Graphene oxide (GO) is an oxygenated functionalized form of graphene that has received considerable attention because of its unique physical and chemical properties that are suitable for a large number of industrial applications. Herein, GO is rapidly obtained directly from the oxidation of graphene using an environmentally friendly modified Hummers method. As the starting material consists of graphene flakes, intercalant agents are not needed and the oxidation reaction is enhanced, leading to orders of magnitude reduction in the reaction time compared to the conventional methods of graphite oxidation. With a superior surface area, the graphene flakes are quickly and more homogeneously oxidized since the flakes are exposed at the same extension to the chemical agents, excluding the necessity of sonication to separate the stacked layers of graphite. This strategy shows an alternative approach to quickly producing GO with different degrees of oxidation that can be potentially used in distinct areas ranging from biomedical to energy storage applications. The presence of functional groups attached to graphene extends the functionalities of this two-dimensional 2D) material. Graphene oxide (GO) is an important form of functionalized graphene that disperses in water and other polar solvents. Because of the presence of oxygenated groups, such as carboxyl, hydroxyl, and epoxy, GO can be further functionalized with various organic molecules, making it a versatile chemical functionalization platform with an extensive potential for a wide range of applications in some of the greatest challenges of our time, such as water treatment, energy storage, environment, and medicine ,2.D materiaBeyond the chemical synthesis, the choice of the carbon source also plays an important role in the final quality of GO. Specifically, different types of graphite and synthesis methods can lead to significant modifications in the physical characteristics of GO, such as the lateral size of the flake, the number of layers, and disorder. As a result, the final properties of GO are sensitive to the materials and methods of production, making every structure suitable for specific applications. For example, highly oxidized GO flakes with sub-micrometer size are of great interest for biomedical applications , whereas4) and sodium nitrate (NaNO3) in a concentrated sulfuric acid (H2SO4) solution [2/N2O4) due to the presence of NaNO3. Additionally, the Hummers method uses not only large amounts of concentrated H2SO4 and KMnO4 to ensure sufficient oxidation of graphite flakes but also huge amounts of water to remove the excessive ions after oxidation. As a result, the process is costly in terms of time, energy, and waste treatment, with several safety and health concerns. Besides, the oxidation is not homogeneous, with the oxidation degree being extremely difficult to control, and some residual graphite is still left unless long sonication periods are used, to produce just a few layers of GO [The Hummers method is the most-used approach for preparing GO and consists of the oxidation of graphite using potassium permanganate (KMnOsolution . Despiters of GO .2FeO4) [3 to avoid the formation of toxic gasses and to facilitate the disposal of wastes due to the absence of Na+ and NO3− ions. However, the elimination of NaNO3 usually demands compensatory strategies to guarantee the efficiency of the Hummers method. Reports including increased amounts of KMnO4 and H2SO4, the introduction of new components, such as phosphoric acid (H3PO4), to the reaction [Recently, numerous strategies have been developed to minimize the issues mentioned above. For example, to reduce the oxidation time, stronger oxidizing agents, such as potassium ferrite (K2FeO4)  or elect2FeO4) . Other sreaction , or reacreaction  have beeN-methyl-2-pyrrolidone, is used to produce monolayer graphene and platelets originally from graphite on large scales [3 is not required, this chemical route is considered eco-friendlier and much less harmful to health and the environment compared to the traditional modified Hummers method. 3 and, consequently, the absence of toxic gasses (NO2/N2O4) and the reduced oxidation time.The liquid phase exfoliation (LPE) in organic solvents, such as e scales . The dire scales  allows ue scales ,13. Here2SO4 , with subsequent cooling up to 2 °C. Next, 2.25 g of KMnO4  was slowly added to the suspension. The system was kept under stirring at room temperature for different periods of time, in which the graphene flakes were oxidized, followed by cooling to 2 °C and further dilution in water. Lastly, the resulting GO suspensions were cleaned with 3 cycles of washing using HCl  10% and dialysis until pH 5.Graphene flakes were supplied by 2D Materials Pte. Ltd. (2DM) (Singapore). The chemical route to obtain graphene oxide directly from graphene consisted of an environmentally friendly simplified Hummers method in which 0.5 g of graphene flakes were added to 17 mL of concentrated HGO with different degrees of oxidations were obtained from Sigma Aldrich  and Abalonyx . The materials were used without further purification and are labelled as commercial GO I and II .2) for Raman spectroscopy and atomic force microscopy (AFM). For XPS analysis, the measurements were performed in a Kratos AXIX Ultra  equipment with a mono-chromatic source Al Kahv = 1486.81 eV. The calibration, Shirley-type background, peak fitting, and quantification were carried out using Casa-XPS software (version 2.1.19) . For the SEM analysis, a FESEM VERIOS 460  with an accelerating voltage of 2.0 kV and a current of 100 pA was used. For Fourier transform infrared (FTIR) spectroscopy, an ALPHA Platinum-ATR , instrument was used. For this, the aqueous suspensions of GO were freeze-dried, and prepared by the KBr disc method, and the spectra were obtained in a in attenuated total reflection (ATR) mode. Atomic force microscopy (AFM) measurements were carried out in a Bruker Dimension Icon Microscope  operated in ScanAsyst tapping mode and scan lines of 512 under ambient conditions. Confocal Raman spectroscopy was performed in a WITec Alpha 300R  with an excitation wavelength of 532 nm and a 100× objective.The samples were drop casted on silicon (Si) substrates for XPS and SEM analyses and on silicon substrate with 300 nm silicon dioxide , as implemented in Quantum ESPRESSO v. 6.5 , with thThe evolution of the increase in oxygenated groups in the graphene structure as a function of the oxidation time is demonstrated in The thickness and lateral size of GO obtained from atomic force microscopy (AFM) images did not change significantly after 5 min of reaction, indicating that most of the modifications occur very quickly, beyond our limit of observation due to the restrictions of the experimental setup conditions. Looking closer at the spectra and comparing the percentages shown in −1 are attributed to the C–OH  stretching, C–O (epoxy) stretching, and O–H (hydroxyl) deformation vibrations, respectively. These are compared with first- principles calculations, which find C–OH bending modes at 1089–1095 cm−1, the epoxy symmetric stretch at 1254 cm−1, and C–OH hydrogen wag mode at 1509 cm−1, which may be the origin of the resonance observed at 1418 cm−1. The carboxylic acid group appears at 1733 cm−1 with the carbonyl (C=O) stretching calculated to lie at 1691 cm−1. The peak at 1620 cm−1 is usually attributed to the non-oxidized domains in the graphene structure [−1. These modes are very close in frequency with a graphene vibrational mode and therefore have significant localization in the carbon atoms as well , which can result in aggregation. After the oxidation, the sharp 2D band at ~2683 cm−1 disappears, and some broad features between 2600 and 3000 cm−1, typical for GO [As expected, after the oxidation, the degree of disorder of the graphene backbone was higher, corroborating the higher relative intensity of the D band and a broader G band in the Raman spectra b. After GO modes . As the ntations . The gral for GO  to the eBy comparing our GO obtained directly from graphene with two commercially available GO obtained from graphite , we see However, as expected, GO from graphite presents larger sheets  compared to GO from graphene since the graphene sheets used in this study had a small lateral size of 1 µm . Importa3, that are commonly used to expand the graphite structure are not required, and toxic gasses, such as NO2/N2O4, are not generated, diminishing several safety and health concerns. Consequently, the reaction time is reduced by the orders of magnitude when compared with conventional methods. As graphene has a comparatively larger surface area per volume, the oxygenated groups are rapidly and homogeneously distributed over the graphene lattice, eliminating steps of sonication to further separate the layers.We proposed an approach to synthesize graphene oxide with a controllable degree of oxidation. The route consists of an environmentally friendly modified Hummers method, in which the carbon source is graphene, not graphite flakes as in the standard processes. For this reason, intercalant agents, such as NaNOOur study opens up new avenues to explore environmentally friendly production routes as well as novel mild oxidation approaches to expand industrial applications of GO, such as chemical sensors , solar c


Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions.We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference.https://github.com/gitter-lab/ssps.Find the full codebase at Bioinformatics online. Signaling pathways enable cells to process information rapidly in response to external environmental changes or intracellular cues. One of the core signaling mechanisms is protein phosphorylation. Kinases add phosphate groups to substrate proteins and phosphatases remove them. These changes in phosphorylation state can act as switches, controlling proteins’ activity and function. A protein’s phosphorylation status affects its localization, stability and interaction partners  to sample from the posterior  structure estimation problem. This family of techniques relies on two core ideas: (i) we can use a DBN to model phosphorylation time series data and (ii) the DBN’s structure translates directly to a directed graph representing the signaling pathway. Rather than identifying a single DBN that best fits the data, these techniques take a Bayesian approach—they yield a osterior . Others We present a new Bayesian DBN-based technique, Sparse Signaling Pathway Sampling (SSPS). It improves on past MCMC methods by using a novel proposal distribution specially tailored for the large, sparse graphs prevalent in biological applications. Furthermore, SSPS makes weaker modeling assumptions than other DBN approaches. As a result, SSPS scales to larger problem sizes and yields superior predictions in comparison to other DBN techniques.Gen probabilistic programming language (PPL). Probabilistic programming is a powerful methodology for building statistical models. It enables the programmer to build models in a legible, modular, reusable fashion. This flexibility was important for prototyping and developing the current form of SSPS and readily supports future improvements or extensions.We implement SSPS using the SSPS makes specific modeling assumptions. We start with the DBN model of G denote a directed graph with vertices V and edges E(G). Graph G will represent a signaling pathway, with vertices V corresponding to proteins and edges E(G) indicating their influence relationships. We use parents of vertex i in G.We first define some notation for clarity’s sake. Let X denote our time series data, consisting of T timepoints. X is a jth column corresponds to the jth variable and the jth graph vertex. As