Analysis of the measurement results demonstrates the proposed sensor network system is capable of automatic and real-time monitoring and can be applied and utilized for both the safety evaluation and precise implementation of buildings under construction.In this study, a practical and integrative SHM system was developed and applied to a large-scale irregular building under construction, where many challenging issues exist. In the proposed sensor network, customized energy-efficient wireless sensing units  were employed and comprehensive communications from the sensor node to the remote monitoring server were conducted through wireless communications. The long-term (13-month) monitoring results recorded from a large number of sensors  indicated that the construction event exhibiting the largest influence on structural behavior was the removal of bents that were temporarily installed to support the free end of the cantilevered members during their construction. The safety of each member could be confirmed based on the quantitative evaluation of each response. Furthermore, it was also confirmed that the relation between these responses ( Applications of SHM to actual buildings largely focus on measuring the responses of in-service buildings to understand their structural conditions and performance.Structural health monitoring (SHM) based on sensor technology has received considerable attention and has successfully replaced traditional visual inspection for damage detection and maintenance of structures subjected to various loadings such as earthquake, wind and service loadings –3. Not o.g., GPS ,14, terr.g., GPS ,16, visi.g., GPS ,18 and l.g., GPS ,20, etc.et al.  are usually used to monitor structural responses. Among these strain-type sensors, FOSs and VWSGs, which are immune to electromagnetic interference and provide superior endurance, are actively studied and employed in SHM -35. HoweThe inclinometer  ) and lasThe sensors automatically and continuously measure the structural responses according to pre-scheduled periods, and the measured data are transmitted to the sensor node through a signal line. 3.1.2.The raw data  measured from each sensor must be converted into structural response values . To obtain usable data information from the raw data, an analog-to-digital converter (ADC), responsible for signal conversion, is needed to downsize, process, and input/output the data information. In the WSN proposed here, the sensor node plays the role of the data reading device, which processes  the data transmitted from the sensors and then transmits the processed data values to a wireless transceiver (repeater node/master node). In other words, sensor node performs three functions—a sensing interface, computational core, and wireless transceiver.Each individual sensor node is developed depending on the sensor type such as a VWSG, inclinometer, or laser displacement sensor. As a sensing interface, a VWSG sensor node is equipped with a system that drives four channels of a VWSG at each node; the inclinometer sensor node can simultaneously process a maximum of three sensors. The transmitted data passing through the signal processing of the sensor node significantly reduces the transmission amount when compared to the direct transmission of raw data. As a result, even when multiple sensors are connected to a single sensor node, this setup is more energy efficient in transmission. The inclinometer and laser displacement sensor have data processing functions included in their sensors. Therefore, the sensor nodes for the inclinometer and laser displacement sensor essentially play the role of a wireless transceiver, transmitting the sampling completed data.3.1.3.The sensors do not possess a transmission function, so a wireless transceiver is required to send the structural response information received from each sensor to the monitoring server. In this system, the transmissions between the sensor nodes and remote server are performed by the repeater node and master node, as shown in The repeater nodes have a relay function to address transmission problems that can occur in various site situations, including direct data transmissions being blocked by limited communication distances between the sensor node and master node or obstructing elements in the wireless communication. The use of repeater nodes allows the final transmission distance is extended and for stable communication.The master node receives all of the measured data wirelessly transmitted from the sensor nodes and the repeater nodes at a short distance. Then, the master node sends the data to the remote monitoring server using long-distance communications. A single master node is able to acquire dozens of sensor data submissions, so a large amount of data transmission is possible with a small number of master nodes through wireless communications.3.1.4.All of the data measured from the numerous sensors extensively installed over the monitoring site are eventually sent to the monitoring server at the monitoring control center through the master node. The monitoring control center (located far from the construction site) can manage several monitoring sites simultaneously. The system suggested in this research utilizes user interface software that can efficiently manage the received data and enable various administrators to easily check the data. This data integrated managing software program then becomes the platform for connecting multiple facilities and multiple maintenance management agents. The registration and removal of managed sites are easily accomplished, and simultaneous access from multiple administrators is possible. Software access levels can be set to limit the range of activity for each administrator.3.2.3.2.1.The data communication from the sensor to the sensor node occurs through the signal line in this system. As shown in 3.2.2.The measured information that is transmitted from the sensor travels through the sensor node and is then transmitted to the master node through a wireless transceiver . A typic3.2.3.The final data transmission process—from the monitored building to the off-site monitoring control center—is performed by the master node. When all of the measured data values at the site are gathered at each master node, the information is transmitted to the monitoring server at the monitoring control center through CDMA communication, which is a digital mobile communications method using spread spectrum technology. CDMA communication is a multi-access technology that transmits coded signals to the entire available bandwidth, so multiple users can gain access in the same frequency band simultaneously. This multi-access configuration spreads the transmission of the signal to a much wider bandwidth than the bandwidth of the signal itself, so it is not interrupted by other communication sources, resulting in multiplexing and high-speed information processing. CDMA communication is resistant to interference and provides added security—the coded signal of the specific user is perceived as noise to other users. There are no distance restrictions, and there is only a low possibility of data noise due to long-distance transmissions. Furthermore, limited resources can be shared by many users, so the signal can be decoded and demodulated using wireless terminals, such as PCs, notebooks, tablets, and mobile phones, on the Internet regardless of the distance from the monitoring site. As a result, an administrator in this system can check the data for a desired location and time, and several administrators can promptly evaluate the structural safety of the site conditions regardless of time and space.3.3.The issue of a stable power supply is a crucial project priority in the stable operation of a SHM ,27. In wi.e., sensor nodes, repeater nodes, and master nodes) to stabilize the operation of each component  from the stage of construction. DPB has numerous complexities in its construction such as various exposed concrete elements using free curvatures and external panels, complicated space frames supporting the panels, an irregular large internal space, and mega members. As shown in Each sensor and wireless sensing unit was installed using a sky-vehicle or temporary structure, as shown in Each sensor and sensor node was covered with a plastic cap for protection from harsh environmental conditions at the construction site. As shown in 4.2.The locations for all measured data are provided in The edge truss in zone C that is connected to the mega truss is a cantilevered structure; before completing the process of welding the separately manufactured mega truss, it was supported by 10 temporary bents arranged in equal intervals along the length of zone C .i.e., bent removal) exhibited the largest influence on the responses of the structural members during the monitoring period.After the welding connections of the fabricated mega truss and edge truss were completed, the temporary bents were removed, as shown in i.e., vertical deflection at the free end) began in the mega truss and edge truss, it had the largest effect on the measured values. Because A6 and C3 are the connecting areas of the mega truss and edge truss, they were areas that required great caution in terms of structural safety when planning the SHM system for DPB. Therefore, the measuring interval of the laser displacement sensors during the bent removal process was changed from 30 min to 1 min to monitor the amount of deflection in real time.Based on the bent removal monitoring results, we can obtain not only the local response of the members (strain) but also the global responses (deflection and inclination) of the structure, thereby evaluating the overall safety of the structure. Furthermore, it is also confirmed that the relationships between these responses can provide information on the global behavior of the structure induced from a specific event .5.i.e., sensor node–repeater node-master node-monitoring server) were conducted through wireless communications by using the ISM band and CDMA method, which were responsible for short- and long-distance communications, respectively. Through these decentralized communications, energy-efficient communication devices operated by battery power were enabled. As a result, wireless communications employed at a construction site drastically reduced cable lengths—a major issue related to in-construction monitoring.This research developed a practical and systematic SHM system that can be applied to a structure under construction, where many challenging issues exist. In this system, customized energy-efficient wireless sensing units  were employed and comprehensive communications from the sensor node to the monitoring server  can provide information about the global behavior of structures induced from specific events .The monitoring system proposed here was applied to a large-scale irregular building under construction that exhibited a very complicated configuration and long span (approximately 118 m) supported by cantilevered edge truss and mega trusses. The long-term monitoring results recorded from a large number of sensors  indicated that the construction event exhibiting the largest influence on structural behavior was the removal of bents that were temporarily installed to support the free end of the cantilevered members (such as the edge truss and mega trusses) during their construction. Bent removal caused a significant vertical deflection of the edge truss and the mega trusses induced from the cantilever behavior. The safety of each member could be confirmed based on the quantitative evaluation of each response. Furthermore, it was also confirmed that the relation between these responses  measurement results demonstrates the proposed sensor network system is capable of automatic and real-time monitoring and can be applied and utilized for both the safety evaluation and precise implementation of buildings under construction. Furthermore, the measured values obtained through this construction monitoring technique can be utilized as fundamental information when analyzing and evaluating the response of a structure subjected to the various loads that occur during the building's service period. This ability is very important for providing continuous information on the construction stage in terms of understanding the behavior of a structure in-service.Finally, we observed the limitations in the proposed WSN throughout the long-term monitoring, including the data loss caused by communication obstacles at the construction site. Therefore, the development of data recovery tools in terms of both hardware and software  is needed to enhance the reliability of monitoring results with data loss. In addition, since the proposed system is applied to measure the static responses in this study, the capability of measuring dynamic response such as acceleration is recognized as an important issue involving stable long-term power supply, which should be resolved to enhance its applicability.


