Multiple Subregions Research Articles

Progressive Parsing and Commonality Distillation for Few-Shot Remote Sensing Segmentation

In recent years, few-shot segmentation (FSS) has received widespread attention from scholars by virtue of its superiority in low-data regimes. Most existing research focuses on natural image processing, and very few studies are dedicated to the practical but challenging topic of remote sensing image understanding. Related experimental results show that directly transferring the previously proposed framework to the current domain is prone to produce unsatisfactory results with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incomplete objects</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">irrelevant distractors</i> . Such phenomena can be attributed to the lack of modules specifically designed for the complex characteristics of remote sensing images, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., great intra-class diversity and low target-background contrast. In this paper, we propose a conceptually simple and easy-to-implement framework to tackle the aforementioned problems. Specifically, our innovative design embodies two main aspects: i) the support mask is progressively parsed into multiple valuable sub-regions that can be further exploited to compute local descriptors with segmentation cues about intractable parts; ii) the base-class memories stored in the meta-training phase are replayed and leveraged for the distillation of novel-class prototypes, where the commonalities between classes are adequately explored, more in line with the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning to learn</i> . These two components, i.e., the progressive parsing module and commonality distillation module, contribute to each other and together constitute the proposed PCNet. We conduct extensive experiments on the standard benchmark to evaluate segmentation performance in few-shot settings. Quantitative and qualitative results illustrate that our PCNet distinctly outperforms previous FSS approaches and sets a new state-of-the-art.

Cortical anatomical variations, gene expression profiles, and clinical phenotypes in patients with schizophrenia

Background and HypothesisSchizophrenia (SZ) patients display significant structural brain abnormalities; nevertheless, the genetic mechanisms regulating cortical anatomical variations and their correlation with the disease phenotype are still ambiguous. Study DesignWe characterized anatomical variation using a surface-based method derived from structural magnetic resonance imaging of patients with SZ and age- and sex-matched healthy controls (HCs). Partial least-squares regression was performed across cortex regions between anatomical variation and average transcriptional profiles of SZ risk genes and all qualified genes from the Allen Human Brain Atlas. The morphological features of each brain region were correlated to symptomology variables in patients with SZ using partial correlation analysis. Study ResultsA total of 203 SZ and 201 HCs were included in the final analysis. We observed significant variation of 55 regions of cortical thickness, 23 regions of volume, 7 regions of area, and 55 regions of local gyrification index (LGI) between SZ and HC groups. Expression profiles of 4 SZ risk genes and 96 genes from all qualified genes showed a correlation to anatomical variability, however, after multiple comparisons, the correlations were no longer significant. LGI variability in multiple frontal subregions was associated with specific symptoms of SZ, whereas cognitive function involving attention/vigilance was linked to LGI variability across nine brain regions. ConclusionsCortical anatomical variation of patients with schizophrenia is associated with gene transcriptome profiles as well as clinical phenotypes.

Ca-STANet: Spatiotemporal Attention Network for Chlorophyll-a Prediction With Gap-Filled Remote Sensing Data

Long-term chlorophyll-a (Chl-a) prediction has the potential to provide an early warning of red tide, and support fishery management and marine ecosystem health. The existing learning-based Chl-a prediction methods mostly predict a single point or multiple points with monitoring data. However, the monitoring data are subject to sparse sampling and difficult to be measured in a large-scale and synchronous way. Moreover, the advanced learning-based models for point Chl-a prediction, such as long short-term memory (LSTM) and convolutional neural network (CNN)-LSTM, are unable to fully mining the spatio-temporal correlation of Chl-a variations. Therefore, by using the satellite remote sensing data with extensive coverage, we design a framework, namely Ca-STANet, to simultaneously predict the Chl-a of all the locations in a large-scale area from the perspective of spatio-temporal field. Specifically in our method, the original data are firstly divided into multiple sub-regions to capture the spatial heterogeneity of large-scale area. Then, two modules are respectively operated to mine the spatial correlation and long-term dependency features. Finally, the outputs from the two modules are integrated by a fusion module to fully mine the spatio-temporal correlations, which are exploited to attain the final Chl-a prediction. In this paper, the proposed Ca-STANet is comprehensively evaluated and compared with the legacy methods based on the OC-CCI Chl-a 5.0 data of the Bohai Sea. The results demonstrate that the proposed Ca-STANet is highly effective for Chl-a prediction and achieves higher prediction accuracy than the baseline methods. Moreover, as the OC-CCI Chl-a 5.0 data have many missing areas, we introduce DINEOF method to fill the data gaps before using them for prediction.

