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One of the key challenges of k-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex data sets such as those obtained from molecular simulation, k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k-means++ will lead to a lack of reproducibility. K-means N-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse data sets and be used as a standalone tool or as part of our MDANCE clustering package.

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BACKGROUND: Child sexual abuse (CSA) and problematic sexual behavior (PSB) are worldwide phenomena that occur across all ages. Kindergarten teachers' proactive involvement can be crucial to the prevention, disclosure and intervention of CSA and PSB. However, research on their experiences of contending with CSA and PSB remains limited. OBJECTIVE: This study examines kindergarten teachers' experiences in Israel with the CSA and PSB of their students. PARTICIPANTS AND SETTING: Semi-structured interviews were conducted with 31 teachers: 11 secular Jewish, seven religious Jewish, nine Druze Arab, and four Muslim Arab. METHODS: A qualitative analysis was conducted using the interview transcripts as data. RESULTS: The analysis revealed three themes illustrating teachers' professional transformations regarding their knowledge of these phenomena: 1) initial shock, uncertainty and sense of responsibility when exposed to CSA and PSB due to missing knowledge, 2) implementation of prevention and intervention strategies regarding CSA and PSB, and 3) embracing a social role to disseminate CSA and PSB knowledge. The findings indicated that the majority of the teachers went from overwhelming shock and fear due to a lack of knowledge in coping with CSA and PSB to a sense of responsibility as a community leader. CONCLUSIONS: The fragmentation of the Israeli education system isolates kindergartens, and the lack of training and education for the teachers left them alone when contending with the CSA and PSB of their students. Nevertheless, the participants exhibited remarkable agency and resourcefulness, gaining the necessary knowledge and acting as knowledge agents within their communities.

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One of the key challenges of k-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex datasets such as those obtained from molecular simulation, k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k-means++ will lead to a lack of reproducibility. K-means N-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse datasets and be used as a standalone tool or as part of our MDANCE clustering package.

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OBJECTIVE: To develop an international consensus statement to advise on designing, delivering and evaluating sport-based interventions (SBIs) aimed at promoting social, psychological and physical well-being in prison. DESIGN: Modified Delphi using two rounds of survey questionnaires and two consensus workshops. PARTICIPANTS: A multidisciplinary panel of more than 40 experts from 15 international jurisdictions was formed, including representation from the following groups and stakeholders: professionals working in the justice system; officials from sport federations and organisations; academics with research experience of prisons, secure forensic mental health settings and SBIs; and policy-makers in criminal justice and sport. RESULTS: A core research team and advisory board developed the initial rationale, statement and survey. This survey produced qualitative data which was analysed thematically. The findings were presented at an in-person workshop. Panellists discussed the findings, and, using a modified nominal group technique, reached a consensus on objectives to be included in a revised statement. The core research team and advisory board revised the statement and recirculated it with a second survey. Findings from the second survey were discussed at a second, virtual, workshop. The core research team and advisory board further revised the consensus statement and recirculated it asking panellists for further comments. This iterative process resulted in seven final statement items; all participants have confirmed that they agreed with the content, objectives and recommendations of the final statement. CONCLUSIONS: The statement can be used to assist those that design, deliver and evaluate SBIs by providing guidance on: (1) minimum levels of competence for those designing and delivering SBIs; (2) the design and delivery of inclusive programmes prioritising disadvantaged groups; and (3) evaluation measures which are carefully calibrated both to capture proposed programme outcomes and to advance an understanding of the systems, processes and experiences of sport engagement in prison.

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The grid inhomogeneous solvation theory (GIST) method requires the often time-consuming calculation of water-water and water-solute energy on a grid. Previous efforts to speed up this calculation include using OpenMP, GPUs, and particle mesh Ewald. This article details how the speed of this calculation can be increased by parallelizing it with MPI, where trajectory frames are divided among multiple processors. This requires very little communication between individual processes during trajectory processing, meaning the calculation scales well to large processor counts. This article also details how the entropy calculation, which must happen after trajectory processing since it requires information from all trajectory frames, is parallelized via MPI. This parallelized GIST method has been implemented in the freely-available CPPTRAJ analysis software.