To better understand the efficacy of various implementation strategies, improved methods for describing and classifying the nature of these strategies are urgently required. The aim of this study was to develop and pilot the feasibility of a taxonomy to classify the nature and content of implementation strategies.A draft implementation taxonomy was developed based on the Cochrane Effective Practice and Organisation of Care (EPOC) data collection checklist. The draft taxonomy had four domains  covering 49 distinct strategies. We piloted the draft taxonomy by using it to classify the implementation strategies described in the conference abstracts of the implementation stream of the 2010 Guideline International Network Conference. Five authors classified the strategies in each abstract individually. Final categorisation was then carried out in a face-to-face consensus meeting involving three authors.The implementation strategies described in 71 conference abstracts were classified. Approximately 15.5% of abstracts utilised strategies that could not be categorised using the draft taxonomy. Of those strategies that could be categorised, the majority were professionally focused (57%). A total of 41% of projects used only one implementation strategy, with 29% using two and 31% three or more. The three most commonly used strategies were changes in quality assurance, quality improvement and/or performance measurement systems, changes in information and communication technology, and distribution of guideline materials .Further refinement of the draft taxonomy is required to provide hierarchical dimensions and granularity, particularly in the areas of patient-focused interventions, those concerned with audit and feedback and quality improvement, and electronic forms of implementation, including electronic decision support. Effective implementation strategies are necessary for improving the uptake and use of clinical practice guidelines by the intended guideline audience ,2. We deThe lack of such a framework inhibits the shared understanding of the exact nature of specific implementation strategies, which makes it difficult to design and conduct effectiveness studies to investigate differences between strategies -6. This The lack of clarity with regards to the nature of implementation strategies obscures the understanding of study outcomes, making it difficult to correlate findings from a range of data sets or to pool data from several studies in a systematic review to determine the effectiveness of a specific strategy . The lacTaxonomies are effective modes of information management that have been used successfully in medicine, business systems, and information technology to describe, classify, and organise items on the basis of shared characteristics ,10. A taA number of strategies have been developed to provide authors with publication guidelines for reporting the outcomes of implementation research. While these are widely used, they do not provide the level of detail required for defining and reporting the nature of implementation strategies ,15. The While these guidelines and other statements have been widely used to provide researchers with a concise and practical method to assess and report implementation strategies, there is still a need for improved reporting of implementation strategies and for more sophisticated guidelines for reporting different strategies ,8. Some Numerous topic-specific taxonomies of implementation strategies have been reported in the literature -23; howeThe aim of our study was to draft an implementation taxonomy and to pilot its usefulness and feasibility as a tool for classifying implementation strategies.http://www.imagingpathways.health.wa.gov.au). The EPOC Data Collection checklist was used as a starting point, which led to an interest in a broader approach of using the EPOC checklist items (see EPOC Data Collection Checklist) to classify the abstracts from the 2008 and 2009 Guidelines International Network (G-I-N) Conferences. G-I-N is a network of guideline organisations, implementers, end-users, researchers, students, and other stakeholders who are working towards improving the efficiency and effectiveness of evidence-based guideline development, adaptation, dissemination and implementation (http://www.g-i-n.net/). The classification of abstracts using the EPOC checklist led to some difficulties, which included difficulty in relating the prose of the checklist items to guideline implementation, repeated efforts to read and re-read checklist items because of the length of the definition and inclusion criteria, some checklist items were irrelevant to guideline implementation, lack of a logical order with checklist items within major categories, checklist items inappropriately categorised, and the absence of checklist items that describe some well-established methods of guideline implementation. Consequently, a revised taxonomy was drafted based on the EPOC checklist, which included 49 items listed under four broad domains: professional, financial, organisational, and regulatory.The development of the taxonomy occurred after correspondence between two of the authors  about taxonomies that could be applied in the implementation of the Diagnostic Imaging Pathways Application (Summary of items from the EPOC Data Collection Checklist in the category of ‘Type of intervention’ (excluding definitions and inclusion criteria)Types of interventionsProfessional interventionsDistribution of educational materialsEducational meetingsLocal consensus processesEducational outreach visitsLocal opinion leadersPatient mediated interventionsAudit and feedbackRemindersMarketingMass mediaOtherFinancial interventionsProvider interventionsFee-for-servicePrepaidCapitationProvider salaried serviceProspective paymentProvider incentivesInstitutional incentivesProvider grant/allowanceInstitution grant/allowanceProvider penaltyInstitution penaltyFormularyOtherPatient interventionsPremiumCo-paymentUser-feePatient incentivesPatient grant/allowancePatient penaltyOtherOrganisational interventionsProvider orientated interventionsRevision of professional rolesClinical multidisciplinary teamsFormal integration of servicesSkill mix changesContinuity of careSatisfaction of providersCommunication and case discussionOtherPatient orientated interventionsMail order pharmaciesPresence and functioning of adequate mechanisms for dealing with patients’ suggestions and complaintsConsumer participation in governance of health care organisationOtherStructural interventionsChanges in the setting/site of service deliveryChanges in physical structure, facilities and equipmentChanges in medical records systemsChanges in scope and nature of benefits and servicesPresence and organisation of quality monitoring mechanismsOwnership, accreditation, and affiliation status of hospitals and other facilitiesStaff organisationOtherRegulatory interventionsChanges in medical liabilityManagement of patient complaintsPeer reviewLicensureOtherTo test its utility and comprehensiveness, we applied our draft taxonomy to a convenience sample of conference abstracts from the implementation stream of the 2010 G-I-N Conference. Abstracts were independently classified by five reviewers  using the draft taxonomy.Reviewers utilised a classification template developed by HB that assessed whether the abstract referred to a research study, whether the study described strategies used to implement a guideline, which of the strategies in the draft taxonomy could be used to categorise the reported interventions, and whether the study described a method for measuring the impact/outcome of implementation. Three of the reviewers  then met face-to-face to establish a consensus regarding the exact implementation strategies used in the relevant abstracts and to highlight areas of the taxonomy requiring further refinement.The draft taxonomy and outcomes from the pilot study were presented to a self-selected group of interested participants who attended a G-I-N meeting workshop in 2011, and feedback was sought from this group about the purpose, design and content of the draft taxonomy.Our draft taxonomy comprises four domains of intervention types: professional, financial, organizational, and regulatory. These domains were preserved from the original EPOC checklist. Within the taxonomy, 49 implementation strategies are described: 15 strategies targeting health professionals; 12 strategies involving financial incentives for guideline implementers (eight) and for patients (four); 18 involving organisational strategies at an implementer level (six), at a patient level (three), and at a structural level (nine); and 4 strategies involving structural change. The draft taxonomy is provided as Additional File The following findings relate to the application of the updated draft taxonomy to the abstracts from the implementation stream of the G-I-N 2010 Conference.A total of 85 abstracts were available for inclusion in the pilot study. Of these, 71 abstracts included a description of guideline implementation strategies and were, therefore, used in the study. Fifty-six abstracts described original research studies involving guideline implementation, and 15 abstracts described non-research studies (projects) involving guideline implementation.A total of 170 implementation strategies were described in the 71 abstracts. Figure All four domains of the taxonomy were utilised in the assessment of the abstracts. However, the implementation strategies were spread disproportionately across the four domains Figure . StrategThe most commonly used strategies are presented in Table The distribution of reported implementation strategies among the domains of the taxonomy are presented in Table Using the EPOC checklist as a starting point to code strategies described in abstracts from the 2008 and 2009 G-I-N Conferences, we drafted an implementation taxonomy, which was subsequently used to classify implementation strategies described in the abstracts from the implementation stream of the 2010 G-I-N Conference. Although all four domains  were utilised in the assessment of the abstracts, our draft taxonomy proved inadequate when attempting to further classify some of the implementation strategies using the subgroups within these domains.All of the domains in our draft taxonomy were successfully applied to the abstracts, which supports their usefulness as broad categorisations. However, further classification of the abstracts using the categories within each domain yielded a high number of strategies that were classified as ‘other’. Use of the ‘other’ category is likely due to the level of detail available in abstracts, which could be overcome by reviewing a full-text article. However, it may also point to the need for better granularity within the taxonomy to allow more accurate classification of strategies. Further investigation is required to identify and understand the types of strategies that have not been included in the current version of the EPOC taxonomy.The language and clarity of our draft taxonomy were assessed using feedback received from attendees at a workshop on the taxonomy, which was held at the G-I-N 2011 meeting. In general, the taxonomy was found to be sufficiently detailed to enable classification. However, in some instances, items were found to be less explicit. For example, the strategy ‘identify barriers’ does not specify the type of barrier or the method used for identification.A number of design-related issues were also identified by the authors when the