Automatically recognizing places of interest from unreliable GPS data using spatio-temporal density estimation and line intersections Stay points are important for recognizing significant places from a mobile user's GPS trajectory. Such places are often located indoors and in urban canyons, where GPS is unreliable. Consequently, mapping a user's stay point to a Place of Interest (POI) using only GPS data is particularly challenging. Our novel algorithm employs both spatio-temporal density estimation and line count inference to predict and rank a user's POI(s) at building level accuracy from noisy time-annotated GPS data points. An experimental study demonstrates the superiority of our algorithm against several baseline approaches with a recall of 96.5% for the top 5 retrieved locations. The rise of GPS-enabled mobile devices has created exciting opportunities for tracking people, objects and vehicles, as well as animals using their digital traces. Such traces or trajectories can be mined to derive knowledge about the behaviour of moving objects for a range of purposes: for health monitoring [1, 2] , offender monitoring [3] , animal migration [4] [5] [6] , traffic and transportation management systems [7] [8] [9] , location-based services and social networks [10] [11] [12] [13] . For example, in case of traffic management systems location traces can be mined to estimate traffic delays on the road [7, 8] . Rich monitoring of traffic and road conditions such as bumps and breaks, can also be performed with the help of additional sensors along with GPS [9] .. Although satellite-based GPS positioning provides good coverage for modern navigation and tracking; it does not work well for indoor places or urban canyons, due to poor line-of-sight transmission between the receiver and the satellites. For a GPS device to be accurate, a clear line of sight to satellites is necessary, so that signals from at least four satellites are available to the GPS receiver for computing its current position [14] .. The complexity of indoor environments, which contain walls, equipment, people and other objects which can influence electro-magnetic waves, causes the multipath propagation of a GPS signal and severely reduces the accuracy of positioning. In urban canyons, the multipath effect and signal loss are also caused by surrounding tall buildings [15] . The High Sensitivity GPS (HSGPS) receiver has been designed to allow tracking in such critical environments. It is claimed a HSGPS receiver is able to acquire signals down to -190 dBW to allow tracking in indoor locations and urban areas [15, 16] . Unfortunately, today's GPS-enabled mobile phones (even those that can use A-GPS) still suffer from poor accuracy when being used in indoor environments or urban canyons. The median accuracy of positioning in such environments is as high as 70 meters [17] . Kjrgaard et al. [15] have investigated the extent to which GPS can be used for positioning in indoor places. Their study showed that if using a state-of-the art GPS receiver (High-Sensitivity-GPS), the accuracy of GPS positioning is adequate (within 10 meters) in many wooden house and brick/concrete buildings. However, the availability and performance of positioning is much lower when using the embedded GPSreceiver in current mobile phones. They showed the performance of GPS positioning for an indoor place not only depends on the type of receiver, but also on nearby building elements and materials, the number of walls, the number of floors and the composition of surrounding buildings.. A range of prior approaches have proposed the use of other technologies such as RFID, Wi-Fi or Bluetooth to improve location accuracy [18] [19] [20] [21] [22] [23] [24] [25] [26] in GPS signal degraded areas. The commercial products like Apple's Maps or Google Maps installed in modern smartphones also mostly rely on Wi-Fi or mobile network connections to perform localization at places where GPS signal is unavailable. The opportunity to use such technologies is dependent on the building-infrastructure and will not be always available. Typically, there is also additional cost for implementation. Moreover, these localization techniques are not designed for emergency critical scenarios such as fire-fighting and earthquake or tsunami response. Other studies have proposed PDR (Pedestrian Dead Reckoning) based techniques for use in indoor locations [27] . D'Souza et al. has proposed techniques for real-time user tracking in indoor environments using wearable motion sensors within a floor-plan and limited wireless sensor network [28] . However these techniques are specialized for indoor environments and are not designed for outdoor use.. Indoor places play an important role in daily life and people spend a large amount of their time in indoor locations such as homes, offices, universities, or restaurants [29] . GPS provides broad coverage for positioning worldwide and provides excellent accuracy for outdoor environments, without incurring any additional infrastructure cost for indoor positioning. The pervasiveness of GPS enabled mobile devices is promoting the generation of increasing volumes of trajectories reflecting people's daily movement behaviour. Prior literature has examined how to extract segments of a GPS trajectory corresponding to user's stay at different significant places, such as the home, office, park or shopping mall [12, [30] [31] [32] [33] [34] [35] [36] . A so-called Stay Point can be computed as the mean of the extracted GPS points. However, significant places are often indoor locations and may correspond to buildings in urban areas where GPS is less accurate. Therefore obtaining the user's Place of Interest (POI) by mapping the stay point to a POI database is not feasible. For example, Figure 1 shows the measured GPS positions by a mobile device for a user's stay in building A at The University of Melbourne. The centroid or mean of the GPS points is shown in orange but the nearest building to the so called stay point does not correspond to the true POI. The problem becomes even more challenging when a user stays at multiple nearby buildings in urban canyons, while being at a significant place. For example, a user's office can be located within a building in a city-centre, but she may have lunch in a nearby restaurant. In a university, a user may spend time in a library and can attend a seminar in another nearby building. In these scenarios, estimating the user's true POIs at the building level becomes impossible using the Stay Point technique, since the measured GPS positions often return a single cluster of GPS points due to poor accuracy. To meet these challenges, we propose a novel algorithm (POI-ID) to obtain a user's POI(s) corresponding to his/her stay at a significant place from a set of highly inaccurate GPS data points. Given a set of time-stamped GPS points and a list of surrounding POIs, POI-ID produces a ranking of the POIs according to the user's most likely location at a building level accuracy. The pervasiveness of GPS enabled mobile devices has created significant volumes of trajectories encapsulating users' movement behaviours. Extracting significant places from these trajectories has attracted considerable research interest due to the large range of applications. Significant places are often indoor locations and may exist in urban canyons where GPS devices can have poor accuracy. Our proposed algorithm, POI-ID, is able to estimate a user's actual POI at a building level accuracy from highly noisy GPS data. The experimental results and comparison against the baseline techniques show that POI-ID outperforms in terms of both identifying (recall) and ranking (NDCG) a user's true POIs, even for urban regions.. POI-ID can successfully predict a user's POI at building level accuracy. However, for situations like fire-fighting and search and rescue operations, we may need to track users in real-time at a room level accuracy where Wi-Fi, RFID based indoor localization may not be useful anymore. Furthermore, the major challenge of using GPS-enabled mobile devices at indoor places is the large power consumption. In the future we plan to extend our research to investigate these