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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type string to null
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2223, in cast_table_to_schema
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2224, in <listcomp>
cast_array_to_feature(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2086, in cast_array_to_feature
return array_cast(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1948, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1451, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 994, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
corpusid
int64 | externalid_arxiv
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13,754,617
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1804.11005
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journals/corr/abs-1804-11005
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Building models for biopathway dynamics using intrinsic dimensionality analysis
September 25, 2015 3 Nov 2018
Emilia M Wysocka
University of Edinburgh
UK
Valery Dzutsati
Arizona State University
USA
Laura Condon
CSIRO
Australia
Colorado School of Mines
USA
Sahil Garg
University of Southern California
USA
INSTITUTE NM
SANTA FE
USA
Building models for biopathway dynamics using intrinsic dimensionality analysis
September 25, 2015 3 Nov 2018COMPLEX SYSTEMS SUMMER SCHOOL 2015
Introduction and project motivations
Extensive development of technologies and methods related to data acquisition, sharing and storage have made analysis and knowledge discovery unprecedentedly challenging. For instance, big data has become increasingly common in social sciences and requires new techniques of analysis, including non linear time series approaches. One of such recent examples of a challenging dataset is the data on rebel violence in the volatile Russian North Caucasus region [26]. The dataset has recordings of incidents of rebel violence on weekly basis for every town and village of the region. Overall, this resulted in over 1 million observations with nearly 200 variables, approximately 200 million data points.
An important task for many if not all the scientific domains is efficient knowledge integration, testing and codification. It is often solved with model construction in a controllable computational environment. In spite of that, the throughput of in-silico simulation-based observations become similarly intractable for thorough analysis. This is especially the case in molecular biology, which served as a subject for this study.
In this project, we aimed to test some approaches developed to deal with the curse of dimensionality. Among these we found dimension reduction techniques especially appealing. They can be used to identify irrelevant variability and help to understand critical processes underlying high-dimensional datasets. Additionally, we subjected our data-sets to nonlinear time series analysis, as those are well established methods for results comparison.
To investigate the usefulness of dimension reduction methods, we decided to base our study on a concrete sample set. The example was taken from the domain of systems biology concerning dynamic evolution of subcellular signalling. Particularly, the dataset relates to the yeast pheromone pathway and is studied in-silico with a stochastic model. The model reconstructs signal propagation stimulated by a mating pheromone.
In the following sections we will elaborate on the reason of multidimensional analysis problem in the context of molecular signalling. Next, we will introduce the model of choice, simulation details and obtained time series dynamics. A description of used methods followed by a discussion of results and their biological interpretation will finalise this report. This study is a preliminary analysis of the dataset, future work will expand on the results presented here. reaction [1]. These highly sophisticated functions produce complex systems embodied by the combinatorial explosion of molecular interactions and states [12,2].
On the lower level, cell signalling depends on formation and interactions of multi-subunit complexes, mainly formed by interacting proteins. They are composed from often numerous and autonomously folding blocks called domains, acting as protein functional interfaces. Importantly, protein activity is determined by multiple post-translational modification sites (phosphorylation, acetylation, ubiquitination), transitionally changing their states. For example, lets consider an ubiquitously present Epidermal Growth Factor Receptor (EGFR), which has 9 sites resulting in 512 possible states (2 9 = 512 , on-and off-state). Furthermore, each site has at least one binding partner rising the value of single receptor protein states to 19,683 possibilities (3 9 = 19, 683). The large number of possible states, even within this relatively simple system is one of the key challenges for mechanistic modelling of signalling networks. Traditional equation-based models are capable of representing only extensively studied and limited size signalling circuits. Any larger integrative models become intractable, impossible to reuse or even proofread [19]. These problems have been addressed by rule-based modelling methods embodied by flexible languages such as Kappa [3] and BioNetGen [6], facilitating the creation of large and complex dynamical models. In contrast to the other modelling techniques, in rule-based models the system emerges with time, often showing unpredictable behaviour arising from elementary reaction rules. However, their construction and analysis often limit their potential application. For instance, even though provided with visualization tools for static and causal analysis, a modeller has to resort to a self-assembled battery of tests trying to unfold the complexity of results [24].
Yeast pheromone response pathway model
In the domain of molecular biology the yeast pheromone cell cycle is an extensively studied example, both in-vivo and as a computational model. It's often used to test hypotheses and investigate details related to mechanisms of signalling processes, such as dynamical pathway adaptation to demanding environmental conditions [20], evolutionary preserved functional units (G-protein coupled receptor signalling [4], mitogene-activated protein kinase [17]), signal-noise decoupling [4] and information transmission [29].
Saccharomyces cerevisiae yeast, is a model species, capable of sexually reproducing in pairs of opposite sexes (type a and α). The mating signal is communicated by either of the cell type through pheromone release (a-factor) [20]. The model used in this study relates to a subcellular signalling activated in the other cell in response to the stimulus [24].
The pathway represents canonical mechanisms of the subcelluar signal propagation, such as G-protein activation via a GPCR, which is stimulated by pheromone ligands. The scaffold protein (Ste5) is recruited to the cell surface. Potential combinations of complexes appearing over the simulation. The red-arrow path represents the possible way of construction of decamer complex. Source: [24] Its major role is to insulate the kinase phosphorylation cascade from activating other related pathways. Ste5 dimerizes and aggregates five more proteins that phosphorylates each other forming an activation cascade. The last one is doubly activated mitogene-activated protein kinase (MAPK, Fus3) that travels to the nucleus and releases the transcription factor (TF) from its inhibitors. In this way TF transcribes genes regulating yeast mating behaviour.
The study is examining the established hypothesis that signals in cells are propagated via well defined complexes of molecular machines rather than loosely assembled and polymorphic ensembles.
As it was shown, even though a conserved structure of decameric complex was hardly present in the ensemble model over repeated simulations, the signal was uninterrupted leading to St4 synthesis. Furthermore, contrary to the machine model, the ensemble model was able to replicate the experimental observation of combinatorial inhibition of phosphorylated Fuss3 (Fus3pp), when a copy-number of St5 was increased 60 fold. Models were built with the rule-based formalism that allows us to sample the sets of possible protein complexes the model can produce, without explicitly imposing the set of species that are formed [24]. More details about the formalism are in the next section. The code with the models' implementation is in the public domain and can be found as one of the attached files to this paper.
Rule-based modelling
The subject of signalling pathways and networks has already been addressed by many modelling formalisms. However, one significant advantage of the rule-based (RB) modelling is that it is able to express an infinite number of reactions with a small and finite number of rules, i.e. a single reaction rule and its parameters generalize a class of multiple interactions. In all of the other modelling methods every chemical species has to be specified in advance which is highly problematic for species with dozens of phosphorylation sites and many possible states. This limiting factor makes these methods inappropriate for modelling large-scale complex dynamical systems.
RB modelling is a method for the formal representation of combinatorially complex signalling systems in both a qualitative and quantitative way. The major idea is to replace equations with interaction rules. A rule representation is a graph-rewriting, where a graph specified on the left-hand-side is a pattern to be matched to instances in the current "mixture" of graphs and transformed into graphs specified on the right-hand-side. Matching should satisfy embedding, i.e. injections on agents (graphs) with the preservation of names, sites, internal states and edges [3]. In the rule-based language nomenclature, "agents" are most elementary molecules and "species" are agent complexes having particular states. A model can be translated into a system of ODE equations or simulated with a stochastic algorithm. In the latter case, system trajectories are created by rule selection at each time step, which is applied probabilistically, based on reaction rates and the initial/current copy number of agents [12]. Immediate consequences of this formulation are different levels of rule contextualisation ("don't care don't write"), without obligation of ad hoc assumptions about the system, modularity, reusability and extensibility of the modelling process [19]. Furthermore, the ability to capture a protein as a graph with (binding) sites (e.g. domains) that have internal state(s) (e.g. phosphorylated) gives a sufficiently expressive system to capture all of the principal mechanisms of signalling processes (e.g. dissociation, synthesis, degradation, binding, complex formation [18]) as well as insight into site-specific details of molecular interactions such as affinities, dynamics of post-translational modifications, domain availability, competitive binding, causality and the intrinsic structure of interactions.
