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We consider a general mathematical model for protein subunit vaccine with a focus on the MF59-adjuvanted spike glycoprotein-clamp vaccine for SARS-CoV-2, and use the model to study immunological outcomes in the humoral and cell-mediated arms of the immune response from vaccination. The mathematical model is fit to vaccine clinical trial data. We elucidate the role of Interferon-𝛾 and Interleukin-4 in stimulating the immune response of the host. Model results, and results from a sensitivity analysis, show that a balance between the T𝐻 1 and T𝐻 2 arms of the immune response is struck, with the T𝐻 1 response being dominant. The model predicts that two-doses of the vaccine at 28 days apart will result in approximately 85% humoral immunity loss relative to peak immunity approximately 6 months post dose 1.

Preterm birth is the leading cause of mortality in children under the age of five worldwide. Despite major efforts, we still lack the ability to accurately predict and effectively prevent preterm birth. While multiple factors contribute to preterm labor, dysregulations of immunological adaptations required for the maintenance of a healthy pregnancy is at its pathophysiological core. Consequently, a precise understanding of these chronologically paced immune adaptations and of the biological pacemakers that synchronize the pregnancy “immune clock” is a critical first step towards identifying deviations that are hallmarks of peterm birth. Here, we will review key elements of the fetal, placental, and maternal pacemakers that program the immune clock of pregnancy. We will then emphasize multiomic studies that enable a more integrated view of pregnancy-related immune adaptations. Such multiomic assessments can strengthen the biological plausibility of immunological findings and increase the power of biological signatures predictive of preterm birth

Cellular-mediated immunity is critical for long-term protection against most viral infections, including coronaviruses. We studied 23 SARS-CoV-2-infected survivors over a one year post symptom onset (PSO) interval by ex vivo cytokine ELISpot assay. All subjects demonstrated SARS-CoV-2-specific IFN-γ, IL-2, and Granzyme B (GzmB) T cell responses at presentation, with greater frequencies in severe disease. Cytokines, mainly produced by CD4+ T cells, targeted all structural proteins (Nucleocapsid, Membrane, Spike) except Envelope, with GzmB > IL-2 > IFN-γ. Mathematical modeling predicted that: 1) cytokine responses peaked at 6 days for IFN-γ, 36 days for IL-2, and 7 days for GzmB, 2) severe illness was associated with reduced IFN-γ and GzmB, but increased IL-2 production rates, 3) males displayed greater production of IFN-γ, whereas females produced more GzmB. Ex vivo responses declined over time with persistence of IL-2 in 86% and of IFN-γ and GzmB in 70% of subjects at a median of 336 days PSO. The average half-life of SARS-CoV-2-specific cytokine-producing cells was modelled to be 139 days (∼4.6 months). Potent T cell proliferative responses persisted throughout observation, were CD4 dominant, and were capable of producing all 3 cytokines. Several immunodominant CD4 and CD8 epitopes identified in this study were shared by seasonal coronaviruses or SARS-CoV-1 in the Nucleocapsid and Membrane regions. Both SARS-CoV-2-specific CD4+ and CD8+ T cell clones were able to kill target cells, though CD8 tended to be more potent.ImportanceOur findings highlight the relative importance of SARS-CoV-2-specific GzmB-producing T cell responses in SARS-CoV-2 control, shared CD4 and CD8 immunodominant epitopes in seasonal coronaviruses or SARS-CoV-1, and indicate robust persistence of T cell memory at least one year after infection. Our findings should inform future strategies to induce T cell vaccines against SARS-CoV-2 and other coronaviruses.

