Multidimensional classification and diagnosis of leukemia in flow cytometry

This research is to establish new optimization methods for pattern recognition and classification of different white blood cells in actual patient data to enhance the process of diagnosis. Beckman-Coulter Corporation supplied flow cytometry data of numerous patients that are used as training sets to exploit the different physiological characteristics of the different samples provided. The methods of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used as promising pattern classification techniques to identify different white blood cell samples and provide information to medical doctors in the form of diagnostic references for the specific disease states, leukemia. The obtained results prove that when a neural network classifier is well configured and trained with cross-validation, it can perform better than support vector classifiers alone for this type of data. Furthermore, a new unsupervised learning algorithm---Density based Adaptive Window Clustering algorithm (DAWC) was designed to process large volumes of data for finding location of high data cluster in real-time. It reduces the computational load to ∼O(N) number of computations, and thus making the algorithm more attractive and faster than current hierarchical algorithms.

An incremental multilinear system for human face learning and recognition

This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

Analysis of fractured terrain using remote sensing and geographic information systems : establishing a correlation between fracture network properties and vegetation

This research analyzed the spatial relationship between a mega-scale fracture network and the occurrence of vegetation in an arid region. High-resolution aerial photographs of Arches National Park, Utah were used for digital image processing. Four sets of large-scale joints were digitized from the rectified color photograph in order to characterize the geospatial properties of the fracture network with the aid of a Geographic Information System. An unsupervised landcover classification was carried out to identify the spatial distribution of vegetation on the fractured outcrop. Results of this study confirm that the WNW-ESE alignment of vegetation is dominantly controlled by the spatial distribution of the systematic joint set, which in turn parallels the regional fold axis. This research provides insight into the spatial heterogeneity inherent to fracture networks, as well as the effects of jointing on the distribution of surface vegetation in desert environments.

Automatic and adaptable registration of live RGBD video streams sharing partially overlapping views

In this thesis, we introduce DeReEs-4v, an algorithm for unsupervised and automatic registration of two video frames captured depth-sensing cameras. DeReEs-4V receives two RGBD video streams from two depth-sensing cameras arbitrary located in an indoor space that share a minimum amount of 25% overlap between their captured scenes. The motivation of this research is to employ multiple depth-sensing cameras to enlarge the field of view and acquire a more complete and accurate 3D information of the environment. A typical way to combine multiple views from different cameras is through manual calibration. However, this process is time-consuming and may require some technical knowledge. Moreover, calibration has to be repeated when the location or position of the cameras change. In this research, we demonstrate how DeReEs-4V registration can be used to find the transformation of the view of one camera with respect to the other at interactive rates. Our algorithm automatically finds the 3D transformation to match the views from two cameras, requires no human interference, and is robust to camera movements while capturing. To validate this approach, a thorough examination of the system performance under different scenarios is presented. The system presented here supports any application that might benefit from the wider field-of-view provided by the combined scene from both cameras, including applications in 3D telepresence, gaming, people tracking, videoconferencing and computer vision.

The ecocultural context and child behavior problems: A qualitative analysis in rural Nepal.

Commonly used paradigms for studying child psychopathology emphasize individual-level factors and often neglect the role of context in shaping risk and protective factors among children, families, and communities. To address this gap, we evaluated influences of ecocultural contextual factors on definitions, development of, and responses to child behavior problems and examined how contextual knowledge can inform culturally responsive interventions. We drew on Super and Harkness' "developmental niche" framework to evaluate the influences of physical and social settings, childcare customs and practices, and parental ethnotheories on the definitions, development of, and responses to child behavior problems in a community in rural Nepal. Data were collected between February and October 2014 through in-depth interviews with a purposive sampling strategy targeting parents (N = 10), teachers (N = 6), and community leaders (N = 8) familiar with child-rearing. Results were supplemented by focus group discussions with children (N = 9) and teachers (N = 8), pile-sort interviews with mothers (N = 8) of school-aged children, and direct observations in homes, schools, and community spaces. Behavior problems were largely defined in light of parents' socialization goals and role expectations for children. Certain physical settings and times were seen to carry greater risk for problematic behavior when children were unsupervised. Parents and other adults attempted to mitigate behavior problems by supervising them and their social interactions, providing for their physical needs, educating them, and through a shared verbal reminding strategy (samjhaune). The findings of our study illustrate the transactional nature of behavior problem development that involves context-specific goals, roles, and concerns that are likely to affect adults' interpretations and responses to children's behavior. Ultimately, employing a developmental niche framework will elucidate setting-specific risk and protective factors for culturally compelling intervention strategies.

