The association of short-term variability in suicidal ideation with subsequent suicidal thoughts and behaviours: Longitudinal cohort study.

Magnetic resonance imaging (MRI)-based volumetry of the fetus, placenta, and amniotic fluid is clinically valuable but rarely used due to labor-intensive manual segmentation of motion-corrupted two-dimensional (2-D) stacks. Existing deep learning approaches are typically limited to single structures and 2-D data, while no robust automated solution exists for whole-uterus volumetry in reconstructed three-dimensional (3-D) MRI, and normative reference ranges are lacking. To develop an automated pipeline for whole-uterus volumetry in 3-D T2-weighted fetal MRI and derive normative growth models for fetal, placental, and amniotic fluid volumes. Motion-corrupted T2-weighted stacks (0.55-3-T field strength) were reconstructed into 3-D isotropic images using deformable slice-to-volume reconstruction, followed by automated segmentation with a 3-D U-Net. The method was applied to 357 normal-control datasets with confirmed term birth (16-41weeks gestational age range) to derive quadratic normative growth curves. Performance and clinical utility were further evaluated on 43 independent datasets. Segmentation was highly accurate (Dice: fetus 0.997, placenta 0.995, amniotic fluid 0.998) with low volume errors (<1%) and minimal manual refinement required in <25% of cases. In the control cohort, fetal and placental volumes increased with gestational age (P<0.001), while amniotic fluid followed a quadratic trend. Longitudinal growth rates were 146.6cc/week (fetus) and 38.8cc/week (placenta). Preterm pregnancies showed significantly lower fetal and placental volumes (P<0.001) and reduced amniotic fluid (P<0.01). This work presents the first automated pipeline for simultaneous whole-uterus volumetry in 3-D fetal MRI and establishes normative growth models across gestation. The approach enables accurate, standardized volumetric assessment and provides a practical tool for detecting abnormal growth patterns in both normal and high-risk pregnancies.

Group graded algebras and varieties with quadratic codimension growth

Examples of open manifolds with almost quadratic volume growth and infinite Betti numbers

Identifying the genetic changes that shaped recent human adaptation depends on our ability to detect selection from genomic data. Summary statistics from haplotype scans have been widely used for that purpose, aggregating genetic signal over windows, though resolution is limited by linkage and their power may diminish as sweeps approach fixation, as in the case of the integrated haplotype score (iHS). Ancient DNA based scans recover signal by analysing time-series trajectories, but the majority of human populations fall outside the geographic range of any existing ancient DNA dataset. Pairwise coalescence times provide a way to complement statistics and can be applied to any modern cohort, yet computing them densely enough at cohort scale poses a computational challenge due to the quadratic growth in the number of haplotype pairs.We introducegamma_smc_cu, a GPU implementation of the Gamma-SMC algorithm (Schweiger and Durbin, 2023) for pairwise time-to-the-most-recent-common-ancestor (TMRCA) inference. Applied to the 1000 Genomes Project (3,202 phased samples, corresponding to 6,404 haplotypes; 829,638 within-population pairs across 26 populations and five different continental ancestries; ∼1012per-site posterior evaluations), it yields a gene-level TMRCA landscape of 17,823 autosomal protein-coding genes after masking for segmental duplications.The scan recovers well-known sweeps (LCT, SLC24A5, EDAR, FADS1, HERC2, ABCC11) and, combined with a depleted-to-enriched variant-class profile, resolves haplotype-block signals down to the gene level. Of seven case studies, two are developed in the main text —GRK2/ADRBK1(chr11q13.2; SAS+EUR) andTREML1/TREM2(chr6p21.1) — and the remaining five (IFIH1chr2q24/IBS,CCDC92chr12q24/CDX,SLC6A15chr12q21/CHS,BPIFA2chr20q11/GIH,CLEC6Achr12p13/CDX) are presented in the Supplementary Information (SI). Notably,TREML1/TREM2is a shared out-of-Africa signal — ranked below the within-population 1% tail in 16 of 19 non-African 1000 Genomes panels that PopHumanScan and five landmark haplotype-based scans miss. A previous 10 kb-windowed-mean iHS scan dilutes the cluster of extreme sites packed inside the ∼5 kb gene bodies, while our own gene-level iHS independently recovers the locus in three South Asian panels (BEB, STU, ITU; top 0.4% genome-wide). We cross-validate the seven cases against the 9.7 million per-variant selection posteriors from a recent West-Eurasian ancient DNA scan.BPIFA2is detected concordantly (s≈ 1.8% per generation).GRK2andCCDC92reach detection threshold in flanking variants but not within their own gene bodies, while theTREML1/TREM2cluster falls below it.To calibrate novelty, we review the candidate landscape against an expanded eight-catalog set spanning curated haplotype scans, the largest current West-Eurasian ancient-DNA leads, and a recent 26-population iHS refinement; the vast majority of our loci overlap at least one prior entry, and only a handful — includingTREML1/TREM2— remain unflagged. The contributions of this work are gene-level resolution, systematic ancient DNA cross-validation, and a reusable TMRCA landscape that complements aDNA panels.

