Joint Regression Research Articles

Applicability of joint regression and biplot models for stability analysis in multi-environment barley (Hordeum vulgare) trials

GGE and AMMI biplot methods with Eberhart and Russell regression model were applied on the set of 18 barley (Hordeum vulgare L.) genotypes grown in 6 environments for quick and relevant method vis-a-vis to delineate genotype by environment interaction, stable genotypes and environmental discrimination. The average grain yield over the locations was depicted as 41.97 q/ha, which ranged from 31.82 (Karnal) to 55.52 q/ha (Bhatinda). The genotype DWRB 91 (47.51 q/ha) exhibited the highest grain yield followed by DWRB 121 (46.35 q/ha), DWRB 123 (46.04 q/ha) and DWRB 128 (44.70 q/ha) over the locations. In Eberhart and Russell model, the genotypes DWRB 124 and PL 880 were found suitable for favourable environments and DWRB 128 for poor environments. In AMMI analysis, IPCA 1 and IPCA 2 altogether captured 74.73% of the interaction mean squares, while in GGE biplot, PC 1 and PC 2 captured 36.51% and 26.44% interaction variation,respectively. The genotypes BH 992, DWRB 121, DWRB 123, RD 2897 and checks BH 902 and DWRB 91 were high yielding and as well as found stable in GGE and AMMI 1 biplot. The test environments Durgapura and Modipuram exhibited different niches, whereas, Hisar, Ludhiana, Bhatinda and Karnal were representative with better discriminating ability. Between biplot models applied, the GGE biplots were clear in visualization for polygon view, genotypic stability and environmental discrimination. The GGE method considered both G+GE for biplot generation and found most suitable for stability analysis.

Tree-Structured Nuclear Norm Approximation with Applications to Robust Face Recognition.

Structured sparsity, as an extension of standard sparsity, has shown the outstanding performance when dealing with some highly correlated variables in computer vision and pattern recognition. However, the traditional mixed (L1, L2) or (L1, L∞) group norm becomes weak in characterizing the internal structure of each group since they cannot alleviate the correla-tions between variables. Recently, nuclear norm has been vali-dated to be useful for depicting a spatially structured matrix variable. It considers the global structure of the matrix variable but overlooks the local structure. To combine the advantages of structured sparsity and nuclear norm, this paper presents a tree-structured nuclear norm approximation (TSNA) model as-suming that the representation residual with tree-structured prior is a random matrix variable and follows a dependent matrix dis-tribution. The Extended Alternating Direction Method of Multi-pliers (EADMM) is utilized to solve the proposed model. An effi-cient bound condition based on the extended restricted isometry constants is provided to show the exact recovery of the proposed model under the given noisy case. In addition, TSNA is connected with some newest methods such as sparse representation based classifier (SRC), nuclear-L1 norm joint regression (NL1R) and nuclear norm based matrix regression (NMR), which can be re-garded as the special cases of TSNA. Experiments with face re-construction and recognition demonstrate the benefits of TSNA over other approaches.

English

Efficacy of pollination bags made of new nonwoven fabrics was compared with the traditional paper bags in sorghum during 2015 using three cultivars comprising BR007B (red seeded), SC283 (white seeded) and 1167048 hybrid with tannin (brown seeded). The five pollination bag treatments were: no bagging, traditional paper bag, paper bag plus plastic screen bag for extra bird protection, duraweb® SG2 polypropylene bag and duraweb® SG1 polyester bag. There was no bird damage on tannin hybrid but birds damaged bags to access grains of the other two varieties. Varieties and bag types differed significantly, and also showed significant interactions for panicle weight (at P<0.06), seed weight and average seed weight per panicle. The tannin hybrid was consistently a better performer for all traits regardless of bag type. The paper bags were the worst for bird damage. Duraweb® SG1 was the best performer for all traits including bird damage followed by duraweb® SG2. The joint regression analysis showed that BR007B performed consistently under all bag types with average response. On the other hand, SC283 improved its response with the increasing quality of bag type at an above average rate for panicle weight and seed traits. It was concluded that new nonwoven fabric bags could replace paper bags in providing better seed production potential and greater protection against bird damage.   Key words: Sorghum, pollination bags, panicle weight, seed weight, bird control.

