Biomarkers for cardiovascular risk assessment in autoimmune diseases

Autoimmune diseases, such as antiphospholipid syndrome, systemic lupus erythematosus, and rheumatoid arthritis, are characterized by a high prevalence of cardiovascular (CV) disease (CVD), which constitutes the leading causes of morbidity and mortality among such patients. Although such effects are partly explained by a higher prevalence of traditional CV risk factors, many studies indicate that such factors do not fully explain the enhanced CV risk in these patients. In addition, risk stratification algorithms based upon traditional CV risk factors are not as predictive in autoimmune diseases as in the general population. For these reasons, the timely and accurate assessment of CV risk in these high-risk populations still remains an unmet clinical need. An enhanced contribution of different inflammatory components of the immune response, as well as autoimmune elements (e.g. autoantibodies, autoantigens, and cellular response), has been proposed to underlie the incremental CV risk observed in these populations. Recent advances in proteomic tools have contributed to the discovery of proteins involved in CVDs, including some that may be suitable to be used as biological markers. In this review we summarize the main markers in the field of CVDs associated with autoimmunity, as well as the recent advances in proteomic technology and their application for biomarker discovery in autoimmune disease.     

Protein post-translational modifications and misfolding: New concepts in heart failure

A new concept in the field of heart-failure (HF) research points to a role of misfolded proteins, forming preamyloid oligomers (PAOs), in cardiac toxicity. This is largely based on few studies reporting the presence of PAOs, similar to those observed in neurodegenerative diseases, in experimental and human HF. As the majority of proteinopathies are sporadic in nature, protein post-translational modifications (PTMs) likely play a major role in this growing class of diseases. In fact, PTMs are known regulators of protein folding and of the formation of amyloid species in well-established proteinopathies. Proteomics has been instrumental in identifying both chemical and enzymatic PTMs, with a potential impact on protein mis-/folding. Here we provide the basics on how proteins fold along with a few examples of PTMs known to modulate protein misfolding and aggregation, with particular focus on the heart. Due to its innovative content and the growing awareness of the toxicity of misfolded proteins, an “Alzheimer's theory of HF” is timely. Moreover, the continuous innovations in proteomic technologies will help pinpoint PTMs that could contribute to the process. This nuptial between biology and technology could greatly assist in identifying biomarkers with increased specificity as well as more effective therapies.     

Liver proteins as sensor of human malignancies and inflammation

In this review we would like to highlight the importance of acute-phase proteins as sensor of diseases. Both acute-phase protein levels and glycosylation have been reported to be altered in inflammation and other diseases including cancer. Factors that promote acute-phase protein synthesis and enhance the expression of specific glycosyltransferases, such as sialyltransferases and fucosyltransferases, may be up-regulated in some tumours and would explain the changes in acute-phase protein levels and the specific N-glycosylation modifications of some acute-phase proteins in cancer. However, further studies are required to define the potential clinical application of these acute-phase protein cancer-specific modifications as possible cancer diagnostic or monitoring tools.     

Shotgun immunoproteomics to identify disease‐associated bacterial antigens: Application to human colon cancer

Circulating antibodies reflect a mirror view of invading antigens that are related to infection and cancer. This was recently exemplified by using serum antibodies to capture Streptococcus bovis antigens followed by MS to generate antigen profiles that were diagnostic for colon cancer. These bacterial antigen profiles have a high potential to aid in the immuno‐diagnosis of this disease, as the magnitude of the immune response to bacterial antigens is, in general, superior to the immune response against tumor (self) antigens. In this study, the identity of individual colon cancer‐associated streptococcal antigens was revealed by enrichment of these “diagnostic” antigens by selected patient antibodies followed by high‐accuracy nanoLC‐MS/MS peptide identification. This showed that both the histone‐like protein HlpA and the ribosomal protein Rp L7/L12 are members of the colon cancer‐associated S. bovis immunome. Both antigens also seem to belong to the group of anchorless surface proteins, like 14 additional proteins that were co‐identified in S. bovis cell wall extracts. Among these were the known streptococcal anchorless surface proteins GAPDH and Enolase. Taken together, these data show that shotgun immunoproteomics, combining immunocapture in‐line with LC MS/MS, is a convenient approach for the rapid identification of disease‐associated bacterial antigens.     