Several decades ago the Proximal Point Algorithm (PPA) started to gain a long-lasting attraction for both abstract operator theory and numerical optimization communities. Even in modern applications, researchers still use proximal minimization theory to design scalable algorithms that overcome nonsmoothness. Remarkable works established tight relations between the convergence behaviour of PPA and the regularity of the objective function. In this manuscript we derive a nonasymptotic iteration complexity of exact and inexact PPA to minimize convex functions under γ-Holderian growth: O ( log ⁡ ( 1 / ϵ ) ) (for γ ∈ [ 1 , 2 ] ) and O ( 1 / ϵ γ − 2 ) (for 2 $ ]]> γ > 2 ). In particular, we recover well-known results on PPA finite convergence for sharp minima and linear convergence for quadratic growth, even under the presence of deterministic noise. Moreover, when a simple Proximal Subgradient Method is recurrently called as an inner routine for computing each IPPA iterate, novel computational complexity bounds are obtained for Restarting Inexact PPA. Our numerical tests show improvements over existing restarting versions of the Subgradient Method.

Current semi-supervised segmentation methods face the following challenges: (1) Cross-branch collaboration: Existing methods typically rely on single-branch pseudo-label generation or simple multi-view fusion strategies, failing to fully exploit the interaction between local details and global structures. This limitation leads to suboptimal performance in the boundary segmentation of complex anatomical structures. (2) Inefficiency in long-range modeling: While Transformer-based methods can capture global dependencies, they suffer from quadratic growth in computational complexity and the risk of overfitting when applied to high-resolution data (e.g., 3D medical images), making it difficult to balance efficiency andaccuracy. To address the above challenges, this article proposes a Tri-branch Collaborative Consistency model based on Mamba long-range modeling (TCC-Mamba), which aims to reduce reliance on annotations while improving segmentation accuracy in complex regions of medicalimages. TCC-Mamba consists of a shared encoder and a tri-branch decoder. Specifically, a tri-branch collaborative supervision mechanism is introduced where three decoders form a closed-loop learning system through cross-pseudo-label supervision, enabling collaborative optimization and information sharing. Additionally, a geometric consistency loss function is incorporated to enhance boundary awareness. Furthermore, we integrate the SpatialTriMamba module, leveraging the efficient long-range dependency modeling of state-space models to achieve dynamic fusion of global context and local features, thereby improving segmentation accuracy for complexboundaries. We conducted experiments on three public datasets: Left Atrium (LA), Pancreas CT, and ACDC, using 10%, 20%, and 30% labeled data. The results demonstrate that our method outperforms the six current advanced semi-supervised methods, achieving better segmentation performance. The TCC-Mamba introduces novel methodologies in medical image segmentation tasks. This model combines the SpatialTriMamba module to capture long-range features and utilizes signed distance maps to enhance the use of geometric information, leading to exceptional results in handling complex anatomical structures. It provides an efficient and reliable solution for semi-supervised medical imagesegmentation.

The impact of social engagement on the mental health development of older adults: A 10-year longitudinal study.