Multivariate Stochastic Process Models for Correlated Responses of Mixed Type

We propose a new model for correlated outputs of mixed type, such as continuous and binary outputs, with a particular focus on joint regression and classification, motivated by an application in constrained optimization for com- puter simulation modeling. Our framework is based upon multivariate stochastic processes, extending Gaussian process methodology for modeling of continuous multivariate spatial outputs by adding a latent process structure that allows for joint modeling of a variety of types of correlated outputs. In addition, we imple- ment fully Bayesian inference using particle learning, which allows us to conduct fast sequential inference. We demonstrate the effectiveness of our proposed meth- ods on both synthetic examples and a real world hydrology computer experiment optimization problem where it is helpful to model the black box objective function as correlated with satisfaction of the constraint.

DEVELOPMENT OF ALOPECIA DURING TREATMENT WITH A TUMOR NECROSIS FACTOR-ALPHA INHIBITOR IN A FEMALE PATIENT WITH PSORIATIC ARTHRITS: A CLINICAL CASE

Objective: to describe a case of the total development of alopecia in a female patient with psoriatic arthritis during treatment with a tumor necrosis factor-αlpha (TNF-α) inhibitor. Materials and methods. Patient I., aged 36 years has been followed up at the Kazan’ Center of Rheumatic Diseases and Osteoporosis since 1998. At approximately the same time, the patient noted the appearance of skin eruptions behind the ears, on the skin of the scalp. She was examined by a dermatologist who diagnosed psoriasis. In 2005, she was admitted to Kazan’ Rheumatology Center, City Clinical Hospital Seven, for the development of obvious synovitis of the knee joint and for the inefficiency of therapy with nonsteroidal anti-inflammatory drugs and diagnosed with psoriatic arthritis. During the prescribed therapy with methotrexate 10 mg/week, evident menstrual irregularities were observed in the patient who stopped using the drug herself. The second pregnancy occurred in 2008. Articular syndrome progression and eruptive psoriasis were recorded in the lactation period. After lactation cessation in 2009, she was hospitalized again. Her examination revealed high laboratory activity (erythrocyte sedimentation rate, as high as 40 mm/hr); magnetic resonance imaging of the knee joints showed the signs of bilateral synovitis; lumbar spine radiography exhibited grade II sacroiliitis. Leflunomide 20 mg/day was recommended as a basic drug. In 2012, the patient used leflunomide, her condition worsened; joint pain progressed; new joints were involved into the process, and cutaneous manifestations were aggravated. To verify a diagnosis and to choose therapy, the patient was referred to a consultation at the Moscow Research Institute of Rheumatology. Results. In connection with the high activity of the disease and with no response to the performed therapy, it was recommended to initiate therapy with biologics, such as infliximab, the drug of choice. Seven infliximab injections were well tolerated: the patient reported lower intensity of joint pains and regression of the skin manifestations of psoriasis. In 2013, after the eighth infliximab injection the patient sought medical advice because of complaints about an obvious exacerbation of psoriasis affecting the trunk, upper and lower extremities, and the development of total alopecia within a week. Discontinuation of cytostatics and biologics was recommended; whether ustekinumab therapy should be used was considered. Conclusion. Practical interest in this clinical case is due to the development of a rare (3.3 %) adverse event in the patient with psoriatic arthritis – hair loss (total development of alopecia) during treatment with a TNF-α inhibitor.

Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients

Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors (LTSs, patients surviving >3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.

Joint analysis of spikes and local field potentials using copula

Recent technological advances, which allow for simultaneous recording of spikes and local field potentials (LFPs) at multiple sites in a given cortical area or across different areas, have greatly increased our understanding of signal processing in brain circuits. Joint analysis of simultaneously collected spike and LFP signals is an important step to explicate how the brain orchestrates information processing. In this contribution, we present a novel statistical framework based on Gaussian copula to jointly model spikes and LFP. In our approach, we use copula to link separate, marginal regression models to construct a joint regression model, in which the binary-valued spike train data are modeled using generalized linear model (GLM) and the continuous-valued LFP data are modeled using linear regression. Model parameters can be efficiently estimated via maximum-likelihood. In particular, we show that our model offers a means to statistically detect directional influence between spikes and LFP, akin to Granger causality measure, and that we are able to assess its statistical significance by conducting a Wald test. Through extensive simulations, we also show that our method is able to reliably recover the true model used to generate the data. To demonstrate the effectiveness of our approach in real setting, we further apply the method to a mixed neural dataset, consisting of spikes and LFP simultaneously recorded from the visual cortex of a monkey performing a contour detection task.

