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To address the limitations of traditional pricing models regarding accuracy and adaptability in high-frequency trading, this study presents a Transformer-based Efficiently-Fused Optimized Bayesian Network (Trans-EFOBN) for financial asset pricing. The framework integrates a masked transformer with temporal logic constraints to extract sequential features and combines a Dynamic Bayesian Network (DBN) to establish hierarchical structural dependencies between macro factors and micro market variables. This design does not aim to establish strict econometric causality but instead leverages an end-to-end learning mechanism to simultaneously optimize feature representation and network parameters. Empirical analyses utilizing minute-level high-frequency data of the CSI 300 constituent stocks from 2019 to 2024 in the Wind database demonstrate substantial performance gains: the mean absolute error (MAE) decreases to 0.037 (approximately 25% lower than the baseline static Bayesian model), while R² attains 0.86. In simulated trading scenarios incorporating transaction costs and slippage, the proposed model yields an annualized return of 14.2% and a Sharpe ratio of 0.95. The results indicate that integrating structural dependency logic with dynamic probabilistic inference significantly enhances asset pricing efficiency and interpretability, providing robust technical support for high-frequency quantitative trading.

Enhancing the energy efficiency for energy-intensive seawater desalination technologies is imperative to sustainably mitigate water scarcity while reducing carbon footprints. This work presents a transformative advance in reverse osmosis desalination technology by fundamentally redefining the long-standing trade-off between energy efficiency and water production efficiency. Through the synergistic integration of bio-inspired ultrapermeable membrane module with state-of-the-art batch reverse osmosis, we demonstrate unprecedented performance-achieving a specific energy consumption of 1.68 kWh m-3 while delivering an average water flux of 95 L m-2 h-1. This represents a 33%-58% reduction in energy demand and a 5-fold improvement in water flux compared with conventional seawater reverse osmosis desalination plants (2.5-4 kWh m-3 and 15 L m-2 h-1), challenging the prevailing assumption that increased membrane permeability offers only marginal efficiency benefits. This work can further guide the development of advanced membrane materials and energy-efficient desalination technologies, with potential applications in desalination and zero/minimal liquid discharge systems.

Body size is an important feature of organisms that correlates strongly with fitness, as it directly or indirectly influences nearly all biological phenomena. The body size of an organism is, in turn, shaped by many biological and physical factors that may not only directly affect the individual but also influence offspring through maternal investment or provisioning and transgenerational mechanisms. Body size differences have widely been observed in adult small hive beetles (Aethina tumida Murray, SHBs), an invasive pest of honey bee colonies; however, little is known about the evolutionary and ecological implications of these variations. We hypothesized that parental body size influences reproductive performance, progeny fitness, and stress tolerance in SHBs. To test this, we paired different adult sizes and sexes of SHBs in rearing containers and compared their reproductive abilities and offspring fitness. We also exposed the progeny beetles to extreme temperatures and measured their thermal tolerance. A clear trade-off emerged: larger beetles generated more offspring with lower fitness, while smaller adults produced fewer but higher-fitness offspring. Additionally, larger SHB females showed greater tolerance to extreme temperatures, while small males were the most vulnerable. This study reveals that parental body size in SHB plays a pivotal role in shaping offspring reproductive traits and thermal stress tolerance. These findings highlight a potential mechanism by which SHB adapts and thrives across diverse and changing environments. Management strategies that exploit these life-history trade-offs could help shift populations toward weaker generations, thereby enhancing long-term control effort.

