Holocene climate and El Nino history as recorded in the sediments of northern coastal Peru [abstract, table, and references]

EXTRACT (SEE PDF FOR FULL ABSTRACT): The history of the El Nino phenomena is recorded in both the fluvial and coastal sediments of northern Peru. The fluvial record was presented at the 1987 PACLIM Workshop and is discussed in detail elsewhere (Wells, 1987). However, the number of radiocarbon dated El Nino events has increased since Wells (1987) was published; this data is presented in Table 1.

Meteorological influences on North America quail populations [abstract, table, and figure]

EXTRACT (SEE PDF FOR FULL ABSTRACT): Comparative study of environmental influences on the population dynamics of three North American species of quail, California quail (Callipepla california), Gambel's quail (C. gambellii), and scaled quail (C. squamata) has lead to identification of differential sensitivity of these species to global weather patterns.

An environmentally based growth model that uses finite difference calculus with maximum likelihood method: its application to the brackish water bivalve Corbicula japonica in Lake Abashiri, Japan

We present a growth analysis model that combines large amounts of environmental data with limited amounts of biological data and apply it to Corbicula japonica. The model uses the maximum-likelihood method with the Akaike information criterion, which provides an objective criterion for model selection. An adequate distribution for describing a single cohort is selected from available probability density functions, which are expressed by location and scale parameters. Daily relative increase rates of the location parameter are expressed by a multivariate logistic function with environmental factors for each day and categorical variables indicating animal ages as independent variables. Daily relative increase rates of the scale parameter are expressed by an equation describing the relationship with the daily relative increase rate of the location parameter. Corbicula japonica grows to a modal shell length of 0.7 mm during the first year in Lake Abashiri. Compared with the attain-able maximum size of about 30 mm, the growth of juveniles is extremely slow because their growth is less susceptible to environmental factors until the second winter. The extremely slow growth in Lake Abashiri could be a geographical genetic variation within C. japonica.

The intraspecific difference of the triose phosphate isomerase ( tim) gene from Giardia lamblia

目的　探讨蓝氏贾第鞭毛虫 (Giardialamblia)磷酸丙糖异构酶基因种内差异。方法　提取虫体总DNA ,对所有虫株磷酸丙糖异构酶 (tim)基因部分片段进行PCR扩增。测定序列后 ,用简约法和NJ法构建系统树进行系统发育分析。结果　共有 12 4个位点存在变异 (占所有测定序列中的 2 3% ) ,且大多数为发生在密码子的同义突变。两种构树方法所得二树的分枝结构相似 ,均将受试的 16株蓝氏贾第虫分为明显的两组。结论　宿主及地理因素对蓝氏贾第虫群体的遗传多样性影响不大。在DNA分子进化水平上 ,自然选择的影响十分显著。可将tim基因作为蓝氏贾第虫群体遗传结构一个十分有效的遗传标记。

Immersed Boundary Method for generalised finite volume and finite difference Navier-Stokes solvers

In Immersed Boundary Methods (IBM) the effect of complex geometries is introduced through the forces added in the Navier-Stokes solver at the grid points in the vicinity of the immersed boundaries. Most of the methods in the literature have been used with Cartesian grids. Moreover many of the methods developed in the literature do not satisfy some basic conservation properties (the conservation of torque, for instance) on non-uniform meshes. In this paper we will follow the RKPM method originated by Liu et al. [1] to build locally regularized functions that verify a number of integral conditions. These local approximants will be used both for interpolating the velocity field and for spreading the singular force field in the framework of a pressure correction scheme for the incompressible Navier-Stokes equations. We will also demonstrate the robustness and effectiveness of the scheme through various examples. Copyright © 2010 by ASME.

