Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The world is in the grip of the COVID-19 pandemic. Public health measures that can reduce the risk of infection and death in addition to quarantines are desperately needed. This article reviews the roles of vitamin D in reducing the risk of respiratory tract infections, knowledge about the epidemiology of influenza and COVID-19, and how vitamin D supplementation might be a useful measure to reduce risk. Through several mechanisms, vitamin D can reduce risk of infections. Those mechanisms include inducing cathelicidins and defensins that can lower viral replication rates and reducing concentrations of pro-inflammatory cytokines that produce the inflammation that injures the lining of the lungs, leading to pneumonia, as well as increasing concentrations of anti-inflammatory cytokines. Several observational studies and clinical trials reported that vitamin D supplementation reduced the risk of influenza, whereas others did not. Evidence supporting the role of vitamin D in reducing risk of COVID-19 includes that the outbreak occurred in winter, a time when 25-hydroxyvitamin D (25(OH)D) concentrations are lowest; that the number of cases in the Southern Hemisphere near the end of summer are low; that vitamin D deficiency has been found to contribute to acute respiratory distress syndrome; and that case-fatality rates increase with age and with chronic disease comorbidity, both of which are associated with lower 25(OH)D concentration. To reduce the risk of infection, it is recommended that people at risk of influenza and/or COVID-19 consider taking 10,000 IU/d of vitamin D3 for a few weeks to rapidly raise 25(OH)D concentrations, followed by 5000 IU/d. The goal should be to raise 25(OH)D concentrations above 40–60 ng/mL (100–150 nmol/L). For treatment of people who become infected with COVID-19, higher vitamin D3 doses might be useful. Randomized controlled trials and large population studies should be conducted to evaluate these recommendations.

Evidence that Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths.

Our Galaxy, the Milky Way, is a benchmark for understanding disk galaxies. It is the only galaxy whose formation history can be studied using the full distribution of stars from faint dwarfs to supergiants. The oldest components provide us with unique insight into how galaxies form and evolve over billions of years. The Galaxy is a luminous (L⋆) barred spiral with a central box/peanut bulge, a dominant disk, and a diffuse stellar halo. Based on global properties, it falls in the sparsely populated “green valley” region of the galaxy color-magnitude diagram. Here we review the key integrated, structural and kinematic parameters of the Galaxy, and point to uncertainties as well as directions for future progress. Galactic studies will continue to play a fundamental role far into the future because there are measurements that can only be made in the near field and much of contemporary astrophysics depends on such observations.

