There has been a significant increase in the popularity of Web-based question-and-answer (Q&A) services that provide health care information for consumers. Large amounts of Q&As have been archived in these online communities, which form a valuable knowledge base for consumers who seek answers to their health care concerns. However, due to consumers’ possible lack of professional knowledge, it is still very challenging for them to find Q&As that are closely relevant to their own health problems. Consumers often repeatedly ask similar questions that have already been answered previously by other users.In this study, we aim to develop efficient informatics methods that can retrieve similar Web-based consumer health questions using syntactic and semantic analysis.We propose the “SimQ” to achieve this objective. SimQ is an informatics framework that compares the similarity of archived health questions and retrieves answers to satisfy consumers’ information needs. Statistical syntactic parsing was used to analyze each question’s syntactic structure. Standardized Unified Medical Language System (UMLS) was employed to annotate semantic types and extract medical concepts. Finally, the similarity between sentences was calculated using both semantic and syntactic features.We used 2000 randomly selected consumer questions to evaluate the system’s performance. The results show that SimQ reached the highest precision of 72.2%, recall of 78.0%, and F-score of 75.0% when using compositional feature representations.We demonstrated that SimQ complements the existing Q&A services of Netwellness, a not-for-profit community-based consumer health information service that consists of nearly 70,000 Q&As and serves over 3 million users each year. SimQ not only reduces response delay by instantly providing closely related questions and answers, but also helps consumers to improve the understanding of their health concerns. Web-based health-related question and answering (Q&A) services are becoming more and more popular. Some consumer health websites receive millions of page views each year, such as NetWellness, WebMed, and EverydayHealth. Thousands of users visit these websites to search for answers related to their health problems . Many ofThe service model of community-based Q&A platforms has several unique advantages. First, users keep their identity anonymous, which protects the users’ privacy and encourages information sharing. For example, many people who feel too stressed or embarrassed to ask certain types of questions during face-to-face physician consultations  can seek help from the online community. Second, the Q&A platform can serve as an information source for acquiring new knowledge. It enhances a user’s understanding of health care on many different topics, such as nutrition, patient care, or disease management. Third, compared to face-to-face physician consultation, a community-based service normally provides a quicker response and a wider range of perspectives. For example, a user who asks a question about “children nutrition” may receive answers from both child-care experts and nutritionists. Finally, the online community provides a platform for consumers to share their health concerns and wellness interests. This creates an environment that not only shares new knowledge, but also provides emotional support for health care consumers. Therefore, a community based Q&A is an excellent way of delivering health care information to a wide range of public users. It could help reduce the time and cost of information delivery, such as those services provided by MIMIR  and YahoDespite the fact that an online community-based health information service has many advantages, there are still many challenging problems that need to be addressed to improve the service’s quality and accessibility . ConsumeMany community-based service platforms have now archived thousands of Q&As, which creates a valuable knowledge base. Berland et al published a study  on evalusemantic similarity analysis method to support the need for retrieving similar questions from NetWellness, that complements existing services, and that enables efficient reuse of the accumulated Q&A knowledge , NP (noun phrase), and VP (verb phrase). The syntactic tree is expanded until all leaves contain a single constituent. Unlike standard syntactic parsing that treats each of the words as a constituent, our method uses the semantic annotation results and treats the UMLS recognizable entities as syntactic constituents. This last step enabled us to retrieve relationships between the identified named entities.To analyze the linguistic structure and the constituents of consumer questions, we parsed the question sentences into syntactic trees. The AQUA parser  was exteWe constructed dependency grammars  from theQ1 and Q2 that are generated from two different consumer questions, we can then calculate the DC and CS similarity scores through the formulas in Dice coefficient and cosine similarity are the algorithms that are employed for calculating similarity in this paper. Dice coefficient (DC) and cosine similarity (CS) see  were useWe use binary representation for both syntactic and semantic features. If a feature appears to a consumer in a question, then it has a value of 1; otherwise, the value is 0. From the binary representation, we can generate vectors containing both syntactic and semantic features to compare the similarity between these two questions as indicated in formulas 1 and 2 in F-score were measured. Also, we created an illustration for the aggregated semantic type patterns of the “Diet and Nutrition” category, which contained 2335 questions (see We applied our method (SimQ) to the consumer questions posted on the Netwellness website, which has archived over 70,000 Q&As and more than 600 health information articles that were written by domain experts. All of the Q&As and articles were categorized into 121 health topics. The performance of the proposed SimQ method was then evaluated by using 2000 random selected NetWellness questions. The precision, recall, and ions see .To evaluate the performance of the SimQ method, 12 seed consumer questions were selected from Yahoo Answers as input questions. These questions were selected from different categories, such as women’s health, diseases and conditions, and mental health. Two biomedical informaticians, who were independent to this project, were recruited to generate a gold standard to evaluate the results of the SimQ question retrieval engine. They were asked to manually select Netwellness questions that were closely similar to the seed questions. A total of 2000 randomly selected Netwellness questions were used as the candidate pool. In total, 246 consumer questions were selected by the evaluators as the positive gold standard to evaluate SimQ’s retrieval performance. The initial agreement between the two evaluators was 83%. However, they were allowed to discuss and reach a final unanimous agreement on all the similar consumer questions, which were then used as the gold standard.We also compared the performance of similarity analysis using different feature representations. F-score, 72.2% precision, and 78.0% recall.In Syntactic parsing is used to facilitate the identification of named entities and to support the construction of semantic features . Part-ofTo demonstrate the use of SimQ, we developed an application that complemented the existing Q&A services on the NetWellness website. This new application enhances the workflow through semantic similarity analysis see , Step 1.At present, one of the most common ways to find health information online is through search engines. Search engines use keyword-based information retrieval techniques , which rThe SimQ method is related to but fundamentally different than Question Answering Machines (QAMs) ,26. QAMsWe observed two types of errors from the SimQ retrieval results: false positive results and false negative results. False positive results, which are incorrectly included questions, were often created by questions with small but important differences. For example, SimQ retrieves the query, “How can I lose weight in one month?” for the question, “How I can gain weight quickly”, because both “lose weight” and “gain weight” have the same semantic type, Findings. Both questions contain the same semantic types, Patient Group and Temporal Concept. The only major difference is seen in the concepts of “lose weight (CUI:C0043096)” and “gain weight (CUI:C0043094)”. One potential solution for this type of error is to incorporate concept importance ranking into the similarity analysis. When generating feature vectors, important concepts have higher weights for calculating the similarity score, which can improve the retrieval results. False negative results (incorrectly excluded questions) are often caused by complex questions. For example, the question “I have a breast lump, could it be a lymph node or tumor?” is semantically close to “Is swelling breast a sign of breast cancer?” by human standards. However, the SimQ similarity score is not very high. To address this problem, we need to add concept reasoning ability to the similarity analysis. In this example, the concept “breast lump (CUI:C0424849)” is a descendent of the concept “swelling (CUI:C0038999)”, and “breast cancer (CUI:C0006142)” is a descendent of “tumor (CUI:C0027651)”.Short ambiguous questions can also lead to both false negative and false positive errors. For example, when analyzing the question, “Vitamin B6 deficiency”, SimQ retrieved the false positive result “Vitamin B12 deficiency?” and the false negative result “What are the symptoms of Vitamin B6 deficiency?” We believe that potential methods to address errors created by short ambiguous questions include weighting the question elements by importance and/or applying a query expansion technique. For example, an intuitive way to expand the question for Netwellness is to include previous answers from similarity analysis. However, the answers usually are much more complex and longer than the question, so it is still challenging to achieve a good result, especially since real-time retrieval response is needed. Integrating both questions and answers to improve retrieval results will be examined in subsequent studies that we plan to undertake.SimQ uses UMLS as a standardized semantic knowledge source. In the future, we plan to exploit other medical knowledge sources for semantic annotation, which could provide finer granularity of the semantic assignment and improve semantic analysis. Additionally, some researchers have pointed out that Consumer Health Vocabularies (CHV)  may faciF-score of 75.0%. We demonstrated a use case application by designing a new Q&A pipeline for the NetWellness website, which retrieves previous Q&As similar to the user’s health care. We designed a new Q&A pipeline for NetWellness, which retrieves previous Q&As similar to the user’s health care question. Then we demonstrated by using a particular case how the additional features of SimQ would be applied to a health consumer’s inquiry and integrated into the existing system to improve the service quality of the Q&A workflow. Therefore, we have shown that SimQ not only reduces response delay by instantly providing closely related question and answers, but also helps consumers improve the understanding of their health concerns.Similarity analysis of consumer health questions can significantly improve the quality and accessibility of online community-based question answering (Q&A) services. In this study, we proposed a new application called SimQ, which analyzes the semantic similarity of consumer health questions by combining natural language processing and semantic pattern techniques. The evaluation results show that our approach effectively retrieves similar questions on NetWellness. The results show that SimQ reached the highest precision of 72.2%, recall of 78.0%, and


