providing detailed examples the paper directly suggests such generalization. Our results from an implementation of the sparsified Reeder and Giegerich algorithm show a significant, presumably even linear, expected work load reduction due to sparsification. As future work, it would be interesting to develop optimizations for the partition function based variants of pseudoknot prediction where sparsification is not directly applicable.The authors declare that they have no competing interests.All authors developed the ideas for this project. MM, RS, and SW elaborated the technical contribution and wrote the paper. RS did the implementation and evaluation. All authors read and approved the final manuscript.


The two main tasks represent extensions of the event extraction model introduced in the BioNLP Shared Task 2009 (ST'09) to two new areas of biomedical scientific literature, each motivated by the needs of specific biocuration tasks. The ID task concerns the molecular mechanisms of infection, virulence and resistance, focusing in particular on the functions of a class of signaling systems that are ubiquitous in bacteria. The EPI task is dedicated to the extraction of statements regarding chemical modifications of DNA and proteins, with particular emphasis on changes relating to the epigenetic control of gene expression. By contrast to these two application-oriented main tasks, the REL task seeks to support extraction in general by separating challenges relating to part-of relations into a subproblem that can be addressed by independent systems. Seven groups participated in each of the two main tasks and four groups in the supporting task. The participating systems indicated advances in the capability of event extraction methods and demonstrated generalization in many aspects: from abstracts to full texts, from previously considered subdomains to new ones, and from the ST'09 extraction targets to other entities and events. The highest performance achieved in the supporting task REL, 58% F-score, is broadly comparable with levels reported for other relation extraction tasks. For the ID task, the highest-performing system achieved 56% F-score, comparable to the state-of-the-art performance at the established ST'09 task. In the EPI task, the best result was 53% F-score for the full set of extraction targets and 69% F-score for a reduced set of core extraction targets, approaching a level of performance sufficient for user-facing applications. In this study, we extend on previously reported results and perform further analyses of the outputs of the participating systems. We place specific emphasis on aspects of system performance relating to real-world applicability, considering alternate evaluation metrics and performing additional manual analysis of system outputs. We further demonstrate that the strengths of extraction systems can be combined to improve on the performance achieved by any system in isolation. The manually annotated corpora, supporting resources, and evaluation tools for all tasks are available from  The biomedical scientific literature is growing at an exponential rate, far outstripping the capacity of individual researchers to fully process in any but the narrowest of subfields. To address the challenges of information overload and to improve access to the wealth of knowledge in this literature, there have been substantial efforts over the previous 15 years to develop automatic methods for the analysis of medical and biomolecular scientific publications .Much of this work has focused on information extraction (IE) and text mining, applying natural language processing (NLP) methods to analyse domain texts, extract structured, computer-readable representations of key information, and compile extracted information into knowledge bases. Until recently, domain IE efforts concentrated primarily on foundational tasks, such as entity mention detection, and on the extraction of simple representations of entity associations, most typically the detection of mentions of names of proteins and the extraction of protein mention pairs representing protein-protein interactions.event representations that capture structured associations of arbitrary numbers of participants in specific roles [However, in recent years there has been increasing interest in the application of more expressive representations in domain IE to address the requirements of tasks such as pathway curation, Gene Ontology term annotation, and semantic literature search ,4. To suic roles -10. The ic roles ,51.Shared tasks have been instrumental in the development of general domain IE technology by introducing new tasks, resources and evaluation standards ,13. Alsotranscription factors in human blood cells, and the event types targeted in the task were chosen by relevance to its topics. These limitations raised the question whether the findings of the shared task and the methods introduced to address the task can generalize beyond this narrow domain. To address such questions, generalization was chosen as the main theme of the second event in the series, the BioNLP Shared Task 2011 (BioNLP ST'11) [Although successful in introducing structured event representations to the general community and promoting the development of practically applicable methods for event extraction, the resources of the BioNLP ST'09 were somewhat limited in their scope. The task data was prepared on the basis of the GENIA corpus ,27, an aP ST'11) . This waIn this paper, we present three of the eight tasks in BioNLP ST'11, including two tasks that attracted the widest participation among newly introduced main tasks, EPI and ID. The Epigenetics and Post-translational Modifications (EPI) task focuses on events relating to epigenetic change and encompasses also common protein post-translational modifications, reactions that are critical for the control or gene expression and protein function. The Infectious Diseases (ID) task concerns the biomolecular mechanisms of infection, virulence and resistance, focusing in particular on the functions of a class of signaling systems that are ubiquitous in bacteria but as of yet incompletely understood. In addition to these two main tasks, we introduce the Entity Relations (REL) supporting task, which seeks to assist extraction in general by separating challenges relating to part-of relations into a subproblem that can be addressed by independent systems whose analyses can then be used to support the recognition of various event extraction targets.Each of these tasks follows the general design of the BioNLP ST'09, providing participants with an extensive, fully annotated corpus with manually curated examples of the extraction targets for method development and training, with evaluation of final submissions received from participants against a separate held-out test set prepared in similar fashion.We extend on the EPI, ID and REL task results previously reported in the BioNLP Shared Task 2011 workshop proceedings -32 in paIn the following, we first briefly motivate each of the EPI, ID and REL tasks and introduce the task setting. We then describe the task data annotation and evaluation criteria before presenting the results of each task. Finally, we present an extended analysis of the outputs of the participating systems.The Epigenetics and Post-translational Modifications (EPI) task is an information extraction task focusing on events relating to epigenetic change, including DNA methylation and histone methylation and acetylation , as Many of the PTMs targeted in the EPI task involve modification of histone, a core protein that forms an octameric complex that has a crucial role in packaging chromosomal DNA. The level of methylation and acetylation of histones controls the tightness of the chromatin structure, and only "unwound" chromatin exposes the gene packed around the histone core to the transcriptional machinery. Since histone modification is of substantial current interest in epigenetics, we designed aspects of the EPI task to capture the full detail in which histone modification events are stated in text. Finally, the DNA methylation of gene regulatory elements controls the expression of the gene by altering the affinity with which DNA-binding proteins (including transcription factors) bind, and highly methylated genes are not transcribed at all ,40. DNA HOSPHORYLATION, which was found to be the single most reliably extracted event type in the task, with the best-performing system for the type achieving 83% F-score in its extraction [The specificity with which protein modifications can be described in text makes them promising IE targets  task is an event extraction task focusing on the biomolecular mechanisms of infectious diseases. The task concentrates on the specific domain of two-component systems , a mechanism widely used by bacteria to sense and respond to the environment . TypicalThe ID task seeks to support efforts to build a systemic understanding of the molecular-level pathways relating to these mechanisms of infectious diseases by adapting the BioNLP ST'09 event extraction model to domain scientific publications. The adaptation of the model originally introduced to represent biomolecular events relating to transcription factors in human blood cells to a domain that centrally concerns both bacteria and their hosts involves a variety of novel challenges, such as events concerning whole organisms, the chemical environment of bacteria, prokaryote-specific concepts (e.g. regulons as units of gene regulation), as well as the effects of biomolecules on larger-scale processes involving hosts, such as virulence. In addition to supporting an application of significant public health interest, the ID task also provides opportunities to study the ability of event extraction technology to generalize in a number of aspects.The Entity Relations (REL) supporting task focuses on the extraction of specific binary relations between biomolecular entities. The motivation for the task draws in part from analysis of the results of the BioNLP ST'09, which suggested that events that involve coreference or entity relations represent particular challenges for extraction . To helpIn this section, we introduce the general representation of the IE tasks, the specific realization of this representation applied in each task, and the