Multi-sensor classification of tennis strokes

In this work, we investigate tennis stroke recognition using a single inertial measuring unit attached to a player’s forearm during a competitive match. This paper evaluates the best approach for stroke detection using either accelerometers, gyroscopes or magnetometers, which are embedded into the inertial measuring unit. This work concludes what is the optimal training data set for stroke classification and proves that classifiers can perform well when tested on players who were not used to train the classifier. This work provides a significant step forward for our overall goal, which is to develop next generation sports coaching tools using both inertial and visual sensors in an instrumented indoor sporting environment.

An investigation on the use of SNR distributions for the optimisation of coarse-fine spectrum sensing for cognitive radio

This thesis investigates the optimisation of Coarse-Fine (CF) spectrum sensing architectures under a distribution of SNRs for Dynamic Spectrum Access (DSA). Three different detector architectures are investigated: the Coarse-Sorting Fine Detector (CSFD), the Coarse-Deciding Fine Detector (CDFD) and the Hybrid Coarse-Fine Detector (HCFD). To date, the majority of the work on coarse-fine spectrum sensing for cognitive radio has focused on a single value for the SNR. This approach overlooks the key advantage that CF sensing has to offer, namely that high powered signals can be easily detected without extra signal processing. By considering a range of SNR values, the detector can be optimised more effectively and greater performance gains realised. This work considers the optimisation of CF spectrum sensing schemes where the security and performance are treated separately. Instead of optimising system performance at a single, constant, low SNR value, the system instead is optimised for the average operating conditions. The security is still provided such that at the low SNR values the safety specifications are met. By decoupling the security and performance, the system’s average performance increases whilst maintaining the protection of licensed users from harmful interference. The different architectures considered in this thesis are investigated in theory, simulation and physical implementation to provide a complete overview of the performance of each system. This thesis provides a method for estimating SNR distributions which is quick, accurate and relatively low cost. The CSFD is modelled and the characteristic equations are found for the CDFD scheme. The HCFD is introduced and optimisation schemes for all three architectures are proposed. Finally, using the Implementing Radio In Software (IRIS) test-bed to confirm simulation results, CF spectrum sensing is shown to be significantly quicker than naive methods, whilst still meeting the required interference probability rates and not requiring substantial receiver complexity increases.

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

CD4+ CD25+ regulatory T cells control T helper cell type 1 responses to foreign antigens induced by mature dendritic cells in vivo.

Recent evidence suggests that in addition to their well known stimulatory properties, dendritic cells (DCs) may play a major role in peripheral tolerance. It is still unclear whether a distinct subtype or activation status of DC exists that promotes the differentiation of suppressor rather than effector T cells from naive precursors. In this work, we tested whether the naturally occurring CD4+ CD25+ regulatory T cells (Treg) may control immune responses induced by DCs in vivo. We characterized the immune response induced by adoptive transfer of antigen-pulsed mature DCs into mice depleted or not of CD25+ cells. We found that the development of major histocompatibility complex class I and II-restricted interferon gamma-producing cells was consistently enhanced in the absence of Treg. By contrast, T helper cell (Th)2 priming was down-regulated in the same conditions. This regulation was independent of interleukin 10 production by DCs. Of note, splenic DCs incubated in vitro with Toll-like receptor ligands (lipopolysaccharide or CpG) activated immune responses that remained sensitive to Treg function. Our data further show that mature DCs induced higher cytotoxic activity in CD25-depleted recipients as compared with untreated hosts. We conclude that Treg naturally exert a negative feedback mechanism on Th1-type responses induced by mature DCs in vivo.

Anti-CTLA-4 treatment induces IL-10-producing ICOS+ regulatory T cells displaying IDO-dependent anti-inflammatory properties in a mouse model of colitis.