Analysis of decoupling performances of an isolation barrier using a mode‐matching method

AbstractThis paper uses a mode‐matching method to analyze the decoupling performance of an isolation barrier that prevents electromagnetic interference between cables. After dividing the overall analyzed space into multiple subregions, potential expressions for each subregion were derived using the Laplace equation in conjunction with the superposition principle. The Dirichlet and Neumann boundary conditions were then applied to the interfaces between subregions to compute the modal coefficients in the previously derived potential expressions. Based on the electric‐field strengths and distributions used in our testing, the geometrical parameters and voltage conditions were varied to find the optimal configuration of the isolation barrier and cables that maximize decoupling performance. The performed study is novel and informative in that the analysis results provide the electromagnetic background to determine the distances between cables separated by an isolation barrier in industrial facilities such as nuclear power plants.

Clarifying water clarity: A call to use metrics best suited to corresponding research and management goals in aquatic ecosystems

Clarifying water clarity: A call to use metrics best suited to corresponding research and management goals in aquatic ecosystems

Microglia are implicated in the development of paclitaxel chemotherapy-associated cognitive impairment in female mice

Chemotherapy remains a mainstay in the treatment of many types of cancer even though it is associated with debilitating behavioral side effects referred to as “chemobrain,” including difficulty concentrating and memory impairment. The predominant hypothesis in the field is that systemic inflammation drives these cognitive impairments, although the brain mechanisms by which this occurs remain poorly understood. Here, we hypothesized that microglia are activated by chemotherapy and drive chemotherapy-associated cognitive impairments. To test this hypothesis, we treated female C57BL/6 mice with a clinically-relevant regimen of a common chemotherapeutic, paclitaxel (6 i.p. doses at 30 mg/kg), which impairs memory of an aversive stimulus as assessed via a contextual fear conditioning (CFC) paradigm. Paclitaxel increased the percent area of IBA1 staining in the dentate gyrus of the hippocampus. Moreover, using a machine learning random forest classifier we identified immunohistochemical features of reactive microglia in multiple hippocampal subregions that were distinct between vehicle- and paclitaxel-treated mice. Paclitaxel treatment also increased gene expression of inflammatory cytokines in a microglia-enriched population of cells from mice. Lastly, a selective inhibitor of colony stimulating factor 1 receptor, PLX5622, was employed to deplete microglia and then assess CFC performance following paclitaxel treatment. PLX5622 significantly reduced hippocampal gene expression of paclitaxel-induced proinflammatory cytokines and restored memory, suggesting that microglia play a critical role in the development of chemotherapy-associated neuroinflammation and cognitive impairments. This work provides critical evidence that microglia drive paclitaxel-associated cognitive impairments, a key mechanistic detail for determining preventative and intervention strategies for these burdensome side effects.