Marine plastic pollution poses significant ecological, economic, and social challenges, requiring innovative monitoring and identification solutions to support effective mitigation and management strategies. Hyperspectral imaging and artificial intelligence have proven to be valuable tools in detecting and identifying macroplastics in aquatic environments. Despite numerous studies focusing on deep learning approaches, many existing models remain computationally heavy and lack adaptability for real-world on-board processing on energy-constrained platforms like drones. This drawback limits their applicability for large-scale monitoring and requires models that are both precise in their predictions and lightweight for efficient computation. First, to improve plastic-type classification performance, this paper proposes an uncertainty-aware fusion approach where the recently proposed patch-based Lightweight Spatial and Spectral Hyperspectral Convolutional Neural Network (LSS-HCNN) is fused with a pixel-based Random Forest (RF) classifier. Second, to improve computational efficiency, this paper investigates two band selection methodologies based on LSS-HCNN Squeeze-and-Excitation (SE) block weights and RF feature importances respectively. This study evaluates the classification of five common polymers (HDPE, LDPE, PET, PP, and PS) supplemented by natural organic matter and background materials. To address material heterogeneity at object boundaries, we evaluate the approach on both pure and mixed-material regions. The results show that LSS-HCNN consistently outperforms traditional Machine Learning (ML) methods, improving performance by more than 4% over the accuracy provided by RF. The proposed uncertainty-aware fusion successfully enhances classification accuracy, achieving 97% on hyperspectral images of plastic debris. Furthermore, a subset of six selected bands, identified by the elbow method as the optimal accuracy-efficiency trade-off, maintains 90% accuracy while reducing computational demands by more than 20 times fewer parameters and floating-point operations. Our findings provide a pathway towards lightweight, accurate, and adaptable models for real-time plastic debris monitoring in aquatic environments.

Endophytic fungi and phytohormone signaling can jointly influence growth-defense trade-offs in medicinal plants by reprogramming metabolism in a stage-dependent manner. To elucidate how the endophytic fungus Trichoderma longibrachiatum FG and exogenous salicylic acid (SA) and methyl jasmonate (MJ) regulate Codonopsis pilosula (C. pilosula), seedlings were assigned to five treatments: control (CK), FG, SA, MJ, and SA + MJ (SM). Assessments were conducted at multiple growth stages using morphological measurements, photosynthetic and physiological assays, antioxidant profiling, and non-targeted metabolomics. All treatments promoted seedling growth, with SM producing the most pronounced improvements in biomass-related traits, including root growth and overall biomass accumulation. FG, SA, and MJ modified photosynthetic traits and redox status, as evidenced by changes in photosynthetic parameters, antioxidant enzyme activities, lipid peroxidation levels, and endogenous signaling molecules (SA, JA, NO), indicating coordinated regulation of primary physiology and stress-related responses. UHPLC/Q-TOF-based metabolomic profiling revealed temporally distinct patterns: FG induced limited early changes but triggered marked metabolic reprogramming at 50 days, whereas SA, MJ, and SM elicited stronger metabolite shifts at 15 days. Differential metabolites were predominantly lipids and lipid-like molecules, with steroid and steroid-derived pathways emerging as key responsive hubs; notably, FG at the late stage was associated with enrichment of intermediates linked to brassinosteroid biosynthesis. Overall, SA-MJ interactions were trait-dependent, showing clearer synergy in biomass-related performance, while FG primarily contributed to late-stage metabolic adjustments. These findings provide a mechanistic basis for optimizing high-quality cultivation and sustainable utilization of C. pilosula.

Enzyme-free nucleic acid amplification circuits, such as the hybridization chain reaction (HCR), hold immense promise for molecular diagnostics but are fundamentally constrained by a persistent trilemma in biological applications: the trade-off between reaction kinetics, probe stability, and manufacturing complexity. Here, we overcome this challenge by introducing a Localized HCR Nanosphere (LHCR-NS), a self-assembling DNA nanodevice that leverages spatial confinement to simultaneously accelerate reaction speed, enhance biostability, and simplify fabrication. The LHCR-NS is constructed from a single palindromic DNA strand that spontaneously folds into a core-shell nanostructure, which then immobilizes hairpin probes. This localized architecture concentrates reactants, boosting the HCR kinetics by over an order of magnitude compared to conventional free-solution systems. The compact spherical structure provides steric shielding, rendering the nanoprobe exceptionally resistant to nuclease degradation even in raw serum. This robust platform achieved an attomolar limit of detection (LOD) for miR-21 with single-nucleotide specificity. Its superior stability and biocompatibility enabled real-time, high-contrast imaging of endogenous miRNA fluctuations within living cancer cells. Critically, the simplified one-pot synthesis and assay workflow allowed for the rapid and accurate quantification of miR-21 in clinical serum samples, perfectly discriminating cancer patients from healthy controls (AUC = 1.0). This work presents a new paradigm in DNA nanoprobe design, where architectural simplicity and physical principles, rather than chemical complexity, are harnessed to create powerful tools for both fundamental cell biology and clinical diagnostics.