a convenient shorthand, let latest X, and let earliest X. Lastly, define jB contains the values of variable j’s parents at the jB may also include columns of non-linear interactions between the parents. We will only include linear terms, unless stated otherwise.Let G from X. In particular, Bayesian approaches seek a posterior distribution prior distribution P(G) and likelihood function In our setting, we aim to infer reference graph inverse temperature λ. This prior gives uniform probability to all subgraphs of λ controls the ‘importance’ given to the reference graph.We derive our model from the one used by et al. choose a Gaussian DBN for their likelihood function. Intuitively, they assume linear relationships between variables and their parents:Hill jβ and noise parameters marginal likelihood function:jβ. For notational simplicity, T. In general, there may be multiple time course replicates with differing lengths. The marginal likelihood generalizes to that case in a straightforward way.A suitable prior over the regression coefficients ters σj2  allows uP(G). We obtain our prior distribution by decomposing edge existence variables Z be the ijz. Then, we can rewrite λ. See Appendix A for a more detailed derivation.In SSPS, we use the same marginal likelihood function [ijc for each edge:C is the matrix of all prior confidences ijc, replacing interpolates the original prior, permitting a continuum of confidences on the interval Rewriting the prior in this form opens the door to generalizations. First, we address a shortcoming in the way reference graph λ inverse temperature variable with a collection of variables, λ variable determined the importance of the reference graph. In the new formulation, each jλ controls the importance of the prior knowledge for vertex j and its parents:We make one additional change to the prior by replacing the shared λ revealed a multimodal posterior that tended to trap λ in high or low values. The introduction of vertex-specific jλ variables yielded faster convergence with weaker modeling assumptions—an improvement in both respects.We introduced Λ primarily to help MCMC converge more efficiently. Experiments with the shared We implicitly relax the model assumptions further via our inference procedure. For sake of tractability, the original exact method of C, introduces vertex-specific inverse temperature variables Λ and places no constraints on vertices’ in-degrees. See the full model in In summary, our model departs from that of Z and (ii) the inverse temperature variables Λ. For each step of MCMC, we loop through these variables and update them in a Metropolis–Hastings fashion.Our method uses MCMC to infer posterior edge existence probabilities. As described in Section 2.1, our model contains two classes of unobserved random variables: (i) the edge existence variables V. For each vertex j, we update its inverse temperature variable jλ and then update its parent set At a high level, our MCMC procedure consists of a loop over the graph vertices, ξ; we typically set For the inverse temperature variables, we use a symmetric Gaussian proposal: neighboring graph uniformly from the set of possible ‘add-edge,’ ‘remove-edge’ and ‘reverse-edge’ actions  the proposal should efficiently traverse the space of directed graphs and (ii) it should favor graphs with higher posterior probability. The most widely used graph proposal distribution selects a  actions . We willFor a given graph vertex add-parent. Select one of vertex j’s non-parents uniformly at random, and add it to remove-parent. Select one of vertex j’s parents uniformly at random, and remove it from swap-parent. A simultaneous application of add-parent and remove-parent. Perhaps surprisingly, this action is not made redundant by the other two. It plays an important role by yielding updates that maintain the size of the parent set. Because the marginal likelihood [sparse graphs. We introduce this preference via the probability assigned to each action. Intuitively, we craft the action probabilities so that when add-parent moves are most probable. When remove-parent moves are most probable. When These three actions are sufficient to explore the space of directed graphs, but we need another mechanism to bias the exploration toward j as follows. As a shorthand, let reference size C to estimate an appropriate reference size for the parent set. Then, the action probabilities areadd-parent is 1; (ii) when remove-parent is 1 and (iii) when We formulate the action probabilities for vertex bability . Beyond Gen provides a helpful interface for computing reverse probabilities. The user can provide an involution function that returns the reverse of a given action. Gen then manages the reverse probabilities without further intervention. This makes it relatively easy to implement Metropolis–Hastings updates with unusual proposal distributions.Recall that Metropolis–Hastings requires us to compute the reverse transition probability for any proposal we make. This could pose a challenge given our relatively complicated parent set proposal distribution. However, We follow the basic MCMC protocols described by failed to converge if its PSRF Upon termination, we assess convergence with two diagnostics: Potential Scale Reduction Factor (PSRF) and effective number of samples  derive and implement the log-probability function and (ii) implement an inference method that operates on the log-probability function. This process of manual derivation and implementation is error-prone and requires a high degree of expertise from the user. In contrast, probabilistic programming only requires the user to express their model in a PPL. The probabilistic programming system manages other details. Probabilistic programming also tends to promote good software engineering principles, such as abstraction, modularity and legibility. Most PPLs organize code into functions, which can be reused by multiple statistical models.Stan  after multiple refinements. Other PPLs do not allow this level of control.We chose the We use a simulation study to answer important questions about SSPS: how does its computational expense grow with problem size? Is it able to correctly identify true edges? What is its sensitivity to errors in the prior knowledge? Simulations allow us to answer these questions in a controlled setting where we have access to ground truth.We generate each simulated dataset as follows:A specifies the structure of a DBN. We choose Sample a random adjacency matrix β for this DBN be drawn from a normal distribution Let the weights M time courses of length T. We imitate the real datasets in Section 2.5 by generating Use the DBN defined by A in two steps: (i) remove A and (ii) add imperfect prior knowledge encountered in reality. The parameters r and a control the ‘false negatives’ and ‘false positives’ in the prior knowledge, respectively.Corrupt the adjacency matrix a, yielding a grid of simulations summarized in We use a range of values for parameters existence confidence to each possible edge. We measure classification performance using area under the precision-recall curve (AUCPR). We use average precision to estimate AUCPR, as opposed to the trapezoidal rule [which tends to be overly-optimistic, see We are primarily interested in SSPS’s ability to correctly recover the structure of the underlying signaling pathway. The simulation study allows us to measure this in a setting where we have access to ground truth. We treat this as a probabilistic binary classification task, where the method assigns an Our decision to use AUCPR is motivated by the sparseness of the graphs. For sparse graphs the number of edges grows linearly with Performance measurements on simulated data come with many caveats. It is most instructive to think of simulated performance as a sanity check. Since our data simulator closely follows our modeling assumptions, poor performance would suggest serious shortcomings in our method.We measure SSPS’s performance on experimental data by following the evaluation outlined by the HPN-DREAM Breast Cancer Network Inference Challenge  to eight stimuli. For each context, there are approximately aggregate priors summarize the participants’ collective knowledge. They were not available to participants in the original challenge, but we use them in our analyses of HPN-DREAM data. We provide them to each of the baseline methods (see Section 2.6), so the resulting performance comparisons are fair. We do not compare any of our scores to those listed by Hill et al. (2016) in the original challenge results.Participants in the original challenge were free to extract prior knowledge from any existing data sources. As part of their analysis, the challenge organizers combined participants’ prior graphs into a set of edge probabilities. These descendant sets against experimentally generated descendant sets. More specifically, the challenge organizers exposed cells to AZD8055, an mTOR inhibitor, and observed the effects on other phosphosites. From this they determined a set of phosphosites downstream of mTOR in the signaling pathway. These include direct substrates of the mTOR kinase as well as indirect targets.The HPN-DREAM challenge aims to score methods by their ability to capture causal relationships between pairs of variables. It estimates this by comparing predicted false positives and false negatives. As we vary a threshold on edge probabilities, the predicted mTOR descendants change, which allows us to make a receiver operating characteristic (ROC) curve. We calculate the resulting area under the ROC curve (AUCROC) with the trapezoidal rule to score methods’ performance on the HPN-DREAM challenge. Comparing predicted descendants of mTOR against experimentally generated descendants of mTOR gives us a notion of dominates another method if its minimum score exceeds that of the other method. Conversely, we say the other method dominates SSPS if its score exceeds SSPS’s maximum score. This dominance comparison has flaws—e.g. its results depend on the sample size K. However, it errs on the side of strictness and suffices as an aid for summarizing the HPN-DREAM evaluation results.Because SSPS is stochastic we run it Our evaluations compare SSPS against a diverse set of baseline methods.reference edges, rather than continuous-valued edge confidences. We overcame this by using a threshold to map edge confidences to 1 or 0. We chose a threshold of 0.25 for the HPN-DREAM challenge evaluation because it yielded a reasonable number of prior edges. We ran Hill et al.’s implementation using MATLAB 2018a.This method was an early inspiration for SSPS and is most similar to SSPS. However, the exact DBN method encounters unique practical issues when we run it on real or simulated data. The method’s computational expense increases rapidly with problem size X, Y have a causal relationship if there exists a functional dependence 2 test against the ‘no functional dependence’ null hypothesis. FunChisq was a strong competitor in the HPN-DREAM challenge, despite the fact that it uses no prior knowledge. In order to use FunChisq, one must first discretize their time course data. We followed Zhang and Song’s recommendation to use 1D k-means clustering for discretization. Detailed instructions are given in the HPN-DREAM challenge Supplementary Materials (FunChisq (v2.4.9.1) and Ckmeans.1d.dp (v4.3.0) R packages.This method is based on the notion that two variables ijc is the prior confidence  for edge . That is, the method uses penalty factors α, using the Bayesian Information Criterion described by GLMNet (GLMNet.jl Julia wrapper (v0.4.2).We included a variant of LASSO regression as a simple baseline. It incorporates prior knowledge into the typical primal formulation:improve