task evaluation criteria.entities, relations, events and event modifications, briefly illustrate the format in which these are stored in the task, and then describe the specific set of annotations involved in each of the tasks.While the EPI, ID and REL tasks differ substantially in the specifics of their extraction targets, the three share the same basic representation of extracted information, an extension of the representation introduced for the BioNLP ST'09  and applROTEIN) and a  offset pair identifying the span of text containing the entity mention. The entity annotations thus mark contiguous sequences of characters. All entity annotations further follow the constraint that no two entities of the same type overlap in their spans, and that the spans of no two overlapping annotations cross. By contrast, entity annotation spans may be nested so that one span completely contains another. Figure All of the tasks build on the basis of entity annotations, which capture mentions of entities of interest in text using a simple typed-span representation. Each entity annotation consists of a type . Gold annotation for PROTEIN entities is provided to participants in all tasks. The generic type ENTITY, by contrast, is defined for marking additional entity annotations generated by participants, such as the specific protein domains or DNA regions involved in modification or binding events. Annotations of this type are only provided in training data, and must thus be detected for test data by systems addressing the full main tasks or the supporting task. The non-specific type ENTITY is selected in part to reduce the demands of this entity recognition component of the tasks by removing the need to differentiate between specific types.The three tasks presented in this paper share the definitions of two entity types, PQUIV. This is a binary, symmetric, transitive relation that defines two entities to be equivalent [Relations are typed binary associations of entities and may be either directed or undirected. While relations are only a target of extraction in the REL task, all of the three tasks involve a specific relation, Euivalent . The relQUIV relation, the REL task defines directed part-of relations that are the targets of extraction in the supporting task, introduced in the section defining the task.In addition to the general En-ary associations of entities or other events, each identified as participating in a specific role. Events are bound to specific expressions in text (the event trigger or text binding) and are primary objects of annotation; that is, annotations may refer to event annotations. Event triggers identify the word or words stating the occurrence of the event in text. Like entities, triggers are represented using a  offset pair. Event types  are drawn from a fixed set separately defined for each task. Each event typically takes one or more arguments (participants in specific roles): for example, an ACETYLATION event may be defined as requiring a single Theme identifying the PROTEIN entity that is acetylated. Events may involve other events as participants, thus creating complex event structures. For example, a REGULATION event may have a BINDING event as its Theme, thus specifying a compound "regulation of binding" event. Figure Events are typed, Theme and Cause as well as the additional argument role Site. As the terms suggest, Theme identifies the participant or participants that undergo the primary effects of the event, and Cause a participant that causes the event to occur. Site identifies a specific part of another participant that is involved in the event, such as the modified residue in a PHOSPHORYLATION event. Event arguments may be specified as being either mandatory or optional, where mandatory arguments must be identified for an event to be extracted. Events typically take a mandatory Theme, reflecting a specificity constraint on the extracted information: while statements regarding e.g. the phosphorylation of specific proteins are targeted for extraction, statements regarding phosphorylation in general are not.The main tasks differ in the details of event arguments, but share the definition of the basic core argument roles The event arguments vary by event type and task, and the specification of event types and arguments largely defines the differences between the different main tasks of the BioNLP ST'11.EGATION and SPECULATION. The former marks an event as being explicitly negated (e.g. H2A is not methylated) and the latter as stated in a speculative context (e.g. H2A may be methylated). An event may be simultaneously marked as both negated and speculated (e.g. H2A may not be methylated). Unlike in the representation used for events and the cue-scope model applied for negation and speculation annotation in e.g. the BioScope corpus and the CoNLL 2010 shared task [Event modification annotations are used to specify further aspects of event statements beyond the core propositional content, for example identifying an event as being negated. Event modifications are represented as simple binary "flags" attached to events. Both of the main event extraction tasks EPI and ID follow the BioNLP ST'09 setting in defining two event modification extraction targets: Nred task ,65, no "The above presentation of the represented information content abstracts away the specific file format in which this information is stored in the task. While secondary to this information content, the specifics of the format may be of interest for assessing the technical requirements of the task; we thus include in Figure ROTEIN and ENTITY, where annotations of the PROTEIN type are provided as part of the input. By contrast to its standard entity definition, the EPI task introduces considerable novelty in the targeted events, involving a total of 14 event types and two participant roles not considered in other BioNLP ST'11 tasks. Table Theme, Cause and Site, EPI defines the task-specific arguments Sidechain and Contextgene.The EPI task focuses on the extraction of information on statements regarding change in the chemical modification state of proteins and DNA. The task involves the two generally applied entity types PSidechain, specific to GLYCOSYLATION and DEGLYCOSYLATION among the targeted events, identifies the moiety attached or removed in the event . Note that while arguments similar to Sidechain could be defined for other event types also, their extraction would provide no additional information: the attached molecule is always acetyl in acetylation, methyl in methylation, etc. Contextgene, specific to ACETYLATION and METHYLATION events and their reverse reactions, identifies the gene whose expression is controlled by these modifications. This argument applies specifically for histone protein modification: the modification of the histones that form the nucleosomes that structure DNA are key to the epigenetic control of the expression of the genes encoded in that segment of DNA. Theme is required for all events in the EPI task, but the Site, Sidechain and Contextgene arguments are not mandatory, and should only be extracted when explicitly stated. For CATALYSIS events, representing the catalysis of protein or DNA modification by another protein, both Theme and Cause are mandatory. Figure HOSPHORYLATION event type targeted in ST'09, and while CATALYSIS is a new event type, it is related to the ST'09 POSITIVE REGULATION type by a class-subclass relation: any CATALYSIS event is a POSITIVE REGULATION event in the ST'09 task terms (but not vice versa).While the EPI task setting has few extraction targets in common with the BioNLP ST'09 and the ST'11 GE task, its entity types follow the same scheme as these tasks and the general definition aims to preserve compatibility with their setting. The basic modification events in EPI are defined similarly to the PEGATION and SPECULATION, and involves EQUIV relations in its evaluation. The task extraction targets do not include any relations.As described in the section on representation, the EPI task targets also the two event modifications NThe ID task concerns the molecular mechanisms of infectious diseases, which involve various associations between multiple types of molecular entities, disease-causing microorganisms and other organisms undergoing the diseases. To support the extraction of information from domain publications, the task extends the basic entity types with multiple new categories. The ID event types extend on those defined in the BioNLP ST'09, broadening the scope of previously defined entities to encompass the new entity types and introducing a new class of events, high-level biological processes.ROTEIN, the ID task defines four additional types of core entities: TWO-COMPONENT-SYSTEM, REGULON-OPERON, CHEMICAL and ORGANISM. As in the other tasks, mentions of names of genes and their products (RNA and proteins) are annotated with the PROTEIN type. Two-component systems, consisting of two proteins, frequently have names derived from the names of the proteins involved (e.g. PhoP-PhoR or SsrA/SsrB). Mentions of TCSs are annotated as TWO-COMPONENT-SYSTEM, nesting PROTEIN annotations if present. Regulons and operons are collections of genes whose expression is jointly regulated. Like the names of TCSs, their names may derive from the names of the involved genes and proteins, and are annotated as embedding PROTEIN annotations when they do. The annotation does not differentiate between the two, marking both with a single type REGULON-OPERON.In addition to PHEMICAL and ORGANISM. All mentions of formal and informal names of atoms, inorganic compounds, carbohydrates and lipids as well as organic compounds other than amino acid and nucleic acid compounds (i.e. gene/protein-related compounds) are annotated as CHEMICAL. Mentions of names of families, genera, species and strains, as well as non-name references with comparable specificity are annotated as ORGANISM. The recognition of these core entities is not part of the ID task: gold annotation for these entities is provided to participants also for test data. As in the other tasks, the non-specific type ENTITY is defined for marking entities that specify additional details of events, ENTITY annotations are not provided for test data and must be detected by participants addressing the full task.In addition to