Search Results (1,156)

Generative artificial intelligence (AI) is transforming biological and medical research and data analysis. Beyond analyzing existing information, these models can learn complex patterns and generate new data such as realistic protein sequences, genetic variants, or clinical notes. In molecular biology, language-like sequence models can read and generate DNA, RNA, and amino acid sequences to predict genetic variant effects, design new proteins, and explore molecular functions. In medicine, large language models (LLMs) trained on biomedical literature and electronic health records (EHRs) can summarize clinical findings, identify patterns, and provide decision support for clinicians and healthcare providers. Additionally, synthetic data generation can help protect patient privacy and augment existing disease datasets. While these advances make tasks that were previously impractical possible at scale, they also carry major risks, including producing convincing but incorrect results, reflecting hidden biases in the training data, and underperforming when real-world conditions change. Full article

Spatially explicit socioeconomic data are critical for regional analysis, yet they remain scarce at subnational scales in many African contexts. This study presents a transparent and reproducible open-data framework to generate high-resolution gridded Gross Domestic Product (GDP) and derived socioeconomic and energy indicators. The approach combines gridded population and Night-Time Light (NTL) through the LitPop method to downscale provincial GDP to 1 km resolution for the Inkomati-Usuthu Water Management Area (IUWMA) in South Africa. The resulting GDP dataset is subsequently used as a spatial proxy to disaggregate compensation of employees, gross capital formation, fixed capital stock, net exports, gross operational surplus and sectoral Total Final Energy Consumption (TFEC). Results show strong consistency with official provincial GDP totals, with deviations ±0.4% after 2017. In 2024, LitPop allocated 4.26 billion constant 2015 USD to the IUWMA, equivalent to 16% of Mpumalanga’s GDP, compared with 47.3% under area-based allocation and 51.3% under population-based allocation. These differences reveal the strong influence of spatially concentrated industrial and energy-intensive activity. The workflow provides a scalable and replicable solution to generate coherent gridded socioeconomic datasets for WEF Nexus modelling, although estimates remain proxy-based and sensitive to NTL-related biases, particularly the overrepresentation of highly illuminated industrial assets and the underrepresentation of less luminous activities. Full article

Multicollinearity reduces the reliability of ordinary least squares by increasing variances and creating unstable estimates. This issue has led to biased and penalized regression methods like ridge-, Liu- and Stein-type estimators. Here, we build existing ridge-type approaches by introducing improved ridge and Liu-type estimators, along with robust variants to handle outliers. We investigate their theoretical properties regarding bias, variance, and mean squared error. We also evaluate their performance through Monte Carlo simulations with different levels of multicollinearity and data contamination. By using several evaluation criteria, including mean squared error, akaike information criterion, mean absolute deviation, and mean absolute percentage error, along with an average-rank comparison framework applied here for the first time, we further validate our results with two health-related datasets. The findings show that the strong estimators provide more stable estimates and improved predictive performance, particularly when dealing with severe multicollinearity and outliers. Full article

Substantial uncertainties remain in climate model simulations of tropical cyclones (TCs), particularly those associated with internal climate variability. While the influence of the El Niño–Southern Oscillation (ENSO) on interannual TC variability is well established, the contribution of the Interdecadal Pacific Oscillation (IPO) to decadal-scale uncertainty is less well constrained. Although models generally reproduce IPO-related variations in tropical cyclone genesis frequency (TCGF) over the eastern North Pacific, large discrepancies persist across the broader North Pacific basin. Clarifying the role of IPO in modulating TCGF uncertainty is therefore essential for improving decadal TC projections. In this study, we analyzed a large ensemble of historical simulations from the MRI-AGCM within the d4PDF (Database for Policy Decision Making for Future Climate Change) framework. Empirical orthogonal function (EOF) analysis is applied to IPO-composited fields to identify the leading modes of intermember (100 members *60 y, 6000 times) simulation uncertainty on a decadal-scale. The results reveal that state-of-the-art models exhibit robust and spatially coherent uncertainty structures in TCGF under different IPO phases. Two leading modes are identified: (1) a South China Sea mode, closely associated with systematic precipitation biases, and (2) a zonal dipole mode between the eastern and western North Pacific, linked to the equatorward propagation of Arctic Oscillation (AO)-related variability. Misrepresentation of AO variability is found to contribute substantially to biases in simulated TCGF patterns. Comparisons with observational datasets further support the proposed mechanisms. These findings highlight the importance of improving the representation of precipitation processes and extratropical–tropical teleconnections in climate models, which is critical for enhancing the reliability of decadal predictions of North Pacific TC activity. 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

