Communities in social networks often reflect close social ties between their members and their evolution through time. We propose an approach that tracks two aspects of community evolution in retweet networks: flow of the members in, out and between the communities, and their influence. We start with high resolution time windows, and then select several timepoints which exhibit large differences between the communities. For community detection, we propose a two-stage approach. In the first stage, we apply an enhanced Louvain algorithm, called Ensemble Louvain, to find stable communities. In the second stage, we form influence links between these communities, and identify linked super-communities. For the detected communities, we compute internal and external influence, and for individual users, the retweet h-index influence. We apply the proposed approach to three years of Twitter data of all Slovenian tweets. The analysis shows that the Slovenian tweetosphere is dominated by politics, that the left-leaning communities are larger, but that the right-leaning communities and users exhibit significantly higher impact. An interesting observation is that retweet networks change relatively gradually, despite such events as the emergence of the Covid-19 pandemic or the change of government. With the ever-growing base of social media users, platforms such as Twitter are becoming a very valuable source of data for social analysis. Users on social media interact with each other, so it is natural to use graphs  to represent the structure of the user base. Nowadays, a lot of research in the field of complex networks is focused on social networks analysis. Due to the social media volatility, temporal analyses are needed for an in-depth understanding of the underlying phenomena. They can provide insights into the patterns and evolution of the social media landscape, and consequently to the society itself.Change in the collective behaviour of groups in networks is referred to as community evolution , where cIn our approach, we proceed by creating overlapping snapshots of the network through time, and detect communities in each snapshot. We then track evolution of relevant communities over time. Several developments are needed to detect community evolution in terms of the flow of members in, out and between the communities, as well as to track the changes in the community influence.We illustrate our approach to community evolution on a set of Slovenian tweets during the last three years, roughly 13 million Twitter posts. Our initial research, where we performed a static community structure analysis of the data showed strong polarization of the detected communities along the political dimension. In the subsequent research, the basis of the current paper, we compared community structures between different manually selected time windows . In the The temporal dimension is very valuable in modern analyses of complex networks. This has implications on how dynamic community discovery is designed and applied.The related approaches mostly depend on the representation of time. One can group them into three types: static/edge-weighted, snapshots, and temporal networks . The firThe snapshot approach crucially depends on the representation of time in the network relations. Two main scenarios were considered so far: perfect memory networks , and limited memory networks. Perfect memory permits only aggregation of nodes and edges, where the old nodes/edges cannot disappear. The limited memory scenario allows for nodes/edges to disappear over time. This is suitable in social network analysis, where the edge disappearance could indicate the decay of social ties. The limited memory networks are implemented with various methods, including static, sliding, or dynamic-sized time windows, each method with its own strengths and weaknesses. In subsection Retweet networks we propose a combined method that circumvents the drawbacks of the existing strategies by building a weight-decaying sliding time window network. We then apply a snapshot selection method, described in subsection Selection of timepoints, where fixed static time windows that contain the most information about the dynamics of the communities are selected.t depend only on the current state of the network at time t. A two-stage approach first detects communities at each snapshot, and then matches the detected communities . The largest negative peak, indicating the highest dissimilarity, F1(P92|P91) = 0.74, occurs between the partitions which end on March 16 and 23, 2020, respectively. These dates closely follow the change of government in Slovenia and first policy reactions to the emergence of the Covid-19 pandemic.The weekly differences between the network partitions are relatively low. The retweet network communities apparently do not change drastically at this relatively high time resolution. Moving to lower time resolution means choosing timepoints which are further apart, and where the network communities exhibit more pronounced differences.P0 and Pn, respectively. For a given k, select k intermediate timepoints such that the differences between the corresponding partitions are maximized, i.e., the F1 scores are minimized:k. We therefore propose a simple heuristic algorithm which finds k approximate timepoints. The algorithm works top-down and starts with the full, high resolution timeline with n + 1 timepoints, t = 0, 1, …, n and corresponding partitions Pt. At each step, it finds a triplet of adjacent partitions Pt−1, Pt, Pt+1 with minimal differences:Pt from the timeline. At the next step, the difference F1(Pt+1|Pt−1) fills the gap of the eliminated timepoint Pt. The algorithm thus finds the k  timepoints in n − 1 − k steps. While efficient, this approach to the relevant timepoint selection is not suitable for incremental, stream-based network processing since it assumes that the final timepoint is fixed.We formulate the timepoint selection task as follows. Let assume that the initial and final timepoints are fixed, corresponding to the partitions k and eventually settled with k = 3 which provides much lower, but still meaningful time resolution. This resulted in the selection of the following network partitions: P0, P22, P68, P91, P132. F1 differences (black line) between the adjacent partitions.For our retweet networks, we experimented with several values of F1 is in the range . Due to these small differences, the timepoint selection procedure is not very robust. The selected timepoints should be considered approximate and can vary for several weeks in both directions. As a consequence, the selected timepoints should not be interpreted as indicators of specific events at specific dates, but should rather help in understanding longer terms qualitative transitions in community evolution.The selected timepoints are on average 26 weeks apart, varying between five and ten months. The differences between the network partitions are increasing with temporal distance, but are still relatively uniform, max-F1 (blue line), where it is assumed that all the common nodes in two adjacent partitions are in one community, and only the intersection size and the number of new and lost nodes affect the score. The max-F1 scores, dropping from 0.77 to 0.63, show increasing fluctuation of nodes in and out of the partitions. In the next subsection we show two visualizations of transitions between these five network partitions.We present two visualizations of transitions between selected network partitions as Sankey diagrams. A Sankey diagram is a type of flow diagram in which the width of the bands is proportional to the flow rate.max-F1  and discussion topics. It turns out that most of the communities are structured around political figures  , and that = 22 did not even make it into the top five:t = 91, 132) did his community grow considerably.The political communities are considerably larger than Sports. Let us first consider some right-leaning communities, which feature the current Slovenian prime minister of the right-wing government, with an anonymized Twitter handle $PM-mar20-now. He was initially a member of relatively small communities that at timepoint C1 community that grows and shrinks with time, but remains by far the largest community throughout the three year period. The former Slovenian prime minister (between September 2018 and March 2020), with an anonymized Twitter handle $PM-sep18-mar20, was a member of C1 for most of the time:t = 68) did he feature prominently in his own community C4. The left-wing Slovenian prime minister before him (until March 2018), with an anonymized Twitter handle $PM-sep14-sep18, was initially a member of smaller communities on the left-wing, and recently joined C1:On the political left-wing, there is the t = 0, 22, the $PM-sep18-mar20 community at t = 68, 91, and the $PM-mar20-now community at t = 132. This is another piece of evidence that retweet communities evolve gradually and that it takes a while before events with a high impact are reflected in a new community structure.It is interesting to observe the official Slovenian government Twitter account @vladaRS. It moves from the left-leaning to the right-leaning communities as the left-wing government is replaced by the right-wing one, but with some delay:To further characterize political polarization and community evolution, we now turn attention from the community membership to the retweet links between the communities.Twitter users differ in how prolific they are in posting tweets, and in the impact these tweets make on the other users. One way to estimate the influence of a Twitter user is to consider how often are its posts retweeted. Similarly, the influence of a community can be estimated by the total number of retweets of their posts. Retweets within the community indicate internal influence, and retweets outside of the community indicate external influence. This approach to characterize influential users and communities was already applied to a wide range of environmental issues discussed on Twitter .Wij denote the sum of all weighted edges between communities Ci and