these three classes relating to genes and proteins, the core entity annotation recognizes the classes CROCESS type. The semantics of these events are defined (as in GE) with reference to the community-standard Gene Ontology [The primary extraction targets of the ID task are the event types summarized in Table Ontology . We refeOntology ,11 for tENE EXPRESSION and TRANSCRIPTION extend on the former definition in recognizing REGULON-OPERON as an alternative unit of expression. LOCALIZATION, taking only PROTEIN type arguments in the ST'09 definition, is allowed to take any core entity argument. This expanded definition remains consistent with the scope of the corresponding GO term (GO:0051179). BINDING is similarly extended, giving it a scope largely consistent with GO:0005488 (binding) but also encompassing GO:0007155 (cell adhesion) (e.g. a bacterium binding another) and protein-organism binding. The three regulation types  likewise allow the new core entity types as arguments, but their definitions are otherwise unchanged from those in ST'09, that is, the GENIA ontology definitions. As in these resources, regulation types are used not only for the biological sense but also to capture statements of general causality [Theme argument: only events involving an explicitly stated theme (of an appropriate type) should be extracted. All other arguments are optional.The definitions of the first four types in Table ausality . As in SROCESS type, new to ID, is used to annotate high-level processes such as virulence, infection and resistance that involve infectious organisms. This type differs from the others in that it has no mandatory arguments: the targeted processes should be extracted even if they have no explicitly stated participants, reflecting that they are of interest even without the further specification. When stated, the involved participants are captured using the generic role type Participant. Figure The PTheme argument as their core argument simple events. In analysis we further differentiate non-regulation events (the first seven) and regulation (the last three). The latter category is known to represent particular challenges for extraction in involving nested event structures where events take events as arguments.We term the first five event types in Table EGATION and SPECULATION are targeted for extraction in the full task, and EQUIV relations are used in evaluation, but no relations are included among extraction targets.The ID task event modifications and relations are defined similarly as for GE and EPI: the two modifications NThe REL task aims to support the main event extraction tasks by isolating the recognition of entity relations as an independent subtask whose results can be used to resolve event structures. Following the results and analysis from previous studies ,68, in tROTEIN annotations) are further included as part of the input of the main tasks, the availability of these annotations could be assumed also in the REL task without introducing additional requirements on the task input. However, in the REL task setting, only one participant in each binary relation is a gene/protein NE, while the other can be either a non-name reference such as promoter or the name of an entity not of the gene/protein type (e.g. a protein complex). Motivated in part by the design goal to avoid introducing additional assumptions on input and the relatively limited number of existing methods for the detection of such entity references, their detection is included in the task: participants must recognize these secondary entities in addition to extracting the relations they participate in. These entities are assigned the generic type ENTITY.The recognition of biologically relevant associations of gene/protein NEs is a key focus of the main event extraction tasks of the shared task. As marking identifying mentions of these entities  involving change in entities, we chose to focus in the REL task on what we have previously termed "static relations" , that isEach of the three tasks makes use of manual annotations created specifically for the shared task, either entirely or in primary part. This section presents the three corpus resources on which these tasks are based.The primary EPI task data were annotated specifically for the BioNLP Shared Task 2011 and are not based on any previously released resource. Before starting this annotation effort, we performed two preparatory studies using related, previously released datasets: one considering the extraction of four protein post-translational modification event types , with reThe texts for the EPI task corpus were drawn from PubMed abstracts. In selecting the primary corpus texts, we aimed to gather a representative sample of all PubMed documents relevant to selected modification events, avoiding bias toward, for example, specific genes/proteins, species, forms of event expression, or subdomains. We primarily targeted DNA methylation and the "prominent PTM types" identified in . We defiAcetylation)1. Select a random sample of PubMed abstracts annotated with the MeSH term corresponding to the target event .Acetylation MeSH term is sufficient to select documents relevant to the modification, not all such documents necessarily concern specifically protein modification, necessitating a filtering step. Following preliminary experiments, we chose to apply the BANNER named entity tagger [MeSH is a controlled vocabulary of over 25,000 terms that is used to manually annotate each document in PubMed. By performing initial document retrieval using MeSH terms it is possible to select relevant documents without bias toward specific expressions in text. While search for documents tagged with e.g. the y tagger  trained y tagger  and to fThis primary corpus text selection protocol does not explicitly target reverse reactions such as deacetylation, and the total number of these events in the resulting corpus was low for many types. To be able to measure the extraction performance for these types, we defined a secondary selection protocol that augmented the primary protocol with a regular expression-based filter removing documents that did not (likely) contain mentions of reverse reactions. This protocol was used to select a secondary set of test abstracts enriched in mentions of reverse reactions. Performance on this secondary test set was also evaluated, but is not part of the primary task evaluation. Results on the secondary test set are reported to provide additional perspective the performance of systems at the EPI task.ROTEIN annotations, but all entity annotations were checked and revised to conform to the specific guidelines for the task  annotation guidelines [Annotation was performed manually. The gene/protein entities automatically detected in the document selection step were provided to annotators for reference for creating Pcriteria , substanidelines , adding ATALYSIS being annotated following the criteria for the POSITIVE REGULATION event type, with the additional constraints that the Cause of the event is a PROTEIN entity and the form of regulation is catalysis of a modification reaction. The manual annotation was performed by three experienced annotators with a molecular biology background. Annotator training and the overall annotation process was organized and supervised by an annotator with extensive experience in domain event annotation (TO).All event annotations were created from scratch without automatic support to avoid bias toward specific automatic extraction methods or approaches. The event annotation follows the GENIA event corpus annotation guidelines  as they ROTEIN annotations as given) for a random sample of 10% of the corpus documents. Comparison of the two manually created sets of event annotations under the primary task evaluation criteria gave an F-score of 82% for the full task and 89% for the core task . We found that CATALYSIS events were particularly challenging, showing just 65% agreement for the core task.After completion of primary annotation, we performed a final check targeting simple human errors using an automatic extraction system as follows: High-confidence system predictions differing from gold annotations were provided to a human annotator for re-evaluation (not used directly to change corpus data). To further reduce the risk of bias, we only informed the annotator of the entities involved, not of the predicted event structure. This correction process resulted in the revision of approximately 2% of the event annotations. To evaluate the consistency of the annotation, we performed independent event annotation  and one in Virginia Bioinformatics Institute (Virginia Tech). The entity and event annotation design was guided by previous studies on NER and event extraction in a closely related domain ,80.The training and test data were drawn from the primary text content of recent full-text PMC open access documents selected by infectious diseases domain experts (Virginia Tech team) as representative publications on two-component regulatory systems. Table Annotation was performed in two primary stages, one for marking core entities and the other for events and secondary entities. As a preliminary processing step, initial sentence segmentation was performed with the GENIA Sentence Splitter . SegmentCore entity annotation was performed from the basis of an automatic annotation created using selected existing taggers for the target entities. The following tools and settings were adopted, with parameters tuned on initial annotation for two documents:ROTEIN: NeMine [Protein types.P: NeMine  trained : NeMine  with thrRGANISM: Linnaeus [OLinnaeus  with "vaHEMICAL: OSCAR3 [C: OSCAR3  with conWO-COMPONENT-SYSTEM: Custom regular expressions.TEGULON-OPERON type or the generic ENTITY type . All automatically generated annotations were at least confirmed through manual inspection, and the majority of the automatic annotations were revised in manual annotation. Table Initial automatic tagging was not applied for entities of the RAnnotation for the task extraction targets - events and event modifications - was created entirely manually without automatic annotation support to avoid any possible bias toward specific extraction methods or approaches. The Tsujii laboratory team organized the annotation effort, with a coordinating annotator with