our knowledge of the true graph. In reality, this improvement is subject to caveats about data quality and model fit.Our most straightforward baseline simply reports the prior edge probabilities, performing no inference at all. Ideally, a Bayesian method should do no worse than the prior—new time course data should only https://github.com/gitter-lab/ssps) and archive it on Zenodo (https://doi.org/10.5281/zenodo.3939287). It includes a Snakemake workflow  grid see .decrease in memory usage between changes in the variables. So the memory consumption of a Markov chain depends not only on acceptance rate of the Metropolis–Hastings proposals. The acceptance rate is smaller for Recall that SSPS differs from more traditional MCMC approaches by nature of its parent set proposal distribution, which is specially designed for sparse graphs (see Section 2.2). When we modify SSPS to instead use a naïve uniform graph proposal, we see a striking difference in sampling efficiency. The uniform graph proposal distribution attains FunChisq and LASSO—are much less computationally expensive. Both finish in seconds and require < 100 MB of memory for each simulated task. This highlights the computationally intense nature of Bayesian approaches. Not every scenario calls for Bayesian inference. However, Bayesian inference is valuable in scientific settings where we are concerned with uncertainty quantification.The other baseline methods—The simulation study provides a setting where we have access to ‘ground truth’—the true simulated graph. We use AUCPR to score each method’s ability to recover the true graph’s edges.r and a. The heat maps are arranged by method and problem size differential performance with respect to the prior knowledge, in a layout analogous to t-statistic of each method’s AUCPR, paired with the prior baseline’s AUCPR. Whenever the prior graph has some informative edges, SSPS outperforms the prior. On the other hand, SSPS’s performance deteriorates whenever the prior contains no true edges (i.e. FunChisq may be a better choice. Since it does not rely on prior knowledge at all, it outperforms the other methods when the prior is totally corrupted. However, we expect that in most realistic settings there exists partially-accurate prior knowledge, in which case we expect FunChisq to perform worse than SSPS.These results confirm SSPS’s ability to identify the true network, given partially-accurate prior knowledge and time series data consistent with the modeling assumptions. SSPS is fairly robust with respect to the prior’s quality and has consistent performance across different problem sizes.We evaluated SSPS on experimental data from the HPN-DREAM challenge. The challenge includes time series phosphorylation data from 32 biological contexts: 8 stimuli applied to 4 breast cancer cell lines. Methods are scored on their ability to correctly identify the experimentally derived descendants of mTOR. FunChisq in 11 contexts and is dominated by FunChisq in 15. This is not surprising because FunChisq was among the top competitors in the original challenge. LASSO, on the other hand, performs poorly. SSPS dominates LASSO in 18 contexts and is dominated in only 6.SSPS performs satisfactorily on this task overall. Employing terminology from Section 2.5, SSPS dominates the exact DBN method in 18 of the 32 contexts, whereas the exact DBN method dominates SSPS in only 9 contexts. Meanwhile, SSPS dominates FunChisq outperforms and is outperformed by the prior knowledge in 11 and 21 contexts, respectively. The aggregate prior’s strong performance is consistent with the results from the original HPN-DREAM challenge; this prior outperformed all individual challenge submissions (FunChisq uses no prior knowledge but was the top performer in the HPN-DREAM challenge’s in silico tasks.More puzzling is the strong performance of the prior knowledge baseline. SSPS dominates the aggregate prior in only 9 contexts and is dominated in 21. This is not isolated to our method. We presented SSPS, a signaling pathway reconstruction technique based on DBN structure estimation. It uses MCMC to estimate the posterior probabilities of directed edges, employing a parent set proposal distribution specially designed for sparse graphs. SSPS is a Bayesian approach. It takes advantage of prior knowledge with edge-specific confidence scores and can provide uncertainty estimates on the predicted pathway relationships, which are valuable for prioritizing experimental validation.SSPS scales to large problems more efficiently than past DBN-based techniques. On simulated data, SSPS yields superior edge predictions with robustness to flaws in the prior knowledge. Our HPN-DREAM evaluation shows SSPS performs comparably to established techniques on a community standard task. It is difficult to make stronger statements in the HPN-DREAM setting because the prior knowledge baseline performs so well and we can only evaluate the predicted mTOR descendants, not the entire pathway. However, SSPS’s scalability among Bayesian methods, strong results in the simulation, and competitive performance in the HPN-DREAM challenge make it an attractive option for further investigation of real phosphorylation datasets.There are several potential limitations of SSPS relative to alternative pathway signaling models. Prior knowledge is not available in some organisms or biological conditions, reducing one advantage of our Bayesian approach. Although SSPS is more scalable than related DBN techniques, it would struggle to scale to proteome-wide phosphoproteomic data measuring thousands of phosphosites. For large datasets, we recommend running SSPS on a pruned version that includes only the highest intensity or most variable phosphosites. SSPS, like most DBN techniques, models only observed variables. It will erroneously exclude important pathway members, such as scaffold proteins, that are not phosphorylated. Latent variable models or background network-based algorithms are better-suited for including unphosphorylated proteins in the pathway. Background network methods can also impose global constraints on the predicted pathway structure, such as controlling the number of connected components or proteins’ reachability from relevant receptors (There are many possible ways to improve SSPS. For example, it could be extended to jointly model related pathways in a hierarchical fashion, similar to btaa861_Supplementary_DataClick here for additional data file.


P value threshold (0.05/14929 = 3.35 × 10–6), such as DSP (PTWAS = 1.35 × 10–29 for lung tissue), MUC5B (PTWAS = 1.09 × 10–28 for lung tissue), and TOLLIP (PTWAS = 1.41 × 10–15 for whole blood). Pathway enrichment analysis identified multiple candidate pathways, such as herpes simplex infection  and antigen processing and presentation . 38 common GO terms and 8 KEGG pathways shared by enrichment analysis of TWAS significant genes and DEGs were identified. In the PPI network, 14 genes  were simultaneously detected by hub gene and module analysis. In conclusion, through integrative analysis of TWAS and mRNA expression profiles, we identified multiple novel candidate genes, GO terms and pathways for IPF, which contributes to the understanding of the genetic mechanism of IPF.Idiopathic pulmonary fibrosis (IPF) is a type of scarring lung disease characterized by a chronic, progressive, and irreversible decline in lung function. The genetic basis of IPF remains elusive. A transcriptome-wide association study (TWAS) of IPF was performed by FUSION using gene expression weights of three tissues combined with a large-scale genome-wide association study (GWAS) dataset, totally involving 2,668 IPF cases and 8,591 controls. Significant genes identified by TWAS were then subjected to gene ontology (GO) and pathway enrichment analysis. The overlapped GO terms and pathways between enrichment analysis of TWAS significant genes and differentially expressed genes (DEGs) from the genome-wide mRNA expression profiling of IPF were also identified. For TWAS significant genes, protein–protein interaction (PPI) network and clustering modules analyses were further conducted using STRING and Cytoscape. Overall, TWAS identified a group of candidate genes for IPF under the Bonferroni corrected  Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by the formation of scar tissue and a progressive, and irreversible decline in lung function , with a Genome-wide association study have significantly succeeded in identifying IPF-related susceptibility genetic loci. However, a great number of genetic variations identified reside in non-coding regions, which are generally difficult to characterize biologically. Indeed, one common sense for GWAS is that most disease-associated genetic variants are located in non-coding regions, resulting in the hypothesis that the underlying biological mechanism of disease may be closely related to gene expression regulation. Furthermore, several expression quantitative trait loci (eQTLs) studies have illustrated that the information on expression regulation may play a pivotal role in disease development . TranscrIn the present study, we leveraged expression imputation from a large-scale IPF GWAS dataset to perform a TWAS analysis in peripheral blood, whole blood and lung tissue. The TWAS significant genes and DEGs identified by mRNA expression profiling of IPF were then subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Through this analysis, the common GO terms and KEGG pathways were identified. Furthermore, for significant genes identified by TWAS for IPF, STRING and Cytoscape software were applied to implement protein–protein interaction (PPI) network and clustering modules analyses. Our results may provide novel insights into the understanding of the molecular mechanisms underlying the development of IPF. The detailed procedure of integrative analysis was displayed in P > 1 × 10–6), and well-imputed  in at least two studies were included. Detailed description related to study participants, genotyping, imputation, association analysis, and quality control can be found in the previous IPF GWAS study     . Gene ex= 1,247)  and Youn= 1,264) , respectn = 11) was obtained from the tissue flanking lung cancer resections. RNA was extracted and hybridized on Affymetrix microarrays. Individual-level gene expression data were included in the mRNA expression profile analysis implemented by LIMMA package  . Briefly package . The DEGus study .2. Note that in the enrichment analysis for IPF-related genes identified by TWAS, we included all the genes with a TWAS P value less than 0.05, rather than those under the Bonferroni corrected P value threshold (0.05/14929 = 3.35 × 10–6), to increase the ability to identify more biological processes relevant to IPF and to make the results more stable by including more input genes  and 1,147 genes with P value < 0.05 (DSP (PTWAS = 1.35 × 10–29 for lung tissue), MUC5B (PTWAS = 1.09 × 10–28 for lung tissue), TOLLIP (PTWAS = 1.41 × 10–15 for whole blood), MAPT (PTWAS = 9.60 × 10–15 for lung tissue), and DEPTOR (PTWAS = 8.58 × 10–9 for lung tissue). The top 30 genes identified by TWAS are summarized in Totally, 14,929 genes were analyzed by TWAS in this study. Overall, TWAS identified 29 genes under the Bonferroni corrected e < 0.05 , such asP value < 0.05 were included in the GO enrichment analysis. Metascape detected 76 GO terms under P value < 0.01 (PTWAS = 1.43 × 10–8), vacuolar part (PTWAS = 5.98 × 10–6), ncRNA metabolic process (PTWAS = 1.61 × 10–5), snRNA transcription by RNA polymerase II (PTWAS = 3.03 × 10–5), and SWI/SNF complex (PTWAS = 5.81 × 10–5). For KEGG pathway enrichment analysis of the genes identified by TWAS, Metascape detected 30 candidate pathways for IPF under P value < 