issues. The related literature can be broadly classified into two categories: 1) place extraction algorithms which extract segments from user's trajectories corresponding to significant places and 2) place ranking algorithms which retrieve and rank the nearby POIs according to a user's current location coordinate. As summarized in Table 1 , previous works cannot identify a user's true POI(s) in an urban region only using GPS data and in the absence of prior knowledge about the user or the place. We perform an initial data preprocessing step based on the instantaneous speed of a mobile user. People either remain still or walk within a confined region in a significant place such as the home, office, university or shopping mall. Therefore we are interested only in the GPS data points where the instantaneous speed is not more than the maximum walking speed of the mobile user. The average human walking speed normally varies from 1.2 to 1.5 meter/sec [52, 53] . However, according to the previous studies [54] [55] [56] , 2 meter/sec is the cut-off speed beyond which people normally prefer to run rather than to walk to save energy. Hence we chose 2 meter/sec as the maximum walking speed of a mobile user while being at a significant place. We remove all the GPS fixes as noise at which a user's speed is greater than 2 meter/sec. A similar preprocessing step is also performed while implementing the baseline techniques in Section 4. The speed at a GPS fix is obtained through i) a direct measurement of the GPS receiver or ii) computation based on the Euclidean distance between two consecutive GPS fixes and the time interval between them.. We propose a two-phase algorithm to estimate a user's most likely POI. In the first phase we rank the POIs according to the spatio-temporal closeness of uncertain GPS data points using trivariate Kernel Density estimation [57] . In the second phase we rank the POIs using a line segment intersection based approach.. Single place datasets Sub-place datasets Distance-to-staypoint (baseline 5) 0.417 0.484 Density-Rank (baseline 6) 0.540 0.65 Point-Count-Rank (baseline 7) 0.559 0.651 Spatio-temporal density rank 0.544 0.675 Line count rank 0.574 0.686. We also performed an experiment to compare the ranking performance of different combinations of Spatiotemporal-density-rank, Point-count-rank and Line-count-rank techniques, since these were the top 3 winners in terms of their overall ranking performance. Table 10 summarizes the average NDCG@5 of all location files for different combinations of the above three techniques. The best performer here is the POI-ID, i.e., combining Spatio-temporaldensity-rank with line-count-rank. Significant improvement in ranking performance is obtained for POI-ID while comparing against the other combinations (p-value < 0.05). Table 10 : Overall NDCG@5 performance of different combinations of ranking techniques for all 100 datasets. The best performance is obtained by combining Spatio-temporal-density-rank and Line-count-rank as in POI-ID Methodology NDCG@5 Spatio-temporal-density-rank + Point-count-rank 0.708 Point-count-rank + Line-count-rank 0.725 Spatio-temporal-density-rank + Point-count-rank + Line-count-rank 0.727 Spatio-temporal-density-rank + Line-count-rank (POI-ID) 0.735

O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Molecular Determinants of the Bacterial Resistance to Fluoroquinolones: A Computational Study Abstract: Quinolones constitute a large class of antibacterial agents whose action is mediated through the formation of a ternary complex with DNA and either, DNA Gyrase or topoisomerase IV, resulting in the inhibition of DNA replication. In order to get a deeper insight into the features of the complex formation, we carried out docking studies of fifteen diverse quinolones to the cleaved topoisomerase IV-DNA complex. Docking studies were performed using the crystal structures of the cleaved complex with levofloxacin and moxifloxacin (pdb entries 3K9F and 2XKK, respectively) using the GOLD software. Ligands dock in positions similar to those of the crystal structures. Analysis of the results reveals that bound quinolones appear intercalated between the two nucleotides that are involved in the DNA cleavage and exhibit hydrogen bonds with Arg 117 and, the latter mediated though a water molecule. Arg 117 has not been described to be involved in resistance, since it is putatively involved in the enzymatic reaction and its mutation would be lethal for the organism. Mutants of Ser 79 exhibit resistance to quinolones which can be explained by the loss of an important anchoring point. Interestingly, quinolone resistance observed in Asp 83 mutants cannot be explained directly on the basis of the loss of a direct interaction, but could be explained on the basis of its involvement at the entrance of the ligands to their binding pocket since the residue is located at the mouth of the pocket. The results of the present study suggest that the 4-keto and 3-carboxyl groups of the fluoroquinolones bind a Mg 2+ before binding to the cleaved topoisomarase IV-DNA complex and use Asp 83 for entry into the binding pocket. Accordingly, mutations that do not conserve the binding capacity for the quinolone-Mg 2+ complex will prevent the binding of this class of ligands. The results we present here are also compared with the structure of PD0305970 a 2,4-dione active against the Ser 79 and Asp 83 mutants. Antibiotic resistance is a major public health concern of modern society. Infections caused by resistant pathogens have serious consequences, including an increased risk of death, hampering of infectious disease control and raising the threat of a return to the pre-antibiotic era [1] . Quinolones constitute a large class of synthetic antimicrobial agents with a 17% share of the world antibiotic market [2] . They are effective for the treatment of many types of infectious diseases caused by bacteria, being clinically used to treat infections of the urinary tract and respiratory system mainly, although they are also used for the treatment of sexually transmitted diseases, bone and joint infections or typhoid fever [3, 4] . Quinolones exhibit properties of the ideal antibiotic: high potency, a broad spectrum of activity, good bioavailability, oral and intravenous formulations, high serum levels, a large volume of distribution indicating concentration in tissues and a potentially low incidence of side-effects. However, after some years of quinolone use, resistance in Enterobacteriaceae has become common, wide spread and non-clonal [5] forcing the need to develop new analogs.. *Address correspondence to this author at the Department of Chemical Engineering, Universitat Politecnica de Catalunya, ETSEIB, Av. Diagonal, 647, 08028 Barcelona, Spain; Tel: +34934016679; Fax: +34934017150; E-mail: juan.jesus.perez@upc.edu. From the chemical point of view, quinolone is a generic term, used loosely to refer to different chemical structures that have a 4-oxo-1, 4-dihydroquinoline skeleton shown in Fig. (1) , most often with a carboxylic acid attached at position 3 [6, 7] . Nalidixic acid (see Fig. 1 ) was the first member of this family to be released in the early sixties, with a moderate antibacterial activity and narrow Gram-negative spectrum. The substitution of a piperazine ring at position R7 and the addition of a fluorine atom at position R6, resulted in an increase of potency and spectrum of activity to Grampositive bacteria, giving rise to a second generation of quinolones, about a decade later. A third generation came a few years later with the popular addition of a cyclopropyl group at position R1 together with substitution of methoxy or chlorine moieties at position R8. The cyclopropyl moiety enhances potency and has some effects on pharmacokinetics. Different structure-activity studies have provided evidence of the effect of different substitutions on the 4-quinolone scaffold. Thus, it is known that bulky substituents at position R2 lower the microbiological activity due to steric hindrance. Only sulfur, incorporated into a small ring as in the case of the thiazetoquinolones, has been able to replace hydrogen at the R2 position. Substituents at R5 position appear to