To create an open‐source visualization program that allows one to find potential cone collisions while planning intracranial stereotactic radiosurgery cases.Measurements of physical components in the treatment room  were incorporated into a script in MATLAB  that produces three‐dimensional visualizations of the components. A localization frame, used during simulation, fully contains the patient. This frame was used to represent a safety zone for collisions. Simple geometric objects are used to approximate the simulated components. The couch is represented as boxes, the gantry head and cone are represented by cylinders, and the patient safety zone can be represented by either a box or ellipsoid. These objects are translated and rotated based upon the beam geometry and the treatment isocenter to mimic treatment. A simple graphical user interface (GUI) was made in MATLAB (compatible with GNU Octave) to allow users to pass the treatment isocenter location, the initial and terminal gantry angles, the couch angle, and the number of angular points to visualize between the initial and terminal gantry angle.The GUI provides a fast and simple way to discover collisions in the treatment room before the treatment plan is completed. Twenty patient arcs were used as an end‐to‐end validation of the system. Seventeen of these appeared the same in the software as in the room. Three of the arcs appeared closer in the software than in the room. This is due to the treatment couch having rounded corners, whereas the software visualizes sharp corners.This simple GUI can be used to find the best orientation of beams for each patient. By finding collisions before a plan is being simulated in the treatment room, a user can save time due to replanning of cases. The dosimetric goal for these treatments is to get adequate target coverage while having the dose falloff as sharply as possible outside the target. Linac‐based SRS is a common technique for delivering these treatments. Multiple, non‐coplanar beam arcs are used with this technique to achieve the sharpest falloff possible. Additionally, stereotactic cones can be used for small and/or spherical targets. Compared to multileaf collimators (MLCs), cones produce a more predictable beam output and improved beam penumbra.A collision with either the patient or treatment couch could result in very serious injuries. This is a possibility that must be checked in person for each arc. Even when checking each arc in person though, there are still two additional issues: the need for replanning a colliding arc and overly conservative arc geometries. Efficiency in a busy clinic is very important. If this collision problem could be addressed before the patient is on the table or before the plan is being physically simulated in the treatment room, there would be great time savings. There are certain table angles that have a very low probability of causing a collision but using only these table angles can produce nonideal dose falloffs.An open‐source visualization program was developed to help with these issues for intracranial cases. First, this program does not replace in‐person collision evaluation, which is a necessity. It is only meant to avoid replanning and overly conservative beam geometries. The program allows for a visual representation of gantry angles, couch angles and spatial positioning of the gantry, cone, and patient. This program can be used at the time of treatment planning to prescreen a treatment geometry for collisions. This will reduce the probability of replanning and could improve dose falloff for some patients. It is estimated that this visualization ability would help find the optimum beam geometry for at least one third of the cases in our clinic. Additionally, even when employing conservative geometries for problematic target locations, it is estimated that one out of ten cases still needed to be replanned due to a collisional issue.Some solutions have been used for other treatments and have even taken into consideration the computed tomography (CT) of the patient.This method is not any faster or more accurate than those presented in literature.22.AMeasurements of physical components are incorporated into a script in MATLAB that calculates results based on inputted parameters to produce 3D visuals. To approximate simulated components graphically, all visualized elements are made from a combination of three different three‐dimensional shapes: boxes, cylinders, and ellipsoids. These objects represent the gantry, treatment couch, and a patient safety zone. Although this approximation compromises the resulting quality, it simplifies the geometric model which translates into fast computation times using very low processing power. Computations for this project were calculated on the order of seconds using an i5‐4300U  processor. Moreover, by using simple models, it allows for simple conversion of code such that the software can be applied toward other combinations of linear accelerators and components. Visuals are produced by incorporating geometric relationships between the components and their positions relative to machine isocenter. A list of variable descriptions and physical measurements for all the equipment used in the simulations of this study is provided in Table 2.Bx points in the patient left direction, the variable y points in the superior direction, and the variable z points in the anterior direction  =  and all had a size of 1 in all directions. The unit box had all sides of length equal to 1. For the cylinder, it had a diameter and height equal to 1 and the ellipsoid was a sphere with a diameter of 1. The second step was to scale and translate these unit objects to represent the linac, couch, and a patient safety zone for the gantry directly overhead, with no couch kick  − yscaled⋅sin(ϕ); yfinalx = scaled⋅sin(ϕ)x + scaled⋅cos(ϕ)y. Rotate for gantry. finalx = scaled⋅cos(θ)x − scaled⋅sin(θ); zfinalz = scaled⋅sin(θ)x + scaled⋅cos(θ)zRotate for couch. Creating the representative objects consisted of three steps. final, yfinal, zfinalx coordinates to the surf command.After these transformations of the unit objects, the locations can be directly visualized by passing the 2.CCalculating the object translations for the couch and safety zone relies on knowing the position of three points in space: the treatment isocenter, the center of the patient safety zone, and a reference point. One of the goals of the software is to display the patient safety zone correctly in relation to the treatment (and machine) isocenter. While the treatment isocenter is readily visible in a treatment planning system, the same is not necessarily true for the center of the safety zone. For this reason, an additional reference point, which is visible on the patient CT image is introduced here. During commissioning, the relationship between the reference point and the safety zone is established. Then, during treatment planning, knowing the relationship between the reference point and the isocenter allows one to place the safety zone and treatment couch correctly in relationship to the isocenter. Figure 2.DCommissioning consists of a set of measurements made to determine the scaling factors, translations, and sign notations for the couch and gantry rotations. Figure The program uses degrees for its angular units. Four data elements are required to characterize the angular conventions of the gantry and couch. The first two are the gantry and couch angle for the setup in Fig. The position for the center of the patient safety zone at commissioning is set with the SZLATCOMM, SZAPCOMM, and the SZSICOMM variables . The position for the reference location is set with the REFLATCOMM, REFAPCOMM, and REFSICOMM variables. The locations of these must be found with a measurement tool in the treatment planning system from a CT image. This allows the coordinate conventions to use the same as those that will appear during clinical usage and bypass the need for any additional sign convention definitions. Here, a CT scan was acquired of an empty mask base, with the localization box in place. The center of the localization box was marked with radiopaque markers for visibility. This CT image was imported into Brainlab Iplan and the location of both the center of the safety zone and reference point were found with a measurement tool.The final information needed to visualize collisions within the software is the sign conventions of the isocenter shifts. A positive shift of the patient safety zone in the x, y, and z directions would move it to the patient left, anterior, and superior. Based on the sign conventions of a treatment planning system, some of these may be reversed. For example, Brainlab conventions for a positive shift in the x, y, and z directions move the patient safety zone to the patient left, posterior, and inferior. For this reason, direction variables  are used to fix this issue. These variables are made to be either 1 or −1 based on sign conventions. A simple test can be used to find these sign notations, which is entering a large shift (such as 200 mm) in the software and evaluating whether each direction has moved correctly. If it has not, reverse the sign. For Brainlab, APDIR = −1, LATDIR = 1, and VERTDIR = −1.Beyond the information needed to visualize the collisions, the color of the different elements can also be changed. Red, green, blue (rgb) values can be varied (from 0 to 1) for the safety zone (SZcolor), couch (CHcolor), cone (Ccolor), and gantry head (Hcolor). In this case, these colors were set to red, dark gray, light gray, and dark yellow, respectively. Additionally, the labels presented to the user for the AP, lateral, and sup/inf directions can be changed. For example, in Brainlab, the AP direction is labeled as “Y,” the lateral direction as “X,” and the sup/inf as “Z.” The final step of commissioning is to validate the software and run end‐to‐end tests (see Section 2.F).2.EThe software is contained fully within one script. If the folder containing the script is within the defined software path, it can be launched by typing the name of the script (SRS_collision in this case). Otherwise, the script can be opened in either MATLAB or GNU Octave and run directly. When the software is first run, a GUI will open requesting the AP, lateral, and sup/inf coordinates for the reference location in the current plan see Fig. , and 6. 2.FIt is necessary to test the coincidence between the GUI and the treatment room components. A simple way to test this coincidence is to find collision points by translating the couch until there are collisions with each corner while rotating the gantry. Once these collision points are found, the shifts can be replicated in the GUI. This should be repeated with all components: corners of the couch and patient safety zone. Another validation test is to replicate a past clinical patient on both the GUI and the linac and check for coincidence in motion of all parts. This includes ensuring the isocenter shifts are properly replicated for each isocenter tested.3clear, (b) the cone is close enough (<1 cm) to cause concern for collisions for the patient setup, and (c) the cone collides.The measurements needed to create this collision software were gathered in 20 min and easily added to the code. Once the code was altered with the proper dimensions, the end‐to‐end validation was shortly followed. The total time spent on commissioning this, including end‐to‐end validation, was 2 h. Three patients and a total of three isocenters were tested for accuracy between the GUI and the treatment room. Each shift was checked to ensure coincidence with the target coordinates provided by BrainLab. Each treatment arc was visualized in the software and verified in the vault. The arc results were recorded as one of three classifications: (a) the cone will Two of the patient plans tested here had collision issues. Tables The GUI was tested for coincidence at multiple isocenters, gantry angles, couch kicks, and collimator angles. Figure 4The GUI provides a fast and simple way to discover collisions in the treatment room before the treatment plan is completed. It is simple to use and can be used to find the best orientation of beams for each patient. By finding collisions before a plan is being simulated in the treatment room, the clinic can save time in the treatment room. This collision software will also provide a better idea of colliding beam geometries to avoid having to make multiple plans.This GUI was tested in several variations and resulted in clear depictions of the room. The shortcomings of this technique are related to the simple shapes utilized to represent the linac, cone, couch, and patient. More specifically, the sharp corners of the virtual model can over predict collisions near the rounded corners of the couch. Similarly, the patient safety zone utilized here is based on the Brainlab localization box, which is designed to fit over anyone’s head. This would also overpredict cone collisions. In the future, this design could include patient outlines for a more exact design but for now, the simplified geometric shapes provide a conservative approximation for the location of the gantry, couch, and patient.The GUI was found to be very accurate in replicating the room geometry but did predict collisions in cases that would clear in the room. The only case that did not fit this trend was a collision which resulted from the cone‐locking mechanism. This locking mechanism included a clip which is not a component on the GUI created. In this particular case, the latch passed closer to the table than 1 cm for one arc. The treatment was not replanned though. The couch and gantry angle remained the same, but the collimator was rotated to avoid a collision between the clip on the cone‐locking mechanism and the table.5This simple GUI can be used by the planner, physician or physicist to find the best orientation of beams for each patient. By finding collisions before a plan is being simulated in the treatment room, the clinic can save time due to replanning of cases and avoid overly conservative beam configurations.No conflict of interest.6Data S1. This is an example SRS cone collision code. It does not replace the need for physical, in‐room cone collision assessment for patients. Those adapting this software for their use must verify the accuracy of these calculations and ensure the safe usage of the code they produce from it.Click here for additional data file.


