Localization is an essential requirement in the increasing prevalence of wireless sensor network (WSN) applications. Reducing the computational complexity, communication overhead in WSN localization is of paramount importance in order to prolong the lifetime of the energy-limited sensor nodes and improve localization performance. This paper proposes an effective Cuckoo Search (CS) algorithm for node localization. Based on the modification of step size, this approach enables the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability for avoiding local convergence. Further, the approach restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. Extensive experiments were conducted to study the effects of parameters like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. In addition, a comparative study was conducted to realize the same localization task using the same network deployment. Experimental results prove that the proposed CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and Particle Swarm Optimization (PSO) algorithm. A wireless sensor network (WSN) is a self-organization network composed of a large number of small-size, low-cost sensor nodes which can monitor physical or environmental condition . Recent Traditionally, each sensor node can be localized by using the BeiDou Navigation Satellite System (BDS) or Global Positioning System (GPS). However, practical considerations such as cost, power consumption and the volume of BDS/GPS receivers make it impossible for the use of BDS/GPS on each sensor node, especially for wide-range WSN. For example, sensor nodes are typically powered by small batteries, which generally cannot be easily changed or recharged. In addition, WSN is commonly deployed in the urban canyon or indoors where the transmission of the satellite signals is adversely blocked .In recent years, more and more researchers tried to overcome the drawbacks by proposing various localization methods. Generally WSN node localization process can be illustrated using As presented in A detailed survey of node localization in WSN has been reported in the literature ,13. NicuIn recent years meta-heuristic algorithms gradually play an important role in engineering optimization ,21. ThisIn 2009, Cuckoo Search (CS) as a highly potential meta-heuristic algorithm was proposed by Xin-She Yang of Cambridge University and Suash Deb of C.V. Raman College of Engineering. Recent work ,36 has fMotivated by the above observations, in this paper, we propose a new modified CS algorithm to obtain positioning results with high precision and rapid convergence. The proposed CS algorithm adopts the modified step size to enable the population to approach global optimal solution rapidly. In addition, to enhance population diversity and thereby avoid local convergence, the fitness of each solution is employed to build mutation probability that determines the chance of it being found and replaced by new random solution. Finally, the modified CS algorithm restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. The modified CS algorithm is studied on the effects of parameters like anchor density, node density and communication range with respect to average localization error and localization success ratio. Experimental results prove that the modified CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and PSO algorithm.The rest of the paper is organized as follows: (1)Each cuckoo lays one egg at a time, and dumps its egg in a randomly chosen nest;(2)The best nests with high-quality eggs will be carried over to the next generations;(3)The number of available host nests is fixed, and the egg laid by a cuckoo is discovered by the host bird with a probability Cuckoo Search , as one of the latest nature-inspired search algorithm, was proposed in 2009 by Xin-She Yang of Cambridge University and Suash Deb of C.V. Raman College of Engineering. CS is based on the brood parasitism of some cuckoo species. To apply CS as an optimization tool, Yang and Deb described the CS by three ideal rules :(1)EachAs a further approximation, this last assumption can be approximated by a fraction For the implementation point of view, we can generate new solutions (cuckoos) by two methods: Firstly, the u and v are drawn from normal distributions as followings:The step length of random walk formed by Briefly speaking, the step length essentially forms a random walk process with a power-law step-length distribution with a heavy tail. Some of the new solutions should be generated by In addition, a fraction In standard CS algorithm, the efficiency of searching global best solution is mainly guided by step size control factor lization ,40. TherOne aspect is the modification about step size Based on the above analysis, we propose that step size As expressed in the Equation (6), step size The other aspect, we employ the fitness of solutions to build the mutation probability According to the standard CS algorithm, mutation probability The above analysis is taken as the basis for the following adjustment about mutation probability From the Equation (7), it can be seen that the fitness of the solution is adopted to adjust Algorithm 1. Modified CS algorithm1. Begin2. Generate initial population of n nests (solutions) 1, x2, …, xd);3. Define objective function f(x); x = (x4. Set the range of 5. Set the range of the nest(solution): 6. Set the maximum number of iterations: N_itertotal7. For all 8. Calculate the fitness 9. End For10. N_iter = 111. While  do12. For all 13. Compute the step size for 14. Generate a new cuckoo 40. End For41. Rank all the solutions and find the current best42. End While43. EndBesides that, the population is restricted in the certain range so that it can prevent the energy consumption caused by meaningless search. That is to say, when the solutions are outside the range, we use the range maximum or minimum to replace them. A modified CS algorithm is proposed herein relying on the above several aspects to find the global optimum as efficiently as possible. The detailed steps about realization of the modified CS algorithm are described in Algorithm 1.The objective of WSN localization is to estimate the coordinates of N unknown nodes based on M anchor nodes. Assuming that all sensor nodes are deployed in a two-dimensional sensor field. The WSN node localization process based on modified CS algorithm is shown in the Step 1:M anchor nodes and N unknown nodes are randomly deployed in a sensor field. The communication range for each sensor node is set to R.Step 2:Anchor nodes broadcast their locations frequently.Step 3:If   ,10, as sThe measured distance As presented in Step 4:Establishing the objective function Step 5:The unknown nodes that get localized will act as anchors in the next iteration, thus the number of anchors increase along with the iteration progress.Step 6:Step 2~Step 5 are conducted repeatedly until no unknown nodes can be localized or termination conditions are reached.Step 7:Computing the average localization error. The average localization error is defined as the average Euclidean distance between the real and estimated locations of sensor nodes, thus average localization error can be calculated via the following Equation (12).In this section, all the simulation experiments are carried out by using MATLAB. The performance of the modified CS algorithm is assessed from the effects of several factors, such as anchor density, communication range, node density in terms of average localization error, localization success ratio and a comparative study with standard CS algorithm and PSO algorithm is conducted to realize the same localization task using the same network deployment. Average localization error is computed using Equation (12) in Localization success ratio, which is defined as the number of unknown nodes which successfully acquire their locations over the total number of sensor nodes whose locations is unknown, is calculated using the following Equation (13). The higher localization success ratio is, the better the localization performance is.2 two-dimensional sensor area using a continuous uniform distribution pseudo-random generator. The total number of sensor nodes, namely node density, is set to be 100, 200, 300 and 400. Anchor nodes account for 10%~60% of all sensor nodes. Anchor nodes are deployed randomly. It is assumed that there is no localization error for anchor nodes. Communication range for all sensor nodes is set to be 10 m~50 m. To eliminate the effects of randomness of topology generation and bio-inspired algorithm, each data point is averaged over 10 different test network and each result for a kind of network topology is averaged by running 30 times repeatedly. Extensive experiments have been conducted to design the parameters in the proposed CS algorithm and Experimental results demonstrate that higher localization precision with less iterations can be achieved when the parameters setting of the proposed CS algorithm are as follows, 2 two-dimensional sensor area, All the sensor nodes are randomly deployed in 100 × 100 mAnchor density is an important parameter affecting the localization performance and cost for WSN. In this subsection, the effects of anchor density on localization performance are evaluated. Anchor ratio is set to be 10%, 20%, 30%, 40%, 50% and 60% of all sensor nodes. The communication range of each sensor node is 25 m.In The simulation results in Communication range is another important parameter determining localization performance and energy consumption of sensor nodes. The impact of communication range on the proposed CS algorithm in terms of average localization error under different node density is shown in 2 sensor field. Anchors are specially deployed in the four corner of sensor area to avoid the collinear problem like the literature [To evaluate the effectiveness of modified CS algorithm, a comparative study was carried out with standard CS algorithm in this subsection. The parameters for standard CS algorithm are set (according to the literature ) as follterature . Their c(1)Population size = 20;(2)Acceleration constants c1 = c2 = 2;(3)Inertia weight w = 0.7;(4)Limits on particle velocity: It has been reported in previous work ,41,42 thTo assess the convergence speed to a minimum average localization error, each algorithm is conducted for thirty iterations. The average localization error with respect to iterations is shown in In this paper, we propose a modified CS algorithm for optimizing node localization in WSN. The algorithm adopts the modified step size to enable the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability to avoid local convergence. In addition, to prevent the energy consumption caused by insignificant search, the approach restricts the population in the certain range. Extensive experiments have been performed to study the impacts of several factors like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. This provides the basis for optimizing node localization using the proposed algorithm in practical WSN applications. Additionally, a comparative study has been conducted and experimental results prove that when compared with standard CS and PSO algorithm, the modified CS algorithm performs better in terms of reducing average localization error and increasing the convergence rate, which is favorable to reduce computational consumption and thus prolong the lifetime of sensor nodes. The effectiveness of the modified CS algorithm was verified through simulation results. In the future, we will focus on designing an experiment system to test the modified CS algorithm in practical applications.