Cj. The average community influence I is defined as:Ci, normalized by its size. The influence I consists of the internal Iint and external Iext component, I = Iint + Iext, wheret = 0, 22, 68, 91, 132. The communities which are politically left- or right-leaning are shown in Ci, Cj is linked by a weighted directed edge from Ci to Cj, with the weight equal to the external influence Iext.In this subsection we focus on community influence and subsequent identification of super-communities. Another measure of individual influence is described in the next subsection Retweet h-index influence. In our retweet networks, the number of retweets is represented by the weighted out-degree of a node. Let The meta-networks in t = 132. However, the influence of the Right-wing is always considerably higher, with the gap even increasing after the right-wing government took over in March 2020 .In Weighted out-degree is a useful measure of influence for communities and super-communities. However, we propose a different measure of influence for individual Twitter users. The user influence is estimated by their retweet h-index, an adaptation of the well known Hirsch index  to Twitth has posted h tweets and each of them was retweeted at least h times. Let RT be the function that returns the number of retweets for each original post. The values of RT are ordered in decreasing order, from the largest to the lowest, and i indicates the ranking position in the ordered list. The h-index is then computed as follows:A user with an index of oponents . Later, oponents , a term t = 0, 22, 68, 91, 132. Two of the top Twitter users, @vladaRS and @ukclj, do not remain in the same super-community, but move from the Left-wing to the Right-wing as the government changed.We compute the h-index and the h-index rank for all the users on Slovenian Twitter during the three year period. For each super-community, Left-wing and Right-wing, we show the top ten most influential users by h-index, ordered by the h-index rank . The useThere is a large difference between the members of the Right-wing and Left-wing super-communities. The Right-wing members consistently take the top h-index ranks, while the Left-wing members barely make it into the top 100 h-index ranks. This reaffirms the super-community influence results from the subsection Identification of super-communities, and is consistent with our previous results. In the case of the European Parliament, higher Twitter activity was observed for the right-wing parties . In the As per PLoS ONE policy, individual Twitter users have to be anonymized. Therefore we replace each individual Twitter handle @User with an anonymous handle $Type. The user types for the top 890 users were determined manually . There aThe Right-wing and Left-wing super-communities are led by the current ($PM-mar20-now) and former ($PM-sep18-mar20) prime minister of Slovenia, respectively. The other top members are either politicians, journalists, or public figures active on Twitter. In the Left-wing, there are some media account , and a political party account .t = 91, and $Public_figure6 at t = 132). They post tweets about the medical issues, drugs and vaccines related to the pandemic. There are other influential users, tracking and commenting on the pandemic development, which emerged recently, but they did not yet make it into the overall top ten h-index list.The only two users in Social media, and Twitter in particular, are a rich source of data that reflects social relations between the users. In the paper we exploit a specific type of networks where retweets are used as links between the users. We demonstrate that in the retweet networks meaningful communities are formed. We show that retweet influence reveals important differences between different communities as well as between individual Twitter users. The main focus of the paper is on the evolution of communities and influence through time, and we address several issues relevant for the field of dynamic networks.One problem is the instability of detected static communities by a standard community detection Louvain algorithm. We propose to run an ensemble of Louvain trials, and detect stable communities through frequent co-occurrence of nodes across the trials. Preliminary evaluations of the Ensemble Louvain algorithm on some benchmark networks with known “ground truth” communities show promising results, and this is certainly one of the directions that needs to be further explored in the future.We study network evolution by taking several static network snapshots with a sliding window. One has to decide on the window size and the temporal resolution between the snapshots. We decided on the 24 weeks window size and an exponential edge weight decay, with half-time of 4 weeks. The edge decay removes the effect of the trailing end of the window, and thus makes the choice of the window size less relevant. The choice of the half-time decay, on the other hand, is subject to experimentation, and depends on the volume of Twitter data. The chosen sliding window of one week provides high temporal resolution, but again the choice of this parameter is not crucial. We propose a temporal zoom-out to a lower time resolution, by computationally efficient selection of more distant timepoints where the network partitions exhibit larger differences. An analysis of how robust is this selection and what are meaningful ranges of distant timepoints is required in the future.F1 score can measure differences between network communities with only partially overlapping set of nodes. This is essential for comparing retweet networks, where new nodes keep appearing and disappearing from the network snapshots. An additional nice property of F1 is that it degenerates into a well-known set comparison coefficient, directly related to the Jaccard index.We apply and extend a measure of community similarity BCubed, which was originally introduced to evaluate quality of document clustering, but does not appear to be used in the field of complex networks. The A specially interesting result of this research is clear identification of super-communities from external influence links between the detected communities. The exiting problem, worth addressing in the future, is how to design a multi-stage super-community detection algorithm. This seems relevant for retweet networks in particular, where a standard community detection algorithm produces a large set of fractured communities.There are two follow-up directions of the current research, already undertaken: classification of tweets by the level of hate speech and detection of discussion topics , and attWe illustrate our approach on a well-defined set of Slovenian tweets, of reasonable size, but not extremely large. Our next step is to apply the same approach on two different, but somehow related sets of Croatian and Serbian tweets. This will reveal which parameters need to be tuned to specific datasets, and what seem to be domain-invariant properties and methods, applicable to a wide range of domains.The three years of comprehensive Slovenian Twitter data cover the period from January 1, 2018 until December 28, 2020. In total, 12,961,136 tweets were collected. We used the TweetCat tool  for TwitThe TweetCat tool is specialized on harvesting Twitter data of less frequent languages. It searches continuously for new users that post tweets in the language of interest by querying the Twitter Search API for the most frequent and unique words in that language. Once a set of new potential users posting in the language of interest are identified, their full timeline is retrieved and the language identification is run over their timeline. If it is evident that specific users post predominantly in the language of interest, they are added to the user list and their posts are being collected for the remainder of the collection period. In the case of Slovenian Twitter, the collection procedure started at the end of 2017 and is still running. As a consequence, we are confident that the full Slovenian tweetosphere is well covered.The Ensemble Louvain algorithm addresses the problem of instability of the Louvain community detection algorithm. The instability is manifested by different results of community detection in the same network, run with different initial seeds. This is due to theoretical issues with modularity maximization, and to heuristic nature of an efficient implementation of the algorithm.run several trials of Louvain on the same network,built a new network where a pair of the original nodes is linked if their total co-membership across all the Louvain trials is above a given threshold ,identify the disjoints sets which represent the resulting communities.We address this instability problem with a new approach called Ensemble Louvain. The steps of the algorithm are as follows:More trials eventually lead to more stable partitioning see , but incWe are not the first to use ensembles for community detection. A combination of several different algorithms to create a refined partitioning was proposed in . Re-sampWe measure the stability of Ensemble Louvain by Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI). An initial comparison between the standard Louvain versus Ensemble Louvain is performed on three well-known datasets: the Football network (115 nodes), the Email EU core (1005 nodes), and a Slovenian retweet network (3992 nodes). 