0.01 (Staphylococcus aureus infection (PTWAS = 3.69 × 10–7), allograft rejection (PTWAS = 4.45 × 10–6), asthma (PTWAS = 7.65 × 10–6), type I diabetes mellitus (PTWAS = 1.32 × 10–5), inflammatory bowel disease (IBD) (PTWAS = 7.37 × 10–5), and herpes simplex infection (PTWAS = 7.93 × 10–5).A total of 1,147 genes with a TWAS e < 0.01 , such ase < 0.01 , such asPTWAS = 1.43 × 10–8, PmRNA = 8.87 × 10–3), lytic vacuole , ncRNA metabolic process , microtubule organizing center , and autophagy . PTWAS = 3.69 × 10–7, PmRNA = 9.17 × 10–3), herpes simplex infection , HTLV-I infection , phagosome , and systemic lupus erythematosus .A total of 2,204 DEGs were identified by mRNA expression profiling analysis of IPF , and werTo evaluate the association of IPF-related genes identified by TWAS, a PPI network was constructed by STRING and visualized by Cytoscape, containing 329 nodes and 893 edges . The topDYNLL1, DYNC1LI1, DYNLL2, HLA-DRB5, HLA-DPB1, HLA-DQB2, HLA-DQA2, HLA-DQB1, HLA-DRB1, POLR2L, CENPP, CENPK, NUP133, and NUP107) were simultaneously detected by both hub gene and module analysis.In addition, module analysis conducted by MCODE plugin in Cytoscape identified several modules in the PPI network. Then, the top four significant modules were selected for subsequent analysis. A significant module, which gained the highest MCODE score, contained 24 nodes and 150 edges. Subsequent functional enrichment analysis indicated that the genes in this module were primarily enriched in antigen processing and presentation of exogenous peptide antigen via MHC class II, chromosome, centromeric region, cytoplasmic dynein complex, and type I interferon signaling pathway . FourteeAlthough the biological basis of IPF has been investigated in the past years, the cellular and molecular mechanisms of IPF are very complicated and remain unclear. In the present study, we performed the first large-scale integrative analysis of TWAS and mRNA expression profiles for IPF, which successfully detected some plausible genes as well as pathways, and can potentially provide novel insights to better understand the molecular mechanisms underlying the development of IPF.DSP and MUC5B. DSP encodes desmoplakin, which is an important component of the desmosome structure. Desmoplakin is involved in the mechanical linkage of cells, stabilization of tissue structure and the process of cell migration, proliferation, and differentiation. Accordingly, DSP is essential to cell-cell adhesion and epithelial barrier function  that were related to IPF , encoded by the human MHC gene complex, plays a critical role in the antigen presentation of peptides and the regulation of immune response . A GWAS d to IPF . Besidesd to IPF , 2015. Td to IPF . In addid to IPF . Besidesd to IPF . Type 1 d to IPF . NaturalStreptococcus OTU1345 and Staphylococcus OTU1348) was positively correlated with the progression of IPF , which is also closely related to the antigen stimulation and immune response, such as herpes simplex and Staphylococcus aureus infection. Previous studies have demonstrated that virus may be involved in disease initiation. And the presence of herpes viral DNA and epithelial cell stress in the lungs of asymptomatic relatives are at risk for the development of familial IPF . A recenn of IPF . In anotn of IPF . In addin of IPF . Haemophfibrosis , suggestThere are several limitations in our study. First, the number of genes that can be accurately imputed in the TWAS analysis is limited by the training cohort sample size, the majority of subjects were of European ancestry, and the results cannot be directly generalized to other ethnic population. Second, there may be tissue bias using lung, peripheral blood and whole blood expression reference panels. Cell-type heterogeneity within or between tissues and cross-tissue pleiotropy may introduce tissue bias. Further investigation can be implemented to address this issue if reference panels for individual cell types or states are available . Third, In conclusion, we conducted a large-scale integrative analysis of TWAS and mRNA expression profiles for IPF. Our results provide novel insights into a better understanding of the genetic mechanism of IPF. Further functional biology studies are warranted to validate our findings and clarify the potential roles of identified genes and pathways in the development of IPF.https://github.com/genomicsITER/PFgenetics. We would like to thank the Collaborative Group of genetic studies of IPF for providing us with the IPF GWAS summary data. The gene expression profile dataset of IPF analyzed for this study can be found in the GEO with accession number GSE110147 (https://www.ncbi.nlm.nih.gov/geo/).The data analyzed in this study is subject to the following licenses/restrictions: Full summary statistics for the genome-wide meta-analysis of IPF can be accessed from ZY conceived and designed the study. WG and PG performed the statistical analysis. PG wrote the manuscript. LL, QG, and ZY provided feasible advice on data analysis and drafting manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cytoplasmic TDP-43 aggregates are a hallmark of amyotrophic lateral sclerosis (ALS). Today, only two drugs are available for ALS treatment, and their modest effect prompts researchers to search for new therapeutic options. TDP-43 represents one of the most promising targets for therapeutic intervention, but reliable and reproducible in vitro protocols for TDP-43-mediated toxicity are lacking. Here, we used HEK293T cells transfected with increasing concentrations of TDP-43-expressing plasmid to evaluate different parameters of toxicity and alterations in cellular metabolism. Overexpression of TDP-43 induced aggregates occurrence followed by the detection of 25- and 35-kDa forms of TDP-43. TDP-43 overexpression decreased cell viability and increased cells arrested at G2/M phase and nuclear fragmentation. Analysis of the energetic metabolism showed a tendency to decrease oxidative phosphorylation and increase glycolysis, but no statistical differences were observed. Metabolomics revealed alterations in different metabolites (mainly sphingolipids and glycerophospholipids) in cells overexpressing TDP-43. Our data reveal the main role of TDP-43 aggregation in cellular death and highlight novel insight into the mechanism of cellular toxicity induced by TDP-43. Here, we provide a simple, sensitive, and reliable protocol in a human-derived cell line to be used in high-throughput screenings of potential therapeutic molecules for ALS treatment. Transactive Response DNA Binding Protein 43 (TDP-43) is a ubiquitous 43 kDa-protein implicated in multiple steps of transcriptional and post-transcriptional regulation. Under physiological conditions, the majority of TDP-43 is localized in the nucleus, where it controls mRNA stability, translation, and nucleocytoplasmic transport promoted by a small proportion of TDP-43 that continuously shuttles between the nucleus and cytoplasm ,2.TARDPB, and in patients with mutations in other genes involved in ALS pathology, like SOD-1 and C9orf72 . Currently, these aggregates are considered the hallmark of ALS. It remains controversial whether TDP-43 aggregation is the principal cause of the motor neuronal degeneration observed in ALS patients, but recent studies shed a light into the key role of TDP-43 aggregates in causing the observed neurodegeneration. Further, TDP-43 aggregates are present in patients with and without any mutations in the TDP-43 encoding gene, Similar pathophysiological mechanisms are described for both genetic and sporadic ALS patients, and as 97% of ALS patients present TDP-43 aggregation, it is plausible to suggest a link between TDP-43 and some of the pathogenic mechanisms . Data puTDP-43-bearing plasmids consisted of human TDP-43. For visualization of TDP-43 expression, the cDNA insert was cloned into pcDNA6.2 N-EmGFP vector  with six histidine residues (6xHis) added to the C terminus of TDP-43. For all the other experiments, TDP-43-6xHis cDNA was cloned into pcDNA3.3 vector (the 6xHis were fused to TDP-43 with the purpose of purification of TDP-43 protein for drug screening protocols). pcDNA6.2 N-EmGFP-6xHis and pcDNA3.3 vectors were used in control conditions.v/v) fetal bovine serum (FBS) at 37 °C and in an atmosphere of 5% CO2. Cells were plated in six-well dishes at a concentration of 2.5 × 105 cells/well and cultured overnight before transfection. Cells were transiently transfected using jetPEI reagent . Cells were transfected with increasing concentrations of TDP-43-containing plasmids . Control plasmids were transfected at the highest concentration (4 µg). Assays were realized 48 h after transfection.HEK293T cells  were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 5%  and centrifuged at 900× g. Cell lysis was induced with ice-cold Pierce-RIPA buffer containing protease inhibitors cocktail (ThermoFisher Scientific) and Benzonase nuclease . Cells were centrifuged at 5000× g, and the half of the supernatant was collected as crude extract. Cells were centrifuged at 15,000× g for collection of the soluble fraction (supernatant) and insoluble fraction (pellet resuspended in RIPA/Urea 6M). Protein content was measured by Lowry method (Bio-Rad). Proteins were separated in 4–20% SDS-PAGE gel (Bio-Rad) and transferred to PVDF membranes. After blocking with 5% milk in TBS-T buffer, membranes were incubated overnight with an antibody anti-TDP-43 C-terminus  and anti-p38 MAPK or anti-phospho p38 MAPK , followed by 1 h incubation with a secondary antibody coupled to a horseradish peroxidase . Chemiluminescence was observed using Chemidoc (Bio-Rad) after incubation with ECL. Bands intensity was measured with Image Lab software (Bio-Rad). Actin  was used as internal control.1 × 10After 48 h of transfection, cells were incubated in 0.5 mg/mL 3--2,5-diphenyltetrazolium bromide (MTT) (Sigma-Aldrich) for 30 min at 37 °C. The tetrazolium ring of MTT can be cleaved by active dehydrogenases in order to produce a precipitated formazan. The medium was withdrawn, the precipitated formazan was solubilized with 500 μL of dimethyl sulfoxide (DMSO), and cellular viability was quantified by spectrophotometry at a wavelength of 570 nm.g. Cells were then resuspended in 1 mL PBS with 1% FBS. 10 µL were used to test for trypan blue 0.4% exclusion . 