The increasing number of open-access ontologies and their key role in several applications such as decision-support systems highlight the importance of their validation. Human expertise is crucial for the validation of ontologies from a domain point-of-view. However, the growing number of ontologies and their fast evolution over time make manual validation challenging.We propose a novel semi-automatic approach based on the generation of natural language (NL) questions to support the validation of ontologies and their evolution. The proposed approach includes the automatic generation, factorization and ordering of NL questions from medical ontologies. The final validation and correction is performed by submitting these questions to domain experts and automatically analyzing their feedback. We also propose a second approach for the validation of mappings impacted by ontology changes. The method exploits the context of the changes to propose correction alternatives presented as Multiple Choice Questions.This research provides a question optimization strategy to maximize the validation of ontology entities with a reduced number of questions. We evaluate our approach for the validation of three medical ontologies. We also evaluate the feasibility and efficiency of our mappings validation approach in the context of ontology evolution. These experiments are performed with different versions of SNOMED-CT and ICD9.The obtained experimental results suggest the feasibility and adequacy of our approach to support the validation of interconnected and evolving ontologies. Results also suggest that taking into account RDFS and OWL entailment helps reducing the number of questions and validation time. The application of our approach to validate mapping evolution also shows the difficulty of adapting mapping evolution over time and highlights the importance of semi-automatic validation. As multiple ontologies can describe the same domain, semantic mappings are often defined to link ontology elements that refer to the same real-world entity but belong to different domain-related ontologies } where semanticTypei∈{≡,≤,≥,≈} such that Relalternatives to formulate the question in the revision of MR category. The alternative relations are proposed from the most precise one to the more general one .Revising the semantic relation We selected a set of ontologies and mappings and designed a series of experiments to evaluate the proposed methods. In this article, we considered medical ontologies and mappings in English language, but our approaches can be applied to other languages as well. We present the obtained results in Sections “Caries Ontology (CO). CO was developed manually by a dentistry expert in our company.Disease-Treatment Ontology (DTO). We constructed an OWL translation of the ontology proposed by Khoo et al. .Mental Diseases Ontology (MDO). This ontology is publicly available.We tested our ontology validation approach on three different medical ontologies that cover different aspects of the medical domains  and constructed using different methods: For the first step of the experiment, Table i.e. more invalid elements), the number of deleted questions will increase.The number of generated questions depends on the ontology size and shows the importance of question ranking and optimization. The results indicate that the optimization method works better in case of ontologies with many instances. For the CO ontology, this strategy helps minimizing the number of submitted questions from 290 to 283 questions with only four NO answers. For the MDO ontology, our method allows asking 239 questions instead of 243 with only two NO answers. In case of ontologies with more NO answers , other methods should be used to optimize the presentation and reduce the time needed to answer the questions. For example, the following presentation methods can be studied: (i) question factorization according to an ontology element  and (ii) logical chaining . Such organization can be effective in helping medical experts understand and answer the questions more quickly.9 (SCT) and ICD-9-CM10 (ICD9) including different versions of official mappings established between them.We evaluate the NL quality of the questions generated automatically to validate mappings. We use two biomedical ontologies SNOMED-CTWe aim to investigate to which degree it is possible to generate NL sentences that can adequately describe mappings. For this purpose, we evaluate the generated questions according to three standard measures in NL generation: grammaticality, fluency and meaning preservation. Since our approach aims to facilitate human intervention in mapping adaptation, we assume that it is relevant to assess the NL quality of the automatically-generated questions.We presented the generated questions to three different human assessors who were asked to associate a score value between 1 and 10 for each dimension and each question. Assessors were ontology experts and familiar with the biomedical domain. We evaluated the approach for the validation of 20 randomly-selected adapted mappings generated from the evolution of mappings between SCT and ICD9.κ measure defined in , [4..6], [7..10]. We use these intervals as categories in the calculation of the κ measure, which corresponds to: We measure the Inter-Assessor Agreement (IAA) for grammaticality, fluency and meaning preservation. IAA corresponds to the average fined in . The κ mP(a) refers to the observed inter-assessor agreement and P(c) is the probability of a chance agreement. κ values range from -1 to 1  the number of returned answer-types for the set of 20 questions according to the different question types and (ii) the number of validation/invalidation according to the observed adapted mappings  , which provides a positive test on the reliability of the assessors’ ratings. In short-term perspectives, further tests will be made with a correlation-based approach to have a more precise view on the levels of agreement.The overall assessment of the generated initial Boolean Questions   or also NO for a specific kind of patient, or under a specific condition. In our experiments the experts answered NO for such cases. Therefore, it would be interesting to give to the expert the possibility to specify a contextual element or an additional condition to their YES/NO answers. A possible solution can be to integrate factual questions as possible question type, which will also contribute to enrich the ontology during the validation process.The experiments on ontology validation showed the need to add other specific types of questions and answer types. In some observed cases an answer can be i.e. concepts with the largest number of links).On the other hand, even if our optimization method may allow significantly reducing the number of questions, it is still challenging to validate very large ontologies with natural language questions. In this context, advanced content selection techniques such as summarization can play an important role. As suggested by Sure et al. , a summaTo validate huge ontologies, we are considering approaches based on summarization. A possible solution can be by using the Key Concepts Extraction (KCE) algorithm which automatically extracts the most representative classes of an ontology. More particularly, summarization can be adapted to the validation task by taking into account several features such as the number of questions needed to validate a given extract or summary and the number of key concepts.On the level of Mapping Validation, the conducted literature survey indicated that the generation of NL questions for mapping validation was not investigated. Our proposal originally evaluated the quality of generated NL questions to help human experts judge the quality of correspondences under evolution. The proposed method uses the context of the source concept to select similar concepts as alternatives in case of invalidated mappings.Are other eye disorders more specific than family history degenerative disorder of macula?”The heterogeneity and length of the literal attributes that led to some inadequacies with the conceived patterns, e.g., “Is other more specific than mechanical complication of suprapubic catheter?”.Errors in the concepts’ attributes (mainly labels), e.g. “ In the conducted experiments on mapping validation, the analysis of quality-deficient Boolean questions produced by the NL generation system highlighted to two main error causes: These observations show the importance of evaluating the linguistic quality of the ontology literals beforehand. This issue is particularly discussed in , where aThe methods for automatic ontology construction have highlighted the problems of validation of ontologies and mappings. Research efforts and interests have grown more and more, but mostly at a formal level. In this article, we addressed the problem of ontology validation including mappings from a conceptual and a semantic point of view.We proposed novel semi-automatic approaches based on the generation of questions and answers interpretation to facilitate the communication with domain experts. The defined methods generates natural language questions from medical ontologies and mappings and uses the answers from the expert to update/correct them.Our approach implementing automatic methods might guide domain experts in the validation process. Relying on domain experts to monitor the validation can lead to various benefits, ensuring reliable communication between heterogeneous systems.We evaluated the proposed methods based on several real datasets from different releases of biomedical ontologies and their associated mappings. The achieved results underscored the feasibility of our general approach for the validation of ontologies and mappings, and its relevant role in the completion of the their adequate evolution over time.1http://ihtsdo.org/snomed-ct/2http://ncit.nci.nih.gov/3http://bioportal.bioontology.org/4http://www.chu-rouen.fr/cismef/5 An international workshop is also held on question generation since 2008.6http://nlp.stanford.edu/software/lex-parser.shtml7http://www.w3.org/TR/rdf-mt/#RDFSRules8 Free textual explanation is considered for future perspectives and is not used in this work.9www.nlm.nih.gov/research/umls/licensedcontent/snomedctarchive.html10www.cdc.gov/nchs/icd/icd9cm.htm


Amongst birds, a single order, Dinornithiformes, comprising the extinct wingless moa of New Zealand, are unique in having no skeletal evidence of forelimb-like structures.The forelimb-specific gene tbx5 in moa, we used a range of PCR-based techniques on ancient DNA to retrieve all nine tbx5 exons and splice sites from the giant moa, Dinornis. Moa Tbx5 is identical to chicken Tbx5 in being able to activate the downstream promotors of fgf10 and ANF. In addition we show that missexpression of moa tbx5 in the hindlimb of chicken embryos results in the formation of forelimb features, suggesting that Tbx5 was fully functional in wingless moa. An alternatively spliced exon 1 for tbx5 that is expressed specifically in the forelimb region was shown to be almost identical between moa and ostrich, suggesting that, as well as being fully functional, tbx5 is likely to have been expressed normally in moa since divergence from their flighted ancestors, approximately 60 mya.To determine the sequence of tbx5 is necessary for the induction of forelimbs, but is not sufficient for their outgrowth. Moa Tbx5 may have played an important role in the development of moa’s remnant forelimb girdle, and may be required for the formation of this structure. Our results further show that genetic changes affecting genes other than tbx5 must be responsible for the complete loss of forelimbs in moa.The results suggests that, as in mice, moa  Limb loss by regressive evolution is common in nature with well-known examples being whales, some salamanders, and snakes. Amongst birds, the loss of forelimbs is best represented amongst ratites, with a general reduction in wing size evident in the rhea and ostrich, and a more severe truncation of wings shown in emu, cassowary, kiwi, and elephant bird. Surprisingly, phylogenetically, New Zealand’s moa group with the fully winged and flighted tinamou , despitetbx5, a highly conserved Tbox motif-containing transcription factor known to be essential for forelimb initiation in nearly