BACKGROUND AND AIMS: Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) has been shown to act as a negative regulator of T cell function and has been implicated in the regulation of T helper 1 (Th1)/Th2 development and the function of regulatory T cells. Tests were carried out to determine whether anti-CTLA-4 treatment would alter the polarisation of naive T cells in vivo. METHODS: Mice were treated with anti-CTLA-4 monoclonal antibody (mAb) (UC10-4F10) at the time of immunisation or colonic instillation of trinitrobenzene sulfonic acid (TNBS). The cytokines produced by lymph node cells after in vitro antigenic stimulation and the role of indoleamine 2,3 dioxygenase (IDO) and of interleukin-10 (IL-10) were tested, and the survival of mice was monitored. RESULTS: Injection of anti-CTLA-4 mAb in mice during priming induced the development of adaptive CD4(+) regulatory T cells which expressed high levels of ICOS (inducible co-stimulator), secreted IL-4 and IL-10. This treatment inhibited Th1 memory responses in vivo and repressed experimental intestinal inflammation. The anti-CTLA-4-induced amelioration of disease correlated with IDO expression and infiltration of ICOS(high) Foxp3(+) T cells in the intestine, suggesting that anti-CTLA-4 acted indirectly through the development of regulatory T cells producing IL-10 and inducing IDO. CONCLUSIONS: These observations emphasise the synergy between IL-10 and IDO as anti-inflammatory agents and highlight anti-CTLA-4 treatment as a potential novel immunotherapeutic approach for inducing adaptive regulatory T cells.

Randomized, controlled trial investigating short-term health-related quality of life with doxorubicin and paclitaxel versus doxorubicin and cyclophosphamide as first-line chemotherapy in patients with metastatic breast cancer: European Organization for Research and Treatment of Cancer Breast Cancer Group, Investigational Drug Branch for Breast Cancer and the New Drug Development Group Study.

PURPOSE: To compare health-related quality of life (HRQOL) in patients with metastatic breast cancer receiving the combination of doxorubicin and paclitaxel (AT) or doxorubicin and cyclophosphamide (AC) as first-line chemotherapy treatment. PATIENTS AND METHODS: Eligible patients (n = 275) with anthracycline-naive measurable metastatic breast cancer were randomly assigned to AT (doxorubicin 60 mg/m(2) as an intravenous bolus plus paclitaxel 175 mg/m(2) as a 3-hour infusion) or AC (doxorubicin 60 mg/m(2) plus cyclophosphamide 600 mg/m(2)) every 3 weeks for a maximum of six cycles. Dose escalation of paclitaxel (200 mg/m(2)) and cyclophosphamide (750 mg/m(2)) was planned at cycle 2 to reach equivalent myelosuppression in the two groups. HRQOL was assessed with the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire C30 and the EORTC Breast Module at baseline and the start of cycles 2, 4, and 6, and 3 months after the last cycle. RESULTS: Seventy-nine percent of the patients (n = 219) completed a baseline measure. However, there were no statistically significant differences in HRQOL between the two treatment groups. In both groups, selected aspects of HRQOL were impaired over time, with increased fatigue, although some clinically significant improvements in emotional functioning were seen, as well as a reduction in pain over time. Overall, global quality of life was maintained in both treatment groups. CONCLUSION: This information is important when advising women patients of the expected HRQOL consequences of treatment regimens and should help clinicians and their patients make informed treatment decisions.

Doxorubicin-paclitaxel: a safe regimen in terms of cardiac toxicity in metastatic breast carcinoma patients. Results from a European Organization for Research and Treatment of Cancer multicenter trial.

BACKGROUND: The potential cardiotoxicity of the doxorubicin-paclitaxel regimen, when paclitaxel is given shortly after the end of the anthracycline infusion, is an issue of concern, as suggested by small single institution Phase II studies. METHODS: In a large multicenter Phase III trial, 275 anthracycline naive metastatic breast carcinoma patients were randomized to receive either doxorubicin (60 mg/m(2)) followed 30 minutes later by paclitaxel (175 mg/m(2) 3-hour infusion; AT) or a standard doxorubicin-cyclophosphamide regimen (AC; 60/600 mg/m(2)). Both treatments were given once every 3 weeks for a maximum of six cycles. Close cardiac monitoring was implemented in the study design. RESULTS: Congestive heart failure (CHF) occurred in three patients in the AT arm and in one patient in the AC arm (P = 0.62). Decreases in left ventricular ejection fraction to below the limit of normal were documented in 33% AT and 19% AC patients and were not predictive of CHF development. CONCLUSIONS: AT is devoid of excessive cardiac risk among metastatic breast carcinoma patients, when the maximum planned cumulative dose of doxorubicin does not exceed 360 mg/m(2).

Doxorubicin and paclitaxel versus doxorubicin and cyclophosphamide as first-line chemotherapy in metastatic breast cancer: The European Organization for Research and Treatment of Cancer 10961 Multicenter Phase III Trial.

