Rhizoma Zingiberis (RZ) on aconitine, an analgesic drug, by quantitatively assessing the influence of RZ on the in vitro intestinal concentration of aconitine using an everted gut sac model and by qualitatively identifying the components in the RZ extract. To quantify aconitine in rat everted gut sacs, both an accurate processing method and a sensitive detection method were required. We developed a three-step sample processing method to protect the components from decomposition and applied ultra-performance liquid chromatography coupled with triple quadrupole mass spectrometry (UPLC/TQMS) to quantify aconitine, glucose and digoxin. In addition, ultra-performance liquid chromatography coupled with linear ion trap mass spectrometry (UPLC/ITMS) was applied to detect the potential antidotal components in the RZ extract. Finally, the RZ extract reduced the level of aconitine in everted gut sacs, and eleven gingerols were successfully identified, which could be considered potential antidotal components for aconitine. This study demonstrated the application of two UPLC/MS methods for analyzing the material basis for the reciprocity between Chinese medicine components in everted gut sacs.This study attempted to clarify the material basis for the detoxification of  The intestinal tract is the main absorptive site for orally administered medicines, and interactions between medicines related to P-glycoproteins might affect their plasma concentration. Although previous studies have demonstrated that Rhizoma Zingiberis is a traditional Chinese medicine that can decrease the toxicity of aconitine ·dC/dt, where V represents the fluid volume in the serosal side, A represents the superficial area of the gut sac, and C0 represents the initial concentration of aconitine outside the gut sac. The calculated Papp value of aconitine is shown in 0 indicated that different gut sacs influenced the level of aconitine, that the P0 value of aconitine for the jejunum sac was greater than in the other three sacs, and that the value for the colon sac was smallest among the four sacs. In addition, the P1 value of aconitine for each gut sac decreased after the Rhizoma Zingiberis extract was added, and the decrease in the duodenum was most significant, which can be observed from its lowest ER value. These results indicate that the Rhizoma Zingiberis extract reduced the level of aconitine in the rat gut sacs, and this reduction was greatest in the duodenum; however, the mechanism could not be elucidated from the above results. Therefore, further experiments were performed to elucidate the possible reason for the reduction in the level of aconitine by the Rhizoma Zingiberis extract.To study the effect of the P-gp is an important glycoprotein in the intestinal mucous membrane epithelia. Because it can help pump medicine from the intestinal serosal side to the mucosal side, P-gp has a negative effect on the level of a majority of medicines. However, for toxic substances, P-glycoprotein might play a positive role. To determine the potential role of intestinal P-gp in the dynamic level of aconitine, verapamil (a known P-gp inhibitor), was added to a K-R solution that contained a high concentration of aconitine. As shown in Rhizoma Zingiberis extract to determine whether this extract could alter the level of digoxin. As shown in Rhizoma Zingiberis extract was added; in other words, the Rhizoma Zingiberis extract decreased digoxin level in the rat intestine. Therefore, it can be deduced that the Rhizoma Zingiberis extract might be an inducer of P-glycoprotein. Furthermore, it can be deduced that the Rhizoma Zingiberis extract might decrease the conitine content by activating P-glycoprotein.Because digoxin is a well-known substrate of P-glycoprotein, it was selected to be incubated with the Rhizoma Zingiberis extract reduced the level of aconitine in the rat gut sacs by inducing intestinal P-gp; however, the material basis was not determined. Therefore, the following experiments were conducted to identify the main components of Rhizoma Zingiberis that affect aconitine levels in the rat gut sacs.The above results demonstrated that the Rhizoma Zingiberis are gingerols [Rhizoma Zingiberis were detected using UPLC/MSn, and the base peak chromatogram was shown in Previous reports have suggested that the major bioactive constituents in ingerols –26. In tRhizoma Zingiberis extract on the level of aconitine in the rat gut sacs” section. The calculated Papp value of aconitine is shown in 2 value is smaller than the P0 value in each gut sac, and the P2 value for the ileum was the smallest among the four gut sacs. From the above results, it could be concluded that 6-gingerol, as well as other gingerols in the Rhizoma Zingiberis extract, contributed to the decreased effect of the extract on the aconitine level.The experiment described in this section was conducted under the same conditions as decribed in the “Effect of the To study the mechanism by which the effect of 6-gingerol on the aconitine level was reduced, 6-gingerol and digoxin were co-incubated. The effect of 6-gingerol on the digoxin concent in various rat gut sacs is clearly visible in Radix Glycyrrhizae and Rhizoma Zingiberis can decrease the toxicity of aconitine. The content of aconitine in the co-decoction of Rhizoma Zingiberis with Radix Aconiti Lateralis Preparata was not less than that in the single decoction of Radix Aconiti Lateralis Preparata, whereas the LD50 value of the co-decoction was larger than that of the single Radix Aconiti Lateralis decoction [Rhizoma Zingiberis might reduce the plasma concentration of aconitine. Based on the above proposal, this study was conducted.Aconitine is a well-known toxic compound that has been the focus of many previous studies. Previous studies have shown that various traditional Chinese medicines, such as ecoction . TherefoRhizoma Zingiberis affects aconitine transport and to determine the main extract components responsible. The findings demonstrate that aconitine is a potential substrate of intestinal P-gp and that the Rhizoma Zingiberis extract is a potential revulsant of P-glycoprotin; in addition, the Rhizoma Zingiberis extract reduced the permeation of aconitine by inducing P-glycoprotein, and gingerols were the main Rhizoma Zingiberis components responsible for the effect of Rhizoma Zingiberis on aconitine transport in the intestine. Early studies showed that the substrate, which comprised at least one basic nitrogen atom and an aromatic ring containing benzoyl groups, could be combined with P-glycoprotein [Rhizoma Zingiberis extract components; these hydroxy groups competed with aconitine to form hydrogen bonds with P-gp. Thus, the induction of P-gp by gingerols present in Rhizoma Zingiberis might be related to a weakening of the hydrogen bonds that are formed between aconitine and P-glycoprotein.Drugs are absorbed into the blood stream mainly in the intestine; therefore, everted rat gut sacs were studied to determine the methanism by which oprotein . Therefooprotein . Large nRhizoma Zingiberis extract, reduced the intestinal aconitine level by inducing intestinal P-gp, which will be verified by our further studies on acute toxicity test. This conclusion is consistent with previous studies using the Caco-2 cell culture model. Moreover, the material basis for the detoxification of Rhizoma Zingiberis on aconitine might be all of the gingerols involved in this study. This study provided a strategy for identifying new P-gp inducers and activators to search for new antidotes to be used in intoxication caused by P-gp sbustrates.This paper demonstrated an effective reduction in the toxicity of aconitine, namely, the


Body size is an extremely important characteristic, impacting on a variety of ecological and life-history traits. It is therefore important to understand the factors which may affect its evolution, and diet has attracted much interest in this context. A recent study which examined the evolution of the earliest terrestrial herbivores in the Late Carboniferous and Early Permian concluded that in the four herbivorous clades examined there was a trend towards increased body size, and that this increase was more substantial than that observed in closely related carnivorous clades. However, this hypothesis was not based on quantitative examination, and phylogenetic comparative methods provide a more robust means of testing such hypotheses. Here, the evolution of body size within different dietary regimes is examined in Captorhinidae, the most diverse and longest lived of these earliest high fibre herbivores. Evolutionary models were fit to their phylogeny to test for variation in rate and mode of evolution between the carnivorous and herbivorous members of this clade, and an analysis of rate variation throughout the tree was carried out. Estimates of ancestral body sizes were calculated in order to compare the rates and direction of evolution of lineages with different dietary regimes. Support for the idea that the high fibre herbivores within captorhinids are being drawn to a higher adaptive peak in body size than the carnivorous members of this clade is weak. A shift in rates of body size evolution is identified, but this does not coincide with the evolution of high-fibre herbivory, instead occurring earlier in time and at a more basal node. Herbivorous lineages which show an increase in size are not found to evolve at a faster rate than those which show a decrease; in fact, it is those which experience a size decrease which evolve at higher rates. It is possible the shift in rates of evolution is related to the improved food processing ability of the more derived captorhinids rather than a shift in diet, but the evidence for this is circumstantial. Body size is among the most important traits of an organism . It inflIn a recent paper, An association between herbivory and large body size has been noticed in other clades e.g. mammals , birds  and lizaThe second explanation for the association between large body size and herbivory, dubbed the abundance-packet size hypothesis by Of the four clades examined by Captorhinus , and both of these do preserve skull material, so an estimate of skull length could be obtained by comparing the length of elements preserved in these taxa to the length of elements preserved in closely related taxa. The skull lengths were log transformed prior to analysis  useful proxies for body size are limited by the available material. Due to the fact that all terminal taxa analysed by ysis see .et al. method is less subjective than the addition of arbitrary amounts of time to zero-length branches. Meanwhile, methods like the Cal3 method  were dropped.The most recent and comprehensive phylogeny of captorhinids  formed t3 method  are not Opisthodontosaurus), 50 of these 100 trees were based on one MPT, 50 the other.To resolve the uncertainty surrounding the age ranges of taxa, 100 time calibrated trees were generated using the method of When examining the evolution of a continuous trait such as body size and comparing it in different clades, ecological groups or time periods, one must consider both the rate and the mode of evolution. Models such as Brownian motion (BM) assume evolution via a statistically random walk with a constant normally distributed deviate from the observed morphology. In macroevolutionary processes, this can result from randomly varying selection a lack of selective pressure in any particular direction and a lack of variation in rate . As suchMaximum likelihood was used to fit three sets of models to the observed size estimates, with the Akaike weights used to deduce which model in each category fits best. The three categories represent 1) models of the evolution of the continuous trait (body size) alone; 2) models of the co-evolution of body size with a dietary regime; 3) models of the evolution of body size subjected to