The topology of the patient flow network in a hospital is complex, comprising hundreds of overlapping patient journeys, and is a determinant of operational efficiency. To understand the network architecture of patient flow, we performed a data-driven network analysis of patient flow through two acute hospital sites of King’s College Hospital NHS Foundation Trust. Administration databases were queried for all intra-hospital patient transfers in an 18-month period and modelled as a dynamic weighted directed graph. A ‘core’ subnetwork containing only 13–17% of all edges channelled 83–90% of the patient flow, while an ‘ephemeral’ network constituted the remainder. Unsupervised cluster analysis and differential network analysis identified sub-networks where traffic is most associated with A&E performance. Increased flow to clinical decision units was associated with the best A&E performance in both sites. The component analysis also detected a weekend effect on patient transfers which was not associated with performance. We have performed the first data-driven hypothesis-free analysis of patient flow which can enhance understanding of whole healthcare systems. Such analysis can drive transformation in healthcare as it has in industries such as manufacturing. Patient flow through a hospital is a key determinant of efficient hospital functioning. Previous studies have focused on linear flow processes, queuing and single patient pathways ,2 therefA hospital can be considered as a network of wards that are connected when patients are transferred between them. By modelling it this way, we sought to understand both the spatial and temporal nature of network activity to help determine where and how bottlenecks form during periods of high activity. Due the complexity of the network, it was difficult to visualise network dynamics changing over time and so we took a network science approach to achieve a global, high-level perspective on hospital network dynamics.The National Health Service (NHS) England's Five Year Forward View proposed that IT will be an essential tool for transforming the NHS . As histAccident and Emergency department (A&E) performance against the UK’s 4-hour waiting time target is a key metric used to assess hospitals, and patient flow through the hospital has been attributed to be a contributing factor . To undeData was derived from the patient administration system (PAS) and the electronic patient record at both acute sites of King’s College Hospital NHS Foundation Trust . The PRUH-site deployed the iSoft i.PM software system  in November 2014 thereby introducing a single unified PAS across both acute sites. Identifiable information on patients was not accessed by any authors. The patient transfer data analysed in this study was exported without any identifiable information from the PAS at Kings College Hospital. There is no potentially identifiable information in the transferred dataset, and identification of individuals would only be possible by linking the data with the original PAS dataset or an external dataset. The dataset cannot be made available without specific information governance approval on a case-by-case basis.A database query was performed to extract all A&E attendances and patient ward transfers between the period of 01/12/2014 and 01/07/2016. Only transfers for non-elective patients admitted via an A&E department at DH or PRUH were included.The project was reviewed by the King’s College Hospital Information Governance committee chaired by the Caldicott Guardian  and approval was granted in June 2016 with continued oversight. The basis of secondary use was analysis for service improvement, operational performance and clinical audit.Patient transfers were modelled as a weighted directed graph ,7 in whie1,e2,…,ek with edge weights w1,w2,…,wk, the edge weights are perfectly balanced if i ≤ k. The observed deviation from the balanced situation normalised to the balanced weight is given by Eq  edges.Unsupervised clustering with principal component analysis was performed in R version 3.2.4  with thewilliam.bernal@nhs.net) subject to local information governance regulatory frameworks.Weekly aggregated transfer data are available in The patient transfers dataset contains 194,410 transfers for 78,400 hospital admissions  over 578 days. The weighted, directed network of all transfers analysed for the DH site contains of 78 nodes (wards) and 1531 edges, and the PRUH site has 72 nodes and 921 edges. The total number of transfers through each edge range from 1 to 11,775 in DH and 1 to 9,353 in PRUH.From the transfer data, it is possible to reconstruct the specific path from admission to exit for each individual patient in the flow network. Many commonly used network statistics are based on the calculation of shortest paths through the network, however in this case shortest paths are not meaningful as the path a patient takes through the network is determined by the requirements of their treatment. We therefore considered the unique observed sequences of wards. Considering only those patients whose entire visit was spent in a single hospital site, there were more unique sequences of wards visited by patients in the DH site (3253) than the PRUH (2263) and the distribution of patient flow over these paths is highly uneven in both sites. Only the top 11 (DH) or 12 (PRUH) most common sequences are required to reach 50% of all visits, corresponding to 0.34% (DH) and 0.53% (PRUH) of all observed sequences of wards. In both sites the single most frequent path was an equivalent sequence from the emergency ward to a clinical decision unit and finally to exit, accounting for 21% of all visits in each case.Transfer data was integrated per calendar month for each of the 19 months included in this study to capture medium-term stability of the network. The average monthly network size for DH is 919 edges (SD 60.4) and 63 nodes (SD 2.08). The PRUH networks have 592 (SD 45.4) edges and 46 (SD 2.50) nodes.In the flow network node in-degree represents the number of other wards sending patients into a ward and out-degree represents the number of other wards patients are sent to. The difference between the in- and out-degree of a node was used as an indication of the structural role that a ward plays in the distribution of flow throughout the network and whether this changes over time . The difFrom the degree difference alone, there is no way to know whether these are high or low degree nodes. th (PRUH) and 6th (DH) lowest output variabilities.The patient flow network is a weighted network. We defined a weight variability score for a node see  that quaEdges that are consistently present over time are likely to be important to the overall functioning of a hospital, in addition to just those edges with the greatest weight. These “core” edges were defined as any edges present in all the monthly transfer networks for each site. For PRUH this leaves a subnetwork with an average of only 16.8% (SD 1.38%) of all edges per month, which accounts for 89.7% (SD 1.37%) of monthly transfers. A similar result was found for DH, where the core edges are 12.6% (SD 0.89%) of the full network on average, but support 83.4% (SD 1.54%) of all transfers. This is consistent with the above finding that that the input and output edge weights of all nodes are unevenly distributed. Therefore, although the monthly networks for both sites are quite large, a small and consistent portion of the network is sustaining the bulk of the transfer activity and is likely to be critical to maintaining patient flow.For the period considered in this study, the average A&E performance against the 4-hour waiting time was higher for DH than PRUH  A&E performance with a differential network analysis approach , focusin−3 in PRUH and 2.6 x 10−3 in DH, and with the 1-day lag of 1.32 x 10−3 in PRUH 2.13 x 10−3 in DH . Edge weights were normalised to represent the proportion of all transfers each day to allow for comparisons between daily flow networks with different numbers of admissions. As shown in nd and 4th components  is correlated with whether the day is a weekday or weekend  but doesIn the PCA analysis for PRUH, the first component (1.89% of variance) did not separate A&E performance or weekday vs weekend. There is insufficient information from our dataset to understand what this PC1-PRUH component reflects but the loadings on surgical wards raises the possibility that it relates to clinical activities specific to surgical wards (e.g. elective surgery pathways interacting with non-elective surgery pathways).We have performed a completely data-driven analysis of patient flow using modern graph theory analysis, which we believe to be the first to be done in a UK healthcare system. Our analysis shows that on a monthly basis the local network structure around wards remains constant . Most waMost of the edges in the flow network lie outside the core sub-network and account for 10–16% of all transfers. These edges may constitute the pathways which have been least optimised. As this residual flow does not travel down the same set of edges every month, these ‘ephemeral’ sub-networks will be much harder to study. One hypothesis is that this flow constitutes less common clinical events that necessitate transfer between clinical areas made up of low-frequency ‘freak’ events  and ‘patient safety’ events . More complex modelling will be needed to study this ephemeral sub-network.Our analysis confirmed prior data showing that A&E attendance rate is poorly correlated with hospital performance statistics . PrincipIt is worth mentioning that caution must be taken when forming general hypotheses around these key pathways, as they may be specific to the local health economy for each site. One possibility is that they relate to the surrounding network of social care and community facilities that is not captured in our dataset. However, the importance of the Clinical Decision Unit to performance was clear in both sites for all network metrics used. The Clinical Decision Units in PRUH and DH are analogous units–they act as units for observation and triage of patients from A&E which may not require admission into hospital but no definite decision had been made yet. This result emphasises the value of dedicated observation and triage units to efficient A&E function, possibly by reducing unnecessary admissions or reducing misallocation of patients to the wrong specialties. One conclusion is that for both sites, well-functioning Clinical Decision Units should be studied in greater detail to understand how they optimise support for A&E.a priori expectations of working patterns within the hospital and in the community. Discharge rates are lower on weekends than weekdays [Unsupervised clustering showed a weekday-weekend effect (PC3) at both hospital sites, indicating a difference in how the hospital flow network functions between weekday and weekends. This finding fits with weekdays . So one However a large scale study showed that while changing the working patterns of senior healthcare staff  did increase weekend discharges from most general medical wards, it did not have any effect on elderly medicine or geriatrics wards . ElderlyIt is important to be mindful that this weekend-weekday effect looks specifically at the question of patient flow and operational efficiency rather than on patient outcomes, and while the question of weekend effect on patient safety and 30-day mortality has been a topical issue in 2016 –16, it ia priori hypothesis about patient flow on weekends, and generated hypotheses on what drives or impedes patient flow. It is also able to identify sub-networks within the hospital where patient flow is most greatly associated with hospital emergency department performance. It is likely that some of the factors we have identified with our analyses are specific to the local health and social care economy, but our technique can be easily exported to other health economies that capture such large volume of data. The approach used in this study could be extended to include dynamic network analysis, extending the data capture to the entire social care economy and including other neighbouring hospital providers, and including data at the level of the single-clinical-episode within the modelling. We envisage a scenario that by pooling Big Data in such a way, it may be possible to use in-silico simulations for accurate forecasting within an entire healthcare economy.In conclusion, this approach validated S1 Fig Mean monthly A&E performance over time for each site, as measured by the percent of patient meeting the 4 hour A&E waiting time target. (c-f) Centrality of wards over time. Here centrality is defined as the proportion of all observed paths each node lies on per month. This measure was chosen as shortest paths do not reflect actual patient trajectories. (e) this general medical ward was re-organised during Dec 2015 to try to cope with winter pressures. R-squared values are shown for the correlation between the monthly centrality of each ward and the monthly performance of the corresponding hospital A&E department. DH = Denmark Hill, PRUH = Princess Royal University Hospital, CDU = Clinical Decision Unit.(PDF)Click here for additional data file.S2 Fig A series of differential patient flow networks (comparing the High (top 10%) and Low (bottom 10%) performance groups) was created for each site by applying an increasingly large lag to the flow data e.g. using the patient flow observed 3 days prior to the days in each performance group. A lag of 0 indicates the same day, a lag of 7 is one week earlier. Networks were generated for all lag values from 0 to 14 days and the standard deviation over all edges in each network is shown. PRUH = Princess Royal University Hospital, DH = Denmark Hill.(PDF)Click here for additional data file.S1 FileThis file contains the total number of patients sent between each pair of wards for every week of the data analysed in this study after applying the pre-processing steps described in the Methods section. Only edges with weight > 0 per month are included. All transfers were aggregated per week, with weeks starting on Mondays. DH = Denmark Hill, PRUH = Princess Royal University Hospital.(CSV)Click here for additional data file.


