The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding Background: Chromatin immunoprecipitation combined with high-throughput sequencing (ChIP-Seq) is the most frequently used method to identify the binding sites of transcription factors. Active binding sites can be seen as peaks in enrichment profiles when the sequencing reads are mapped to a reference genome. However, the profiles are normally noisy, making it challenging to identify all significantly enriched regions in a reliable way and with an acceptable false discovery rate.Results: We present the Triform algorithm, an improved approach to automatic peak finding in ChIP-Seq enrichment profiles for transcription factors. The method uses model-free statistics to identify peak-like distributions of sequencing reads, taking advantage of improved peak definition in combination with known characteristics of ChIP-Seq data. Conclusions: Triform outperforms several existing methods in the identification of representative peak profiles in curated benchmark data sets. We also show that Triform in many cases is able to identify peaks that are more consistent with biological function, compared with other methods. Finally, we show that Triform can be used to generate novel information on transcription factor binding in repeat regions, which represents a particular challenge in many ChIP-Seq experiments. The Triform algorithm has been implemented in R, and is available via http://tare.medisin.ntnu.no/triform. It is challenging to evaluate ChIP-Seq peak-finding methods because we normally do not know the true solution to a given experiment. The intensity of a given peak reflects the frequency of interaction between the TF and the genomic region, and thus the interaction strength. It is therefore tempting to focus on the strong peaks, assuming that these represent the most important regulatory interactions. However, this may be misleading for several reasons. First, the apparent binding strength may be affected in many different ways, including posttranslational modifications and protein-protein interactions involving the relevant TF. The formation of cis-regulatory modules, including indirect binding, may also affect the efficiency of immunoprecipitation and pull-down of the relevant genomic fragments. The mapping of reads to the reference genome may also be biased, for example by differences between the sequenced genome and the reference genome used for read mapping. This means that we also have to include minor peaks in the analysis, which makes the peak finding more challenging. In addition, many protein-protein interactions lead to more complicated peak shapes, making the problem even more challenging.. Triform attempts to improve peak finding by identifying and using essential features of typical ChIP-Seq peaks, including peak shape and sequencing-induced peak shifts. This has been implemented in a rigorous model-free statistical framework, making the classification both robust and sensitive. In particular, Triform achieves greater sensitivity, specificity, and control of the FDR than other methods by utilizing the Hoel test to detect significant Poisson inhomogeneities, as could be seen in its comparison with, for example, FindPeaks.. As Triform gains performance by addressing specific properties of ChIP-Seq peaks, it could be argued that Triform may lead to model overfitting by favoring features that are important mainly in the benchmark set. However, the selected features represent completely general ChIP-Seq peak features, and the number of parameters in the Triform implementation has been reduced to a minimum. In combination with the statistical framework, we believe that this makes the algorithm more resistant to overfitting.. The excellent performance of Triform has been confirmed by the tests described here, including benchmarking, statistical overrepresentation analysis, and motif discovery for novel motifs. In all cases, the evaluation was limited by the fact that no perfect solution is available as a reference. However, in our opinion, all these tests indicate that Triform is at least as good as any of the methods it has been compared with, and in many cases is better.. However, it is important to be aware that although the Triform approach uses a quite general framework, the implementation is adapted to peak finding in ChIP-Seq experiments for TFs. It is likely that application to other types of ChIP-Seq experiments, for example for chromatin structure, will require a modified approach. Chromatin immunoprecipitation combined with highthroughput sequencing (ChIP-Seq) is currently the method of choice for genome-wide mapping of binding sites for transcription factors (TFs) on DNA [1] [2] [3] . This is achieved by using DNA fragmentation after DNAbinding proteins have been cross-linked to the genome. The fragmentation is followed by selection of fragments bound by the TF of interest, using an antibody targeting this factor. The selected genomic fragments are then released, sequenced, and mapped to a reference genome. There, the genomic locations bound by the TF will be enriched with matching sequencing reads.. An essential step in the analysis of ChIP-Seq data is the genome-wide identification of enriched regions. Several software tools have been developed to perform this task [4, 5] , but benchmarking has demonstrated that there is room for improvement in the existing approaches [6] . There are alternative definitions of what constitutes a peak, and useful characteristics of ChIP-Seq profiles may not be fully utilized. A typical example is the shift property [7] , which occurs because the full sequence fragments, typically with an average length around 200 bp, are sequenced for only 25-50 bp from each side. Other examples are the use of independent control samples [8] , separation of overlapping enrichment profiles [9, 10] , or optimal use of statistical approaches to separate signal from noise [11] . Our conclusion in a previous study [6] was that different programs focus on different characteristics of the ChIP-Seq data, but that no program takes all the characteristics into account. Though several programs achieve a high coverage of true enrichment profiles, the trade-off has often been an intolerably high false discovery rate (FDR).. A major limitation in the development of improved approaches has been the lack of proper benchmarks to compare the performance of different methods [12] . Because of this, different strategies for performance evaluation have been used, the most common being motif occurrences in the proximity of the ChIP-Seq peaks [4, 5, 8, 13] , and overlap with experimentally confirmed qPCR sites [4, 5] . However, both evaluation methods have important limitations [6, 12] , and when looking at a limited number of binding regions, the preferred evaluation method remains visual assessment of local enrichment profiles in a genome browser. To compensate for the lack of benchmarks in ChIP-Seq analysis, we have previously manually curated a benchmark data set based on visual inspection of ChIP-Seq profiles from three different TFs: NRSF (also known as REST), SRF, and MAX [6] . Individual regions were visually assessed relative to relevant criteria, including peak-like shape, peak shift, lack of signal in control sample, and motif occurrence in peak region, and the regions were classified either as real peaks or as noise or artifacts. The idea was that such a benchmark can be used to evaluate and improve both new and existing efforts for the automatic analysis of ChIP-Seq data for TFs.. In this study, we present an improved approach for automatic identification of peaks in ChIP-Seq enrichment profiles, called the Triform method. Triform uses robust genome-wide statistical tests to detect three different forms of peak-like enrichment profiles, taking advantage of the profile characteristics mentioned above. Overfitting is precluded by the fact that Triform is based on model-free statistical tests and uses a minimal number of preset parameters based on the general properties of the ChIP-Seq data sets. The Triform algorithm is model free in the sense that it relies on the Hoel test [14] which is fully nonparametric, i.e. free from any assumed relationships or fitted parameters. In particular, the Hoel test is free from any assumed background model and is therefore more robust than model-based tests, which depend on locally uniform background models and fitted background parameters. When evaluated on the benchmark data set of peak profiles, Triform outperformed both newly developed and previously evaluated programs for the automatic detection of enrichment profiles, for all three TFs studied. The good performance of Triform was further confirmed using tests on motif enrichment in significant peak regions.. Since TFs often co-regulate genes that are involved in specific biological processes, we would expect such processes to be overrepresented in the annotation of genes associated with regions for TF binding from the ChIP-Seq experiment. We therefore used statistical overrepresentation analysis on peak sets from the main peak-finding programs that are compared here, and showed that peak sets from Triform in most cases give the most significant overrepresentation of relevant annotation terms.. To illustrate the potential of improved peak finding to generate novel information, we further analyzed the DNA sequence motifs identified de novo within the Triform SRF peak regions. In addition to the canonical SRF motif, we discovered a significant co-occurrence of SRF, ELK1, and NFY motifs within LTR/ERV1/MER57 repeats, and these particular repeats were significantly co-located with genes associated with cancer. This exemplifies how optimal identification of peak regions may generate novel information.