external influences at specific times.The models in the first category included Brownian motion, Brownian motion with trend, Ornstein Uhlenbeck, the TM1 model and the SURFACE model. Under the TM1 model the trait evolves by Brownian motion, but one or more heritable shifts in rate may occur at any node. If a shift occurs at a node, an increase or decrease in rate is deduced for all lineages descended from that node . Under tThe models in the second category allow different rates or modes of evolution to occur under different dietary regimes. Three such models are tested: BM-V OU-M and OU-MV. Under the BM-V model, body size evolves by Brownian motion but with rates of evolution differing between the carnivorous lineages and the herbivorous lineages. The OU-M model represents body size evolution under an OU process, but with the different dietary regimes drawn to different adaptive optima. The OU-MV model is similar to the OU-M model, but allows a change in rate of evolution as well as adaptive optimum under different dietary regimes. All three of these models were fit using the functions in the R package OUwie.The third category of models tests for the possibility of extrinsic influences on the evolution of body size in captorhinids; that is, changes in rate or mode are related to a change in the organism’s environment (both biotic and abiotic) rather than any evolutionary innovation within the clade itself. Were this to be the case, one would expect a shift in rate or mode to occur at a specific point in time, and affect all lineages after this point, rather than affecting all taxa descended from a specific node. The captorhinids evolved at a time of great changes in environment. Throughout the late Carboniferous and Permian, there was a trend towards a warmer, dryer climate . At the Having found the best fitting model in each of these three categories, these three models were compared to find a single model which overall best fits the evolution of body size in captorhinids.When considering the evolution of a trait such as size, one cannot only consider shifts in the rate. Increasing the rate of evolution under a BM model increases the rate of evolution in both directions, towards larger and smaller. In order to examine whether there is indeed a greater tendency towards larger body size in the herbivorous captorhinids, one must ascertain: 1) whether rates of increase in size of herbivores are greater than rates of increase in carnivores; 2) whether rates of decrease in size of herbivores are less than rates of decrease in carnivores; and 3) whether rates of increase in size of herbivores are greater than rates of decrease. Such an examination requires a method which can assess rate variation along every branch in the phylogeny. The method of Along with the rate values calculated using the  method, Captorhinus, Captorhinikos valensis, Labidosaurus and the Moradisaurinae (Captorhinus. This indicates the uncertainty surrounding the ages of taxa is influencing the results.The model from the first category (models of evolution independent of diet or extrinsic events) which best fits the body size data and phylogeny of captorhinids is the TM1 model representing a rate shift at a specific node in the tree . This mosaurinae , althougThe best model from the second category (coevolution of body size and diet) which best fits the body size data and phylogeny of captorhinids is, in all 100 of the time calibrated phylogenies, the OU-M model . In all The best model from the third category (coevolution of body size and diet) is the Rate shift model, with a rate increase occurring at the end of the Sakmarian stage . The medWhen the three best fitting models from each category are compared, it is the TM1 model with a rate increase at the node indicated in The variable rates analysis indicates that the mean rate of size increase in herbivores is higher than that of carnivores, and the Mann Whitney U test suggests the difference is significant . HoweverCaptorhinus and Labidosaurus meachami  is Gansurhinus. Again, this herbivore is found to be experiencing a rapid decrease in body size.The results of the variable rates analysis in BayesTraits may be represented as a heat map , in whicdecrease . AnotherMoradisaurus, Rothianiscus and Labidosaurikos, all of which have skull lengths above 200 mm, extremely rapid rates of decreasing body size are also observed in herbivorous taxa such as Captorhinikos valensis and Gansurhinus .These results allow the rejection of the Jarman-Bell Principle as governing body size in Captorhinidae. The Jarman-Bell Principle posits a selective pressure towards large body size within an already herbivorous lineage. Therefore, were it applicable to captorhinids, one would expect the OU-M model, with the herbivorous captorhinids being drawn to a higher body size than carnivores, to best fit the captorhinid data, and one would expect the herbivorous captorhinids to show higher rates of increase in body size than decrease. Neither of these predictions is borne out by the data. The OU-M model cannot be completely rejected due to the uncertainty surrounding the ages of taxa, but it is not the best fitting model in most cases. Meanwhile, herbivorous captorhinids show some extremely rapid decreases in body size, while most of the herbivorous lineages which increase in body size do so gradually  when fitted to each of the 100 time calibrated phylogenies.Click here for additional data file.10.7717/peerj.1555/supp-4Supplemental Information 4Parameters of the OU-M model (the best fitting of the category 2 models) when fitted to each of the 100 time calibrated phylogenies.Click here for additional data file.10.7717/peerj.1555/supp-5Supplemental Information 5Parameters of the Rate Shift model with a shift ath the end of the Sakmarian (the best fitting of the category 3 models) when fitted to each of the 100 time calibrated phylogenies.Click here for additional data file.


Information regarding bacteria biotopes is important for several research areas including health sciences, microbiology, and food processing and preservation. One of the challenges for scientists in these domains is the huge amount of information buried in the text of electronic resources. Developing methods to automatically extract bacteria habitat relations from the text of these electronic resources is crucial for facilitating research in these areas.We introduce a linguistically motivated rule-based approach for recognizing and normalizing names of bacteria habitats in biomedical text by using an ontology. Our approach is based on the shallow syntactic analysis of the text that include sentence segmentation, part-of-speech (POS) tagging, partial parsing, and lemmatization. In addition, we propose two methods for identifying bacteria habitat localization relations. The underlying assumption for the first method is that discourse changes with a new paragraph. Therefore, it operates on a paragraph-basis. The second method performs a more fine-grained analysis of the text and operates on a sentence-basis. We also develop a novel anaphora resolution method for bacteria coreferences and incorporate it with the sentence-based relation extraction approach.We participated in the Bacteria Biotope (BB) Task of the BioNLP Shared Task 2013. Our system (Boun) achieved the second best performance with 68% Slot Error Rate (SER) in Sub-task 1 (Entity Detection and Categorization), and ranked third with an F-score of 27% in Sub-task 2 . This paper reports the system that is implemented for the shared task, including the novel methods developed and the improvements obtained after the official evaluation. The extensions include the expansion of the OntoBiotope ontology using the training set for Sub-task 1, and the novel sentence-based relation extraction method incorporated with anaphora resolution for Sub-task 2. These extensions resulted in promising results for Sub-task 1 with a SER of 68%, and state-of-the-art performance for Sub-task 2 with an F-score of 53%.Our results show that a linguistically-oriented approach based on the shallow syntactic analysis of the text is as effective as machine learning approaches for the detection and ontology-based normalization of habitat entities. Furthermore, the newly developed sentence-based relation extraction system with the anaphora resolution module significantly outperforms the paragraph-based one, as well as the other systems that participated in the BB Shared Task 2013. This illustrates the difficulty of manual curation for developing a comprehensive database that stores and provides easy access to information about bacteria and their habitats. An important step towards the creation and population of such a database is developing text mining methods to automatically recognize and normalize mentions of bacteria and habitats in text, as well to identify the relations among them.Identifying and characterizing the habitats where bacteria live (i.e. bacteria biotopes) is crucial for gaining a better understanding of bacterial infections, which in turn can lead to the development of novel disease prevention, prediction, and treatment methods. Besides health sciences, information about the relations of bacteria with their environments is also important for research areas such as microbiology, agronomy, and food processing and preservation. One of the challenges that researchers in these areas face is the absence of a comprehensive database that stores the relationships among bacteria and their habitats in a structured format. Most of the bacteria habitat information is only available in an unstructured textual format in electronic resources such as scientific publications and web pages of bacteria sequencing projects . For insThe Bacteria Biotope (BB) Task in the BioNLP Shared Task 2013 addressed the problems of identifying locations where bacteria live and semantically annotating them using an ontology -6), the Sub-task 1 involved the recognition of habitat names in text and their categorization with concepts from the OntoBiotope (MBTO) Ontology ["Bifidobacterium" is a bacteria entity, whereas "human" and "human gastrointestinal tract" are habitat entities. The concept that is associated with the "human gastrointestinal tract" habitat in the OntoBiotope ontology is "digestive tract", and the one associated with the "human" habitat is "human".The BB task consisted of three sub-tasks. Ontology . Figure Bacteria, Habitat, Geographical), and positions of the entities in text the goal of Sub-task 2 was to extract the localization relations between bacteria and habitat  pairs, as well as PartOf relations between habitat pairs. A PartOf relation between a pair of habitats holds if one of them is a living organism , and the other one is part of this organism . The relation between "Bifidobacterium" and "human gastrointestinal tract", as well as the one between "Bifidobacterium" and "human" are among the localization relations described in the text shown in Figure "human" and the host part "human gastrointestinal tract" is one of the PartOf relations described in Figure Given the names, types  and Sub-task 2  in the BB Task of the BioNLP Shared Task 2013 , as wellin, of, and with (e.g. "infected child in Germany"). In this article, we also introduce and compare two different approaches for relation extraction. Our original submission to Sub-task 2 was based on the first approach, which assumes that discourse changes with a new paragraph, and associates the habitat entities with the first bacteria mention in the paragraph. After the official shared task evaluation, we developed a novel sentence-based approach that incorporates an anaphora resolution module to handle bacteria coreferences.As improvements to the Sub-task 1 system, we investigate expanding the OntoBiotope ontology using the training set and extending the noun phrases with their modifiers including