A Functional Region Decomposition Method to Enhance fNIRS Classification of Mental States

Functional near-infrared spectroscopy (fNIRS) classification of mental states is of important significance in many neuroscience and clinical applications. Existing classification algorithms use all signal-collected brain regions as a whole, and brain sub-region contributions have not been well investigated. This paper proposes a functional region decomposition (FRD) method to incorporate brain sub-region contributions and enhance fNIRS classification of mental states. Specifically, the method iteratively decomposes the brain region into multiple sub-regions to maximize their contributions with respect to the validation accuracy and coverage of brain sub-regions. Then for the fNIRS data in brain sub-regions, features are extracted and classified to output the predictions. The final predictions are determined by fusing predictions from multiple brain sub-regions with stacking. Experiments on a publicly available fNIRS dataset showed that the proposed functional region decomposition method led to 9.01% and 10.58% increase of classification accuracy for the methods related to slope-based features and mean concentration change features, respectively. Therefore, the proposed method can decompose the brain region into sub-regions with respect to their functional contributions and fundamentally enhance the performance of mental state classification.

Embedded parallel Fourier ptychographic microscopy reconstruction system.

Fourier ptychographic microscopy (FPM) has attracted a wide range of focus for its ability of large space-bandwidth product and quantitative phase imaging. It is a typical computational imaging technique that jointly optimizes imaging hardware and reconstruction algorithms. The data redundancy and inverse problem algorithms are the sources of FPM's excellent performance. But at the same time, this large amount of data processing and complex algorithms also evidently reduce the imaging speed. To accelerate the FPM reconstruction speed, we proposed a fast FPM reconstruction framework consisting of three levels of parallel computation and implemented it with an embedded computing module. In the conventional FPM framework, the sample image is divided into multiple sub-regions to process separately because the illumination angles and defocus distances for different sub-regions may also be different. Our parallel framework first performs digital refocusing and high-resolution reconstruction for each sub-region separately and then stitches the complex sub-regions together to obtain the final high-resolution complex image. The feasibility of the proposed parallel FPM reconstruction framework is verified with different experimental results acquired with the system we built.

Some mathematical aspects of Anderson localization: boundary effect, multimodality, and bifurcation

Anderson localization is a famous wave phenomenon that describes the absence of diffusion of waves in a disordered medium. Here we generalize the landscape theory of Anderson localization to general elliptic operators and complex boundary conditions using a probabilistic approach, and further investigate some mathematical aspects of Anderson localization that are rarely discussed before. First, we observe that under the Neumann boundary condition, the low energy quantum states are localized on the boundary of the domain with high probability. We provide a detailed explanation of this phenomenon using the concept of extended subregions and obtain an analytical expression of this probability in the one-dimensional case. Second, we find that the quantum states may be localized in multiple different subregions with high probability in the one-dimensional case and we derive an explicit expression of this probability for various boundary conditions. Finally, we examine a bifurcation phenomenon of the localization subregion as the strength of disorder varies. The critical threshold of bifurcation is analytically computed based on a toy model and the dependence of the critical threshold on model parameters is analyzed.

Automatic Contour Refinement of Inaccurate Auto-Segmentation Using an Active Contour Model for MR-Guided Adaptive Radiotherapy

<h3>Purpose/Objective(s)</h3> One of the bottleneck problems of MR-guided adaptive radiotherapy (MRgART) is the impractically long time required to segment the patient's anatomy of the day. Deep learning auto-segmentation (DLAS) has a limited success for complex structures such as those in abdomen. We have previously proposed an active contour model (ACM) to automatically refine the inaccurate contours from DLAS. This study aims to improve the performance of the ACM method by further optimizing the method and testing it using large and diversified abdominal MRI datasets. <h3>Materials/Methods</h3> An improved ACM method that does not require any manual parameter adjustment was implemented. The ACM utilizes the probability maps generated from DLAS models to establish 2D parameter maps and to initialize contour evolution. The performance of the ACM method was tested on the inaccurate DLAS contours of abdominal organs from 91 MRI sets (76 sets of T2-HASTE sequence from a 3T MR-simulator and 15 sets of BTFE sequence from a 1.5T MR-Linac). Bowels were divided into multiple subregions with each including a bowel loop. Levels of contour inaccuracies were divided into two groups: (1) major error group with Dice similarity coefficient (DSC) <0.5 or mean distance to agreement (MDA) >8 mm, and (2) minor error group with remaining contours of 0.5≤ DSC<0.8 or 3 mm<MDA≤8 mm. The performance of the ACM was evaluated based on various metrics, e.g., DSC, MDA, and the added path length (APL, a metric that is correlated with contour editing time). Manually delineated contours were used as ground truth. <h3>Results</h3> As shown in the table below, the average DSC, MDA and APL values for all the contours were improved after the ACM correction. By applying this ACM, approximately half of the contours in major error group were improved to minor error group, and 12-40% of the contours became practically acceptable per TG-132 recommendation (DSC>0.8 and MDA<3 mm). The largest improvement was seen in the major error group of pancreases, where APL reduced by one third, indicating substantial reductions in the subsequent contour editing effort. The execution time of this method to correct one contour was around 1 second. <h3>Conclusion</h3> The ACM method was successfully implemented to automictically and quickly correct for inaccurate contours from MRI-based DL auto-segmentation for complex anatomy, e.g., bowels and pancreas, in abdomen. The ACM method may be used to reduce the manual contour editing workload and accelerate the segmentation process for MRgART.

