Search Results (14,375)

Double-skin façades (DSFs) can moderate heat transfer and airflow between the outdoor environment and the building interior; however, their performance in hot-arid climates is highly dependent on cavity geometry, ventilation arrangement, and the interaction between the airflow and any active cooling surfaces. The objective of this study is to establish, under a single idealised extreme hot-arid design point, how sealed, ventilated and actively cooled double-skin façade cavities differ in their predicted temperature, velocity and turbulent kinetic energy fields, and which arrangements merit controlled follow-up study. The four configurations are treated as an idealised comparative case study rather than as validated building-performance predictions. This exploratory study uses computational fluid dynamics (CFD) to compare the aerothermal behaviour of four DSF cavity configurations under prescribed external air and outer-wall temperatures of 50 °C, an inner-wall temperature of 24 °C, and an external inlet velocity of 3.06 m/s. The configurations comprise a sealed 0.4 m cavity (M1), a wind-driven ventilated 0.4 m cavity (M2), the same ventilated cavity with six 25 mm cooling pipes at 10 °C (M3), and a concept-stage lateral-flow arrangement combining a 0.10 m cavity, a 0.025 m slit and four 80 mm cooling pipes at 10 °C (M4). The simulations employ the standard k-ε turbulence model with fixed thermal boundary conditions. Along the reported sampling lines, M1 exhibited a nearly uniform air temperature of approximately 45.7 °C, whereas M2 remained close to the imposed 50 °C external-air temperature. M3 produced lower temperatures in the immediate vicinity of the cooling pipes, but most of the sampled profile remained near ambient conditions. M4 exhibited a broader spanwise temperature range of approximately 26.9–50 °C, with local pipe-adjacent air temperatures approaching 24 °C and cooler regions developing along parts of the lateral flow path. The findings provide preliminary concept-screening evidence and support further controlled parametric analysis, higher-fidelity modelling, and experimental validation. Full article

Depression is often associated with functional disturbances in the visual system. However, the fundamental features underlying these visual system aberrations in depression remain to be fully elucidated. In particular, in the first stage of visual processing, how different retinal output neuron types change their intrinsic properties and output features in response to specific light stimulation remains unclear. Here, by adopting a mouse model of depression induced by chronic unpredictable stress (CUS), we found that depression is associated with reduced blood perfusion in the retinal inner plexiform layer (IPL). The hypoperfusion in the IPL is paralleled by a remarkable reduction of retinal ganglion cell (RGC) density, with more cell loss in ipRGCs than in the general RGCs. The surviving RGCs—particularly, ipRGCs—changed their intrinsic electrical properties, exhibiting decreased membrane input resistance, more depolarized resting membrane potential, and altered spiking properties. Additionally, these cells showed stimulus-size-dependent increases in light-evoked responses. Together, our findings demonstrate that depression is associated with the retinal IPL hypoperfusion and RGC impairments (particularly ipRGCs) and suggest retinal layer- and RGC-type-specific susceptibilities, furthering our understanding of retinal pathophysiology in depression. Full article

The fate of heavy metals in agricultural soils is governed by organo-mineral–microbial interactions, which predictive models often fail to capture. The competitive sorption mechanisms of Cd(II) and Cu(II) on montmorillonite/kaolinite composites (Mont/Kao) functionalized with humic acid (HA) and Pseudomonas putida (P. p), a model system representative of contaminated agricultural soils, were investigated. Batch experiments, XRD, FTIR, and thermodynamic analysis reveal that metal retention is a non-additive function of competing interfacial processes. Bacterial biomass dominated sorption, accounting for >50% of total metal uptake, with capacity ranked as: P. p > Mont/Kao-P. p > Mont/Kao-HA-P. p > Mont/Kao-HA > Mont/Kao. Humic acid exerts a dual, concentration-dependent role: Low levels enhanced adsorption via mineral dispersion, while high levels induced surface masking, suppressing bacterial binding sites. Competition was highly asymmetric: Cd(II) reduced Cu(II) maximum adsorption capacity by 75.5% in the Mont/Kao-HA system by preferentially occupying montmorillonite interlayer sites, whereas Cu(II) inhibited Cd(II) below pH 6. Single-metal sorption was characterized by positive ΔS° (32.96–58.89 J·mol⁻¹·K⁻¹), indicative of inner-sphere complexation, while negative ΔS° under competitive conditions signals a transition to outer-sphere complexation. This work provides mechanistic insights into site masking, competitive displacement, and ternary cation bridging controlling metal immobilization in organo-mineral assemblages. Full article

