ABSTRACT Feminist studies have shown how societal discourse constructs entrepreneurship as a gendered field, privileging a masculine image of entrepreneurship. Grounded in this literature, we examine business incubators and accelerators (BIAs) as implicit gatekeepers for new entrants into entrepreneurship, and as heavily understudied intermediary actors within the broad entrepreneurship ecosystem. Through a critical discourse analysis of 166 Canadian BIA websites, we examine how these texts construct entrepreneurship and study these constructions through the lens of gender inclusion or exclusion. Analysing identity and relational functions of the texts we find three overarching discursive practices, two that reproduce the status quo (a masculine gendered entrepreneurship), and a third that creates a counter-discourse, pointing to the possibility for change. Based on these practices and how they are combined, we develop a critical typology of BIAs’ website discourses comprising: 1) an exclusionary discourse perpetuating a masculine gendered system that we label ‘closed entrepreneurship’; 2) a less prevalent inclusive discourse that disturbs existing structures and shifts them to ‘open entrepreneurship’, and 3) a hybrid discourse, permitting variance while sustaining the gendered status quo. Our work extends entrepreneurship discourse studies to the organizational level of analysis and has implications for ‘inclusive entrepreneurship’ theory and practice.

Inner emptiness is a transdiagnostic experience linked to self-injurious and suicidal behaviors, yet its underlying structure and psychosocial correlates remain poorly understood, particularly in nonclinical young adults. This mixed-methods study examined the structure of emptiness and its associations with identity and personality functioning, health risk behaviors, and coping strategies in community young adults, a developmental period marked by identity and personality consolidation, and increased vulnerability to maladaptive behaviors. A total of 737 young adults (Mage = 21.70; 66.6% female) completed online self-report measures and wrote personal narratives about their experience of emptiness. Exploratory and confirmatory factor analyses were conducted on the Subjective Emptiness Scale and the Multidimensional Sense of Emptiness Scale. Quantitative analyses assessed associations with psychosocial factors, and narrative data were thematically analyzed for subjective experiences of emptiness and coping strategies in response to emptiness. Factor analyses revealed a three-factor structure: Feelings of Inner Emptiness, Absence of Relatedness, and Meaninglessness. Narrative analysis supported this structure. Experiences of emptiness were often described in physical terms and varied in frequency, chronicity, and triggers. Feelings of Inner Emptiness were strongly associated with identity disturbances, personality dysfunction, and greater engagement in self-harm and disordered eating. Furthermore, emptiness was linked to greater use of externalizing and distancing coping strategies, alongside adaptive strategies such as seeking support. These findings offer a more nuanced understanding of emptiness beyond clinical populations and highlight its connections to self- and interpersonal dysfunction, engagement in internalizing health risk behaviors, and avoidance-focused coping strategies. Longitudinal research is needed to investigate temporal pathways. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

ABSTRACT This article challenges conventional understandings of maritime power. Using case studies from the Arabian/Persian Gulf region, it argues that maritime power serves not only material and strategic purposes but also ontological ones. Drawing on Ontological Security Theory (OST), this study introduces the concept of Ontological Maritime Security (OMS) to explain how Gulf states, namely Iraq, Kuwait, Bahrain, Oman, and Saudi Arabia, use maritime power to perform distinct identity functions. Iraq seeks to reclaim its Gulfness and to become a rightful maritime actor in the Gulf; Kuwait uses maritime engagement to rebuild confidence after the 1990 invasion; Bahrain uses maritime power to project unity at home amid the Iranian threat; Oman uses maritime power to maintain neutrality; and Saudi Arabia constructs maritime self-reliance, signaling a shift to autonomous maritime power. The findings suggest that smaller and medium-sized Gulf states possess significant agency in shaping international security narratives and, thus, offer critical insights into how states use maritime power as a domain of symbolic performance and legitimacy.