Old proteins and the Achilles heel of mass spectrometry. The role of proteomics in the etiology of human cataract

Proteomics may have enabled the root cause of a major human-blinding condition, age-related cataract, to be established. Cataract appears to result from the spontaneous decomposition of long-lived macromolecules in the human lens, and recent proteomic analysis has enabled both the particular crystallins, and the specific sites of amino acid modification within each polypeptide, to be identified. Analysis of proteins from cataract lenses has demonstrated that there are key sites on some structural proteins that show a consistently greater degree of deterioration than age-matched normal lenses. Proteomic analysis, using MS, revealed that the most abundant posttranslational modification of aged lens proteins is racemization. This is somewhat ironic, since structural isomers can be viewed as the “Achilles heel” of MS and there are typically few, if any, differences in the MS/MS spectra of tryptic peptides containing one d -amino acid. It is proposed that once a certain level of spontaneous PTM at key sites occurs, that protein–protein interactions are disrupted, and binding of complexes to cell membranes takes place that impairs cell-to-cell communication. These findings may apply more widely to age-related human diseases, in particular where the deterioration of long-lived proteins is a crucial component in the etiology.     

An approximation of the activity duration distribution in PERT

PERT is widely used as a tool for managing large-scale projects. The traditional PERT approach uses the beta distribution as the distribution of activity duration and estimates the mean and the variance of activity duration based on the “pessimistic”, “optimistic” and “most likely” time estimates. Several authors have modified the original three point PERT estimators to improve the accuracy of the estimates. This article proposes new approximations for the mean and the variance of activity based on “pessimistic”, “optimistic” and “most likely” time estimates. By numerical comparison with actual values, the proposal is shown as more accurate than the original PERT estimates and its modifications. Another advantage of the proposed approximation is that it requires no assumptions about the parameters of the beta distribution as in the case of existing ones.Scope and purposeThe traditional PERT model uses beta distribution as the distribution of activity duration and estimates the mean and the variance of activity duration using “pessimistic”, “optimistic” and “most likely” time estimates proposed by an expert. In the past several authors have modified the original PERT estimators to improve the accuracy. This article proposes new approximations for the mean and the variance of activity duration which are more accurate than the original PERT estimates and their modifications. Another advantage of the proposal is that it is not based on any assumptions about the parameters of the beta distribution as in the case of the existing ones.     

Proteomic analysis of circulating immune complexes in juvenile idiopathic arthritis reveals disease-associated proteins

Juvenile idiopathic arthritis reflects a group of clinically heterogeneous arthritides hallmarked by elevated concentrations of circulating immune complexes. In this study, the circulating immune complex proteome was examined to elucidate disease-associated proteins that are overexpressed in patients with an aggressive, and at times destructive, disease phenotype. To solve this proteome, circulating immune complexes were isolated from the sera of patients with chronic, erosive or early-onset, aggressive disease and from patients in medical remission or healthy controls subsequent to protein separation by 2-DE. Thirty-seven protein spots were overexpressed in the circulating immune complexes of the aggressive disease groups as compared to controls, 28 of which have been confidently identified to date. Proteolytic fragments of glyceraldehyde-3-phosphate dehydrogenase, serotransferrin, and α-1-antitrypsin have been identified among others. In total, these 28 putative disease-associated proteins most definitely contribute to immune complex formation and likely have a significant role in disease etiology and pathogenesis. Moreover, these proteins represent markers of aggressive disease, which could aid in diagnosis and management strategies, and potential therapeutic targets to prevent or control disease outcome. This is the first in-depth analysis of the circulating immune complex proteome in juvenile idiopathic arthritis.     

