Recently, device-free human activity–monitoring systems using commercial Wi-Fi devices have demonstrated a great potential to support smart home environments. These systems exploit Channel State Information (CSI), which represents how human activities–based environmental changes affect the Wi-Fi signals propagating through physical space. However, given that Wi-Fi signals either penetrate through an obstacle or are reflected by the obstacle, there is a high chance that the housing environment would have a great impact on the performance of a CSI-based activity-recognition system. In this context, this paper examines whether and to what extent housing environment affects the performance of the CSI-based activity recognition systems. Activities in daily living (ADL)–recognition systems were implemented in two typical housing environments representative of the United States and South Korea: a wood-frame apartment (Unit A) and a reinforced concrete-frame apartment (Unit B), respectively. The experimental results show that housing environments, combined with various environmental factors , generate a significant difference in the accuracy of the applied CSI-based ADL-recognition systems. This outcome provides insights into how such ADL systems should be configured for various home environments. In order to advance smart home environments that can deliver elderly healthcare, energy savings, and home security, systems must first be able to accurately recognize human activity in daily living (ADL) ,2. TradiIn recent years, device-free activity-recognition approaches have been the focus of ADL-recognition systems , with WiWi-Fi signal-based ADL-recognition approaches exploit fine-grained Wi-Fi signal signatures. Specifically, Wi-Fi signals propagate from a transmitter to receivers, so human activity may affect the signals’ propagation paths, which in turn cause the signals to be changed at receivers. For example, Received Signal Strength (RSS) has been used for fingerprint-based localization systems ,10,11,12Unlike RSS which has a superimposition layer of multipath signals, Channel State Information (CSI) exploits channel information between the AP and the receiver at the individual subcarrier level. Specifically, CSI represents a channel response to the physical environmental changes, which depicts the multipath effects of signals. Consequently, CSI is more stable and robust than RSS, and CSI data can be captured from commodity Wi-Fi devices using a Linux CSI 802.11n tool . RecentlDespite these advantages, these CSI-based ADL-recognition systems environments still need further verification in terms of their performance in various real-world housing environments; Most previous studies ,20,21,22Recently, exploiting Wi-Fi signals for activity detection has risen in popularity due to the availability and ubiquitous distribution of these wireless networks and their corresponding commercial devices. Human-body movements cause Wi-Fi signals to change when the signals are reflected from the body, which results in variations at the Wi-Fi receiver and, consequently, opportunities to estimate human activities by analyzing the signal variance. This approach further benefits from the fact that Wi-Fi signal–based ADL-recognition systems do not require LOS for the activities since Wi-Fi signals propagate through walls. Thus, systems such as Wi-Vi  and WiSeWhile such RSS approaches have been successfully used for gross-level ADL recognition, CSI manifests even greater sensitivity to fine-activity differentiation. CSI is more stable and robust than RSS , and theTo-date, CSI-based ADL-recognition systems have been applied to various activity-detection tasks. Firstly, CSI has been used for indoor localization. Location detection indoors is required in various settings, such as in hospitals (patient tracking) and disaster areas (personnel locating). Wu et al.  comparedAnother CSI-based approach, Wi-Chase , recogniFor more diverse in-place activities and for walking-direction recognition, Wang et al.  proposedBuilding upon these successes, researchers have applied CSI-based ADL-recognition solutions to various smart home healthcare systems. Wang et al.  proposedIn keeping with the healthcare applications, CSI has been exploited to detect vital signs such as respiration and heartbeat rates. PhaseBeat  used CSICSI-based ADL-recognition systems have also been used in place of human-device interactions. Various smart home gadgets control such home appliances as TVs, laptops, and mobile phones. Alternatively, Nandakumar et al.  used CSIThese successfully developed and verified CSI-based ADL-recognition systems benefit from CSI’s stability and accessibility. However, Wi-Fi signals are affected by various environmental factors, which can in turn have impact on the performance of CSI-based ADL-recognition systems. Thus, in order to advance the opportunities for applying CSI to human ADL recognition in smart homes, studies must verify that these systems’ performances will yield consistent accuracy in different housing environments.As Wi-Fi signals propagate in physical space, the signals reach receivers (Wi-Fi devices) through various routes, a concept known as multipath. Mathematically, the CSI matrix, tion (1) .(1)Yi=HCSI has several subcarriers that are divided by Orthogonal Frequency Division Multiplexing (OFDM) . Thus, tIn order to examine whether and how CSI-based ADL-recognition systems can be resistant to varying housing environment, two different housing environments were selected: A wood-frame, low-rise apartment in the United States (Unit A), and a reinforced concrete-frame, high-rise apartment in South Korea (Unit B). These two housing environments show clear differences in construction materials and population densities. The exterior walls in Unit A were composed of wood framing and insulation, and wood sheathing, and its interior walls were also composed of wood framing and drywalls with painting finishes. Unit B was built with reinforced concrete structure and its interior walls were masonry and drywall, and wallpapers were used for the interior wall finishing. While Unit A is also a part of low-rise multi-family housing, the population density of the high-rise apartment complex that Unit B belongs to is much higher. The layouts of two units present clear differences, as shown in Two subjects were recruited for the experiments at each Unit; Recruiting for the experiment at each Unit was conducted separately due to the geographical distance. All the four subjects participating in both Units were male having similar physical characteristics; Their heights ranged from 175 cm to 180 cm, while their weights range from 68 kg to 70 kg. During the experiments, the subjects were instructed to perform the identical activities, and tests were performed by one subject at a time. Each subject walked in ten rounds, as indicated by the arrows, and performed eating and typing in ten rounds in the dining room and living room, respectively. Each round required 10 s of activity and 20 s of no-activity. The 20 s interval between activities clearly differentiated the multiple activity rounds. During the test, the activities were recorded using a camera and were labelled with time stamps for the activities to establish the ground truth. As shown in 11n tool , the CSIThe raw CSI data contains high frequency noise, outliers, and artifacts introduced by rate adaptation rather than by human activities—high frequency noise manifests when the radios are switched to different modulation and coding schemes. Thus, a second-order, low-pass Butterworth filter was used to remove high frequency noise from the raw CSI data. The filter was configured to keep the sampling rate at 10 packets/s, and the cut-off frequency was set at 1 Hz. Although the walking activity causes higher Doppler shift than 1 Hz, the phase shift caused by occupant’s activity would be rotated due to hardware imperfection. Thus, the 1 Hz filtered CSI still contained the variation caused by walking activity. The variation in the filtered CSI was distinguished from other activities . Figure As shown in As developing high-efficiency activity-segmentation algorithms is beyond the scope of this study, to increase recognition accuracy, the walking and in-place activities were manually segmented by referring to ground truth. A total of 400 data samples were segmented.Activity-classification approaches exploit various activity-recognition models to classify the segmented activity data. In this study, two different algorithms were used for activity classification: SVM and EMD reconstructed from literature ,15,18,30Unlike an SVM, an EMD finds the minimum cost of matching one distribution into another and thereby represents to what extent two distributions are similar to each other. Notably, under EMD, the same activity will have similar distributions across the CSI amplitudes, whereas different activities will have distinctive distributions. In this study, to determine the impact of environmental factors on the accuracy of two different CSI-based ADL-recognition systems, two subjects performed a walking activity and two in-place activities in different housing environments. To recognize activities, the authors used two different activity-recognizing approaches, an SVM-based model and an EMD-based model. The activity-recognition accuracy of the SVM-based model was 94.38% for Unit A and 87.50% for Unit B, whereas the EMD-based model showed 68.75% and 60.25% accuracy levels for Unit A and Unit B, respectively. Under the SVM-based model, features used for activity classification related to the amplitude of signal variance. Thus, even though an activity signal forms a specific waveform, its amplitude appeared to be low, so the SVM classifier considered the activity as no activity. The confusion matrix for Unit B showed 15 ‘no-activity’ data were predicted as examples of ‘typing activity,’ which mainly reduced the model’s accuracy. ‘Typing activity’ is one of the smallest movements in daily life, since when people type, they only move their fingers and arms a small amount. Thus, the signal variance of the ‘typing activity’ has a low amplitude, and if the signal power is reduced by environmental factors, the ‘typing activity’ signal may be too weak to differentiate from a ‘no activity’ signal. Accordingly, we expected—and observed—that Unit B environment would have a greater impact on signal propagation than Unit A environment. Under the EMD-based model, the distribution of the signals’ amplitudes has more impact on the activity classification than the amplitudes’ variance within the signal, since the EMD-based model classifies unknown activities based on the distributions’ similarity to known distributions. As shown in The results also show that the EMD-based model has lower average accuracies compared to the SVM-based model. The SVM-based approach exploits 60% of the total 400 activities data to train the model and classified the remaining 40% activity data—160 samples. On the other hand, the EMD-based approach does not need training data, and consequently evaluates activity data for the full 400 samples. Thus, the actual number of recognized activities evaluated by the EMD-based model is greater than the number recognized by SVM-based model, which means the EMD-based model has more opportunities to make false predictions than the SVM-based model. Also, the SVM-based model used 60% of the total data as reference data, whereas the EMD-based model exploits only one reference datum for each activity to calculate the similarity of the known activity against the unknown activity data. Consequently and foreseeably, the EMD-based model does not cope with the variability of unknown activity data as well as the SVM-based model does.To investigate the effect of time-dependency or subject-dependency of the result, we split data into each subject data and classified activities for each subject data; Each subject performed the experiment in different days. The results in different data segmentation showed that the accuracy of Unit A was generally higher than Unit B and the accuracy of SVM was higher than EMD, meaning that the time dependency and subject dependency did not have much impact on this result.It is important to note that rather than identify effective ADL-recognition methods, this study compares the effect different environmental factors have on the performance of CSI-based ADL-recognition systems. Even though the SVM-based model and EMD-based model both show different accuracy levels for activity recognition, the results clearly indicate that both CSI-based ADL-recognition systems show less accuracy for the Unit B environment, meaning that the Unit B environment mediates the accuracy of the CSI-based ADL-recognition systems more than the Unit A environment does. We further discuss this important finding about the environmental effects mediating performance of the CSI-based ADL-recognition systems in the next section.The results indicate that both of the tested CSI-based ADL-recognition models are quite sensitive to the difference of housing environment and yield significantly different accuracies between two units. Here, we mainly discuss the potential factors that affect the performance of the CSI-based ADL-recognition systems. While these factors may appear to be limitations to the present study, they serve—in fact—as a further defense of the justification of this study, since smart-home engineers must mediate these factors when designing tools within different environments.One potential factor contributing the performance of ADL recognition is the difference of building materials. The main building materials of Unit B were reinforced concrete and concrete masonry, which is one of the hardest building materials for wireless signals to penetrate , while UThe household item  is another potential factor for Wi-Fi signal propagation. Depending on the materials of household items, Wi-Fi signal is reflected from the object more or a lot of energy is absorbed. Unit B had more small and medium sized household items, which were not all made of metal, the more reflective objects compared to the wooden or plastic materials. Thus, the small and medium sized objects absorbed more energy of the Wi-Fi signal in Unit B than Unit A, which resulted in a reduced amplitude of the CSI. The decreased variation in the CSI, then, became hard to distinguish from no-activity or other activities in Unit B. The other contributing factor is the difference of population densities. Just as people can access home networks from outside their walls, people outside of a house can affect the Wi-Fi network environment inside. Thus, neighbors’ activities or outdoor movements will conceivably affect the performance of CSI-based ADL-recognition systems. Unit B had more nearby units than Unit A. Significantly, for Unit B, neighboring households shared walls on both sides of the apartment, whereas Unit A only had neighbors five meters away on one side of the testbed. Also, the elevator located next to Unit B continuously ran, which influenced the signals within Unit B; Unit A had no elevator. Thus, more movements unrelated to the test activities took place in proximity to Unit B, which could affect the received signals used for activity detection. Unit B was in a higher population apartment complex than Unit A testbed, meaning that there were more Wi-Fi networks around to influence the test. Thus, the surrounding Wi-Fi signals had a higher chance to interfere with the testing Wi-Fi signals in Unit B than Unit A. However, as mentioned in the previous section, such interference from surrounding Wi-Fi signals might have been alleviated by the building materials of Unit B, although Unit B had many openings.In this section, we will discuss possible solutions for coping with the housing environmental effects influencing the accuracy of the CSI-based ADL-recognition system, including (1) using multiple receivers; (2) filtering external noise; and (3) using Wi-fi-friendly construction materials.While our experimental setting used only one receiver, using additional receivers would improve the recognition accuracies of CSI-based systems by augmenting the variance of the received signals. In larger houses or those with multiple rooms, the strength of the signals arriving at the receivers becomes weaker, as the signals have to penetrate through multiple walls ,23,24. UIn order to address the effects resulting from a neighbor’s activity, advanced filtering methods must be proposed and used. While such filtering would represent a great challenge, developers may conceivably be able to distinguish the signal changes due to a neighbor’s activities in another unit, especially as such signals would have to penetrate multiple walls  and a neighbor’s activities may manifest different patterns in the frequency domain . Lee et al.  demonstrAs smart homes become more popular, Wi-Fi-friendliness will play an important role in the design and selection of construction materials . The perAs this study intended to examine the effect of actual housing environments of existing homes on CSI-based ADL-recognition systems, various environmental factors were less controlled, and the results from such the experimental settings represent the combined effects of all possible environmental factors. Thus, the effect of each individual environmental factor is still unknown, although the experiment results confirm the significant effect of housing environment on CSI-based ADL-recognition systems. In addition, although two representative ADL-recognition algorithms were used in this study, the effect of housing environment could be largely dependent on algorithms.The two robust CSI-based activity-recognition algorithms yielded lower accuracy in Unit B than Unit A, a result that speaks to the mediating influence of environment on CSI-based ADL-recognition systems. This result highlights that the effects of the housing environments should be considered when designing and implementing CSI-based ADL-recognition systems in various home environments. However, further research is necessary to analyze the isolated effects of various housing environmental factors and understand the impact of possible mitigation strategies on such environmental effects.


Recent BOLD-fMRI studies have revealed spatial distinction between variability- and mean-based between-condition differences, suggesting that BOLD variability could offer complementary and even orthogonal views of brain function with traditional activation. However, these findings were mainly observed in block-designed fMRI studies. As block design may not be appreciate for characterizing the low-frequency dynamics of BOLD signal, the evidences suggesting the distinction between BOLD variability and mean are less convincing. Based on the high reproducibility of signal variability modulation between continuous eyes-open (EO) and eyes-closed (EC) states, here we employed EO/EC paradigm and BOLD-fMRI to compare variability- and mean-based EO/EC differences while the subjects were in light. The comparisons were made both on block-designed and continuous EO/EC data. Our results demonstrated that the spatial patterns of variability- and mean-based EO/EC differences were largely distinct with each other, both for block-designed and continuous data. For continuous data, increases of BOLD variability were found in secondary visual cortex and decreases were mainly in primary auditory cortex, primary sensorimotor cortex and medial nuclei of thalamus, whereas no significant mean-based differences were observed. For the block-designed data, the pattern of increased variability resembled that of continuous data and the negative regions were restricted to medial thalamus and a few clusters in auditory and sensorimotor networks, whereas activation regions were mainly located in primary visual cortex and lateral nuclei of thalamus. Furthermore, with the expanding window analyses we found variability results of continuous data exhibited a rather slower dynamical process than typically considered for task activation, suggesting block design is less optimal than continuous design in characterizing BOLD variability. In sum, we provided more solid evidences that variability-based modulation could represent orthogonal views of brain function with traditional mean-based activation. In traditional task-based functional magnetic resonance imaging (fMRI) studies, activation regions were typically identified by comparing the temporal mean of a task related time course with baseline. Signal variability was usually conceived as uninformative noise. However, recent evidences have suggested that fMRI signal variability may also be functionally relevant  and primary sensorimotor cortex (PSMC), showed significantly decreased signal power (or variability) between continuous eyes-open (EO) and eyes-closed (EC) states. Such findings are highly consistent across different groups  were recruited. Each provided written informed consent and was screened with a questionnaire to ensure no history of brain injury, neurological illness or psychiatric disorders. This study was approved by the ethics committee of Institutional Review Board of the Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University.There were totally four sessions for each participant, each lasting 8 min. Specifically, an EC resting state session was firstly acquired . Then, two continuous resting state sessions, EO and EC, were acquired and counter-balanced across participants. In these sessions, the participants were instructed to lie in the scanner quietly with their eyes open or closed, not to fall asleep, to be as motionless as possible, and not to think about anything in particular. Finally, the participants underwent an EO/EC block-designed session, in which subjects were instructed to alternately open and close their eyes for 30 s in respond to an acoustic signal via earphones. This session contained 8 EO and 8 EC blocks. A black and blank screen without fixation was always presented. All participants reported that they had not fallen asleep during the scanning.2. For the purpose of spatial normalization, we acquired a 3D T1-weighted image for each subject using a spoiled gradient-recalled pulse sequence  = 450 ms, in-plane resolution = 256 × 256, FOV = 256 × 256 mm2).MRI data was acquired using a GE Discovery MR750 3-Tesla scanner at the Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University. The functional images were obtained by using an echo-planar imaging (EPI) sequence with the following parameters: 33 axial slices, slice thickness/gap = 3.5/0.7 mm, flip angle = 90°, TR = 2,000 ms, TE = 30 ms, in-plane resolution = 64 × 64, field of view (FOV) = 224 × 224 mmhttp://rfmri.org/DPABI, Yan et al., The EPI data were preprocessed by using the toolbox for Data Processing & Analysis for Brain Imaging . For each individual, task regressor representing alternated EO and EC blocks was generated by convolving a boxcar function with the canonical hemodynamic response function as well as its time and dispersion derivatives. Group-level statistical analysis was performed on the individual-level beta images by using permutation test implemented by PALM   analysis to reveal differences in mean signal level between EO and EC  by using the SPM12 software , respectively. And then individual signal-mean maps were entered into group-level statistical analysis. The statistical and multiple comparison correction procedures were the same as described above. Of note, image intensity scaling during preprocessing may bias the differences in signal mean, in this section we also analyzed data without intensity scaling.In this study, we used standard deviation (SD) to quantify the variability of BOLD signal. A variety of metrics have been utilized to characterize BOLD variability, such as power spectrum  to reveal the EO/EC differences. Multiple comparison correction was performed by using the same procedures as described before. The corrected To evaluate whether block deign is suited for BOLD variability analysis, we used expanding window approach .For the block-designed data, the activation regions were mainly located in PVC, lateral thalamus and a few voxels in right PSMC and PAC , some spatially discrete voxels showed decreased SD in PSMC and PAC for the data analyzed with GMN  was quite prominent in PVC and lateral thalamus. We did not found the widespread deactivation of bilateral PSMC as have detected in darkness  gradually became larger as the window lengths increased both for the data analyzed with and without GMN. Our findings suggest that changes of SD in PAC and PSMC is a rather slow process and thus cannot be fully captured within the short blocks. It should be noted that the distinction of SD results between continuous and block-designed data is not due to differences in sample lengths, since the length of concatenated block-designed data, on which SD was calculated, is more than 200 s whereas the decreases of SD in bilateral PSMC and PAC in continuous data were evident as soon as nearly 90 s after the scan onset. The slow dynamics of SD results are in line with recent findings that more than 5 min is necessary for functional connectivity (FC) metrics to reach stable  might be involved in the visual processing in a more indirect way. The decreases of SD in PAC and PSMC during EO may reflect active or passive suppression of excitability in these non-visual modalities in order to facilitate the processing of incoming visual stimuli (Baier et al., The observation of decreased SD in non-visual modalities is quite similar with the phenomenon of cross-modal compensation which has been widely reported in the literature of blind (Stevens et al., By using BOLD-fMRI and comprehensive comparisons, we provided more solid evidences to confirm that BOLD variability could represent orthogonal views of brain function with conventional activation, thus highlighted the importance of variability analysis in task fMRI studies. Furthermore, it could exhibit rather slow dynamics that cannot be fully captured by typical block design. Its behavior significance requires further investigation.D-QL designed research. P-WZ and X-JQ performed research. P-WZ, X-JQ, S-FQ, X-BW, R-DW, Q-YL, S-YL, LC, and D-QL wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Hyperspectral dimensionality reduction (HDR), an important preprocessing step prior to high-level data analysis, has