ABSTRACT Acoustic computed tomography plays an important role in assessing the health and structural integrity of trees, and has applications in forestry management and environmental conservation. Traditionally, the precise measurement of sensor distances within tree trunks using manual methods, such as rulers, has been the standard practice. In this study, a novel approach utilizing 3D scan technology to measure sensor distances within tree trunks was introduced, offering a comparative assessment of its advantages over traditional methods. A schematic representation of sensor distance measurement using both a ruler and a 3D scanner, revealing differences in measurement lengths, is presented. Additionally, the methodology for determining sensor positions via 3D scanning is detailed, including the use of markers and precise distance measurements. The results demonstrate that a 3D scan yields sensor positions that closely approximate those obtained via traditional ruler measurements. Comparative analyses reveal nearly identical maps of velocity rays and minimal relative errors, reinforcing the reliability of the 3D scanning technique. These findings further support the use of 3D scanning as a viable alternative for sensor position measurement in the acoustic tomography of tree samples.

Introduction Against the backdrop of population aging and rapid digitalization in China, the new generation of older adults faces a growing paradox of digital inclusion. While digital participation can generate important benefits, it may also produce physical discomfort, health risks, psychological strain, and behavioral dependence. Methods To achieve healthy and sustainable digital participation, this study develops an integrated framework drawing on dramaturgical, Motivation-Opportunity-Ability (MOA), and optimal distinctiveness theories and proposes a pathway linking digital inclusion, identity reconstruction, and digital addiction. Identity reconstruction is conceptualized as a five-stage process comprising identity formulation, identification, dissemination, co-construction, and maintenance. Then algorithmic perception, platform pressure, and digital reflection are further introduced as moderating variables. Using a mixed-methods design that combines structural equation modeling and fuzzy-set qualitative comparative analysis, this study validates the proposed framework and identifies three patterns of digital addiction: identity operation, identity reinforcement, and norm-guided. Results The findings indicate that digital inclusion does not directly trigger digital addiction but indirectly influences sustained use through multiple stages of identity reconstruction. Moreover, algorithmic perception, platform pressure, and digital reflection exert differentiated moderating effects on the relationship between identity reconstruction and digital addiction. The three identified patterns move beyond conventional explanations based on limited digital skills, cognitive decline, or insufficient self-control, and instead reveal the central role of differentiated identity mechanisms in the formation of digital addiction. Discussion These findings advance our understanding of the unintended consequences of digital inclusion and point to the need for multilevel interventions that better align identity needs, platform structures, and social support.

Multi-omics profiling-spanning proteomics, transcriptomics, and additional omics data types-is rapidly advancing, providing increasingly detailed maps of cellular identity and function. Yet, identifying rare cell populations while maintaining computational tractability remains a major challenge in large-scale multi-omics clustering. Here, we introduce the supercell paradigm, in which expression-coherent cells are grouped into intermediate units that preserve weak but biologically meaningful local structure across omics layers, thereby improving sensitivity to rare populations that are often masked at the conventional cluster level. Supercells are constructed using angle-aware similarity metrics and second-order co-occurrence neighbors, with impurity cells pruned by degree centrality. Building on this idea, we develop scHG, a high-order graph learning framework with an omics-weighted optimizer that adaptively balances contributions from gene expression, surface proteins, and chromatin accessibility while remaining scalable on large datasets through sparse matrix optimization and iterative graph refinement. Across six benchmark datasets (up to 30672 cells), scHG consistently outperforms state-of-the-art methods, improving mean ARI and NMI by 3.97% and 3.54%, respectively, while reducing runtime by 26.40%. Beyond overall clustering accuracy, scHG resolves fine-grained heterogeneity within conventionally defined T-cell populations and, importantly, uncovers rare populations-including dendritic-cell populations and NK-like B cells-that remain hidden under standard clustering pipelines. These results demonstrate that supercells provide not only an efficient intermediate representation for large-scale multi-omics integration, but also a practical mechanism for rare-cell detection.