Early establishment response of different Pinus nigra ssp. salzmanii seed sources on contrasting environments: Implications for future reforestation programs and assisted population migration

Forest restoration constitutes an important issue within adaptive environmental management for climate change at global scale. However, effective implementation of these programs can only be achieved by revising current seed transfer guidelines, as they lack inherent spatial and temporal dynamics associated with climate change. In this sense, provenance trials may provide key information on the relative performance of different populations and/or genotypes under changing ecological conditions. This study addresses a methodological approach to evaluate early plantation performance and the consequent phenotypic plasticity and the pattern of the adaptation of different seed sources in contrasting environments. To this end, six seed sources of Salzmann pine were tested at three contrasting trial sites testing a hypothetical assisted population migration. Adaptation at each site was assessed through Joint Regression and Additive Main effect and Multiplication Interaction (AMMI) models. Most of the observed variation was attributed to the environment (above 90% for all traits), even so genotype and genotype by environment interaction (GxE) were significant. Seedlings out-planted under better site conditions did not differ in survival but in height growth. However, on sites with higher constraints, survival differed among seed sources and diameter growth was high. The adaptation analyses (AMMI) indicated that the cold-continental seed source ‘Soria’ performed as a generalist seed source, whereas ‘Cordilleras Béticas’, the southernmost seed source, was more adapted to harsh environments (frost and drought) in terms of survival. The results supported partially the hypothesis that assisted migration of seed sources makes sense within limited transfer distances, and this was reinforced by the GxE results. The present study could be valuable to address adaptive transfer of seedings in ecological restoration and to determine the suitable seed sources for reforestation programs and assisted population migration under climatic changes. The reported results are based on 3 years' data and need to be considered in this context.

Missense Variant in MAPK Inactivator PTPN5 Is Associated with Decreased Severity of Post-Burn Hypertrophic Scarring.

BackgroundHypertrophic scarring (HTS) is hypothesized to have a genetic mechanism, yet its genetic determinants are largely unknown. The mitogen-activated protein kinase (MAPK) pathways are important mediators of inflammatory signaling, and experimental evidence implicates MAPKs in HTS formation. We hypothesized that single-nucleotide polymorphisms (SNPs) in MAPK-pathway genes would be associated with severity of post-burn HTS.MethodsWe analyzed data from a prospective-cohort genome-wide association study of post-burn HTS. We included subjects with deep-partial-thickness burns admitted to our center who provided blood for genotyping and had at least one Vancouver Scar Scale (VSS) assessment. After adjusting for HTS risk factors and population stratification, we tested MAPK-pathway gene SNPs for association with the four VSS variables in a joint regression model. In addition to individual-SNP analysis, we performed gene-based association testing.ResultsOur study population consisted of 538 adults (median age 40 years) who were predominantly White (76%) males (71%) admitted to our center from 2007–2014 with small-to-moderate-sized burns (median burn size 6% total body surface area). Of 2,146 SNPs tested, a rare missense variant in the PTPN5 gene (rs56234898; minor allele frequency 1.5%) was significantly associated with decreased severity of post-burn HTS (P = 1.3×10−6). In gene-based analysis, PTPN5 (P = 1.2×10−5) showed a significant association and BDNF (P = 9.5×10−4) a borderline-significant association with HTS severity.ConclusionsWe report PTPN5 as a novel genetic locus associated with HTS severity. PTPN5 is a MAPK inhibitor expressed in neurons, suggesting a potential role for neurotrophic factors and neuroinflammatory signaling in HTS pathophysiology.

Regression Guided Deformable Models for Segmentation of Multiple Brain ROIs.

