Sequential Aggregation Research Articles

WWOX dysfunction induces sequential aggregation of TRAPPC6AΔ, TIAF1, tau and amyloid β, and causes apoptosis.

Aggregated vesicle-trafficking protein isoform TRAPPC6AΔ (TPC6AΔ) has a critical role in causing caspase activation, tau aggregation and Aβ generation in the brains of nondemented middle-aged humans, patients with Alzheimer’s disease (AD) and 3-week-old Wwox gene knockout mice. WWOX blocks neurodegeneration via interactions with tau and tau-phosphorylating enzymes. WWOX deficiency leads to epilepsy, mental retardation and early death. Here, we demonstrated that TGF-β1 induces shuttling of endogenous wild-type TPC6A and TPC6AΔ in between nucleoli and mitochondria (~40–60 min per round trip), and WWOX reduces the shuttling time by 50%. TGF-β1 initially maximizes the binding of TPC6AΔ to the C-terminal tail of WWOX, followed by dissociation. TPC6AΔ then undergoes aggregation, together with TIAF1 (TGF-β1-induced antiapoptotic factor), in the mitochondria to induce apoptosis. An additional rescue scenario is that TGF-β1 induces Tyr33 phosphorylation and unfolding of WWOX and its the N-terminal WW domain slowly binds TPC6AΔ to block aggregation and apoptosis. Similarly, loss of WWOX induces TPC6AΔ polymerization first, then aggregation of TIAF1, amyloid β and tau, and subsequent cell death, suggesting that a cascade of protein aggregation leads to neurodegeneration.

Background Context Augmented Hypothesis Graph for Object Segmentation

In this paper we address the problem of semantic segmentation. Inspired by the significant role of the context information, in this task our solution makes use of semantically meaningful overlapping object hypotheses augmented by contextual information, which is obtained from a novel background mining procedure. More precisely, a fully connected conditional random field is considered over a set of overlapping segment hypotheses and the unlabeled background regions are learned from a training set and applied in the unary terms corresponding to the foreground regions. The final segmentation result is obtained via maximum a posteriori inference, in which the segments are merged based on a sequential aggregation followed by morphological hole filling and superpixel refinement serving as postprocessing. Moreover, by incorporating other kinds of contextual cues, such as global image classification and object detection cues, a new state-of-the-art performance is achieved by our proposed solution as experimentally verified on the challenging PASCAL Visual Object Class (VOC) Challenge 2012 and MSRC-21 object segmentation data sets.

Minimizing response time in time series classification

Providing a timely output is one of the important criteria in applications of time series classification. Recent studies have been motivated to explore models of early prediction, prediction based on truncated temporal observations. The truncation of input improves the response time, but generally reduces the reliability of the prediction. The trade-off between the earliness and the accuracy is an inherent challenge of learning an early prediction model. In this paper, we present an optimization-based approach for learning an ensemble model for timely prediction with an intuitive objective function. The proposed model is comprised of time series classifiers with different response time, and a sequential aggregation procedure to determine the single timing of its output. We formalize the training of the ensemble classifier as a quadratic programming problem and present an iterative algorithm which minimizes an empirical risk function and the response time required to achieve the minimal risk simultaneously. We conduct an empirical study using a collection of behavior and time series datasets to evaluate the proposed algorithm. In the comparisons of the traditional and time-sensitive performance measures, the ensemble framework showed significant advantages over the existing methods on early prediction.

Somatic Mutation Spectrum in Monoclonal Gammopathy of Undetermined Significance Compared to Multiple Myeloma