Multiple target localization & data association for frequency-only widely separated MIMO radar

The multiple target localization using only Doppler frequencies in Multiple-Input Multiple-Output (MIMO) radar is a problem of great interest. The Doppler frequencies include all relevant information about the location, velocity and direction of the targets. Hence, these frequencies can be used efficiently for data association and target localization. The MIMO configuration and the frequency diversity of the system enable us to easily determine the number of moving targets using only the Doppler frequencies. Then, the data association is achieved for the known number of targets by using the estimated target velocities, directions and positions which can be utilized together or separately for data association. Using these parameters, three data association methods are proposed, namely “Position & Velocity Based”, “Direction Based” and “Position, Velocity & Direction Based” Data Associations. Moreover, when probability of detection is less than unity, data association can still be achieved with some modifications. Besides that, another algorithm, referred to as “Frequency Reduction & Association”, is proposed to eliminate spurious frequencies when they appear in the spectrum. These are the first data association methods for Doppler-only systems and they can be used efficiently when Doppler frequencies are only available information in the MIMO radar context. After received frequencies are associated with the correct targets, multiple target localization can be performed using the associated Doppler frequencies.     

Stochastic Budget Optimization in Internet Advertising

Internet advertising is a sophisticated game in which the many advertisers “play” to optimize their return on investment. There are many “targets” for the advertisements, and each “target” has a collection of games with a potentially different set of players involved. In this paper, we study the problem of how advertisers allocate their budget across these “targets”. In particular, we focus on formulating their best response strategy as an optimization problem. Advertisers have a set of keywords (“targets”) and some stochastic information about the future, namely a probability distribution over scenarios of cost vs click combinations. This summarizes the potential states of the world assuming that the strategies of other players are fixed. Then, the best response can be abstracted as stochastic budget optimization problems to figure out how to spread a given budget across these keywords to maximize the expected number of clicks. We present the first known non-trivial poly-logarithmic approximation for these problems as well as the first known hardness results of getting better than logarithmic approximation ratios in the various parameters involved. We also identify several special cases of these problems of practical interest, such as with fixed number of scenarios or with polynomial-sized parameters related to cost, which are solvable either in polynomial time or with improved approximation ratios. Stochastic budget optimization with scenarios has sophisticated technical structure. Our approximation and hardness results come from relating these problems to a special type of (0/1, bipartite) quadratic programs inherent in them. Our research answers some open problems raised by the authors (in Algorithmica, 58(4):1022–1044, 2010).     

In-silico bonding schemes to encode chemical bonds involving sharing of electrons in molecular structures

Encoding of covalent and coordinate covalent bonds in molecular structures using ground state valence electronic configuration is achieved. The bonding due to electron sharing in the molecular structures is described with five fundamental bonding categories viz. uPair–uPair, lPair–uPair, uPair–lPair, vPair–lPair, and lPair–lPair. The involvement of lone pair electrons and the vacant electron orbitals in chemical bonding are explained with bonding schemes namely “target vacant promotion”, “source vacant promotion”, “target pairing promotion”, “source pairing promotion”, “source cation promotion”, “source pairing double bond”, “target vacant occupation”, and “double pairing promotion” schemes. The bonding schemes are verified with a chemical structure editor. The bonding in the structures like ylides, PCl₅, SF₆, IF₇, N-Oxides, BF₄⁻, AlCl₄⁻ etc. are explained and encoded unambiguously. The encoding of bonding in the structures of various organic compounds, transition metals compounds, coordination complexes and metal carbonyls is accomplished.     

Data complexity measured by principal graphs

How to measure the complexity of a finite set of vectors embedded in a multidimensional space? This is a non-trivial question which can be approached in many different ways. Here we suggest a set of data complexity measures using universal approximators, principal cubic complexes. Principal cubic complexes generalize the notion of principal manifolds for datasets with non-trivial topologies. The type of the principal cubic complex is determined by its dimension and a grammar of elementary graph transformations. The simplest grammar produces principal trees.We introduce three natural types of data complexity: (1) geometric (deviation of the data’s approximator from some “idealized” configuration, such as deviation from harmonicity); (2) structural (how many elements of a principal graph are needed to approximate the data), and (3) construction complexity (how many applications of elementary graph transformations are needed to construct the principal object starting from the simplest one).We compute these measures for several simulated and real-life data distributions and show them in the “accuracy–complexity” plots, helping to optimize the accuracy/complexity ratio. We discuss various issues connected with measuring data complexity. Software for computing data complexity measures from principal cubic complexes is provided as well.     

Cardiac myofilaments: from proteome to pathophysiology

This review addresses the functional consequences of altered post-translational modifications of cardiac myofilament proteins in cardiac diseases such as heart failure and ischemia. The modifications of thick and thin filament proteins as well as titin are addressed. Understanding the functional consequences of altered protein modifications is an essential step in the development of targeted therapies for common cardiac diseases.     