The Galaxy in Context: Structural, Kinematic, and Integrated Properties

Aims. The aim of this paper is to explore and map the age and abundance structure of the stars in the nearby Galactic disk. Methods. We have conducted a high-resolution spectroscopic study of 714 F and G dwarf and subgiant stars in the Solar neighbourhood. The star sample has been kinematically selected to trace the Galactic thin and thick disks to their extremes, the metal-rich stellar halo, sub-structures in velocity space such as the Hercules stream and the Arcturus moving group, as well as stars that cannot (kinematically) be associated with either the thin disk or the thick disk. The determination of stellar parameters and elemental abundances is based on a standard analysis using equivalent widths and one-dimensional, plane-parallel model atmospheres calculated under the assumption of local thermodynamical equilibrium (LTE). The spectra have high resolution (R = 40 000-110 000) and high signal-to-noise)S/V = 150-300) and were obtained with the FEROS spectrograph on the ESO 1.5 in and 2.2 in telescopes, the SOFIN and PIES spectrographs on the Nordic Optical Telescope, the LIVES spectrograph on the E50 Very Large Telescope, the HARPS spectrograph on the ESO 3.6 m telescope, and the MIKE spectrograph on the Magellan Clay telescope. The abundances from individual Fe I lines were were corrected for non-LTE effects in every step of the analysis. Results. We present stellar parameters, stellar ages, kinematical parameters, orbital parameters, and detailed elemental abundances for 0, Na, Mg, Al, Si, Ca, Ti, Cr, Fe, Ni, Zn, Y. and Ba for 714 nearby 12 and G dwarf stars. Our data show that there is an old and a-enhanced disk population, and a younger and less a-enhanced disk population. While they overlap greatly in metallicity between 0.7 < [Fe/HI] less than or similar to +0.1, they show a bimodal distribution in [alpha/Fe]. This bimodality becomes even clearer if stars where stellar parameters and abundances show larger uncertainties (T-eff less than or similar to 5400 K) are discarded, showing that it is important to constrain the data set to a narrow range in the stellar parameters if small differences between stellar populations are to be revealed. In addition, we find that the a-enhanced population has orbital parameters placing the stellar birthplaces in the inner Galactic disk while the loss-alpha stars mainly come from the outer Galactic disk, fully consistent with the recent claims of a short scale-length for the alpha-enhanced Galactic thick disk. We have also investigated the properties of the Hercules stream and the Arcturus moving group and find that neither of them presents chemical or age signatures that could suggest that they are disrupted clusters or extragalactic accretion remnants from ancient merger events. Instead, they are most likely dynamical features originating within the Galaxy. We have also discovered that a standard 1D. LTE analysis, utilising ionisation and excitation balance of Fe I and Fen lines produces a flat lower main sequence. As the exact cause for this effect is unclear we chose to apply an empirical correction. Turn-off stars and more evolved stars appear to be unaffected. (Less)

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Exploring the Milky Way stellar disk - A detailed elemental abundance study of 714 F and G dwarf stars in the solar neighbourhood

This work aims to present our current best physical understanding of common-envelope evolution (CEE). We highlight areas of consensus and disagreement, and stress ideas which should point the way forward for progress in this important but long-standing and largely unconquered problem. Unusually for CEE-related work, we mostly try to avoid relying on results from population synthesis or observations, in order to avoid potentially being misled by previous misunderstandings. As far as possible we debate all the relevant issues starting from physics alone, all the way from the evolution of the binary system immediately before CEE begins to the processes which might occur just after the ejection of the envelope. In particular, we include extensive discussion about the energy sources and sinks operating in CEE, and hence examine the foundations of the standard energy formalism. Special attention is also given to comparing the results of hydrodynamic simulations from different groups and to discussing the potential effect of initial conditions on the differences in the outcomes. We compare current numerical techniques for the problem of CEE and also whether more appropriate tools could and should be produced (including new formulations of computational hydrodynamics, and attempts to include 3D processes within 1D codes). Finally we explore new ways to link CEE with observations. We compare previous simulations of CEE to the recent outburst from V1309 Sco, and discuss to what extent post-common-envelope binaries and nebulae can provide information, e.g. from binary eccentricities, which is not currently being fully exploited.

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Common envelope evolution: where we stand and how we can move forward

Aims. We reanalyze FEROS observations of the star HIP 11952 to reassess the existence of the proposed planetary system.Methods. The radial velocity of the spectra were measured by cross-correlating the observed spectrum with a synthetic template. We also analyzed a large dataset of FEROS and HARPS archival data of the calibrator HD 10700 spanning over more than five years. We compared the barycentric velocities computed by the FEROS and HARPS pipelines.Results. The barycentric correction of the FEROS-DRS pipeline was found to be inaccurate and to introduce an artificial one-year period with a semi-amplitude of 62 m s-1 . Thus the reanalysis of the FEROS data does not support the existence of planets around HIP 11952.

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Reanalysis of the FEROS observations of HIP 11952

Over the past decade, significant data have shown that obese men experience a survival detriment after treatment for prostate cancer. While methods to combat obesity are of utmost importance for the prostate cancer patient, newer data reveal the overall metabolic improvements that accompany increased activity levels and intense exercise beyond weight loss. Along these lines, a plethora of data have shown improvement in prostate cancer-specific outcomes after treatment accompanied with these activity levels. This review discusses the metabolic mechanisms in which increased activity levels and exercise can help improve both outcomes for men treated for prostate cancer while lowering the side effects of treatment.

