BMP receptor IA is required in the mammalian embryo for endodermal morphogenesis and ectodermal patterning

AbstractBMPRIA is a receptor for bone morphogenetic proteins with high affinity for BMP2 and BMP4. Mouse embryos lacking Bmpr1a fail to gastrulate, complicating studies on the requirements for BMP signaling in germ layer development. Recent work shows that BMP4 produced in extraembryonic tissues initiates gastrulation. Here we use a conditional allele of Bmpr1a to remove BMPRIA only in the epiblast, which gives rise to all embryonic tissues. Resulting embryos are mosaics composed primarily of cells homozygous null for Bmpr1a, interspersed with heterozygous cells. Although mesoderm and endoderm do not form in Bmpr1a null embryos, these tissues are present in the mosaics and are populated with mutant cells. Thus, BMPRIA signaling in the epiblast does not restrict cells to or from any of the germ layers. Cells lacking Bmpr1a also contribute to surface ectoderm; however, from the hindbrain forward, little surface ectoderm forms and the forebrain is enlarged and convoluted. Prechordal plate, early definitive endoderm, and anterior visceral endoderm appear to be expanded, likely due to defective morphogenesis. These data suggest that the enlarged forebrain is caused in part by increased exposure of the ectoderm to signaling sources that promote anterior neural fate. Our results reveal critical roles for BMP signaling in endodermal morphogenesis and ectodermal patterning

Phenotypic landscape inference reveals multiple evolutionary paths to C4_4 photosynthesis

C$_4$ photosynthesis has independently evolved from the ancestral C$_3$ pathway in at least 60 plant lineages, but, as with other complex traits, how it evolved is unclear. Here we show that the polyphyletic appearance of C$_4$ photosynthesis is associated with diverse and flexible evolutionary paths that group into four major trajectories. We conducted a meta-analysis of 18 lineages containing species that use C$_3$, C$_4$, or intermediate C$_3$-C$_4$ forms of photosynthesis to parameterise a 16-dimensional phenotypic landscape. We then developed and experimentally verified a novel Bayesian approach based on a hidden Markov model that predicts how the C$_4$ phenotype evolved. The alternative evolutionary histories underlying the appearance of C$_4$ photosynthesis were determined by ancestral lineage and initial phenotypic alterations unrelated to photosynthesis. We conclude that the order of C$_4$ trait acquisition is flexible and driven by non-photosynthetic drivers. This flexibility will have facilitated the convergent evolution of this complex trait

2010-2011 Philharmonia Season Program

Philharmonia No. 1 October 9, 2010 at 7:30 PM and October 10, 2010 at 4:00 PM Albert-George Schram, music director and conductor ; Elmar Oliveira, violin Feierlicher Einzug der Ritter des Johanniterordens / Richard Strauss, arranged by Karl Kramer -- Violin Concerto in E Minor, op. 64 / Felix Mendelssohn -- Symphony No. 5 in C-sharp Minor / Gustav Mahler Philharmonia No. 2 November 6, 2010 at 7:30 PM and November 7, 2010 at 4:00 PM Albert-George Schram, music director and conductor ; Tao Lin, piano Overture to Ruslan and Lyudmila / Mikhail Glinka -- Piano Concerto No. 25 in C Major, K. 503 / Wolfgang Amadeus Mozart -- Symphohy No. 2 in D Major, op. 43 / Jean Sibelius Philharmonia No. 3 December 4, 2010 at 7:30 PM and December 5, 2010 at 4:00 PM Concerto Competition Winners Philharmonia No. 4 January 29, 2011 at 7:30 PM and January 30, 2011 at 4:00 PM Gunther Schuller, guest conductor ; Lisa Leonard, piano ; Marc Reese, trumpet Die Vorstellung des Chaos from Die Schöpfung (The Representation of Chaos from the Creation) / Joseph Haydn -- Concerto for Piano, Trumpet and Strings in C Minor, op. 35 / Dmitri Shostakovich -- Symphony No. 3 in F Major, op. 90 / Johannes Brahms Philharmonia No. 5 February 19, 2011 at 7:30 PM and February 20, 2011 at 4:00 PM Jon Robertson, guest conductor ; Roberta Rust, piano Piano Concerto No. 5 in E-flat Major, op. 73 ( Emperor ) / Ludwig van Beethoven -- Symphony No. 6 in D Major, op. 60 / Antonín Dvořák Philharmonia No. 6 March 26, 2011 at 7:30 PM and March 27, 2011 at 4:00 PM Albert-George Schram, music director ; Amanda Hall, soprano ; Christin-Marie Hill, mezzo-soprano ; Scott Ramsey, tenor ; Wayne Shepperd, bass-baritone ; Joshua Habermann, Master Chorale of South Florida artistic director and conductor ; Master Chorale of South Florida Messa da Requiem / Giusepe Verdihttps://spiral.lynn.edu/conservatory_philharmonia/1022/thumbnail.jp

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state‐of‐the‐art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water and natural hazard management are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing