An overview of functional and stereological evaluation of spermatogenesis and germ cell transplantation in fish

Although there are almost thirty-thousand species of fish living in a great variety of habitats and utilizing vast reproductive strategies, our knowledge of morphofunctional and quantitative aspects of testis structure and spermatogenesis is still incipient for this group of vertebrates. In this review, we discuss aspects that are important to better understanding of testis structure and function, and of the development of germ cells (GC) during spermatogenesis. To achieve this, we have recently completed a number of studies presenting morphometric and functional data related to the numbers of GC and Sertoli cells (SC) per each type of spermatogenic cyst, the number of spermatogonial generations, the SC efficiency, and the magnitude of GC loss that normally occurs during spermatogenesis. We also investigated SC proliferation and the relationship of this important event to early spermatogenic cysts. The available data strongly suggest that SC proliferation in sexually mature tilapia is the primary factor responsible for the increase in testis size and for determination of the magnitude of sperm production. The influence of temperature on the duration of spermatogenesis in tilapia was also evaluated and we have used this knowledge to deplete endogenous spermatogenesis in this teleost, in order to develop an experimental system for GC transplantation. This exciting technique results in new possibilities for investigation of spermatogenesis and spermatogonial stem cell biology, creating also an entirely new and promising scenario in biotechnology - transgenic animal production and the preservation of the genetic stocks of valuable animals or endangered species. © Springer Science+Business Media B.V. 2008.

A multivariate approach for processing magnetization effects in triggered event-related functional magnetic resonance imaging time series

Triggered event-related functional magnetic resonance imaging requires sparse intervals of temporally resolved functional data acquisitions, whose initiation corresponds to the occurrence of an event, typically an epileptic spike in the electroencephalographic trace. However, conventional fMRI time series are greatly affected by non-steady-state magnetization effects, which obscure initial blood oxygen level-dependent (BOLD) signals. Here, conventional echo-planar imaging and a post-processing solution based on principal component analysis were employed to remove the dominant eigenimages of the time series, to filter out the global signal changes induced by magnetization decay and to recover BOLD signals starting with the first functional volume. This approach was compared with a physical solution using radiofrequency preparation, which nullifies magnetization effects. As an application of the method, the detectability of the initial transient BOLD response in the auditory cortex, which is elicited by the onset of acoustic scanner noise, was used to demonstrate that post-processing-based removal of magnetization effects allows to detect brain activity patterns identical with those obtained using the radiofrequency preparation. Using the auditory responses as an ideal experimental model of triggered brain activity, our results suggest that reducing the initial magnetization effects by removing a few principal components from fMRI data may be potentially useful in the analysis of triggered event-related echo-planar time series. The implications of this study are discussed with special caution to remaining technical limitations and the additional neurophysiological issues of the triggered acquisition.

PENALIZED FUNCTIONAL REGRESSION

We develop fast fitting methods for generalized functional linear models. An undersmooth of the functional predictor is obtained by projecting on a large number of smooth eigenvectors and the coefficient function is estimated using penalized spline regression. Our method can be applied to many functional data designs including functions measured with and without error, sparsely or densely sampled. The methods also extend to the case of multiple functional predictors or functional predictors with a natural multilevel structure. Our approach can be implemented using standard mixed effects software and is computationally fast. Our methodology is motivated by a diffusion tensor imaging (DTI) study. The aim of this study is to analyze differences between various cerebral white matter tract property measurements of multiple sclerosis (MS) patients and controls. While the statistical developments proposed here were motivated by the DTI study, the methodology is designed and presented in generality and is applicable to many other areas of scientific research. An online appendix provides R implementations of all simulations.

