A Machine Learning Method of Determining Causal Inference applied to Shifts in Voting Preferences between 2012-2016

This research investigates the application of machine learning techniques to assist in the execution of a synthetic control model. This model was performed to analyze counties within the United States that showed a voter shift from a majority of Democratic voter share to Republican between the 2012 and 2016 election cycles. The following study applies two steps of machine learning analysis. The first, which is the treatment discovery process, leverages a Random Forest to evaluate feature importance. The second step was the execution of the synthetic control model with two predictor variable lists. The first was the parametric method: a hand curated predictor variable list based on domain knowledge. The second was the non-parametric method: all available predictor (descriptive) variables were used. The Random Forest treatment discovery process resulted in two uncommon variables applied as treatment effects: WIC women enrollment and a decrease of vegetable farm acreage. The opportunity to research these atypical treatment variables allows for the potential of surfacing counterfactual arguments for further research. The use of the parametric and non-parametric methods offers a system of comparison for the research in this paper. The result from the decrease in vegetable farm acreage treatment variable was negative for the non-parametric model. However, the parametric model did show strong statistical evidence towards a treatment effect from the decrease in farm acreage. It is likely that the decrease in vegetable farm acreage is a proxy for poverty or a population density metric. These data results suggest that this model was likely suffering from omitted variable bias for representation of one or both of these metrics in the predictor variable list. The WIC women enrollment treatment variable investigation resulted in the synthetic control model having difficulty in forming a synthetic control comparison. These results suggest there is a fundamental difference between those counties used to create the synthetic control and the other counties that saw a treatment effect. Additional research needs to be performed, and it could result in a different application of the data for use in a synthetic control model. The results of this study, while not surfacing causal inference, did open questions for further research. Given the opportunity these joined causal inference and machines learning practices could continue and potential offer assistance to traditional causal modeling methods. Allowing researchers to understand data and relationships between the data more intimately, theoretically allowing for new causal inferences to be discovered

Finding middle ground between intellectual arrogance and intellectual servility: Development and assessment of the limitations-owning intellectual humility scale

Recent scholarship in intellectual humility (IH) has attempted to provide deeper understanding of the virtue as personality trait and its impact on an individual's thoughts, beliefs, and actions. A limitations-owning perspective of IH focuses on a proper recognition of the impact of intellectual limitations and a motivation to overcome them, placing it as the mean between intellectual arrogance and intellectual servility. We developed the Limitations-Owning Intellectual Humility Scale to assess this conception of IH with related personality constructs. In Studies 1 (n= 386) and 2 (n = 296), principal factor and confirmatory factor analyses revealed a three-factor model – owning one's intellectual limitations, appropriate discomfort with intellectual limitations, and love of learning. Study 3 (n = 322) demonstrated strong test-retest reliability of the measure over 5 months, while Study 4 (n = 612) revealed limitations-owning IH correlated negatively with dogmatism, closed-mindedness, and hubristic pride and positively with openness, assertiveness, authentic pride. It also predicted openness and closed-mindedness over and above education, social desirability, and other measures of IH. The limitations-owning understanding of IH and scale allow for a more nuanced, spectrum interpretation and measurement of the virtue, which directs future study inside and outside of psychology

Ectopic models for endochondral ossification: comparing pellet and alginate bead culture methods

Key aspects of native endochondral bone development and fracture healing can be mimicked in mesenchymal stem cells (MSCs) through standard in vitro chondrogenic induction. Exploiting this phenomenon has recently emerged as an attractive technique to engineer bone tissue, however, relatively little is known about the best conditions for doing so. The objective of the present study was to compare the bone-forming capacity and angiogenic induction of hypertrophic cell constructs containing human adipose-derived stem cells (hASCs) primed for chondrogenesis in two different culture systems: high-density pellets and alginate bead hydrogels. The hASC constructs were subjected to 4 weeks of identical chondrogenic induction in vitro, encapsulated in an agarose carrier, and then implanted subcutaneously in immune-compromised mice for 8 weeks to evaluate their endochondral potential. At the time of implantation, both pellets and beads expressed aggrecan and type II collagen, as well as alkaline phosphatase (ALP) and type X collagen. Interestingly, ASCs in pellets formed a matrix containing higher glycosaminoglycan and collagen contents than that in beads, and ALP activity per cell was higher in pellets. However, after 8 weeks in vivo, pellets and beads induced an equivalent volume of mineralized tissue and a comparable level of vascularization. Although osteocalcin and osteopontin-positive osteogenic tissue and new vascular growth was found within both types of constructs, all appeared to be better distributed throughout the hydrogel beads. The results of this ectopic model indicate that hydrogel culture may be an attractive alternative to cell pellets for bone tissue engineering via the endochondral pathway

A re-evaluation of the morphology of Sorapilla (Bryophyta: Sorapillaceae) based on Sorapilla papuana

Sorapilla Spruce & Mitt. is a taxonomically problematic genus of mosses. In this paper one of the two known species, Sorapilla papuana Broth. & Geh., is described and illustrated in detail for the first time, and its morphological peculiarities are discussed. It is monoicous, rather than dioicous as had been assumed. The presence of an annulus on the capsule and sparse hairs on the calyptra, both suggested previously, are confirmed. Paraphyllia, which had been reported for Sorapilla, are not present. The phylogenetic position of Sorapilla remains uncertain