On the integer sets with identical representation functions

Camera-based 3D occupancy prediction commonly relies on bird’s-eye-view (BEV) representations, yet two limitations remain: optimization instability when inserting new modules into pre-trained BEV encoders, and height-agnostic BEV-to-voxel lifting that fails to preserve elevation-aware scene structure. We propose GSH-Occ (Gradient-Shielded and Height-Aware BEV Occupancy Network), a framework that tackles both issues through two complementary mechanisms. Gradient-Shielded Residual Dual Attention (GS-RDA) introduces a zero-initialized residual gate that preserves the identity mapping at initialization, allowing new attention modules to be grafted onto pre-trained encoders without disturbing learned features. Height-Aware Adaptive Lift (HAL) replaces naive channel replication with per-voxel adaptive fusion of BEV features and learnable height embeddings, followed by 3D convolutional refinement to capture vertical structure. On the Occ3D-nuScenes validation benchmark, GSH-Occ achieves 46.92 mIoU, outperforming FlashOcc by mIoU. Ablation studies confirm that GS-RDA and HAL target distinct failure modes and yield complementary improvements.

Efficient, low-cost atmospheric CO2 capture is essential for scaling negative-emission technologies. Moisture-swing carbon capture─which adsorbs CO2 from dry air and releases it under humid conditions─offers a low-energy alternative, yet the structure-property relationships governing its performance remain underexplored. Here, we systematically investigate humidity-driven capture on strong-base ion-exchange resins (IERs), varying polymer backbones (acrylic vs styrenic), ammonium functionality (Type I vs Type II), pore architecture (gel-type vs macroporous), and counteranion (dibasic phosphate vs carbonate) across 10 commercial resins. Thermodynamic and kinetic behaviors were assessed via closed-loop cycling with ambient CO2 at 20-70% RH. Morphological and chemical properties were characterized by SEM/EDS, N2 sorption, NMR cryoporometry, and solid-state NMR and FTIR spectroscopies. Macroporous IERs outperformed gel-type analogues in capacity and kinetics, but only when possessing intermediate-sized, well-connected pores, with the phase lag scaling inversely with the pore size. Ion identity and ammonium functionality acted jointly: Type I and Type II IERs exhibited higher swing capacities with phosphate and carbonate loadings, respectively. Anion choice also governed the kinetics, with phosphate slowing adsorption and carbonate slowing desorption. Acrylic backbones drove greater water uptake than styrenic ones. Solid-state NMR revealed humidity-driven protonation, consistent with the proposed swing mechanism. Together, these findings provide practical design rules for improving direct air capture via moisture-swing sorbents.

Abstract Hair plays a central role in self-perception, social identity, and interpersonal functioning, particularly among men experiencing androgenetic alopecia. While hair transplantation is typically conceptualized as a cosmetic intervention, emerging evidence suggests associated psychological and psychosexual benefits. This longitudinal clinical study examined changes in self-esteem, relationship satisfaction, and sexual functioning among 40 male patients undergoing hair transplantation at Vera Clinic. Assessments were conducted at baseline (T0), 6 months (T1), and 12 months (T2) using validated instruments (RSES, RAS, ISS). Results indicated statistically significant improvements across all domains (p < 0.001), with large effect sizes (Cohen’s d = 1.30–1.85). Significant correlations between variables suggest an interrelated pattern between self-esteem, relational satisfaction, and sexual functioning. These findings indicate that hair transplantation is associated with improvements in psychological well-being and relational functioning over time. The results are discussed within a biopsychosocial framework, highlighting potential mechanisms related to body image and social feedback processes.

