Involutions and the Gelfand character

The Gelfand representation of $\mathcal{S}_n$ is the multiplicity-free direct sum of the irreducible representations of $\mathcal{S}_n$. In this paper, we use a result of Adin, Postnikov, and Roichman to find a recursive generating function for the Gelfand character. In order to find this generating function, we investigate descents of so-called $\lambda$-unimodal involutions

Simulating counterfactual representation

We show how to use multilevel modeling and post-stratification to estimate legislative outcomes under counterfactual representation schemes that, for example, boost the representation of women or translate votes into seats differently. We apply this technique to two research questions: (1) Would the U.S. Congress be less polarized if state delegations were formed according to the principle of party proportional representation? (2) Would there have been stronger support for legalizing same-sex marriage in the U.K. House of Commons if Parliament more closely reflected the population in gender and age

On the Temperature Dependence of the Lifetime of Thermally Isolated Metastable Clusters

The temperature dependence of the lifetime of the thermally isolated metastable N8 cubane up to its decay into N2 molecules has been calculated by the molecular dynamics method. It has been demonstrated that this dependence significantly deviates from the Arrhenius law. The applicability of the finite heat bath theory to the description of thermally isolated atomic clusters has been proved using statistical analysis of the results obtained.Comment: 14 pages, 4 figure

Motor Function and Dopamine Release Measurements in Transgenic Huntington’s Disease Model Rats

Huntington’s disease (HD) is a fatal, genetic, neurodegenerative disorder characterized by deficits in motor and cognitive function. Here, we have quantitatively characterized motor deficiencies and dopamine release dynamics in transgenic HD model rats. Behavioral analyses were conducted using a newly-developed force-sensing runway and a previously-developed force-plate actometer. Gait disturbances were readily observed in transgenic HD rats at 12 to 15 months of age. Additionally, dopamine system challenge by ip injection of amphetamine also revealed that these rats were resistant to the expression of focused stereotypy compared to wild-type controls. Moreover, dopamine release, evoked by the application of single and multiple electrical stimulus pulses applied at different frequencies, and measured using fast-scan cyclic voltammetry at carbon-fiber microelectrodes, was diminished in transgenic HD rats compared to age-matched wild-type control rats. Collectively, these results underscore the potential contribution of dopamine release alterations to the expression of motor impairments in transgenic HD rats

A dynamic network approach for the study of human phenotypes

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.Comment: 28 pages (double space), 6 figure

Veterinary dairy herd fertility service provision in seasonal and non-seasonal dairy industries - a comparison

The decline in dairy herd fertility internationally has highlighted the limited impact of traditional veterinary approaches to bovine fertility management. Three questionnaire surveys were conducted at buiatrics conferences attended by veterinary practitioners on veterinary dairy herd fertility services (HFS) in countries with a seasonal (Ireland, 47 respondents) and non-seasonal breeding model (The Netherlands, 44 respondents and Portugal, 31 respondents). Of the 122 respondents, 73 (60%) provided a HFS and 49 (40%) did not. The majority (76%) of all practitioners who responded stated that bovine fertility had declined in their practice clients' herds with inadequate cow management, inadequate nutrition and increased milk yield as the most important putative causes. The type of clients who adopted a herd fertility service were deemed more educated than average (70% of respondents), and/or had fertility problems (58%) and/or large herds (53%). The main components of this service were routine postpartum examinations (95% of respondents), fertility records analysis (75%) and ultrasound pregnancy examinations (69%). The number of planned visits per annum varied between an average of four in Ireland, where breeding is seasonal, and 23 in Portugal, where breeding is year-round. The benefits to both the practitioner and their clients from running a HFS were cited as better fertility, financial rewards and job satisfaction. For practitioners who did not run a HFS the main reasons given were no client demand (55%) and lack of fertility records (33%). Better economic evidence to convince clients of the cost-benefit of such a service was seen as a major constraint to adoption of this service by 67% of practitioners

Attribution of space-time variability in global-ocean dissolved inorganic Carbon

The inventory and variability of oceanic dissolved inorganic carbon (DIC) is driven by the interplay of physical, chemical, and biological processes. Quantifying the spatiotemporal variability of these drivers is crucial for a mechanistic understanding of the ocean carbon sink and its future trajectory. Here, we use the Estimating the Circulation and Climate of the Ocean-Darwin ocean biogeochemistry state estimate to generate a global-ocean, data-constrained DIC budget and investigate how spatial and seasonal-to-interannual variability in three-dimensional circulation, air-sea CO2 flux, and biological processes have modulated the ocean sink for 1995–2018. Our results demonstrate substantial compensation between budget terms, resulting in distinct upper-ocean carbon regimes. For example, boundary current regions have strong contributions from vertical diffusion while equatorial regions exhibit compensation between upwelling and biological processes. When integrated across the full ocean depth, the 24-year DIC mass increase of 64 Pg C (2.7 Pg C year−1) primarily tracks the anthropogenic CO2 growth rate, with biological processes providing a small contribution of 2 (1.4 Pg C). In the upper 100 m, which stores roughly 13 (8.1 Pg C) of the global increase, we find that circulation provides the largest DIC gain (6.3 Pg C year−1) and biological processes are the largest loss (8.6 Pg C year−1). Interannual variability is dominated by vertical advection in equatorial regions, with the 1997–1998 El Niño-Southern Oscillation causing the largest year-to-year change in upper-ocean DIC (2.1 Pg C). Our results provide a novel, data-constrained framework for an improved mechanistic understanding of natural and anthropogenic perturbations to the ocean sink. © 2022. The Authors

The ECCO-Darwin Data-Assimilative Global Ocean Biogeochemistry Model: Estimates of Seasonal to Multidecadal Surface Ocean \u3cem\u3ep\u3c/em\u3eCO\u3csub\u3e2\u3c/sub\u3e and Air-Sea CO\u3csub\u3e2\u3c/sub\u3e Flux

Quantifying variability in the ocean carbon sink remains problematic due to sparse observations and spatiotemporal variability in surface ocean pCO2. To address this challenge, we have updated and improved ECCO-Darwin, a global ocean biogeochemistry model that assimilates both physical and biogeochemical observations. The model consists of an adjoint-based ocean circulation estimate from the Estimating the Circulation and Climate of the Ocean (ECCO) consortium and an ecosystem model developed by the Massachusetts Institute of Technology Darwin Project. In addition to the data-constrained ECCO physics, a Green\u27s function approach is used to optimize the biogeochemistry by adjusting initial conditions and six biogeochemical parameters. Over seasonal to multidecadal timescales (1995–2017), ECCO-Darwin exhibits broad-scale consistency with observed surface ocean pCO2 and air-sea CO2 flux reconstructions in most biomes, particularly in the subtropical and equatorial regions. The largest differences between CO2 uptake occur in subpolar seasonally stratified biomes, where ECCO-Darwin results in stronger winter uptake. Compared to the Global Carbon Project OBMs, ECCO-Darwin has a time-mean global ocean CO2 sink (2.47 ± 0.50 Pg C year−1) and interannual variability that are more consistent with interpolation-based products. Compared to interpolation-based methods, ECCO-Darwin is less sensitive to sparse and irregularly sampled observations. Thus, ECCO-Darwin provides a basis for identifying and predicting the consequences of natural and anthropogenic perturbations to the ocean carbon cycle, as well as the climate-related sensitivity of marine ecosystems. Our study further highlights the importance of physically consistent, property-conserving reconstructions, as are provided by ECCO, for ocean biogeochemistry studies