been garnering growing attention in the remote sensing community. Although a variety of methods, both unsupervised and supervised models, have been proposed for this task, yet the discriminative ability in feature representation still remains limited due to the lack of a powerful tool that effectively exploits the labeled and unlabeled data in the HDR process. A semi-supervised HDR approach, called iterative multitask regression (IMR), is proposed in this paper to address this need. IMR aims at learning a low-dimensional subspace by jointly considering the labeled and unlabeled data, and also bridging the learned subspace with two regression tasks: labels and pseudo-labels initialized by a given classifier. More significantly, IMR dynamically propagates the labels on a learnable graph and progressively refines pseudo-labels, yielding a well-conditioned feedback system. Experiments conducted on three widely-used hyperspectral image datasets demonstrate that the dimension-reduced features learned by the proposed IMR framework with respect to classification or recognition accuracy are superior to those of related state-of-the-art HDR approaches. A general but effective solution to these issues is dimensionality reduction, also referred to as subspace learning. In this process, we expect to compress the HSI to a low-dimensional subspace along the spectral dimension while preserving the highest possible spectral discrimination.Recently, hyperspectral imaging in sensing techniques has garnered growing attention for many remote sensing tasks , such asWith the significant support in both theory and practice as well as a fact that the learning-based strategy is somehow superior to the manually-designed feature extraction , a consiThe classic principal component analysis (PCA)  is a useIn supervised HDR strategies, the main consideration is the discrimination between intra-class and inter-class, where different discriminative rules are followed: local discriminative analysis (LDA) , local fMost previously-proposed HDR methods adhere to either the unsupervised or the supervised strategy, yet the labeled and unlabeled information is less frequently taken into consideration. A straightforward way to consider the unlabeled samples is the graph-based label propagation (GLP) , which h1.1Although these proposed semi-supervised approaches have been proven to be effective in handling the issue of HDR to some extent, yet their graph structures for unlabeled samples are constructed either from the similarity measurement  or from the pseudo-labels inferred by GLP or pre-trained classifier. The resulting features by using this type of graph construction strategy is neither robust nor generalized, due to the noisy data and labels as well as the scarce labeled samples. Also, these semi-supervised algorithms, as often as not, attempt to find a single transformation that connects the original data and the subspace to be estimated. On account of the complexity in the learning process, the optimal subspace search is hardly accomplished only by a single transformation. On the other hand, in spite of being guided by label information, there is still lack of an explicit and direct connection between the learned subspace and the label space in the subspace learning strategy interpreted by a single projection, further causing the performance bottleneck. In addition, these subspace-learning-based models are commonly treated as a disjunct feature learning step before classification. In other words, it is unknown what kinds of features in the learning process may be capable of improving classification accuracy.According to these factors, our objectives in this paper can be summarized as follows: 1) to bridge the to-be-estimated subspace with the label information more explicitly and effectively; 2) to introduce many unlabeled samples for improving the model’s generalization ability; 3) and to refine the quality of class indicators of unlabeled samples for high discriminative HDR.1.2soft-graph structure from the data rather than utilizing a hard-graph (fixed graph) constructed manually or generated by additional algorithms, yielding a high-performance and more generalized label propagation. In the meantime, to facilitate the use of pseudo-labels more effectively, the learned graph can be updated after each outer iteration ends, and the pseudo-labels accordingly refined, thereby enabling the learned features to be progressively optimized. More specifically, the main contributions of this work can be highlighted as follows.•We propose a JL-based variant: a novel iterative multitask regression (IMR) framework by simultaneously considering few labeled samples and unlabeled samples in quantity, with the application to semi-supervised HDR.•We adaptively learn the connectivity (graph structure) between samples by aligning the labeled and unlabeled samples in the estimated subspace.•We deeply integrate the adaptive graph learning with the proposed multitask regression framework in an iterative manner, making it possible for pseudo-labels to be gradually updated using the learned graph in each outer iteration.•We also design a general solver that originates from the alternating direction method of multipliers (ADMM) optimizer for the solution of our proposed IMR method.Towards the aforementioned goals, a novel regression-induced learning model motivated by the joint learning (JL) framework  is propo2In this section, we start with a brief review of our model’s cornerstone, the JL framework, and then extend it to a variant of multitask learning by synchronously regressing the labeled and unlabeled data. We will further introduce the proposed iterative multitask regression (IMR) model by integrating the JL framework with the advanced graph learning technique, which more effectively propagates labels. Finally, an ADMM-based optimizer is used for the IMR solution. 2.1d bands by N pixels (or samples), and l classes by N pixels. We model the original JL problem  regularization is formulated by optimizing the following objective function.2.2Earth Vision is extremely costly and time-consuming, as the remote sensing images have a larger-scale and more complex visual field. This leads to a limited number of labeled samples, which further hinders improvement of the model’s learning and generalization capability. To this end, we effectively utilize the information of unlabeled samples that are largely available by making a regression between the unlabeled samples and pseudo-labels in the form of multitask learning.Labeling in 2.2.1In the multitask framework, we propose a learning-based graph regularization instead of a fixed graph artificially constructed with the known kernels , in order to depict the connectivity (or similarity) between samples. Accordingly, a multitask regression framework is proposed for semi-supervised HDR by optimizing the following objective function.pair-wise distance matrix (term-wise Schur-Hadamard product.To solve trix see  in red. By means of Eq. point-wise weighted-norm with respect to the variable of Algorithm 1Iterative Multitask Regression (IMR) In Eq. 2.2.2In Eq. 2.3Unlike the previous HDR methods following the graph embedding framework  that sol2.3.1Intuitively, the optimization problem for solving the variable A closed-form solution of Eq. 2.3.2The variable The orthogonality-constrained regression problem in Eq. 2.3.3k-th class.In the sub-problem, we learn the connectivity (or similarity) between samples from the data rather than using certain existing distance measurements. Therefore, the resulting optimization problem can be formulated as2.3.4th step, and the labeled , Average Accuracy (AA), and Kappa Coefficient , are computed to quantify the experimental results using two simple but effective classifiers: nearest neighbor (NN) and linear SVM (LSVM). In our case, the two classifiers were selected because those more powerful classifiers  tend to result in confusing evaluation, as it is unknown whether the performance improvement originates from either these advanced classifiers or the features itself.With the input of different dimension-reduced features, we adopt the pixel-wise classification as a potential application for quantitative evaluation in terms of classification or recognition accuracy. More specifically, three commonly-used indices, 3.2.2•Non-HDR: original spectral features (OSF);•Supervised HDR: feature space discriminant analysis (FSDA) (s (FSDA) , joint ls (FSDA) ;•Semi-supervised subspace learning for HDR: semi-supervised local discriminant analysis (SELD) (s (SELD) , collabos (SELD) ;•GLP-based semi-supervised HDR: soft-label LDA (SL-LDA) ((SL-LDA) , semi-su(SL-LDA) .We evaluate the performance of the proposed IMR model visually and quantitatively in comparison with eight state-of-the-art baselines, including.3.2.3k); the standard deviation  than when using several iterations. Intuitively, this proves the superiority of the iterative strategy by gradually optimizing the pseudo-labels. It is worth noting, however, that the performance gain starts to slow down after two iterations and then remains essentially stable in the follow-up iterations, as the variable According to the model’s stopping criteria in 3.33.3.1Using the NN classifier, there is basically the same classification performance in OSF and FSDA. Despite an improved supervised criteria, FSDA still yields poor classification accuracy, since directly projecting the original data into a discriminative subspace with the limited amount of labeled samples is very challenging, especially when dealing with noisy data  with various spectral variabilities. Overall, the classification performance by considering the unlabeled samples is better than that without considering them. It should be noted, however, that inspired by latent subspace learning, the JL model dramatically outperforms FSDA . It is worth noting that the regression-based JL model is provided with nearly identical performance to those semi-supervised HDR approaches using both NN and LSVM classifiers, even though the powerful GLP is utilized . As expected, the performance of the IMR framework, which optimizes the pseudo-labels in an iterative fashion, is dramatically superior to that of others with the OA’s increase of approximately 8Deciduous Trees, Bare Earth, Water, Crosswalks, Highways, Unpaved Parking, and Stadium Seats), the IMR makes a significant performance gain , OSF only holds a 53.97Commercial, thereby further revealing the IMR’s advantages in low-dimensional representation learning.Beyond the two subspace-based (SELD and CDME) and two GLP-based (SL-LDA and SSFLDA) semi-supervised strategies, we propose to iteratively optimize the pseudo-labels and feed them into a multitask regression framework in order to find a latent optimal subspace where the final decision boundary for different classes can be easily determined. On the other hand, our proposed IMR for each of the classes in the studied image exceeds the vast majority of compared methods except the material of 3.43.4.1Implementation Preparation). Thus, the cross-validation strategy can be effectively used to determine the model’s parameters so that other researchers can produce the results for their tasks.The quality of low-dimensional features extracted by the proposed IMR model is, to some extent, sensitive to the selection of three regularization parameters  and a 32 GB random access memory (RAM). Herein, we assess the operational efficiency of the compared HDR approaches in terms of running time, as listed in In general, the running time of supervised HDR is much less than that of semi-supervised HDR, such as between supervised discriminant analysis (FSDA) and semi-supervised discriminant analysis . The conclusion is just as much applicable to another group, that is, JL and our proposed IMR. Remarkably, although the newly-proposed IMR model seems to be operationally complex compared to other HDR methods, yet as it turns out, the IMR shows the computationally efficiency and the time cost is acceptable, mainly owing to the fast matrix-based computing power in regression-based techniques.4To facilitate the use of unlabeled samples effectively and efficiently, we propose a novel regression-based semi-supervised HDR model, called iterative multitask regression (IMR), which 1) simultaneously bridges the labeled and unlabeled samples with the labels and pseudo-labels in a multitask regression framework; and 2) progressively updates the pseudo-labels in an iterative fashion. This model provides us a new insight into the solutions of HDR-related problems. We conducted extensive experiments on three convincing and challenging HSI datasets, demonstrating that our method (IMR) is capable of extracting more discriminative features by allowing for the unlabeled samples and by optimizing the pseudo-labels.It should be noted, however, that while there has been a desirable performance boost in IMR, it is still limited to working well only by linearly learning the low-dimensional feature representations for complex nonlinear cases. For this reason, our future work will address the HDR issue in a more complex scene and extend our framework to a nonlinear one with possible spatial information modeling.


