A violation of the uncertainty principle implies a violation of the second law of thermodynamics

Uncertainty relations state that there exist certain incompatible measurements, to which the outcomes cannot be simultaneously predicted. While the exact incompatibility of quantum measurements dictated by such uncertainty relations can be inferred from the mathematical formalism of quantum theory, the question remains whether there is any more fundamental reason for the uncertainty relations to have this exact form. What, if any, would be the operational consequences if we were able to go beyond any of these uncertainty relations? We give a strong argument that justifies uncertainty relations in quantum theory by showing that violating them implies that it is also possible to violate the second law of thermodynamics. More precisely, we show that violating the uncertainty relations in quantum mechanics leads to a thermodynamic cycle with positive net work gain, which is very unlikely to exist in nature.Comment: 8 pages, revte

Gestational diabetes as a risk factor for pancreatic cancer: a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>Diabetes is known to be associated with cancer of the pancreas, though there is some debate as to whether it is a cause or a consequence of the disease. We investigated the incidence of pancreatic cancer in a cohort of 37926 Israeli women followed for 28–40 years for whom information on diabetes had been collected at the time they gave birth, in 1964–1976, in Jerusalem. There were 54 cases of pancreatic cancer ascertained from the Israel Cancer Registry during follow-up.</p> <p>Methods</p> <p>We used Cox proportional hazards models to adjust for age at baseline and explore effects of other risk factors, including ethnic groups, preeclampsia, birth order and birth weight of offspring.</p> <p>Results</p> <p>We observed no cases of pancreatic cancer in the women with insulin dependent diabetes; however, there were five cases in the women with gestational diabetes. The interval between the record of diabetes in pregnancy and the diagnosis of pancreatic cancer ranged from 14–35 years. Women with a history of gestational diabetes showed a relative risk of pancreatic cancer of 7.1 (95% confidence interval, 2.8–18.0).</p> <p>Conclusion</p> <p>We conclude that gestational diabetes is strongly related to the risk of cancer of the pancreas in women in this population, and that gestational diabetes can precede cancer diagnosis by many years.</p

Predicting the Impact of Climate Change on Threatened Species in UK Waters

Global climate change is affecting the distribution of marine species and is thought to represent a threat to biodiversity. Previous studies project expansion of species range for some species and local extinction elsewhere under climate change. Such range shifts raise concern for species whose long-term persistence is already threatened by other human disturbances such as fishing. However, few studies have attempted to assess the effects of future climate change on threatened vertebrate marine species using a multi-model approach. There has also been a recent surge of interest in climate change impacts on protected areas. This study applies three species distribution models and two sets of climate model projections to explore the potential impacts of climate change on marine species by 2050. A set of species in the North Sea, including seven threatened and ten major commercial species were used as a case study. Changes in habitat suitability in selected candidate protected areas around the UK under future climatic scenarios were assessed for these species. Moreover, change in the degree of overlap between commercial and threatened species ranges was calculated as a proxy of the potential threat posed by overfishing through bycatch. The ensemble projections suggest northward shifts in species at an average rate of 27 km per decade, resulting in small average changes in range overlap between threatened and commercially exploited species. Furthermore, the adverse consequences of climate change on the habitat suitability of protected areas were projected to be small. Although the models show large variation in the predicted consequences of climate change, the multi-model approach helps identify the potential risk of increased exposure to human stressors of critically endangered species such as common skate (Dipturus batis) and angelshark (Squatina squatina)

Rapid automatized naming as an index of genetic liability to autism

This study investigated rapid automatized naming (RAN) ability in high functioning individuals with autism and parents of individuals with autism. Findings revealed parallel patterns of performance in parents and individuals with autism, where both groups had longer naming times than controls. Significant parent-child correlations were also detected, along with associations with language and personality features of the broad autism phenotype (retrospective reports of early language delay, socially reticent personality). Together, findings point towards RAN as a potential marker of genetic liability to autism

A Survey on Continuous Time Computations

We provide an overview of theories of continuous time computation. These theories allow us to understand both the hardness of questions related to continuous time dynamical systems and the computational power of continuous time analog models. We survey the existing models, summarizing results, and point to relevant references in the literature

Widespread range expansions shape latitudinal variation in insect thermal limits

I thank the authors of previous studies on global variation in insect thermal tolerances who have generously provided open access use of their data sets.Peer reviewedPostprin