Diabetes mellitus is frequently associated with gastrointestinal dysfunction, in which alterations in the enteric nervous system (ENS) are considered important contributing factors. Neuropeptide Y (NPY) is a key enteric neuromodulator involved in the regulation of gastrointestinal motility, secretion, and blood flow; however, its response to diabetes remains insufficiently characterized, particularly in large animal models. This study investigated the effect of experimental diabetes on the distribution of NPY-immunoreactive enteric neurons in the porcine gastrointestinal tract. Diabetes was induced in juvenile female pigs by streptozotocin administration. Six weeks later, the stomach, duodenum, jejunum, ileum, and descending colon were collected, and the population of NPY-immunoreactive neurons in the myenteric and submucosal plexuses was evaluated by double-label immunofluorescence. Experimental diabetes significantly increased the population of NPY-immunoreactive neurons in the myenteric plexus of the stomach, jejunum, ileum, and descending colon, whereas no statistically significant changes were detected in the duodenum after correction for multiple comparisons. In the submucosal plexuses, significant increases were restricted to the inner and outer submucosal plexuses of the descending colon. These findings demonstrate that diabetes induces region- and plexus-specific neurochemical plasticity of NPY-immunoreactive enteric neurons in pigs. Given the predominantly inhibitory effects of NPY on gastrointestinal motility and secretion, together with its vasoconstrictive actions, the observed changes may contribute to altered neural regulation of gastrointestinal function during diabetes and may represent one of the mechanisms involved in diabetic gastroenteropathy. Full article

Grid-forming converters are pivotal for stability support in modern power systems, where their interactive behavior is significantly determined by control parameters. However, prevalent impedance-based analysis is confined to individual control schemes, lacking a unified basis for comparative assessment and generalized parameter impact analysis. To bridge this gap, this paper develops a generalized sequence impedance model that consolidates major multi-loop GFM control strategies, structurally mapping each control loop to specific impedance components. Utilizing this generalized representation, the interactive effects of inner-loop parameters are analytically disentangled. Based on the found effects, a general parameter-tuning rule is proposed to improve the interactive stability of the GFM converter connected to different grids. Experimental validation confirms the model’s accuracy and demonstrates its utility for the systematic, stability-oriented design of GFM converters under diverse grid conditions. Full article

High-frequency induction hardening enhances the surface wear resistance and contact fatigue life of high-speed linear guides, but simultaneously produces an inhomogeneous, layered cross-sectional structure comprising a high-strength, low-ductility outer hardened layer and a low-strength, high-ductility inner core. This structural heterogeneity renders conventional straightening stroke prediction models—predicated on homogeneous material assumptions—fundamentally inadequate. Moreover, the iterative trial-bending operations ubiquitous in industrial practice progressively accumulate plastic strain, causing guide rails to exhibit erratic positive-to-negative deflection reversal during sequential straightening passes. To address these critical challenges, this study proposes a novel two-stage hybrid straightening strategy based on inverse finite element analysis (FEA) and closed-loop experimental feedback. An equivalent hardened layer depth (HD₀) is introduced as a parametric descriptor to construct a layered elastoplastic finite element model, and an inverse simulation strategy is developed to generate a comprehensive three-dimensional stroke–residual deflection prediction dataset encompassing both vertical and lateral straightening conditions across multiple support spans. Displacement-controlled three-point bending experiments validate the layered model and elucidate the mechanism by which cumulative plasticity progressively amplifies cross-sectional plastic sensitivity under repeated loading. Grounded in this physical insight, a hybrid straightening algorithm is formulated, combining dataset-driven initial stroke prediction for rapid large-deformation elimination with an upper-bound constraint and a measurement-feedback-driven sequential reduction compensation scheme for fine-tuning. Comparative experiments demonstrate that the proposed strategy effectively suppresses the oscillatory over-straightening characteristic of conventional empirical trial-and-error approaches, consistently reducing residual deflection below 0.05 mm within two to three loading cycles. This work bridges the gap between theoretical simulation and the complex physical state of actual machining, substantially improving both the efficiency and precision of straightening for guide rails with induction-hardened layers. Full article