RB modelling originates from concurrency system representations and as such has the ability to capture dependencies, causality and conflicts in biological interactions (overlooked by concentration-based ODEs).
In other words, precedences occurring along trajectories (stories) reveal competing events leading to a final state [3]. In the Kappa language, this feature is supported by the syntax for graphical analysis provided in the simulation tool. Among these are diagrams with causal flows, flux and influence maps as well as contact maps [ Figure 2] that facilitate the process of modelling. The causal flow diagram shows dependencies and conflicts in tracking indicated species and the flux maps [ Figure 13], negative/positive activity transfers between rules with the quantitative contributions on edge weights, both generated on the fly during a simulation [7]. At any time of a simulation, a snapshot can be taken to record the collection of species existing at that time.
Datasets and simulations
Our time series datasets report changes of indicated molecular species' copy-number over 13,800 time points. Stochastic simulations were run for 4,600 sec with 3 time points recorded per second. The system was first simulated over 1,000 sec to reach a steady state and the initial mixture of protein complexes. Afterwords, a pheromone stimulus was introduced and the system was simulated for another 3,600 sec.
Variables in the rule-based syntax are called "observables" ( %obs:) and are specified in a separate code block. A single observable can be mapped to one or more rules conditioned on the level of its particularity. Hence, all the types of created species not indicated as observables, become intractable. For instance, the observable %obs: Fus3PPFus3(T180~p,Y182~p), which is a double phosphorylated MAPK kinase Fus3, is associated with 14 rules of the following form:
• Fus3(dock!1,T180~p,Y182~p),Sst2(S539,mapk!1) -> Fus3(dock,T180~p,Y182~p),Sst2(S539,mapk) • Ste7(ste5!2,S359_T363~pp,mapk!1),Ste5(ste7!2), Fus3(dock!1,T180~p,Y182~p) -> Ste7(ste5,S359_T363~pp,mapk),Ste5(ste7), Fus3(dock,T180~p,Y182~p) • Fus3(dock!1,T180~p,Y182~p),Ste11(mapk!1) -> Fus3(dock,T180~p,Y182~p),Ste11(mapk) • Ste5(ste7!1),Ste7(mapk!2,ste5!1,S359_T363~pp), Fus3(dock!2,T180~p,Y182~u) -> Ste5(ste7!1),Ste7(mapk!2,ste5!1,S359_T363~pp), Fus3(dock!2,T180~p,Y182~p) • ...etc.
However, as it is with the model specification, as it is infeasible to observe all potentially important variations of species, we have to resort to what we know we want to observe. Therefore, the considered dataset consists of standard 31 variables, patterned after the original paper. There is also an extended 977 variable set but it has yet to be explored with parallel computations. This number was dictated by the snapshot of all existing species at the pick of the simulation (~1,000 sec after the stimulus appearance) used then as a list of observables in the simulation.
Perturbed model
To compare the outcome of applied methods, the model was simulated in two states, which are called here "perturbed" and "unperturbed". By the unperturbed model we call the standard "wild type" pathway dynamics. The perturbed one relates to an experimentally observed phenomenon of combinatorial inhibition. It occurs when the copy-number of protein scaffold is largely increased and impossible to fully assemble the complex that doubly activates Fus3 because all available members of the complex are used up on too many scaffold proteins.
Simulator
Models written in Kappa language are supported by KaSim simulator. By default, reaction rates are computed applying the law of mass action [2] but can easily be adjusted to follow any kinetic law (e.g. Michaelis-Menten, Hill's Law). What can be found under the hood is a direct particle-based variant of Gillespie's method. A general version of Gillespie's method, also called exact stochastic simulation algorithm (SSA) or kinetic Monte Carlo is a common simulation method for modelling time-evolution of stochastic chemical reaction systems. Numerical stochastic simulations are known to be computationally intensive and a lot of efforts have been made to improve their efficiency [10]. The most popular and effective solution, implemented in KaSim, is called "network-free" because rules transforming reactant into products are applied directly at runtime to advance the state of a system. As a result, it does not have to generate the full reaction network beforehand and is therefore independent of its size [13].
Applied methods and results
Correlation Explanation
Choice rationales
Since a rule representation may vary in generalization, it can be applied to more then one reaction that satisfies it. In other words rules serve as the reaction and species generator. It results in the unpredictability of species types and their importance emerging over time. Especially, if the model is of a large magnitude.
On the other hand, the intrinsic modularity of Kappa syntax opens the path to large integrative models, gradually assembled from the collections of reusable rule-based syntactic modules.
However, models are currently built in a fully controllable and stringent fashion. It leaves the notion of modularity and its experimental aspect risky and unexplored. Thinking ahead, the rule notation can be understood as an updatable, machine readable and executable knowledge representation and storage, replacing the usually manual revision of papers required in the model construction [15]. We could allow for uncontrollable, collective model growth in a form of rule stacks and then verify inner links and hierarchies in the system. That could guide an automatic trimming of the model size. Hence, the question is whether and how we could restrict a model to only these rules which are most informative. Likewise the question lies what exactly does it mean to say a species is "important" or "informative" and what do groups of biologically important species share with each other? Are they strongly interlinked modules of the system? Could they guide the rule-based modularity idea? The last question is especially important, since the scope of elementary parts of RB model is not yet clear. Are these three, four, five reaction rules? Is there any other measure of mechanisms granularity?
Facing these kind of questions, we opted to test one of recently realised methods Correlation Explanations (CorEx) that applies information theoretic objective to learn a hierarchy of more abstract representations of the data.
Having the hierarchy of latent variables, we can pose more precise questions, such as:
• What subset of species has common underlying dynamics?
• How strong is the correlation between species grouped under the same latent variable?
• How many underlying hidden causes can be identified out of the observed high-dimensional species dynamics?
and finally:
• What could be the meaning of these "hidden causes" for molecular signalling?
The algorithm was previously applied in a biological context for identification of targets for a cancer therapy [23]. Furthermore, a similar method mentioned in CorEx paper, called the information bottleneck, was previously applied for trimming of gene ontology (GO) [14] to compress the data into a smaller representation. In contrast, in CorEx the redundant information is preserved ignoring uncorrelated random variables [27].
Method description
In this section, we discuss an information theoretic approach for building a model on dynamics of the species concentrations. This method, proposed recently for a general domain [27,28], learns a hierarchy of latent variables that maximally inform correlation between the observed species dynamics. Herein, correlation refers to mutual information between a set of variables, and not just a linear correlation.
Before we delve into the details of the method for our specific settings, it should be noted that we disregard the time series nature of species copy-number dynamics in this method application.
Let G be a set of random variables representing copy-numbers for all the species. Then, X G is a joint random variable on G. For a species i, all the copy-number values of the time series are assumed to be independent samples of a random variable X i . As such, we can see that there is a contradiction since consecutive samples in the time series would have a correlation (not i.i.d.). For obtaining uncorrelated samples, one can take sub-samples of the time series, either at uniform interval or using any other relevant technique.
Following the notations in [27], total correlation T C(.) between a set of variables X G is defined as below.
T C(X G ) = i∈G H(X i ) − H(X G )(1)
T C(X G ) = I(X 1 ; · · · ; X g )
Here H(X i ) is entropy on a random variable X i ; and H(X G ) is a joint entropy on X G . Another interpretation of T C(X G ) is that it is mutual information, I(.), between all the variables in the set G. Typically mutual information is expressed between a pair where each element of the pair can be a set of random variables. Here, we are instead expressing mutual information between a g dimensional triplet of random variables, where g is a number of random variables in the set G.