draft taxonomy was used to classify the abstracts. The level of detail included in each subgroup of the taxonomy was, in some instances, not sufficiently detailed to distinguish differences between two items on the checklist. For example, problems arose when trying to differentiate reminders and recalls from other forms of electronic implementation and when differentiating audit and feedback from other quality improvement activities. These issues support the need for improving the granularity of the taxonomy.The focus on interventions for health professionals in this taxonomy also overlooks other groups of individuals, such as patients and key decision makers, who play a critical role in the health system. There is an increasing recognition of the benefits of patient involvement in healthcare, particularly in patient-centred health care and shared decision-making -30. As sIn addition to health professionals and patients, knowledge translation in health care involves other groups such as policy makers, government officials, membership bodies, and non-government organisations. The present structure of the taxonomy does not permit consideration of the specific roles of these groups within the healthcare system and, as such, would require revision to ensure that implementation strategies aimed at behaviour change in a range of participants within the healthcare system are included in the framework.Following the process of classifying implementation strategies using our draft taxonomy, we found that the majority of abstracts reported the use of two or more strategies. The use of multiple strategies by some guideline implementers (with some abstracts describing six or more activities) suggests that implementers are cautious about confining themselves to a select number of strategies. Alternatively, it suggests that the use of multiple strategies is assumed to be more effective because it addresses more barriers . NeverthIt is also currently unclear how guideline implementers select the number of strategies required to facilitate the implementation of a guideline. While a number of studies have demonstrated improved patient outcomes after multifaceted interventions , more evThe results of our study showed that the commonly used implementation strategies described within the abstracts were either professional or organisational. These results may reflect the fact that: there is more evidence to support the use of these strategies; these strategies are more accessible to guideline implementers in general; or these strategies are more accessible to the cohort of presenters at the G-I-N Conference who are likely to be researchers . ConversWe identified a few limitations in our study. The review of abstracts provided a lower level of detail compared to that of a full-text article. In some instances, this lower level of detail made it difficult to accurately classify the implementation strategies described in the abstract. In these instances, it is difficult to ascertain if the inability to classify a strategy is due to the detail and accuracy of the abstract or the draft taxonomy itself. Nevertheless, abstracts are usually the first ‘screening point’ for published papers and, therefore, it is important that abstracts, as well as full-text articles, include a sufficient level of detail to inform the reader about strategies that were used.Another limitation of this study is the use of abstracts from the G-I-N Conference. Applying the taxonomy on these abstracts may yield optimistic depictions of guideline implementation, because the participants at this conference are likely to be more conscientious about the implementation of guidelines than people in the broader academic or healthcare community. Nevertheless, these abstracts provide a good basis for testing our draft taxonomy as they are screened and selected by an expert panel and, therefore, the reporting of details in these abstracts is likely to be of a higher standard than those published in the wider literature. Further development of our draft taxonomy will include its application on full-text articles, which would greatly enhance its utility, validity, and relevance.This first draft of the taxonomy utilised a flat structure to create a blueprint of a potential hierarchical format of the taxonomy. Knowledge translation can, however, occur in three dimensions: linear, cyclic, and multi-dimensional , and guiBehavioural change techniques may also intersect with guideline implementation strategies. It is therefore important to consider the reliability of language and coding for implementation strategies used within the guideline, as these could contribute to guideline implementation but could also be classified as behavioural change . Future Further development of our draft implementation taxonomy should include a review of other taxonomies for supplementary items that may be relevant to our current taxonomy and the inclusion of strategies that were classified as ‘other’ in the current draft. This could result in a more useful tool for guideline implementers and researchers. A discussion of the strengths and weaknesses of our taxonomy in comparison to other published taxonomies, including those related to specific healthcare settings , would ahttp://www.mcmasterhealthforum.org/healthsystemsevidence-en), which is a database that contains research evidence about governance, finance, and delivery arrangements of healthcare systems, as well as implementation strategies. Improving the accuracy and granularity of the MeSH vocabulary [While there continue to be issues relating to the reporting of interventions , the availability of a taxonomy moves us one step closer to providing structure for reporting and assessing implementation strategies. Guideline implementers and researchers would also benefit from the availability of a well-indexed database of implementation studies, which would facilitate the search for and identification of suitable implementation strategies. One such example is the Health Evidence Database Classification .Click here for file


Peripheral odontoma arising in the extraosseous soft tissues is rare and if not removed early, may enlarge over time and eventually erupt in the oral cavity.A 15-year-old girl presented with “denticles on the gingiva”. During the intraoral examination, seven small tooth-like structures were found. These were exposed in the anterior left gingiva between the permanent maxillary lateral incisor and canine teeth, and the left first premolar was absent. Radiographic examination revealed irregular tooth-like structures without evidence of bone involvement.The lesion was surgically removed, and the specimens were analyzed histopathologically. The diagnosis of compound odontoma was established.This is the twelfth reported case of peripheral odontoma in the gingiva and the first one that erupted in the oral cavity. Odontomas are the most common type of odontogenic tumors and they are often associated with permanent or temporary tooth eruption disturbances . AccordiClinically, odontomas are either intraosseous or extraosseous -5. The iWe report a case of a 15-year-old girl with a peripheral compound odontoma characterized by multiple rudimentary teeth which erupted spontaneously in the anterior maxillary region. Based in the related English literature, this is the first reported case of peripheral compound odontoma with several teeth erupted in the gingiva.The patient, a 15-year-old girl was referred to the Stomatology Clinic of Alfenas Federal University with the complaint of “denticles on the gingiva”. The patient reported that when she was 12 years old, she perceived the emergence of small asymptomatic mass in the anterior maxillary region and one year later multiple denticles erupted at the injury site. During the intraoral examination, the residual root of a deciduous tooth that had been destroyed by caries lesions and six small tooth-like structures were found exposed in the anterior left gingiva between the permanent maxillary lateral incisor and canine teeth Figure A. These Postoperatively there was no complication and the suture was removed ten days after surgery. At this time, there was satisfactory healing of the operated region. After one month, the patient showed complete healing of the lesion and the gingival contour in the region of the lateral incisor and canine teeth had been fully restored Figure . After tOdontomas are generally asymptomatic lesions and they constitute causal findings in the course of routine radiological studies, particularly in the second and third decades of life . CompounPeripheral or soft tissue odontomas are extremely uncommon with about twelve compound odontomas reported in the related English literature, including the present case, as summarized in Table Although peripheral odontomas have a limited potential for growth, Ide; Shimoyama; Horie, (2000), the case here reported reinforces the idea that if the lesion had not been surgically removed in early developmental stage, it might have erupted into the oral cavity. The eruptive mechanism of peripheral odontoma remains uncertain and it appears to be different from tooth eruption because of the absence of periodontal attachment in odontomas .The majority of erupted odontomas described are intraosseous and related to non-erupted teeth -5. ThereOn the other hand, the exposure of denticles from the peripheral compound odontoma in the gingiva, as illustrated in the present case, could represent a tendency to exfoliation, as suggested by Shekar et al., due to its growth over time, rather than a real process of tooth eruption.Histopathological analysis showed that the tooth-like structures were composed of enamel, dentin, pulp chamber, and cementum in the same order of arrangement as that in a normal tooth. These structures were surrounded by thin epithelium and embedded in dense fibrous connective tissue, demonstrating their peripheral origin. The rudimentary denticles that had erupted from the odontoma had a fragile insertion into the gingival tissue which was clinically confirmed by accentuated tooth mobility. The fibrous connective tissue contained mononuclear inflammatory infiltrate and bacterial colonies was clinically characterized by inflammation of the gums due to the presence of dental biofilm, probably caused by the difficulty of cleaning in the region.In summary, due to the rare occurrence of peripheral odontomas, the present case is the first report of an erupted peripheral odontoma. However, it is necessary for dentists to be aware that peripheral odontomas, if not removed early, may enlarge over time and eventually erupt in the oral cavity, compromising both periodontal health and esthetics. Moreover, it is important to note that the increased tooth mobility caused by these lesions may result in the spontaneous exfoliation of these denticles, which may lead to the patient swallowing them.This is the twelfth reported case of peripheral odontoma in the gingiva and the first one that erupted in the oral cavity.Written informed consent was obtained from the patient for publication of this Case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.The authors declare that they have no competing interests.JACH operated and monitored the patient. DTO performed the microscopic diagnosis and assisted in drafting of paper. NGG accompanied the clinical case and drafted the article. MRGS accompanied the clinical case and drafted the article. AACP assisted in the surgery. All authors read and approved the final manuscript.


