Erratum: [Corrigendum] Relationship between changes in mitochondrial function and hippocampal neuronal apoptosis after recurrent convulsion during developmental stage.

[This corrects the article DOI: 10.3892/etm.2018.6147.].

InSAR and machine learning reveal new understanding of coastal subsidence risk in the Yellow River Delta, China.

Coastal subsidence is a geological disaster that has devastating consequences. However, an accurate understanding of its risks involves more than simply assessing the amount or rate of land subsidence. The existing methods used to evaluate geological disaster risks depend on extensive data collection, entail substantial workloads, suffer from error estimation challenges, and lack regional adaptability. These limitations prevent us from fully understanding coastal subsidence risks in estuarine deltas. Therefore, in this study, we propose a new subsidence risk assessment method that addresses the challenges of traditional geological risk assessments in terms of spatial coverage, spatiotemporal resolution, and data collection difficulty. First, Sentinel-1 multitemporal interferometric synthetic aperture radar (MT-InSAR) and cluster analysis were used to estimate the subsidence hazards. Subsequently, Landsat-8 imagery and a random forest (RF) classifier were used to obtain land use and land cover (LULC), and the analytic hierarchy process (AHP) was used to obtain settlement vulnerability. Thereafter, subsidence susceptibility was derived from the sediment layer thickness. By combining subsidence hazard, vulnerability, and susceptibility, the first subsidence risk map with a 30 m resolution was generated. The results showed that 4.54 % of the Yellow River Delta (YRD) area was high-risk, 8.75 % was medium-risk, and 10.14 % was low-risk. Notably, the risk map shows a clear overlap between high-risk and saltwater mining areas in the YRD. The proposed method is expected to improve our understanding of the coastal subsidence risk in estuarine deltas. Considering that the risk in high-value economic areas in the YRD is increasing, whereas the risk in low-value economic areas may change owing to human activity, early preventive measures are required.

Correlation analysis between plasma fibrinogen and nerve electrophysiological changes in type 2 diabetic peripheral neuropathy.

INTRODUCTION: The aim of the study was to investigate the pathogenesis of diabetic peripheral neuropathy (DPN) and the value of fibrinogen (FIB) in the early diagnosis of DPN. MATERIAL AND METHODS: A total of 121 patients with type 2 diabetes mellitus (T2DM) and DPN hospitalized in the Endocrinology Department of the 923 Hospital of the People's Liberation Army of China were randomly selected between May and October 2020 and divided into a T2DM asymptomatic (no peripheral neuropathy-related symptoms) group (66 cases) and a T2DM symptomatic group (55 cases) according to the presence or absence of clinical neurological symptoms and signs. Forty healthy volunteers were selected as a normal control group. In addition to plasma FIB and nerve electrophysiological tests, all included subjects were electrophysiologically tested for nerve conduction velocity (NCV), terminal motor latency (DML), sensory nerve action potential (SNAP) amplitude, and compound muscle action potential (CMAP) amplitude. RESULTS: Compared with the control group, NCV was slowed down in T2DM patients, DML was prolonged, and the amplitude of CMAP and SNAP were decreased. Compared with asymptomatic T2DM patients, symptomatic patients had slower NCV, longer DML, lower CMAP amplitude of median nerve, ulnar nerve and tibial nerve, and significantly lower SNAP amplitude of median nerve and ulnar nerve. CMAP amplitudes were decreased, and median and ulnar nerve SNAP amplitudes were also significantly decreased ( p < 0.05). The plasma FIB concentration of asymptomatic patients with T2DM was higher than that of the control group, and the plasma FIB concentration of symptomatic patients with T2DM was higher than that of asymptomatic patients with T2DM ( p < 0.01). The NCV and DML of asymptomatic patients with T2DM slowed down and prolonged as the FIB level increased; the NCV of T2DM symptomatic patients also slowed down as FIB increased, and median and ulnar nerve DML increased as FIB increased. There was no correlation between NCV and DML and the plasma FIB level in the control group. SNAP amplitudes of symptomatic and asymptomatic patients with T2DM decreased as plasma FIB increased, while CMAP amplitudes of the tibial nerve and the T2DM symptomatic ulnar nerve decreased as FIB increased in the control group. CONCLUSIONS: FIB may be a contributing factor for diabetic neuropathy and could be used as an indicator in the early screening and diagnosis of peripheral neuropathy in patients with T2DM.