In our problem of learning a model of species dynamics, evaluating mutual information (or total correlation) between all random variables would not be of much value. We are instead interested in evaluating mutual information between some subsets of species. However there are two problems along these lines: i) we do not know for which subsets of species we should evaluate mutual information and there can be a large number of permutations to explore (depending on the size of a subset and the G set); ii) non-parametric estimation of mutual information between random variables is a hard problem [16,25,22,9]. To tackle these, we formulate our algorithm such that; i) we assume the individual species copy-number variables X i to be Gaussian; however, we do not assume that the set of variables has to be Gaussian (the later is a stronger assumption); ii) we are interested in only those subsets where variables have low mutual information conditioning on a latent variable (or high mutual information between variables explained by a latent variable).
Along these lines, let us define a new information theoretic quantity T C(X G ; Y F ).
T C(X G ; Y F ) = T C(X G ) − T C(X G |Y F )(3)
T C(X G ; Y F ) is a total correlation (or mutual information) in the set of random variables X G explained by a set of latent variables Y F . T C(X G |Y F ) is a total correlation between the random variables X G that can not be explained by Y F , i.e. conditional total correlation (conditional mutual information). If the latent variables Y F can explain the total correlation in X G perfectly, then T C(X G |Y F ) = 0. Ideally, we would like to learn Y F if exists. Thus intuitively, optimal Y would correspond to minimum of T C(X G |Y F ). In our formulation, we can express optimization of Y F as optimizing conditional distributions P Y |X . Let us first consider optimization of a single latent variable Y , and then generalize it later.
arg max Y :p(y|x G ) T C(X G ; Y ) s.t. |Y | = k (4)
Here Y is a discrete random variable; x G is a sample of the random variable X G and y is sample of Y . We optimize Y by learning the conditional distribution p(y|x G ). Now, we further extend it for multiple latent variables, where each latent variable explains total correlation in a subset of the species concentration variables.
arg max
G j ,p(y j |x G j ) m j=1 T C(X G j ; Y j )(5)
We have introduced m latent variables here with Y j explaining correlation between random variables in the corresponding subset G j ⊂ G. Here these subsets can have an overlap.
Optimizing the above objective function seems hard. However, as explained in detail in [27,28], it can be solved very efficiently for practical purposes. We omit these optimization details and refer readers to the original papers introducing this algorithm for the first time [27,28]. Computational complexity of the method is linear with respect to the number of samples and number of variables in the set G. Furthermore, as an unsupervised method, it requires no assumption about the learned model. The code implementation for this algorithm is publicly available from the original authors 1 .
Results
The CorEx algorithm was applied both to perturbed and unperturbed datasets and yielded two results with a single layer of hidden variables. In both cases, presented results were the maximal values the data sets could be divided to. Further increase of the number of hidden units did not change their values.
For both sets [ Figures 6 & 5] CorEx found 6 latent variables, where 8-9 out of 31 biologically plausible variables were expected.Biologically plausible variables were thought to be all these observables that contained Ste5 scaffold protein, known to be a nucleation point of the system [24]. However, the preliminary intuitions did not align perfectly with the algorithm results.
In the unperturbed data tree we can distinguish two important groups, '0' and '1'. They are recognisable in the perturbed data tree as they preserved half of their members from the former set. However, contrary to the unperturbed data tree, where the members of both groups have similarly balanced strong relations, the perturbed set shows far uncertain correlations, mostly concentrated in the group '0'.
Interpretation and analysis
The interpretation of the results was conducted on two levels. The first one is based on the biological knowledge about the process. The second one is supported by the dynamic analytical tools provided by the KaSim simulator.
Generally speaking, the CorEx algorithm successively subsets data into a defined number of latent variables guided by species dynamics. Results appears to be consistent with the differences between perturbed [Figures 9 & 10] and unperturbed models [ Figures 7 & 8]. The group ordering, referring to the strength of inter-correlations, shows which event takes the lead in two cases. Earliest events upstream to the formation of signalling cascade appeared to be the leading ones in the perturbed simulation. This is consistent with the fact that phosphorylation of Fus3 kinase distinctively drops when the amount of Ste5 protein scaffolds competing for binding kinases increases [ Figure 4 in Section 2.4.2]. As the Fus3 phosphorylation was not entirely blocked, the second latent variable relates to events leading to Fus3 phosphorylation. Thus it is more consistent with the group '0' apart from transcription in the nucleus, which was inhibited in the perturbed data.
Owing to the static causal analysis provided by the simulation software, we can ask whether important observables relate to frequently executed rules. The most powerful visualization output is a flux map, which tracks the overall influence of rule applications on each other [7]. It is a directed and weighted graph with rules as vertices and edges annotated with positive or negative weights [ Figure 13]. Dependent on simulation parameters (selected time or number of events), a flux map might vary in structure (for details of our simulation parameters see section 2.4).
Both untrimmed graphs for unperturbed and perturbed models had 233 vertices but they differ from each other in the edge number (unperturbed-2,753, perturbed-2,422). Weights range from 0 to 407,172,203. An important note is that vertices are rules. Hence, to compare them with the output of CorEx, thus subsets of observables, first observables had to be mapped to rules they referred to [ Figure 11 & 12]. The weight cutoff varies with inverse proportion to the number of observables in flux map subgraphs. Therefore, we compared different subgraphs by gradually removing less and less vertices given a set of thresholds for weight values. The aggregated results are presented in the Figure 13. As we can observe, the frequency of rule application relates to subsets obtained with the CorEx algorithm but cannot explain them fully.
We stated some questions in the Section 3.1.1, which we would like to comment on or even answer to in the following part. We have learned that the algorithm used on time series datasets divided the species into most important ones Figure 5: Result of 31-variable dataset without perturbation. Intrinsic dimensionality was found to be 6. Variable numbers are shown in the middle node of each group. Edge weights leading from a group centre to its member are dictated by its explanatory contribution to remaining group members over the entire course of time series, with results depended from the outcome of signalling process. Hence, it did not inform us about intrinsic modularity of the system, what would relate to more "horizontal" division of time courses (when looking at the process diagram). Perhaps the considered system is far too small thus interlinked to observe invariable modules among species (encapsulations). Hence, the result might be then more correctly named as a form of "compression". Furthermore, given the limited number of experiments and the model size and its character, we are not yet ready to precisely answer the question of biological meaning related to the importance and informativeness indicated by the algorithm.
Chaos Time Series Analysis
To compare results with the outcome of CorEx algorithm and discover other aspects hidden in our data, we applied methods of nonlinear time series analysis [11]. Similarly, we used both the perturbed and unperturbed datasets (for more details about used datasets see Section 2.4). To bypass an obvious division into a pre-and post-pheromonal stimulation, we cut the beginning 1,000 sec and used Figure 8: On the left, a fragment of unperturbed data-tree with Group '1'. On the right, the process diagram for a comparison. The second highly scored group indicates less vital events, related to dimerization, and the impact of Kss1 kinase on the Ste4 activation. Figure 9: On the left, a fragment of perturbed data-tree with Group '0'. On the right, the process diagram for a comparison. The strongest group indicates the earliest events located upstream to the Fus3 poshorylation (observable called Fus3PP), preceding the complexation step Figure 10: On the left, a fragment of perturbed data-tree with Group '1'. On the right, the process diagram for a comparison. The second strongest group of perturbed data tree reflects weak correlation between members and the unsuccessful activation of transcription factor St4. Figure 11: An example of two flux map subgraphs mapping observables to related rules. The perturbed dataset with weights > 1,000,000, blue nodes denote observables, red nodes rule names. Figure 12: An example of two flux map subgraphs mapping observables to related rules. The perturbed dataset with weights > 5,000,000, blue nodes denote observables, red nodes rule names. Figure 13: Three top-scored groups of latent variables (G0, G1, G2) found with CorEx, both for perturbed (red) and unperturbed (blue) simulations and five flux map subgraphs with weights above (starting from left on x-axis) 10 000 000, 1 000 000, 100 000, 10 000, 1 000 units (two last sets in the perturbed set overlap). The comparison of changes in the number of intersecting observables with decrease of stringency in rule importance shows that perturbed system gives seemingly higher overlap between compared groups than the non-perturbed dataset.
only the part after the stimulation. Furthermore, to cap the computation time, we cut the data from original 10,801 (three events per second) observations to 3,600 (one event per second).
