Search Results (840)

Artificial intelligence is reshaping employment in smart cities, yet centralized hiring platforms can deepen exclusion for persons with disabilities through privacy risk, biased models, weak multilingual support, and limited accommodation awareness. Because disability-related records are highly sensitive, no single institution holds enough representative data to train fair models, and centralizing such data is rarely permissible across borders. We propose FedAgent-Chain, a framework that integrates federated learning, blockchain-based auditability, multilingual processing, rule-based agentic services, and human-in-the-loop governance, extended with an education-to-employment module that builds individualized, accessible job-readiness pathways. Institutions across Saudi Arabia, the United States, China, and Europe train shared models without exchanging raw data. In a prototype evaluation on synthetic records over five seeds, the framework reached a mean F1 of 0.7207 (95% CI: [0.6506, 0.7909]), comparable to a centralized logistic-regression baseline while preserving data locality, with a formal $(\epsilon =3.2,\delta ={10}^{-5})$ differential-privacy guarantee after 20 rounds. Multi-dimensional fairness regularization lowered disability-category and work-mode disparity by 32.3% and 40.3% relative to local-only training. We report the fairness behavior transparently, including a case where the penalty does not outperform standard FedAvg on disability-category disparity, and we position cross-institutional integration with accountable governance, rather than raw metric superiority, as the central contribution. Full article

Implicit bias, automatic attitudes or stereotypes outside conscious awareness, may influence clinicians’ communication, diagnosis, and treatment decisions, contributing to inequities in care. We conducted a scoping review to map measurement strategies used to assess implicit bias among health professionals and students in healthcare and training settings. Using Joanna Briggs Institute guidance and PRISMA-ScR, we searched PubMed, Embase, BVS, Google Scholar, and institutional repositories for studies to November 2025; two reviewers independently screened and charted data (protocol was developed a priori but submitted internal in organization, and then uploaded in OSF. Of 1864 records, 93 studies from 28 countries were included. We identified 57 bias domains, most often race/ethnicity, weight, and sexual orientation. Across studies, 42 unique instruments were reported; the Implicit Association Test was most common, while psychometric validation and administration details were frequently limited, constraining comparability and interpretation. Evidence gap mapping showed concentration in academic and hospital settings, with fewer studies in primary care or community contexts and limited attention to age, disability, and intersectionality-related biases. The evidence base is growing but fragmented; future work should prioritize standardized administration and reporting, stronger validation, and tools that better capture automatic responding across diverse identities and care settings to support education and equity-oriented interventions. Full article

Background/Objectives: Clinical foundation models (CFMs) are increasingly being applied to analyze unstructured clinical text, supporting tasks such as clinical reasoning, clinical documentation generation, and information extraction. However, their evaluation remains limited and lacks standardization, which represents a significant challenge for their safe integration into healthcare systems. This scoping review aimed to examine how performance, clinical utility, safety, and generalization are assessed in current evaluation frameworks and methodologies for CFMs applied to electronic health record text and to identify existing gaps and challenges in their evaluation. Methods: This scoping review was reported in accordance with PRISMA-ScR guidance, and the study selection process was summarized using a PRISMA flow diagram. A total of 448 records were identified in the initial search, of which 16 met the eligibility criteria and were included in the final synthesis. Results: The findings were organized into three categories: methodological frameworks, performance and clinical utility, and ethical and safety considerations. The results show progress in structured methods, evaluation schemes, and simulated environments. Nevertheless, inconsistencies persist in operational robustness, particularly regarding clinical reasoning and hallucinations. The literature also highlights important ethical concerns, including potential biases and regulatory gaps. Conclusions: Although CFMs show strong potential to transform clinical text analysis, no comprehensive framework currently guides their evaluation and safe use in real clinical settings. These findings highlight the need for a structured conceptual framework that integrates methodological, technical, clinical, and ethical criteria to support responsible implementation in clinical environments. Full article

