Does Logic Help Us Beat Monty Hall?

The classical Monty Hall problem entails that a hypothetical game show contestant be presented three doors and told that behind one door is a car and behind the other two are far less appealing prizes, like goats. The contestant then picks a door, and the host (Monty) is to open a different door which contains one of the bad prizes. At this point in the game, the contestant is given the option of keeping the door she chose or changing her selection to the remaining door (since one has already been opened by Monty), after which Monty opens the chosen door and the contestant wins the prize which lies behind it. Inspired by the work of Morrow, Oman and Salminen (2016, “Game Show Shenanigans: Monty Hall Meets Mathematical Logic,” Elemente der Mathematik 71(4), pp. 145-155), we consider several logic-themed variants of this problem. Among these are versions where d doors and p prizes reside behind some p of these doors, and the contestant is permitted to present Monty with q random true/false questions concerning the location of the prizes, to which Monty must respond truthfully. Our results extend those of the original paper, and involve a combination of probabilistic techniques and exhaustive computation using a computer program

The impact of a supplementary medication review and counselling service within the oncology outpatient setting

The impact on the care of breast cancer patients, of a pharmacy technician-led medication review and counselling clinic, provided in an outpatient setting, was investigated using a controlled randomised study. Compared to the controls, clinic patients showed a significantly improved level of understanding of their chemotherapy support medication (95% CI for difference in mean knowledge rating scores=2.165–2.826, P<0.001) and a significant reduction in the median number of support items required (two compared to five in the control, P<0.001). This resulted in a significant reduction in mean medication expenditure per patient (£26.70 vs £10.20, 95% CI for the mean difference in cost £6.72–£26.26, P<0.001). The clinic was also associated with significant reductions in chemotherapy delays (P<0.001) and dose reductions due to side effects (P=0.003). Other benefits from the clinic were a reduction in pharmacy dispensing time and a highly significant reduction in pharmacy time spent resolving post-clinic prescription queries (P<0.001). Taking into account the initial technician training cost, the scheme represented an annual saving to the Trust of over £15 000. The clinic serves as a model for those wishing to improve outpatient services to breast cancer patients

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

World scientists' warnings into action, local to global

‘We have kicked the can down the road once again – but we are running out of road.’ – Rachel Kyte, Dean of Fletcher School at Tufts University. We, in our capacities as scientists, economists, governance and policy specialists, are shifting from warnings to guidance for action before there is no more ‘road.’ The science is clear and irrefutable; humanity is in advanced ecological overshoot. Our over exploitation of resources exceeds ecosystems’capacity to provide them or to absorb our waste. Society has failed to meet clearly stated goals of the UN Framework Convention on Climate Change. Civilization faces an epochal crossroads, but with potentially much better, wiser outcomes if we act now. What are the concrete and transformative actions by which we can turn away from the abyss? In this paper we forcefully recommend priority actions and resource allocation to avert the worst of the climate and nature emergencies, two of the most pressing symptoms of overshoot, and lead society into a future of greater wellbeing and wisdom. Humanity has begun the social, economic, political and technological initiatives needed for this transformation. Now, massive upscaling and acceleration of these actions and collaborations are essential before irreversible tipping points are crossed in the coming decade. We still can overcome significant societal, political and economic barriers of our own making. Previously, we identified six core areas for urgent global action – energy, pollutants, nature, food systems, population stabilization and economic goals. Here we identify an indicative, systemic and time-limited framework for priority actions for policy, planning and management at multiple scales from household to global. We broadly follow the ‘Reduce-Remove-Repair’ approach to rapid action. To guide decision makers, planners, managers, and budgeters, we cite some of the many experiments, mechanisms and resources in order to facilitate rapid global adoption of effective solutions. Our biggest challenges are not technical, but social, economic, political and behavioral. To have hope of success, we must accelerate collaborative actions across scales, in different cultures and governance systems, while maintaining adequate social, economic and political stability. Effective and timely actions are still achievable on many, though not all fronts. Such change will mean the difference for billions of children and adults, hundreds of thousands of species, health of many ecosystems, and will determine our common future

Supernova / Acceleration Probe: A Satellite Experiment to Study the Nature of the Dark Energy

The Supernova / Acceleration Probe (SNAP) is a proposed space-based experiment designed to study the dark energy and alternative explanations of the acceleration of the Universe's expansion by performing a series of complementary systematics-controlled measurements. We describe a self-consistent reference mission design for building a Type Ia supernova Hubble diagram and for performing a wide-area weak gravitational lensing study. A 2-m wide-field telescope feeds a focal plane consisting of a 0.7 square-degree imager tiled with equal areas of optical CCDs and near infrared sensors, and a high-efficiency low-resolution integral field spectrograph. The SNAP mission will obtain high-signal-to-noise calibrated light-curves and spectra for several thousand supernovae at redshifts between z=0.1 and 1.7. A wide-field survey covering one thousand square degrees resolves ~100 galaxies per square arcminute. If we assume we live in a cosmological-constant-dominated Universe, the matter density, dark energy density, and flatness of space can all be measured with SNAP supernova and weak-lensing measurements to a systematics-limited accuracy of 1%. For a flat universe, the density-to-pressure ratio of dark energy can be similarly measured to 5% for the present value w0 and ~0.1 for the time variation w'. The large survey area, depth, spatial resolution, time-sampling, and nine-band optical to NIR photometry will support additional independent and/or complementary dark-energy measurement approaches as well as a broad range of auxiliary science programs. (Abridged)Comment: 40 pages, 18 figures, submitted to PASP, http://snap.lbl.go