all animals. Tbx5 is an essential member of a tightly regulated gene network that includes the Hox genes , characterized by reduction of the forelimbs and associated heart anomalies )D. novaezealandiae, D. robustus, E. curtus, E. crassus, and M. didinus  according to the manufacturers instructions. Genomic DNA was recovered from extant ratite blood by standard phenol/chloroform extraction and ethanol precipitation . AncientApproximately 5 ug of total RNA was reverse transcribed at 41°C for 60 min in 20 ul volumes with random 7-mer or oligodT primers and purified by phenol:chloroform extraction and ethanol precipitation.tbx5 intron/exon boundaries required to allow primer design for moa amplification. These included: Single primer PCR - A single primer was used in a standard PCR reaction at low annealing temperatures for one cycle to allow random priming and then amplified for ~35 cycles with the annealing temperature set at the primer’s Tm. Hairpin primer ligation PCR - hairpin primers containg a PstI compatible overhang were ligated to PstI-digested DNA before standard PCRs were carried out using the hairpin primer and a tbx5-specific primer. Medium range PCR - amplification across the smaller tbx5 introns was carried out usng Elongase® (Invitrogen) or Expand Long Template PCR System (Roche) as outlined by the manufacturer. Inverse PCR - Inverse PCR was carried out to obtain intron sequences directly from moa. Biefly, moa aDNA was denatured, then dephosphorylated with shrimp alkaline phosphatase (SAP) before fresh phosphates were added with T4 Polynucleotide Kinase. The aDNA was then circularised with CircligaseTM ssDNA Ligase (Epicentre®) and subjected to rolling circle amplification using TemplifyTM (Amersham). The amplified aDNA was then subjected to standard PCR using inverse primers.DNA was amplified from approximately 1–20 ng of DNA as outlined in . A numbetbx5 sequences we initially obtained full-length tbx5 cDNA sequences for kiwi and ostrich. Comparison with the chicken genome identified the intron/exon boundaries and primers were then designed to recover moa tbx5 exon sequences. To obtain moa tbx5 intron/exon boundaries, these boundaries were first isolated from kiwi. Primers were then designed to kiwi tbx5 intron sequences and conserved ostrich/kiwi/chicken tbx5 exon sequences to obtain moa tbx5 intron/exon boundary sequences. Most primers designed to kiwi tbx5 intron sequences successfully amplified from moa aDNA. For a few introns however, primers successful for amplification from moa required additional intron sequences from rhea, and/or ostrich.To isolate moa 20) and ex2R2 (5’- GGTGAGCGACTTGCTGGTG), followed by H5F (5’- AATCGGACAAACTGGTCCTTGCAAC) and ex2R3 (5’- CAAAGCCTTCCTCCGTAT). Amplified products were TA cloned into pGEM®T-Easy (Promega) and sequenced with m13F (5’-TGTAAAACGACGGCCAGT) or m13R (5’-CAGGAAACAGCTATGACC). Full-length forelimb-specific exon 1 transcripts were then obtained by cDNA walking using primers designed to conserved upstream regions of the chicken genome.Total RNA isolated from embryonic ostrich and kiwi forelimb and kiwi heart were A-tailed with terminal transferase (Invitrogen) and amplified with H5FdT (5’- AATCGGACAAACTGGTCCTTGCAACdTTM 5.0 (Gene Codes Corporation).PCR products were purified by centrifugation through dry Sephacryl S200HR, sequenced using ABI BigDye® Terminator v3.1 chemistry, then analysed and aligned in SequencherIn accordance with criteria suggested for the verification of aDNA sequences , a numbefgf10 and ANF promoters was determined using a luciferase assay. HEK293T cells were seeded into 24-well plates at a density of 5 × 104 cells/well twenty four hours before transfection. The following vectors were transfected into HEK293T cells using XtremeGENE HP (Roche); 6 kb of mouse sequence immediately upstream of fgf10.Supporting tables and figure are available as additional files. The authors declare that they have no competing interests.LH, TS, TO, YW, and SM carried out the molecular genetic studies. LH, DML, CDM, CS, and MH helped conceive the study and participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.Moa tbx5; materials, methods and sequences. Detailed descriptions of materials and methods used, nucleotide and amino acid sequences.Click here for file


One intriguing feature of the human communication system is the interactional infrastructure it builds on. In both dyadic and multi-person interactions, conversation is highly structured and organized according to set principles  Foundations of turn-taking, (2) Signals and mechanisms for prediction and timing, (3) Planning next turns in conversation, (4) Effects of context and function on timing, (5) Turn-taking in signed languages, and (6) Development of turn-taking skills.Levinson and Torreira review behavioral and cognitive findings specifying the parameters of the processes underlying the human turn-taking system. This empirical evidence is synthesized into a model claiming that intention ascription and response planning begin as early as possible during the incoming turn, running through all the serial stages of speech production à la Levelt . These become available to listeners as turns unfold in conversation and often allow next speakers to predict the content of ongoing turns. Himbert et al. throw light on yet another source of information that speakers in interaction may use for timing their turns: their analysis demonstrates that interlocutors adapt their turn-taking rhythms to one another, which they argue is facilitated by the alignment of semantic and syntactic processes.One of the central debates on the cognitive processes involved in turn-taking focuses on the role played by prediction. Part of this debate is the issue of which kinds of cues adults may use for predicting the end of turns, allowing them to come in on time. The article by Roberts et al. explore the value of both psycholinguistic factors  and conversational structures  as explanatory factors when modeling the timing of turns in conversation. Their results show that both sets of factors significantly contribute to explaining variation in turn timing. Torreira et al. study pre-answer in-breaths in a dialogue setting using insights from acoustic and inductive plethysmography recordings. They demonstrate that the occurrence of an in-breath is dependent on the length of an answer, suggesting that answers are planned prior to these in-breaths. Since the pre-answer in-breaths in their data were launched close to the end of question turns, the data provide evidence for the concurrence of comprehension and next utterance planning.The contributions in this section explore some of the cognitive processes involved in preparing next turns in conversation. Applying a cutting edge statistical approach (“random forests”) to data from a large conversational corpus, Kendrick shows that turns dealing with problems of speaking, hearing, and understanding  are governed by different timing principles and can thus break the common pattern of minimal gaps between turns. As the analysis reveals, the longer gaps characteristic of repair sequences tend to be used by participants as opportunities to either allow the producer of the trouble source to resolve the issue before repair is initiated, to allow themselves to resolve their problems in understanding before initiating repair, or to signal problems in understanding through visual displays  before initiating repair verbally. The article by Gardner and Mushin provides evidence from Garrwa, an indigenous Australian language, for turns that are followed by substantially longer gaps than one would ordinarily expect based on prior work on English conversations. In these cases, however, it is not repair that drives the longer turn transition times; the environment in which they occur is slow-paced conversation, appearing to reduce the pressure for gap minimization. Stevanovic and Peräkylä discuss perspectives on the intersection of two different systems of temporal organization, that of turns at talk and that of emotional reciprocity—the former favoring sequential organization, the latter affording simultaneity and immediate uptake through emotional contagion and mimicry.Three articles investigate the interplay of turn-taking rules with other principles shaping human behavior in specific conversational contexts. De Vos et al. analyze the timing of turns in Sign Language of the Netherlands (NGT), showing that the timing of turns in signed conversation looks remarkably similar to that of spoken interaction  when considering not simply onset and offset of manual movements but individual movement phases . Girard-Groeber examines turn-taking principles in multi-party conversations in Swiss German Sign Language (DSGS), focusing on the occurrence of overlaps. She, too, finds striking similarities with spoken interactions: the examples provided illustrate a strong orientation to the “one at a time” principle, an orientation of participants toward points of possible completion in the sign stream, and a set of principles that appear to determine deviations from this rule (such as repair initiations or strong disagreements). Manrique and Enfield focus on a particular type of turn transition environment—other-initiated-repair—in Argentine Sign Language (LSA), thus complementing Kendrick's work on repair in spoken interaction (this volume). However, their focus is on how repair is elicited in visual question-answer sequences rather than on the timing of turns in the repair environment, revealing the frequent use of a visual display form termed the “freeze-look.” Next to clearly unique features, the three articles point toward some striking similarities regarding the timing and organization of turns in spoken and signed languages.The research presented in this section investigates the principles of turn-taking and sequence organization in signed languages where communication is constrained to the visual modality. Gratier et al. demonstrate that already at 8–21 weeks babies are active participants in, as well as initiators of, turn-taking sequences, but also that at this early stage of development mothers play a core role in the timing of turns by adapting their behavior to the infant. Hilbrink et al. provide a longitudinal study showing that turn-timing skills continue to develop continuously from 3 to 18 months, with some regressive slowing down as language comprehension kicks in around the “9 month revolution”  verbal and gestural responses to questions. The pattern they find nicely fits with the temporal slowing down in vocal turn-timing caused by the challenge of having to master language—while verbal responses often occurred with long delays, the child frequently produced gestural responses preceding speech. The following two articles examine children's use of linguistic cues for anticipating upcoming next turns when observing dyadic conversations. Keitel and Daum find that three but not 1 year olds are able to make use of intonational cues for predicting upcoming next turns. In line with this, Lammertink et al. find that 2 year olds make use of prosodic cues for predicting upcoming next turns, but that they make use of lexicosyntactic cues, too, even weighing these more strongly—just like adults do.Convergent findings regarding principles governing turn-taking across languages in different modalities hint at the possibility of a shared cognitive infrastructure underlying all human communicative interaction. This cognitive infrastructure may also account for the ease with which young children appear to acquire the necessary skills to interact with others. The contributions included in this section focus on the acquisition of turn-taking in very young infants and in children as they start to master spoken language. The first two articles suggest that temporal turn-taking skills are learned early on in infancy. The authors were supported through the Max Planck Gesellschaft and European Research Council (Advanced grant #269484 INTERACT awarded to SCL) during the preparation of the editorial and the research topic as a whole.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.