Background/Objectives: Assessment of cognitive function in patients with dementia is often hindered by functional and environmental barriers. Although caregiver reports are an alternative source, their clinical utility for estimating patients’ cognitive function remains uncertain. This study aimed to estimate cognitive function using caregiver-reported data combined with patient demographics and to evaluate its clinical utility. Methods: This retrospective cross-sectional study enrolled participants who visited a memory clinic and completed the Montreal Cognitive Assessment (MoCA) for cognitive assessment, together with caregiver-reported questionnaires for activities of daily living (ADL) and neuropsychiatric symptoms (NPS). Multivariable linear regression models were constructed to predict the MoCA score, with Model 1 including demographics, ADL, and NPS as covariates and Model 2 further incorporating clinical diagnosis. The intraclass correlation coefficient, Bland–Altman analysis, and regression error characteristic curves were assessed. Results: Among 2650 participants (56.5% women; mean age, 70.4 years), the NPS variable was excluded from both models. Model 1, which included demographics and ADL, explained 65.4% of the variance, whereas Model 2, which incorporated clinical diagnosis, explained 75.9%. Model 2 yielded an intraclass correlation coefficient of 0.853, compared to 0.778 for Model 1. At a 4-point error tolerance, Model 2 yielded an accuracy of 75.5%. Bland–Altman biases were near zero, with 95% limits of agreement of approximately ±7 points for Model 2. Conclusions: MoCA scores can be estimated using caregiver-reported ADL scores, demographics, and clinical diagnosis. NPS scores provided no additional predictive value when these factors were included. These models provide valid quantitative tools for indirect cognitive assessment when in-person testing is impossible. Full article

The increased use of technology-assisted distance counseling practices is one result of COVID’s impact on behavioral health, including in counselor education and the delivery of supervision. First, technology-assisted distance supervision needed for “real time” communication grew. Furthermore, there is an emergence of artificial intelligence (AI) technologies that have the potential to contribute to aspects of supervision; however, current evidence remains emerging, context-dependent, and at times mixed, warranting cautious interpretation of their effectiveness. The article offers an overview of using AI in clinical supervision, examines the benefits and potential concerns of AI from different perspectives, and considers the significance of using AI in counseling supervision. The role of AI is discussed as applied to counseling supervision including the use of AI tools, such as chatbots and reasoning AI, to detect and track sessions, note behavioral and emotional cues, aid/monitor communication and feedback, while also attending to ethical and legal consideration for its use. The article will report a range of benefits for supervisors and trainees using AI—for example, by enhancing data-driven supervision decisions, analyzing feedback trends, providing more efficient administrative monitoring, flexible/remote support, skill development, and promoting ethical decisions and self-reflection. Special attention is given to the challenges of using AI in supervision, including risks of undervaluing intuition and qualitative insights, potential for algorithms to reinforce systemic biases, risks of replacing human interaction, as well as non-compliance with HIPAA, FERPA, and ethical guidelines in data storage and privacy. The article will discuss privacy concerns, depersonalized feedback, and increased judgment-driven anxiety despite needed empathy when using AI as a tool for clinical supervision. Recommendations will also be offered for effective, ethical integration of AI in counseling supervision. Full article

Ticks are haematophagous ectoparasites and vectors of a diverse range of pathogens, exerting substantial impacts on wildlife, domestic animals and public health. In Australia, despite the country’s rich and unique biodiversity, a comprehensive understanding of ticks and tick-borne pathogens associated with wildlife remains limited. Environmental change, urban expansion and climate variability are increasingly disrupting wildlife habitats, potentially intensifying interactions between wildlife hosts, ticks and humans. A broad evidence synthesis of studies published between January 1940 and March 2024 was conducted, retrieving 133 eligible records from Web of Science, CABI Abstracts and PubMed databases. Fifty tick species parasitising 160 wildlife species were identified, predominantly from the genera Ixodes, Amblyomma and Haemaphysalis. The most commonly reported hosts included marsupials, particularly bandicoots, wallabies and possums, with notable tick species being Ixodes tasmani, Ixodes holocyclus and Amblyomma triguttatum. Microorganism records were relatively limited and mostly represented molecular detections or reported associations, including Babesia, Borrelia, Coxiella, Rickettsia and Theileria species, rather than confirmed vector competence, reservoir status or pathogenicity. Key limitations included geographic sampling biases towards eastern Australia, limited molecular identification of ticks and infrequent pathogen screening, particularly regarding the ecology, epidemiology and molecular diversity of host–vector–microorganism interactions. Improved surveillance, expanded molecular characterisation, and integrated One Health investigations are required to better understand the ecological and public health significance of these host–vector–microorganism interactions. Full article