extensive experience in event annotation leading annotator training and annotation scheme development, similarly as for the EPI corpus annotation. Detailed annotation guidelines  extendinInitial annotation was produced for the selected sections in 33 full-text articles, of which 30 were selected for the final dataset as representative of the extraction targets. These documents were split into training, development and test sets of 15, 5 and 10 documents, respectively. Participants were provided with all training and development set annotations and test set core entity annotations. The overall statistics of the datasets are given in Table As the corpus consists of full-text documents, it contains a somewhat limited number of articles, but in other terms it is of broadly comparable size to the largest of the BioNLP ST corpora: the corpus word count, for example, corresponds to that of a corpus of approximately 800 PubMed abstracts, and the core entity count is comparable to that in the ST'09 data. However, for reasons that may relate in part to the domain, the event count is approximately a third of that for the ST'09 data. In addition to having less training data, the entity/event ratio is thus considerably higher (i.e. there are more candidates for each true target), suggesting that the ID data could be expected to provide a more challenging extraction task.The REL task dataset consists of new annotations for the GENIA corpus , buildinROTEIN-COMPONENT relation is more than twice that for SUBUNIT-COMPLEX. Thus, at least for methods based on machine learning, we might generally expect to find higher extraction performance for the former relation.For compatibility with the ST'09 and the ST'11 GE task, the REL task training/development/test set division of the GENIA corpus abstracts matches that of the ST'09 data. The statistics of the corpus are presented in Table F1, the harmonic mean of precision and recall, is used to combine precision and recall and used as the primary ranking criterion in all tasks. The F1 score is referred to as F-score for short throughout.Evaluation for all tasks is based on comparison of submissions from participants against gold standard data prepared in advance for each task. Performance measurement is instance-oriented - that is, each individual annotation (event or relation) is considered separately - and based on the standard precision, recall and F-score metrics. Specifically, approximate span matching and approximate recursive matching. Under approximate span matching, text-bound annotations (event triggers and ENTITY type entities) in a submission are considered to match a corresponding gold annotation if their span is contained within the expansion of the gold span by one word to both the left and the right. Under approximate recursive matching, events that refer to other events as arguments are considered to match if the Theme arguments of the recursively referred events match, that is, non-Theme arguments are ignored for recursively referred events. For an extended discussion of these evaluation criteria, we refer to their definition in the BioNLP ST'09 overview [The primary evaluation criteria for determining whether a predicted event matches a gold event in the EPI and ID tasks are the same as in the BioNLP ST'09 and the GE task. These criteria relax exact matching in two aspects, incorporating overview .single partial penalty. Under the primary criteria, when a predicted event matches a gold event in some of its arguments but lacks one or more arguments of the gold event, the submission is arguably given a double penalty: the predicted event is counted as a false positive (FP), and the gold event is counted as a false negative (FN). Under the single partial penalty evaluation criterion, predicted events that match a gold event in all their arguments but do not contain all the arguments of the gold event are not counted as FP, although the corresponding gold event still counts as FN . Analogously, gold events that partially match a predicted event are not counted as FN, although the corresponding predicted event with "extra" arguments counts as FP. This criterion is intended to provide a more nuanced view of performance for partially correctly predicted events.In addition to the primary evaluation criteria, we consider a new relaxed event evaluation criterion that we term core and additional) as well as the detection of event modifications (NEGATION and SPECULATION). These aspects of event extraction are treated as separate subtasks in the BioNLP ST'09 and the GE task, where the identification of additional event arguments is subtask 2 and the detection of negated and speculated events subtask 3. While explicit subtasks are not defined for ID and EPI, both tasks specify in addition to the full task targets also minimal core extraction targets, consisting of events with only their core arguments and excluding event modifications. Results are then reported for each submission separately for evaluation against the full gold standard data (full task) and for evaluation where both the gold standard data and the submission events are reduced to only core arguments, event modifications are removed, and resulting duplicate events removed (core task). In terms of the subtask structure of the BioNLP ST'09 and the GE task, the core task is analogous to subtask 1 and the full task analogous to the combination of subtasks 1-3.The full ID and EPI tasks involve many partially independent challenges, requiring the extraction of all event arguments  is entirely contained within the span of the gold annotation. This corresponds largely to the approximate span matching criterion for events, although the REL criterion is slightly stricter in not involving testing against an extension of the gold entity span. Relation matching is exact: for a submitted relation to match a gold one, both its type and the related entities must match.The statistical significance of the differences in system performance is evaluated using the approximate randomization method with 9,999 repetitions ,88.Final results to each of the EPI and ID main tasks were successfully submitted by seven participants and results for the REL supporting task by four participants.Table Several participants compiled dictionaries of event trigger words and two dictionaries of hedge words from the data. The use of dictionary-based approaches instead of machine learning for these components may reflect challenges in training with the trigger and hedge annotations. Trigger annotation is not exhaustive as triggers are not included in gold data e.g. for event statements where the participants fall out of scope of the entity annotation, and negation and speculation are annotated without specific trigger/cue words, precluding direct training for modifier cue detection. Despite the existence of rich lexical resources in the domain, only two groups applied databases such as UMLS in their systems. The results indicate that such resources are not critical for success at the task, a somewhat surprising result that may merit further investigation. In addition to manually curated external resources, ones derived from large-scale unannotated data through unsupervised methods were used by three groups for event extraction and one for relation extraction.HOSPHORYLATION annotations included in the GE corpus [Table E corpus , no partWhile several participants made use of supporting syntactic analyses provided by the organizers , none apWe find a remarkable number of similarities between the approaches also for the REL supporting task, with all four utilizing full parsing and a dependency representation of the syntactic analysis, and the three highest-ranking further using the same parser and specific dependency representation. While UTurku  and VIBGTable We note that only two teams, UTurku  and ConcTheme and Site arguments is consistently higher than for other event types, with absolute F-score differences of over 10% points for many systems. Similar notable performance differences are seen between the addition events, for which ample training data was available, and the removal types for which data was limited .Table The full ID task requires the extraction of additional arguments and event modifications and involves multiple novel challenges from previously addressed event extraction tasks including a new subdomain, full-text documents, several new entity types and a new event category. Nevertheless, extraction performance for the top systems is comparable to the state-of-the-art results for the established BioNLP ST'09 task  as well ROCESS, while UTurku [We note that there is substantial variation in the relative performance of systems for different entity types. For example, Stanford  has relae UTurku  results ROCESS approaches or exceeds 70% F-score, arguably approaching a sufficient level for user-facing applications of the extraction technology. By contrast, BINDING and regulation events, found challenging in ST'09 and GE, remain problematic also in the ID task, with best overall performance below 50% F-score. As in the EPI task, only the UTurku and ConcordU [The best performance for simple events and for PConcordU  teams atWith the exception of ConcordU, all systems clearly favor precision over recall Table , left, iROTEIN in GE), challenges related to nested entity annotations (not appearing in GE), or the new PROCESS type. A possible ID-specific strength of the three top-ranking systems is the use of GE data for training: Riedel and McCallum [The five top-ranking systems participated also in the GE task , which iMcCallum  report aMcCallum  a 3% poiMcCallum  estimatep < 0.05). By contrast to the results for EPI, where differences of over 30% points were observed between full and core task results, there are only modest and largely consistent differences to the corresponding full task results for ID, reflecting in part the relative sparseness of additional arguments: in the training data, for example, only approximately 3% of instances of event types that can potentially take additional arguments had one or more additional arguments. While event modifications represent a further 4% of full task extraction targets not required for the core task, the overall low extraction performance for additional arguments and modifications limits