This paper proposes a novel method of using regression-guided deformable models for brain regions of interest (ROIs) segmentation. Different from conventional deformable segmentation, which often deforms shape model locally and thus sensitive to initialization, we propose to learn a regressor to explicitly guide the shape deformation, thus eventually improves the performance of ROI segmentation. The regressor is learned via two steps, (1) a joint classification and regression random forest (CRRF) and (2) an auto-context model. The CRRF predicts each voxel's deformation to the nearest point on the ROI boundary as well as each voxel's class label (e.g., ROI versus background). The auto-context model further refines all voxel's deformations (i.e., deformation field) and class labels (i.e., label maps) by considering the neighboring structures. Compared to the conventional random forest regressor, the proposed regressor provides more accurate deformation field estimation and thus more robust in guiding deformation of the shape model. Validated in segmentation of 14 midbrain ROIs from the IXI dataset, our method outperforms the state-of-art multi-atlas label fusion and classification methods, and also significantly reduces the computation cost.

Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis

Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis

Distinctness, Uniformity and Stability Testing in Maize Inbreds

A set of 21 maize inbreds were evaluated for 29 traits for studying the Distinctness, Uniformity and Stability parameters as per the guidelines issued by the Protection of Plant Varieties and Farmers Rights Authority (PPV&FRA), India. Out of 29 traits, five traits were monomorphic, 18 traits were dimorphic, while six traits were polymorphic. The studied quantitative traits were independent and their interaction with environment as well as themselves were negligible as revealed by modified joint regression analysis. The combine over year analysis revealed that five inbreds were completely uniform, for all the MS or MG traits, while rest of the inbreds were non-uniform for 1–4 traits, but within the acceptable limit of uniformity test as per PPV&FRA guideline. Combine over year distinct analysis revealed that all the studied inbreds were found distinct to each other except VQL 1 and CM 212, which showed non-distinctiveness to each other but showed distinctiveness to other 19 inbreds. The present data can be used to develop database for inbreds and also safeguard the ownership issue.

A novel relational regularization feature selection method for joint regression and classification in AD diagnosis.

In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response-response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ2,1-norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods.

The effect of genotype by environment interaction, phenotypic plasticity and adaptation on Pinus halepensis reforestation establishment under expected climate drifts

Genotype by environment interaction (GEI) is becoming an important issue within the proactive adaptive silviculture oriented to global changes. However, there is a considerable lack of information on how GEI and phenotypic plasticity may affect early establishment (survival and early growth) performance in many non-commercial forest species, such as Aleppo pine, a key species in semiarid forest restoration programs. The objectives of this study were to (1) evaluate the phenotypic plasticity and adaptation in the broad context of GEI of eleven Aleppo pine seed sources regarding survival, height and diameter growth after outplanting in contrasting core and marginal habitats representing core-to-dry and cold-to-core drifts and to (2) compare the efficiency of joint regression and Additive Main effect and Multiplication Interaction (AMMI) models in elucidating the pattern of the adaptation of the eleven seed sources regarding these traits. Even though phenotypic plasticity was low, more plasticity was observed in the core drift than the dry drift. Specific adaptation to extreme environments was coupled with lower phenotypic plasticity. Among traits, plasticity was lower for survival and height than for diameter in the dry drift and the opposite for the core drift. There were also significant environment, genotype and GEI effects. AMMI models revealed higher capabilities than joint regression in determining seed sources adaptation across environments. Specifically, seed sources with higher plasticity performed better on the core habitat conditions. Southern seed sources of ‘Bética Septentrional’ and ‘La Mancha’ suited more to the dry environment. However, ‘Maestrazgo Los Serranos’ seed source grew better under the cooler local conditions. ‘Levante Interior’ seed source performed as a generalist genotype adapted to both drifts. These results make a significant contribution towards reforestation programs with practical implications for abiotic stress tolerance and assisted population migration in response to climate change.

The Effect of Using Different Imputation Methods for Economic Variables in Aging Surveys

We study the sensitivity of analyses on income and wealth to the methods of imputation for missing data. We do so by implementing a conditional hot-deck imputation, based on the method used by the English Longitudinal Study of Ageing, in the Health and Retirement Study. We compare marginal and joint distributions and regression models estimated from data using these imputations to the same statistics estimated from the RAND HRS data, which use a much more elaborate imputation method, and to estimates from data using an intermediate method. Although many results are qualitatively or quantitatively similar, there are also notable differences.

Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis.

Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract features independently from each modality, and then simply concatenate them into a long vector for classification, without appropriate consideration of the correlation among modalities. In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation, by canonical correlation analysis. We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. In our experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we use Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer's disease diagnosis. The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.