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AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

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While there is a burgeoning body of literature on visual methods in ethnography, including drawing and illustration as method for collecting and exploring data, little has been written about how artistic illustrations can be used as a representational method for finished ethnographic texts. Based on an illustrated ethnography-a PhD thesis on sport pedagogy in youth detention-this paper explores what artistic illustrations can do for representations of ethnographic texts. An important starting point of the paper is that artistic illustrations are purpose-full-they can be used strategically to highlight some details over others, thus making it possible for researchers to selectively accomplish several aims when it comes to representation. Particularly, we focus on ethical, affective, and descriptive purposes for using artistic illustrations when publishing research on sport in total institutions. We present selected illustrations from the thesis together with analytical and procedural commentary to shed light on some strategic thinking behind the production of the illustrations. Finally, we conclude with some reflections on the methodology and discuss some further considerations for illustrating ethnographic texts in terms of benefits, risks, and possibilities.

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Molecular dynamics (MD) simulations are now able to routinely reach timescales of microseconds and beyond. This has led to a corresponding increase in the amount of MD trajectory data that needs to be stored, particularly when those trajectories contain explicit solvent molecules. As such, it is desirable to be able to compress trajectory data while still retaining as much of the original information as possible. In this work, we describe compressing MD trajectory data using the NetCDF4/HDF5 file format, making use of quantization of the original positions to achieve better compression ratios. We also analyze the affect this has on both the resulting positions and the energies calculated from post-processing these trajectories, and recommend an optimal level of quantization. Overall we find the NetCDF4/HDF5 format to be an excellent choice for storing MD trajectory data in terms of speed, compressibility, and versatility.

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BACKGROUND: Schools serve a central role in prevention, disclosure and intervention in cases of child sexual abuse (CSA). As school principals often face CSA cases in their daily work, they may hold the key to making social change on this front. However, research on principals' experiences of contending with CSA remains limited. OBJECTIVE: The current study is part of a larger qualitative research project examining various Israeli educators' coping with CSA among their students in diverse cultural contexts. In this study, we specifically focused on principals. The research questions were: (1) What are the unique ways in which school principals cope with cases of CSA during their course of daily work? (2) Do their cultural contexts and cultural affiliations shape their coping, and if so, how? METHODS: In-depth interviews were conducted with 25 principals from multiple cultural groups (secular, religious and ultra-Orthodox Jews, and Arab-Muslims), which were analyzed using a thematic approach. RESULTS: The findings indicated that principals demonstrate three types of coping strategies in response to encounters with CSA in the course of their work: they may act as "navigators" (exclusively responsible); "sharers" (rely on teamwork); or "balancers" (negotiating between cultural and legal demands). Furthermore, two contextual factors affected their construction of coping: ongoing professional experience in cases of CSA and personal experiences, including being a CSA survivor. CONCLUSIONS: This study highlights the importance of principals in identifying and leading interventions for CSA cases. It also raises the need for training to combine reflective, experience-based practice alongside evidence-informed practice.

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Setting up molecular dynamics simulations from experimentally determined structures is often complicated by a variety of factors, particularly the inclusion of carbohydrates, since these have several anomer types which can be linked in a variety of ways. Here we present a stand-alone tool implemented in the widely-used software CPPTRAJ that can be used to automate building structures and generating a "ready to run" parameter and coordinate file pair. This tool automatically identifies carbohydrate anomer type, configuration, linkage, and functional groups, and performs topology modifications (e.g., renaming residue/atom names) required to build the final system using state of the art GLYCAM force field parameters. It will also generate the necessary commands for bonding carbohydrates and creating any disulfide bonds.

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Grid Inhomogeneous Solvation Theory (GIST) maps out solvation thermodynamic properties on a fine meshed grid and provides a statistical mechanical formalism for thermodynamic end-state calculations. However, differences in how long-range nonbonded interactions are calculated in molecular dynamics engines and in the current implementation of GIST have prevented precise comparisons between free energies estimated using GIST and those from other free energy methods such as thermodynamic integration (TI). Here, we address this by presenting PME-GIST, a formalism by which particle mesh Ewald (PME)-based electrostatic energies and long-range Lennard-Jones (LJ) energies are decomposed and assigned to individual atoms and the corresponding voxels they occupy in a manner consistent with the GIST approach. PME-GIST yields potential energy calculations that are precisely consistent with modern simulation engines and performs these calculations at a dramatically faster speed than prior implementations. Here, we apply PME-GIST end-state analyses to 32 small molecules whose solvation free energies are close to evenly distributed from 2 kcal/mol to -17 kcal/mol and obtain solvation energies consistent with TI calculations (R2 = 0.99, mean unsigned difference 0.8 kcal/mol). We also estimate the entropy contribution from the second and higher order entropy terms that are truncated in GIST by the differences between entropies calculated in TI and GIST. With a simple correction for the high order entropy terms, PME-GIST obtains solvation free energies that are highly consistent with TI calculations (R2 = 0.99, mean unsigned difference = 0.4 kcal/mol) and experimental results (R2 = 0.88, mean unsigned difference = 1.4 kcal/mol). The precision of PME-GIST also enables us to show that the solvation free energy of small hydrophobic and hydrophilic molecules can be largely understood based on perturbations of the solvent in a region extending a few solvation shells from the solute. We have integrated PME-GIST into the open-source molecular dynamics analysis software CPPTRAJ.

