Women in the Public School: A Problem in Discrimination and Motivation

Sex-typing wastes talent, contends the author of this outspoken article. Worse (she maintains), Whereas men are unsexed by failure, women seem to be unsexed by success. Her solution is two-sided

Cloning of the Complete Gene for Carcinoembryonic Antigen

Carcinoembryonic antigen (CEA) is a widely used tumor marker, especially in the surveillance of colonic cancer patients. Although CEA is also present in some normal tissues, it is apparently expressed at higher levels in tumorous tissues than in corresponding normal tissues. As a first step toward analyzing the regulation of expression of CEA at the transcriptional level, we have isolated and characterized a cosmid clone (cosCEA1), which contains the entire coding region of the CEA gene. A close correlation exists between the exon and deduced immunoglobulin-like domain borders. We have determined a cluster of transcriptional starts for CEA and the closely related nonspecific cross-reacting antigen (NCA) gene and have sequenced their putative promoters. Regions of sequence homology are found as far as approximately 500 nucleotides upstream from the translational starts of these genes, but farther upstream they diverge completely. In both cases we were unable to find classic TATA or CAAT boxes at their expected positions. To characterize the CEA and NCA promoters, we carried out transient transfection assays with promoter-indicator gene constructs in the CEA-producing adenocarcinoma cell line SW403, as well as in nonproducing HeLa cells. A CEA gene promoter construct, containing approximately 400 nucleotides upstream from the translational start, showed nine times higher activity in the SW403 than in the HeLa cell line. This indicates that cis-acting sequences which convey cell type-specific expression of the CEA gene are contained within this region

Salt Appetite and its Effects on Cardiovascular Risk in Primary Aldosteronism

First described in 1955 by Jerome W. Conn, primary aldosteronism (PA) today is well established as a relevant cause of secondary hypertension and accounts for about 5–10 % of hypertensives. The importance of considering PA is based on its deleterious target organ damage far beyond the effect of elevated blood pressure and on PA being a potentially curable form of hypertension. Aside the established contributory role of high dietary salt intake to arterial hypertension and cardiovascular disease, high salt intake is mandatory for aldosterone-mediated deleterious effects on target-organ damage in patients with primary aldosteronism. Consequently, counselling patients on the need to reduce salt intake represents a major component in the treatment of PA to minimize cardiovascular damage. Unfortunately, in PA patients salt intake is high and far beyond the target values of 5 g per day, recommended by the World Health Organization. Insufficient patient motivation for lifestyle interventions can be further complicated by enhancing effects of aldosterone on salt appetite, via central and gustatory pathways. In this context, treatment for PA by adrenalectomy results in a spontaneous decrease in dietary salt intake and might therefore provide further reduction of cardiovascular risk in PA than specific medical treatment alone. Furthermore, there is evidence from clinical studies that even after sufficient treatment of PA dietary salt intake remains a relevant prognostic factor for cardiovascular risk. This review will focus on the synergistic benefits derived from both blockade of aldosterone-mediated effects and reduction in dietary salt intake on cardiovascular risk

Gene Expression Analysis of the Hepatotoxicant Methapyrilene in Primary Rat Hepatocytes: An Interlaboratory Study

Genomics technologies are used in several disciplines, including toxicology. However, these technologies are relatively new, and their applications require further investigations. When investigators apply these technologies to in vitro experiments, two major issues need to be clarified: a) can in vitro toxicity studies, in combination with genomics analyses, be used to predict the toxicity of a compound; and b) are the generated toxicogenomics data reproducible between laboratories? These questions were addressed by an interlaboratory study with laboratories of four pharmaceutical companies. We evaluated gene expression patterns from cultured rat primary hepatocytes after a 24-hr incubation with methapyrilene (MP). Extensive data analysis showed that comparison of genomics data from different sources is complex because both experimental and statistical variability are important confounding factors. However, appropriate statistical tools allowed us to use gene expression profiles to distinguish high-dose–treated cells from vehicle-treated cells. Moreover, we correctly identified MP in an independently generated in vitro database, underlining that in vitro toxicogenomics could be a predictive tool for toxicity. From a mechanistic point of view, despite the observed site-to-site variability, there was good concordance regarding the affected biologic processes. Several subsets of regulated genes were obtained by analyzing the data sets with one method or using different statistical analysis methods. The identified genes are involved in cellular processes that are associated to the exposure of primary hepatocytes to MP. Whether they are specific for MP and are cause or consequence of the toxicity requires further investigations