100 separate experiment runs show that Ensemble Louvain yields significantly more stable results, especially on the larger networks, where the variation between possible solutions grows.The Football network,standard Louvain: NMI = 0.88±0.015 and ARI = 0.78±0.041,Ensemble Louvain: NMI = 0.92±0.008 and ARI = 0.89±0.019.The Email EU Core network,standard Louvain: NMI = 0.58±0.016 and ARI = 0.32±0.032,Ensemble Louvain: NMI = 0.72±0.005 and ARI = 0.52±0.012.We measure the performance with respect to the “ground truth” for the Football and Email EU Core networks. The initial results (presented by the mean ± standard deviation of the scores) show a significant improvement of Ensemble Louvain over the standard Louvain:The BCubed measure was originally proposed to evaluate effectiveness of document clustering . Its proPre) and recall (Rec) are then combined into the F1 score:F1 score is a special case of Van Rijsbergen’s effectiveness measure [F1 score as the harmonic mean. We first define the BCubed measure for a node, and then proceed with definitions of core-F1, standard F1, and theoretical max-F1.The BCubed measure is applicable to individual nodes, communities, and network partitions in general. It decomposes the evaluation into calculating the precision and recall associated with each node in the network. The precision  denote the “ground truth” community and C(n) the detected community of the node n, n ∈ L(n), C(n). Pre and Rec for a node are defined as follows:Let core-F1. Let Ls = {Li} denote a set of “ground truth” communities Li, and Cs = {Ci} a set of detected communities Ci. Constituent Pre and Rec for the partition Cs with respect to Ls are defined as:F1 measure proposed by Rossetti [core-F1. In our case, the Pre and Rec are computed with respect to all the communities Ci and Li, while Rossetti computes the Pre and Rec just between a pair of communities with the largest overlap.Let first assume a special case when a pair of network partitions consist of the same set of nodes. In this case, we name the BCubed measure Rossetti  is a speP0, P1 has some overlapping nodes, and some nodes that are present in only one of the partitions. Let Ls, Cs denote communities with overlapping nodes, and R0, R1 the nodes specific to the respective partitions P0, P1. We have:Pre and Rec of partition P1 with respect to the “ground truth” partition P0 are then computed as follows:In general, a pair of partitions F1 can be computed under the assumption that all the overlapping nodes of the two partitions P0, P1 form one community. Let C = L denote the community with the intersecting nodes, R0 extra nodes in P0 (w.r.t. P1), and R1 extra nodes in P1 (w.r.t. P0):Pre and Rec of P1 with respect to the “ground truth” P0 are computed as:max-F1 score is then:P0 and P1, is also known as Sørensen-Dice coefficient [F1 is as follows:A theoretical maximum value of fficient , 42. It F1 measure therefore has two special cases, core-F1 for comparing completely overlapping network partitions, and max-F1 for comparing two partitions with emerging (new) and disappearing (lost) nodes. The later case is specially relevant in evolving retweet networks, when new users appear and some users leave the network at different time windows.The BCubed-based F1 can be compared to standard community evaluation measures, such as Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). In F1 in this case is actually the core-F1, compatible to ARI and NMI. ARI and NMI cannot be applied in the case when network partitions differ in the sets of respective nodes.The


Objective: Food-related attentional bias has been defined as the tendency to give preferential attention to food-related stimuli. Attentional bias is of interest as studies have found that increased attentional bias is associated with obesity; others, however, have not. A possible reason for mixed results may be that there is no agreed upon measure of attentional bias: studies differ in both measurement and scoring of attentional bias. Additionally, little is known about the stability of attentional bias over time. The present study aims to compare attentional bias measures generated from commonly used attentional bias tasks and scoring protocols, and to test re-test reliability.Methods: As part of a larger study, 69 participants  completed two food-related visual probe tasks at baseline: lexical (words as stimuli), and pictorial (pictures as stimuli). Reaction time bias scores  for each task were calculated in three different ways: by subtracting the reaction times for the trials where probes replaced (1) neutral stimuli from the trials where the probes replaced all food stimuli, (2) neutral stimuli from the trials where probes replaced high caloric food stimuli, and (3) neutral stimuli from low caloric food stimuli. This resulted in three separate attentional bias scores for each task. These reaction time results were then correlated. The pictorial visual probe task was administered a second time 14-days later to assess test-retest reliability.Results: Regardless of the scoring use, lexical attentional bias scores were minimal, suggesting minimal attentional bias. Pictorial task attentional bias scores were larger, suggesting greater attentional bias. The correlation between the various scores was relatively small (r = 0.13–0.20). Similarly, test-retest reliability for the pictorial task was poor regardless of how the test was scored (r = 0.20–0.41).Conclusion: These results suggest that at least some of the variation in findings across attentional bias studies could be due to differences in the way that attentional bias is measured. Future research may benefit from either combining eye-tracking measurements in addition to reaction times. Attentional bias is the tendency to give preferential attention to stimuli which are personally, motivationally and emotionally relevant . It is oAlthough attentional bias for food has been linked to obesity , numerouWhile various tasks have been used to assess attentional bias, the visual probe task is considered “gold standard” by some researchers, remaining the most extensively used in attentional bias research . HoweverAttentional bias is of theoretical interest to obesity researchers, but to advance the field researchers must understand more about the reliability of the tests being used. Without consistency across measures, it is possible that the observed differences across studies could be due to differences in the way that attentional bias is assessed from study to study. The aim of the present study was to assess the reliability of the visual probe task. As such, the present study was conducted: (i) to compare attentional bias scores obtained from the lexical and pictorial version of the visual probe task using the different methods of scoring, and (ii) to assess the test-retest reliability of the pictorial probe task. We chose to examine the visual probe task as it is the most commonly implemented task assessing food-related attentional bias. Given the exploratory nature of this study, no specific hypotheses were tested.1. The full data set and the code book can be accessed here: https://rdp.utas.edu.au/metadata/2c3122be-fc62-48d0-a42d-41875d21e71b. In addition to completing a series of laboratory attentional bias tasks—the focus of this paper—, participants in the larger study were also required to track their eating and drinking using a smartphone application during a 2-week real-world monitoring period , of whom 35% were in the healthy weight range (BMI = 18.5–24.9), 29% in the overweight range (BMI = 25.0–29.9), and 36% in the obese range (BMI > 30).Potential participants were recruited through a mixture of social media advertisements see:  and flyeThe full procedure for the present study has been described elsewhere  and mirr2. After testing, participants were thanked and reimbursed AU$60 for their time.During this initial study visit, participants were asked to complete the pictorial probe task followed by the lexical probe task (described in greater detail below). Participants were then issued with a study-specific electronic diary for a field-based monitoring component of the study (data not reported here). Approximately 14 days after this initial session, participants returned to the lab and completed the pictorial probe task for a second time. Tasks at both sessions were completed seated approximately 50 cm in front of a 21.5 inch monitor using Inqusit 5 . ParticiThe task consisted of 20 food words and 60 animal words. Food words included both high-caloric , and low-caloric  words. Animal words were made up of species generally not consumed in Western cultures . The critical trials were made up of 20 food words paired with animal words, whilst the control trials were made up of animal words paired with other animal words. Based on previous research using this task , all worThe pictorial task mirrored the lexical task, however, using pictures of food instead of words. All picture pairs were matched for ratings of valence and arousal  were included.To ensure participants had complied with instructions to eat a light meal up to 2 h prior testing and were satiated upon arrival, a modified version of the hunger scale  was admianimal—RTall food) “high-caloric”  and “low-caloric” . For all three calculations, positive values indicated attentional bias toward food related stimuli. Mean attentional bias scores for both tasks and each of the different stimulus types are shown in In accordance with standard protocols , mean reTo address our first aim, we compared attentional bias scores obtained from our two different visual probe tasks , and three common scoring methods , using data gathered during the initial study visit. r = 0.41; high-caloric: r = 0.20; all food: r = 0.40). As can be seen in t(68) = 5.62, p < 0.001] and animal  stimuli in session 2 compared to session 1. A paired samples t-test confirmed that hunger levels remained consistent across both sessions .To address our second study aim, we compared attentional bias scores obtained from the pictorial probe task at the initial study visit to those obtained when the task was re-administered at the final study visit (∼14-days later). Again, we used the three different attentional bias scoring procedures for the task, yielding attentional bias scores for all food stimuli, high-caloric food stimuli and low-caloric food stimuli. Regardless of the scoring procedure used, the test-retest reliability of the task was poor  and high-caloric food (r = 0.611) RT indices. It is possible that the improved reliability was due to the longer stimulus presentation time of 3,000 ms, as it has been suggested that longer presentation times can improve reliability of time-reaction tasks (The present findings are in contrast to a recent study which found high test-retest reliability of attentional