182 research outputs found
Death Anxiety in a National Sample of United States Funeral Directors and its Relationship with Death Exposure, Age, and Sex
The purpose of this study is to examine the relationship between the level of death anxiety among a national sample of United States funeral directors with varying levels of death exposure, age, and sex. Utilizing the Multidimensional Fear of Death Scale (MFODS), the results showed a significant, but weak negative relationship between levels of death anxiety and the participants\u27 reported number of funerals attended per year. The correlation between death anxiety scores and the number of reported embalming cases performed yearly was, however, not significant. We found a significant negative correlation between death anxiety and age in both men and women funeral directors. The difference in the death anxiety scores between men (n = 166) and women (n = 38) funeral directors was not significant. There was a significant negative correlation with age in both men and women in several fears of death including fear of the dying process, fear for significant others, and fear of premature death. The significant negative correlations were stronger for women than men across all three subscales. Results, direction for further research, and implications of the findings for mental health workers are discussed
A `Primer’ in Conceptual Metaphor for Counselors
Conceptual metaphor provides a potentially powerful counseling framework, generalizable across theoretical orientations. According to the conceptual perspective, metaphor is not merely a matter of language, but is an indispensable dimension of human understanding and experience whereby more abstract ideas (like relationships) are understood in terms of more concrete experiences (like journeys). Consequently, when a couple in counseling says, “we're just spinning our wheels,” they are not only using a common colloquial expression, but also giving information about how they conceptualize their relationship. This article provides a theoretical foundation for use of conceptual metaphor and offers examples of its potential for counseling
The what and where of adding channel noise to the Hodgkin-Huxley equations
One of the most celebrated successes in computational biology is the
Hodgkin-Huxley framework for modeling electrically active cells. This
framework, expressed through a set of differential equations, synthesizes the
impact of ionic currents on a cell's voltage -- and the highly nonlinear impact
of that voltage back on the currents themselves -- into the rapid push and pull
of the action potential. Latter studies confirmed that these cellular dynamics
are orchestrated by individual ion channels, whose conformational changes
regulate the conductance of each ionic current. Thus, kinetic equations
familiar from physical chemistry are the natural setting for describing
conductances; for small-to-moderate numbers of channels, these will predict
fluctuations in conductances and stochasticity in the resulting action
potentials. At first glance, the kinetic equations provide a far more complex
(and higher-dimensional) description than the original Hodgkin-Huxley
equations. This has prompted more than a decade of efforts to capture channel
fluctuations with noise terms added to the Hodgkin-Huxley equations. Many of
these approaches, while intuitively appealing, produce quantitative errors when
compared to kinetic equations; others, as only very recently demonstrated, are
both accurate and relatively simple. We review what works, what doesn't, and
why, seeking to build a bridge to well-established results for the
deterministic Hodgkin-Huxley equations. As such, we hope that this review will
speed emerging studies of how channel noise modulates electrophysiological
dynamics and function. We supply user-friendly Matlab simulation code of these
stochastic versions of the Hodgkin-Huxley equations on the ModelDB website
(accession number 138950) and
http://www.amath.washington.edu/~etsb/tutorials.html.Comment: 14 pages, 3 figures, review articl
Hundreds of variants clustered in genomic loci and biological pathways affect human height
Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
Stochastically Gating Ion Channels Enable Patterned Spike Firing through Activity-Dependent Modulation of Spike Probability
The transformation of synaptic input into patterns of spike output is a
fundamental operation that is determined by the particular complement of ion
channels that a neuron expresses. Although it is well established that
individual ion channel proteins make stochastic transitions between conducting
and non-conducting states, most models of synaptic integration are
deterministic, and relatively little is known about the functional consequences
of interactions between stochastically gating ion channels. Here, we show that a
model of stellate neurons from layer II of the medial entorhinal cortex
implemented with either stochastic or deterministically gating ion channels can
reproduce the resting membrane properties of stellate neurons, but only the
stochastic version of the model can fully account for perithreshold membrane
potential fluctuations and clustered patterns of spike output that are recorded
from stellate neurons during depolarized states. We demonstrate that the
stochastic model implements an example of a general mechanism for patterning of
neuronal output through activity-dependent changes in the probability of spike
firing. Unlike deterministic mechanisms that generate spike patterns through
slow changes in the state of model parameters, this general stochastic mechanism
does not require retention of information beyond the duration of a single spike
and its associated afterhyperpolarization. Instead, clustered patterns of spikes