SN 2022jli: a type Ic supernova with periodic modulation of its light curve and an unusually long rise

We present multi-wavelength photometry and spectroscopy of SN 2022jli, an unprecedented Type Ic supernova discovered in the galaxy NGC 157 at a distance of $\approx$ 23 Mpc. The multi-band light curves reveal many remarkable characteristics. Peaking at a magnitude of $g=15.11\pm0.02$, the high-cadence photometry reveals 12.5$\pm0.2\$day periodic undulations superimposed on the 200 day supernova decline. This periodicity is observed in the light curves from nine separate filter and instrument configurations with peak-to-peak amplitudes of $\simeq$ 0.1 mag. This is the first time that repeated periodic oscillations, over many cycles, have been detected in a supernova light curve. SN 2022jli also displays an extreme early excess which fades over $\approx$ 25 days followed by a rise to a peak luminosity of $L_{\rm opt} = 10^{42.1}$ erg s$^{-1}$. Although the exact explosion epoch is not constrained by data, the time from explosion to maximum light is $\gtrsim$ 59 days. The luminosity can be explained by a large ejecta mass ($M_{\rm ej}\approx12\pm6$M$_{\odot}$) powered by $^{56}$Ni but we find difficulty in quantitatively modelling the early excess with circumstellar interaction and cooling. Collision between the supernova ejecta and a binary companion is a possible source of this emission. We discuss the origin of the periodic variability in the light curve, including interaction of the SN ejecta with nested shells of circumstellar matter and neutron stars colliding with binary companions.Comment: Accepted in ApJ

AT2022aedm and a new class of luminous, fast-cooling transients in elliptical galaxies

We present the discovery and extensive follow-up of a remarkable fast-evolving optical transient, AT2022aedm, detected by the Asteroid Terrestrial impact Last Alert Survey (ATLAS). AT2022aedm exhibited a rise time of $9\pm1$ days in the ATLAS $o$-band, reaching a luminous peak with $M_g\approx-22$ mag. It faded by 2 magnitudes in $g$-band during the next 15 days. These timescales are consistent with other rapidly evolving transients, though the luminosity is extreme. Most surprisingly, the host galaxy is a massive elliptical with negligible current star formation. X-ray and radio observations rule out a relativistic AT2018cow-like explosion. A spectrum in the first few days after explosion showed short-lived He II emission resembling young core-collapse supernovae, but obvious broad supernova features never developed; later spectra showed only a fast-cooling continuum and narrow, blue-shifted absorption lines, possibly arising in a wind with $v\approx2700$ km s$^{-1}$. We identify two further transients in the literature (Dougie in particular, as well as AT2020bot) that share similarities in their luminosities, timescales, colour evolution and largely featureless spectra, and propose that these may constitute a new class of transients: luminous fast-coolers (LFCs). All three events occurred in passive galaxies at offsets of $\sim4-10$ kpc from the nucleus, posing a challenge for progenitor models involving massive stars or massive black holes. The light curves and spectra appear to be consistent with shock breakout emission, though usually this mechanism is associated with core-collapse supernovae. The encounter of a star with a stellar mass black hole may provide a promising alternative explanation.Comment: Accepted in ApJ

Who Shares? Who Doesn't? Factors Associated with Openly Archiving Raw Research Data

Many initiatives encourage investigators to share their raw datasets in hopes of increasing research efficiency and quality. Despite these investments of time and money, we do not have a firm grasp of who openly shares raw research data, who doesn't, and which initiatives are correlated with high rates of data sharing. In this analysis I use bibliometric methods to identify patterns in the frequency with which investigators openly archive their raw gene expression microarray datasets after study publication

SN 2022jli: A Type Ic Supernova with periodic modulation of its light curve and an unusually long rise

We present multiwavelength photometry and spectroscopy of SN 2022jli, an unprecedented Type Ic supernova discovered in the galaxy NGC 157 at a distance of ≈ 23 Mpc. The multiband light curves reveal many remarkable characteristics. Peaking at a magnitude of g = 15.11 ± 0.02, the high-cadence photometry reveals periodic undulations of 12.5 ± 0.2 days superimposed on the 200-day supernova decline. This periodicity is observed in the light curves from nine separate filter and instrument configurations with peak-to-peak amplitudes of ≃ 0.1 mag. This is the first time that repeated periodic oscillations, over many cycles, have been detected in a supernova light curve. SN 2022jli also displays an extreme early excess that fades over ≈25 days, followed by a rise to a peak luminosity of L opt = 1042.1 erg s−1. Although the exact explosion epoch is not constrained by data, the time from explosion to maximum light is ≳ 59 days. The luminosity can be explained by a large ejecta mass (M ej ≈ 12 ± 6 M ⊙) powered by 56Ni, but we find it difficult to quantitatively model the early excess with circumstellar interaction and cooling. Collision between the supernova ejecta and a binary companion is a possible source of this emission. We discuss the origin of the periodic variability in the light curve, including interaction of the SN ejecta with nested shells of circumstellar matter and neutron stars colliding with binary companions