The maternal and neonatal disposition of pethidine and bupivacaine administered in childbirth

Imperial Users onl

Reinterpretation of evidence advanced for neo-oogenesis in mammals, in terms of a finite oocyte reserve

The central tenet of ovarian biology, that the oocyte reserve in adult female mammals is finite, has been challenged over recent years by proponents of neo-oogenesis, who claim that germline stem cells exist in the ovarian surface epithelium or the bone marrow. Currently opinion is divided over these claims, and further scrutiny of the evidence advanced in support of the neo-oogenesis hypothesis is warranted - especially in view of the enormous implications for female fertility and health. This article contributes arguments against the hypothesis, providing alternative explanations for key observations, based on published data. Specifically, DNA synthesis in germ cells in the postnatal mouse ovary is attributed to mitochondrial genome replication, and to DNA repair in oocytes lagging in meiotic progression. Lines purported to consist of germline stem cells are identified as ovarian epithelium or as oogonia, from which cultures have been derived previously. Effects of ovotoxic treatments are found to negate claims for the existence of germline stem cells. And arguments are presented for the misidentification of ovarian somatic cells as de novo oocytes. These clarifications, if correct, undermine the concept that germline stem cells supplement the oocyte quota in the postnatal ovary; and instead comply with the theory of a fixed, unregenerated reserve. It is proposed that acceptance of the neo-oogenesis hypothesis is erroneous, and may effectively impede research in areas of ovarian biology. To illustrate, a novel explanation that is consistent with orthodox theory is provided for the observed restoration of fertility in chemotherapy-treated female mice following bone marrow transplantation, otherwise interpreted by proponents of neo-oogenesis as involving stimulation of endogenous germline stem cells. Instead, it is proposed that the chemotherapeutic regimens induce autoimmunity to ovarian antigens, and that the haematopoietic chimaerism produced by bone marrow transplantation circumvents activation of an autoreactive response, thereby rescuing ovarian function. The suggested mechanism draws from animal models of autoimmune ovarian disease, which implicate dysregulation of T cell regulatory function; and from a surmised role for follicular apoptosis in the provision of ovarian autoantigens, to sustain self-tolerance during homeostasis. This interpretation has direct implications for fertility preservation in women undergoing chemotherapy

An Investigation of the Cellular Stress Response in Cells Infected With Herpes Simplex Virus

This study entailed an investigation of the cellular stress response in secondary chick embryo fibroblasts (CEF) during infection by HSV. It was established that infections at a+ NPT with temperature-sensitive mutants of HSV-1 which are defective in immediate-early viral polypeptide Vmw IE 175 (i.e. tsD, tsK and tsT) cause the stress response to be induced, as manifest by a marked stimulation of synthesis of stress proteins. Induction by tsK was shown to be dependent upon the synthesis of immediate-early viral polypeptides. Infections with other mutants of HSV-1 (tsl201, tsB, tsE, tsG and MDK/2) at a NPT and, to a lesser extent, the revertant of tSK, ts+K, or with wt HSV-1 or wt HSV-2, all of which are non-defective in Vmw IE 175 or the HSV-2 equivalent, Vmw IE 182, cause synthesis of stress proteins to be increased. To account for these observations a hypothesis was advanced, that cells are subjected to stress during infection with wt or mutant HSV, owing to the expresssion of viral functions, and that induction of the stress response is subsequently inhibited depending upon the characteristics of the infecting virus: inhibition is most effective in cells infected with wt viruses, and least effective in cells infected at a NPT with temperature-sensitive mutants of HSV-1 defective in Vmw IE 175

Macroscopic self standing SWCNT fibers as efficient electron emitters with very high emission current for robust cold cathodes

A novel of self-standing nanotube-based cold cathode is described. The electron emitter is a single macroscopic fibre spun from neat single wall carbon nanotubes and consists of an ensemble of nanotube bundles held together by van der Waals forces. Field emission measurements carried out using two different types of apparatus demonstrated the long working life of the realised cathode. The system is able to emit at very high current densities, up to 13 A/cm2, and shows very low values of both turn on and threshold field, 0.12 V/lm and 0.21 V/lm, respectively. Such easy to handle self-standing electron sources assure good performances and represent an enabling technology for a scalable production of cold cathodes. 2012 Elsevier Ltd. All rights reserved. 1. Introduction Due to a unique combination of properties, including high electrical and thermal conductivity, and high mechanical/ chemical/thermal stability, carbon nanotubes (CNTs) have been recognised as ideal candidate materials for application in microelectronics [1]. Moreover, the high aspect ratio characterising this intriguing material makes possible to significantly strengthen electric fields into the vicinity of nanotubes tips