Conventional photodetectors and image sensors deliver high-fidelity digital outputs but face a growing data-movement bottleneck: the energy and latency cost of transferring raw pixel streams to off-chip memory and processors increasingly dominates over both sensing and computation in modern machine-vision pipelines. An emerging response is the neuromorphic photodetector, a class of optoelectronic device that converts incident light into an electrical signal while simultaneously storing, modulating, and pre-processing that signal in a manner inspired by biological synapses and retinas. Over the past decade, demonstrations have spanned at least eight material platforms—organic semiconductors, organic–carbon-nanotube hybrids, perovskite and perovskite hybrids, metal oxides (including ultra-wide-bandgap and printable variants), wide-bandgap III-nitrides and 4H-SiC, two-dimensional materials, photo-memristors, and silicon CMOS in-sensor compute architectures—and have been realised through four distinct architectural families: phototransistor synapses, photo-memristors, heterojunction in-sensor compute, and linear photovoltaic neural networks. Here, we provide a quantitative cross-platform benchmark across forty in-scope articles, identify persistent photoconductivity as a near-universal device-physical substrate underlying synaptic functionality, characterise the responsivity–speed–energy trade-off structure observed across platforms, and present a critical assessment of energy-reporting practice in the field. We further identify three best-practice exemplars from three independent material platforms that converge on operating biases of 0.01–0.1 V and energies of 0.07–0.8 fJ per event, and we propose a unified reporting framework to enable meaningful cross-platform benchmarking of next-generation neuromorphic photodetectors. Full article

Introduction: The European catfish (Silurus glanis) has expanded rapidly across Europe, significantly impacting native freshwater biodiversity. Despite its well-documented ecological and economic effects as a top predator, reproductive biology data from non-native populations remain scarce, limiting the development of effective management strategies. Objective: This study examines key reproductive traits, sex ratio, size at first maturity, spawning period, fecundity, and oocyte diameter, of an invasive European catfish population in the Lower Tagus River (LTR), Portugal, approximately 15 years after its establishment. Methodology: A total of 674 individuals were collected monthly from January 2022 to November 2023 using electrofishing, gill nets, baited hook-lines, and catches from professional fishermen. Sex and reproductive stage were assessed via gonadal analysis. Size at first maturity was estimated using logistic regression. Fecundity was determined by the gravimetric method, and oocyte stage and diameter were assessed histologically. The gonadosomatic index (GSI) was used to characterise the reproductive cycle. Results: The sex ratio was significantly female-biased (1.4:1). Size at first maturity (TL₅₀) was 72.9 cm TL for females and 68.8 cm TL for males. The spawning season extended from February to June, coinciding with water temperatures of 11–23 °C, with the highest GSI values reported to date for this species (GSI max = 22.5%). Histological analysis confirmed asynchronous oocyte development. Absolute fecundity ranged from 8364 to 319,000 oocytes per female and was positively correlated with total length and body weight. Mean mature oocyte diameter ranged from 1.50 to 3.21 mm. Conclusions: The European catfish in the LTR exhibits high reproductive plasticity, early maturity, a prolonged spawning season, and elevated fecundity, likely facilitated by warm water temperatures and abundant prey resources. Crucially, these parameters reveal earlier maturation and greater reproductive investment relative to native populations, demonstrating an extreme phenotypic plasticity characteristic of successful invasions in southern European aquatic ecosystems. These findings provide essential biological parameters for targeted management, including selective removal of large females, intensified fishing effort during the spawning season, and population monitoring to prevent compensatory reproductive responses. Full article

