Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled activation subunits, while the DA was modeled using uncoupled activation subunits. Implementations of DA with coupled subunits, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable - allowing an easy and efficient DA implementation. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13 Submissions were made by three free-modelling methods which combine the predictions of three neural networks. All three systems were guided by predictions of distances between pairs of residues produced by a neural network. Two systems assembled fragments produced by a generative neural network, one using scores from a network trained to regress GDT_TS. The third system shows that simple gradient descent on a properly constructed potential is able to perform on-par with more expensive traditional search techniques and without requiring domain segmentation. In the CASP13 free-modelling assessors' ranking by summed z-scores, this system scored highest with 68.3 vs 48.2 for the next closest group. (An average GDT_TS of 61.4.) The system produced high-accuracy structures (with GDT_TS scores of 70 or higher) for 11 out of 43 free-modelling domains. Despite not explicitly using template information, the results in the template category were comparable to the best performing template-based methods

Cryptosporidium Priming Is More Effective than Vaccine for Protection against Cryptosporidiosis in a Murine Protein Malnutrition Model

Cryptosporidium is a major cause of severe diarrhea, especially in malnourished children. Using a murine model of C. parvum oocyst challenge that recapitulates clinical features of severe cryptosporidiosis during malnutrition, we interrogated the effect of protein malnutrition (PM) on primary and secondary responses to C. parvum challenge, and tested the differential ability of mucosal priming strategies to overcome the PM-induced susceptibility. We determined that while PM fundamentally alters systemic and mucosal primary immune responses to Cryptosporidium, priming with C. parvum (106 oocysts) provides robust protective immunity against re-challenge despite ongoing PM. C. parvum priming restores mucosal Th1-type effectors (CD3+CD8+CD103+ T-cells) and cytokines (IFNγ, and IL12p40) that otherwise decrease with ongoing PM. Vaccination strategies with Cryptosporidium antigens expressed in the S. Typhi vector 908htr, however, do not enhance Th1-type responses to C. parvum challenge during PM, even though vaccination strongly boosts immunity in challenged fully nourished hosts. Remote non-specific exposures to the attenuated S. Typhi vector alone or the TLR9 agonist CpG ODN-1668 can partially attenuate C. parvum severity during PM, but neither as effectively as viable C. parvum priming. We conclude that although PM interferes with basal and vaccine-boosted immune responses to C. parvum, sustained reductions in disease severity are possible through mucosal activators of host defenses, and specifically C. parvum priming can elicit impressively robust Th1-type protective immunity despite ongoing protein malnutrition. These findings add insight into potential correlates of Cryptosporidium immunity and future vaccine strategies in malnourished children

Attachment, infidelity, and loneliness in college students involved in a romantic relationship: the role of relationship satisfaction, morbidity and prayer for partner

This study examined the mediating effects of relationship satisfaction, prayer for a partner, and morbidity in the relationship between attachment and loneliness, infidelity and loneliness, and psychological morbidity and loneliness, in college students involved in a romantic relationship. Participants were students in an introductory course on family development. This study examined only students (n = 345) who were involved in a romantic relationship. The average age of participants was 19.46 (SD = 1.92) and 25 % were males. Short-form UCLA Loneliness Scale (ULS-8), (Hays and DiMatteo in J Pers Assess 51:69–81, doi:10.1207/s15327752jpa5101_6, 1987); Relationship Satisfaction Scale (Funk and Rogge in J Fam Psychol 21:572–583, doi:10.1037/0893-3200.21.4.572, 2007); Rotterdam Symptom Checklist (De Haes et al. in Measuring the quality of life of cancer patients with the Rotterdam Symptom Checklist (RSCL): a manual, Northern Centre for Healthcare Research, Groningen, 1996); Prayer for Partner Scale, (Fincham et al. in J Pers Soc Psychol 99:649–659, doi:10.1037/a0019628, 2010); Infidelity Scale, (Drigotas et al. in J Pers Soc Psychol 77:509–524, doi:10.1037/0022-3514.77.3.509, 1999); and the Experiences in Close Relationship Scale-short form (Wei et al. in J Couns Psychol 52(4):602–614, doi:10.1037/0022-0167.52.4.602, 2005). Results showed that relationship satisfaction mediated the relationship between avoidance attachment and loneliness and between infidelity and loneliness. Physical morbidity mediated the relationship between anxious attachment and psychological morbidity. Psychological morbidity mediated the relationship between anxious attachment and physical morbidity. The present results expand the literature on attachment by presenting evidence that anxious and avoidant partners experience loneliness differently. Implications for couple’s therapy are addressed. Future research should replicate these results with older samples and married couples.Acknowledgments This research was supported by Grant Number 90FE0022 from the United States Department of Health and Human Services awarded to the last author

Self-assessment and students’ study strategies in a community of clinical practice: A qualitative study