The evolution of drug resistant bacteria is a severe public health problem, both in hospitals and in the community. Currently, some countries aim at concentrating highly specialized services in large hospitals in order to improve patient outcomes. Emergent resistant strains often originate in health care facilities, but it is unknown to what extent hospital size affects resistance evolution and the resulting spillover of hospital-associated pathogens to the community. We used two published datasets from the US and Ireland to investigate the effects of hospital size and controlled for several confounders such as antimicrobial usage, sampling frequency, mortality, disinfection and length of stay. The proportion of patients acquiring both sensitive and resistant infections in a hospital strongly correlated with hospital size. Moreover, we observe the same pattern for both the percentage of resistant infections and the increase of hospital-acquired infections over time. One interpretation of this pattern is that chance effects in small hospitals impede the spread of drug-resistance. To investigate to what extent the size distribution of hospitals can directly affect the prevalence of antibiotic resistance, we use a stochastic epidemiological model describing the spread of drug resistance in a hospital setting as well as the interaction between one or several hospitals and the community. We show that the level of drug resistance typically increases with population size: In small hospitals chance effects cause large fluctuations in pathogen population size or even extinctions, both of which impede the acquisition and spread of drug resistance. Finally, we show that indirect transmission via environmental reservoirs can reduce the effect of hospital size because the slow turnover in the environment can prevent extinction of resistant strains. This implies that reducing environmental transmission is especially important in small hospitals, because such a reduction not only reduces overall transmission but might also facilitate the extinction of resistant strains. Overall, our study shows that the distribution of hospital sizes is a crucial factor for the spread of drug resistance. The increasing spread of bacteria, which are resistant to antibiotics, is a serious threat to clinical care. Currently, several countries aim at concentrating highly specialized services in large hospitals in order to improve patient outcomes. However, empirical studies have shown that resistance levels correlate with hospital size. To illustrate this correlation, we analyze two published datasets from the US and Ireland and controlled for antimicrobial usage, disinfection and length of stay. The proportion of patients acquiring both sensitive and resistant infections in hospitals strongly correlated with hospital size. Moreover, we observe the same pattern for both the percentage of resistant infections and the temporal increase of hospital-acquired infections. To investigate to what extent hospital size can directly affect the prevalence of antibiotic resistance, we use mathematical models describing the epidemic spread of resistance in hospitals and the community. We find that small hospitals typically lead to considerably lower resistance levels than large hospitals. However, this beneficial effect of small hospital size may be reduced if bacteria are transmitted indirectly via the environment. Therefore, reducing environmental transmission might be particularly important in small hospitals. Overall, our findings suggest that the short-term benefits of larger hospitals may come at the price of increasing resistance in the long term. The last decades have shown that the introduction of an antibiotic agent is almost inevitably followed by the spread of resistance mutations that jeopardize the beneficial effect of this agent The epidemic spread of antibiotic resistance can be strongly affected by the structure of the human host populationThe size distribution of hospitals is an important determinant for the population structure generated through the hospital-community interaction. In small hospitals, bacterial population sizes and frequencies are subject to strong stochastic effects and populations may frequently become extinct. It has been empirically shown that resistance levels tend to be lower in small hospitals The model presented here extends the basic models for the spread of antibiotic resistance Admission & Discharge: Hospitals are characterized by the turnover-rate of patients (Turnr), the expected patient population size for which the hospital has been conceived (number of beds)(HS) and the actual number of patients at a given time point (HN). Admission and discharge events occur at rates H rTurnS and H rTurn,N respectively, so that HS corresponds to the average number of patients. At an admission event a randomly chosen patient from the community enters the hospital and at a discharge event a randomly chosen patient leaves the hospital and reenters the community. We assume that the colonization status does not affect the admission or discharge rates. This is a reasonable approximation for many endogenous infections, where organisms that are usually part of the commensal microflora enter previously sterile body compartments.Colonization: Uncolonized patients in the hospital and the community become colonized with strain i (here and below i can be either i  =  s for the sensitive or i  =  r for the resistant strain) with a rate H,i βH (1-si)N and C,i βC (N1i),- s respectively. Here H,IN  denotes the number of patients in the hospital (community) infected with strain H (βC)i, β denotes the transmission rate in the hospital (community), and is the fitness costs of the mutations carried by strain i .Clearance: In the hospital, patients colonized with strain i are cleared with a rate Baseliner+Hu if strain i is susceptible and with a rate Baseliner is strain i is resistant. Here, Baseliner denotes the baseline clearance and Hu the usage frequency of the antibiotic in the hospital. For the community, clearance rates are obtained by substituting the usage frequency in the hospital by the usage frequency in the community (Cu).Mutation: A mutation from a strain i to a strain j becomes fixed in a patient if it increases replicative fitness, i.e. if it either confers resistance in a treated patient or a growth advantage in an untreated patient. In the first case the mutation occurs with a rate H,j μ uHN and in the second case it occurs with a rate H,j μ (si -sj)N for the hospital and analogously for the community.H, uCu) increase linearly in the first five years until they reach their final value, which is kept constant for the remaining 25 years of the treatment period. In the following, values given for antibiotic usage frequencies always refer to this final value.Each simulation run consists of a burn-in period followed by a treatment period. In the burn-in period, the model is simulated for 30 years in absence of treatment . In the treatment period, antibiotic usage frequencies  in R), hospital size was the parameter which overall had the most significant impact. Unsurprisingly, the amount of overall antibiotic usage also strongly correlated with both absolute and relative resistance levels. Nevertheless, the model fits . To test our findings obtained with the Irish dataset, we used surveillance data from the Pennsylvania Health Care Cost Containment Council (PHC4) 5 individuals with 1000 hospital beds.We mainly consider two simple settings see , in whicWe find that, if drug use is high in the hospital and low in the community, the level of resistance increases with hospital size . This efWhen varying the usage frequencies of antibiotics, we find three basic effects : 1) ReduThese effects and the parameter combinations at which they occur can be understood as follows: If antibiotic usage is low in the community and high in the hospital, selection acts strongly against antibiotic resistance in the community and in favor of antibiotic resistance in the hospital. In this case, frequent extinctions in the hospital, caused by a small population size, weaken the resistance-favoring selection in the hospital compared to the resistance-disfavoring selection in the community. This occurs because, when the resistant strain is extinct in the hospital, selection has no diversity to act on and is therefore ineffectiveβ (β’) denotes the transmission rate of the sensitive (resistant strain), t the turnover of infections in the setting considered, s the fitness cost of drug resistance, and u the usage frequency of the antibiotic. It follows that resistance is selected (i.e. R0'/R0>1) if the usage frequency is above a threshold given by d−1 vs. 1/300 d−1) the same applies to these thresholds and hence resistance becomes selected for at much lower usage frequencies in the community than in the hospital. The white lines in In this context it is important to note that, because of different turnover rates, a level of drug usage that would lead to selection against drug resistance in the hospital can lead to selection for drug resistance in the community. Selection in the hospital or the community on its own, favors resistance if the fitness of the resistant strain (R0') exceeds the fitness of the sensitive strain (R0). The ratio of these fitnesses is given byIf the community is subdivided as well, the beneficial effects of small hospital size are increased further . This isNosocomial infections do not only spread by direct (patient-to-patient) transmission but also by indirect transmission via the environment . Hospitals exhibit a high turnover of patients and hence in the absence of influx or transmission, the number of infections decays rapidly and eventually the pathogen may become extinct. By contrast, the environment can potentially act as a reservoir Up to this point we have approximated the patient population in a given hospital as panmictic; i.e. we have assumed that the transmission probability is the same for all pairs of patients. Hospitals, however, often exhibit strong population structure imposed by hospital wards. The opposite extreme of the panmictic hospital model considered so far is a hospital model consisting of completely subdivided hospital wards. This latter extreme is formally identical to a model in which every ward of a given size is replaced by hospital of the same size. The population structure of real hospitals most likely lies between these extremes: it is characterized by ward structure but also by frequent migration between wards. For instance Leverstein-VanHall et al. found, for patients infected with resistant strains, interward migration rates of 0.03–0.07 per patient-dayIt has been argued that concentrating highly specialized services in large hospitals will both improve patient outcomes as well as reduce health care costs We used a stochastic epidemiological model in order to describe both the spread of drug resistance in a hospital setting as well as the interaction between one or several hospitals and the community. Like all theoretical models, this is a very simplified description of complex interactions. For example, the epidemiological fitness costs of resistance are inherently difficult to determine. We used estimates of the epidemiological costs of resistance in tuberculosis Theoretically, the impact of hospital size on the evolution of antibiotic resistance can be explained by the meta-population dynamics characterized by local extinctions and