0.3 × 106 cells were incubated with 10 µg/mL PI in PBS with 1% FBS for 30 min at 37 °C. Cells were then analyzed by flow cytometry on a Becton Dickinson Accuri™ C6 Plus flow cytometer, and results are presented as the percentage of positive cells (% Pos) in relation to the control group (cells transfected with the empty pcDNA3.3 vector).For the quantification of live cells (measurement of propidium iodide (PI) incorporation (Sigma Aldrich) and trypan blue exclusion test), cells were washed with PBS and incubated with Trypsin (Gibco) for 5 min at 37 °C and centrifuged at 900× g for 5 min to pellet any cell debris, and frozen at −20 °C until analysis. Absorbance was measured in the Roche/Hitachi cobas® according to the manufacturer’s instructions.Lactate dehydrogenase (LDH) activity was measured in the medium of the different transfection conditions. Briefly, the medium (2 mL) was collected from the wells, centrifuged at 900× 6 HEK293T cells were washed, permeabilized, and stained with PI after RNA elimination. Samples were immediately analyzed by flow cytometry (Becton Dickinson Accuri™ C6 flow cytometer). At least 50,000 events were collected for each condition. Cells in phases G0/G1, S, and G2/M, as well as Sub-G1 events , were analyzed as described before [Cell cycle analysis was performed with the BD Cycletest Plus DNA kit (BD Biosciences), according to the manufacturer’s instructions. Briefly, 102DCFDA) . Cells (0.3 × 106) were washed with PBS with 1% FBS and stained with 10 μM CM-H2DCFDA for 30 min at 37 °C. Cells were then analyzed by flow cytometry (Becton Dickinson Accuri™ C6 flow cytometer).Intracellular reactive oxygen species (ROS) levels were measured using 5- (and-6-)chloromethyl-2′,7′-dichlorodihydrofluorescein diacetate, acetyl ester (CM-H6) were washed with PBS with 1% FBS and stained with 20 nM TMRM for 30 min at 37 °C. Cells were analyzed by flow cytometry (Becton Dickinson Accuri™ C6 flow cytometer).Tetramethylrhodamine methyl ester  is a cell-permeant probe that accumulates in mitochondria with intact membrane potentials. Cells , and supplemented with glutamine (2 mM). Cells were then incubated at 37 °C in a non-CO2 incubator for 1 h. Sequential injection of glucose (10 mM), oligomycin (1 μM), dinitrophenol , or rotenone/antimycin A (0.5 mM) were added according to the supplier’s technical specifications to determine the principal metabolic parameters (Sigma-Aldrich). Specific mitochondrial and glycolytic ATP productions were assessed using Seahorse XF Real-Time ATP Rate Assay Kit (Seahorse Bioscience) according to the manufacturer protocol. Results were normalized in relation to DNA content per well using CyQuant dye (ThermoFisher Inc.).The cellular oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were determined using a Seahorse™ XF96 Flux analyzer . Experiments were performed according to the manufacturer’s instructions. Briefly, HEK293T cells were seeded in XF96 cell culture plates at 106 cells resuspended in ice-cold ethanol/0.01M PBS subjected to freeze/thaw cycles. Samples were loaded onto a filter paper and dried in a stream of nitrogen for derivation with a solution of phenyl-isothiocyanate 5%. Subsequently, dried residues were extracted with methanol containing 5 mM ammonium acetate. The MetIDQ® software (Biocrates) is used to calculate the concentrations of individual metabolites. Quality controls (QC) were analyzed regularly on the plate (every 8 samples) to ensure the stability of the mass system over time.A targeted, quantitative approach was used based on the AbsolutIDQ™ p180 kit  using a Flow Injection Analysis and High-Performance Liquid Chromatography (HPLC-) Mass Spectrometry (MS/MS) assay. This assay kit enables the quantification of 188 metabolites . Cell lyhttp://www.metaboanalyst.ca). The classification method was based on Partial Least-Squares Discriminant Analysis (PLS-DA). Values of variable importance in projection [For metabolomics analysis, both multivariate and univariate analyses were performed using MetaboAnalyst version 4.0 . Cytometry results were analyzed using FlowJo VX software, and representative graphs from all analyses are shown in HEK293T cells transfected with increasing quantities of TDP-43-encoding plasmid showed an expected increase in the number of positive cells expressing GFP-fused TDP-43. As can be observed in For the following experiments, we turned to a plasmid containing only TDP-43-6xHis, as GFP-induced expression was toxic in preliminary experiments  was significantly smaller in cells transfected with increasing concentrations of TDP-43 compared to control cells  in almost all TDP-43 conditions, but no statistical differences were found  pathway is usually activated in cases of cell apoptosis, we decided to investigate if TDP-43 overexpression could alter the activation of p38 pathway. Western blot analysis revealed a trend of TDP-43 to induce increase in the ratio Phosphorylated-p38/Total p38 MAPK, although no statistical significance was observed between groups C.Analyses of energy metabolism, that is, oxidative phosphorylation  and glycolysis , revealed a tendency of dose–response effect of TDP-43 overexpression. Data in PLS-DA scatter plot showed an excellent intragroup variability and discrimination of the four groups  with a clear separation of the control group through the first component B,C.Interestingly, analysis of the metabolites commonly altered in all concentrations of TDP-43 tested revealed alterations in levels of mono-unsaturated fatty acids (MUFA), poly-unsaturated fatty acids (PUFA), and saturated fatty acids, among others . These fIn the present study, we showed that TDP-43 overexpression in HEK293T cells with different concentrations of TDP-43 plasmid induce alterations in cell function, expression in the cytoplasm, and aggregation in a concentration-dependent way. Further, it induces expression of cleaved TDP-43 forms as found in ALS patients, namely 25- and 35-kDa fragments. Transfection of HEK293T cells (250.000 cells) one day after plating with 4 µg of TDP-43-encoding plasmid induced a decrease in cell viability. This feature was shown by diverse complementary findings, as reduction in the number of alive, attached cells, increase in cells arrested at G2/M phase, and increase in nuclear fragmentation (sub-G1 events). TDP-43 overexpression altered cells’ morphology in a dose-dependent way. Interestingly, cells transfected with both 3 µg and 4 µg of TDP-43-enconding plasmid showed a decrease in MTT reduction and increase in PI staining, while TDP-43 4 µg increased the LDH released into the medium. TDP-43 overexpression did not alter mitochondrial membrane potential nor ROS levels. Evaluation of mitochondrial metabolism suggested a concentration-dependent tendency of TDP-43 in decreasing basal and maximal respiration followed by increased non-mitochondrial respiration, glycolysis, and glycolytic capacity. As demonstrated by metabolomics analysis, TDP-43 4 µg presents the most discriminant profile of metabolites when compared to control group and the other TDP-43 concentrations tested.In our cellular model, the overexpression of TDP-43 revealed common characteristics observed in ALS patients as the presence of CTFs with 25- and 35-kDa. Although there is controversy regarding whether TDP-43 is overexpressed in ALS patients , the preAlteration in cell viability associated with TDP-43 was shown here in several complementary protocols, from reduction in the number of alive cells, decrease in the capacity of cells to metabolize MTT, increased release of LDH into the medium, and increase in cells permeant to PI. The similar profile of reduced cellular viability was demonstrated here by different protocols: (1) strongly confirmed the effect of TDP-43 in decreasing cell viability and (2) broadened the number of techniques researchers can apply, adapted to the material available . Microscopic examination of cells transfected with increasing concentrations of TDP-43 also evidenced the toxicity caused by TDP-43 overexpression in a concentration-dependent way. Intriguingly, transfection with 2 µg or 3 µg of TDP-43-encoding plasmid induced a decrease in the number of cells arrested in S phase, but only the condition of TDP-43 3µg was followed by a decrease in cell viability. The increase in the number of cells arrested in the G2/M phase was followed by an increase in nuclear fragmentation when cells were transfected with the higher concentration of TDP-43 tested here, meaning that TDP-43 at 4 µg inhibited cellular proliferation that then entered apoptosis pathways. These results could also explain the alterations in cellular morphology and the decreased number of adherent cells when analyzing cells under the microscope. Our findings are in accordance with a previous study performed in SH-SY5Y cells . HoweverActivation of p38 MAPK pathway is known to occur in cases of cellular stress and DNA damage. p38 MAPK pathway may markedly contribute to neurodegenerative disorders, including Parkinson’s and Alzheimer’s disease and ALS . In ALS Our data presented here suggested that overexpression of TDP-43 can induce an alteration in mitochondrial membrane polarization, but further analysis revealed no statistical differences between TDP-43-overexpressing cells and control cells. We also observed a tendency of alterations in the oxidative phosphorylation and glycolysis capacities of cells overexpressing TDP-43, in a concentration-dependent way. Again, no significant differences could be observed. Despite our data, impaired mitochondrial function was shown to be closely related to ALS pathophysiology . Mitochondrial dysfunction is another toxic effect mediated by TDP-43 according to independent reports ,24, and Although mitochondrial impairment is closely related to an increase in the levels of ROS, and several reports already demonstrated that TDP-43 induces increase in the ROS levels, our results do not support such findings. These discrepancies could be due to technical reasons, since our results showed large variances, or to the fact that there are several ways to measure oxidative stress. For example, Wang et al. (2019) use the fluorescent probe mitoSOX  and fixate cells . Other sWe also realized metabolomics analyses to investigate the effect of TDP-43 overexpression over the cell metabolome to evaluate if these alterations reflect the alterations found by metabolomics studies performed in ALS patients . AlthougWhile most studies focus on mutations in the TDP-43 protein, in the present study we highlight the toxic role of the wild type, full-length form of TDP-43 protein—the most common in ALS cytoplasmic aggregates. Here, we provided a fast and reproducible protocol for researchers to evaluate potential therapeutic candidates against TDP-43-mediated toxicity by means of protein localization and aggregation, cell viability , and cell cycle arrest . Further


This study is semiexperimental with an intragroup design. A number of 10 military marksmen (30-42 years old) with at least 10 years of experience in shooting performed under four attentional focus strategies in a counterbalanced design. In each strategy, two blocks  were conducted. Shooters' performance was recorded using SCATT device and analyzed using the factorial variance analysis with repeated measure.  Results showed that the interactional effects of internal-external/relevant-irrelevant focuses of attention were significant on shooting record, shooting accumulation, and stability on the target center. Results suggest that the external-relevant attentional focus strategies were more effective than other focus strategies.  