Compositions and mineralogies of unmelted polar micrometeorites: Similarities and differeces with IDPs and meteorites

Micrometeorites from 60 to 1200μm in diameter constitute ∿80% of the continuous, planet-wide accretion of meteoritic material onto the Earth. Although interplanetary dust particles (IDPs)1cm are sampled as meteorites, the 60 to 1200μm size range has, thus far, only been sampled by the recovery of meteoritic particles from polar ices and lakes and from the sea. Polar micrometeorites have major element abundances consistent with chondritic meteorites and IDPs, except that Ni and S are strongly depleted. Synchrotron X-Ray Fluorescence trace element abundance measurements of twelve Antarctic and four Greenland micrometeorites from 50 to 150μm show enrichments by 2 to 5 times over CI abundances for Cu, Zn, and Ga, however Ni, Ge, and Se are depleted relative to CI. This abundance pattern differs significantly from both chondritic meteorites and IDPs, including IDPs heated on atmospheric entry. There is no correlation of element enrichment or depletion with nebula condensation temperature. Olivine, pyroxene, magnetite, chromite, spinel, and glass, which commonly occur in IDPs and meteorites, were identified in these particles by analytical transmission electron microscopy. However, layer-lattice silicates, also common in IDPs, CI and CM carbonaceous meteorites, were not identified in any of these polar micrometeorites. Textural relationships and mineral compositions suggest these polar micrometeorites were strongly heated during atmospheric entry. The chemical compositions and mineralogies of the polar micrometeorites show similarities to both the IDPs and the chondritic meteorites, but they are not identical to either type of material

SMART (Stochastic Model Acquisition with ReinforcemenT) learning agents: A preliminary report

We present a framework for building agents that learn using SMART, a system that combines stochastic model acquisition with reinforcement learning to enable an agent to model its environment through experience and subsequently form action selection policies using the acquired model. We extend an existing algorithm for automatic creation of stochastic strips operators [9] as a preliminary method of environment modelling. We then define the process of generation of future states using these operators and an initial state and finally show the process by which the agent can use the generated states to form a policy with a standard reinforcement learning algorithm. The potential of SMART is exemplified using the well-known predator prey scenario. Results of applying SMART to this environment and directions for future work are discussed

Chapter 11 - Near-term climate change: Projections and predictability

This chapter assesses the scientific literature describing expectations for near-term climate (present through mid-century). Unless otherwise stated, "near-term" change and the projected changes below are for the period 2016-2035 relative to the reference period 1986-2005. Atmospheric composition (apart from CO2; see Chapter 12) and air quality projections through to 2100 are also assessed

Comprehensive evaluation of deconvolution methods for human brain gene expression

Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing.Gavin J. Sutton, Daniel Poppe, Rebecca K. Simmons, Kieran Walsh, Urwah Nawaz, Ryan Lister, Johann A. Gagnon-Bartsch, Irina Voineag