Introduction: Malignant transformation of normal to tumour cells is a multistep process followed by sequential aggregation of hits at different molecular levels. Genetic events including single nucleotide variants (SNVs), insertion-deletion changes (indels) as well as copy number variants (CNVs) affect the phenotype of the tumour population and consequently patient prognosis. Transformation from a symptomless state, monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM) can be used as a unique model for cancer development studies. To date, there is very little data regarding the mechanisms leading to disease progression at molecular level. In our study, we performed exome sequencing together with SNP array analysis on 33 MGUS patients to describe the premalignant phenotype and compared these to advanced tumour cells at the DNA level. We hypothesised that increased genetic instability indicated MGUS patients with a high risk of progression to MM.Methods: 33 MGUS patients (M:F 1.5:3; median age 61, range: 35-86) were included in this study. Plasma cells were isolated from bone marrow by FACSAria (BD Biosciences) system using CD138, CD19 and CD56 markers to obtain a pure abnormal plasma cell population with a purity >90%. Tumour DNA was isolated using Gentra Puregene Kit and amplified using REPLI-g Midi Kit (both Qiagen); control DNA was gained from peripheral white blood cells by MagNA Pure System (Roche Diagnostics). For exome sequencing, NEBNext kit (NEB) and SureSelect Human All Exon V5 (Agilent Technologies) were used and samples were sequenced by HiSeq2000 (Illumina) using 76-bp paired end reads. Unbalanced CNVs were tested by SurePrint G3 CGH+SNP, 4x180K (Agilent Technologies). Results were compared to 463 MM patients.Results: In our analysis, we found acquired SNVs in 100% (33/33) MGUS patients with a median of 89 (range 9-315) SNVs per patient. Non-synonymous SNVs (NS-SNVs) were present in 97% (32/33) cases with a median 19 (range 0–70) NS-SNVs per patient. Overall, 42 genes were recurrently mutated in at least 2 patients and 6 genes were mutated in at least 3 cases including MUC16, IGK, TTN, KLHL6, AKAP9 and NPIPL2. We identified 7 genes which were significantly mutated in MM in our previous study including KRAS (n=2), HIST1H1E (n=2) and NRAS, DIS3, EGR1, LTB, PRKD2 (all n=1). IGH translocations were identified in 27% (9/33) of patients: t(11;14) in 12% (4/33), t(4;14) in 9% (3/33), t(14;16) in 3% (1/33) and t(14;20) in 3% (1/33). We did not find any translocations involving MYC (8q24.21) or the light chain loci IGK (2p12) and IGL (22q11.2). Using SNP arrays, unbalanced CNVs were presented in 67% (22/33) of MGUS patients and detected CNVs showed similarity to MM across the cohort. As previously described in MM, only one type of IGH translocation was found per patient and all 9 cases with IGH translocation did not have additional hyperdiploidy. Furthermore, we identified a patient with two CCND1 (p.K50T, p.E51D) mutations and a t(11;14), a case with a DIS3 (p.D488N) mutation and a 13q loss. Moreover, we noticed a co-segregation of cases t(4;14) and t(14;16) who all had a 13q loss (100%, 4/4). In contrast none of the patients (0/5) with a t(11;14) or a t(14;20) had a 13q loss. Of note 29% (7/24) patients without any IGH translocation had a 13q loss. Sixty seven percent (2/3) of patients with a t(4;14) and the one case with a t(14;16) also had a 1q gain. In comparison, none of patients with a t(11;14) (0%; 0/4) had a 1q gain. Unlike what has previously been described in MM, neither of the 2 MGUS patients with a KRAS (p.Q61L and p.A146T) mutations had a t(11;14). We also identified a patient with both a KRAS (p.Q61L) and an NRAS (p.G13R) mutation which are although not mutually exclusive, negatively correlated in MM. Importantly, we did not find any mutations in TP53, ATM, ATR and ZFHX4 genes involved in DNA repair pathway alterations which were identified as unfavourable factors in survival of MM patients.Summary: We have performed the first comprehensive analysis of 33 MGUS patients using exome sequencing together with SNP arrays and described the main genetic events that are already present in this premalignant state. We found similarities to MM in terms of SNVs, CNVs and their correlations. We identified 6 MGUS cases with NS-SNVs in potential key genes that could indicate a potential high risk to progression.Support: IGA MH CZ NT13492, OPVK CZ.1.07/2.3.00/20.0183. DisclosuresWalker:Onyx Pharmaceuticals: Consultancy, Honoraria.

A short identity-based proxy ring signature scheme from RSA

Identity-based proxy ring signature concept was introduced by Cheng et al. in 2004. This primitive is useful where the privacy of proxy signers is required. In this paper, the first short provably secure identity-based proxy ring signature scheme from RSA assumption has been proposed. In addition, the security of the proposed scheme tightly reduces to the RSA assumption, and therefore, the proposed scheme has a proper advantage in security reduction compared to the ones from RSA. The proposed scheme not only outperforms the existing schemes in terms of efficiency and practicality, but also does not suffer from the proxy key exposure attack due to the use of the sequential aggregation paradigm.

Sequential aggregation of bags

This study introduces and develops a new approach to sequential aggregation of bags. It generalizes several related approaches known from the literature. The approach is based on a system of aggregation functions with property (SA) – a weakened form of symmetry. It generalizes symmetric associative aggregation. In the paper, several methods of constructing sequential aggregations, as well as extended aggregation functions with property (SA), are introduced and exemplified. Moreover, an example of a real-world application, which illustrates the proposed sequential aggregation procedure, is included.