PURPOSE: To compare the efficacy and tolerability of the combination of doxorubicin and paclitaxel (AT) with a standard doxorubicin and cyclophosphamide (AC) regimen as first-line chemotherapy for metastatic breast cancer. PATIENTS AND METHODS: Eligible patients were anthracycline-naive and had bidimensionally measurable metastatic breast cancer. Two hundred seventy-five patients were randomly assigned to be treated with AT (doxorubicin 60 mg/m(2) as an intravenous bolus plus paclitaxel 175 mg/m(2) as a 3-hour infusion) or AC (doxorubicin 60 mg/m(2) plus cyclophosphamide 600 mg/m(2)) every 3 weeks for a maximum of six cycles. A paclitaxel (200 mg/m(2)) and cyclophosphamide (750 mg/m(2)) dose escalation was planned at cycle 2 if no grade >or= 3 neutropenia occurred in cycle 1. The primary efficacy end point was progression-free survival (PFS). Secondary end points were response rate (RR), safety, overall survival (OS), and quality of life. RESULTS: A median number of six cycles were delivered in the two treatment arms. The relative dose-intensity and delivered cumulative dose of doxorubicin were lower in the AT arm. Dose escalation was only possible in 17% and 20% of the AT and AC patients, respectively. Median PFS was 6 months in the two treatments arms. RR was 58% versus 54%, and median OS was 20.6 versus 20.5 months in the AT and AC arms, respectively. The AT regimen was characterized by a higher incidence of febrile neutropenia, 32% versus 9% in the AC arm. CONCLUSION: No differences in the efficacy study end points were observed between the two treatment arms. Treatment-related toxicity compromised doxorubicin-delivered dose-intensity in the paclitaxel-based regimen

Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

Latent Stick-Breaking Processes.

We develop a model for stochastic processes with random marginal distributions. Our model relies on a stick-breaking construction for the marginal distribution of the process, and introduces dependence across locations by using a latent Gaussian copula model as the mechanism for selecting the atoms. The resulting latent stick-breaking process (LaSBP) induces a random partition of the index space, with points closer in space having a higher probability of being in the same cluster. We develop an efficient and straightforward Markov chain Monte Carlo (MCMC) algorithm for computation and discuss applications in financial econometrics and ecology. This article has supplementary material online.

Delimiting species without nuclear monophyly in Madagascar's mouse lemurs.

BACKGROUND: Speciation begins when populations become genetically separated through a substantial reduction in gene flow, and it is at this point that a genetically cohesive set of populations attain the sole property of species: the independent evolution of a population-level lineage. The comprehensive delimitation of species within biodiversity hotspots, regardless of their level of divergence, is important for understanding the factors that drive the diversification of biota and for identifying them as targets for conservation. However, delimiting recently diverged species is challenging due to insufficient time for the differential evolution of characters--including morphological differences, reproductive isolation, and gene tree monophyly--that are typically used as evidence for separately evolving lineages. METHODOLOGY: In this study, we assembled multiple lines of evidence from the analysis of mtDNA and nDNA sequence data for the delimitation of a high diversity of cryptically diverged population-level mouse lemur lineages across the island of Madagascar. Our study uses a multi-faceted approach that applies phylogenetic, population genetic, and genealogical analysis for recognizing lineage diversity and presents the most thoroughly sampled species delimitation of mouse lemur ever performed. CONCLUSIONS: The resolution of a large number of geographically defined clades in the mtDNA gene tree provides strong initial evidence for recognizing a high diversity of population-level lineages in mouse lemurs. We find additional support for lineage recognition in the striking concordance between mtDNA clades and patterns of nuclear population structure. Lineages identified using these two sources of evidence also exhibit patterns of population divergence according to genealogical exclusivity estimates. Mouse lemur lineage diversity is reflected in both a geographically fine-scaled pattern of population divergence within established and geographically widespread taxa, as well as newly resolved patterns of micro-endemism revealed through expanded field sampling into previously poorly and well-sampled regions.

The evolutionary origin of Indian Ocean tortoises (Dipsochelys).

Today, the only surviving wild population of giant tortoises in the Indian Ocean occurs on the island of Aldabra. However, giant tortoises once inhabited islands throughout the western Indian Ocean. Madagascar, Africa, and India have all been suggested as possible sources of colonization for these islands. To address the origin of Indian Ocean tortoises (Dipsochelys, formerly Geochelone gigantea), we sequenced the 12S, 16S, and cyt b genes of the mitochondrial DNA. Our phylogenetic analysis shows Dipsochelys to be embedded within the Malagasy lineage, providing evidence that Indian Ocean giant tortoises are derived from a common Malagasy ancestor. This result points to Madagascar as the source of colonization for western Indian Ocean islands by giant tortoises. Tortoises are known to survive long oceanic voyages by floating with ocean currents, and thus, currents flowing northward towards the Aldabra archipelago from the east coast of Madagascar would have provided means for the colonization of western Indian Ocean islands. Additionally, we found an accelerated rate of sequence evolution in the two Malagasy Pyxis species examined. This finding supports previous theories that shorter generation time and smaller body size are related to an increase in mitochondrial DNA substitution rate in vertebrates.

