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

https://hal.archives-ouvertes.fr/hal-00457602/document

A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests

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.

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Climate Change 2007: The Physical Science Basis.

This chapter should be cited as: Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural Radiative Forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Coordinating Lead Authors: Gunnar Myhre (Norway), Drew Shindell (USA)

Anthropogenic and Natural Radiative Forcing

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0033589419000334

The potential to strengthen temperature reconstructions in ecoregions with limited tree line using a multispecies approach

The Pacific ocean–atmosphere system exerts an important influence on the climate of Asia and North America, but the limited length of the observational record prevents a complete understanding of its bidecadal and multidecadal time scales. Paleoclimate reconstructions provide one source of information on longer time scales, although they differ in their estimation of the behavior of the Pacific decadal oscillation (PDO) prior to the instrumental period. Forced general circulation model simulations offer complementary long-term perspectives on both the history and dynamics of this important mode of variability. Here, we analyze the PDO in the ensemble of CMIP5/PMIP3 last millennium (past1000 + historical) simulations. We evaluate the modeled spatial, temporal, and spectral characteristics of this mode, as well as teleconnections between North Pacific variability and global climate. All models produce a mode of North Pacific variability over the last millennium with spatial patterns and spectral power density similar to observations. CCSM, FGOALS, and IPSL best reproduce observed spatial patterns, spectral characteristics, and teleconnections to terrestrial regions used in paleoclimate proxy reconstructions. In these simulations, the PDO shows no consistent response to solar or volcanic forcing.

North Pacific decadal variability in the CMIP5 last millennium simulations

Spatially extensive and persistent drought episodes have repeatedly influenced human history, including the ?Strange Parallels? drought event in monsoon Asia during the mid-18th century. Here we explore the dynamics of sustained monsoon failure using observed and tree-ring reconstructed drought patterns and a 1300-year pre-industrial community earth system model control run. Both modern observational and climate model drought patterns during years with extremely weakened South Asian monsoon resemble those reconstructed for the Strange Parallels drought. Model analysis reveals that this pattern arises during boreal spring over Southeast Asia, with decreased precipitation and moisture flux, while related summertime climate anomalies are confined to the Indian subcontinent. Years with simulated South Asian drying exhibit canonical El Ni?o conditions over the Pacific and associated shifts in the Walker circulation. In contrast, multi-year drought periods, resembling those sustained during the Strange Parallels drought, feature anomalous Pacific warming around the dateline, typical of El Ni?o Modoki events.

/pdf/multi-scale-drought-and-ocean-atmosphere-variability-in-2ii5rl39dy.pdf

Multi-scale drought and ocean–atmosphere variability in monsoon Asia

A narrow window of summer temperatures associated with shrub growth in Arctic Alaska

Climate information may help smallholder farmers in Southeast Asia takeinformed risks and reduce their climate vulnerability. Model-based seasonalforecasts are limited in the skill they offer with longer lead times, and intheir accessibility to farmers in remote regions. Climate reconstructions derivedfrom tree rings can complement model-based forecasts, providing probabilisticestimates of future climate at an annual resolution. This study demonstratesthe skill of PDSI reconstructions derived from the Monsoon Asia DroughtAtlas (MADA) to forecast a climate signal several years into the future, andillustrates the advantage of such information vis-a-vis the simple strategy ofassuming climate to persist from year to year. This work shows the potentialfor such climate reconstructions to improve inter-annual decisions on SoutheastAsian farms, and highlights the social research necessary to transmute thispotential into a valuable climate decision aid.In much of Southeast Asia the capacity of smallholder farmers to predict andprotect against drought is limited. Farmers commonly employ conservative, risk-averse strategies to farm planning and operation that reduce losses to climate-relateddamages, but that also reduce surpluses in years of favorable climate (Meinke andStone 2005). Farmers operating close to thresholds of sustenance and self-sufficiencywho fail to take advantage of these rents can fall into ‘poverty traps’ (Holling 2001;Sachs 2005), unable to lift themselves from degraded land conditions and a lackof capital. To help smallholders become informed risk takers and capitalize on the

Kevin J. Anchukaitis

Papers

The potential to strengthen temperature reconstructions in ecoregions with limited tree line using a multispecies approach

North Pacific decadal variability in the CMIP5 last millennium simulations

Multi-scale drought and ocean–atmosphere variability in monsoon Asia

A narrow window of summer temperatures associated with shrub growth in Arctic Alaska

Repurposing climate reconstructions for drought prediction in Southeast Asia