Behavioral and neural studies of selective attention have consistently demonstrated that explicit attentional cues to particular perceptual features profoundly alter perception and performance. The statistics of the sensory environment can also provide cues about what perceptual features to expect, but the extent to which these more implicit contextual cues impact perception and performance, as well as their relationship to explicit attentional cues, is not well understood. In this study, the explicit cues, or attentional prior probabilities, and the implicit cues, or contextual prior probabilities, associated with different acoustic frequencies in a detection task were simultaneously manipulated. Both attentional and contextual priors had similarly large but independent impacts on sound detectability, with evidence that listeners tracked and used contextual priors for a variety of sound classes . Further analyses showed that listeners updated their contextual priors rapidly and optimally, given the changing acoustic frequency statistics inherent in the paradigm. A Bayesian Observer model accounted for both attentional and contextual adaptations found with listeners. These results bolster the interpretation of perception as Bayesian inference, and suggest that some effects attributed to selective attention may be a special case of contextual prior integration along a feature axis. It is an active process that is very sensitive to numerous factors, including attentional state and expectations. For example, explicit attentional cues can rapidly enhance perception of expected events  and will bias perception. We tested this prediction by way of a well-studied effect of auditory selective attention, the When listeners are asked to detect the presence of a tone (any tone) in noise, a high explicit attentional prior placed on a particular tone frequency via cueing causes an attentional band of perceptual sensitivity around that frequency. This means that tone detection rates fall off as a function of frequency distance from the expected tone(s) –23. SimiA second prediction of a Bayesian model of auditory scene analysis is that listeners collect and use contextual priors optimally. In a dynamic environment where context changes and the prior probabilities associated with different tone frequencies vary over time, a Bayesian model predicts that past evidence will be weighted in such a way as to provide the best estimates of the future. This prediction was tested by analyzing tone detection rates as a function of prior probability as computed over different windows sizes and decay parameters to determine how contextual priors are integrated over time.The final prediction of a Bayesian model of auditory scene analysis tested here is that listeners use explicit attentional cues and implicit contextual cues simultaneously and independently. Both attentional and contextual cues have been shown to bias perception, but (as will be demonstrated) Bayesian observer models predict contextual priors have the same effect on detection sensitivity whether there is an effective attentional prior, an ineffective attentional prior, or no attentional prior at all. This prediction was tested by explicitly cuing listeners to expect that a 250Hz frequency would regularly  occur, forming a simple and explicit attentional prior during the prior probability manipulation.likely to appear in that trial, with corresponding examples and practice. The specific instructions given were: “The cue sound will often be the same sound that appears in the trial. Occasionally the cue sound will not be the same sound that appears in the trial”. The cue, two intervals, and an opportunity for response were indicated by text appearing sequentially on a monitor at the appropriate times.The procedure closely followed the probe-signal method used by Dai and colleagues . Each truninformative cue version in which the detection tones were as described but an uninformative cue was presented at 777 Hz and related cue instructions were omitted, (2) the standard informative cue version for additional probe distances of 1.75 ERB (350 Hz) and 3.75 ERB (487 Hz) to more fully fit the attentional band.Cue sounds were presented at clearly audible levels, and the sound presented for detection was attenuated at its threshold level in each trial and with equal probability in the two intervals (550 ms or 1200 ms after cue offset). One of four different detection sounds were presented in each trial: a sound matching the cue in pitch, or a probe sound differing from the cue by one of three distances along the frequency axis. For all three stimulus classes tested , the cued pitch was 250 Hz and probe pitches were .75 Equal Rectangular Bandwidths  was presented in the remaining proportion of trials  in a given condition. Each condition was made up of blocks of 75 trials each, and prior probabilities were calculated over this length. In this way, each condition contained two detection sounds: one with a pitch of 250 Hz presented with some probability p and another with a different pitch presented with a probability of 1 –p  “Did you notice anything in particular about how often the different sounds were played? If so, what did you notice?” (2) “Did you use any particular strategy to detect the sound? If so, can you describe your strategy?” No listener responded in any way consistent with conscious awareness of the probability manipulation or any deliberate detection strategy related to tone probability.All stimuli had a duration of 300ms, a sampling rate of 16 kHz, began and ended at zero crossings, and were presented with 25ms cosine rise and fall times. Simple tones (single sinusoids) and harmonic spectra  were generated in MATLAB and vowels (/a:/ as in father) were generated with the Klatt Synthesizer. While the same formant parameters were used to generate all vowels , differing F0s resulted in slight variations in formants (on the order of 30 mels). Low-pass filtered white noise  was presented at a level of approximately 60 dB SPL (participants were permitted to adjust noise volume) continuously throughout threshold and target-probe procedures. Participants listened through Sennheiser HD595 headphones while seated in an ETS-Lindgren soundproof chamber and stimulus delivery and response collection was controlled from a Windows computer (ASUS Xonar Essence STX sound card) with custom software written in MATLAB.Seventy-seven naïve volunteers  self-identified as having no hearing difficulties successfully completed testing . The experiments were conducted in accordance with the declaration of Helsinki and after approval by the Johns Hopkins University Homewood institutional review board. All subjects gave written informed consent to participate in the study and were compensated $20 for participation in threshold and probe-signal procedures lasting two hours. Volunteers were assigned to one of several identical probe-signal tasks that used matched stimuli of different acoustic classes as summarized in s1 and s2 to an array of N tonotopically-organized neurons. Both s1 and s2 were N×1 vectors representing N possible frequency channels whose activity is randomly drawn from a standard uniform distribution. One of the two stimuli had an additional activation at channel k representing a tone embedded in noise background. Here, N was arbitrarily set to 100 and k set to 30 for target frequency and 40,55,70,80,95 for probe frequencies. The tone amplitude was set at signal to noise ratio -11.5dB. Both stimuli were processed through N neurons; with Gaussian fi(s) = N tuning curves . Variations of the number of channels and shapes of tuning curves (within reason) did not quantitatively change the function of the model.The model replicated the two-interval forced choice detection paradigm by presenting two stimuli sj, neuron i fired ri spikes following a Poisson process with mean fi(s). An estimated neural log likelihood function was obtained as rifi(s); then integrated across all N neurons to yield an estimate of the stimulus log likelihood π(s) by summing the log of both quantities. We also explored alternative implementations for integrating priors with neural responses; by multiplying the tuning curve of each neuron with a scaled version of the prior distribution π(s). This case would mimic changes to the tuning properties of the neural array as a result of contextual prior accumulation. Computationally, both implementations were equivalent and yielded comparable estimates of the posterior probability Θ(s|r).For each stimulus tions in ,28. The s1,s2} with highest mode (highest maximum a posteriori estimator) was chosen as the interval with correct target or probe tone. For the informative cue case, a weighted posterior mean estimator with a bell shaped weight function W(s) was centered on the location of the cue, and modeled as N. Again, the interval with the highest peak was flagged. For both informative and uninformative conditions, the location of the detected tone was used to update the distribution of priors . The update function used a temporal decay term (α = 0.12) such that For the uninformative cue condition, the interval {2p) values. All error bars displayed are 1 standard error of the mean calculated for repeated measures , further suggesting that contextual priors condition sensitivity independently from attentional priors.Prior-driven adaptation in the absence of cued attention. If attentional and contextual prior probabilities are genuinely independent factors, the effects of probe probability reported above should be closely replicated even in the absence of an attentional manipulation. To test this claim, the procedure was replicated with an uninformative cue (at 777 Hz). The uninformative cue replaced the informative cue in all trials, and instructions made no mention of using the cue to guide attention, effectively eliminating any attentional prior . In the absence of cued attention, there was a clear effect of contextual priors on detection accuracy   and vowels (Group 6), with target and probe pitches for these stimuli matched to the tone frequencies reported above. Significant main effects of probe prior were found for both classes of stimuli [harmonic: Fp = .56] . No attew) that yielded, on average, the most accurate (best correlated) estimates of the underlying probe priors used to generate trials in the informative cue tone paradigm. Using the actual trial sequences participants experienced, probe prior estimates were made for each trial, for each participant, and for each w between 5 and 150 preceding trials.Listeners collect priors near-optimally. Prior probabilities can be time-invariant, such that new information is integrated with the entire unweighted historical record. Alternatively, they can be time-dependent, such that prior distributions are either estimated for a discrete and limited historical window, or weighted to place more importance on recent experiences. A weighting of the past would be optimal if it gives the most predictive estimate of the future. Here, an optimal integration window size was defined as the number of preceding trials , to rectify for correctly scored guesses. In this way, prior estimates were updated only on a corrected proportion of correctly scored detections. In addition, since probe priors were inversely proportional to target priors (i.e. blocks with more probes had fewer targets), identical analyses were performed using targets to calculate prior probability. The model that computed priors based on only the truly detected probe trials (i.e. rectified correct probes) had the best overall agreement with the window size actually used by listeners , where t is a time point or trial, and f is a tone frequency: fπ = 1. Similar to the integration window results reported above, the optimal and functional α values were very similar: 0.132 and 0.122, respectively  to estimate probe priors and determine the optimal and functional decay weights. The coefficient α refers to the weight of the most recent trial relative to the historical prior distribution, such that smaller values closer to 0 correspond to slower decay rates (long memory) and higher values closer to 1 correspond to higher decay rates (short memory). This coefficient is used in computing prior probability, ectively .Because the discrete window-based and continuous decay-based models yielded almost