First we examined our data by creating recurrence plots for dynamical systems [5]. The recurrence plot is an array of dots in a N xN square, where a dot is placed at (i, j) whenever x(j) is sufficiently close to x(i). For the purposes of this study we selected an embedding dimension of 10 and time delay 5 to keep the computational time within reasonable limits.
In general, the recurrent plot shows the times at which a phase space trajectory visits roughly the same area in the phase space [21]. The authors [5] defined small and large scale patters, textures and topologies respectively, to ease their interpretation.
The resulting figures [16 & 15] are densely grey without distinctive textures or patterns. However, the unperturbed set is seemingly brighter away from the diagonal and distinctively darker along it. This gradient is interpreted as the occurrence of a progressive decorrelation at large time intervals involving a linear trend or drift. The perturbed model presents dynamics pushed a bit more towards randomness.
Next, we created plots, showing the average mutual information index (AMI) of a given time series for a specified number of lags [8].
S = − ij p ij (τ )ln p ij (τ ) p ij(6)
Next, we created a sample correlation integral plot [11]. The correlation integral can be approximated by the correlation sum. The correlation sum counts the number of pairs ( − → x (i), − → x (j)) in a given set of vectors that are at most apart. As the perturbation involved a single parameter and demonstrated naturally occurring phenomenon (not randomised), differences between these two plots were not expected to be extreme. Nonetheless, the results are coherent both with the understanding of process and the CorEx algorithm. However, for our purposes, these methods present a more distanced view on the system dynamics, missing a decoupling problem of individual species relations.
C( ) = 1 N (N − 1) N i=1 N j=i+1 Θ( − − → x (i) − − → x (j) ), − → x (i) ∈ R m(7)
Conclusions
Overall, this project offered a fruitful chance for an exploration of multivariate time series analysis. We have learned that the approach offered by the CorEx method might be very promising in analysis of rule-based models. However, it requires further testing with models that incorporate multiple randomly modified parameters and represent larger advanced processes. Further, we applied some nonlinear time series methods to our dataset. Though powerful, they offered a bird's-eye view understanding of system dynamics missing species-related details. However, both methods correctly interpreted the process offering a useful insight otherwise inaccessible.
Figure 1 :
1A: Usual scheme of hierarchically structured molecular machines B:
Figure 2 :
2Contact map defined without running the simulation with KaSa software accompanying KaSim4.0 simulation tool. Yellow circles denote agents sites, green circles agent states, and edges all potential connections between species.
Figure 3 :
3Flux map for pheromone pathway model in steady state simulated with KaSim4.0
Figure 4 :
4Model dynamics in unperturbed and perturbed states for characteristic protein species. The perturbed ensemble model showed a decrease in Fus3 activation (Fus3PP) being a key observation of the combinatorial inhibition. As we can see, the synthesis of St4, which happens in the nucleus, was inhibited under perturbed state (plot with a flat line).
Figure 6 :Figure 7 :
67Result of 31-variable dataset with perturbation. Intrinsic dimensionality was found to be 6. Variable numbers are shown in the middle node of each group. Edge weights leading from a group centre to its member are dictated by its explanatory contribution to remaining group members On the left, a fragment of unperturbed data-tree with Group '0'. On the right, the process diagram for a comparison. A biological interpretation of the strongest group '0' refers to the most important steps indicating critical events in the successful signal propagation. As the authors argued, the assembly of decamer involving Ste5 dimerization does not belong to the most crucial events guaranteeing the signal transfer.
Figure 14 :
14The largest intersection between the network of most frequently executed rules and the latent groups is apparently more visible in the perturbed model, which confirms a lack of coherence in species behaviour.
Figure 15 :Figure 17 :
1517Unperturbed Unperturbed data Figure 18: Perturbed model The larger time lag the smaller is the value of AMI. In case of Figure 19 AMI drops down almost diagonally, as opposed to the Figure 20. Thus, the perturbed model is far more unpredictable, showing randomized dynamics and less interdependent relation between events.
Figure 19 :Figure 20 :
1920Unperturbed Perturbed data
https://github.com/gregversteeg/CorEx
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8 GHz electromagnetic field induces permeability of Gram- positive cocci
Published: 16 June 2015
Hong Phong Nguyen
School of Science
Swinburne University of Technology
MelbourneAustralia
Yury Shamis
School of Science
Swinburne University of Technology
MelbourneAustralia
Rodney J Croft
Illawarra Health and Medical Research Institute
WollongongAustralia
Australian Centre for Electromagnetic Bioeffects Research
Australia
Andrew Wood
Australian Centre for Electromagnetic Bioeffects Research
Australia
Robert L Mcintosh
Australian Centre for Electromagnetic Bioeffects Research
Australia
Russell J Crawford
School of Science
Swinburne University of Technology
MelbourneAustralia
Elena P Ivanova [email protected]
School of Science
Swinburne University of Technology
MelbourneAustralia
Australian Centre for Electromagnetic Bioeffects Research
Australia
8 GHz electromagnetic field induces permeability of Gram- positive cocci
Published: 16 June 201510.1038/srep10980Received: 27 October 2014 Accepted: 08 May 20151 and 4 School of Health Sciences. Correspondence and requests for materials should be addressed to E.P.I. (email: OPEN
The effect of electromagnetic field (EMF) exposures at the microwave (MW) frequency of 18 GHz, on four cocci, Planococcus maritimus KMM 3738, Staphylococcus aureus CIP 65.8 T , S. aureus ATCC 25923and S. epidermidis ATCC 14990 T , was investigated. We demonstrate that exposing the bacteria to an EMF induced permeability in the bacterial membranes of all strains studied, as confirmed directly by transmission electron microscopy (TEM), and indirectly via the propidium iodide assay and the uptake of silica nanospheres. The cells remained permeable for at least nine minutes after EMF exposure. It was shown that all strains internalized 23.5 nm nanospheres, whereas the internalization of the 46.3 nm nanospheres differed amongst the bacterial strains (S. epidermidis ATCC 14990 T~ 0%; Staphylococcus aureus CIP 65.8 T S. aureus ATCC 25923, ~40%; Planococcus maritimus KMM 3738, 80%). Cell viability experiments indicated that up to 84% of the cells exposed to the EMF remained viable. The morphology of the bacterial cells was not altered, as inferred from the scanning electron micrographs, however traces of leaked cytosolic fluids from the EMF exposed cells could be detected. EMF-induced permeabilization may represent an innovative, alternative cell permeability technique for applications in biomedical engineering, cell drug delivery and gene therapy.Cell permeability is the route by which the passage of different types of exogenous ions, molecules/ macromolecules can pass through the cell membrane 1-5 . This process is of substantial significance in biomedical engineering, drug delivery and gene therapy applications 1,5-9 . One of the proposed causes of cell permeability is the formation of pores on the bacterial cell membrane, which is often called 'membrane poration' 1-5 . Cell membrane poration is known to be caused by the rearrangement of the molecular structure of the membrane, together with interactions taking place at the aqueous-lipid interface, which can be physically induced through the application of external shocks such as mechanical stress 10,11 , ultrasound (sonoporation) 2,9,12 , electric fields (electroporation) 1,3,5,6,13-15 , and laser (photoporation or optoporation) 4,[16][17][18][19]. Inducing pores in the membrane is believed to lead to the relaxation of the surface tension of the membrane 20 , together with a consequent change in the osmotic pressure that is due to the passage of internal and external components through the pores that are formed 1,13,20 . The initial rupture of the membrane leads to the formation of cylindrical pores that become lined with phospholipid head groups; these continue to increase in size until a state of zero surface tension is reached 11,20 . Such membrane pores can either be temporary, resealing after a given elapsed period, or continue to expand and eventually rupture the membrane. The effect that results is dependent on the degree of external shock, duration of exposure, and the characteristics of the cells under consideration 6,15,21 .It was recently reported that exposing Escherichia coli cells to EMFs at 18 GHz (with a resultant temperature of 40 °C) caused the internalization of large macromolecules such as dextran (150 kDa)[22][23][24]. It was suggested that in contrast to low-frequency and traditional EMF exposures, high-frequency EMFs
amplify and enhance the electro-kinetic processes, and do so without damaging the cells 22,25,26 . Since it remains unclear as to whether other bacterial taxa with different cell wall structures and compositions to that of Gram-negative E. coli [27][28][29][30][31][32] (e.g., Gram-positive bacteria) would be affected in a similar way, the aim of this study was to investigate whether the application of EMF exposures at the microwave (MW) frequency of 18 GHz would induce permeability in the membranes of Gram-positive cocci; Planococcus maritimus KMM 3738, Staphylococcus aureus ATCC 25923, S. aureus CIP 65.8 T and S. epidermidis ATCC 14990 T . In this work, propidium iodide 13,33 , large (23.5 nm and 46.3 nm) silica nanosphere uptake assays, Confocal Laser Scanning Microscopy (CLSM), TEM and SEM were employed to assess whether the cells could be made permeable under certain carefully defined experimental conditions. We also aimed to determine the size of the polymer nanocarriers that can be delivered into the cytosol via this method.