the ones that are attached with the prepositions Due to the continued rapid increase in the number of scientific articles published in the biomedical domain, it has become difficult for scientists to reach and make use of the knowledge contained in the biomedical scientific literature. Therefore, developing text mining systems for automatically extracting the biologically useful information from biomedical text has become crucial . A numbeThe Bacteria Biotope Task is the first shared task targeting the extraction of information about bacteria and their habitats. This task was first conducted in the BioNLP Shared Task 2011 -14. AmonBoun 2 obtained 68% SER on Sub-task 1, and 53% F-score on Sub-task 2. The details of our official submission as well as the improvements developed after the shared task are described in the following sections.The Bacteria Biotope (BB) Task in the BioNLP 2013 Shared Task gave another opportunity to scientists to address the task of extracting information about bacteria and their habitats from text and evaluate their approaches on a common platform  based onSub-task 1 of the BB Task is related to the general problem of named entity recognition (NER) and automatic semantic annotation by ontologies. Rule-based approaches (e.g. ), as welSub-task 2 of the BB Task is related to the general problem of relation extraction. A number of different methods including entity co-occurrence based approaches ,37 and pWe developed a linguistically motivated rule-based system for Sub-task 1 (Entity Detection and Categorization), the workflow of which is displayed in Figure In the preprocessing step, we used the Genia Sentence Splitter (GeniaSS)  to segmeMBTO:00001875 has the name "mummy tissue" in the ontology. This entry does not have any synonyms. However, in the training set the term "tissues of ancient mummies" is labeled with this concept. Therefore, "tissues of ancient mummies" is added as a synonym of the "mummy tissue" concept in the ontology.In this step, the annotated training data set is used to expand the OntoBiotope ontology. If a term in the training set is labeled with an OntoBiotope ontology concept, it is included in the ontology as a synonym of that concept, unless it is already defined as a name or as a synonym of that concept. For example, the ontology concept with ID mummy tissuethe " and "small intestineits ". The simplified noun phrases are obtained by removing the determiner "the" from the first noun phrase and the possessive pronoun "its" from the second noun phrase. Thus, the simplified noun phases are "mummy tissue" and "small intestine", respectively. The preprocessing, noun phrase extraction and simplification processes are illustrated in Figure In the noun phrase extraction and simplification step, first, the noun phrases are extracted based on the constituent categories of the words identified by the Genia Tagger. Next, the extracted noun phrases are simplified by removing the words that do not contain informative information regarding bacteria habitats. The non-informative words are identified based on their POS tags. For instance, determiners and possessive pronouns are non-informative and thus, are not included in the boundaries of the habitat entities. Consider the noun phrases ""ground and surface water" contains two overlapping entities, namely "ground water" and "surface water" ["and". Such noun phrases are split into two sub-phrases from the conjunction "and". If the two sub-phrases map to two concepts in the OntoBiotope ontology, which have the same direct ancestor represented with a common is-a relation, then the habitats are identified according to the structure of the noun phrase as follows. Each sub-phrase is considered to be a separate habitat entity, if both of the sub-phrases consist of single words tagged as nouns. Otherwise, the two sub-phrases constituting the noun phrase are identified as a single habitat entity. On the other hand, if the mapped two concepts in the OntoBiotope ontology don't have a common direct ancestor, then the corresponding two sub-phrases are considered to be two separate habitat entities. Our approach for discontinuous entity handling is described in more detail below through the example phrases "pharyngeal and gut mucosa" , "iron-rich and wet environment", "plants and animals", and "mouse and cheese".Some habitat entity spans in text may be discontinuous. For example, the phrase e water" . Our sys"pharyngeal and gut mucosa", the two generated sub-phrases are "pharyngeal mucosa" and "gut mucosa". The direct ancestor of "pharyngeal mucosa" in the OntoBiotope ontology is "respiratory tract part", whereas the direct ancestors of "gut mucosa" are "digestive tract part" and "mucosal tissue". Since the OntoBiotope ontology concepts corresponding to the two sub-phrases don't have a common direct ancestor, these sub-phrases are identified as two different habitat entities, namely "pharyngeal mucosa" and "gut mucosa"  corresponding to the entire noun phrase is generated . However, the boundary of the habitat entity is the entire phrase, namely "infected infant in Germany". The shallow parser labels "infected infant" and "Germany" as two separate noun phrases and "in" is labeled as a preposition. After the official evaluation, our system has been extended to handle the habitat entities that contain modifiers. If a noun phrase (NP) is followed by a preposition (prep) and then by another noun phrase, the entire NP prep NP sequence is identified by the noun phrase extraction and simplification module as a candidate habitat entity. Besides the prepositional phrases that contain "in", the ones that contain "of"  and "with" (e.g. "2-year-old girl with tick-bourne relapsing fever") are also handled using the same approach. However, as discussed in the Results section this extension degraded the performance of the system.The data set for the Bacteria Biotopes shared task has been annotated by including the modifiers that describe the habitats in the boundaries of the habitat entities . ConsideTo identify whether the phrases extracted in the previous steps correspond to habitat entities and to determine the boundaries of the habitat entities, exact or partial matching against the names and synonyms of the concepts in the OntoBiotope ontology is performed."the animal bodily fluid". In the noun phrase simplification step, this phrase is simplified as "animal bodily fluid", which is searched against the OntoBiotope ontology for exact or partial matches. As shown in Figure "body fluid" due to the partial match with the exact synonym: "bodily fluid". Similarly, it is mapped to the concept "animal" due to the partial match with the concept name: animal.Consider the extracted noun phrase The boundaries of the habitat entities are identified by using the following manually designed syntactic rules.• If there is an exact match between an ontology concept and a candidate phrase, the candidate phrase is identified as a habitat entity and the entity boundary is set as the boundary of the candidate phrase."animal bodily fluid" matches with the name: "animal" of the ontology concept for animal. Therefore, the habitat entity "animal" is identified and normalized to the ontology concept with MBTO:00001660.• If there is a partial match between a candidate phrase and an ontology concept such that the match begins from the first word of the candidate phase, but does not cover the entire phrase, the matching sub-phrase of the candidate phrase is identified as a habitat entity and the entity boundary is set as the boundary of the matching sub-phrase. For instance, as shown in Figure "animal bodily fluid" matches with the exact synonym: "bodily fluid" of the ontology concept for body fluid, starting with the second word of the candidate phrase. Therefore, the entire candidate phase "animal bodily fluid" is identified as a habitat entity and normalized to the ontology concept with MBTO:00000921.• If there is a partial match between a candidate phrase and an ontology concept such that the match does not begin from the first word of the candidate phrase, the candidate phrase is identified as a habitat entity and the boundary of the entity is set as the boundary of the phrase. For instance, in Figure "animal" against its plural form "animals", we performed lemmatization on the candidate phrases by using the Genia Tagger, and applied the same methodology that is explained above not only to the surface forms of the candidate phrases, but also to their lemmatized forms.In order to match the different inflected forms of the habitat names such as matching the habitat name In this section we describe the systems that we developed for extracting bacteria localization and habitat PartOf relations."Bordetella. This group of organisms is capable of invading the respiratory tract of animals and causing severe diseases.", contains information about "Bordetella" bacteria that lives in the "respiratory tract of animals". Therefore, there are localization relations between the "Bordetella" bacteria entity and the "respiratory tract of animals" and "animals" habitat entities, which must be extracted automatically.One of the two types of relations that have to be extracted for Sub-task 2 is the localization relations between bacteria and habitat entities. For example, the following excerpt from an input text file In order to extract localization relations between bacteria and habitat entities, we propose two different systems. The paragraph-based system is the official system which was submitted to BioNLP Shared Task 2013 . The sen"strain". In this case, the habitat entities are associated with the first occurrence of the corresponding bacterium name that does not contain the "strain" term. For example, in a paragraph that starts with the sentence "Bordetella petrii strain DSM12804 was initially isolated from river sediment", a relation is set between the habitat entity "river sediment" and the bacterium entity "Bordetella petrii DSM12804" that occurs earlier in the document, instead of "Bordetella petrii strain DSM12804", which is the first bacterium name in the given paragraph.This system is based on the assumption that the bacteria name that occurs first in a paragraph is the topic of that paragraph. Therefore, after identifying the bacteria and habitat entities in a paragraph, the bacterium that appears first in the paragraph is associated with all habitat entities in that paragraph. If this bacterium entity occurs earlier in the document as well, then its first occurrence in the document is associated with the habitat entities in the paragraph. A special rule is applied to bacteria names that contain the term "Bordetella petrii DSM12804" is a specific bacteria name, whereas the terms "bacteria" and "bacterium" are not specific bacteria names, even though they are tagged as bacteria entities in the text documents.The workflow of the sentence-based system is shown in Figure One of the challenges for extracting bacteria localization relations is that the corpus contains a large number of anaphora. In general, each document in the corpus is about a specific bacterium species . After aAnaphora type 1: We compiled a keyword list consisting of 23 anaphoric expressions such as "the bacterium", "this organism", "this species", "this genus", and "this group of organisms" by manually analyzing the training set. If a sentence does not contain a bacteria name, but contains an anaphoric expression included in the keyword list, the antecedent of the anaphor is set as the first bacteria name that occurs in the previous sentence. Then, localization relations are identified between the habitats in the sentence and the detected antecedent of the anaphoric expression. For example, although the sentence "This bacterium is highly infectious, and can be spread through the contact with the infected animal products or through the