The demandingness of Nozick’s ‘Lockean’ proviso

Interpreters of Robert Nozick’s political philosophy fall into two broad groups concerning his application of the ‘Lockean proviso’. Some read his argument in an undemanding way: individual instances of ownership which make people worse off than they would have been in a world without any ownership are unjust. Others read the argument in a demanding way: individual instances of ownership which make people worse off than they would have been in a world without that particular ownership are unjust. While I argue that the former reading is correct as an interpretive matter, I suggest that this reading is nonetheless highly demanding. In particular, I argue that it is demanding when it is expanded to include the protection of nonhuman animals; if such beings are right bearers, as more and more academics are beginning to suggest, then there is no nonarbitrary reason to exclude them from the protection of the proviso

Bridging topological and functional information in protein interaction networks by short loops profiling

Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.This research was supported by the Biotechnology and Biological Sciences Research Council (BB/H018409/1 to AP, ACCC and FF, and BB/J016284/1 to NSBT) and by the Leukaemia & Lymphoma Research (to NSBT and FF). SSC is funded by a Leukaemia & Lymphoma Research Gordon Piller PhD Studentship

Giant pedunculated hepatocellular carcinoma with hemangioma mimicking intestinal obstruction

<p>Abstract</p> <p>Background</p> <p>Pedunculated hepatocellular carcinoma (P-HCC) has rarely been reported and is characteristically large and encapsulated. Only sporadic cases have been published, in which P-HCC was combined with other liver tumors (mostly benign), making the diagnosis difficult.</p> <p>Case presentation</p> <p>We report a patient who was admitted to our hospital with clinical features of intestinal obstruction and a palpable mass in the right iliac fossa. Ultrasound, computed tomography and magnetic resonance imaging demonstrated an encapsulated mass of unclear origin and characteristics of liver hemangioma. Laboratory tests revealed elevated α-fetoprotein (> 800 ng/ml) and cancer antigen 125 (> 51.2 U/ml). With a possible diagnosis of giant liver hemangioma, we proceeded to surgery. During surgery, a giant pedunculated tumor was discovered on the inferior surface of the right lobe of the liver, hanging free in the right abdominal cavity towards the right iliac fossa. The macroscopic appearance of the tumor was compatible with liver hemangioma. Tumor resection was performed at a safe distance, including the pedicle. The rest of the liver appeared normal. Histopathological examination revealed grade II and III HCC (according to Edmondson-Steiner's classification) with nodular configuration, central necrosis, and infiltration of the capsule. Underneath the tumor capsule, residual tissue of a cavernous hemangioma was recognized. The resection margins were free of neoplastic tissue.</p> <p>Conclusion</p> <p>This rare presentation of a giant P-HCC combined with a hemangioma with features of intestinal obstruction confirmed the diagnostic difficulties of similar cases, and required prompt surgical treatment. Therefore, patients benefit from surgical resection because both the capsule and the pedicle prevent vascular invasion, therefore improving prognosis.</p

Genome wide prediction of protein function via a generic knowledge discovery approach based on evidence integration

BACKGROUND: The automation of many common molecular biology techniques has resulted in the accumulation of vast quantities of experimental data. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system (e.g. knowledge of genes and their products, and the biological roles of proteins, their molecular functions, localizations and interaction networks). We present a technique called Global Mapping of Unknown Proteins (GMUP) which uses the Gene Ontology Index to relate diverse sources of experimental data by creation of an abstraction layer of evidence data. This abstraction layer is used as input to a neural network which, once trained, can be used to predict function from the evidence data of unannotated proteins. The method allows us to include almost any experimental data set related to protein function, which incorporates the Gene Ontology, to our evidence data in order to seek relationships between the different sets. RESULTS: We have demonstrated the capabilities of this method in two ways. We first collected various experimental datasets associated with yeast (Saccharomyces cerevisiae) and applied the technique to a set of previously annotated open reading frames (ORFs). These ORFs were divided into training and test sets and were used to examine the accuracy of the predictions made by our method. Then we applied GMUP to previously un-annotated ORFs and made 1980, 836 and 1969 predictions corresponding to the GO Biological Process, Molecular Function and Cellular Component sub-categories respectively. We found that GMUP was particularly successful at predicting ORFs with functions associated with the ribonucleoprotein complex, protein metabolism and transportation. CONCLUSION: This study presents a global and generic gene knowledge discovery approach based on evidence integration of various genome-scale data. It can be used to provide insight as to how certain biological processes are implemented by interaction and coordination of proteins, which may serve as a guide for future analysis. New data can be readily incorporated as it becomes available to provide more reliable predictions or further insights into processes and interactions

Effect of Crystallographic Texture on Magnetic Characteristics of Cobalt Nanowires