Cerebral palsy (CP) is a neuro-developmental disorder. Spastic CP is the most common type of CP and is characterized by contractures of the extremities. Selective Percutaneous Myofascial Lengthening (SPML) is a minimally invasive procedure practiced by a handful of physicians in the US, and it decreases contractures and increases the range of motion in individuals with spastic CP. This study wanted to examine if there was an association between CP severity, socioeconomic status, and reoperation rates. This study used electronic medical records (EMR) to include 626 patients with spastic CP who had surgeries between January 2006 and December 2012. The zip codes from the EMR were used to determine the inflation-adjusted mean income and educational qualification (a high school education or higher) of the community via the US Census Bureau. Disease severity before the initial surgery was determined by using the functional mobility scale in the EMR to compute the Gross Motor Function Classification System (GMFCS) level. Then the data was graphed and averages were taken for the reoperation versus the no-reoperation populations, and Student's t-tests were run to determine statistical significance. The data showed that communities with higher education and income tended to reoperate more often. The higher number of reoperations in affluent communities could mean that either more affluent communities are better educated and know the benefits of bringing their children back for reoperation or that they require further education about physical therapy after the initial surgery to decrease the incidence of reoperation. This retrospective study is a level 2 study looking at the socioeconomic and educational backgrounds and disease severity and their association with reoperation rates. Cerebral palsy (CP) is a group of non-progressive permanent disorders that affect movement and are attributed to non-progressive disturbances that occurred in the developing fetal or infant brain. CP can be accompanied by other secondary disturbances, such as communication difficulties . SpasticThe goal of this retrospective study was to examine if there was any association between the SPML reoperation rates and the disease severity of the patients as well as their educational and socioeconomic backgrounds. The socioeconomic status of the patient may affect the outcome of SPML surgery and the need for reoperation. Socioeconomic status can also affect access to resources such as physical therapy and orthotic services and can lead to challenges with the transition to such services. A cross-sectional study in Brazil showed that as the severity of CP increases, the functional performance decreases more significantly in lower socioeconomic levels . To our We compared the University of Texas Medical Branch (UTMB) electronic medical records (EMR) of children with SPML surgery without a reoperation and children with SPML surgery with a reoperation at UTMB. This study included 626 patients with spastic CP who had SPML surgeries between January 2006 and December 2012. Of those patients, 107 had reoperations (17%). The zip codes from addresses before the initial surgery were used to determine the inflation-adjusted mean income and educational qualification (a high school education or higher) of the community via US Census Bureau 5-year estimates for 2012 [Our results showed that there was no preoperation difference in GMFCS levels in the spread of the individuals who received reoperation vs. those that did not. The mean GMFCS level of the reoperation population was 3.2 in comparison to 2.98 in the no-reoperation population; the p-value was 0.087 Figure .We found that the reoperation community income was higher. The mean community income of the reoperation population was USD 82,698 vs. 74,424 in the no-reoperation population; the p-value was 0.011 , which suggests that if reoperation is necessary, they could benefit from further education about reoperation after their initial surgery. It could also mean that patients from lower educated and lower socioeconomic communities partake in more physical therapy and other services, decreasing the need for reoperation. It is suggested that more affluent individuals should be educated about ways that they can ensure that the patient will be less likely to need reoperation. Also, there seems to be a trend that fewer patients with private insurance receive reoperation. It is not clear whether this is an education issue or if it could be attributed to denial of coverage.A future study could be done to look at ethnicity and community access to green space for physical therapy to see if there is a correlation between these factors and reoperation rates. This study was retrospective and has limitations, such as relying on available documentation in the EMR by different providers. Also, the two groups that we studied were not of the same size in terms of the number of individuals, and the follow-up was limited to our chart-search period.SPML can significantly increase the quality of life in CP patients and increase their range of motion, but it may sometimes require reoperation. Our data demonstrated that more affluent communities tended to have a higher incidence of SPML reoperations. The higher number of reoperations in affluent communities could mean that either more affluent communities are better educated and know the benefits of bringing their kids back for reoperation or they require further education about physical therapy after the initial surgery to decrease the incidence of reoperation.