Integration of electrical neuroimaging with other functional imaging methods

Integrating evidence from different imaging modalities is important to overcome specific limitations of any given imaging method, such as insensitivity of the EEG to unsynchronized neural events, or the lack of fMRI sensitivity to events of low metabolic demand. Processes that are visible in one modality may be related in a nontrivial way to other processes visible in another modality and insight may only be obtained by integrating both methods through a common analysis. For example, brain activity at rest seems to be at least partly determined by an interaction of cortical rhythms (visible to EEG but not to fMRI) with sub-cortical activity (visible to fMRI, but usually not to EEG without averaging). A combination of EEG and fMRI data during rest may thus be more informative than the sum of two separate analyses in both modalities. Integration is also an important source of converging evidence about specific aspects and general principles of neural functions and their dysfunctions in certain pathologies. This is because not only electrical, but also energetic, biochemical, hemodynamic and metabolic processes characterize neural states and functions, and because brain structure provides crucial constraints upon neural functions. Focusing on multimodal integration of functional data should not distract from the privileged status of the electric field as the primary direct, noninvasive real-time measure of neural transmission. The preceding chapters illustrate how electrical neuroimaging has turned scalp EEG into an imaging modality which directly captures the full temporal dynamics of neural activity in the brain.

Systematic Review and Cumulative Analysis of Oncologic and Functional Outcomes After Robot-assisted Radical Cystectomy

CONTEXT Although open radical cystectomy (ORC) is still the standard approach, laparoscopic radical cystectomy (LRC) and robot-assisted radical cystectomy (RARC) are increasingly performed. OBJECTIVE To report on a systematic literature review and cumulative analysis of pathologic, oncologic, and functional outcomes of RARC in comparison with ORC and LRC. EVIDENCE ACQUISITION Medline, Scopus, and Web of Science databases were searched using a free-text protocol including the terms robot-assisted radical cystectomy or da Vinci radical cystectomy or robot* radical cystectomy. RARC case series and studies comparing RARC with either ORC or LRC were collected. A cumulative analysis was conducted. EVIDENCE SYNTHESIS The searches retrieved 105 papers, 87 of which reported on pathologic, oncologic, or functional outcomes. Most series were retrospective and had small case numbers, short follow-up, and potential patient selection bias. The lymph node yield during lymph node dissection was 19 (range: 3-55), with half of the series following an extended template (yield range: 11-55). The lymph node-positive rate was 22%. The performance of lymphadenectomy was correlated with surgeon and institutional volume. Cumulative analyses showed no significant difference in lymph node yield between RARC and ORC. Positive surgical margin (PSM) rates were 5.6% (1-1.5% in pT2 disease and 0-25% in pT3 and higher disease). PSM rates did not appear to decrease with sequential case numbers. Cumulative analyses showed no significant difference in rates of surgical margins between RARC and ORC or RARC and LRC. Neoadjuvant chemotherapy use ranged from 0% to 31%, with adjuvant chemotherapy used in 4-29% of patients. Only six series reported a mean follow-up of >36 mo. Three-year disease-free survival (DFS), cancer-specific survival (CSS), and overall survival (OS) rates were 67-76%, 68-83%, and 61-80%, respectively. The 5-yr DFS, CSS, and OS rates were 53-74%, 66-80%, and 39-66%, respectively. Similar to ORC, disease of higher pathologic stage or evidence of lymph node involvement was associated with worse survival. Very limited data were available with respect to functional outcomes. The 12-mo continence rates with continent diversion were 83-100% in men for daytime continence and 66-76% for nighttime continence. In one series, potency was recovered in 63% of patients who were evaluable at 12 mo. CONCLUSIONS Oncologic and functional data from RARC remain immature, and longer-term prospective studies are needed. Cumulative analyses demonstrated that lymph node yields and PSM rates were similar between RARC and ORC. Conclusive long-term survival outcomes for RARC were limited, although oncologic outcomes up to 5 yr were similar to those reported for ORC. PATIENT SUMMARY Although open radical cystectomy (RC) is still regarded as the standard treatment for muscle-invasive bladder cancer, laparoscopic and robot-assisted RCs are becoming more popular. Templates of lymph node dissection, lymph node yields, and positive surgical margin rates are acceptable with robot-assisted RC. Although definitive comparisons with open RC with respect to oncologic or functional outcomes are lacking, early results appear comparable.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Length of Stay, Discharge Destination, and Functional Improvement: Utility of the Australian National Subacute and Nonacute Patient Casemix Classification