Compression and intelligence: social environments and communication

Compression has been advocated as one of the principles which pervades inductive inference and prediction - and, from there, it has also been recurrent in definitions and tests of intelligence. However, this connection is less explicit in new approaches to intelligence. In this paper, we advocate that the notion of compression can appear again in definitions and tests of intelligence through the concepts of `mind-reading¿ and `communication¿ in the context of multi-agent systems and social environments. Our main position is that two-part Minimum Message Length (MML) compression is not only more natural and effective for agents with limited resources, but it is also much more appropriate for agents in (co-operative) social environments than one-part compression schemes - particularly those using a posterior-weighted mixture of all available models following Solomonoff¿s theory of prediction. We think that the realisation of these differences is important to avoid a naive view of `intelligence as compression¿ in favour of a better understanding of how, why and where (one-part or two-part, lossless or lossy) compression is needed.We thank the anonymous reviewers for their helpful comments, and we thank Kurt Kleiner for some challenging and ultimately very helpful questions in the broad area of this work. We also acknowledge the funding from the Spanish MEC and MICINN for projects TIN2009-06078-E/TIN, Consolider-Ingenio CSD2007-00022 and TIN2010-21062-C02, and Generalitat Valenciana for Prometeo/2008/051.Dowe, DL.; Hernández Orallo, J.; Das, PK. (2011). Compression and intelligence: social environments and communication. En Artificial General Intelligence. Springer Verlag (Germany). 6830:204-211. https://doi.org/10.1007/978-3-642-22887-2_21S2042116830Chaitin, G.J.: Godel’s theorem and information. 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Conf. on Computational Intelligence & multimedia applications (ICCIMA 1998), Gippsland, Australia, pp. 101–106 (February 1998)Hernández-Orallo, J.: Beyond the Turing Test. J. Logic, Language & Information 9(4), 447–466 (2000)Hernández-Orallo, J.: Constructive reinforcement learning. International Journal of Intelligent Systems 15(3), 241–264 (2000)Hernández-Orallo, J.: On the computational measurement of intelligence factors. In: Meystel, A. (ed.) Performance metrics for intelligent systems workshop, pp. 1–8. National Institute of Standards and Technology, Gaithersburg, MD, U.S.A (2000)Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010)Hernández-Orallo, J., Minaya-Collado, N.: A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In: Proc. Intl Symposium of Engineering of Intelligent Systems (EIS 1998), pp. 146–163. ICSC Press (1998)Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (2007)Lewis, D.K., Shelby-Richardson, J.: Scriven on human unpredictability. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition 17(5), 69–74 (1966)Oppy, G., Dowe, D.L.: The Turing Test. In: Zalta, E.N. (ed.) Stanford Encyclopedia of Philosophy, Stanford University, Stanford (2011), http://plato.stanford.edu/entries/turing-test/Salomon, D., Motta, G., Bryant, D.C.O.N.: Handbook of data compression. Springer-Verlag New York Inc., Heidelberg (2009)Sanghi, P., Dowe, D.L.: A computer program capable of passing I.Q. tests. In: 4th International Conference on Cognitive Science (and 7th Australasian Society for Cognitive Science Conference), vol. 2, pp. 570–575. Univ. of NSW, Sydney, Australia (July 2003)Sayood, K.: Introduction to data compression. Morgan Kaufmann, San Francisco (2006)Scriven, M.: An essential unpredictability in human behavior. In: Wolman, B.B., Nagel, E. (eds.) Scientific Psychology: Principles and Approaches, pp. 411–425. Basic Books (Perseus Books), New York (1965)Searle, J.R.: Minds, brains and programs. Behavioural and Brain Sciences 3, 417–457 (1980)Solomonoff, R.J.: A formal theory of inductive inference. Part I. Information and control 7(1), 1–22 (1964)Sutton, R.S.: Generalization in reinforcement learning: Successful examples using sparse coarse coding. Advances in neural information processing systems, 1038–1044 (1996)Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. The MIT Press, Cambridge (1998)Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950)Veness, J., Ng, K.S., Hutter, M., Silver, D.: A Monte Carlo AIXI Approximation. 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What is the effect of intergenerational activities on the wellbeing and mental health of older people?: A systematic review

Background Opportunities for social connection between generations have diminished over the last few decades around the world as a result of changes in the way that we live and work. The COVID-19 pandemic has exacerbated loneliness for many with young and old being kept apart for safety. The Public Health England prevention concordat for better mental health (Office for Health Improvement and Disparities) aims to bring a prevention-focused approach to improving public mental health. The concordat promotes evidence-based planning and commissioning to increase the impact on reducing health inequalities using sustainable and cost-effective interventions that impact on the wider determinants of mental health and wellbeing for children and young people and older people. Intergenerational activities could provide an opportunity to support both populations. In 2023, we produced an evidence and gap map to illustrate the amount and variety of research on intergenerational interventions and the gaps in research that still exist in this area. The review conducted here is based on the evidence in that map. Objectives This systematic review examines the impact of intergenerational interventions on the wellbeing and mental health of older people and identifies areas for future research as well as key messages for service commissioners. Search Methods We searched an evidence and gap map published in 2022 (comprehensive searches conducted July 2021 and updated June 2023) to identify randomised controlled trials of intergenerational interventions that report mental health and wellbeing outcomes for older people. Selection Criteria Randomised controlled trials of intergenerational interventions that involved unrelated younger and older people with at least one skipped generation between them and reported mental health or wellbeing outcomes for older people were included in this review. Data Collection and Analysis We used standard methodological procedures expected by The Campbell Collaboration. We conducted data extraction and Cochrane risk of bias assessments in EPPI reviewer. Where data allowed meta-analyses were conducted in STATA. Main Results This review includes 14 trials from six different countries. The trials had some important methodological weaknesses. Interventions were mainly delivered in-person and often in groups. They included visiting programmes, school volunteering programmes, music-based interventions and task-oriented interventions such as activities set in a multigenerational park, reminiscing activities, aggression management programmes, learning a language, making local environmental changes and in-school project work. Intergenerational interventions showed a small positive trend towards improving self-esteem (effect size [ES]: 0.33, 95% confidence interval [CI]: −0.35, 1.01) and depression (ES: 0.19, 95% CI: −0.23, 0.60) for older people participating. However, due to the small study sizes and low number of studies available, we cannot be confident about any effects. The results for other mental health and wellbeing outcomes are reported but due to little overlap in similar assessments across the studies, we could not combine them to assess the strength of evidence. There were no data about social isolation, spiritual health or sense of community. There are no long-term studies and no data on equity. We still know very little about what works and how or why. Whilst some interventions do use theories and logic to inform their development others do not. More exploration of this is needed. Authors’ Conclusions Commissioners and intervention developers should ensure interventions provide sufficient theoretical evidence for the logic behind the proposed intervention and should improve their consideration of equity within the interventions Research on intergenerational interventions need more consistent and agreed measures for reporting outcomes including community outcomes (core outcome sets). More understanding is needed on how best to measure ‘community’ outcomes. Research on intergenerational interventions should measure outcomes for BOTH the older and younger population engaged in the intervention—these may or may not be the same outcomes reflected in both populations. Further research is needed on the long-term impact of interventions on outcomes (whether participants need to keep being involved in an ‘intervention’ to continue to benefit) and sustainability of interventions beyond the initial funding of the research project. Supporting this our stakeholders highlighted that interventions that are initiated for research and then end (usually within a year) are not helpful