re-colonizations. According to this interpretation, the beneficial effect of small hospital size is the result of a simple evolutionary mechanism: Selection typically acts in different directions in the hospital and in the community: resistance is selected for in the hospital and selected against in the community. Thus any mechanism, which weakens the effectiveness of selection in the hospital relative to that in community, will lead to an increase in the level of resistance. According to Fisher's fundamental theorem, the effectiveness of selection acting on a trait (here trait  =  resistance) is proportional to the variance in that trait. Small hospital size decreases the variance in resistance through frequent extinctions of the resistance-conferring allele. In other words, if the resistance-conferring allele is extinct in a given hospital, selection  is completely ineffective because the population consists only of the sensitive strain and selection can only favor one strain over the other if they coexist. In addition to the hospital-community setting described here, many more instances of population structure might cause similar meta-population effects that hinder the epidemic spread of antibiotic resistance mutations. Indeed, we made the same observations when considering inter-ward transfer. Further examples include: household structure, caring facilities, schools etc. Thus the effects described here are likely to extend beyond the hospital-community setting.In this context it should be noted that for a real hospital of a given size, the magnitude of stochastic effects will typically be considerably stronger than what might be expected from a simple population-biological model like the one presented here. The reason for this is that several processes such as population structure, fluctuating population sizes, and variability in transmission rates (e.g. super-spreaders) strongly enhance stochasticity. Thus, a real hospital with a given number of patients will typically behave like an idealized hospital, which is much smaller. .In contrast to the typical beneficial effect of small hospital size, we also found that under some circumstances small hospitals may increase the prevalence of antibiotic resistance. This effect occurs if antibiotic usage in the community reaches similar magnitude than in the hospital. Such a setting might be uncommon for nosocomial infections, which are mainly treated in the hospital. However, it might occur for other infections, which occur frequently in both the hospital and the community , can substantially reduce the spread of antibiotic resistance. Furthermore, we find that this beneficial effect of small hospital-size is substantially reduced if a substantial fraction of infection events is not acquired directly from other patients , but, indirectly, via a slowly decaying environmental compartment. This latter point indicates that reducing such environmental transmission might be especially important in small hospitals as this might not only reduce the force of infection but also the burden of antibiotic resistance by promoting the stochastic extinction of resistant pathogen strains.


In vitro primary cultures of dissociated invertebrate neurons from locust ganglia are used to experimentally investigate the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. At all the different stages of the culture's development, identification of neurons' and neurites' location by means of a dedicated software allows to ultimately extract an adjacency matrix from each image of the culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main network's characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graph's micro- and meso-scale properties emerge. Finally, we identify the main physical processes ruling the culture's morphological transformations, and embed them into a simplified growth model qualitatively reproducing the overall set of experimental observations. To that purpose, dissociated and randomly seeded neurons are initially prepared, and the spontaneous and self-organized formation of connections is tracked up to their assembling into a two dimensional clustered network.In this paper, we experimentally investigate the self-organization into a network of an Most existing studies in neuronal cultures restricted their attention to functional networks  and not to the physical connections supporting the functionality of the network in vitro neuronal networks tend to develop from a random network state toward a particular networking state, corresponding to a small-world configuration, in which several relevant graph's micro- and meso-scale properties emerge. Our approach also unveils the main physical processes underlying the culture's morphological transformation, and allows using such information for devising a proper growth model, qualitatively reproducing the set of our experimental evidence.Here, instead, we focus on intermediate neurons' densities, and provide a full tracking of the most relevant topological features emerging during the culture's evolution. In particular, we show experimentally that longitudinal study of the network structure highlights as yet unknown self-organization properties of cultured neural networks, such as i) a large increase in both local and global network's efficiency associated to the emergence of the small-world configuration, and ii) the setting of assortative degree-degree correlation features.Together with confirming several results of previous works on functional connectivity Schistocerca gregaria species. In all cases, a same protocol was used, involving animals that were daily fed with organic wheat grass and maintained under a 12∶12 h light∶dark cycle from their fifth nymph growth to their early adult stage of development. At this latter stage, we followed the dissection and culturing protocol thoroughly described in In this paper, we report on six cultured networks, which were grown from independent initial sets of dissociated neurons extracted from the frontal ganglion of adult locusts of the in vitro (DIV). During the entire experiment, the culture medium was not changed.The density at which cultures are seeded determines the maturation rate and the spatial organization at the mature state High-resolution and large scale images of the whole culture were acquired daily using a charge coupled device camera  mounted on a phase contrast microscope , with automated control of a motorized XYZ stage .A typically observed growth evolution is shown in er 3 DIV  many celer 3 DIV . At thisThe latter process favors neuron migration, giving rise to clusters of neurons, and the fusion of parallel neurites into thicker bundles together with the retraction of those branches which did not target any neuron  over time. In such a sparse connectivity regime, we quantify how our networks constrained in 2D space percolate. To do so, we measure the size A deeper information on the culture evolution can be gathered by monitoring the behavior of a subset of local and network-wide quantities. For that purpose, we calculated several topological properties of the extracted adjacency matrices  were characterized by networks with very few nodes and links see . After Din vitro. To properly asses the significance of this finding and isolate the influence of the variable network size and density, we calculated the values of The associated networks statistics sheds light on the transition from random to non-random properties with a progression of both the clustering coefficient and the average path length  as a function of age see . The firorks see . In doinin vitro neuronal networks at a single developmental stage during the graph development , we also measured the global and local efficiency, as introduced by Latora and Marchiori in These results are in agreement with previous morphological characterizations of Turning now our attention to network statistics at the micro-scale, we investigated how the node degree distributions evolved during maturation process. At all ages, cultures appeared to belong to the class of single-scale networks, displaying a well defined characteristic mean node degree. Caenorhabditis elegansThe data were fitted to an exponential scaling law While the number of neighbors (the degree) is a quantity retaining information at the level of a single node, one can go further to inspect degree-degree correlations, i.e. to quantify whether the degrees of two connected nodes are correlated. It is known, indeed, that biological networks feature generally disassortative network structures in vitro cultured neuronal network, and such an evidence actually links to other studies where assortativity was found in simple in vivo neuronal systems, like the C. elegans neural network structure It is important to remark here that, to the best of our knowledge, this is the first report of assortativity in an A series of previous studies Our model is schematically illustrated in As for the process of cell migration and clusterization, cells or clusters whose distance is less than rate see .In order to validate our model, we ran a large number of simulations for different values of the model parameters ents see .Despite its relative simplicity, it is remarkable that the model offers a rather good qualitative verification of the trends of all the structural network characteristics measured in the experiments. In particular, Though it would have been unrealistic to expect a perfect quantitative agreement between model and observations, the fact that the model reproduces the main qualitative scenarios of the experiments indicates that it captures the main processes underlying the observed morphological evolution and self-organization of the cultures.in vitro primary cultures of dissociated invertebrate neurons from locust ganglia. At all stages of the culture's development, we were able to identify neurons' and neurites' location in automated way, and extract the adjacency matrix that fully characterizes the connectivity structure of the networking neurons. A systematic statistical analysis of a group of topological observables has later allowed tracking of the main network characteristics during the self-organization process of the culture, and drawing important conclusions on the nature of the processes involved in the culture' structuring. At early stages of development  the fact that a small-world configuration spontaneously emerges in connection to a large increase in both local and global network's efficiency, and ii) the evidence that cultures tend to organize in a regime of non trivial degree mixing which, in turn, is characterized by assortative degree-degree correlation features. The evolution from an initial random to a small-world topology has also been reported recently in the context of a functional network of a cortical culture Our results extend previous studies where network properties of cultures were investigated at a particular developmental stage and for a lesser number of nodes Given the absence of external chemical or electrical stimulations, we conclude that such complex network evolution and morphological structuring is indeed an intrinsic property of neuronal maturation. Our study therefore contributes to the understanding of the complex processes ruling the morphological structuring of cultured neuronal networks as they self-organize from collections of separated cells into clustered graphs, and may help identifying culture development stages in new, specific and targeted, experiments.