In recent years, artificial intelligence (AI) has been increasingly used in criminal justice systems across the world. To achieve objectives set out through Sustainable Development Goals (SDGs), adoption of technology is inevitable and undeniable. The press release dated 25 February 2025 from India’s Ministry of Law and Justice, quoting Prime Minister of India Narendra Modi to make a “justice system that will be fully future-ready”, confirmed that the Indian law enforcement agencies are integrating AI into policing and law enforcement to enhance crime detection, criminal investigation, etc. It is intended to enhance their capabilities in solving criminal cases and delivering justice speedily and more efficiently. However, the usage of AI tools in such contexts presents a double-edged sword, as evidenced by their application in a number of cases across the world like Christopher Gatlin, Nijeer Parks, the Harm Assessment Risk Tool (HART), and in India during the 2020 Delhi riots cases. As reported by the Washington Post, in Christopher Gatlin’s case it was found that the police arrested him on the basis of the facial recognition programme matching his face with the captured video footage. He spent 17 months in jail before his release by the court, observing that the police failed to conduct fair investigation. A similar incident was reported by NJ.com and CNN Business. In the investigations following the 2020 Delhi riots, Delhi Police effected over 1900 arrests in 758 riot-related cases, relying predominantly on AI-driven facial recognition matches. Subsequent court scrutiny in decided cases raised questions about reliability, leading to widespread acquittals and discharges of the accused in 82% of decided cases as of early 2025. In certain cases, AI-driven solutions have failed, leading to criminal prosecutions of innocent people based on AI-generated evidence. This study examines the reliability, validity, and ethics of AI technology in the criminal justice system in India’s unique socio-legal and political environment. The researchers analyse three interrelated axes. First, a comprehensive review of the international algorithmic policing literature to identify successes and failures. In addition, cases of AI-assisted investigations during the Delhi riots show how facial recognition systems and other AI techniques were used for inquiry. Finally, stakeholders’ perspectives, including a preliminary survey of 27 legal experts showing strong consensus on classifying AI-FRT outputs strictly as corroborative evidence and highlighting BSA insufficiencies for addressing opacity and explainability, help identify practical, procedural, and normative fault lines. Researchers noted that while AI has the potential to revolutionise resource-constrained investigative agencies, its unquestioning and uncritical adoption risks amplify pre-existing biases, undermine presumptions of innocence, and shift the burden of refuting algorithmic inference onto the accused. Independent algorithmic audits, transparent documentation of error rates and confidence thresholds, statutory guidelines on AI tool use and admissibility, and sustained capacity-building throughout the justice delivery chain are needed to integrate it into the Indian criminal justice system. Without such measures, the very tools designed and introduced to enhance accuracy threaten to undermine the fundamental norms of the criminal justice system such as fairness and due process. This fills a gap in doctrinal analysis of AI-specific evidentiary admissibility in non-Western contexts like India. This study aims to propose policy reforms, enhance judicial discourse, and promote a more circumspect trajectory for AI adoption in Indian law enforcement by mapping the potential and risks of algorithmic evidence in a non-Western legal order. Full article

The transition from sinusoidal to pulse width-modulated (PWM) voltage excitation introduces high-frequency ripple, generating small remagnetization cycles within the main magnetization cycle and increasing total iron losses. Soft magnetic materials are essential for constructing many electrical devices, and accurate loss data are critical for reliable design and thermal dimensioning. However, magnetic material data are typically available only under sinusoidal excitation, and there is no generally accepted method for calculating PWM-induced losses during the design phase. To address this issue, loss measurements under DC-biased magnetization were performed on laminated ring cores, and the data were collected in the form of three-dimensional (3D) loss maps defined by the variables $\Delta B$, ${\displaystyle \frac{\mathrm{d}B}{\mathrm{d}t}}$ and $B_{\mathrm{bias}}$. Based on these maps, a method referred to as 3DLMB is proposed to calculate the contribution of PWM-induced losses to total iron losses by comparing minor-loop variables obtained from AC excitation with those measured under DC bias conditions. The method is experimentally validated on three ring cores with different geometrical parameters, showing agreement between calculated and measured total AC losses within ±5% over a range of switching frequencies. The reported agreement applies to the investigated M400-50A material, ring-core geometries, and operating range, while applying it to other materials or geometries requires constructing the corresponding DC-bias 3D loss map. Full article

