Overlap Measure Research Articles

Overlap Analysis in Progressive Hybrid Censoring: A Focus on Adaptive Type-II and Lomax Distribution

This article explores the adaptive type-II progressive hybrid censoring scheme, introduced by Ng et al. (2009), which is used to make inferences about three measures of overlap: Matusita's measure ($\rho $), Morisita's measure ($\lambda $), and Weitzman's measure ($\Delta $) for two Lomax distributions with different parameters. The article derives the bias and variance of these overlap measures' estimators. If sample sizes are limited, the precision or bias of these estimators is difficult to determine because there are no closed-form expressions for their variances and exact sampling distributions, so Monte Carlo simulations are used. Also, confidence intervals for these measures are constructed using both the bootstrap method and Taylor approximation. To demonstrate the practical significance of the proposed estimators, an illustrative application is provided by analyzing real data.

Stability analysis of patient-specific 4DCT- and 4DCBCT-based correspondence models.

Surrogate-based motion compensation in stereotactic body radiation therapy (SBRT) strongly relies on a constant relationship between an external breathing signal and the internal tumor motion over the course of treatment, that is, a stable patient-specific correspondencemodel. This study aims to develop methods for analyzing the stability of correspondence models by integrating planning 4DCT and pretreatment 4D cone-beam computed tomography (4DCBCT) data and assessing the relation to patient-specific clinicalparameters. For correspondence modeling, a regression-based approach is applied, correlating patient-specific internal motion (vector fields computed by deformable image registration) and external breathing signals (recorded by Varian's RPM and RGSC system). To analyze correspondence model stability, two complementary methods are proposed. (1) Target volume-based analysis: 4DCBCT-based correspondence models predict clinical target volumes (GTV and internal target volume [ITV]) within the planning 4DCT, which are evaluated by overlap and distance measures (Dice similarity coefficient [DSC]/average symmetric surface distance [ASSD]). (2) System matrix-based analysis: 4DCBCT-based regression models are compared to 4DCT-based models using mean squared difference (MSD) and principal component analysis of the system matrices. Stability analysis results are correlated with clinical parameters. Both methods are applied to a dataset of 214 pretreatment 4DCBCT scans (Varian TrueBeam) from a cohort of 46 lung tumor patients treated with ITV-based SBRT (planning 4DCTs acquired with Siemens AS Open and SOMATOM go.OPEN Pro CT scanners). Consistent results across the two complementary analysis approaches (Spearman correlation coefficient of between system matrix-based MSD and GTV-based DSC/ASSD) were observed. Analysis showed that stability was not predominant, with 114/214 fraction-wise models not surpassing a threshold of for the GTV, and only 14/46 patients demonstrating a in all fractions. Model stability did not degrade over the course of treatment. The mean GTV-based DSC is (mean ASSD of ) and the respective ITV-based DSC is (mean ASSD of ). The clinical parameters showed a strong correlation between smaller tumor motion ranges and increasedstability. The proposed methods identify patients with unstable correspondence models prior to each treatment fraction, serving as direct indicators for the necessity of replanning and adaptive treatment approaches to account for internal-external motion variations throughout the course oftreatment.

Observation of Replica Symmetry Breaking in Standard Mode-Locked Fiber Laser.

We report the first experimental demonstration of the replica symmetry breaking (RSB) phenomenon in a fiber laser system supporting standard mode-locking (SML) regime. Though theoretically predicted, this photonic glassy phase remained experimentally undisclosed so far. We employ an ytterbium-based mode-locked fiber laser with a very rich phase diagram. Two phase transitions are observed separating three different regimes: cw, quasi-mode-locking (QML), and SML. The regimes are intrinsically related to the distinct dynamics of intensity fluctuations in the laser spectra. We set the connection between the RSB glassy phase with frustrated modes and onset of L-shaped intensity distributions in the QML regime, which impact directly the replica overlap measure.