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Fortifying the Treatment of Prostate Cancer with Physical Activity.

Breast cancer (BC) patients often ask for a healthy diet. Here, we investigated a healthy standard diet (SD), a low carb diet (LCD), and a ketogenic diet (KD) for BC patients during the rehabilitation phase. KOLIBRI was an open-label non-randomized one-site nutritional intervention trial, combining inpatient and outpatient phases for 20 weeks. Female BC patients (n = 152; mean age 51.7 years) could select their diet. Data collected were: Quality of life (QoL), spiroergometry, body composition, and blood parameters. In total 30, 92, and 30 patients started the KD, LCD, and SD, respectively. Of those, 20, 76, and 25 completed the final examination. Patients rated all diets as feasible in daily life. All groups enhanced QoL, body composition, and physical performance. LCD participants showed the most impressive improvement in QoL aspects. KD participants finished with a very good physical performance and muscle/fat ratio. Despite increased cholesterol levels, KD patients had the best triglyceride/high-density lipoprotein (HDL) ratio and homeostatic model assessment of insulin resistance index (HOMA-IR). Most metabolic parameters significantly improved in the LCD group. SD participants ended with remarkably low cholesterol levels but did not improve triglyceride/HDL or HOMA-IR. In conclusion, both well-defined KDs and LCDs are safe and beneficial for BC patients and can be recommended during the rehabilitation phase.

Low Carb and Ketogenic Diets Increase Quality of Life, Physical Performance, Body Composition, and Metabolic Health of Women with Breast Cancer.

Ketogenic therapy in the form of ketogenic diets or calorie restriction has been proposed as a metabolic treatment of high grade glioma (HGG) brain tumors based on mechanistic reasoning obtained mainly from animal experiments. Given the paucity of clinical studies of this relatively new approach, our goal is to extrapolate evidence from the greater number of animal studies and synthesize it with the available human data in order to estimate the expected effects of ketogenic therapy on survival in HGG patients. At the same time we are using this analysis as an example for demonstrating how Bayesianism can be applied in the spirit of a circular view of evidence. A Bayesian hierarchical model was developed. Data from three human cohort studies and 17 animal experiments were included to estimate the effects of four ketogenic interventions (calorie restriction/ketogenic diets as monotherapy/combination therapy) on the restricted mean survival time ratio in humans using various assumptions for the relationships between humans, rats and mice. The impact of different biological assumptions about the relevance of animal data for humans as well as external information based on mechanistic reasoning or case studies was evaluated by specifying appropriate priors. We provide statistical and philosophical arguments for why our approach is an improvement over existing (frequentist) methods for evidence synthesis as it is able to utilize evidence from a variety of sources. Depending on the prior assumptions, a 30–70% restricted mean survival time prolongation in HGG patients was predicted by the models. The highest probability of a benefit (> 90%) for all four ketogenic interventions was obtained when adopting an enthusiastic prior based on previous case reports together with assuming synergism between ketogenic therapies with other forms of treatment. Combinations with other treatments were generally found more effective than ketogenic monotherapy. Combining evidence from both human and animal studies is statistically possible using a Bayesian approach. We found an overall survival-prolonging effect of ketogenic therapy in HGG patients. Our approach is best compatible with a circular instead of hierarchical view of evidence and easy to update once more data become available.

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Rainer J. Klement

Papers

Reanalysis of the FEROS observations of HIP 11952

Fortifying the Treatment of Prostate Cancer with Physical Activity.

Low Carb and Ketogenic Diets Increase Quality of Life, Physical Performance, Body Composition, and Metabolic Health of Women with Breast Cancer.

Application of Bayesian evidence synthesis to modelling the effect of ketogenic therapy on survival of high grade glioma patients.

Kinematics of Stellar Populations with RAVE data