the practical effect of these annotation categories on the performance difference between systems addressing only the core targets and those addressing the full task.Table ROTEIN-COMPONENT relation extraction as tentatively predicted from the relative numbers of training examples, but their rule-based approach suggests training data size is likely not the decisive factor. While the limited amount of data available prevents strong conclusions from being drawn, overall the lack of correlation between training data size and extraction performance suggests that performance may not be primarily limited by the size of the available training data.Table Table Table Evaluation using single partial penalty allows additional perspectives into both the absolute and the comparative performance of systems. The results also indicate possible directions for seeking more meaningful criteria for event extraction system evaluation. While there are no single "correct" answers, further basis for evaluating candidate answers can be provided through manual evaluation reflecting the perceived quality of system outputs, considered in the following.Finally, Table n combined systems, where n is a parameter ranging between one and the total number of systems. For determining event equality for the combination, we applied the primary evaluation metrics (approximate span and recursive matching). As the majority of systems only addressed the core tasks, we measured the performance of system combinations only on the core extraction targets.The event extraction system analyses created by the participants represent a rich resource for further study of the task. To explore one opportunity presented by this data, we studied whether the outputs of various systems could be beneficially combined to further improve extraction performance. We performed a straightforward combination of the output of all systems participating in the EPI and ID main tasks using a strategy where the combination system only outputs an event if it is found in the output of at least n ∈  the combinations outperform the highest standalone result for the core task (68.86%) in terms of F-score, improving on the standalone result by over 3% points in F-score for n = 3, thus providing an approximately 10% relative decrease in error.Table n in calculating the combination permit the choice of various points along a precision-recall curve spanning nearly the full precision range, giving nearly 97% precision for n = 7. However, no combination permits for comparably high values of recall: even for n = 1, corresponding to the union of all submissions, recall remains below 80%. There thus remains a substantial number of events that none of the systems can retrieve.Different choices of n = 2 and n = 3. The advantage is somewhat less than for EPI, approximately 2.5% points or an approximately 6% decrease in error. The more limited benefit from system combination for ID may be explained in part by the greater variance in the performance of individual systems for the task.Table We note that the system combination strategies explored here are comparatively simple and make no use of information such as the relative reliability of the different systems , which cEvaluation against a fixed gold standard provides a stable, objective basis for the comparison of systems and, when feasible, is often the preferred choice for the study of new tasks such as those introduced in this study. A fixed, fully annotated gold standard has the further benefit of allowing the results of experiments performed after its original introduction to be directly compared against those of the original work on an even basis.translation from natural language into the semantic representation. Tightly specified annotation guidelines can (and in gold standard annotation arguably should) be used to rule out all but one candidate interpretation (cf. controlled language). However, from the perspective of many applications such constraints do not change the basic issue that declaring one out of several potentially acceptable analyses "correct" and others "incorrect" can lead to a distorted picture of system (or human annotator) performance.Nevertheless, comparison against gold standard annotation such as that applied in the BioNLP Shared Tasks also has its drawbacks when compared to alternatives such as direct evaluation of system outputs. One such drawback is the difficulty of assessing degrees of correctness for system outputs that differ from gold annotation. For any non-trivial representation aiming to capture (part of) the meaning of complex text there will almost unavoidably be cases where more than one set of annotations are at least acceptable; where the annotation task takes on aspects of A affects the regulation of B is marked with two REGULATION annotations . This effect is consistent also when examining the individual samples of 100 events for each system separately. Accepting (for the moment) the manual evaluation as "ground truth", this result indicates that approximately 20% of events appearing as false positive in evaluation against gold are in fact correct.For ID, while the average precision for the sample of 700 events is 49.2% in comparison against gold, the manual evaluation suggests that 64.7% of the events are correct. This discrepancy between the two evaluations is larger than that for EPI also in relative terms: with reference to the results of the manual evaluation, 30% of "false positive" events in gold evaluation are correct. Unlike in the EPI evaluation, a nontrivial amount of ID events were marked acceptable; the evaluation suggests that less than 30% of all event predictions are incorrect, a striking contrast to the over 50% estimate from comparison to gold.Some of these differences are almost certainly due to events missing from gold. However, given the very high precision of gold events measured also in this evaluation, the lack of factors that could make errors of omission up to an order of magnitude more common than errors of commission in gold annotation, and the support from the relative number of "accurate" markings, we find it likely that the observed discrepancy is primarily due to cases that genuinely permit more than one acceptable analysis.While the trend that this evaluation shows was expected and similar results have been reported previously (e.g. ), the spWe have presented the preparation, resources, results and analysis of three tasks of the BioNLP Shared Task 2011: the main tasks on Epigenetics and Post-translational modifications (EPI) and Infectious Diseases (ID) and the supporting task on Entity Relations (REL).The two main tasks were each based on newly annotated corpus resources developed for the purposes of the shared task. For the EPI task, we annotated 1,200 publication abstracts selected as a balanced sample of all PubMed citations regarding the selected event types. For ID, a corpus of 30 full-text publications on the two-component systems subdomain of infectious diseases was created in a collaboration of event annotation and domain experts, adapting and extending the BioNLP'09 Shared Task (ST'09) event representation to the domain. Data for the supporting task REL was created by extending previously introduced GENIA corpus annotations.The EPI task results demonstrated that the core extraction target of identifying statements of 14 different gene/protein modification types can be reliably addressed by current event extraction methods, with two systems approaching 70% F-score for this task. We further demonstrated that a simple system combination can further improve on this level of performance, reducing error rate by approximately 10%. Challenges remain in detecting statements regarding the catalysis of modification events as well as in resolving the full detail of such events, a task attempted by only one EPI task participant and at which performance remains below 55% F-score.For the ID task, despite the novel challenges of full papers, four new entity types, extension of event scopes and the introduction of a new event category for high-level processes, the highest results achieved by the seven participating teams were comparable to the state-of-the-art performance on the established ST'09 data, showing that the event extraction approach and present systems generalize well and demonstrating the feasibility of event extraction for the infectious diseases domain. Analysis of the results suggested further opportunities for improving extraction performance, and a system combination experiment demonstrated that even comparatively simple combination approaches can realize further benefits. Finally, manual evalutation of system outputs suggested that many events identified as false positives in evaluation against the gold standard data may nevertheless be acceptable from a user perspective.Both the EPI and ID main tasks were designed from the perspective of supporting specific biocuration tasks. We believe successful EPI systems have potential to offer further support for existing protein modification database curation as well as for novel efforts targeting specifically epigenetics-related modifications. The resources, tools and methods developed in the ID task are currently being adopted in the development of the Pathosystems Resource Integration Center (PATRIC) . Presenthttp://www.bionlp-st.org/.As was done after the BioNLP'09 shared task, we have made the data, tools and resources for all three task introduced in this paper open to all interested parties to encourage further study of event extraction. The tasks continue as an open shared challenges accessible through the shared task website, The authors declare that they have no competing interests.SP and TO conceived of the tasks. SP coordinated the ID and REL task organization, drafted the manuscript, and implemented evaluation and support tools. TO coordinated the EPI task organization and led the EPI, ID and REL annotation efforts. RR, DS, CM and CW contributed to the design of the ID task, RR created the ID task initial automatic annotation, and DS, CM, CW and TO created the ID task manual annotation. BS and SA participated in the design and coordination of the ID task and JT participated in the design and coordination of the overall shared task. All authors read and approved the final manuscript.


