Daily Fluctuations in Hormonal and Performance Markers in Collegiate Weightlifters

The purpose of this study was to assess relationships between daily fluctuations in hormonal and performance markers in weightlifters. Nine male collegiate weightlifters gave daily pre-practice salivary samples for one week and were tested daily for standing broad jump distance; first jump (BJ1) and best jump (BJB) were recorded. Volume-load was heavy on Monday (47%), light on Tuesday (13%), and medium-heavy on Wednesday (40%). To determine if variables differed by day, RM ANOVAs were used with partial-eta squared effect sizes (η2 p) to calculate meaningful changes. RM ANOVA models suggest daily differences occurred for T (F=4.027, p=.024, η2 p =.402), T/C (F=11.735, p=.019, η2 p =.898), and BJ1 (F=6.229, p=.004, η2 p =.509), but not for C (F=1.623, p=.219, η2 p =.213) nor BJB (F=1.088, p=.379, η2 p =.154). Daily fluctuations in BJ1 shared a moderate inverse relationship with daily fluctuations in C (r = -0.42), whereas BJB revealed no association with hormonal markers. T, T/C, and BJ1 appeared to be meaningfully affected by the previous day’s training stress in collegiate weightlifters, suggesting that BJ1 may be indicative of hormonal status and that a one-day reduction in VL may enhance acute athlete readiness

A First Look at the Auriga-California Giant Molecular Cloud With Herschel and the CSO: Census of the Young Stellar Objects and the Dense Gas

We have mapped the Auriga/California molecular cloud with the Herschel PACS and SPIRE cameras and the Bolocam 1.1 mm camera on the Caltech Submillimeter Observatory (CSO) with the eventual goal of quantifying the star formation and cloud structure in this Giant Molecular Cloud (GMC) that is comparable in size and mass to the Orion GMC, but which appears to be forming far fewer stars. We have tabulated 60 compact 70/160um sources that are likely pre-main-sequence objects and correlated those with Spitzer and WISE mid-IR sources. At 1.1 mm we find 18 cold, compact sources and discuss their properties. The most important result from this part of our study is that we find a modest number of additional compact young objects beyond those identified at shorter wavelengths with Spitzer. We also describe the dust column density and temperature structure derived from our photometric maps. The column density peaks at a few x 10^22 cm^-2 (N_H2) and is distributed in a clear filamentary structure along which nearly all the pre-main-sequence objects are found. We compare the YSO surface density to the gas column density and find a strong non-linear correlation between them. The dust temperature in the densest parts of the filaments drops to ~10K from values ~ 14--15K in the low density parts of the cloud. We also derive the cumulative mass fraction and probability density function of material in the cloud which we compare with similar data on other star-forming clouds.Comment: in press Astrophysical Journal, 201

Benthic Foraminiferal response to sea level change in the mixed siliciclastic-carbonate system of southern Ashmore Trough (Gulf of Papua)

Ashmore Trough in the western Gulf of Papua (GoP) represents an outstanding modern example of a tropical mixed siliciclastic-carbonate depositional system where significant masses of both river-borne silicates and bank-derived neritic carbonates accumulate. In this study, we examine how benthic foraminiferal populations within Ashmore Trough vary in response to sea level–driven paleoenvironmental changes, particularly organic matter and sediment supply. Two 11.3-m-long piston cores and a trigger core were collected from the slope of Ashmore Trough and dated using radiocarbon and oxygen isotope measurements of planktic foraminifera. Relative abundances, principal component analyses, and cluster analyses of benthic foraminiferal assemblages in sediment samples identify three distinct assemblages whose proportions changed over time. Assemblage 1, with high abundances of Uvigerina peregrina and Bolivina robusta, dominated between ∼83 and 70 ka (early regression); assemblage 2, with high abundances of Globocassidulina subglobosa, dominated between ∼70 and 11 ka (late regression through lowstand and early transgression); and assemblage 3, with high abundances of neritic benthic species such as Planorbulina mediterranensis, dominated from ∼11 ka to the present (late transgression through early highstand). Assemblage 1 represents heightened organic carbon flux or lowered bottom water oxygen concentration, and corresponds to a time of maximum siliciclastic fluxes to the slope with falling sea level. Assemblage 2 reflects lowered organic carbon flux or elevated bottom water oxygen concentration, and corresponds to an interval of lowered siliciclastic fluxes to the slope due to sediment bypass during sea level lowstand. Assemblage 3 signals increased off-shelf delivery of neritic carbonates, likely when carbonate productivity on the outer shelf (Great Barrier Reef) increased significantly when it was reflooded. Benthic foraminiferal assemblages in the sediment sink (slopes of Ashmore Trough) likely respond to the amount and type of sediment supplied from the proximal source (outer GoP shelf)