Objectives: Endocrine neoplasms, as a general rule, show systemic, neuro-inflammatory and metabolic consequences, known as paraneoplastic syndrome. The comorbidity of thyroid tumors with neurological and autoimmune diseases prompt looking for common neuro-immuno-endocrine mechanisms of these disorders. While most TCs are well described, there is a gap in the literature after the isolation of oncocytic/Hürthle cell carcinoma (HCC), as a unique type due to immunoendocrine and metabolic features (low TSH-receptor expression and radioiodine avidity). The aim of this study was to collect clearly defined reports of HCC (as a separate entity) and to attempt determining common clinical symptoms and the usefulness of various diagnostic techniques (comprehensive critical review). This may be an introduction to modern treatment (patient-centered care) since the main cause of mortality is not local progression or metastases. Results: Until now, due to misnomenclature and data misinterpretation, HCC has been treated according to general standards (with overuse of TSH-ST and RIA). High thyroglobulin level, decreased total thyroxin (with normal FT3 and spontaneous decrease in TSH), hypercalcemia, as well as the “reverse flip-flop” phenomenon, as common symptoms, indicate the neuroendocrine origin of HCC. Sparse, well-documented lymph node metastases are another feature, although from few studies. Most studies omit the N stage. Whole-body ¹³¹iodine and ¹⁸F-fluorodeoxyglucose scintigraphy may be useful before FNAB. Fine-needle aspiration biopsy (FNAB), as a “gold standard” in early diagnosis of thyroid nodules, delays HCC diagnosis because of the inability to determine a benign/malignant nature. Conclusions: Final HCC outcome may be affected by various overlapping immunoendocrine factors (paraneoplastic effects). Due to very few thyroid function tests performed in HCC, we have proposed a set of basic laboratory analyses, core biopsy in HCC differentiation, and diagnostic chain for standardization. According to the review, adaptation and treatment of HCC based on existing standards for other thyroid cancers seem to be insufficient, and the risks outweigh the benefits. The key recommendations resulting from the 5th edition of the WHO Classification of Endocrine Neoplasms are only the beginning of refuting many myths and biases. Full article

Stream sediment geochemistry is a widely used reconnaissance tool in early-stage mineral exploration, particularly in regions where direct evidence of mineralisation is limited. Because stream sediment anomalies provide indirect geochemical signatures and are typically constrained by limited ground-truth information, labelled datasets are often scarce and spatially biased. This limitation restricts the applicability of supervised learning approaches and highlights the need for robust unsupervised methods. In this study, six unsupervised techniques, Principal Component Analysis (PCA), Non-negative Matrix Factorisation (NMF), Uniform Manifold Approximation and Projection (UMAP), Autoencoder (AE), Deep Embedded Clustering (DEC), and an Averaged Ensemble Index (AVE), were evaluated for integrating multivariate stream sediment geochemical data and delineating gold prospectivity zones. Eight gold-related elements (Au, As, Ag, B, Hg, Mo, Sb, and W) were selected based on regional metallogenic characteristics and previously reported geochemical associations. To facilitate direct comparison, all model outputs were normalised to a fuzzy membership scale ranging from 0 to 1. Model performance was quantitatively assessed using Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) and Matthews Correlation Coefficient (MCC) metrics based on independently verified mineralised and non-mineralised locations. The results indicated that DEC and AE consistently outperformed the other methods investigated, achieving the highest ROC–AUC and MCC values, whereas UMAP exhibited comparatively weaker performance. The findings demonstrated that unsupervised representation learning approaches, particularly DEC and AE, provided a more effective framework for integrating multivariate geochemical data and delineating gold-related anomalies in data-limited exploration environments than conventional dimensionality reduction and heuristic integration methods. Full article

Microwave-based remote sensing possesses the capability to penetrate through atmospheric obstructions such as cloud layers and fog, making it extensively utilized for estimating parameters including soil water content, atmospheric moisture levels, and terrestrial surface temperatures. Extended temporal datasets serve as fundamental requirements for climatological investigations; however, individual satellite operational lifespans remain constrained and prove inadequate for establishing multi-decade temporal sequences. Consequently, conducting comparative analyses and implementing cross-calibration procedures across measurements obtained from distinct sensors exhibiting comparable operational features becomes imperative. The FengYun (FY)-3G spacecraft, deployed into orbit during April 2023, hosts China’s most recent orbiting microwave radiometric instrument, designated as the Microwave Radiation Imager–Rainfall Mission (MWRI-RM). The FY-3G satellite’s unique drifting equator crossing time orbit plays a critical role in the calibration behavior of the MWRI-RM instrument, representing a key novelty of this study. The reliability of its brightness temperature (TB) observations has attracted considerable attention. Within this investigation, we conduct comparative assessments of orbital TB observations acquired from FY-3G/MWRI-RM against corresponding measurements obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2) installed on the Global Change Observation Mission–Water 1 (GCOM-W1) platform, and establish a straightforward linear inter-calibration methodology. Both sensing systems show strong consistency, with correlation coefficients exceeding 0.9 for all corresponding channels and systematic biases ranging from −1.40 K to −0.14 K. FY-3G/MWRI-RM generally reports lower TB values than GCOM-W1/AMSR2. The inter-sensor differences vary with frequency, land cover type, and TB range. Larger negative biases are mainly observed at 23.8 GHz and over water bodies, whereas the biases at 89 GHz are generally close to zero for most surface types. Latitude-dependent TB biases are most evident at 10.65 and 18.7 GHz, especially for vertical polarization at high latitudes, while orbit-dependent differences are more pronounced for vertically polarized low- and mid-frequency channels. After applying an inter-calibration procedure using AMSR2 as the reference, the agreement between FY-3G/MWRI-RM and GCOM-W1/AMSR2 is improved substantially, with mean biases below 0.25 K and RMSE values below 2 K for all channels. Validation using independent datasets further supports the stability of the calibration. The calibrated FY-3G/MWRI-RM TB data provide a basis for constructing long-term passive microwave brightness temperature records and for retrieving land and atmospheric parameters. Full article