bias for food using the visual probe task . van Enson tasks . Howeveron tasks  reflectson tasks . Theoreton tasks  regardinon tasks .The task parameters used in the present study were based on previous research with a stimulus presentation time of 500 ms . AlthougContrary to previous findings e.g., , particiThe fact that this is a secondary analysis of a larger study examining real-world eating patterns also leads to limitations. As part of the larger study, participants underwent an intensive ∼14 day monitoring period during which they recorded all food and drink intake. It is possible that the monitoring may have influenced participants’ performance at the final attentional bias assessment. To conclude, the present study found correlations between the lexical and pictorial probe tasks to be weak. Furthermore, the test-retest reliability of the pictorial task was poor—regardless of how the attentional bias scores were calculated. Going forward, alternate measures of attentional bias should be explored . Finallhttps://rdp.utas.edu.au/metadata/2c3122be-fc62-48d0-a42d-41875d21e71b.The full dataset and the code book can be accessed here: The studies involving human participants were reviewed and approved by Tasmanian Social Sciences Human Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.SFr and SFe developed the study concept and contributed to the study design. SFr and AM collected the data. SFr performed the data analysis and interpretation under the supervision of SFe. SFr drafted the manuscript. All authors provided revisions and approved the final version of the manuscript for submission.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


An optimized doping amount at 1:1 weight content of the graphene to urea precursors could exhibit the best catalytic performance. The present work paves the way for developing novel and efficient nitrogen-doped graphene monolithic catalysts for oxidative dehydrogenation reactions of propane.It’s of paramount importance to develop renewable nanocarbon materials to replace conventional precious metal catalysts in alkane dehydrogenation reactions. Graphene-based materials with high surface area have great potential for light alkane dehydrogenation. However, the powder-like state of the graphene-based materials seriously limits their potential industrial applications. In the present work, a new synthetic route is designed to fabricate nitrogen-doped graphene-based monolith catalysts for oxidative dehydrogenation of propane. The synthetic strategy combines the hydrothermal-aerogel and the post thermo-treatment procedures with urea and graphene as precursors. The structural characterization and kinetic analysis show that the monolithic catalyst well maintains the structural advantages of graphene with relatively high surface area and excellent thermal stability. The homogeneous distributed nitrogen species can effectively improve the yield of propylene (5.3% vs. 1.9%) and lower the activation energy (62.6 kJ mol The oxi2-C and breaking the inertness of π electrons through electron transfer , a graphene derivative, can be transferred into a 3D porous graphene monolith aerogel  via bothtransfer . Whereastransfer .2 functional groups has strong interactions with the oxygen functional groups on GO, which effectively separates graphene layers from restacking and benefits nitrogen element to dope into the graphene. Meanwhile, the released NH3 during hydrothermal and pyrolysis process leads to the generation of rich hierarchical pores resulting in the enhancement of the surface area. The as-prepared NG aerogel monolith catalyst exhibits excellent thermal stability and high catalytic performance in the oxidative dehydrogenation reaction of propane. This work not only creates an efficient ODH nanocarbon catalyst but also addresses the problems regarding the powder-like nature of carbon materials, providing a new method to fabricate monolith carbon catalyst with enhanced heat and mass transfer ability, which has potential in practical heterogeneous catalysis.Aiming at above scientific challenges, herein, we fabricated a series of porous nitrogen doped graphene monolith catalyst (NG) with GO and urea as precursors through self-assembly and hydrothermal method. During the hydrothermal process, the dopant urea containing -NH2SO4 and 20 ml of H3PO4 were firstly mixed together at 50°C with magnetic stirring. Then 9 g of KMnO4 was slowly added with vigorous stirring for about 6 h. Finally, 100 ml of H2O2 (30 wt%) was dropped into above solution, and its color turned from dark brown to yellow with vigorous stirring for another 3 h. The suspension was centrifuged and washed with deionized water until the PH reaches 7. Finally, the GO dispersion in the concentration of 5 mg/ml was kept in 15°C for later use.The GO was prepared with a modified Hummers’ method . TypicalThe N-doped graphene (NG) monolith was synthesized by the hydrothermal reduction of GO with urea as the N precursors and the following freeze-drying and further annealing process. Briefly, a certain amount of urea was dissolved in 120 ml of 5 mg/ml GO water dispersion, followed with vigorous stirring for about 20 min and the mixture was transferred into a 180 ml Teflon-lined autoclave maintaining at 180°C for 12 h. Then, the formed NG hydrogel was immersed in deionized water for 20 min to remove the residual agents. Next, the NG monolith was obtained by vacuum freeze-drying for 48 h. Finally, the NG monolith catalysts were treated at 550°C with the heating rate of 5°C/min under Argon atmosphere for 2 h. NG monolith were prepared with three different mass ratios of GO to urea  to control the doping amount of nitrogen in some extent, and the synthesized samples were denoted as NG-0.5 (1:0.5), NG-1 (1:1), NG-2 (1:2), respectively. For comparison, the reduced graphene oxide monolith without nitrogen doping was also prepared by the same method without introducing urea, which is denoted as G.Transmission electron microscopy (TEM) images of the catalysts were obtained by using FEI Tecnai T12 transmission electron microscope operating at 200 kV with a space resolution of 0.24 nm. The crystal structures were analyzed by XRD , Raman measurements were carried out with HORIBA LabRam HR800 Raman spectrometer equipped with the laser source at the wavelength of 532 nm, and the scanning electron microscopy (SEM) measurements were carried out with FEI Nano 450. The Brunauer-Emmett-Teller (BET) specific surface area (SSA) measurements were carried out at −196°C (ASAP3020). Thermogravimetric analysis was performed using NETZSCH STA 449 C with an air flow of 100 ml/min, and the catalysts were heated up to 900°C keeping for 20 min. XPS (X-ray photoelectron spectroscopy) analysis was carried out with ASCALAB 250 spectrometer equipped with a semispherical electron analyzer and Mg Kα (hν = 1253.6 eV) 300 W X-ray source. The binding energies were referenced to the C 1s line at 284.6 eV. The half-peak width of each oxygen or nitrogen species is controlled at 1.2–1.8 eV. The attenuated total reflectance FTIR measurements (ATR-IR) were recorded on a Thermo Nicolet iS10 ATR-FTIR system equipped with a liquid nitrogen-cooled MCT detector.3H8 and 2% O2 with He (99.999%) balance at a total flow rate of 15 ml/min and the space velocity is 2.25*104 mlgasgcat−1h−1. The contact time is 4.44*10−5 gcat h mlgas−1. The carbon balance for all the reactions were observed maintaining at 100 ± 5%. The kinetic analysis was performed under the kinetic region, where the propane conversion was controlled below 10% without the influence of diffusion limitation. All the reactants and products were analyzed by gas chromatography (Agilent 7890B) equipped with the flame ionization detector (FID) and thermal conductivity detector (TCD). The conversion of reactant (Con.), selectivity (S) and yield (Y) of product, formation rate of product (Rf) and carbon balance (Cb) were determined using 0 representing the number of carbon atoms and the initial molarity of propane. The letter p, r, I, and m stand for product, reactant, summation notation and mass of catalyst, respectively.The oxidative dehydrogenation reaction of propane was performed at 450°C in a fixed bed quartz tube reactor with 10 mm outside diameter. 40 mg catalyst was mounted into the reactor with certain amount of quartz wool as the cover and holder. The apparent activation energy test was carried out at 400–450°C with 40 mg catalyst. The reactant mixture containing 4% CThe fabrication process of the nitrogen doped graphene monolith is illustrated in 50, which refers to the temperature at which the weight loss reaches 50% of the initial mass . It is obvious that the signal of C=O functional group become weaker after nitrogen doping, and which is mainly because of the reducing effect of urea resulting in the decrease of oxygen amount. In addition, there are several obvious peaks in the nitrogen doped graphene aerogel at 1537, 1401 and 1201 cm−1, corresponding to the stretching vibration modes of C-N bond, the stretching vibration modes of C-N bond and the symmetrical stretching vibration modes C=N=C bond, respectively, which indicates that nitrogen element was indeed doped into graphene layer after the hydrothermal and calcination process (FTIR spectra are shown in  process .2 adsorption–desorption isotherms with the detailed information of specific surface area (SSA), pore size distribution (PSD) and pore volume. As shown in 2 adsorption–desorption profiles of NG-0.5, NG-1 and NG-2 exhibit a typical type IV isotherm in the medium to high pressure regions (p/p0 = 0.35–0.99), indicating the existence of rich mesoporous structures. In addition, two small unobvious steep peaks at low and high pressure  can also be observed implying the presence of micropores and macropores . Comparing with metal oxide catalysts  under the same reaction conditions. The promotion effect is mainly because of the increase amount of the active sites, namely the surface content of ketone C=O and quinone C=O (O1) species on NG surface after the introduction of nitrogen species. Especially the quinone C=O groups, which is