Paroxysmal sympathetic hyperactivity (PSH) is a serious complication of traumatic brain injury (TBI), characterized by episodic hypertension (HTN), tachycardia, hyperthermia, hyperhidrosis, and dystonia. It is associated with prolonged mechanical ventilation (MV), extended ICU and hospital stays, and worse outcomes. Current guidelines lack prophylactic recommendations. This single-center randomized controlled trial (NCT05427474) enrolled 90 adults with moderate-to-severe TBI glasgow coma scale (GCS 3-12). Participants were randomized to: standard care (Group I, n&#x2009;=&#x2009;30); standard care plus propranolol (40&#xa0;mg/12&#xa0;h, Group II, n&#x2009;=&#x2009;30); or standard care plus propranolol (40&#xa0;mg/12&#xa0;h) and gabapentin (100&#xa0;mg/8&#xa0;h, Group III, n&#x2009;=&#x2009;30). The primary endpoint was PSH incidence. Secondary endpoints included ventilator days, ICU and hospital length of stay (LOS), and mortality. PSH incidence was lowest in Group III (10%) vs. Group II (33.3%) and Group I (60%) (p&#x2009;<&#x2009;0.001). Group II showed the shortest MV duration (5.92&#x2009;&#xb1;&#x2009;5.15 days) vs. Group III (9.42&#x2009;&#xb1;&#x2009;6.99 days, p&#x2009;=&#x2009;0.047) and Group I (12.92&#x2009;&#xb1;&#x2009;5.98 days, p&#x2009;<&#x2009;0.001). ICU LOS was shortest in Group II (9.6&#x2009;&#xb1;&#x2009;5.32 days) vs. Group III (14.69&#x2009;&#xb1;&#x2009;8.35 days, p&#x2009;=&#x2009;0.017) and Group I (19.5&#x2009;&#xb1;&#x2009;8.19 days, p&#x2009;<&#x2009;0.001). Mortality and GCS improvement did not differ significantly (p&#x2009;>&#x2009;0.05). Prophylactic propranolol significantly reduces PSH incidence, shortens MV duration, and decreases ICU stay in moderate-to-severe TBI. Although adding gabapentin further reduces PSH, it prolongs recovery time, suggesting a trade-off between efficacy and sedative effects. These findings suggest that propranolol monotherapy is a promising prophylactic strategy, with gabapentin potentially reserved for refractory cases. However, given the study's limitations, these results should be considered hypothesis-generating and warrant confirmation in larger, multicenter trials. Mortality and neurological outcomes were comparable across groups. The trial was prospectively registered at ClinicalTrials.gov (NCT05427474) on June 22, 2022.

The clinical outcome predictions of conventional in vitro and in vivo models are often inaccurate because they cannot replicate the tumor microenvironment (TME) complexity. Existing 3D models encounter challenges regarding TME complexity replication, engineering constraints, and limited capacity in analyzing immune-cancer interactions. This study employs 3D embedded bioprinting to develop a heterogeneous lung spheroid (HLS) model, incorporating key stromal factors to better reflect the TME. Transcriptomic profiling via RNA sequencing reveals gene signatures associated with extracellular matrix remodeling, immune suppression, and tumor progression, demonstrating substantial similarity to patient-derived lung tumor samples and validating the biological fidelity of the model. Functional assays demonstrate that the model effectively replicated TME dynamics, as evidenced by reduced CAR-NK cell infiltration, cytotoxicity, and cytokine secretion with increasing model complexity, indicative of a highly immunosuppressive environment. Advanced CAR-NK cells expressing chemokine receptors are utilized to overcome this immune barrier and enhance migration and infiltration within the physiologically relevant lung TME model. Overall, this model replicates critical features of the lung TME, showing potential for evaluating next-generation immunotherapies targeting complex solid tumors.