Deep learning prediction of two-dimensional ocean dynamics with wavelet-compressed data.

This study addresses the challenge represented by the application of deep learning models to the prediction of ocean dynamics using datasets over a large region or with high spatial or temporal resolution In a previous study by the authors of this article, they showed that such a challenge could be met by using a divide and conquer approach. The domain was in fact split into multiple sub-regions, which were small enough to be predicted individually and in parallel with each other by a deep learning model. At each time step of the prediction process, the sub-model solutions would be merged at the boundary of each sub-region to remove discontinuities between consecutive domains in order to predict the evolution of the full domain. This approach led to the growth of non-dynamical errors that decreased the prediction skill of our model. In the study herein, we show that wavelets can be used to compress the data and reduce its dimension. Each compression level reduces by a factor of two the horizontal resolution of the dataset. We show that despite the loss of information, a level 3 compression produces an improved prediction of the ocean two-dimensional data in comparison to the divide and conquer approach. Our method is evaluated on the prediction of the sea surface height of the most energetic feature of the Gulf of Mexico, namely the Loop Current.

Forward and Backward Multibeam Scanning Controlled by a Holographic Acoustic Metasurface

Acoustic metasurfaces have great potential in the field of beam-forming acoustic antennas due to their thin, two-dimensional, and miniature artificial materials that can freely control sound waves. Here, we propose an acoustic metasurface, with multibeam acoustic radiation serving as leaky-wave antennas used for acoustic beam steering. Based on the holographic principle, we pattern a sinusoidal modulated admittance surface, which is designed as a periodic arrangement of cylindrical holes with varying depth profiles. An omnidirectional sound generated from a tiny hole located at the center of the patterned plate creates acoustic surface waves on the modulated surface while simultaneously generating leaky-wave radiation. For multibeam steering, the holographic admittance surfaces are designed with multiple subregions of the metasurface, with each subregion emitting single-beam radiation in the desired direction. Furthermore, the holographic metasurfaces are programmed separately to have forward and backward leaky-wave radiation. In this study, we use three-dimensional additive fabrication to print acoustic holograms to craft the surface-admittance variation at subwavelength resolution. Experimental results on these printed holograms show multidirectional acoustic beam steering in close agreement with the theoretical analysis and numerical results. Forward and backward multibeam frequency scanning is also demonstrated by frequency variation. Thus, we expect that this planar surface can be used in applications such as acoustic communications, levitation, and imaging.

Basal Forebrain-Dorsal Hippocampus Cholinergic Circuit Regulates Olfactory Associative Learning.