SummaryBackgroundThe lipid nanoparticle (LNP)-formulated mRNA vaccines are a widely adopted two-dose vaccination public health strategy to manage the COVID-19 pandemic. Clinical trial data has described the immunogeneicity of the vaccine, albeit within a limited study time frame. Our aims were to use a within-host mathematical model for LNP-formulated mRNA vaccines, informed by available clinical trial data, to project a longer term understanding of humoral immunity as a function of vaccine type, dosage amount, age, and sex.MethodsWe developed a mathematical model describing the immunization process of LNP-formulated mRNA vaccines, and fit our model to twenty-two clinical humoral and cytokine BNT162b2 or mRNA-1273 human two-dose vaccination data sets. We incorporated multi-dose effects in our model to specify whether the dosage is standard or low-dose. We further specify the age groups 18-55, 56-70, and 70+ in our fits for two-standard doses of mRNA-1273, and sex in our fits for two-standard doses of BNT162b2. We used non-linear mixed effect models to fit to all similar data types (e.g. standard two-dose BNT162b2 or mRNA-1273, or two low-dose mRNA-1273). Therefore, in our fits all estimated parameters are statistically correlated, which allowed us to determine the underlying ‘population-dynamics’ structure common to a data type. We therefore made accurate long-term predictions informed by all clinical data used in this study.FindingsWe estimate that two standard doses of either mRNA-1273 or BNT162b2, with dosage times separated by the company-mandated intervals, results in individuals loosing more than 99% humoral immunity relative to peak immunity by eight months following the second dose. We predict that within an eight month period following dose two (corresponding to the CDC time-frame for administration of a third dose), there exists a period of time longer than one month where an individual has less then 99% humoral immunity relative to peak immunity, regardless of which vaccine was administered. We further find that age has a strong influence in maintaining humoral immunity; by eight months following dose two we predict that individuals aged 18-55 have a four-fold humoral advantage compared to aged 56-70 and 70+ individuals. We find that sex has little effect on the vaccine uptake and long-term IgG counts. Finally, we find that humoral immunity generated from two low doses of mRNA-1273 decays substantially slower relative to peak immunity gained than compared to two standard doses of either mRNA-1273 or BNT162b2.InterpretationFor the two dose mRNA vaccines, our predictions highlight the importance of the recommended third booster dose in order to maintain elevated levels of antibodies. We further show that age plays a critical role in determining the antibody levels. Hence, a third booster dose may confer an immuno-protective advantage in older individuals.FundingThis research is supported by NSERC Discovery Grant (RGPIN-2018-04546), NSERC COVID-19 Alliance Grant ALLRP 554923-20, CIHR-Fields COVID Immunity Task Force, NRC Pandemic Response Challenge Program Grant No. PR016-1.

During the SARS-CoV-2 global pandemic, several vaccines, including mRNA and ade-novirus vector approaches, have received emergency or full approval. However, supply chain logistics have hampered global vaccine delivery, which is impacting mass vaccination strategies. Recent studies have identified different strategies for vaccine dose administration so that supply constraints issues are diminished. These include increasing the time between consecutive doses in a two-dose vaccine regimen and reducing the dosage of the second dose. We consider both of these strategies in a mathematical modeling study of a non-replicating viral vector adenovirus vaccine in this work. We investigate the impact of different prime-boost strategies by quantifying their effects on immunological outcomes based on simple ordinary differential equations. The boost dose is administered either at a standard dose (SD) of 1000 or at a low dose (LD) of 500 or 250 vaccine particles. Simulated Second dose fractionation highlights previously shown dose-dependent features of the immune mechanism. In agreement with clinical characteristics of 175 COVID-19 recovered patients, the model predictions for either SD/SD or SD/LD regimens mainly show that by stretching the prime-boost interval until 18 or 20 weeks, the minimum promoted antibody (Nab) response is comparable with the neutralizing antibody level of COVID-19 recovered patients. The minimum stimulated antibody in SD/SD regimen is identical with the high level of clinical trial data. It is at the same range of the medium-high level of Nab in SD/LD, where the second dose is half or quarter of the standard dose.

Stroke is a leading cause of cognitive impairment and dementia, but the mechanisms that underlie post-stroke cognitive decline are not well understood. Stroke produces profound local and systemic immune responses that engage all major innate and adaptive immune compartments. However, whether the systemic immune response to stroke contributes to long-term disability remains ill-defined. We used a single-cell mass cytometry approach to comprehensively and functionally characterize the systemic immune response to stroke in longitudinal blood samples from 24 patients over the course of 1 year and correlated the immune response with changes in cognitive functioning between 90 and 365 days post-stroke. Using elastic net regularized regression modelling, we identified key elements of a robust and prolonged systemic immune response to ischaemic stroke that occurs in three phases: an acute phase (Day 2) characterized by increased signal transducer and activator of transcription 3 (STAT3) signalling responses in innate immune cell types, an intermediate phase (Day 5) characterized by increased cAMP response element-binding protein (CREB) signalling responses in adaptive immune cell types, and a late phase (Day 90) by persistent elevation of neutrophils, and immunoglobulin M(+) (IgM(+)) B cells. By Day 365 there was no detectable difference between these samples and those from an age- and gender-matched patient cohort without stroke. When regressed against the change in the Montreal Cognitive Assessment scores between Days 90 and 365 after stroke, the acute inflammatory phase Elastic Net model correlated with post-stroke cognitive trajectories (r = −0.692, Bonferroni-corrected P = 0.039). The results demonstrate the utility of a deep immune profiling approach with mass cytometry for the identification of clinically relevant immune correlates of long-term cognitive trajectories.

Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. The model derived in the Stanford cohort was highly significant (p = 3.9E–15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E–01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E–21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E–02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E–454, R = 0.92) and Detroit cohorts (p = 1.1.E–92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences. Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the “same” clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women's physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI): [0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC = 0.87, 95% CI: [0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC = 0.90, 95% CI: [0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia.

This diagnostic/prognostic study describes the use of cell-free transcriptomics, urine metabolomics, and plasma proteomics for identifying the biological measurements associated with preterm birth., Key Points Question What maternal biological modalities are associated with preterm birth (PTB)? Findings In this diagnostic/prognostic study of 81 pregnant women from 5 birth cohorts in low- and middle-income countries, several correlates of preterm birth in urine and blood were found to be associated with PTB. Although cohort-specific signatures were present, a machine learning algorithm was able to generate a model that was capable of predicting PTB across the cohorts. Meaning Results of this study suggest that most PTBs can be predicted using blood and urine samples collected early in the pregnancy, providing opportunities for interventions., Importance Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. Objective To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. Design, Setting, and Participants This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Exposures Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. Main Outcomes and Measures The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Results Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. Conclusions and Relevance This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

In many cities, worldwide public transit companies use smart card system to manage fare collection. Analysis of this acquisitive information provides a comprehensive insight of user's influence in the interactive public transit network. In this regard, analysis of temporal data, describing the time of entering to the public transit network is considered as the most substantial component of the data gathered from the smart cards. Classical distance-based techniques are not always suitable to analyze this time series data. A novel projection with intuitive visual map from higher dimension into a three-dimensional clock-like space is suggested to reveal the underlying temporal pattern of public transit users. This projection retains the temporal distance between any arbitrary pair of time-stamped data with meaningful visualization. Consequently, this information is fed into a hierarchical clustering algorithm as a method of data segmentation to discover the pattern of users.

Chronic periodontitis (ChP) is a prevalent inflammatory disease affecting 46% of the US population. ChP produces a profound local inflammatory response to dysbiotic oral microbiota that leads to destruction of alveolar bone and tooth loss. ChP is also associated with systemic illnesses, including cardiovascular diseases, malignancies, and adverse pregnancy outcomes. However, the mechanisms underlying these adverse health outcomes are poorly understood. In this prospective cohort study, we used a highly multiplex mass cytometry immunoassay to perform an in-depth analysis of the systemic consequences of ChP in patients before ( n = 28) and after ( n = 16) periodontal treatment. A high-dimensional analysis of intracellular signaling networks revealed immune system–wide dysfunctions differentiating patients with ChP from healthy controls. Notably, we observed exaggerated proinflammatory responses to Porphyromonas gingivalis–derived lipopolysaccharide in circulating neutrophils and monocytes from patients with ChP. Simultaneously, natural killer cell responses to inflammatory cytokines were attenuated. Importantly, the immune alterations associated with ChP were no longer detectable 3 wk after periodontal treatment. Our findings demarcate systemic and cell-specific immune dysfunctions in patients with ChP, which can be temporarily reversed by the local treatment of ChP. Future studies in larger cohorts are needed to test the boundaries of generalizability of our results.

Nowadays, tremendous data, are continuously gathering from the smart card in public transport domain. Such data, conveying two viable distinct information, can ensue designing intelligent transportation. More specifically, users behavior in a public transport system, can be investigated, as one of the data mining and machine learning applications. The first component of the data, provides the spatial feature, indicates the geographical coordinates of bus stops or subway stations. The second component of the data, deals with the temporal feature, being the time of the trips that public transport is used. Hence, it is necessary to distill the data, in order to get the advantages of the data analysis techniques and extract the essential knowledge from the data. Due to the massive data storage and the diversity of the data analysis methods, various challenges are arisen during the process of exploiting the hidden patterns of the data. We review a couple of scenarios and suggest a solution to overcome a number of the raised challenges. Moreover, the other aspects of this problem, are remaining as the open problems for the future research.