have the capacity to alter overall steric configuration (planar structure) of the molecule, which is why changes here are thought to affect activity. Modestly sized additions, such as an amino, hydroxyl, or methyl groups can markedly increase in vitro activity against Grampositive bacteria as well as enhance potency. It is also known that a fluorine atom at position R6 controls bacterial potency or that the presence of a bulky ring at position R7 controls potency spectrum and pharmacokinetics. Finally, the heterocycle scaffold can also be modified. Thus, when position R8 is changed for nitrogen, the family of napthylridines produced exhibit also high antibiotic activity. Similarly, quinazolines, quinazolinediones [8] [9] [10] or isothioquinolones [11] have also proved to have high antibiotic activity.. The therapeutical targets of quinolones are two bacterial type II topoisomerases: DNA gyrase and topoisomerase IV [12] . Type II topoisomerases regulate the overwinding or underwinding of DNA by creating transient breaks on the two DNA strands (known as the cleaved complex) and reseal them, after passing another unbroken DNA double strand through it. Both are tetrameric proteins formed by two (duplicated) subunits. In the case of gyrase they are called GyrA and GyrB subunits, whereas in the case of topoisomerase IV they are called ParC and ParE subunits. Gyrase tends to be the primary target in Gram-negative bacteria, while the activity of topoisomerase IV is preferentially inhibited by most quinolones in Gram-positive organisms, but this may vary with the drug. Quinolones bind to the transient topoisomerases-DNA complex preventing religation of DNA ends. The subsequent release of DNA from protein constraints fragments chromosomes, causing cell death [13] .. Regular uses of quinolones in the past more than forty years have raised resistant strains against them. Resistance arises from mutations that either alter drug uptake and efflux or affect binding interactions between the target enzyme and the quinolones, being this latter the main mechanism of bacterial resistance [14] . Mutations affecting the binding of quinolones more frequently affect a region of the protein called quinolone-resistant determining region located either in the N-terminal domains of ParC and GyrA or in the Cterminal domain of ParE. In ParC subunit of Streptococcus pneumonia topoisomerase IV, the most common mutations are S79A, S79W and D83Y, D83G, D83H, D83N, and D83A [15] .. Knowledge of the structural features that control ligandreceptor interaction at the atomic level is crucial for designing new quinolones to be active to resistant strains. The recent release of the crystallographic structures of the cleaved topoisomerase IV-DNA-quinolone ternary complexes with levofloxacin shown in Fig. 1 (pdb entry 3K9F) [16] ; moxifloxacin (see Fig. 1 ) (pdb entry 2XKK) [17] , ciprofloxacin, shown in Fig. 1 (pdb entry 2XCT) [18] and PD0305970, shown in Fig. 1 (pdb entry 3LTN) [16] have contributed to a better understanding of quinolone mechanism of action. Superimposition of the crystallographic structures of the four cleaved DNAtopoisomerase IV-quinolone complexes (shown in Fig. 3) , shows the ligands occupying the same binding pocket and oriented in the same pose, suggesting that the modification from a 3-carboxylic acid, 4-ketone to a 2,4-dione does not alter the binding mode of the ligand to the target protein.. Bound quinolones are found intercalated between the two nucleotides involved in the DNA cut, and oriented in such a way that some moieties interact with specific residues of the ParC subunit, including Arg 117 and Ser 79 . Specifically, the four molecules exhibit a hydrogen bond with Arg 117 : in the case of the dione involving the carbonyl located in R2, whereas in the case of the fluoroquinolones the carboxylic group in R3. The three fluoroquinolones also exhibit a hydrogen bond interaction with the side chain of Ser 79 . In the case of ciprofloxacin is a direct interaction, whereas in the case of levofloxacin and moxifloxacin it is mediated through a water molecule. Furthermore, analysis of the moxifloxacin and ciprofloxacin structures reveals that the Ser 79 side chain is involved in an interaction with a divalent ion (Mg 2+ in the former and Mn 2+ in the latter) mediated through a water molecule. No divalent ion is found in the structure with levofloxacin [16] . On the opposite region of the ligand structure, the bulky moiety in R7 projects into a large solvent-accessible volume. This region in topoisomerase IV contains several polar interconnected residues, including [19] .. In spite of the wealth of information provided by the crystallographic structures, there are uncertainties surrounding the exact details of the quinolone-DNA and quinolone-protein interaction in part associated with the degree of resolution of the different structures. In addition, it is very likely that different quinolones will have different specific interactions with their target, and these will also vary depending on the target (gyrase or topoisomerase IV). An intriguing question remaining regards the role played by Mg 2+ in quinolone binding. Thus, in addition to its well established role for DNA cleavage [20, 21] , it is known that the divalent ion is necessary for quinolone action [21, 22] . In fact, two of the three crystallographic structures of the cleaved topoisomerase IV-DNA-quinolone ternary complex available show a divalent ion (Mn 2+ in the complex with ciprofloxacin) bound to the C3/C4 keto acid moiety of the quinolone molecule with four water molecules saturating the rest of its octahedral coordination shell. Inspection of the structures suggests that the loss of activity of quinolones when Ser 79 is mutated can be attributed to the corresponding loss of the interaction serine side chain-water-ion [17, 22] . However, it is not clear if this hypothesis can be extended to explain the observed resistance when Asp 83 is mutated? Furthermore, why the ion bound to the quinolones is not directly bound to Ser 79 and Asp 83 side chains in contrast to the one found in the catalytic site? Why the crystallographic structure of the complex with levofloxacin does not exhibit any ion bound to the quinolone?. Thus, a deeper understanding of the structure-activity relationships exhibited by this family of compounds, as well as characterization of the structural reasons for certain mutations to make strains resistant or the mechanism of quinolone binding at the atomic level, will be useful for designing new analogs. Computational techniques represent an adequate alternative and companion to experimental methodologies in providing insightful atomic level structural information and interactions knowledge. Accordingly, we have analyzed the structural features of 15 complexes of different quinolone analogs bound to the cleaved DNAtopoisomerase IV and the results of this comparative analysis are reported in the present work. These molecules have been selected to cover the maximum structural diversity of the quinolones so far described. In this work we performed molecular modeling studies of the binding features of quinoloes using the available crystal structures of the topoisomerase IV-DNA cleaved complex with levofloxacin and moxifloxacin (pdb entries 3K9F and 2XKK, respectively) to account for the known structureactivity features of the quinolones and explain subsequently the resistance arose from amino acid alteration in the target enzymes, addressing simultaneously the role of Mg 2+ in the process of quinolone binding. Specifically, we performed docking studies of a series of fifteen quinolones using both crystallographic structures alternatively. Analysis of the docked structures reveals that the binding of the different quinolones does not differ much from the poses of levofloxacin and moxifloxacin found in the crystal structures. Moreover, bound orientation of the ligands is not altered by the presence or not of a quinolone Mg 2+ , suggesting that the magnesium ion may already be bound to the quinolone before binding to the enzyme. This hypothesis provides an explanation for the resistance exhibited by different Asp 83 mutants. Since the residue is