The basal forebrain, an anatomically heterogeneous brain area containing multiple distinct subregions and neuronal populations, innervates many brain regions including the hippocampus (HIP), a key brain region responsible for learning and memory. Although recent studies have revealed that basal forebrain cholinergic neurons (BFCNs) are involved in olfactory associative learning and memory, the potential neural circuit is not clearly dissected yet. Here, using an anterograde monosynaptic tracing strategy, we revealed that BFCNs in different subregions projected to many brain areas, but with significant differentiations. Our rabies virus retrograde tracing results found that the dorsal HIP (dHIP) received heavy projections from the cholinergic neurons in the nucleus of the horizontal limb of the diagonal band (HDB), magnocellular preoptic nucleus (MCPO), and substantia innominate (SI) brain regions, which are known as the HMS complex (HMSc). Functionally, fiber photometry showed that cholinergic neurons in the HMSc were significantly activated in odor-cued go/no-go discrimination tasks. Moreover, specific depletion of the HMSc cholinergic neurons innervating the dHIP significantly decreased the performance accuracies in odor-cued go/no-go discrimination tasks. Taken together, these studies provided detailed information about the projections of different BFCN subpopulations and revealed that the HMSc-dHIP cholinergic circuit plays a crucial role in regulating olfactory associative learning.

Transcriptional substrates underlying functional connectivity profiles of subregions within the human sensorimotor cortex.

The human sensorimotor cortex has multiple subregions showing functional commonalities and differences, likely attributable to their connectivity profiles. However, the molecular substrates underlying such connectivity profiles are unclear. Here, transcriptome-neuroimaging spatial correlation analyses were performed between transcriptomic data from the Allen human brain atlas and resting-state functional connectivity (rsFC) of 24 fine-grained sensorimotor subregions from 793 healthy subjects. Results showed that rsFC of six sensorimotor subregions were associated with expression measures of six gene sets that were specifically expressed in brain tissue. These sensorimotor subregions could be classified into the polygenic- and oligogenic-modulated subregions, whose rsFC were related to gene sets diverging on their numbers (hundreds vs. dozens) and functional characteristics. First, the former were specifically expressed in multiple types of neurons and immune cells, yet the latter were not specifically expressed in any cortical cell types. Second, the former were preferentially expressed during the middle and late stages of cortical development, while the latter showed no preferential expression during any stages. Third, the former were prone to be enriched for general biological functions and pathways, but the latter for specialized biological functions and pathways. Fourth, the former were enriched for neuropsychiatric disorders, whereas this enrichment was absent for the latter. Finally, although the identified genes were commonly associated with sensorimotor behavioral processes, the polygenic-modulated subregions associated genes were additionally related to vision and dementia. These findings may advance our understanding of the functional homogeneity and heterogeneity of the human sensorimotor cortex from the perspective of underlying genetic architecture.

Dynamic dispatch of connected taxis for large-scale urban road networks with stochastic demands: An MFD-enabled hierarchical and cooperative approach

The dynamic dispatch of taxis is an important way to improve the service efficiency of taxis and reduce cruising distance. However, in a complex urban road network with extensive and stochastic roadside service demands, current macroscopic dispatch methods can hardly establish accurate and real-time matching relationships between taxis and passengers, and microscopic dispatch methods have the disadvantage of excessive computational complexity and nonupdatable match relationships. To this end, this paper proposes a hierarchical and cooperative macroscopic and microscopic dynamic dispatching approach for real-time urban network taxis in a connected taxi (CTaxi) information environment. First, network traffic flow dynamic evolution is described based on a macroscopic fundamental diagram (MFD), and the optimal macroscopic dispatching model is proposed to optimize the distribution of CTaxis for large-scale urban road networks with multiple MFD sub-regions. Second, to avoid the phenomenon of CTaxis blindly searching for passengers, a microscopic and relationship-updatable dispatching strategy between CTaxis and passengers is established within each MFD sub-region considering stochastic service demands and the signal timing of intersections. Finally, numerical experiments are conducted in a real road network to compare the performance of four methods: no CTaxi dispatch (NCD), conventional CTaxi dispatch (CCD), MPC dispatching (MPCD) and the dispatching strategy cooperating macro and micro (DSCMM) proposed in this paper. The results show that the DSCMM method can reduce the idle driving distance by 26.8%, 10.8% and 8.3% and the passenger waiting time by 11.0%, 13.4% and 6.4% compared to NCD, CCD and MPCD, respectively. Additionally, the market allocation of CTaxis under four scenarios is discussed, and the results demonstrate that the proposed DSCMM method is effective in improving the efficiency of CTaxis operation and reducing the waste of service resources in urban road networks with different service scales.

