Binscatter Regressions

We introduce the \texttt{Stata} (and \texttt{R}) package \textsf{Binsreg}, which implements the binscatter methods developed in \citet*{Cattaneo-Crump-Farrell-Feng_2019_Binscatter}. The package includes the commands \texttt{binsreg}, \texttt{binsregtest}, and \texttt{binsregselect}. The first command (\texttt{binsreg}) implements binscatter for the regression function and its derivatives, offering several point estimation, confidence intervals and confidence bands procedures, with particular focus on constructing binned scatter plots. The second command (\texttt{binsregtest}) implements hypothesis testing procedures for parametric specification and for nonparametric shape restrictions of the unknown regression function. Finally, the third command (\texttt{binsregselect}) implements data-driven number of bins selectors for binscatter implementation using either quantile-spaced or evenly-spaced binning/partitioning. All the commands allow for covariate adjustment, smoothness restrictions, weighting and clustering, among other features. A companion \texttt{R} package with the same capabilities is also available

On Binscatter

Binscatter is very popular in applied microeconomics. It provides a flexible, yet parsimonious way of visualizing and summarizing large data sets in regression settings, and it is often used for informal evaluation of substantive hypotheses such as linearity or monotonicity of the regression function. This paper presents a foundational, thorough analysis of binscatter: we give an array of theoretical and practical results that aid both in understanding current practices (i.e., their validity or lack thereof) and in offering theory-based guidance for future applications. Our main results include principled number of bins selection, confidence intervals and bands, hypothesis tests for parametric and shape restrictions of the regression function, and several other new methods, applicable to canonical binscatter as well as higher-order polynomial, covariate-adjusted and smoothness-restricted extensions thereof. In particular, we highlight important methodological problems related to covariate adjustment methods used in current practice. We also discuss extensions to clustered data. Our results are illustrated with simulated and real data throughout. Companion general-purpose software packages for \texttt{Stata} and \texttt{R} are provided. Finally, from a technical perspective, new theoretical results for partitioning-based series estimation are obtained that may be of independent interest

Multi-variable weakening buffer operator and its application

To weaken the disturbances of multi-variable and reveal the real situation, it is proved that the essence of the weakening buffer operator (abbreviated as WBO) can weaken the disturbance of one variable. According to this, the multi-variable weakening buffer operator is put forward. The multi-variable weakening buffer operator can satisfy the desire to use the freshest data and its buffer effect is obvious when the sample size is small. Four real cases show that the proposed multi-variable weakening buffer operator has higher forecasting performances

CD133, Stem Cells, and Cancer Stem Cells: Myth or Reality?

CD133, a member of the prominin family, is found in a variety of tissues with at least three variants. The function of CD133 is not well understood, but its expression is subject to changes in the microenvironment cues including bioenergetic stress. Knockout of CD133 does not affect renewal, but mammary gland branching. A point mutation of CD133 (R733C) leads to retinal disorder. CD133 is found in embryonic stem cells, normal tissue stem cells, stem cell niches, and circulating endothelial progenitors as well as cancer stem cells. Maintenance of stemness in cancer may be attributable to asymmetric cell division in association with a set of embryonic expression signatures in CD133+ tumor cells. CD133 could enrich cancer stem cells, which are associated with chemo- and radiation resistance phenotype. High CD133 is associated with poor survival in a variety of solid tumors, including lung, colon, prostate, etc. Monitoring CD133+ cells in peripheral blood, and targeting CD133 in cancer, may further predict and improve the clinical outcomes

Characterize the assembly of dark matter halos with protohalo size histories: I. Redshift evolution, relation to descendant halos, and halo assembly bias