located at the entrance of the binding pocket, it could be considered this residue to be putatively involved in the early recognition step of quinolones being able to bind to the quinolone-Mg 2+ complex.. The importance of Arg 117 and Ser 79 for quinolone binding is reinforced by the computational work of the present report. Interestingly, the former is putatively involved in the mechanism of the enzymatic reaction and its mutation would be lethal, whereas the latter has been found mutated providing quinolone resistance. Comparison with the structure of PD0305970, a 2,4-dione analog was also performed. This compound is active against mutants at positions Ser 79 and Asp 83 . In the case of the former it can be explained because its interaction with the ligand is not as important as in quinolones. In the case of the latter, it suggests that this ligand does not require this amino acid for entry to the binding pocket, possibly due to the lack of chelating capacity of the ligand.. The key to understand quinolone action lies on our knowledge of the binding process. Thus, the future of quinolone research is entwined with the detailed understanding of the formation of quinolone-protein-DNA complex. The present study also raises a question on the future of quinolones as therapeutic agents. Knowledge of the structure of the complex and a plausible mechanism of binding provides us with important information to design new quinolones with improved drug-target interactions. The ternary cleaved of DNA-topoisomerase IV structures from Streptococcus pneumoniae and Acinetobacter baumannii with levofloxacin and moxifloxacin bound (pdb entries 3K9F and 2XKK) respectively, (with their respective ligands removed) were used as templates for the modeling studies described in the present work. The templates were pre-processed using the Molecular Operating Environment (MOE) [23] software, including assignment of side change ionization states, addition of missing residues and hydrogens. Crystallographic waters were kept to perform the present study. In order to characterize the features of the binding pocket, the MOE software was used to compute interaction maps using probes of different nature. Maps produced by a hydrophobic, proton acceptor and proton donor probes were used to supervise the results of subsequent docking studies.. Docking of a group of diverse fifteen fluoroquinolones (including levofloxacin, ciprofloxacin and moxifloxacin) was carried out. The group of quinolones includes clinafloxacin, garenoxacin, gatifloxacin, gemifloxacin, ofloxacin, prulifloxacin, sitafloxacin, trovafloxacin, temafloxacin, grepafloxacin, sparfloxacin, and tosufloxacin, whose structures are shown in Fig. (2) . Fluoroquinolones were docked into the binding pocket of the templates using the Genetic Optimization for Ligand Docking software (GOLD) version 5.1 with the goldscore scoring function [24, 25] .. In addition, in order to understand the effect of resistant mutations on quinolone binding, mutants S79A and D83Y as well as the double mutation (S79A+D83Y) were generated using the topoisomerase IV-moxifloxacin complex (pdb entry 3K9F). These proteins were used to construct fifteen different complexes of each, using the ligands of Fig. (2) together with moxifloxacin, ciprofloxacin and levofloxacin. Docking studies were carried out as described above.

O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Locator/Identifier Separation: Comparison and Analysis on the Mitigation of Worm Propagation As a basic prerequisite for worm detection based on computational intelligence in networks with locator/identifier separation, it is well worth considering the influence on worm propagation due to the incoming locator/identifier separation. In this paper, according to the characteristics of locator/identifier separation, we systematically analyze the mitigation of worm propagation in three aspects: address semantics, address space and mapping delay. By applying the classical AAWP and SIR worm propagation models, we give a quantitative comparison between today's Internet and networks with locator/identifier separation. In particular, our research results show that, the characteristics of locator/identifier separation can help to markedly mitigate worm propagation, and networks with locator/identifier separation are more resistant to worm propagation than today's Internet. In today's Internet, computational intelligence seems promising to detect a wide variety of threats and attacks, and has been increasingly applied in the area of network and information security, such as Bot-nets detection using network forensics and artificial intelligence techniques 1 and multi-agent intrusion detection system 2 .. However, in networks with locator/identifier separation, since locator/identifier separation has changed the basic network architecture and communication mechanism, it will definitely exert some influences on network security, especially worm propagation. From this point, to develop worm detection based on computational intelligence more efficiently in networks with locator/identifier separation, the study on the mitigation of worm propagation due to the incoming architectural change is absolutely necessary and beneficial. It has been pointed out in many recent researches that, the current Internet routing architecture is facing serious scalability issues 3, 4 , which are basically caused by the overloading of IP address semantics. That is, an IP address represents not only the identity but also the location of an end host. Therefore, a promising solution, locator/identifier separation [5] [6] [7] [8] , has been proposed by many research communities to resolve the dual semantics of IP address. That is to say, the existing IP addresses are separated into two independent namespaces: a locator namespace and an identifier namespace. Literally speaking, the locator namespace, used in the network layer to forward packets, represents the location of an end hosts. On the other hand, the identifier namespace, used in the transport and upper layers to identify nodes, represents the identity of an end host. However, in networks with locator/identifier separation, a critical challenge is how to decouple locators from identifiers. Based on dissimilar hypotheses, a great many identifier-to-locator mapping services [9] [10] [11] [12] [13] [14] have been put forward by both academia and industry to map locators onto identifiers. Also, all these mapping services have one common basic characteristic: the identifier-to-locator mappings of all end hosts are stored and maintained in one or some organized nodes which are referred to as the resolvers in the rest of this paper for ease of expression. When a tunnel router (TR) tries to resolve an identifier-to-locator mapping for some identifier, it first needs to send a mapping request to the corresponding resolver. Once the TR receives this identifier-to-locator mapping from the resolver, it can encapsulate and forward the packet by using the received locator. In this paper, we focus on worm spread in networks with locator/identifier separation, and systematically analyze the mitigation of worm propagation according to the ordinary characteristics of locator/identifier separation. In particular, we just utilize the commonness of networks with locator/identifier separation, and how to accomplish the separation or how to design a scalable mapping service is outside the scope of this paper. When the paradigm of locator/identifier separation comes to being, it is worth arguing whether this paradigm can provide better security capability for the Internet. From this point, we pay more attention to the damage of worm spread, which cannot be evaded in either today's Internet or networks with locator/identifier separation. Our main purpose is to demonstrate obvious mitigation of worm propagation by virtue of the incoming locator/identifier separation, and to provide a basic prerequisite for worm detection based on computational intelligence in networks with locator/identifier separation. In today's Internet, worms exist as a great threat to its dependability due to their ability to infect millions of end hosts in a very short period of