Early Onset Diffusion Abnormalities in Refractory Headache Disorders.

ObjectiveThis study sought to determine if individuals with medically refractory migraine headache have volume or diffusion abnormalities on neuroimaging compared to neurotypical individuals.BackgroundNeuroimaging biomarkers in headache medicine continue to be limited. Early prediction of medically refractory headache and migraine disorders could result in earlier administration of high efficacy therapeutics.MethodsA single-center, retrospective, case control study was performed. All patients were evaluated clinically between 2014 and 2018. Individuals with medically refractory migraine headache (defined by ICDH-3 criteria) without any other chronic medical diseases were enrolled. Patients had to have failed more than two therapeutics and aura was not exclusionary. The initial MRI study for each patient was reviewed. Multiple brain regions were analyzed for volume and apparent diffusion coefficient values. These were compared to 81 neurotypical control patients.ResultsA total of 79 patients with medically refractory migraine headache were included and compared to 74 neurotypical controls without headache disorders. Time between clinical diagnosis and neuroimaging was a median of 24 months (IQR: 12.0–37.0). Comparison of individuals with medically refractory migraine headache to controls revealed statistically significant differences in median apparent diffusion coefficient (ADC) in multiple brain subregions (p < 0.001). Post-hoc pair-wise analysis comparing individuals with medically refractory migraine headache to control patients revealed significantly decreased median ADC values for the thalamus, caudate, putamen, pallidum, amygdala, brainstem, and cerebral white matter. No volumetric differences were observed between groups.ConclusionsIn individuals with medically refractory MH, ADC changes are measurable in multiple brain structures at an early age, prior to the failure of multiple pharmacologic interventions and the diagnosis of medically refractory MH. This data supports the hypothesis that structural connectivity issues may predispose some patients toward more medically refractory pain disorders such as MH.

The thalamus and its subregions – a gateway to obsessive-compulsive disorder

IntroductionHigher thalamic volume has been found in children with obsessive-compulsive disorder (OCD) and children with clinical-level symptoms within the general population (Boedhoe et al. 2017, Weeland et al. 2021a). Functionally distinct thalamic nuclei are an integral part of OCD-relevant brain circuitry.ObjectivesWe aimed to study the thalamic nuclei volume in relation to subclinical and clinical OCD across different age ranges. Understanding the role of thalamic nuclei and their associated circuits in pediatric OCD could lead towards treatment strategies specifically targeting these circuits.MethodsWe studied the relationship between thalamic nuclei and obsessive-compulsive symptoms (OCS) in a large sample of school-aged children from the Generation R Study (N = 2500) (Weeland et al. 2021b). Using the data from the ENIGMA-OCD working group we conducted mega-analyses to study thalamic subregional volume in OCD across the lifespan in 2,649 OCD patients and 2,774 healthy controls across 29 sites (Weeland et al. 2021c). Thalamic nuclei were grouped into five subregions: anterior, ventral, intralaminar/medial, lateral and pulvinar (Figure 1).ResultsBoth children with subclinical and clinical OCD compared with controls show increased volume across multiple thalamic subregions. Adult OCD patients have decreased volume across all subregions (Figure 2), which was mostly driven by medicated and adult-onset patients.Conclusions Our results suggests that OCD-related thalamic volume differences are global and not driven by particular subregions and that the direction of effects are driven by both age and medication status.DisclosureNo significant relationships.