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Majority-vote model with heterogeneous agents on square lattice We study a nonequilibrium model with up-down symmetry and a noise parameter q known as majority-vote model of M.J. Oliveira 1992 with heterogeneous agents on square lattice. By Monte Carlo simulations and finite-size scaling relations the critical exponents β/ν, γ/ν, and 1/ν and points qc and U * are obtained. After extensive simulations, we obtain β/ν = 0.35(1), γ/ν = 1.23(8), and 1/ν = 1.05(5). The calculated values of the critical noise parameter and Binder cumulant are qc = 0.1589(4) and U * = 0.604(7). Within the error bars, the exponents obey the relation 2β/ν + γ/ν = 2 and the results presented here demonstrate that the majority-vote model heterogeneous agents belongs to a different universality class than the nonequilibrium majority-vote models with homogeneous agents on square lattice. A community of people where each person has a characteristic (for example, an opinion on a particular subject and this opinion can be expressed in a binary form, in favor (+1) or against (−1) a particular issue in question, and this opinion can be influenced by the vicinity of this individual) can be modeled using some simple models as the equilibrium Ising model 1,2 that has become an excellent tool to study models of social application 3 . Many works these nature are well described in a thorough review 4 , a more recent summary by Stauffer 5 and the following papers in these special issues on sociophysics in this journal. The majority-vote model (MVM) of Oliveira 6 is a nonequilibrium model of social interaction: individuals of a certain population make their decisions based on the opinion of the majority of their neighbors. This model has been studied for several years by various researchers in order to model social and economic systems 7,8,9,10,11 in regular structures 12,13,14,15 and various other complex networks 16,17,18,19,20,21,22,23 . There are also applications to real elections in which similar models of opinion dynamics have been explored in the literature, such as Araújo et al. 24 .. In the present work, we study the critical properties of MVM with random noise on a square lattice SL. Here, we start with each individual or agent having their characteristic noise q i randomly selected within a range from 0 to q. Thus each agent does not have an opinion in the presence of a constant noise q as in the traditional MVM 6 , but instead each agent has intrinsic resistance, q i , to the opinion of their neighborhood on SL. The effective dimension using the exponents ratio β/ν and γ/ν is also determined for MVM with random noise. Finally, the critical exponents calculated for this model are compared with the results obtained by Oliveira 6 . Finally, we remark that our MC results obtained on SL for MVM with random noise show that critical exponent ratios β/ν = 0.35(1) and γ/ν = 1.01 (9) (7) are similar to the MVM for regular lattice 6 . We also showed that the effective dimension D ef f (within error bars) is close to 2. The agreement in D ef f and 1/ν but not in the two exponent ratios β/ν and γ/ν remains to be explained. γ/ν=1.01(9), 1/ν=1.05(5) Fig. 8 . Data collapse of the susceptibility for the lattice size L = 300,400, 500, and 1000 for SL. The exponents used here were γ/ν = 1.01(9) and 1/ν = 1.05(5).

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siRNA Produced by Recombinant Dicer Mediates Efficient Gene Silencing in Islet Cells RNA interference (RNAi) is emerging as a powerful and convenient tool for studying gene function and genetic variation. RNAi is mediated by 21- to 23-nucleotide-long, small interfering RNAs (siRNA) produced from larger double-stranded RNAs in vivo by the RNase III family enzyme Dicer. To overcome the problems associated with the use of predesigned synthetic siRNA molecules, a novel method utilizing the in vitro activity of recombinant Dicer has been developed recently. In nonislet cells, it has been demonstrated that a pool of siRNA, generated by Dicer from in vitro transcribed dsRNA (d-siRNA), mediates convenient, efficient, and reproducible gene silencing in various cell types. The aim of this study was to evaluate the ability of d-siRNA to silence endogenous gene expression in pancreatic islet cells. We observed that liposomal transfection mediates efficient transport of siRNA in up to 90% of dispersed islet cells and that d-siRNA mediates almost complete and nontoxic silencing of an endogenous mRNA, the messenger coding for the nonreceptor tyrosine kinase c-Abl. The approach described here using d-siRNA provides an important tool for elucidating gene function in further studies of pancreatic islets and diabetes pathophysiology. The d-siRNA technique can successfully be used for the silencing of genes in primary -cells. Gleevec treatment can prevent -cell death in response to various stress-inducing agents. Inhibition of JNK can in part explain the protective action of Gleevec. Another protective effect could be mediated via ischemic preconditioning. Gleevec can prevent diabetes development in the NOD mouse model of type 1 diabetes. The adaptor protein Shb and c-Abl can form a complex, that is implicated in -cell stress responses. The methodology of this thesis is hereby in this section explained in general terms. For more detailed descriptions, see individual papers.

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Combining Feature Methods for Content-Based Classification of Mammogram Images Abstract: Breast cancer is among the leading cause of death among females. Studies show that early detection allows for a better prognosis. Mammography is one of the successful ways for early detection of breast cancer. It mostly involves manual reading of mammograms, a process that is difficult and error-prone. This paper discusses a classification model for mammograms based on microcalcification characteristics, as a way of helping radiologists make quick and accurate diagnostic decisions by availing to them similar past cases. The images are pre-processed by Gaussian smoothing and median filtering with 5 × 5 and 3 × 3 kernels respectively. Gabor and Haralick features are then extracted to form the image signatures over which similarity measurements are made. Experimental results show an average precision value between 0.5 and 0.61 using Haralick features, 0.49 and 0.57 using Gabor features, and 0.51 and 0.78 using combination of Gabor and Haralick features. Breast cancer is a high-mortality disease, and one of the leading causes of death among women [1] . If detected early, this disease is manageable, and can be cured in some instances. Mammography is one of the methods used to detect early signs of cancer, and has proven to be very effective [23] . It involves generating images of the breast through X-ray photography, enabling the visualization of the internal breast structure for analysis that can expose any abnormality. Traditionally, mammogram analysis has been manually done by radiologists through visual inspection. The amount of medical images being generated is increasing exponentially. According to Geneva radiology, at Geneva University and Hospitals, images in excess of 30000 are being produced daily [1] . A large image database increases the referential space, providing a solid foundation for solving new cases easily. However, the large size increases the time needed for processing of the images. Speed of decision making is important, especially in medical diagnosis.. Computer Aided Diagnosis (CADx) employs techniques in image processing and artificial intelligence to assist pathologists arrive at objective conclusions about a given image [3] . It is commonly used for identification of suspicious regions in a mammogram, as well as for determination of malignancy. A major part of malignancy determination involves extracting and computing features that are used to characterise the image. Accurate characterization of these Mammography allows the detection of breast cancer in its early stages, which makes possible early remedial measures that can reduce the high mortality rates associated with the disease. This paper discussed a content-based classification model for mammogram images, with the objective of availing a second opinion to a radiologist for reference during diagnosis. It implemented the Gabor filter and Haralick features for textural analysis and description for similarity assessment. This work evaluated Haralick features at five distances, k ∈ [1, 3, 5, 7, 9] and four orientations East, North, South and West. The best value is attained using a combined Gabor and Haralick feature set, with a score of 0.79 at the distance of k = 1, with lowest value as 0.49 at the distance k = 9 using Gabor features only. The moderate precision value could be attributed to the impact of non-calcification breast areas, as well as redundant and less discriminating features. Work is underway to remove redundant features as well as those features that do not discriminate well with respect to microcalcifications. The first results of the experimental runs are given in Figures 3, 4 and 5, show the Haralick results for single, double and combined class query image sets, respectively. The figures 6, 7 and 8, show the Gabor filter results for single, double and combined class query image sets, respectively. The figures 9, 10 and 11 show the results for single, double and combined class query image sets, respectively, using combined Gabor and Haralick features.. The first results ( Figures 3, 4 and 5), show precision values obtained by querying the database using 8 images randomly selected from the "Normal" class. The querying process (section 4.2) is evaluated over five rounds. The lowest precision value of 0.71 is scored