Background and Purpose - Although implemented in 1998, no research has examined how well the Australian National Subacute and Nonacute Patient (AN-SNAP) Casemix Classification predicts length of stay (LOS), discharge destination, and functional improvement in public hospital stroke rehabilitation units in Australia. Methods - 406 consecutive admissions to 3 stroke rehabilitation units in Queensland, Australia were studied. Sociode-mographic, clinical, and functional data were collected. General linear modeling and logistic regression were used to assess the ability of AN-SNAP to predict outcomes. Results - AN-SNAP significantly predicted each outcome. There were clear relationships between the outcomes of longer LOS, poorer functional improvement and discharge into care, and the AN-SNAP classes that reflected poorer functional ability and older age. Other predictors included living situation, acute LOS, comorbidity, and stroke type. Conclusions - AN-SNAP is a consistent predictor of LOS, functional change and discharge destination, and has utility in assisting clinicians to set rehabilitation goals and plan discharge.

Functional characterisation of an engineered multidomain human P450 E1 by molecular Lego

The human cytochrome P450s constitute an important family of monooxygenase enzymes that carry out essential roles in the metabolism of endogenous compounds and foreign chemicals. We present here results of a fusion between a human P450 enzyme and a bacterial reductase that for the first time is shown does not require the addition of lipids or detergents to achieve wild-type-like activities. The fusion enzyme, P450 2E1-BMR, contains the N-terminally modified residues 22-493 of the human P450 2E1 fused at the C-terminus to residues 473-1049 of the P450 BM3 reductase (BMR). The P450 2E1-BMR enzyme is active, self-sufficient and presents the typical marker activities of the native human P450 2E1: the hydroxylation of p-nitrophenol (K (M)=1.84 +/- 0.09 mM and k (cat) of 2.98 +/- 0.04 nmol of p-nitrocatechol formed per minute per nanomole of P450) and chlorzoxazone (K (M)=0.65 +/- 0.08 mM and k (cat) of 0.95 +/- 0.10 nmol of 6-hydroxychlorzoxazone formed per minute per nanomole of P450). A 3D model of human P450 2E1 was generated to rationalise the functional data and to allow an analysis of the surface potentials. The distribution of charges on the model of P450 2E1 compared with that of the FMN domain of BMR provides the ground for the understanding of the interaction between the fused domains. The results point the way to successfully engineer a variety of catalytically self-sufficient human P450 enzymes for drug metabolism studies in solution.

The implementation of a functional query language front-end to a relational database system. Available in 2 volumes.

Database systems have a user interface one of the components of which will normally be a query language which is based on a particular data model. Typically data models provide primitives to define, manipulate and query databases. Often these primitives are designed to form self-contained query languages. This thesis describes a prototype implementation of a system which allows users to specify queries against the database in a query language whose primitives are not those provided by the actual model on which the database system is based, but those provided by a different data model. The implementation chosen is the Functional Query Language Front End (FQLFE). This uses the Daplex functional data model and query language. Using FQLFE, users can specify the underlying database (based on the relational model) in terms of Daplex. Queries against this specified view can then be made in Daplex. FQLFE transforms these queries into the query language (Quel) of the underlying target database system (Ingres). The automation of part of the Daplex function definition phase is also described and its implementation discussed.