identical best fits, these results cannot endorse one of these representations of priors over the other; though both models agree that the average listeners appeared to be collecting a distribution of priors optimally over the course of this experiment. Furthermore, optimal window sizes and temporal decay coefficients were not manipulated, and so whether the functional window sizes or decay rates used by participants would track different optimums, and how robust these results are to different presentation paradigms, cannot be determined from this data.s, an array of N tonotopically-organized non-deterministic neurons produce ri,{i = 1, …, N} spikes over the duration of the stimulus. The firing rate of each neuron i follows Poisson statistics and reflects the match between the input and the neuron’s tuning curve fi(s). The ability of the perceptual system to detect a target in the stimulus depends on the fidelity of its sensory mapping; i.e. its ability to give the best estimate of the stimulus from a readout of an inherently noisy neural population. An estimate of the likelihood of stimulus s based on the population response of N neurons is modeled as a weighted integration of responses of individual neurons into a single observation, or likelihood function Lr(s). This function, or its equivalent log likelihood, was previously shown to yield an optimal decoding of sensory information given noisy measurements [ri weighted by its corresponding tuning function fi(s).A Bayesian Observer model accounts for attentional and perceptual adaptation. The model tests the intuition that the behavioral results reported here can be captured by an optimal decision rule based on a Bayesian regime. The model estimates the detectability of a tone embedded in a noise background . Given surements ,28,30; aπ(s) to yield a posterior estimate of the probability of s given the observed neural response:Θ(s|r) ∝ Lr(s)π(s).In a frequentist non-Bayesian setting, this function gives an estimate of likely stimuli that could have given rise to the observed population response. Its peak (i.e. location with highest likelihood) corresponds to the expected position of the target to be detected  ,32. It aOnce a posterior is estimated, a perceptual decision uses a selection rule to determine the presence of a target note embedded in the noise. In case of complete uncertainty (i.e. uninformative cue), the decision stage relies on a uniform decision function. Here, we use a standard 0–1 loss function  of a probe tone is proportional to its frequency distance from the expected or target tone . Additioperceptual bands. Perceptual bands—perceptual attenuation proportional to the distance from relatively expected feature levels—can be understood as a simple consequence of the kind of attentional or contextual prior integration along a feature axis  described in this report and others [Alternatively, the attentional band in auditory frequency listening could be characterized as a special case of modality-general d others ,45,46, aTo illustrate this concept, consider a simple attentional prior represented as some bell-shaped distribution along the frequency axis. According to the distribution, prior probability falls off proportional to the distance from the most likely frequency. Integrating this distribution with incoming sensory information results in a bell-shaped perceptual band of tone frequency sensitivities. Certainly prior distributions will often be more complex than this simple attentional prior, but perceptual bands can form at any local maximum in the prior distribution through a variety of mechanisms. For example, high priors associated with particular levels of a feature can act as perceptual magnets or attractors in feature estimation , producior priors they are naïve to, it may not be necessary to appeal to selective attention  to explain these results. What’s more, it may be necessary to control for the effects of implicit contextual priors when interpreting studies of selective attention.Others have interpreted perceptual bands as evidence for explicit attention, but as suggested here, perceptual bands may occur without observer awareness. In their study of priors associated with direction of motion, Chalk and colleagues  reportedThe effect of priors on perceptual sensitivity likely extends beyond relatively simple perceptual bands. Although we used prior probability distributions limited to a single perceptual dimension, studies of selective attention suggest that auditory perception is sensitive to multimodal and joint prior distributions. Perceptual bands have been observed when priors are spread across four different tone frequencies , suggestWhile there are many indications that complex, multidimensional prior distributions can be represented and used to improve perception, several issues must be addressed before this work can be extended to perception of real-world or natural scenes. First, the vast majority of related studies have measured prior-based perceptual adaptation only at near-threshold levels. As a result, the degree to which priors influence the speed and accuracy of perception at more typical levels is unknown. Second, to fully exploit the statistical structure in the environment, encoded prior distributions should incorporate contextual information . Context-specific statistical learning has been widely demonstrated ,55, but This research was supported by National Science Foundation grant IIS-0846112, National Institutes of Health grants 1R01AG036424 and T32 DC 23–27, and Office of Naval Research grants N00014-12-1-0740 and N000141010278. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank research assistants Katherine Simeon and Nicha Apichitsopa for their valuable suggestions and contributions to data collection.


L1-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased, especially in genomic research. Recently, we gained a large amount of molecular interaction information about the disease-related biological processes and gathered them through various databases, which focused on many aspects of biological systems. In this paper, we use an enhanced L1/2 penalized solver to penalize network-constrained logistic regression model called an enhanced L1/2 net, where the predictors are based on gene-expression data with biologic network knowledge. Extensive simulation studies showed that our proposed approach outperforms L1 regularization, the old L1/2 penalized solver, and the Elastic net approaches in terms of classification accuracy and stability. Furthermore, we applied our method for lung cancer data analysis and found that our method achieves higher predictive accuracy than L1 regularization, the old L1/2 penalized solver, and the Elastic net approaches, while fewer but informative biomarkers and pathways are selected.Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on  Identifying molecular biomarker or signaling pathway involved in a phenotype is a particularly important problem in genomic studies. Logistic regression is a powerful discriminating method and has an explicit statistical interpretation which can obtain probabilities of classification regarding the class label information.L1-norm type regularization methods. However, L1 regularization may yield inconsistent selections when applied to variable selection in some situations [L1 regularization has a gap to meet these requirements. Thus, a further improvement of regularization is urgently required. Lq (0 < q < 1) regularization can assuredly generate more sparse and precise solutions than L1 regularization. Moreover, L1/2 penalty can be taken as a representative of Lq (0 < q < 1) penalty and has demonstrated many attractive properties which do not appear in some L1 regularization approaches, such as unbiasedness, sparsity, and oracle properties [A key challenge in identifying diagnosis or prognosis biomarkers using the logistic regression model is that the number of observations is much smaller than the size of measured biomarkers in most of the genomic studies. Such limitation causes instability in the algorithms used to select gene marker. Regularization methods have been widely used in order to deal with this problem of high dimensionality. For example, Shevade and Keerthi proposed the sparse logistic regression based on the Lasso regularization , 2. Meietuations  and oftetuations . In manyoperties –8.So far, we observed dense molecular interaction information about the disease-related biological processes and gathered it through databases focused on many aspects of biological systems. For example, BioGRID records collected various biological interactions from more than 43,468 publications . These rL1/2 penalty following the framework established by [L1/2  regularization, which is more efficient than the old half thresholding approach in the literature [Inspired by the aforementioned methods and ideas, here, we define a network-constrained logistic regression model with ished by , where tterature , 15, 16.L1/2 regularization. In L1/2 regularization and the corresponding coordinate descent algorithm. In The rest of the paper is organized as follows. In D has n samples, D = {, ,…, }, where Xi =  is ith sample with p genes and yi is the corresponding variable that takes a value of 0 or 1. Define a classifier f(x) = ex/(1 + ex) and the logistic regression is defined asβ =  are the coefficients to be estimated. We can obtain β by minimizing the log-likelihood function of the logistic regression. Following [G = , where V is the set of genes that meet p explanatory variables and E is the set of edges. If gene u and gene v are connected, then there is an edge between gene u and gene v, which is denoted by Euv = 1; else Euv = 0. wuv denotes the weight of edge Euv. The normalized Laplacian matrix L for G is defined bydu and dv are the degrees of genes u and v, respectively. The degrees of gene u (or v) describe the number of the edges that connected with u (or v). For λ ≥ 0, the network-constrained logistic regression model is presented asβ on the graph.Generally, assuming that dataset ollowing , to combsented asLλ,β=−∑i=p ≫ the sample size n. Therefore, the regularization approach is vitally needed. When adding a regularization term to  which has the regularization term P(β) = ∑j=1p|βj| to penalize (L1) regularization is not good enough for interpretation, especially in genomic research. Besides this, L1 regularization is asymptotically biased [L1 regularization to Lq penalties. In mathematics, Lq  (0 < q < 1) type regularization |β|q = ∑|β|q with the lower value of q would lead to better solutions with more sparsity and gives asymptotically unbiased estimates [L1/2 penalty can be taken as a representative of Lq  (0 < q < 1) penalty and has permitted an analytically expressive thresholding representation [L1/2 net approach based on L1/2 regularization to penalize the network-constrained logistic regression model, as shown in β|1/2 = ∑j=1p|βj|1/2.Directly computing  performs term to , the spag et al. , the autterm to , to partially optimize the target function with respect to βj, and fix the remaining elements βk at their most recently updated values. The specific form of updating β depends on the thresholding operator of the penalty.gorithms  for solvgorithms , MCP [21gorithms . Since tL1/2 thresholding operator for the coordinate descent algorithm:φλ(ω) = arccos((λ/8)(|ω|/3)−3/2), π = 3.14, βj.In this paper, we present an enhanced Remark. This enhanced L1/2 thresholding operator L1/2 thresholding (3/4)(λ)2/3 introduced in [λ. Based on this enhanced L1/2 thresholding operator, when λ is chosen by some efficient strategies for the parameter tuning, such as cross validation, the convergence of algorithm  ← 0  and y∗, X∗, and set m ← 0, γ chosen by cross validation.Step 2. Calculate Z(m) and W(m) and approximate the loss function (β(m).function  based onStep 3. Update each βj(m) and cycle over j = 1,…, p, until βj(m) does not change.Step 3.1. Compute Zij)((m) ← ∑i=1nWi(m)(Zi(m) − ∑k≠jxik∗βk(m)) and Step 3.2. Update βj(m) ← Enhanced_Half(ωj(m), γ).Step 4. Let m ← m + 1, β(m + 1) ← β(m).