The Perspectives of Gay, Bisexual, and Queer Adolescent Males with Parent-Child Sex Communication

Problem: Gay, bisexual, and queer (GBQ) adolescent males are disproportionately affected by negative sexual health outcomes compared to their heterosexual counterparts. Their sex education needs are not sufficiently addressed in the home and the larger ecological systems. The omission of their sex education needs at a time when they are forming a sexual identity during adolescence compels GBQ males to seek information in unsupervised settings. Evidence-based interventions aimed at ensuring positive sexual health outcomes through sex communication cannot be carried out with these youth as research on how parents and GBQ males discuss sex in the home has been largely uninvestigated.

Methods: This naturalistic qualitative study focused on the interpretive reports of 15- to 20-year-old GBQ males’ discussions about sex-related topics with their parents. From a purposive sample of 30 male adolescents who self-identified as GBQ, participants who could recall at least one conversation about sex with their parents were recruited for one-time interviews and card sorts. This strategy revealed, using Bronfenbrenners’ Bioecological Theory, their perceptions about sex communication in the context of their reciprocal relationship and the ecological systems that GBQ males and their parents navigate.

Results: Parents received poor ratings as sex educators, were generally viewed as not confident in their communication approach, and lacked knowledge about issues pertinent to GBQ sons. Nevertheless, participants viewed parents as their preferred source of sex information and recognized multiple functions of sex communication. The value placed by GBQ youth on sex communication underscores their desire to ensure an uninterrupted parent-child relationship in spite of their GBQ sexual orientation. For GBQ children, inclusive sex communication is a proxy for parental acceptance.

Results show that the timing, prompts, teaching aids, and setting of sex communication for this population are similar to what has been reported with heterosexual samples. However, most GBQ sons rarely had inclusive guidance about sex and sexuality that matched their attraction, behavior, and identities. Furthermore, the assumption of heterosexuality resulted in the early awareness of being different from their peers which led them to covertly search for sex information. The combination of assumed heterosexuality and their early reliance on themselves for applicable information is a missed parental opportunity to positively impact the health of GBQ sons. More importantly, due to the powerful reach of new media, there is a critical period of maximum receptiveness that has been identified which makes inclusive sex communication paramount in the pre-sexual stage for this population. Our findings also indicate that there are plenty of opportunities for systemic improvements to meet this population’s sexual education needs.

Investigating metabolomic biomarkers of hypoxic ischaemic encephalopathy

Background An early objective biomarker to predict the severity of hypoxic-ischaemic encephalopathy (HIE) and identify infants suitable for intervention remains elusive. This thesis aims to progress metabolomic markers of HIE through a pipeline of biomarker discovery and validation by employing a novel untargeted mass spectrometry metabolomic method. Methodology Term infants with perinatal asphyxia were recruited, all having umbilical cord blood (UCB) drawn and biobanked within three hours of birth. HIE was defined by Sarnat score at 24hours and continuous multichannel-EEG. Infant neurodevelopment was assessed at 36-42 months using the Bayley Scales of Infant and Toddler Development Ed. III (BSID-III). Untargeted metabolomic analysis of UCB was performed using direct injection FT-ICR mass spectrometry (DI FT-ICR MS). Putative metabolite annotations and lipid classes were assigned and pathway analysis was performed. Results Untargeted metabolomic analysis: Thirty enrolled infants were diagnosed with HIE, including 17 mild, 8 moderate, and 5 severe cases. Pathway analysis revealed that ΔHIE was associated with a 50% and 75% perturbation of tryptophan and pyrimidine metabolism respectively, alongside alterations in amino acid pathways. Significant metabolite alterations were detected from six putatively identified lipid classes including fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids and prenol lipids. Outcome prediction: Metabolite model scores significantly correlated with outcome R=0.429 (model A) and R=0.549 (model B) respectively. Model B demonstrates the potential to predict both severe outcome (AUROC of 0.915) and intact survival (AUROC of 0.800). The effect of haemolysis: On average 5% of polar and 1.5% of non-polar features were altered between paired haemolysed and clean samples. However unsupervised multivariate analysis concluded that the preanalytical variability introduced by haemolysis was negligible compared with the inherent biological inter-individual variability. Conclusion This research has employed untargeted metabolomics to identify potential early cord blood biomarkers of HIE and has performed the technical validation of previously proposed markers.

