Carnap and the Ontology of Mathematics

In this thesis I investigate Rudolf Carnap's philosophy of mathematics. Most philosophers assume that the nature of mathematics raises deep philosophical questions, which call for theories about how we manage to know about and interact with abstract objects. Carnap's position, in contrast, is deflationary: he aims to show that we can take mathematics at face value without having to answer questions about the metaphysical status of mathematical objects. If Carnap is right, there is thus no need for a philosophy of mathematics as it is usually understood at all. The main argument of my thesis is that Carnap's position is unstable, since his own commitments force him to make at least some ontological assumptions about syntax, i.e. entities such as letters, strings, and proofs. My claim that Carnap needs to accept some ontological questions as being in good shape goes against the received view in the secondary literature. Since the late 1980s interest in Carnap's philosophy of mathematics has been growing, mostly in the wake of important papers by Michael Friedman, Warren Goldfarb, and Thomas Ricketts. These and other scholars have forcefully defended Carnap against objections by, among others, Kurt Gödel, W. V. Quine, and Hilary Putnam. The most powerful challenge to Carnap's view, however, can actually be found in a less well-known paper by the logician E. W. Beth. The core of my thesis is thus a new interpretation of what I call Beth's argument from non-standard models, which relies on Gödel's incompleteness theorems and targets Carnap's claim that mathematics is analytic. I show that my reconstruction of Beth's argument is more charitable to the text than competing interpretations in the secondary literature, and argue that it is also more powerful since extant defences of Carnap cannot be applied.Funding was provided by the Arts and Humanities Research Council, the Cambridge Trust, the Aristotelian Society, the Royal Institute of Philosophy, and the German-American Fulbright Commission

Earthquakes in Switzerland and surrounding regions during 2005

Abstract.: This report of the Swiss Seismological Service summarizes the seismic activity in Switzerland and surrounding regions during 2005. During this period, 611 earthquakes, 96 quarry blasts and two landslides were detected and located in the region under consideration. With 19 events with ML ≥ 2.5, the seismic activity in the year 2005 was below the average over the last 30 years. However, with the earthquake of Vallorcine (ML 4.9) located just across the border to France, between Martigny and Chamonix, and the two earthquakes of Rumisberg and Brugg (ML 4.1), located in the lower crust beneath the Jura Mountains of northern Switzerland, the year 2005 saw three events that produced shaking of intensity IV and V (EMS98). Of the 611 recorded earthquakes more than 110 events are aftershocks of the Vallorcine quake. Moreover, 51 events occurred within two days at the end of August during a period of very intense rainfalls. The epicenters of these events were concentrated in several clusters distributed over a wide area of central Switzerland, and their focal depths were shallow, so that they most likely constitute a case of precipitationinduced seismicit

Genetic targeting of B-Raf(V600E) affects survival and proliferation and identifies selective agents against BRAF-mutant colorectal cancer cells

Background: Colorectal cancers carrying the B-Raf V600E-mutation are associated with a poor prognosis. The purpose of this study was to identify B-Raf(V600E)-mediated traits of cancer cells in a genetic in vitro model and to assess the selective sensitization of B-Raf(V600E)-mutant cancer cells towards therapeutic agents. Methods: Somatic cell gene targeting was used to generate subclones of the colorectal cancer cell line RKO containing either wild-type or V600E-mutant B-Raf kinase. Cell-biologic analyses were performed in order to link cancer cell traits to the BRAF-mutant genotype. Subsequently, the corresponding tumor cell clones were characterized pharmacogenetically to identify therapeutic agents exhibiting selective sensitivity in B-Raf(V600E)-mutant cells. Results: Genetic targeting of mutant BRAF resulted in restoration of sensitivity to serum starvation-induced apoptosis and efficiently inhibited cell proliferation in the absence of growth factors. Among tested agents, the B-Raf inhibitor dabrafenib was found to induce a strong V600E-dependent shift in cell viability. In contrast, no differential sensitizing effect was observed for conventional chemotherapeutic agents (mitomycin C, oxaliplatin, paclitaxel, etoposide, 5-fluorouracil), nor for the targeted agents cetuximab, sorafenib, vemurafenib, RAF265, or for inhibition of PI3 kinase. Treatment with dabrafenib efficiently inhibited phosphorylation of the B-Raf downstream targets Mek 1/2 and Erk 1/2. Conclusion: Mutant BRAF alleles mediate self-sufficiency of growth signals and serum starvation-induced resistance to apoptosis. Targeting of the BRAF mutation leads to a loss of these hallmarks of cancer. Dabrafenib selectively inhibits cell viability in B-Raf(V600E) mutant cancer cells

Computational pan-genomics: status, promises and challenges

International audienceMany disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains

Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism.

OBJECTIVE From January 1, 2018, until July 31, 2020, our hospital network experienced an outbreak of vancomycin-resistant enterococci (VRE). The goal of our study was to improve existing processes by applying machine-learning and graph-theoretical methods to a nosocomial outbreak investigation. METHODS We assembled medical records generated during the first 2 years of the outbreak period (January 2018 through December 2019). We identified risk factors for VRE colonization using standard statistical methods, and we extended these with a decision-tree machine-learning approach. We then elicited possible transmission pathways by detecting commonalities between VRE cases using a graph theoretical network analysis approach. RESULTS We compared 560 VRE patients to 86,684 controls. Logistic models revealed predictors of VRE colonization as age (aOR, 1.4 (per 10 years), with 95% confidence interval [CI], 1.3-1.5; P < .001), ICU admission during stay (aOR, 1.5; 95% CI, 1.2-1.9; P < .001), Charlson comorbidity score (aOR, 1.1; 95% CI, 1.1-1.2; P < .001), the number of different prescribed antibiotics (aOR, 1.6; 95% CI, 1.5-1.7; P < .001), and the number of rooms the patient stayed in during their hospitalization(s) (aOR, 1.1; 95% CI, 1.1-1.2; P < .001). The decision-tree machine-learning method confirmed these findings. Graph network analysis established 3 main pathways by which the VRE cases were connected: healthcare personnel, medical devices, and patient rooms. CONCLUSIONS We identified risk factors for being a VRE carrier, along with 3 important links with VRE (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations require data maturity, and potential confounding factors must be considered