Did grandmothers enhance reproductive success in historic populations? : Testing evolutionary theories on historical demographic data in Scandinavia and North America

Human reproductive success requires both producing children and making investments in the development of offspring. To a large extent these investments are made by the parents of the child, but researchers are now looking beyond the nuclear family to understand how extended kin, notably grandmothers, enhance reproductive success by making transfers to progeny of different kinds. The extent to which kin influence fertility and mortality outcomes may vary across different socio-economic and geographic contexts; as a result, an international comparative framework is used here to sharpen our understanding of the role of kin in reproduction. This chapter assesses the role of grandmothers in fertility outcomes in a comparative historical demographic study based on data from Scandinavia and North America. The individual-level data used are all longitudinal and multigenerational, allowing us to address the impact of maternal and paternal grandmothers on the fertility of their daughters and daughters-in-law, while attending to heterogeneous effects across space and time as well as within-family differences via the use of fixed effects models. We discover broader associations of the paternal grandmother with higher fertility across the four regions. We also find a general fertility advantage associated with the post-reproductive availability or recent death of the maternal grandmother in the four populations. Important variations across regions nevertheless exist in terms of the strength of the association, the importance of the grandmother’s proximity, and the results derived by using fixed effects models. Our interpretation is that grandmothers were generally associated with high-fertility outcomes, but that the mechanism for this effect was co-determined by family configurations, resource allocation and the advent of fertility control

Phase 1/2a trial of intravenous BAL101553, a novel controller of the spindle assembly checkpoint, in advanced solid tumours

Background: BAL101553 (lisavanbulin), the lysine prodrug of BAL27862 (avanbulin), exhibits broad anti-proliferative activity in human cancer models refractory to clinically relevant microtubule-targeting agents. Methods: This two-part, open-label, phase 1/2a study aimed to determine the maximum tolerated dose (MTD) and dose-limiting toxicities (DLTs) of 2-h infusion of BAL101553 in adults with advanced or recurrent solid tumours. The MTD was determined using a modified accelerated titration design in phase I. Patients received BAL101553 at the MTD and at lower doses in the phase 2a expansion to characterise safety and efficacy and to determine the recommended phase 2 dose (RP2D). Results: Seventy-three patients received BAL101553 at doses of 15–80 mg/m2 (phase 1, n = 24; phase 2a, n = 49). The MTD was 60 mg/m2; DLTs observed at doses ≥60 mg/m2 were reversible Grade 2–3 gait disturbance with Grade 2 peripheral sensory neuropathy. In phase 2a, asymptomatic myocardial injury was observed at doses ≥45 mg/m2. The RP2D for 2-h intravenous infusion was 30 mg/m2. The overall disease control rate was 26.3% in the efficacy population. Conclusions: The RP2D for 2-h infusion of BAL101553 was well tolerated. Dose-limiting neurological and myocardial side effects were consistent with the agent’s vascular-disrupting properties. Clinical trial registration: EudraCT: 2010-024237-23

Monkey Steering Responses Reveal Rapid Visual-Motor Feedback

The neural mechanisms underlying primate locomotion are largely unknown. While behavioral and theoretical work has provided a number of ideas of how navigation is controlled, progress will require direct physiolgical tests of the underlying mechanisms. In turn, this will require development of appropriate animal models. We trained three monkeys to track a moving visual target in a simple virtual environment, using a joystick to control their direction. The monkeys learned to quickly and accurately turn to the target, and their steering behavior was quite stereotyped and reliable. Monkeys typically responded to abrupt steps of target direction with a biphasic steering movement, exhibiting modest but transient overshoot. Response latencies averaged approximately 300 ms, and monkeys were typically back on target after about 1 s. We also exploited the variability of responses about the mean to explore the time-course of correlation between target direction and steering response. This analysis revealed a broad peak of correlation spanning approximately 400 ms in the recent past, during which steering errors provoke a compensatory response. This suggests a continuous, visual-motor loop controls steering behavior, even during the epoch surrounding transient inputs. Many results from the human literature also suggest that steering is controlled by such a closed loop. The similarity of our results to those in humans suggests the monkey is a very good animal model for human visually guided steering

Mean step response by fixation condition and step size.

<p>Each trace shows the mean response to a given step size. Each fixation condition is represented by different traces.</p

Parameters extracted from a step response.

<p>a) latency, b) time to peak, c) peak amplitude, d) amplitude of the overshoot correction.</p

Numbers of target steps used for analysis.

<p>Numbers of target steps used for analysis.</p

Initial acceleration as a function of step amplitude.

<p>a) Mean slope of the initial response by monkey and step amplitude. The slope of the response corresponds to the initial acceleration of the monkey's turn (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011975#s2" target="_blank">Methods</a>). Error bars represent the standard error of the mean. b) Distribution of slopes for monkey F by step amplitude. Similar results were found for monkeys J and M.</p

Peak response to a target step.

<p>a) Mean peak velocity by monkey and step size. Error bars represent the standard error of the mean. b) Median time to the peak response by monkey and step size. Error bars span the first and third quartiles of the data.</p

Mean response and change in trajectory for a target step.

<p>Top) Mean steering response in degrees per second to a target step from −25° to 25° for monkeys F, M and J. Bottom) Mean change in trajectory during a step response.</p

Task design and geometry.

<p>a) Schematic of the steering task. The monkey viewed the scene from the position of the blue arrow, which was 50 cm above the simulated ground plane. In our task, the monkey translated at a constant speed of 2.1 m/s across the ground plane, but could control its yaw. The monkey's goal was to turn until its current trajectory was toward the target (red dot). b) Scene as viewed from the primate chair. Upon seeing the target stepped from the center of the screen, the monkey was trained to correct its trajectory so that it would follow a path that led to the target (red dot). The red arrow illustrates one possible path to the target. c) Example traces from a single steering epoch. Top: target bearing (solid) and subject heading (dashed) over time; bearing and heading are relative to an arbitrary reference frame. Middle: steering error over time. Steering error is defined as the target bearing relative to the subject's heading. Bottom: Steering response over time.</p