Marine Vertebrates and Low Frequency Sound: Technical Report for LFA EIS

Over the past 50 years, economic and technological developments have dramatically increased the human contribution to ambient noise in the ocean. The dominant frequencies of most human-made noise in the ocean is in the low-frequency range (defined as sound energy below 1000Hz), and low-frequency sound (LFS) may travel great distances in the ocean due to the unique propagation characteristics of the deep ocean (Munk et al. 1989). For example, in the Northern Hemisphere oceans low-frequency ambient noise levels have increased by as much as 10 dB during the period from 1950 to 1975 (Urick 1986; review by NRC 1994). Shipping is the overwhelmingly dominant source of low-frequency manmade noise in the ocean, but other sources of manmade LFS including sounds from oil and gas industrial development and production activities (seismic exploration, construction work, drilling, production platforms), and scientific research (e.g., acoustic tomography and thermography, underwater communication). The SURTASS LFA system is an additional source of human-produced LFS in the ocean, contributing sound energy in the 100-500 Hz band. When considering a document that addresses the potential effects of a low-frequency sound source on the marine environment, it is important to focus upon those species that are the most likely to be affected. Important criteria are: 1) the physics of sound as it relates to biological organisms; 2) the nature of the exposure (i.e. duration, frequency, and intensity); and 3) the geographic region in which the sound source will be operated (which, when considered with the distribution of the organisms will determine which species will be exposed). The goal in this section of the LFA/EIS is to examine the status, distribution, abundance, reproduction, foraging behavior, vocal behavior, and known impacts of human activity of those species may be impacted by LFA operations. To focus our efforts, we have examined species that may be physically affected and are found in the region where the LFA source will be operated. The large-scale geographic location of species in relation to the sound source can be determined from the distribution of each species. However, the physical ability for the organism to be impacted depends upon the nature of the sound source (i.e. explosive, impulsive, or non-impulsive); and the acoustic properties of the medium (i.e. seawater) and the organism. Non-impulsive sound is comprised of the movement of particles in a medium. Motion is imparted by a vibrating object (diaphragm of a speaker, vocal chords, etc.). Due to the proximity of the particles in the medium, this motion is transmitted from particle to particle in waves away from the sound source. Because the particle motion is along the same axis as the propagating wave, the waves are longitudinal. Particles move away from then back towards the vibrating source, creating areas of compression (high pressure) and areas of rarefaction (low pressure). As the motion is transferred from one particle to the next, the sound propagates away from the sound source. Wavelength is the distance from one pressure peak to the next. Frequency is the number of waves passing per unit time (Hz). Sound velocity (not to be confused with particle velocity) is the impedance is loosely equivalent to the resistance of a medium to the passage of sound waves (technically it is the ratio of acoustic pressure to particle velocity). A high impedance means that acoustic particle velocity is small for a given pressure (low impedance the opposite). When a sound strikes a boundary between media of different impedances, both reflection and refraction, and a transfer of energy can occur. The intensity of the reflection is a function of the intensity of the sound wave and the impedances of the two media. Two key factors in determining the potential for damage due to a sound source are the intensity of the sound wave and the impedance difference between the two media (impedance mis-match). The bodies of the vast majority of organisms in the ocean (particularly phytoplankton and zooplankton) have similar sound impedence values to that of seawater. As a result, the potential for sound damage is low; organisms are effectively transparent to the sound – it passes through them without transferring damage-causing energy. Due to the considerations above, we have undertaken a detailed analysis of species which met the following criteria: 1) Is the species capable of being physically affected by LFS? Are acoustic impedence mis-matches large enough to enable LFS to have a physical affect or allow the species to sense LFS? 2) Does the proposed SURTASS LFA geographical sphere of acoustic influence overlap the distribution of the species? Species that did not meet the above criteria were excluded from consideration. For example, phytoplankton and zooplankton species lack acoustic impedance mis-matches at low frequencies to expect them to be physically affected SURTASS LFA. Vertebrates are the organisms that fit these criteria and we have accordingly focused our analysis of the affected environment on these vertebrate groups in the world’s oceans: fishes, reptiles, seabirds, pinnipeds, cetaceans, pinnipeds, mustelids, sirenians (Table 1).

Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.