Before matching, NHT, tumor volume percentage, and extracapsular extension were significant factors for resection margin positivity (p≤0.001); however, after matching, NHT became insignificant in the multivariate analysis (p=0.084). In the survival analysis, NHT was not associated with BCR or OS before and after matching . Conversely, PSA level , extracapsular extension , and lymphovascular invasion  were significant factors for BCR , but none were significant factors for OS in the propensity-score matching analysis (p≥0.948). Therefore, NHT was not a significant factor for resection margin positivity, BCR-free survival, and OS before and after propensity-score matching in patients with LAPC.This study aimed to investigate the effect of neoadjuvant hormone therapy (NHT) on resection margin positivity, biochemical-recurrence- (BCR-) free survival, and overall survival (OS) in 176 patients with locally advanced prostate cancer (LAPC) treated with radical prostatectomy using propensity-score matching, including 79 (44.9%) patients treated with the NHT. Fifty pairs of one-to-one propensity-score matching were matched to investigate the pure effect of NHT on resection margin positivity, BCR, and OS with a statistical significance of Prostate cancer (PC) results from an androgen-dependent tumor in males. The androgen-producing mechanism is a key therapeutic objective in the treatment of PC. In 1964, Scott and colleagues introduced the idea of encouraging recurrence-free survival in men with advanced PC by treating with radical prostatectomy (RP) and androgen deprivation therapy or androgen blocking hormonal therapy (HT). Presently, HT is one of the standard therapeutic options for PC in order to inhibit the growth of PC, especially for recurred PC after prostatectomy or advanced PC  receptor antagonist) versus luteinizing hormone-releasing hormone (LHRH) agonist in patients with PC prior to RP, neoadjuvant degarelix alone was associated with higher levels of intratumoral dihydrotestosterone compared to the use of LHRH agonist and bicalutamide, despite similar testosterone levels . AnotherLast important consideration for the use of HT is HT-associated complications. HT has its own local and systemic side effects, secondary to the blockade of androgen hormone , 29. TheThis study had limitations. First, this study had a small sample from a single institution with a short follow-up period. Second, the variations of the specific hormonal treatments, the duration of NHT, timing of treatments, the different antiandrogen agents and no adjustment of lymph node status or other pathological parameters in PS matching might have affected the results from the analyses. However, the clinical significance of this study is the potential to minimize the different baseline clinicopathological characteristics of tumor burden and disease status between groups using PS matching. Further studies with a large number of subjects and long-term follow-up are required, and the effect of secondary hormonal agents should also be investigated for their prognostic efficacy with and without androgen deprivation therapy.This study showed that NHT was not a significant factor in predicting RM positivity, BCR-free survival, and OS before and after PS matching in patients with locally advanced or high risk localized PC who underwent RP.


This work aims to comprehensively characterize hotspots and frontier landscapes concerning diabetes-specific distress from 2000 to 2018.  Firstly, diabetes-specific distress-related literature was retrieved and downloaded from the Web of Science Core Collection (WoSCC). Secondly, WoSCC self-contained toolkits and GraphPad Prism7 were conducted to analyze general characteristics, including literature products, countries, institutes, authors, and journal resource. Finally, CiteSpace V Toolkits was put forward to implement advanced analysis, consisting of keyword-term frequency and co-occurrence, references-cited frequency and co-occurrence, and burst detection for keyword terms and references cited, which uncovers the hotspots and frontiers of diabetes-specific distress. diabetic medicine delivered the largest number of documents. The United States occupied the leading positions, and the most productive institution was the University of California System in terms of literature products. Fisher L. has the highest references-cited frequency. Prevalence of diabetes-specific distress, diabetes-specific distress and glycemic control, diabetes-specific distress and depression comorbidity, and diabetes-specific distress and risk factors were the research hotspots, whereas the measure of diabetes-specific distress and latent and serious/severe diabetes-specific distress was the research frontiers.  After preprocessing, our study included a total of 1051 papers concerning diabetes-specific distress. Publication outputs increased smoothly year by year. Compared with other journals,  Overall, our study may inspire researchers to show great interest in diabetes-specific distress in the next few years. An overwhelmingly increasing number of publications over the past decades have suggested that diabetes mellitus is one of the most frequently diagnosed noninfectious chronic diseases and ranks first on the incidence of complications in noncommunicable chronic disorders –4. Most Fortunately, the association between diabetes-related psychological disorders and prognosis of diabetes has been widely studied during the past 30 years –12. HoweRecently, bibliometrics relying on literature databases has been widely applied in medical research . BibliomIn the present study, we retrieved diabetes-specific distress-related publications from 2000 to 2018 and explored the research hotspots and frontiers by means of CiteSpace V Toolkits. Taken together, we aimed to identify hotspots and frontiers by time and warrants further studies in the diabetes-specific distress filed.To avoid the discrepancies resulted from database daily updates, diabetes-specific distress-related publication was retrieved through the Science Citation Index-Expanded (SCI-E) of the Web of Science Core Collection (WoSCC)  on January 6, 2019. The search expressions were constructed as follows: TS = ((“diabetes distress”) OR (“diabetes” AND “distress”) OR  OR (“diabetes-related distress”) OR (“diabetes-specific distress”) OR  OR  OR  AND Language = English AND Time range = 2000–2018). In this study, only the original articles and reviews were included.Two authors (Dan Li and Fu-Min Dai) were independently in charge of literature retrieval, download, extraction, and verification. Any controversies were discussed, standardized, and unified by all authors. The literature contents including full records and references cited from WoSCC were downloaded in a text plain format, compliant with software input format. Then, literature profiling was imported into CiteSpace V  for data preprocessing, including deduplication and sorting.The WoSCC self-contained toolkits were used to analyze literature's general characteristics, including the number of annual publications, countries, institutes, author's information, and journal sources. GraphPad Prism7.0  was conducted to depict and visualize publication outputs and growth trends by means of a polynomial regression model.CiteSpace V was carried out to decode keyword-term frequency and co-occurrence, references-cited frequency and co-occurrence, detect keyword terms with the strongest citation burst, and construct visualization maps, thereby uncovering diabetes-specific distress-related hotspot and frontiers. Generally speaking, nodes standing for keyword terms and references cited with high frequency and centrality are identified as hotspots, while nodes with strong citation burst are considered as frontiers , 32.The criteria for selecting publications are shown as follows : (1) timR2) equals 0.95) by the year of publication. It is estimated from curve fitting that document circulations will reach approximately 213 in 2019. In light of the above, diabetes-specific distress is a relatively new field and is still in the developing phase at home and abroad. Therefore, there is also a need to pay more attention to diabetes-specific distress over the days ahead.According to the inclusion and exclusion criteria, a total of 1,051 publications were included in this study  published the most papers, followed by diabetes care (IF = 13.397), diabetes research and clinical practice (IF = 2.548), and diabetes educator (IF = 1.736).Despite the disadvantages, the impact factor (IF) is also a relatively objective index to evaluate the journal's quality and can reflect the academic level of journals at a certain degree at the present time . In totaThe premise that keywords can reflect research hotspots is that researchers carefully and accurately select professional terms. This team is inclined to integrate keywords and terms as bibliometrics research objects, which ensured that subsequent analysis maximizes the coverage degree of major points of a diabetes-specific distress domain. In this part, the keyword-term cluster view, timeline view, and frequency distribution were drawn/plotted via CiteSpace V. The parameters of CiteSpace V were configured as follows: time slicing , node types (term and keyword), selection criteria (top 30), and visualization .As was presented in Q and Mean Silhouette were 0.3891 and 0.5106, which demonstrated that clustering quality was acceptable. Top 5 keyword-term clusters were illustrated as follows: “diabetes distress,” “cohort study,” “anti-depression medication use,” “controlled trial,” and “risk factor management outcome.”The keyword-term cluster view  and timeReferences cited have always been considered as a core component of the bibliometrics research. Meanwhile, the CiteSpace toolkit has an absolute advantage in references-cited analysis. Therefore, CiteSpace V was performed to analyze references cited frequently and drawing reference-cited cluster view and timeline view. The parameters of CiteSpace V were configured as follows: node types (references cited). Other parameters are set as default.Q and Mean Silhouette were 0.7435 and 0.322, which indicated that cluster quality was reasonable. Top 5 references-cited clusters were presented as follows: “diabetes distress,” “adult type,” “care intervention,” “psychological risk factor,” and “nurse case management.”As shown in Keyword-term burst detection and references-cited burst detection were considered as indicators of research frontiers or emerging trends over time. diabetes care (IF = 13.397) had an IF higher than 10. Moreover, among the top ten authors, each person has published at least 17 papers; they were regarded as prolific authors, especially Snoek F. J. In addition, Fisher L. owned the highest references-cited frequency.In our study, we analyzed 1,051 publications that originated from the WoSCC database and diabetes-specific distress-related publication outputs maintained a steady overall growth trend