Nuclear-L1 norm joint regression for face reconstruction and recognition with mixed noise

Occlusion, real disguise and illumination are still the common difficulties encountered in face recognition. The sparse representation based classifier (SRC) has shown a great potential in handling pixel-level sparse noise, while the nuclear norm based matrix regression (NMR) model has been demonstrated to be powerful for dealing with the image-wise structural noise. Both methods, however, might be not very effective for handling the mixed noise: the structural noise plus the sparse noise. In this paper, we present two nuclear-L1 norm joint matrix regression (NL1R) models for face recognition with mixed noise, which are derived by using MAP (maximum a posteriori probability estimation). The first model considers the mixed noise as a whole, while the second model assumes the mixed noise is an additive combination of two independent componenral nts: sparse noise and structuoise. The proposed models can be solved by the alternating direction method of multipliers (ADMM). We validate the effectiveness of the proposed models through a series of experiments on face reconstruction and recognition.

Written on the Face: Self- and Expert-Rated Impairments in Personality Functioning Are Differently Related to the Expression of Disgust Toward an Interviewer.

Although current theories suggest that impairments in personality functioning are at the core of personality pathology, there is a lack of research on how these impairments play out behaviorally. The aim of the present study was to investigate disgust expressions as indicators of personality dysfunction. Facial expressions were investigated in a sample of 78 female participants during an in-depth clinical interview and coded with the Facial Action Coding System. Personality dysfunction was assessed with self- and expert ratings. By applying a joint regression analysis, the authors found that disgust expressions toward the interviewer were positively associated with expert ratings but negatively associated with self-ratings. In other words, disgust expressions were indicative of an underestimation of personality dysfunction by participants as compared with experts. This suggests that interactional expressions of disgust might be a behavioral marker of personality dysfunction when individuals are unaware of or deny impairments.

Copula regression analysis of simultaneously recorded frontal eye field and inferotemporal spiking activity during object-based working memory.

Inferotemporal (IT) neurons are known to exhibit persistent, stimulus-selective activity during the delay period of object-based working memory tasks. Frontal eye field (FEF) neurons show robust, spatially selective delay period activity during memory-guided saccade tasks. We present a copula regression paradigm to examine neural interaction of these two types of signals between areas IT and FEF of the monkey during a working memory task. This paradigm is based on copula models that can account for both marginal distribution over spiking activity of individual neurons within each area and joint distribution over ensemble activity of neurons between areas. Considering the popular GLMs as marginal models, we developed a general and flexible likelihood framework that uses the copula to integrate separate GLMs into a joint regression analysis. Such joint analysis essentially leads to a multivariate analog of the marginal GLM theory and hence efficient model estimation. In addition, we show that Granger causality between spike trains can be readily assessed via the likelihood ratio statistic. The performance of this method is validated by extensive simulations, and compared favorably to the widely used GLMs. When applied to spiking activity of simultaneously recorded FEF and IT neurons during working memory task, we observed significant Granger causality influence from FEF to IT, but not in the opposite direction, suggesting the role of the FEF in the selection and retention of visual information during working memory. The copula model has the potential to provide unique neurophysiological insights about network properties of the brain.

Consensus on State and Time: Decentralized Regression With Asynchronous Sampling

An implicit assumption made in several studies on sensor systems is that the time and frequency at which sensor measurements are taken is consistent across all the distributed sensing sites. In reality, the times of measurement often lack consistency and integrity, and this is an intrinsic vulnerability of wide area sensor system. Data logs coming from different analog to digital converters (ADCs) are not in phase and may differ also in the sampling rate, in some cases because heterogeneity in the sensors and in others because the data are simply not refreshed in the data historians with the same frequency. Lack of good synchronization in sensing may be the result of a malfunction or also due to intentional delay attacks. This premise motivates our work, where we advance the area of decentralized signal processing and consider explicitly timing errors and nonhomogenous sampling rates in least square estimation problems with distributed sensing. For linear observations models, we provide a necessary and sufficient condition for identifiability of the time offsets. We propose an algorithm for the joint regression on the state vector and time offsets. The algorithm also exploits the asynchrony and redundancy in the spatial sampling to attain sub-Nyquist sampling resolution of the slow sensor feeds. Importantly, this also leads to the development of a novel decentralized algorithm. The efficacies of the proposed decentralized algorithm are shown by both convergence analysis and numerical simulations.