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

Aim To clarify the high variability in COVID-19-related deaths during the first wave of the pandemic, we conducted a modeling study using publicly available data. Materials and methods We used 13 population- and country-specific variables to predict the number of population-standardized COVID-19-related deaths in 43 European countries using generalized linear models: the test-standardized number of SARS-CoV-2-cases, population density, life expectancy, severity of governmental responses, influenza-vaccination coverage in the elderly, vitamin D status, smoking and diabetes prevalence, cardiovascular disease death rate, number of hospital beds, gross domestic product, human development index and percentage of people older than 65 years. Results We found that test-standardized number of SARS-CoV-2-cases and flu vaccination coverage in the elderly were the most important predictors, together with vitamin D status, gross domestic product, population density and government response severity explaining roughly two-thirds of the variation in COVID-19 related deaths. The latter variable was positively, but only weakly associated with the outcome, i.e., deaths were higher in countries with more severe government response. Higher flu vaccination coverage and low vitamin D status were associated with more COVID-19 related deaths. Most other predictors appeared to be negligible. Conclusion Adequate vitamin D levels are important, while flu-vaccination in the elderly and stronger government response were putative aggravating factors of COVID-19 related deaths. These results may inform protection strategies against future infectious disease outbreaks.

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Identifying factors associated with COVID-19 related deaths during the first wave of the pandemic in Europe


 Background: Breast Cancer (BC) patients often ask for a healthy diet. Here, we compared three different diets which could support BC patients during the rehabilitation process: a healthy standard diet (SD), a low carb diet (LCD) and a ketogenic diet (KD).Patients and Methods:KOLIBRI was a one-site nutritional intervention trial, combining inpatient and outhouse 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, blood biochemistry. 30, 92 and 30 patients started the KD, LCD and SD, respectively. Of those, 20, 72 and 25 completed the final examination.Results:Patients rated all diets as feasible in daily life. No adverse effects occurred in any diet group. KD offered the highest amount of energy and protein. All groups improved in QoL, body composition and physical performance. KD was superior in improving fatigue, insomnia and physical functioning. KD participants finished with the best physical performance and the highest muscle/fat ratio. Despite their increased cholesterol, KD patients had the best triglyceride/HDL ratio and 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.Conclusions:A well-defined KD and LCD are safe and beneficial for BC. Both diets could thus be recommended for patients wanting to self-support their rehabilitation process.Trial registration IDNCT02092753 Protocol Registration date:02/26/2014Ketogenic Or LOGI Diet In a Breast Cancer Rehabilitation Intervention (KOLIBRI)URL: https://clinicaltrials.gov/ct2/show/NCT02092753?cond=KOLIBRI&draw=2&rank=1

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Low Carb (LCD) and Ketogenic (KD) Diets Increase Quality of Life, Physical Performance, Body Composition and Metabolic Health of Women with Breast Cancer Better Than a Standard Diet (SD)

Patients with head and neck cancer (HNC) are at risk of malnutrition, especially during radiochemotherapy. We aimed to study the impact of a ketogenic diet (KD) versus an unspecified standard diet (SD) on body composition and survival in HNC patients undergoing radio(chemo)therapy. As part of a controlled clinical trial, non-metastasized HNC patients were enrolled into either a KD (N = 11) or an SD (N = 21) group between May 2015 and May 2021. Body composition was measured weekly by bioimpedance analysis and analyzed using linear mixed effects models. Overall and progression-free survival was assessed during regular follow-up. A total of 7 KD and 21 SD patients completed the study and were eligible for comparative analysis. Chemotherapy was significantly associated with declines in all body composition parameters, while the KD had opposing, yet nonsignificant effects. In patients receiving chemotherapy, average weekly reductions of body mass (BM) and skeletal muscle mass (SMM) were 0.9 kg and 0.31 kg in the KD group versus 1.2 kg and 0.57 kg in the SD group, respectively. Patients in the KD group receiving no chemotherapy achieved an average increase of 0.04 kg BM and 0.12 kg SMM per week. After a median follow-up of 42 months (range 6.7–78 months) there were no significant differences in progression-free or overall survival between the groups. The KD may partially counteract the detrimental effects of radiochemotherapy on body composition in HNC patients. This should encourage further research into KDs in frail cancer patient populations and motivate their implementation as complementary therapy for selected patients. 

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Impact of a ketogenic diet intervention during radiotherapy on body composition: V. Final results of the KETOCOMP study for head and neck cancer patients

Hintergrund und Fragestellung: Fasten ist möglicherweise eine e ektive Unterstützung, um Nebenwirkungen der Chemotherapie (CHT) zu verringern ohne deren E ektivität gegenüber Tumorzellen zu beeinträchtigen – oder diese sogar noch zu steigern. In der Arbeit von Tanya Dor und Kollegen wurde nun über klinische Ergebnisse bei Krebspatienten berichtet, welche vor ihrer CHT eine Fastenperiode durchlaufen hatten [1].

Fasten als Nebenwirkungsmanagement

https://www.zora.uzh.ch/id/eprint/208527/1/Stiefel-2021-33659717.pdf

Rainer J. Klement

Papers

Identifying factors associated with COVID-19 related deaths during the first wave of the pandemic in Europe

Low Carb (LCD) and Ketogenic (KD) Diets Increase Quality of Life, Physical Performance, Body Composition and Metabolic Health of Women with Breast Cancer Better Than a Standard Diet (SD)

Impact of a ketogenic diet intervention during radiotherapy on body composition: V. Final results of the KETOCOMP study for head and neck cancer patients

Fasten als Nebenwirkungsmanagement

High-dose re-irradiation of intracranial lesions - Efficacy and safety including dosimetric analysis based on accumulated EQD2Gy dose EQD calculation.