Regulatory T (Treg) cells expressing Forkhead Box P3 (FOXP3) play crucial roles in maintaining immune tolerance and tissue integrity. EZH2, a Histone H3 lysine 27 (H3K27) methyltransferase, is known as a key regulator of Treg cell identity and suppressive function upon activation. Here, we demonstrate that the H3K27 lysine demethylase KDM6B, which catalyzes the opposing reaction to EZH2, was also required for Treg cell identity and function after activation. Treg-specific deletion of Kdm6b impaired tissue Treg cell fate and function. KDM6B was upregulated following T cell antigen receptor (TCR) signaling in Treg cells and contributed to the regulation of Treg-associated gene expression through both direct and indirect mechanisms. A subset of Treg functional genes were direct targets of KDM6B and were co-occupied by FOXP3 at cis-regulatory regions, where KDM6B recruitment limited H3K27me3 accumulation. More broadly, KDM6B-dependent H3K27 demethylation facilitated Treg gene expression programs that supported tissue Treg homeostasis.

Gliomas are among the most severe types of brain tumors and can be life-threatening without early detection. Accurate and timely segmentation of brain tumors from MRI scans is crucial for effective treatment planning; however, it remains challenging due to significant variation in tumor shape, size, and location. This paper proposes a 2D Patch-wise Deep Residual U-Net with 102 convolutional layers for automatic tumor segmentation. The approach divides MRI scans into uniform, non-overlapping patches to achieve precise localization and better preserve local features. Residual blocks with identity mapping help mitigate vanishing gradient issues, while dropout layers reduce overfitting during training. T1, T2, and FLAIR modalities from the BraTS 2019 and 2020 datasets were used to evaluate the model. Experimental results show high segmentation accuracy on BraTS 2020 and the Dice Similarity Coefficients (DSC) achieved were 0.9136 (WT), 0.7143 (TC), and 0.7028 (ET). The paper demonstrates that patch-wise deep residual architectures, even with limited training data, can deliver reliable and robust brain tumor segmentation.

Abstract The marine deep biosphere harbors microbial communities that drive organic matter transformations and biogeochemical cycles. Previous work on these communities has focused either on genomic characterization or metabolic activity measurements. However, to understand microbial ecophysiology in the deep biosphere taxonomic identity and metabolic function must be connected on both single-cell and ecosystem scales. In this work, we optimized a bioorthogonal non-canonical amino acid tagging fluorescence-activated cell sorting (BONCAT-FACS) workflow for low-biomass deep-biosphere sediments obtained during International Ocean Discovery Program Expedition 385 (IODP 385). BONCAT-FACS with 16S rRNA gene amplicon sequencing as well as metagenomics of sediment communities was applied to characterize translationally active communities in hydrothermally altered subsurface sediments of the Guaymas Basin. Our results revealed a heterotrophic microbial population throughout all sediments examined, with taxa translationally active down to our deepest sampling point, 154 meters below the seafloor. Based on 16S rRNA gene identities, the translationally active microbial community was dominated by heterotrophic members of the Gammaproteobacteria, Bacilli, Deinococci, and Alphaproteobacteria. These taxa are likely key contributors to cycling the large quantities of hydrothermally altered organic matter in Guaymas Basin sediments. To further elucidate the metabolic capacity of active taxa, we mapped 16S rRNA gene amplicons to metagenome assembled genomes (MAGs) previously obtained from IODP 385. These MAGs contained genes associated with C1 metabolism, carbohydrate degradation, and fermentation, indicating that active taxa leverage these metabolisms for energy conservation. Our results demonstrate that BONCAT-FACS provides high-throughput and single-cell insights into the metabolic activity of microbes in the low-biomass marine subsurface.