time 15 , and the infected end hosts can be recruited as the bots or zombies who can be manipulated to cause unprecedented damage, such as DDoS attacks 16 . At the same time, the easy access and widespread usage of the Internet make it a powerful means for propagating the malicious worms. In general, worms are self-propagated programs throughout the Internet by exploiting the vulnerabilities or policy flaws of computers. When a worm aims at infecting other end hosts, it can find the vulnerabilities of these end hosts by mean of scanning self-generated IP address pools, and then directly comprises these end hosts by implanting the copies of worm into the vulnerable target computers. In addition, the newly infected end hosts will automatically and continuously attempt to infect other end hosts in the same way until the worm spreads to the entire IP address space. Given the fast spread and the substantial damage caused by worms, it is significant to develop monitoring and detection mechanisms against worms. In today's Internet, computational intelligence has figured prominently in many solutions to the worm detection problem, and this prominence and popularity will continue to shine in networks with locator/identifier separation. Therefore, the study on the mitigation of worm propagation can help to develop worm detection based on computational intelligence. In this paper，we concentrate our study on the scanbased worm propagation in today's Internet and networks with locator/identifier separation. Indeed, the pronounced change of worm propagation caused by locator/identifier separation should deserve wide attention and discussion. In order to address this issue, this paper summarizes the characteristics of locator/identifier separation, and appropriately selects the classical Analytical Active Worm Propagation (AAWP) and Susceptible-Infected-Removed (SIR) models to illustrate the mitigation of worm propagation. By means of the quantitative comparison between today's Internet and networks with locator/identifier separation, we can draw the conclusion: locator/identifier separation can be markedly conducive to alleviating worm propagation. To be precise, we systematically analyze the mitigation of worm propagation in the following three aspects:. Firstly, the change of address semantics can impact on worm propagation. In today's Internet, some types of worms can purposefully spread according to BGP routing information or Class A address space. However, locator/identifier separation resolves the dual semantics of IP address, and conceals the location information of end hosts. Besides, the feasible flat identifiers also make worms more difficult to spread.. Secondly, since IP address space has been divided into the identifier space and the locator space, worms are quite possible to scan the locator space which has no significance for worm propagation.. Thirdly, in networks with locator/identifier separation, when worms attempt to scan some identifier, the TR must send a mapping request to resolve the corresponding locator for the identifier. Therefore, the mapping delay may play an important role to mitigate worm propagation.. Not surprisingly, we also give some discussions on worm detection based on computational intelligence in networks with locator/identifier separation, and analyze some benefits provided by locator/identifier separation. This paper aims to argue the influence on worm propagation due to the incoming locator/identifier separation. The most significant benefit of our study is that it systematically analyzes the mitigation of worm propagation in networks with locator/identifier separation, and provides a basic prerequisite for worm detection based on computational intelligence in networks with locator/identifier separation. In this paper, we first introduce networks with locator/identifier separation by a general network model. Then, compared with today's Internet, we mathematically model the mitigation of worm propagation in networks with locator/identifier separation, with focus on the following three aspects: address semantics, address space and mapping delay. Last but not least, by the numerical analysis, we give a quantitative comparison between today's Internet and networks with locator/identifier separation. We find that, the characteristics of locator/identifier separation could contribute a lot to mitigating worm propagation in networks with locator/identifier separation. In this section, we present some related work from both academia and industry. First of all, we briefly introduce various locator/identifier separation approaches and some corresponding mapping services. And then, we sum up the related researches on worm propagation, including the existing worms and worm propagation models.

B I O O O O O O O B I O O O O O O O O O B I O O O B I O O O O O O O O B I O O O O B I O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O B O B B I O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O B I O O O B I O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O B I O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O B I O O O O O O O O O B I O O O O O O B I O O O O O B I O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O B I B O O B O O O O O O B I O O O O O O O O O O O O B I O O O O O B I O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O B I O O O O O O B I O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O B I O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O B I O


A novel function prediction approach using protein overlap networks Background: Construction of a reliable network remains the bottleneck for network-based protein function prediction. We built an artificial network model called protein overlap network (PON) for the entire genome of yeast, fly, worm, and human, respectively. Each node of the network represents a protein, and two proteins are connected if they share a domain according to InterPro database.The function of a protein can be predicted by counting the occurrence frequency of GO (gene ontology) terms associated with domains of direct neighbors. The average success rate and coverage were 34.3% and 43.9%, respectively, for the test genomes, and were increased to 37.9% and 51.3% when a composite PON of the four species was used for the prediction. As a comparison, the success rate was 7.0% in the random control procedure. We also made predictions with GO term annotations of the second layer nodes using the composite network and obtained an impressive success rate (>30%) and coverage (>30%), even for small genomes. Further improvement was achieved by statistical analysis of manually annotated GO terms for each neighboring protein.Conclusions: The PONs are composed of dense modules accompanied by a few long distance connections. Based on the PONs, we developed multiple approaches effective for protein function prediction. The protein overlap networks have different properties compared with the domain co-occurrence networks. The PON of an entire genome contains many disconnected sub-graphs with one main sub-graph, which comprises 50% of proteins except for the relatively small PON of yeast. The logarithmic relationship between the number of nodes and the corresponding degree value does not follow a typical power law distribution of scalefree network. The clustering coefficient of the networks is exceptionally high (>0.9). Using a PON, protein functions can be predicted with the domain-based method or gene ontology annotation database. The GOA database method usually shows a better performance than the domain-based method. The composite PON of multiple genomes can be used to enhance the performance for the domain-based method, especially in prediction for small genomes. But the GOA database method achieves the best accuracy for a protein when the singlegenome PON of the same species is used. The success rate of the domain-based method is close to that of the GOA method in prediction for proteins without genome-wide sequence information and domain annotations. Moreover, the domain-based method can be optimized to predict the functions for an uncharacterized domain from the annotated GO terms of other domains. We built PONs for protein