Online social media has greatly affected the way in which we communicate with each other. However, little is known about what fundamental mechanisms drive dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and that can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena that, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network. In the era of big data, online social networks offer unprecedented opportunities for studying collective human behavior. One important question pertains to the characteristics of human interactions that lead to some items of information (“memes”) becoming massively popular via online sharing. The standard approach to such a question involves large-scale longitudinal data analysis, which has yielded many important clues about underlying mechanisms. Here, we present the first modeling approach that provides insight into the distinct roles of network connectivity structure (who connects to whom) and the memory time of users .The attention of users is a valuable commodity in both cyberspace and the real world, and competition between memes for attention leads to characteristic signatures in popularity distributions. We focus on nearly one million Twitter user IDs and the popularity of hashtags related to a protest movement that occurred in 2011 in Spain. We assume that all memes—which can be thought of as ideas or hashtags—are attractive to the same degree. We show that the resulting meme popularity distributions are fat tailed, limiting to power laws. Our analytically tractable model incorporates long memory times of users, which is an improvement over previous models. Our probabilistic model yields formulas that enable the model to be rapidly fitted to large-scale data from social networks.We expect that our findings will provide insights into the fundamental drivers of popularity on social networks. I.Recent advances in communication technologies and the emergence of social media have made it possible to communicate rapidly on a global scale. However, since we receive pieces of information from multiple sources, this has also made the information ecosystem highly competitive; in fact, users’ influence and visibility are highly heterogeneous, and topics strive for users’ attention in online social systems. Although several studies have described the dynamics of information flow in popular communication media Information often spreads through a social network as a cascade: A person adopts a new behavior, installs a new app, or sends a news item or rumor to his friends . The avalanche spreads if the friends decide to also adopt the new behavior and in turn pass on the social influence effect to their own friends, who may further propagate the behavior. Following the usage in the review To enable a succinct general description, we use Dawkins’s term A notable characteristic of many meme popularity distributions is that they are very fat-tailed: If a power-law distribution is fitted to the data, then the power-law exponent To address this problem, we develop a theoretical framework that models how users choose among multiple sources of incoming information and affect the spreading of memes on a directed social network, like Twitter We phrase the model in terms of meme propagation on a directed social network (like Twitter) and interpret a “meme” to be any distinct piece of information that is easily copied and transmitted . However, it should be clear that the model and its results can also be extended to the other examples of viral phenomena discussed above. For the adoption of apps on Facebook, for example, the memes are the notifications sent when a user installs an app The remainder of the paper is structured as follows. The model is introduced in Sec. II.In online communication platforms like Twitter, users follow (receive the broadcasts or “tweets” of) other users. In graph-theoretical terms, these relationships constitute directed links from the followed node (user) to the follower Fig. . The netEach user has a “stream” that records all tweets received by the user, time stamped by their arrival time. We assume that only a fraction When a user decides, at time see Fig. : With prThe model as described is a “neutral model” A branching process approximation III.A.We define see Fig. . To obta1User The likelihood that meme 2The factor 3There are To determine the size of trees originating from meme see Fig. . In eachTo calculate the total size of the tree seeded by copying from the The popularity of a meme, as observed at time see Fig. . IntroduB.It is worth noting that all agents in the model have constant activity rates, so the actions of each individual agent constitute a Poisson process. A Poisson process is characterized by an exponential distribution of interevent times, where each event corresponds to an innovation or a retweeting action. This assumption is contrary to studies such as Refs. In particular, if the memory-time distribution IV.A.A branching process may be classified by the expected (mean) number We identify the parent in the process as a meme that was accepted into the stream  of a  cf. Eq.  KjkThus, we have shown that the branching process underlying the model is critical when It is worth noting that the result of Eq. Recalling that the activity rate of user B.The value never retweeted and thus have popularity The C.The age dependence of the mean popularity . However, if  Setting To consider the large-age asymptotics of The preceding analysis all assumes that the seed node  is chosen at random from all the network users, with probability weighted by the user activity rate. It is straightforward to repeat the steps of the calculations for the case where the seed node is known to have D.In the infinite-age (steady-state) limit (i) Case 1: 5The values of The large-(ii) Case 2: E.In Appendix V.To confirm the accuracy of the branching-process approximation and to explore the interactions of the network structure and the memory-time distribution, here we compare numerical simulations of the model with the theoretical predictions of Sec. Figure Panels (b) and (c) of Fig. The mean popularity  see Eq. , while aVI.A.To test the ability of the model to fit real-world data, we use a 1-year data set comprised of the popularities of The out-degree distribution The model parameter del Eqs.  to find . a Fig.  and the The data do, however, provide an upper bound on the value of the innovation probability B.Using the empirical Twitter out-degree distribution Despite these successes, it was not possible to successfully fit the rve Fig.  using thC.As shown in Fig. As we saw in Sec.  see Eq. , the divD.Although our analysis methods are quite general, in order to focus on understanding the combined effects of memory and out-degree distribution, most of our results thus far are specialized to the case of uniform user activity rates, The mean popularity is given in the general case by Eq. If we additionally assume that the user activity rates saturate to a constant level To demonstrate the effect of heterogeneous activity rates, we consider a model for VII.As we have demonstrated, the analytical tractability of the null model enables it to be fitted to time-dependent data on meme popularity. However, we were required to make a number of assumptions to obtain analytical results, and in this section, we briefly highlight the most important assumptions and discuss possible extensions to the model.The network structure is assumed to be that of a directed configuration-model graph defined by the joint probability A more unrealistic simplification of the configuration model is the fact that it generates networks that are locally treelike, with few short cycles. In particular, our model does not include bidirectional edges (i.e., reciprocated following relationships, where user An important assumption of the null model is that all memes have equal fitness. This is consistent with random-copying models of human decision making Perhaps the most unrealistic aspect of the current model is the assumption that all users have constant activity rates, so their tweeting activity is described by a Poisson process (see the discussion in Sec. The heuristic calculation of the branching number considered at the end of Sec. Finally, our focus here has been on the statistical physics of the model, but for completeness, we should note the difficulties inherent in applying the model to data sets where memes may not be as simple to recognize and track as hashtags are. In Ref. A related question is whether the popularity of online memes has any implications in terms of mass social movements in the offline world. This is a complex question that lies beyond the scope of this paper, but we note that Fig. 3 of Ref. VIII.The extremely wide range of popularities achieved by items on social media poses many challenges for complex systems researchers. These include the identification of the causes In this paper, we have introduced and analyzed a null model of meme spreading that is analytically tractable yet realistic enough to reproduce several characteristic features of empirical data. The model is sufficiently general to incorporate heterogeneous user activity rates and a joint distribution The analytical tractability enables fast fitting of the model to data, as demonstrated in Sec. As noted in the Introduction, and expanded upon in Sec.


VOLUME 282 (2007) PAGES 31060–31067A, some lanes with irrelevant samples were deleted. In the composition of the final figure, the panel for the loading control Actin was erroneously assembled, resulting in the duplication of one of the bands. We now include an amended figure with the correct Actin panel, which should replace Figure 3A. The legend should be amended to state that: “Dashed lines in A indicate position of lanes removed during figure composition.” This error does not affect any of the conclusions in the article. The authors deeply regret this unintended error and they apologize for any confusion it may have caused to the readers.During the preparation of the immunoblot image in Figure 3


Spatial neighboring analysis is an indispensable part of geo-raster spatial analysis. In the big data era, high-resolution raster data offer us abundant and valuable information, and also bring enormous computational challenges to the existing focal statistics algorithms. Simply employing the in-memory computing framework Spark to serve such applications might incur performance issues due to its lack of native support for spatial data. In this article, we present a Spark-based parallel computing approach for the focal algorithms of neighboring analysis. This approach implements efficient manipulation of large amounts of terrain data through three steps: (1) partitioning a raster digital elevation model (DEM) file into multiple square tile files by adopting a tile-based multifile storing strategy suitable for the Hadoop Distributed File System (HDFS), (2) performing the quintessential slope algorithm on these tile files using a dynamic calculation window (DCW) computing strategy, and (3) writing back and merging the calculation results into a whole raster file. Experiments with the digital elevation data of Australia show that the proposed computing approach can effectively improve the parallel performance of focal statistics algorithms. The results also show that the approach has almost the same calculation accuracy as that of ArcGIS. The proposed approach also exhibits good scalability when the number of Spark executors in clusters is increased. Spatial neighboring analysis, or focal statistics, is a raster-based spatial modeling method. It performs a neighborhood operation that computes an output raster, where the value for each output cell is a function of the values of all the input cells that are in a specified neighborhood around that location. The algorithms performed on the input raster include block statistics, filter, and terrain surface analysis (such as slope and aspect) and so on. They have been widely used in the estimation of surface water volume , predictAdvances in data collection techniques (such as LiDAR and IfSAR) have brought us much more accessible three-dimensional mapping products in the form of digital elevation model (DEMs), and have also made the volume of digital terrain data reach an unprecedented scale. For instance, the global SRTM1 data with a resolution of one arc-second in latitude and longitude have 1 points . Since m1 points ,7. It isNeighboring analysis applies to algorithms that are based on a fixed cell size and sliding analysis window, such as cellular automata , digitalIn the last decade, distributed computing frameworks, like Hadoop MapReduce and Apache Spark, have been widely accepted for scalable processing of large spatial datasets. Existing solutions for big spatial data processing on Hadoop or Spark include Hadoop-GIS , SpatialSpark has been widely accepted as the data processing platform for big datasets, and provides better performance than Hadoop MapReduce due to its large set of operators and support for cyclic data flow. However, Spark provides a general data model for processing any type of data, and does not provide native support for geo-raster spatial data. So, users have to implement special operators for raster-based data processing. Encouragingly, recent studies have shown that it is feasible to achieve desirable parallel acceleration for geo-raster spatial analysis using Spark. These research efforts to improve the computational efficiency of raster-based algorithms include the edge extraction from remote sensing images  and terrIn this article, a parallel computing approach implementing the slope algorithm  using Spark is presented. First, we propose a tile-based data partitioning strategy to address the problem of utilizing the Hadoop Distributed File System (HDFS) to manage raster data. Then, we design a dynamic calculation window (DCW) computing strategy to optimize the data communication overhead brought by the halo phenomenon  of spatiThe remainder of the article is organized as follows. The distributed focal statistics algorithms synthesize a tile-based