Agile software development gains requirements from the direct discussion with customers and the development staff each time, and the customers evaluate the appropriateness of the requirement. If the customers divide the complicated requirement into individual requirements, the engineer who is in charge of software development can understand it easily. This is called division of requirement. However, the customers do not understand how much and how to divide the requirements.This paper proposes the method to divide a complicated requirement into individual requirements. Also, it shows the development of requirement specification editor which can describe individual requirements.The engineer who is in charge of software development can understand requirements easily. Generally, the software is developed according to a life-cycle model Boehm . Any lifRecently, MBT model-based testing , Time(When), Location(Where), Reason(Why), Object of target(What)), and SRS has the tag which 5W1H are written as semantic information. We developed the Software requirement specification(SRS) editor system with semantic information. Since the system which we developed can have semantic information, the engineer who takes charge of SRS and the engineer who takes charge of software test have the same understanding.A part of 1.2 scope shows that the people who belong to university can send and receive a mail. However, this requirement sentence does not show who these people are clearly. Figure In order to divide a complicated requirement into individual requirements, this chapter suggests the method to describe the individual requirements. Sugimoto  can be divided into six parts, 5W1H, (Actor(Who), Time(When), Location(where), Reason(Why), Object of target(What) and requirement specificaton have tag which 5W1H was written as semantic information. This paper also describes how to given the degree of requirement, and describes the methods to have tag which the degree of requirement was written as semantic information. This paper describes the Requirement Specification editor system which we developed with semantic information, the function which reuses the inputted the description, the function which shows the relationship of the descriptions.


Plecotus auritus indicated that the positioning of the tail membrane (uropatagium) can significantly influence flight control. Adjusting tail position by increasing the angle of the legs ventrally relative to the body has a two-fold effect; increasing leg-induced wing camber  and increasing the angle of attack of the tail membrane. We also used our model to examine the effects of flying with and without a tail membrane. For the bat model with a tail membrane increasing leg angle increased the lift, drag and pitching moment (nose-down) produced. However, removing the tail membrane significantly reduced the change in pitching moment with increasing leg angle, but it had no significant effect on the level of lift produced. The drag on the model also significantly increased with the removal of the tail membrane. The tail membrane, therefore, is potentially important for controlling the level of pitching moment produced by bats and an aid to flight control, specifically improving agility and manoeuvrability. Although the tail of bats is different from that of birds, in that it is only divided from the wings by the legs, it nonetheless, may, in addition to its prey capturing function, fulfil a similar role in aiding flight control.Wind tunnel tests conducted on a model based on the long-eared bat  In recent years it has become established that bird tails have important effects upon their flight. For example, bird tails are known to produce lift during flight In contrast to birds, relatively little research has investigated the aerodynamic function(s) of a bat's tail membrane (uropatagium). Although previous authors have hypothesised that bat tail membranes perform similar aerodynamic functions to bird tails Of the 17 families of bats Plecotus auritus). The use of simple physical modelling in biomechanics is a valuable technique as it allows variables to be manipulated in a manner not possible using comparative in vivo methods. This approach also allows the performance consequences of each variable to be thoroughly explored Current understanding of the flight aerodynamics of animal tails is polarised. For example, although delta-wing theory has been used to predict tail performance in birds Pipistrellus pipistrellusThe bat model presented here is necessarily a simplification of a real bat in flight, representing a small gliding bat in steady state aerodynamic conditions. At first glance this may appear at odds with bat biology as most bat species, with the exception of some large bats P. auritus,Plecotus auritus is a slow flying, highly manoeuvrable species which gleans prey from amongst vegetation P. auritus specimen, from which the measurements where taken, represents a typical method of stretching out wings of both bird and bats in the field for calculating wing span and area. The model, therefore, provides our best possible representation of the posture of a gliding bat, in the absence of detailed P. auritus flight footage, and is consistent with previous work on vertebrate aerodynamics  of a brown long-eared bat (mics see . Latex smics see . Hencefoβ) was set relative to the body and the body angle (ϕ) was set relative to the oncoming air stream (4 to 2.8×104) determined using a pitot tube, which is at the higher end of the natural flight speeds of many insectivorous bats P.auritus (around 3 m/s) P. auritus the aerodynamic coefficients (i.e. lift coefficient) measured in the wind tunnel will also be applicable to the natural flight of P. auritus. Indeed, aerodynamic coefficients are often quoted as being relevant over Reynolds numbers of several orders of magnitude The force and moment measurements were made using a 6-component force torque transducer . Prior to testing the calibration of the transducer was checked using small weights applied in the direction of each axis. Data was acquired using a National Instruments card  plugged into a desktop computer. The transducer is manufactured to be accurate down to increments of 0.0125 N (forces) and 0.0625 Nmm (torques). The bat model was mounted onto the transducer via small wooden discs and a thin structural support. This arrangement was then attached to the mast of the wind tunnel at the Université of Liège, Belgium. The wind tunnel working section area of 2×1.5 m is significantly larger than the bat model, removing the potential for unwanted aerodynamic effects induced by the tunnel walls r stream . All angL is the lift, D the drag, xF and zF the transducer forces and ϕ the body angle . The location of the centre of mass on the bat model corresponded well with methods used to estimate the centre of mass of live bats Where ansducer . The locCL, CD and CM are the lift, drag and pitching moment coefficients respectively, ρ is the air density, V the air speed, A the wing area and c the average wing chord. Finally the lift to drag (L/D) ratio of the model was calculated for each test position since this ratio gives a good indication of overall aerodynamic performance.Where ANCOVA was used to determine whether the different model configurations  changed the relationship between aerodynamic parameters and leg angle. Tukey's post hoc tests were used to indentify specific differences between the four model configurations used. All statistical tests were preformed using the statistics toolbox for MATLAB® R2009a .During wind tunnel testing little aero-elastic deformation of the model's latex wing membranes or wing struts was observed. There was also no obvious fluttering of the trailing edge of the membrane. The only deformation of the latex membrane observed was the local increase in wing camber (leg-induced wing camber) due to the repositioning of the legs , previouCL and CD produced by the bat model with a tail membrane follow similar general trends with body angle and leg angle  and also changed with bat model configuration (body angle and presence/absence of the tail membrane), and the incremental change (i.e. the slope) in CL with leg angle differed between the model configurations . Furthermore, it is clear from CL and leg angle was similar for the model with and without a tail membrane; only body angle had an effect. The CL produced by the bat model is, therefore, increased by both leg angle and body angle, but the removal of the tail membrane has no impact.The eg angle . As leg n ANCOVA  confirmeCD was always positive and tended to increase with body and leg angle . Accordingly, simplifying the ANCOVA to assume parallel lines  showed that CD increased with leg angle and changed with model configuration . CL, CD is increased by the removal of the tail membrane from bat model. The CD, therefore, increases with both increasing leg angle and body angle, and further increases with the removal of the tail membrane from the model.The eg angle . ANCOVA L/D ratio . Removal of the tail membrane from the model has a profound affect on the pitching moment produced. (Decreasing body angle and increasing leg angle caused the pitching moment coefficient to decrease . This doroduced. . Tukey'sP. auritus flight performance.Changes in leg angle had a significant impact on the aerodynamic performance of the bat model . These lM = 0) and lift equals weight (L-W = 0). Then in general, a statically stable bat would be defined byOne of the most critical issues of flight performance is the trade-off between stability and manoeuvrability (i.e. the