Working at the interface of phylogenetics and population genetics: a biogeographical analysis of Triaenops spp. (Chiroptera: Hipposideridae).

New applications of genetic data to questions of historical biogeography have revolutionized our understanding of how organisms have come to occupy their present distributions. Phylogenetic methods in combination with divergence time estimation can reveal biogeographical centres of origin, differentiate between hypotheses of vicariance and dispersal, and reveal the directionality of dispersal events. Despite their power, however, phylogenetic methods can sometimes yield patterns that are compatible with multiple, equally well-supported biogeographical hypotheses. In such cases, additional approaches must be integrated to differentiate among conflicting dispersal hypotheses. Here, we use a synthetic approach that draws upon the analytical strengths of coalescent and population genetic methods to augment phylogenetic analyses in order to assess the biogeographical history of Madagascar's Triaenops bats (Chiroptera: Hipposideridae). Phylogenetic analyses of mitochondrial DNA sequence data for Malagasy and east African Triaenops reveal a pattern that equally supports two competing hypotheses. While the phylogeny cannot determine whether Africa or Madagascar was the centre of origin for the species investigated, it serves as the essential backbone for the application of coalescent and population genetic methods. From the application of these methods, we conclude that a hypothesis of two independent but unidirectional dispersal events from Africa to Madagascar is best supported by the data.

BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES.

Technological advances in genotyping have given rise to hypothesis-based association studies of increasing scope. As a result, the scientific hypotheses addressed by these studies have become more complex and more difficult to address using existing analytic methodologies. Obstacles to analysis include inference in the face of multiple comparisons, complications arising from correlations among the SNPs (single nucleotide polymorphisms), choice of their genetic parametrization and missing data. In this paper we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations, MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level, with the prior distribution on SNP inclusion in the model providing an intrinsic multiplicity correction. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally "validated" in independent studies. We examine sensitivity of the NCOCS results to prior choice and method for imputing missing data. MISA is available in an R package on CRAN.

Association between DNA damage response and repair genes and risk of invasive serous ovarian cancer.

BACKGROUND: We analyzed the association between 53 genes related to DNA repair and p53-mediated damage response and serous ovarian cancer risk using case-control data from the North Carolina Ovarian Cancer Study (NCOCS), a population-based, case-control study. METHODS/PRINCIPAL FINDINGS: The analysis was restricted to 364 invasive serous ovarian cancer cases and 761 controls of white, non-Hispanic race. Statistical analysis was two staged: a screen using marginal Bayes factors (BFs) for 484 SNPs and a modeling stage in which we calculated multivariate adjusted posterior probabilities of association for 77 SNPs that passed the screen. These probabilities were conditional on subject age at diagnosis/interview, batch, a DNA quality metric and genotypes of other SNPs and allowed for uncertainty in the genetic parameterizations of the SNPs and number of associated SNPs. Six SNPs had Bayes factors greater than 10 in favor of an association with invasive serous ovarian cancer. These included rs5762746 (median OR(odds ratio)(per allele) = 0.66; 95% credible interval (CI) = 0.44-1.00) and rs6005835 (median OR(per allele) = 0.69; 95% CI = 0.53-0.91) in CHEK2, rs2078486 (median OR(per allele) = 1.65; 95% CI = 1.21-2.25) and rs12951053 (median OR(per allele) = 1.65; 95% CI = 1.20-2.26) in TP53, rs411697 (median OR (rare homozygote) = 0.53; 95% CI = 0.35 - 0.79) in BACH1 and rs10131 (median OR( rare homozygote) = not estimable) in LIG4. The six most highly associated SNPs are either predicted to be functionally significant or are in LD with such a variant. The variants in TP53 were confirmed to be associated in a large follow-up study. CONCLUSIONS/SIGNIFICANCE: Based on our findings, further follow-up of the DNA repair and response pathways in a larger dataset is warranted to confirm these results.