Cobalt nanowires with controlled diameters have been synthesized using electrochemical deposition in etched ion-track polycarbonate membranes. Structural characterization of these nanowires with diameter 70, 90, 120 nm and length 30 μm was performed by scanning electron microscopy, high-resolution transmission electron microscopy, and X-ray diffraction techniques. The as-prepared wires show uniform diameter along the whole length and X-ray diffraction analysis reveals that [002] texture of these wires become more pronounced as diameter is reduced. Magnetic characterization of the nanowires shows a clear difference of squareness and coercivity between parallel and perpendicular orientations of the wires with respect to the applied field direction. In case of parallel applied field, the coercivity has been found to be decreasing with increasing diameter of the wires while in perpendicular case; the coercivity observes lower values for larger diameter. The results are explained by taking into account the magnetocrystalline and shape anisotropies with respect to the applied field and domain transformation mechanism when single domain limit is surpassed

Ranked retrieval of Computational Biology models

<p>Abstract</p> <p>Background</p> <p>The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind.</p> <p>Results</p> <p>Here we present an improved search approach for computational models of biological processes. It is based on existing retrieval and ranking methods from Information Retrieval. The approach incorporates annotations suggested by MIRIAM, and additional meta-information. It is now part of the search engine of BioModels Database, a standard repository for computational models.</p> <p>Conclusions</p> <p>The introduced concept and implementation are, to our knowledge, the first application of Information Retrieval techniques on model search in Computational Systems Biology. Using the example of BioModels Database, it was shown that the approach is feasible and extends the current possibilities to search for relevant models. The advantages of our system over existing solutions are that we incorporate a rich set of meta-information, and that we provide the user with a relevance ranking of the models found for a query. Better search capabilities in model databases are expected to have a positive effect on the reuse of existing models.</p

Improving Cancer Classification Accuracy Using Gene Pairs

Recent studies suggest that the deregulation of pathways, rather than individual genes, may be critical in triggering carcinogenesis. The pathway deregulation is often caused by the simultaneous deregulation of more than one gene in the pathway. This suggests that robust gene pair combinations may exploit the underlying bio-molecular reactions that are relevant to the pathway deregulation and thus they could provide better biomarkers for cancer, as compared to individual genes. In order to validate this hypothesis, in this paper, we used gene pair combinations, called doublets, as input to the cancer classification algorithms, instead of the original expression values, and we showed that the classification accuracy was consistently improved across different datasets and classification algorithms. We validated the proposed approach using nine cancer datasets and five classification algorithms including Prediction Analysis for Microarrays (PAM), C4.5 Decision Trees (DT), Naive Bayesian (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN)

Finite Size Effects in Simulations of Protein Aggregation

It is becoming increasingly clear that the soluble protofibrillar species that proceed amyloid fibril formation are associated with a range of neurodegenerative disorders such as Alzheimer's and Parkinson diseases. Computer simulations of the processes that lead to the formation of these oligomeric species are starting to make significant contributions to our understanding of the determinants of protein aggregation. We simulate different systems at constant concentration but with a different number of peptides and we study the how the finite number of proteins affects the underlying free energy of the system and therefore the relative stability of the species involved in the process. If not taken into account, this finite size effect can undermine the validity of theoretical predictions regarding the relative stability of the species involved and the rates of conversion from one to the other. We discuss the reasons that give rise to this finite size effect form both a probabilistic and energy fluctuations point of view and also how this problem can be dealt by a finite size scaling analysis

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date

Total 18F-dopa PET tumour uptake reflects metabolic endocrine tumour activity in patients with a carcinoid tumour

Positron emission tomography (PET) using 6-[(18)F]fluoro-L-dihydroxyphenylalanine ((18)F-dopa) has an excellent sensitivity to detect carcinoid tumour lesions. (18)F-dopa tumour uptake and the levels of biochemical tumour markers are mediated by tumour endocrine metabolic activity. We evaluated whether total (18)F-dopa tumour uptake on PET, defined as whole-body metabolic tumour burden (WBMTB), reflects tumour load per patient, as measured with tumour markers. Seventy-seven consecutive carcinoid patients who underwent an (18)F-dopa PET scan in two previously published studies were analysed. For all tumour lesions mean standardised uptake values (SUVs) at 40% of the maximal SUV and tumour volume on (18)F-dopa PET were determined and multiplied to calculate a metabolic burden per lesion. WBMTB was the sum of the metabolic burden of all individual lesions per patient. The 24-h urinary serotonin, urine and plasma 5-hydroxindoleacetic acid (5-HIAA), catecholamines (nor)epinephrine, dopamine and their metabolites, measured in urine and plasma, and serum chromogranin A served as tumour markers. All but 1 were evaluable for WBMTB; 74 patients had metastatic disease. (18)F-dopa PET detected 979 lesions. SUV(max) on (18)F-dopa PET varied up to 29-fold between individual lesions within the same patients. WBMTB correlated with urinary serotonin (r = 0.51) and urinary and plasma 5-HIAA (r = 0.78 and 0.66). WBMTB also correlated with urinary norepinephrine, epinephrine, dopamine and plasma dopamine, but not with serum chromogranin A. Tumour load per patient measured with (18)F-dopa PET correlates with tumour markers of the serotonin and catecholamine pathway in urine and plasma in carcinoid patients, reflecting metabolic tumour activity