Results
EMF induced permeability in Gram-positive coccoid bacterial cells. The CLSM analysis of EMF exposed bacterial cells showed that the EMF induced membrane permeability of the cells of the four Gram-positive strains tested (E. coli was used as a reference strain, Supplementary Fig. S1), as confirmed by the uptake of propidium iodide ( Fig. 1 and Supplementary Fig. S1). It was also found that approximately 97% ± 5% of P. maritimus, 99% ± 4% of S. aureus ATCC 25923, 99% ± 3% of S. aureus CIP 65.8 T and 99% ± 5% of S. epidermidis cells were able to internalize 23.5 nm silica nanospheres (Fig. 2). A similar effect was observed for the E. coli cells (98% ± 4%) ( Supplementary Fig. S2). CLSM analysis indicated that the internalization of the nanospheres could continue for up to approximately 9 min after the initial EMF exposure (data not shown), whereas no uptake of the nanospheres was detected when the bacteria were exposed to the nanospheres 10 min after the EMF exposure (Fig. 2). The same bacterial cells in the respective control groups were subjected to conventional heating, replicating the temperature profiles of the cells being subjected to the EMF exposure (using a Peltier plate, Supplementary Fig. S1 and S3). These reference group cells were not able to internalize the propidium iodide, however, it should be noted that up to approximately 5% of these reference cells were observed to be capable of internalizing nanospheres, most likely due to the presence of damaged or dead cells, which are often present in cell populations 34 . TEM analysis confirmed the uptake of 23.5 nm-nanospheres by the EMF exposed cells (Fig. 3). Within the cross-sectioned cells, it can be seen that some of the nanospheres were located around the cell membrane and others within the cells themselves, whilst the majority were found in the cytosol. In contrast, non-EMF exposed cells remained intact ( Supplementary Fig. S4), with the majority of the cells (95%) showing no internalisation of either form of nanosphere.
While all of the strains tested were found to internalize the 23.5 nm-nanospheres, the extent of uptake of the 46.3 nm-nanospheres was variable amongst the strains studied (Table 1). For example, a large proportion of the P. maritimus cells (80% ± 9%), and almost half of the S. aureus ATCC 25923 and S. aureus CIP 65.8 T cells (40% ± 7% and 44% ± 7%, respectively) were able to internalize these nanospheres, however none of the S. epidermidis cells were able to do so. The nanosphere loading capacity of a single bacterium was evaluated for each of the bacterial strains studied (Table 1). It was found that up to approximately 261 of the 23.5 nm nanospheres and 114 of the 46.3 nm nanospheres could be internalized by a single coccoid cell after exposure to EMF. EMF effect on cell morphology. The SEM analysis of EMF exposed bacterial cells did not reveal any significant change in the morphology of the cocci; however, the some traces of leaked cytosolic fluid could be seen surrounding the cells of each strain studied (Fig. 2). The morphology of the non-treated and Peltier heat-treated reference cells were also unchanged ( Supplementary Fig. S3).
Effect of EMF on cell viability. Cell viability experiments were performed via the direct counting of colony forming units and were conducted for the EMF exposed, Peltier plate heated and non-treated cells. The results showed that above 84% of each of the strains studied (85% ± 8% P. maritimus, 85% ± 5% S. aureus ATCC 25923, 89% ± 5% S. aureus CIP 65.8 T and 84% ± 9% of the S. epidermidis cells) remained viable after the EMF exposures (Fig. 4). The Peltier plate heated cells maintained their viability (99% ± 6% P. maritimus, 98% ± 7% S. aureus ATCC 25923, 99% ± 9% S. aureus CIP 65.8 T and 99% ± 8% of the S. epidermidis cells). A statistical analysis of the data did not reveal a statistically significant difference between the viability of the Peltier heated and untreated cells (P. maritimus (p > 0.05), S. aureus ATCC 25923 (p > 0.05), S. aureus CIP 65.8 T (p > 0.05) and S. epidermidis (p > 0.05)) ( Fig. 4). Although the cell viability of EMF exposed cells was only slightly affected, this difference was found to be statistically significant in comparison to the controls (p < 0.05).
Discussion
The results reported here provide evidence that the exposure of cocci to EMF at 18 GHz, with a specific energy absorption rate (SAR) value approximately 5.0 kW kg −1 , induced cell permeability in each strain being investigated. The SAR was determined experimentally in this work because it has been suggested that it is a more accurate estimation energy absorption for biological material 33,[35][36][37] , because the variations in specific heat within biological matter are usually much smaller than corresponding variations in conductivity, resulting in a much more uniform temperature than electric field distribution [35][36][37] . It is of interest to note that certain biological effects of SAR values in the range of 4.0 kW kg −1 at 8.53 GHz and 4.85 kW kg −1 at 2.45 GHz, have been described, e.g., three-dimensional conformational changes in green fluorescent protein (GFP) 25 and increased citrate synthase binding efficiency 26 , respectively; however, no comparable data are available regarding bacterial cells.
Here, the induced permeability of bacterial cells was confirmed by propidium iodide intake, as well as TEM and CLSM microscopy which allowed visualization of the internalized nanospheres. The propidium iodide assay has been applied as the standard technique used for confirming electroporation of bacterial membranes 13,33 . Propidium iodide does not normally pass through intact membranes 13,33 , however, when a cell membrane is disrupted, the propidium cation (Pr 2+ ) can pass through the membrane and bind to the nucleic acids within the cell, which will eventually fluoresce 33 . Further examination of CLSM and TEM micrographs revealed that indeed after exposure to an 18 GHz EMF, the bacterial cells were capable of internalizing both propidium iodide and nanospheres as a direct consequence of membrane traffic modulation (Figs. 1-3). The consistent internalization of both propidium iodide and nanospheres was observed in different cocci species, despite the variations in cell wall structure between species 38,39 . Since no internalisation of either propidium iodide or nanospheres was observed for the bacterial cells subjected to conventional heat treatment (Peltier plate heating, Supplementary Fig. S1-S4), it can be assumed that the EMF-induced cell permeabilization could not be attributed simply to the bulk temperature rise of the cells, and therefore must have arisen as a direct result of either the interaction of EMF with the bacterial cell membrane and its components (e.g., phospholipids, membrane proteins, etc), or microthermal changes not detectable at the macro level.
The great efficiency (97%) with which the bacteria populations that were exposed to EMF were able to internalize 23.5 nm nanospheres is an important characteristic; for example, the efficiency of the photoporation-induced permeability has been reported to be 85-100% with up to 80% cell viability 4 , and the permeabilization efficiency of sonoporation reported to be 78% with 82% cell viability 2 . Furthermore, a pore lifetime of 9 min appears to be comparable with that obtained using electroporation or photoporation processes 40 . For example, Saulis et al. reported that under the influence of a single electric field pulse, human red blood cells were able to be permeabilized (allowing the internalizing of ascorbic acid and mannitol), but that the membrane barrier function partially recovered after 3 min and that complete resealing of the pores was attained after 10 min 40 . Similarly, Schneckenburger et al. employed the focused beam of an argon ion laser (488 nm) to photoporate Chinese hamster ovary (CHO-K1) cells 41 . These authors reported the formation of small circular black spots of approximately the same size as the focused beam, which disappeared within about 5 min 41 .