As the primary peripheral relay station for vibrissal tactile information, the trigeminal ganglion (TG) features heterogeneous three-dimensional (3D) cytoarchitecture that eludes full characterization using conventional two-dimensional methodologies. A high-resolution 3D imaging and reconstruction pipeline is thus required to unveil TG structural organization and define the spatial framework of target-related sensory neurons. Herein, we established a fluorescence micro-optical sectioning tomography (fMOST)-based workflow for 3D cytoarchitectural mapping of TG anatomy and validated its utility for profiling the distributions of TG neurons innervating vibrissae via single-axon tracing. fMOST imaging coupled with propidium iodide (PI) staining was applied to acquire whole-head anatomical data encompassing the vibrissae and the TG at cellular resolution. Based on clearly resolved cellular morphology and the spatial distribution of neuronal somata, we delineated the soma distribution of TG neurons and revealed a spatially heterogeneous 3D organization pattern, from which we operationally defined two anatomically distinct subdomains: the neuronal soma-rich region (NSRR) and the fiber-rich region (FRR). Furthermore, with retrograde viral/genetic labeling combined with neuronal tracing, TG neurons innervating the C2, D3, and δ vibrissae were observed in both NSRR and FRR, showing partially overlapping yet spatially biased distributions consistent with previous population-level observations of vibrissa-row-dependent topography. Notably, TG neurons innervating the δ vibrissa occupied a comparatively broader spatial extent along the anteroposterior plane in our dataset. Overall, this study facilitates an in-depth mechanistic and anatomical understanding of TG cytoarchitectural organization and underlying functional mechanisms. 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

Risk assessment tools are used in criminal justice to evaluate an individual’s likelihood of reoffending. There is a growing discussion around the use of artificial intelligence (AI) and machine learning (ML) in algorithmic risk assessment (ARA). This survey examines the use of and the potential for bias in the use of ARA in criminal justice. Through a structured interdisciplinary review of recent research on the impact of ARA, this investigation examines the tools currently being used and whether there is evidence that ARA tools contribute to bias. Included papers were collected from Google Scholar and the ACM Digital Library and have been published since 2015, discuss AI, and focus on the adult justice system in the US, yielding 56 studies. In total, 79% of the surveyed literature concluded that AI and ML can or do contribute to biased performance in risk assessment. The two most recorded sources of bias were the use of historical court records as training data and the use of variables or features that correlate strongly with race, gender, age, or other protected attributes, while noting that this result relies heavily on a small number of real-world observations, most notably the COMPAS dataset collected in Broward County. The recorded benefits of ARA included efficiency and resource utilization. The use of AI-derived risk assessment tools is growing and holds the potential to affect a lot of lives. It is important to examine and consider the implications of their use, especially involving bias and fairness in criminal justice decision-making. Full article

Spaceborne precipitation products such as NASA IMERG V07 provide sub-hourly data required for hydrological modelling, but systematic biases in wet-event frequency and extreme-intensity representation limit their reliability for applications sensitive to precipitation extremes. This study develops a three-stage calibration pipeline combining probability-of-precipitation frequency adaptation, empirical quantile mapping of the distribution body, and Generalised Pareto Distribution tail modelling with constrained blending. The approach is calibrated against 202 Roshydromet stations using 3-hourly observations and evaluated on 15 spatially independent stations over a 9-year validation period. At the station-optimal blending weight, the proposed pipeline reduces median absolute percentage bias at the P99 quantile from 43.9% to 10.2%, while maintaining comparable volume balance (|PBIAS| 6.5%). To suppress a disaggregation artefact arising from amplification of multi-hour accumulations, the operational gridded R-factor product instead adopts a more conservative blend (|PBIAS@P99| = 24.9%) together with an empirically constrained accumulation cap, although the absence of sub-hourly calibration data remains the principal limitation. The calibrated dataset is applied to derive a 24-year (2001–2024) rainfall erosivity climatology for the Volga region, yielding a domain-mean R-factor of 254 ± 55 MJ mm ha⁻¹ h⁻¹ yr⁻¹ with no detectable monotonic trend. The proposed framework improves the representation of precipitation extremes and provides a transferable preprocessing approach for hydrological modelling applications. Full article

In recent years, explainable artificial intelligence has received increasing attention in the field of bearing fault diagnosis. However, existing interpretability methods, such as Shapley Additive Explanations (SHAP), often rely on the quality of input features. To achieve high diagnostic accuracy, researchers often extract a large number of features from vibration signals across multiple domains, leading to feature redundancy. This redundancy not only increases the computational cost and risk of overfitting in diagnostic models but also dilutes the contributions of core features during interpretability analysis, resulting in biased explanations. To address this challenge, we propose a two-stage interpretable fault diagnosis approach. In the first stage, the Explainable Boosting Machine (EBM) selects core features to reduce redundancy. In the second stage, EBM is enhanced by Random Forest (RF) through residual learning to form the RF-EBM diagnostic model. EBM and SHAP are further used for dual interpretability analysis. Experimental results on public laboratory benchmark datasets demonstrate that the proposed approach achieves good diagnostic performance and outperforms traditional EBM. Overall, the approach reduces redundancy through feature selection, improves diagnostic performance, and makes the decision-making process more transparent, providing a useful methodological reference for trustworthy fault diagnosis in industrial applications. Full article