A data-driven framework for selecting recycling processes of end-of-life PCBs

LAUREA MAGISTRALEIl riciclo delle schede elettroniche a fine vita (PCBs) rappresenta una sfida e un’opportunità sempre più rilevante nel contesto dei rifiuti elettronici (RAEE) e dell’economia circolare. Questa tesi affronta il problema ancora poco esplorato della selezione dei processi ottimali di riciclo delle PCB, proponendo un approccio decisionale orientato ai dati. Con una literature review su più livelli e un’analisi dei gap esistenti, viene evidenziata l’assenza di sistemi informativi specifici a supporto della selezione dei processi di trattamento dei PCB e la mancanza di una prospettiva olistica nella valutazione delle diverse tecniche di riciclo. In risposta a queste lacune, la tesi sviluppa un modello di database relazionale e un Decision Support System strutturato per consentire valutazioni basate sulla simulazione. Attenzione particolare è rivolta alla definizione dell’insieme di dati necessari peralimentare tali simulazioni, inclusi attributi specifici dei PCB, condizioni di mercato e parametri operativi a livello di processo, oltre alle fonti di questi dati. Per valutare la fattibilità e la rilevanza del modello, sono state condotte interviste con ricercatori e professionisti del settore. L’analisi tematica ha evidenziato ostacoli chiave all’implementazione, come la mancanza di dati a livello di componente, la resistenza alla condivisione delle informazioni e la scarsa diversificazione industriale nei processi di riciclo, nonché alcuni fattori abilitanti, come la maggiore trasparenza dei dati favorita dalle politiche europee o l’innovazione tecnologica nei processi di trattamento. Il framework supporta un processo decisionale strutturato tramite Analytic Hierarchy Process (AHP). Le interviste agli esperti hanno permesso di attribuire i pesi ai criteri dell’AHP, identificando la redditività e la maturità tecnologica come i criteri più rilevanti. Sebbene l’esecuzione delle simulazioni sia fuori dallo scopo di questa tesi, l’architettura proposta pone le basi per una futura implementazione. Il modello contribuisce a colmare il divario tra ricerca accademica e processi decisionali nel contesto reale della selezione dei trattamenti per il riciclo delle PCB.The recycling of waste printed circuit boards (PCBs) presents a growing challenge and opportunity within the broader context of electronic waste (WEEE) and circular economy strategies. This thesis addresses the underexplored problem of selecting optimal recycling processes for PCBs by proposing a data-oriented decision support approach. Through a multi-level literature review and gap analysis, it identifies the absence of specific information systems to support the selection of PCB processes, and a lack of a holistic perspective when evaluating different recycling techniques. In response, the thesis develops a relational database model and a decision support system structured to enable simulation-based assessments. Particular focus is placed on defining the full spectrum of data required to inform such simulations, including PCB-specific attributes, market conditions, and process-level operational parameters, along with their potential sources. To assess the model’s feasibility and relevance, interviews were conducted with academic researchers and industry professionals. Thematic analysis revealed key implementation barriers, such as the lack of component-level data, resistance to information sharing, and limited industrial recycling diversity, as well as enablers like policy-driven data transparency or recycling process technology innovation. The framework supports structured decision-making using the Analytic Hierarchy Process (AHP). Through interviews and expert input, the weights of the AHP criteria were assigned, with profitability and technology readiness emerging as the most important. Although simulation execution is outside the thesis scope, the proposed architecture creates an initial basis for future implementation. The resulting model contributes to bridging the gap between academic research and real-world decision-making in PCB recycling process selection