Mobile robots and autonomous vehicles require tightly regulated direct current (DC) power under rapidly varying load conditions, motivating control strategies that combine fast nonlinear regulation with predictive optimization. This paper proposes a Hybrid Sliding Mode Control and Model Predictive Control (Hybrid SMC + MPC) strategy for a DC-DC buck converter supplying a representative mobile-robot mission load. The controller employs a cascade SMC structure for fast inner-loop regulation and an MPC component that provides finite-horizon duty-cycle correction using planned load information. The MPC problem is formulated in condensed form and solved analytically without an external optimization solver. A Lyapunov-based analysis establishes a sufficient reaching condition for the sliding variable under the ideal averaged-model assumptions, and the condition is verified for the simulated mission. The proposed approach is evaluated in MATLAB using a 10-phase, 10 s load profile with resistance varying from 7 Ω to 100 Ω and is compared with SMC-only, MPC-only, PID, constant-duty, and reconstructed fuzzy-logic benchmarks. In the averaged-model study, the Hybrid SMC + MPC achieves a maximum absolute voltage deviation of 0.388 V, an RMSE of 0.0115 V, and a final-phase mean absolute error of 0.0076 V. It provides the lowest maximum voltage deviation among the principal closed-loop controllers, while PID achieves the lowest RMSE and final-phase error and SMC-only exhibits the shortest mean settling time. Relative to MPC-only, the Hybrid controller reduces the maximum voltage deviation by approximately 43.6% and the mean settling time by approximately 66.1%. An ablation study shows that the MPC contribution substantially improves overall and steady-state regulation accuracy, while load preview primarily reduces the worst-case voltage deviation. Switching-level MATLAB/Simulink validation with explicit 20 kHz PWM and converter parasitics confirms that the output remains within ±2% of the 25 V reference throughout the complete mission, with a maximum absolute deviation of 0.443 V and a maximum steady-state switching ripple of 21.6 mV peak-to-peak. These results demonstrate that the proposed Hybrid SMC + MPC architecture provides a favorable balance between worst-case transient regulation, steady-state accuracy, and predictive control capability for dynamically varying robotic power loads. Full article

Parkinson’s disease (PD) is associated with alterations in gait symmetry and plantar loading that can be examined using vertical ground reaction force (VGRF) recordings. This study presents a confidence-stratified, physiology-anchored multiple-instance learning framework with concept-bottleneck-inspired pathways (implementation identifier: DRO-PAS-MIL-CBM; hereafter, PAS-MIL) for retrospective session-level PD-versus-control classification. Each gait session is represented as a bag of temporal windows. Eight predefined bilateral signal descriptors are combined with eight learned latent temporal dimensions, aggregated through attention-based pooling, and processed by concept-guided, prototype, anchor-only, and static-feature expert pathways. The evaluation used five-fold person-grouped cross-validation on 306 sessions from 165 participants in the PhysioNet Gait in Parkinson’s Disease database.Inner person-grouped out-of-fold ExtraTrees probabilities were used to construct the confidence strata and distillation targets. PAS-MIL achieved a pooled session-level area under the receiver operating characteristic curve of 0.771, average precision of 0.890, and a mean fold AUC of $0.826\pm 0.041$. Relevance analysis identified C05 (asymmetry variability) and C08 (bilateral change mismatch) as the highest-weighted predefined physiological anchor descriptors. Protocol-stratified sensitivity analysis showed variation across the three source sub-studies, with AUCs ranging from 0.740 to 0.790. Probability calibration remained suboptimal after temperature scaling (mean per-fold ECE, $0.291\pm 0.042$). The results demonstrate the feasibility of integrating physiology-informed descriptors, temporal representation learning, and session-level aggregation. The study is a retrospective proof of concept and does not establish external robustness or clinical deployment readiness. Full article

This paper proposes an annular suction caisson specifically designed to reinforce in-service monopiles and upgrade existing offshore wind farms to accommodate larger-capacity wind turbines. During penetration of the annular suction caisson into clay, the existing monopile restricts the inward migration of soil into the internal space of the caisson, promoting upward soil displacement and consequently increasing the height of the soil plug formed inside the caisson. In addition, the strain-softening behavior causes varying degrees of strength degradation in the clay along the caisson wall. The softened zones extend approximately one caisson wall thickness on the inner side and 1.2 times the wall thickness on the outer side of the caisson. Both effects should be considered for accurately predicting the penetration resistance of annular suction caissons. Therefore, three-dimensional large-deformation finite element analyses were performed to investigate the penetration behavior of annular suction caissons in strain-softening clay. A comprehensive parametric study was conducted to quantify the soil plug heave and overall penetration resistance. Meanwhile, the soil flow mechanism at the caisson tip, the evolution of clay strength along the caisson wall, and the formation characteristics of the internal soil plug were systematically examined. Based on the numerical results, a theoretical approach was developed to evaluate the penetration resistance of annular suction caissons. Full article