A quadratic mindlin element using shear constraints

“Shear constraints” are used to derive a displacement-based bending element for the analysis of thin and moderately thick plates of general plan form. As a starting point, the eight serendipity modes are adopted for the normal rotations and the nine Lagrangian modes for the transverse displacement, w. Subsequently, the shear constraints are used to eliminate the mid-side and central w variables so that the final element has three degrees-of-freedom at the corners and two at each mid-side. The bending energy is integrated using the standard formulation for the serendipity Mindlin element (with two-point Gaussian integration) so that the only modifications to that element involve the shear strain-displacement matrix. The constraints, which are used to implement these modifications, involve explicit algebraic expressions rather than numerical integration or matrix manipulation. A Fortran subroutine is provided for implementing these changes in a general quadrilateral. Using hierarchical displacement functions, the mid-side displacement variables Δw, that are missing from the standard serendipity element, may be simply constrained to zero as “boundary conditions”. Numerical experiments are presented which show that the element does not “lock” and that it gives excellent results for both thin and moderately thick plates. It also passes the patch test for a general quadrilateral.     

Tracking humoral responses using self assembling protein microarrays

The humoral immune response is a highly specific and adaptive sensor for changes in the body's protein milieu, which responds to novel structures of both foreign and self antigens. Although Igs represent a major component of human serum and are vital to survival, little is known about the response specificity and determinants that govern the human immunome. Historically, antigen-specific humoral immunity has been investigated using individually produced and purified target proteins, a labor-intensive process that has limited the number of antigens that have been studied. Here, we present the development of methods for applying self-assembling protein microarrays and a related method for producing 96-well formatted macroarrays for monitoring the humoral response at the proteome scale. Using plasmids encoding full-length cDNAs for over 850 human proteins and 1700 pathogen proteins, we demonstrate that these microarrays are highly sensitive, specific, reproducible, and can simultaneously measure immunity to thousands of proteins without a priori protein purification. Using this approach, we demonstrate the detection of humoral immunity to known and novel self-antigens, cancer antigens, autoimmune antigens, as well as pathogen-derived antigens. This represents a powerful and versatile tool for monitoring the immunome in health and disease.     

The role of proteomics in understanding biological mechanisms of sepsis

Sepsis is a systemic inflammatory state caused by infection. Complications of this infection with multiple organ failure lead to more lethal conditions, such as severe sepsis and septic shock. Sepsis is one of the leading causes of US deaths. Novel biomarkers with high sensitivity and specificity may be helpful for early diagnosis of sepsis and for improvement of patient outcomes through the development of new therapies. Mass spectrometry-based proteomics offers powerful tools to identify such biomarkers and furthermore to give insight to fundamental mechanisms of this clinical condition. In this review, we summarize findings from proteomics studies of sepsis and how their applications have provided more understanding into the pathogenesis of septic infection. Literatures related to “proteomics”, “sepsis”, “systemic inflammatory response syndrome”, “severe sepsis”, “septic infection”, and “multiple organ dysfunction syndrome” were searched using PubMed. Findings about neonatal and adult sepsis are discussed separately. Within the adult sepsis studies, results are grouped based on the models (e.g., human or animal). Across investigations in clinical populations and in rodent and mammalian animal models, biological pathways, such as inflammatory and acute phase response, coagulation, complement, mitochondrial energy metabolism, chaperones, and oxidative stress, are altered at the protein level. These proteomics studies have discovered many novel biomarker candidates of septic infection. Validation the clinical use of these biomarker candidates may significantly impact the diagnosis and prognosis of sepsis. In addition, the molecular mechanisms revealed by these studies may also guide the development of more effective treatments.     

Immunoproteomics: From biomarker discovery to diagnostic applications

Circulating antibodies reflect a molecular imprint of antigens that are related to autoimmune diseases, cancer or infection. Importantly, serum antibodies are useful clinical markers as they carry diagnostic information from all around the human body. Moreover, the amplification cascade governed by the humoral immune system causes a surplus of circulating antibodies after appearance of the corresponding (low abundance) antigen. In combination with the fact that antibodies are highly stable compared to many other serum proteins, they seem ideal to be implemented in clinical diagnostic assays for the detection of antigen-associated diseases. This review summarises advances in immunoproteomics with respect to technologies for biomarker discovery, with special emphasis on recently developed gel-free MS-based approaches, and looks forward to potential immunoproteomic applications in diagnostic medicine.     

