Timing of human rhythmic jumping with vertical floor vibrations tends towards mechanically efficient solutions.

Jumping on vibrating platforms is described not only by the frequency of jumping (FoJ) but also by the timing of key events in a cycle of jumping relative to vibrations. This study aimed to capture timing and efficiency-related adaptations during jumping on vertically vibrating platforms. Whole body kinematic and kinetic data were collected as ten participants jumped on a sinusoidally vibrating platform of 2.

Blocking the Dkk1-LRP6 interaction prevents acute amyloid-β-driven cognitive impairment.

Synapse loss driven by amyloid-β (Aβ) is an early event in Alzheimer's disease (AD). Although the mechanism by which Aβ drives synapse loss remain poorly understood data indicate that a disruption of Wnt signalling plays an important part. We have shown that Aβ exerts its effects on synapses through Dickkopf-1 (Dkk1), a secreted protein that acts upon Wnt signalling via a direct interaction with the canonical Wnt pathway co-receptor proteins, LRP5 and LRP6, preventing their interaction with the receptor Frizzled.

Perinatal nutritional effects on postpubertal secretion of gonadotropins and feedback responsiveness to estradiol-17β in sexually mature heifers.

Developmental changes in response to nutritional extremes may in some cases be manifested later in adult life. Objectives of experiments reported herein were to test the hypotheses that maternal nutrition during mid- to late-gestation interacts with postnatal nutrition during the juvenile period in heifers to impact 1) tonic secretion of gonadotropins, and 2) estradiol-17β (E2) negative and positive feedback responsiveness in adulthood. Heifers were selected from a larger population programmed nutritionally using a 3 × 2 factorial arrangement of pre- and postnatal diets.

Evaluation of the epidemic intelligence from open sources (EIOS) system for the early detection of outbreaks and health emergencies in the African region.

Introduction: Public health today is challenged by a wide array of hazards that threaten humans, often resulting in high rates of morbidity and mortality when they strike. These events should be detected and responded to as early as possible to save lives and minimize their impact. The Epidemic Intelligence from Open Sources (EIOS) system leverages natural language processing and machine learning techniques for the early detection of public health events from open-source information using an all-hazards approach.