There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues

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

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

/pdf/molecular-classification-of-cancer-class-discovery-and-class-22p4huwudl.pdf

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

This paper reviews our most recent contributions in the field of cardiac deformation imaging, which includes a motion estimation framework based on the conservation of the image phase over time and an open pipeline to benchmark algorithms for cardiac strain imaging in 2D and 3D ultrasound. The paper also shows an original evaluation of the proposed motion estimation technique based on the new benchmarking pipeline.

/pdf/the-role-of-the-image-phase-in-cardiac-strain-imaging-113xhdnzdb.pdf

The role of the image phase in cardiac strain imaging

Large scale precipitation of calcium carbonate in the oceans by coccolithophorids is a phenomenon that plays an important role in carbon sequestration. However, there is a controversy on the effect of an increase in atmospheric CO2 concentration on both calcification and photosynthesis of coccolithophorids. Indeed recent experiments, performed in conditions of nitrogen limitation, revealed that the associated fluxes may be slowed down, while other authors claim the reverse response. We have designed models to account for various scenarii of calcification and photosynthesis regulation in chemostat cultures of Emiliania huxleyi, based on different hypotheses of regulation mechanism. These models, which are kept at a general and generic level, consider that either carbon dioxide, bicarbonate, carbonate or pH is the regulating factor. These models are calibrated to predict the same carbon fluxes in nowadays pCO2, but they turn out to respond differently to an increase of CO2 concentration. Thus, we simulated a bloom of Emiliania huxleyi using the four considered regulation scenarii. For high biomass concentration, the coccolithophorids can significantly affect the inorganic carbon and the pH in their environment, thus leading to a feedback in their growth rate which is, depending on the model, positive or negative. It results that the prediction of the carbon fixed during the bloom varies by a factor 2, depending on the assumed regulating mechanism hypothesized for growth and calcification.

Predictions of Carbon Fixation during a Bloom of Emiliania huxleyi as a Function of the Regulating Inorganic Species

Resume – Le probleme traite concerne la segmentation d'images echocardiographiques par contour actifs implicites utilisant des descripteurs statistiques de region bases sur les K-distributions. La quasi totalite des auteurs traitant ce sujet exploite la statistique de Rayleigh, qui est un modele limite pour la caracterisation de certain milieux tissulaires, tel que le myocarde. Ainsi, l'originalite de la methode proposee reside en l'exploitation du formalisme recemment developpe par Jehan-Besson et al. afin d'utiliser un parametre statistique issu du modele des K-distributions qui reflete plus fidelement les proprietes des images ultrasonores du myocarde.

http://documents.irevues.inist.fr/bitstream/handle/2042/13829/A139_38683.pdf?sequence=1

Segmentation d'images échocardiographiques par contours actifs implicites : exploitation de descripteurs statistiques de régions

Algebraic cryptanalysis of the shortest vector problem in ideal lattices. (Cryptanalyse algébrique du problème du plus court vecteur dans les réseaux idéaux)

The bioreactor for the production of biogas, comprises a liquid phase bioreactor medium activated by biological degradation of organic substances in anaerobic conditions for producing fuel gas and carbon dioxide, a gas phase containing a biogas product in exchange with the liquid phase through liquid-gas, a control unit for regulating the production of biogas with the introduction of the chemical substance in liquid phase, and a movable injector. The control unit is equipped with a unit comprising a predetermined function that calculates a resulting value. The bioreactor for the production of biogas, comprises a liquid phase bioreactor medium activated by biological degradation of organic substances in anaerobic conditions for producing fuel gas and carbon dioxide, a gas phase containing a biogas product in exchange with the liquid phase through liquid-gas, a control unit for regulating the production of biogas with the introduction of the chemical substance in liquid phase, and a movable injector. The control unit is equipped with a unit comprising a predetermined function that calculates a resulting value with respect to the carbon dioxide content present in the biogas. The resulting value represents a desired content of carbon dioxide dissolved in the liquid medium. The control unit is arranged for controlling a parameter such as flow of chemical substance in a direction to reduce the content of carbon dioxide. The flow is controlled in an integral proportional mode based on a difference between the content of carbon dioxide measured in the biogas and a determined value, and in a static manner based on the alkalinity in liquid phase, a content of carbon dioxide dissolved in liquid phase and the content of carbon dioxide measured in the biogas. The movable injector is placed at a level of a recirculation loop. The chemical substance is an alkaline agent, and an acid agent. The bioreactor medium comprises microorganisms capable of synthesizing methane. An independent claim is included for a process for producing biogas in a bioreactor.

Bioreactor for producing biogas, comprises liquid phase bioreactor medium activated by biological degradation of organic substances in anaerobic conditions, gas phase containing biogas product, control unit, and movable injector

Olivier Bernard

Papers

The role of the image phase in cardiac strain imaging

Predictions of Carbon Fixation during a Bloom of Emiliania huxleyi as a Function of the Regulating Inorganic Species

Segmentation d'images échocardiographiques par contours actifs implicites : exploitation de descripteurs statistiques de régions

Algebraic cryptanalysis of the shortest vector problem in ideal lattices. (Cryptanalyse algébrique du problème du plus court vecteur dans les réseaux idéaux)

Bioreactor for producing biogas, comprises liquid phase bioreactor medium activated by biological degradation of organic substances in anaerobic conditions, gas phase containing biogas product, control unit, and movable injector