Background Early detection of maladaptive processes underlying pregnancy-related pathologies is desirable because it will enable targeted interventions ahead of clinical manifestations. The quantitative analysis of plasma proteins features prominently among molecular approaches used to detect deviations from normal pregnancy. However, derivation of proteomic signatures sufficiently predictive of pregnancy-related outcomes has been challenging. An important obstacle hindering such efforts were limitations in assay technology, which prevented the broad examination of the plasma proteome. Objective The recent availability of a highly multiplexed platform affording the simultaneous measurement of 1310 plasma proteins opens the door for a more explorative approach. The major aim of this study was to examine whether analysis of plasma collected during gestation of term pregnancy would allow identifying a set of proteins that tightly track gestational age. Establishing precisely timed plasma proteomic changes during term pregnancy is a critical step in identifying deviations from regular patterns caused by fetal and maternal maladaptations. A second aim was to gain insight into functional attributes of identified proteins and link such attributes to relevant immunological changes. Study Design Pregnant women participated in this longitudinal study. In 2 subsequent sets of 21 (training cohort) and 10 (validation cohort) women, specific blood specimens were collected during the first (7–14 weeks), second (15–20 weeks), and third (24–32 weeks) trimesters and 6 weeks postpartum for analysis with a highly multiplexed aptamer-based platform. An elastic net algorithm was applied to infer a proteomic model predicting gestational age. A bootstrapping procedure and piecewise regression analysis was used to extract the minimum number of proteins required for predicting gestational age without compromising predictive power. Gene ontology analysis was applied to infer enrichment of molecular functions among proteins included in the proteomic model. Changes in abundance of proteins with such functions were linked to immune features predictive of gestational age at the time of sampling in pregnancies delivering at term. Results An independently validated model consisting of 74 proteins strongly predicted gestational age (P = 3.8 × 10–14, R = 0.97). The model could be reduced to 8 proteins without losing its predictive power (P = 1.7 × 10–3, R = 0.91). The 3 top ranked proteins were glypican 3, chorionic somatomammotropin hormone, and granulins. Proteins activating the Janus kinase and signal transducer and activator of transcription pathway were enriched in the proteomic model, chorionic somatomammotropin hormone being the top-ranked protein. Abundance of chorionic somatomammotropin hormone strongly correlated with signal transducer and activator of transcription-5 signaling activity in CD4 T cells, the endogenous cell-signaling event most predictive of gestational age. Conclusion Results indicate that precisely timed changes in the plasma proteome during term pregnancy mirror a proteomic clock. Importantly, the combined use of several plasma proteins was required for accurate prediction. The exciting promise of such a clock is that deviations from its regular chronological profile may assist in the early diagnoses of pregnancy-related pathologies, and point to underlying pathophysiology. Functional analysis of the proteomic model generated the novel hypothesis that chrionic somatomammotropin hormone may critically regulate T-cell function during pregnancy.

The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling-based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2-dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies.

A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company’s daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the mixture of MLP experts (ME) scheme, the recognition rate is strongly increased to 86.35%. A gain and loss analysis is also used, showing the relative forecasting success of the ME method with and without rejection criteria, compared to a simple buy and hold approach. In experiment II, the time series data are the daily closing prices of 37 companies on the Tehran stock exchange. This experiment is conducted to verify the results of experiment I and to show the efficiency of the ME method compared to stacked generalization, modified stacked generalization and ANFIS.

In this paper a holistic method and a local method based on decision template ensemble are investigated. In addition by combining both methods, a new hybrid method for boosting the performance of the system is proposed and evaluated with respect to robustness against small sample size problem in face recognition. Inadequate and substantial variations in the available training samples are the two challenging obstacles in classification of an unknown face image. At first in this novel multi learner framework, a decision template is designed for the global face and a set of decision templates is constructed for each local part of the face as a complement to the previous part. The prominent results demonstrate that, the new hybrid method based on fusion of weighted multiple decision templates is superior to the other classic combining schemes for both ORL and Yale data sets. In addition when the global and the local components of the face are combined together the best performance is achieved.