Monitoring biodiversity in coastal and marine ecosystems is essential for supporting conservation strategies, sustaining ecosystem services, and meeting policy commitments at multiple scales, including the European Union’s Habitats Directive, Sustainable Development Goal 14 (SDG 14, Life Below Water), and the Kunming–Montreal Global Biodiversity Framework (GBF). However, many benthic habitats remain insufficiently mapped or monitored due to the spatial, temporal, and logistical limitations of traditional field-based approaches. Optical Remote Sensing (ORS), based on the use of optical sensors to retrieve spectral information from shallow-water environments, has emerged as a powerful tool for mapping and monitoring these ecosystems. This study presents a systematic review aimed at providing a comprehensive synthesis of above-water ORS applications for benthic biodiversity and habitat monitoring over the period 2014–2023. A total of 179 peer-reviewed studies were analyzed to identify temporal trends, geographic patterns, target ecosystems, and methodological workflows. The review considered observation platforms including satellite, airborne, unmanned aerial vehicles (UAVs), and field spectrometry systems, together with key preprocessing procedures required for reliable benthic detection, such as atmospheric correction, water column correction, and sunglint removal, alongside validation using independent measurements. The analysis reveals a rapid expansion of ORS applications, with a strong geographic concentration in tropical and subtropical regions. Studies focusing on specific benthic groups predominantly target coral reefs and seagrass ecosystems, although many adopt integrative benthic habitat classifications that incorporate multiple benthic components at the habitat level. However, significant limitations persist, including inconsistent preprocessing workflows, limited reporting transparency, and the underrepresentation of several ecologically important taxa (e.g., annelids, mollusks, echinoderms). Despite these challenges, ORS has become a cornerstone of large-scale and repeatable coastal monitoring. By analyzing methodological practices, ecological targets, and geographic biases, this review provides a critical foundation for improving the robustness, scalability, and global applicability of ORS in benthic habitat mapping, biodiversity monitoring, and ecosystem-based management. Full article

The northern pike (Esox lucius) is an economically important cold-water fish species in northern China. It exhibits pronounced sexual dimorphism, yet the molecular mechanism underlying its sex differentiation remains unclear, which hinders the development of aquaculture. Whole-transcriptome sequencing is a powerful approach for screening sex-related genes; however, no such study has been reported for this species to date. In this study, gonadal tissues from three female and three male E. lucius were collected for whole-transcriptome sequencing. A total of 14,941 differentially expressed messengerRNAs, 119 differentially expressed microRNAs, 229 differentially expressed circularRNAs, and 2055 differentially expressed long non-codingRNAs were identified. Functional enrichment analysis revealed that the differentially expressed genes were significantly enriched in pathways closely associated with sex differentiation, such as steroid hormone biosynthesis and oocyte meiosis. Several key sex-biased genes were identified, including female-biased genes (FANCL, DDX5, SRSF5B) and male-biased genes (STAR, FDX1B, ITGA2B). Furthermore, a competing endogenous RNA (ceRNA) regulatory network involving dre-miR-107b was constructed, which may represent a candidate for further investigation into sex differentiation in E. lucius. This study provides the first comprehensive whole-transcriptome dataset of female and male gonads in E. lucius, identifies key sex-biased genes and core pathways involved in its sex differentiation, and thereby identifies the dre-miR-107b-centered ceRNA network and key sex-biased genes (FANCL, DDX5, SRSF5B, STAR, FDX1B, ITGA2B) as core molecular players in sex differentiation of this species. Full article