The results of the study support the hypothesis that external-relevant attentional focus produced better scores, better accumulation, more stability at the target center, and less average fluctuation. Therefore, this attentional focus strategy improves performance precision of military elite shooters. Shooting activity requires consistency and accuracy . MilitarVarious factors affect shooters' performance precision. Physiological factors, sensory factors , and motor factors such as recruitment pattern of muscles undoubtedly play roles in shooting performance . TherefoAttentional focus influences muscle and nervous activities and ultimate performance outcomes . Minor dAttentional focus has been investigated from different perspectives and has been characterized as either associative  or dissociative 7  . It has Although in their research, Beilock et al. and Castaneda and Gary emphasized the supremacy of the internal attentional focus –10, depeThe advantage of an external focus of attention over an internal focus has been well documented across a wide variety of skills. For example Zachry et al. showed that in basketball free throw, an external focus of attention produced better results comparing to an internal one . SimilarWulf et al. outlined the mechanism for superiority of external attentional focus over internal attentional focus using constrained action hypothesis . AccordiAmong other mechanisms of evaluating the superiority of external attentional focus, Prinz's theory of common coding can be pointed out. According to this view, performance is more efficient when it is represented by the result of the motion, which is achievable by an external attentional focus . In otheIn recent years, constraints-led perspective also explains the underlying mechanism of external attentional strategies . Based oTask-relevant attentional strategy refers to condition in which the performer deliberately focuses on task-relevant aspects such as movement of the opponent or the movement of the object. In contrast, in the task-irrelevant terms, performer considers task-irrelevant aspects of information such as the audience' noise. There are evidences indicating that elite performers display their best performance in the task-irrelevant attentional situation; in contrast, beginners do their best in task-relevant attentional circumstances. Explicit monitoring theory  states tThough most studies examined only one dimension of attentional strategies, few studies simultaneously considered both dimensions and investigated their interaction . Castañeda and Gray concluded that batting performance for highly skilled players was best in the irrelevant/external condition and worst in the relevant/internal condition. Performance of less-skilled batters was significantly better in task-relevant conditions regardless of being external or internal . Also, tIt is often argued that an external focus of attention produces better outcomes because it results in better organization of the motor plan. For this to be true, it is important to show that the opposite focus of attention does not improve performance as much as an external focus actually does. On the other hand, we need to keep in mind that the explanations given by each line of research are mutually exclusive: results supporting one hypothesis will provide evidence against the other. In order to improve the understanding of how different attentional focus strategies operate in military shooting performance, researchers need to integrate these approaches to observe the effect of the interaction of these strategies on performance.Therefore, the purpose of present study was to examine the effect of various attentional strategies  on precision performance of military elite shooters.A number of 10 elite military shooters  and 9.8 years (SD: 7.13) of experience as a military shooter) volunteered to participate in this study. Due to the high professional standards in this skill, there were no more than 10 participants available for this study. All participants were trained in firearms safety and familiar with shooting a Kalashnikov AK-101 7.62 caliber. All participants self-reported their right hand as their dominant hand and had no history of physical or mental disabilities or injury. The University's Institutional Review Board approved the study, and all participants filled out consent forms.The present study was conducted in an indoor shooting range with individual section lanes, with solitary fighting weapons used for every participant. Participants' shooting performance was tested using SCATT kit, on the same day. SCATT is a kit used to analyze shooting; it is comprised of a software (version 602) installed on a computer as well as hardware . This system displays the target image on the screen of the computer which simultaneously displays the trace of the point of aim by the optical sensor on the barrel on the screen. When the weapon trigger is activated, the point of impact is displayed on the screen. The sequence of the attentional conditions was the same for each participant ((A) external-relevant, (B) internal-relevant, (C) external-irrelevant, and (D) internal-irrelevant) .X-axis was aligned horizontally, and the Z-axis was aligned vertically on the target. The manufacturers quote the accuracy of this system as ±0.1 mm [The nomenclature presents the parameters used to indicate shooting performance and aim-point fluctuation . All aim ±0.1 mm .X) axis, and error in the vertical (Z) axis. These parameters are included in the nomenclature. The maximum score of 10.9 is consistent with the scoring protocol during finals in international shooting competitions, in which the point of shot on target is measured to one decimal place. Aim-point fluctuation parameters were quantified from 1 s to shot [Shooting performance was indicated by score (out of 10.9), error in the horizontal . Then, in a within-group design, the participants took 80 counterbalanced trials in four conditions ((A) external-relevant, (B) internal-relevant, (C) external-irrelevant, and (D) internal-irrelevant). Therefore, each condition consisted of 20 shots. Participants took a period of 2-minute rest after 5 shots. In the internal-relevant condition, participants were required to mentally focus on the movement of their forearm, wrist, and fingers. In the external-relevant condition, they were asked to mentally focus on the target. In the internal-irrelevant condition, participants were required to mentally focus on their breathing rhythm and judge about the number of inhale and exhale in each trial. In the external-irrelevant condition, the participants had to mentally focus on an auditory stimulus which was simultaneously and randomly presented by the program designed in the Mat-lap software and then judge about the bass or treble nature of that sound. To improve the effectiveness of the attention strategies, the experimenter reminded the participants to focus according to the mentioned attentional strategy before and during the trials and do their best to obtain the highest shooting scores. Before each trial, the participants read aloud the attentional strategy instruction relevant to their states, written on a sheet of paper. This was to increase the effectiveness of the attentional strategies.The Shapiro–Wilk test was used to assess the normality of data distribution for each variable. The Mauchly's test of sphericity was also used to test the assumption of sphericity, the Box's M test was used to test the homogeneity of covariance matrices, and eventually, the Levene test was used to assess the equality of variances. To data analysis, a 2  × 2 (relevant − irrelevant focus) repeated measure ANOVA was used. Data analysis was performed using the SPSS v. 20 software at a significance level equal or less than 0.05.This study received ethical approval from the Institutional Review Board of the Baqiyatallah University of Medical Sciences (IR.BMSU.REC.1396.642), and written informed consent was obtained from all participants.The second table  illustraThe epsilon value was lying between 1/(4-1) and 1, where 4 is the number of levels ((1) external-relevant, (2) internal-relevant, (3) internal-irrelevant, and (4) external-irrelevant) on which our dependent variable differential attentional focus strategies have been measured.F value for “2  × 2 (relevant − irrelevant focus)” factor, its associated significance level, and effect size .The next table  shows anP = 0.001), accumulation (P = 0.001), average fluctuations (P = 0.001), horizontal fluctuations (P = 0.001), vertical fluctuations (P = 0.001), and stability at the target center (P = 0.001)) of participants in any of the internal and external focus conditions, it can be concluded that the participants performed significantly better in the external-relevant and external-irrelevant attentional strategies comparing to internal-relevant and internal-irrelevant focus conditions.The main effect of internal-external attentional strategic conditions in precision performance was statistically significant. Considering the mean precision performance (record (P = 0.005), accumulation (P = 0.001), average fluctuations (P = 0.001), and stability at the target center (P = 0.001)), and the participants performed better in the relevant focus conditions rather than relevant focus.The main effect of relevant-irrelevant focus was also statistically significant in precision performance (record (P = 0.001), accumulation (P = 0.001), average fluctuations (P = 0.001), vertical fluctuations (P = 0.002), and stability at the target center (P = 0.001)) was also significant (The interaction effect of relevant-irrelevant and internal-external attentional strategies in precision performance (record (nificant .Figures This study examined the effect of different attentional focus strategies on performance of military elite shooters. Two types of attentional focus strategies were employed: “internal/external” and “task-relevant/task irrelevant.” The general hypothesis was “the interaction of various attentional strategies would significantly affect performance”; and the external-relevant focus of attention would produce the best results.The results of this experiment showed that external-relevant focus of attention produced better scores, better accumulation, more stability at the target center, and less average fluctuation. Therefore, the results of the study retain the hypothesis of the study and indicate that external-relevant attentional focus improves performance precision of military elite shooters . AlthougThe constrained action hypothesis suggests that the external focus of attention prevents from top-down constraints on the coordination of movement ; therefoThe self-invoking trigger hypothesis was proposed by Wulf and Lewthwaite who suggested that an internal focus of attention triggers people to engage in self-evaluation and self-regulatory processes in an attempt to gain control over their thoughts and feelings. If the addition of these processes exceeds the attentional capacity, automatic control of the motor program may become disrupted and leads to declines in motor performance .Also, results of the present study may well be explained by constrain-led perspective . The parAmong the various attentional strategies presented in this study, internal-relevant strategy presented the lowest score, lowest accumulation, lowest stability at the target, and the highest fluctuations, which is significantly inferior to the external-relevant attentional strategy. This is compatible with the proposed principles in the constraint action hypothesis which indicate that attempts to consciously control the movement in the form of the internal attentional focus confine the motor system, prevent processes that control the movement, and ultimately disturb automatic control of the motor system, therefore weaken the motor performance .Based on what we have learnt from explicit processes theory , one mayStudying performance of the elite always has limitations. First, it is the limited number of participants. Authors were not able to induce voluntarily participation of any more elite military shooters; there is no need to mention that their total number is apparently limited too. This was somewhat justified by the limited sample size in previous similar studies , 31. TheThis study expanded the previous findings by demonstrating the greater effectiveness of external, relevant attentional strategy over other attentional strategies for elite shooting. Results of this study indicate the significance of using standard attentional strategies during training or assessing shooting skill. As the results show, changing attentional strategy leads to remarkable performance variations. Depending on the skill level of the participants, these strategies could differ. In general, given the importance and necessity of considering perceptive-cognitive skills in shooting, especially at the military level, findings of this study suggest that employing external attentional strategy, yet irrelevant to the task, would lead to improvement in the performance. Therefore, along with related hypothesis and theories in the field, findings of the present study suggest instructors to direct elite performers' attention to the external, yet irrelevant cues to the task.


