A census of human soluble protein complexes.

Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.

Detecting overlapping protein complexes in protein-protein interaction networks.

We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.

GFam: a platform for automatic annotation of gene families.

We have developed GFam, a platform for automatic annotation of gene/protein families. GFam provides a framework for genome initiatives and model organism resources to build domain-based families, derive meaningful functional labels and offers a seamless approach to propagate functional annotation across periodic genome updates. GFam is a hybrid approach that uses a greedy algorithm to chain component domains from InterPro annotation provided by its 12 member resources followed by a sequence-based connected component analysis of un-annotated sequence regions to derive consensus domain architecture for each sequence and subsequently generate families based on common architectures. Our integrated approach increases sequence coverage by 7.2 percentage points and residue coverage by 14.6 percentage points higher than the coverage relative to the best single-constituent database within InterPro for the proteome of Arabidopsis. The true power of GFam lies in maximizing annotation provided by the different InterPro data sources that offer resource-specific coverage for different regions of a sequence. GFam's capability to capture higher sequence and residue coverage can be useful for genome annotation, comparative genomics and functional studies. GFam is a general-purpose software and can be used for any collection of protein sequences. The software is open source and can be obtained from http://www.paccanarolab.org/software/gfam/.

Improving GO semantic similarity measures by exploring the ontology beneath the terms and modelling uncertainty.

MOTIVATION: Several measures have been recently proposed for quantifying the functional similarity between gene products according to well-structured controlled vocabularies where biological terms are organized in a tree or in a directed acyclic graph (DAG) structure. However, existing semantic similarity measures ignore two important facts. First, when calculating the similarity between two terms, they disregard the descendants of these terms. While this makes no difference when the ontology is a tree, we shall show that it has important consequences when the ontology is a DAG-this is the case, for example, with the Gene Ontology (GO). Second, existing similarity measures do not model the inherent uncertainty which comes from the fact that our current knowledge of the gene annotation and of the ontology structure is incomplete. Here, we propose a novel approach based on downward random walks that can be used to improve any of the existing similarity measures to exhibit these two properties. The approach is computationally efficient-random walks do not need to be simulated as we provide formulas to calculate their stationary distributions. RESULTS: To show that our approach can potentially improve any semantic similarity measure, we test it on six different semantic similarity measures: three commonly used measures by Resnik (1999), Lin (1998), and Jiang and Conrath (1997); and three recently proposed measures: simUI, simGIC by Pesquita et al. (2008); GraSM by Couto et al. (2007); and Couto and Silva (2011). We applied these improved measures to the GO annotations of the yeast Saccharomyces cerevisiae, and tested how they correlate with sequence similarity, mRNA co-expression and protein-protein interaction data. Our results consistently show that the use of downward random walks leads to more reliable similarity measures.

Understanding how adherence goals promote adherence behaviours: a repeated measure observational study with HIV seropositive patients.

BACKGROUND: The extent to which patients follow treatments as prescribed is pivotal to treatment success. An exceptionally high level (> 95%) of HIV medication adherence is required to suppress viral replication and protect the immune system and a similarly high level (> 80%) of adherence has also been suggested in order to benefit from prescribed exercise programmes. However, in clinical practice, adherence to both often falls below the desirable level. This project aims to investigate a wide range of psychological and personality factors that may lead to adherence/non-adherence to medical treatment and exercise programmes. METHODS: HIV positive patients who are referred to the physiotherapist-led 10-week exercise programme as part of the standard care are continuously recruited. Data on social cognitive variables (attitude, intention, subjective norms, self-efficacy, and outcome beliefs) about the goal and specific behaviours, selected personality factors, perceived quality of life, physical activity, self-reported adherence and physical assessment are collected at baseline, at the end of the exercise programme and again 3 months later. The project incorporates objective measures of both exercise (attendance log and improvement in physical measures such as improved fitness level, weight loss, improved circumferential anthropometric measures) and medication adherence (verified by non-invasive hair analysis). DISCUSSION: The novelty of this project comes from two key aspects, complemented with objective information on exercise and medication adherence. The project assesses beliefs about both the underlying goal such as following prescribed treatment; and about the specific behaviours such as undertaking the exercise or taking the medication, using both implicit and explicit assessments of patients' beliefs and attitudes. We predict that i) the way people think about the underlying goal of their treatments explains medication and exercise behaviours over and above the effects of the behaviour-specific thinking and ii) the relationship between adherence to exercise and to medical treatment is stronger among those with more favourable views about the goal. Results from this study should identify the key contributing factors to inform subsequent adherence research and afford a more streamlined assessment matrix. The project also aims to inform patient care practices. UK CLINICAL RESEARCH NETWORK REGISTRATION NUMBER: UKCRN 7842.

