Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.Comment: 33 pages, 12 figure

Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt

Cerebral involvement in a patient with Goodpasture's disease due to shortened induction therapy: a case report

<p>Abstract</p> <p>Introduction</p> <p>Goodpasture's disease is a rare immunological disease with formation of pathognomonic antibodies against renal and pulmonary basement membranes. Cerebral involvement has been reported in several cases in the literature, yet the pathogenetic mechanism is not entirely clear.</p> <p>Case presentation</p> <p>A 21-year-old Caucasian man with Goodpasture's disease and end-stage renal disease presented with two generalized seizures after a period of mild cognitive disturbance. Blood pressure and routine laboratory tests did not exceed the patient's usual values, and examination of cerebrospinal fluid was unremarkable. Cerebral magnetic resonance imaging (MRI) revealed multiple cortical and subcortical lesions on fluid-attenuated inversion recovery sequences. Since antiglomerular basement membrane antibodies were found to be positive with high titers, plasmapheresis was started. In addition, cyclophosphamide pulse therapy was given on day 13. Encephalopathy and MRI lesions disappeared during this therapy, and antiglomerular basement membrane antibodies were significantly reduced. Previous immunosuppressive therapy was performed without corticosteroids and terminated early after 3 months.</p> <p>The differential diagnostic considerations were cerebral vasculitis and posterior reversible encephalopathy syndrome. Vasculitis could be seen as an extrarenal manifestation of the underlying disease. Posterior reversible encephalopathy syndrome, on the other hand, can be triggered by immunosuppressive therapy and may appear without a hypertensive crisis.</p> <p>Conclusion</p> <p>A combination of central nervous system symptoms with a positive antiglomerular basement membrane test in a patient with Goodpasture's disease should immediately be treated as an acute exacerbation of the disease with likely cross-reactivity of antibodies with the choroid plexus. In our patient, a discontinuous strategy of immunosuppressive therapy may have favored recurrence of Goodpasture's disease.</p

Endothelin-1 Predicts Hemodynamically Assessed Pulmonary Arterial Hypertension in HIV Infection.

BackgroundHIV infection is an independent risk factor for PAH, but the underlying pathogenesis remains unclear. ET-1 is a robust vasoconstrictor and key mediator of pulmonary vascular homeostasis. Higher levels of ET-1 predict disease severity and mortality in other forms of PAH, and endothelin receptor antagonists are central to treatment, including in HIV-associated PAH. The direct relationship between ET-1 and PAH in HIV-infected individuals is not well described.MethodsWe measured ET-1 and estimated pulmonary artery systolic pressure (PASP) with transthoracic echocardiography (TTE) in 106 HIV-infected individuals. Participants with a PASP ≥ 30 mmHg (n = 65) underwent right heart catheterization (RHC) to definitively diagnose PAH. We conducted multivariable analysis to identify factors associated with PAH.ResultsAmong 106 HIV-infected participants, 80% were male, the median age was 52 years and 77% were on antiretroviral therapy. ET-1 was significantly associated with higher values of PASP [14% per 0.1 pg/mL increase in ET-1, p = 0.05] and PASP ≥ 30 mmHg [PR (prevalence ratio) = 1.24, p = 0.012] on TTE after multivariable adjustment for PAH risk factors. Similarly, among the 65 individuals who underwent RHC, ET-1 was significantly associated with higher values of mean pulmonary artery pressure and PAH (34%, p = 0.003 and PR = 2.43, p = 0.032, respectively) in the multivariable analyses.ConclusionsHigher levels of ET-1 are independently associated with HIV-associated PAH as hemodynamically assessed by RHC. Our findings suggest that excessive ET-1 production in the setting of HIV infection impairs pulmonary endothelial function and contributes to the development of PAH

Estimating measures of interaction on an additive scale for preventive exposures

Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95%CI: −0.30; 0.82], AP = 0.30 [95%CI: −0.28; 0.88], S = 0.35 [95%CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = −0.37 [95%CI: −1.23; 0.49], AP = −0.29 [95%CI: −0.98; 0.40], S = 0.43 [95%CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding

Synthesis and Thermoelectric Properties of Bi2Se3 Nanostructures

Bismuth selenide (Bi2Se3) nanostructures were synthesized via solvothermal method. The crystallinity of the as-synthesized sample has been analyzed by X-ray diffraction, which shows the formation of rhombohedral Bi2Se3. Electron microscopy examination indicates that the Bi2Se3 nanoparticles have hexagonal flake-like shape. The effect of the synthesis temperature on the morphology of the Bi2Se3 nanostructures has also been investigated. It is found that the particle size increases with the synthesis temperature. Thermoelectric properties of the Bi2Se3 nanostructures were also measured, and the maximum value of dimensionless figure of merit (ZT) of 0.096 was obtained at 523 K