multifile storing strategy, a DCW computing strategy, and a write-back and merging strategy, which will be expatiated in the following. The flowchart of the proposed implementation of the neighboring analysis using Spark is illustrated in Focal algorithms calculate a selected statistic for each input cell of the values within a specified neighborhood around it. For example, a square neighborhood is defined by eight points that are on a rectangle centered at a point for which a terrain topographic attribute  is to be computed from the elevation data. As each cell in the input is processed, the neighborhood is essentially a moving window that shifts along with it until an output raster dataset is produced. The size of the neighborhood specifically determines which cells surrounding the processing cell should be used in the calculation of each output value. The most typical neighborhood is a 3 × 3 cell window, as illustrated in x and y directions, respectively, a, b, c, g, h, and i, respectively, and x and y directions, respectively.Slope is a commonly used topographic attribute for terrain analysis , which iO  Get the metadata of the original raster file, and calculate the rows of tiles and columns of tiles using a predefined (2) Partition the raster file into multiple tile files according to the method shown in (3) Write metadata information for subsequent calculations to a metadata file. (4) Upload the metadata file and multiple tile files to the HDFS.The computing strategy is comprised of the method for parallel reading of multiple tile files and the DCW computing strategy for the neighboring analysis. The flowchart of the proposed computing strategy is illustrated in It firstly calculates the number of computing iterations according to the input DCW size. Then, it repeatedly reads the multiple tile files of each DCW window in parallel and performs the Spark-based slope calculation on them.The tile-based partitioning method could effectively solve the problem of utilizing the HDFS to store raster data, but also produces a great number of small tile files when processing large terrain data. Although having so many files introduces additional I/O reading costs, it provides the possibility to read data in parallel.A resilient distributed dataset (RDD) is a fundamental data structure of Spark that expresses a directed acyclic graph (DAG) with RDD lineage. Spark provides transformations and actions to build the RDD lineage and explicitly expresses the algorithmic logic of applications . In SparTo address this problem, a parallel reading method for multiple tile files based on Spark was designed, as illustrated in In (1) The Definition and Implementation of the DCWAlthough the computing performance of raster-based analysis could be improved by using Spark, there are still some problems to be solved. For example, the traditional pixel-based calculating process brings frequent data I/O consumption, which also increases the scheduling time of the master node and the waiting time of the computing node. Therefore, the DCW computing strategy was proposed to accelerate the execution speed of the Spark-based slope algorithm. It adopts the DCW as a minimum computing unit and uses the raster tile as a minimum parallel task processing unit.The DCW is defined as follows:(a) If the upper-left corner of a tile is regarded as its location origin , a DCW refers to a tile dataset composed of one or more rows of tiles whose upper-left corners have the same latitude.(b) The DCW size refers to the number of rows of tiles contained in the DCW. The forming principle of the DCW is illustrated in m denotes the number of columns of tiles, n denotes the number of DCW. When a job is submitted, Spark will start a driver process and create SparkContext in the process. A SparkContext is the entry point for low-level API functionality. It takes on the task of creating RDDs, accumulators, and broadcast variables. In the proposed DCW computing strategy, SparkContext is in charge of reading multiple tile files to form a More details of the Spark-based slope algorithm are described in Algorithm 1.The Spark-based slope algorithm incorporates three main steps, as follows:Step 1: Read tile data in parallel: In this step, the algorithm gets the pathname set of multiple tile files to construct the Step 2: Calculate the data that need to be broadcast: After reading the tile data, the algorithm selects a set of cells in each tile for other neighboring tiles to calculate, and broadcasts the cell set to each Spark worker node.Step 3: Filter the tiles to be calculated and calculate the slope value: The algorithm enables each tile to acquire the cell set needed for neighboring analysis, and then the RDD of the slope analysis result is obtained by performing the map operation named Algorithm 1 Slope algorithm based on SparkInput: multiple tile files T, the size of DCW SOutput: the slope analysis result RDD <index, value>  1: get the number of iterations based on S  2: get metadata information of DEM  3:   4: functionreadData.23:   24:   /* Step 3: Filter the tiles to be calculated and calculate the slope value*/25:   26:   27:   doSlope)28: end for29: Output the (2) The Solution to the Halo PhenomenonDuring the execution of the neighboring analysis, the computation of a tile requires not only its own cells, but also adjacent cells from other tiles. These extra cell data of other tiles form a halo around the original tile , which iThere are primarily two factors affecting the communication overhead in the DCW computing strategy. One is the tile size, and the other is the DCW size. The tile size affects the data size that needs to be broadcast. The smaller the tile size, the more data that need to be broadcast, and the time for network communication also increases. On the other hand, the DCW size affects the number of broadcasts of Spark broadcast variables. This number should be as small as possible to make the accumulated broadcast data as close to the number of cells that need to be broadcast as possible to reduce re-computing. Therefore, the DCW size should be as large as possible if cluster memory resources permit. These two factors will be further discussed in subsequent experiments.R denotes the total number of cells to be broadcast in the row direction, C denotes the total number of cells to be broadcast in the column direction, N denotes the number of cells that need to be broadcast.In order to solve the halo problem in the DCW computing strategy, the total number of cells to be broadcast should be determined. Its calculation formula is defined as follows:In addition to data organization and computation, the persistent storage of the slope results is also important. After the calculation is completed, the results saved in memory in the form of tiles should be merged into a whole file on the HDFS. The problem of the sequence of data write-back and merging needs to be considered. This is because large data shuffles would be triggered if data merging operations are performed on Spark first, which may cause a lot of communication overhead and lead to memory overflow. Therefore, this article chose to firstly perform the data write-back strategy on Spark, and then to perform the data merging strategy on the HDFS.First of all, the goal of data write-back is to write the data back to the HDFS in parallel. The slope analysis results are stored in a local node after the calculation is completed. If the local node is a data node, the HDFS will use it to store the first copy. In this process, the tasks on different nodes do not affect each other. This is beneficial in improving the efficiency of data write-back operations. The implementation of the data write-back strategy is shown in The main steps of the strategy include: (1) transforming the slope results from key-value RDDs into a byte array, (2) creating empty files named with TileNo, and (3) saving the byte array to its corresponding file.i and j denote the row and column numbers of a grid cell of a tile, In the second place, the goal of data merging is responsible for merging multiple tile files into a whole file. It can be regarded as the inverse process of data partitioning. In this process, the local position of each cell of a tile file is converted into the global position of the slope result file according to the following formula:The large number of I/O operations would directly affect the implementation performance during data merging. In order to improve the efficiency of data merging operations, a row/tile-based data merging strategy was designed, as shown in Algorithm 2 Data merging algorithmInput: multiple slope results’ tile files TOutput: a whole slope result’s raster file R  1: create an empty file R  2: open R through output stream  3: for each do  4:   retrieve tiles in T  5:   open   6:   for each do  7:    for each do  8:     if i ×then  9:      read 10:      write the data to file R11:     end if12:    end if13:   end if14:   close the 15: end if16: close the output stream of R17: Output the file RMore details of the implementation of the data merging algorithm are described in Algorithm 2.Here, https://data.gov.au/data/dataset/da926e47-1cd8-4dc9-b859-cbc18c29d858) were selected as the main data source. To evaluate the impact of the amount of data on the approach proposed in this article, four elevation datasets of different sizes were used to test the effectiveness of the proposed approach through data resampling. All dataset files listed in The digital terrain data of Australia  To investigate the performance of the proposed calculation method, both the spatial analysis tool of ArcGIS on the workstation and the slope algorithm implemented on the cluster were executed using four datasets.(2) To study the impact of the size of a raster tile and a DCW on the overall performance of the focal algorithms, the slope algorithm was also carried out on the four datasets with different tile sizes and DCW sizes.(3) To verify the calculation accuracy of the Spark-based slope algorithm, the slope analysis tool of ArcGIS implemented on the workstation was chosen as a reference. They were both executed using four datasets to obtain the measurement results of accuracy.(4) To test the scale-up performance of the proposed approach, the Spark-based slope algorithm was implemented with an incremental number of Spark executors.S is the standard deviation, and n is the number of raster grid cells.The accuracy metric was selected as an indicator to evaluate the calculation approach proposed in this article. It is defined as the mean and standard deviation  calculatThe slope map of the experimental result is shown in In The computing performance results are shown in In this experiment, the slope algorithm was implemented using the Grid3 dataset. The time spent on communication and computing was recorded and compared using different tile sizes. The results for time consumption shown in In this experiment, the slope algorithm was again carried out using the Grid3 dataset. The time spent on communication and computing was recorded in six DCW sizes from 10 to 60. The results for time consumption in In this experiment, the slope analysis method of ArcGIS was selected as a reference because of its popular application in practice. The subtraction matrix of the calculation result of ArcGIS and that of the Spark-based algorithm were firstly obtained, and then the mean and the standard deviation were calculated according to the matrix. The experimental differences shown in In this experiment, the Spark-based slope algorithm was implemented using the Grid3 dataset with an incremental number of Spark executors. The execution time results shown in Spatial neighboring analysis is an important component of raster-based geospatial analytics. Its execution efficiency is strongly influenced by the amount of raster data. Focal statistics algorithms can achieve desirable computational performance for processing large amounts of terrain data by exploiting the distributed computing capability of Apache Spark. In this article, we presented a Spark-based parallel computing approach for neighboring analysis algorithms, which integrates a tile-based multifile storing strategy, a dynamic calculation window (DCW) computing strategy, and a data write-back and merging strategy. First, the basic idea of the proposed tile-based multifile storing strategy is to take a raster tile as the minimum processing unit while performing the transformation and action functions of Spark, which is helpful in reducing the data transfer overhead of the distributed computation. Then, the DCW computing strategy is beneficial for optimizing communication overhead to improve the parallel performance of the neighboring analysis. Last, the proposed write-back and merging strategy contributes to the persistent storage of the massive analysis results. The experimental results showed that the Spark-based focal statistics algorithms dramatically reduced the computing time, achieved satisfactory performance and good accuracy, and scaled reasonably well as the data volume and the number of Spark executors of clusters increased.In future work, we will first collect more high-resolution elevation data to further measure the parallel performance of the proposed computing approach. The experiment on the scale-up performance of the proposed approach was not sufficient due to the constraints of the cluster hardware environment. We will conduct the experiment in a cluster that is composed of better configured computational nodes. Thirdly, through the experiments, we found that the overhead of calculation was higher than that of the communication, and a proper DCW size should be determined based on the current cluster computing resources. We will also investigate how to further optimize our strategies to gain higher efficiency. Last but not least, existing geo-distributed computing frameworks presented in the literature  focus on big geo-vector data processing, and rarely involve geo-raster analysis. We will continue to search for some suitable geo-raster computational frameworks and conduct quantitative comparisons with them.