Step kinematic variability has been characterized during gait using spatial and temporal kinematic characteristics. However, people can adopt different trajectory paths both between individuals and even within individuals at different speeds. Single point measures such as minimum toe clearance (MTC) and step length (SL) do not necessarily account for the multiple paths that the foot may take during the swing phase to reach the same foot fall endpoint. The purpose of this study was to test a step-by-step foot trajectory area (SBS-FTA) variability measure that is able to characterize sagittal plane foot trajectories of varying areas, and compare this measure against MTC and SL variability at different speeds. We hypothesize that the SBS-FTA variability would demonstrate increased variability with speed. Second, we hypothesize that SBS-FTA would have a stronger curvilinear fit compared with the CV and SD of SL and MTC. Third, we hypothesize SBS-FTA would be more responsive to change in the foot trajectory at a given speed compared to SL and MTC. Fourth, SBS-FTA variability would not strongly co-vary with SL and MTC variability measures since it represents a different construct related to foot trajectory area variability.We studied 15 nonimpaired individuals during walking at progressively faster speeds. We calculated SL, MTC, and SBS-FTA area.SBS-FTA variability increased with speed, had a stronger curvilinear fit compared with the CV and SD of SL and MTC, was more responsive at a given speed, and did not strongly co-vary with SL and MTC variability measures.SBS foot trajectory area variability was sensitive to change with faster speeds, captured a relationship that the majority of the other measures did not demonstrate, and did not co-vary strongly with other measures that are also components of the trajectory. Step kinematic variability has been characterized during gait using spatial and temporal kinematic characteristics such as step length, step height, step time, and cadence –4. PartiPast studies have demonstrated that foot trajectory variability, between individuals that walk more slowly compared to individuals that do not walk as slowly, can be an important marker for reduced CWS and increased fall risk. For example, greater variability of step kinematics is associated with decreased gait stability and increased risk of falls in fall risk populations , 13–16. T). Further, to capture differences in initial swing phase versus terminal swing, we divided that area measurement into two regions from foot off to midswing (H1), and midswing to initial foot contact (H2). The foot trajectory sub-phases between individuals are relatively consistent for healthy adults. However, we divided by these sub-phases because part of this current study is to establish a metric using healthy controls for individuals where these sub-phases are less consistent (chronic poststroke hemiparesis).We sought to capture the bi-dimensionality of foot trajectory in the sagittal plane by a curvilinear approach into an area measurement on a step-by-step basis. Therefore, the purpose of this study was to test a step-by-step foot trajectory area (SBS-FTA) variability measure that is able to characterize foot trajectories of varying areas, and compare this measure against MTC and SL variability at different speeds. The SBS-FTA variable was quantified by measuring step-by-step changes in the area under the toe marker during swing phase (HWith this paper we assessed which measure of variability was most responsive to change across a range of speeds. The types of variability responsiveness tested were SL variability and MTC variability using standard deviation (SD) and CV, and SBS-FTA variability. We hypothesize that the SBS-FTA variability would demonstrate increased variability with speed. Second, we hypothesize that SBS-FTA would have a stronger curvilinear fit compared with the CV and SD of SL and MTC. Third, we hypothesize SBS-FTA would be more responsive to change in the foot trajectory at a given speed compared to SL and MTC. Fourth, that SBS-FTA variability would not strongly co-vary with SL and MTC variability measures since it represents a different construct related to foot trajectory area variability. This can provide more information about how this endpoint control is modified by some scaling factor on a step-by-step basis, like walking speed, and may provide information that can be beneficial to study impaired populations whose endpoint control is affected.We recruited fifteen non-impaired individuals . Inclusion criteria were: older than 19 years of age, ability to walk independently, medically stable, and able to provide written informed consent. Exclusion criteria were: history of serious medical conditions, uncontrolled respiratory or metabolic disorders, uncontrolled blood pressure , presence of cerebellar and brainstem deficits, severe cognitive disorder, inability to follow simple commands, and major or acute musculoskeletal problems. This study was performed at the University of Alabama at Birmingham (UAB) and written informed consent was obtained from all participants. This study was approved by UAB’s Institutional Review Board under protocol number: F141212010. The study was conducted in accordance with the principles of the latest version of the Declaration of Helsinki. Recruitment and experimental procedures were completed by the investigators.Passive reflective markers were placed bi-laterally on the feet on the second and fifth metatarsal heads, lateral ankle malleoli, and the calcaneus and were collected by an 8 camera Qualisys motion capture system sampling at 100Hz. Marker positions were tracked using QTM 2.2 offline. Marker position data was processed via Visual3D software .This current study is a sub-analysis of data from a larger dataset that also involved participants that have chronic post-stroke hemiparesis. The larger study design involved the use of a robotic safety system to allow people, at risk for falls, to experience walking at very fast speeds without risk of harm if they were to lose their balance. The device has a fall harness applied at the waist. The KineAssist (KA) Gait and Balance Robotic SystemTM  was coupled to a Bertec treadmill (Bertec Corporation) to have similar conditions to the participants with chronic post-stroke hemiparesis. The pelvic harness of the KineAssist allowed 6-degrees of freedom at the pelvis and was used as a safety mechanism as the device would detect a loss of balance as a drop in the height of their pelvis, catching the individual in the harness system and simultaneously ceased from moving forward. The biomechanics of nonimpaired individuals walking in the KA has been described in detail in a previous study .After providing consent, the participant completed an over ground 10-meter walk test for their CWS. Then, participants were prepped for the kinematic collection and placed in the KA harness. Participants underwent a short familiarization process of walking in the KA coupled to the treadmill at comfortable speeds controlled by one of the investigators. We asked participants to decide when they felt comfortable walking in the KA and then concluded the acclimation process.We then asked people to walk at least thirty strides at a range of eleven different speeds . Order of presentation of speeds was randomized to reduce any order bias and to fulfill assumptions underlying tests for statistical inference. Rest breaks were provided as needed and, if not requested, were enforced every 5 trials.nd metatarsal head. Step kinematics of ‘foot on’ and ‘foot off’ were identified using Visual3D’s gait events algorithm using the 5th metatarsal head and calcaneus’s trajectory to isolate individual steps for analysis, and the data were visually inspected to ensure accurate representation of gait events. SL was calculated by dividing the belt speed by the step frequency on a step-by-step basis. The MTC was calculated by locating the local minima of each step during the swing phase. The coefficient of variation and standard deviation (SD) were calculated for SL and MTC for each trial as below  for any of the speeds. Trials where the individual adopted a gait other than walking were identified, and removed from analysis.1 and H2 SBS-FTA variability was calculated by bisecting the swing phase with the weighted centroid position. The weighted centroid position for each step was calculated to divide the anterior-posterior trajectory at mid-swing. The weighted centroid position was calculated by (1) locating data points from foot off to foot on, (2) the median A/P coordinate was identified and used to create the centroid position (Hposition .1) and the second half of swing (H2) were calculated. They were calculated for each half using Matlab  code for polyarea that returns the area of the polygon specified by the vertices of each data point in the fore-aft and vertical direction as seen in below  and 2) the second half of swing (H2), 3) SD of SL and 4) MTC, and 5) CV of SL and 6) MTC variability for each participant) across speed as the independent variable. The best-fit curve was determined by first fitting a linear model and then a quadratic model. If the quadratic model added a significant increase in R2 (significant F change between models), compared to the lower order model, then we chose the higher order relationship. We then compared H1 and H2 SBS-FTA, MTC, and SL changes on a step-by-step basis using paired t-tests to compare the slowest speed tested to the fastest speed (without a flight phase tested) to determine which measures had the greatest magnitude of change from a slow speed to a fast speed.Cadence and step length data were plotted against speed to determine the extent to which participants increased speed by linearly increasing step length and/or cadence and to ensure the data looked normal. Mean descriptive statistics for all measures at all speeds were calculated. We conducted simple linear and quadratic function regressions for each dependent variable , H1 SBS-FTA variability, H2 SBS-FTA variability, step length variability, and minimum toe clearance variability across the range of speeds to establish to what extent the variables were associated with each other. We used an alpha level of .05 for all statistical tests. Corrections were used for multiple comparisons where applicable. All statistical analyses were performed using SPSS v.24.We conducted Kendall Tau-b correlation analyses on total SBS-FTA variability (H2 = 0.95) and cadence and speed (R2 = 0.95).We characterized the way that each participant increased speed on the treadmill by calculating step length and cadence. Every participant exhibited strong positive linear relationships between step length and speed , CV SL had a negative linear fit, however, the quadratic model was a significantly better fit (CV SL sig. F change <0.01). SD SL did not have a significant fit for linear nor quadratic models. MTC increased monotonically but did not have a significant fit for any variability measure  measures using CV. HT, H1 and H2 had more variability relative to its mean compared to SL & MTC , we expressed relative variability magnitude for SL, MTC, HSL & MTC . H1 and 1 area magnitude was 11.9% (IQR = 8.34–14.1%), H2 area magnitude was 15.9% (IQR = 11.0–22.8%), HT area magnitude was 12.4% (IQR = 9.8–15.0%), while SL magnitude was 2.0% (IQR = 1.9–2.9%), and MTC magnitude was 4.4% (IQR = 3.1–5.8%)  .τ was to 1, the stronger the correlation [1 and H2 were strongly and positively correlated with each other (rτ >0.6), and weakly (rτ <0.4) and positively correlated with SD of SL and MTC, and CV of MTC  What does it mean that SBS-FTA increases with speed and why is it so responsive to speed? 