Long-term time series forecasting (LTSF) is critical for modern power systems, energy management, and grid planning. Yet virtually all existing forecasting models employ stationary activation functions that apply identical nonlinear mappings regardless of temporal context-a fundamental mismatch with real-world load data, which exhibits strongly regime-dependent dynamics such as summer demand peaks, winter heating patterns, and overnight low-load periods. We address this gap by proposing TC-KAN (Time-Conditioned Kolmogorov-Arnold Network), the first forecasting architecture to augment KAN activation functions with position-aware coefficient parameterisation. The core innovation replaces the static polynomial coefficients in standard KAN activations with position-conditioned coefficients produced by a lightweight positional-embedding MLP, providing additional learnable capacity beyond standard KAN while adding negligible parameter overhead. TC-KAN further integrates a dual-pathway processing block-combining depthwise convolution for local temporal pattern extraction with the time-conditioned KAN layer for enhanced nonlinear transformation-within a channel-independent framework with Reversible Instance Normalisation. Experiments were conducted on four standard ETT benchmark datasets and the high-dimensional Weather dataset. TC-KAN achieves superior or competitive accuracy in most configurations while requiring merely 51K parameters-approximately 40% of DLinear and ∼100× fewer than iTransformer. On ETTh2, TC-KAN reduces the mean squared error by up to 61.4% over DLinear, and matches the current state-of-the-art iTransformer on ETTm2 at a fraction of the computational cost. This extreme parameter reduction circumvents the steep memory bottlenecks endemic to massive Transformer models, positioning TC-KAN as a highly practical architecture tailored precisely for resource-constrained edge deployments-such as on-device load forecasting inside smart grid sensors and industrial IoT controllers.

Interlocked transcription factor feedback loops maintain and restore adult touch sensation.

A wealth of noncoding regulatory elements has been described across mammalian cell types, yet determining their functional role remains a challenge. Regulatory control of gene expression is critical during active processes such as the adaptive immune response. Upon antigen presentation, a naive CD4+ T cell undergoes major transcriptional and structural reorganization necessary for establishment of subset identity and immune function. In this study, we systematically measure the regulatory potential of candidate regulatory elements associated with open chromatin across five mouse CD4+ T cell subsets. Using ATAC-STARR-seq, we found that approximately one quarter of open chromatin regions demonstrate regulatory activity. Most exhibit shared functional potential across subsets, though we identify enhancers with activity that is restricted to specific cellular contexts. To distinguish regulatory potential from endogenous function, we performed CRISPR-based epigenome editing screens at noncoding regions of Th17 cells and identified a set of core elements essential for subset polarization. Integrating Region Capture Micro-C, we resolved precise 3D chromatin topologies that explain functional regulatory networks via physical contacts. We characterize examples of active regulatory hubs formed through multiple CTCF-independent interactions organized in a hierarchical architecture. Furthermore, we discover a critical Batf enhancer that operates via these contacts. Using targeted perturbations, we disrupt local chromatin topology and gene expression with profound consequence to downstream Th17 cell phenotypes. We confirm the physiological necessity of these functional enhancers in vivo, demonstrating the importance of noncoding elements for Th17 cell identity. Together, this work reveals how DNA sequence and chromatin cooperate to shape the regulatory logic of Th17 cells, with implications for cis-regulatory principles beyond the immune system.

Extracellular spike waveforms provide critical insights into neuronal activity, morphology, and function. Their shape can reveal cell-type identity, excitatory versus inhibitory function, and afferent projections from distal regions. The development of dense, high-channel-count probes now permits recordings from thousands of sites simultaneously, revealing a wider diversity of waveform types than previously appreciated. These advances provide an unprecedented opportunity to link waveform shape to the underlying biophysical processes of neurons and their spatial arrangement relative to the recording electrode. This review examines and catalogs the diversity of extracellular waveforms (including negative, triphasic, and positive spike waveforms), focusing on their biophysical origins and roles in neural compartments. We also discuss classification strategies, ranging from traditional feature-based approaches that use specific waveform features (such as spike duration and peak-to-trough ratios) to emerging machine learning and multimodal methods that integrate waveform shape with firing dynamics and anatomical localization. These new approaches reveal novel neuronal populations but also highlight a pressing need for standardized classification frameworks to ensure reproducibility and facilitate cross-study comparisons. Finally, we review how experimental factors such as filtering, sampling biases, and spike-sorting algorithms shape the observed diversity of extracellular waveforms. By consolidating recent progress in both experimental and computational approaches, this review provides a comprehensive resource for interpreting extracellular recordings. A deeper understanding of waveform diversity will advance basic neuroscience and accelerate applications in brain-machine interfaces, diagnostics, and neural prosthetics.