We propose a novel method to quantify the assembly histories of dark matter halos with the redshift evolution of the mass-weighted spatial variance of their progenitor halos, i.e. the protohalo size history. We find that the protohalo size history for each individual halo at z~0 can be described by a double power-law function. The amplitude of the fitting function strongly correlates to the central-to-total stellar mass ratios of descendant halos. The variation of the amplitude of the protohalo size history can induce a strong halo assembly bias effect for massive halos. This effect is detectable in observation using the central-to-total stellar mass ratio as a proxy of the protohalo size. The correlation to the descendant central-to-total stellar mass ratio and the halo assembly bias effect seen in the protohalo size are much stronger than that seen in the commonly adopted half-mass formation time derived from the mass accretion history. This indicates that the information loss caused by the compression of halo merger trees to mass accretion histories can be captured by the protohalo size history. Protohalo size thus provides a useful quantity to connect protoclusters across cosmic time and to link protoclusters with their descendant clusters in observations.Comment: 19 pages, 12 + 8 figures, comments are welcome

Micropatterned Cell–Cell Interactions Enable Functional Encapsulation of Primary Hepatocytes in Hydrogel Microtissues

Drug-induced liver injury is a major cause of drug development failures and postmarket withdrawals. In vitro models that incorporate primary hepatocytes have been shown to be more predictive than model systems which rely on liver microsomes or hepatocellular carcinoma cell lines. Methods to phenotypically stabilize primary hepatocytes ex vivo often rely on mimicry of hepatic microenvironmental cues such as cell–cell interactions and cell–matrix interactions. In this work, we sought to incorporate phenotypically stable hepatocytes into three-dimensional (3D) microtissues, which, in turn, could be deployed in drug-screening platforms such as multiwell plates and diverse organ-on-a-chip devices. We first utilize micropatterning on collagen I to specify cell–cell interactions in two-dimensions, followed by collagenase digestion to produce well-controlled aggregates for 3D encapsulation in polyethylene glycol (PEG) diacrylate. Using this approach, we examined the influence of homotypic hepatocyte interactions and composition of the encapsulating hydrogel, and achieved the maintenance of liver-specific function for over 50 days. Optimally preaggregated structures were subsequently encapsulated using a microfluidic droplet-generator to produce 3D microtissues. Interactions of engineered hepatic microtissues with drugs was characterized by flow cytometry, and yielded both induction of P450 enzymes in response to prototypic small molecules and drug–drug interactions that give rise to hepatotoxicity. Collectively, this study establishes a pipeline for the manufacturing of 3D hepatic microtissues that exhibit stabilized liver-specific functions and can be incorporated into a wide array of emerging drug development platforms.National Institutes of Health (U.S.) (Grant UH2 EB017103)National Institutes of Health (U.S.) (Grant R01 EB008396)National Institutes of Health (U.S.) (Grant R01 DK85713)National Cancer Institute (U.S.) (Koch Institute Support (Core) Grant P30-CA14051)American Gastroenterological Association (Research Scholar Fellowship)National Science Foundation (U.S.). Graduate Research Fellowship (1122374

A novel phosphatidylinositol(3,4,5)P3 pathway in fission yeast

The mammalian tumor suppressor, phosphatase and tensin homologue deleted on chromosome 10 (PTEN), inhibits cell growth and survival by dephosphorylating phosphatidylinositol-(3,4,5)-trisphosphate (PI[3,4,5]P3). We have found a homologue of PTEN in the fission yeast, Schizosaccharomyces pombe (ptn1). This was an unexpected finding because yeast (S. pombe and Saccharomyces cerevisiae) lack the class I phosphoinositide 3-kinases that generate PI(3,4,5)P3 in higher eukaryotes. Indeed, PI(3,4,5)P3 has not been detected in yeast. Surprisingly, upon deletion of ptn1 in S. pombe, PI(3,4,5)P3 became detectable at levels comparable to those in mammalian cells, indicating that a pathway exists for synthesis of this lipid and that the S. pombe ptn1, like mammalian PTEN, suppresses PI(3,4,5)P3 levels. By examining various mutants, we show that synthesis of PI(3,4,5)P3 in S. pombe requires the class III phosphoinositide 3-kinase, vps34p, and the phosphatidylinositol-4-phosphate 5-kinase, its3p, but does not require the phosphatidylinositol-3-phosphate 5-kinase, fab1p. These studies suggest that a pathway for PI(3,4,5)P3 synthesis downstream of a class III phosphoinositide 3-kinase evolved before the appearance of class I phosphoinositide 3-kinases