The FALCON decision support system: Preparing communities for weapons of opportunity

Since September 11, 2001, awareness of potential terrorist targets has increased greatly. Industrial chemicals, either in storage or transport, are now considered dangerously accessible materials that could be used to cause substantial harm. In response to this new threat, emergency organizations are beginning to plan for such possible chemical releases. Currently there is no tool that allows a community to track, analyze, query, and display data about these chemical “weapons of opportunity” and the readiness of the communities around them.Decision support systems are computer environments designed to assist decision makers within a particular problem-solving context. A particular type of DSS, environmental decision support systems (EDSS), assists environmental scientists and planners in making environmental management decisions. A hazardous materials decision support system called “FALCON” will assist emergency organizations by integrating information describing chemical inventories, security, health readiness, geography, and population into one information system. Emergency organizations will be able to assess response readiness of a community for chemical releases and prioritize antidote stockpiling, training, and security. Emergency organizations and first responders will use the FALCON DSS to simulate and prepare for real-time events, assess possible casualties, and receive emergency contact information. And with the help of FALCON, law enforcement and security personnel will be able to evaluate and augment protection of the most dangerous facilities.     

An Empirical Study of Home IoT Services in South Korea: The Moderating Effect of the Usage Experience

This article examines the responses of users to home Internet of Things (IoT) services in South Korea, which is taking progressive steps in the field of IoT. It is important to investigate the user’s response because home IoT users are the core users of the IoT business. To this end, the research model includes two trust constructs — “trust in the service provider” and “institutional trust”; two risk constructs — “perceived security risk” and “perceived privacy risk”; and “perceived benefit” construct. This study has two main objectives: (1) to establish the functional relationship among the five constructs listed above; (2) to examine the moderating role of home IoT usage experience in these relationships. The study first reviews the literature on home IoT services and describes the Korean situation. Data were collected from residents living in a smart apartment complex. They were made aware of not only the benefits of home IoT but also the security and privacy risks before they moved into their new homes. The research model was empirically analyzed with structural equation modeling (SEM) using Amos 22.0. The results show that (1) “trust in the service provider” negatively influences “perceived security risk” and “perceived privacy risk” while “institutional trust” does not have a significant influence on them, (2) “perceived security risk” and “perceived privacy risk” negatively influence “perceived benefit,” and (3) “trust in service provider” does not directly influence “perceived benefit” while “institutional trust” has a positive and direct influence on it. In addition, there is a significant moderating effect of home IoT usage experience on some paths. Finally, the study’s findings and limitations are discussed, and potential avenues for future research are suggested.     

Combined elastoplastic and limit analysis via restricted basis linear programming

In many engineering situations rigid-perfectly plastic limit analysis turns out to be insufficient and unreliable. On the other hand, elastic-plastic “historical” analysis is often laborious. For trusslike frames and suitably discretized structures the former analysis is traditionally cast into the mathematical model of linear programming; the latter has led to various formulations and solution methods explicitly or implicitly related to quadratic programming.In this paper the complete stress and strain response of elastic-perfectly plastic structures subjected to proportional loading up to collapse is determined by a method essentially consisting of a linear programming procedure supplemented by an additional rule which enforces a complementarity relation among variables at each pivotal step (“restricted basis entry”). Possible local unloadings are allowed for by means of suitable modifications of the current tableau. The method proposed provides a whole “exact” deformation history of the structure, the safety factor and the collapse mechanism. Lack of uniqueness in the deformation history and “limited mechanisms” prior to collapse are discussed. Nonholonomic (irreversible) analysis and holonomic (reversible nonlinear-elastic) analysis are compared.The computational effort is assessed and checked by examples and turns out to be roughly equivalent to that required by the familiar rigid-plastic limit analysis by the static approach via standard linear programming. Extensions of the method to strain-hardening behaviour, to nonproportional loading processes and to broader categories of structures are envisaged.