For higher activity levels, or when homeostatic mechanisms are impaired, the homeostatic mechanisms fail in maintaining ion concentrations close to baseline, and the edPR model diverges from the PR model as it accounts for effects of concentration changes on neuronal firing. We envision that the edPR model will be useful for the field in three main ways. Firstly, as it relaxes commonly made modeling assumptions, the edPR model can be used to test the validity of these assumptions under various firing conditions, as we show here for a few selected cases. Secondly, the edPR model should supplement the PR model when simulating scenarios where ion concentrations are expected to vary over time. Thirdly, being applicable to conditions with failed homeostasis, the edPR model opens up for simulating a range of pathological conditions, such as spreading depression or epilepsy.Uncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https//pypi.org) and from github (https//github.com/opencobra/Medusa), and comprehensive documentation is available at https//medusa.readthedocs.io/en/latest.Animals often exhibit dramatically behavioral plasticity depending on their internal physiological state, yet little is known about the underlying molecular mechanisms. The migratory locust, Locusta migratoria, provides an excellent model for addressing these questions because of their famous phase polyphenism involving remarkably behavioral plasticity between gregarious and solitarious phases. Here, we report that a major insect hormone, juvenile hormone, is involved in the regulation of this behavioral plasticity related to phase change by influencing the expression levels of olfactory-related genes in the migratory locust. We found that the treatment of juvenile hormone analog, methoprene, can significantly shift the olfactory responses of gregarious nymphs from attraction to repulsion to the volatiles released by gregarious nymphs. In contrast, the repulsion behavior of solitarious nymphs significantly decreased when they were treated with precocene or injected with double-stranded RNA of JHAMT, a juvenile hormone acid O-methyltransferase. Further, JH receptor Met or JH-response gene Kr-h1 knockdown phenocopied the JH-deprivation effects on olfactory behavior. RNA-seq analysis identified 122 differentially expressed genes in antennae after methoprene application on gregarious nymphs. Interestingly, several olfactory-related genes were especially enriched, including takeout (TO) and chemosensory protein (CSP) which have key roles in behavioral phase change of locusts. Furthermore, methoprene application and Met or Kr-h1 knockdown resulted in simultaneous changes of both TO1 and CSP3 expression to reverse pattern, which mediated the transition between repulsion and attraction responses to gregarious volatiles. Our results suggest the regulatory roles of a pleiotropic hormone in locust behavioral plasticity through modulating gene expression in the peripheral olfactory system.Plasmids, when transferred by conjugation in natural environments, must overpass restriction-modification systems of the recipient cell. We demonstrate that protein ArdC, encoded by broad host range plasmid R388, was required for conjugation from Escherichia coli to Pseudomonas putida. Expression of ardC was required in the recipient cells, but not in the donor cells. Besides, ardC was not required for conjugation if the hsdRMS system was deleted in P. putida recipient cells. ardC was also required if the hsdRMS system was present in E. coli recipient cells. Thus, ArdC has antirestriction activity against the HsdRMS system and consequently broadens R388 plasmid host range. The crystal structure of ArdC was solved both in the absence and presence of Mn2+. ArdC is composed of a non-specific ssDNA binding N-terminal domain and a C-terminal metalloprotease domain, although the metalloprotease activity was not needed for the antirestriction function. We also observed by RNA-seq that ArdC-dependent conjugation triggered an SOS response in the P. putida recipient cells. Our findings give new insights, and open new questions, into the antirestriction strategies developed by plasmids to counteract bacterial restriction strategies and settle into new hosts.BACKGROUND The prognostic role of axillary lymph node ratio (LNR) after neoadjuvant chemotherapy (NAC) in breast cancer has not been illuminated. This study was designed to investigate the prognostic role of LNR in breast cancer compared with traditional ypN stage. MATERIAL AND METHODS A total of 306 breast cancer patients diagnosed with positive axillary lymph nodes from January 2007 to December 2014 were eligible for this retrospective analysis. All enrolled patients were treated with a median of 4 cycles of NAC followed by mastectomy and level I, II, and III axillary lymph node dissection (ALND). https://www.selleckchem.com/pharmacological_epigenetics.html RESULTS The median duration of follow-up was 78 months (range, 7-147 months). Univariate analysis indicated that both the LNR category (P less then 0.001) and ypN stage (P less then 0.001) were significant associated with event-free survival (EFS) and overall survival (OS). However, multivariate analysis indicated that the LNR category was independently associated with EFS (P less then 0.001) and OS (P less then 0.