emerge in the stochastic model of stellate neurons as a result of a transient
increase in firing probability driven by activation of HCN channels during
recovery from the spike afterhyperpolarization. Using this model, we infer
conditions in which stochastic ion channel gating may influence firing patterns
in vivo and predict consequences of modifications of HCN
channel function for in vivo firing patterns
Astrocytes Optimize the Synaptic Transmission of Information
Chemical synapses transmit information via the release of neurotransmitter-filled vesicles from the presynaptic terminal. Using computational modeling, we predict that the limited availability of neurotransmitter resources in combination with the spontaneous release of vesicles limits the maximum degree of enhancement of synaptic transmission. This gives rise to an optimal tuning that depends on the number of active zones. There is strong experimental evidence that astrocytes that enwrap synapses can modulate the probabilities of vesicle release through bidirectional signaling and hence regulate synaptic transmission. For low-fidelity hippocampal synapses, which typically have only one or two active zones, the predicted optimal values lie close to those determined by experimentally measured astrocytic feedback, suggesting that astrocytes optimize synaptic transmission of information
Accurate and Fast Simulation of Channel Noise in Conductance-Based Model Neurons by Diffusion Approximation
Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically detailed model neurons calls for the availability of fast numerical methods. In fact, exact techniques employ the microscopic simulation of the random opening and closing of individual ion channels, usually based on Markov models, whose computational loads are prohibitive for next generation massive computer models of the brain. In this work, we operatively define a procedure for translating any Markov model describing voltage- or ligand-gated membrane ion-conductances into an effective stochastic version, whose computer simulation is efficient, without compromising accuracy. Our approximation is based on an improved Langevin-like approach, which employs stochastic differential equations and no Montecarlo methods. As opposed to an earlier proposal recently debated in the literature, our approximation reproduces accurately the statistical properties of the exact microscopic simulations, under a variety of conditions, from spontaneous to evoked response features. In addition, our method is not restricted to the Hodgkin-Huxley sodium and potassium currents and is general for a variety of voltage- and ligand-gated ion currents. As a by-product, the analysis of the properties emerging in exact Markov schemes by standard probability calculus enables us for the first time to analytically identify the sources of inaccuracy of the previous proposal, while providing solid ground for its modification and improvement we present here
Stochastic Ion Channel Gating in Dendritic Neurons: Morphology Dependence and Probabilistic Synaptic Activation of Dendritic Spikes
Neuronal activity is mediated through changes in the probability of stochastic transitions between open and closed states of ion channels. While differences in morphology define neuronal cell types and may underlie neurological disorders, very little is known about influences of stochastic ion channel gating in neurons with complex morphology. We introduce and validate new computational tools that enable efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes. Comparison of five morphologically distinct neuronal cell types reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of stochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location. For typical neurons, the amplitude of membrane potential fluctuations depends on channel kinetics as well as open probability. Using a detailed model of a hippocampal CA1 pyramidal neuron, we show that when intrinsic ion channels gate stochastically, the probability of initiation of dendritic or somatic spikes by dendritic synaptic input varies continuously between zero and one, whereas when ion channels gate deterministically, the probability is either zero or one. At physiological firing rates, stochastic gating of dendritic ion channels almost completely accounts for probabilistic somatic and dendritic spikes generated by the fully stochastic model. These results suggest that the consequences of stochastic ion channel gating differ globally between neuronal cell-types and locally between neuronal compartments. Whereas dendritic neurons are often assumed to behave deterministically, our simulations suggest that a direct consequence of stochastic gating of intrinsic ion channels is that spike output may instead be a probabilistic function of patterns of synaptic input to dendrites
First Dark Matter Search Results from the LUX-ZEPLIN (LZ) Experiment
The LUX-ZEPLIN (LZ) experiment is a dark matter detector centered on a
dual-phase xenon time projection chamber operating at the Sanford Underground
Research Facility in Lead, South Dakota, USA. This Letter reports results from
LZ's first search for Weakly Interacting Massive Particles (WIMPs) with an
exposure of 60 live days using a fiducial mass of 5.5 t. A profile-likelihood
ratio analysis shows the data to be consistent with a background-only
hypothesis, setting new limits on spin-independent WIMP-nucleon, spin-dependent
WIMP-neutron, and spin-dependent WIMP-proton cross-sections for WIMP masses
above 9 GeV/c. The most stringent limit is set at 30 GeV/c, excluding
cross sections above 5.9 cm at the 90\% confidence level.Comment: 9 pages, 6 figures. See https://tinyurl.com/LZDataReleaseRun1 for a
data release related to this pape
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