Making ends meet: revisiting the consensual income approach to measuring poverty

Conventional (income-based) poverty measures have been criticised for being narrowly focused and failing to provide evidence that those identified as poor have an unacceptable standard of living. The consensual approach to deprivation addresses both weaknesses by drawing on community perceptions of what items are essential for all and establishing that those who do not have them cannot afford them. In contrast, the consensual income approach maintains the role of income as a key determinant of poverty but uses community perceptions of how much is needed to make ends meet to set a poverty line. Although perceptions vary widely, it is possible to estimate the income level at which people with that income would say that their current income is just enough to make ends meet. This article re-examines this approach drawing on recent developments in poverty research and using new data for Australia. The consensual poverty lines produced are shown to have similarities with those used in existing poverty studies, but also to exhibit important differences. An overlap measure is developed that includes those in consensual poverty who also indicate that their current income is not enough for them to make ends meet. The poverty rates produced by this overlap measure is shown to align with those based on the 50 per cent of median-income OECD equivalised poverty line used in most Australian studies. It is argued that the consensual income approach has the potential to advance our understanding of poverty beyond that provided by conventional income-based measures.

Fibre orientation atlas guided rapid segmentation of white matter tracts.

Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.

Interval estimation of the overlapping coefficients in an exponential family of distributions based on upper record values

This paper investigates interval estimation for measures of overlap, namely Matusita's measure, Weitzman's measure and based on Kullback–Leibler. Two types of sampling procedures, namely, Simple Random Sample and Upper Record Values from two exponential populations. Bootstrap method series approximation is used to construct confidence intervals for the overlap measures. To illustrate the performance of the likelihood confidence intervals obtained for this overlapping coefficient, under our proposal, we carry out some simulation studies that yield adequate coverage frequencies, this study conducts for comparing the performances of various competing estimators. A real data set is analysed to exemplify our proposal.

Metrics for Quantifying Shareability in Transportation Networks: The Maximum Network Flow Overlap Problem

Cities around the world vary in terms of the structure of their transportation networks and travel demand patterns. Despite these variations significantly impacting opportunities for travelers to share trips, no metrics are available to jointly characterize a region’s transportation network and travel demand in terms of the potential for travelers to share space with each other on network links, or what we call person-trip shareability. This study proposes metrics to quantify the shareability of person-trips in a city subregion, as a function of three inputs—the underlying transportation network, origin–destination (OD) travel demand, and a maximum permissible detour parameter. After defining person-trip shareability and providing principles for operationalizing this definition, we formulate a fundamental metric, called flow overlap. Flow overlap measures, for a person-trip traversing a given path, the weighted (by link distance) average number of other person-trips sharing the links along the original person-trip’s path. We then formulate the Maximum Network Flow Overlap Problem (MNFLOP), a math program that assigns person-trips to network paths that maximize network-wide flow overlap. We utilize the MNFLOP output to calculate shareability metrics at various levels of aggregation: person-trip level, OD level, origin or destination level, network level, and link level. The study applies the MNFLOP and associated shareability metrics to different OD demand scenarios in the Sioux Falls network. The computational results verify that MNFLOP (i) assigns person-trips to paths such that flow overlaps significantly increase relative to shortest path assignment, (ii) can meaningfully differentiate between different OD trip matrices in terms of flow overlap, and (iii) metrics can meaningfully quantify demand dispersion from a single network node/location considering the underlying road network. Finally, we validate MNFLOP’s ability to quantify shareability by showing that demand patterns with higher flow overlap are strongly associated with lower mileage routes for a last-mile microtransit service. The paper includes an extensive discussion of other potential future uses of the MNFLOP and its associated shareability metrics.

Topologically Overlapped Fused LASSO Measure for Reconstructing Gene Regulation Networks

Identifying target genes in Gene Regulation Network (GRN) models has always been challenging in Systems Biology. In this regard, indirect gene regulatory hierarchical architectures may be promising enough, considering varied topological structures and unknown gene regulation factors. Such causal regulations can be investigated, keeping in force all perturbation experiments of a dataset. Contemporary research primarily highlights direct interaction networks, which mostly forego the inevitable presence of a third entity, if any, towards varied forms of causal regulations. In this article, we have developed a hierarchical algorithm that unveils the genetic wiring through the Fused Least Absolute Shrinkage and Selection Operator (Fused-LASSO) technique with a Topological Overlap (TO) measure as the interaction structure. The potential power of this proposed model is studied over YEAST Cell Cycle, and Human cancer cell line data (HeLa). In this connection, the different statistically significant hierarchical regulation outcomes maintaining parity with the direct interaction structures, if any, to the target genes may throw new light on gene regulation statistics.