Gene amplification chiefly manifests as homogeneously stained regions (HSRs) or double minutes (DMs) in cytogenetically and extrachromosomal DNA (ecDNA) in molecular genetics. Evidence suggests that gene amplification is becoming a hotspot for cancer research, which may be a new treatment strategy for cancer. DMs usually carry oncogenes or chemoresistant genes that are associated with cancer progression, occurrence and prognosis. Defining the molecular structure of DMs will facilitate understanding of the molecular mechanism of tumorigenesis. In this study, we re‐identified the origin and integral sequence of DMs in human colorectal adenocarcinoma cell line NCI‐H716 by genetic mapping and sequencing strategy, employing high‐resolution array‐based comparative genomic hybridization, high‐throughput sequencing, multiplex‐fluorescence in situ hybridization and chromosome walking techniques. We identified two distinct populations of DMs in NCI‐H716, confirming their heterogeneity in cancer cells, and managed to construct their molecular structure, which were not investigated before. Research evidence of amplicons distribution in two different populations of DMs suggested that a multi‐step evolutionary model could fit the module of DM genesis better in NCI‐H716 cell line. In conclusion, our data implicated that DMs play a very important role in cancer progression and further investigation is necessary to uncover the role of the DMs. Cytogenetically, gene amplification chiefly manifests as homogeneously stained regions (HSRs) or double minutes (DMs).Currently, it has been documented that extrachromosomal chromatins arose from circularization of co‐amplified DNA fragments from multiple chromosomal loci.Because of the diversity of extrachromosomal chromatins in size, amount, molecular composition and organization in single or multiple cells,The human colorectal adenocarcinoma cell line NCI‐H716 carries DMs, and the molecular structure of them has not been explicitly defined.22.12 at 37°C. Cells were authenticated by short tandem repeat profiling analysis .The human colorectal cancer cell line NCI‐H716 with spontaneous DMs was purchased from American Type Culture Collection . The cells were cultured in RPMI‐1640 medium  supplemented with 10% foetal bovine serum (Invitrogen) in a humidified atmosphere of 5% CO2.2Metaphase chromosomes of the NCI‐H716 cells were prepared as previously described.2.3https://earray.chem.agilent.com/) for interrogating the genome of DMs with high‐resolution and accuracy. Data were quality controlled and extracted using Feature Extraction , and subsequently analysed by Agilent Genomic Workbench .DNA processing, microarray handling and data analysis were performed according to the protocol by manufacturer  with minor modifications. Oligonucleotide‐based Human Genome Microarrays (Agilent Technologies) containing 60 K, 180 K, 400 K and 1 M features were used for hybridization, among which 2 × 400 K format was used to examine genome‐wide copy number alteration Figure . The 4 ×2.4Genomic coordinates of DMs were provided by high‐resolution array‐CGH analysis. Customized NimbleGen 2.1M sequence capture arrays were fabricated using SeqCap v2 software. The captured library preparation and NimbleGen Sequence Capture Arrays were performed according to the manufacturer's protocol, which was adapted from the company's application notes . The captured DNAs were generated by ligating PacBio SMRTbell™ adapters to both ends of linear DNA fragments. These DNA fragments were sequenced on the PacBio RS as a continuous circle. Then, clear data were mapped onto reference human genome (GRCh37/hg19) using Burrows‐Wheeler Aligner (BWA) to filter out the reads without SVs. Subsequently, all the identified fusion sequences were also verified with the BLAST‐Like Alignment Tool (BLAT) and Basic Local Alignment Search Tool (BLAST).2.5Genomic DNA of the NCI‐H716 cells was extracted, quantified and purified with HiSeq X Ten protocol . DNA fragments were ligated with adaptor oligonucleotides to form paired‐end DNA libraries with an insert nucleotide of 500 bp. This library was amplified by PCR with adaptor‐specific primers and sequenced by Illumina HiSeq X Ten instrument to obtain up to ∼150 million reads . Reads that aligned to genomic regions were collected for mutation identification and subsequent analysis. Samtools mpileup and bcftools were used to do variant calling and identify single nucleotide polymorphisms (SNPs), insertions and deletions (INDELs). Control‐FREEC was utilized to do copy number variations (CNVs) detection. And BreakDancer was applied to detect SV information.2.6Sequences of Junction VII and Junction VIII were acquired using GenomeWalker universal kit and Advantage 2 PCR Kit  according to the manufacturer's instructions. Normal human DNA was used as control. All the junction sequences were validated by Sanger sequencing.33.16. However, a dramatically higher amplification level was observed in sub‐amplicons , twice of the major amplicons  on chromosome 8q24.12‐21 and Amplicon H4 on 10q26.13 Figure . Based o3.2Since DMs in the cells contained sequences from different chromosome fragments, we speculate that the way in which DMs arranged might be crucial to cell evolution. To this end, we chose six available BACs based on amplification level within these amplicons, as representative FISH probes to localize the sub‐amplicons in the cells. The amplified regions on DMs were verified by M‐FISH analyses, and co‐localization of the four amplified DNA fragments was shown in Figure Furthermore, we observed duplicated signals of H1a, H2d and H3c located within one individual DMs from Population one, indicating a more complex molecular structure of DMs similar to a ‘diploid’ structure  and identified a total number of 3,120,201 single nucleotide polymorphisms (SNPs), which including 1,689,436 heterozygote SNPs and 1,430,765 homozygote SNPs. We selected a total of 34 heterozygote SNPs in amplified regions from chromosome 8 for the study Table  and foun3.3Because of heterogeneity of DMs, we hypothesized that break‐rejoin model might be a possible mechanism to induce the occurrence of DMs. To elucidate the mechanism, we designed a NimbleGen capture array to detect the sequence of DMs. First, DMs DNA was enriched with a NimbleGen capture array and sequenced with PacBio RS system with average read length of 1100 bp on the basis of circular consensus sequencing. The sequencing reads were then aligned to the human genome to obtain the breakpoint sequences. On the basis of the CGH microarray results, 15 breakpoints within the above four major amplicons were identified and named J1‐J15 in sequential order Figure , and theTo acquire the entire sequence map of DMs, junction sequences must be fully ascertained. Therefore, we cloned the rearrangement breakpoints J7, J8, J10, and J12 of DMs by chromosome walking technique starting from the boundary sequence obtained through CGH microarray and high‐throughput sequencing. Then, the rejoining patterns were manifested by alignment of the cloned sequence to the human reference genome Figure . As show3.42 ratios ≈ 7) than those in the other amplification regions .The regions of each amplicon in DMs were validated through high‐density array CGH. The amplified regions on DMs were verified by M‐FISH analyses, and the results confirmed two populations of DMs in NCI‐H716 cells. Precise boundaries and junction sequences of each amplicon were identified by high‐throughput sequencing and chromosome walking analysis. Based on our research data, we reconstructed the molecular structure of DMs Figure . In deta4Previous studies have reported the existence of endogenous DMs carrying fragments from chromosome 8 and 10 in NCI‐H716 cell line,Our study employed the high‐throughput sequencing technique, providing an unprecedented platform to detect DMs in cancer cells.In this study, we aimed to reveal the mechanisms of DMs formation by deciphering the junction events. As a result, we found three ‘junctions’ Figure  in DM PoTill now, several different models have been proposed for the formation of DMs.In NCI‐H716 cells, DM Population one might have participated in the formation of DM Population two. It can be speculated from the following evidence. First, DM Population one included three integral amplicons ; in contrast, DM Population two only contained partial sequences of these amplicons . Second, more powerful evidence was from the formation of Junction IV (green bar), which does not exist on chromosomes, shared by both populations Figure . These SIn summary, we re‐identified two populations of DMs in human colorectal adenocarcinoma cell line NCI‐H716, confirming their heterogeneity in cancer cells and managed to construct their molecular structure, which were not investigated before. Based on our analysis, we propose that both NHEJ/MMEJ and FoSTeS/MMBIR pathways may mediate the rearrangements in DMs, and the complex structure of DMs in NCI‐H716 cell line may be generated by multi‐step evolutionary process involving various mechanisms. Further anatomy on DMs will enhance our understanding the biological significance of extrachromosomal chromatins in cancer.The authors declare that they have no competing interests.Xueyuan Jia: Conceptualization ; Methodology ; Software (lead); Writing – original draft (lead); Writing – review and editing (lead). Rongwei Guan: Data curation (lead); Software . Xiaobo Cui: Data curation ; Software ; Writing – review and editing . Jing Zhu: Methodology ; Software ; Validation . Peng Liu: Methodology ; Validation . Ling Zhang: Visualization (lead). Dong Wang: Methodology ; Software . Yang Zhang: Conceptualization ; Methodology . Kexian Dong: Methodology ; Software . Jie Wu: Validation ; Visualization . Wei Ji: Methodology ; Validation . Guohua Ji: Resources . Jing Bai: Project administration ; Supervision . Jingcui Yu: Resources ; Supervision . Yang Yu: Conceptualization ; Supervision . Wenjing Sun: Data curation ; Resources (lead); Validation (lead); Writing – review and editing . Feng Zhang: Conceptualization (lead); Funding acquisition ; Methodology (lead); Supervision . Songbin Fu: Funding acquisition (lead); Project administration (lead); Supervision (lead); Writing – review and editing .Supplementary MaterialClick here for additional data file.