2) Why does FTA capture the relationship with speed stronger than MTC and SL? 3) What are the implications that SBS-FTA do not strongly co-vary with SL and MTC if SL and MTC are points in the trajectory?SL variability values observed with this experiment were like those reported in the literature. For SL SD, Sekiya, Nagasaki  demonstrMTC variability values, observed with this experiment, were also similar to those reported in the literature. For MTC SD does not significantly change with increasing speed according to the model, but the mean increases monotonically with speed , 25. Sim1, foot off to midswing, the initiation of swing is important since the resulting swing trajectory is thought to be directed by initial momentum of the swing [2, midswing to foot on, the foot starts to travel with maximum horizontal velocity [What does it mean that SBS-FTA increases linearly with speed and why is it so responsive to speed? Increasing variability with speed could be viewed as walking performance becoming unstable or needing to transition to a new gait state , 27. Conhe swing . Initiathe swing ,30. For velocity  while movelocity , 33. In Why does SBS-FTA capture more variability relative to its mean compared to SL & MTC at a given speed? SBS-FTA captures the changing area under the toe marker, while SL and MTC are singular spatially tied data points that act independent of each other. SBS-FTA has more possible changes in area solely based on the number of trajectories that the foot can travel through on a step-by-step basis allowing us to observe more variability relative to a mean value. This may be indicative of observing more variability in the foot trajectory that the other measures may not capture.1 and H2 only weakly positively correlated with SD of SL and MTC and with CV of MTC indicating that the H variables appear to capture a unique construct of foot trajectory area variability. The combination of the measures not strongly covarying with H1 and H2, and H1 and H2 SBS variability increasing with speed, indicates that the numerous trajectories possible are not completely dependent on MTC nor SL.Is SBS-FTA a different construct? We hypothesized that SBS variability would not strongly co-vary with SL and MTC variability measures. We observed that SBS H1 and H2 SBS-FTA variability may be a better measure to use when examining changes in variability at faster speeds when compared to SL and MTC variability. MTC and SL provide information about points along that trajectory, but they do not provide information on the behavior of the continuous trajectory of the foot on a step-by-step basis. SBS-FTA may be a tool to acquire more sensitive information about endpoint control of the foot. Additionally, while the MTC and step length variability may be appropriate for non-impaired individuals, for individuals poststroke MTC is often not systematically identifiable and the different areas that the trajectory may generate for individuals poststroke are not accounted for [Is there a case where one of the variability measures would be better to use than the others? Hnted for , 35. Tho1 and H2 SBS foot trajectory area variability measure we used is a relatively new measure, which captured the change in area under the trajectory curve of the toe marker during the swing phase. We developed this measure in order to combine the variability of foot trajectory in both the vertical and fore-aft directions. Breaking the trajectory into halves allowed us to observe the combined fore-aft and vertical trajectory during the initial and terminal swing phases. Additionally, the data that was used for this analysis was only in the sagittal plane and did not account for medial-lateral movement. Due to the primary interest of comparing H1 and H2 SBS foot trajectory area variability to SL and MTC, we decided that it was more appropriate to utilize only sagittal plane data.There are a few study limitations that should be noted. The HSBS-FTA variability was sensitive to change with faster speeds, captured a relationship that the majority of the other measures did not demonstrate, and did not co-vary strongly with other measures that are also components of the foot trajectory. SBS-FTA foot trajectory area variability was sensitive to change with faster speeds, captured a relationship that the majority of the other measures did not demonstrate, and did not co-vary strongly with other measures that are also components of the foot trajectory. Future studies may be performed to determine if these measures may be useful in identifying people at risk for loss of control at faster speeds .


Anguilla japonica, has declined in recent decades possibly due to both anthropogenic and ocean-atmosphere factors. The potential impact of ocean circulation on the decreasing Japanese eel catches in the western North Pacific was examined based on a three-dimensional particle-tracking method, in which virtual larvae (v-larvae) were programmed to swim horizontally and vertically, in addition to being transported by ocean currents after being released in their North Equatorial Current (NEC) spawning area. Transport patterns varied among years between 1993 and 2013, and dispersion of v-larvae towards East Asia decreased in the last two decades, especially for the western Taiwan and Japan regions. In recent years, instead of entering the Kuroshio and moving towards East Asia as in the 1990s’, more v-larvae tended to enter the southern areas due to the weakening of the NEC and strengthening of subsurface southward flow near the spawning area. Changes in ocean circulation in the western Pacific appear to be caused by the weakening of subtropical and tropical wind stress curl in the past two decades. This suggests that decadal changes in ocean circulation have occurred that affect the larval migration success of the Japanese eel to their recruitment areas.Recruitment of Japanese eels,  Their adults migrate offshore to spawn over deep water and their larvae, called leptocephali have a long larval dispersal stage that varies among tropical and temperate species5. World eel recruitment has significantly declined in the past few decades6, raising concerns about what are the major causes. Overfishing and habitat loss due to human activities were considered to be one of the primary reasons causing the eel recruitment decline7. Changes in ocean circulation or oceanic conditions may also significantly affect eel recruitment15, or both types of factors have likely contributed16.Anguillid eels are widely distributed in the Indo-Pacific and North Atlantic regions where the juveniles live in freshwater and estuarine habitatsAnguilla japonica, is one of the most important eel species for fisheries and aquaculture, and it has been listed as endangered on the IUCN red list17. Among the Northern Hemisphere species of eels that have all experienced declines, the Japanese eel seems to have shown the earliest recruitment declines that started in the 1970s18. After about 2010, the annual recruitment had decreased by as much as 90% compared to eel catches in the 1960s17.The Japanese eel, 20. The NEC forms a zone of westward flow from about 10–20°N, which then bifurcates into northward flow (the Kuroshio) and southward flow (the Mindanao Current)22. The eel larvae are carried primarily by the NEC and Kuroshio toward their growth habitats in East Asia, although it is possible that some active swimming occurs5. The maturing silver eels migrate thousands of kilometers to return to their spawning area for reproduction3. The migration process of Japanese eels, however, is not well understood due to limited direct observational evidence on adult migration23 or larval dispersal24. Numerical modelling methods, therefore, have been used to simulate the potential migration paths of Japanese eels, and to evaluate their linkages to ocean-atmosphere changes26.The Japanese eel is distributed across East Asia in areas that are adjacent to the western Pacific Ocean. Their spawning area is located along the West Mariana Ridge within the westward flowing North Equatorial Current (NEC)12. Other recent modelling studies evaluated the interannual migration success of Japanese eel larvae from their spawning area to East Asia and suggested that more complex ocean circulation factors related to the NEC, Kuroshio, and eddies can impact the migration, which are connected to the Philippine-Taiwan Oscillations (PTO)15. The strength and position of the NEC appear to be important factors27, and tropical rainfall affecting the position of the salinity front that can form in the spawning area may also affect recruitment in some years27. Therefore, ocean-current and climate-related factors need further investigation using long-term simulations.Previous numerical modelling studies on Japanese eel leptocephali examined the effect of ocean circulation and ocean-atmosphere conditions on larval transport. For example, some studies suggested that the transport of larvae carried by currents from the NEC to the Kuroshio is lowest in the El Niño years compared to the non-El Niño periods28. The western North Pacific Ocean is characterized by major oceanic features, such as the NEC, the Kuroshio, the Subtropical Countercurrent (STCC), and eddies, which will likely vary during a changing climate. For example, the Kuroshio has warmed two times faster than the global warming rate in the past century29. The NEC bifurcation latitude is shifting southward in the past 60 years30. The Kuroshio along the East China Sea has gradually moved onshore31, and the Luzon Strait intrusion into the South China Sea is weakening in the last two decades32.The western Pacific Ocean is one of the most variable ocean regions, having the fastest sea level rise in the worldThe recruitment of the Japanese eel has been declining during these years even after the some of the major anthropogenic impacts have been reduced in at least some areas, so the effects of the changing ocean need further exploration. The present study investigates the potential impact of ocean circulation on the decreasing Japanese eel catches in the western North Pacific. The 22-year (1993–2013) common period for model reanalysis and annual glass eel catches is used. Note that the present work does not strictly obey the definition of the time period required to study climate effects due to the limitation of data. The long-term (1993–2013) dispersal of virtual larvae (v-larvae) in the western Pacific is simulated based on a three-dimensional (3D) particle tracking method, in which swimming behavior is included in addition to transport by ocean currents. Biological factors affecting mortality  are not considered in the present work that focuses on the effects of ocean circulation. In addition to the yearly variation that was reported by the earlier studies, a long-term trend of decreasing dispersal towards parts of East Asia is observed in our simulations. We also tentatively examine the possible causes of recent ocean circulation changes that could be connected to climate changes.25. Ocean drifter data were obtained from the Global Drifter Program (http://www.aoml.noaa.gov/phod/dac/index.php). Surface drifters released in the NEC region from May to September during two periods (1993–1997 and 2009–2013) were selected. There were 17 and 29 drifters in the early and later periods, respectively.Annual Japanese glass eel (recruitment-stage early juveniles) catch data from 1993 to 2013 for Taiwan and Japan were obtained from the Taiwan Fisheries Yearbook  and Japan Aquaculture Information News , respectively33. The model domain of JCOPE2 encompasses the western North Pacific (10.5–62°N and 108–180°E), with a horizontal resolution of 1/12° (8–9 km) and 46 vertical layers. The external forcing to drive JCOPE2 includes wind stresses and net heat/freshwater fluxes at the sea surface converted from the six-hourly atmospheric reanalysis produced by the National Centers for Environmental Prediction/National Center for Atmospheric Research. Satellite and in situ temperature and salinity data were