A robust interpolation-based method for forensic soil provenancing: A Bayesian likelihood ratio approach

Soil is a complex and spatially variable material that has a demonstrated potential as a useful evidence class in forensic casework and intelligence operations. Here, the capability to spatially constrain police search areas and prioritise resources by triaging areas as low and high interest is advantageous. Conducted between 2017 and 2021, a forensically relevant topsoil survey (0–5 cm depth; 1 sample per 1 km2) was carried out over Canberra, Australia, aiming to document the distribution of chemical elements in an urban/suburban environment, and of acting as a testbed for investigating various aspects of forensic soil provenancing. Geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for trace elements) following a total digestion (HF + HNO3) of the fused XRF beads were obtained from the survey’s 685 topsoil samples (plus 138 additional quality control samples and six “Blind” simulated evidentiary samples). Using those “Blind” samples, we document a likelihood ratio approach where for each grid cell the analytical similarity between the grid cell and evidentiary sample is attributed from a measure of overlap between the two Cauchy distributions, including appropriate uncertainties. Unlike existing methods that base inclusion/exclusion on an arbitrary threshold (e.g., ± three standard deviations), our approach is free from strict binary or Boolean thresholds, providing an unconstrained gradual transition dictated by the analytical similarity. Using this provenancing model, we present and evaluate a new method for upscaling from a fine (25 m x 25 m) interpolated grid to a more appropriate coarser (500 m x 500 m) grid. In addition, an objective method using Random Match Probabilities for ranking individual variables to be used for provenancing prior to receiving evidentiary material was demonstrated. Our results show this collective procedure generates more consistent and robust provenance maps when applied to two different interpolation algorithms (e.g., inverse distance weighting, and natural neighbour), with different grid placements (e.g., grid shifts to the north or east) and by different theoretical users (e.g., different computer systems, or forensic geoscientists).

Trust in virtual ingroup or outgroup members relies on perceived self-other overlap.

Why do we trust each other? We carried out three experiments to test whether interpersonal trust depends on perceived self-other overlap. As previous studies suggest that enfacing (feeling ownership for, and include more into oneself of the face of) an avatar might make one trust this avatar more, we exposed participants to faces of ingroup and outgroup avatars that moved in synchrony or out of synchrony with the participant's own facial movements, and assessed the impact of synchrony on self-other overlap and trust measures. Experiment 1 used ingroup faces and successfully showed that synchrony (manipulated within-participants) increased self-other overlap and trust, which we assessed by means of the Trust Game and the Implicit Association Test (IAT). In Experiment 2, which used outgroup faces and a within-participants design, synchrony still increased scores in the Trust Game but the IAT was no longer affected. Experiment 3 replicated Experiment 2 but with synchrony varying between participants, which eliminated the synchrony effect in both trust measures. Importantly, Inclusion of Other in the Self (IOS) ratings were found to predict the IAT effect in synchronous conditions. Taken altogether, our findings suggest that interpersonal trust is mainly driven by perceived self-other overlap. Besides group identification, appearance, and voluntary movement, synchrony is just one of several sources contributing to perceived self-other overlap.

Object Detection of UAV Images from Orthographic Perspective Based on Improved YOLOv5s