function prediction. Our function prediction algorithm is based on two observations. Firstly, if two domains (or domain combinations), B and C, combine with the domain A to form two proteins: AB and AC, it is possible that the domain B and C share the same GO terms. Actually, for all connected pairs, e.g., AB and AC, in the human PON, a GO term associated with domain B is also associated with domain C in 24.4% of cases compared with 4.7% in the random control procedure. In fact, this indirect functional association was also observed at protein level in early studies. Chua et al. found proteins interacting with the same partners had a greater likelihood of sharing similar physical or biochemical characteristics [14] . Secondly, a GO term that is associated with A is also the annotated GO term of B in 35.4% cases, i.e., domains within a protein may share the same GO term annotations, which is the basis for domain function prediction with a DCN [32] . It should be noted that the GO terms shared by B and C are not due only to the fact that B shares GO terms with A and A shares GO terms with C (35.4% × 35.4% <24.4%). Wang et al. made protein function predictions by integrating the GO terms predicted for each of its domains using a single-genome DCN [32] . If all the conditions were equally set, the domain-based PON methods and the DCN methods should produce exactly the same results for protein function prediction. Nevertheless, PONs contain richer information than DCNs, which makes it possible to yield better prediction results. For example, the current Pfam 26.0 has a sequence coverage of only 69.7% and an amino acid coverage of 44.4% for the human genome [38] . For the DCN method, the prediction completely relies on the Pfam annotation. However, the complete GO term annotations of a protein could be obtained from GOA database [41] and used for function prediction with a PON even if only a part of the protein is annotated with Pfam domains.. GO term comparison is a yes-or-no binary criterion in this study even though two different GO terms may share functional similarity. Nevertheless, the mean value of success rates calculated with the binary criterion strongly correlates with that of the similarity scores in large-scale calculations like those presented here. For example, the mean success rates predicted and evaluated with the GOA database were 48.3, 41.1, and 35.5% respectively, for yeast proteins using the single-genome PON, the composite PON of four species, and the composite PON excluding yeast. When the semantic score between the predicted GO term set and the annotated set of each protein was calculated with the online method G-sesame [45] , the mean values correspondingly increased to 0.709, 0.678, and 0.638, respectively.. The currently proposed methods can be used for the prediction of new functions of a protein in addition to the annotated GO terms, or a protein with unknown function. In fact, there are 3.2% yeast proteins that are not annotated but predictable with the domain based method using the single-genome PON ( Table 2 ). The number increased to 7.5% by using the composite network of yeast, fly, worm, and human. In future, more genomes could be used to build the composite network to improve the prediction accuracy and coverage for the domain-based prediction method. Results from multiple approaches such as the domain-based method, prediction with the GOA database, and analysis of the second layer neighbors could also be combined to produce a comprehensive prediction. Proteins are basic functional components in any biological process. Discovery of the functions of an individual protein is therefore a critical step towards understanding biological processes and the complete biological system. Besides experimental studies, computational prediction plays an important role in current protein function investigation [1] . The general biochemical function of a protein can be inferred if the amino acid sequence or 3-D structure of the protein resembles another protein whose function is known [2] [3] [4] [5] [6] [7] . The rationale for the homology-based method is that two proteins with a similar sequence or structure could evolve from a common ancestor and thus have similar functions. Although this homology-based method remains the most widely utilized computational tool for function assignment, some proteins identified from genome sequencing do not have any homologs that were functionally characterized in the previous studies. On the other hand, homologous proteins might also acquire different functions in evolution [8] . Thus computational methods other than the homology-based method are much in demand to improve the accuracy and coverage for protein function prediction.. Graph theoretical analysis can be used for predicting the function of uncharacterized proteins [9] [10] [11] . Briefly, genes or the products of genes, i.e. proteins, are represented as nodes in the network and two nodes are connected if they are in some type of association, e.g., protein-protein interaction (PPI). Possible functions of a protein can be assigned based on the frequently observed known functions of its immediate interacting partners [12] . Advanced approaches, such as defining the neighborhood of a protein with a radius of n [13] , considering the shared neighborhood of a pair of proteins [14] , transferring common annotations in a module to the uncharacterized members [15] [16] [17] [18] [19] , were also developed over the years.. Unlike function inference methods, construction of a reliable network remains the bottleneck for network-based function prediction. High-throughput experimental methods such as two-hybrid techniques [20] , affinity precipitation [21] , and synthetic lethal screening [22, 23] have been developed to construct PPI networks. Gene expression networks were built from gene pairs showing significant correlation of expression in microarray experiments [24, 25] . In spite of wide applications, the high-throughput experimental methods often sacrifice specificity for scale. The constructed networks have a high level of false positives [26] and the predicted protein functions are even noisier.. The so-called genomic context (GC) methods, namely, gene fusion [27] , gene order conservation [28] , phylogenetic profiles [29] , and operon rearrangement [30, 31] exploit the genome sequences themselves to predict protein-protein associations. The derived information can be used for the construction of a PPI network. As a result, the network based approach showed better performance than the raw scores of GC-based methods in protein function prediction [10] .. To a less extent, domain co-occurrence networks (DCN) were investigated for the prediction of protein and domain functions [32] . A protein domain is a continuous sequence within the protein that represents a structural, functional, and evolutional unit. A simple protein may contain a single domain, whereas 70% of eukaryotic proteins are composed of multiple domains [33] . In a DCN, two domains are connected by an edge if there is one protein containing both of them. Previous studies showed that DCNs are scale-free and small-world networks [34] . The domain function could be predicted by counting the occurrence frequency of GO terms associated with the neighboring nodes in a single-genome DCN while the function of a multipledomain protein could be derived by integrating the predicted function of each domain. The prediction results were not improved by sophisticated algorithms such as χ 2 method or support vector machine (SVM) learning [32] .. As a counterpart to DCN, the protein overlap network (PON) provides another way for function prediction. In a PON, each node represents a protein and two nodes are connected with an edge if the proteins share a common domain. While a few efforts have been made to study the properties of DCNs [32, [34] [35] [36] , to our knowledge, there has been no detailed research report but a few brief comments about PONs [35] . In this study, we constructed the PONs with the entire genomes of individual organisms, studied the network properties, and made predictions of protein function with the composite PON of multiple genomes.