SCPS: a fast implementation of a spectral method for detecting protein families on a genome-wide scale.

BACKGROUND: An important problem in genomics is the automatic inference of groups of homologous proteins from pairwise sequence similarities. Several approaches have been proposed for this task which are "local" in the sense that they assign a protein to a cluster based only on the distances between that protein and the other proteins in the set. It was shown recently that global methods such as spectral clustering have better performance on a wide variety of datasets. However, currently available implementations of spectral clustering methods mostly consist of a few loosely coupled Matlab scripts that assume a fair amount of familiarity with Matlab programming and hence they are inaccessible for large parts of the research community. RESULTS: SCPS (Spectral Clustering of Protein Sequences) is an efficient and user-friendly implementation of a spectral method for inferring protein families. The method uses only pairwise sequence similarities, and is therefore practical when only sequence information is available. SCPS was tested on difficult sets of proteins whose relationships were extracted from the SCOP database, and its results were extensively compared with those obtained using other popular protein clustering algorithms such as TribeMCL, hierarchical clustering and connected component analysis. We show that SCPS is able to identify many of the family/superfamily relationships correctly and that the quality of the obtained clusters as indicated by their F-scores is consistently better than all the other methods we compared it with. We also demonstrate the scalability of SCPS by clustering the entire SCOP database (14,183 sequences) and the complete genome of the yeast Saccharomyces cerevisiae (6,690 sequences). CONCLUSIONS: Besides the spectral method, SCPS also implements connected component analysis and hierarchical clustering, it integrates TribeMCL, it provides different cluster quality tools, it can extract human-readable protein descriptions using GI numbers from NCBI, it interfaces with external tools such as BLAST and Cytoscape, and it can produce publication-quality graphical representations of the clusters obtained, thus constituting a comprehensive and effective tool for practical research in computational biology. Source code and precompiled executables for Windows, Linux and Mac OS X are freely available at http://www.paccanarolab.org/software/scps.

Convergence and divergence are mostly reciprocated properties of the connections in the network of cortical areas.

Cognition is based on the integrated functioning of hierarchically organized cortical processing streams in a manner yet to be clarified. Because integration fundamentally depends on convergence and the complementary notion of divergence of the neuronal connections, we analysed integration by measuring the degree of convergence/divergence through the connections in the network of cortical areas. By introducing a new index, we explored the complementary convergent and divergent nature of connectional reciprocity and delineated the backward and forward cortical sub-networks for the first time. Integrative properties of the areas defined by the degree of convergence/divergence through their afferents and efferents exhibited distinctive characteristics at different levels of the cortical hierarchy. Areas previously identified as hubs exhibit information bottleneck properties. Cortical networks largely deviate from random graphs where convergence and divergence are balanced at low reciprocity level. In the cortex, which is dominated by reciprocal connections, balance appears only by further increasing the number of reciprocal connections. The results point to the decisive role of the optimal number and placement of reciprocal connections in large-scale cortical integration. Our findings also facilitate understanding of the functional interactions between the cortical areas and the information flow or its equivalents in highly recurrent natural and artificial networks.

Fuzzy communities and the concept of bridgeness in complex networks.

We consider the problem of fuzzy community detection in networks, which complements and expands the concept of overlapping community structure. Our approach allows each vertex of the graph to belong to multiple communities at the same time, determined by exact numerical membership degrees, even in the presence of uncertainty in the data being analyzed. We create an algorithm for determining the optimal membership degrees with respect to a given goal function. Based on the membership degrees, we introduce a measure that is able to identify outlier vertices that do not belong to any of the communities, bridge vertices that have significant membership in more than one single community, and regular vertices that fundamentally restrict their interactions within their own community, while also being able to quantify the centrality of a vertex with respect to its dominant community. The method can also be used for prediction in case of uncertainty in the data set analyzed. The number of communities can be given in advance, or determined by the algorithm itself, using a fuzzified variant of the modularity function. The technique is able to discover the fuzzy community structure of different real world networks including, but not limited to, social networks, scientific collaboration networks, and cortical networks, with high confidence.

Worldwide food recall patterns over an eleven month period: a country perspective.

