抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

/pdf/climate-change-2007-the-physical-science-basis-1x93ttz9jt.pdf

Climate Change 2007: The Physical Science Basis.

Whether the characteristics of tropical cyclones have altered, or will alter, in a changing climate has been subject of considerable debate. An overview of recent research indicates that greenhouse warming will cause stronger storms, on average, but a decrease in the frequency of tropical cyclones. Whether the characteristics of tropical cyclones have changed or will change in a warming climate — and if so, how — has been the subject of considerable investigation, often with conflicting results. Large amplitude fluctuations in the frequency and intensity of tropical cyclones greatly complicate both the detection of long-term trends and their attribution to rising levels of atmospheric greenhouse gases. Trend detection is further impeded by substantial limitations in the availability and quality of global historical records of tropical cyclones. Therefore, it remains uncertain whether past changes in tropical cyclone activity have exceeded the variability expected from natural causes. However, future projections based on theory and high-resolution dynamical models consistently indicate that greenhouse warming will cause the globally averaged intensity of tropical cyclones to shift towards stronger storms, with intensity increases of 2–11% by 2100. Existing modelling studies also consistently project decreases in the globally averaged frequency of tropical cyclones, by 6–34%. Balanced against this, higher resolution modelling studies typically project substantial increases in the frequency of the most intense cyclones, and increases of the order of 20% in the precipitation rate within 100 km of the storm centre. For all cyclone parameters, projected changes for individual basins show large variations between different modelling studies.

/pdf/tropical-cyclones-and-climate-change-4lh90c9xt5.pdf

Tropical cyclones and climate change

This chapter assesses long-term projections of climate change for the end of the 21st century and beyond, where the forced signal depends on the scenario and is typically larger than the internal variability of the climate system. Changes are expressed with respect to a baseline period of 1986-2005, unless otherwise stated.

Chapter 12 - Long-term climate change: Projections, commitments and irreversibility

. The modeling of paleoclimate, using physically based tools, is increasingly seen as a strong out-of-sample test of the models that are used for the projection of future climate changes. New to CMIP6 is the Tier 1 lig127k experiment, designed to address the climate responses to stronger orbital forcing than the midHolocene experiment, using the same state-of-the-art models and following a common experimental protocol. We present a multi-model ensemble of 17 climate models, all of which (except for two) have also completed the CMIP6 DECK experiments. The Equilibrium Climate Sensitivity (ECS) of these models varies from 2.1 to 5.6 °C. The seasonal character of the insolation anomalies results in strong warming over the Northern Hemisphere (NH) continents in the lig127k ensemble as compared to the piControl in June–July–August and a much-reduced minimum (August–September) summer sea ice extent in the Arctic. The multi-model results indicate enhanced summer monsoonal precipitation and areal extent in the Northern Hemisphere and reductions in the Southern Hemisphere. These responses are greater in the lig127k than midHolocene simulations as expected from the larger insolation anomalies at 127 ka than 6 ka. New syntheses for surface temperature and precipitation, targeted for 127 ka, have been developed for comparison to the multi-model ensemble. The lig127k model ensemble and data reconstructions are in good agreement for summer temperature anomalies over Canada, Scandinavia, and the North Atlantic and precipitation over the Northern Hemisphere continents. The model-data comparisons and mismatches point to further study of the sensitivity of the simulations to uncertainties in the specified boundary conditions and of the uncertainties and sparse coverage in current proxy reconstructions. The CMIP6-PMIP4 lig127k simulations, in combination with the proxy record, have potential implications for confidence in future projections of monsoons, surface temperature, Arctic sea ice, and the stability of the Greenland ice sheet.

Large-scale features of Last Interglacial climate: Results from evaluating the lig127k simulations for CMIP6-PMIP4

Abstract. Experiment outputs are now available from the Coupled Model Intercomparison Project's sixth phase (CMIP6) and the past climate experiments defined in the Palaeoclimate Modelling Intercomparison Project's fourth phase (PMIP4). All of this output is freely available from the Earth System Grid Federation (ESGF). Yet there is overhead in analysing this resource that may prove complicated or prohibitive. Here we document the steps taken by ourselves to produce ensemble analyses covering past and future simulations. We outline the strategy used to curate, adjust the monthly calendar aggregation and process the information downloaded from the ESGF. The results of these steps were used to perform analysis for several of the initial publications arising from PMIP4. We provide post-processed fields for each simulation, such as climatologies and common measures of variability. Example scripts used to visualise and analyse these fields are provided for several important case studies.


Analysing the PMIP4-CMIP6 collection: a workflow and tool (pmip_p2fvar_analyzer v1)

The Pliocene was characterized by a weak equatorial sea surface temperature gradient in the Pacific, confusingly reminiscent of that seen fleetingly during an El Ni no. Data also show interannual variability in the Pliocene, raising questions about ENSO’s dependence on the mean climate state.

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ENSO behavior before the Pleistocene

1 Human Evolution, Department of Organismal Biology, Uppsala University, Sweden 2 Palaeo-Research Institute, University of Johannesburg, South Africa 3 SciLifeLab Uppsala, Sweden 4 Department of Genetics, University of Cambridge, UK 5 Extreme Events Research Group, Max Planck Institute for Chemical Ecology, Jena, Germany. 6 Department of Genetics and Genome Biology & School of Archaeology and Ancient History, University of Leicester, UK 7 Department of Genetics, Evolution and Environment, University College London, UK 8 Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland 9 Department of Life Sciences and Biotechnology, University of Ferrara, Italy 10 Evolution & Diversité Biologique lab, CNRS, Univ. Toulouse 3, France Instituto Gulbenkian de Ciência, Oeiras, Portugal 11 Center for Genomics, Evolution and Medicine (cGEM), University of Tartu, Estonia 12 Department of Zoology, University of Cambridge, UK 13 Departments of Genetics and Biology, University of Pennsylvania, Philadelphia, PA USA 14 Pan-African Evolution Research Group, Max Planck Institute for the Science of Human History, Jena, Germany 15 Department of Geography, University College London, UK Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 17 November 2019 doi:10.20944/preprints201911.0193.v1

/pdf/human-origins-in-southern-african-palaeo-wetlands-strong-uso93fjs62.pdf

Human Origins in Southern African Palaeo-wetlands? Strong Claims from Weak Evidence

The Paris Agreement aims to limit the increase in global average temperature to 1.5 °C above preindustrial. A natural question for the public to ask is “But how much warmer than preindustrial is where I live?” We develop a pattern-scaling technique to present local annually-resolved, gridded temperature anomalies prior to the industrial burning of fossil fuels. On average the past 5 years, 2014-2018, was 1.13 °C above preindustrial (with a likely range of 1.00-1.26 °C). When accounting for the distribution of the human population and urban heat island effect, we find that people experienced an average warming of 1.61 °C (1.43-1.79 °C) over the same period. When the Paris Agreement was signed in 2015, the majority of the global population was exposed to local, annual temperatures warmer than 1.5 °C above preindustrial.

https://eartharxiv.org/repository/object/994/download/2216/

Chris Brierley

Papers

Large-scale features of Last Interglacial climate: Results from evaluating the lig127k simulations for CMIP6-PMIP4

Analysing the PMIP4-CMIP6 collection: a workflow and tool (pmip_p2fvar_analyzer v1)

ENSO behavior before the Pleistocene

Human Origins in Southern African Palaeo-wetlands? Strong Claims from Weak Evidence

Half the worlds population already experiences years 1.5°C warmer than preindustrial