commonly regarded as the active sites for propane ODH reactions. At the same time, the apparent activation energy (Ea) for CO2 formation on NG-1, calculated based on the formation rate of CO2, is much larger than that of pure graphene (112.1 ± 1.2 kJ/mol vs. 64.6 ± 4.3 kJ/mol) indicating that it’s more difficult to produce CO2 than propylene for NG-1 comparing to that for pure graphene catalyst in ODH reaction. This finding also explains why the active NG-1 has shown higher propylene selectivity in the presence of oxygen at high temperature.The apparent activation energy (Ea) for ODH on NG-1 is determined at 62.6 ± 1.9 kJ molD/IG value varying from 1.93 to 2.15), which means that the slight alternation of the structure defect degree may be not the main reason for the catalytic performance enhancement, although it may contribute to the catalytic performance improvement in some degree. On the other hand, some experimental studies and related theoretical calculation results (It is reported  that str results  (Hu et a results  have sho−1 vs. 80.1 kJ mol−1). The optimized doping amount at 1:1 weight content of the graphene to urea precursors exhibits the best promotion effect of the catalyst. The present work paves the way for developing nitrogen doped graphene monolith catalysts for ODH reactions with high efficiency. The detailed reaction mechanism for the promotion effect and the basic structure–function relations for these series of carbon catalysts still need further exploration.In summary, we have designed a new synthetic route to fabricate nitrogen doped graphene-based monolithic carbon catalysts via the sequent self-assembly, hydrothermal and post thermo-treatment procedures with urea and graphene oxide as precursors. During the hydrothermal process, amino functional groups in urea interact with the oxygen functional groups in graphene resulting in a slight decrease of total oxygen content but an increase of quinone C=O (O1) and ketone C=O amount. The FTIR and XPS characterizations indicate that the nitrogen element indeed doped into the graphene aerogel and the surface nitrogen content is up to 6.88 at % (NG-2). The catalytic performances of these samples are evaluated in oxidative dehydrogenation of propane reaction. The homogeneously distributed nitrogen species effectively improve the yield of propylene (5.3% vs. 1.9%) and lower the activation energy of propane ODH reactions (62.6 kJ mol


The structure and morphology of the conductive polymer (Gr/PPy)–BiPO4 materials were analyzed using an X-ray diffractometer, scanning electron microscopy, transmission electron microscopy, and energy-dispersive X-ray spectroscopy. Moreover, properties such as hysteresis loop, impedance, sensing response, and response and recovery time were calculated and evaluated using an inductance–capacitance–resistance analyzer. The data expressed that PPy/BiPO4, as prepared in this study, exhibited excellent sensing properties, with impedance changing by only a few orders of range. Furthermore, the response time and time of recovery were 340 s and 60 s, respectively, and negligible humidity hysteresis occurred at different relative humidities. Therefore, conductive PPy/BiPO4, as prepared in the present study, is an excellent candidate for application in humidity sensors.In this particular experiment, a chain of conductive polymer graphene/polypyrrole (Gr/PPy) and BiPO Recenhumidity . Mallickthe TiO2 . Duong eomposite . Arularaomposite . Using tto Al2O3 . Ye et aof Fe3O4 . Xie et steresis . Manikanery time . Manikanry times . Douani tability . Arrizabtability . Hu et atability . Ko et atability . Among ttability ,27,28; ttability ,30,31.4 is an attractive material to researchers due to its economic cost, chemically stable structure, nontoxicity, photocatalytic characteristics, extraordinary optics, and electronic properties [4 composite-resistive humidity sensors. In the present research, a series of Gr/PPy-modified BiPO4 was successfully synthesized, characterized, and investigated as sensing materials for humidity sensors. Moreover, the sensing mechanism, sensing properties, and morphology of as-prepared Gr/PPy-modified BiPO4 were examined.BiPOoperties ,33. Grap3)3·5H2O) bismuth(III)nitrate pentahydrate, trisodium phosphate (Na3PO4), pyrrole (C4H5N), cetyltrimethylammonium bromide (CTAB), ammonium persulfate (NH4)2S2O8), and polyvinyl alcohol ((C2H4O)x) were obtained from Sigma-Aldrich Co., Inc. . Graphene was purchased from National Chiayi University in Taiwan. All materials were utilized without any purification. DI water was drawn from a system water purification provided by the Milli-Q system, processed, and used in all experiments.The 97% pure (Bi(NO3)3·5H2O and 2.5 mmol of Na3PO4 were dissolved in 46 mL of HNO3 (0.3 M) under continuous stirring for 1 h to obtain a homogeneous aqueous solution. The resulting solution was transferred into a Teflon-lined stainless steel autoclave. Subsequently, the autoclave was tightly sealed and maintained in a temperature-controlled electric oven at 160 °C for 24 h. Then, after it was filtered and washed with deionized water and ethanol five times, the resulting white product was dried at 70 °C for 12 h to harvest BiPO4. Next, an amount of 0.5 g as-prepared BiPO4 was added to 300 mL of an aqueous solution containing 1% CTAB under ultrasound for 0.5 h to form solution A, whereas solution B was formed by dissolving 0.03 mL of C4H5N in solution A under ultrasound for 10 min. Thereafter, 100 mL of 10% (NH4)2S2O8 and solution B were mixed under ultrasound for 0.5 h and maintained under temperatures ranging from 1 °C to 5 °C for 24 h to form a black precipitate. Finally, the as-obtained precipitate was centrifuged, washed with deionized water and ethanol several times, and dried at 70 °C for 2 h to obtain PPy/BiPO4. Additionally, precalculated amounts of as-prepared BiPO4 and graphene were added to 10 mL of 95% ethanol under ultrasound for 1 h to produce a black suspension. The obtained suspension was centrifuged and washed with ethanol and deionized water and then dried at 60 °C for 12 h to obtain graphene/BiPO4 [As is typical in this procedure, 5 mmol of Bi method—the Shimadzu XRD-6000 (operated at 35 kV and 35 mA) with a (λ = 1.5404 Å) Cu source. The particle size of the as-obtained prepared photocatalysts was averaged and explained using the Debye–Scherrer equation. Transmission electron microscopy (TEM) images were acquired using a JEOL JEM2010 transmission electron microscope  at an accelerating voltage of 200 kV. The samples for TEM analysis were suspended in ethanol with the assistance of ultrasound and dispersed on a copper grid. The chemical composition of the as-synthesized samples was analyzed using an energy-dispersive X-ray spectroscope (EDS). The morphology of different as-prepared materials was observed using field emission scanning electron microscopy , with the microscope operated at 30 kV.2; rotational speed, 1000 rpm). The chips were subsequently heated at 80 °C for 0.5 h and calcined at 300 °C for 4 h.A solution of 10% PVA was used as the binder. The sensing chips were fabricated by dip-coating them on a pair of comb-like gold electrodes on an alumina substrate , (200), (102), and (211) compared to JCPDS, which demonstrate a similar hexagonal phase.To investigate the structural features of the as-fabricated samples, XRD measurement was performed. The XRD patterns of the samples presented in nd 50.9° , respectnd 50.9° ,41. Thes4 (200) lattice plane, which is about 0.329 nm in terms of lattice displacement. TEM images of BiPO4 are shown at low magnification, at a scale of 100 nm, as well as at a higher magnification. The figure also shows a TEM image of a single BiPO4 nanorod 569.53 nm in length and 97.92 nm in width. 4 indicated the presence of C, O, N, P, and Bi atoms as major components in the as-prepared PPy/BiPO4.Surface morphology plays an essential role in the humidity-sensing properties of sensing materials, as illustrated in 4, PPy, Gr, PPy/BiPO4, and Gr/BiPO4) were inversely related to RH changes. Additionally, PPy/BiPO4 exhibited the most significant change within the RH range, from 12% to 90% , suggesting that PPy/BiPO4 can absorb more water molecules and is thus more sensitive to humidity.The roles of the as-prepared samples in the humidity control were measured and investigated at the optimal operating frequency (1 kHz) . Figure 4 exhibited the highest humidity in response (S = 24.6) to changes in RH (es in RH .4 had minor humidity hysteresis loss and a relatively large hysteresis loop (0.70%) that occurred at RH levels less than 50%; this indicated that a fast equilibrium could be achieved between the adsorption and desorption process for PPy/BiPO4.Humidity hysteresis, defined as the maximum difference in measured values of RH when the humidity sensor is exposed to adsorption and desorption processes, is an essential parameter for evaluating the reliability of a humidity sensor. 4 were 340 s and 60 s between 12% RH and 90% RH, respectively. The observed sensing parameters were compared with the previously reported sensors [4 sensor exhibited an excellent sensing response across the whole humidity range; however, its response and recovery times were longer than those of other sensors, as the PPy/BiPO4 sensor was tested using materials with different sensitivities. Selectivity is an essential parameter for the practical application of humidity sensors. The cross-sensitivity of PPy/BiPO4 has been studied for various gases at room temperatures, such as carbon monoxide (CO), ammonia (NH3), nitrogen dioxide (NO2), and nitric oxide (NO). As 4 sensor had the highest selectivity for humidity and the lowest responsiveness to other types of gases.Crucial parameters such as time of recovery and response data are vital for evaluating the performance of humidity sensors. These parameters represent humidity sensors’ speed in measuring RH in various environments.  