O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Investigation of routing reliability of vehicular ad hoc networks In intelligent transportation systems, the cooperation between vehicles and the road side units is essential to bring these systems to fruition. Vehicular ad hoc networks (VANETs) are a promising technology to enable the communications among vehicles on one hand and between vehicles and road side units on the other hand. However, it is a challenging task to develop a reliable routing algorithm for VANETs due to the high mobility and the frequent changes of the network topology. Communication links are highly vulnerable to disconnection in VANETs; hence, the routing reliability of these ever-changing networks needs to be paid special attention. In this paper, we propose a new vehicular reliability model to facilitate the reliable routing in VANETs. The link reliability is defined as the probability that a direct communication link between two vehicles will stay continuously available over a specified time period. Furthermore, the link reliability value is accurately calculated using the location, direction and velocity information of vehicles along the road. We extend the well-known ad hoc on-demand distance vector (AODV) routing protocol to propose our reliable routing protocol AODV-R. Simulation results demonstrate that AODV-R outperforms significantly the AODV routing protocol in terms of better delivery ratio and less link failures while maintaining a reasonable routing control overhead. Recently, it has been widely accepted by the academic society and industry that the cooperation between vehicles and road transportation systems can significantly improve driver's safety and road efficiency and reduce environmental impact. In light of this, the development of vehicular ad hoc networks (VANETs) has received more attention and research efforts. Much work has been conducted to provide a common platform to facilitate inter-vehicle communications (IVCs) [1] . IVC is necessary to realize traffic condition monitoring, dynamic route scheduling, emergency-message dissemination and, most importantly, safe driving [2] . It is supposed that each vehicle has a wireless communication equipment to provide ad hoc network connectivity. VANETs are considered as a special class of mobile ad hoc networks (MANETs), yet they have several key features distinguishing them. Network nodes in VANETs are highly mobile, thus the network topology is ever-changing. Accordingly, the communication link condition between two vehicles suffers from fast variation, and it is prone to disconnection due to the vehicular movements and the unpredictable behaviour of drivers. Fortunately, their mobility can be predictable along the road because it is subject to the traffic networks and its regulations. VANETs have normally higher computational capability and higher transmission power than MANETs.. Those unique characteristics of VANETs raise important routing challenging issues that should be resolved before deploying these networks effectively [3] . The most challenging issue is potentially the high mobility and the frequent changes of the network topology [4, 5] . The topology of vehicular networks could vary when the vehicles change their velocities and/or lanes. These changes depend on the drivers, road situations and traffic status, and are not scheduled in advance. The proposed routing protocols and mechanisms that may be employed in VANETs should adapt to the rapidly changing topology. Besides that, they must be efficient and provide qualityof-service support to permit different transmission priorities according to the data traffic type. The existing routing protocols as they are designed for MANETs are not suitable for VANETs.. In this paper, we propose a new reliability based routing scheme to establish a more reliable route between the source and the destination nodes. The novelty of this work lies in its unique design of a reliable routing protocol that considers the mathematical distribution of vehicular movements and the link breakages. This work is based on the scenario, where vehicles move at a variant velocity along two directions on the highway. Extensive simulation experiments are performed to evaluate the performance of our proposed scheme in comparison to ad hoc on-demand distance vector (AODV) routing protocol. Packet delivery ratio, average number of link failures, average end-to-end delay and routing control overhead are the performance metrics considered in our evaluation process.. The rest of this paper is organized as follows: Related works summarizes the related works in this field. Background of vehicular communications briefs a background of vehicular communication technology. Vehicular reliability model presents the proposed vehicular reliability model and explains how our mathematical model is developed to get calculate the link reliability value. The reliability based routing protocol AODV-R explains our reliability based routing protocol AODV-R. Simulation setup and results presents the simulation scenario setup and results. Finally, Conclusion concludes the paper. In this paper, we developed a link reliability model based on the vehicular velocity distribution on highways. A hybrid approach combining both macroscopic and microscopic traffic flow models is used in our highway mobility model. We applied the link reliability model to the routing process in VANETs to have a reliability-based routing scheme. We showed the advantages of using the link reliability model to improve the performance of the current routing protocol in VANETs. The vehicular reliability model is incorporated into AODV routing protocol to create our AODV-R routing protocol. The evaluation results reveal that AODV-R has a better delivery ratio compared to the conventional AODV since it chooses the most reliable route among all possible routes to the destination. Although the proposed AODV-R scheme has a slightly higher computational cost, it results much lower link failures while providing slightly higher average end-to-end delays than AODV.. The developed link reliability model considered the vehicular movements as the main cause for link breakages. Wireless channel congestion and/or noise errors [32] could be other possible causes for link breakages as well. The impact of wireless channel congestion/noise errors on the link reliability model and considering more routing constraints such as delay in our developed routing protocol will be our future extensions. The literature on routing reliability mainly addresses MANETs (e.g. [6, 7] ). For VANETs, Taleb et al. [8] propose a scheme that uses the information on vehicle headings to predict a possible link breakage prior to its occurrence. Vehicles are grouped according to their velocity vectors. When a vehicle shifts to a different group and a route involving the vehicle is to break, the proposed scheme searches for a more stable route that includes other vehicles from the same group.. In [9] , the authors propose a velocity-aided routing protocol which determines its packet forwarding scheme based on the relative velocity between the forwarding node and the destination node. The region for packet forwarding is determined by predicting the future trajectory of the destination node based on its location information and velocity.. The authors of [10] present a prediction-based routing (PBR) protocol for VANETs. It is specifically designed for the mobile gateway scenario and takes advantage of the predictable mobility pattern of vehicles on highways. PBR predicts route lifetimes and preemptively creates new routes before the existing ones fail. The link lifetime is predicted based on the range of communication, vehicles' location, and corresponding velocities of vehicles.. Since a route is comprised of one or more links, the route lifetime is the minimum of all its link lifetimes. PBR allows the processing of multiple routing requests to check all the available routes to the destination. If the source node receives multiple replies, then it uses the route that has the maximum predicted route lifetime.. The movement prediction based routing (MOPR) algorithm is proposed in [11] . This algorithm predicts the future position of the vehicle and searches for a stable route to avoid link breakage. If several potential routes between the source vehicle and the destination vehicle exist, MOPR chooses the route that is the most stable when considering the movement conditions of the intermediate nodes with respect to source and destination nodes. This is done using the location, direction and velocity information of each vehicle. An extension to the routing table in each node is needed to fulfill the requirements of this algorithm.

