Endogenous Truncation Research Articles

Investment-q Sensitivity under Endogenous Truncation

Investment-q Sensitivity under Endogenous Truncation

DIPG-53. CHARACTERIZING THE ROLE OF PPM1D MUTATIONS IN THE PATHOGENESIS OF DIFFUSE INTRINSIC PONTINE GLIOMAS (DIPGS)

INTRODUCTIONWe have previously found that up to 15% of all DIPGs harbor mutations in PPM1D, resulting in the expression of an activated and truncated PPM1D (PPM1Dtr). Here we evaluate the mechanisms through which PPM1Dtr enhances glioma formation and identify its associated therapeutic vulnerabilities.METHODSWe have developed multiple in vitro and in vivo models of PPM1D-mutant DIPGs and applied quantitative proteomic and functional genomic approaches to identify pathways altered by PPM1Dtr and associated dependencies.RESULTS PPM1D mutations are clonal events that are anti-correlated to TP53 mutations. We find ectopic expression of PPM1Dtr to be sufficient to enhance glioma formation and to be necessary in PPM1D-mutant DIPG cells. In addition, endogenous truncation of PPM1D is sufficient to enhance glioma formation in the presence of mutant H3F3A and PDGFRA. PPM1Dtr overexpression attenuates g-H2AX formation and suppresses apoptosis and cell-cycle arrest in response to radiation treatment. Deep scale phosphoproteomics analyses reveal DNA-damage and cell cycle pathways to be most significantly associated with PPM1Dtr. Furthermore, preliminary analysis of genome-wide loss-of-function CRISPR/Cas9 screens in isogenic GFP and PPM1Dtr overexpressing mouse neural stem cells reveal differential dependency on DNA-damage response genes in the PPM1Dtr overexpressing cells. Consistent with PPM1D’s role in stabilizing MDM2, PPM1D-mutant DIPG models are sensitive to a panel of MDM2 inhibitors (Nutlin-3a, RG7388, and AMG232).CONCLUSIONOur study shows that PPM1Dtr is both an oncogene and a dependency in PPM1D- mutant DIPG, and there are novel therapeutic vulnerabilities associated with PPM1D that may be exploited.

Semiparametric Wavelet-Based JPEG IV Estimator for Endogenously Truncated Data

A new and an enriched JPEG algorithm is provided for identifying redundancies in a sequence of irregular noisy data points which also accommodates a reference-free criterion function. Our main contribution is by formulating analytically (instead of approximating) the inverse of the transpose of JPEGwavelet transform without involving matrices which are computationally cumbersome. The algorithm is suitable for the widely-spread situations where the original data distribution is unobservable such as in cases where there is deficient representation of the entire population in the training data (in machine learning) and thus the covariate shift assumption is violated. The proposed estimator corrects for both biases, the one generated by endogenous truncation and the one generated by endogenous covariates. Results from utilizing 2,000,000 different distribution functions verify the applicability and high accuracy of our procedure to cases in which the disturbances are neither jointly nor marginally normally distributed.

Semiparametric Correction for Endogenous Truncation Bias With Vox Populi-Based Participation Decision

We synthesize the knowledge present in various scientific disciplines for the development of semiparametric endogenous truncation-proof algorithm, correcting for truncation bias due to endogenous self-selection. This synthesis enriches the algorithm's accuracy, efficiency and applicability. Improving upon the covariate shift assumption, data are intrinsically affected and largely generated by their own behavior (cognition). Refining the concept of Vox Populi (Wisdom of Crowd) allows data points to sort themselves out depending on their estimated latent reference group opinion space. Monte Carlo simulations, based on 2,000,000 different distribution functions, practically generating 100 million realizations, attest to a very high accuracy of our model.