sensors ,43,44,454 surface, as illustrated in 4 composite was low, so only the outer particles came into contact with the water molecules, as shown in 2O or H3O+ onto the discontinuous water layer was challenging [4, Gr/BiPO4, and PPy/BiPO4 composite, the impedance was 2.15 × 107 Ω, 9.18 × 10–1 Ω, 2.71 × 107 Ω, 2.03 × 107 Ω, 1.49 × 107 Ω, respectively. As the RH level increased, the physisorption of water molecules occurred on the chemisorbed layer through hydrogen bonding, and the formation of a water multilayer, as illustrated in 2O or H3O+. The ion transfer mechanism presented by Grotthuss [2O or H3O+ on serial water layers H2O + H3O+ → H3O+ + H2O, as shown in 4 composites exhibited humidity-sensing properties that are more favorable than those of the pure PPy or BiPO4 samples.The humidity-sensing mechanism can be explained by the chemical and physical adsorption of water molecules on the PPy/BiPOllenging . Thereforotthuss  and Casarotthuss  involves4 materials were successfully synthesized and used to detect RH. The structure and morphology samples were characterized by XRD, TEM, FESEM, and EDX. In this study, a humidity sensor based on Gr/BiPO4 and PPy/BiPO4 was prepared under room temperature conditions. The experimental results show that the PPy/BiPO4 composite exhibited excellent humidity-sensing capabilities, including negligible humidity hysteresis, sensing response (S = 24.6), and high selectivity. Moreover, the response was 340 s and the recovery time was 30 s. Compared to pure PPy, pure graphene, and pure BiPO4, as well as Gr/BiPO4 and PPy/BiPO4 composites, these results indicate that PPy/BiPO4-based humidity sensors are good candidates for humidity sensor applications.In this paper, various polymer (Gr/PPy)–BiPO


C. elegans germline syncytial organization by stabilizing intercellular bridges in embryos and adults . We recently demonstrated that many of these regulators are enriched at intercellular bridges in the small rachis (proto-rachis) of L1-stage larvae . We sought to assess whether these contractility regulators are functionally required for stability of intercellular bridges and maintenance of the primordial germ line syncytial architecture in L1-stage C. elegans animals. Here we report that temperature-sensitive alleles, RNAi-mediated depletion and latrunculin A treatment are largely ineffective to perturb actomyosin function in the L1-stage primordial germ line.Cytokinesis, the separation of daughter cells at the end of mitosis, relies on the coordinated activity of several regulators of actomyosin assembly and contractility . These include the small GTPase RhoA (RHO-1) and its guanine-nucleotide exchange factor Ect2 (ECT-2), the scaffold protein Anillin (ANI-1), the non-muscle myosin II (NMY-2), the formin CYK-1 and the centralspindlin complex components ZEN-4 and CYK-4. These regulators were also shown to be required for maintenance of  Previous work demonstrated that the initial stages of germline expansion occur normally in cyk-4(ts) and zen-4(ts) animals raised at restrictive temperature from the L1 stage . We found that primordial germ line organization in cyk-1(ts), nmy-2(ts), cyk-4(ts) or zen-4(ts) L1 larvae maintained at restrictive temperature for 12h was no different than control . Furthermore, the first primordial germ cell (PGC) division occurred normally upon feeding these animals at restrictive temperature with typical bacterial food (E. coli OP50). As noted previously , germ line disorganization and sterility were observed in all cases when animals reached adulthood .To perturb actomyosin function in the primordial germ line, we first monitored germ line organization in L1-stage animals bearing temperature-sensitive (ts) alleles in genes encoding actomyosin regulators and that were reported to interfere with cytokinesis during embryogenesis . This demonstrates that the RISC complex had effectively been engaged by dsRNA treatment at the L1 stage but that the phenotype only manifested itself later in development. Similar results  were obtained when we soaked RNAi-hypersensitive rrf-3(pk1426) mutants in dsRNA against ani-1, rho-1 or ect-2 for 24h .We then used RNAi to deplete actomyosin regulators in L1 larvae expressing NMY-2::GFP or mNG::ANI-1, as well as fluorescent markers for membrane and histone. We found that soaking L1 animals for 24h in a dsRNA solution against ). As shown previously , latrunculin A treatment of L4 larvae (even with a lower dose of 25 µM) resulted in an extensive collapse of the germ cell intercellular bridges , demonstrating that the drug is effective.Finally, we treated L1 larvae expressing mNG::ANI-1 or LifeAct::mKate2 (marking F-actin) with the actin depolymerizing drug latrunculin A and scored primordial germ line organization. We found that incubating L1 larvae for 3-5 hours in a solution of 100 µM latrunculin A did not result in significant primordial germ line disorganization and the fluorescence levels of either marker at the proto-rachis remained unchanged compared to control , our results demonstrate that perturbing the function of actomyosin contractility regulators in the C. elegans primordial germ line is difficult to achieve at the L1 stage by means of ts alleles, RNAi or latrunculin A treatment. The reasons for this are unclear and could vary depending on the treatment, yet we consider it unlikely that these gene products are dispensable for germline development. Notably, RNAi depletion in PGCs was previously achieved for regulators of the spindle assembly checkpoint , and our finding that RNAi treatment at the L1 stage results in phenotypes later in development indicates that the RNAi machinery can be engaged in L1 animals. One possibility is that actomyosin regulators within the primordial germ line are organized in a very compact and/or stable manner that makes perturbation difficult, a situation perhaps analogous to microtubule organization at the midbody prior to abscission . While other approaches for gene depletion could be more effective (e.g. degron-based), this phenomenon will require further investigation.Together with previous work (Lee C. elegans strain maintenanceE. coli strain OP50 and maintained at 20°C as described (Brenner 1974), with the exception of temperature-sensitive strains and rrf-3(pk1426) mutants that were maintained at 15°C. First stage (L1) larvae were obtained by dissolving gravid hermaphrodites in sodium hypochlorite solution  and hatching recovered embryos for 24h at room temperature (or at 15°C for ts strains) in M9 buffer .Animals were grown on NGM plates seeded with ImagingAnimals were immobilized in M9 buffer supplemented with 0.2% tetramisole, mounted on an agarose pad (3% for L4s/adults and 5% L1s), and a coverslip was applied and sealed with VaLaP . With one exception, images were acquired with a GaAsP detector at 16-bit depth mounted on a Zeiss LSM880 laser-scanning confocal microscope, controlled by ZEN black 2.1 SP3 software, and using a Plan-Apochromat 63x/1.4 oil DIC M27 objective; images of adult animals in Temperature-sensitive strain upshiftsE. coli OP50 at 26°C, for 2-3h to image 2-cell germ lines and 5-6h to image 3-cell germ lines. For controls, unfed L1 animals were left at 15°C for 12h, then plated on NGM plates seeded with E. coli OP50 at 15°C for 4-5h to image 2-cell germ lines and 9-10h to image 3-cell germ lines.Newly hatched and unfed L1 animals were upshifted at 26°C for 12h in M9 buffer, then transferred to NGM plates seeded with dsRNA productionnmy-2 (sjj_F20G4.3), ani-1 (sjj_Y49E10.19), rho-1 (cenix:169-h12) and ect-2 (sjj_T19E10.1a) as well at the L4440 empty vector we used as template in PCR reactions and individual inserts flanked by T7 promoters were amplified using T7 promoter-specific primers. PCR products were purified on columns (Qiagen) and used as template for in vitro transcription reactions using the T7 Ribomax Express RNAi System (Promega).Bacterial clones targeting the genes RNA Interference2HPO4, 5.5 mM KH2PO4, 2.1 mM NaCl, 4.7 mM NH4Cl, 6.3 mM spermidine, 0.11% gelatin) supplemented with 8-20 µg of dsRNA targeting nmy-2, ani-1, rho-1 or ect-2, as described . Animals were then washed 3 times with M9 buffer and allowed to recover in M9 buffer for 24h at 15°C. Animals were either imaged immediately or grown at 15°C on NGM plates seeded with E. coli OP50 and imaged after first PGC division or after having reached the adult stage.First larval stage (L1) animals were soaked for 24h at 15°C in 2-4 µl of buffer . For controls, animals were incubated in M9 buffer supplemented with solvent alone .unc-119(ed3) was in the parental strain but may not be present in this strain.*


The most common idiopathic interstitial lung disease (ILD) is idiopathic pulmonary fibrosis (IPF). It can be identified by the presence of usual interstitial pneumonia (UIP) via high-resolution computed tomography (HRCT) or with the use of a lung biopsy. We hypothesized that a CT-based approach using handcrafted radiomics might be able to identify IPF patients with a radiological or histological UIP pattern from those with an ILD or normal lungs. A total of 328 patients from one center and two databases participated in this study. Each participant had their lungs automatically contoured and sectorized. The best radiomic features were selected for the random forest classifier and performance was assessed using the area under the receiver operator characteristics curve (AUC). A significant difference in the volume of the trachea was seen between a normal state, IPF, and non-IPF ILD. Between normal and fibrotic lungs, the AUC of the classification model was 1.0 in validation. When classifying between IPF with a typical HRCT UIP pattern and non-IPF ILD the AUC was 0.96 in validation. When classifying between IPF with UIP  and non-IPF ILD, an AUC of 0.66 was achieved in the testing dataset. Classification between normal, IPF/UIP, and other ILDs using radiomics could help discriminate between different types of ILDs via HRCT, which are hardly recognizable with visual assessments. Radiomic features could become a valuable tool for computer-aided decision-making in imaging, and reduce the need for unnecessary biopsies. Idiopathic pulmonary fibrosis (IPF) is the most common progressive form of interstitial lung disease (ILD) with an unknown etiology, usually