Multi-Institutional Dosimetric Evaluation of Modern Day Stereotactic Radiosurgery (SRS) Treatment Options for Multiple Brain Metastases.

Purpose/Objectives: There are several popular treatment options currently available for stereotactic radiosurgery (SRS) of multiple brain metastases: 60Co sources and cone collimators around a spherical geometry (GammaKnife), multi-aperture dynamic conformal arcs on a linac (BrainLab Elements™ v1.5), and volumetric arc therapy on a linac (VMAT) calculated with either the conventional optimizer or with the Varian HyperArc™ solution. This study aimed to dosimetrically compare and evaluate the differences among these treatment options in terms of dose conformity to the tumor as well as dose sparing to the surrounding normal tissues. Methods and Materials: Sixteen patients and a total of 112 metastases were analyzed. Five plans were generated per patient: GammaKnife, Elements, HyperArc-VMAT, and two Manual-VMAT plans to evaluate different treatment planning styles. Manual-VMAT plans were generated by different institutions according to their own clinical planning standards. The following dosimetric parameters were extracted: RTOG and Paddick conformity indices, gradient index, total volume of brain receiving 12Gy, 6Gy, and 3Gy, and maximum doses to surrounding organs. The Wilcoxon signed rank test was applied to evaluate statistically significant differences (p \u3c 0.05). Results: For targets ≤ 1 cm, GammaKnife, HyperArc-VMAT and both Manual-VMAT plans achieved comparable conformity indices, all superior to Elements. However, GammaKnife resulted in the lowest gradient indices at these target sizes. HyperArc-VMAT performed similarly to GammaKnife for V12Gy parameters. For targets ≥ 1 cm, HyperArc-VMAT and Manual-VMAT plans resulted in superior conformity vs. GammaKnife and Elements. All SRS plans achieved clinically acceptable organs-at-risk dose constraints. Beam-on times were significantly longer for GammaKnife. Manual-VMATA and Elements resulted in shorter delivery times relative to Manual-VMATB and HyperArc-VMAT. Conclusion: The study revealed that Manual-VMAT and HyperArc-VMAT are capable of achieving similar low dose brain spillage and conformity as GammaKnife, while significantly minimizing beam-on time. For targets smaller than 1 cm in diameter, GammaKnife still resulted in superior gradient indices. The quality of the two sets of Manual-VMAT plans varied greatly based on planner and optimization constraint settings, whereas HyperArc-VMAT performed dosimetrically superior to the two Manual-VMAT plans

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Modification of right hepatectomy results in improvement outcome: a retrospective comparative study

AbstractObjectiveTo evaluate any change in the operative and survival outcomes in patients undergoing a right hepatectomy after adoption of the no-clamp technique using a radiofrequency dissecting sealer (TissueLink™) in liver resection.MethodsIn all, 58 consecutive patients who underwent a right hepatectomy from July 2003 to December 2007 (Group 1) were compared with 66 consecutive patients who underwent a right hepatectomy from January 1999 to June 2003 (Group 2). In group 1, a liver transection was performed with a cavitron ultrasonic surgical aspirator (CUSA) and TissueLink™ without hilar clamping whereas in group 2, a liver transection was performed with CUSA and diathermy with routine continuous hilar clamping.ResultsFor the operative outcomes, there was significantly less blood loss (median 450 vs. 900ml, P < 0.001) in group 1. The complication rate was also significantly lower in group 1 (22.4% vs. 47.0%, P= 0.004). In subgroup analysis for patients with hepatocellular carcinoma (HCC), the overall survival rate was significantly better in group 1; 1-, 3- and 5-year survival rates were 78%, 72% and 57% in group 1 vs. 72%, 44% and 39% in group 2, respectively (P= 0.048).ConclusionsWhen compared with the retrospective cohort, a right hepatectomy utilizing TissueLink™ without hilar clamping was feasible with potential benefits in surgical outcomes

AusTraits: a curated plant trait database for the Australian flora

INTRODUCTION AusTraits is a transformative database, containing measurements on the traits of Australia’s plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from &gt; 250 distinct sources, describing &gt; 400 plant traits and &gt; 26,000 taxa. To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project’s GitHub repository, http://traitecoevo.github.io/austraits.build/. Further information on the project is available in the associated publication and at the project website austraits.org. Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 CONTRIBUTORS The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from &gt; 300 contributors from over &gt; 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University. We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment, Land, Water and Planning, Victoria. AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their “Transformative data collections” (https://doi.org/10.47486/TD044) and “Data Partnerships” (https://doi.org/10.47486/DP720) programs; fellowship grants from Australian Research Council to Falster (FT160100113), Gallagher (DE170100208) and Wright (FT100100910), a grant from Macquarie University to Gallagher. The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS). ACCESSING AND USE OF DATA The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community. A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors: Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 In addition, we encourage users you to cite the original data sources, wherever possible. Note that under the license data may be redistributed, provided the attribution is maintained. The downloads below provide the data in two formats: austraits-3.0.2.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python. austraits-3.0.2.rds: data as compressed R object. Suitable for users of R (see below). Both objects contain all the data and relevant meta-data. AUSTRAITS R PACKAGE For R users, access and manipulation of data is assisted with the austraits R package. The package can both download data and provides examples and functions for running queries. STRUCTURE OF AUSTRAITS The compiled AusTraits database has the following main components: austraits ├── traits ├── sites ├── contexts ├── methods ├── excluded_data ├── taxanomic_updates ├── taxa ├── definitions ├── contributors ├── sources └── build_info These elements include all the data and contextual information submitted with each contributed datasets. A schema and definitions for the database are given in the file/component definitions, available within the download. The file dictionary.html provides the same information in textual format. Full details on each of these components and columns are contained within the definition. Similar information is available at http://traitecoevo.github.io/austraits.build/articles/Trait_definitions.html and http://traitecoevo.github.io/austraits.build/articles/austraits_database_structure.html. CONTRIBUTING We envision AusTraits as an on-going collaborative community resource that: Increases our collective understanding the Australian flora; and Facilitates accumulation and sharing of trait data; Builds a sense of community among contributors and users; and Aspires to fully transparent and reproducible research of the highest standard. As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at traitecoevo/austraits.build. Here are some of the ways you can contribute: Reporting Errors: If you notice a possible error in AusTraits, please post an issue on GitHub. Refining documentation: We welcome additions and edits that make using the existing data or adding new data easier for the community. Contributing new data: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at http://traitecoevo.github.io/austraits.build/articles/contributing_data.html

AusTraits, a curated plant trait database for the Australian flora

International audienceWe introduce the austraits database-a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual-and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist"X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io.</p

Reproducibility of fluorescent expression from engineered biological constructs in E. coli

We present results of the first large-scale interlaboratory study carried out in synthetic biology, as part of the 2014 and 2015 International Genetically Engineered Machine (iGEM) competitions. Participants at 88 institutions around the world measured fluorescence from three engineered constitutive constructs in E. coli. Few participants were able to measure absolute fluorescence, so data was analyzed in terms of ratios. Precision was strongly related to fluorescent strength, ranging from 1.54-fold standard deviation for the ratio between strong promoters to 5.75-fold for the ratio between the strongest and weakest promoter, and while host strain did not affect expression ratios, choice of instrument did. This result shows that high quantitative precision and reproducibility of results is possible, while at the same time indicating areas needing improved laboratory practices.Peer reviewe