A provably secure identity‐based proxy ring signature based on RSA

AbstractProxy ring (anonymous proxy) signatures allow an entity to delegate its signing capability to a group of entities (proxy group) such that only one of the members in the proxy group can generate a proxy signature on behalf of the delegator, while privacy of the proxy signer is protected. Identity‐based versions of proxy ring signatures employ identity strings in place of randomly generated public keys. Our contribution is twofold. First, we formalize a security model for identity‐based proxy ring signatures. We note that there exists no formal security model for identity‐based proxy ring signatures prior to our work. Second, we present the first provably secure identity‐based proxy ring signature scheme using a new paradigm called sequential aggregation. The construction is proved secure, under the one‐wayness assumption of RSA, in the random oracle model by presenting a new forking lemma. We should highlight that the proxy key exposure attack cannot be applied to our scheme. Further, in contrast to the existing schemes that are based on pairings, our scheme is based on RSA; therefore, it outperforms the existing schemes in terms of efficiency and practicality. Copyright © 2014 John Wiley & Sons, Ltd.

Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM10 on the Prev'Air platform

This paper presents the application of an ensemble forecasting approach to the Prev’Air operational platform. This platform aims at forecasting maps, on a daily basis, for ozone, nitrogen dioxide and particulate matter. It relies on several air quality models which differ by their physical parameterizations, their input data and numerical strategies, so that one model may perform better with respect to observations for a given pollutant, at a given time and location. We apply sequential aggregation methods to this ensemble of models, which has already proved good potential in previous research papers. Compared to these studies, the novelties of this paper are the variety of models, the real operational context, which requires robustness assessment, and the application to several pollutants. In this paper, we first introduce the ensemble forecasting methods and the operational platform Prev’Air along with its models. Then, the sequential aggregation performance and robustness are assessed using two different data sets.The results with the discounted ridge regression method show that the errors of the forecasts are respectively reduced by at least 29%, 35% and 19% for hourly, daily and peak O3 concentrations, by 19%, 26% and 20% for hourly, daily and peak NO2 concentrations, and finally by 17%, 19% and 11% for hourly, daily and peak PM10 concentrations. At last, we give a first insight of the ensemble ability to forecast threshold exceedances.

N-Terminal Protein Tails Act as Aggregation Protective Entropic Bristles: The SUMO Case

The formation of β-sheet enriched amyloid fibrils constitutes the hallmark of many diseases but is also an intrinsic property of polypeptide chains in general, because the formation of compact globular proteins comes at the expense of an inherent sequential aggregation propensity. In this context, identification of strategies that enable proteins to remain functional and soluble in the cell has become a central issue in chemical biology. We show here, using human SUMO proteins as a model system, that the recurrent presence of disordered tails flanking globular domains might constitute yet another of these protective strategies. These short, disordered, and highly soluble protein segments would act as intramolecular entropic bristles, reducing the overall protein intrinsic aggregation propensity and favoring thus the attainment and maintenance of functional conformations.

Minimax filtering for sequential aggregation: Application to ensemble forecast of ozone analyses

This paper presents a new algorithm for sequential aggregation of an ensemble of forecasts. At any forecasting step, the aggregation consists of (1) computing new weights for the ensemble members represented by different numerical models and (2) forecasting with a weighted linear combination of the ensemble members. We assume that the time evolution of the weights is described by a linear equation with uncertain parameters and apply a minimax filter (and also Kalman filter, for comparison) in order to estimate the vector of weights given “observations”. The “observation” equation for the filter compares the aggregated forecast with the analysis determined in a data assimilation cycle together with its variance. The minimax approach allows one to work with flexible uncertainty description: deterministic bounding sets for uncertain parameters in weight's equation, and error covariance matrices for the “observational” errors. Our key contribution is an uncertainty estimate of the aggregated forecast, for which we introduce an evaluation test. The performance of the method is assessed for the forecast of ground‐level ozone daily peaks over Europe, for the year 2001. Compared to forecasts generated by classical data assimilation, the root mean square error is decreased by 16% for prediction of the analyses and by 20% for prediction of the observations.

Bribery in Voting With Soft Constraints

We consider a multi-agent scenario where a collection of agents needs to select a common decision from a large set of decisions over which they express their preferences. This decision set has a combinatorial structure, that is, each decision is an element of the Cartesian product of the domains of some variables. Agents express their preferences over the decisions via soft constraints. We consider both sequential preference aggregation methods (they aggregate the preferences over one variable at a time) and one-step methods and we study the computational complexity of influencing them through bribery. We prove that bribery is NPcomplete for the sequential aggregation methods (based on Plurality, Approval, and Borda) for most of the cost schemes we defined, while it is polynomial for one-step Plurality.

Random versus sequential pathway of molecular self-assembly

Two important assumptions are often made in the analysis of molecular self-assembly at equilibrium, viz., that sequential is preferred to random aggregation and that the equilibrium constants at each stage of aggregation are equal, though both assumptions have not been justified strictly. In the present work we show that molecular self-assembly leading to formation of linear polymers and proceeding in a random manner appears to be less entropically favored than sequential aggregation, which provides a physical background for assuming sequential aggregation when studying molecular self-assembly in solution. Exact equations for analysis of experimental data for molecular assembly proceeding in a sequential manner were derived by taking strict account of the profile of the equilibrium constant, which provides a physically more correct approach than that using the conventional indefinite equilibrium constant (EK) model.

Self‐aggregating mutant TRAPPC6A from partial exon 1 gene deletion activates caspases, binds TIAF1, and generates amyloid beta in hippocampus

TGF‐β‐induced factor TIAF1 forms aggregates in the hippocampus of normal individuals, and this may lead to the generation of amyloid beta (Aβ) and plaques as shown in Alzheimer's disease (AD). Here, we determined that TRAPPC6A gene deletion occurs at approximately a 3‐time higher incidence in AD than in normal controls. TRAPPC6A (TPC6A), a component of transport protein particle complex, forms aggregates with TIAF1 in the normal hippocampus, and the TIAF1/TPC6A complexes possess increasing amounts of Aβ in the AD hippocampus. TGF‐β induces aggregation of wild type TPC6A (TPC6Awt) and an N‐terminal deletion mutant (TPC6AΔ). Aggregated TPC6AΔ, but not TPC6Awt, upregulates p53 expression, causes caspase activation and Aβ superproduction in vitro. Tumor suppressor WWOX or WOX1 is frequently downregulated in AD. TPC6A physically interacts with WOX1, and TGF‐β1 dissociates the complex for causing TPC6A aggregation. Wwox−/− MEF cells were generated and prone to possess TPC6A and TIAF1 aggregates. Thus, downregulation of WOX1 is likely to elicit a sequential aggregation of TPC6A and TIAF1, respectively, for leading to the formation of Aβ and plaques in the brain. (Supported by NSC and NHRI, Taiwan, and DoD, USA)

Sequential aggregation of verifiable information

We introduce the notion of verifiable information into a model of sequential debate among experts who are motivated by career concerns. We show that self-censorship may hamper the efficiency of information aggregation, as experts withhold evidence contradicting the conventional wisdom. In this case, silence is telling and undermines the prevailing view over time if this view is incorrect. As a result, withholding arguments about the correct state of the world is only a temporary phenomenon, and the probability of a correct decision always converges to one as the group of experts becomes large. For small groups, a simple mechanism the principal can use to improve decision-making is to appoint a devil's advocate.

Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation

Sequential aggregation is an ensemble forecasting approach that weights each ensemble member based on past observations and past forecasts. This approach has several limitations: The weights are computed only at the locations and for the variables that are observed, and the observational errors are typically not accounted for. This paper introduces a way to address these limitations by coupling sequential aggregation and data assimilation. The leading idea of the proposed approach is to have the aggregation procedure forecast the forthcoming analyses, produced by a data assimilation method, instead of forecasting the observations. The approach is therefore referred to as ensemble forecasting of analyses. The analyses, which are supposed to be the best a posteriori knowledge of the model's state, adequately take into account the observational errors and they are naturally multivariable and distributed in space. The aggregation algorithm theoretically guarantees that, in the long run and for any component of the model's state, the ensemble forecasts approximate the analyses at least as well as the best constant (in time) linear combination of the ensemble members. In this sense, the ensemble forecasts of the analyses optimally exploit the information contained in the ensemble. The method is tested for ground‐level ozone forecasting, over Europe during the full year 2001, with a 20‐member ensemble. In this application, the method proves to perform well with 28% reduction in root‐mean‐square error compared to a reference simulation, to be robust in time and space, and to reproduce many spatial patterns found in the analyses only.