We believe, however, that the nature of the permeabilization that arises from the exposure of the bacterial cells to EMF at 18 GHz is very different to that in previously-reported pore formation phenomena 1,3,4,13,14,18,19,22,42 : Electroporation induces pore formation in the cells that are exposed to an electric field 1,6,14,15,43 , during which, cells have to be placed in close proximity between two electrodes. The presence of an electric field changes the electrochemical potential across the cell membrane, locally inducing instabilities through the formation of defects in one of the leaflets of the membrane 1,6,7,14,15,44 . Photoporation occurs when tightly focused laser light is used to induce the reversible poration of the cellular membrane, allowing exogenous materials to enter the cell 4, [16][17][18][19]41 . Laser light with wavelengths in the ultraviolet (UV), visible (VIS), and infrared (IR) ranges, both as pulsed (ns or fs) and continuous waves (CW), have been used for photoporation 19 . For CW lasers, the poration mechanism would most likely occur as a result of localized heating of the cellular membrane by the laser irradiation 4,16,41 . It is thought that high frequency electromagnetic fields, through their resultant high frequency vibrations, generate an external mechanical stimulation to the cell membrane 45 . The modulation of the latter results in enhanced membrane trafficking via exocytosis/endocytosis, as was reported, for example, for several (eukaryotic) cell types 45,46 . Other studies, including Karshafian et al., reported the presence of sonoporation-induced membrane-pore like defects of the murine fibrosarcoma cell line KHT-C 2 . These authors estimated the ultrasound-induced pore size (in the presence of micro-bubbles) to be in the range of 20 nm to 500 nm, based on their ability to internalize different molecular weight markers (10 kDa to 2 MDa FITC-dextran) 2 . Similarly, Zhou et al. reported that the ultrasound-induced pores of the Xenopus laevis oocyte cell membrane were of a diameter in the order of 220 nm (with a standard deviation of 80 nm, due the changes of the trans-membrane current (TMC) of a single cell under voltage clamp) 47 . The size of the exogenous materials internalized into eukaryotic cells by exposure to an electric field of a similar field strength or ultrasound was reported to be much smaller (i.e., less than 6 nm [1][2][3][4]13,14,41,42,48 ), however, no direct confirmation of the actual pores formed in the plasma membrane were presented in these studies.
The present findings suggest that the mechanical stimulation of cellular membranes resulting from exposure to the high frequency vibrations resulting from exposure to 18 GHz EMF radiation changes the membrane tension, causing it to deform, inducing an endocytosis-like process in the bacterial cell walls. Endocytosis is an endomembrane dynamic feature of eukaryotes that has not been previously reported for bacteria, with the exception of recently discovered subcellular compartmentalization in two bacterial taxa, Planctomycetes and Verrucomicrobia [49][50][51][52][53] . For example, Lonhienne et al. have shown an endocytosis-like green fluorescence protein (GFP) being taken up by Gemmata obscuriglobus bacterial cells 53 . Our results thus raise the possibility that EMF might induce subcellular compartmentalization. The variation in the bacterial cells' ability to internalize the 46.3 nm nanospheres might be due to the differences in cell wall structure and/or the phospholipid composition of the cell membranes of different bacteria. For example, it is well documented that Staphylococcus species vary in the type of teichoic acids present, one of the essential components of the peptidoglycan in the Gram-positive cell wall [54][55][56] . Endl et al. reported that poly(glycerolphosphate) teichoic acids are characteristic components of the S. epidermidis peptidoglican, whereas poly(ribitolphosphate) teichoic acids are characteristic of the S. aureus cell wall 55 . It may be speculated that due to the differences in the molecular weight and three-dimensional organization of glycerol and ribitol molecules, the ability to internalize nanospheres may have been affected to such an extent that the S. epidermidis cells were unable to internalize the 46.3 nm nanospheres. In contrast, the P. maritimus cells showed a greater propensity for the internalization of 46.3 nm nanospheres (80% susceptible cells). P. maritimus is a member of the genus Planococcus, which represents a bacterial lineage of peculiar irregular morphology 57 , adopting a coccoid-like shape due to being Gram-variable motile cocci 58,59 , and characteristic cell-wall structure whose chemical composition is yet to be determined.
To the best of our knowledge, this is the first time the physical internalization of large polymeric carriers and biomolecules by bacterial cells via an endocytosis-like process has been reported. We hypothesize that the modulation of the membrane permeability, induced by the high-frequency vibrations induced by 18 GHz EMF, is electro-kinetic in nature due to the resulting increased conductivity, diffusion and ion mobility induced in the cell membrane 24,40,41 . EMF-induced permeabilization may represent an innovative alternative cell permeability technique for applications in biomedical engineering, cell drug delivery and gene therapy 1,6-9 . Further studies are required to investigate the 18 GHz EMF-induced permeability in eukaryotic cells and to elucidate the mechanism/s by which EMF interacts with the microbial cell walls and/or cell membranes.
Materials and methods
Bacterial strains, cultivation procedures and sample preparation. Four strains of coccoid bacteria, P. maritimus KMM 3738, S. aureus ATCC 25923, S. aureus CIP 65.8 T , and S. epidermidis ATCC 14990 T , were studied. E. coli ATCC 15034 was used as a reference strain. Bacterial strains were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA), the Culture Collection of the Pasteur Institute (CIP, Paris, France) and the Collection of Marine Microorganisms (KMM, Vladivostok, Russian Federation). All strains were routinely grown on nutrient agar (NA, Oxoid, Basingstoke, England) or marine agar (MA, Becton Dickinson). Prior to each experiment, the Staphylococcus spp. strains were grown overnight at 37 °C and P. maritimus grown at 25 °C. All strains were collected at their stationary phase of growth (as confirmed by growth curves, data not shown) in order to utilize mature cells for the experiments 34 . Working bacterial suspensions were freshly prepared for each independent experiment. The cell density was adjusted to OD 600 = 0.1 in phosphate buffered saline (PBS), 10 mM, pH 7.4, using a spectrophotometer (Dynamica Halo RB-10 UV-Vis, Precisa Gravimetrics, Dietikon, Switzerland). EMF exposure. The samples for EMF exposure comprised of 2 mL of bacterial cells suspension in a micro Petri dish (35 mm diameter, Griener Bio One, Frickenhausen, Germany). The EMF apparatus used for all experiments was a Vari-Wave Model LT 1500 (Lambda Technologies, Morrisville, USA) (the EMF configuration is shown in Fig. 5a) with a fixed frequency of 18 GHz using the settings detailed elsewhere 22 .
Each sample was subjected to three consecutive EMF exposures (resulting in a temperature increase in the samples ranging from 20 °C to 40 °C at a heating rate of 20 °C per min) for 1 minute, allowing the sample to cool to 20 °C on ice (at a rate of 10 °C per minute) between exposures. In order to obtain a uniform temperature gradient and avoid "hot spot" effects, the samples were placed onto a ceramic pedestal PD160 (Pacific Ceramics, Sunnyvale, CA, USA, ε ' = 160, loss tangent < 10 −3 ) within the same position that had been identified, using electric field modelling using CST Microwave Studio 3D Electromagnetic Simulation Software (CST MWS) (CST of America, Framingham, MA, USA), as being the position that provided the most consistent heating environment (Fig. 5b). Since the average dielectric constant of the bacterial suspension was assumed to be that of water at EMF frequencies of 18 GHz, the dielectric loss tangent describing the energy dissipation was also assumed to be that of water at 25 °C and 18 GHz. The calculated wavelength of the EMF in water was determined to be 2.34 mm, which is greater than the dimensions of each bacterial cell. The depth of penetration was calculated to be 1.04 mm, which is also greater than the thickness of the bacterial suspension in the Petri dish. Hence, the possibility of subjecting the samples to non-even heating due to the presence of a non-uniform field distribution was considered negligible. The bulk temperature rise of the bacterial suspension was monitored via a built-in temperature probe, a Luxtron Fiber Optic Temperature Unit (LFOTU) (LumaSense Technologies, Santa Clara, CA, USA), of which the tip was placed at various positions (0 and 0.5 mm from the bottom of the plate) in the Petri dish, as shown in Fig. 5b. According to the manufacturer's specifications, the tip of the probe is less than 1 mm thick and operates with an accuracy of ± 0.2 °C and 250 ms (in water) response time. The temperature was also carefully monitored after EMF exposure and during cooling using a portable Cyclopes 330S infrared/thermal monitoring camera (Minolta, Osaka, Japan). A total of 60 measurements were collected from 10 positions in the Petri dish, which included five different locations and two different depth levels within the bacterial suspension). It was found that the temperature variation was only in the range of ± 0.2 °C. The temperature profiles are reproduced in Fig. 5d and the heating rate arising from the EMF exposure is shown in Fig. 5e.