at distance k = 3, with the highest value of 0.88 scored at the distance k = 1. The system gives a low score for images identified positive for microcalcifications . The highest score of 0.375 is recorded at distance k = 1, and the lowest score of 0.13 scored at distance k = 9. For mixed class query images (Figure 4 ), 1−Nearest Neighbor gives the best precision score at 0.69, with the 9−Nearest 508 K. Chikamai, S. Viriri, J-R. Tapamo Comparison of retrieval performance was done considering a mixed Gabor and Haralick feature set ( Figures 9, 10 and 11 ), which gave mixed results for various values of k. The combined feature set gave the highest score for queries involving normal classified images, with a score of 0.88 at k = 9 ( Figure 9 ). It also registered the highest scores for k ∈ [1, 7] , with the Gabor vector giving the highest values for the remaining values of k. This set also gave high values for the calcification and mixed class query images at 0.625 and 0.79 respectively, both values attained at k = 1. Summarily, the combined feature set of Gabor and Haralick features enhances retrieval performance for all classes of query images. The best consistent performance is achieved at k = 1 for all query classes.. For comparison, Wei et al. [19] implement a GLCM-based mammogram retrieval system for comparison with their algorithm. Regions of interest (ROIs) are cropped from mammogram images of multiple pathologies. The images are then Gabor-filtered before calculation of Haralick features. The GLCM matrix is calculated over three distances and four orientations. Their average precision values range between 0.33 and 0.64. Our system proves to be more discriminating towards images not containing microcalcifications than for those containing microcalcifications. A possible explanation is that a lot of unnecessary breast information is being included for similarity calculations. This means that the algorithm needs to be enhanced more to reduce the impact of non-calcification regions. The high dimension of features might also have a negative effect on the accuracy of the algorithm by introducing redundancies. Our precision value is however not far-off the one obtained in [19] . This adds to the fact that the proposed model automates ROI selection. Mammogram images lack color information, and usually exhibit low intensity ranges as well as noise occlusions. Overlying vessels and tissues also present a lot of challenges to the detection of malignant objects. This restricts the type of applicable features to those that exploit shape and textural characteristics of objects, with the requirement that these features be stable and robust against the mentioned limitations.. Researchers have attempted Haralick features for the determination of malignancy in mammograms. Hamid et al. [12] attempted a comparison between wavelet, Haralick and shape features for classification of benign and malignant tumors in mammograms. Pre-processing phase included segmentation using adaptive filtering banks described in [14] . Martins et al. [13] combined Haralick features with shape features as input to the K-means and SVM classifier, achieving considerable success rate of 85%.. Muller et al. [4] look at developments in content-based image retrieval (CBIR) in medical domain and present some future promising research directions. The authors note that speed as an evaluation parameter is rarely mentioned yet is important for an interactive system. They also propose that performance comparison for different feature sets needs to be done to identify well performing visual features and their optimal applications. In pointing out future research directions, the study revealed that availability of good quality features could increase accuracy in data mining and related applications. Specialization is also proposed as a means of including the domain knowledge as a measure of improving accuracy.. Wei et al. [15] analytically look at the potential of CBIR in Medical image database retrieval, and discuss the benefits and feasibility of applying it, or extending the current techniques in order to apply them to daily medical practice. They review the limitations of the current non-CBIR approach, as well as obstacles of the application of CBIR to medical image retrieval. They propose a textural analysis approach based on Grey-level Co-Occurrence Matrices for CBIR in Mammography as a case study. The method involves two stages: image analysis and image retrieval. Image analysis determines the discriminating textural features that best act as descriptors for the image, later to be used for the subsequent image retrieval process. Twelve GLCMs were constructed in four directions at three distances. Eleven Haralick features [11] were then calculated for the 12 GLCMs giving a total of 132 features for each Region of Interest (ROI). The L2 norm was used for similarity measurement, with the smallest distance indicating most similarity. A total of 329 ROIs were used for evaluation from images sourced from the Mammographic Images Analysis Society (MIAS). Precision and recall were used to test accuracy, with the system achieving 51% and 19% as the highest scores respectively. Both these values were scored at a GLCM distance of 5. The study identified a number of research issues, which include: semantic gap, systems integration, usability and performance evaluation. The major problems identified with current retrieval systems include subjectivity, financial and time costliness and Combining Feature Methods for Content-Based Classification of Mammogram Images 501 inefficiency of image data representation. Obstacles to CBIR application include image noise and many image formats available. Sixteen images were selected from the database to form the query image set. These images were taken from both classes of pathology, i.e., normal and malignant. Normal images in this experiment were defined as those images not containing Microcalcifications. This definition covers images diagnosed as positive for other malignancy indicators such as circumscribed masses and asymmetries. Results were then collected for each round for every query image. For generalized results, precision values calculated are averaged over all query images instead of one. The query process was repeated ten times (ten iterations) using a randomized set of 8 images from the normal class, the average precision values obtained in each round were then averaged over the ten iterations to give the statistical base for reporting. This process was done for every value of k ∈ [1, 3, 5, 7, 9].

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The Bulk-Synchronous Parallel Random Access Machine The model of bulk-synchronous parallel (BSP) computation is an emerging paradigm of general-purpose parallel computing. Originally, BSP was defined as a distributed memory model. Shared-memory style BSP programming had to be provided by PRAM simulation. However, this approach destroys data locality and therefore may prove inefficient for many practical problems. In this paper we present a new BSP-type model, called BSPFCAM, which reconciles sharedmemory style programming with efficient exploitation of data locality. BSPRAM can be optimally simulated by BSP for a broad range of algorithms. We identify some characteristic properties of such algorithms: obliviousness, slackness, granularity. Finally, we illustrate these concepts by presenting BSPRAM algorithms for butterfly dag computation, cube dag computation, dense matrix multiplication and sorting. @ 1998-Elsevier Science B.V. All rights reserved The model of bulk-synchronous parallel (BSP) computation (see [27, ) is intended to provide a simple and practical framework for general-purpose parallel computing. Its main goal is to support the creation of architecture-independent and scalable parallel software. The key features of BSP are the treatment of the communication medium as an abstract fully connected network, and explicit and independent costing of communication and synchronisation. Many other communication complexity models have been proposed for parallel computing. One of the main divisions among the models is by the type of memory organisation: distributed or shared. Models based on shared memory are appealing from arise from imperfect matching of integer parameters. For example, when we write "divide an array of size n into p regular blocks", the value n may not be an exact multiple of p, and therefore the blocks may differ in size by f 1. Such effects need not be considered in the abstract description of algorithms, since they can be easily accounted for during implementation. A new model for bulk-synchronous parallel computing, the BSPRAM, has been presented. The model enables the shared-memory style BSP programming with efficient exploitation of data locality. The BSP model can simulate BSPRAM optimally for a broad range of algorithms. The use of BSPRAM was illustrated on the examples of butterfly dag computation, cube dag computation, matrix multiplication and sorting. The corresponding values of the BSP cost, the type of BSPRAM used, and the characteristics of the obtained algorithms are summarised in Table 1 . The BSPRAM approach encourages natural specification of the problems: the input and output data are assumed to reside in the main memory, and no assumptions on data distribution are necessary.. The design and analysis of algorithms are also simplified, since all communication is performed via the shared memory.. In future we plan to develop new BSPRAM algorithms and to analyse their costs. This may lead to identifying new algorithm properties connecting BSPRAM and BSP, in addition to obliviousness, slackness and granularity. We also plan to develop a programming model and an implementation of BSPRAM.