BACKGROUND: Following the World Health Organization Forum in November 2007, the Beijing Declaration recognized the importance of food safety along with the rights of all individuals to a safe and adequate diet. The aim of this study is to retrospectively analyze the patterns in food alert and recall by countries to identify the principal hazard generators and gatekeepers of food safety in the eleven months leading up to the Declaration. METHODS: The food recall data set was collected by the Laboratory of the Government Chemist (LGC, UK) over the period from January to November 2007. Statistics were computed with the focus reporting patterns by the 117 countries. The complexity of the recorded interrelations was depicted as a network constructed from structural properties contained in the data. The analysed network properties included degrees, weighted degrees, modularity and k-core decomposition. Network analyses of the reports, based on 'country making report' (detector) and 'country reported on' (transgressor), revealed that the network is organized around a dominant core. RESULTS: Ten countries were reported for sixty per cent of all faulty products marketed, with the top 5 countries having received between 100 to 281 reports. Further analysis of the dominant core revealed that out of the top five transgressors three made no reports (in the order China > Turkey > Iran). The top ten detectors account for three quarters of reports with three > 300 (Italy: 406, Germany: 340, United Kingdom: 322). CONCLUSION: Of the 117 countries studied, the vast majority of food reports are made by 10 countries, with EU countries predominating. The majority of the faulty foodstuffs originate in ten countries with four major producers making no reports. This pattern is very distant from that proposed by the Beijing Declaration which urges all countries to take responsibility for the provision of safe and adequate diets for their nationals.

Optimized flocking of autonomous drones in confined environments.

We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.

Doping in Two Elite Athletics Competitions Assessed by Randomized-Response Surveys.

BACKGROUND: Doping in sports compromises fair play and endangers health. To deter doping among elite athletes, the World Anti-Doping Agency (WADA) oversees testing of several hundred thousand athletic blood and urine samples annually, of which 1-2% test positive. Measures using the Athlete Biological Passport suggest a higher mean prevalence of about 14% positive tests. Biological testing, however, likely fails to detect many cutting-edge doping techniques, and thus the true prevalence of doping remains unknown. METHODS: We surveyed 2167 athletes at two sporting events: the 13th International Association of Athletics Federations Word Championships in Athletics (WCA) in Daegu, South Korea in August 2011 and the 12th Quadrennial Pan-Arab Games (PAG) in Doha, Qatar in December 2011. To estimate the prevalence of doping, we utilized a "randomized response technique," which guarantees anonymity for individuals when answering a sensitive question. We also administered a control question at PAG assessing past-year use of supplements. RESULTS: The estimated prevalence of past-year doping was 43.6% (95% confidence interval 39.4-47.9) at WCA and 57.1% (52.4-61.8) at PAG. The estimated prevalence of past-year supplement use at PAG was 70.1% (65.6-74.7%). Sensitivity analyses, assessing the robustness of these estimates under numerous hypothetical scenarios of intentional or unintentional noncompliance by respondents, suggested that we were unlikely to have overestimated the true prevalence of doping. CONCLUSIONS: Doping appears remarkably widespread among elite athletes, and remains largely unchecked despite current biological testing. The survey technique presented here will allow future investigators to generate continued reference estimates of the prevalence of doping.

Estimating the prevalence of socially sensitive behaviors: attributing guilty and innocent noncompliance with the single sample count method.

Prevalence estimation models, using randomized or fuzzy responses, provide protection against exposure to respondents beyond anonymity and represent a useful research tool in socially sensitive situations. However, both guilty and innocent noncompliance can have a profound impact on prevalence estimations derived from these models. In this article, we introduce the maximum-likelihood extension of the single sample count (SSC-MLE) estimation model to detect and attribute noncompliance through testing 5 competing hypotheses on possible ways of noncompliance. We demonstrate the ability of the SSC-MLE to estimate and attribute noncompliance with a single sample using the observed distribution of affirmative answers on recent recreational drug use from a sample of university students (N = 1,441). Based on the survey answers, the drug use prevalence was estimated at 17.62% (± 6.75%), which is in line with relevant drug use statistics. Only 2.51% (± 1.54%) were noncompliant, of which 0.55% (± 0.44%) was attributed to guilty noncompliance (i.e., have used drugs but did not admit) and 2.17% (± 1.44%) to innocent noncompliers with no drug use in the past 3 months to hide. The SSC-MLE indirect estimation method represents an important tool for estimating the prevalence of a broad range of socially sensitive behaviors. Subsequent applications of the SSC-MLE to a range of transgressive behaviors with varying sensitivity will contribute to establishing the SSC-MLE's performance properties, along with obtaining empirical evidence to test the underlying assumption of independence of noncompliance from involvement. Freely downloadable, user-friendly software to facilitate applications of the SSC-MLE model is provided.

Flocking algorithm for autonomous flying robots.