Marine reserves are an effective tool for protecting biodiversity locally, with potential economic benefits including enhancement of local fisheries, increased tourism, and maintenance of ecosystem services. However, fishing communities often fear short-term income losses associated with closures, and thus may oppose marine reserves. Here we review empirical data and develop bioeconomic models to show that the value of marine reserves (enhanced adjacent fishing + tourism) may often exceed the pre-reserve value, and that economic benefits can offset the costs in as little as five years. These results suggest the need for a new business model for creating and managing reserves, which could pay for themselves and turn a profit for stakeholder groups. Our model could be expanded to include ecosystem services and other benefits, and it provides a general framework to estimate costs and benefits of reserves and to develop such business models. This model incorporates both fishery and tourism benefits over time following the designation of a marine reserve.A review of peer-reviewed studies on 124 marine reserves in 29 countries showed that, on average, marine reserves cause increases of 21% in the number of species, 28% in the size of organisms, 166% in density , and a remarkable 446% in biomass, relative to unprotected areas nearby Fisheries may benefit from reserves when they help replenish nearby habitats through spillover of adult organisms and dispersal of larvae. The increase in the biomass of commercial species inside marine reserves has been shown to increase reproductive output e.g., , as longMarine reserves can provide economic benefits through tourism , fishing (increase or stabilization of catch around reserves), and other services, some of which are difficult to quantify . A primary concern among fishermen is the loss of fishing grounds and yields that may occur when marine reserves are implemented; these effects may not be offset by the increase in spillover and dispersal of larvae provided by the reserves The increase in marine life inside marine reserves, in particular large fish, is the main attraction for divers and other tourists, which can bring revenue disproportionately higher than fishing . In the Well-enforced marine reserves can increase adjacent fishery catches . At smalAn additional value for fishing of marine reserves concerns catch-and-release recreational fishing inside the reserves, which may be compatible with the reserves provided that ecological impacts can be minimized Recreational fishing outside reserves may also benefits from spillover. In Florida, the no-take areas in the Merritt Island National Wildlife Refuge have supplied increasing numbers of world record–sized fish to adjacent recreational fisheries since the 1970s Marine reserves help preserve and restore biodiversity at many levels  By protecting coastal ecosystems such as mangroves, marshes and seagrass beds that are threatened by coastal development, aquaculture, agriculture and wood production, marine reserves can play a significant role in protecting some of the most efficient natural carbon sinks on the planet Despite the increasing amount of evidence of the benefits provided by marine reserves, there are issues that have impeded the creation of marine reserves as a tool that yields economic profitability. The major economic arguments against marine reserve creation are short-term loss of fishing catch and revenue because of the closure of a fraction of the fishing grounds, and displacement of fishing effort to unprotected areas. The latter has not been a significant issue to date on a global scale because only less than 1% of the ocean is protected in marine reserves. Marine reserves are also criticized as insufficient tools for managing fisheries. It is important to note that the current research does not suggest the replacement of alternative fishery management tools. Marine reserves provide the myriad benefits described above and may further complement traditional fishery management measures in the long run. The next section presents a simulation model of the time path of marine reserve benefits and costs.Creating marine reserves can be an economically optimal solution when the combined value derived from tourism, the enhancement of local fisheries (via spillover from the reserve) and other services (see above) outweighs the value of any single use in isolation in the now-protected area. In what follows we develop a general dynamic model to simultaneously analyze these quantifiable effects of marine reserves on economic welfare. In particular, we develop a bio-economic model to simulate the value of a fishery and the value of tourism over time for a fishery that implements a no-take zone. We then develop a focused case study to illustrate the economic effects of marine reserves.i at the beginning of year t+1 is i after adult movement in year t, k and older, k and older, k is the age when the fish can reproduce,i in year t. The model accounts for two age classes, adults and recruits. All adults for a given species have the same vulnerability to fishing pressure and reproduce. This model assumes that each species is homogeneous across all areas for all biological parameters.We use a delay difference model to simulate the population dynamics of a single species, and parameterize the model to examine the effects on several species with different characteristics. We assume a linear coastline divided into 100 areas. This model tracks the species biomass in each area each year and accounts for growth of average individuals. Using the Deriso-Schnute biomass model The simulations begin at equilibrium biomass  in patch tFV in year t is given by:i.The fishery profit Tourism values are often neglected in bioeconomic analyses of marine reserves. We model the marginal value of additional site visits (or “dives”) as follows: surplus . We expet (OPt) is calculated to maximize the tourism revenue in year t. Tourism revenue is defined as the product of the fee per dive and the number of dives in the reserve:An optimal fee per dive in year t is:By taking the derivative of equation 10 with respect to The optimal fee per dive that maximizes tourism revenue is then calculated by:To illustrate the dynamics of the bio-economic model, we present a simulation based on the characteristics from the Medes Islands fishery in Spain. This case study builds from the bio-economic analysis by Merino et al. The Medes Islands Marine Reserve was created in 1983, and it includes a no-take zone of 51 ha, and partially-protected area of 460 ha where seven local artisanal fishing vessels have exclusive access Mullus surmuletus) to represent the species important to the fishery and European seabass (Dicentrarchus labrax) to represent the species that divers are interested in seeing in the water.To simulate this reserve system, we use three harvest rates, one for the no-take zone, one for the partial reserve and one for the area with no reserve. The relative size of these areas matches the relative areas of the Medes Islands zones . In these areas we simulate the biological dynamics of two representative species and their change in biomass over time. We use the striped red mullet  While we have omitted these additional sources of value, including them would only serve to further increase the benefit of the reserve  and a reduced rate for nontargeted fish that are caught as bycatch (European seabass). After the implementation of the reserve system, the fishing pressure is assumed to drop to zero in the no-take zone, is reduced in half in the partially-protected area, and remains the same outside of the reserves. Estimates for fishing costs were based on personal communications with local fishers. Prices per kg of fish were based on prices for the red mullet The output of the model was remarkably accurate. When we capped diver numbers at the 1991 level, we obtained 63,000 dives per year, with a revenue generated by diving fees of € 221,000 . The actAn increasing number of studies show that the combined economic benefits of marine reserves  outweigh the costs of creating and maintaining the reserves Our bio-economic model shows that fishing revenue increases after the creation of a reserve, and also that tourism revenue surpasses the revenues from fishing. It is worth noting that the total value of the reserve is larger than the pre-reserve value within only five years of protection. This result is in agreement with data on the rapid biological recovery of reserves Posidonia oceanica beds in the reserve could increase income in the reserve; these and other benefits could be added to the model. As our bio-economic model predicts, the aggregate economic value of the Medes Islands Marine Reserve is larger than the costs, and suggests that other reserves in locations with similar tourism opportunities could be designed as revenue and job creators. Our model provides a general framework to estimate costs and benefits and plan consequently.In the Medes Islands Marine Reserve example, before the creation of the reserve, only four diving centers took tourists to the islands, generating a revenue of about € 0.5 million. Presently, the increased abundance of marine life in the reserve supports a thriving tourism industry including diving centers, snorkeling, glass bottom boats, and kayaks. The current diver access fee of € 3.5 per dive  brings in € 234,500 per year, which covers half of the annual budget for the reserve Uncertainty of future benefits may be one of the larger barriers to reserve formation. The simulation model, paired with local biological and economic data could reduce uncertainty regarding long-run financial benefits of a potential marine reserve. The tourism literature is rich with methodologies to estimate the price-elasticity of demand. The model parameter Other constraints may exist to reserve creation such as capital constraints and the ability of potential beneficiaries to coordinate with those fishers bearing the short-term costs. Essentially, potentially profitable reserves may suffer from incomplete markets. Our example from Medes suggests that even for fisheries alone, the reserve will ultimately have a positive effect. However, in many cases, fishers, the current users might oppose reserve formation even when models and data produce expectations of future profits. The short-term losers may face capital constraints and may have little reason to expect to share in future tourism benefits. Even when future fishery benefits are credible, current fishing interests may not hold secure claims to those future benefits.There are many potential mechanisms that might resolve capital and coordination constraints. In some cases, improved legal structures guaranteeing current fishers' shares in future benefits may suffice. In other cases, the creation of markets for conservation may be appropriate. Finally, external organizations may wish to speed the formation of reserves by offering a buy-out to reduce fishing effort in the fishing zone, or by financing the loss in fishery value during the time gap between reserve implementation and fishery recovery. Possible financing mechanisms include private investments and public/private partnerships, some of which have proven successful in other social initiatives and businesses. In addition to facilitating the transition from open access to a system with a fraction of the fishing grounds closed as reserves, these mechanisms could cover the costs of creation and management of reserves, making them self-sustaining.Metrics for success will be critical for long-run benefits to be realized. We cannot conclude that a reserve is failing only because it is not enhancing the catch of one or more species around it e.g., . That maThe economic benefits of marine reserves may be enhanced by additional management around their borders