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Using social information to guide action: Infants' locomotion over slippery slopes In uncertain situations such as descending challenging slopes, social signals from caregivers can provide infants with important information for guiding action. Previous work showed that 18-month-old walking infants use social information selectively, only when risk of falling is uncertain. Experiment 1 was designed to alter infants' region of uncertainty for walking down slopes. Slippery Teflon-soled shoes drastically impaired 18-month-olds' ability to walk down slopes compared with walking barefoot or in standard crepe-soled shoes, shifting the region of uncertainty to a shallower range of slopes. In Experiment 2, infants wore Teflon-soled shoes while walking down slopes as their mothers encouraged and discouraged them from walking. Infants relied on social information on shallow slopes, even at 0°, where the probability of walking successfully was uncertain in the Teflon-soled shoes. Findings indicate that infants' use of social information is dynamically attuned to situational factors and the state of their current abilities. In situations of uncertainty, infants often look to their caregivers to decide how to respond. Is the stranger friend or foe? Is the new toy funny or scary? Is the unfamiliar room inviting or threatening? Parents' social signals can tell infants whether to approach or avoid a new person, novel object, or questionable situation.. In the most commonly used paradigms in the social referencing literature, researchers observe infants' responses to an ''ambiguous'' toy (e.g., a motorized robot) or a strange person (Baldwin & Moses, 1996; Feinman, Roberts, Hsieh, Sawyer, & Swanson, 1992; Stenberg, 2009; Stenberg & Hagekull, 2007) . Infants solicit information from their mothers by looking toward them or engaging them in social interaction. Mothers, in turn, provide infants with social information about how to interpret the stimulus by varying their facial expressions and vocalizations. By 12 months of age, infants change their affective displays and proximity to mother, toy, or person in accordance with the valence of mothers' messages (Hirschberg & Svejda, 1990; Hornik, Risenhoover, & Gunnar, 1987; Mumme, Fernald, & Herrera, 1996; Stenberg, 2009; Stenberg & Hagekull, 1997) . The traditional approach to motor development is overly simplistic. Clearly social information plays a role in guiding locomotion. But, the importance of social information in infants' decisions about action should not be overstated. When infants are presented with conflicting information -social signals from their caregivers versus perceptual information generated by their own exploratory activity -the latter generally wins the day. In no study thus far have infants been found to defer to social information across the board. Such a conclusion is probably good news. In everyday situations, parents cannot be so vigilant that they can protect infants from every potential danger. With the advent of independent locomotion comes increasing autonomy. Although mothers' advice can often be useful, especially under conditions of uncertainty, infants must eventually learn to navigate the world on their own. Participants. Families were recruited from the greater metropolitan area through visits to the maternity ward of local hospitals and purchased mailing lists. All infants were healthy and born at term. Most families were white, middle-class, and educated. Parents received framed photographs of their infants to commemorate their visit.. Seventy-eight infants participated in one of three walking conditions: Teflon-soled shoes (13 boys, 9 girls), barefoot (18 boys, 22 girls), and crepe-soled shoes (8 boys, 8 girls). Due to practical constraints of recruiting participants, infants wearing Teflon-soled shoes were slightly older (M = 18.82 months, SD = 0.41) than infants walking barefoot (M = 18.04 months, SD = 0.27) and infants wearing crepe-soled shoes (M = 18.07 months, SD = 0.16); F (2, 60) = 53.13, p < .001. Post hoc comparisons confirmed these age differences (p < .001). Infants in the barefoot and crepe-soled groups were of similar ages (p > .05). Parents reported their infants' walking experience (the first day that they witnessed infants walking 10 ft without stopping or falling) in the context of a structured interview, using calendars and baby books to augment their memories. As a consequence of being a few weeks older, infants wearing Teflon-soled shoes had slightly more walking experience (M = 6.54 months, SD = 1.63) than infants wearing rubber-soled shoes (M = 5.08 months, SD = 1.45); F (2, 60) = 3.77, p < .05. Post hoc comparisons confirmed this difference in walking experience (p < .05). Walking experience in the barefoot group (M = 5.89 months, SD = 1.69) did not differ from either of the other groups. We considered the slight age and experience advantage of infants in the Teflon group to be acceptable because any impairment due to the Teflon-soled shoes would be conservatively biased-that is, if anything, slightly older and more experienced infants would be expected to show smaller impairments due to the slippery shoes. Seventeen (Teflon = 4, barefoot = 10, crepe = 3) additional infants did not contribute data due to fussiness (N = 12) or experimenter error (N = 5).. Sloping walkway and slippery shoes. We tested infants' walking skill on a wooden walkway with an adjustable slope (Fig. 2) . A sloping ramp (86 cm wide×91 cm long) was connected with piano hinges to flat starting (86 cm wide × 182 cm long) and landing platforms (86 cm wide ×91 cm long) to form a continuous walking surface. An electric motor, operated with a push-button remote, adjusted the slope of the ramp from 0°to 90°in 2°increments by raising and lowering the height of the landing platform from 116 to 25 cm. The height of the starting platform remained fixed at 116 cm. A protractor on one side of the walkway indicated the degree of slope. Nets spanned the sides of the walkway as a safety precaution, and the surface was lined with plush carpet. A raised catwalk on one side of the walkway provided the experimenter with easy access to infants in case they fell.. We constructed Teflon-and crepe-soled shoes from flat-soled, commercial infant sneakers (infant sizes 3-9). We attached pieces of slippery Teflon material (1.75 mm thick, 20 g) or standard shoe rubber (2 mm thick, 16 g) to the toe and heel sections of the shoe sole, allowing the shoe to flex around the arch when infants bent their feet. All of the shoes looked and felt like typical infants' sneakers. It was only apparent to infants whether the shoe/carpet interface was slippery after they began walking.. Procedure. We used a psychophysical procedure developed in earlier work to estimate each infant's ability to walk down slopes (Adolph, 1997; Adolph et al., 2008; Tamis-LeMonda et al., 2008) . Trials began with infants standing on the starting platform. Mothers stood at the end of the landing platform and encouraged their infants to walk down, offering toys and dry cereal as incentives. An experimenter stood on the raised catwalk and followed alongside infants to ensure their safety. An assistant panned a camera from a side view of the walkway. A second camera recorded the degree of slant from the protractor. Both camera views were mixed onto a single video frame. Trials lasted until infants reached the landing platform or 30 s if they avoided descent. Sessions lasted approximately 90 min.. Protocols began with four consecutive trials at 0°to teach the infants the game of crossing the walkway to their caregivers and to acclimatize them to the height of the starting platform. Then, beginning with an easy baseline slope of 4°, the experimenter increased or decreased the degree of slope based on the