assimilated into the model based on a three-dimension variational method33. The daily JCOPE2 reanalysis fields cover the period from January 1993 to the present. Comparison of simulated trajectories of passive particles carried by JCOPE2 and observed trajectories was performed in a previous study14, which showed the satisfactory performance of JCOPE2 in simulating the 3D circulation over the western North Pacific Ocean.The data-assimilative ocean circulation model known as the Japan Coastal Ocean Predictability Experiment 2 (JCOPE2) provides the three-dimensional currents and hydrological fields that were used in particle tracking in the present study. JCOPE2 was constructed from the Princeton Ocean Model with a generalized coordinate systemhttps://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml).The wind data used for calculating wind stress curl and Sverdrup transport was based on the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis products  particle-tracking method was used to simulate the movement of virtual eel larvae (v-larvae). The particles were carried by ocean currents in addition to having their own swimming behavior. The 3D particle-tracking scheme developed by Ohashi and Sheng39; i.e., they remain in upper surface waters at night and swim to deeper waters probably to avoid predators during the daytime. The age-dependent DVM and horizontal swimming followed a previous study40. V-larvae at night stayed at a fixed depth of 50 m. The swimming speed and daytime diving depth linearly increased with age. Swimming speed increased by 0.0006 m s−1 per day, and daytime DVM deepened by 0.75 m per day. . The maximum swimming speed was 0.14 m s−1, and the maximum daytime diving depth was 230 m to the end of the simulation. A random walk displacement was included to represent unresolved sub-grid turbulent flow and other local processes34. The estimated maximum horizontal and vertical displacements due to the random walk were 600 m and 20 m, respectively. The duration of day and night were determined by the time of sunrise (6 am) and sunset (6 pm) throughout the simulation. The swimming direction of the v-larvae was set to be the same as the local flow in the open ocean with water depths greater than 100 m as in the previous study to allow intercomparison of the results14. When the v-larvae approached the coastal and shelf waters with water depths shallower than 100 m, they were set to search for lower salinity and swim toward coastal fresher waters41. As actual eel larvae likely do not swim continuously throughout their early life history and may use a directional swimming orientation5, an additional simulation using different larval behaviors was conducted as a control to establish that larval behavior was not affecting the overall trend of decreasing transport to East Asia over time (see Discussion section).Two important swimming behaviors were considered in this study: diel vertical migration (DVM) and horizontal swimming. Eel larvae likely show DVM behavior3. The particles (v-larvae) were released at locations spread over the region of 140 to 143°E and 12 to 15°N with a separation distance of 10 km in both zonal and meridional directions, and one being released at each location. Leptocephali estimated to be 10–40 days old were found in June and July42. The releases time were set from May 1 to July 31 and each release was staggered by a time interval of 5 days during the three-month period. About 18,000 v-larvae were released each year. The Japanese eels born in summer likely reach the East China Sea and south of Japan in winter and early spring24. The migration period estimated from observations is about six to eight months. The tracking duration was set to be eight months to track v-larvae during their migration14.Numerical experiments were conducted to examine the interannual variation of v-larvae transport during a simulation period from 1993 to 2013. The release region and time of v-larvae were chosen on the basis of the observed spawning area and season based on the collections of Japanese eel eggs, newly hatched preleptocephali, and spawning-condition adults along of the southern West Mariana RidgeThe number of v-larvae reaching each of 8 designated regions Fig.  were calThe linear trend (percentage per year) for observed glass eel recruitment and simulated v-larvae dispersion referred to the 22-year (1993–2014) mean. The significance of linear trends was examined based on Mann-Kendall tests. Significance was defined as p < 0.05.The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.25, such as ocean-atmosphere conditions. In addition to year-to-year variations, a long-term decreasing trend can be seen (p < 0.01). The annual eel catches in Taiwan and Japan showed overall decreases of about 5% per year in the past two decades.The observed annual glass eel catches in Japan and Taiwan showed inter-annual fluctuations Fig. . The eelSimulated v-larvae transport paths were widely distributed in the western North Pacific during each of the 21 simulation years with some clear variations among years Fig. . The gre14. V-larvae in the Kuroshio associated regions (region 2–6) were all correlated with each other. The v-larvae entry into the areas south of Japan (region 6) was significantly associated with v-larvae variation near Taiwan (region 2 and 3), similar to the relationship observed in glass eel catches  and the western boundary current 5°N Fig. . The cor5°N Fig. . The sub5°N Fig. . The str15. Those studies and the present study examined the importance of ocean circulation to Japanese eel larvae dispersion, however we focused on the possible effect of changing winds. Our findings further suggest that changes in ocean circulation could be playing a role in the decreasing trend of glass eel catches in Japan and Taiwan using long-term simulation results. An important factor in the decreases in recruitment could be that instead of entering the Kuroshio and moving northward, more larvae may be moved southward due to weakening of the westward flow of the NEC or a southward shifting of its position and strengthening of subsurface southward flow near the spawning area in recent years. These changes in ocean circulation may be in response to changes in winds resulting from a weakening of subtropical and tropical wind stress curl in the past two decades. Other factors, such as changes in horizontal temperature or salinity balanced by the changes of vertical shear may also lead to the strengthening of subsurface southward current. A detailed dynamical analysis is necessary for understanding the actual cause for change of ocean circulation.The present study explored the potential impact of western North Pacific Ocean circulation on the declining and fluctuating Japanese eel catches in East Asia based on a three-dimensional particle-tracking method, in which v-larvae were programmed to swim , in addition to movements resulting from transport by ocean currents. The simulated v-larvae dispersion to different regions of the western North Pacific showed clear variations among years, and in recent decades there was a decrease in the number of v-larvae reaching areas near Japan and Taiwan where the species recruits. Previous larval transport modelling studies have found similar interannual variability and that ocean-atmosphere factors such as El Niño or the Philippines–Taiwan Oscillation (PTO) appear to influence the number of larvae that may successfully reach their recruitment areas46, and climate projections have also suggested that weakening of the NEC and the upstream Kuroshio (south of 25°N) could be associated with the increase of greenhouse gases47. However, the actual dynamical forcing and responses remain unclear, and uncertainty also exists within different models47. Moreover, the global temperature warmed up rapidly starting in the 1980′s, yet the wind stress curl showed decadal oscillations prior to the 1990’s. The 22-years of ocean reanalysis available for our study could be too short to fully represent all aspects of the long-term climate variation, therefore, the role of the warming climate in changing atmospheric conditions that influence ocean circulation requires more exploration.The weakening of wind stress curl was proposed to be connected to global warming48. That could affect the number of larvae being transported out of the spawning area. The fishing effort could also vary year by year, which could potentially influence the amount of annual recruitment. Another factor that appears to be unique for the Japanese eel is that the latitude of their spawning locations appear to be influenced by the latitude of a shallow salinity front that often forms in the NEC as a result of tropical rainfall50. When a distinct front is present, spawning seems to occur south of the front, but when there is no front, the spawning occurs over a wider range of latitude51. It is also possible that changes in productivity might affect early larval survival through reductions in their marine snow food source16. Changes in productivity-related indicators have changed in the NEC region53, and chlorophyll-a has steadily decreased and surface temperatures have increased in the North Pacific subtropical gyre since 199854. Distinguishing the possible contributions of these types of biologically linked factors and the effects of changing currents will require further detailed research efforts.Although the present study and previous transport modelling studies illustrate the possible role of ocean circulation in causing distinct variability in the transport patterns that could affect recruitment levels, there are other factors that could be involved in the actual levels of recruitment each year. For example, the general decreasing trends of the time-series of glass eel catches in Taiwan and Japan were similar to the number of larvae reaching those areas in the simulations, but their year by year variations were not significantly correlated (p > 0.1). Other factors that could be involved include the number of adult eels migrating to the spawning area each year, which has the potential to be influenced by climatic factors affecting the freshwater habitats where the juveniles live5. In order to confirm that the general findings of dispersion decreases were not sensitive to the particular swimming strategy used in the simulations, different swimming behaviors were examined. Considering that the 24-hour swimming used in the present study and previous ones26 could be energy consuming, and therefore not biologically accurate, and the newborn larvae may just drift with ocean currents while they are feeding and growing, an additional experiment (Exp. A) set v-larvae to start swimming when they reached 30-days old, and they only swam at night when they probably stop feeding. The linearly increasing swimming speed, DVM and swimming direction were the same as the standard simulation run. Another experiment (Exp. B) used the same strategy as Exp. A, but set a fixed swimming direction of northwestward for v-larvae to facilitate movement towards and across the Kuroshio. Both experiments showed decreasing trends of reaching areas near East Asia (Fig. Another biological factor that could be more important than previously considered is that of active swimming by the larvae, whose bodies are filled with energy storage material that could be used for swimmingsia Fig. , especiaA. marmorata, A. bicolor pacifica, and A. luzonensis in the western Pacific as well as the eel species in the other subtropical gyres where equatorial currents and western boundary currents are important for transporting larvae5. How changes in the ocean circulation or in biological factors are influencing larval survival will be essential to help efforts to conserve these interesting species in a changing ocean-atmosphere system in the future.Further research on the effects of ocean current changes and the influence of larval behaviors are needed not only for the Japanese eel, but also on other species, such as


