Evolution in Quantum Causal Histories

We provide a precise definition and analysis of quantum causal histories (QCH). A QCH consists of a discrete, locally finite, causal pre-spacetime with matrix algebras encoding the quantum structure at each event. The evolution of quantum states and observables is described by completely positive maps between the algebras at causally related events. We show that this local description of evolution is sufficient and that unitary evolution can be recovered wherever it should actually be expected. This formalism may describe a quantum cosmology without an assumption of global hyperbolicity; it is thus more general than the Wheeler-DeWitt approach. The structure of a QCH is also closely related to quantum information theory and algebraic quantum field theory on a causal set.Comment: 20 pages. 8 figures. (v3: minor corrections, additional references [2,3]) to appear in CQ

Physics, Topology, Logic and Computation: A Rosetta Stone

In physics, Feynman diagrams are used to reason about quantum processes. In the 1980s, it became clear that underlying these diagrams is a powerful analogy between quantum physics and topology: namely, a linear operator behaves very much like a "cobordism". Similar diagrams can be used to reason about logic, where they represent proofs, and computation, where they represent programs. With the rise of interest in quantum cryptography and quantum computation, it became clear that there is extensive network of analogies between physics, topology, logic and computation. In this expository paper, we make some of these analogies precise using the concept of "closed symmetric monoidal category". We assume no prior knowledge of category theory, proof theory or computer science.Comment: 73 pages, 8 encapsulated postscript figure

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

Fast X-Ray Fluorescence Microtomography of Hydrated Biological Samples

Metals and metalloids play a key role in plant and other biological systems as some of them are essential to living organisms and all can be toxic at high concentrations. It is therefore important to understand how they are accumulated, complexed and transported within plants. In situ imaging of metal distribution at physiological relevant concentrations in highly hydrated biological systems is technically challenging. In the case of roots, this is mainly due to the possibility of artifacts arising during sample preparation such as cross sectioning. Synchrotron x-ray fluorescence microtomography has been used to obtain virtual cross sections of elemental distributions. However, traditionally this technique requires long data acquisition times. This has prohibited its application to highly hydrated biological samples which suffer both radiation damage and dehydration during extended analysis. However, recent advances in fast detectors coupled with powerful data acquisition approaches and suitable sample preparation methods can circumvent this problem. We demonstrate the heightened potential of this technique by imaging the distribution of nickel and zinc in hydrated plant roots. Although 3D tomography was still impeded by radiation damage, we successfully collected 2D tomograms of hydrated plant roots exposed to environmentally relevant metal concentrations for short periods of time. To our knowledge, this is the first published example of the possibilities offered by a new generation of fast fluorescence detectors to investigate metal and metalloid distribution in radiation-sensitive, biological samples

Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma

BACKGROUND Papillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. METHODS We performed comprehensive molecular characterization of 161 primary papillary renal-cell carcinomas, using whole-exome sequencing, copy-number analysis, messenger RNA and microRNA sequencing, DNA-methylation analysis, and proteomic analysis. RESULTS Type 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with MET alterations, whereas type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2'antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH). CONCLUSIONS Type 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renalcell carcinoma consisted of at least three subtypes based on molecular and phenotypic features

Prognostic factors in prostate cancer

Prognostic factors in organ confined prostate cancer will reflect survival after surgical radical prostatectomy. Gleason score, tumour volume, surgical margins and Ki-67 index have the most significant prognosticators. Also the origins from the transitional zone, p53 status in cancer tissue, stage, and aneuploidy have shown prognostic significance. Progression-associated features include Gleason score, stage, and capsular invasion, but PSA is also highly significant. Progression can also be predicted with biological markers (E-cadherin, microvessel density, and aneuploidy) with high level of significance. Other prognostic features of clinical or PSA-associated progression include age, IGF-1, p27, and Ki-67. In patients who were treated with radiotherapy the survival was potentially predictable with age, race and p53, but available research on other markers is limited. The most significant published survival-associated prognosticators of prostate cancer with extension outside prostate are microvessel density and total blood PSA. However, survival can potentially be predicted by other markers like androgen receptor, and Ki-67-positive cell fraction. In advanced prostate cancer nuclear morphometry and Gleason score are the most highly significant progression-associated prognosticators. In conclusion, Gleason score, capsular invasion, blood PSA, stage, and aneuploidy are the best markers of progression in organ confined disease. Other biological markers are less important. In advanced disease Gleason score and nuclear morphometry can be used as predictors of progression. Compound prognostic factors based on combinations of single prognosticators, or on gene expression profiles (tested by DNA arrays) are promising, but clinically relevant data is still lacking

Defining a standard set of patient-centered outcomes for men with localized prostate cancer

Background Value-based health care has been proposed as a unifying force to drive improved outcomes and cost containment. Objective To develop a standard set of multidimensional patient-centered health outcomes for tracking, comparing, and improving localized prostate cancer (PCa) treatment value. Design, setting, and participants We convened an international working group of patients, registry experts, urologists, and radiation oncologists to review existing data and practices. Outcome measurements and statistical analysis The group defined a recommended standard set representing who should be tracked, what should be measured and at what time points, and what data are necessary to make meaningful comparisons. Using a modified Delphi method over a series of teleconferences, the group reached consensus for the Standard Set. Results and limitations We recommend that the Standard Set apply to men with newly diagnosed localized PCa treated with active surveillance, surgery, radiation, or other methods. The Standard Set includes acute toxicities occurring within 6 mo of treatment as well as patient-reported outcomes tracked regularly out to 10 yr. Patient-reported domains of urinary incontinence and irritation, bowel symptoms, sexual symptoms, and hormonal symptoms are included, and the recommended measurement tool is the Expanded Prostate Cancer Index Composite Short Form. Disease control outcomes include overall, cause-specific, metastasis-free, and biochemical relapse-free survival. Baseline clinical, pathologic, and comorbidity information is included to improve the interpretability of comparisons. Conclusions We have defined a simple, easily implemented set of outcomes that we believe should be measured in all men with localized PCa as a crucial first step in improving the value of care. Patient summary Measuring, reporting, and comparing identical outcomes across treatments and treatment centers will provide patients and providers with information to make informed treatment decisions. We defined a set of outcomes that we recommend being tracked for every man being treated for localized prostate cancer