The potential of water-soluble membrane proteins (wsMPs) has not been fully realized. In this article, we exploit the nearly identical functionality of wsMPs with their membrane-bound counterparts and show that we can create water-soluble membrane proteins that incorporate into the plasma membranes of cells and alter their fate. As a proof of concept, we demonstrate the functional properties of water-soluble engineered pore-forming proteins, K+ ionic channels (MthK), and constitutively active GPCRs-among them frizzled receptors-both in vitro and in vivo. We call this method in vivo deployment of recombinant viable MPs, iDRIVE. Furthermore, we demonstrate that our strategy mediates the unidirectional insertion of MPs into the plasma membrane, and through constitutively active receptors, we present evidence for similar signaling pathway activation between small molecules and our water-soluble proteins using model phenotypes and molecular signaling assays. We present three examples where wsMPs are functional in dictating cellular fate, both in vitro and in vivo. Lastly, we show the induction of similar differential methylation via the activation of the Wnt signaling pathway using the conventional small molecule agonist, CHIR99021, or our wsFrizzled receptors (iDRIVE-FZD) in human embryonic kidney (HEK 293) embryoid spheroids (ESs). Additionally, we show that Wnt activation via wsFrizzled receptors results in even more biologically relevant epigenetic changes than via the small molecule CHIR99021. Future work will employ iDRIVE to differentiate stem cells in the production of research and clinically relevant organoids.

ABSTRACT Collective memories are shared representations of a group’s past. For nations, these memories serve important purposes: they shape national identity, promote social cohesion and guide future decisions. Although extensive research has examined collective memory in Europe and the United States, less is known about countries outside these regions, such as Japan. Cultural tightness and other societal differences may influence the extent to which collective memories serve these functions. To address this issue, we first asked Japanese participants to nominate nationally important collective memories (Study 1), and then asked both Japanese and American participants to report the extent to which their country’s collective memories serve directive, social and identity functions (Study 2). Surprisingly, Japanese participants showed agreement on relatively few collective memories and rated those memories as serving these functions to a lesser degree than did Americans. These findings raise questions about how cultural tightness, institutional influences and educational systems shape collective memory and its functions. We suggest that in Japan, national identity may rely more on structural and cultural continuity than on shared recollections of specific historical events.

Lipid nanoparticles (LNPs) have become the leading platform for delivering genetic material, gaining global recognition through the success of mRNA-based COVID-19 vaccines such as mRNA-1273 (SpikeVax, Moderna) and BNT162b2 (Comirnaty, BioNTech/Pfizer). Yet, while RNA-LNPs have reached clinical maturity, their DNA counterparts remain comparatively underexplored, despite holding great promise for gene replacement and genome-editing therapies. In this review, we turn the spotlight on DNA-loaded LNPs, examining how their structure, composition, and biological behavior differ from RNA-LNPs, their natural point of reference, and from earlier lipid-based systems such as cationic liposome/DNA complexes (lipoplexes). DNA-LNPs tend to form larger, more heterogeneous, and often multilamellar particles due to the intrinsic stiffness and high charge density of DNA. These distinctive features call for dedicated design strategies, including the use of cationic lipids, pre-condensation agents, and optimized PEGylation schemes. Moreover, DNA profoundly influences the biomolecular corona that forms in biological fluids, which in turn shapes immune recognition, circulation, and tissue targeting. By highlighting these unique physical and biological challenges, this review underscores the need to move beyond simply adapting RNA-based formulations. Instead, a cargo-informed design approach will be key to unlocking the full therapeutic potential of DNA-LNPs in next-generation gene delivery.