slope of the equation describing the relationship between the pitching moment coefficient and body angle should be negative). In this statically stable case, an increase in body angle due to, for example atmospheric turbulence will be corrected by an accompanying decrease in pitching moment and the bat will return to equilibrium position. However, the results of the pitching moment  for the i.e. the slope of the relationship between pitching moment coefficient (and therefore the pitching moment) and body angle is positive. In this case any increase in body angle will tend to be exacerbated by the increase in pitching moment, which will in turn increase body angle further making the bat model statically unstable. Second, the pitching moment results show that the slope of the surface is not significantly affected by the leg angle . In otheP. auritus configuration is statically unstable. A lack of static stability is not necessarily undesirable. Acrobatics aircraft are often neutrally stable (on the border between static stability and instability) as this increases their flight agility and the ability of the pilot to perform stunts The most obvious explanation for the static instability of the bat model is that the centre of pressure of the wing (the point where the aerodynamic forces act) lies in front of the centre of mass. Of course, real bats can flap and deform their wings in a complex manner P. auritus, particularly around the pitch axis. The wings of bats are well positioned to produce the necessary rolling and yawing moments around the centre of mass required for many manoeuvres Eptesicus nilssoni and Pipistrellus pipistrellus) have specialisations in wing morphology, such as broad wing tips, to enhance the aerodynamic rolling moment generated by their wings Repositioning the tail membrane by increased leg angle, increases the pitching moment coefficient produced by the model, compared to the model without a tail membrane . TherefoCL produced by the model  has a flight control function and hypothesise that:Our model data here presents the first experimental evidence for a flight function of the bat tail membranes and provides a foundation for future research efforts. It would, for example, be very interesting to study whether bats actively control their leg position during flight as the model results suggests since potentially this is similar to a bird's control of tail position and furl which allows them to actively influence their aerodynamic performance. The alternative to active control is passive positioning of the legs and tail membrane driven by the inherent aerodynamic and inertial loads from the wings and body of the bat. From the results of the bats will actively control leg position (and hence tail position and leg-induced wing camber), since this will allow greater control over their flight and consequently, their foraging performance.bats will rapidly reposition their legs and tail, coincident with aerial manoeuvres.bats with the longest legs and largest tail membranes will be the most manoeuvrable.


Second, we distinguish between the immediate effect of singing on speech production and the long-term effect of the entire program on language recovery. Many results in the MIT literature can be explained by this temporal perspective. Finally, we propose that MIT can be viewed as a treatment of apraxia of speech more than aphasia. This issue should be explored in future experimental studies.We present a critical review of the literature on melodic intonation therapy (MIT), one of the most formalized treatments used by speech-language therapist in Broca’s aphasia. We suggest basic clarifications to enhance the scientific support of this promising treatment. First, therapeutic protocols using singing as a speech facilitation technique are not necessarily MIT. The goal of MIT is to restore propositional speech. The rationale is that patients can learn a new way to speak through singing by using language-capable regions of the right cerebral hemisphere. Eventually, patients are supposed to use this way of speaking permanently but not to sing overtly. We argue that many treatment programs covered in systematic reviews on MIT’s efficacy do not match MIT’s therapeutic goal and rationale. Critically, we identified two main variations of MIT: the French  Melodic intonation therapy MIT; , 2] is a is a2)] Because MIT is one of the most formalized treatments in speech-language therapy , it is pThere is no consensus in the literature on the definition of MIT, which has sometimes been reduced to the intoned-speech facilitation technique rather than the entire program, similar to describing a recipe as one of the ingredients. Consequently, so-called MIT programs vary widely. Moreover, it is important to distinguish between the immediate effects of the technique on speech accuracy and the longer-term effects of the entire program on language recovery. In our review, we discuss the implications of the main deviations from the original MIT. A second objective is to consider the plausible mechanisms involved in the MIT and to suggest novel approaches in order to understand better these mechanisms.First, we will define the basic goal of MIT according to its designers and how they developed MIT’s rationale with this in mind –3.Melodic intonation therapy was developed to improve propositional language , 2, or tBecause MIT targets generative speech, the American Academy of Neurology recommends that efficacy studies should measure the effect of the program on non-trained material, standard language tests, and spontaneous connected speech . IdeallyThe original MIT is based on the observation that people with aphasia are able to sing familiar songs , but thaintoned speech. Intoned speech is made of pitch and rhythm features based on the exaggeration of normal speech prosody , unison production with the clinician, and lip-reading Figure . AlthougIn the following section, we review some variations made to the original MIT program and its facilitation technique.We examined 14 publications Table , 8. We fpalliative versions of MIT, and certain generalizations may be made. For example, Hough  are also associated with a departure from the rationale of the original MIT. At least theoretically, these modifications could have an impact on the mechanisms by which MIT works on language recovery.The initial hypothesis concerning the role of the right cerebral hemisphere to the MIT efficacy remains debated. Table First, only three of the seven studies used the original MIT , 18, 31.Second, not all studies used pre- and post-therapy acquisition. Belin and colleagues  only repThird, the functional neuroimaging paradigms differed across studies. One study used a covert action naming task , anotherIndividual differences constitute another important factor to consider. Lateralization of brain activation related to language tasks likely varies depending on the time elapsed since a stroke . MoreoveDespite the difficulty of properly comparing the different studies in terms of the brain substrates involved in MIT, three consistent observations may be made, based on four reports that used a similar repetition paradigm of bisyllabic words and/or short phrases with aphasic participants in the chronic stage , 31, 32.Some evidence point to an association of formulaic expressions with right frontotemporal areas, the right basal ganglia and, possibly, the right cerebellum –44. AlthThus, the role of the cerebral hemispheres in the MIT mechanism could be better determined by distinguishing the facilitation technique effect and the program effect. How these two effects interact remains to be understood, and could be explored within the broader issue of brain reorganization after stroke. To date, brain imaging data acquired pre- and post-MIT are only available for 12 participants Table , 18, 31 One of the most compelling reasons for using singing in a speech facilitation technique in MIT  is that singing along . Interestingly, this is the condition that the authors of MIT deemed the most clinically facilitating for teaching the intoned-speech technique to patients.In a thorough study of the facilitation effect of singing in aphasia, Racette and colleagues  measuredsinging along over singing alone and (2) the finding of singing along over speaking along. According to the authors, singing in unison promoted better speech output than singing alone, because it allowed patients to synchronize with a stable model, reducing the task-associated memory load compared to sung repetition alone. They speculated that unison production had a positive impact on the accuracy of motor speech planning and performance through the involvement of the mirror neuron system (Racette and colleagues further discuss two results of their study: (1) the finding of n system , 56 or tn system –59. Thesn system  and musin system . With ren system  suggesten system . Howevern system  providesIn the next section, we address the importance of rhythmic aspects  in MIT’s main facilitation technique.Several studies have attempted to determine the relative contribution of the pitch and rhythmic components as used in MIT’s main facilitation technique  recommeThe renewed interest in MIT-like interventions should provide new opportunities to clarify aspects that have important clinical implications for patients. Therapeutic protocols using singing as a speech facilitation technique are not necessarily MIT. Among the protocols that have been considered as MIT, we distinguish in fact three main types of treatment based on different therapeutic goals: original MIT, TMR, and palliative