The Potential of Sentinel-1A Data for Identification of Debris-Covered Alpine Glacier Based on Machine Learning Approach

Microwave remote sensing is one of the main approaches to glacier monitoring. This paper provides a comparative analysis of how different types of radar information differ in identifying debris-covered alpine glaciers using machine learning algorithms. Based on Sentinel-1A data, three data suites were designed: A backscattering coefficient (BC)-based data suite, a polarization decomposition parameter (PDP)-based data suite, and an interference coherence coefficient (ICC)-based data suite. Four glaciers with very different orientations in different climatic zones of the Tibetan Plateau were selected and classified using an integrated machine learning classification approach. The results showed that: (1) The boosted trees and subspace k-nearest neighbor algorithms were optimal and robust; and (2) the PDP suite (63.41–99.57%) and BC suite (55.85–99.94%) both had good recognition accuracy for all glaciers; notably, the PDP suite exhibited better rock and debris recognition accuracy. We also analyzed the influence of the distribution of glacier surface aspect on the classification accuracy and found that the more asymmetric it was about the sensor orbital plane, the more difficult it was for the BC and PDP suites to recognize the glacier, and a large slope could further reduce the accuracy. Our results suggested that during the inventory or classification of large-scale debris-covered alpine glaciers, priority should be given to polarization decomposition features and elevation information, and it is best to divide the glaciers into multiple subregions based on the spatial relationship between glacier surface aspect and radar beams.

Relationships between species richness and ecosystem services in Amazonian forests strongly influenced by biogeographical strata and forest types

Despite increasing attention for relationships between species richness and ecosystem services, for tropical forests such relationships are still under discussion. Contradicting relationships have been reported concerning carbon stock, while little is known about relationships concerning timber stock and the abundance of non-timber forest product producing plant species (NTFP abundance). Using 151 1-ha plots, we related tree and arborescent palm species richness to carbon stock, timber stock and NTFP abundance across the Guiana Shield, and using 283 1-ha plots, to carbon stock across all of Amazonia. We analysed how environmental heterogeneity influenced these relationships, assessing differences across and within multiple forest types, biogeographic regions and subregions. Species richness showed significant relationships with all three ecosystem services, but relationships differed between forest types and among biogeographical strata. We found that species richness was positively associated to carbon stock in all biogeographical strata. This association became obscured by variation across biogeographical regions at the scale of Amazonia, resembling a Simpson’s paradox. By contrast, species richness was weakly or not significantly related to timber stock and NTFP abundance, suggesting that species richness is not a good predictor for these ecosystem services. Our findings illustrate the importance of environmental stratification in analysing biodiversity-ecosystem services relationships.

Indicator-Based Evolutionary Algorithm for Solving Constrained Multiobjective Optimization Problems

To prevent the population from getting stuck in local areas and then missing the constrained Pareto front fragments in dealing with constrained multiobjective optimization problems (CMOPs), it is important to guide the population to evenly explore the promising areas that are not dominated by all examined feasible solutions. To this end, we first introduce a cost value-based distance into the objective space, and then use this distance and the constraints to define an indicator to evaluate the contribution of each individual to exploring the promising areas. Theoretical studies show that the proposed indicator can effectively guide population to focus on exploring the promising areas without crowding in local areas. Accordingly, we propose a new constraint handling technique (CHT) based on this indicator. To further improve the diversity of population in the promising areas, the proposed indicator-based CHT divides the promising areas into multiple subregions, and then gives priority to removing the individuals with the worst fitness values in the densest subregions. We embed the indicator-based CHT in evolutionary algorithm and propose an indicator-based constrained multiobjective algorithm for solving CMOPs. Numerical experiments on several benchmark suites show the effectiveness of the proposed algorithm. Compared with six state-of-the-art constrained evolutionary multiobjective optimization algorithms, the proposed algorithm performs better in dealing with different types of CMOPs, especially in those problems that the individuals are easy to appear in the local infeasible areas that dominate the constrained Pareto front fragments.