Concept drift detection in high-dimensional spaces

LAUREA MAGISTRALENel realizzare modelli di classificazione, analisi predittiva, etc., l’assunzione di relazioni statiche tra i dati di input e output - cio`e che le relazioni tipo y = f(x) rimangano vere nel tempo - spesso fallisce. Il fenomeno si può chiamare “concept drift”, o deriva del concetto, indicando cambiamenti fra le quantità e relazioni statistiche. Considerando flussi di dati multivariati, comuni in settori come l’IoT e l’analisi testuale, l’identificazione di cambiamenti significativi è estremamente complicata. Tecniche di riduzione della dimensionalità come l’Analisi delle Componenti Principali (PCA) offrono - a volte - una soluzione ma comportano anche altri problemi e costi computazionali. Questo studio osserva le analisi dell’algoritmo QuantTree (QT) di dataframe ad alta dimensionalità, estende l’osservazione alla sua versione generalizzata Kernel-QT (KQT) e alla sua variante online, QT-EWMA. Viene proposto l’algoritmo Kernel-QuantTree Exponentially Weighted Moving Average (KQT- EWMA). Nel costruire sistemi di monitoraggio, consideriamo l’equilibrio tra il tasso di veri positivi (TPR) e il tasso di falsi positivi (FPR), con particolari attenzioni per quest’ultimo, e altri criteri quantitativi come il tempo di esecuzione, che abbiamo sempre registrato lungo questi esperimenti. La formulazione del problema si fonda sui tre elementi principali di un algoritmo per concept drift detection: un modello della distribuzione iniziale, una statistica derivata da questo e una regola decisionale che valuti un cambiamento come tale. Assumiamo sconosciute sia la distribuzione iniziale che le derive che questa subisce nel tempo. Esploriamo le due modalità batch-wise (offline) e online, indagando limiti e vantaggi delle diverse tecniche di riduzione della dimensionalità d, incluse PCA e Random Projections, proiezioni randomiche. Lo studio mostra l’impatto di d sulle prestazioni di diversi algoritmi insieme alla disponibilità (in generale scarsa) di dati di training. Continuiamo l’analisi di KQT considerando quasi-norme l_p come funzioni kernel alternative, noto che valori di p ≤ 1 possono essere adatti in spazi ad alta dimensionalità. Con l’introduzione di KQT-EWMA, un algoritmo online e non parametrico, estiendiamo alcuni risultati teorici precedenti; ne confrontiamo le prestazioni con metodi esistenti. KQT-EWMA è comparabile allo stato dell’arte, superiore in ambienti controllati (con un sufficiente numero di punti di training). Le complessità affrontate in questo ambiente dinamico portano senz’altro la necessità di soluzioni semplici.In predictive modeling, the assumption of static relationships between input and output data often fails. The phenomenon can be addressed as \textit{concept drift}, signifying shifts in data patterns over time. In high-dimensional multivariate data streams, common in fields like IoT and text analysis, the curse of dimensionality complicates the identification of meaningful changes. Feature reduction techniques, such as Principal Component Analysis (PCA), offer a solution but come with computational costs. This study addresses the performance of the QuantTree (QT) algorithm in high-dimensional dataframes, extending its exploration to the generalized Kernel-QT (KQT) and its online variant, QT-EWMA. A novel addition to this landscape is proposed—Kernel-QuantTree Exponentially Weighted Moving Average (KQT-EWMA). In evaluating and building a monitoring system, we consider the balance between True Positive Rate (TPR) and False Positive Rate (FPR) to be crucial. We also consider other quantitative criteria such as execution time. The problem formulation revolves around a change detection algorithm's three main components: a model of the initial distribution, a statistic derived from it, and a decision rule. We assume both the initial distribution and the changing distribution to be unknown. The study employs a QuantTree-like histogram fitted on the distribution to estimate the model. Two modes of drift detection - batch-wise and online - are explored. The study investigates the limitations and advantages of different dimensionality reduction techniques, including PCA and Random Projections. The study uncovers the impact of dimensionality together with the availability of training data on QT's performance. We extend the analysis to incorporate $l_p$ norms as alternative kernel functions for KQT, considering quasi-norms with $p \leq 1$ that might be suitable in high dimensional dataframes. Introducing KQT-EWMA, an online nonparametric change-detection algorithm, we extend some previous theoretical results and compare its performance against existing methods; KQT-EWMA stands out as comparable or superior with respect to the state of the art. The complexities addressed in this dynamic environment underscore the ongoing challenges and the need for simple solutions in the ever-evolving landscape of data analysis

Transport mechanism in granular Ni deposited on carbon nanotubes fibers

We investigate the transport properties of granular nickel electrodeposited on carbon nanotube fibers by measuring the electrical resistance and the current voltage characteristics as a function of the temperature. The bare fiber is governed by a three-dimensional variable range hopping transport mechanism, however, a semiconducting to metallic transition is observed after the Ni deposition as a consequence of the evolution from weak to strong coupling between the deposited nickel grains. The experimental results indicate that the charge transport in the Ni-coated fiber develops from hopping governed by the Coulomb blockade in the case of small grains dimensions to a metallic electron phonon interaction mechanism for large grains dimensions. Tunneling enhanced by thermal fluctuation is responsible for the transport in the intermediate conductivity range. The role of the fiber and the effects due to the magnetic nature of the nickel grains are also discussed

The use of computer models to predict temperature and smoke movement in high bay spaces

The Building and Fire Research Laboratory (BFRL) was given the opportunity to make measurements during fire calibration tests of the heat detection system in an aircraft hangar with a nominal 30.4 (100 ft) ceiling height near Dallas, TX. Fire gas temperatures resulting from an approximately 8250 kW isopropyl alcohol pool fire were measured above the fire and along the ceiling. The results of the experiments were then compared to predictions from the computer fire models DETACT-QS, FPETOOL, and LAVENT. In section A of the analysis conducted, DETACT-QS AND FPETOOL significantly underpredicted the gas temperature. LAVENT at the position below the ceiling corresponding to maximum temperature and velocity provided better agreement with the data. For large spaces, hot gas transport time and an improved fire plume dynamics model should be incorporated into the computer fire model activation routines. A computational fluid dynamics (CFD) model, HARWELL FLOW3D, was then used to model the hot gas movement in the space. Reasonable agreement was found between the temperatures predicted from the CFD calculations and the temperatures measured in the aircraft hangar. In section B, an existing NASA high bay space was modeled using the CFD model. The NASA space was a clean room, 27.4 m (90 ft) high with forced horizontal laminar flow. The purpose of this analysis is to determine how the existing fire detection devices would respond to various size fires in the space. The analysis was conducted for 32 MW, 400 kW, and 40 kW fires