from 2000 to 2018. Among countries, the United States had an absolute advantage in the number of paper outputs. And the top ten institutes were all from the United States. Remarkably, China was the only one from the developing country, showing its vast progress in diabetes-specific distress over the past decade. Among academic journals, Prevalence of Diabetes-Specific Distress. More and more attention has been paid to explore the prevalence of diabetes-specific distress in recent years. A meta-analysis including fifty-five original articles suggested that 36% people with type 2 diabetes suffer from diabetes-specific distress [distress . Furtherdistress . More stdistress . Taken tDiabetes-Specific Distress and Glycemic Control. Glycemic control has always been an essential indicator of the treatment and prognosis of diabetes. Long-term controllable glycemic control can greatly improve patient's quality of life. Data from several sources have identified that diabetes-specific distress has a strong negative correlation with glycemic control [ control –38. More control ,40. BaseDiabetes-Specific Distress and Depression Comorbidity. Common psychological comorbidities, including depression, anxiety, and diabetes-specific distress, are widely screened and are closely associated with poor outcomes in patients with diabetes [diabetes . For exadiabetes . Moreovediabetes . HoweverDiabetes-Specific Distress and Risk Factors. A number of studies have suggested that the prevalence of diabetes-specific distress is caused by many risk factors. In terms of gender, the females are more likely to suffer from diabetes-specific distress than male peers [le peers . Moreovele peers . In addile peers . TherefoDiabetes-Specific Distress and Management and Intervention. Precise management and the personalized intervention of diabetes-specific distress play a vital role in controlling blood sugar and preventing complications in patients with diabetes [diabetes . A systediabetes . Remarkadiabetes . HoweverHerein, considering the findings from keyword-term frequency distribution and co-occurrence and references-cited frequency distribution and co-occurrence, we summarized the following diabetes-specific distress-related research hotspots:Measurement of Diabetes Distress. The vigorous advance of diabetes distress benefits from the development, evaluation, and validation of the corresponding diabetes-specific distress scale. To the best of our knowledge, diabetes-specific distress has been measured in diverse scales so far. Polonsky et al. first described the diabetes-specific distress measurement and introduced the Problem Areas in Diabetes Scale (PAID) in 1995 [ in 1995 . Subsequ in 1995 . Althoug in 1995 . Moreove in 1995 . AlthougLatent and Serious/Severe Diabetes-Specific Distress. Thus far, there have been no studies focusing on the analysis of the latent diabetes-specific distress based on the existing literature. The current domestic academic research on severe diabetes-specific distress is only sporadic discussion [scussion ,48. In aWith views of burst detection for keyword terms and references cited, the research frontier can be shown as follows:To the best of our knowledge, this study is one of the first attempts to comprehensively draw the outline of diabetes-specific distress-related hotspots and frontiers based on a bibliometric method. Our findings will provide a new direction for research on diabetes-specific distress from multiple dimension.However, the generalizability of findings above is subject to certain limitations, including several biases: (1) Language bias: all publications included in this study were written in English. Therefore, there are many shortcomings such as much relevant information included other languages. (2) Literature-type bias: this study only includes the original articles and reviews published between 2000 and 2018, which may not be enough to represent all diabetes-specific distress literature. (3) Database selection bias: the literature included in this study was downloaded from WoSCC. However, there is no doubt that other databases such as PubMed, Google Scholar, Baidu Scholar, Scopus, and EMBASE may include a broader and deeper range of documentary coverage. (5) Literature-cited bias: document-cited counts need to be excluded from man-made malicious manipulation. (6) Bibliometrics and corresponding software bias: owing to limitations of the software, there is no uniform standard for setting parameters in CiteSpace. In spite of its limitations, this study certainly adds to our understanding of the research hotspots and frontiers. However, this study finding still needs further confirmation.The present study suggested that publication outputs of diabetes-specific distress have been rising over the past quarter century. Although diabetes-specific distress develops very late, we can foresee that the quantity and quality of publication have been expanded and promoted in recent years. More and more researchers show their interest in this fresh and promising discipline. Besides, our findings have a number of important implications for future clinical practice.


Background: Subjective age (SA) has been found to be a biopsychosocial marker of aging, yet little is known about factors that influence SA development. This study examined factors influencing SA using longitudinal data of community-dwelling older Japanese. Methods: Data drawn from the Kusatsu Longitudinal Study were collected during annual health check-ups in 2017 and 2018 from participants (aged 65-95) who completed all the measurement items used for this analysis (N=981). SA was indexed by asking participants to specify in years how old they felt. Proportional discrepancy scores /chronological age ×100) were calculated to indicate younger or older SAs and used as a dependent variable. As influencing factors of SA, chronological age, sex, years of schooling, history of smoking, cognitive function , depressive symptoms, physical function (gait speed), and social function (employment status) were examined. Analyses were performed with random-effects GLS regression models. Results: Significant partial regression coefficients were found for cognitive function , years of schooling , depressive symptoms , and chronological age . Implications: This study found that older age and longer years of schooling were associated with younger SA, while better cognition and depressive symptoms were linked to older SA. Better cognition being associated with older SA was inconsistent with existing studies. This may be due in part to the association of better cognition and the level of satisfaction influenced by awareness of age-related physical/social changes.


The aim was to evaluate the quality and acceptability of patient-reported outcome measures used to assess fatigue in patients with axial spondyloarthritis.A two-stage systematic review of major electronic databases (1980–2017) was carried out to: (i) identify measures; and (ii) identify evaluative studies. Study and measurement quality were evaluated following international standards. Measurement content was appraised against a conceptual model of RA-fatigue.From 387 reviewed abstracts, 23 articles provided evidence for nine fatigue-specific measures: 6 multi-item and 3 single-item. No axial spondyloarthritis-fatigue-specific measure was identified. Evidence of reliability was limited, but acceptable for the Multi-dimensional Fatigue Inventory  and Short Form 36-item Health Survey Vitality subscale . Evidence of construct validity was moderate for the Functional Assessment of Chronic Illness Therapy-Fatigue and 10 cm visual analog scale, limited for the SF-36 VT and not available for the remaining measures. Responsiveness was rarely evaluated. Evidence of measurement error, content validity or structural validity was not identified. Most measures provide a limited reflection of fatigue; the most comprehensive were the Multi-dimensional Assessment of Fatigue, Multi-dimensional Fatigue Inventory-20, Functional Assessment of Chronic Illness Therapy-fatigue and Fatigue Severity Scale.The limited content and often poor quality of the reviewed measures limit any clear recommendation for fatigue assessment in this population; assessments should be applied with caution until further robust evidence is established. Well-developed, patient-derived measures can provide essential evidence of the patient’s perspective to inform clinical research and drive tailored health care. The collaborative engagement of key stakeholders must seek to ensure that future fatigue assessment is relevant, acceptable and of high quality. Fatigue is important to patients, but the quality and acceptability of assessment are limited.Fatigue assessment is limited by methodological quality and limited relevance to patients.Future guidance should be co-produced with patients, ensuring both assessment relevance and methodological rigor.Pain, stiffness, reduced mobility and fatigue are cardinal features of axial spondyloarthritis (axSpA), including AS . HoweverThis review will systematically appraise, compare and synthesize published evidence of the quality and acceptability of clearly defined single- and multi-item PROMs used in fatigue assessment in axSpA to establish the quality and acceptability of fatigue measures. The review will provide a transparent assessment of the evidence with which to inform PROM selection for future application in axSpA research and clinical practice.Medical subject headings and free text searching reflected: (i) population: axSpA/AS; (ii) construct: fatigue; (iii) assessment type: PROMs; and (iv) measurement and practical properties . Five daOne author (N.A.P.) assessed all titles and abstracts; agreement was independently checked on a 10% subset by a second author (K.L.H.). A third author (J.C.P.) double-assessed all abstracts relating to PsA. Any conflicts were resolved through discussion.Studies were included if they contained a clearly identifiable and reproducible patient-reported assessment of fatigue, reported evidence of development and/or evaluation after completion by axSpA patients, and were written in English. Studies were excluded if they were available only as abstracts, fatigue assessment was not patient reported, clearly identifiable or reproducible, or the study described PROM application only.PROMs were included if they were fatigue specific, assessed fatigue as a separate domain within a multidomain measure, or were single or multi-item assessments. Clinician-reported assessments were excluded.Rheumatology Advances in Practice online). Practical properties included evidence of feasibility  and acceptability (patient relevance). Evidence of fatigue conceptualization and information pertaining to patient involvement was extracted and recorded. The RA-fatigue conceptual model .Disclosure statement: The authors declare no conflict of interest.Rheumatology Advances in Practice online.Supplementary DataClick here for additional data file.