Semiparametric Maximum Likelihood Sieve Estimator for Correction of Endogenous Truncation Bias

Semiparametric correction for a sample selection bias in the presence of endogenous truncation is known to be much more difficult in the case of a binary selection variable than in the case of a continuous selection variable. This paper proposes a simple bandwidth-free semiparametric methodology to correct for a self-selection bias in a truncated sample, without any prior knowledge of the marginal density functions of the selection model’s random disturbances. Each of the unknown marginal density functions is estimated using Sieve estimator, utilizing Hermite polynomials as basis functions. The aforementioned procedure is appropriate for both binary and continuous selection variables cases under the covariate shift assumption. We consider a double hurdle model, which is a combination of two selection rules. The first is propagated by a truncation in the dependent variable of the substantive equation. The second is propagated by endogenous self-selection. The suggested correction procedure produces estimates that are of high accuracy and consistent based on Monte Carlo simulations. The random disturbances are not restricted to being symmetric and their marginal distribution functions are unknown. Thus, in the data generation process we verify the applicability of our procedure to cases in which the disturbances are neither jointly nor marginally normally distributed. These disturbances are constructed as realizations of non-symmetric distribution functions.

Semiparametric Wavelet-Based Jpeg IV Estimator for Endogenously Truncated Data

We show that when data are endogenously truncated the widely-used IV fails to render the relationship causal as well as introduces bias into the exogenous covariates. We offer a newly-introduced semiparametric biorthogonal wavelet-based JPEG IV estimator and its associated symmetry preserving kernel, which is closely related to object recognition methods in Artificial Intelligence. The newly-introduced enriched JPEG algorithm is a denoising tool amenable for identifying redundancies in a sequence of irregular noisy data points which also accommodates a reference-free criterion function for optimal denoising. This is suitable for situations where the original data distribution is unobservable such as in the case of endogenous truncation. This estimator corrects both biases, the one generated by endogenous truncation and the one generated by endogenous covariates, by means of denoising. We introduce a multifocal variant of the local GMM (MFGMM) estimator to establish jointly the entire parameter set asymptotic properties. Using Monte Carlo simulations attest to very high accuracy of our offered semiparametric JPEG IV estimator as well as high efficiency as reflected by √n consistency. These results have emerged from utilizing 2,000,000 different distribution functions, generating 100 million realizations to construct the various data sets.

Semi-Parametric Correction for Endogenous Truncation Bias with Vox Populi Based Participation Decision

Not having access to the entire data distribution entails severe biases in the resulting parameter estimates of any empirical investigation and which hampers the understanding and analysis of the data generation process. We integrate and synthesize the knowledge present in various scientific disciplines for the development of semiparametric endogenous truncation-proof algorithms correcting for truncation bias due to endogenous self-selection. Thus, computer science (pattern recognition), machine (unsupervised) learning, electrical engineering (signal extraction), economics, psychology, and management science all contribute to the ideas and techniques embedded in the offered algorithms developed. This synthesis enriches the algorithms' accuracy, efficiency and applicability. Under truncation the unobserved portion of the distribution is similar to the unobserved pixels problems in pattern recognition dealt with in computer science, machine learning and artificial intelligence algorithms. However, data in the social sciences are intrinsically affected and largely generated by their own behavior (cognition). Thus, incorporating behavioral aspects from economics, psychology and management science to play a role in the model allows for endogeneity to take place, be modeled and controlled. The algorithms offered improve upon the covariate-shift assumption in machine learning in that, each data point's decision to truncate itself out from the original distribution is an important building block generating the estimation algorithms. Refining the concept of Vox Populi (Wisdom of Crowd) allows data points to sort themselves out depending on their estimated latent reference group opinion space. The opinion space is composed of experts' observed and unobserved characteristics. The later are captured by latent classes. The emerging algorithms are semiparametric and are of the orthonormal polynomials sequence family (such as Fourier) known to have flexibility and are useful in non-parametric analysis. The most attractive feature of this estimator for our purpose is that it intrinsically prevents potential multicollinearity problems, a feature the kernel estimator does not possess. Each datum is generated by a different distribution function characterized as a finite mixture of various continuous distribution functions which are not restricted to be unimodal or symmetric. Thus, the proposed algorithm is distribution free. The number of reference groups is not arbitrarily imposed but rather estimated using SCAD (smoothly clipped absolute deviation) penalization mechanism. Monte-Carlo simulations based on 2,000,000 different distribution functions, practically generating 100 millions realizations which are not i.i.d and attest to a very high accuracy of our model.