Exakte L∞-Nächster-Nachbar-Suche in hohen Dimensionen

Cover and Contents 1 Introduction 1 2 Nearest-Neighbor Search without preprocessing 5 2.1 The `CUBE METHOD` 5 2.2 The `ADAPTIVE METHOD` 15 3 Nearest-Neighbor Search with preprocessing 19 3.1 Speeding up the `CUBE METHOD` by rejecting points 19 3.2 Speeding up the `CUBE METHOD` by using monotone sequences 34 4 Extensions of the `CUBE METHOD` 53 4.1 The `GROWING-CUBE` variant 53 4.2 k-Nearest-Neighbor Search 62 4.3 Other distributions 69 4.4 External-Memory Nearest-Neighbor Search 73 4.5 Experiments 78 5 Time-Space Tradeoffs for Nearest-Neighbor Search 81 5.1 The data structure 81 5.2 The expected runtime and expected space complexity 82 Conclusions 89 Bibliography 91 Curriculum Vitae 96 Zusammenfassung 97In this thesis we consider the nearest-neighbor problem, which is defined as follows: given a fixed set P of n data points in some metric space X, build a data structure such that for each given query point q a data point from P closest to q can be found efficiently. The underlying metric space is usually the d-dimensional real space Rd together with one of the Lp-metrics, 1<= p <=∞. In many applications, the dimension d of the search space is quite high and can reach several hundreds or even several thousands. Therefore, running times and storage requirements exponential in d are prohibitive in these cases. Because of their exponential dependence on the dimension, all known techniques for exact nearest-neighbor problem are in fact in high dimensions not competitive with the brute-force method, which just determines the distance of q to each point in P and selects the minimum. This thesis presents algorithms for solving the high-dimensional exact nearest-neighbor problem with respect to the L∞-distance. We analyze the average-case situation when the data points are chosen independently at random under uniform distribution. The algorithms considerably improve the brute- force method, they are simple and easy to implement. In Chapter 2 we consider query algorithms that need no preprocessing and require storage only for the point set P. Their average running time is O( n+(nd / ln(n)) ). In Chapter 3 we present two strategies which speed up the search by using preprocessing. The query algorithm introduced in Section 3.1.2 requires linear storage and has an expected running time of O(n ln(d / ln( n)+1)+n). The data structure developed in Section 3.2 is based on a preprocessed partition of the data set into sequences, which are monotone with respect to some of the dimensions. The query algorithm has an expected running time of O( √dn1-1/√dln(n)) for dimensions d<(ln(n)/ln(ln(n)))2. Chapter 4 presents several generalizations, in particular to the important problem of finding the k nearest neighbors to a query point. We generalize the analysis of the considered algorithms to other "well-behaved" probability distributions. Furthermore, we develop extensions of the algorithms which work efficiently in the external-memory model of computation. In Chapter 5 we present a method which provides tradeoffs between the space complexity of the data structure and the time complexity of the query algorithm.Das Thema dieser Dissertation ist die exakte Nächster-Nachbar-Suche. Das Problem besteht darin, effiziente Datenstrukturen und Algorithmen zu entwickeln, die zu einer festen Menge P von n Punkten aus Rd und einem beliebigen Anfragepunkt q in Rd, den oder die nächsten Nachbarn aus P zu q finden. Der Abstand wird meist in einer der Minkowski-Metriken L1, L2 ,...,L∞ definiert. Die Dimension d des Suchraumes ist in vielen Anwendungen dieses Problems sehr groß; sie liegt in der Grössenordnung von Hunderten bis Tausenden. Die bekannten Algorithmen für das exakte Nächster-Nachbar-Problem benötigen eine in d exponentielle Laufzeit oder einen in d exponentiellen Speicherplatz. Sie sind daher ineffizient für hochdimensionale Anwendungen, und können mit der brute-force Methode nicht konkurrieren, welche alle n Distanzen zu dem Anfragepunkt berechnet und den Punkt mit minimaler Distanz auswählt. In dieser Dissertation werden effiziente Algorithmen für die exakte Nächster- Nachbar-Suche in einem hochdimensionalen (Rd, L∞) Raum entwickelt. Wir untersuchen die erwartete Laufzeit unserer Algorithmen unter der Voraussetzung, dass die Punkte aus P gleichverteilt im d-dimensionalen Einheitswürfel sind. Die Algorithmen sind einfach zu implementieren und verbessern die brute-force Methode bezüglich der erwarteten Laufzeit wesentlich. Kapitel 2 stellt Methoden für das exakte Nächster-Nachbar Problem vor, die keine Vorverarbeitung und nur Speicherplatz für die n Punkte benötigen. Deren erwartete Laufzeit ist O( n+(nd / ln(n)) ). Wir entwickeln in Kapitel 3 Algorithmen, die auf der Basis einer einfachen Vorverarbeitung eine effizientere Suche ermöglichen. Der im Abschnitt 3.1.2 entwickelte Suchalgorithmus hat eine erwartete Laufzeit von O(n ln(d / ln( n)+1)+n), einen linearen Speicherbedarf und O(nd ln(n)) Vorverarbeitungszeit. Im Abschnitt 3.2 geben wir einen Suchalgorithmus an, der eine in der Vorverarbeitung berechnete Zerlegung der Punktmenge P benutzt. Die Zerlegung besteht aus Folgen von Punkten, die monoton in Rd sind. Die erwartete Laufzeit beträgt O( √dn1-1/√dln(n)) für Dimensionen d<(ln(n)/ln(ln(n)))2. Die vorgestellten Methoden und deren Laufzeitanalyse werden in Kapitel 4 für das k-Nächste-Nachbarn-Problem verallgemeinert. Außerdem werden die erwarteten Laufzeiten der entwickelten Methoden bei Zugrundelegung von anderen Wahrscheinlichkeitsverteilungen analysiert. Weiterhin betrachten wir die Anpassung der Algorithmen für die L∞-Nächster-Nachbar-Suche unter Verwendung von externen Speicher. In Kapitel 5 wird eine Methode entwickelt, die einen Tradeoff zwischen dem erwarteten Speicherbedarf der Datenstruktur und der erwarteten Laufzeit des dazugehörigen Suchalgorithmus erlaubt