Guillain-Barré syndrome (GBS) is a rare but severe autoimmune disease and the usage of the Brighton criteria can be a source of great help in resource-limited settings. It encompasses all ages. Late diagnosis of GBS can have a significant negative impact on the prognosis. The Brighton criteria are used to assist in the diagnosis of GBS and help distinguish between low-risk and high-risk patients. In this article, we have discussed the challenges regarding the diagnosis of the GBS and the possible solutions that can help in the early diagnosis and management of GBS. Guillain-Barré syndrome (GBS) is a rare autoimmune disease having a potential of life-threatening complications. It is acute in onset and involves inflammatory polyneuropathy. In this disease, immune-mediated damage of the peripheral nervous system occurs with the destruction of the layer of insulation around the nerve axon, the myelin sheath, and rapidly developing muscle weakness.GBS is a disease that encompasses all ages but is rarely seen in infants. A number of studies have found the incidence of GBS to be 0.16 to 4/100000 per year [The diagnosis of GBS is usually a result of the clinical evaluation. The development of the Brighton criteria by the Brighton Collaboration has assisted in making the diagnosis of GBS easier for clinicians. The criteria enlist all of the inceptive symptoms inherent to the disease, as well as the diagnostic tools that help in its identification and monitoring . The difThe sensitivity of the Brighton criteria correlates with the levels of the criteria. Roodbol et al. deduced in a study that Levels 1, 2, and 3 had sensitivities of 72%, 96%, and 98%, respectively . In a stThe Brighton criteria are used as a tool to assist in diagnosing GBS and help distinguish between low-risk and high-risk patients. They aid in the early and prompt diagnosis of the disease. The criteria also help in outlining the course of treatment required by the particular number of patients diagnosed with GBS. In an ill-equipped clinical setup, the Brighton criteria play a prime role in the detection of GBS.Currently, few physicians, specialists, residents, and medical students have knowledge regarding the Brighton criteria as a means to diagnose GBS. The lack of awareness and application of the Brighton criteria has made it difficult for physicians to identify clinically suspected cases of GBS. This has resulted in increasing rates of misdiagnosis that eventually poses the risk of fatal complications to the patient. This leads to increased mortality and morbidity resulting in increases in the burden of responsibility on the physician and healthcare facility. To prevent the occurrence of such situations, it is essential that all healthcare-associated individuals be brought up to date regarding the new diagnostic criteria for all diseases, and in this case, GBS. Events of missed diagnosis of GBS can be overcome if knowledge about the Brighton criteria is made available to impending doctors from an early stage of their career. Textbooks that are part of the curriculum of a medical school should mention the Brighton criteria as a means to diagnose and manage GBS at an early onset. Any patient diagnosed with GBS, observed on clinical rotations or ward rounds, is an opportunity to provide medical students and residents with the knowledge of the Brighton criteria and how the criteria should be applied on a case-by-case basis.For physicians and specialists, it is essential that they should be upgraded regarding the latest diagnostic tools and measures used for diagnosing GBS. The importance of the review of the literature regarding GBS cannot be emphasized for every future and practicing medical practitioner. There is a need to increase awareness of Brighton criteria among physicians. This can be done through available modes of communication like conduction of seminars, workshops, case presentations, pamphlets, articles, and oral discussions among groups of doctors at different hospitals across many different geographic locations. The collective efforts of all physicians together, in increasing the awareness of the Brighton criteria, may help to lower the morbidity and mortality associated with GBS and somewhat lessen the burden of this disease.


N = 189 adults (79% men) with mild intellectual disability or borderline intellectual functioning who were residents of a treatment facility in the Netherlands.This study examined the psychometric properties of the Group Climate Instrument (GCI) in a sample of Construct validity of the GCI was examined by means of confirmatory factor analysis. Also, reliability and convergent validity of the GCI were examined. We also examined the variability in perception of the living group climate between and within living groups by computing intraclass correlation coefficients.The model contained four first‐order factors  and a second‐order factor overall climate, providing preliminary support for construct validity of the GCI. Reliability coefficients were good for all factors. Preliminary evidence for convergent validity was found in significant moderate associations between subscales and single item ratings for the factors of group climate. The intraclass correlation coefficients indicated that a considerate proportion of variance can be attributed to between‐group differences.The GCI might be used to assess perception of the living group climate for individuals with mild intellectual disability or borderline intellectual functioning in psychiatric and forensic care settings, although further development of the GCI and replication of our findings seem necessary. There has been an extensive history of research into living group climate in (secure) residential settings for more than 50 years Tonkin . The notet al. 1Throughout this paper the term ‘sociotherapist’ is used to describe the role of professional caregiver. and clients and processes between clients make up most of the living group climate within the group  living group climate, by contrast, is characterised by unfulfilled basic psychological needs as a result of rivalry, aggression, and insecurity among sociotherapists and clients and among clients on the living group. In such, climate sociotherapists are inclined towards restricting clients' autonomy, and excessive control instead of support, connectedness and flexibility towards clients. Furthermore, a closed living group climate is characterised by a lack of responsivity by sociotherapists, insufficient prospects for growth, an oppressive atmosphere in the living group and aggression among clients and among clients and sociotherapists Harvey . Also, aet al.  who were residents of a secure treatment facility. As transactional processes between clients and sociotherapists and between clients make up a large part of the climate, we propose the perception of the living group climate to quality to be most salient in a living group level as opposed to individual or facility level. It is important that changes to working practices are made on the basis of the perspective of clients for which the GCI provides an important tool.The aim of this study is to examine the psychometric properties  of the GCI  secure treatment facility for individuals with MID‐BIF located in the northern and eastern part of the Netherlands. All 441 residents were invited to participate in the study. In total, 208 residents participated (47% response rate). Data of 19 participants (9% of 208) were excluded from the analyses because of missing data of intellectual functioning (IQ), resulting in a sample of 189 participants. Participants (79% men) were aged between 18 and 69 years . Of the participants, 44% had a mild ID (IQ 50–69) and 56% had borderline intellectual functioning (IQ 70–85) . Besides MID‐BIF, participants had severe problem behaviour in combination with mental health problems and/or serious problems in all areas of life, often with a history of substance use. Comorbidity is high: most of the participants were diagnosed with more than one disorder, not including MID‐BIF. For most of the participants, the type of problem behaviour was the reason for admittance. Most of the participants were admitted because of externalising behaviour problems  and/or internalising problems (like self‐injurious behaviour and suicide attempt) offending.The facility consists of 58 living groups, and the modal ward size is eight beds. The mean treatment duration at Trajectum is 2 years and 4 months, although this is largely dependent on the participants' legal status and risk level. During treatment, participants move to wards with different levels of restrictions. Participants of this sample resided on wards which had different security levels; 59% of the participants resided on a low secure ward, 20% of the participants resided on a medium secure ward and 21% of the participants resided on a high secure ward.Data were collected in the context of routine monitoring of the ward's climate within the facility. The study received approval from the local institutional review board. Each year, participants who resided in the facility were individually interviewed and completed the GCI. For the purpose of exploring psychometric properties of the instrument, only data from the first wave were used which were collected in March and April 2016. Participation was on a