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.

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

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

Climate in the Gulf of Alaska (GOA) reflects large-scale ocean-atmosphere variability of the North Pacific climate system. Ring-width (RW) records from the GOA have yielded a valuable long-term perspective for North Pacific changes on decadal to longer time scales in prior studies, but express a broad winter to late summer seasonal response. Similar to the highly climate-sensitive maximum latewood density (MXD) proxy, the Blue Intensity (BI) parameter has recently been shown to correlate well with year-to-year warm-season temperatures for a number of sites at northern latitudes. Since BI records are much less expensive and labor intensive to generate than MXD, such data hold great potential value for future tree-ring studies in the GOA and other regions at mid-to-high latitudes. Here we highlight the potential for improving tree-ring based reconstructions using combinations of RW and BI-related parameters (latewood BI (LWB) and delta BI (DB)) from an experimental sub-set of samples from eight mountain hemlock (Tsuga mertensiana) sites along the GOA. This is the first such study for the hemlock genus using BI data. We find that using either LWB or DB can improve the amount of explained temperature variance by > 10 % compared to RW alone although the optimal target season changes to June–September, which may have implications for studying ocean-atmosphere variability in the region. However, one challenge in building these BI records is that resin extraction did not remove colour differences between the heartwood and sapwood, so long term trend biases, expressed as relatively warm temperatures in the 18th century, were noted when using the LWB data. Using DB appeared to overcome these trend biases resulting in a reconstruction expressing 18th–19th century temperatures ca. 0.5 °C cooler than the 20th/21st centuries. This cool period agrees well with previous dendroclimatic studies and the glacial advance record in the region. Continuing BI measurement in the GOA region must focus on sampling more trees per site (> 20) and more sites to overcome site specific factors effecting climate response while sub-fossil material will extend the reflectance records back over 1000 years. DB appears to capture long term secular trends that agree with other proxy archives in the region but great care is needed when implementing different detrending options. Finally, more experimentation is needed to assess the utility of DB for different conifer species around the Northern Hemisphere.

/pdf/blue-intensity-based-experiments-for-reconstructing-17x0q2x8du.pdf

Blue Intensity based experiments for reconstructing NorthPacific temperatures along the Gulf of Alaska

Does signal-free detrending increase chronology coherence in large tree-ring networks?

Past, present, and future western North American drought in models and paleoclimate data

On the AD 1815 Tambora eruption and the matter of misplaced tree rings

Abstract Extreme summer temperatures are increasingly common across the Northern Hemisphere and inflict severe socioeconomic and biological consequences. In summer 2021, the Pacific Northwest region of North America (PNW) experienced a 2-week-long extreme heatwave, which contributed to record-breaking summer temperatures. Here, we use tree-ring records to show that summer temperatures in 2021, as well as the rate of summertime warming during the last several decades, are unprecedented within the context of the last millennium for the PNW. In the absence of committed efforts to curtail anthropogenic emissions below intermediate levels (SSP2–4.5), climate model projections indicate a rapidly increasing risk of the PNW regularly experiencing 2021-like extreme summer temperatures, with a 50% chance of yearly occurrence by 2050. The 2021 summer temperatures experienced across the PNW provide a benchmark and impetus for communities in historically temperate climates to account for extreme heat-related impacts in climate change adaptation strategies. 

https://www.nature.com/articles/s41612-023-00340-3.pdf

Kevin J. Anchukaitis

Papers

Blue Intensity based experiments for reconstructing NorthPacific temperatures along the Gulf of Alaska

Does signal-free detrending increase chronology coherence in large tree-ring networks?

Past, present, and future western North American drought in models and paleoclimate data

On the AD 1815 Tambora eruption and the matter of misplaced tree rings

Unprecedented 21st century heat across the Pacific Northwest of North America