Does Logic Help Us Beat Monty Hall?

The classical Monty Hall problem entails that a hypothetical game show contestant be presented three doors and told that behind one door is a car and behind the other two are far less appealing prizes, like goats. The contestant then picks a door, and the host (Monty) is to open a different door which contains one of the bad prizes. At this point in the game, the contestant is given the option of keeping the door she chose or changing her selection to the remaining door (since one has already been opened by Monty), after which Monty opens the chosen door and the contestant wins the prize which lies behind it. Inspired by the work of Morrow, Oman and Salminen (2016, “Game Show Shenanigans: Monty Hall Meets Mathematical Logic,” Elemente der Mathematik 71(4), pp. 145-155), we consider several logic-themed variants of this problem. Among these are versions where d doors and p prizes reside behind some p of these doors, and the contestant is permitted to present Monty with q random true/false questions concerning the location of the prizes, to which Monty must respond truthfully. Our results extend those of the original paper, and involve a combination of probabilistic techniques and exhaustive computation using a computer program

The Rice Miniature Inverted Repeat Transposable Element mPing Is an Effective Insertional Mutagen in Soybean

Insertional mutagenesis of legume genomes such as soybean (Glycine max) should aid in identifying genes responsible for key traits such as nitrogen fixation and seed quality. The relatively low throughput of soybean transformation necessitates the use of a transposon-tagging strategy where a single transformation event will produce many mutations over a number of generations. However, existing transposon-tagging tools being used in legumes are of limited utility because of restricted transposition (Ac/Ds: soybean) or the requirement for tissue culture activation (Tnt1: Medicago truncatula). A recently discovered transposable element from rice (Oryza sativa), mPing, and the genes required for its mobilization, were transferred to soybean to determine if it will be an improvement over the other available transposon-tagging tools. Stable transformation events in soybean were tested for mPing transposition. Analysis of mPing excision at early and late embryo developmental stages revealed increased excision during late development in most transgenic lines, suggesting that transposition is developmentally regulated. Transgenic lines that produced heritable mPing insertions were identified, with the plants from the highest activity line producing at least one new insertion per generation. Analysis of the mPing insertion sites in the soybean genome revealed that features displayed in rice were retained including transposition to unlinked sites and a preference for insertion within 2.5 kb of a gene. Taken together these findings indicate that mPing has the characteristics necessary for an effective transposon-tagging resource

Real-time individualized training vectors for experiential learning.

Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning

Short communication: Landlab v2.0: a software package for Earth surface dynamics

umerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon–Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components – modular implementations of single physical processes – and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components – for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab

Transcriptional and Cellular Diversity of the Human Heart

Background: The human heart requires a complex ensemble of specialized cell types to perform its essential function. A greater knowledge of the intricate cellular milieu of the heart is critical to increase our understanding of cardiac homeostasis and pathology. As recent advances in low-input RNA sequencing have allowed definitions of cellular transcriptomes at single-cell resolution at scale, we have applied these approaches to assess the cellular and transcriptional diversity of the nonfailing human heart. Methods: Microfluidic encapsulation and barcoding was used to perform single nuclear RNA sequencing with samples from 7 human donors, selected for their absence of overt cardiac disease. Individual nuclear transcriptomes were then clustered based on transcriptional profiles of highly variable genes. These clusters were used as the basis for between-chamber and between-sex differential gene expression analyses and intersection with genetic and pharmacologic data. Results: We sequenced the transcriptomes of 287 269 single cardiac nuclei, revealing 9 major cell types and 20 subclusters of cell types within the human heart. Cellular subclasses include 2 distinct groups of resident macrophages, 4 endothelial subtypes, and 2 fibroblast subsets. Comparisons of cellular transcriptomes by cardiac chamber or sex reveal diversity not only in cardiomyocyte transcriptional programs but also in subtypes involved in extracellular matrix remodeling and vascularization. Using genetic association data, we identified strong enrichment for the role of cell subtypes in cardiac traits and diseases. Intersection of our data set with genes on cardiac clinical testing panels and the druggable genome reveals striking patterns of cellular specificity. Conclusions: Using large-scale single nuclei RNA sequencing, we defined the transcriptional and cellular diversity in the normal human heart. Our identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases

Exploring the Variable Sky with the Sloan Digital Sky Survey

We quantify the variability of faint unresolved optical sources using a catalog based on multiple SDSS imaging observations. The catalog covers SDSS Stripe 82, which lies along the celestial equator in the Southern Galactic Hemisphere (22h 24m < {alpha}{sub J2000} < 04h 08m, -1.27 < {delta}{sub J2000} < +1.27, {approx}290 deg{sup 2} ), and contains 58 million photometric observations in the SDSS ugriz system for 1.4 million unresolved sources that were observed at least 4 times in each of the gri bands (with a median of 10 observations obtained over {approx}5 years). In each photometric bandpass we compute various low-order lightcurve statistics such as root-mean-square scatter (rms), {chi}{sup 2} 2 per degree of freedom, skewness, minimum and maximum magnitude, and use them to select and study variable sources. We find that 2% of unresolved optical sources brighter than g = 20.5 appear variable at the 0.05 mag level (rms) simultaneously in the g and r bands. The majority (2/3) of these variable sources are low-redshift (< 2) quasars, although they represent only 2% of all sources in the adopted ux-limited sample. We find that at least 90% of quasars are variable at the 0.03 mag level (rms) and confirm that variability is as good a method for finding low-redshift quasars as is the UV excess color selection (at high Galactic latitudes). We analyze the distribution of lightcurve skewness for quasars and find that is centered on zero. We find that about 1/4 of the variable stars are RR Lyrae stars, and that only 0.5% of stars from the main stellar locus are variable at the 0.05 mag level. The distribution of lightcurve skewness in the g-r vs. u-g color-color diagram on the main stellar locus is found to be bimodal (with one mode consistent with Algol-like behavior). Using over six hundred RR Lyrae stars, we demonstrate rich halo substructure out to distances of 100 kpc. We extrapolate these results to expected performance by the Large Synoptic Survey Telescope and estimate that it will obtain well-sampled 2% accurate, multi-color lightcurves for {approx}2 million low-redshift quasars, and will discover at least 50 million variable stars

A Neutralizing Monoclonal Antibody Targeting the Acid-Sensitive Region in Chikungunya Virus E2 Protects from Disease.

The mosquito-borne alphavirus, chikungunya virus (CHIKV), has recently reemerged, producing the largest epidemic ever recorded for this virus, with up to 6.5 million cases of acute and chronic rheumatic disease. There are currently no licensed vaccines for CHIKV and current anti-inflammatory drug treatment is often inadequate. Here we describe the isolation and characterization of two human monoclonal antibodies, C9 and E8, from CHIKV infected and recovered individuals. C9 was determined to be a potent virus neutralizing antibody and a biosensor antibody binding study demonstrated it recognized residues on intact CHIKV VLPs. Shotgun mutagenesis alanine scanning of 98 percent of the residues in the E1 and E2 glycoproteins of CHIKV envelope showed that the epitope bound by C9 included amino-acid 162 in the acid-sensitive region (ASR) of the CHIKV E2 glycoprotein. The ASR is critical for the rearrangement of CHIKV E2 during fusion and viral entry into host cells, and we predict that C9 prevents these events from occurring. When used prophylactically in a CHIKV mouse model, C9 completely protected against CHIKV viremia and arthritis. We also observed that when administered therapeutically at 8 or 18 hours post-CHIKV challenge, C9 gave 100% protection in a pathogenic mouse model. Given that targeting this novel neutralizing epitope in E2 can potently protect both in vitro and in vivo, it is likely to be an important region both for future antibody and vaccine-based interventions against CHIKV

Deep learning enables genetic analysis of the human thoracic aorta

Genome-wide association analyses identify variants associated with thoracic aortic diameter. A polygenic score for ascending aortic diameter was associated with a diagnosis of thoracic aortic aneurysm in independent samples. Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests and human aortic single nucleus RNA sequencing prioritized genes including SVIL, which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank participants (hazard ratio = 1.43 per s.d., confidence interval 1.32-1.54, P = 3.3 x 10(-20)). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images

Short communication: Landlab v2.0: a software package for Earth surface dynamics

Numerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon-Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components - modular implementations of single physical processes - and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components - for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab.</p