: Self-assessment is recognized as a necessary skill for lifelong learning. It is widely reported to offer numerous advantages to the learner. The research evaluated the impact of students’ and supervisors’ self-assessment and feedback training on students’ perceptions and practices of self-assessment. Moreover, it evaluated the effect of self-assessment process on students’ study strategies within a community of clinical practice.: We conducted a qualitative phenomenological study from May 2008 to December 2009. We held 37 semi-structured individual interviews with three different cohorts of undergraduate medical students until we reached data saturation. The cohorts were exposed to different contexts while experiencing their clinical years’ assessment program. In the interviews, students’ perceptions and interpretations of ‘self-assessment practice’ and ‘supervisor-provided feedback’ within different contexts and the resulting study strategies were explored.: The analysis of interview data with the three cohorts of students yielded three major themes: strategic practice of self-assessment, self-assessment and study strategies, and feedback and study strategies. It appears that self-assessment is not appropriate within a summative context, and its implementation requires cultural preparation. Despite education and orientation on the two major components of the self-assessment process, feedback was more effective in enhancing deeper study strategies.: This research suggests that the theoretical advantages linked to the self-assessment process are a result of its feedback component rather than the practice of self-assessment isolated from feedback. Further research exploring the effects of different contextual and personal factors on students’ self-assessment is needed

Why simulation can be efficient: on the preconditions of efficient learning in complex technology based practices

<p>Abstract</p> <p>Background</p> <p>It is important to demonstrate learning outcomes of simulation in technology based practices, such as in advanced health care. Although many studies show skills improvement and self-reported change to practice, there are few studies demonstrating patient outcome and societal efficiency.</p> <p>The objective of the study is to investigate if and why simulation can be effective and efficient in a hi-tech health care setting. This is important in order to decide whether and how to design simulation scenarios and outcome studies.</p> <p>Methods</p> <p>Core theoretical insights in Science and Technology Studies (STS) are applied to analyze the field of simulation in hi-tech health care education. In particular, a process-oriented framework where technology is characterized by its devices, methods and its organizational setting is applied.</p> <p>Results</p> <p>The analysis shows how advanced simulation can address core characteristics of technology beyond the knowledge of technology's functions. Simulation's ability to address skilful device handling as well as purposive aspects of technology provides a potential for effective and efficient learning. However, as technology is also constituted by organizational aspects, such as technology status, disease status, and resource constraints, the success of simulation depends on whether these aspects can be integrated in the simulation setting as well. This represents a challenge for future development of simulation and for demonstrating its effectiveness and efficiency.</p> <p>Conclusion</p> <p>Assessing the outcome of simulation in education in hi-tech health care settings is worthwhile if core characteristics of medical technology are addressed. This challenges the traditional technical versus non-technical divide in simulation, as organizational aspects appear to be part of technology's core characteristics.</p

Computational identification of ubiquitylation sites from protein sequences

<p>Abstract</p> <p>Background</p> <p>Ubiquitylation plays an important role in regulating protein functions. Recently, experimental methods were developed toward effective identification of ubiquitylation sites. To efficiently explore more undiscovered ubiquitylation sites, this study aims to develop an accurate sequence-based prediction method to identify promising ubiquitylation sites.</p> <p>Results</p> <p>We established an ubiquitylation dataset consisting of 157 ubiquitylation sites and 3676 putative non-ubiquitylation sites extracted from 105 proteins in the UbiProt database. This study first evaluates promising sequence-based features and classifiers for the prediction of ubiquitylation sites by assessing three kinds of features (amino acid identity, evolutionary information, and physicochemical property) and three classifiers (support vector machine, <it>k</it>-nearest neighbor, and NaïveBayes). Results show that the set of used 531 physicochemical properties and support vector machine (SVM) are the best kind of features and classifier respectively that their combination has a prediction accuracy of 72.19% using leave-one-out cross-validation.</p> <p>Consequently, an informative physicochemical property mining algorithm (IPMA) is proposed to select an informative subset of 531 physicochemical properties. A prediction system UbiPred was implemented by using an SVM with the feature set of 31 informative physicochemical properties selected by IPMA, which can improve the accuracy from 72.19% to 84.44%. To further analyze the informative physicochemical properties, a decision tree method C5.0 was used to acquire if-then rule-based knowledge of predicting ubiquitylation sites. UbiPred can screen promising ubiquitylation sites from putative non-ubiquitylation sites using prediction scores. By applying UbiPred, 23 promising ubiquitylation sites were identified from an independent dataset of 3424 putative non-ubiquitylation sites, which were also validated by using the obtained prediction rules.</p> <p>Conclusion</p> <p>We have proposed an algorithm IPMA for mining informative physicochemical properties from protein sequences to build an SVM-based prediction system UbiPred. UbiPred can predict ubiquitylation sites accompanied with a prediction score each to help biologists in identifying promising sites for experimental verification. UbiPred has been implemented as a web server and is available at <url>http://iclab.life.nctu.edu.tw/ubipred</url>.</p

Inferring Epidemic Contact Structure from Phylogenetic Trees

Contact structure is believed to have a large impact on epidemic spreading and consequently using networks to model such contact structure continues to gain interest in epidemiology. However, detailed knowledge of the exact contact structure underlying real epidemics is limited. Here we address the question whether the structure of the contact network leaves a detectable genetic fingerprint in the pathogen population. To this end we compare phylogenies generated by disease outbreaks in simulated populations with different types of contact networks. We find that the shape of these phylogenies strongly depends on contact structure. In particular, measures of tree imbalance allow us to quantify to what extent the contact structure underlying an epidemic deviates from a null model contact network and illustrate this in the case of random mixing. Using a phylogeny from the Swiss HIV epidemic, we show that this epidemic has a significantly more unbalanced tree than would be expected from random mixing