On The Dirichlet Distribution

The Dirichlet distribution is a multivariate generalization of the Beta distribution. It is an important multivariate continuous distribution in probability and statistics. In this report, we review the Dirichlet distribution and study its properties, including statistical and information-theoretic quantities involving this distribution. Also, relationships between the Dirichlet distribution and other distributions are discussed. There are some different ways to think about generating random variables with a Dirichlet distribution. The stick-breaking approach and the Pólya urn method are discussed. In Bayesian statistics, the Dirichlet distribution and the generalized Dirichlet distribution can both be a conjugate prior for the Multinomial distribution. The Dirichlet distribution has many applications in different fields. We focus on the unsupervised learning of a finite mixture model based on the Dirichlet distribution. The Initialization Algorithm and Dirichlet Mixture Estimation Algorithm are both reviewed for estimating the parameters of a Dirichlet mixture. Three experimental results are shown for the estimation of artificial histograms, summarization of image databases and human skin detection.

Revisiting the Late Jurassic-Early Cretaceous of the NW South Iberian Basin: new ages and sedimentary environments

The study of the Upper Jurassic-Lower Cretaceous deposits (Higueruelas, Villar del Arzobispo and Aldea de Cortés Formations) of the South Iberian Basin (NW Valencia, Spain) reveals new stratigraphic and sedimentological data, which have significant implications on the stratigraphic framework, depositional environments and age of these units. The Higueruelas Fm was deposited in a mid-inner carbonate platform where oncolitic bars migrated by the action of storms and where oncoid production progressively decreased towards the uppermost part of the unit. The overlying Villar del Arzobispo Fm has been traditionally interpreted as an inner platform-lagoon evolving into a tidal-flat. Here it is interpreted as an inner-carbonate platform affected by storms, where oolitic shoals protected a lagoon, which had siliciclastic inputs from the continent. The Aldea de Cortés Fm has been previously interpreted as a lagoon surrounded by tidal-flats and fluvial-deltaic plains. Here it is reinterpreted as a coastal wetland where siliciclastic muddy deposits interacted with shallow fresh to marine water bodies, aeolian dunes and continental siliciclastic inputs. The contact between the Higueruelas and Villar del Arzobispo Fms, classically defined as gradual, is also interpreted here as rapid. More importantly, the contact between the Villar del Arzobispo and Aldea de Cortés Fms, previously considered as unconformable, is here interpreted as gradual. The presence of Alveosepta in the Villar del Arzobispo Fm suggests that at least part of this unit is Kimmeridgian, unlike the previously assigned Late Tithonian-Middle Berriasian age. Consequently, the underlying Higueruelas Fm, previously considered Tithonian, should not be younger than Kimmeridgian. Accordingly, sedimentation of the Aldea de Cortés Fm, previously considered Valangian-Hauterivian, probably started during the Tithonian and it may be considered part of the regressive trend of the Late Jurassic-Early Cretaceous cycle. This is consistent with the dinosaur faunas, typically Jurassic, described in the Villar del Arzobispo and Aldea de Cortés Fms.

Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.

Insights into the multistep transformation of MGUS to myeloma using microarray expression analysis.

To define specific pathways important in the multistep transformation process of normal plasma cells (PCs) to monoclonal gammopathy of uncertain significance (MGUS) and multiple myeloma (MM), we have applied microarray analysis to PCs from 5 healthy donors (N), 7 patients with MGUS, and 24 patients with newly diagnosed MM. Unsupervised hierarchical clustering using 125 genes with a large variation across all samples defined 2 groups: N and MGUS/MM. Supervised analysis identified 263 genes differentially expressed between N and MGUS and 380 genes differentially expressed between N and MM, 197 of which were also differentially regulated between N and MGUS. Only 74 genes were differentially expressed between MGUS and MM samples, indicating that the differences between MGUS and MM are smaller than those between N and MM or N and MGUS. Differentially expressed genes included oncogenes/tumor-suppressor genes (LAF4, RB1, and disabled homolog 2), cell-signaling genes (RAS family members, B-cell signaling and NF-kappaB genes), DNA-binding and transcription-factor genes (XBP1, zinc finger proteins, forkhead box, and ring finger proteins), and developmental genes (WNT and SHH pathways). Understanding the molecular pathogenesis of MM by gene expression profiling has demonstrated sequential genetic changes from N to malignant PCs and highlighted important pathways involved in the transformation of MGUS to MM.

Compared with specialist registrars, experienced staff nurses shorten the duration of weaning neonates from mechanical ventilation

OBJECTIVE: To compare the overall performance of specially trained neonatal nurses acting autonomously, unsupervised, and without a protocol with specialist registrars when weaning neonates from mechanical ventilation.

DESIGN: Prospective, randomized, controlled trial.

SETTING: A single neonatal intensive care unit.

PATIENTS: Neonates requiring conventional mechanical ventilation (n = 50).

INTERVENTIONS: Infants on conventional ventilation were randomly assigned to receive either nurse-led (n = 25) or registrar-led (n = 23) weaning. A total of 48 infants completed the study (two infants in the registrar group were excluded when their parents withdrew consent).

MEASUREMENTS AND MAIN RESULTS: The main outcome measure, median weaning time, was 1200 mins (95% confidence interval [CI], 621-1779 mins) in the nurse group and 3015 mins (95% CI, 2650-3380 mins) in the registrar group (p = .0458). The median time from treatment assignment to the first ventilator change was 60 mins (95% CI, 52-68 mins) in the nurse group and 120 mins (95% CI, 103-137 mins) in the registrar group (p = .35). On average, the nurses made ventilator changes every 4.5 hrs (95% CI, 2.9-6 hrs) and the registrars every 7.2 hrs (95% CI, 5.4-9 hrs; p = .003). The median number (range) of backward steps taken per infant was 0 (0-5 steps) in the nurse group and 1 (0-5 steps) in the registrar group (p = .019).

CONCLUSIONS: The findings of this study suggest that additional domains of neonatal critical care could be reviewed for their potential transfer to appropriately prepared nurses.

New developments on VCA unmixing algorithm

Hyperspectral sensors are being developed for remote sensing applications. These sensors produce huge data volumes which require faster processing and analysis tools. Vertex component analysis (VCA) has become a very useful tool to unmix hyperspectral data. It has been successfully used to determine endmembers and unmix large hyperspectral data sets without the use of any a priori knowledge of the constituent spectra. Compared with other geometric-based approaches VCA is an efficient method from the computational point of view. In this paper we introduce new developments for VCA: 1) a new signal subspace identification method (HySime) is applied to infer the signal subspace where the data set live. This step also infers the number of endmembers present in the data set; 2) after the projection of the data set onto the signal subspace, the algorithm iteratively projects the data set onto several directions orthogonal to the subspace spanned by the endmembers already determined. The new endmember signature corresponds to these extreme of the projections. The capability of VCA to unmix large hyperspectral scenes (real or simulated), with low computational complexity, is also illustrated.