Role of Fibrinogen in Type-2 Diabetes Mellitus with Diabetic Neuropathy and its Preliminary Mechanism.

INTRODUCTION: Diabetic peripheral neuropathy (DN) is the most common complication of type 2 diabetes mellitus (T2DM). OBJECTIVE: This study aimed to explore the role of fibrinogen (FIB) in T2DM neuropathy and its preliminary mechanism. METHODS: Ten male Sprague-Dawley rats were divided into a normal control group (NC group) and a T2DM neuropathy model group (DN group). The DN group was given a high-energy diet and streptozotocin, while the NC group was given a normal diet and a citric acid buffer. The expression levels of related proteins were analysed. RESULTS: Electrophysiology: Compared with the NC group, the conduction latency of the somatosensory-evoked potential and nerve conduction velocity was prolonged in the DN group, while the motor nerve action potential was decreased. As seen under a light microscope, the peripheral nerve fibres in the DN group were swollen, and the nerve fibres in the posterior funiculus of the spinal cord were loose or missing. Moreover, as seen under an electron microscope, the peripheral nerve demyelination of the DN group was severe, with microvascular blood coagulation, luminal stenosis, and collapse. Compared with the NC group, in the DN group, the expression of FIB was positively correlated with the expression of both ionised calcium-binding adaptor molecule-1 and glial fibrillary acidic protein. Compared with the NC group, in the DN group, the expression of platelet/endothelial cell adhesion molecule-1 and B-cell lymphoma 2 was negatively correlated. CONCLUSION: The increased concentration of FIB may be the cause of neuropathy, and its mechanism may be related to its promotion of inflammatory response, blood coagulation, and vascular stenosis.

Hypoxia inducible factor-1α responds to freezing, anoxia and dehydration stresses in a freeze-tolerant frog.

The wood frog, Rana sylvatica (aka Lithobates sylvaticus) is the main model for studies of natural freeze tolerance among amphibians living in seasonally cold climates. During freezing, ∼65% of total body water can be converted to extracellular ice and this imposes both dehydration and hypoxia/anoxia stresses on cells. The current study analyzed the responses of the alpha subunit of the hypoxia-inducible transcription factor (HIF-1), a crucial oxygen-sensitive regulator of gene expression, to freezing, anoxia or dehydration stresses, examining six tissues of wood frogs (liver, skeletal muscle, brain, heart, kidney, skin). RT-PCR revealed a rapid elevation hif-1α transcript levels within 2 h of freeze initiation in both liver and brain and elevated levels of both mRNA and protein in liver and muscle after 24 h frozen. However, both transcript and protein levels reverted to control values after thawing except for HIF-1 protein in liver that dropped to ∼60% of control. Independent exposures of wood frogs to anoxia or dehydration stresses (two components of freezing) also triggered upregulation of hif-1α transcripts and/or HIF-1α protein in liver and kidney with variable responses in other tissues. The results show active modulation of HIF-1 in response to freezing, anoxia and dehydration stresses and implicate this transcription factor as a contributor to the regulation of metabolic adaptations needed for long term survival of wood frogs in the ischemic frozen state.

Relationship of binding-site occupancy, transthyretin stabilisation and disease modification in patients with tafamidis-treated transthyretin amyloid cardiomyopathy.

BACKGROUND: Tafamidis inhibits progression of transthyretin (TTR) amyloid cardiomyopathy (ATTR-CM) by binding TTR tetramer and inhibiting dissociation to monomers capable of denaturation and deposition in cardiac tissue. While the phase 3 ATTR-ACT trial demonstrated the efficacy of tafamidis, the degree to which the approved dose captures the full potential of the mechanism has yet to be assessed. METHODS: We developed a model of dynamic TTR concentrations in plasma to relate TTR occupancy by tafamidis to TTR stabilisation. We then developed population pharmacokinetic-pharmacodynamic models to characterise the relationship between stabilisation and measures of disease progression. RESULTS: Modelling individual patient data of tafamidis exposure and increased plasma TTR confirmed that single-site binding provides complete tetramer stabilisation in vivo. The approved dose was estimated to reduce unbound TTR tetramer by 92%, and was associated with 53%, 56% and 49% decreases in the rate of change in NT-proBNP, KCCQ-OS, and six-minute walk test disease progression measures, respectively. Simulating complete TTR stabilisation predicted slightly greater reductions of 58%, 61% and 54%, respectively. CONCLUSIONS: These findings support the value of TTR stabilisation as a clinically beneficial treatment option in ATTR-CM and the ability of tafamidis to realise nearly the full therapeutic benefit of this mechanism. CLINICALTRIALS.GOV IDENTIFIER: NCT01994889.