impacting older adults ,2. In 20Diagnosing IPF comes about using a multidisciplinary discussion (MDD) of the clinical, radiological, and, if available, pathological data showing a usual interstitial pneumonia (UIP) pattern which is the most common histopathological form of diffuse lung fibrosis ,4. The dSurgical lung biopsy (SLB), which is recommended when no UIP pattern is present on the HRCT ,7, is anThe interest in radiomics, pioneered in 2012, has increased in recent years . DiffereWe hypothesize that radiomic features are able to decode biological information from specified regions of interest within the lung that can be used to diagnose IPF with UIP pattern. The aims of this study are two-fold: (1) to evaluate the use of radiomics, to differentiate between normal lung tissue and ILDs; (2) to evaluate the use of radiomics to distinguish IPF with a typical or less typical (biopsy-proven) UIP pattern related to IPF from HRCT patterns related to non-IPF ILDs. We also conjecture, based on the literature , that trhttps://ltrcpublic.com/ (accessed on 19 September 2018)). Images from patients with ostensibly healthy lungs (database B) were collected from the publicly available Radiomics Imaging Archive ) (G4). Information was also gathered from patients, such as the demographic  and clinical data (body mass index—BMI), as well as the measurements of pulmonary function tests (PFT) (forced expiratory volume in 1s (FEV1), forced vital capacity (FVC), and diffusion capacity of carbon monoxide (DLCO). The so-called gender, age, and pulmonary function (GAP) score and staging system that was developed by Ley et al. in 2012 [The study protocol was registered on Clinicaltrials.gov (identifier: NCT04430491), approved by the ethics committee of the Erasme University hospital (ref: P2017/411). The electronic medical records at Erasme University hospital (center i) were searched between 2011 and 2018 for patients diagnosed with ILD. The inclusion criteria were: (i) the availability of HRCT with slices of less than 1.5 mm; (ii) the availability of a high-confidence diagnosis . The exclusion criteria were (i) the use of contrast enhancements in HRCT; (ii) images containing metal or motion artifacts; and (iii) images reconstructed with a slice thickness larger than 1.5 mm . At leas in 2012  was calcFor center (i), the HRCTs were acquired on a 64- or 128-detector row CT system . For database A, HRCT images were acquired using 4 different CT vendors , , , and . For database B, all scans were acquired from the same scanner . The slice thickness of all scans varied between 0.5 and 1.5 mm.The process of delineating a region of interest (ROI) that will be utilized to extract handcrafted radiomic features is known as segmentation. A workflow for radiomics from segmentation to data analysis is depicted in 3 voxel size, using linear interpolation to address the disparate reconstruction settings found in the datasets [3 was the maximum voxel size available in the dataset [https://www.radiomics.bio (accessed on 23 June 2020)), which calculates radiomics features in compliance with the Imaging Biomarkers Standardization Initiative (IBSI) [To minimize the effects of the variations in image voxel size, all HRCT images were resampled into 1 × 1 × 1 mmdatasets . 1 × 1 × dataset . Radiomie (IBSI) . Voxel ie (IBSI) . The exte (IBSI) .For the first aim, i.e., normal vs. ILDs , the data from center (i) and database B was combined and split into training and validation datasets, with a ratio of 0.8:0.2. For the second aim, i.e., IPF/UIP vs. non-IPF ILDs (G1 and 2 vs. G3), datasets from center (i) were randomly divided into training and validation dataset, using a ratio of 0.8:0.2, and data from database A was used as an external validation dataset.To avoid any information leaking, all of the feature selection and model training was conducted in the training dataset alone. In order to reduce feature dimensionality, several steps were applied. Firstly, features with (near) zero variance  were excluded. Next, feature pairs with Spearman correlation (r ≥ 0.90) were considered to be highly correlated, and the feature with the highest average correlation with all other features was removed. Then, the remaining features were fed into the Boruta dimension-reduction and feature-elimination algorithm, with the maximal number of important sources, runs set to 1000. The Boruta algorithm is a wrapper method based on random forest classification . Afterwahttps://www.R-project.org/ (accessed on 10 January 2022)). Comparisons between datasets were summarized using a Wilcoxon rank-sum test for the continuous variables and an X2 Fisher exact test for categorical variables. A Spearman correlation was used to evaluate the correlation between radiomic features.All statistical analyses were performed using R on RStudio  from the receiver operating characteristic (ROC) analysis was used and a 95% confidence interval (CI) was reported. To estimate the goodness-of-fit of the models, the Hosmer–Lemeshow test was used, and calibration plots were generated to visualize the consistency of models. This study was assessed using a Radiomics Quality Score  that conp < 0.001), whereas no significant differences were noticed regarding age (p = 0.06), and lung function tests . A total of 328 patients were included in the study after the application of the exclusion criteria . A groupOriginal features were extracted (n = 170) for the whole and sectorized lung. Shape features and features with little or zero variance were excluded (n = 33). A list of the selected features after removing the highly correlated features, applying the Boruta algorithm, and Gini decrease can be found in p < 0.001) between the control, IPF/UIP, and ILDs other than IPF patients , and also between IPF/ UIP and ILD (non-IPF) (p < 0.001) (p = 0.59), or the GAP index . Following the feature selection, the volume of the trachea was selected as an important feature for all models, except for the classification between normal and ILDs.The volume of the trachea was observed to differ significantly (< 0.001) . In addiWhen classifying between a normal lung  and a lung with ILDs (G1 + G2 + G3) from center (i), an AUC of 1.0 (CI: 1.0–0.1) was achieved in validation (M1) . For theWhen combining G1 and G2 to distinguish the results from G3 (center (i)), an AUC of 0.82  and 0.66  in validation and test dataset (database A) were achieved using whole lungs respectively. When 40% of the test dataset (from database A) is introduced to the training dataset, and retaining the remaining 60% as testing, an AUC of 0.77 (95% CI: 0.69–0.85) was achieved (M5).The detailed sensitivity and specificity of the models for validation/testing dataset are summarized in p = 0.68, 0.32, and 0.07, respectively  extracted from HRCT to classify fibrotic lung disease. A random forest classifier was used to differentiate between (1) normal lungs and interstitial lung diseases (ILDs); (2) idiopathic pulmonary fibrosis (IPF)  radiological presentation), and non-IPF ILDs . Briefly stated, we were able to demonstrate that radiomic features derived from HRCT images can be used to distinguish between a normal state and ILDs, as well as between IPF with a UIP pattern and ILDs with no UIP pattern verified by surgical biopsy. The inclusion of biopsy-proven non-IPF ILDs patients strengthens the study, as well as making it unique .Differentiating between normal and ILD lung tissues might seem a trivial task. However, it is a time-consuming process since the clinician has to go through all the scans. Developing an automated approach that differentiates between normal and abnormal lungs would decrease the amount of time a clinician needs to assess images on a daily basis. A previous study presented a novel texture analysis method that incorporates texture matching with histogram features analysis . This stMany ILDs have characteristics and changes in the lungs similar to those of IPF/UIP on HRCT, making the diagnosis very difficult—even for experienced radiologists . Visual By assessing the potential of handcrafted radiomics to differentiate between IPF with typical UIP presentation on HRCT and ILDs other than IPF, we discovered another benefit of automation similar to that achieved by differentiating between normal and abnormal lung tissue. It could serve mainly as a decision-aiding tool that would increase the diagnostic accuracy of the disease, reduce the need for invasive lung biopsies, and decrease the time needed to conduct routine scans.IPF is also associated with wide parenchymal and airway conditions, such as those found in the trachea wall, which leads to pathological changes . RatwaniThe decrease in model performance in the test dataset might be explained by the presence of variation in acquisition and reconstruction parameters. When the random forest algorithm learned part of the test dataset in the training dataset (M4.1), the model AUC increased from 0.66 to 0.77. Such findings indicate the need for addressing the challenges associated with differences in imaging parameters.This study has some limitations. Firstly, we did have the additional categories of UIP patterns  in the training dataset but not in the test dataset. Therefore, we only used the test dataset when we combined G1 and G2. Secondly, the healthy CT scans (G4) were obtained only from one center (center iii). Thirdly, the CT acquisition parameters of HRCT varied between and within the centers, and radiomic features are known to be influenced by different CT acquisition and reconstruction parameters ,49,50. FAt present, there is minimal radiomics research on ILDs. Our findings are, nonetheless, promising and underline the strong potential of HRCT-based radiomics for the identification of ILDs. The classification between IPF/UIP and other ILDs using radiomics might capture features indicating different types of ILDs in HRCT, which are hardly recognizable via visual assessment. The radiomic features extracted from HRCT, along with clinical features, might aid in the assessment of ILDs and be used as a valuable tool for computer-aided decision-making in imaging.


