Dissecting the regulatory strategies of NF-kB RelA target genes in the inflammatory response reveals differential transactivation logics

Nuclear factor κB (NF-κB) RelA is the potent transcriptional activator of inflammatory response genes. We stringently defined a list of direct RelA target genes by integrating physical (chromatin immunoprecipitation sequencing [ChIP-seq]) and functional (RNA sequencing [RNA-seq] in knockouts) datasets. We then dissected each gene’s regulatory strategy by testing RelA variants in a primary-cell genetic-complementation assay. All endogenous target genes require RelA to make DNA-base-specific contacts, and none are activatable by the DNA binding domain alone. However, endogenous target genes differ widely in how they employ the two transactivation domains. Through model-aided analysis of the dynamic time-course data, we reveal the gene-specific synergy and redundancy of TA1 and TA2. Given that post-translational modifications control TA1 activity and intrinsic affinity for coactivators determines TA2 activity, the differential TA logics suggests context-dependent versus context-independent control of endogenous RelA-target genes. Although some inflammatory initiators appear to require co-stimulatory TA1 activation, inflammatory resolvers are a part of the NF-κB RelA core response

Orienting Patients to Greater Opioid Safety: Models of Community Pharmacy-Based Naloxone

The leading cause of adult injury death in the USA is drug overdose, the majority of which involves prescription opioid medications. Outside of the USA, deaths by drug overdose are also on the rise, and overdose is a leading cause of death for drug users. Reducing overdose risk while maintaining access to prescription opioids when medically indicated requires careful consideration of how opioids are prescribed and dispensed, how patients use them, how they interact with other medications, and how they are safely stored. Pharmacists, highly trained professionals expert at detecting and managing medication errors and drug-drug interactions, safe dispensing, and patient counseling, are an under-utilized asset in addressing overdose in the US and globally. Pharmacies provide a high-yield setting where patient and caregiver customers can access naloxone—an opioid antagonist that reverses opioid overdose—and overdose prevention counseling. This case study briefly describes and provides two US state-specific examples of innovative policy models of pharmacy-based naloxone, implemented to reduce overdose events and improve opioid safety: Collaborative Pharmacy Practice Agreements and Pharmacy Standing Orders

Letting nature do the work : managing wildfires for resource objectives in New Mexico

2 pagesIn millions of acres of fire-adapted landscapes across the West, the need for forest restoration and wildfire mitigation outpaces capacity to respond, posing risks to homes, communities, and forest health. Land managers are increasingly looking for tools to help address these risks. One approach is to manage naturally ignited wildfires at appropriate intensities and severities to reduce fuel loads and improve forest health. This fact sheet describes managing naturally ignited wildfires for resource objectives and how multiple public, private and nongovernmental entities are working on wildfire mitigation, pre-planning, and suppression in northern New Mexico to foster the necessary conditions for this approach.FUNDER: Joint Fire Science Program

Machine learning using digitized herbarium specimens to advance phenological research

Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth

A less disastrous disaster: Managing response to climate-driven hazards in the Cayman Islands and NE Brazil

This paper explores the relationship between disaster risk reduction and long-term adaptive capacity building in two climate vulnerable areas—the Cayman Islands in the Caribbean and Ceará, in NE Brazil. Drawing on past applications of the disaster risk reduction framework, we identify four critical factors that have led to reductions in risk: flexible, learning-based, responsive governance; committed, reform-minded and politically active actors; disaster risk reduction integrated into other social and economic policy processes; and a long-term commitment to managing risk. We find that while the presence of these factors has reduced overall risk in both regions, in Ceará, disaster response as it is currently practiced, has fallen short of addressing the fundamental causes of vulnerability that leave those prone to hazards able to cope in the short term, yet enmeshed in poverty and at risk from the longer-term changes associated with climate change. Although calls for integration of disaster risk management with poverty eradication are not new, there has been insufficient attention paid in the literature on how to foster such integration. Based on the two case studies, we argue that the adoption of good governance mechanisms (such as stakeholder participation, access to knowledge, accountability and transparency) in disaster risk reduction policy may create the policy environment that is conducive to the kind of structural reform needed to build long-term adaptive capacity to climate-driven impacts. We conclude that without a synergistic two-tiered approach that includes both disaster risk reduction and structural reform, disaster risk reduction, in the face of climate changes, will prove to be an expensive and ineffective palliative treatment of changing risks

Enhancer Reprogramming Confers Dependence on Glycolysis and IGF Signaling in KMT2D Mutant Melanoma.

Histone methyltransferase KMT2D harbors frequent loss-of-function somatic point mutations in several tumor types, including melanoma. Here, we identify KMT2D as a potent tumor suppressor in melanoma through an in vivo epigenome-focused pooled RNAi screen and confirm the finding by using a genetically engineered mouse model (GEMM) based on conditional and melanocyte-specific deletion of KMT2D. KMT2D-deficient tumors show substantial reprogramming of key metabolic pathways, including glycolysis. KMT2D deficiency aberrantly upregulates glycolysis enzymes, intermediate metabolites, and glucose consumption rates. Mechanistically, KMT2D loss causes genome-wide reduction of H3K4me1-marked active enhancer chromatin states. Enhancer loss and subsequent repression of IGFBP5 activates IGF1R-AKT to increase glycolysis in KMT2D-deficient cells. Pharmacological inhibition of glycolysis and insulin growth factor (IGF) signaling reduce proliferation and tumorigenesis preferentially in KMT2D-deficient cells. We conclude that KMT2D loss promotes tumorigenesis by facilitating an increased use of the glycolysis pathway for enhanced biomass needs via enhancer reprogramming, thus presenting an opportunity for therapeutic intervention through glycolysis or IGF pathway inhibitors

Concert recording 2016-11-15

[Track 1]. Subjugation. Connection [Track 2]. Captivation / Durgan Maxey -- [Track 3]. Fight / Bryce Owens -- [Track 4]. Overture to Stay / Joshua Bland -- [Track 5]. A cellist\u27s legacy. Part I [Track 6]. Part II / Eric Dreggors -- [Track 7]. Evening prayer / Robbie Baker -- [Track 8]. Elegy / Brandon Wade -- [Track 9]. The grotesques trio. Gargoyles [Track 10]. Chimera [Track 11]. Grotesques / Marissa Johnson -- [Track 12]. Crosshair / Joshua Bland -- [Track 13]. Nightwind sings / L. Coley Pitchford -- [Track 14]. Six reflections through poetry. Memories (Walt Whitman) [Track 15]. The musician\u27s wife (Weldon Kees) [Track 16]. The road not taken (Robert Frost) [Track 17]. Lessons (Whitman) [Track 18]. Stronger lessons (Whitman) [Track 19]. O me! O life! (Whitman) / Nick Vecchio -- [Tracks 20-21]. String quartet #1 / Jeremiah Flannery -- [Track 22]. Tides. Morning tide [Track 23]. Bore tide / Elizabeth Greener -- [Track 24]. Shepherd\u27s contemplation / Robbie Baker -- Green grass / arranged by Eva Martin -- [Track 25]. Urbe fracta est II. A prayer for Jerusalem / Joshua Bland

NetKet: A machine learning toolkit for many-body quantum systems

We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used as a variational ansatz for quantum wavefunctions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wavefunction data, and ground state searches for a wide range of customizable lattice models. Our aim is to provide a common platform for open research and to stimulate the collaborative development of computational methods at the interface of machine learning and many-body physics