This study examined the association between distalization and lateralization of implant components and revision outcomes and patient-reported outcome measures (PROMs) following primary total stemmed reverse shoulder arthroplasty (rTSA) using data from a national arthroplasty registry. All primary total stemmed modular rTSA procedures (excluding custom implants) recorded between January 1, 2015, and December 31, 2023, were identified. A total of 25,837 primary rTSA procedures were included. Procedures were classified into 6 construct groups based on humeral and glenoid component features associated with distalization and/or lateralization. Kaplan-Meier methods were used to estimate cumulative percent revision. Cox proportional hazards models estimated adjusted hazard ratios, controlling for age, sex, American Society of Anesthesiologists score, body mass index, primary diagnosis, glenosphere size, and glenoid morphology. Subgroup analyses were performed for inlay and onlay humeral configurations. PROMs collected from November 2018 were analyzed in a subset of patients. Constructs incorporating humeral distalization demonstrated higher revision rates beyond 1.5 years compared with standard components (P = .002). Augmented glenoid baseplates were associated with lower early revision rates within 3 months (P = .002). Onlay designs incorporating distalization and/or lateralization showed increased revision risk during specific post-operative periods. Instability or dislocation was the most common indication for revision, while infection was more frequent in constructs combining distalization and lateralization. All groups demonstrated significant improvements in EuroQol Visual Analogue Scale and Oxford Shoulder Score at 6 months. Humeral distalization was associated with higher medium-term revision rates despite early improvements in PROMs, highlighting trade-offs in implant selection.

Safe and Sustainable by Design (SSbD) and life&#x2011;course exposome science share a primary&#x2011;prevention goal: reducing harmful exposures by intervening early in the chemical and product life cycle. Here we propose an integrated, replacement&#x2011;first decision workflow that links life&#x2011;course exposure considerations to SSbD stage&#x2011;gates using New Approach Methodologies (NAMs). The framework combines: (i) problem formulation informed by susceptible windows and populations; (ii) AOP&#x2011;guided selection and curation of mechanistic in vitro bioactivity evidence (e.g., ToxCast/Tox21, transcriptomics); (iii) physiologically based pharmacokinetic (PBPK) modelling with quantitative in vitro&#x2011;to&#x2011;in vivo extrapolation to translate in vitro potency into internal dose anchors; and (iv) transparent multi&#x2011;criteria synthesis to support early design trade&#x2011;offs. We illustrate this approach with an endocrine&#x2011;relevant substitution scenario comparing bisphenol A (BPA) with a structurally similar alternative (BPAP) and a bio&#x2011;based monomer candidate (isosorbide). Estrogen receptor (ER) bioactivity anchors derived from curated HTS assays are contrasted with PBPK&#x2011;predicted internal concentration ranges to generate an internal margin for stage&#x2011;gate decisions. The example shows how candidates with weak or absent pathway&#x2011;relevant bioactivity can be advanced, while structurally similar alternatives with ER potency approaching predicted internal concentrations can be deprioritised or redesigned pending improved exposure controls. By explicitly mapping NAM outputs to AOP key events and life&#x2011;course windows, the workflow operationalises the 3Rs by replacing broad exploratory animal testing with targeted, human&#x2011;relevant evidence and reducing unnecessary in vivo studies through early prioritisation.

Antibiotic combination therapy is often used to broaden the antimicrobial spectrum, limit resistance and improve treatment efficacy. Several antibiotics show collateral effects where resistance to one antibiotic increases susceptibility to another. In intensive care units (ICUs), antibiotic treatments are frequently adjusted based on patient outcomes, without considering collateral effects. This provides a setting to study these effects in Pseudomonas aeruginosa (PA), a highly adaptable, multidrug-resistant (MDR), nosocomial pathogen. We compared longitudinal PA isolates from twenty-five ventilated ICU patients receiving various antibiotics to laboratory strains undergoing in vitro adaptive evolution under four antipseudomonal monotherapies. Prolonged exposure to certain antibiotics produced resistance with collateral effects. In vitro, increasing antibiotic pressure drove distinct mutational trajectories. In patients, the number of antibiotics administered did not correlate with resistance changes to those antibiotics, suggesting that switching may reduce persistence of resistance. Notably, an inverse correlation between resistance to non-administered antibiotics and the number of different antibiotic classes administered, aligns with the principles of collateral susceptibility driven by multi-class exposure. This study provides s real-world evidence that empirical antibiotic mixing in ICU patients leverages evolutionary trade-offs. Consequently, diversifying antibiotic pressure via multi-class exposure may attenuate the fixation and persistence of MDR phenotypes in critical care.