Object detection methods of UAV (Unmanned Aerial Vehicle) images are greatly improved with the development of UAV technology. In comparison, the existing object detection methods of UAV images lack outstanding performance in the face of challenges such as small targets, dense scenes, sparse distribution, occlusion, and complex background, especially prominent in the task of vehicle detection. This paper proposed an improved YOLOv5s method to perform vehicle detection of UAV images. The CA (Coordinate Attention) is first applied to the neck of YOLOv5s to generate direction-aware and position-sensitive feature maps, respectively, to improve the detection accuracy of sparsely distributed vehicle targets in complex backgrounds. Then, an improved PAFPN (Path Aggregation Feature Pyramid Network) at the neck of YOLOv5s is proposed for more efficient detection of small and dense vehicle targets. Finally, the CIoU (Complete Intersection Over Union) loss function was used to calculate the bounding box regression to obtain a more comprehensive overlap measure to accommodate different shapes of vehicle targets. We conducted extensive experiments on the self-built UAV-OP (Unmanned Aerial Vehicle from Orthographic Perspective) dataset. The experimental results show that our method achieves the best detection accuracy with a small quantity of calculation increase compared with YOLOv5s. The mAP50 improved by 3%, and the mAP50:95 improved by 1.7% with a 0.3 GFlops increase.

Similarity between structural and proxy estimates of brain connectivity

Functional magnetic resonance and diffusion weighted imaging have so far made a major contribution to delineation of the brain connectome at the macroscale. While functional connectivity (FC) was shown to be related to structural connectivity (SC) to a certain degree, their spatial overlap is unknown. Even less clear are relations of SC with estimates of connectivity from inter-subject covariance of regional F18-fluorodeoxyglucose uptake (FDGcov) and grey matter volume (GMVcov). Here, we asked to what extent SC underlies three proxy estimates of brain connectivity: FC, FDGcov and GMVcov. Simultaneous PET/MR acquisitions were performed in 56 healthy middle-aged individuals. Similarity between four networks was assessed using Spearman correlation and convergence ratio (CR), a measure of spatial overlap. Spearman correlation coefficient was 0.27 for SC-FC, 0.40 for SC-FDGcov, and 0.15 for SC-GMVcov. Mean CRs were 51% for SC-FC, 48% for SC-FDGcov, and 37% for SC-GMVcov. These results proved to be reproducible and robust against image processing steps. In sum, we found a relevant similarity of SC with FC and FDGcov, while GMVcov consistently showed the weakest similarity. These findings indicate that white matter tracts underlie FDGcov to a similar degree as FC, supporting FDGcov as estimate of functional brain connectivity.

Hospital-physician Integration and Value-based Payment: Early Results From MIPS.

Hospital-physician integration is often justified as a driver of clinical quality improvement due to joint resources covering a broad spectrum of care. Value-based programs, such as the Medicare Merit-Based Incentive Payment System (MIPS), are intended to tie financial incentives to clinical quality, which may confer an advantage on such integrated practices. We assessed the relationship between hospital-physician integration and MIPS performance by comparing hospital-integrated practices and independent practices. This was a cross-sectional study using data from the Quality Payment Program for the performance year 2020. Physician practices with a valid MIPS composite score in performance year 2020. Hospital integration was based on whether at least 75% of a practice's physicians either billed most of their services using hospital outpatient department codes or billed through a hospital tax identifier. The primary outcome was the MIPS quality category score, and the secondary outcomes were the specific quality measures reported by practice groups. Of the 20 most frequently reported measures, 14 were common in both groups. No difference was observed in the quality category score between hospital-integrated practices and independent practices in either unadjusted comparisons or after adjusting for practice characteristics, including practice size, geography, specialty mix, and case mix. In the secondary outcome models for specific quality measures, hospital-integrated practices achieved higher scores on most overlap measures but not all. The findings on quality category score suggest that hospital integration does not confer much advantage in the context of MIPS quality performance.

MSSA-based adaptive low-frequency noise reduction using spectrum overlap measure

A multistage singular spectrum analysis based method is presented to extract the useful component from the residue stage by stage. In every stage of the decomposition, the sum of the signal-dominated SSA components is treated as the denoised signal. The signal-to-noise ratios of the denoised signals first increase and then decrease. A measurement called spectrum overlap factor (SOF) is proposed to estimate the optimal stage which achieves the highest SNR. First, the factor is calculated to measure the spectrum overlap degree between the residue and the denoised signal in every stage. Then, the curve of the SOF with respect to the number of stages is analyzed. Further, the minimum of the SOFs, which indicates the less spectrum overlap, allows for the estimation of the optimal stage. The proposed strategy avoids inappropriate parameter selection effectively since the estimation of the optimal stage is automatic. Besides, simulation results show that the proposed method has satisfactory denoising performance in different test scenarios.