air," does not include any explicit bacteria entity names, it describes localization relations between the bacteria anaphor "This bacterium" and the habitats "animal products" and "air". In this case, the anaphora resolution module looks at the previous sentence, which is "Brucella canis." and assigns the habitat entities to this bacteria entity. If there is no bacteria name in the previous sentence, then the first bacteria entity in the document is assigned to the habitat entities, since in general each document is about a specific bacterium species, and the mention of this species occurs first in the document.Anaphora type 2: If there is no specific bacteria name in the given sentence, but the sentence begins with the anaphoric pronoun "it", then our system looks at the previous sentence and a localization relation is set with the first bacteria in the previous sentence and the habitats in the given sentence. For example, given the sentence "It was isolated from Ixodes scapularis in 1982,", our system looks at the previous sentence "Borrelia burgdorferi" and sets a localization relation between the "Borrelia burgdorferi" bacteria entity and the "Ixodes scapularis" habitat entity.Anaphora type 3: If a sentence begins with the "This strain" anaphoric expression, then similarly to the paragraph-based system, the bacteria entity that occurs first in the document is assigned as the antecedent of the anaphor. Consequently, the habitat entities in the sentence are assigned to this antecedent."This strain was isolated from infant feces", the habitat entity "infant feces" is a part of the habitat "infant". For habitat PartOf relation extraction we introduce a shallow syntactic analysis dependent rule-based approach. The first rule with the preposition "of" was developed for the official shared task submission. The remaining rules were developed after the shared task. Our rules are based on the assumption that a habitat is likely to be a part of another habitat, if the mention of the second habitat in text contains the mention of the first habitat, and in addition the syntactic rules described below are met.PartOf relations between habitat entities is the second relation type targeted in the BB Shared Task. For example, in the sentence Syntax rule 1: If one habitat contains the other one, and the second habitat follows one of the prepositions "of", "in", "from", then the relation that the first habitat is PartOf the second habitat is extracted. For example, the habitat mention "rhizosphere of plants" contains the "plants" habitat mention. Since the first habitat phrase contains the preposition "of", and the second habitat phrase "plants" occurs right after this preposition, the relation "rhizosphere of plants" is PartOf "plants" is extracted. As another example, the habitat mention "oral cavity in humans" contains the "humans" habitat mention. Since the first habitat mention contains the preposition "in", and the second habitat mention "humans" follows this preposition, the relation "oral cavity in humans" is PartOf "humans" is extracted. Finally, "skin lesion from a Lyme disease patient in Europe" and "Lyme disease patient in Europe" are overlapping habitat entities, one of which contains "from", which is succeeded by the second habitat mention. Then, the relation "skin lesion from a Lyme disease patient in Europe" is PartOf "Lyme disease patient in Europe" is extracted.Syntax rule 2: If two habitat mentions overlap in text like in the example "Aeschynomene stem nodule" and "Aeschynomene", by looking at their positions we infer a PartOf relation between them. For example, "Aeschynomene stem nodule" is PartOf "Aeschynomene".The training, development, and test sets provided by the BB shared task organizers contain 52, 26, and 26 documents, respectively. The gold standard annotations for the training and development sets were provided to the participants, whereas the evaluations on the test set were performed by using the online evaluation tool released by the shared task organizers. The documents in the corpus consist of web pages obtained from a number of web sites such as from the web sites of bacteria sequencing projects or MicrobeWiki .(SER) [S, D, and I correspond to the number of substitutions, deletions, and insertions, respectively. N is the total number of habitats in the reference. If a reference entity does not match exactly or partially with any of the predicted entities, then this corresponds to a deletion, i.e., to a false negative. On the other hand, if a predicted entity does not match exactly or partially with any of the reference entities, then this corresponds to an insertion, i.e., to a false positive. D and I are the numbers of false negatives and false positives, respectively.The main evaluation metric used for Sub-task 1 is Slot Error Rate S is shown in Equation 2.The computation of M is the similarity between two entities. It is computed by using Equation 3. The more similar two entities are, the lower their substitution score is.Here, J in Equation 3 is the Jaccard coefficient similarity between the predicted and reference entities [J equals 1 for the pair. The less the entities overlap, the lower the value of J is. W is a parameter that measures the similarity between the ontology concepts of the reference and the predicted entities [W is 1 if the predicted entity and the reference entity are assigned to the same concept in the ontology, and it is less than 1 if they are assigned to different entities. The higher the value of W, the more similar the two concepts are to each other.entities . If the entities . It is bBoun) with which we participated in the BB shared task and the results of the improved system (Boun 2) developed after the official evaluation are presented.The evaluation metrics used for Sub-task 2 are precision, recall, and f-score. The details of the evaluation metrics and the official evaluation results are available in . In the Boun and Boun 2 systems. The workflows of both systems are the same  and higher number of matches, which leads to higher recall and F-score values. While the SER value does not change, due to the increase in the number of false positives , the precision of the system decreases.Table Boun and Boun 2 systems, and the other systems that participated in the Bacteria Biotope 2013 Sub-task 1. The Boun system that we submitted to the official evaluation ranked second among four systems in terms of the SER evaluation metric. The Boun 2 system also achieves a SER value (68%) which is close to the LIPN system that ranked first in the shared task. In addition, the precision and recall values of the Boun and Boun 2 systems are relatively more balanced compared to the other systems except the LIPN system.Table Boun 2 system, whereas the second column shows the results obtained by removing the discontinuous entity handling module from the system. These results demonstrate that performing discontinuous entity handling leads to a lower SER value, i.e., to a better performance on the training and development sets. On the other hand, the discontinuous entity handling module does not make any particular change in the SER value of the system on the test set.Table Boun 2 system, whereas the subsequent rows show the results obtained by extending the Boun 2 system by including a mechanism to handle the modifiers attached to the noun phrases with the prepositions in, of, and with. Due to the fact that the SER values obtained by the system with the entity modifier handling module are not lower than the Boun 2 system for the training and development sets, this module is not included in the final system. The results reveal that the introduced entity modifier handling approach reduces the performance of the system, due to the prepositional phrase attachment ambiguity problem. For example, consider the sentence "This species was isolated from a Lyme disease patient in Europe". Our entity modifier handling approach correctly identifies the habitat "Lyme disease patient in Europe" by extending the "Lyme disease patient" noun phrase with its modifier "in Europe". However, given the sentence "This species was isolated from a Lyme disease patient in 1993", the habitat is incorrectly identified as "Lyme disease patient in 1993". The prepositional phrase "in 1993" is incorrectly attached to the noun phrase, whereas it should have been attached to the verb "isolated". Handling complex nominals and resolving such prepositional phrase attachment problems can be possible by using a full syntactic parser, rather than a partial parser.Table Boun) with which we participated in the BB Shared Task Sub-task 2 , as well as the newly developed sentence-based system with the anaphora resolution module (Boun 2). Table Boun and Boun 2 systems with each other. The results demonstrate that the Boun 2 system performs significantly better than the Boun system.This section provides the evaluation results obtained by the paragraph-based system , which leads to improved F-score performances on the training and test sets. The two syntax rules have similar effects. In general, they lead to an increase in recall, which can improve F-score if the drop in precision is relatively less (e.g. on the training and test sets).Tables "human gastrointestinal tract" and "human"). A deeper syntactic analysis can enable identifying long-distance relations between habitat entities . Furthermore, the lower accuracy of the PartOf relations may also be caused by the fact that our system does not take into account whether the candidate habitat entities are hosts or host parts. For example, the habitat entity "fresh water" is neither a host nor a host-part. Therefore, it should not be considered for a PartOf relation. Including a module that can pre-identify the habitats which can act as hosts or host-parts in advance, may improve the performance of the system for PartOf relation extraction.These results demonstrate that performing a more fine-grained analysis of the text at the sentence level and incorporating an anaphora resolution module to handle relations that span multiple sentence is an effective approach for extracting relations in the bacteria biotopes domain. Our improved system achieves state-of-the-art results. However, there is still a lot of room for improvement. Our current approach assumes that if a specific bacteria (or its coreference) occur in the same sentence with a habitat entity, there is a localization relation between them. Deeper syntactic and semantic analysis of the sentences by using full or dependency parsing strategies can enhance the accuracy of the system. The PartOf relation extraction method that we proposed is only able to identify PartOf relations between habitat entities that overlap , which is close to the SER value of the system that ranked first in the shared task.This paper shows that our approaches based on the shallow syntactic analysis of the text and linguistically-motivated hand-coded rules are as effective as machine learning approaches for named entity detection, ontology-based normalization, and relation extraction in the bacteria biotopes domain. Our future directions for research include employing a full syntactic parsing approach to better identify the modifiers of the entities in Sub-task 1 and making use of dependency parsing in Sub-task 2 to handle long-distance PartOf relations, as well as to more accurately identify localization relations. We also plan to investigate the adaptation and evaluation of our proposed approaches for extracting bacteria biotope information from scientific publications.The authors declare that they have no competing interests.IK: Design and implementation of the algorithms, evaluation of the results, and drafting of the manuscript; AO: Design of the algorithms, evaluation of the results, and drafting of the manuscript. All authors read and approved the final manuscript.