Bayesian lasso binary quantile regression

In this paper, a Bayesian hierarchical model for variable selection and estimation in the context of binary quantile regression is proposed. Existing approaches to variable selection in a binary classification context are sensitive to outliers, heteroskedasticity or other anomalies of the latent response. The method proposed in this study overcomes these problems in an attractive and straightforward way. A Laplace likelihood and Laplace priors for the regression parameters are proposed and estimated with Bayesian Markov Chain Monte Carlo. The resulting model is equivalent to the frequentist lasso procedure. A conceptional result is that by doing so, the binary regression model is moved from a Gaussian to a full Laplacian framework without sacrificing much computational efficiency. In addition, an efficient Gibbs sampler to estimate the model parameters is proposed that is superior to the Metropolis algorithm that is used in previous studies on Bayesian binary quantile regression. Both the simulation studies and the real data analysis indicate that the proposed method performs well in comparison to the other methods. Moreover, as the base model is binary quantile regression, a much more detailed insight in the effects of the covariates is provided by the approach. An implementation of the lasso procedure for binary quantile regression models is available in the R-package bayesQR

Disruption of a GATA4/Ankrd1 Signaling Axis in Cardiomyocytes Leads to Sarcomere Disarray: Implications for Anthracycline Cardiomyopathy

Doxorubicin (Adriamycin) is an effective anti-cancer drug, but its clinical usage is limited by a dose-dependent cardiotoxicity characterized by widespread sarcomere disarray and loss of myofilaments. Cardiac ankyrin repeat protein (CARP, ANKRD1) is a transcriptional regulatory protein that is extremely susceptible to doxorubicin; however, the mechanism(s) of doxorubicin-induced CARP depletion and its specific role in cardiomyocytes have not been completely defined. We report that doxorubicin treatment in cardiomyocytes resulted in inhibition of CARP transcription, depletion of CARP protein levels, inhibition of myofilament gene transcription, and marked sarcomere disarray. Knockdown of CARP with small interfering RNA (siRNA) similarly inhibited myofilament gene transcription and disrupted cardiomyocyte sarcomere structure. Adenoviral overexpression of CARP, however, was unable to rescue the doxorubicin-induced sarcomere disarray phenotype. Doxorubicin also induced depletion of the cardiac transcription factor GATA4 in cardiomyocytes. CARP expression is regulated in part by GATA4, prompting us to examine the relationship between GATA4 and CARP in cardiomyocytes. We show in co-transfection experiments that GATA4 operates upstream of CARP by activating the proximal CARP promoter. GATA4-siRNA knockdown in cardiomyocytes inhibited CARP expression and myofilament gene transcription, and induced extensive sarcomere disarray. Adenoviral overexpression of GATA4 (AdV-GATA4) in cardiomyocytes prior to doxorubicin exposure maintained GATA4 levels, modestly restored CARP levels, and attenuated sarcomere disarray. Interestingly, siRNA-mediated depletion of CARP completely abolished the Adv-GATA4 rescue of the doxorubicin-induced sarcomere phenotype. These data demonstrate co-dependent roles for GATA4 and CARP in regulating sarcomere gene expression and maintaining sarcomeric organization in cardiomyocytes in culture. The data further suggests that concurrent depletion of GATA4 and CARP in cardiomyocytes by doxorubicin contributes in large part to myofibrillar disarray and the overall pathophysiology of anthracycline cardiomyopathy

Characterisation and expression analysis of the Atlantic halibut (Hippoglossus hippoglossus L.) cytokines: IL-1β, IL-6, IL-11, IL-12β and IFNγ

Genes encoding the five Atlantic halibut (Hippoglossus hippoglossus L.) cytokines; interleukin (IL)-1β, IL-6, IL-11b, IL-12βc, and interferon (IFN) γ, were cloned and characterised at a molecular level. The genomic organisation of the halibut cytokine genes was similar to that seen in mammals and/or other fish species. Several mRNA instability motifs were found within the 3′-untranslated region (UTR) of all cytokine cDNA sequences. The putative cytokine protein sequences showed a low sequence identity with the corresponding homologues in mammals, avian and other fish species. Nevertheless, important structural features were presumably conserved such as the presence, or absence in the case of IL-1β, of a signal peptide, secondary structure and family signature motifs. The relative expression pattern of the cytokine genes was analyzed in several halibut organs, revealing a constitutive expression in both lymphoid and non-lymphoid organs. Interestingly, the gills showed a relatively high expression of IL-1β, IL-12βc and IFNγ. The real time RT-PCR data also showed that the mRNA level of IL-1β, IL-6, IL-12βc and IFNγ was high in the thymus, while IL-11b was relatively highly expressed in the posterior kidney and posterior gut. Moreover, the halibut brain showed a relatively high level of IL-6 transcripts. Anterior kidney leucocytes in vitro stimulated with imiquimod showed a significant increase in mRNA level of the five halibut cytokine genes. The sequence and characterisation data presented here will be useful for further investigation of both innate and adaptive immune responses in halibut, and be helpful in the design of vaccines for the control of various infectious diseases