Although wireless fingerprinting has been well researched and widely used for indoor localization, its performance is difficult to quantify. Therefore, when wireless fingerprinting solutions are used as location updates in multi-sensor integration, it is challenging to set their weight accurately. To alleviate this issue, this paper focuses on predicting wireless fingerprinting location uncertainty by given received signal strength (RSS) measurements through the use of machine learning (ML). Two ML methods are used, including an artificial neural network (ANN)-based approach and a Gaussian distribution (GD)-based method. The predicted location uncertainty is evaluated and further used to set the measurement noises in the dead-reckoning/wireless fingerprinting integrated localization extended Kalman filter (EKF). Indoor walking test results indicated the possibility of predicting the wireless fingerprinting uncertainty through ANN the effectiveness of setting measurement noises adaptively in the integrated localization EKF. The location information has become essential for the location-based service (LBS) applications. Global navigation satellite systems , BeiDou navigation satellite systems (BDS), GLONASS and Galileo) have achieved great success and commercialization during the past decades for outdoor applications. However, their performances may be degraded in indoor environments. To fill the gap for indoor mass-market localization, indoor localization with consumer portable devices has been researched.Among various indoor localization techniques, wireless localization has been widely used due to the promotion of access points  for wireless technologies such as low-power wide-area network (LPWAN), cellular network, wireless local area network (WiFi), Bluetooth low energy (BLE), ZigBee, ultra-wide-band (UWB), and radio frequency identification (RFID). Among various wireless measurements  , angle-oFor large-scale mass-market indoor localization applications, specific devices or networks may not be affordable. Thus, the existing wireless APs in public areas are used. Additionally, to lower the requirement for the costly database training process, methods such as crowdsourcing  and simuMethod #1: Integration with inertial sensors. The dead-reckoning (DR) solutions from inertial sensors are used to enhance the continuity and robustness of localization.Method #2: Setting the wireless localization uncertainty adaptively in data fusion. To achieve this objective, machine learning (ML) methods are used to predict the wireless localization uncertainty, which is further used to set the weight of wireless position updates.Therefore, two methods are used in this research to enhance indoor wireless localization:For Method #1, DR is self-contained to provide seamless outdoor/indoor localization solutions . HoweverFor Method #2, there are methods that use adaptive Kalman filters based on variables such as residuals  and innoANN has been introduced into the localization area over a decade ago  but has When investigated on the types of ANN used, CNN  is the mBesides ANN, there are ML methods that have been used for indoor fingerprinting. These approaches include nearest neighbors , GD-baseCompared to the existing wireless fingerprinting works, the main contributions of this paper areAlthough wireless fingerprinting has been widely used for indoor localization, its performance is difficult to quantify. Thus, this paper predicts fingerprinting-based location uncertainty by given RSS measurements. Two ML methods, including an ANN-based method and the GD-based method, are applied.Compared to the existing ML works, this paper uses ML from a new perspective. Specifically, instead of directly estimating the location or navigation states, this paper uses ML to learn and predict the relation between RSS and localization uncertainty. The ML-predicted location uncertainty is further used to set the measurement noises in the dead-reckoning/wireless fingerprinting integrated localization extended Kalman filter (EKF).This paper is organized as follows. As shown in ith RP in the database and i. j at RP i. i.Fingerprinting consists of the training and localization steps. The training step is conducted to generate a  database that consists of a set of reference points (RPs) with known coordinates and RSS from available APs, while the localization step finds the closest match between the measured RSS and those stored in the database. The reference fingerprint in the database can be written asThe objective for localization is to find the reference fingerprint ith RP  of the neurons in the output layer asith training example and m is the number of training examples.This paper applies the multi-layer perceptron (MLP) , which iAn important factor in back-propagation is to find the optimum values for weights and biases of the ANN in order to achieve desirable outputs from given inputs. The weights and biases are updated using the four standard back-propagation equations in . These eb-frame) to the local level frame , predicted by the inertial navigation system (INS) mechanization [n-frame, and n-frame with respect to the Earth frame , respectively; As shown in nization ; fn is tTo mitigate DR errors, the pedestrian velocity model  is used Refer to ,59 for dWhen Wireless fingerprinting position solutions are available, they are used as EKF location updates. The measurement model isWith the above system and measurement models, the EKF is applied to estimate the state vector in real time. The EKF works by predicting the process states and then obtaining feedback from noisy measurements. Refer to  for the Indoor walking tests were conducted in the MacEwan Student Hall, at the University of Calgary. Five Android smart devices were used, including a Samsung Galaxy S4, a Galaxy S7, a Huawei P10, a Lenovo Phab 2 Pro smartphone, and a Nexus 9 tablet.All devices were equipped with three-axis inertial sensors and a WiFi receiver. The data rates were 20 Hz for for inertial sensors and 0.5 Hz for WiFi.The test area was an indoor shopping mall environment, which had a size of approximately 160 m by 90 m. During both the training and testing periods, a Lenovo Phab 2 Pro smartphone was carried by each tester to collect red-green-blue-depth (RGB-D) images and compute SLAM solutions, so as to generate the reference trajectories. Refer to  for detaer AP in  and 40 mer AP in . For praAs illustrated in To train the MLP, the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm is applied. Refer to  for moreThe predicted location uncertainties were further used to set the wireless location measurement noises in the EKF. Compared to the DR/WiFi-CN strategy, the DR/WiFi-GD and DR/WiFi-ANN strategies had reduced the error RMS values by 27.9% and 23.3%, respectively, and had reduced the 95% errors by 32.3% and 28.7%. Thus, both the DR/WiFi-GD and DR/WiFi-ANN strategies are effective in reducing the location errors for DR/WiFi integration. This phenomenon indicates the effectiveness of adaptively setting location measurement noises in the EKF.Finally, to visualize the improvement by using DR/WiFi fingerprinting integration and using the ML-predicted location uncertainty in the EKF, This paper has verified the possibility for predicting wireless fingerprinting uncertainty through machine learning. Compared to the traditional DR/WiFi fingerprinting integrated method that uses a constant measurement noise setting for the wireless fingerprinting-based location update, the proposed method, which sets the measurement noise adaptively by using machine learning-based approaches, had reduced the indoor localization errors by 23.3 % to 32.3 %. Therefore, it is suggested to investigate not only the location solution, but also the corresponding location uncertainty from fingerprinting, so as to achieve more reliable indoor localization solutions.


The amygdala, which is involved in human social information processing and socio-emotional response neuronal circuits, is segmented into three subregions that are responsible for perception, affiliation, and aversion. Though there is different functional and effective connectivity (EC) among these networks, age-related structural changes and associations between structure and function within the amygdala remain unclear. Here, we used diffusion tensor imaging (DTI) data (106 participants) to investigate age-related structural changes in fractional anisotropy (FA) of amygdalar subregions. We also examined the relationship between FA and EC within the subregions. We found that the FA of the amygdalar subregions exhibited inverted-U-shape trends with age. Moreover, over the human lifespan, there were negative correlations between the FA of the right ventrolateral amygdala (VLA.R) and the Granger-based EC (GC) of VLA.R → perception network (PerN), the FA of the VLA.R and the GC of the net flow from VLA.R → PerN, and the FA of the left dorsal amygdala (DorA.L) and the GC of the aversion network (AveN). Conversely, there was a positive correlation between the FA of the DorA.L and the GC of the net flow from DorA.L → AveN. Our results suggest that age-related changes in the function of the brain are constrained by the underlying white matter architectures, while the functional information flow changes influence white matter structure. This work increases our understanding of the neuronal mechanisms in the maturation and aging process. The amygdala is involved in human social information processing and socio-emotional response neuronal circuits, and it plays an important role in dynamic emotional changes during human life. As a component of the “social brain,” the amygdala is a subcortical structure Adolphs, . Along wMyriad studies focus on functional connectivity (FC) between the amygdala and its related networks based on the NKI-RS databaseAccording to different cognitive and emotional representations in social information processing, the amygdala, which contains multiple nuclei, is segmented into three subregions. The ventrolateral amygdala (VLA) is involved in the social perception network (PerN), which includes the lOFC, FFA, rostral STS, ventromedial temporal cortex, temporal pole, and subgenual ACC. The medial amygdala (MedA) is related to the affiliation network (AffN), which includes the vmPFC, ventral medial striatum of the nucleus accumbens, ventromedial hypothalamus, adjoining subgenual and rACC, dorsomedial temporal pole, and medial temporal lobe. The dorsal amygdala (DorA) is associated with the aversion network (AveN), which includes the caudal ACC, ventrolateral striatum, anterior insula, somatosensory operculum, caudolateral hypothalamus, thalamus, and brainstem  to obtain age-related structural changes. Next, based on our previous age-related EC study in the amygdala . All participants are right-handed and not diagnosed with any mental abnormalities.The sample consisted of 106 participants , and 64-direction diffusion-weighted images .All three-dimensional T1, T2, and DTI imaging data were obtained with a 3-T Siemens Trio scanner. High-resolution T1-weighted structural data were obtained with magnetization-prepared rapid gradient echo sequence . T2-weighted functional data were obtained with a single-shot, gradient-recalled echo-planar sequence . DTI was conducted with sequence parameters , non-diffusion-weighted images  with no diffusion weighting; (2) evaluating diffusion tensor metrics and FA on each voxel; (3) registering all the individual FA images to the FA template in the MNI space; and (4) spatial smoothing using a 6-mm full-width at half-maximum Gaussian kernel for high signal-to-noise ratio, and resampling to 2 × 2 × 2 mm3 voxels.DTI data were preprocessed using a sequence of standard steps based on voxel-based analysis (VBA), and processing was conducted with the SPM8r) between each ROI and the left and right hemispheres, respectively. The correlation results were transformed to z (r) maps using Fisher’s r-to-z transformation, and the whole amygdala