Corona discharge at the tip of buildings in a thunderstorm environment is an important factor causing changes in the near-ground electric field, but the influence of a quadratic growth law and quantitative research on the parameters is still rare. Therefore, based on the three-dimensional corona discharge model, this paper studies the influence of positive and negative symmetrical triangular wave electric fields with different amplitudes on the corona discharge of an independent lightning rod. Studies have shown that the corona current is synchronized with the peak of the background electric field. Studies have shown that the corona current is synchronized with the peak of the background electric field. When the polarity of the electric field changes from positive to negative, the positive charge accumulated in the positive half-cycle promotes the subsequent negative corona, so the negative corona starts in advance when the polarity reverses. Compared with unipolar discharge, the amplitude of the negative current and the number of negative charges have significantly improved. However, due to the counteraction of neutralization between positive and negative charges, the total corona charge is at a low level, which shows a net negative polarity result. The corona current and the amount of charge increase nonlinearly with an increase in the background electric field amplitude. Under the symmetrical triangular wave electric field, the quantitative fitting relationship between the peak value of the negative corona current in the second half-cycle and the amount of charge is established for the 5 m high independent lightning rod, which is I− = −0.0532 − 0.153 E − 0.0682 E2, Q− = −3.18 × 10−3 + 7.762 × 10−4E − 4.671 × 10−5 E2, respectively. The increase in the background electric field amplitude will aggravate the disturbance of the corona discharge to the near-surface electric field. When the direction of the electric field has reverted to zero, the existence of the space charge will lead to a significant change in the strength and polarity of the ground electric field. When the thunderstorm background electric field changes from positive to negative, the corona effect reverses the polarity of the ground electric field in advance, and the larger the peak value of the background electric field, the larger the advance. The corona interference mechanism revealed by this study can provide an important reference for correcting the electric field monitoring data and improving the accuracy of lightning warnings.

While Graph Convolutional Networks (GCNs) have revolutionized skeleton-based action recognition, existing methods face a critical efficiency–accuracy dilemma: state-of-the-art approaches achieve high performance through computationally expensive multi-stream fusion (joint, bone, joint motion, and bone motion) and deep architectures, limiting real-world deployment on resource-constrained devices. We propose LST-AGCN (Lightweight Spatial–Temporal Attention Graph Convolutional Network), introducing three technical contributions that address this challenge: (1) Unified Attention Module (UAM)—a framework that integrates channel, spatial, and temporal attention through a single compact operation, significantly reducing attention parameters compared to separate attention mechanisms; (2) Depthwise Separable Attention Mechanism (DSAM)—a factorization using depthwise separable convolutions that achieves linear complexity reduction from O(C2) to O(C) in attention operations; and (3) Efficient Topology-Aware Fusion (ETAF)—an adaptive Joint-wise Attention strategy that captures fine-grained spatial relationships without quadratic complexity growth. Extensive experiments on NTU RGB+D 60 and NTU RGB+D 120 datasets demonstrate that LST-AGCN achieves strong performance using only joint modality (86.14%/94.0% and 79.5%/82.0% Top-1 accuracy with 99.0% Top-5 on cross-view) while requiring 14.11 M parameters and 19.02 GFLOPs, delivering efficient inference suitable for edge deployment.

IntroductionSexually diverse adolescents report higher suicidality (ideation and attempts) than their heterosexual peers, but information on the onset and course of suicidality from early adolescence to young adulthood among contemporary sexually diverse individuals remains limited. This study traces suicidality trajectories across this critical developmental period, comparing sexually diverse and heterosexual adolescents both overall and by sex assigned at birth.MethodsData was drawn from the Quebec Longitudinal Study of Child Development, an ongoing population-based prospective birth cohort. This study included 1,505 participants who self-reported their sexual attraction at ages 15-17 (2013-2015) and past-year suicidal ideation and attempts using 3-items at ages 13, 15, 17 and 23 (2011-2021) from which a suicidality severity score was derived.Results11.5% of the sample (n = 173, 60.5% female) reported sexually diverse attraction. Growth curve modelling tested the random effect of sexually diverse (vs. heterosexual) attraction on suicidality severity intercept, linear and quadratic latent growth factors. Age-specific contrasts in suicidality severity between sexually diverse and heterosexual adolescents were also examined. Compared to heterosexual adolescents, sexually diverse adolescents showed a steeper increase in suicidality severity from ages 13 to 17, and declining yet persisting disparities from ages 17 to 23. Although developmental trajectories differed across sexually diverse males and females, both experienced greater suicidality severity during adolescence compared with heterosexual peers.DiscussionOur findings point to a developmental trajectory in which sexually diverse adolescents, particularly females assigned at birth, experience elevated suicidality that persists into young adulthood. Targeted and timely interventions during this critical developmental period are essential for suicide prevention.

Abstract We consider the problem of minimizing a smooth and convex function over the n -dimensional spectrahedron — the set of real symmetric $$n\times n$$ n × n positive semidefinite matrices with unit trace, which underlies numerous applications in statistics, machine learning and additional domains. Standard first-order methods often require high-rank matrix computations which are prohibitive when the dimension n is large. The well-known Frank-Wolfe method on the other hand, only requires efficient rank-one matrix computations, however suffers from worst-case slow convergence, even under conditions that enable linear convergence rates for standard methods. In this work we present the first Frank-Wolfe-based algorithm that only applies efficient rank-one matrix computations and, assuming quadratic growth and strict complementarity conditions, is guaranteed, after a finite number of iterations, to converge linearly, in expectation, and independently of the ambient dimension.