Using petrolatum gel as an antitranspirant on the flowers of California poppy and giant bindweed, we show that transpiration provides a large contribution to floral humidity generation.Calystegia silvatica and Eschscholzia californica. Floral humidity production of flowers that underwent an antitranspirant treatment, petrolatum gel which blocks transpiration from treated tissues, is compared to flowers that did not receive such treatments. Gel treatments reduced floral humidity production to approximately a third of that produced by untreated flowers in C. silvatica, and half of that in E. californica. This confirms the previously untested inferences that transpiration has a large contribution to floral humidity generation and that this contribution may vary between species.Floral humidity, an area of elevated humidity in the headspace of flowers, is believed to be produced predominantly through a combination of evaporation of liquid nectar and transpirational water loss from the flower. However, the role of transpiration in floral humidity generation has not been directly tested and is largely inferred by continued humidity production when nectar is removed from flowers. We test whether transpiration contributes to the floral humidity generation of two species previously identified to produce elevated floral humidity, The online version contains supplementary material available at 10.1007/s00425-022-03864-9. Floral humidity, an area of elevated humidity in the headspace of the flower (relative to the environment), has been detected in several flower species across different families  or without (gel-treated flowers) the contribution of petal transpiration.Calystegia silvatica (Kit.) Griseb. and California poppy Eschscholzia californica Cham.. These species are appropriate choices for demonstrating the role of transpiration in floral humidity generation, as both produce larger amounts of floral humidity compared to other flower species  to produce little in the way of nectar rewards . Flowers were cut on the stem, so no leaf remained on the cutting, but sepals were retained, and individual ivy leaves were cut with the majority of the petiole remaining attached. Immediately after cutting, the stems attached to samples were stuck through a hole in the cap of a 24 cmSamples were collected to allow two samples (two flowers or two leaves) to be presented to the robot in pairs . When selecting flowers and leaves that would be presented for sampling as ‘Gel’ and ‘Untreated’ sampling pairs (see below for treatment details), care was taken to collect flowers of approximately the same size, measured using a ruler across either the horizontal span of a flower, or the width of the leaf’s widest point. To control for size effects, flowers and leaves presented as sampling pairs of one ‘Gel’ and one ‘Untreated’ sample were within 10 mm of their partner’s span. For sampled pairs of ‘Unhandled’ treatments, size was allowed to vary to capture as wide a range of the diversity as possible. Flowers and leaves presented for sampling in the same pair were always collected from the same site, so that flowers had grown in comparable conditions prior to being collected.C. silvatica can have differing flower orientations dependent on where it grows. Thus, fixing the orientation of the flowers is unlikely to negatively affect the flowers. Flowers were fixed in position with a cylinder of rigid paper-card  fixed with tape  to the outside of the horticultural tube, so the flowers rested flat facing vertically upwards . This tape was placed over the base of the leaf and the hole in the horticultural tube’s lid and continued down the side of the tube Fig. c and d. Two further ‘control samples’ were conducted. These were the ‘Dry tube’ control, where a card support cylinder was attached around an empty horticultural tube (protruding 1 cm above the lid), and the ‘Full tube’ control which was the same but with the tube filled with water to within 2 cm of the lid, similar to leaf and flower samples  was applied evenly by hand over the abaxial and adaxial surface of petals, and over the flower sepals Fig. b. Gel waThe humidity sampling sequence was conducted on a pair of samples of the same type . The pair of samples presented to the robot were prepared, so that they were either: a sample that had undergone the ‘Gel’ treatment and sample that had undergone the ‘Untreated’ treatment; or two flower samples that had undergone the ‘Unhandled’ treatment. This allowed us to balance our comparisons with respect to time of sampling and any other environmental changes in the sampling area that might influence humidity production.Humidity within sample headspace  was measured using a modified version of the robot humidity transect method used in Harrap et al. , 2021; uHumidity sampling was conducted by a Staubli RX 160 robot arm . Here, background humidity was measured by a humidity probe  placed within the lab space, the ‘background’ probe. Sample headspace humidity measurements were taken with an identical probe mounted to the robot arm, the ‘focal’ probe Fig. . Consequx-axis transect  and then a z-axis transect . During these two transects, the arm stops to take humidity measurements at set measurement points , which accounted for variation between probes in humidity estimation of the same humidity levels . During 200 s, each probe could sample humidity approximately 100 times. Change in humidity, ΔRH, between the focal and background probe was then calculated asΔRH indicates the focal probe has detected an increase in humidity in the flower’s headspace relative to the background humidity. This same measurement procedure of 30 s waiting time and 200 s measurement was also carried out during probe control measurements.The arm stopped for 230 s at each measurement point on both transects Fig. , b. The Although similar, the humidity sampling procedure used here differs in several ways from that in Harrap et al. , 2021. HΔRH, as measured by the robot sampling procedure, has been found to be highly consistent within each measurement period, the c.100 measurements made at each sampling point, on each replicate, on each sample  was included in all models, as a random factor influencing humidity intensity (model intercept). The most complex models allowed humidity to vary with replicate transects and show a quadratic structure in the x-axis and a logarithmic structure in the z-axis. All other models were simplified versions of these. How well these different models described humidity structure was compared using Akaike information criterion (AIC) to identify the best-fitting model of x- and z-axis humidity structure for each sample type, using the model section criteria described in Richards  and Tz0 (z-axis) models; an effect on humidity intensity only (model intercept), the T1 and Tz1 models; an effect on humidity structure only, the T2 and Tz2 models; an effect on both humidity intensity and structure the T3 and Tz3 models; or an effect on both humidity intensity and structure as well as how humidity changes with replicate effects, the T4 and Tz4 models. Where the best-fitting model of humidity structure for a sample, as selected above, did not include changes in humidity with replicate effects, the T4 and Tz4 models were not fitted to the data . Depending on the best-performing humidity structure model, either the T3 and Tz3 or the T4 and Tz4 represent the ‘full’ model selected in the humidity structure step .In the second part of the analysis, the effects of treatment on humidity production in each sample type were evaluated. Here, versions of the best-fitting model of humidity structure with different treatment effects were fitted to the data of each sample type. These models differed in how humidity production changed with treatments, treatment had either: no effect on humidity production, the T3TCwP describes treatment effects on both the humidity intensity and structure, but groups the ‘Gel’ and ‘Untreated’ treatments together; thus, only the ‘Unhandled’ treatment differs. The T0 and Tz0 models describe no treatment effects; thus, there were no further variants of these models. The structure of treatment effect models is described in Supplementary Information S2 and Suppl. Table S3.In both tube and leaf samples, there were only two treatments (‘Untreated’ and ‘Gel’), so the models described up to this point identify the nature of treatment’s effects. However, in flower samples, there were three treatments . To further assess the effects of these three different treatments additional variants of the treatment effects models described above were fitted to the data that grouped together the effects of different treatments. These were identified using a subscript after the model names described above: a lack of a subscript entry indicating all treatments differ; ‘TCwP’, Untreated and Gel treatments are grouped together; ‘TCwU’, Untreated and Unhandled treatments are grouped together; ‘TPwU’, Gel and Unhandled treatments are grouped together. For example, model x- and z-axis for each sample type, using criteria described in Richards (x-axis transect) were calculated according to the best-fitting treatment model for each treatment of each sample and each replicate transect when it was included in the best-fitting model. These summary values, respectively, give a conservative estimate of humidity structure symmetry and humidity intensity produced by each sample and treatment. For further detail on summary values and their calculation, see Harrap et al.  was mounted on a tripod viewing the sample pair from an elevated side on angle. A small (c. 10 × 20 cm) aluminium foil multidirectional mirror was placed in view of the camera against with the bottom of the horticultural tube rack. This fully charged thermal camera was automated to take a thermal image upon activation just before humidity sampling began and every subsequent 15 min using the FLIR Tools software live feed functionality and autoclicker software (written within AutoHotkey) running on an attached laptop placed within the sampling area. Neither the thermal camera nor the laptop generated considerable heat or had fan components that might influence turbulence within the sampling area.Floral transpiration can play an important role in floral temperature regulation . Due to constraints of the software, loss of the PC connection often occurred before the end of floral sampling. Thermal images captured more than 151 min after the start of sampling were also discarded as sampling had finished by this point. All remaining thermal images were included in our analysis of floral temperature over the sampling period, regardless of when camera connection was lost.The temperature of samples was taken from the thermal images in FLIR tools using a manually placed point measurement at the centre of the visible portion of each flower or control tube. During thermographic measurements, target emissivity was set to 0.98, a value appropriate for floral and vegetative tissue , and the Dry Tube controls (‘Untreated’ and ‘Gel’), the temperatures of each flower species and the Dry tube controls were analysed separately to avoid rank deficiencies and avoiding complex three factor interactions. For both species and the Dry Tube controls, the effects of treatment and time elapsed during sampling  were assessed using a repeated-measures ANOVA, including both flower or control identity and the pairings of samples  as a random factors.x or z transects. This led to models without treatment effects (T0 and Tz0) being favoured, which indicates that the presence of gel on its own did not produce or reduce the humidity of Dry Tube controls.In the Dry Tube control, we saw little change in the humidity during sampling Fig. . Best-fiT1 treatment model) or a small change in humidity structure (T2 treatment model) performed best. However, these models were comparable in terms of AIC , humidity production increased, as seen by the slightly elevated entclass1pt{minimaes Table c, indicaentclass1pt{minimaz-axis transect, both control samples showed a (log-)linear structure according to the best-fitting humidity structure model  was approximately 0%, with humidity rising by small amounts (c.0.1–0.2%) with increased distance. Treatments had no effect on humidity production in the z-axis transect in either control, leading to models with no treatment effects (Tz0 models) being favoured in both control samples  led to a small increase in humidity production compared to flowers of the same species that had not been handled  reduced floral humidity production compared to other treatments . Furthermore, tubes did not show a change in temperature over time . That gel treated tubes did not differ in temperature suggests the gel itself does not generate heat. Gel treatment likewise had no effect on floral surface temperature of E. californica flowers . E. californica flowers began sampling at approximately the temperature of the sampling room  and cooled slightly (at a rate of − 8.5 × 10–4 °C/min) during humidity sampling . However, gel treatment did affect the temperature of C. silvatica flowers, which showed a similar change in temperature with time regardless of treatment , with flowers cooling gradually (at a rate of − 6.1 × 10–4 °C/min) over time . Nevertheless, gel treatment had a small but significant effect on the overall temperature of the flowers . Post hoc Tukey tests revealed that this was due to ‘Gel’ treated flowers being, on average, 0.3 °C hotter than ‘Untreated’ flowers , ‘Untreated’ flowers again beginning sampling at approximately room temperature . No significant temperature differences were found between other treatment group pairings of C. silvatica flowers .Temperature monitoring of Dry Tube controls during humidity sampling revealed application of gel had no detectable effect on tube surface temperature  were gel treated. These structures may also transpire and this may contribute to the remaining humidity production of gel-treated flowers. Additionally, the elevated humidity detected in the floral headspace of gel-treated flowers still includes humidity from sources extraneous to the flower. These ‘extraneous sources’ may account for differences between focal and background probes of up to 0.31% relative humidity (that of the ‘Untreated’ full tube). However, leaf and flower samples also block the hole in the horticultural tube lid. Consequently, extraneous humidity sources are likely to be lowered in a similar manner as seen in Harrap et al. Below is the link to the electronic supplementary material.