Scientific equipment, such as high-end electron microscopes, entails considerable costs for acquisition, maintenance and operation. Efficient use of these instruments requires thoughtful allocation of access among multiple users. This task may be aided by a thorough statistical analysis that combines cost modelling and queueing theory. In this work, we propose a framework for estimating the hourly access cost and the average waiting time, based on equipment characteristics (price, lifespan and operating hours), user patterns (cycle and acquisition times) and staffing assumptions. In this way, we derive expressions that help estimate the optimal number of users and service congestion levels. The methodology is general and applicable to many types of shared scientific infrastructure. We illustrate its use with realistic cryo-electron microscopy scenarios. To facilitate adoption, we provide an open-access online calculator implementing the model at https://i2pc.es/coss/Programs/equipmentCost.html. This tool can support evidence-based decision-making in equipment planning and policy design.

Reasoning large language models are increasingly considered for healthcare-related artificial intelligence applications, but their practical value depends not only on diagnostic accuracy, but also on responsiveness and operational reliability. In this study, we benchmarked six model settings on 1,000 questions from the MedQA dataset: DeepSeek-R1, its distilled 8-billion-parameter local variant DeepSeek-R1:8b, ChatGPT o3-mini-high, and their knowledge-base-augmented counterparts. We evaluated performance across three dimensions: diagnostic accuracy, response latency, and first-attempt connection reliability. DeepSeek-R1 achieved the highest accuracy (89.5%, 95% CI: 87.4-91.2) but showed substantially longer response times (median 26.54&#x2009;s) and higher connection failure rates (4.6%). ChatGPT o3-mini-high responded faster (median 10.05&#x2009;s) and showed the most favorable tail-latency profile, but its accuracy (78.2%, 95% CI: 75.5-80.7) was lower than that of DeepSeek-R1. The locally deployed DeepSeek-R1:8b demonstrated markedly stronger connection reliability (failure rate 0.2%, 95% CI: 0.0%-0.5%) but substantially reduced accuracy (55.0%, 95% CI: 51.9%-58.5%). Knowledge-base augmentation did not consistently improve performance; for DeepSeek-R1, it significantly reduced accuracy by 4.36% ( p = 0.0002 ), while no significant benefit was observed for the other models. These findings show that reasoning model performance in medical question answering is best understood as a trade-off among accuracy, latency, connection reliability, and deployment mode, and that retrieval augmentation is not universally beneficial. More broadly, this study provides deployment-relevant benchmarking evidence for evaluating reasoning models in healthcare-related settings, while also indicating the need for richer knowledge resources and more realistic task environments before such systems can be meaningfully assessed for real-world clinical use.

High-nighttime-temperature (HNT) poses a major challenge to tomato (Solanum lycopersicum L.) growth and productivity. To elucidate the molecular basis of HNT responses, this study systematically examined the morphological and transcriptomic changes in tomato seedlings under prolonged HNT stress. We observed that HNT suppressed plant growth and chlorophyll content while triggering H2O2 accumulation in new leaves; concurrently, it promoted thermomorphogenesis-related adaptations like reduced leaf angles and lower leaf trichome density, traits potentially facilitating heat dissipation. Transcriptome profiling identified 4,551 differentially expressed genes (DEGs), comprising 2,104 up-regulated and 2,447 down-regulated genes. Functional enrichment analysis revealed that up-regulated DEGs were primarily involved in glycosyl transfer, flavonoid biosynthesis, mismatch repair, and protein processing, whereas down-regulated DEGs were enriched in photosynthesis, metabolic, and immune signaling. These changes suggest a strategic trade-off, with down-regulated photosynthetic and metabolic activities potentially enabling the reallocation of resources toward stress resilience mechanisms. As a central heat shock response (HSR) mechanism, the SlHSPs-SlHSFs system responded to HNT, with 10-day stress inducing distinct expression patterns of SlHSP70/90 genes alongside concurrent suppression of SlHSFs. qPCR analysis unveiled a transcriptional shift in SlHSFs from an initial shock phase, marked by pronounced expression changes at 1-day HNT, to a sustained acclimation phase. Prolonged HNT also triggered gene-specific expression changes in the unfolded protein response (UPR) pathway, as well as in genes involved in ROS homeostasis and hormone signaling. In addition, it increased alternative splicing in genes associated with antioxidant defense, DNA repair, and protein processing. Collectively, these transcriptomic alterations reflect a systemic reprogramming that prioritizes energy conservation, redox homeostasis, and macromolecular stability to support nocturnal heat acclimation. Our findings provide novel insights into tomato adaptation to HNT and offer valuable genetic resources and a theoretical foundation for breeding HNT-resilient tomato varieties.