Animal swarms displaying a variety of typical flocking patterns would not exist without the underlying safe, optimal and stable dynamics of the individuals. The emergence of these universal patterns can be efficiently reconstructed with agent-based models. If we want to reproduce these patterns with artificial systems, such as autonomous aerial robots, agent-based models can also be used in their control algorithms. However, finding the proper algorithms and thus understanding the essential characteristics of the emergent collective behaviour requires thorough and realistic modeling of the robot and also the environment. In this paper, we first present an abstract mathematical model of an autonomous flying robot. The model takes into account several realistic features, such as time delay and locality of communication, inaccuracy of the on-board sensors and inertial effects. We present two decentralized control algorithms. One is based on a simple self-propelled flocking model of animal collective motion, the other is a collective target tracking algorithm. Both algorithms contain a viscous friction-like term, which aligns the velocities of neighbouring agents parallel to each other. We show that this term can be essential for reducing the inherent instabilities of such a noisy and delayed realistic system. We discuss simulation results on the stability of the control algorithms, and perform real experiments to show the applicability of the algorithms on a group of autonomous quadcopters. In our case, bio-inspiration works in two ways. On the one hand, the whole idea of trying to build and control a swarm of robots comes from the observation that birds tend to flock to optimize their behaviour as a group. On the other hand, by using a realistic simulation framework and studying the group behaviour of autonomous robots we can learn about the major factors influencing the flight of bird flocks.

Hierarchical self-organization of non-cooperating individuals.

Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network) can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score) of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks.

The multi-player performance-enhancing drug game.

This paper extends classical work on economics of doping into a multi-player game setting. Apart from being among the first papers formally formulating and analysing a multi-player doping situation, we find interesting results related to different types of Nash-equilibria (NE). Based mainly on analytic results, we claim at least two different NE structures linked to the choice of prize functions. Linear prize functions provide NEs characterised by either everyone or nobody taking drugs, while non-linear prize functions lead to qualitatively different NEs with significantly more complex predictive characteristics.

The Procrustean bed of EU food safety notifications via the Rapid Alert System for Food and Feed: does one size fit all?

Previous reports demonstrated wide variations in contributions by EU Member States (MS) to the Rapid Alert System for Food and Feed (RASFF) especially for border notifications, emphasising that MS with major entry points play a vital role in ensuring that EU food imports meet EU standards. To further explore the variation this paper aimed to examine notification practices among MS by comparing the levels of detection as a function of the total food imported and population. RASFF notifications issued between 2003 and 2007 were analysed using descriptive statistics and network analysis for differences in notification practice between MS. Major variations in contributions to the RASFF database were observed, which did not correlate with MS size or population. For the key contrast of ratio: 'border: non-border notifications', variations between 7%:89% were observed for the average monthly contributions and, import tonnage per border notification revealed up to 129-fold differences between MS. In conclusion, wide variations in food safety practice exist between MS, including both number and type of contributions to the RASFF database, with some MS being relatively highly active in the key class of border notifications. These findings should inform EU food safety enforcement policies and practices; and central resource allocations.

Interactive network analytical tool for instantaneous bespoke interrogation of food safety notifications.

BACKGROUND: The globalization of food supply necessitates continued advances in regulatory control measures to ensure that citizens enjoy safe and adequate nutrition. The aim of this study was to extend previous reports on network analysis relating to food notifications by including an optional filter by type of notification and in cases of contamination, by type of contaminant in the notified foodstuff. METHODOLOGY/PRINCIPAL FINDINGS: A filter function has been applied to enable processing of selected notifications by contaminant or type of notification to i) capture complexity, ii) analyze trends, and iii) identify patterns of reporting activities between countries. The program rapidly assesses nations' roles as transgressor and/or detector for each category of contaminant and for the key class of border rejection. In the open access demonstration version, the majority of notifications in the Rapid Alert System for Food and Feed were categorized by contaminant type as mycotoxin (50.4%), heavy metals (10.9%) or bacteria (20.3%). Examples are given demonstrating how network analytical approaches complement, and in some cases supersede, descriptive statistics such as frequency counts, which may give limited or potentially misleading information. One key feature is that network analysis takes the relationship between transgressor and detector countries, along with number of reports and impact simultaneously into consideration. Furhermore, the indices that compliment the network maps and reflect each country's transgressor and detector activities allow comparisons to be made between (transgressing vs. detecting) as well as within (e.g. transgressing) activities. CONCLUSIONS/SIGNIFICANCE: This further development of the network analysis approach to food safety contributes to a better understanding of the complexity of the effort ensuring food is safe for consumption in the European Union. The unique patterns of the interplay between detectors and transgressors, instantly revealed by our approach, could supplement the intelligence gathered by regulatory authorities and inform risk based sampling protocols.