β(m) dose not converge, then repeat Steps 2 and 3.If L1 regularization (L1 net), L1/2 regularization with old thresholding value (3/4)(λ)2/3 (L1/2 net) and with the enhanced thresholding value L1/2 net), and the Elastic net regularization approach (Elastic net). We first simulated the graph structure to mimic gene regulatory network: assuming that the graph consists of 200 independent transcription factors (TFs) and each TF regulates 10 unlike genes, so there are a total of 2200 variables, X = , p = 2200. The training and the independent test data sets include the sample sizes of 100, respectively. Each TF xn and its regulated genes xm were generated by the normal distribution N. We set the correlation rate between xn and its regulated gene xm as 0.75, xm = (1 − 0.75) × xm + (0.75) × xn. The binary responder yi (1 ≤ i ≤ 100), which is associated with the matrix X of TFs and their regulated genes, is calculated based on the following formula and rule:ε ~ N.We evaluate the performance of four methods: the network-constrained logistic regression models with Model 2 was defined similar to Model 1, except that we considered the case when the TF can have positive and negative effects on its regulated genes at the same time: L1/2 net, L1/2 net, and L1 net. Both penalized parameters for L1 and ridge regularization in the Elastic net were tuned by the 10-fold cross validation on the two-dimensional parameter surfaces. We repeated the simulations over 100 times and then computed the misclassification error, the sensitivity, and the specificity averagely for each net model on the test datasets.In these two models, the 10-fold cross validation approach was conducted on the training datasets to tune the regularization parameters of the enhanced L1/2 net model achieved the smallest misclassification errors in Models 1 (9.22%) and 2 (10.76%) compared with the other regularization methods including the old L1/2 thresholding method (9.85% for Model 1 and 10.83% for Model 2), L1 net (11.81% for Model 1 and 13.21% for Model 2), and the Elastic net (13.12% for Model 1 and 14.14% for Model 2). Meanwhile, the enhanced L1/2 net resulted in the highest sensitivity in Model 1 (98.5%) compared with the other methods. Moreover, the enhanced L1/2 net obtained the best specificity in Model 2 (98.7%) amongst the other approaches. To sum up, the enhanced L1/2 net outperforms the other three algorithms in terms of prediction accuracy, sensitivity, and specificity.http://thebiogrid.org/) with a lung cancer (LC) gene-expression dataset [L1/2 net method. The gene-expression dataset contains the expression profiles of 22284 genes for 107 patients, in which 58 had lung cancer. To test the generalization ability of the proposed method, we divided the dataset into the training set  which covered 2/3 samples of the dataset and the test set  which covered the other 1/3 specimens of the dataset. The 10-fold cross validation approach was conducted on the training dataset to tune the regularization parameters. By combining the gene-expression data with the PPI network, the final PPI network includes 8619 genes and 28293 edges.In this section, we merged the protein-protein interaction (PPI) network , and x-axis at the top (degrees of freedom) means the number of nonzero coefficients of beta. y-axis is the values of the coefficients beta which measure the gene importance. The predictive model builds from the training set and then tests its predictive performance on the test set. The detailed results were represented in Figures L1/2 net selected the fewest number of genes and edges compared to L1/2 net, L1 net, and the Elastic net. Meanwhile, the predictive performance of the enhanced L1/2 net outperforms the other three regularization net algorithms.As shown in L1/2 net procedure, we report its capacity of identifying the biomarkers related to lung cancer. NK2 homeobox 1 (Nkx2-1) protein regulates transcription of genes specific for lung. It is used as a biomarker to determine lung cancer in anatomic pathology. It also has a critical role in maintaining lung tumor cells [L1/2 net . As these gene express significantly and continuously, it promotes lung cancer progression. On the other hand, this group is linked to ENO1. We are unable to get a clear testimony to sustain this relationship by looking at PPI database. However, a recent report [Except to identify these two significant biomarkers (EGFR and Nkx2-1), the enhanced t report  has demoL1/2 net is more reliable than L1/2 net, L1 net, and the Elastic net approaches for selecting key markers from high-dimensional genomic data. Another advantage of our proposed method is that it has the ability to recognize novel and potential relationships with biologic significance. It is mentionable that our proposed method is inclined to identify fewer but more informative genes (or edges) than L1 net and the Elastic net approaches in genomic data and that means the proposed method has allowed the researcher to more easily concentrate on the key targets for functional studies or downstream applications.All these results reveal that the enhanced L1/2 solver to penalized network-constrained logistic regression model to integrate lung cancer gene-expression with protein-to-protein interaction network. We develop the corresponding coordinate descent algorithm as a novel biomarker selection approach. This algorithm is extremely fast and easy to implement. Both simulation and real genomic data studies showed that the enhanced L1/2 net is a ranking procedure compared with L1/2 net (using the old thresholding operator), L1 net, and the Elastic net in the selection of biomarker and subnetwork.In biological molecular research, especially for cancer, the analysis of combining biological pathway information with gene-expression data may play an important role to search for new targets for drug design. In this paper, we use the enhanced We successfully identified several important clinical biomarkers and subnetwork that are driving lung cancer. The proposed method has provided new information to investigators in biological studies and can be the efficient tool for identifying cancer related biomarker and subnetwork.“Sub-networks identified by the L_1 net and the Elastic net for lung cancer datasets (only those genes that are linked on the PPI network are plotted). Nodes colored based on higher (red) to lower (green) coefficients in the model.”


Photoreceptor cGMP Phsophodiesterase 6 (PDE6) is the effector molecule of visual signal transduction and mediates fast response of light signals. The rod holo-PDE6 comprises catalytic  and two identical inhibitiory  subunits. The catalytic subunits comprise N-terminal tandem GAF domains followed by C-terminal catalytic domains and isoprenylations for membrane-association. Contrary to activation of other tandem GAF comprising PDEs, PDE6 activation does not occur via cGMP-induced concerted conformational changes. Rather two copies of the α-subunit of retinal G-Protein (Gα*), transducin, activate PDE6 by partially displacing the inhibitory subunits. The activation of PDE6 has therefore been described as a “de-inhibition”. The affinity of Gα* to PDE6 and the enzymatic activity of the intermediary 1:1 complex is highly disputed, therefore a conclusive activation model is lacking so far.Our combined structural, enzymatic and computational investigations deal with the activation-mechanism of PDE6. Our cryo electron microscopy (EM) structure of PDEαβ catalytic core shows an elongated bell-shaped structure with symmetric side-by-side arrangement of the two subunits with flexible membrane-binding domains. A comparison with nearly full-length inactive PDE2A structure  suggests


Humidity measurement is one of the most significant issues in various areas of applications such as instrumentation, automated systems, agriculture, climatology and GIS. Numerous sorts of humidity sensors fabricated and developed for industrial and laboratory applications are reviewed and presented in this article. The survey frequently concentrates on the RH sensors based upon their organic and inorganic functional materials, e.g., porous ceramics (semiconductors), polymers, ceramic/polymer and electrolytes, as well as conduction mechanism and fabrication technologies. A significant aim of this review is to provide a distinct categorization pursuant to state of the art humidity sensor types, principles of work, sensing substances, transduction mechanisms, and production technologies. Furthermore, performance characteristics of the different humidity sensors such as electrical and statistical data will be detailed and gives an added value to the report. By comparison of overall prospects of the sensors it was revealed that there are still drawbacks as to efficiency of sensing elements and conduction values. The flexibility offered by thick film and thin film processes either in the preparation of materials or in the choice of shape and size of the sensor structure provides advantages over other technologies. These ceramic sensors show faster response than other types. To have a desirable surrounding atmosphere, it is essential to monitor, detect and control the ambient humidity under different conditions ranging from low temperature to high or in mixtures with other gases by precise and provident sensors ,22. Utili.e., composite or single-species in nano-scale processing system are among the principal considerations. Synthesis and preparation of metal oxides as ceramic type hygrometers and film based humidity sensors are further assessed. To provide a clear comprehension of operation principles, the universally observed sensing mechanism of porous matters which is of an ionic type, will be detailed. Nanocomposites incorporating ceramics, ceramics/polymers and polymers/carbon nanotubes with nanoporous, nanofiber and nanowire forms are amongst the most promising materials for future applications, and their preparation processes deserve mention.This paper reviews reported studies on different humidity sensor fabrication technologies, principles of materials, applications, and moisture sensing mechanisms. Further, recent developments of dampness-sensitive materials, The rest of this article is organized as follows: in Section 2, the fundamental definitions of humidity and the procedures for parameter measurement are described. Then, a classification of humidity sensors and the related applications are given in Section 3. The principle of ceramic humidity sensor operation based upon hydrogen permeation and water protonation is detailed in Section 4. Whilst Section 5 provides a survey of impedance type sensors, Section 6 covers capacitive type transducers. A summary of the review is given in Section 7.2.Humidity is defined as the amount of water vapour in an atmosphere of air or other gases. Humidity parameters are stated in diverse ways and the corresponding units are based on the measurement technique used. The most commonly used terms are “Relative Humidity (RH)”, “Parts Per Million (PPM)” by weight or by volume and “Dew/Frost Point (D/F PT)”, in which the two latter are subclasses of “Absolute Humidity (AB)”. Absolute Humidity units are applicable for the primary measurement results inasmuch as one is able to directly measure the value of the water vapour content. In contrast, Relative Humidity is true for the secondary measurement results, since measurement of the water vapour values is mediated in some fashion.1/7000 pound lb) and expressed as:AB is the absolute humidity (g/m3 or grains/ft3), wm is the mass of water vapour (gram or grain) and v is the volume of air (m3 or ft3).Absolute Humidity (vapour density) is defined as a ratio of the mass of water vapour in air to the volume of air, with the unit of grams per cubic meter or grains per cubic foot .Relative Humidity (abbreviated as RH) is defined as ratio of the amount of moisture content of air to the maximum (saturated) moisture level that the air can hold at a same given temperature and pressure of the gas. RH is a temperature dependent magnitude, and hence it is a relative measurement. The RH measurement is stated as a percentage and determined by the expression:SH is the saturation humidity (g/m3), wsm is mass of water vapour at saturation (g) and v is the volume of air (m3). The saturation humidity is a function of temperature and can provide the maximum amount of moisture content (mass) in a unit volume of gas at a given temperature.Saturation Humidity is defined as the ratio of the mass of water vapour at saturation to the volume of air:According to Dew point is defined as a temperature (above 0 °C) at which the water vapour content of the gas begins to condense into liquid