The SAR, given in kW kg −1 , was calculated in the medium using Equation 1:
c SAR T t 1 t 0 = × ∂ ∂ ( ) =
where c is the specific heat capacity of the medium (kJ kg −1 °C −1 ), and T t t 0
∂ ∂ =
is the time derivative of the temperature determined at t = 0 s (°C s −1 ). For the 10 measurement locations, the SAR was calculated using Equation 1 to be 5.0 ± 1.3 kW kg −1 . It was assumed that the specific heat capacity of the bacterial suspension was the same as that of water at 25 °C, which is 4.18 kJ kg −1 °C −1 .
The liquid evaporation that took place during the EMF exposures was found to be approximately 1.5% using an analytical balance (Cheetah Scientific, France) and therefore regarded as negligible.
Bulk heat treatment. A Peltier plate heating/cooling system (TA Instruments, New Castle, DE, USA) was used to replicate the bulk temperature profiles during the EMF exposures according to the method previously described 22 . Briefly, a 2 mL volume of bacterial suspension was applied directly onto the Peltier plate sample platform (Fig. 5c), and subjected to heating from 20 °C to 40 °C for 1 min, followed by cooling to 20 °C for 2 min between heating treatments. The diameter of the Peltier plate sample platform was 65 mm and the bacterial suspension layer thickness was calculated to be 0.6 mm. All Peltier plate heated samples were performed in triplicate, and in parallel with EMF exposure experiments. The bulk temperatures during heating and cooling were monitored using the portable infrared/thermal monitoring camera Cyclopes 330 S (Fig. 5d). Working bacterial suspensions that were not exposed to either EMF exposures or heat treatment were used as the negative controls for all experiments.
Scanning Electron Microscopy (SEM).
A field emission scanning electron microscope FeSEM -SUPRA 40VP (Carl Zeiss, Jena, Germany) with a primary beam energy of 3 kV was used. A 100 μ L aliquot of bacterial suspension was placed on a glass cover slip (ProSciTech, Kirwan, Australia) in duplicate for each condition. After one min, the glass cover slips were washed with nanopure H 2 O (with resistivity of 18.2 MW cm −1 ), dried with 99.99% purity nitrogen gas, then exposed to gold sputtering (6 nm thick gold film) using a NeoCoater MP-19020NCTR (JEOL, Tokyo, Japan). The remainder of the bacterial suspensions, which were left to stand for 9 and 10 min, were subjected to the same treatment. Approximately ten SEM images were taken at 5,000 × and 70,000 × magnifications for each sample and analyzed.
Confocal Laser Scanning Microscopy (CLSM). Propidium iodide (1.0 mg mL −1 solution in water;
Life Technologies Australia, Mulgrave, Australia) was used at a concentration of 500 nM to determine the viability of the bacterial cells 33 . Non-treated cells, cells inactivated by boiling (100 °C) and heat-treated cells using the Peltier plate (40 °C) were used as three different types of control. Heat inactivated cells were prepared by boiling the bacterial suspension for 15 min, followed cooling in a 25 °C water bath for 30 min. PI was added to all the bacterial suspensions at 1 min and 10 min after EMF exposures. The PI remained in contact with the bacteria throughout the experiment.
Two types of fluorescent silica nanospheres 23.5 ± 0.2 nm (FITC) and 46.3 ± 0.2 nm (Rhodamine B) (Corpuscular, Cold Spring, NY, USA) were added to the bacterial suspensions at a concentration of 15 μ g mL −1 after periods of 1, 9 and 10 min following EMF exposures and heat treatment. The controls for these experiments comprised an untreated bacterial suspension that was mixed with the fluorescent silica nanospheres and Peltier heated bacterial suspensions mixed with the fluorescent silica nanospheres using the Peltier plate up to 40 °C. These were processed in parallel with the EMF exposed bacterial suspensions. Each suspension was then washed twice, followed by centrifugation at 4500 rpm for 5 min. The supernatant was then removed and re-suspended in PBS. A 100 μ L aliquot of each sample was then analyzed using a Fluoview FV10i-W inverted microscope (Olympus, Tokyo, Japan). Approximately 5 CLSM images per treatment group were obtained, with each containing at least 50 bacterial cells per image. immediately cooled to 4 °C by refrigerating for 30 min, then cut into 1 mm 3 cubes and fixed with 1 mL of 1% osmium tetroxide (OsO 4 ) for 1 h. The cubes were washed twice in nanopure H 2 O (with resistivity of 18.2 MW cm −1 ) for 15 min. The cells were dehydrated by passing the cubes through a graded ethanol series (20%, 40%, and 60%) (2 mL) for 15 min and stained for 8 h with 2% uranyl acetate in 70% ethanol (2 mL). After staining, the bacterium was further dehydrated by passing the cubes through another graded ethanol series (80%, 90% and 100%) for 15 min (2 mL) 60,61 .
Transmission Electron
The embedding medium was prepared from araldite, dodecenyl succinic anhydride (DDSA) and benzyldimethylamine (BDMA) (ProSciTech) and stir thoroughly 62 . In order to embed, each cube was washed twice with 100% acetone (2 mL) for 20 min, then incubated in 2 mL of acetone and embedding medium (1:1 ratio) for 8 h, followed by transfer to acetone and embedding medium (1:3 ratio) for 8 h and finally transfer into pure embedding medium for 8 h. The cube was transferred to embedding mould containing fresh pure embedding medium, which was then polymerized for 24 h at 60 °C 63 . The final block was trimmed and cut into ultrathin sections (80 nm thickness) with Leica EM UC7 ultramicrotome (Leica Microsystems, Wetzlar, Germany) and glass knife. Sections were put on 200 mesh copper grids and examined in JEM 1010 (JEOL).
Quantification of internalized nanospheres. The nanosphere loading capacity of the four EMF exposed bacterial strains was quantified according to the fluorescence intensity of internalized silica nanospheres. A POLARstar Omega microplate reader (BMG Labtech, Ortenberg, Germany) was used to measure the fluorescence intensity of nanospheres in each bacterial suspension (Supplementary method). Each sample was prepared according to the method used for CLSM analysis.
Cell Viability. A 100 μ L volume of Staphylococcus spp. suspension was spread onto NA and a 100 μ L volume of P. maritimus was spread onto MA; the Staphylococcus spp. were incubated for 24 h at 37 °C and the P. maritimus was incubated for 48 h at 25 °C. Cells inactivated by boiling (100 °C) were also examined. All the cell viability tests were conducted right after treatments and in parallel. Ten plates were used for each type of treatments in three independent experiments for each sample, to allow a statistical analysis to be performed. Three independent Student t-tests were performed to compare the consistency of decontamination rates across conditions, experiments and bacterial test strains.
All statistical data processing was performed using SPSS 21.0 software (SPSS, Chicago, IL, USA).
Figure 1 .
