Hospice Capacity to Provide General Inpatient Care: Emergency Department Utilization and Live Discharge Among Cancer Patients.

General inpatient (GIP) hospice care is used only minimally for hospice patients, and more than a quarter of Medicare hospice facilities do not provide GIP care. To determine the impact of hospices' capacity to provide on emergency department use during hospice enrollment and live discharge from hospice, we used Surveillance, Epidemiology, and End Results-Medicare linked data and CMS Provider of Services data from 2007 to 2013 from ten states and two metropolitan regions. Grouping hospices into three GIP care provision categories: 1) no-GIP; 2) GIP-contract; and 3) GIP-IHF where hospices directly provide GIP care in their own inpatient hospice facility (IHF), we built a multilevel logistic model that accounted for unobserved hospice characteristics. Nearly 9% of the study sample received GIP care, of which 82% received such care in the last week of discharge. GIP-IHF hospices had lower live discharge rates than no-GIP hospices (AOR: .61; 95% CI: .47-.79; P < .001) and GIP-contract hospices (AOR: .84; 95% CI: .70-1.00; P < .05). Similarly, GIP-contract hospices were also associated with a decreased risk of live discharge, compared to no-GIP hospices (AOR: .76; CI: .62-.92; P < .05). There was no difference in emergency department use between no-GIP hospices and hospices with such capacity. Our results suggest that hospices capable of providing GIP care have lower live discharge rates than their counterparts. However, the fact that GIP care tends to be provided too close to death limits its effectiveness in preventing avoidable emergency department use.

Elucidating the Temporal Patterns of Gene Expression in the Inferred Regulatory Interactions of GmCOL1b in Glycine max.

The CONSTANS ( CO ) gene in Arabidopsis thaliana has a central role in photoperiodic regulation of flowering. However, the roles of CO genes in mediating flowering in soybeans ( Glycine max ) remain uncertain. We previously inferred regulatory interactions of a soybean CO homolog, GmCOL1b , using in-house RNA-seq data and the network inference algorithm package CausNet. Here, we identify potential GmCOL1b downstream genes and experimentally verify them by expressing GmCOL1b in soybean protoplast cells. Temporal expression patterns of these genes indicate the regulatory effects of GmCOL1b on the expression of the circadian clock genes GmLCL1 and GmLCL4 and the flowering regulator GmTEM1a .

Clarifying the Temporal Dynamics of the Circadian Clock and Flowering Gene Network Using Overexpression and Targeted Mutagenesis of Soybean EARLY FLOWERING 3-1 ( GmELF3-1 ).

With progressing climate fluctuations, an understanding of the molecular mechanisms of crop plants that regulate their flowering responses to environments is crucial. To achieve this goal, we aimed at clarifying the gene regulatory networks among the circadian clock and flowering genes in soybean ( Glycine max ). Based on our network inference approach , we hypothesize that GmELF3-1 , one of the Evening Complex (EC) gene homologs in soybean's circadian clock, may have an integrative role in transcriptional regulation of the circadian clock and flowering gene network. In this study, we verify GmELF3-1 ' s regulatory roles in its potential downstream genes by modulating the activity of GmELF3-1 using overexpression and CRISPR-Cas9 in soybean protoplasts. Our results indicate that GmELF3-1 may control the expression of the PRR genes in the circadian clock and the flowering gene GmCOL1a .

Experimental Verification of Inferred Regulatory Interactions of EARLY FLOWERING 3 ( GmELF3-1 ) in Glycine max.

Understanding the roles of evening complex (EC) genes in the circadian clock of plants can inform how diurnal transcriptional loops in the clock gene network function to regulate key physiological and developmental events, including flowering transition. Gene regulatory interactions among soybean's circadian clock and flowering genes were inferred using time-series RNA-seq data and the network inference algorithmic package CausNet. In this study, we seek to clarify the inferred regulatory interactions of the EC gene GmELF3-1. A gene expression analysis using soybean protoplasts as a transient model indicated regulatory roles of GmELF3-1 in expression of selected flowering genes.