Identifying Extrinsic versus Intrinsic Drivers of Variation in Cell Behavior in Human iPSC Lines from Healthy Donors

Large cohorts of human induced pluripotent stem cells (iPSCs) from healthy donors are a potentially powerful tool for investigating the relationship between genetic variants and cellular behavior. Here, we integrate high content imaging of cell shape, proliferation, and other phenotypes with gene expression and DNA sequence datasets from over 100 human iPSC lines. By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we extracted factors that captured the effects of intrinsic (genetic concordance between different cell lines from the same donor) and extrinsic (cell responses to different fibronectin concentrations) conditions. We identify genes that correlate in expression with intrinsic and extrinsic PEER factors and associate outlier cell behavior with genes containing rare deleterious non-synonymous SNVs. Our study, thus, establishes a strategy for examining the genetic basis of inter-individual variability in cell behavior

The Desiring Algorithm. The Sex Appeal of the Inorganic

Each new era brings with it new technologies and new challenges to civilization. The impetuous technological development is changing the economy and work more and more every day; to the point of making the passage from agrarian society to industrial society seem like a trifle. Nevertheless, if there is widespread awareness of the general issues of society, there is definitely less on the way in which anthropomorphic robots (increasingly in an emotional and cognitive relationship with human beings) are radically modifying intimate and exclusive spheres such as sexuality and love. An epochal passage is being announced from an organic sexuality (driven by desire and pleasure) to an inorganic, artificial sexuality, suspended in an abstract excitement, always at hand and disengaged from categories such as beauty, forms and age. In this paper, we will discuss the plausibility of an emotional relationship between robots and human beings and its ethical implications. It is probable, in fact, that soon there will be self-aware robots, capable of experiencing feelings such as love, joy, suffering, pain and pleasure. If this scenario is realized, will we continue to think that they are only machines, or will we recognize the moral relevance that they do not have today

Feminized behavior and brain gene expression in a novel mouse model of Klinefelter Syndrome.

Klinefelter Syndrome (KS) is the most common sex chromosome aneuploidy in men and is characterized by the presence of an additional X chromosome (XXY). In some Klinefelter males, certain traits may be feminized or shifted from the male-typical pattern towards a more female-typical one. Among them might be partner choice, one of the most sexually dimorphic traits in the animal kingdom. We investigated the extent of feminization in XXY male mice (XXYM) in partner preference and gene expression in the bed nucleus of the stria terminalis/preoptic area and the striatum in mice from the Sex Chromosome Trisomy model. We tested for partner preference using a three-chambered apparatus in which the test mouse was free to choose between stimulus animals of either sex. We found that partner preference in XXYM was feminized. These differences were likely due to interactions of the additional X chromosome with the Y. We also discovered genes that differed in expression in XXYM versus XYM. Some of these genes are feminized in their expression pattern. Lastly, we also identified genes that differed only between XXYM versus XYM and not XXM versus XYM. Genes that are both feminized and unique to XXYM versus XYM represent strong candidates for dissecting the molecular pathways responsible for phenotypes present in KS/XXYM but not XXM. In sum, our results demonstrated that investigating behavioral and molecular feminization in XXY males can provide crucial information about the pathophysiology of KS and may aid our understanding of sex differences in brain and behavior

Sex-Specific Effects of Testosterone on the Sexually Dimorphic Transcriptome and Epigenome of Embryonic Neural Stem/Progenitor Cells

The mechanisms by which sex differences in the mammalian brain arise are poorly understood, but are influenced by a combination of underlying genetic differences and gonadal hormone exposure. Using a mouse embryonic neural stem cell (eNSC) model to understand early events contributing to sexually dimorphic brain development, we identified novel interactions between chromosomal sex and hormonal exposure that are instrumental to early brain sex differences. RNA-sequencing identified 103 transcripts that were differentially expressed between XX and XY eNSCs at baseline (FDR = 0.10). Treatment with testosterone-propionate (TP) reveals sex-specific gene expression changes, causing 2854 and 792 transcripts to become differentially expressed on XX and XY genetic backgrounds respectively. Within the TP responsive transcripts, there was enrichment for genes which function as epigenetic regulators that affect both histone modifications and DNA methylation patterning. We observed that TP caused a global decrease in 5-methylcytosine abundance in both sexes, a transmissible effect that was maintained in cellular progeny. Additionally, we determined that TP was associated with residue-specific alterations in acetylation of histone tails. These findings highlight an unknown component of androgen action on cells within the developmental CNS, and contribute to a novel mechanism of action by which early hormonal organization is initiated and maintained

Noise Induces Biased Estimation of the Correction Gain

The detection of an error in the motor output and the correction in the next movement are critical components of any form of motor learning. Accordingly, a variety of iterative learning models have assumed that a fraction of the error is adjusted in the next trial. This critical fraction, the correction gain, learning rate, or feedback gain, has been frequently estimated via least-square regression of the obtained data set. Such data contain not only the inevitable noise from motor execution, but also noise from measurement. It is generally assumed that this noise averages out with large data sets and does not affect the parameter estimation. This study demonstrates that this is not the case and that in the presence of noise the conventional estimate of the correction gain has a significant bias, even with the simplest model. Furthermore, this bias does not decrease with increasing length of the data set. This study reveals this limitation of current system identification methods and proposes a new method that overcomes this limitation. We derive an analytical form of the bias from a simple regression method (Yule-Walker) and develop an improved identification method. This bias is discussed as one of other examples for how the dynamics of noise can introduce significant distortions in data analysis