Under-five mortality has been a major public health challenge from time immemorial. In response to this challenge, the World Health Organization and the United Nations Children's Fund developed the Integrated Management of Childhood Illnesses (IMCI) strategy and presented it to the whole world as a key approach to reduce child morbidity and mortality. Botswana started to implement the IMCI strategy in 1998. Reductions in the under-five mortality rate (U5MR) have been documented, although the reduction is not on par with the expected Millennium Development Goal 4 predictions.A quantitative study was done to identify the problems IMCI implementers face when tending children under 5 years in the Gaborone Health District of Botswana. The study population was made up of all the IMCI-trained and registered nurses, and systematic sampling was used to randomly select study participants. Questionnaires were used to collect data.The study findings indicated challenges related to low training coverage, health systems, and the unique features of the IMCI strategy.The comprehensive implementation of the IMCI strategy has the potential to significantly influence the U5MR in Botswana. Santana sings, ‘Let the children play’, but in our civilised world children are dying without ever having had a chance to play. Globally, every 3 seconds a child dies . In the In sub-Saharan Africa, children under the age of five are 15 times more likely to die, compared to children in developed countries . BotswanThe 1996 analysis published under the title ‘The global burden of disease’  , BotswanThe government of Botswana, through the Ministry of Health (MoH), introduced the Accelerated Child Survival and Development (ACSD) strategic plan intervention with a specific focus on reducing the U5MR. One of the high-impact interventions for reducing the U5MR is the Integrated Management of Childhood Illness (IMCI) strategy. The IMCI strategy was formulated by the WHO and the United Nations Children's Fund (UNICEF) and was presented to the world in 1996 as the principal strategy to reduce child mortality and improve child health , 8. MoreThe WHO  defines Reducing infant mortalityReducing the incidence and seriousness of illnesses and health problems that affect boys and girlsImproving growth and development during the first 5 years of a child's life , 11The main goal of IMCI is to contribute to healthy growth and development, during the first 5 years of life. This goal comprises three objectives:The IMCI strategy consists of three components . The firThe second component entails the health system or service component . It targThe community component, as the third component , seeks tAccording to the IMCI Health Facility Survey (HFS) , the IMCChild and Adolescent Health Annual Report in the Gaborone Health District who were trained in the health worker component of the IMCI strategy and were tending children under 5 years of age in their current station of duty.An explorative, descriptive, quantitative study with qualitative enhancement was conducted to identify challenges faced by the IMCI-1-trained, registered nurses in the Gaborone district when implementing the guidelines and procedures established by the IMCI-1 strategy. The target population was all the registered nurses ], the sample size (n), with a confidence level of 90% and a margin error of 10%, calculated to 39 participants. Systematic sampling was performed and a slightly larger sample of 45 participants was randomly selected from all of the 15 clinics in Gaborone Health District where the IMCI-trained nurses were deployed.Using Slovin's formula [A structured questionnaire was used to extract information about the possible challenges faced by the IMCI-trained, registered nurses in implementing the IMCI strategy and to solicit relevant recommendations on how to improve the implementation of this strategy. About 32 participants completed the questionnaire. Data from the closed questions were statistically analysed. Descriptive statistics such as frequency distributions, measures of central location, and measures of dispersion were used to describe and summarise the data.The results were originally statistically analysed. However, they were also thematised and categorised as illustrated in The three themes identified indicated specific challenges related to the unique features of the IMCI strategy, low training coverage, and health system and client expectations.Challenges related to the unique features of IMCI were that IMCI consultations are time-consuming and lead to longer patient-waiting queues. If the queues get too long, the non-IMCI-trained nurses take care of some children, and as a result IMCI is only partially implemented. The study participants suggested that this challenge could be resolved by addressing the problem of understaffing, improving the patient–nurse ratio and advocating for more IMCI implementation resources.Low IMCI training coverage of registered nurses posed a challenge, which was attributed to inadequate funding and the high cost of IMCI training courses. The study participants suggested that this challenge could be addressed by increasing the number of health workers trained in the IMCI strategy . The second recommendation was that IMCI training should be extended to lower-level cadres, such as healthcare assistants, to increase the number of health workers able to deliver IMCI services. However, the lower-level cadres can only assist with non-clinical aspects of the IMCI protocol, such as counselling the caregiver.Study participants indicated that the layout of the health facility was not conducive or fully equipped to support the practice of IMCI procedures. They recommended that the health system provide a spacious facility layout and give time to apply IMCI skills and procedures (i.e. observation of first-dose treatment and rehydration corners). Another health-related system was lack of support from supervisors and colleagues. Study participants proposed that IMCI training be extended to senior managers to boost their confidence in supervising IMCI implementers. Yet another challenge related to health systems was the lack of IMCI follow-up training and visits. Respondents recommended that the health system ensure follow-up training and supervision of IMCI implementers by IMCI facilitators. They asserted that follow-up visits were important, as they could help newly trained practitioners to transfer their new skills to practice.Issues related to client expectations have been attributed to the lack of uniformity between the IMCI-trained and the non-IMCI-trained health workers, in their case management practices. Non-IMCI-trained nurses take less time because they use single-diagnosis approaches, which do not view the client holistically and do not consider health problems other than the reason for the visit. As a result, caregivers of under-five patients prefer to be seen by them. This defeats the entire purpose of introducing the integrated approach. Two recommendations were proposed to standardise the practices of all registered nurses who tend under-five patients. The first recommendation was that all nurses tending under-five patients be IMCI-trained. The second recommendation was that IMCI training be the main criterion for the allocation of daily duties and the deployment of nurses to duty stations. This measure would help to ensure that only IMCI-trained nurses tend patients under the age of 5 years.The participants made relevant recommendations that could be applied to promote and improve the use of the IMCI strategy. Recommendations for improving the use of the strategy included adopting short-duration IMCI training courses, scaling up both pre-service and in-service training, and extending IMCI training to lower-level cadres, as well as addressing the challenges related to health systems and the unique features of the strategy.If adopted, the proposed measures could lead to the promotion and improvement of the use of the IMCI strategy. This improvement could, in turn, lead to a decrease in the current morbidity and mortality rates in children under the age of five in Botswana. Such a decrease would contribute to the realisation of the goals articulated in the third pillar of Botswana's Vision 2016, as well as in MDG4.The recommendations, although originally crafted for Botswana, can be adapted and used in various regions and countries to enhance the implementation of the IMCI strategy. If these challenges could be addressed, then maybe our children will have an opportunity to play.


