Single fibre action potentials in skeletal muscle related to recording distances

Single muscle fibre action potentials (SFAPs) are considered to be functions of a bioelectrical source and electrical conductivity parameters of the medium. In most model studies SFAPs are computed as a convolution of the bioelectrical source with a transfer function. Calculated peak-to-peak amplitudes of SFAPs decrease with increasing recording distances. In this paper an experimental validation of model results is presented. Experiments were carried out on the m. extensor digitorum longus (EDL) of the rat. Using a method including fluorescent labelling of the active fibre, the distance between the active fibre and the recording electrode was derived. With another method, the decline of the peak-to-peak amplitude of SFAPs detected along a multi-electrode was obtained. With both experimental methods, in general peak-to-peak amplitudes of SFAPs decreased with increasing recording distances, as was found in model results with present volume conduction theory. However, this behaviour was not found in all experiments. The rate of decline of the peak-to-peak amplitudes with recording distance was always less than in models

The Highlander: Volume 2, Number 10- November 16, 1936

https://digitalmaine.com/highlander/1009/thumbnail.jp

Bayesian clustering of multiple zero-inflated outcomes

Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'

Single-Domain Parvulins Constitute a Specific Marker for Recently Proposed Deep-Branching Archaeal Subgroups

Peptidyl-prolyl cis/trans isomerases (PPIases) are enzymes assisting protein folding and protein quality control in organisms of all kingdoms of life. In contrast to the other sub-classes of PPIases, the cyclophilins and the FK-506 binding proteins, little was formerly known about the parvulin type of PPIase in Archaea. Recently, the first solution structure of an archaeal parvulin, the PinA protein from Cenarchaeum symbiosum, was reported. Investigation of occurrence and frequency of PPIase sequences in numerous archaeal genomes now revealed a strong tendency for thermophilic microorganisms to reduce the number of PPIases. Single-domain parvulins were mostly found in the genomes of recently proposed deep-branching archaeal subgroups, the Thaumarchaeota and the ARMANs (archaeal Richmond Mine acidophilic nanoorganisms). Hence, we used the parvulin sequence to reclassify available archaeal metagenomic contigs, thereby, adding new members to these subgroups. A combination of genomic background analysis and phylogenetic approaches of parvulin sequences suggested that the assigned sequences belong to at least two distinct groups of Thaumarchaeota. Finally, machine learning approaches were applied to identify amino acid residues that separate archaeal and bacterial parvulin proteins from each other. When mapped onto the recent PinA solution structure, most of these positions form a cluster at one site of the protein possibly indicating a different functionality of the two groups of parvulin proteins

The testis-specific Cα2 subunit of PKA is kinetically indistinguishable from the common Cα1 subunit of PKA

Background The two variants of the α-form of the catalytic (C) subunit of protein kinase A (PKA), designated Cα1 and Cα2, are encoded by the PRKACA gene. Whereas Cα1 is ubiquitous, Cα2 expression is restricted to the sperm cell. Cα1 and Cα2 are encoded with different N-terminal domains. In Cα1 but not Cα2 the N-terminal end introduces three sites for posttranslational modifications which include myristylation at Gly1, Asp-specific deamidation at Asn2 and autophosphorylation at Ser10. Previous reports have implicated specific biological features correlating with these modifications on Cα1. Since Cα2 is not modified in the same way as Cα1 we tested if they have distinct biochemical activities that may be reflected in different biological properties. Results We show that Cα2 interacts with the two major forms of the regulatory subunit (R) of PKA, RI and RII, to form cAMP-sensitive PKAI and PKAII holoenzymes both in vitro and in vivo as is also the case with Cα1. Moreover, using Surface Plasmon Resonance (SPR), we show that the interaction patterns of the physiological inhibitors RI, RII and PKI were comparable for Cα2 and Cα1. This is also the case for their potency to inhibit catalytic activities of Cα2 and Cα1. Conclusion We conclude that the regulatory complexes formed with either Cα1 or Cα2, respectively, are indistinguishable

Understanding public support for COVID-19 pandemic mitigation measures over time:Does it wear out?

Background: COVID-19 mitigation measures intend to protect public health, but their adverse psychological, social, and economic effects weaken public support. Less favorable trade-offs may especially weaken support for more restrictive measures. Support for mitigation measures may also differ between population subgroups who experience different benefits and costs, and decrease over time, a phenomenon termed “pandemic fatigue.” Methods: We examined self-reported support for COVID-19 mitigation measures in the Netherlands over 12 consecutives waves of data collection between April 2020 and May 2021 in an open population cohort study. Participants were recruited through community panels of the 25 regional public health services, and through links to the online surveys advertised on social media. The 54,010 unique participants in the cohort study on average participated in 4 waves of data collection. Most participants were female (65%), middle-aged [57% (40–69 years)], highly educated (57%), not living alone (84%), residing in an urban area (60%), and born in the Netherlands (95%). Results: COVID-19 mitigation measures implemented in the Netherlands remained generally well-supported over time [all scores >3 on 5-point scale ranging 1 (low)−5 (high)]. During the whole period studied, support was highest for personal hygiene measures, quarantine and wearing face masks, high but somewhat lower for not shaking hands, testing and self-isolation, and restricting social contacts, and lowest for limiting visitors at home, and not traveling abroad. Women and higher educated people were more supportive of some mitigation measures than men and lower educated people. Older people were more supportive of more restrictive measures than younger people, and support for more socially restrictive measures decreased most over time in higher educated people or in younger people. Conclusions: This study found no support for pandemic fatigue in terms of a gradual decline in support for all mitigation measures in the first year of the pandemic. Rather, findings suggest that support for mitigation measures reflects a balancing of benefits and cost, which may change over time, and differ between measures and population subgroups