voluntary basis. The researcher gave oral and written information to participants concerning data collection, study aims and objectives. All participants and their legal guardians were informed that the research was strictly confidential and anonymous; data were only reported on a ward level and – upon approval – signed an informed consent form. In addition, the multidisciplinary treatment team determined whether a participant was able to give informed consent to participate. The active consent method was used; explicit consent was given by all of the participants. Questionnaires were given a code to guarantee anonymity of the participants. The names of participating participants were replaced by a code to ensure privacy.Students of Windesheim University of Applied Sciences and (assistant) researchers of Trajectum were trained to conduct the questionnaire and signed a written statement of confidentiality. Most participants were assisted to complete the questionnaire by a student or (assistant) researcher who read the questions and answering categories out loud and explained the questions to the participant if necessary. Alternative scripted phrases to enable questions to be explained in a different way were part of the training they received. If used, this would provide an additional way of checking participants' understanding whilst preventing students and researchers from projecting their interpretation of the questions on to participants. The completed questionnaires were returned to the researcher (first author), after which the scores were entered into SPSS version 24  for analyses. Data on participant and context characteristics  were extracted from the records of the participants and added to the SPSS database.et al. The quality of the living group climate was measured with the revised GCI which was adapted for individuals with MID‐BIF by using simpler wording compared to the original version  support, (2) growth, (3) atmosphere and (4) repression. A total scale score is a combined score in which the subscale scores are added . The GCI was used to assess whether the living group climate is more open or more closed. The four factors are evident in both a closed and an open living group climate score. The balance between these two (i.e. open vs. closed) is decisive in terms of the quality of the climate. The outcomes produced by the GCI provide an in‐depth insight into living group climate from the clients' perspective and are being used to guide clinical practice and improve quality of client care.Support subscale contains 11 items and measures the responsivity of the sociotherapists towards the needs of participants, including giving attention to participants, taking complaints seriously and providing respect and trust. An example item of the growth subscale is ‘The sociotherapists treat me with respect’. The Growth subscale consists of six items and measures the degree to which participants feel they learn, gain hope for the future and comprehend the benefit of their stay at the ward. An example item of the growth subscale is ‘I learn the right things here’. The Repression subscale has seven items and measures the experience of strictness and control, unfair and coincidental rules and a lack of flexibility in the living group. An example item of the repression subscale is ‘You need to ask permission for everything here’. The Atmosphere subscale consists of five items and measures the degree to which participants trust one another, feel safe and secure towards one another (both clients and sociotherapists), are able to find rest and receive sufficient daylight. An example item of the atmosphere subscale is: ‘We trust one another here’.The GCI consists of 29 items that can be scored on a Likert‐scale ranging from 1 (‘not applicable’) to 5 (‘entirely applicable’). The In addition to filling out the GCI, participants were asked to evaluate the various factors of living group climate by giving a report mark (single item rating) between 1 (very poor) and 10 (excellent) to a statement, corresponding to the four subscales of the GCI. The statement ‘The support you receive from sociotherapists’ corresponded with the subscale support; the statement ‘What you learn here’ corresponded with the subscale growth; the statement ‘The atmosphere at the ward’ corresponded with the subscale atmosphere; and the statement ‘The rules at the ward’ corresponded with the subscale repression.χ2 statistic and the degrees of freedom (df) lower than 2.5 indicates a close fit to the data . We used the lavaan package Rosseel  in the Rr correlations between the subscales of the GCI and the report marks (between 1 and 10). A positive moderate to strong correlation between the subscales support, growth and atmosphere and the corresponding report marks is seen as indicative of convergent validity of the three subscales. A negative moderate to strong correlation between the repression subscale and the corresponding report mark for repression indicates convergent validity of the subscale repression. Pearson's correlations of r = .10–.30 are seen as small, r = .30–.50 are seen as a moderate and r > .50 are seen as a large  = 457.152 (P < .001); CFI = .931; TLI = .922; RMSEA = .048 (90% CI = .036–.058); SRMR = .071. The ratio between the χ2 statistic and the degrees of freedom was 1.37. One item of the repression subscale (i.e. ‘Clients must ask permission for everything’) did not load significantly on the repression factor and was deleted from the model to improve model fit. Further analyses were conducted with 28 items. Table Results for the GCI indicated a good fit to the data. CFA was conducted on all 29 GCI items. Results showed factor loadings ranging from .234 to .828 Table . The modr was used to calculate correlations between the subscales of the GCI and the report marks (between 1 and 10). We found that repression had a negative correlation with the statement ‘the rules at the ward’ . Positive correlations were found between support and ‘the support you receive from sociotherapists’ , growth and ‘what you learn here’  and atmosphere and ‘the atmosphere at the ward’   Table . All corThe GCI was found to be internally consistent, with alpha's ranging between .642 and .882. The ICCs for the group climate subscales and the total climate scale ranged from .193 to .385. These results indicate that a considerate proportion of variance (roughly 19–39%) can be attributed to between‐group differences (i.e. the group level). Means, standard deviations, ICCs and results of reliability coefficients in terms of Cronbach's alpha (α) and Guttman's Lambda‐2 (λ‐2) are displayed in Table et al. et al. et al. The aim of this study was to explore the psychometric properties of the GCI for individuals with MID‐BIF who resided in a (forensic) secure treatment facility. We used conventional single level CFA to examine the factor structure of the GCI. The present study provides preliminary evidence for the construct validity and reliability of the GCI for individuals with MID‐BIF. Results showed an adequate fit for a first‐order and second‐order model, which indicates construct validity of the GCI. Reliability coefficients for all scales were satisfactory. The support subscale loaded highest on the overall group climate scale, which indicates that support is the most important indicator of group climate for individuals with MID‐BIF. One item of the repression subscale (i.e. ‘Clients must ask permission for everything’) did not load significantly on the repression factor as a result of which it was deleted from the model to improve model fit. This finding may be related to the fact that the repression subscale had relatively lower loadings on the overall climate scale but also to the heterogeneity among the items in order to adequately capture the multifaceted nature of the construct scale scores of the GCI. Future studies should examine possible differences in perceived living group climate between different subgroups, addressing within‐group  and between‐group  variables. It cannot be ruled out that the participants did not understand some of the questions. However, because there were no dropouts and no missing data, we believe this did not influence our results. In order to keep the level of interviewing as high as possible, monthly meetings were organised to align with all interviewers how to present and explain information to participants unambiguously. Neither the possibility of socially desirable answers can be excluded. Consistency in answering patterns, the fact that the questionnaire contains both positively and negatively formulated items and interviewers were not in any way involved in treatment, suggests that the influence of social desirability was minimised.et al. et al. et al. et al. et al. A further limitation is that we used a single item measure to assess convergent validity of the GCI. This may yield biased results, because the statements corresponding to the subscales may not capture all relevant aspects of the different factors of living group climate. Future studies should assess convergent validity of the GCI with a validated GCI, such as the EssenCES  of the Radboud University (ECSW2017‐3001‐471).The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.This research was funded in part by aNWOSia‐Raak grant: ‘Met kleine stapjes maar wel vooruit’.