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Visualizing data generated from molecular dynamics simulations can be difficult, particularly when there can be thousands to millions of trajectory frames. The creation of a 3D grid of atomic density (i.e. a volumetric map) is one way to easily view the long-time average behavior of a system. One way to generate volumetric maps is by approximating each atom with a Gaussian function centered on that atom and spread over neighboring grid cells. However the calculation of the Gaussian function requires evaluation of the exponential function, which is computationally costly. Here we report on speeding up the calculation of volumetric maps from molecular dynamics trajectory data by replacing the expensive exponential function evaluation with an approximation using interpolating cubic splines. We also discuss the errors involved in this approximation, and recommend settings for volumetric map creation based on this.

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Before beginning the production phase of molecular dynamics simulations, i.e., the phase that produces the data to be analyzed, it is often necessary to first perform a series of one or more preparatory minimizations and/or molecular dynamics simulations in order to ensure that subsequent production simulations are stable. This is particularly important for simulations with explicit solvent molecules. Despite the preparatory minimizations and simulations being ubiquitous and essential for stable production simulations, there are currently no general recommended procedures to perform them and very few criteria to decide whether the system is capable of producing a stable simulation trajectory. Here, we propose a simple and well-defined ten step simulation preparation protocol for explicitly solvated biomolecules, which can be applied to a wide variety of system types, as well as a simple test based on the system density for determining whether the simulation is stabilized.

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As we age, sleep patterns undergo severe modifications of their micro and macrostructure, with an overall lighter and more fragmented sleep structure. In general, interventions targeting sleep represent an excellent opportunity not only to maintain life quality in the healthy aging population, but also to enhance cognitive performance and, when pathology arises, to potentially prevent/slow down conversion from e.g. Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Sleep abnormalities are, in fact, one of the earliest recognizable biomarkers of dementia, being also partially responsible for a cascade of cortical events that worsen dementia pathophysiology, including impaired clearance systems leading to build-up of extracellular amyloid-ß (Aß) peptide and intracellular hyperphosphorylated tau proteins. In this context, Noninvasive Brain Stimulation (NiBS) techniques, such as transcranial electrical stimulation (tES) and transcranial magnetic stimulation (TMS), may help investigate the neural substrates of sleep, identify sleep-related pathology biomarkers, and ultimately help patients and healthy elderly individuals to restore sleep quality and cognitive performance. However, brain stimulation applications during sleep have so far not been fully investigated in healthy elderly cohorts, nor tested in AD patients or other related dementias. The manuscript discusses the role of sleep in normal and pathological aging, reviewing available evidence of NiBS applications during both wakefulness and sleep in healthy elderly individuals as well as in MCI/AD patients. Rationale and details for potential future brain stimulation studies targeting sleep alterations in the aging brain are discussed, including enhancement of cognitive performance, overall quality of life as well as protein clearance.

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The selectivity filter (SF) of bacterial voltage-gated sodium channels consists of four glutamate residues arranged in a C4 symmetry. The protonation state population of this tetrad is unclear. To address this question, we simulate the pore domain of bacterial voltage-gated sodium channel of Magnetococcus sp. (Nav Ms) through constant pH methodology in explicit solvent and free energy perturbation calculations. We find that at physiological pH the fully deprotonated as well as singly and doubly protonated states of the SF appear feasible, and that the calculated pKa decreases with each additional bound ion, suggesting that a decrease in the number of ions in the pore can lead to protonation of the SF. Previous molecular dynamics simulations have suggested that protonation can lead to a decrease in the conductance, but no pKa calculations were performed. We confirm a decreased ionic population of the pore with protonation, and also observe structural symmetry breaking triggered by protonation; the SF of the deprotonated channel is closest to the C4 symmetry observed in crystal structures of the open state, while the SF of protonated states display greater levels of asymmetry which could lead to transition to the inactivated state which possesses a C2 symmetry in the crystal structure. We speculate that the decrease in the number of ions near the mouth of the channel, due to either random fluctuations or ion depletion due to conduction, could be a self-regulatory mechanism resulting in a nonconducting state that functionally resembles inactivated states.