Triggering and recovery of earthquake accelerated landslides in Central Italy revealed by satellite radar observations.

Earthquake triggered landslides often pose a great threat to human life and property. Emerging research has been devoted to documenting coseismic landslides failed during or shortly after earthquakes, however, the long-term seismic effect that causes unstable landslides only to accelerate, moderately or acutely, without immediate failures is largely neglected. Here we show the activation and recovery of these earthquake accelerated landslides (EALs) in Central Italy, based on satellite radar observations. Unlike previous studies based on single or discrete landslides, we established a large inventory of 819 EALs and statistically quantified their spatial clustering features against a set of conditioning factors, thus finding that EALs did not rely on strong seismic shaking or hanging wall effects to occur and larger landslides were more likely to accelerate after earthquakes than smaller ones. We also discovered their accelerating-to-recovering sliding dynamics, and how they differed from the collapsed 759 coseismic landslides. These findings contribute to a more comprehensive understanding of the earthquake-triggering landslide mechanism and are of great significance for long-term landslide risk assessment in seismically active areas.

Retrospective Study of the Relationship between Variable Benign Epilepsy of Childhood with Centrotemporal Spikes and the Changes of Zinc, MRS, VEEG, and IQ Test.

Benign epilepsy in childhood with centrotemporal spikes (VBECT) has been associated with electroencephalography (EEG), but the relationship of VBECT with zinc, magnetic resonance spectroscopy (MRS), and intelligence quotient (IQ) tests is unclear. The aim of this study was to investigate the association of VBECT with zinc, MRS, EEG, and IQ tests. In this retrospective study, we selected 58 children with variable benign epilepsy with centrotemporal spikes as the experimental group. A total of 120 children with typical benign childhood epilepsy with centrotemporal spikes were selected as the control group. The zinc, MRS, EEG, and IQ test results of 178 children were measured and analyzed. The results showed that the zinc, MRS, and IQ test results of the patients in the experimental group decreased significantly. The spinal slow wave results in the experimental group showed a significant upward trend. Linear correlation analysis of zinc with MRS, EEG, and IQ tests showed that 13 pairs of indicators were significantly negatively correlated. Our results suggest the importance of zinc, MRS, EEG, and IQ during VBECT.

Distributed Generalized Nash Equilibrium Seeking and Its Application to Femtocell Networks.

In this article, distributed algorithms are developed to search the generalized Nash equilibrium (NE) with global constraints. Relations between the variational inequality and the NE are investigated via the Karush-Kuhn-Tucker (KKT) optimal conditions, which provide the underlying principle for developing the distributed algorithms. Two time-varying consensus schemes are proposed for each agent to estimate the actions of others, by which a distributed framework is established. The algorithm with fixed-gains is designed with certain system knowledge, while the adaptive algorithm is proposed to address the problem when the system parameters are not available. The asymptotic convergence to the NE is established through the Lyapunov theory and the consensus theory. The power control problem in a femtocell network is formulated as a Nash game and is solved by the proposed algorithms. The simulation results are provided to verify the effectiveness of theoretical development.

Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG.

Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moments) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, data-driven methods have emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. Simultaneously, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.

Bearing-Only Formation Control With Prespecified Convergence Time.

This article considers the bearing-only formation control problem, where the control of each agent only relies on relative bearings of their neighbors. A new control law is proposed to achieve target formations in finite time. Different from the existing results, the control law is based on a time-varying scaling gain. Hence, the convergence time can be arbitrarily chosen by users, and the derivative of the control input is continuous. Furthermore, sufficient conditions are given to guarantee almost global convergence and interagent collision avoidance. Then, a leader-follower control structure is proposed to achieve global convergence. By exploring the properties of the bearing Laplacian matrix, the collision avoidance and smooth control input are preserved. A multirobot hardware platform is designed to validate the theoretical results. Both simulation and experimental results demonstrate the effectiveness of our design.

Identifying barriers to sustainable apple production: A stakeholder perspective.