water, and Frost point is the temperature (below 0 °C) at which the water vapour in a gas condenses into ice. D/F point parameters are functions of the pressure of the gas, but independent of temperature and are amongst the absolute humidity measurements. In other words, dew point is the temperature at which the saturation water vapour pressure is equal to the partial pressure of the water vapour (in an air atmosphere). The difference between the ambient temperature and the dew point temperature is a measure of the ambient relative humidity ,39.3.Humidity sensing studies have progressed rapidly and humidity sensors—regardless of fabrication technique—have been widely employed in industrial and household applications as instrumentation equipment or for human comfort issues. Due to the different operating conditions of moisture sensors in different areas of application ranging from indoor to open air uses, various types of humidity sensing instruments have been developed based on different work principles and diverse hygroscopic sensing materials –45. AmonAs most of the commercially available humidity sensors in use are relative humidity sensors, these can be grouped based upon their sensing material types and according to their operating principle. In the 1980s the different sensing elements were proximately classified into three main groups of electrolytes, porous ceramics and organic polymers conforming to the Yamazoe and Shimizu classification . Around At the present time, plenty of different brand humidity sensors on the market or under development in laboratory studies are RH sensors, which are further categorized into three classes, including ceramic type (semiconductor), organic polymer-based sensors, and organic/inorganic hybrid sensors (polymer/ceramic). By reviewing the published results in this field, the authors have determined that well-nigh 80% of these three types are based on the electrolytic properties of the sensing matter, by virtue of the inner water electrolytes. Humidity sensors using nanowires, nanofibers, nanorods and p-n heterojunctions are subclasses of the ceramic (inorganic) type. Regarding the intrinsic properties of sensing elements, ceramic types can be designed by utilizing either semiconducting or dielectric metal oxide composites. Furthermore, polymeric types in turn can be based on conducting or non-conducting (dielectric) polyelectrolytes.All three categories of such sensors , utilize changes in the physical and electrical properties of the sensitive elements when exposed to the different atmospheric humidity conditions of the surrounding environment, and provide a measure of the humidity due to some amount of adsorption and desorption of water vapour molecules. It has been found that porous films exhibit higher humidity sensitivity than the nonporous counterparts . The preIn 1937 a electrolytic humidity sensor based on lithium chloride (LiCl) developed by Dunmore , became Impedance-sensitive type humidity sensors which encompass all three classes, are further classified into ionic and electronic (charge carriers) conduction type sensor devices. This classification is determined by the mechanism of the electrical transport. Electrolyte-based sensors being special cases that only follow the ionic transport mechanism. Later we present and classify the different types of sensing materials, working principles (impedance ionic or impedance electronic or capacitance type) with humidity and temperature ranges of various hygrometer sensors.Traditionally, the organic polymer film humidity sensors fall into the fundamental categories of resistive type (impedance type) ,54 and cCeramic type humidity sensors based on metal oxides have exhibited some superior advantages in comparison to polymer films from the viewpoints of their mechanical strength, thermal capability, physical stability and their resistance to chemical attack, which reveals them to be the most promising materials for electrochemical humidity sensor applications . This clBasically, metal-oxide ceramics used in humidity sensor applications are prepared by conventional  and adva4.i.e., ionic conduction, electronic conduction, solid-electrolyte and capacitive type, relies on the superficial water vapour adsorption based on chemical adsorption (chemisorption), physical adsorption (physisorption) and capillary condensation processes  trimethylammonium chloride) to an interdigitated gold electrode (pretreated with 3-mercaptopropionic acid) on a PET substrate by a peptide chemical protocol. N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride (EDC) was employed as a peptide coupling reagent. The sensors have shown good humidity dependence over wide humidity ranges (20%–90% RH) with good long term stability. The water stability of the anchored poly-MMA-MAPTAC to the MPA/Au contact area was influenced by the amount of added EDC  trimethyl ammonium chloride) resistive humidity sensors were fabricated by Su et al. from composite thin films on a flexible polyester substrate through in situ photo-polymerization of precursors  (BCB) dielectric material (0.8 μm thick) was recently designed and configured on a flexible polyimide substrate (8 μm thick). To optimize the sensing performance, the electrode structure has been sketched based on numerical simulations. In testing at 10% to 90% RH, the sensor showed an excellent linear response (0.996) to capacitance variations .6.2.2O3) with a porous structure is the most applicable material for these type of sensors [2O3 with small pore radius is very sensitive to very low humidity levels [2O3 only the two phases γ-Al2O3 (amorphous) and α-Al2O3 (corundum) are commonly used in humidity sensing applications; further the prior is more sensitive than the latter one owing to its higher porosity [2O3 humidity sensor fabrication, due to its low cost and process simplicity.Aluminium oxide TiO3 on silicon substrate [x Ti1−xO3 [3 [2. The (Sr1−x Lax)TiO3 humidity sensor reported by Li et al. with MIS structure consisted of a silicon substrate, aluminium evaporated interdigitated top electrodes, thick film of (1 μm) Sr1−xLaxTiO3 and SiO2 as an insulator layer. The n-type semiconducting Sr1−xLaxTiO3 ceramic film was deposited on the SiO2 oxide layer via argon ion-beam sputtering and annealed at 400 °C in nitrogen [xTi1−xO3). The presented film has shown a higher grain boundary barrier which could be explained because of substitution of Ti4+ by Nb5+ in the B sites instead of Sr2+ by La3+ in the A sites, and consequently a higher activation energy [Other groups of ceramic-type capacitive moisture sensors are based on perovskite metal oxides ,323 suchubstrate , SrNbx T Ti1−xO3  and BaTi1−xO3 [3 . The comnitrogen . To furtn energy .Based on the parallel plate  or inter2 core/shell nanorod capacitive thin film humidity sensors were fabricated on glass substrates by Gu et al. Shells of sol gel processed anatase titanium oxide (TiO2) were deposited on hydrothermally grown zinc oxide (ZnO) nanorod cores. Morphological characterization showed that the primarily formed zinc oxide nanorods were coated with anatase titanium oxide shells as a second layer. Compared with those sensors based on individual ZnO and TiO2, the ZnO/TiO2 nanocomposite (ZTNA) sensors exhibited considerably enhanced sensitivity at 95% RH . Further, the core/shell arrays' capacitance varied from 101 to 106 pF over the whole humidity range of 11% to 95% RH at room temperature [Highly sensitive ZnO/TiOperature .i.e., cubic sillenite and monoclinic types synthesized through a hydrothermal method were investigated by Sheng and co-workers with respect to their capacitive characteristics [3+ cations incorporated with moderate PO dopants. The sensing mechanisms have been reported to be of two main types: “non-Debye” types at low RH and “proton transport” mechanisms at high RH.Recently, the humidity sensing properties of two types of bismuth phosphates eristics . Based oLike the sensing mechanism of previous types, the pore properties of capacitive type sensors are responsible for their humidity sensing performance and influence the physisorption and chemisorption of water vapour molecules as well as capillary condensation .Proton-exchange membrane fuel cells (PEMFCs) are one of the pragmatic applications of humidity sensors . The ambet al. were motivated to design and fabricate a home-made MEMS-based novel flexible multi-functional micro sensor integrated on a stainless steel foil substrate which was able to sense temperature, humidity and voltage. The proposed multi-functional micro sensors could be embedded in the cathode flow channel of a proton-exchange membrane fuel cell (PEMFC) to measure the variations of internal voltage, temperature and humidity. Fabricated micro sensors have benefited from numerous desirable advantages, encompassing high flexibility due to their stainless steel foil substrate, high temperature resistance, high sensitivity, tiny size, precision of measurement, and capability to operate in high moisture (high RH) conditions along with long term stability. To reduce the temperature dependence of the humidity sensor, a capacitive principle of construction based on an interdigitated structure with a polyimide sensitive film has been adopted instead of a resistive type. Furthermore, to eliminate a long time response of the micro humidity sensor, it has been integrated with a micro heater [Lee o heater .7.The moisture sensing properties, manufacturing technologies and operating mechanisms of various humidity sensors based on applications and different sensing elements consisting of ceramic, organic polymer and hybrid composites have been classified and comprehensively described. Synthesis and preparation methods of precursor metal oxides and polymers for hygrometric applications were also explained. To provide an overall perspective, a review of state-of-the-art humidity transducers has been rendered in table form. Regarding the mechanism of sensing, a protonic conduction type has been discussed in detailed as a generally accepted mechanism in the majority of sensors (more than 85%) at room temperature. The key factors such as design configuration, structural characteristics and applied precursor quantities that influence efficiency, stability and sensitivity have been discussed.Among all humidity sensor design configurations, the impedance- (resistive) and capacitive-based sensors are revealed to be the best suited and most popular ones in advanced applications such as laboratory research or automated industries. This popularity could be due to their ability to fulfil the general requirements such as simpler structure, free choice of sensing elements among various available types, cost, adaptability to different types of circuits, and ease of fabrication and measurement setup.Nowadays, film-based humidity sensors are widely applied because of their advantages such as cost-effectiveness, acceptable gasketing, design flexibility and quick deposition rate. Among the various types of the humidity sensors, semiconducting metal oxide and metal oxide/polymer based sensors, dominantly produced by thick film and thin film deposition techniques, are noteworthy due to their variety of sensitive element choices, post-processing and greater response characteristics. In comparison with polymer-based thin or thick film humidity sensors, the synthesis process of the ceramics is simpler and they frequently reveal very fast response times, however polymers are lower in cost. These sensors are operational over a wide range of humidity with instrumental response characteristics, and possess the potential of being commercially available for medium to high volume demands.Recent developed humidity sensors in both university research and industry, especially those incorporated with nano-scale elements, provide promising performance with the high impact contribution to an improvement of accuracy, reliability, and economic efficiency. However, in the real conditions of practical environments, there still remain challenges to be solved for enhancing the sensor efficiency and response characteristics. In the future outlook of the humidity sensors design process, the nanocrystalline composites incorporating ceramics and ceramic/polymer are among the most promising materials, which may be applied for humidity sensing applications due to the better performance prospects they exhibit.