ABSTRACT Properly determining the area at risk for coal spontaneous combustion (CSC) is essential for the proactive prevention of fire disasters in goaf. This study employs the discrete element method-computational fluid dynamics (DEM-CFD) coupling approach to develop a numerical model for CSC. This model incorporates the oxygen consumption characteristics of coal-oxygen reaction and the porosity distribution of the goaf. Furthermore, it investigates the impact of coal thickness and air leakage within the goaf on the distribution of the CSC hazard area. The results indicate that the goaf porosity generated by lower coal seam mining surpasses that of single coal seam mining. Increases in porosity of 16%, 23%, and 19% are observed in the open-off cut area, the middle area, and near the working face area of the goaf, respectively. The CSC hazard area extends deeper into the goaf with increases in coal thickness, working face wind speed, and interlayer air leakage. Its maximum width follows a quadratic function growth pattern as these factors increase, with the growth rate gradually slowing. Based on this quantitative relationship, a mathematical function for predicting the location of CSC hazard area has been established. The function demonstrates relatively high predictive accuracy, thereby providing a foundation for optimizing the placement of fire prevention measures in goaf.

Objective : Suicidal thoughts and behaviors (STB) constitute high public health concerns. Understanding the longitudinal trajectories of STB from adolescence to adulthood as a function of sex, ancestry, and genetic liability would improve our knowledge and the design of population-based prevention/intervention. Methods : We used data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) and included participants of European (EA, N = 4,905) and African (AA, N = 1,654) ancestry. We evaluated the growth trajectories of suicide ideation and suicide attempt from age 12 to 40 separately in participants of EA and AA and assessed the roles of sex and genetic liability, indexed using polygenic scores (PGS). Results : Quadratic growth models including an age-by-sex interaction fit the data best in both ancestral groups. Results indicated an overall decrease of STB from adolescence to adulthood, a stabilization between ages 25–35, and a tendency to increase after age 35. Sex differences were evidenced by higher baseline levels of STB and sharper decreases across ages in females. Including PGS improved the model fit and was related to baseline levels of STB. Conclusions : Adolescence constitutes a high-risk period for the development of STB, particularly in females and those with high genetic liability. Though we observed a stabilization of STB in adulthood, another risk period may arise after age 35.

We study a good shape property of boundary sections of convex solutions to the oblique boundary value problem for the Monge–Ampère equation \det D^{2}u =f(x) \quad \text{in }\Omega, \quad D_{\beta}u = \phi(x) \quad \text{on }\partial \Omega. In two dimensions, we prove a global C^{2,\alpha} estimate for solutions. For dimensions n \geq 3 , we show that this estimate remains valid provided the solution satisfies a quadratic growth condition in tangential directions. We also prove an existence result for convex solutions to the Monge–Ampère equation with an oblique Robin boundary condition.

The integration of blockchain technology into Cyber–Physical Systems (CPS) offers decentralized resilience against data manipulation. This also introduces stochastic consensus latencies that threaten real-time control stability. We present a Stochastic-Aware Blockchain Predictive Control (SAB-PC) framework, which models blockchain-induced jitter as a state-dependent Markovian process, and embeds it within a Markovian Jump Linear System (MJLS) formulation. Using mode-dependent Linear Matrix Inequalities (LMIs), we derive Mean Square Stability (MSS) conditions, which capture the interaction between decentralized consensus dynamics and closed-loop control behavior. The framework is validated on the Tennessee Eastman Process (TEP) benchmark, using a calibrated stochastic delay model that reflects realistic blockchain congestion patterns. Our results show that standard blockchain-mediated control architectures become unstable under Practical Byzantine Fault Tolerance (PBFT)-induced quadratic latency growth, whereas SAB-PC maintains stable operation across decentralized networks up to 60 validator nodes. The predictive Safety Runway effectively masks long-tail delay distributions, ensuring real-time feasibility and preserving safe Reactor Pressure trajectories. Under coordinated False Data Injection (FDI) attacks, SAB-PC limits pressure deviations to only 1.2 kPa despite an 8.0 kPa adversarial bias, demonstrating cryptographic and control-theoretic resilience.