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The quantitative assessment of uncertainty and sampling quality is essential in molecular simulation. Many systems of interest are highly complex, often at the edge of current computational capabilities. Modelers must therefore analyze and communicate statistical uncertainties so that "consumers" of simulated data understand its significance and limitations. This article covers key analyses appropriate for trajectory data generated by conventional simulation methods such as molecular dynamics and (single Markov chain) Monte Carlo. It also provides guidance for analyzing some 'enhanced' sampling approaches. We do not discuss systematic errors arising, e.g., from inaccuracy in the chosen model or force field.

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Advances in biomolecular simulation methods and access to large scale computer resources have led to a massive increase in the amount of data generated. The key enablers have been optimization and parallelization of the simulation codes. However, much of the software used to analyze trajectory data from these simulations is still run in serial, or in some cases many threads via shared memory. Here, we describe the addition of multiple levels of parallel trajectory processing to the molecular dynamics simulation analysis software CPPTRAJ. In addition to the existing OpenMP shared-memory parallelism, CPPTRAJ now has two additional levels of message passing (MPI) parallelism involving both across-trajectory processing and across-ensemble processing. All three levels of parallelism can be simultaneously active, leading to significant speed ups in data analysis of large datasets on the NCSA Blue Waters supercomputer by better leveraging the many available nodes and its parallel file system. © 2018 Wiley Periodicals, Inc.

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An experimentally well-studied model of RNA tertiary structures is a 58mer rRNA fragment, known as GTPase-associating center (GAC) RNA, in which a highly negative pocket walled by phosphate oxygen atoms is stabilized by a chelated cation. Although such deep pockets with more than one direct phosphate to ion chelation site normally include magnesium, as shown in one GAC crystal structure, another GAC crystal structure and solution experiments suggest potassium at this site. Both crystal structures also depict two magnesium ions directly bound to the phosphate groups comprising this controversial pocket. Here, we used classical molecular dynamics simulations as well as umbrella sampling to investigate the possibility of binding of potassium versus magnesium inside the pocket and to better characterize the chelation of one of the binding magnesium ions outside the pocket. The results support the preference of the pocket to accommodate potassium rather than magnesium and suggest that one of the closely binding magnesium ions can only bind at high magnesium concentrations, such as might be present during crystallization. This work illustrates the complementary utility of molecular modeling approaches with atomic-level detail in resolving discrepancies between conflicting experimental results.

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The cyanobactin prenyltransferases catalyze a series of known or unprecedented reactions on millions of different substrates, with no easily observable recognition motif and exquisite regioselectivity. Here we define the basis of broad substrate tolerance for the otherwise uncharacterized TruF family. We determined the structures of the Tyr-prenylating enzyme PagF, in complex with an isoprenoid donor analog and a panel of linear and macrocyclic peptide substrates. Unexpectedly, the structures reveal a truncated barrel fold, wherein binding of large peptide substrates is necessary to complete a solvent-exposed hydrophobic pocket to form the catalytically competent active site. Kinetic, mutational, chemical, and computational analyses revealed the structural basis of selectivity, showing a small motif within peptide substrates that is sufficient for recognition by the enzyme. Attaching this 2-residue motif to two random peptides results in their isoprenylation by PagF, demonstrating utility as a general biocatalytic platform for modifications on any peptide substrate.

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Long time scale molecular dynamics (MD) simulations of biological systems are becoming increasingly commonplace due to the availability of both large-scale computational resources and significant advances in the underlying simulation methodologies. Therefore, it is useful to investigate and develop data mining and analysis techniques to quickly and efficiently extract the biologically relevant information from the incredible amount of generated data. Wavelet analysis (WA) is a technique that can quickly reveal significant motions during an MD simulation. Here, the application of WA on well-converged long time scale (tens of µs) simulations of a DNA helix is described. We show how WA combined with a simple clustering method can be used to identify both the physical and temporal locations of events with significant motion in MD trajectories. We also show that WA can not only distinguish and quantify the locations and time scales of significant motions, but by changing the maximum time scale of WA a more complete characterization of these motions can be obtained. This allows motions of different time scales to be identified or ignored as desired.

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