MIT. The original MIT aims to restore propositional speech through a functional reorganization of language production. MIT variations such as TMR train patients to use a facilitation technique in case of speech struggle, while palliative versions of MIT help patients with the most severe expressive deficits produce a limited set of useful, readymade phrases.The treatment mechanisms may depend on the therapeutic approach. Future studies could more clearly distinguish between the facilitation effect of a technique and the therapeutic effect of a treatment. Currently, the mechanisms of the intoned-speech technique appear to involve the rhythmic component of the melody along with left hemisphere peri-lesional regions. However, the brain correlates of MIT’s therapeutic effect on language recovery are still unclear, even at the gross level of brain hemispheric lateralization. The combination of rhythm and pitch variation seems to be key elements in the original program but the relative role of rhythm and pitch in MIT’s therapeutic effect has not been tested yet. Moreover, other components of the treatment could also have a significant impact on language and/or speech recovery, especially in TMR and palliative variations of MIT. This deserves investigation in order to improve the efficacy of speech and language therapy in different clinical profiles of patients with non-fluent aphasia.Although MIT is regarded as a language treatment for Broca’s aphasia, it could also act on motor speech deficits in this aphasic syndrome. Future experimental studies could explore this issue more thoroughly.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In the 30 simulated samples, the Hausdorff distances with respect to the Ground Truth are significantly reduced after restoration, signifying that the algorithm can restore geometrical accuracy of motion-affected LV models. In the 20 in vivo patient-specific models, the results show that our method is able to restore the shape of LV models without altering the general shape of the model. The magnitudes of in-plane translations are also consistent with existing registration techniques and experimental findings.This paper describes an automatic algorithm that uses a geometry-driven optimization approach to restore the shape of three-dimensional (3D) left ventricular (LV) models created from magnetic resonance imaging (MRI) data. The basic premise is to restore the LV shape such that the LV epicardial surface is smooth after the restoration and that the general shape characteristic of the LV is not altered. The Maximum Principle Curvature ( Breath-hold cine Magnetic Resonance Imaging (MRI) is an advanced imaging technique for cardiac morphological and functional assessment in clinical practice. While conventional methods of evaluation are based on MRI images, several recent methods However, factors such as respiration and patient movement contribute to misalignments in the MRI data which results in inaccuracies in the 3D models. MRI data acquired over different breath-hold positions also induce errors in the reconstructed models. The main dilemma of using an image registration approach to restore the shape of the LV is that errors induced by motion are already embedded in the images. While multi-view image registration techniques could potentially reduce such errors, these methods are essentially using data which contain error for self-correction. In our work, we make use of morphological knowledge of the LV to drive the shape restoration. Instead of using image-based parameters, such as gray values, our LV shape restoration method is based on geometrical consideration. The basic premise is that the LV epicardial surface must be smooth after the restoration, which is a reasonable assumption based on observed morphology, that the myocardial surface tension or force of the LV wall forces the hearts to minimize the wall surface area, creating a smooth epicardial surface. In addition, the general shape of the LV cannot be lost in the process such as its skewness and asymmetrical configuration. The Maximum Principle Curvature in vivo patient-specific models to test the performance of the algorithm, followed by a discussion on the implications of the experimental results, and finally conclude the paper.The input to our algorithm is an initial 3D LV mesh model In this section, we briefly outline the formulation of The value of The basic premise of the shape restoration is the assumption that the LV epicardial surface is smooth. From a shape characterization perspective, this implies that it should have minimum local concavity. The objective is then to find the optimal modifications in the LV model such that its total concavity is at a global minimum. Computationally, we can calculate the maximum and minimum principle curvatures  for Human Research. All enrolled participants gave written informed consent. The MRI scan was performed using breath-held steady-state free precession technique on a 1.5T Siemens scanner . TrueFISP (fast imaging with steady-state precession) MR pulse sequence with segmented k-space and retrospective electrocardiographic gating were used to acquire 2D cine images of the LV in the long-axis (LA) plane, as well as a parallel stack of 2D cine images of the LV in the SA plane, from the LV base to apex . Each slice was acquired in a single breath hold, with 25 temporal phases per heart cycle. The epicardial borders of contiguous SA-slices were manually delineated by an experienced cardiologist using commercially-available software CMRtools . Both SA- and LA-views were utilized to carry out 3D LV reconstruction at the end-diastole phase. in vivo patient-specific model, we assess the performance of our restoration algorithm by comparing the mean contour displacement values of our method with those of existing image registration techniques As it is difficult to obtain the corresponding ground truth for each of the 20 To further validate the applicability of our method, we study the performance of our method on a set of LV data with simulated motion. This is done by selecting a gold standard to act as a reference or ground truth for comparison. Ten healthy volunteers underwent breath hold cine-MRI scan. The sample with the smoothest epicardial surface is selected as the gold standard. To do this, the respective 3D models of the LV were reconstructed for each of the 10 volunteers and we subject these 3D models to a computational algorithm to assess the quantitative value of the local curvature. In addition, we apply our shape restoration method to these 3D models. The model which yields the minimum concavity and has the least modification in the restoration process is selected as the Ground Truth.This Ground Truth model is then used as a reference to investigate our algorithm's ability in performing LV shape restoration. We proceed to construct a set of 30 random samples from the ground truth model by translating three randomly selected but consecutive slices to simulate motion artifacts. They are translated within the range of ±20 mm. These 30 randomized samples are then restored using our proposed algorithm and compared against the ground truth model for validation. To evaluate the accuracy of the restoration, we make use of The Metro Software in vivo patient-specific models. While we do not have the ground truth for each of the patient-specific model, we compared the results of the mean contour displacements and observed that the values lie within the range reported in existing literatures. In addition, we attempted to account for this limitation by performing an experiment with simulated motion and observed significant improvement in terms of Hausdorff distance and curvedness.As mentioned in the above section, the limitation of this study is in the validation of the restoration results of the 20 We would also like to highlight that the derivation of the optimization function that drives the shape restoration is based on the observed morphology that the surface tension of the LV wall physically forces the heart to minimize the wall surface area, creating a smooth epicardial surface. However there are questions if this assumption still holds true for severe pathological conditions such as acute myocardial infarction. In a study of ventricular shape after myocardial infarction by Mitchell et al. in vivo LV epicardial datasets at end-diastole are used as inputs to investigate the performance of our shape restoration algorithm. The results showed that there were significant improvements in the smoothness of the LV mesh both visually and quantitatively . Also, our algorithm was successful in preserving the overall shape of the LV mesh without over smoothing. Our results are also consistent with results of existing image registration techniques. Another 30 samples of data sets generated from a smooth patient set (i.e. Ground Truth), were restored and the difference in Hausdorff distance and curvedness values w.r.t the Ground Truth were smaller after restoration.In this paper, we presented an automatic algorithm to restore the shape of a 3D LV mesh model using a geometry-driven optimization approach. The method used an analytical surface fitting method to approximate the geometry of the LV mesh and computed the minimum principal curvature as a quantification of the surface smoothness. Next, a limited memory quasi-Newton algorithm, L-BFGS-B, was used to correct the positions of all SA-slices to achieve an optimal shape with minimal concavity. To retain the overall shape of the LV mesh, such as its asymmetrical configuration, we performed optimization using