PROTOCOL: What is the effect of intergenerational activities on the wellbeing and mental health of older people?

This is the protocol for a Campbell systematic review. The objectives are as follows: This systematic review will examine the impact of intergenerational interventions on the mental health and wellbeing of older people and will identify areas for future research as well as key messages for service commissioners

Choice of tracers for the evaluation of spray deposits

Tracer substances, used to evaluate spraying effectiveness, ordinarily modify the surface tension of aqueous solutions. This study aimed to establish a method of using tracers to evaluate distribution and amount of spray deposits, adjusted to the surface tension of the spraying solution. The following products were tested: 0.15% Brilliant Blue, 0.15% Saturn Yellow in 0.015% Vixilperse lignosulfonate, and 0.005% sodium fluorescein, and mixtures of Brilliant Blue plus Saturn Yellow and Brilliant Blue plus sodium fluorescein at the same concentrations. Solutions were deposited on citrus leaves and stability was determined by measuring fluorescence and optical density of solutions without drying, dried in the dark and exposed to sunlight for 2, 4 and 8 h. These values were compared to those obtained directly in water. The static surface tension of the tracer solution was determined by weighing droplets formed during a period of 20 to 40 seconds. The Brilliant Blue and Saturn Yellow mixture at 0.15% was stable under all conditions tested. It was not absorbed by the leaves and maintained the same surface tension as that of water, thus permitting concentration adjustment to the same levels used for agrochemical products, and allowing the development of a qualitative method based on visual evaluation of the distribution of the pigment under ultraviolet light and of a quantitative method based on the determination of the amount of the dye deposited in the same solution. Spray deposition could be evaluated at different surface tensions of the spraying solution, simulating the effect of agrochemical formulations

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P = 0.04 for lesions ≤ 2 cm; P = 0.02 for lesions >2 to≤ 5 cm) as with the entire data set (P-value = 0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine

First upper limits from LIGO on gravitational wave bursts

We report on a search for gravitational wave bursts using data from the first science run of the LIGO detectors. Our search focuses on bursts with durations ranging from 4 ms to 100 ms, and with significant power in the LIGO sensitivity band of 150 to 3000 Hz. We bound the rate for such detected bursts at less than 1.6 events per day at 90% confidence level. This result is interpreted in terms of the detection efficiency for ad hoc waveforms (Gaussians and sine-Gaussians) as a function of their root-sum-square strain h_{rss}; typical sensitivities lie in the range h_{rss} ~ 10^{-19} - 10^{-17} strain/rtHz, depending on waveform. We discuss improvements in the search method that will be applied to future science data from LIGO and other gravitational wave detectors.Comment: 21 pages, 15 figures, accepted by Phys Rev D. Fixed a few small typos and updated a few reference