There is strong evidence for a phylogenetic signal in the degree to which species share co‐evolved biotic partners and in the outcomes of biotic interactions. This implies there should be a phylogenetic signal in the outcome of feedbacks between plants and the soil microbiota they cultivate. However, attempts to identify a phylogenetic signal in plant–soil feedbacks have produced mixed results.Here we clarify how phylogenetic signals could arise in plant–soil feedbacks and use a recent compilation of data from feedback experiments to identify: whether there is a phylogenetic signal in the outcome of plant–soil feedbacks; and whether any signal arises through directional or divergent changes in feedback outcomes with evolutionary time.We find strong evidence for a divergent phylogenetic signal in feedback outcomes. Distantly related plant species show more divergent responses to each other’s soil microbiota compared with closely related plant species. The pattern of divergence implies occasional co‐evolutionary shifts in how plants interact with soil microbiota, with strongly contrasting feedback responses among some plant lineages.Our results highlight that it is difficult to predict feedback outcomes from phylogeny alone, other than to say that more closely related species tend to have more similar responses. A direcet al., et al., et al., et al., et al., et al., et al., et al., Evidence for a directional trend is mixed. Studies have variously found that species perform better, worse or much the same when exposed to soil microbiota cultivated by close compared to distant relatives : logeA1B1 and logeA2B2, where A1 is the biomass of species A when grown with soil community 1. The log transformation ensures that a proportional difference in biomass has the same magnitude whether positive or negative.Consider two soil microbial communities, 1 and 2. If we conduct a pairwise experiment where we grow two plant species, A by 20% relative to soil community 1, we expect the same proportional reduction (or increase) in biomass for species B if it responds the same way as species A. Because we measure relative differences, this holds regardless of any absolute difference in biomass between species A and B, and independent of the origin of the two soil communities. A difference in the log ratios indicates that plant species differ in their response to the two soil communities, with the magnitude of difference a measure of the dissimilarity in response: a large difference indicates a more divergent response. For example, relative to soil community 1, if the net effect of soil community 2 is to reduce the biomass of species A by 20% but reduce the biomass of species B by 10%, the difference in log ratio is 0.12. If the difference in response is more pronounced, such that the net effect of soil community 2 is to reduce the biomass of species A by 20% but increase the biomass of species B by 30% relative to soil community 1, the difference in log ratio increases to 0.49.If the two species respond in the same way to the different soil communities, we expect the two log ratios to be equal. That is, if the net effect of soil community 2 is to reduce (or increase) the biomass of species r, to soil microbiota cultivated by one species relative to that cultivated by the other as the difference in the log ratios of the biomass of species A and B in association with each soil microbiota:Pairwise feedback experiments measure the performance of two species in association with their own soil microbiota and with the microbiota of the other species. We can calculate the dissimilarity in response, aA is the biomass of species A grown with its associated soil community a. The log transformation ensures that the magnitude of r is the same regardless of which species is chosen as the numerator and which as the denominator. However, for whichever species is chosen as the numerator, how we interpret the direction of r (positive or negative) depends on whether the numerator in the left‐hand log ratio denotes performance in conspecific . Specifying the numerator in the left‐hand log ratio as performance in conspecific soil means that a positive value of r is associated with species performing better overall in conspecific relative to heterospecific soil, which is the usual definition of a positive soil feedback. Equation 1 is the dissimilarity measure that Crawford et al. (r increasingly diverge from zero with greater phylogenetic distance in a consistent positive or negative direction) and a divergent trend in feedback outcomes , and quantify the pattern of divergence.where d et al.  analysedr, to test for a phylogenetic signal in plant–soil feedbacks by comparing the fit of six models to the data, with the different models specifying different types of phylogenetic signal , using Eqn σrij2, of each ijr using the formula in Crawford et al. .where σ2+ktj, where k measures the rate of change in the variance of rj∗ with increasing phylogenetic distance.Model 3 , gradual change coupled with occasional major co‐evolutionary shifts in some lineages could constrain feedback responses, leading to a more peaked distribution with more extreme outcomes, and thus heavier tails, than captured by a normal distribution , scale parameter s2 and parameter υ controlling the degree of kurtosis, with smaller values of υ implying a more heavy‐tailed distribution  with higher variance. We allowed the kurtosis, and hence the variance, to change with phylogenetic distance, specified by rate parameter k, with smaller values of k indicating a more heavy‐tailed distribution with higher variance:Model 5 Fig.  specifieModel 6 Fig.  was the r appears to increase among species pairs separated by more than 300 million years , models 5 and 6 were the best performing, with both having similar predictive accuracy. Models 5 and 6 specified heavy‐tailed distributions and, for model 6, the parameter estimates identified a clear negative directional trend  in model 7 was substantially less than in model 6, with 95% credible intervals that overlapped zero  and Ericaceae (heaths) form specialized associations with orchid and ericoid mycorrhizal fungi, Fabaceae (legumes) rely on soil bacteria (rhizobia) for nitrogen fixation, and Poaceae (grasses) cultivate distinct microbial communities and are more responsive to those communities than other life‐forms  should be directed to the Notes S1 R code used to perform the analyses and draw the figures.Click here for additional data file.


Brass wind instruments with long sections of cylindrical pipe, such as trumpets and trombones, sound “brassy” when played at a fortissimo level due to the generation of a shock front in the instrument. It has been suggested that these shock fronts may increase the spread of COVID-19 by propelling respiratory particles containing the SARS-CoV-2 virus several meters due to particle entrainment in the low pressure area behind the shocks. To determine the likelihood of this occurring, fluorescent particles, ranging in size from 10–50 μm, were dropped into the shock regions produced by a trombone, a trumpet, and a shock tube. Preliminary results indicate that propagation of small airborne particles by the shock fronts radiating from brass wind instruments is unlikely. I.The current Centers for Disease Control guidelines recommend maintaining a distance of approximately 2 m between people to reduce the spread of SARS-CoV-2, the virus that is responsible for COVID-19.In many regions of the world the entertainment industry is a major contributor to the local economy, and as the entertainment industry begins to function again one of the primary beneficiaries will be musicians. Although musicians are critical to the economies of many communities, there appears to be little research into the requirements for effective distancing from musical instruments to reduce the spread of disease. In many cases, the requirement of maintaining approximately 2 m between people will likely be adequate. However, the amount of air exhaled from brass instruments, combined with the known nonlinear effects that cause wave steepening, indicates the possibility that some members of the brass wind family of instruments have the potential to spread an airborne virus significantly farther. Additionally, it is possible that exhaled droplets emitted by the musician or anyone in the vicinity of the instrument may be propagated much further than a few meters due to the shock wave exiting the bell of these instruments, as suggested by recent studies on particle entrainment in shock waves.7 m/s2, with terminal velocities exceeding 150 m/s for shock fronts produced by Mach numbers as small as 1.2.It has been known for many years that when brass wind instruments with long cylindrical sections of pipe are played at a high volume, a shock wave is created inside the instrument that is subsequently radiated into the air. This shock wave is responsible for the “brassy” sound produced by trumpets and trombones. The presence of the shock wave from these instruments has been verified by direct observation,In the event of a single passing shock front, the displacement of a particle will be on the order of millimeters. However, in the case of brass wind instruments, the shock wave is not a single event. Rather, it is a series of shock fronts occurring hundreds or thousands of times per second. Under these conditions an estimated travel of several meters is not unreasonable, given that it has been shown that even weak spherical shock fronts can persist several hundred meters from the source.It is also known that large water droplets can be broken into smaller droplets when a shock wave passes.II.To determine if it is necessary to revise the physical distancing requirements for trumpets and trombones during performance, we attached a compression driver to the mouthpiece of a trombone with the slide fully extended. The extension of the slide ensured that the maximum cylindrical tubing was available to create the shock front. The input to the trombone mouthpiece was sinusoidal, and the frequency was varied between 500 Hz and 2.5 kHz, with the sound pressure level (SPL) measured 1 m from the bell varying from approximately 110 dB at 500 Hz to approximately 119 dB at 2.5 kHz. The radiated pressure at 2 kHz is shown as a function of time in Fig. The axis of the trombone was oriented parallel to the floor, with the center of the bell approximately 80 cm above it. The bell and floor area were enclosed in a wooden box to ensure that ambient air currents did not affect the results. The floor was covered in black paper. Fluorescent chalk dust was dropped approximately 10 cm in front of the bell to simulate the presence of small aspirated particles. The particles were dropped from approximately 20 cm above the bell, both by hand and through a piece of filter paper with an approximately 1 mm diameter hole. The method of dropping the particles did not affect the results.μm. In addition to the chalk dust, strontium aluminate crystals impregnated with fluorescent dye were used to study the effects on larger particles. The particle sizes of the crystals were stated by the manufacturer as ranging from 15–50 μm; however, we found no crystals smaller than 20 ± 3 μm. It is believed that the transmission of COVID-19 occurs primarily through drops with diameter greater than 5 μm;The sizes of the particles were measured on the floor after the experiment using a microscope with a calibrated reticle. The smallest particles were measured to be 10 ± 3 Strontium aluminate and chalk dust both have sizes commensurate with aspirant particles; however, aspirant droplets are largely made of water, and the densities of both are significantly different than water. The specific gravity of strontium aluminate is approximately 3.5 and chalk dust has a specific gravity of approximately 2.5. The difference in mass between these substances and water droplets will affect the time the particle is suspended in the air as well as any acceleration by the shock front. The time that the particles are suspended in the air is a function of the atmospheric drag, which, given the sizes of the particles, is assumed to be linear. Assuming a spherical particle, the terminal velocity is approximately proportional to the density. The horizontal distance traveled is also linearly proportional to the density. Therefore, we assume that any asymmetric propagation observed due to the shock front must be multiplied by the square of the specific gravity to approximate the result achieved for an aspirant particle. This is likely an overestimation due to the non-spherical nature of chalk and strontium aluminate crystals, but a factor of 12 is probably a reasonable upper bound.The chalk dust and crystals were each dropped while the signal from the compression driver produced oscillations in the air column within the trombone. The compression driver was driven by a sine wave from a function generator, and the frequency was varied by hand between 100 Hz and 2.5 kHz while observing the falling particles. Additionally, the signal was held constant at 500 Hz, and 1, 2 and 2.5 kHz for several seconds. Following the experiments, the paper under the trombone bell was examined using an ultraviolet light to determine the extent of the dispersion of the particles. The ultraviolet light caused the particles to fluoresce, allowing easy identification as well as the ability to distinguish the particles of interest from dust and dirt. A typical dispersion pattern of the chalk dust is shown in Fig. Although there was some small dispersion of the particles below the bell, resulting from random drift attributable to the non-spherical nature of the particles, and possibly some slight initial off-axis velocity of the particles when they were dropped, it was consistent with what was observed when particles were dropped in front of the bell without the shock fronts present. The maximum extent of the particles from the center of the pattern was approximately 10 cm in both cases, representing a symmetrical 6° cone from the point at which they were dropped. In no case did the pattern exhibit a measurable preference for directionality. Similar experiments with a trumpet produced the same result.et al.et al.The waveform shown in Fig. et al.To produce a shock front more similar to that produced during musical performance, the experiments described above were repeated with the compression driver attached to a 3.1 m long cylindrical pipe. Although the input signal is different from that produced by lips, and the bore shape is different from a trombone, the radiated waveform is similar. The waveform from the cylindrical pipe produced by a 2 kHz input sine wave is shown in Fig. The pipe was 25.4 mm in diameter and the shock front was driven by the compression driver at several frequencies between 1 and 2.5 kHz. The SPL measured 1 m from the end of the pipe was approximately 107 dB between 1 and 2 kHz, and 112 dB at 2.5 kHz.The results of the experiments using the shock tube were similar to the results found when the trombone was used. We could find no difference between the dispersion pattern of the particles on the floor in front of the pipe when the shock wave was present and when there was no sound. It is interesting to note that when chalk dust was placed inside the pipe near the open end, particles were expelled from the pipe when the driving frequency matched an acoustic resonance of the pipe, as one might expect. At resonance frequencies above 1 kHz a significant amount of chalk dust was expelled, but the forward motion was not maintained after leaving the pipe. Typically, the chalk dust stopped its horizontal motion within approximately 5 cm and fell onto the floor without traveling any farther.III.et al.The lack of propagation of small particles by the shock front is probably due to the fact that the shock is relatively weak. It is proposed by PrestridgeIt will be necessary to complete several experiments with professional musicians under controlled conditions to conclusively demonstrate that there is no significant propagation of small particles due to the shock fronts produced by cylindrical brass wind instruments during performance. The role of the air flow exiting the instrument and how it interacts with the shock front is also unknown and needs to be carefully investigated, as does the appropriateness of applying the theory proposed by PrestridgeThe results of these experiments indicate that the danger of spreading SARS-CoV-2 by brass wind musicians due to the presence of the radiated shock front is probably minimal. Further research is required to conclusively demonstrate this is true during performance; however, based on the results of the experiments described here, we find no justification for imposing more stringent distancing requirements for brass wind players simply due to the presence of the shock waves produced during loud play. To our knowledge, however, the appropriate distancing requirements that will reduce the spread of COVID-19 has yet to be determined for any musical instrument.