Neural Field Models: A mathematical overview and unifying framework

Rhythmic electrical activity in the brain emerges from regular non-trivial interactions between millions of neurons. Neurons are intricate cellular structures that transmit excitatory (or inhibitory) signals to other neurons, often non-locally, depending on the graded input from other neurons. Often this requires extensive detail to model mathematically, which poses several issues in modelling large systems beyond clusters of neurons, such as the whole brain. Approaching large populations of neurons with interconnected constituent single-neuron models results in an accumulation of exponentially many complexities, rendering a realistic simulation that does not permit mathematical tractability and obfuscates the primary interactions required for emergent electrodynamical patterns in brain rhythms. A statistical mechanics approach with non-local interactions may circumvent these issues while maintaining mathematically tractability. Neural field theory is a population-level approach to modelling large sections of neural tissue based on these principles. Herein we provide a review of key stages of the history and development of neural field theory and contemporary uses of this branch of mathematical neuroscience. We elucidate a mathematical framework in which neural field models can be derived, highlighting the many significant inherited assumptions that exist in the current literature, so that their validity may be considered in light of further developments in both mathematical and experimental neuroscience.Comment: 55 pages, 10 figures, 2 table

Eigenvalue spectral properties of sparse random matrices obeying Dale's law

Understanding the dynamics of large networks of neurons with heterogeneous connectivity architectures is a complex physics problem that demands novel mathematical techniques. Biological neural networks are inherently spatially heterogeneous, making them difficult to mathematically model. Random recurrent neural networks capture complex network connectivity structures and enable mathematically tractability. Our paper generalises previous classical results to sparse connectivity matrices which have distinct excitatory (E) or inhibitory (I) neural populations. By investigating sparse networks we construct our analysis to examine the impacts of all levels of network sparseness, and discover a novel nonlinear interaction between the connectivity matrix and resulting network dynamics, in both the balanced and unbalanced cases. Specifically, we deduce new mathematical dependencies describing the influence of sparsity and distinct E/I distributions on the distribution of eigenvalues (eigenspectrum) of the networked Jacobian. Furthermore, we illustrate that the previous classical results are special cases of the more general results we have described here. Understanding the impacts of sparse connectivities on network dynamics is of particular importance for both theoretical neuroscience and mathematical physics as it pertains to the structure-function relationship of networked systems and their dynamics. Our results are an important step towards developing analysis techniques that are essential to studying the impacts of larger scale network connectivity on network function, and furthering our understanding of brain function and dysfunction.Comment: 18 pages, 6 figure

Repressive Interactions Between Transcription Factors Separate Different Embryonic Ectodermal Domains.

The embryonic ectoderm is composed of four domains: neural plate, neural crest, pre-placodal region (PPR) and epidermis. Their formation is initiated during early gastrulation by dorsal-ventral and anterior-posterior gradients of signaling factors that first divide the embryonic ectoderm into neural and non-neural domains. Next, the neural crest and PPR domains arise, eithe

Beliefs around luck : confirming the empirical conceptualization of beliefs around luck and the development of the Darke and Freedman beliefs around luck scale

The current study developed a multi-dimensional measure of beliefs around luck. Two studies introduced the Darke and Freedman beliefs around luck scale where the scale showed a consistent 4 component model (beliefs in luck, rejection of luck, being lucky, and being unlucky) across two samples (n = 250; n = 145). The scales also show adequate reliability statistics and validity by ways of comparison with other measures of beliefs around luck, peer and family ratings and expected associations with measures of personality, individual difference and well-being variables

Autoregressive models for biomedical signal processing

Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially, this data is subject to measurement errors as well as uncertainties in the underlying system model. As a result, standard signal processing using autoregressive model estimators may be biased. We present a framework for autoregressive modelling that incorporates these uncertainties explicitly via an overparameterised loss function. To optimise this loss, we derive an algorithm that alternates between state and parameter estimation. Our work shows that the procedure is able to successfully denoise time series and successfully reconstruct system parameters. This new paradigm can be used in a multitude of applications in neuroscience such as brain-computer interface data analysis and better understanding of brain dynamics in diseases such as epilepsy