Receiving a higher weight-adjusted dose of the prophylactic antibiotic cefazolin was associated with lower SSIs risk OR 0.967, 95% CI 0.94-0.99.In conclusion, a high rate of SSIs following caesareans was detected, and modifiable risk factors of SSIs should be incorporated into targeted policies aiming to reduce the rate of SSIs.Surgical site infections (SSIs) following caesarean surgeries are common. The present study aimed to evaluate the frequency of SSIs following caesareans at Jordan University Hospital during the 30 postoperative days and to identify factors associated with increased SSIs risk. Data regarding the occurrence of SSIs were collected both prospectively via follow-up phone calls and retrospectively via reviewing wound culture results and clinical notes. SSI cases were subsequently determined utilizing predefined criteria. Data relating to possible risk factors of SSIs were collected from patient interviews and hospital records. Risk factors for SSIs were identified via logistic regression. A high rate of SSIs (14.4%) was detected; implicated factors included body mass index ≥36 kg/m Of the different types of surgical wound infections, caesarean wound infections are of particular interest, as they are of the main infectious caesarean complications and are associated with elevated health care costs and maternal morbidity2. Another factor that emphasizes the clinical significance of such infections is the rising worldwide rates of caesarean deliveries3; this growing rate predicts a parallel increase in caesarean surgical site infection cases. Jordan, a middle-eastern developing country, is no exception to these rising rates, as the Jordanian caesarean rate increased from 18.2% in 2002 to 30.3% in 20124.Based on world health organization (WHO) data, one in three patients receiving a surgical procedure in low-income/middle-income countries is predisposed to be affected by surgical site infections (SSIs)8, depending on differences in methodological issues such as the length of follow-up, the use of prophylactic antibiotics, and various patient-specific and surgery-related factors. To detect a notable decrease in the rate of SSIs at any centre, the rate of SSIs needs to be determined accurately, and risk factors associated with SSIs need to be investigated. This would allow policymakers to design targeted plans to eliminate modifiable risk factors of SSIs.The reported rate of SSIs varies considerably amongst the studies conducted at different centres around the world (2.1–48.0%)et al. reported that the rate of wound infections during hospital admission in 1,319 caesareans conducted between 1998 and 2002 was 8.1%9. In fact, this rate underestimates the true prevalence of caesarean wound infections, as the onset of such infections is usually detected post-discharge. For example, in a large-scale study conducted in England only 5.3% of the total infections were detected during the initial hospital stay10, while the rest were detected following discharge during the 30 postoperative days. This result pinpoints the importance of post-discharge surveillance systems in identifying SSI cases.There are few Jordanian studies relating to SSIs following caesareans. Kaplan Considering the fact that there is no active post-discharge surveillance policy for monitoring SSIs implemented by our hospital’s infection control team, limited data are available relating to the development of SSIs during the first postoperative month. The present study was designed to evaluate the frequency of surgical site infection during the initial 30 postoperative days, utilizing multiple surveillance methods. Furthermore, this study aimed to identify factors associated with an increased risk of SSIs.The present research was conducted at the Jordan University Hospital (JUH) - Department of Obstetrics and Gynaecology for a period of eleven months from July 2015 to May 2016. The protocol of the study was approved by the Department of Biopharmaceutics and Clinical Pharmacy of the University of Jordan and then by the institutional review board of the JUH. This study was conducted in accordance with JUH’s ethical policy for preserving the rights of patients involved in research studies (policy number: Adm P021).11 and smoking status. Surgery-related factors were also included such as type of anaesthesia, type of surgery (planned vs. emergent) and duration of surgery. Finally, factors related to the practice of administering prophylactic antibiotics prior to the operation were also investigated. The infection control team at JUH adapted the American Society of Health-System Pharmacists (ASHP) clinical practice guidelines for antimicrobial prophylaxis12 as their surgical antibiotic prophylaxis protocol. For caesareans in absence of β-lactam allergy, the ASHP recommends administering a single 2-g cefazolin dose to women weighing less than 120-kg, within a period of 60 minutes prior to the incision. In the present study, we examined the impact of non-compliance with two components of the ASHP guidelines, which are the dose of cefazolin (ASHP recommended 2-g dose vs. 1-g dose) and the duration of antibiotic administration (single preoperative dose vs. multiple doses) in relation with SSIs.All women who underwent a caesarean operation at JUH were eligible for the study unless they fulfilled one of the following criteria: a. Women receiving multiple doses of antibiotics for an established infection during their preoperative hospital stay; b. Women who had another operation during the same admission requiring prophylactic antibiotics. Patient enrolment in the study and follow-up in addition to the data collection process is summarized in Fig. Multiple approaches were utilized to collect data relating to the development of SSIs in the abdominal wound during the 30 postoperative days. First, all available wound culture results for the included patients were retrieved from the computer centre of the hospital and positive cultures were recorded; at JUH, cultures are usually ordered by the attending physician, as part of routine care, when upon clinical examination of the wound signs of infection are noted. Second, all available computerized clinical notes from outpatient visits were also retrieved, and patients diagnosed with SSIs by the hospital physicians during the study period were identified. Finally, all patients who agreed to be re-contacted were called 30 days following the operation. In total, there were three phone call attempts for each patient, and patients who did not answer the first time were called two additional times on two different days. Patients who did not answer after three phone calls were not contacted again. A summary of questions asked during the phone interview is shown in Supplementary information Table Each phone call record was transcribed by three different researchers into an analysable format via a custom made Excel form; then, the main researcher checked the results of the three transcriptions and resolved any disagreement.14:A surgical site infection was diagnosed by the attending physician at the hospital’s outpatient clinics during the first 30 days postoperation.Results of culture indicate the presence of microorganisms.et al.10, as shown in Supplementary Table All patients who agreed to be re-contacted were called 30 days following the operation. Women were then allocated to the SSI group if they met one of the criteria for identifying patient reported surgical site infection adapted with modifications from Wloch The data obtained from the three methods were pooled together and patients were allocated to the surgical site infection group based on fulfilling one the following criteria adapted from the Centres for Disease Control and Prevention (CDC) definition of SSIshttp://www.raosoft.com/samplesize.html). An increase in sample size was desired because it is widely accepted that at least 10 events per predictor are required in a regression analysis to increase the stability of estimates16. All data were coded into Excel and then analysed via SPSS version 22 .The minimum required sample size for the present analysis was 323 patients, based on a maximum annual population size of 2,000 caesareans, 5% margin of error, 95% confidence level, and 50% response distribution as calculated by the Raosoft sample size calculator  of these women agreed to be re-contacted via a phone call one month after the operation and provided the researchers with their personal phone number. The overall rate of follow-up via phone calls was 72.0%. Reasons for loss of follow-up included wrong phone number, disconnected, unreachable number or travelling abroad at the time of follow-up. Phone calls were in general brief, lasting only a few minutes. Sixty women met one of the previously specified exclusion criteria. Of the remaining 1,173 women, a total of 312 women were excluded due to the absence of follow-up or incomplete follow-up  during the initial postpartum period was low; less than 2.0% of the 1,173 women were readmitted.Utilizing the multiple approaches described previously, a total of 861 participants were followed-up for the entire 30 postoperative days. Wound infections were identified in 124 cases, constituting 14.4% of the included participants. The detected infections were identified by the physicians during outpatient visits or through positive cultures in 37.1% of infected patients, while the remaining infections were identified only via the phone interviews. When the infections were detected in the hospital (predominantly through outpatient clinics), they were identified on median postoperative day 10.8 . Of the 312 women who were lost to follow-up, there were 83 women who did not answer phone calls and showed no signs of infection at last follow-up, which was on median postoperative day 9.6 , these women were excluded from the infection analysis as it was not possible to determine whether they had developed an infection after the last follow-up. Furthermore, five patients with no follow-up of any type during the 30 postoperative days who visited the outpatient clinic or who had negative culture results after 30 days were also excluded from the analysis, as no data were available regarding SSIs during the 30 postoperative days. Culture results for the first 30 postoperative days were available for 41 of the infected patients. More than 10 types of pathogens were identified in the culture results. In 22 cases, more than one organism was identified in the culture results of the wound; therefore, the total number of infection-causing microorganisms isolated from the cultures was 67. The frequency of identified microorganisms is shown in Table Table th week, and a hospital stay longer than 3.5 days. The study participants were under the care of ten consultants; when adjusting for this variable (categorized into two categories based on consultants’ obstetric experience), the results remained unchanged. The multiple regression analysis results are given in Table During the regression analysis, several factors were found to be associated with infection; these factors included a higher BMI prior to pregnancy, lower weight-adjusted cefazolin dose, delivery beyond the 406, high rates of 16% and 23.5% in Irish and Brazilian studies, respectively7, and to a rate as high as 48% in a resource-limited Tanzanian hospital8. Similar to other studies, staphylococcal microorganisms were the predominate type of infecting pathogens10, and other common pathogens included enterococcal bacteria. Enterococcal infections are not only common in caesareans12 but they are also thought to be the third most common cause of nosocomial infections globally, and special infection control measures are required in managing such infections17.The present study determined the rate