was depicted based on Harvard–Oxford Subcortical Structural Atlas probabilistic maps with thresholds at 50%. After excluding gender and age as covariates, we conducted one-sample t-tests [p < 0.05 with familywise error (FWE) correction] on z (r)-transformed maps. Finally, we identified amygdalar subregions by identifying which voxels in amygdala showed the strongest connectivity with each ROI on the statistically salient brain regions using paired t-tests  associated with amygdalar subregions, three seed regions of interest (ROIs) in lOFC, vmPFC, and cACC were created . We then extracted the mean FA values for each constructed ROI from the segmented map.t-tests .Within-group analyses of the left and right hemispheric VLA, MedA, and DorA FA were conducted using one-sample 2 as predictors. Therefore, the model can be written as:We built an age-related generalized linear model (GLM) to explore the FA alterations in left and right amygdalar subregions across life. Next, multiple linear regression equations for the GLM were constructed with gender as a covariate and with age and ageY denotes the fitted value and a0–a3 denote regression coefficients. The prediction model was based on Akaike’s information criterion .We calculated the Pearson’s correlation coefficient between the VBA-based FA maps and GCA-based GC maps as mentioned above for each hemispheric amygdalar subregion  and demonstrated an age-related inverted-U trajectory. For the right VLA FA, the quadratic term was significant  and showed an inverted-U trend with age. The left MedA FA presented a significant quadratic age effect  and demonstrated an age-related inverted-U trend. The right MedA FA exhibited a significant quadratic age effect  and demonstrated an age-related inverted-U trend. The left DorA FA exhibited a significant quadratic age term  and showed an inverted-U trend with aging. However, there was no significant correlation between right DorA and age. We obtained specific patterns that described age-related changes in mean amygdalar subregion FA degree vs. age . Considep < 0.05, FDR corrected). The GC influence from the right VLA (VLA.R) to PerN was negatively correlated with the VLA.R FA . The GC net flow from the VLA.R to PerN was also negatively related with the VLA.R FA . For GC, the AveN influence to left DorA (DorA.L) was negatively correlated with the DorA.L FA . Regarding GC, the net flow from the DorA.L to AveN was positively correlated with the DorA.L FA . None of the other hemispheric amygdalar subregions exhibited significant correlations with their related networks.After calculating the EC between amygdalar subregions and their related networks based on GCA, we acquired the correlation between the GC and FA for hemispheric amygdalar subregions  and was not very well distributed across all age ranges. The number and period might affect the explanation of results when applying cross-sectional designs to age-related research (Lindenberger et al., via a correlation between structure and function. The results demonstrated both consistency and discrepancy with regard to structural and functional changes. While structural architecture changes influence emotional functions, information flow also impacts amygdalar subregion structural alterations. This study could provide a theoretical foundation for researchers to understand the process of brain maturation and aging.In the current study, we examined the three segmented amygdalar subregions from the angles of social information processing and emotional control. We also explored age-related changes in white matter integrity of amygdalar subregions. Interestingly, we discovered an age-related effect in the amygdala YT: experiments design, conception and writing. YT, ZW and YJ: data analysis and the draft writing, discussed the design, data analysis and results. YT and YJ: contributed reagents, materials and analysis tools.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


ModelTest-NG is one to two orders of magnitude faster than jModelTest and ProtTest but equally accurate and introduces several new features, such as ascertainment bias correction, mixture, and free-rate models, or the automatic processing of single partitions. ModelTest-NG is available under a GNU GPL3 license at  It is well known that the use of distinct probabilistic models of evolution can change the outcome of phylogenetic analyses . Not surData and models supported: ModelTest-NG supports both nucleotide and amino acid models. It uses statistical criteria for selecting the best-fit substitution models such as AIC Partitioned MSAs: ModelTest-NG can automatically perform model selection on single, nonoverlapping partitions, as specified by the user .Phylogenetic templates: Users can select so-called templates for popular phylogenetic inference tools like RAxML  . This library encapsulates all compute- and memory-intensive phylogenetic likelihood computations and fully leverages the capabilities of modern x86 processors by using the AVX and AVX2 vector instruction sets. PLL also incorporates a recent algorithmic technique for accelerating likelihood calculations .ulations . All reqParallel execution: ModelTest-NG can take advantage of multicore desktop computers and clusters using PThreads and MPI (Message Passing Interface)  using simulated as well as empirical data sets. We measured model selection accuracy  using the simulated data sets, as well as run times. In all cases, we used the default model selection parameter settings. The experimental setup is described in detail in the ModelTest-NG found the true generating model for 81% of the simulated DNA MSAs  and for 85% of the simulated protein MSAs  . In geneIn terms of speed, on simulated DNA data, ModelTest-NG was 110.77 times faster than jModelTest but slower than ModelFinder (the latter was 1.59 times faster). On empirical DNA data, ModelTest-NG yielded average speedups of 510.13 over jModelTest and of 1.24 over ModelFinder  online. To ensure a fairer comparison with SMS, which only considers a subset of the models, we conducted a separate set of experiments comparing only ModelTest-NG and SMS on empirical data. For DNA data, both tools selected the same model 80% of the time, while ModelTest-NG was 95.53 times faster. For protein data, ModelTest-NG selected the same model as SMS 86.7% of the time and was 17.20 times faster.The thoroughness of the model parameter optimization routines in ModelTest-NG can be controlled by the user. In additional experiments, we found that the more thoroughly we optimize the likelihood score the more accurate the selected model becomes see  online. ModelTest-NG represents a substantial improvement over our previous tools, jModelTest and ProtTest. Although being equally accurate, it is up to two orders of magnitude faster on empirical data. Compared with ModelFinder, we observed similar run times for empirical data sets, but ModelFinder was faster on synthetic MSAs, particularly on DNA data. However, the accuracy of ModelFinder on DNA data was substantially lower than for ModelTest-NG . In future versions of ModelTest-NG, we intend to introduce new methods to dynamically determine the optimal speed/accuracy tradeoff for the data set at hand. ModelTest-NG is particularly well suited for analyzing large data sets.msz189_Supplementary_DataClick here for additional data file.


Rootstock has a significant impact on plant growth and development, including fruit maturation. However, the existence of mutual interaction between scion and rootstock is often neglected. To explore the origin of different fruit quality traits in citrus, we studied the effect of rootstock and the reciprocal interaction between scion and rootstock of nine combinations; three mandarin varieties grafted on three different rootstocks. We analyzed the metabolic profile of juice via gas and liquid chromatography-mass spectrometry . Additionally, we profiled phloem sap composition in the scion and the rootstock. Quality traits of fruit and their physio-chemical characteristics were also evaluated.For all three cultivars, rootstock was found to affect fruit yield and biochemical fruit quality parameters (sugar and acidity) in interactions with the scions. In mandarin juice, eight of 48 compounds (two primary and six secondary) were related directly to the rootstock, and another seven (one primary and six secondary) were interactively affected by scion and rootstock. In scion and rootstock sap, six and 14 of 53 and 55 primary metabolites, respectively, were directly affected by the rootstock, while 42 and 33 were affected by rootstock interactively with scion, respectively.In this work, we show for the first time a reciprocal effect between rootstock and scion. Based on our results, the scion and rootstock interaction might be organ, distance or time dependent. Grafting is a well-developed technique in fruit trees, which combines a scion and a rootstock to form a new plant with a blend of characteristics . SeveralCitrus aurantium L.) is known to produce moderate yields of orange and grapefruit with average fruit size and good quality  rootstock gives good-quality fruit and exhibits tolerance to CTV and citrus blight, but is salt-sensitive , Volkameriana lemon (designated Volka) [C. volkameriana (Ten. and Parq)] and SB-812 (designated 812) [C. sunki (Hort. ex Tan.) × Poncirus trifoliate (L.)]. Trees were planted in five randomized blocks of four trees (spaced 3 x 6 m) per block of the examined scion/rootstock combinations. The orchard comprises nine scion/rootstock combinations of mandarin. Three different easy-peeler Fruit yield data were collected from full-grown trees for three successive years, 2008, 2009 and 2010. The total fruit yield from each tree  was weighed separately, and the effect of scion/rootstock interaction on average accumulated yield per tree was calculated.The fruit was harvested at full maturity, at the peak of the commercial harvest season (December 2014). Fifty fruit from each of the nine combinations were randomly sampled , acidity (%), and ascorbic acid (100 mg/ml) analyses, and about 40 ml was stored at -80°C for gas chromatography-mass spectrometry (GC-MS) and ultra-performance liquid chromatography (UPLC-MS) analysis.Total soluble sugar content in the juice was determined with a digital Brix refractometer . Acid content was assessed by standardization of NaOH using phenolphthalein as an indicator by titration method. Vitamin C (ascorbic acid) was tested with DCPIP  as described by Raveh et al .n-dodecane, n-pentadecane, n-nonadecane, n-docosane, n-octacosane, n-dotracontane, and n-hexatriacontane dissolved in pyridine (0.0075% H2O)  and N-methyl-N-[trimethylsilyl] trifluoroacetamide .Metabolic profiling was carried out using a GC-MS based approach . The jui® data software (version 2.0.7) against RI libraries from the Max-Planck Institute for Plant Physiology in Golm, Germany (http://www.mpimp-golm.mpg.de/mms-library/) and finally normalized by respective ribitol amount. Results were elaborated and analyzed by multivariate data analysis approaches using the software package MetaboAnalyst 3.0 [For GC-MS analysis, one μL of the derivatized sample was injected in splitless mode onto a 30-m VF-5ms GC column with 0.25 mm i.d., a film thickness of 0.25 μm, and +10 m EZ-Guard (Agilent). The GCMS system consisted of an AS 3000 autosampler, a Trace GC Ultra gas chromatograph, and a DSQII quadrupole mass spectrometer (Thermo-Fisher ltd). The parameters of the machine were as previously described . Annotatlyst 3.0 .http://www.chemspider.com) and Metlin (https://metlin.scripps.edu) online databases. The quantification of the compounds was based on the relative peak response area of each mass signal after Pareto scaling in the chromatograms and normalized to the internal standard ampicillin [The extraction of metabolites was described above, as a combined GC-MS/LC-MS extraction protocol was used . After epicillin .A strip (about 5 by 7cm) of phloem tissue (phloem and its adjacent bark) was removed using a chisel from scion and rootstock parts (10 cm above and below the grafting zone) in the second week of August 2015. The tissue removal was done in the summer season because in this period the cambium tissues are active, allowing a precise separation between the xylem and the phloem parts. The phloem strips were cut into 1–1.5 cm pieces, and 3 to 5 pieces were placed vertically in a 0.5 ml tube. A small hole was made at the bottom of the tube, which was placed into a 2 ml tube for centrifugation.  