1Propidium iodide internalization by the bacterial cells after EMF exposure. CLSM images showing propidium iodide internalization after 1 min (first row) following EMF exposure. Phase contrast micrographs showing bacterial cells in the same field of view. No propidium iodide was observed in any tested cell types after 10 min (same field of view insets in the second row). Scale bars in all fluorescence images are 5 μ m. Scientific RepoRts | 5:10980 | DOi: 10.1038/srep10980
Figure 2 .
2Bacterial cell response to EMF exposure. Typical scanning electron micrographs of P. maritimus KMM 3738, S. aureus ATCC 25923, S. aureus CIP 65.8 T and S. epidermidis ATCC 14990 T cells at 1 min and 10 min after EMF exposure. No significant change of cell morphology was observed (insets). Scale bars are 10 μ m, inset scale bars are 200 nm. CLSM images showing intake of 23.5 nm nanospheres (second row) and 46.3 nm nanospheres (third row), 1 min after EMF exposure. After 10 min both types of nanospheres were not able to be internalized by the cells. The phase contrast images in the bottom row show the bacterial cells in the same field of view. Scale bars are 5 μ m.
Figure 3 .
3Nanosphere (23.5 nm) internalization by the bacterial cells following EMF exposure. Typical TEM images of thin-sectioned (80 nm) cells of P. maritimus KMM 3738, S. aureus ATCC 25923, S. aureus CIP 65.8 T and S. epidermidis ATCC 14990 T showing the uptake of 23.5 nm nanospheres. Scale bars are 200 nm.
Figure 4 .
4Effect of EMF exposure and bulk heat on the bacterial cell viability. The recovery rate (%) of EMF exposed cells (shaded white), Peltier heated 40 °C (grey) and untreated cells (white). Cells inactivated by boiling (100 °C) were non-viable. Data are means ± standard deviation (SD) and representative of 3 independent experiments with 10 replicates each. *p < 0.05 versus the corresponding controls.Scientific RepoRts | 5:10980 | DOi: 10.1038/srep10980
Figure 5 .
5EMF configuration schematic and Peltier heating dynamics during treatments (MW and Peltier plate). (a) Schematic diagram of the EMF system; (b) Electric field and absorbed power modeling using CST Microwave Studio 3D Electromagnetic Simulation Software and inset images of the tip positions of the temperature probe in media (top and side view); (c) Peltier heating stage (diameter = 65 mm, suspension depth = 0.6 mm) with position used for applying the bacterial suspension; (d) The temperature profiles of bacterial suspensions during three consecutive EMF exposures and Peltier Plate heating/cooling control; (e) The heating rate of the bacterial suspension: increasing at a rate of approximately 1 °C per s during first 3 s and then reducing to a rate of approximately 0.3 °C per s for the remainder of the time.
Microscopy (TEM). Bacterial suspensions with the 23.5 nm nanospheres were pelleted by centrifugation at 4800 rpm for 5 min at 25 °C. The cells were washed twice with PBS to remove all the unbound nanospheres. The pellets were suspended in 2 mL of 4% glutaraldehyde in PBS (10 mM, pH 7.4) for 30 min. The cells were then washed twice in PBS for 5 min. After the final washing step, the cells were mixed thoroughly in 0.5 mL of 5% agarose gel by stirring. The agar was Scientific RepoRts | 5:10980 | DOi: 10.1038/srep10980
AcknowledgementsThis work was partly supported by the Australian Centre for Electromagnetic Bioeffects Research (ACEBR), an National Health & Medical Research Council Centre of Research Excellence.Author Contributions
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}
|
279,145,679
| null | null | null |
40496547v1
|
12151614
| null |
10.1155/crra/9912392
| {"pdfurls":null,"pdfsha":"349b6c526fc613835ec9adc4c0ac4f8fbabda0f7","oainfo":{"license":"CCBY","open(...TRUNCATED)
| "\nNeurofibromatosis Type 1: Clinical and Imaging Perspectives From a Pediatric Case\n\n\nPuneet Kum(...TRUNCATED)
| {"abstract":"[{\"end\":1891,\"start\":1078}]","author":"[{\"end\":204,\"start\":85},{\"end\":354,\"s(...TRUNCATED)
|
250,243,703
|
2207.00581
| null | null | null | null |
conf/nips/RammalAGDS22
|
10.48550/arxiv.2207.00581
| {"pdfurls":["https://arxiv.org/pdf/2207.00581v1.pdf"],"pdfsha":"273ed70682fa85ab1465e5cdc1ecb52f925a(...TRUNCATED)
| "\nOn Leave-One-Out Conditional Mutual Information For Generalization\n\n\nMohamad Rida Rammal ridar(...TRUNCATED)
| {"abstract":"[{\"end\":1184,\"start\":306},{\"end\":1184,\"start\":306}]","author":"[{\"end\":112,\"(...TRUNCATED)
|
244,838,167
| null | null | null | null | null | null |
10.1088/1742-6596/2083/3/032076
| {"pdfurls":null,"pdfsha":"c5c37f142e7a5b60da787fe3fc617b83f699c88a","oainfo":{"license":null,"openac(...TRUNCATED)
| "\nStudy on afterbody-effects of multi-stage separation at high- speed\nIOP PublishingCopyright IOP (...TRUNCATED)
| {"abstract":"[{\"start\":\"1255\",\"end\":\"2088\"}]","author":"[{\"start\":\"113\",\"end\":\"238\"}(...TRUNCATED)
|
247,160,102
| null | null | null |
35300415
|
8921082
| null |
10.3389/fcell.2022.786268
| {"pdfurls":null,"pdfsha":"fd79728203152574627935a56743ee4ff4af4e09","oainfo":{"license":"CCBY","open(...TRUNCATED)
| "\nApplying Sodium Carbonate Extraction Mass Spectrometry to Investigate Defects in the Mitochondria(...TRUNCATED)
| {"abstract":"[{\"end\":3826,\"start\":2363}]","author":"[{\"end\":282,\"start\":120},{\"end\":442,\"(...TRUNCATED)
|
1,646,684
| null |
2134600210
| null |
24049571
|
3774944
| null |
10.4047/jap.2013.5.3.296
| {"pdfurls":null,"pdfsha":"bf091b878b77c44ed68a807d576247155e0edb7f","oainfo":{"license":"CCBYNC","op(...TRUNCATED)
| "\nEffect of polishing and glazing on the color and spectral distribution of monolithic zirconia\n20(...TRUNCATED)
| {"abstract":"[{\"end\":2669,\"start\":1043}]","author":"[{\"end\":252,\"start\":101},{\"end\":398,\"(...TRUNCATED)
|
76,143,054
| null |
2329015274
| null | null | null | null |
10.21032/jhis.2016.41.1.43
| {"pdfurls":["https://e-jhis.org/upload/pdf/jhis-41-1-43.pdf"],"pdfsha":"a6b59b1021e93a00ae23e9909d8c(...TRUNCATED)
| "\n우리나라의 요일별 출생 빈도에 관한 연구, 1995-2012\n\n\n박상화 \nInstitute of(...TRUNCATED)
| {"abstract":"[{\"end\":2695,\"start\":1278}]","author":"[{\"end\":278,\"start\":37},{\"end\":477,\"s(...TRUNCATED)
|
219,576,134
| null | null | null |
28854777
|
5582295
| null |
10.5713/ajas.2005.1080c
|
{
"pdfurls": null,
"pdfsha": null,
"oainfo": null
}
| null | {"abstract":null,"author":null,"authoraffiliation":null,"authorfirstname":null,"authorlastname":null(...TRUNCATED)
|
251,018,475
|
2207.11106
| null | null |
36512727v1
| null | null |
10.1021/jacs.2c09947
| {"pdfurls":["https://export.arxiv.org/pdf/2207.11106v1.pdf"],"pdfsha":"99b998cdf3936a8bda5e287ed61b6(...TRUNCATED)
| "\nStrong structural and electronic coupling in metavalent PbS moiré superlattices\n22 Jul 2022\n\n(...TRUNCATED)
| {"abstract":"[{\"end\":2987,\"start\":1691}]","author":"[{\"end\":291,\"start\":94},{\"end\":404,\"s(...TRUNCATED)
|
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