Rhinogobio nasutus, an endangered fish species endemic to the upper and middle reaches of the Yellow River in China, lacks essential genomic information on gonadal development, hindering research into its reproductive biology. To address this, mRNA-seq and miRNA-seq datasets derived from adult testis (n = 3) and ovary (n = 3) were integrated to characterize sex-biased expression profiles and potential regulatory mechanisms. A total of 34,813 genes and 68,623 transcripts were detected, and 16,105 differentially expressed genes (DEGs) were identified between testis and ovary, including 9365 testis-biased and 6740 ovary-biased genes. Small-RNA profiling identified 51 differentially expressed miRNAs (DEMs: 31 testis-biased; 20 ovary-biased). The sex-biased mRNA profiles highlighted conserved candidate genes associated with germ-cell maintenance, somatic regulation, ovarian differentiation, and oocyte maturation, including vasa, piwi, dmrt1, amh, cyp19a1a, zar1, zar1l, and rbpms2. Integrated miRNA–mRNA analysis further predicted potential interactions involving key sex-related genes, suggesting that DEMs may contribute to post-transcriptional regulation during gonadal differentiation. Functional enrichment (GO and KEGG analyses) highlighted pathways associated with gonadal differentiation, germline maintenance, and signal transduction pathways. qRT-PCR validation of nine mRNAs and nine miRNAs showed expression patterns consistent with the sequencing results. Collectively, these results provide an integrated mRNA and miRNA resource for R. nasutus gonads and identify candidate genes and miRNAs for future studies on sex-biased gonadal development, reproductive regulation, and artificial propagation. 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

Artificial intelligence (AI) is increasingly embedded in development systems, enabling new capabilities for poverty prediction, social protection targeting and service delivery optimisation. However, the implications of these technologies for poverty governance—the institutional mechanisms for designing and delivering poverty reduction strategies—remain fragmented in the literature. This study conducted a PRISMA 2020-guided systematic review of peer-reviewed journal articles and scholarly book chapters published between 2015 and 2025 and retrieved from Scopus, Web of Science and DOAJ. Following title/abstract screening, full-text eligibility assessment and quality appraisal, 48 studies were selected, thematically identifying cross-cutting patterns related to system performance, implementation processes, governance considerations and contextual constraints. The reviewed literature indicates that AI can improve poverty governance through multimodal data integration, enhanced targeting accuracy and automated administrative processes. However, persistent challenges include biased datasets, infrastructural limitations, regulatory gaps and ethical risks such as algorithmic bias and digital exclusion, which may reinforce structural inequalities. The review contributes an integrated evidence base and introduces a conceptual framework for understanding AI in poverty governance, highlighting that developmental gains depend on robust data governance, inclusive digital infrastructure, context-sensitive design, algorithmic transparency and institutional capacity. Future research should prioritise impact evaluation, fairness-aware AI, participatory design and scalable approaches for low-resource environments. Full article

LAI is a critical parameter for forest management and global ecosystem monitoring. GEDI provides global-scale vegetation structure data, yet its L2B LAI product often exhibits systematic biases. This study investigates the Maoer Mountain forest in China, utilizing a total of 60 validated GEDI footprints as the primary dataset. To address the limitations of the standard GEDI L2B algorithm, which assumes a horizontally uniform canopy, we integrated a four-scale geometric optical model to characterize canopy clumping effects. This model was employed to simulate the geometric proportions of sunlit/shaded canopy and ground components within each footprint to derive a footprint-specific clumping index, thereby refining the gap rate estimates. The accuracy of the revised leaf area index was rigorously verified by using the measured data from the sample plots in the Maoer Mountain area. The results indicate that the original GEDI L2B data underestimates LAI, with a mean absolute error (MAE) of 1.79 m²/m², a root mean square error (RMSE) of 1.47 m²/m², and a bias of −1.25 m²/m². After correcting for canopy clumping, accuracy improved significantly, reducing the MAE to 0.65 m²/m² and the RMSE to 0.82 m²/m², while effectively mitigating underestimation. These findings demonstrate that accounting for non-uniform canopy distribution effectively reduces errors, providing a robust methodological basis for high-precision LAI retrieval using spaceborne lidar. Despite these improvements, this method still has certain limitations: the model’s performance is constrained in extremely steep terrain due to waveform aliasing and in fragmented vegetation areas where sub-footprint heterogeneity is high. Future research should incorporate topographic corrections and multi-source data fusion to enhance the model’s robustness in complex landscapes. Full article

Despite their demonstrated effectiveness in modeling sensor interaction networks for industrial fault diagnosis, graph neural networks (GNNs) still encounter two key limitations: black-box operation that lacks transparency in fault identification and propagation analysis, and unreliable attention mechanisms whose weights fail to faithfully reflect the genuine relevance of sensors or their interactions. To tackle these challenges, we put forward the Stochastic Mask Causal Graph Network, a novel framework that integrates a learnable stochastic masking mechanism guided by the information bottleneck principle. Unlike conventional attention-based or post-hoc approaches, our method automatically suppresses label-irrelevant graph components while preserving causally relevant structures, thereby providing faithful inherent interpretability without biased assumptions and effectively removing spurious correlations to enhance generalization. Comprehensive experiments on realistic complex industrial system datasets demonstrate that the proposed method achieves superior diagnostic accuracy and enhanced interpretability compared with existing advanced approaches. Full article