Evidence of exploitative competition between honey bees and native bees in two California landscapes.

Human-mediated species introductions provide real-time experiments in how communities respond to interspecific competition. For example, managed honey bees Apis mellifera (L.) have been widely introduced outside their native range and may compete with native bees for pollen and nectar. Indeed, multiple studies suggest that honey bees and native bees overlap in their use of floral resources. However, for resource overlap to negatively impact resource collection by native bees, resource availability must also decline, and few studies investigate impacts of honey bee competition on native bee floral visits and floral resource availability simultaneously. In this study, we investigate impacts of increasing honey bee abundance on native bee visitation patterns, pollen diets, and nectar and pollen resource availability in two Californian landscapes: wildflower plantings in the Central Valley and montane meadows in the Sierra. We collected data on bee visits to flowers, pollen and nectar availability, and pollen carried on bee bodies across multiple sites in the Sierra and Central Valley. We then constructed plant-pollinator visitation networks to assess how increasing honey bee abundance impacted perceived apparent competition (PAC), a measure of niche overlap, and pollinator specialization (d'). We also compared PAC values against null expectations to address whether observed changes in niche overlap were greater or less than what we would expect given the relative abundances of interacting partners. We find clear evidence of exploitative competition in both ecosystems based on the following results: (1) honey bee competition increased niche overlap between honey bees and native bees, (2) increased honey bee abundance led to decreased pollen and nectar availability in flowers, and (3) native bee communities responded to competition by shifting their floral visits, with some becoming more specialized and others becoming more generalized depending on the ecosystem and bee taxon considered. Although native bees can adapt to honey bee competition by shifting their floral visits, the coexistence of honey bees and native bees is tenuous and will depend on floral resource availability. Preserving and augmenting floral resources is therefore essential in mitigating negative impacts of honey bee competition. In two California ecosystems, honey bee competition decreases pollen and nectar resource availability in flowers and alters native bee diets with potential implications for bee conservation and wildlands management.

L1 variation and L2 acquisition: L1 German /eː/-/ɛː/ overlap and its effect on the acquisition of L2 English /ɛ/-/æ/.

A person's first language (L1) affects the way they acquire speech in a second language (L2). However, we know relatively little about the role different varieties of the L1 play in the acquisition of L2 speech. This study focuses on German (L1) learners of English (L2) and asks whether the degree to which German speakers distinguish between the two vowels /eː/ and /ɛː/ in their L1 has an impact on how well these individuals identify /æ/ and discriminate between the two English vowels /ɛ/ and /æ/. These two English vowels differ in both vowel quality and duration (/æ/ is longer than /ɛ/). We report on an identification and a discrimination experiment. In the first study, participants heard a sound file and were asked to indicate whether they heard "pen" or "pan" (or "pedal" or "paddle"). The stimuli differed from each other in terms of both vowel quality (11 steps on a spectral continuum from an extreme /æ/ to an extreme /ɛ/) and duration (short, middle, long). In the second study, participants had to signal whether two sound files they were exposed to differed from each other. We modeled the percentage of /æ/ ("pan," "paddle") selection (identification task only, binomial logistic regression), accuracy (discrimination task only, binomial logistic regression), and reaction time (identification and discrimination tasks, linear mixed effects models) by implementing the German Pillai score as a measure of vowel overlap in our analysis. Each participant has an individual Pillai score, which ranges from 0 (= merger of L1 German /eː/ and /ɛː/) to 1 (=maintenance of the contrast between L1 German /eː/ and /ɛː/) and had been established, prior to the perception experiments reported here, in a production study. Although the findings from the discrimination study remain inconclusive, the results from the identification test support the hypothesis that maintaining the vowel contrast in the L1 German leads to a more native-like identification of /æ/ in L2 English. We conclude that sociolinguistic variation in someone's L1 can affect L2 acquisition.