of SSIs during the 30 postoperative days and identified possible risk factors for the development of SSIs. The rate of surgical site infections detected in the present study was considerably high; approximately 3 out of 20 women undergoing a caesarean operation developed a SSI. Although this rate is high, it was within the range of reported rate of caesarean SSIs worldwide; the worldwide rates vary between low rates of infection of 2.1% in the United States18.The preoperative dose of cefazolin was changed from 1 g during the initial period of the study to the ASHP recommended 2-g dose for non-obese women, although the 2-g dose was only marginally associated with a lower rate of SSIs compared with the 1-g dose, the higher term weight-adjusted cefazolin dose correlated with a lower risk for SSIs, as the odds ratio was 0.967; therefore, a 1-unit increase in the weight-adjusted cefazolin dose resulted in a 3.3% reduction in the risk of infection. Based on a median term weight of 80 kg, a change in dosing from 1 g to 2 g, in a typical pregnant woman, would result in an increase of 13 mg/kg units, which corresponds to a 35.6% reduction in the risk of wound infections. This result demonstrates the strong impact that a simple change in preoperative practice could have on the risk of SSIs. Furthermore, it is in line with the findings of a pharmacokinetic-pharmacodynamic simulation study that revealed that 2-g doses were more successful in maintaining blood levels at or above the target minimum inhibitory concentration of susceptible gram-positive bacteria during surgery19 revealed that a major problem in the practice of surgical antibiotic prophylaxis at JUH is the prolonged administration of postoperative antibiotics. In light of the present results, an important modification to the current practice of surgical antibiotic prophylaxis administration at JUH would be the cessation of non-indicated postoperative antibiotic therapy, because we have not detected a difference in the rate of SSIs between women who received a single dose of preoperative cefazolin and those who continued to receive antibiotics postoperatively. This recommendation is supported by the ASHP guidelines and by several authors who have concluded that utilizing a single prophylactic dose (regardless of the type of antibiotic) was at least as effective, if not superior, to administering multiple doses in reducing infectious caesarean complications22. Obese women, on the other hand, constitute a special subpopulation that needs extensive research, particularly in light of the alarmingly high rates of obesity amongst Jordanian women and women of western countries24. New evidence arising from a recent randomized clinical trial indicates that, compared with placebo, obese women could benefit from a 2-day course of cephalexin and metronidazole25. Whether this approach is superior to a high single dose of cefazolin needs to be determined. Our results indicated a 4-fold increase in the risk of SSIs in obese women (BMI ≥36); the correlation between obesity and an increased risk of wound infection could be explained by a lower tissue concentration of the drug in obese patients, due to increases in the volume of distribution and the clearance of cephalosporins, necessitating an increase in the dose26.A recent study conducted by our groupth week found that both maternal and neonatal risk increase if delivery occurred at the 40th week or beyond; such complications included chorioamnionitis, perineal laceration of 3rd and 4th degree, and non-reassuring foetal heart rate27. The present study adds a new complication for such types of deliveries, which is wound infection. Chaim et al.28 investigated the impact of having a caesarean at the gestational age of less than 37 weeks as opposed to greater than 37 weeks on the rate of SSIs, and found no significant difference in the rate of wound infections between the two groups. Similar results were obtained in our study when 37 weeks was used as a cutoff point . However, when 40 weeks was used as a cutoff value, the risk of infection doubled in women delivering beyond 40 weeks.A study conducted on 119,254 women who delivered following completion of the 3729, other researchers reported an increased risk of SSIs with prolonged total hospital stay and pre-hospital stay in general surgeries, probably due to an increase in the chance of antibiotic-resistant bacterial colonization with prolonged stays31.The total duration of hospital stay in our population was relatively short (3.4 days on average). A prolonged postoperative stay was frequently related to patients’ attitude, as women refused to be discharged until their infant was released from the neonatal intensive care unit. Hospitalization longer than 3.5 days doubled the risk of subsequent SSIs development. Although a prolonged hospital stay was not associated with an increased risk of infection in gynaecological surgeries such as hysterectomyet al.10, however, this identification proved difficult, as some questions relating to this type of infection  from patient-reported symptoms, as previously described by Wloch on Table : Q8–10 w33.In conclusion, the present study measured for the first time at a Jordanian hospital the rate of surgical site infection following caesarean operations during the initial 30 postoperative days and identified several risk factors for SSIs. Although some detected variables are non-modifiable, such as obesity, special measures can be taken at the clinical setting to provide optimal care and follow-up for this subpopulation. Other risk factors of SSIs detected in the present study can be easily modified; for instance, the authors recommend that caregivers should be instructed to administer 2-g cefazolin instead of 1-g in women weighing less than 120-kg. Hospital pharmacists should be given a central role in managing the practice of surgical antibiotic prophylaxis, as handling such a responsibility to pharmacists has recently been proven to be efficacious in enhancing optimal prophylactic antibiotic utilization34. This could be accomplished by developing a special information technology infrastructure to create a hospital database that automatically links different possible sources of SSIs identification, including microbiological data, inpatient wards, outpatient clinics and coded transcribed phone interviews in addition to important patient-specific, procedure-specific and therapy-related factors that could influence SSIs. In this manner, hospitals could audit their infection rate regularly and take appropriate measures to optimize their clinical service.Another aspect of improvement is developing a post-discharge surveillance system. There is no ideal post-discharge surveillance system, but utilizing multiple approaches could help in identifying the largest possible proportion of these infectionsSupplementary information


The Siberian larch forests, taking up about a fifth of the global boreal biome, are different from the North American boreal forests in that they generally do not undergo a secondary succession. While wildfires in the boreal forests in North America have been shown to exert a cooling effect on the climate system through a sharp increase in surface albedo associated with canopy removal and species composition change during succession, the magnitude of the surface forcing resulting from fire-induced albedo change and its longevity in Siberia have not been previously quantified. Here we show that in contrast to previous expectations, stand-replacing fires exert a strong cooling effect similar in magnitude to that in North America. This cooling effect is attributable to the increase in surface albedo during snow-on periods. However, the observed earlier snowmelt in the region, and subsequently a longer snow-free season, has resulted in a warming effect which has the potential to offset the fire-induced cooling. The net albedo-induced forcing of the Siberian larch forests in the future would hinge on the interaction between the fire-induced cooling effect and the climate-induced warming effect, both of which will be impacted by the expected further warming in the region. Its influence on climate is exerted mainly through two sets of mechanisms: the biogeochemical effects, primarily through the carbon cycle, and the biogeophysical effects including albedo, evapotranspiration, and surface roughness. The boreal forest has a strong impact on global temperatures which is largely attributable to the increased absorption of solar radiation caused by the considerably lower albedo of the boreal forest during the snow season, relative to other land cover types such as tundra and bare ground4. The albedo-driven warming effect likely overshadows the cooling resulting from the carbon sequestration in the boreal zone and is the dominant driver controlling the net climatic impact of the boreal forest6.The boreal forest is one of the largest biomes in the world10. Wildfire impacts have been studied extensively in the boreal forests of North America, where wildfires have been found to impose a general cooling effect after fire15. This is attributable to two factors. First, wildfires in the North American boreal forests are generally stand-replacing. Thus the loss of the forest canopy results in considerably higher albedo during the snow season13. Second, burned forests recover slowly over a prolonged successional period when forest composition is dominated by deciduous broadleaf species, which typically have higher albedo than needleleaf evergreen species18. Randerson et al.11 constructed a radiative forcing trajectory induced by post-fire albedo changes in the burned black spruce (Picea mariana) forest stands in interior Alaska. Their results indicated that albedo-induced radiative forcing in the burned forests became negative one year after fire and remained so for 50 years. The magnitude of the cooling effect increased rapidly in the first 10 years after fire when it peaked at −8 ± 3 Wm−2 and then slowly decreased to reach the pre-fire levels 50 years after the fire occurrence. The cumulative cooling effect of the fire-induced albedo changes outweighed the combined warming effect of the CO2 and CH4 emissions, resulting in a net cooling effect estimated to last for more than 150 years. Rogers et al.14 also established an albedo-induced surface forcing (SF) trajectory for the burned forests in North America. Based on MODIS data, their results, although limited to an 11-year time span, were well-aligned with those of Randerson et al.11.Because albedo of the boreal biome exerts a strong control over climate, any forces that significantly change forest albedo, including wildfire, can potentially carry strong climatic implications. Wildfire is a dominant natural disturbance agent in the boreal forest that controls species composition, stand age, and successional patterns19, the forests of Eastern Siberia are an important component of the boreal biome. Underlain by continuous permafrost and dominated by larch (deciduous needleleaf species), these forests are strikingly different from their North American and European counterparts20. Larch species dominate forest composition at all recovery stages and generally do not undergo secondary broadleaf successional stages. This implies that the knowledge gained about the climatic impact of the fire-induced albedo changes in the North American boreal forests cannot be readily applied to the larch forests of Siberia. A recent study conducted by Rogers et al.14 indicates that the post-fire stands in boreal Northern Eurasia also produce an albedo-induced cooling effect although of a much smaller magnitude compared to those in the North American boreal forests. However, considering the widespread occurrence of surface fires in Siberia - a major difference in fire regime between the two boreal zones21 - a more focused examination of the impacts