Apple is one of the most important cash crops in China. However, negative economic, environmental and social impacts are associated with its production. This study aims to apply a holistic systems perspective to understand existing problems associated with apple production in China and use this information to improve its sustainability. A structured survey was administered to farmers (n = 245) in Shandong and Shanxi provinces, combined with semi-structured interviews with apple supply chain stakeholders (n = 25). Themes, dimensions and relationships were identified based on an inductive thematic analysis of interview data, and then triangulated against the survey data. Interpretive Structural Modelling and Cross-Impact Matrix Multiplication Applied to Classification methods were applied to investigate interrelationships and effects of the elicited elements within the system. The results indicated that various environmental, economic and social problems are associated with apple production in China, including environmental and health risks associated with synthetic input applications, yield instability, deterioration of apple quality, farmers' uncertainty about accessing routes to market, and the ageing farming workforce. The interaction of socio-economic and supply chain issues has contributed to the system "lock-in" to unsustainable practices within the apple production system. Existing agricultural policies were ineffective as they did not include policy leverage to mitigate the multiple factors driving lock-in to unsustainable practices within the system. The research has provided evidence to enable policymakers to develop effective and targeted strategies to facilitate sustainable production within the apple production system. In particular, the future policy mix should consider the entirety of the food system including perspectives and requirements of different stakeholders. The three-stage approach applied has demonstrated its feasibility of investigating sustainability issues facing a particular industry within a specific cultural and policy context.

Elevated Expression of Glycerol-3-Phosphate Phosphatase as a Biomarker of Poor Prognosis and Aggressive Prostate Cancer.

The limitations of the biomarker prostate-specific antigen (PSA) necessitate the pursuit of biomarkers capable of better identifying high-risk prostate cancer (PC) patients in order to improve their therapeutic management and outcomes. Aggressive prostate tumors characteristically exhibit high rates of glycolysis and lipogenesis. Glycerol 3-phosphate phosphatase (G3PP), also known as phosphoglycolate phosphatase (PGP), is a recently identified mammalian enzyme, shown to play a role in the regulation of glucose metabolism, lipogenesis, lipolysis, and cellular nutrient-excess detoxification. We hypothesized that G3PP may relieve metabolic stress in cancer cells and assessed the association of its expression with PC patient prognosis. Using immunohistochemical staining, we assessed the epithelial expression of G3PP in two different radical prostatectomy (RP) cohorts with a total of 1797 patients, for whom information on biochemical recurrence (BCR), metastasis, and mortality was available. The association between biomarker expression, biochemical recurrence (BCR), bone metastasis, and prostate cancer-specific survival was established using log-rank and multivariable Cox regression analyses. High expression of G3PP in PC epithelial cells is associated with an increased risk of BCR, bone metastasis, and PC-specific mortality. Multivariate analysis revealed high G3PP expression in tumors as an independent predictor of BCR and bone metastasis development. High G3PP expression in tumors from patients eligible for prostatectomies is a new and independent prognostic biomarker of poor prognosis and aggressive PC for recurrence, bone metastasis, and mortality.

Soluble Low-density Lipoprotein Receptor-related Protein 1 in Juvenile Idiopathic Arthritis.

OBJECTIVES: This study aimed to expand knowledge about soluble low-density lipoprotein receptor-related protein 1 (sLRP1) in juvenile idiopathic arthritis (JIA) by determining associations of sLRP1 levels in nonsystemic JIA patients with clinical and inflammatory biomarker indicators of disease activity. METHODS: Plasma sLRP1 and 44 inflammation-related biomarkers were measured at enrollment and 6 months later in a cohort of 96 newly diagnosed Canadian patients with nonsystemic JIA. Relationships between sLRP1 levels and indicators of disease activity and biomarker levels were analyzed at both visits. RESULTS: At enrollment, sLRP1 levels correlated negatively with age and active joint counts. Children showed significantly higher levels of sLRP1 than adolescents (mean ranks: 55.4 and 41.9, respectively; P = 0.02). Participants with 4 or fewer active joints, compared to those with 5 or more active joints, had significantly higher sLRP1 levels (mean ranks: 56.2 and 40.7, respectively; P = 0.006). At enrollment, considering the entire cohort, sLRP1 correlated negatively with the number of active joints (r = -0.235, P = 0.017). In the entire cohort, sLRP1 levels at enrollment and 6 months later correlated with 13 and 6 pro- and antiinflammatory biomarkers, respectively. In JIA categories, sLRP1 correlations with inflammatory markers were significant in rheumatoid factor-negative polyarticular JIA, oligoarticular JIA, enthesitis-related arthritis, and psoriatic arthritis at enrollment. Higher sLRP1 levels at enrollment increased the likelihood of absence of active joints 6 months later. CONCLUSION: Plasma sLRP1 levels correlate with clinical and biomarker indicators of short-term improvement in JIA disease activity, supporting sLRP1 as an upstream biomarker of potential utility for assessing JIA disease activity and outcome prediction.