Computability and dynamical systems

In this paper we explore results that establish a link between dynamical systems and computability theory (not numerical analysis). In the last few decades, computers have increasingly been used as simulation tools for gaining insight into dynamical behavior. However, due to the presence of errors inherent in such numerical simulations, with few exceptions, computers have not been used for the nobler task of proving mathematical results. Nevertheless, there have been some recent developments in the latter direction. Here we introduce some of the ideas and techniques used so far, and suggest some lines of research for further work on this fascinating topic

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Background: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation

Assembling proteomics data as a prerequisite for the analysis of large scale experiments

<p>Abstract</p> <p>Background</p> <p>Despite the complete determination of the genome sequence of a huge number of bacteria, their proteomes remain relatively poorly defined. Beside new methods to increase the number of identified proteins new database applications are necessary to store and present results of large- scale proteomics experiments.</p> <p>Results</p> <p>In the present study, a database concept has been developed to address these issues and to offer complete information via a web interface. In our concept, the Oracle based data repository system SQL-LIMS plays the central role in the proteomics workflow and was applied to the proteomes of <it>Mycobacterium tuberculosis</it>, <it>Helicobacter pylori</it>, <it>Salmonella typhimurium </it>and protein complexes such as 20S proteasome. Technical operations of our proteomics labs were used as the standard for SQL-LIMS template creation. By means of a Java based data parser, post-processed data of different approaches, such as LC/ESI-MS, MALDI-MS and 2-D gel electrophoresis (2-DE), were stored in SQL-LIMS. A minimum set of the proteomics data were transferred in our public 2D-PAGE database using a Java based interface (Data Transfer Tool) with the requirements of the PEDRo standardization. Furthermore, the stored proteomics data were extractable out of SQL-LIMS via XML.</p> <p>Conclusion</p> <p>The Oracle based data repository system SQL-LIMS played the central role in the proteomics workflow concept. Technical operations of our proteomics labs were used as standards for SQL-LIMS templates. Using a Java based parser, post-processed data of different approaches such as LC/ESI-MS, MALDI-MS and 1-DE and 2-DE were stored in SQL-LIMS. Thus, unique data formats of different instruments were unified and stored in SQL-LIMS tables. Moreover, a unique submission identifier allowed fast access to all experimental data. This was the main advantage compared to multi software solutions, especially if personnel fluctuations are high. Moreover, large scale and high-throughput experiments must be managed in a comprehensive repository system such as SQL-LIMS, to query results in a systematic manner. On the other hand, these database systems are expensive and require at least one full time administrator and specialized lab manager. Moreover, the high technical dynamics in proteomics may cause problems to adjust new data formats. To summarize, SQL-LIMS met the requirements of proteomics data handling especially in skilled processes such as gel-electrophoresis or mass spectrometry and fulfilled the PSI standardization criteria. The data transfer into a public domain via DTT facilitated validation of proteomics data. Additionally, evaluation of mass spectra by post-processing using MS-Screener improved the reliability of mass analysis and prevented storage of data junk.</p

Relational Concurrent Refinement II: Internal Operations and Outputs

Two styles of description arise naturally in formal specification: state-based and behavioural. In state-based notations, a system is characterised by a collection of variables, and their values determine which actions may occur throughout a system history. Behavioural specifications describe the chronologies of actions -- interactions between a system and its environment. The exact nature of such interactions is captured in a variety of semantic models with corresponding notions of refinement; refinement in state based systems is based on the semantics of sequential programs and is modelled relationally. Acknowledging that these viewpoints are complementary, substantial research has gone into combining the paradigms. The purpose of this paper is to do three things. First, we survey recent results linking the relational model of refinement to the process algebraic models. Specifically, we detail how variations in the relational framework lead to relational data refinement being in correspondence with traces-divergences, singleton failures and failures-divergences refinement in a process semantics. Second, we generalise these results by providing a general flexible scheme for incorporating the two main ''erroneous'' concurrent behaviours: deadlock and divergence, into relational refinement. This is shown to subsume previous characterisations. In doing this we derive relational refinement rules for specifications containing both internal operations and outputs that corresponds to failures-divergences refinement. Third, the theory has been formally specified and verified using the interactive theorem prover KIV