outcome of the previous trial. Trials were coded online as a success (walked safely), failure (tried to walk but fell), or refusal to walk (slid down or avoided going). After each successful trial, the experimenter increased the slope by 6°. After failures, the experimenter presented infants with the baseline 4°slope to renew their motivation to walk, and then re-entered the protocol with a slope 4°shallower than the previous success. After refusals, infants received the baseline slope and then trials were repeated, so that thresholds were determined based only on the ratio of successes to failures. The rule of ''plus 6°, minus 4°'' continued until meeting a ≥0.67 stopping criterion for estimating the walking threshold: the steepest slope at which the ratio of successes to failures was at least 0.67 and <.67 at the next 2°, 4°, and 6°i ncrements. Fewer trials were required for shallower thresholds and more trials were required for steeper thresholds and protocols with frequent refusals to walk. The total number of trials ranged from 17 to 63, M = 33.14.. We obtained useable protocols from every infant in the Teflonsoled shoe group-presumably because the slippery shoes caused them to fall as slopes became challenging. However, on 12 protocols in the barefoot group and 3 protocols in the crepe-soled shoe group, infants never fell; these protocols were not analyzed because the walking thresholds would be underestimated. Infants who never fell did not differ from infants who did exhibit failures in terms of their age or walking experience. Thus, the final sample consisted of 63 infants, 22 wearing Teflon-soled shoes, 28 walking barefoot, and 13 wearing crepe-soled shoes.. Video coding. A primary coder rescored trials from video to verify the online scoring. A computerized video coding system, OpenSHAPA (openSHAPA.org), allowed frame-by-frame viewing of the video (Sanderson et al., 1994) . Trials were rescored as success, failure, and refusal, as described above. A second coder scored 25% of the trials to verify inter-rater reliability. Inter-rater agreement was 96.7% (κ = .95, p < .001). Disagreements were resolved through discussion. Only threshold estimates derived from video coding were used in analyses.. Participants. Twenty-four 18-month-olds (M = 18.44 months, SD = 0.44; 12 boys and 12 girls) and their mothers were recruited as in Experiment 1. Walking experience (M = 5.93 months, SD = 1.77) was similar to that of the infants in Experiment 1. The infants were relatively experienced walkers. They were primarily white and from middle-class homes. Six additional infants did not complete testing due to fussiness (N = 5) or experimenter error (N = 1).. Walkway and procedure. Infants wore the same slippery, Teflon-soled shoes and walked over the same carpeted, sloping walkway used in Experiment 1. As before, an experimenter followed alongside infants to ensure their safety. Sessions lasted approximately 90 min. Because the range of thresholds was so compressed in the Teflon-soled group in Experiment 1, for Experiment 2, it was not necessary to estimate thresholds on an individualized basis before testing the effects of social signals (as in Adolph et al., 2008; Tamis-LeMonda et al., 2008) . Instead, we could safely assume that infants would only be able to manage very shallow slopes. Thus, testing consisted of four blocks of 11 trials distributed in 5°increments from 0°to 50°in 1 of 4 random trial orders. The shallowest slopes (0°, 5°, 10°, and 15°) included 100% of walking thresholds from infants in the Teflon group in Experiment 1-presumably uncertain for infants in Experiment 2. The midrange of slopes (20°, 25°, 30°, and 35°) included 93% of thresholds from infants in the barefoot and crepe groups in Experiment 1-safe under normal footgear conditions, but impossible while wearing the slippery shoes. The steep range of slopes (40°, 45°, and 50°) was impossible for all but one barefoot child in Experiment 1-that is, impossible in any footgear.. Trial blocks alternated between encourage and discourage conditions. Condition order and trial presentation orders were counterbalanced across infants. Trials lasted 30 s or until infants initiated descent, whichever occurred first. An assistant panned a camera from the side of the walkway to record infants' whole body movements, a second assistant recorded a close-up view of infants' faces from the bottom of the walkway, and a third camera recorded the degree of slant from the protractor attached to the walkway. A shotgun microphone mounted on the ceiling recorded infants' vocalizations. The camera views and audio track were mixed onto a single video frame.. Protocols began with four consecutive warm-up trials on the flat 0°walkway to ensure that they could walk in the slippery shoes and to acclimatize them to the raised walkway. As in previous work Tamis-LeMonda et al., 2008) , on test trials, mothers were instructed to encourage/discourage their infants to walk down the slopes using words, vocal intonations, facial expressions, gestures, and body language in whatever way felt most natural. In the encourage condition, mothers were instructed to ''get their infants to try to walk down the slopes''; they should ''treat the situation as if they were encouraging their infants to tackle a new challenge such as taking their first walking steps or jumping into a caregivers' arms at the swimming pool''. In the discourage condition, mothers were instructed to ''prevent their infants from trying to walk down the slopes''; they should ''treat the slope as if it were a sheet of ice that would jeopardize infants' safety''. Mothers were told to disregard the steepness of the slopes when delivering their messages. Previous work verified that the mothers' behaviors varied by social incentive condition but not by slant Tamis-LeMonda et al., 2008) .. Mothers sat on a raised platform adjacent to the landing platform, at infants' eye level but out of reach (Fig. 4) . At the beginning of each trial, the experimenter held infants on the starting platform. After infants faced their mothers, an assistant rang a bell to signal mothers to begin encouraging or discouraging. The experimenter released the infant after 2 s and mothers continued to deliver their social message.. Video coding. A primary coder scored each trial from video using OpenSHAPA. A secondary coder scored 25% of each infant's data to verify inter-rater reliability. Disagreements between coders were resolved through discussion. Attempts to walk were scored as successes, failures, and refusals, as described above. Coders agreed on 96.5% of trials (κ = .94). On trials scored as refusals, coders determined whether infants slid down (sitting, head first prone, or feet first backing) or avoided descent for the duration of the trial (98.5% agreement, κ = .98). Latency to descend was scored from the start of the trial until infants initiated descent, and included the time infants explored slopes by looking and touching. Latency could range from 0 s (immediate decision) to 30 s (maximum trial length). The correlation between coders' scores was r(184) = .98, p < .001. Infants nearly always used their feet, not their hands, to explore slopes by touching. Thus, tactile exploration included rubbing the feet against the slope, rocking back and forth with feet straddling the brink, or taking tiny steps at the edge of the slope (99.5% agreement, κ = .83).. Coders scored negative vocalizations as whining, whimpering, and crying (97.0% agreement, κ = .90) and negative facial expressions as frowns, pouts, and downward curls of the mouth (94.5% agreement, κ = .83). All other vocalizations and facial expressions were scored as positive/neutral (92.0% agreement, κ = .84; and 100% agreement κ = 1.0, respectively). Both negative and positive/neutral social expressions could occur in one trial.