Recent Chromosome Conformation Capture (3C) measurements performed with high throughput sequencing (Hi-C) and molecular dynamics studies show that there is a large correlation between colocalization and coregulation of genes, but these important researches are hampered by the lack of biologists-friendly analysis and visualisation software. Here, we describe NuChart, an R package that allows the user to annotate and statistically analyse a list of input genes with information relying on Hi-C data, integrating knowledge about genomic features that are involved in the chromosome spatial organization. NuChart works directly with sequenced reads to identify the related Hi-C fragments, with the aim of creating gene-centric neighbourhood graphs on which multi-omics features can be mapped. Predictions about CTCF binding sites, isochores and cryptic Recombination Signal Sequences are provided directly with the package for mapping, although other annotation data in bed format can be used (such as methylation profiles and histone patterns). Gene expression data can be automatically retrieved and processed from the Gene Expression Omnibus and ArrayExpress repositories to highlight the expression profile of genes in the identified neighbourhood. Moreover, statistical inferences about the graph structure and correlations between its topology and multi-omics features can be performed using Exponential-family Random Graph Models. The Hi-C fragment visualisation provided by NuChart allows the comparisons of cells in different conditions, thus providing the possibility of novel biomarkers identification. NuChart is compliant with the Bioconductor standard and it is freely available at  The three-dimensional conformation of chromosomes in the nucleus is important for many cellular processes related to gene expression regulation, including DNA accessibility, epigenetics patterns and chromosome translocations ,2,3.In recent years, many experimental techniques have been developed to study the nuclear organization at an unprecedented resolution. In particular, the Chromosome Conformation Capture (3C) technology ,5 and thThe combination of high-throughput sequencing with these techniques, which is generally called Hi-C, allows the characterization of long-range chromosomal interactions genome-wide ,11,12. HHi-C is useful to identify active and non-active genome domains, because chromosomal territories fold distinctively, interact hierarchically as independent units, and contain several genes with correlated expression profiles ,18. BothThe 3D information is relevant also for the generation of the immunological diversity, which is possible through the V(D)J recombination mechanism that assembles gene segments into functional immunoglobulin (Ig) and T-cell receptor (TCR) genes. This rearrangement is directed by Recombination Signal Sequences (RSSs), which flank each of the hundreds of potential donor gene segments. DNA repair activities then re-join the breaks at two distant cuts to generate functional genes through chromosomal rearrangements . Errors Also isochores, large regions of DNA (greater than 300 Kb) with high uniformity in guanine (G) and cytosine (C) content, are probably associated to chromosomal rearrangements because they show a high correlation with DNA breakpoints . NotablyIn the last 12 months few software , NuChart analyses the structure of a neighbourhood graph and also the relation of its topology with respect to the mapped multi-omics features.A typical question the software attempts to answer is the following: what are the most important genomic features in the space nearby a gene of interest? Given a list of genes or a specific pathway, we can identify their neighbourhood in the 3D nucleus organization by following Hi-C fragments and then identifying the chromosomal domains that relate to the input. This software could also provide information on spatial proximity of gene promoters and the density of highly expressed nearby genes that may point to some important questions such the co-proximity of genes coding for protein interacting pairs with respect to the nuclear pore localisation.The NuChart package has been designed to provide a novel gene-centric, pathway-oriented, multi-omics tool for the representation and the statistical analysis of Hi-C data. The package contains four sets of functions: (i) the first group to load and normalize data; (ii) the second to create neighbourhood graphs; (iii) the third to map genomic features and expression data on graphs; (iv) and the last one to compare and statistically analyse graphs of different cells or experimental conditions.load_HITCSAM_file, which loads data in the Sequence Alignment Map (SAM) format as provided by the Hicup [load_HITCSAM_file function creates a data frame, the main data structure of the NuChart package, on which all the other package functions rely. By employing the convert_HITCSAM_matrix function, this data structure can be turned at any time into the corresponding contact map, in order to be analysed with other standard software.The main function to import data into the R environment is he Hicup  softwarenormalize_CONTACT_map.From the normalization point of view, NuChart presents a modified version of the Hu et al.  approachgraph_interaction_genes function that creates the neighbourhood graph of the genes provided as input. This function creates a graph in which the vertices are the genes and the edges are the Hi-C contacts represented by the reads. Beside the input genes and the sequencing results, the user specifies the restriction enzymes used for the experiment and the related digested fragments . These data are used to identify which fragments belong to the input genes. Then, using the association matrix represented by the coupled reads, each fragment is associated with another fragment in a different genomic region. If the identified fragment is within a gene an edge is created on the graph between the starting gene and the novel detected one. If the identified fragment is intergenic, the corresponding genomic position is represented on the graph as a singularity point (red dot) that, if the user desires, can be then connected with the nearest upstream and downstream genes in terms of genomic coordinates, which can be very important for the subsequent analysis of colocalization and coregulation.The core of NuChart is the graph_interaction_genes makes an extensive use of the package biomaRt . Using this approach it is possible to identify the neighbourhood graph of the genes provided by the user, according to the Hi-C experiment under analysis, overcoming the problem of intergenic contacts by expanding these singularity points to the nearby genes. As an example, In other words, starting from the genomic coordinates of fragments belonging to the input genes, and using experimental associated reads as a cross-reference table, Hi-C connected genes are mapped on the graph. In order to compare genomic coordinates of reads, fragments, and genes, the function graph_interaction_genes function is implemented to take into account genome-wide data, which allows searching both for inter and intra chromosomal fragments. Moreover, the function can deal with multiple connections between genes and also with loops inside single genes, in order to provide a complete genome-wide description of the chromosomal three-dimensional conformations.As mentioned, the connections established by fragments are mapped as edges in the output graph, while vertices represent the genes connected by fragments. This procedure is performed for all the input genes and can be automatically repeated many times, making the output of one neighbourhood analysis the input for the following iteration. The possibility of iterating this analysis permit to follow progressively the connections provided by Hi-C data, enabling the opportunity to explore increasingly the chromosomal territories that concern the initial set of genes. Noteworthy, the graph_interaction_genes function is able to generate, relying on the igraph package, the neighbourhood graph by iterating the Hi-C contact research process according to a user-selected parameters, which allows to extend the analysis far away from the input genes or to focus the attention only to the near proximity of them. The neighbourhood graph can be drawn using the common plot function, which can redirect static graphs to any R device, or using tkplot, that enables a user-interactive graph drawing facility. Although igraph contains a lot of functions and many different options to represent graphs, if the user would like to use a different tool for visualization  the graph can be exported in many formats, by employing the function write. graph. Moreover, beside the graph, NuChart provides as output two tables describing respectively the vertices (genes) and the edges (Hi-C contacts) of the neighbourhood graph.In other words, the graph_interaction_pathways, enables the creation of neighbourhood graphs for full-annotated networks, thanks to the possibility of querying both KEGG (using KEGGREST) and REACTOME (using biomaRt) to download the list of genes of a user-selected pathway. Considering the importance that Hi-C profiles can have in describing the spatial conformation of a specific genomic region, NuChart implements also a function for mapping the neighbourhood of specific chromosomal regions. In detail, the function graph_interaction_coord allows to select a specific genomic interval in order to create a graph of the Hi-C contacts that involve genes in that region. This feature can be particularly interesting to highlight differences in the organization of chromosomes from a cytogenetic point of view, potentially providing the possibility of identifying novel biomarkers related to the spatial conformations of specific genomic regions.Based on this core implementation, NuChart provides two important functions. The first, graph_interaction_genes function must perform for creating the neighbourhood graph, in particular while working with a large number of genes or considering a whole pathway, the main computational routine has been implemented to exploit, if present, the multicore package. In detail, there is a switch controlling the entrance in the iterative search for Hi-C contacts in the neighbourhood of a selected genes: if the multicore package is loaded into the R environment the function exploits all the available cores for parallelizing the procedure, otherwise the common sequential approach is performed.Due to the large number of operations the get_expression function, it is possible to download expression data from these repositories (using respectively the R/Bioconductor packages ArrayExpress and GEOquery), to perform a standard normalization and a differential expression analysis of them (using limma or samr), in order to identify down-regulated and up-regulated genes in the neighbourhood. These data are used to provide different weights (according to the logarithm of the fold change) and colours to the vertices of the graph (respectively green is used for down-regulation and red for up-regulation).Considering the importance that chromosomal territories assume in gene expression coregulation, NuChart provides