O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Remote temperature monitoring device using a multiple patients-coordinator set design approach Newcastle University ePrints -eprint.ncl.ac.uk Chin CS, Atmodihardjo W, Woo WL, Mesbahi E. Remote temperature monitoring device using a multiple patients-coordinator set design approach.Abstract This paper aims to develop a prototype for a web-based wireless remote temperature monitoring device for patients. This device uses a patient and coordinator set design approach involving the measurement, transmission, receipt and recording of patients' temperatures via the MiWi wireless network. The results of experimental tests on the proposed system indicated a wider distance coverage and reasonable temperature resolution and standard deviation. The system could display the temperature and patient information remotely via a graphical-user interface as shown in the tests on three healthy participants. By continuously monitoring participants' temperatures, this device will likely improve the quality of the health care of the patients in normal ward as less human workload is involved. This paper presents a web-based MiWi wireless remote temperature-monitoring device using temperature sensors for the axillary measurement. The temperature data were sent via the MiWi wireless protocol to the nurse's station connected to internet. This approach was facilitated by a patient and coordinator set design that connects the process of monitoring, transmitting, alerting and recording of the temperature. The experimental tests on the proposed device indicated that it provides long-distance coverage, a temperature resolution of 0.3 to 0.5°C and small standard deviation of approximately 0.3°C. Importantly, the system will likely to improve the quality of care through 24-hour continuous monitoring of the temperature of patients and reducing the workload of the nursing staff in normal ward.. The remote temperature monitoring device will be further developed into a low-cost product with plug-andplay feature that enable untrained nurse or doctors to use with minimal assistance. The software interface for users will be improved and made easy to be installed and downloaded. For future work involving remote monitoring, the use of different wireless network protocols, such as ZigBee and WiFi, will be compared. A standard medical temperature probe, such as a tympanic probe, and standard disposable tape will be used for final implementation in the hospital. Further evaluation of the proposed system against current measurement techniques needs to be performed. The privacy and security protection and export to clinical information system (CIS) will be studied. During the severe acute respiratory syndrome (SARS) outbreak in 2003, hospitals became treatment centres in most countries. Because a patient's core body temperature is one vital parameter for monitoring the progress of the patient's health, it is often measured manually at a frequency ranging from once every few hours to once a day [1] . However, such manual measurement of the temperature of patients requires the efforts of many staff members. In addition, when the patients suffer from conditions that result in abrupt changes of the core body temperature, e.g., due to infection at a surgical site after surgery, the staff on duty will not know such a temperature change occurred until the next temperature measurement. Such a delay may lead to patients being unnoticed while their health conditions worsen, which is dangerous because a difference of 1.5 degrees Celsius can result in adverse outcomes [2] . Furthermore, there is always a need to have a monitoring system to improve the quality of health care [3] , such as temperature monitoring of elderly and challenged persons using a wireless remote temperature monitoring system. Body temperature can be used to monitor the pain level of a patient following an operation [4] or after shoulder endoprosthesis [5] . In some cases, the tissue transient temperature was monitored during microwave liver ablation [6] for the treatment of liver metastases. Instead of using a temperature sensor, pulse-echo ultrasound [7] was used to visualize changes in the temperature of the patient's body. In addition, a non-contact temperature-measuring device, such as a thermal imaging camera [8] , was successfully used to detect human body temperature during the SARS outbreak. However, it can be quite expensive to equip each patient room with a thermal imaging camera. In addition, there are a few wireless temperature-monitoring systems (e.g., CADIT™, Primex™, and TempTrak™) on the market that are used to monitor and store a patient's temperature for medical research by using body sensor networks [9] . Most of these systems consist of an electronic module and a temperature-sensing device. The systems include a stand-alone electronic module with a display screen that allows the temperature sensor data to be transmitted over a secure wireless network.. However, these systems can be difficult to reconfigure to suit the current database system used in the hospital. In addition, the current systems using short message service (SMS)-based telemedicine [10] systems with hardware equipment were developed to monitor the mobility of patients. However, proper hardware and software to manage the messages and the patient's temperature for display on mobile phones are not widely available.. Hence, a medical device to continuously measure the body temperature of patients using a wireless temperature sensor [4, 11, 12] is required. With such a wireless temperature sensor system, nurses will no longer have to manually measure the temperature of patients, which will free their time for other tasks and also reduce the risk associated with coming into contact with patients with contagious diseases, such as SARS. The readings will be transmitted wirelessly to the central nurse station, where they can be monitored by the staff-on-duty. In addition, the current and past history of the body temperature measurements can be stored in an online database, which allows the medical staff to access the database when they are not in the hospital.. To the best of our knowledge, a MiWi wireless (besides using the Zigbee [11] ) temperature-monitoring system using a patient and coordinator set design that provides remote internet access to the temperature database has not been reported in any publication. The objective is therefore to develop and implement a prototype temperature-monitoring system for patients using a MiWi wireless remote connection to the nurse's station for frequent real-time monitoring. The temperature monitoring system was designed based on a proposed patient and coordinator set design approach. The proposed temperature-monitoring system for use in normal ward will likely to improve the quality of the health care of the patients as the nursing workload is reduced. In this paper, the discussion on medical regulations and policy will not be included. Design concepts of the wireless remote temperaturemonitoring system. The core body temperature of a human is one of the vital statistics used to determine individual health. Normal human body temperature (normothermia) is approximately 37°C, with variations depending on factors such as age and geological factor [13] . When the body fails to regulate itself by dissipating heat, it increases the core body temperature above the normal condition. This condition is called hyperthermia. The highest recorded surviving core body temperature is 45°C [14] . Generally, there are four locations [15] that can be used to measure human core body temperature, rectal (anus), oral (mouth), tympanic (ear) and axillary (armpit), with the rectal measurement considered to be the most accurate in measuring the core body temperature. Rectal measurement of body temperature is the preferred method to perform a core body temperature measurement. In order to track a patient's core body temperature wirelessly (assuming the patient is not moving), the axillary measurement was chosen for the initial trial. The proposed measurement system must be able to measure within the temperature deviation [16, 17] of 0.3-0.5°C and detect changes in the human body temperature (at minimum resolution of 0.1°C).

B B B O O O O O O O O O O O O O O O O O O O O B B B O O O O O O O O O O O O O O O O O B B B B B O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B B O O O B O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O B B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B B O O O O O O O B B B B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O B B O O O O O O O O O B B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B I O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O B O B B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O