The correlation of mechanical strains in graphene layer with the substrate morphology is established via Raman mapping. Apparent vertical piezoresponse from the single-layer graphene supported by underlying SiO2 structure is observed by piezoresponse force microscopy. The calculated vertical piezocoefficient is about 1.4 nm V−1, that is, much higher than that of the conventional piezoelectric materials such as lead zirconate titanate and comparable to that of relaxor single crystals. The observed piezoresponse and achieved strain in graphene are associated with the chemical interaction of graphene's carbon atoms with the oxygen from underlying SiO2. The results provide a basis for future applications of graphene layers for sensing, actuating and energy harvesting.Electromechanical response of materials is a key property for various applications ranging from actuators to sophisticated nanoelectromechanical systems. Here electromechanical properties of the single-layer graphene transferred onto SiO Pristine graphene is not piezoelectric, but adsorbing dopant atoms can induce a piezoresponse by breaking the material's inversion symmetry. Here the authors demonstrate strong out-of-plane piezoelectricity in graphene on a silica substrate, providing a system for two-dimensional sensing and actuation. Recent discovery of piezoelectricity in two-dimensional 2D) materials1D materia5729101112et al.2 grating  was periodically localized doping induced by chemical interaction of graphene with SiO2 surface141516Lee 2 calibration grating substrates by piezoresponse force microscopy (PFM) and confocal Raman spectroscopy. We experimentally confirm the existence of electric polarization induced in graphene deposited on SiO2 via direct measurements of converse piezoelectric effect by PFM. Piezoelectric activity was mainly observed on the supported graphene regions where van der Waals and/or chemical interaction between the SiO2 surface and graphene layer can induce an anisotropic strain and detectable PFM signal. The in-plane strain in graphene was evaluated by polarization-dependent Raman spectroscopy.In this work, we present the results of experimental study of single-layer graphene (SLG) deposited on SiO2 calibration grating TGZ4 by the company 2D-Tech (UK) The substrate was a commercially available standard calibration grating for atomic force microscopy (AFM) purchased from NT-MDT (Russia). It consisted of a grid of parallel rectangular pitches with the height 1,317±10 nm and period 1,500±10 nm. The thickness of the thermally grown SiO2 layer was 1,400±10 nm.SLG was grown by chemical vapour deposition on a copper substrate and then transferred by the wet process on top of the Si/SiO−1) and CSG30/Pt . Standard topography mapping was carried out in a tapping mode. PFM and conductive AFM were performed using CSG30/Pt cantilevers in the contact mode. PFM measurements were carried out in a quasi-static regime at the frequencies <100 KHz with the 0–5 V amplitude of a.c. excitation signal under a vacuum ∼10−1–10−2 torr. Frequency-dependent measurements of PFM amplitude were carried out in the 10–900 kHz frequency range. Raman measurements were performed using a confocal Raman microscope WiTec alpha300AR . A 488-nm solid-state excitation laser with polarization along the grating stripes was focused by a 100 × objective . Spectral resolution of the used diffraction grating was 3.19 cm−1. Raman spectra were acquired by a charge-coupled device camera with 0.2 s integration time.Topography and PFM measurements were performed using a Ntegra Prima AFM  equipped with the lock-in amplifier  and function generator . The measurements were carried out using Pt-coated cantilevers purchased from NT-MDT (Russia): NSG30/Pt  were performed using a conventional AFM tapping mode . In thisBy contrast, scanning in a contact mode required by PFM a deleterious effect on the structure of both supported and suspended areas could be seen. Graphene layer could be destroyed after a single scan even at zero deflection level . Such behaviour demonstrated sufficiently low stability of graphene under an influence of lateral (in-plane) force and special care  was taken to prevent early degradation of the SLG during measurements.2 calibration grating substrate with the SLG on top is shown in Representative topography of the Si/SiOThe mechanical strain state of graphene layer was studied by Raman spectroscopy. Analysis of G-band frequency shift allowed estimating the value and spatial distribution of mechanical strains, as well as strain gradients .Spatial distribution of the strains (strain map) is shown in Tensile strains occur in defect-free regions of the graphene. The largest values of such strains fall on supported graphene  applied between the tip and the substrate to detect tiny (pm level) piezoelectric vibrations of the graphene surface. Despite the partial damage, we could find areas where the topography did not change during scanning. The cross-section of the PFM (mixed) signal across such an area is shown in 2 substrate and, thus, provides a natural reference to the PFM signal observed on graphene.Zero PFM signal on the right part of It is seen that clear negative piezoresponse is observed on both supported (shaded areas) and suspended graphene, and the signal on the supported graphene is four times higher than on the suspended one. The amplitude of the electric field-induced vibrations (first harmonic) was about 0.02–0.03 nm. The maximum strain attained and corresponding piezoelectric coefficient will be estimated below.To avoid further degradation of the SLG, voltage spectroscopy measurements18191920To further prove the piezoelectric nature of the response, we measured the frequency dependence of the signal under different a.c. voltage levels. Resonance amplification at the cantilever fundamental resonance was used to get a.c. voltage dependence at low biases and to verify the linearity of the response. Conventional resonance spectra were observed with the fundamental resonance at 262 kHz . Signal 2 substrate2 complexes on the surface layer, the C+–O− dipoles partly deviate from the substrate , we can excite and detect surface deflection directly under the tip. When the tip is in contact with the suspended area (with higher conductivity), the electric field is applied over the area covered by the graphene sheet, reaches the dipoles in the supported regions and excites mechanical vibrations. These mechanical vibrations from the supported regions are transferred by the graphene membrane and drive the tip.2. It is known that the strain in graphene or/and additional oxygen-containing functional groups can transfer the graphene into semi-conducting or insulating state122 surface can also induce the gap opening2.Significant frequency shift of the G-band seen in the Raman spectra on the supported regions  associatE perpendicular to the surface plane can be described as follows:Strain induced by the electric field P is the sum of the induced, Pi, and spontaneous, Ps, polarization: P=Pi+Ps=(ɛ−1)ɛ0E+Ps. Then Here the electric polarization Q33(ɛ−1)ɛ0PsE corresponds to the piezoelectric effect, with the piezoelectric coefficient d33=2Q33(ɛ−1)ɛ0Ps. The last term, E (first and last terms):The first term 2E is a combination of a.c. Ea.c. and d.c. Ed.c. components: E=Ed.c.+Ea.c.. Since the applied Ea.c.=E0 cos (ωt+φ0), then normal to the surface displacement, h is thickness of the carbon-oxide dipole layer. The amplitude of the first harmonic thus takes the form:The applied electric field E0 is the magnitude of a.c. electric component, Ea.c.. For thin carbon-oxide dipole layer with h<<R0, where R0 is the tip radius, we can consider electric field below the tip in the layer as uniform. Using the relation for tip potential  out-of-plane converse piezoelectric effect on supported graphene induced by underlying SiO2. This effect is directly related to a huge number of possible applications such as motors, actuators, resonators and micro- and nanomechanical systems based on graphene. Naturally, the displacement level can be increased by fabricating thin SiO2 membrane or bridge structures. As such, a high value of the out-of-plane piezoelectric coefficient observed in our work provides a basis for such applications.When this manuscript was prepared for the submission, the paper by Wang 2 calibration grating substrates. Mapping the strain distribution in graphene was performed via confocal Raman measurements and converse piezoelectric effect was measured locally by PFM. The piezoelectric activity in graphene layers was attributed to the chemical interaction of graphene atoms with underlying oxygen from SiO2 substrate. Piezoelectric effect is sufficiently high . We foresee a number of emergent applications when using graphene/SiO2 structures as a platform for future sensors and actuators.In summary, we observed a strong piezoelectric activity of the SLG deposited on Si/SiOHow to cite this article: da Cunha Rodrigues, G. et al. Strong piezoelectricity in single-layer graphene deposited on SiO2 grating substrates. Nat. Commun. 7:7572 doi: 10.1038/ncomms8572 (2015).Supplementary Figures 1-5, Supplementary Notes 1-2 and Supplementary References