A Machine Learning Method of Determining Causal Inference applied to Shifts in Voting Preferences between 2012-2016

This research investigates the application of machine learning techniques to assist in the execution of a synthetic control model. This model was performed to analyze counties within the United States that showed a voter shift from a majority of Democratic voter share to Republican between the 2012 and 2016 election cycles. The following study applies two steps of machine learning analysis. The first, which is the treatment discovery process, leverages a Random Forest to evaluate feature importance. The second step was the execution of the synthetic control model with two predictor variable lists. The first was the parametric method: a hand curated predictor variable list based on domain knowledge. The second was the non-parametric method: all available predictor (descriptive) variables were used. The Random Forest treatment discovery process resulted in two uncommon variables applied as treatment effects: WIC women enrollment and a decrease of vegetable farm acreage. The opportunity to research these atypical treatment variables allows for the potential of surfacing counterfactual arguments for further research. The use of the parametric and non-parametric methods offers a system of comparison for the research in this paper. The result from the decrease in vegetable farm acreage treatment variable was negative for the non-parametric model. However, the parametric model did show strong statistical evidence towards a treatment effect from the decrease in farm acreage. It is likely that the decrease in vegetable farm acreage is a proxy for poverty or a population density metric. These data results suggest that this model was likely suffering from omitted variable bias for representation of one or both of these metrics in the predictor variable list. The WIC women enrollment treatment variable investigation resulted in the synthetic control model having difficulty in forming a synthetic control comparison. These results suggest there is a fundamental difference between those counties used to create the synthetic control and the other counties that saw a treatment effect. Additional research needs to be performed, and it could result in a different application of the data for use in a synthetic control model. The results of this study, while not surfacing causal inference, did open questions for further research. Given the opportunity these joined causal inference and machines learning practices could continue and potential offer assistance to traditional causal modeling methods. Allowing researchers to understand data and relationships between the data more intimately, theoretically allowing for new causal inferences to be discovered

Two Corpora Lutea Seen at 6-13 Weeks' Gestation Infers Dizygosity Among Spontaneous Same-Sexed Dichorionic Twins

Current ultrasound techniques can accurately determine the chorionicity of twins, but not zygosity. We previously proposed that the zygosity of spontaneously conceived twins can be determined at early ultrasound, where 2 corpora lutea infers dizygosity, and 1 implies monozygosity. Here we did a case series, comparing zygosity predicted using this method with definitive DNA genotyping of twins after birth. We retrospectively identified 14 ultrasound reports of spontaneous twin pregnancies at 6(+0 days) to 13+6 weeks' gestation, where both ovaries were seen and the number of corpora lutea documented. We visited all twin pairs, obtained buccal smears, and determined zygosity by genotyping 9 independent microsatellite markers. All 8 cases where 2 corpora lutea were seen were dizygotic pregnancies. One further case where 3 corpora lutea were seen was also dizygotic. All 3 sets of monozygotic twins had 1 corpus luteum. There were 2 cases incorrectly assigned, where 1 corpus luteum was seen in dizygotic pregnancies. We conclude if 2 corpora lutea are seen at a first trimester ultrasound of spontaneously conceived dichorionic twins, they appear to be almost certainly dizygotic. However, if I corpus luteum is seen in dichorionic twins, zygosity cannot be determined with certainty since it is either monozygotic, or dizygotic where a second corpus luteum has been missed

Epigenetic inactivation of a cluster of genes flanking MLH1 in microsatellite-unstable colorectal cancer

Biallelic promoter methylation and transcriptional silencing of the MLH1 gene occurs in the majority of sporadic colorectal cancers exhibiting microsatellite instability due to defective DNA mismatch repair. Long-range epigenetic silencing of contiguous genes has been found on chromosome 2q14 in colorectal cancer. We hypothesized that epigenetic silencing of MLH1 could occur on a regional scale affecting additional genes within 3p22, rather than as a focal event. We studied the levels of CpG island methylation and expression of multiple contiguous genes across a 4 Mb segment of 3p22 including MLH1 in microsatellite-unstable and -stable cancers, and their paired normal colonic mucosa. We found concordant CpG island hypermethylation, H3-K9 dimethylation and transcriptional silencing of MLH1 and multiple flanking genes spanning up to 2.4 Mb in microsatellite-unstable colorectal cancers. This region was interspersed with unmethylated genes, which were also transcriptionally repressed. Expression of both methylated and unmethylated genes was reactivated by methyltransferase and histone deacetylase inhibitors in a microsatellite-unstable colorectal carcinoma cell line. Two genes at the telomeric end of the region were also hypermethylated in microsatellitestable cancers, adenomas, and at low levels in normal colonic mucosa from older individuals. Thus, the cluster of genes flanking MLH1 that was specifically methylated in the microsatellite-unstable group of cancers extended across 1.1 Mb. Our results show that coordinate epigenetic silencing extends across a large chromosomal region encompassing MLH1 in microsatellite-unstable colorectal cancers. Simultaneous epigenetic silencing of this cluster of 3p22 genes may contribute to the development or progression of this type of cancer