Final State Interactions and CP Violation in KL→π+π−e+e−K_L \to \pi^+ \pi^- e^+ e^-

Using chiral perturbation theory we calculate the imaginary parts of the $K_L \rightarrow \pi^+ \pi^- e^+ e^-$ form factors that arise from $\pi \pi \rightarrow \pi^+ \pi^-$ and $\pi \pi \rightarrow \pi^+ \pi^- \gamma^*$ rescattering. We discuss their influence on CP violating variables in $K_L \rightarrow \pi^+ \pi^- e^+ e^-$.Comment: ; 12 pages, 2 figures, TeX format; uses epsf.tex, tables.tex, and phyzzx.te

Wavelength-Gated Photochemical Synthesis of Phenalene Diimides

Herein, we pioneer a wavelength‐gated synthesis route to phenalene diimides. Consecutive Diels–Alder reactions of methylisophthalaldehydes and maleimides afford hexahydro‐phenalene‐1,6‐diol diimides via 5‐formyl‐hexahydro‐benzo[f]isoindoles as the intermediate. Both photoreactions are efficient (82–99 % yield) and exhibit excellent diastereoselectivity (62–98 % d.r.). The wavelength‐gated nature of the stepwise reaction enables the modular construction of phenalene diimide scaffolds by choice of substrate and wavelength. Importantly, this synthetic methodology opens a facile avenue to a new class of persistent phenalenyl diimide neutral radicals, constituting a versatile route to spin‐active molecules

Multi-transcriptome analysis following an acute skeletal muscle growth stimulus yields tools for discerning global and MYC regulatory networks

Myc is a powerful transcription factor implicated in epigenetic reprogramming, cellular plasticity, and rapid growth as well as tumorigenesis. Cancer in skeletal muscle is extremely rare despite marked and sustained Myc induction during loading-induced hypertrophy. Here, we investigated global, actively transcribed, stable, and myonucleus-specific transcriptomes following an acute hypertrophic stimulus in mouse plantaris. With these datasets, we define global and Myc-specific dynamics at the onset of mechanical overload-induced muscle fiber growth. Data collation across analyses reveals an under-appreciated role for the muscle fiber in extracellular matrix remodeling during adaptation, along with the contribution of mRNA stability to epigenetic-related transcript levels in muscle. We also identify Runx1 and Ankrd1 (Marp1) as abundant myonucleus-enriched loading-induced genes. We observed that a strong induction of cell cycle regulators including Myc occurs with mechanical overload in myonuclei. Additionally, in vivo Myc-controlled gene expression in the plantaris was defined using a genetic muscle fiber-specific doxycycline-inducible Myc-overexpression model. We determined Myc is implicated in numerous aspects of gene expression during early-phase muscle fiber growth. Specifically, brief induction of Myc protein in muscle represses Reverbα, Reverbβ, and Myh2 while increasing Rpl3, recapitulating gene expression in myonuclei during acute overload. Experimental, comparative, and in silico analyses place Myc at the center of a stable and actively transcribed, loading-responsive, muscle fiber–localized regulatory hub. Collectively, our experiments are a roadmap for understanding global and Myc-mediated transcriptional networks that regulate rapid remodeling in postmitotic cells. We provide open webtools for exploring the five RNA-seq datasets as a resource to the field

Past as global trade governance prelude: reconfiguring debate about reform of the multilateral trading system

This paper peers backwards into the history of the multilateral trading system and its development over the past half century as a means of considering what may lie beyond the horizon for the future of global trade governance. Its purpose is to underscore the necessity and urgency for root-and-branch reform of the multilateral trading system. It achieves this by comparing and contrasting the global trading system of 50 years ago with its modern-day equivalent and its likely future counterpart half-a-century hence. In so doing, the paper throws into sharp relief not only the inadequacies of global trade governance today but also the damaging consequences of not fundamentally reforming the system in the near future, with a particular emphasis on the past, present and future development of the world’s poorest and most marginalised countries

Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization

Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization

The genomes of two key bumblebee species with primitive eusocial organization

Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats. Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits. Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation

SAD phasing using iodide ions in a high-throughput structural genomics environment

The Seattle Structural Genomics Center for Infectious Disease (SSGCID) focuses on the structure elucidation of potential drug targets from class A, B, and C infectious disease organisms. Many SSGCID targets are selected because they have homologs in other organisms that are validated drug targets with known structures. Thus, many SSGCID targets are expected to be solved by molecular replacement (MR), and reflective of this, all proteins are expressed in native form. However, many community request targets do not have homologs with known structures and not all internally selected targets readily solve by MR, necessitating experimental phase determination. We have adopted the use of iodide ion soaks and single wavelength anomalous dispersion (SAD) experiments as our primary method for de novo phasing. This method uses existing native crystals and in house data collection, resulting in rapid, low cost structure determination. Iodide ions are non-toxic and soluble at molar concentrations, facilitating binding at numerous hydrophobic or positively charged sites. We have used this technique across a wide range of crystallization conditions with successful structure determination in 16 of 17 cases within the first year of use (94% success rate). Here we present a general overview of this method as well as several examples including SAD phasing of proteins with novel folds and the combined use of SAD and MR for targets with weak MR solutions. These cases highlight the straightforward and powerful method of iodide ion SAD phasing in a high-throughput structural genomics environment

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

Novel diagnostic DNA methylation episignatures expand and refine the epigenetic landscapes of Mendelian disorders

Overlapping clinical phenotypes and an expanding breadth and complexity of genomic associations are a growing challenge in the diagnosis and clinical management of Mendelian disorders. The functional consequences and clinical impacts of genomic variation may involve unique, disorder-specific, genomic DNA methylation episignatures. In this study, we describe 19 novel episignature disorders and compare the findings alongside 38 previously established episignatures for a total of 57 episignatures associated with 65 genetic syndromes. We demonstrate increasing resolution and specificity ranging from protein complex, gene, sub-gene, protein domain, and even single nucleotide-level Mendelian episignatures. We show the power of multiclass modeling to develop highly accurate and disease-specific diagnostic classifiers. This study significantly expands the number and spectrum of disorders with detectable DNA methylation episignatures, improves the clinical diagnostic capabilities through the resolution of unsolved cases and the reclassification of variants of unknown clinical significance, and provides further insight into the molecular etiology of Mendelian conditions