Semi-Parametric Correction for Endogenous Truncation Bias with Vox Populi Based Participation Decision

We synthesize the knowledge present in various scientific disciplines for the development of semiparametric endogenous truncation-proof algorithm, correcting for truncation bias due to endogenous self-selection. This synthesis enriches the algorithm's accuracy, efficiency and applicability. Improving upon the covariate shift assumption, data are intrinsically affected and largely generated by their own behavior (cognition). Refining the concept of Vox Populi (Wisdom of Crowd) allows data points to sort themselves out depending on their estimated latent reference group opinion space. Monte Carlo simulations, based on 2,000,000 different distribution functions, practically generating 100 million realizations, attest to a very high accuracy of our model.

Cellular Models of Aggregation-dependent Template-directed Proteolysis to Characterize Tau Aggregation Inhibitors for Treatment of Alzheimer Disease

Alzheimer disease (AD) is a degenerative tauopathy characterized by aggregation of Tau protein through the repeat domain to form intraneuronal paired helical filaments (PHFs). We report two cell models in which we control the inherent toxicity of the core Tau fragment. These models demonstrate the properties of prion-like recruitment of full-length Tau into an aggregation pathway in which template-directed, endogenous truncation propagates aggregation through the core Tau binding domain. We use these in combination with dissolution of native PHFs to quantify the activity of Tau aggregation inhibitors (TAIs). We report the synthesis of novel stable crystalline leucomethylthioninium salts (LMTX®), which overcome the pharmacokinetic limitations of methylthioninium chloride. LMTX®, as either a dihydromesylate or a dihydrobromide salt, retains TAI activity in vitro and disrupts PHFs isolated from AD brain tissues at 0.16 μM. The Ki value for intracellular TAI activity, which we have been able to determine for the first time, is 0.12 μM. These values are close to the steady state trough brain concentration of methylthioninium ion (0.18 μM) that is required to arrest progression of AD on clinical and imaging end points and the minimum brain concentration (0.13 μM) required to reverse behavioral deficits and pathology in Tau transgenic mice.

Functional Characterization of a Naturally Occurring Cx50 Truncation

Lens connexins undergo proteolytic cleavage of their C termini during fiber maturation. Although the functional significance of this is unknown, cleavage has been correlated with changes in channel-gating properties. This study evaluates the functional consequences of this endogenous truncation by characterizing the properties of a C-terminal truncated Cx50 protein. Murine and human Cx50 were truncated at amino acids 290 and 294, respectively, before expression in paired Xenopus oocytes or mammalian cells. Protein expression was evaluated by immunocytochemistry. Dual whole-cell voltage clamp techniques were used to analyze macroscopic and single-channel conductance, voltage-gating properties, and kinetics; pH gating sensitivity was measured by superfusion with 100% CO2-saturated media. Cx50tr290 channels exhibited an 86% to 89% reduction in mean macroscopic conductance compared with full-length Cx50. Heterotypic channels formed functional gap junctions, displayed an intermediate level of coupling, and exhibited unaltered voltage-gating properties. C-terminal truncation did not alter single-channel gating characteristics or unitary conductance. Interestingly, truncated and full-length Cx50 channel conductances were reversibly blocked by cytoplasmic acidification. C-terminal truncation of Cx50 did not inhibit the formation of homotypic or heterotypic channels. However, a significant decrease in conductance was observed for truncated channels, a phenomenon independent of alterations in voltage-gating sensitivity, kinetics, or chemical gating. These results provide a plausible explanation for the 50% decrease in junctional coupling observed during lens fiber maturation.