During the last few years graphene has emerged as a potential candidate for electronics and optoelectronics applications due to its several salient features. Graphene is a smart material that responds to any physical change in its surrounding environment. Graphene has a very low intrinsic electronic noise and it can detect even a single gas molecule in its proximity. This property of graphene makes is a suitable and promising candidate to detect a large variety of organic/inorganic chemicals and gases. Typical solid state gas sensors usually requires high operating temperature and they cannot detect very low concentrations of gases efficiently due to intrinsic noise caused by thermal motion of charge carriers at high temperatures. They also have low resolution and stability issues of their constituent materials  in harsh environments. It accelerates the need of development of robust, highly sensitive and efficient gas sensor with low operating temperature. Graphene and its derivatives could be a prospective replacement of these solid-state sensors due to their better electronic attributes for moderate temperature applications. The presence of extremely low intrinsic noise in graphene makes it highly suitable to detect a very low concentration of organic/inorganic compounds (even a single molecule ca be detected with graphene). In this article, we simulated a novel graphene nanoribbon based field effect transistor (FET) and used it to detect propane and butane gases. These are flammable household/industrial gases that must be detected to avoid serious accidents. The effects of atmospheric oxygen and humidity have also been studied by mixing oxygen and water molecules with desired target gases (propane and butane). The change in source-to-drain current of FET in the proximity of the target gases has been used as a detection signal. Our simulated FET device showed a noticeable change in density of states and IV-characteristics in the presence of target gas molecules. Nanoscale simulations of FET based gas sensor have been done in Quantumwise Atomistix Toolkit (ATK). ATK is a commercially available nanoscale semiconductor device simulator that is used to model a large variety of nanoscale devices. Our proposed device can be converted into a physical device to get a low cost and small sized integrated gas sensor. Gas sensing has been a critical subject for wide range of applications such as medical, industrial environment, military and aerospace applications. The presence of hazardous and toxic gases may lead to some serious accidents in industrial as well as household environments. There must be some tool to detect the presence of these gases effectively ,2. The uAlthough, with an increase in the demand of gas sensors, still there is a need to develop robust, highly sensitive and reversible sensors that work at low temperatures. Gas sensors based on conventional semiconductor materials usually require high operating temperatures. In order to cope with these issues, several efforts are being made to modify the shape and orientations of such materials . NanomatGraphene based gas and inorganic/organic molecule detectors detect the presence of these molecules with different mechanisms. The most popular detection mechanisms are the resistive method, field effect transistors (FET) method and micro-electromechanical system (MEMS) based method. In the resistive method, the change in electrical conductivity in the presence of target molecules is used as a detection signal . In FET Graphene based field effect transistor (FET) can be a potential candidate to detect the presence of a wide range of chemicals, toxic compounds, biomolecules and gases with better sensitivity compared to that of solid-state sensors. The sensitivity range of these graphene based FETs usually range from parts per billion (ppb) to parts per million (ppm) ,22,23. TMoreover, propane and butane are the most commonly used fuel for household/industrial environments . These aIn this article, we simulated a novel FET device based on graphene nanoribbons with nanoscale semiconductor device simulator, Quantumwise Atomistix Toolkit (ATK). This simulated device has been used to detect the presence of propane and butane gases. The change in electric current through FET device at different gate voltages in the presence of these gases has been used as a detection mechanism. To the best of our knowledge, this type of device has not been reported in the literature for the detection of propone and butane gases.The simulations of GNR based FET for the detection of propane and butane gases have been carried out with Quantumwise Atomistix Toolkit (ATK) software package. Graphical user interface of ATK is called Virtual Nano Lab (VNL). ATK-VNL allows atomic scale modeling of nano-systems. This software uses several in-built calculators to solve and calculate transportation properties of quantum systems. Density functional theory (DFT) calculator has been used for the simulations of our proposed device in ATK-VNL. The work flow and mathematical formalism used by ATK-DFT has been given in . All theThe constituent materials of simulated graphene field effect transistor are armchair graphene nanoribbons (AGNR) and zigzag graphene nanoribbons (ZGNR). The termination pattern of the edge of these structures defines the type of nanoribbon either armchair edge or zigzag edge, as shown in Furthermore, the two probe model of GNR based FET has been simulated by using AGNR and ZGNR in ATK-VNL. The builder tool of ATK-VNL is used to simulate FET. All the details have been given in next lines. First of all, the central region of the device have been simulated. The central region of FET consists of AGNR. AGNR with width of four atoms has been created using Nanoribbon Plugin Tool of ATK Software. Afterwards, this GNR has been extended 5 times along C-axis (the repetition pattern) as shown in εo and a very thin metallic layer has been deposited on it to form a gate electrode of field effect transistor. The lengths of source and drain electrodes are approximately 7 Å. In next step, three molecules of propane, three molecules of butane and both propane & butane (four molecules of butane and one molecule of propane) have been exposed to the FET device in three different experiments, as shown in After that, the next step is to join the central region  with theX, Y, Z coordinates) the target molecules with respect to the simulated FET device. The position and geometry of each butane molecule along with X, Y, Z axis have been shown as an example in Furthermore, an in-built Merger Tool of ATK-VNL has been used to expose the target molecules  to the simulated FET device d. ATK-VNIn this section, the results obtained from simulations have been presented and discussed in detail. The DOS and IV-characteristics of simulated FET based sensor have been calculated. A significant and distinct change in the DOS of FET device have been observed in the presence of different target gas molecules. A comparison of DOS of simulated FET device in the absence of any target gas molecules and in the presence of three propane gas molecules has been shown in Furthermore, the presence of three butane gas molecules affected DOS of FET differently compared to that of propane gas molecules, as shown in dsI) and voltage (dsV) curves of simulated FET have been calculated for different applied gate voltages. The simulated device (in absence of any target gas molecules) showed depletion mode MOSFET like behavior. A decrease in dsI versus dsV has been observed with an increase in negative gate to source voltage (gsV), as shown in Drain to source current (dsV), but also on gate voltage. The adsorbed target gas molecules act as donors or acceptors of charge carriers for the graphene. If they act as donors, they change (increase) the concentration of charge carriers in graphene. Consequently, the electric current through the graphene based device increases in the presence of adsorbed target molecules under same bias condition. When the foreign target molecules behave like acceptors of charge carriers, they decrease the current through the graphene based device [dsV on the IV-characteristics of FET based sensor.In our previously published work , we usedd device . In resigateV = −0.1 V and at a fixed dsV, shown with a solid black line in dsI has been observed in the presence of propane as a target gas, at gateV = −0.1 V, as shown with orange dotted line in dsI in presence of butane gas molecules compared to that of reference device. The range of the dsI is between −26 nA to −134 nA approximately in the presence of butane gas, shown with blue color dashed line in dsI has been observed when FET device is exposed to four butane and one propane gas molecules simultaneously. The range of dsI in this case is between −93 nA to −167 nA at gateV = −0.1 V, which is quite high compared to that of the reference device. This high amount of current through the device may be due to the donor like dominated effect of four butane molecules compared to that of one propane molecule (acceptor like behavior), which are exposed to the device simultaneously. This curve has been shown with a dotted-dashed green colored line in Furthermore, our simulated device exhibited a considerable change in IV-characteristics in the presence of the target gases. The reference FET device showed a current range between −24 nA to −66 nA at dsI through reference FET, as it is a depletion mode n-channel FET. A similar trend of increase and decrease in dsI at gateV = −0.3 V for the same dsV has been observed with respect to the reference FET device, as shown in dsI at this gateV has been observed compared to that of reference FET. The range of dsI in this case is between −20 nA to −57 nA, approximately. Whereas butane gas increased the dsI compared to that of reference FET and the range of current is between −18 nA to −106 nA. The simultaneous presence of both gases increased the dsI in similar manner that is observed in previous cases. The range of electric current under this bias condition is between −187 nA to −220 nA, which is quite high compared to that of the device at gateV = −0.1 V, as shown in gateV = −0.5 V, as shown in However, an increase in negative bias gate voltage reduced the overall gateV = −0.1 and fixed dsV have been calculated. A slight decrease in dsI of the simulated FET has been observed in the presence of two oxygen molecules, as shown in dsI has been observed, when water and oxygen molecules are mixed with propane gas molecules, as shown in dsI, as shown in dsI has been observed in the presence of mixture of butane and oxygen molecules, as shown in In The presence of both oxygen and water molecules degraded the device performance in terms of reduction of detectable current for propane and butane gases. A careful calibration of such physical sensor is required to nullify the environmental effects on the actual reading of the device in the presence of desired target molecules. The presence of oxygen content in the atmosphere has a strong influence on the electrical properties of graphene. The oxygen content of the air strongly affects the electrical resistance of graphene which depends on the exposure time. The electrical conductance of graphene reduces with passage of time at its exposure to the oxygen content of the air. Therefore, the change in device performance may be expected as exposure time increases . Similar2) and carbon dioxide (CO2) on the electrical conduction properties of graphene based sensors are negligible. Whereas, water molecules have strong influence on the conductivity of graphene. The considerable response of graphene to the water molecules makes it highly suitable for humidity sensor applications [2 and CO2 gases have not been considered in these simulations. The purpose of these simulations is to investigate the feasibility of simulated FET device for the detection of solely propane and butane molecules for household and industrial environments. However, the concentration of methane (CH4) gas is increasing day by day due to green house effect. Several efforts have been done for the development of CH4 gas sensors [4 with our proposed device for climatic and household applications can be considered as future work. These simulation results are convincing in terms of the ranges of detectable electric current values. In our previous work [Moreover, in ambient atmospheric conditions, the effect of nitrogen (Nications . Therefo sensors ,56,57,58ous work , the difous work . The phyThe goal of the work that has been presented in this article is to access the potential of graphene nanoribbon based FET for the detection of propane and butane gases. Armchair graphene nanoribbons and zigzag nanoribbon have been used to develop FET device. In the absence of any target gas molecules, the IV-characteristics of the simulated device are similar to n-channel depletion mode FET. In the proximity of target gas molecules, a change in source-to-drain current of FET at different gate voltages has been observed. This change is distinct for each specific target gas i.e., propane and butane. Our simulated FET device also exhibited a noticeable change in the density of states in the proximately of these target gases. The change in source-to-drain current of FET in the presence of target gas molecules has been used as a detection signal for the leakage detection of these gases. The influence of atmospheric factors like the presence of water and oxygen molecules on the proposed device have also been investigated. It has been observed that the presence of these molecules also affected the IV-characteristics of the device. A careful calibration of such physically fabricated device is required to nullify the effect of atmospheric factors and get the correct reading for desired target gases. Our proposed device could be a promising candidate to replace conventional solid-state gas sensors due to its exceptional electronic properties and compact size. Theoretically, it is possible to simulate such type of FET sensors. However, the physical fabrication of these kind of device could be a challenge due to extremely small dimensions of graphene nanoribbons that make it difficult to handle at atomic levels. Moreover, during physical fabrication of our proposed FET, there is a need of some suitable substrate like SiC or Si for the deposition of graphene. Our nearest future intension is to develop graphene based FET device for the detection of propane and butane gases.