After ceMetabolic profiling was carried out using a GC-MS based approach. Fifty μl of sap samples were used to perform the extraction process prior to the GC-MS run. Extraction was done using the protocol described in juice analysis previously. Extracted sap sample, 200 μl, was reduced to dryness in a vacufuge. The derivatization process was carried out in the same way as it was performed for juice, as mentioned before.P < 0.05) using JMP 8 and Prism program, version 5.0. Comparisons among the combinations based on the scion/rootstock reciprocal interaction on fruit quality and phloem sap were performed. Principal Component Analysis (PCA) was performed with the software package MetaboAnalyst 3.0 using the default weighted covariance estimation functions [P<0.05).Fruit yield and quality statistical analyses were done by Tukey test , and on another seven compounds in interaction with scion, total of 15 of 48 juice metabolites . To deteFor secondary metabolite profiling, we used a LC-MS-QTOF protocol. Eighteen metabolites were annotated, of which six were affected only by scion, five were affected by both scion and rootstock, and six metabolites changed among scions according to rootstock (interaction effect) . In totaA total of 53 metabolites were annotated in phloem sap of scions in all nine scion/rootstock combinations. In scion sap, one compound was affected only by rootstock, four only by scion, five by both rootstock and scion, and 42 changed in scion sap depending on specific scion/rootstock interaction . RootstoA total of 55 metabolites were annotated in phloem sap of rootstocks in all nine scion/rootstock combinations. Six were affected only by rootstock, eight only by scion, eight by both rootstock and scion, and 33 changed in rootstock sap depending on specific scion/rootstock interaction . In totaIn order to determine the scion-rootstock interaction in citrus trees, we studied their relation through analysis of fruit juice in addition to phloem sap samples from above and below the graft, as well as yield and chemical characteristic of the fruit of nine a grafted citrus combinations. Based on our results, we found that in addition to known effects of rootstocks on scion performances, scion and rootstock also interact to affect fruit juice and phloem sap composition.Regarding rootstock effect on fruit yield, Volka enhanced the yield of Michal, and also of Orra compared to SB-812. At the same time, SB-812 and SO had low yields in combination with both Merav and Orra. However, no rootstock had a significant effect on yield in combination with Merav. Volkameriana lemon is considered to produce vigorous trees producing large quantities of fruits . EarlierThe physio-chemical results strongly indicated that the effect of rootstock on mandarin fruit juice quality is rather a complex phenomenon that depends on a specific interaction between the rootstock and each scion variety. For instance: while Volka negatively affected TSS levels in Merav and Orra, it did not have such effect in Michal; while SO positively affected Michal and Orra TSS levels, it did not affect Merav; and while Volka induced TSS/TA ratio in Michal, the opposite was true for Merav. Additionally, Volka induced vitamin C contents in Orra, but hindered its production in Merav and Michal. Scion-rootstock interactions thus affect both fruit taste quality and its nutritional value.Our results show that fruit juice composition was mainly affected by scion, as of 48 metabolites only 15 were affected by rootstock (eight directly and seven through scion interaction). Of these, eight were related to flavonoids and their biosynthesis: quinic acid, vicenin-2, didymin, narirutin, tangeritin, rutin, diosmin and the amino acid phenylalanine. Another four compounds were limonoid related , two were amino acids (proline and tryptophan) and one organic acid (threonic acid). The health-related effects of citrus flavonoids have been vastly reported, including anti-oxidative, anti-carcinogenic and anti-diabetic effects, and thuBy analyzing phloem sap composition above and below the grafting zone  we were able to learn about scion and rootstock interaction in grafted trees. As opposed to fruit juice, both scion sap and rootstock sap were chiefly affected by rootstock and by scion-rootstock interactions: rootstock affected 48 of 53 metabolites in scion sap (six directly and 42 through scion interactions), and 47 of 55 compounds in rootstock sap (14 directly and 33 through scion interactions). It is unclear whether the inconsistency between fruit juice and phloem sap results from a dilution effects occurring along the stem, different time of sampling or organ-dependent gene expressionWhen considering directly affected metabolites in the sap (from both scion and rootstock), results show that levels of all these metabolites were low in SB-812. These include ribonic acid, malate, fumarate, phosphoric acid, cysteine, glycine, and pyroglutamate. Juice directly affected metabolites followed the same trend of low levels in SB-812, including quinic acid, nomilin, nomilinic acid-17-beta-D-glucopyranoside and vicenin-2. Additionally, the levels of interactively affected scion sap compounds are also lower in SB-812 in comparison to both other rootstocks, irrespective of the scion, and with not much interaction with the scion grafted . RootstoIn contrast to SB-812, SO rootstock had high levels of all directly affected metabolites, in both scion and rootstock sap, including valine, ferulic acid, fructose, and glucose. Volka rootstock showed a mixed trend, with low levels of glucose 6P, xylose lyxose and phosphoric acid, and high levels of ribonic acid, maleate, malate and galactopyranose. Both rootstocks demonstrated much interaction with the scion, presenting different profiles for each rootstock-scion combination. Specifically such combinations can be classified according to the degree of scion-rootstock of interaction, manifested by high levels of metabolites (number and intensity). Metabolically strong rootstock-scion interaction is favorable, as a more interactive scion-rootstock system is presumably better in means of plant physiological state, reflecting a more fluent connection between the two organisms of the system. As such, it enables a reciprocal and coherent communication, expressed as metabolite transfer between the two parts, and possibly allowing more flexibility in response to environmental conditions, including stress, resulting in long-lasting trees.Based on that parameter, it is evident in scion sap that the Orra-Volka combination has the lowest interaction , followed by Merav-SO, Merav-Volka and Orra-SO. The Michal-SO and Michal-Volka are the best combinations in regard to rootstock-scion interaction. In rootstock sap  it can bAlthough the number of interactive metabolites is higher in scion sap than in rootstock sap (47 vs.33), the degree of interaction is greater in the rootstock sap, while metabolites in the scion sap are less affected. Together with the larger number of directly affected metabolites in rootstock sap (14) compared to scion sap (6) and juice (8) , this suggests a distance-dependent effect for the rootstock. The rootstock imposes its effect locally, and thus it is greatest in rootstock sap, followed by scion sap, and lowest in juice, as shown by the number of directly and interactively affected compounds, and the degree of influence.Notably, among interactive metabolites are important transporters of carbon, such as sucrose and raffinose  and of nInterestingly, a small number of metabolites were affected by neither scion, rootstock or their interaction. In juice these were apigenin, myo-inositol, sucrose and asparagine, and tartaric acid in scion sap. These compounds might play an important role in citrus, as suggested for tartaric acid in citrus as a source of ascorbic acid  or for mTaken together, our results clearly suggest that rootstock-scion combination is a reciprocal system, crucial to fruit quality and juice and sap composition, with possible impact over tree physiology. Our work suggests a reciprocal relationship between the scion and roots in a grafted tree, whereby a mutual alteration/acclimation process in the metabolism is developing in the forced connection between two different genotypes. Furthermore, characteristics of either a scion or a rootstock cannot be determined for themselves, but rather only in interaction with each scion/ rootstock. Accordingly, choosing a rootstock-scion combination should first take into consideration the required characteristics of either the scion or the rootstock , followed by a careful choice of its compatible partner.Specifically, our results indicate that SB-812 has the lowest level of scion-rootstock interaction, while Michal-Volka, Michal-SO and Orra-SO combinations have the highest level of interaction. Further research is required in order to determine the biochemical mechanisms underlie this phenomenon, and should include a more detailed analysis of the interaction, including tree-physiological parameters, in addition to possibly identifying specific chemical and physiological markers to indicate the quality of interaction, in order to examine new combinations. Additionally, to better understand the locality attribute of scion-rootstock interaction, more sap measurements should be done alongside the grafting zone, in varying distances.Principally, every rootstock- scion combination is kind of a new organiam by itself. As a result, all the metabolic pathways are potentially affected by the grafing in both rootstock and scion. Yet, some of the metabolic pathways are strongly affected by rootstock (branced chain amino acid metabolism) while others by scion (fatty acid metaolism and secondary metabolites metabolism). This makes sense since its well known that some of the hormones that move along the plant are dominantly produced by the roots (e.g. cytokinins) while other are produced by the shoots (e.g. auxin). Therefore whenever we choose to create a new rootstock-scion combination, its potential effect on metabolic pathways should be taken into consideration. The effect on the pathways may vary along season and based on tree's age. The scion-rootstock union unite intimately, providing a viable system for the uptake and translocation of minerals, water, assimilates, and hormones throughout the entire lifespan of the tree –46. ImprIn summary, in this work we show for the first time a reciprocal interaction between rootstock and scion. Some of the rootstock effect that was found on the rootstock phloem sap also appeared in scion phloem sap, but not in fruit. Such inconsistency between the phloem sap and fruit might be due to the distance from the grafting zone (a dilution effect with distance), to sampling timing, or could arise from organ-dependent gene expression. The distance, organ and timing effects are currently investigated.S1 TableSmall letters- scion statistics; Capital letters-rootstock statistics; bold letters-interaction statistics.(XLSX)Click here for additional data file.S2 TableSmall letters- scion statistics; Capital letters-rootstock statistics; bold letters-interaction statistics.(XLSX)Click here for additional data file.S3 TableSmall letters- scion statistics; Capital letters-rootstock statistics. FC-fold change.(XLSX)Click here for additional data file.S4 TableSmall letters- scion statistics; Capital letters-rootstock statistics. FC-fold change.(XLSX)Click here for additional data file.