of wildfires in Siberia is needed. In this study we aim to isolate the impacts of stand-replacing fires within the Siberian larch forests on SF using a recently developed stand age map of the Siberian larch forests22 along with the full record of the collection 6 MODIS albedo product23. We focus on understanding the temporal trajectory of SF resulting from stand-replacing fires as well as quantifying the impact of those fires on the SF produced by the Siberian larch forests as a whole between 2001 and 2012.With the total area of about a fifth of the global boreal forest8 ha. The delineation of the study area was based on the deciduous needleleaf forest class in the 500-m MODIS Land Cover Type Product  . Within this region, wildfires are the most dominant disturbance agent, accounting for 87% of forest loss between 2001 and 201222.The study area of the current project is the Siberian larch forests, which is the forests in Eastern Siberia that are dominated by larch. It is located within 50°–66° N and 94°–140° E and the total area is 1.93 × 10et al.23) and used to calculate annual mean BSA and WSA. The calculation of the annual mean albedo for each pixel was hierarchical, where the monthly mean values were calculated first, based on which the annual mean values were then calculated. The extremely low solar angles result in very few valid albedo observations between November and February. Consequently, the mean albedo for those months would further hinder the calculation of the annual mean values. To compute the annual mean albedo across the study area, in cases when January and February monthly mean albedo was not available, the mean albedo values of those two months were set to the first valid albedo value in March of the corresponding year . Similarly, the mean albedo values for November and December, were set to the last valid albedo observation in October . After the annual mean albedo was calculated, a three-year mean filter was applied to smooth the high inter-annual variability. The annual mean blue-sky albedo (BA) was calculated with the direct radiation ratio and annual mean BSA/WSA for each year. We used the MODIS Level-3 atmosphere monthly global product  to calculate the direct radiation ratio at the earth’s surface. The monthly average solar zenith angle, aerosol optical depth, cloud optical depth, cloud fraction and water vapor were used to calculate the direct radiation ratio using the libRadtran27. The following values were used as entries in the radiative transfer simulations: solar zenith angle , Cloud Optical Depth , Aerosol Optical Depth at 550 nm , and water vapor . The direct radiation ratio was calculated at the spatial resolution of one degree and then interpolated to 500 m.Daily shortwave black-sky (BSA) and white-sky (WSA) albedo values between 2001 and 2015 within the study area were extracted from the MODIS albedo product , respectively, and 28 was used to compute 28. Since the spatial resolution of the surface flux data is 1°, it was resampled to 500-m to reconcile with the MODIS albedo data. The SF values were calculated for all areas that experienced stand-replacing fires between 2001 and 2012 (referred to as burned forests in this manuscript) as mapped in Chen et al.22. The extracted values were averaged according to year since fire based on which a post-fire SF trajectory was established. In order to show seasonal variation in SF changes, we also generated the monthly SF trajectories following the same methodology using corresponding monthly mean BA and downward shortwave surface flux. Monthly mean total downward shortwave surface flux within the study area between 2001 and 2015 was also calculated to show the seasonal pattern of incoming solar radiation.We then calculated SF for the entire study area based on all possible year combinations allowing for the calculation of SF following the equation:2 = 0.99, Supplementary Fig. With the available datasets, the maximum range of the SF trajectories was 14 years. However, by using the 24-year stand age map, post-fire albedo dynamics can be assessed over a longer period. Therefore the albedo trajectories within all known burns as a function of time since burning were constructed and compared. In this analysis, BSA rather than BA was used to represent albedo because of very high correlation between annual mean BSA and WSA within the study area during 2001–2015  was calculated for all forests that did not experience stand-replacing fires between 2001 and 2012. The BSA anomalies were then computed by subtracting each of the 8-day mean BSA for the forests that did not burned during 2001–2012 from the corresponding 8-day burned BSA for each year.29. Once a pixel switched from snow-on to snow-free and remained so for 7 consecutive days, the first day of this 7-day window was considered the snow melt date for that pixel in that year. Due to the frequent occurrence of cloud cover, the qualified 7-day window was allowed to have no more than 5 cloudy days. The snow onset dates were calculated in a similar fashion, however, instead of searching for the snow-off windows, the algorithm searched for the snow-on windows. The first day of the identified 7-day window was considered the snow onset date for that pixel in that year. Generalized linear regression was fitted based on the snow onset and melt dates, respectively, of 150,000  randomly selected sample points with respect to year.Finally, we examined the spatial distribution of SF for the entire study area calculated between 2001 and 2013, which reflects the combined effect of all stand-replacing fires that occurred between 2001 and 2012. To summarize the findings, we divided the study area into 6 zones, each corresponding to 10° in longitude and 7.5° in latitude. We further compared SF for burned and unburned forests between 2001 and 2012 and all forests within each zone accounting for fraction of area burned. To aid the interpretation of revealed patterns of the spatial distribution of SF, the daily snow cover for each pixel within the study area between 2001 and 2015 was tracked using the MODIS MOD10A1 (Collection 6) daily snow cover product−2). The cooling effect, however, grows rapidly and continuously until the 11th year when it peaks at −9.60 ± 0.03 Wm−2, after which it gradually lessens to reach −7.15 ± 0.03 Wm−2 by year 14  establishes that the mean annual SF of the burned forests is consistently below zero after fire for at least 14 years . During the first year after the fire event, the mean SF is slightly negative , it appears that the distribution of areas with significant cooling effects (SF < −2 Wm−2) is consistent with the areas that experienced stand-replacing fires between 2001 and 2012. This is well illustrated through the comparison in Fig. −2, the net 2001–2013 SF for three  out of six zones is found to be positive  ive Fig.  shows thive Fig. .Figure 6et al.14 have shown that the albedo-induced cooling effect caused by wildfires in Northern Siberia is much smaller than that in North America, with the net climatic impact of wildfires in Siberia potentially resulting in a small warming or neutral effect. However, it is important to consider that this observed pattern reflects only present-day or even very recent past fire regimes in North America and Siberia. As mentioned in the introduction, in contrast to the dominance of stand-replacing fires within the North American forests21, stand-replacing fires in Siberia represent a much smaller proportion of the total estimated fire activity30. However, even within the last 30 years, early evidence of an increase in the amount of stand-replacing fire in Siberia has emerged22. Coupled with the expected further increase in extent and particularly severity of fire events under the projected climate change in boreal ecosystems31, stand-replacing fire events in Siberia are likely to play a more prominent role in influencing the Earth’s radiation budget compared to the current conditions and should be modeled explicitly moving forward. Our study aims to augment the results of Rogers et al.14 by offering an isolated evaluation of the albedo-induced forcing of stand-replacing fires within the Siberian larch forests which, in terms of fire ecology, are more compatible with those in the North American boreal forests. The SF and albedo trajectories established for the forests that burned between 2001 and 2012 showed that stand-replacing fires in this region impose a strong cooling effect for at least 26 years after fire and the magnitude is directly comparable to that reported for North America14. According to our results, the peak SF of stand-replacing fires on average exceeds −9 Wm−2.In a recent study, Rogers et al.14 estimates. The stand age map that we used to provide the locations of the stand-replacing fires was derived based on a series of Landsat-based remotely sensed datasets33. Its spatial resolution is 30 m which may have translated into better delineation of stand-replacing fires than the MODIS-based burned area product analyzed by Rogers et al.14. In addition, the Collection 5 MCD43A3 albedo product, used in the 2015 assessment, has been reported to suffer from the degradation of MODIS Terra sensor34. We encountered the impact of sensor degradation in a preliminary analysis, where the cooling effect of the burned forests estimated using the Collection 5 data was up to 2 Wm−2 smaller compared to estimates derived from the Collection 6 MODIS albedo product, which was corrected for the inconsistency and used in the current study. Moreover, the CERES downward shortwave surface flux data that we used is more accurate than the reanalysis dataset35 used by Rogers et al. in terms of both spatial resolution (i.e. 1° vs. 2.5°) and production basis .In addition to examining stand-replacing fires separately from surface fires, this study used different input datasets for estimating both burned area and surface albedo which likely also contributed to the differences in our and Rogers 37. As a result, forest albedo is typically higher than pre-fire and remains elevated for a considerable amount of time. According to Liu and Randerson17, it may take more than 60 years for forest albedo to recover to the pre-fire level in the black spruce forests in Alaska. In the Siberian larch forests, however, there is typically no secondary succession, and larch remains the dominant genus very early in the burned sites40. As a result, the duration of the fire-induced cooling is primarily determined by the time it takes for the larch stands to reach pre-fire levels of canopy closure. The results suggest that the forest canopy in the study area does not recover fully within at least 26 years, which is consistent with several field-based estimates of forest recovery in the Siberian larch forests42. According to Zyryanova et al.40, larch forests reach maturity around 50 to 90 years after a disturbance. However, ecological maturity is a more complex term than canopy closure, which may be reached sooner during the recovery process.The similarity in the magnitude of the albedo-induced cooling effects of stand-replacing fires between the Siberian larch forests and the North American boreal forests likely reflects a common driver of cooling immediately after fire: forest canopy removal. The post-fire albedo anomalies between 2001 and 2015  generally coincide spatially with stand-replacing fires. Within burned forests, the cooling effect dominates  and environmental (e.g. the length of snow-free season) changes within the Siberian larch forests are needed to quantify the overall impact of wildfire on regional and global climate system.The spatial analysis of the net 12-year SF in the Siberian larch forests reveals a general warming effect which particularly noticeable within the three zones  which were least impacted by stand-replacing fires between 2001 and 2012 Fig. . We attrSupplementary Figure