O O O O O O O O O O O O O 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 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 O 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 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 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 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 O O O O O O 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 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 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 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Modeling and systematic analysis of biomarker validation using selected reaction monitoring Background: Discovery and validation of protein biomarkers with high specificity is the main challenge of current proteomics studies. Different mass spectrometry models are used as shotgun tools for the discovery of biomarkers. Validation of a set of selected biomarkers from a list of candidates is an important stage in the biomarker identification pipeline. Validation is typically done by triple quadrupole (QQQ) mass spectrometry (MS) running in selected reaction monitoring (SRM) mode. Although the individual modules of this pipeline have been studied, there is little work on integrating the components from a systematic point of view. Results: This paper analyzes the SRM experiment pipeline in a systematic fashion, by modeling the main stages of the biomarker validation process. The proposed models for SRM and protein mixture are then used to study the effect of different parameters on the final performance of biomarker validation. Sample complexity, purification, peptide ionization, and peptide specificity are among the parameters of the SRM experiment that are studied. We focus on the sensitivity of the SRM pipeline to the working parameters, in order to identify the bottlenecks where time and energy should be spent in designing the experiment. Conclusions: The model presented in this paper can be utilized to observe the effect of different instrument and experimental settings on biomarker validation by SRM. On the other hand, the model would be beneficial for optimization of the work flow as well as identification of the bottlenecks of the pipeline. Also, it creates the required infrastructure for predicting the performance of the SRM pipeline for a specific setting of the parameters. General facts can be gleaned from the results reported above in the paper on the relative importance of each http://bsb.eurasipjournals. parameter to the sensitivity of biomarker validation performance using the QQQ-based SRM system.. • Purification critically increases the efficiency of the whole pipeline by reducing the background high-abundance proteins.. • On the other hand, peptide ionization efficiency also plays an important role in the success of biomarker validation experiment.. • A high value of modification noise can greatly compromise the performance of the system, as measured by the decreases of the TPR and ranking power value.. • Likewise, a decrease of peptide specificity reduces the TPR and ranking power to a great extent.. The results emphasize the importance of the correct selection of peptides in an SRM experiment. If the selected peptides are not unique to the targeted protein, it is hard to have high-precision quantification of the abundance of the targeted peptides, which will show itself in the unsuccessful protein validation results. An additional factor is of course sample size, which not surprisingly showed a clear effect on the performance of the biomarker discovery pipeline. In this paper, the key components of the typical SRMbased biomarker validation workflow were reviewed, modeled, and analyzed. Based on the synthetic data, the process was simulated and the effect of different parameter setting on the performance was studied. Ranking power and the TPR were used as two different metrics to assess the performance of the biomarker validation process as a function of the parameters of the model. The goal of this study was not the determination of the exact value of each parameter for reaching a given performance value but rather to investigate the effect of the different parameters, namely, sample purification, peptide ionization efficiency, peptide specificity, modification noise, and sample size, on the overall performance of the SRM experiment utilized for biomarker validation.. The model presented here can not only be utilized to observe the effect of different instrument and experimental settings on biomarker validation by SRM but also could be useful for experimental design, providing an insight on the working range of the important parameters of the SRM pipeline. It creates the required infrastructure for studying the inverse problem, where one can use the model to set the parameters of the entire experiment to reach the highest performance considering technical, experimental and financial constraints. Also, the model has the advantage of being flexible to future possible extension in order to include more detailed modules of the SRM pipeline. In spite of the widespread application of SRM in the protein biomarker validation process, there is little work on the integration of the different modules in SRM workflow, and their systematic study to assess the impact of different parameters on the overall biomarker validation pipeline. A model-based approach toward the SRM experiment will help us to have a better understanding of the characteristics of the different modules of the SRM-based biomarker validation process. Here, the SRM pipeline is modeled as a noisy channel affecting the underlying protein abundance signal; a model for the noise channel is proposed and used to analyze the effect of different parameters and experimental settings on the final performance of the SRM-based biomarker validation pipeline and the ability of SRM to detect true biomarkers among a set of candidate ones. Although the aim of the SRM model proposed here is not to determine the exact value of each parameter, it will be useful in providing a systematic view towards studying the individual components of the SRM experiment. We perform a total of 5,000 Monte Carlo runs in this experimental study, using the parameter settings given in Table 1 , and compute average performance metrics over all the runs.. The performance metrics used to evaluate SRM performance are the percentage of peptides correctly identified and the ranking power [27] . The former is computed by applying the t-test as a feature selection method to find the best discriminant set of features, and computing the ratio of true biomarkers detected in that list. The latter defines a measure of goodness based on how close the estimate-based feature sets are to optimality. Let A best be the best feature set relative to the feature-label distribution, ε 0 be the true error of the classifier for A best designed on the sample, and A (1) , A (2) sorted from lowest to highest. The ranking power of the list is defined by n,r. for r > 0. The ranking power gives the probability that at least one feature set in the list has error within r of the best feature set. The closer n,r D,d is to 1, the better the performance is (as long as m is small; here, m = 10 is used).. The pseudocode for computing the power rank is described in Algorithm 1.

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


Branch and price for the vehicle routing problem with discrete split deliveries and time windows The Discrete Split Delivery Vehicle Routing Problem with Time Windows (DSDVRPTW) consists of designing the optimal set of routes to serve, at least cost, a given set of customers while respecting constraints on vehicles' capacity and customer time windows. The delivery request of a customer is discrete since it consists of several items that cannot be split further. The problem belongs to the class of split delivery problems since each customer's demand can be split in orders, i.e. feasible combinations of items, and each customer can be visited by more than one vehicle. In this work, we model the DSDVRPTW assuming that all feasible orders are known in advance and that each vehicle can serve at most one order per customer. Remarkably, service time at customer's location depends on the serviced combination of items, which is a modeling feature rarely found in literature. We present a mixed integer program for the DSDVRPTW based on arcflow formulation, we reformulate it via Dantzig-Wolfe and we apply column generation. We propose a branch-and-price algorithm, implemented using state-of-the-art techniques for the pricing and the master problem. Computational results on instances based on Solomon's data set are presented and discussed.2 The capacitated Vehicle Routing Problem (VRP) consists of designing the optimal routes for a set of vehicles with given capacity in order to serve a set of customers. Customer's demand must be delivered by exactly one vehicle and vehicles' capacity must cannot be violated (exceeded).. The Split Delivery Vehicle Routing Problem (SDVRP) is a relaxed version of the classical capacitated VRP in which the number of visits to customer locations is no longer constrained to be at most one. In the SD-VRP each customer can be visited by more than one vehicle which serves a fraction of its demand. It has been shown that this relaxation could yield to substantial savings on the total traveled distance, up to 50% in some instances, as well as on the number of required vehicles (Archetti et al., 2006a; Archetti et al., 2008a) . The problem and some properties have been introduced by Dror and Trudeau (1989) and Dror and Trudeau (1990) , who solve the problem using heuristic schemes. Next, Dror et al. (1994) introduce a mathematical formulation based on integer programming, solved through a cutting plane approach. Lower bounds have been proposed by Belenguer et al. (2000) ; exact methods (Gueguen, 1999; Jin et al., 2007) as well as heuristic algorithms (Archetti et al., 2006b; Chen et al., 2007; Jin et al., 2008; Archetti et al., 2008b) have been proposed. Gendreau et al. (2006) and Desaulniers (2008) address the problem with time windows and present exact approaches based on column generation and branch-and-bound techniques. Lower bounds have been studied by Ceselli et al. (2009b) and a tabu search algorithm has been proposed by Ho and Haugland (2004) .. In the Discrete Split Delivery Vehicle Routing Problem (DSDVRP) the demand of a customer consists of several items which cannot be split further. The problem belongs to the class of split delivery problems since each customer's demand can be fractionated and each customer can be visited by more than one vehicle. Nakao and Nagamochi (2007) present the problem and propose a dynamic programming based heuristic. The algorithm is compared to other existing heuristics for the VRP and computational