However, the conceptual three-dimensional invisibility cloak that exactly follows specifications of transformation optics is formidably difficult to implement, while the simplified cylindrical cloak that has been experimentally realized is inherently visible. On the other hand, the recent carpet cloak model has acquired remarkable experimental development, including a recently demonstrated full-parameter carpet cloak without any approximation in the required constitutive parameters. In this paper, we numerically investigate the electromagnetic radiation from a charged particle passing through a perfect carpet cloak and propose an experimentally verifiable model to demonstrate symmetry breaking of transformation optics.It has been shown that a spherical invisibility cloak originally proposed by Pendry  Invisibility cloaking has been proposed as a representative application of transformation optics (TO)1This asymmetry has been utilized to propose an approach of detecting a perfect spherical cloak electromagnetically689101113For simplicity, we perform derivation, computation, and experimental design in a two-dimensional (2D) geometry where all physical quantities are invariant along the third dimension. where where the minus (positive) sign applies to the region of In this 2D geometry, a photon that goes through the cloak in the physical space will follow a bent path as in In the physical space, the current density of the fast-moving 2D line charged particle can be expressed asTo avoid potential confusion, we will use the upper-case It will excite the following magnetic field in a homogeneous medium with permittivity where the positive (negative) sign applies to the region of where the positive (negative) sign applies to Because the trajectory of the current in the virtual space is bent, to facilitate analysis, we cut each finite current segment  into small pieces and use a finite dipole array with a uniform phase difference where where where where To summarize, the current density in the virtual space in Reflection of the fields at the ground plane can be taken into account by making use of equivalent image sources. The current densities of the image sources of the fast-moving 2D line charged particle as well as the dipole arrays for corresponding current segments areBy labeling the fields generated by these image sources as The field inside the cloak in the physical space can be obtained by applying coordinate transformation to the field in the virtual space. In the numerical modeling of the particle, we use a Gaussian profile To fix the parameters of the cloak model in simulation, we set the background here to be Since the trajectory of the particle in the virtual space is bent while the time remains the same as that in the physical space, the speed in the virtual space increases by a factor of The above analysis of the charged particle shows TR from the charged particle when its velocity is relatively low. When the velocity of the charged particle is sufficiently high, another type of radiation, CR will be involved. Here, we choose the velocity of the particle to be u-v-z orthogonal coordinates , the effective parameters are x-y plane going through the carpet cloak that is composed of realistic metamaterials as shown in To verify the results analyzed above, we propose a full-parameter carpet cloak that is implementable in reality. To facilitate observation, the radiation from the charged particle can be simulated in a microwave experiment by monitoring only one spectral component. It can be seen from equation We let the spectral component of the current equation with freIn We analyzed in this paper the radiation generated by a moving charged particle passing through a perfect carpet cloak with uniform velocity. The radiation can reveal the asymmetry of transformation optics applied to motions of photons and charges. We proposed an experimentally verifiable model to demonstrate this asymmetry effect.X.S., F.G. and X.L. performed the calculation and analyzed the results, B.Z. supervised the progress.How to cite this article: Shi, X. et al. Electromagnetic Detection of a Perfect Carpet Cloak. Sci. Rep.5, 10401; doi: 10.1038/srep10401 (2015).


Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem. Swarm Intelligence (SI) is defined as the collective problem-solving capabilities of social animals . SI is tet al. [etc.). Finally, there are multiple interactions between individuals. There should be a minimum number of individuals who are capable of interacting with each other to turn their independent local-level activities into one interconnected living organism [Self-organization is created by four elements as were suggested by Bonabeau et al. . Positivet al. . The secet al. . Randomnorganism . As a reorganism .There are many different species of animal that benefit from similar procedures that enable them to survive and to create new and better generations. Honey bees, ants, flocks of birds and shoals of fish are some of the examples of this efficient system in which individuals find safety and food. Moreover, even some other complex life forms follow similar simple rules to benefit from each other’s strength . To somei.e., the Genetic Algorithm), the Particle Swarm Optimization (PSO) [Swarm-based optimization algorithms (SOAs) mimic nature’s methods to drive a search towards the optimal solution. A key difference between SOAs and direct search algorithms such as hill climbing and random walk is that SOAs use a population of solutions for every iteration instead of a single solution. As a population of solutions is processed as iteration, the outcome of each iteration is also a population of solutions . If an oon (PSO)  Artificion (PSO) ,6 the Anon (PSO) . Common The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, introduced by Pham , inspireIn this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The paper is organized as follows: Swarm-optimization algorithms is given in Swarm Optimization Algorithms (SOAs) mimic the collective exploration strategy of the swarms in the nature on optimization problems . These aEvolutionary Algorithms (EAs) are well-known SOAs, and inspired from the natural selection, mutation and recombination of the biological mechanism. Several forms of these algorithms have been introduced such as Evolutionary Strategy, Evolutionary Programming, Genetic Algorithm and so on. In EAs, the main strategy is to find the optimal points by utilizing the stochastic search operators such as natural selection, mutation and recombination to the population. The algorithms work with a random population of solutions. The algorithm efficiently exploits historical information to speculate on new search areas with improved performance . When apParticle Swarm Optimization (PSO) utilizes the social behavior of the groups of population in nature such as animal herds or bird flocking, or schooling of fish. PSO consists of a population called swarm and each member of the swarm is called a particle . The parAnt Colony Optimization (ACO) was inspired by the pheromone-based strategy of ants foraging in nature. The foraging behavior of ants is based on finding the shortest path between source and their nests . During In nature, honey bees have several complicated behaviors such as mating, breeding and foraging. These behaviors have been mimicked for several honey bee based optimization algorithms.One of the famous mating and breeding behavior of honey bees inspired algorithm is Marriage in Honey Bees Optimization (MBO). The algorithm starts from a single queen without family and passes on to the development of a colony with family having one or more queens. In the literature, several versions of MBO have been proposed such as Honey-Bees Mating Optimization (HBMO) , Fast MaThe other type of bee-inspired algorithms mimics the foraging behavior of the honey bees. These algorithms use standard evolutionary or random explorative search to locate promising locations. Then the algorithms utilize the exploitative search on the most promising locations to find the global optimum. The following algorithms were inspired from foraging behavior of honey bees; Bee System (BS), Bee Colony Optimization (BCO), Artificial Bee Colony (ABC) and The Bees Algorithm (BA). Bee System is an improved version of the Genetic Algorithm (GA) . The maiBee Colony Optimization (BCO) was proposed to solve combinatorial optimization problems by . BCO haset al. [Artificial Bee Colony optimization (ABC) was proposed by Karaboga et al. ,29,30. Tet al.  to startet al. [The Bees Algorithm was proposed by Pham et al. , which iA colony of honey bees can exploit a large number of food sources in big fields and they can fly up to 11 km to exploit food sources ,32. The The scout bees inform their peers waiting in the hive as to the quality of the food source, based amongst other things, on sugar levels. The scout bees deposit their nectar and go to the dance floor in front of the hive to communicate to the other bees by performing their dance, known as the “waggle dance” .The waggle dance is named based on the wagging run (in which the dancers produce a loud buzzing sound by moving their bodies from side to side), which is used by the scout bees to communicate information about the food source to the rest of the colony. The scout bees provide the following information by means of the waggle dance: the quality of the food source, the distance of the source from the hive and the direction of the source ,33.The waggle dance path has a figure of eight shape. Initially the scout bee vibrates its wing muscles which produces a loud buzz and runs in a straight line the direction which is related to the vertical on the hive and indicates the direction of the food source relative to the sun’s azimuth in the field, see The BA has both local and global search capability utilizing exploitation and exploration strategies, respectively. The BA uses the set of parameters given in n scout bees randomly to selected sites  and (2). The third parameter is called “Repetition Number for the Enhancement”, denoted rep_nenh. This parameter defines the number of repetitions until the end of the shrinking process, and the beginning of the enhancement process as shown in Equations (1) and (3) [rep_naban, which denotes the “Repetition Number for Abandonment Process”. Hence a non-productive site is abandoned and it is stored in aband_site list. If there is no better solution than the abandoned site at the end of the searching process, this is the final solution.This section describes the proposed improvements to the BA by applying adaptive change to the neighborhood size and site abandonment approach simultaneously. Combined neighborhood size change and site abandonment (NSSA) strategy has been attempted on the BA by Koc  who foun and (3) . The enhIn this sub-section, the ANSSA-based BA was tested on benchmark functions and the results were compared with those obtained using the basic BA and other well-known optimization techniques such as Evolutionary Algorithm (EA), Particle Swarm Optimization (PSO) (standard PSO), Artificial Bee Colony (ABC). There are several differences between these algorithms as given in Feasibility of finding global optimum for several problems,Availability to combine the hybrid algorithms with EA and others,Implementation with several optimization problems,Availability for real and binary problems.Advantages:Slow convergence rate,Stability and convergence of algorithm is based on recombination and mutation rates,The algorithm may converge to a sub-optimal solution (risk of premature convergence),Algorithm has a weakness on local search,It has a difficult encoding scheme.Disadvantages:EA has been implemented on several optimization problems, however this algorithm has advantages and disadvantages as given below :AdvantaThe algorithm can easily be implemented;The global search of the algorithm is efficient,The dependency on the initial solution is smaller,It is a fast algorithm,The algorithm has less parameter for tuning.Advantages:The algorithm has a weakness regarding local search,It has a slow convergence rate,It may get trapped in local minima for hard optimization problems.Disadvantages;PSO has the following advantages and disadvantages ;AdvantaThe algorithm has strength in both local and global searches ,Implemented with several optimization problems ,Advantages;Random initialization,The algorithm has several parameters,Parameters need to be tuned,Probabilistic approach in the local search.Disadvantages;ABC is also the same as the other algorithms in that it has advantages and disadvantages:The algorithm has local search and global search ability,Implemented with several optimization problems,Easy to use,Available for hybridization combination with other algorithms.Advantages:Random initialization,The algorithm has several parameters,Parameters need to be tuned.Disadvantages:The BA also has advantages and disadvantages compared to the other algorithms :AdvantaTo analyze the behavior of each algorithm, the benchmark functions have been tested. The ten benchmark functions used for the test are given in The proposed algorithm was run a hundred times for each function. The performance of the algorithm was assessed according to the accuracy and the average evaluation numbers . ExperimFurther, the t-test was utilized to measure the statistical significance of the proposed algorithm and the basic Bees Algorithm. The results are given in In this paper neighborhood search in the BA was investigated. The focus was on improving the BA by utilizing the adaptive neighborhood sizes and site abandonment (ANSSA) strategy. The accuracy of the algorithm was computed with average absolute differences of the best results. According to this, the more accurate results were closer to zero. The proposed algorithm performed significantly better on high dimensional functions. For example, accuracy for 10D-Rastrigin was 0.0002 whereas the accuracy of the basic BA was 24.8499, and for 10D-Ackley it was 0.0063, whereas the accuracy of the basic BA was 1.2345. On another hand the proposed algorithms have less performance on lower dimensional problems, for example the accuracy of the algorithm for 2D-Schwefel function was 0.0003, which was lower than the basic BAs result given in As it is shown in i.e., no significant difference between the performances of the two algorithms. An α value of less than 0.05 in A t-test was carried out on the results obtained for the basic BA and the improved BA in order to see if there was any significant difference between the performances of the two methods. This was done by looking for evidence for the rejection of the null hypothesis In this paper, an optimization algorithm inspired by the natural foraging behavior of honey bees, called the Bees Algorithm, has been discussed, and an enhanced version called ANSSA-based Bees Algorithm has been proposed.The proposed ANSSA-based has been successfully applied on continuous type benchmark functions and compared with other well-known optimization techniques. To test the performance of the proposed algorithm, the following comparison approaches have been utilized: accuracy analysis, average evaluation and t-test. According to the results of the accuracy analysis and the average evaluation, the proposed algorithm performed better on higher dimensional than lower dimensional functions.Finally, the statistical significance of proposed algorithm has been computed with a t-test and the results were compared with the basic Bees Algorithm. Based on the t-test results it can be concluded that the results of the proposed algorithm are statistically significant than the results of basic Bees Algorithm. Thus the proposed algorithm performed better than the basic Bees Algorithm on higher dimensional functions such as, Ackley (10D) and Griwank (10D).


Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors , wireless signal strength indicators , and visual sensors . People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design. In recent years, researchers have developed various approaches for mobile-device  user-positioning in GPS-denied indoor environment. To name a few, radio frequency (RF) fingerprinting techniques, motion-sensor-based pedestrian dead reckoning (PDR) techniques, and visual-sensor-based feature matching techniques, are some of the most popular approaches in indoor positioning. However, all of them have their own limitations. RF fingerprinting techniques  algorithm, which combines state hypotheses from multiple filter models to get a better state estimate of targets with changing dynamics [In previous work , we adapov model . Rossi erference . To solvdynamics . The filA graph structure Specifically, we make the following contributions: Under the HMM framework, we propose a graph structure to store the model constructed by multiple sensors in the offline training phase, and a multimodal particle filter to efficiently fuse the information during the online tracking phase. The particle filter is able to handle the motion sensor drift problem during the resampling step. The WiFi signal strength fluctuation problem is mitigated using the motion sensor information to guide the particle propagation towards the higher likelihood field. Based on our algorithm, we develop an indoor positioning system on the iOS platform. To the best of our knowledge, our iOS application is the first one to achieve accurate, robust, and highly integrated indoor positioning by seamlessly fusing the information from the multiple sensors on board.This paper is organized as follows: In the next section, we describe, in detail, the offline training phase. Then, in In this section, we will discuss every detail of our preparation for online tracking.A detailed 3D model of the indoor environment is constructed by fusing the data from a camera and a 2D line-scan LiDAR. Both devices are mounted rigidly on a robotic servo, which sweeps vertically to cover the third dimension . FiduciaAs shown in As shown in (1)Find the inliers for the corners of checkerboard based on RANSAC algorithm.(a)Randomly select three pairs of points from the LiDAR intensity image and camera image to estimate a fitting model.(b)Calculate the transformation matrix (c)Change the The RANSAC algorithm follows these steps:(2)Choose the transformation matrix (3)Use all inlier point pairs to compute a refined transformation matrix The algorithm for generating the transformation matrix is summarized below:After generating the transformation matrices, we are able to stitch the camera panoramas together and fuse them with one LiDAR intensity image by applying the transformation matrices. Then, we back project the textured 2D points to a 3D color point cloud.The extrinsic calibration result is applied to a large indoor environment. The LiDAR-camera scan system is mounted on a pushcart in order to record the data in stop-and-go mode. By manually aligning the data in each survey point, we can get a detailed 3D model of the indoor environment. At the same time, the 3D model is partitioned, based on the survey point locations. The key idea of constructing a graph structure is to represent the indoor environment using a graph A vertex By introducing a graph as the data structure, we are able to restrict the user movement and predict the user location at the next moment. Since the user can only move around connected vertices, we will only have limited amount of candidate vertices for the next move. If the space involved in a vertex is small enough, finding the user’s location is approximated as locating the vertex. This is similar to grid-based localization. However, the smaller the grid is, the higher the computational cost will be. In practice, we choose the grid size as the survey area covered during each 3D scan. Thus, locating the vertex gives a coarse estimation of the user’s location. A finer positioning within the vertex is achieved using particle filtering. The use of the graph structure also increases the system’s robustness. In the case of crowded environments, where the sensor signal fidelity may not be reliable, the constraints in the graph can help detect a sensor failure if the prediction based on the sensor’s measurement violates the constraints.A Gaussian process (GP) essentially estimates a posterior probability distribution over functions from training data (details can be found in ). We wilLet us first define a function, To estimate the posterior distribution over function Here, the parameters Since we only have access to the noisy observations Here, Note that the covariance between the observations is written as a function of the inputs, emphasizing the non-parametric nature of Gaussian process regression.Now we can generate the posterior distribution over functions The predicted mean and variance are:The parameters To apply GP in WiFi signal strength modeling, the input values The motion dynamic model is defined as the user’s position changes over time, which are represented by the distance traveled and the heading movement. The built-in motion sensors in the mobile device, including an accelerometer, gyroscope and magnetometer, are used to track the user’s movement. We first take a look at the accelerometer measurement. If the user is standing still, it is expected that their mobile device will register little acceleration. Therefore, the standard deviation in the magnitude of acceleration is selected to detect the walking/stopped transition. If  walking .In order to determine whether the user actually enters the walking mode, we calculate the auto-correlation of the acceleration signal If the user is walking, then the auto-correlation will spike the periodicity of the walker. We define Once we have determined that the state is walking, step counting and stride estimation are performed to calculate the walking distance of the user. As shown in Because the human stride is not constant during walking, the stride size is determined by dynamically checking the acceleration sequence. We apply an empirical equation, based on , to estiIn order to detect the user’s orientation, we apply a gyroscope and magnetometer sensor to detect angle change. According to reference , the rapTo determine the new position, we first need to calculate the step number and stride length, and then estimate the movement as follows:The motion dynamic model fuses the information provided by different motion sensors, and indicates a higher likelihood field in particle filtering. There are four scenarios depending on the access of different kinds of motion sensors. When an accelerometer is available, the distance traveled can be estimated, based on step counting and stride length estimation. Otherwise, the distance is estimated with an empirical maximum speed, for example, 1 m/s. If a gyroscope and a magnetometer are available, the user heading is detected, if not, the heading remains unknown. Assuming an open space, the calculation of the likelihood field is shown in A general HMM characterizes a physical system with a state space model. In the problem of position tracking, the HMM model represents the temporal correlation of a user’s position and orientation. The observation model describes the measurement likelihood of making an observation at different positions in the indoor environment. Our observation includes WiFi received signal strength, motion sensor readings, and captured image.The WiFi signal strength measurement likelihood model uses the mean and variance of the signal at each position, calculated by Gaussian process regression.The camera measurement likelihood model is computed by pairwise pixel comparison between the current view and the view of a particle . In the A predefined threshold determines the maximal color difference for two pixels, and a normalized color space is adopted to alleviate the effect of illumination.The motion sensor measurement likelihood model compares the motion model Bayesian filtering is used to estimate the posterior state (1)Particle InitializationWe implement Bayesian filtering using a particle filter, which represents posterior over the state The initial position is calculated through the weighted (2)Particle propagationNext, we apply the state transition model to guide particle propagation. During the state transition process, we observe that the particle propagation plays an important role, which represents the state transition probability. The more accurate the particles propagate towards the right position, the better the positioning performance will be. We generate new particles by sampling (3)Particle weight updateAfter particle propagation in each epoch, we weight the sample (4)Particle resamplingOnce the particle weights are updated, we perform importance resampling to update the particles’ state by pickinga random sample (5)Position estimationAfter the resampling process, the estimated position is calculated as the mean of all the resampled particles’ positions. To further increase the positioning accuracy, we perform Direct Linear Transformation (DLT)  between We realize the indoor positioning algorithm on the iOS platform and build an app to test the system performance. The system workflow is shown in The training and testing are conducted in an office building corridor area and a library’s open space. In total, we have 24 survey points in the corridor and 21 survey points in the library. Positioning tests are conducted on a predefined path. The average length of the path is about 90 m in the library’s open space and 100 m in the corridor area. The position update is performed every 5 s, which means the particle filtering step can be completed in 5 s. The memory usage is under 40 MB RAMsince the iOS app will be forced to shut down if it exceeds this limit. During the traverse on the path, we measured the error distance between the estimated position and the ground truth position. The ground truth position is based on manual annotation of waypoints. Whenever the tester reaches a waypoint, the timestamp is recorded and the estimated position with the actual one are compared. To gain insight into the positioning error distribution, As we can see from In this paper, we have demonstrated an indoor localization system based on a graph structure and multimodal particle filtering technique. The implementation on the iOS platform, and the test in a real world situation proved that our application is a reliable indoor localization system. To the best of our knowledge, this is the first iOS app delivering such accurate, highly integrated indoor localization system on a small mobile device. Based on our system, many position-aware applications will be able to function properly indoors, providing more convenient service to people’s daily lives.In the future, we will focus on fusing the visual sensors on board into our real time localization system. We believe the key technology for future localization systems lies in how to efficiently fuse the information provided by various sensors. We are also looking to integrate our system into other platforms, for example, a vehicular infotainment platform, so that we have access to more sensor information and can function both indoors and outdoors.