the possibility to map on the neighbourhood graph expression data from ArrayExpress and Gene Expression Omnibus (GEO). Using the map_FEATURES_file function that can be used to load any kind of annotations provided as bed file to characterize the neighbourhood graph. In particular, the package comes with data concerning predicted cryptic RSSs [map_RSS_computed), CTCF binding sites (map_CTCF_computed), and enriched GC-contents (map_ISO_computed). In order to exploit the computation on the fly of these features, the user must load the BSgenome package concerning the genome under analysis. This is essential while working on species different from human and mouse, but it can be also exploited, by forging a user-defined specific BSgenome data package, to analyse genomes rich of genomic variations, which can have altered genomic features with respect to the normal ones.NuChart also provides the possibility of mapping on the edges of the neighbourhood graph genomic features that are known to be involved in chromosomal recombination, looping and stability. Noteworthy, the software contains the tic RSSs , indicattic RSSs , and isotic RSSs . All thetic RSSs  Hi-C datgraph_statistics computes global indexes such as density, connectivity and diameter which describe the graph as a whole, and local indexes, such as node degree, betweenness and closeness that describe in detail the neighbourhood of each gene of the graphs. The second function, graph_correlation has been implemented to correlate graph, by transforming them into adjacency matrices and then calculating their Pearson correlation. This is a key aspect because it allows the comparison of the spatial conformation of a gene neighbourhood between different sequencing runs performed on the same cells, between the same cells under different conditions, or between different types of cells. A third function, graph_ergm, has been designed to statistically analyse the structure of the neighbourhood graph and the relation between its topology and the annotated multi-omics features. In particular, this function relies on the package ergm that provides an integrated set of tools to fit and analyse networks based on the Exponential-family Random Graph Models. Therefore, NuChart can be used to create statistical models of neighbourhood graphs by implementing maximum likelihood estimators, which are calculated using Markov Chain Monte Carlo (MCMC) [igraph data structure.The last group of functions has been designed to describe, compare and statistically analyse graphs. The first function o (MCMC) . AlthougNuChart has been designed to improve both analysis and representation of Hi-C data, by employing a gene-centric, pathway-oriented approach to the treatment of chromosome capture information A first task addressed by the package is the normalization of data, which still represents an open issue in Hi-C data analysis. We propose a combination of methods that enable an unmet flexibility for the biologist, which can both perform a read-based normalization, selecting the most suitable threshold according to his experience and sensibility, and convert data into a contact maps to combine, if desired, also a matrix based normalization. An example of the reproducibility of the Hu et al.  approachThe representation of Hi-C data using a graph approach, oriented at overcoming the common view relying on genomic coordinates, is a step forward the available representation tools, in particular considering the capability of mapping multi-omics features on the graph. The possibility of representing expression profiles on the graph allows analysing the co-expression of mapped genes, providing a strongest correlation than the one provided by the mere chromosomal coordinates. As an example, we show in NuChart can be also very useful for the description of the DNA organization while looking at full chromosomal regions or cytobands. The idea of correlating different states, for example physiological and pathological conditions, is in hand by employing the Hi-C technology. The option of creating a map for a specific cytoband is very innovative and allows the discernment of different cell states at cytological level, potentially providing to researcher novel powerful biomarkers. In The possibility of describing graphs through statistics and, even more, the capability of correlating them through the adjacency matrix representation is a powerful tool to highlight similarities and differences in different Hi-C runs, in different cell conditions or in different cell types. In By employing ERGMs, NuChart allows the option of statistically analyse the structure of the neighbourhood graph, implementing a stochastic model of the network and using MCMC to create an estimator trough a likelihood function. These models can be used to compute simple statistics about the significance of some graph characteristics, such as the topology of the edges (edges), the vertex tendency to be reciprocal , the distribution of the vertex degree in the graph (degree), or the measure of vertex clustering attitude (triangle). On the other hand, by choosing more complex modelling functions and exploiting the mapped multi-omics features, the user can test the probabilities that edges are function of a specific genomic feature (nodecov) or the significance of having edges in relation to a particular vertex property (absdiff). In In order to confirm the quality of the model estimator for the neighbourhood graph, it is possible to investigate the stochastic simulation that produced goodness-of-fit diagnostics presented above. In Thanks to ERGMs, it is possible to understand if a particular genomic feature has a significant effect on the presence of an edge. For example, we mapped data concerning CTCF binding sites, isochores, cryptic RSSs and DNase hypersensitive sites on the achieved graph and then we used the proposed model for analysing the correlation between edges and the distribution of these genomic features. By exploring the model with a Monte Carlo simulation, we achieved in all simulations a high level of confidence . Noteworthy, there is a significant positive effect of DNase hypersensitive sites and CTCF binding sites (nodecov.dnase = 0.86291 ± 0.07961 and nodecov.ctcf = 0.52386 ± 0.04158) on the probability of an edge to be in the graph see , while cNuChart is designed to study Hi-C data in a systems biology oriented view, with the aim of correlating the spatial distribution of genes with the mechanism of their coregulation. NuChart combines a graph based representation of Hi-C contacts , the analysis of expression of colocalized genes, the possibility of annotating the neighbourhood graph with multi-omics features, and the statistical analysis of the results, providing a complete tool that can be very helpful to identify novel regulatory mechanisms and can bring at the identification of novel biomarkers.ftp://fileserver.itb.cnr.it/nuchart with all the example datasets employed for the experiments and a detailed manual of the package. Future directions are in providing more embedded features to map on the graph and in the implementation of more sophisticate methods for graphs analysis and comparison. From the computational point of view, we aim at improving the quality of representations and the speed of graph computations by employing high performance on-chip architectures for the analysis, in order to produce more complete nuclear charts.The software is freely available from Statistical Analysis S1Full description of the stochastic simulations and the related statistical analyses performed for the creation of the neighbourhood graph estimators presented in the results and discussion section.(PDF)Click here for additional data file.


Traditional mattress sutures prove difficult to remove when the knot becomes buried in swollen tissue. We describe a suture with the benefits of a mattress suture that can be removed more easily:Starting on the ‘near-side’, cross the wound twice as per a regular horizontal mattress suture, leaving the loop proud.Pass one free end through the loop and pull to the near-side of the wound.Tie on the near-side, ensuring suture material of the loop bridges the outside of the wound.This will remain easily accessible for removal regardless of swelling .


The influence of noise on oscillatory motion is a subject of permanent interest, both for fundamental and practical reasons. Cells respond properly to external stimuli by using noisy systems. We have clarified the effect of intrinsic noise on the dynamics in the human cancer cells following gamma irradiation. It is shown that the large amplification and increasing mutual information with delay are due to coherence resonance. Furthermore, frequency domain analysis is used to study the mechanisms. In fact, any oscillating chemical system, with or without delayed dynamics, will demonstrate dephasing between different realizations, and it isn't an artifact of the delayed dynamics themselves, although this will undoubtedly cause further decorrelation of different realizations at later time, which causes the damped behavior at the population level . Essentially, the value which the cell population converges to is simply approximately the mean of the invariant distribution of the chemical species for one cell, multiplied by the number of cells in the population of interest. This can be shown more rigorously for large populations using the ergodic property of the system.The peak frequency corresponds to the characteristic frequency of the solution of Eqs. (12), which represents the mean frequency of Fourier transform The resultant Mutual information (MI) is meaningful to discuss resonant phenomena et al.et al.MI is zero if and only if the two random variables are strictly independent To describe the nonlinear dynamics more clearly, we use frequency domain analysis method to study the mechanisms of the p53 network motif. Our model can be described by a set of chemical Langevin equations corresponding to Eqs. (1),In order to analyze our model in the frequency domain, we first replace Since we are discussing the solution in the oscillatory scheme, here the signal The Fourier transformations of Eqs. (18) take the formSince Eqs. (23) are integral equations, they can be solved by interpolation method and truncated at a specific order, the following calculation includes convolutions in the spectral presentation replacing the nonlinear items in the temporal one and truncating them, e.g., we can first solve the linear equationBy defining the intermediate variables,The parameters If we remove the nonlinear terms in Eqs. (21), Eqs. (34) becomeIt is worthwhile to mention that a module, which consists of two components, has been discussed recently Another characteristic feature of Eqs. (34) is that when In our numerical calculation, we use the fourth-order stochastic Runge-Kutta method for integrating the chemical Langevin equations (18), and Gaussian integration method to calculate the integrations in Eqs. (34). The numerical results have shown that the correlation functions Bioscience and nanoscience provide pretty examples of nonequilibrium and nonlinear dynamics in which noise can be expected to have unavoidable effects. The methods developed over years to deal with the effects in physical systems will help us to further our understanding of the mechanisms ascribed to nonlinearity and noise.The stochastic p53 circuit was characterized by a Monte Carlo method called the exact DSSA. Numerical integration of the equations was carried out using Matlab software.