results on real-world instances are provided. Ceselli et al. (2009a) present an exact approach to a real-world VRP in which customers' orders can be split among several vehicles in a discrete fashion. The authors propose a three level order aggregation which end up, at the last level, in considering any possible combination of items. The VRP with splittable and discrete demand arises in some practical applications, such as the routing of helicopters for crew exchanges on off-shore locations (Sierksma and Tijssen, 1998) and the Field Technician Scheduling Problem (Xu and Chiu, 2001) ; however, authors do not specifically relate their problems to the DSDVRP.. In the reminder of the paper we study the Discrete Split Delivery Vehicle Routing Problem with Time Windows (DSDVRPTW). We assume that demand can be split in orders, i.e. feasible combinations of items, that each vehicle can serve at most one order per customer and that service time at customer's location depends on the delivered combination of items. Remarkably, this is a modeling feature rarely found in literature, where service times are usually assumed to be independent of the delivered quantities. We refer e.g. to Gendreau et al. (2006) and Desaulniers (2008) , who make the simplifying assumption of constant service times: this is indeed the case in applications where the unloading time is negligible, but it is not an appropriate modeling assumption for applications where the unloading time is largely affected by the size of the delivery. In Section 2 we recall some known properties of split deliveries. Section 3 provides an arc-flow formulation for the DSDVRPTW. In Section 4 we reformulate the problem using Dantzig-Wolfe decomposition and we illustrate the column generation scheme. The branch-and-price implementation is presented in Section 5 and computational results are discussed in Section 6. Section 7 concludes the paper. Analyzing the results, we can conclude that obtaining optimal solutions is difficult, even with a small number of orders per customer. Furthermore, only a limited number of instances with 50 customers could be solved.. We guess that the bottleneck is in the pricing problem. Indeed, the underlying ESPPRC network is huge, since, in the worst case scenario, for every customer i ∈ N we have that set C i corresponds to the set of all subsets of R i and therefore its size grows exponentially with the number of items. Computational results show that solving the ESPPRC on such a network may be impractical. Therefore, more efficient solution techniques need to be investigated. Table 6 : Optimal solutions for class RC1, n = 50 customers. We were able to solve 88, 67 and 47 out of 174 instances for scenarios A, B and C, respectively. The difficulty of solving the instances increases with the size of |C|: 75, 125 and 175 orders with 25 customers and 150, 250, and 350 orders with 50 customers for scenarios A, B and C, respectively. This difficulty also increases with the number of customers: we were able to solve 76% (A), 60% (B) and 48% (C) of instances with n = 25, whereas only 25% (A), 17% (B) and 6% (C) of instances with n = 50 were solved at optimality. The average computational time is also affected by the size of |C| and the number of customers.. For n = 25 customers, instances of class R1 are the easiest to solve. There are 36 (A), 36 (B) and 32 (C) solved instances out of 36 for class R1; 20 (A), 8 (B) and 2 (C) solved instances out of 27 for class C1; 10 (A), 8 (B) and 8 (C) solved instances out of 24 for class RC1. On average, 96% of instances were solved in class R1, 37% in class C1 and 36% in class RC1.. For n = 50 customers, class RC1 seems easier to solve than class R1 (on average, 42% versus 11% of solved instances), while no instances in class Optimal solutions are detailed in tables 2, 3, 4, 5 and 6. For each instance, we provide the value of the optimal integer solution (z IP ), the number of vehicles (veh) and the computational time in seconds (t). The three DSDVRPTW scenarios A, B, C and compared to the unsplittable VRPTW scenario O: figures highlighted in bold denote savings due to split deliveries. Instances that are not feasible for the unsplittable case because of insufficient capacity are denoted by "Q < d". Instances not solved at optimality within 1 hour of computational time are denoted by "x".. We can observe that split deliveries are more frequent for instances with small Q values, although they also occur for certain instances with Q = 100.. In a few cases, split deliveries not only decrease the total traveling costs but also allow to save one vehicle.

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


Personalized routing for multitudes in smart cities Human mobility in a city represents a fascinating complex system that combines social interactions, daily constraints and random explorations. New collections of data that capture human mobility not only help us to understand their underlying patterns but also to design intelligent systems. Bringing us the opportunity to reduce traffic and to develop other applications that make cities more adaptable to human needs. In this paper, we propose an adaptive routing strategy which accounts for individual constraints to recommend personalized routes and, at the same time, for constraints imposed by the collectivity as a whole. Using big data sets recently released during the Telecom Italia Big Data Challenge, we show that our algorithm allows us to reduce the overall traffic in a smart city thanks to synergetic effects, with the participation of individuals in the system, playing a crucial role. Rapid development of wireless communication and mobile computing technologies call new research that explores the responses of urban systems to the flow of instant information. Thus, the analysis of spatial signals becomes an increasingly important research theme.. The required four steps to model trips consist of calculating trip generation, trip distribution, modal split and route assignments. The sources to inform these steps traditionally have come from travel diaries and census data [] . However, the presence of new information and communication technologies (ICT) provide big data sources that are allowing novel research and applications related to human mobility. Recent studies have advanced the knowledge on trip generation by studying the number of different locations visited by individuals through mobile phones and quantifying their frequent return to previously visited locations. These have demonstrated that the majority of travels occur between a limited number of places, with less frequent trips to new places outside an individual radius [, ] . In the domain of trip distributions, new models have helped us to predict number of commuting trips when lacking data for calibration [] .. An important topic is to explore route assignments in the context of smart multimodal systems [, ] , where individual daily trips follow recommendations based on personal and global constraints. This is of special interest towards efficient cities, where individuals could be automatically routed reducing the probability of traffic congestion and at the same time reducing the environmental impact. From the individual's point of view, for instance, one might want to choose a trip which minimizes the amount of traffic along the route, or to avoid routes across areas with high criminality level, or to favorite routes across more touristic areas, etc. On the other hand, the choices of certain routes at individual level, without accounting for the state of the system, often leads to traffic congestion [, ] which, in turn, is responsible for increasing pollution while decreasing the quality of the environment, with evident impact on the community.. In this work we model the trips in an urban system as interacting particles with datadriven origin-destination pairs that can be routed in their trips. Their route choices are based in a time-varying potential energy landscape that seeks to satisfy individual's and community's requirements simultaneously. Main streams methods for distributed routing seek to avoid congestion by global travel time reduction based on optimization methods [, ]. More recently, adaptive path optimization on networks (London underground network and global airport network) related the problem to physics of interacting polymers [] . In this work we go one step forward in that direction and use a framework based on potential energy landscapes to integrate diverse layers of constraints to favor certain routes and to study the effects of the level of adoption of the proposed recommendations. In this work our main focus is to explore a new framework of analysis to study routing strategies for urban mobility, while the road network constrains are left to further studies. We have presented a strategy to route individuals between pairs of points of interest according to constraints of different type. Our method accounts for the simultaneous interplaying between personalized constraints, as avoiding specific areas of the city because of personal choices, and collective constraints, from pollution reduction in certain areas of the city to the presence of adverse atmospherical conditions requiring targeted intervention. We have shown that the synergy plays a fundamental role in designing a smart city: only when all individuals take part in the routing system and move according to the recommended routes, the overall traffic in the city is closer to the most ideal mobility scenario. In the presence of real time information, our method allows to monitor the state of the city in real time, automatically identifying areas that are experiencing a temporary congestion and giving authorities the possibility to intervene timely.. Finally, the potential applications of our routing strategy are multiple. For instance, for certain values of the parameters (i.e., a = b = , leading to γ (t) = ), we obtain a routing strategy from an origin and without a fixed destination, while accounting for specified constraints. This case could be useful to perform automated routing of objects or individuals through the city. For instance, it would be possible to route cars or drones which are collecting data about the city (as Google cars) and to route people in charge of social services like cleaning the streets or performing targeted intervention, as disseminating salt in areas with snow. An additional application could be in the field of social security, to route police cars in areas with high crimes rate. Finally, our framework can help decision-makers to real-time application of urban mobility policies in responses to crisis, e.g. the emergence of hotspots of infection in specific areas of the city (or a larger area) can be incorporated into the model to avoid people passing through dangerous areas before physical quarantine is employed.