Mass appraisal is widely considered an advanced frontier in the real estate valuation field. Performing mass appraisal entails the need to get access to base information conveyed by a large amount of transactions, such as prices and property features. Due to the lack of transparency of many Italian real estate market segments, our survey has been addressed to gather data from residential property advertisements.The dataset specifically focuses on property offer prices and dwelling energy efficiency. The latter refers to the label expressed and exhibited by the energy performance certificate. Moreover, data are georeferenced with the highest possible accuracy: at the neighborhood level for a 76.8% of cases, at street or building number level for the remaining 23.2%.Data are related to the analysis performed in Bonifaci and Copiello  Specifications TableValue of the data•Raw data included here are suitable to be processed by means of a variety of analytical methods, both quantitative, as is the well-established multiple regression based hedonic price model •Georeferenced data lend themselves to be processed by means of spatial statistical analyzes, such as those based on interpolation models •The research branch devoted to evaluating the customers׳ willingness to pay for more efficient dwellings is growing interest, although the achieved outcomes are still conflicting and debated 1.The dataset features are as follows: 1,042 records, namely single unique properties, and 16 fields, namely property characteristics or other variables. Within the dataset, coding systems adopted for the variables based upon nominal and ordinal scales does not rely on a preconceived scheme, instead they are driven by the information usually available in residential property advertisements.22.1The city of Padua locates in Northern Italy and has been chosen for the survey because of its dynamic real estate market. According to cadastral statistics The dynamism of the local real estate sector is further witnessed by the amount of transactions recorded in the national observatory on the property market The maintenance of a real estate market monitor is among the commitments undertaken by the Italian Revenue Agency. This monitor provides a full coverage of the national territory, by dividing it into homogeneous zones. The zones are identified by means of codes composed by a progressive number following a capital letter. In turn, the letters are assigned according to five kinds of locations: B stands for downtown areas; C for inner-ring areas; D for outer-ring areas; E for the outskirts and R for the rural areas. Usually, C, D, and E zones encompass both residential and industrial settlements. The real estate market monitor divides Padua into 22 zones, as represented in Owing to the growing interest in building energy efficiency, several regulations have been enacted in Italy since the mid-seventies. When the Law 373/1976 entered into force, it constituted the first attempt to impose constraints on the building energy consumptions. Subsequently, a major enhancement to deal with the energy saving issue was represented by the Law 10/1991. Most recently, trying to meet the increasing consumers׳ demand for market transparency, the Decree-Law 63/2013 – implementing the European Directive 2010/31/EU – has introduced the legal obligation to include energy-related information within real estate advertisements. The data which are to be exhibited refer to the global energy performance index and the consequent energy label, as they are reported in the mandatory energy performance certificate The Italian coding system of energy label relies on eight classes, from A+, which is the best one, to G, the worst. Each class correlates to the energy performance index, depending on the climate zone. The city of Padua is characterized by a yearly amount of Heating Degree Days  equal to 2,383. Therefore, according to the classification of the national territory , it falls within the E climatic zone . For this zone, The data provided here are useful to investigate the relationship between the energy performance of housing and the expected selling price by owners. Such a relationship is predicted to be positive by the literature; nevertheless, its functional form – linear or not – and magnitude are still debated. Moreover, georeferenced data allow to analyze the potential spatial patterns of both real estate prices and building energy performances 2.2The data have been gathered within a research branch devoted to exploring the relationships among building energy performances, the prices in the real estate market, the costs in the construction sector and the provision of affordable dwellings Main real estate advertisement-posting websites have been inspected daily during the survey period. They have been cross-checked to avoid considering the duplicate items. When an old advertisement has been found to have been posted again without changes, only initial data have been kept. On the contrary, reissued advertisements with changes led to discarding the prior ones, hence oldest data have been replaced by the newest.Advertisements are usually concise; moreover, they may contain different kinds of information and make use or not of supporting materials, such as pictures of interiors and exteriors, or floor plans. The sample here provided includes only records characterized by complete information with regard to the previously mentioned variables see . On the Based on the location declared in the advertisements, data have been georeferenced but they are characterized by heterogeneous levels of accuracy. The sample implements the exact geographical position for dwellings whose advertisement contained the full address. The dwellings whose advertisement provided the street name, but not the building number, have been located by referring to the midpoint of the self-same street. Otherwise, advertisements providing only succinct information about the location have entailed the need to refer to the midpoint of neighborhoods.


The inclusion of this layer of information was shown to improve conformational space sampling and apply for the first time a coarse-grained normal mode analysis method to predict the effect of single point mutations on protein dynamics and thermostability resulting from vibrational entropy changes. Here we present a web server that allows non-technical users to have access to ENCoM calculations to predict the effect of mutations on thermostability and dynamics as well as to generate geometrically realistic conformational ensembles. The server is accessible at:  Proteins are dynamic objects with movements ranging from sub-rotameric side-chain movements to domain movements intrinsically associated to their function. Among the main computational techniques to study protein dynamics are molecular dynamics (MD) and normal mode analysis (NMA). The following properties are common to both techniques: (i) both can be used to explore the conformational space; (ii) may use the same force fields and the accuracy of the simulation depends on the quality of the potential; (iii) both techniques are as exact descriptions of the dynamics as the level of detail of the representation of the protein structure and the force field used permits. The major difference between MD and NMA is that the former produces an actual trajectory in conformational space while the later produces a basis set of movements described as a set of normal modes (Eigenvectors) and associated frequencies  with which individual points in conformational space can be sampled. Every possible conformational change of a protein from the starting equilibrium structure can be described as a linear combination of all Eigenvectors modulated by specific amplitudes. Therefore, NMA produces the set of possible movements whereas MD provides an actual trajectory. The modes associated to the slowest frequencies are the most energetically accessible.Coarse-grained NMA methods use reduced amino acids representations, for example, one point mass per amino acid. Different levels of simplification in the representation of protein structures exist. However, with the exception of ENCoM  and VAMMOur group recently introduced ENCoM, a coarse-grained NMA method that accounts for the nature of amino-acids through the inclusion of a pairwise atom-type term proportional to the surface area in contact between heavy-atoms in the potential. The more realistic representation of intramolecular interactions in ENCoM results in more accurate predictions of conformational changes in terms of calculated squared overlap . Specifically, compared to ANM using the PSCDB , a databE. Coli. Although this mutation has only a modest effect on protein stability and is 15 Å away from the binding site, it disrupts catalytic activity 200 fold through allosteric effects. ENCoM calculations are consistent with experimentally observed variations in NMR S2 values  to the time taken by jobs with similar parameters. Thus, as the ENCoM server usage increases, running time estimates will improve.The mutation interface is built to allow high-throughput analysis of single-point mutations with the implementation of a simple command line interface. The user inputs the residue number or range of residues to mutate, the chain and the type of mutations. Whereas a large number of mutations may be specified at once in the input, the ENCoM server is restricted to the analysis of single mutations and models each mutation in turn as a single-point mutation. The ENCoM server utilizes the same methodology employed in our recent papers ,16, nameAs stabilizing mutations are more rare, the possibility to input a large number of mutations at once  in the ENCoM server web interface simplifies the search for such mutations that are particularly relevant in the context of protein design. With the exception of PoPMusic, all other web-servers dedicated to the prediction of the effect of mutations on stability require that users input every position to be mutated (and the nature of the corresponding residue in the wild type) manually, making the task of exploring the effect of mutations unnecessarily time consuming.et al. data set .The ENCoM server offers easy access to powerful NMA-based predictions of the effect of mutations on thermostability and dynamics of single residues and generates comprehensive, geometrically realistic conformational ensembles.Supplementary Data are available at NAR Online.