The most frequently encountered clinical situation, either in the maxillary or mandibular arch, is of a missing first premolar and first molar, where the canine and the second molar are known as terminal abutments and second premolar is a pier abutment. This clinical situation poses challenge to prosthodontist in rehabilitation phase. It has been postulated that terminal abutment has a rocking movement when in function, whereas pier abutment acts as a fulcrum. This will lead to debonding of the less retentive terminal retainer. In order to overcome this, utilization of nonrigid connectors has been advised. This paper presents a clinical case report which describes incorporation of nonrigid connector to rehabilitate pier abutment case. There are some important variables that are responsible for the longevity of a fixed partial denture (FPD) and its abutments such as proper occlusion, span length, bone loss surrounding the abutment, and the quality of the periodontium . ExcessiA FPD with the pontic rigidly fixed to the retainer provides adequate strength and stability to the prosthesis and also minimizes the stresses associated with the restoration. But if an edentulous space occurs on both sides of a tooth, creating a pier abutment then physiologic tooth movement, arch position of the abutment, and a difference in the retentive capacity of the retainers can make a five-unit FPD a less than ideal for the treatment .Biomechanical factors such as overload, leverage, torque, and flexing induce abnormal stress concentration in a FPD . Stress Rigid connectors are made by different procedures such as casting, soldering, and welding. The cast connectors are to be properly shaped in wax patterns. The connector that permits limited movement between the otherwise, independent members of the FPDs is the nonrigid connector. The nonrigid connector could be made by incorporation of prefabricated inserts, by use of a custom-milling machine, or by use of the prefabricated plastic patterns . The purA 43-year-old male patient reported to the Department of Prosthodontics in our dental college in Chennai, India with a chief complaint of missing teeth in the right upper back tooth region for a duration of six months. He also complained of difficulty in mastication as well as esthetic problem. There is no relevant past medical history and past dental history revealed that the patient had undergone extraction of the badly carious tooth in left maxillary first premolar and first molar three months back. On intraoral examination we found missing left maxillary first premolar and first molar with second premolar acting as a pier abutment Figure .Different treatment options were discussed with the patient, and with the patient’s consent, we decided to rehabilitate the edentulous space using nonrigid connector in the distal aspect of the pier abutment. The following clinical steps were carried out for oral rehabilitation. The patient preferred metal ceramic restoration. Hence tooth preparation was done on left maxillary canine, second premolar, and second molar Figure  with equThe gingival retraction was done with gingival retraction cord and final impression was made using elastomeric impression material with two-stage putty wash technique. Interocclusal record was made using bite registration material to obtain good occlusion of the patient. Provisional temporary restoration was given using tooth colored auto polymerizing resin and was cemented using noneugenol temporary cement. Type IV dental stone was used to pour cast. After the material was completely set, it was retrieved and die cutting was done and the die pins were placed. Master cast was then mounted on an articulator with the help of interocclusal record.Wax pattern Figure  was fabr Surveying was done using Ney surveyor which helps in checking the position and parallelism of female and male component placed within the contours of the pier abutment. Male pattern was later retrieved from the female pattern, so that the female pattern is free of wax in the recess area. After the casting is recovered from the investment, the mandrel and any excess on the top portion of the key are carefully reduced so the key and keyway were in flush with each other.After casting, metal trimming was done and extension of female pattern was cut accordingly. Later metal try-in of the anterior segment  was done in the laboratory Figure  in the wThe posterior segment  was seated in the casted female portion, then wax pattern was fabricated for left maxillary first molar and second molar. Similar casting procedures were carried out Figure  and the Both male and female portions were placed and the metal fit was checked in the lab Figure .Metal try-in of both anterior and posterior segment was done clinically to verify properly the marginal fit of the restoration Figure . Then itCompleted five-unit FPD is shown in Figure Clinically, three-unit segment containing the left maxillary canine, first premolar, and second premolar (pier abutment) with keyway on its distal aspect was cemented Figure  first foThen the excess cement is removed and the occlusion was verified using articulating paper. The patient was advised to use dental floss regularly to maintain good oral hygiene and interdental brush if needed. The patient was reviewed after one week Figure  to furthThe existence of pier abutment promotes a fulcrum-like situation that can cause the weakest of the terminal abutments to fail and may cause the intrusion of a pier abutment . Shear sThe rigid and nonrigid connectors can increase the lifespan of an abutment in the five-unit FPD as it transfers less stress on the abutments. Further it allows physiologic tooth movement and eliminates any hindrance against a fixed restoration with all rigid connectors . FPDs arNonrigid connectors are indicated in places where there is existence of pier abutment, and in cases like misaligned abutment where the use of intracoral attachments as connectors is indicated . It is a Nonrigid connectors are contraindicated when there is significant mobility in abutment teeth where splinting also cannot produce good prognosis . It is aAccording to literature there are four types of nonrigid connectors such as Tenon-Mortise type connectors, Cross-pin and Wing type connector, Split type connector, and Loop type connector. The most widely used type is Tenon-Mortise type where accurate position of the Mortise is technique sensitive as it has to establish the parallelism for accurate path of withdrawal of a distal retainer. Selection of the right type of connector makes the difference between success and failure of the restoration . Under cThough nonrigid connectors have the advantage of minimizing the torquing forces and shear stresses, they have a disadvantage also. They involve more tooth reduction clinically in order to facilitate engagement of male and female components. It is more technique sensitive and requires precision in work. When there is no occlusal stability, lifting of key from key is also reported. Shillinburg and Fischer in 1973 suggested that patrix should be prepared within the contours of the retainer and matrix attached to the distal of pontic. The pier abutment should have the keyway of the connector in the distal and the key should be placed on the mesial of the distal most pontic. If the keyway of the connector is placed on the distal side of the pier abutment, mesial movements seats the key into the keyway . Oruc etGill in 1952 suggested that each tooth must function both as an individual and as a part of a collective unit which makes each tooth an important factor in the function of the entire mouth . Adams iThus the factors such as physiologic tooth movement, arch position of the abutments, and retentive capacity of retainers make the rigid connectors a less ideal plan of treatment in cases involving pier abutments particularly five-unit bridges. Broken-stress measures serve as “safety valves” against the tremendous leverage forces created by the rigid attachment to two or more teeth. The size, shape, and type of connectors also play a key role in future success of a FPD. The employment of nonrigid connector increases the life of the restoration as it minimizes the stresses on the abutments. The stress distributions and values of a FPD and a pier abutment are affected by the location of nonrigid connector. Apart from the advantages of nonrigid connectors, the increased laboratory time and expense should be ignored while considering the augmented life of the restoration.