Mercury (Hg) is a global contaminant that biomagnifies in food webs, raising concerns for food safety, fisheries exploitation, and wildlife conservation. Fish, including apex predators like sharks, are the primary source of human Hg exposure, yet species-specific speciation data remain scarce. Most studies rely on total Hg (THg) as a proxy for methylmercury (MeHg), but direct MeHg measurement is essential for accurate risk assessment due to neurotoxicity and bioavailability. This study presents a comprehensive assessment, quantifying THg-MeHg in 18 species from the Mediterranean, Indian, and Atlantic Oceans, nine measured for the first time. Concentrations varied widely, with deep-sea and pelagic sharks showing highest levels. THg and MeHg strongly correlated (R2 = 0.99), but MeHg-THg ranged 65-101%, demonstrating substantial interspecific variability and challenging the assumption of near-complete methylation. Bioaccumulation increased with body size and trophic level, and biomagnification was pronounced in Mediterranean deep-sea assemblages. Nearly half of the species exceeded the 1 mg kg-1-EU Hg limit. Target Hazard Quotients exceeded 1 for deep-sea and large pelagic sharks, highlighting tangible health risks. Elevated MeHg levels in commercial fillets confirm consumer exposure. Species with the highest MeHg burdens are heavily exploited and threatened, identifying globally traded sharks as hotspots of human Hg exposure.

Dynamic control in artificial intelligence hardware is increasingly emphasized for the co-integration of learning and computing. However, existing approaches to dynamic mode modulation often require impractical structures or parasitic external signals, which pose significant challenges for realistic implementation. This study presents an &#x3b1;-In2Se3 device with ambipolar transport behavior, achieved through surface charge transfer doping (SCTD). The p-type conduction arises from In2O3, which acts as the highest occupied molecular orbital (HOMO), and Se vacancies, which serve as acceptors. Adaptive synaptic and logic functions are enabled through the control of ferroelectric polarization. The excitatory/inhibitory synaptic mode reconfiguration was successfully realized at an ultralow energy consumption from approximately 200 aJ to 20 fJ. The reversible ferroelectric switching serves as the underlying mechanism for the tunability of the inverter trip point. This study is an effort to break away from dedicated stationary applications and move closer to a future prototype device than previous works in integrating neuromorphic computing and logical functionalities.

Alloy-disordered I-III-VI quantum dots often trade spectral stability for efficiency, limiting photon-transport devices through reabsorption. Here, we confine Cu(I) dopants inside Ag-In-Ga-S cores during GaSx overgrowth, verified by Cu-valence fingerprints and quantitative elemental mapping showing predominantly core-enriched Cu distribution, and obtain red emission spectrally pinned with a constant Stokes shift (&#x223c;140 meV) across growth and maturation. Single-dot spectroscopy resolves symmetric Lorentzian lines down to &#x223c;62 meV, showing that the broad ensemble band is dominated by population inhomogeneity rather than an intrinsically broad dopant transition. First-principles calculations identify substitutional CuAg as a low-energy defect forming an acceptor-like, Cu-S p-d hybridized valence-edge manifold, rationalizing the pinning. The resulting dots deliver photoluminescence quantum yields up to 85% and enhance luminescent solar concentrators to an optical efficiency of 7.33% by mitigating reabsorption.

Prisons, as total institutions organized around the sex/gender binary, are highly gendered institutions. Individual experiences of prison, therefore, vary dramatically by gender identity due to pains of imprisonment experienced differently by people experiencing incarceration. Building on the gendered pains of imprisonment literature, we use the public-use 2016 Survey of Prison Inmates to expand on previous research exploring the disproportionate mental health burden experienced by transgender and gender diverse (TGD) individuals who are incarcerated relative to cisgender men and women who are incarcerated. We disaggregate the TGD group to examine the different mental health burdens of TGD individuals and how the methodological task of categorizing gender identities can affect our understanding of mental health in prison. Results highlight the theoretical importance of acknowledging the stressful carceral environment and its impacts on psychological well-being and the methodological importance of understanding how decisions around the categorization of gender identity can differently impact our understanding of diverse individuals within the already-marginalized TGD population in prisons.