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


The Research of Differentiated Service and Load Balancing in Web Cluster Abstract: Differentiated service, as a key solution to meet the heterogenicity of Web clients' QoS requirements, has been widely used to optimize the server utilization without over-providing resources. Based on the relative differentiated service, this paper treats the application of proportional delay as a optimal control problem, and focuses on the cluster-side architecture improvement as well as QoS controller design. A load balancing Web cluster architecture supported differentiated service is proposed and implemented. By system identification and resource optimal control, the frontend dispatcher could adjust the resource quotas assigned to different classes in every single back-end server, and Multi-class based Maximum Idle First load balancing strategy is designed to ensure a fair resource consumption among back-end nodes. As a result, the end-to-end delay is controlled and proportional delay is guaranteed. The experiments demonstrate that no matter using Round-Robin, Least Connection Scheduling or Maximum Idle First load balancing strategy, the proposed resource optimal controller could hold the relationship among different classes. Compared to Round-Robin and Least Connection First Scheduling, Maximum Idle First strategy increases the cluster throughput by 33% and reduces the average delay by 21%. With the dramatic explosion of online information, the Internet is undergoing a transition from a data communication infrastructure to a service intergraded utility. The increase of Web applications, Web clients and HTTP requests on the Internet makes the Web server systems often suffer from huge pressure of heavy workload. The Deployment of Web cluster system keeps increasing to meet the demand for availability, scalability and stability of the diversified performance demands of clients.. Web cluster organizes a number of Web servers as a logical entirety to enhance the storage and processing capacity. The cluster also shows a good expansibility, whose capability can be easily tuned by changing the number of back-end server, which are connected by high speed local area network. Clients' HTTP requests are well-proportioned and transparently dispatched to back-end. Server nodes work concurrently and the responsiveness as well as the reliability of Web sites are improved (see [17] ).. To take full use of every server's processing resources, a major issue is how to arrange each server node appropriate requests according to its capability. However, the cluster system scale is limited by the financial cost of Web sits and IDC (Internet Digital center), and the load characteristics of Web sites is often affected by the browsing habits, geographic distribution and breaking news. It is impossible to accurately predict the peak load and prepare enough 2 The Overview of Cluster Figure 1 gives the framework of Web cluster.The front-end is called Dispatcher, which is the entrance of the cluster system. The clients' HTTP requests first reach dispatcher, then are distributed to back-end servers according to the load balancing strategy. The the dispatcher can select a back-end according to the Request-URL in HTTP Message and/or other information in entity-header fields, such as User-Agent, From, Host, etc. According to the layer of dispatcher, clusters can be divided into three types (see [17] The dispatcher should establish TCP connection with clients and back-ends concurrently. Although compared with the hardware-accelerated forwarding method used by L4/2 and L4/3 cluster, L7 cluster has the limitation of larger processing overhead. But it is still a promising implementation of Web server cluster (see [11] ). It could not only combine L4/2, L4/3 forwarding technology (see [3] ), but also take usage of application information to enforce the content-aware dispatcher and combine the priority scheduling with the processing/threads-based Web QoS control scheme (see [18] ). In this paper, we design a feedback control mechanism to achieve Proportional Delay Differentiation Service and Load Balancing in a Web Cluster System. By recursive least square estimation and F-test, PDDS-LB is model as a second-order liner time-invariant system. We construct the cost function and obtain the optimal law by derivation of cost function. Experimental evaluations have shown that our mechanism achieves PDDS, while the low priority class is not over-sacrificed. With the aid of MIF, we maximize the resource utilization of the cluster system. However, our experiments are just in the condition of ideal transport layer, in the real network, link status is complex and changeable ,the factors that influence QoS interweave each other, such as bandwidth, cache, I/O etc. As part of out ongoing work, we are exploring a integrated resource management to adapt to more complex environment. The current related work mainly focus on the load balancing strategy and algorithm. While the researches of L4 cluster are just on how to uniformly spread the requests. Besides this, L7 cluster considers the contents of the request as well.. Typical L4 load balancing strategies are as follows: Round Robin (RR), i.e. executes (i + 1) mod n for every request and selects back-end according to the result; Weighted Round Robin (WRR), i.e. every back-end servers' processing capacity is in accordance with its weight. The larger weight the more times requests be sent; Least Connection Scheduling (LCS) and Weighted Least Connection Scheduling (WLCS), which forward the requests to the server with least active TCP connections; Source Hashing Scheduling (SHS) and Destination Hashing Scheduling (DHS) are statics mapping algorithms, the IP source address or destination address of HTTP request is hashed and mapped to a certain back-end server.. There are two common scheduling strategy used in L7, Client-aware Policy (CAP) (see [4] ) and Locality Aware Request Distribution (LARD)(see [13] ). CAP is actually a kind of RR scheduling supported client-side classification. Same kind of requests are spreading uniformly without considering their content. LARD is server-status based load balancing strategy, in the threshold of stability, the same URL are forwarding to the same server to achieve a higher hit rate.. However, no matter L4 or L7 cluster research does not concern the issue of differentiated service and the related study is few. [21] gives the Demand-driven Service Differentiation(DDSD) approach, which improves Web Cluster model into a multi-queueing system, such as M/M/1/∞ and M/G/1/∞ models proposed by [19, 20] . By selecting Stretch Factor, it treats the re-allocation of cluster resources as a SLAs constrained optimization problem. However, because queueing model is based on the premise of Poisson arrival rate and exponential distribution of processing time, queueing model can not accurately describe the of HTTP traffic feature when the arrival rate and leave rate does not match(see [2, 9] ). Dynamic Partitioning (DynamicPart) algorithm is proposed by Casalicchio (see [1] ). By feedback control, the back-ends is dynamically servicing different kinds of requests, which transforms a best-effort Web cluster into a QoS-enhanced system. But the performance isolation is enforced at the level of server host, it can only provide coarse-grain control at host-level, which disturbs the on-line extension and the resources utilization.. Our research purpose is to provide a differentiated services to increase the resources utilization of Web cluster at the level of processing/thread. First, the resource consumption of every backend should be coordinated and load-balanced. Then the resource management and scheduling 664 A. Gao, Q. Pan, Y. Hu should meet the QoS requirement (see [5] ).

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