OsWRKY22, a monocot WRKY gene, plays a role in the resistance response to blast.

With the aim of identifying novel regulators of host and nonhost resistance to fungi in rice, we carried out a systematic mutant screen of mutagenized lines. Two mutant wrky22 knockout lines revealed clear-cut enhanced susceptibility to both virulent and avirulent Magnaporthe oryzae strains and altered cellular responses to nonhost Magnaporthe grisea and Blumeria graminis fungi. In addition, the analysis of the pathogen responses of 24 overexpressor OsWRKY22 lines revealed enhanced resistance phenotypes on infection with virulent M. oryzae strain, confirming that OsWRKY22 is involved in rice resistance to blast. Bioinformatic analyses determined that the OsWRKY22 gene belongs to a well-defined cluster of monocot-specific WRKYs. The co-regulatory analysis revealed no significant co-regulation of OsWRKY22 with a representative panel of OsWRKYs, supporting its unique role in a series of transcriptional responses. In contrast, inquiring a subset of biotic stress-related Affymetrix data, a large number of resistance and defence-related genes were found to be putatively co-expressed with OsWRKY22. Taken together, all gathered experimental evidence places the monocot-specific OsWRKY22 gene at the convergence point of signal transduction circuits in response to both host and nonhost fungi encountering rice plants.

Methodological considerations regarding response bias effect in substance use research: is correlation between the measured variables sufficient?

Efforts for drug free sport include developing a better understanding of the behavioural determinants that underline doping with an increased interest in developing anti-doping prevention and intervention programmes. Empirical testing of both is dominated by self-report questionnaires, which is the most widely used method in psychological assessments and sociology polls. Disturbingly, the potential distorting effect of socially desirable responding (SD) is seldom considered in doping research, or dismissed based on weak correlation between some SD measure and the variables of interest. The aim of this report is to draw attention to i) the potential distorting effect of SD and ii) the limitation of using correlation analysis between a SD measure and the individual measures. Models of doping opinion as a potentially contentious issue was tested using structural equation modeling technique (SEM) with and without the SD variable, on a dataset of 278 athletes, assessing the SD effect both at the i) indicator and ii) construct levels, as well as iii) testing SD as an independent variable affecting expressed doping opinion. Participants were categorised by their SD score into high- and low SD groups. Based on low correlation coefficients (<|0.22|) observed in the overall sample, SD effect on the indicator variables could be disregarded. Regression weights between predictors and the outcome variable varied between groups with high and low SD but despite the practically non-existing relationship between SD and predictors (<|0.11|) in the low SD group, both groups showed improved model fit with SD, independently. The results of this study clearly demonstrate the presence of SD effect and the inadequacy of the commonly used pairwise correlation to assess social desirability at model level. In the absence of direct observation of the target behaviour (i.e. doping use), evaluation of the effectiveness of future anti-doping campaign, along with empirical testing of refined doping behavioural models, will likely to continue to rely on self-reported information. Over and above controlling the effect of socially desirable responding in research that makes inferences based on self-reported information on social cognitive and behavioural measures, it is recommended that SD effect is appropriately assessed during data analysis.

Metal ions affecting the gastrointestinal system including the liver.

In the present context, metal ions can be categorized into several classes including those that are essential for life and those that have no known biological function and thus can be considered only as potentially hazardous. Many complexities arise with regard to metal toxicity and there is a paucity of studies relating to many metals which are frequent components of the diet. For many people ingestion of mineral supplements is considered a risk-free health choice despite growing evidence to the contrary. Numerous approaches have been developed to assess risk associated with ingestion of metal ions. These include straightforward estimation of safe limits such as oral reference dose which are often based on data derived from animal experiments. More convoluted approaches such as the Target Hazard Quotient involve assessment of hazard with frequent exposure over long durations such as a lifetime. The latter calculation also affords facile consideration of the effects of many metals together. In many cases, rigorous data are unavailable, hence, large factors of uncertainty are employed to relate risk to humans. Owing to the nature of metal toxicity, data pertaining to the gastrointestinal tract and liver are often acquired from diseases of metal homeostasis or episodes of considerable metal overload. Whilst these studies provide evidence for mechanisms of metal-induced toxicity such as enhancing oxidative stress, extrapolation of these results to healthy individuals or patients with chronic inflammatory diseases is not straightforward. In summary, the diverse nature of metals and their effects on human tissues along with a paucity of studies on the full range of their effects, warrant further in-depth studies on the association of metals to ageing, chronic inflammatory diseases, and cancer.