A multi-layer modelling approach for mining versatile ports of a global maritime transportation network

ABSTRACT International ports play critical roles in maintaining transportation flows and sustaining the effectiveness of global maritime logistics. Although the concept of versatile ports has been introduced to represent the specific patterns that these ports play at the regional and global levels, there is still a need for the design and development of computational approaches that support these notions. This paper introduces a flexible multi-layer network approach for an international maritime network whose interest is that it offers a flexible model that favours the identification of the different transportation flows and versatile ports. The model is complemented by a series of structural indices complemented by a new overlap measure that evaluates the specific role of a given port across various transportation trades. The whole approach is implemented and experimented with using massive AIS maritime data that supports the automatic generation of the multi-layer network, and derivation of the structural measures while maintaining a flexible view of the maritime network. The experiments applied to the global maritime transportation network identify key versatile ports and highlight significant differences at the regional and trade flow levels.

Quantum pattern recognition on real quantum processing units

One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We find that with a two-qubit protocol, swap test can efficiently detect the similarity between two patterns with good fidelity, though for three or more qubits, the noise in the real devices becomes detrimental. To mitigate this noise effect, we resort to destructive swap test, which shows an improved performance for three-qubit states. Due to limited cloud access to larger IBMQ processors, we take a segment-wise approach to apply the destructive swap test on higher dimensional images. In this case, we define an average overlap measure which shows faithfulness to distinguish between two very different or very similar patterns when run on real IBMQ processors. As test images, we use binary images with simple patterns, grayscale MNIST numbers and fashion MNIST images, as well as binary images of human blood vessel obtained from magnetic resonance imaging (MRI). We also present an experimental set up for applying destructive swap test using the nitrogen vacancy (NVs) center in diamond. Our experimental data show high fidelity for single qubit states. Lastly, we propose a protocol inspired from quantum associative memory, which works in an analogous way to supervised learning for performing quantum pattern recognition using destructive swap test.

Institutional Overlap in Global Governance and the Design of Intergovernmental Organizations

How does the increasingly dense network of overlapping institutions in global governance affect the design of intergovernmental organizations (IGOs)? We argue that institutional overlap can unleash mimicking dynamics whereby states design new IGOs using the design of existing organizations that engage in similar issue areas and perform similar governance tasks for similar member states as templates. Using design templates from the reference group of overlapping institutions is a strategy for boundedly rational designers in situations of complexity because it reduces uncertainty and lowers the costs of identifying suitable institutional solutions. Overlap therefore increases the design similarity between new and pre-existing IGOs, specifically where pre-existing organizations have institutional designs that made them endure. Introducing a new measure of institutional overlap in global governance and new data on the design and governance tasks of the 534 IGOs from the Correlates of War Project, we corroborate our argument using regression analyses. Our results hold important lessons for theories of institutional design, regime complexity, and global governance more broadly.

Genetic associations between bipolar disorder and brain structural phenotypes.

Patients with bipolar disorder (BD) and their first-degree relatives exhibit alterations in brain volume and cortical structure, whereas the underlying genetic mechanisms remain unclear. In this study, based on the published genome-wide association studies (GWAS), the extent of polygenic overlap between BD and 15 brain structural phenotypes was investigated using linkage disequilibrium score regression and MiXeR tool, and the shared genomic loci were discovered by conjunctional false discovery rate (conjFDR) and expression quantitative trait loci (eQTL) analyses. MiXeR estimated the overall measure of polygenic overlap between BD and brain structural phenotypes as 4-53% on a 0-100% scale (as quantified by the Dice coefficient). Subsequent conjFDR analyses identified 54 independent loci (71 risk single-nucleotide polymorphisms) jointly associated with BD and brain structural phenotypes with a conjFDR < 0.05, among which 33 were novel that had not been reported in the previous BD GWAS. Follow-up eQTL analyses in respective brain regions both confirmed well-known risk genes (e.g. CACNA1C, NEK4, GNL3, MAPK3) and discovered novel risk genes (e.g. LIMK2 and CAMK2N2). This study indicates a substantial shared genetic basis between BD and brain structural phenotypes, and provides novel insights into the developmental origin of BD and related biological mechanisms.