Optimizing Global Navigation Satellite Systems network real-time kinematic infrastructure for homogeneous positioning performance from the perspective of tropospheric effects.

Real-time centimetre-level precise positioning from Global Navigation Satellite Systems (GNSS) is critical for activities including landslide, glacier and coastal erosion monitoring, flood modelling, precision agriculture, intelligent transport systems, autonomous vehicles and the Internet of Things. This may be achieved via the real-time kinematic (RTK) GNSS approach, which uses a single receiver and a network of continuously operating GNSS reference stations (CORS). However, existing CORS networks have often been established simply by attempting regular spacing or in clusters around cities, with little consideration of weather, climate and topography effects, which influence the GNSS tropospheric delay, a substantial GNSS positional error and which prevents homogeneous RTK accuracy attainment. Here, we develop a framework towards optimizing the design of CORS ground infrastructure, such that tropospheric delay errors reduce to 1.5 mm worth of precipitable water vapour (PWV) globally. We obtain average optimal station spacings of 52 km in local summer and 70 km in local winter, inversely related to the atmospheric PWV variation, with denser networks typically required in the tropics and in mountainous areas. We also consider local CORS network infrastructure case studies, showing how after network modification interpolated PWV errors can be reduced from around 2.7 to 1.4 mm.

An EMG-Driven Musculoskeletal Model for Estimating Continuous Wrist Motion.

EMG-based continuous wrist joint motion estimation has been identified as a promising technique with huge potential in assistive robots. Conventional data-driven model-free methods tend to establish the relationship between the EMG signal and wrist motion using machine learning or deep learning techniques, but cannot interpret the functional relationship between neuro-commands and relevant joint motion. In this paper, an EMG-driven musculoskeletal model is proposed to estimate continuous wrist joint motion. This model interprets the muscle activation levels from EMG signals. A muscle-tendon model is developed to compute the muscle force during the voluntary flexion/extension movement, and a joint kinematic model is established to estimate the continuous wrist motion. To optimize the subject-specific physiological parameters, a genetic algorithm is designed to minimize the differences of joint motion prediction from the musculoskeletal model and joint motion measurement using motion data during training. Results show that mean root-mean-square-errors are 10.08°, 10.33°, 13.22° and 17.59° for single flexion/extension, continuous cycle and random motion trials, respectively. The mean coefficient of determination is over 0.9 for all the motion trials. The proposed EMG-driven model provides an accurate tracking performance based on user's intention.

A Physiologically Based in Silico Tool to Assess the Risk of Drug-Related Crystalluria.

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule's physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure-activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs.

Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery.

This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites.

Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network.

The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (MRDED) network is proposed, which integrates Convolutional Neural Network (CNN), Residual Network (ResNet), Dense Convolutional Network (DenseNet), Global Convolutional Network (GCN), and Convolutional Long Short-Term Memory (ConvLSTM). MRDED contains three parts: the Gray Level Gradient Co-occurrence Matrix (GLGCM), the Encoder network, and the Decoder network. GLGCM is used to extract low-level features, which are further processed by the Encoder. The Encoder network employs ResNet to extract features at different resolutions. There are two components of the Decoder network, namely, the Multi-level Features Extraction and Fusion (MFEF) and Score maps Fusion (SF). We implement two versions of MFEF, named MFEF1 and MFEF2, which generate separate score maps. The difference between them lies in that the Chained Residual Pooling (CRP) module is utilized in MFEF2, while ConvLSTM is adopted in MFEF1 to form the Improved Chained Residual Pooling (ICRP) module as the replacement. The two separate score maps generated by MFEF1 and MFEF2 are fused with different weights to produce the fused score map, which is further handled by the Softmax function to generate the final extraction results for water and shadow areas. To evaluate the proposed framework, MRDED is trained and tested with large SAR images. To further assess the classification performance, a total of eight different classification frameworks are compared with our proposed framework. MRDED outperformed by reaching 80.12% in Pixel Accuracy (PA) and 73.88% in Intersection of Union (IoU) for water, 88% in PA and 77.11% in IoU for shadow, and 95.16% in PA and 90.49% in IoU for background classification, respectively.