This study conducts a comprehensive bibliometric analysis regarding the application of ultrasound biomicroscopy in glaucoma research over the past two decades. Bibliometric analysis was performed on relevant literature published between 2005 and 2024. Data pertaining to authorship, affiliations, countries of origin, journals, keywords, and cited references were extracted. Visualization and analysis were executed utilizing VOSviewer and CiteSpace software. Bibliometric analysis of 557 articles revealed a quadratic growth trend in annual publication volume. The most productive authors, institutions, countries, and journals were identified and collaboration network maps were drawn. Keyword co-occurrence analysis identified five primary research clusters: angle-closure glaucoma pathogenesis, cataract surgery & complications, glaucoma surgery, ciliary body & iris, and iridocorneal angle. Burst detection and timeline analysis highlighted emerging research frontiers, including deep learning, 3D reconstruction, vitreous zonule imaging, and minimally invasive glaucoma surgery. These findings signal a shift in research priorities towards computational approaches and novel clinical applications. This bibliometric analysis delineates the dynamic evolution and broadening scope of ultrasound biomicroscopy application in glaucoma research over the past two decades. Collectively, these findings affirm the enduring value of ultrasound biomicroscopy and delineate promising avenues for future investigation.

As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to continuously optimize assortments over time. In this article, we consider the dynamic assortment problem with dual contexts—user and item features. In high-dimensional scenarios, the quadratic growth of dimensions complicates computation and estimation. To tackle this challenge, we introduce a new low-rank dynamic assortment model to transform this problem into a manageable scale. Then we propose an efficient algorithm that estimates the intrinsic subspaces and uses the upper confidence bound approach to address the exploration-exploitation tradeoff in online decision making. Theoretically, we establish a regret bound of O ˜ ( ( d 1 + d 2 ) r T ) , where d 1 , d 2 represent the dimensions of the user and item features, respectively, r is the rank of the parameter matrix, and T denotes the time horizon. This bound represents a substantial improvement over prior literature, achieved by leveraging the low-rank structure. Extensive simulations and an application to the Expedia hotel recommendation dataset further demonstrate the advantages of our proposed method. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Small-scale dynamos (SSDs) amplify magnetic fields in turbulent plasmas. Theory predicts nonlinear magnetic energy growth E_{mag}∝t^{p_{nl}}, but this scaling has not been tested across flow regimes. Using a large ensemble of SSD simulations spanning subsonic to supersonic turbulence, we measure linear growth (p_{nl}=1) in subsonic flows and quadratic growth (p_{nl}=2) in supersonic flows. In all cases, the nonlinear dynamo converts a nearly constant fraction approximately equal to 1/100 of the turbulent kinetic energy flux into magnetic energy, and the nonlinear phase has a characteristic duration Δt≈20t_{0}, where t_{0} is the outer-scale turnover time. By isolating the onset of magnetic backreaction in SSDs, our statistical ensemble approach identifies a robust efficiency and duration for the nonlinear SSD that can be used to interpret more complex astrophysical and laboratory plasmas.

High-dimensional quantum systems leverage an expanded Hilbert space to enhance resilience against decoherence and noise. However, standard quantum teleportation is fundamentally limited by the quadratic growth of measurement complexity and high classical communication overhead, requiring the resolution of $d^2$ Bell states and $2\log_2 d$ classical bits. In this study, we propose a resource-efficient high-dimensional coherence teleportation (REHDCT) protocol. By designing $d$ sets of specialized positive operator-valued measure (POVM) bases, our protocol achieves a 50\% reduction in classical communication by utilizing one of the $d$ designed POVM sets, which effectively scales the measurement complexity from $O(d^2)$ to $O(d)$. Furthermore, we demonstrate that by utilizing initial phase engineering to align the target qudit with the measurement basis, theoretically perfect teleportation of quantum coherence can be achieved for arbitrary qudit states. A quantitative robustness analysis reveals that the protocol remains highly resilient to operational errors, maintaining an efficiency above 99.6\% even under a 0.1 rad phase deviation for $d=16$. Our analysis under various noise models (amplitude damping, phase flip, depolarizing, and dit-flip) confirms that high-dimensional systems exhibit an expanding quantum advantage window as dimensionality increases. Notably, under dit-flip noise, perfect coherence teleportation can be restored through the optimal selection of the POVM basis. These findings establish REHDCT as a practical, hardware-friendly framework for resource-efficient quantum communication in future high-dimensional networks.