Functional characterization of two human receptor activity-modifying protein 3 variants

Adrenomedullin (AM) and amylin are involved in angiogenesis/lymphangiogenesis and glucose homeostasis/food intake, respectively. They activate receptor activity-modifying protein (RAMP)/G protein-coupled receptor (GPCR) complexes. RAMP3 with the calcitonin receptor-like receptor (CLR) forms the AM(2) receptor, whereas when paired with the calcitonin receptor AMY(3) receptors are formed. RAMP3 interacts with other GPCRs although the consequences of these interactions are poorly understood. Therefore, variations in the RAMP3 sequence, such as single nucleotide polymorphisms or mutations could be relevant to human health. Variants of RAMP3 have been identified. In particular, analysis of AK222469 (Homo sapiens mRNA for receptor (calcitonin) activity-modifying protein 3 precursor variant) revealed several nucleotide differences, three of which encoded amino acid changes (Cys40Trp, Phe100Ser, Leu147Pro). Trp56Arg RAMP3 is a polymorphic variant of human RAMP3 at a conserved amino acid position. To determine their function we used wild-type (WT) human RAMP3 as a template for introducing amino acid mutations. Mutant or WT RAMP3 function was determined in Cos-7 cells with CLR or the calcitonin receptor (CT((a))). Cys40Trp/Phe100Ser/Leu147Pro RAMP3 was functionally compromised, with reduced AM and amylin potency at the respective AM(2) and AMY(3(a)) receptor complexes. Cys40Trp and Phe100Ser mutations contributed to this phenotype, unlike Leu147Pro. Reduced cell-surface expression of mutant receptor complexes probably explains the functional data. In contrast, Trp56Arg RAMP3 was WT in phenotype. This study provides insight into the role of these residues in RAMP3. The existence of AK222469 in the human population has implications for the function of RAMP3/GPCR complexes, particularly AM and amylin receptors.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.

Data analysis with merge trees

Today’s data are increasingly complex and classical statistical techniques need growingly more refined mathematical tools to be able to model and investigate them. Paradigmatic situations are represented by data which need to be considered up to some kind of trans- formation and all those circumstances in which the analyst finds himself in the need of defining a general concept of shape. Topological Data Analysis (TDA) is a field which is fundamentally contributing to such challenges by extracting topological information from data with a plethora of interpretable and computationally accessible pipelines. We con- tribute to this field by developing a series of novel tools, techniques and applications to work with a particular topological summary called merge tree. To analyze sets of merge trees we introduce a novel metric structure along with an algorithm to compute it, define a framework to compare different functions defined on merge trees and investigate the metric space obtained with the aforementioned metric. Different geometric and topolog- ical properties of the space of merge trees are established, with the aim of obtaining a deeper understanding of such trees. To showcase the effectiveness of the proposed metric, we develop an application in the field of Functional Data Analysis, working with functions up to homeomorphic reparametrization, and in the field of radiomics, where each patient is represented via a clustering dendrogram.

Satisfação no trabalho e capacidade para o trabalho entre docentes universitários

O objetivo principal deste estudo foi verificar a correlação entre satisfação no trabalho e capacidade para o trabalho de docentes universitários. Este é um estudo transversal de abordagem quantitativa. Dele fizeram parte 154 docentes, dos quais 50,6% eram homens e 49,4% eram mulheres e cuja idade média era de 39,25 anos. A coleta de dados incluiu três questionários: 1. Dados sociodemográficos e funcionais, 2. Escala de satisfação no trabalho; 3. Índice de capacidade para o Trabalho - ICT. A satisfação no trabalho e o ICT mostraram correlação estatisticamente significativa (r=0,23, p<0,01). Conclui-se que o aumento da satisfação no trabalho pode melhorar a capacidade para o trabalho entre os docentes.

Formal Contracts for Runtime Verification Support in the Ada Programming Language

Poster presented in The 28th GI/ITG International Conference on Architecture of Computing Systems (ARCS 2015). 24 to 26, Mar, 2015. Porto, Portugal.

Design and implementation of 3D buildings integration for a Webgl-Based Virtual Globe: a case study of Valencian Cadastre and Fide Building Mode

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.