Sequential Aggregation of Verifiable Information

We introduce the notion of verifiable information into a model of sequential debate among experts who are motivated by career concerns. We show that self-censorship may hamper the efficiency of information aggregation, as experts withhold evidence contradicting the conventional wisdom. In this case, silence is telling and undermines the prevailing view over time if this view is incorrect. As a result, withholding arguments about the correct state of the world is only a temporary phenomenon, and the probability of the correct state of the world being revealed always converges to one as the group of experts becomes large. For small groups, a simple mechanism the principal can use to improve decisionmaking is to appoint a devil’s advocate.

Sequential Aggregation of Verifiable Information

We introduce the notion of verifiable information into a model of sequential debate among experts who are motivated by career concerns. We show that self-censorship may hamper the efficiency of information aggregation, as experts withhold evidence contradicting the conventional wisdom. In this case, silence is telling and undermines the prevailing view over time if this view is incorrect. As a result, withholding arguments about the correct state of the world is only a temporary phenomenon, and the probability of the correct state of the world being revealed always converges to one as the group of experts becomes large. For small groups, a simple mechanism the principal can use to improve decisionmaking is to appoint a devil’s advocate.

A Method for Elasticity Modulus Calculation in Porous Media with Known Geometry Using the Monte Carlo Technique

This study introduces a method for a computational calculus of the Elasticity Modulus (E) of simulated porous media using the Monte Carlo technique. The porous media of known geometry is simulated as an elastic network of central forces, to which a known deformation is applied. The minimum strain energy is calculated applying the Monte Carlo technique. The Elasticity Modulus is obtained from the theoretical relations between the elastic energy of a system and its deformation. The computational method is validated by applying it in systems of known analytic solution and over porous media generated through aggregation algorithm in two dimensions i.e. Random Sequential Aggregation and Diffusion Limited Cluster-Cluster Aggregation (RSA and DLCA respectively). The latter used to simulate the structure of silica aerogels. As for the range of concentrations studied for the DLCA and RSA systems, it was found that the elasticity modulus E decreases as the porosity of the system increases, being the E value higher for the DLCA system with respect to RSA. The method used is able to differentiate the elastic properties for two different aggregation models. Being E values different for equal porosities, the coordination number (Z) was the geometric parameter that best explains the behavior of the Elasticity Modulus.

Ozone ensemble forecast with machine learning algorithms

We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrations. On the basis of past observations and past model forecasts, the learning algorithms produce a weight for each model. A convex or linear combination of the model forecasts is then formed with these weights. This process is repeated for each round of forecasting and is therefore called sequential aggregation. The aggregated forecasts demonstrate good results; for instance, they always show better performance than the best model in the ensemble and they even compete against the best constant linear combination. In addition, the machine learning algorithms come with theoretical guarantees with respect to their performance, that hold for all possible sequences of observations, even nonstochastic ones. Our study also demonstrates the robustness of the methods. We therefore conclude that these aggregation methods are very relevant for operational forecasts.

Multistep Aggregation Pathway of Human Interleukin-1 Receptor Antagonist: Kinetic, Structural, and Morphological Characterization

The complex, multistep aggregation kinetic and structural behavior of human recombinant interleukin-1 receptor antagonist (IL-1ra) was revealed and characterized by spectral probes and techniques. At a certain range of protein concentration (12–27 mg/mL) and temperature (44–48°C), two sequential aggregation kinetic transitions emerge, where the second transition is preceded by a lag phase and is associated with the main portion of the aggregated protein. Each kinetic transition is linked to a different type of aggregate population, referred to as type I and type II. The aggregate populations, isolated at a series of time points and analyzed by Fourier-transform infrared spectroscopy, show consecutive protein structural changes, from intramolecular (type I) to intermolecular (type II) β-sheet formation. The early type I protein spectral change resembles that seen for IL-1ra in the crystalline state. Moreover, Fourier-transform infrared data demonstrate that type I protein assembly alone can undergo a structural rearrangement and, consequently, convert to the type II aggregate. The aggregated protein structural changes are accompanied by the aggregate morphological changes, leading to a well-defined population of interacting spheres, as detected by scanning electron microscopy. A nucleation-driven IL-1ra aggregation pathway is proposed, and assumes two major activation energy barriers, where the second barrier is associated with the type I → type II aggregate structural rearrangement that, in turn, serves as a pseudonucleus triggering the second kinetic event.