Coal-based solid wastes, including coal gangue and fly ash, can be extensively utilised in cemented backfill materials. However, the slump, bleeding rate, and mechanical strength of these materials depend nonlinearly on the mixture composition, particle size, solids concentration, and curing conditions, complicating the multi-performance mixture design. This study developed an ensemble-learning framework for the target-specific performance prediction and empirical-uncertainty-aware inverse design of coal-based solid-waste cemented backfill materials. A literature-derived database containing 720 observations and 11 predictors was established. After the target-specific filtering of missing responses, 214 observations were available for the slump, 284 for the bleeding rate, and 711 for the uniaxial compressive strength (UCS). Support vector regression (SVR), Bagging-SVR, AdaBoost-SVR, and Stacking-SVR were evaluated using 20 repeated random 80:20 holdout partitions to assess the within-database predictive performance. Bagging-SVR achieved the lowest mean inner-cross-validation RMSE for all three responses. Its mean test R² values were 0.969, 0.871, and 0.965 for the slump, bleeding rate, and UCS, respectively, with corresponding RMSE values of 2.228 cm, 1.206 percentage points, and 1.575 MPa. SHAP analysis showed that the coal-gangue particle size and solids concentration received the largest model attributions for the slump and bleeding-rate predictions, whereas the cement content and curing time received the largest attributions for the UCS prediction. The selected Bagging-SVR models were subsequently coupled with multi-objective differential evolution incorporating empirical prediction bounds, component mass balance, and target-specific five-nearest-neighbour applicability-domain constraints. The selected compromise candidate had a solids concentration of 79.46% and coal-gangue, fly-ash, and cement dry-solid mass fractions of 63.29%, 24.95%, and 11.76%, respectively. Its predicted slump, bleeding rate, and 28 d UCS were 21.19 cm, 1.85%, and 6.36 MPa, respectively. The nominal empirical upper bound of the bleeding rate was 3.83%, and the lower bound of the UCS was 3.74 MPa, both satisfying their prescribed limits. However, the nominal slump interval of 15.70–26.65 cm was not fully contained within the prescribed range of 18–26 cm. Full article

Background: Predicting recurrence after liver transplantation (LT) for hepatocellular carcinoma (HCC) remains important for post-transplant risk stratification and surveillance planning. The Milan criteria discriminate only moderately and some machine-learning re-analyses report overly optimistic results because of information leakage. Aim: The current study aimed to re-evaluate a previously published transplant cohort under a leakage-free survival-analysis framework and to benchmark post-transplant, explant-informed survival learners against the Milan criteria as a fixed pre-transplant reference. We hypothesised moderate rather than near-perfect discrimination, similar performance across learners of differing complexity, and better discrimination than the Milan criteria. Methods: This secondary analysis included 356 patients with HCC who underwent LT. The primary endpoint was recurrence-free survival, analysed from the observed event indicator and follow-up time rather than from a derived risk label. Seven survival learners were benchmarked with repeated nested cross-validation, using three repeats of a five-fold outer loop with a three-fold inner tuning loop. All data-dependent preprocessing, including robust multivariable outlier handling and imputation, was fitted within training folds only. Performance was assessed by the concordance indices of Harrell and Uno, the time-dependent area under the curve, the integrated Brier score, calibration, decision-curve analysis and descriptive competing-risk assessment. Results: Recurrence developed in 183 of the 356 patients over a median follow-up of 52 months. Discrimination was moderate rather than near-perfect and similar across learners; the random survival forest ranked highest and the Elastic-Net Cox model performed comparably. All learners showed higher descriptive concordance than the Milan criteria, and dependency-corrected comparisons supported higher concordance for the full-feature Cox model than for the Milan criteria, whereas the random survival forest and Cox did not differ materially. Out-of-fold calibration of the Elastic-Net Cox model at 36 months was acceptable, decision-curve analysis indicated positive net benefit across clinically relevant thresholds, and tumour size and alpha-fetoprotein were the leading contributors to prediction. Findings were stable in ablation and threshold-sensitivity analyses. Conclusions: Leakage-free survival modelling gave moderate but internally validated prediction of post-transplant recurrence and higher concordance than the Milan criteria in this cohort, supporting the stated hypotheses. Careful study design may matter more than architectural complexity in this setting, and leakage-free survival analysis is a practical standard for prognostic modelling in transplant oncology. Full article

Graphite ore grade sorting is a key step toward intelligent mineral processing; however, it faces three representational contradictions: ambiguous classification posteriors at grade boundaries, asymmetric multi-scale feature interaction, and the mismatch between class-agnostic self-distillation assignment and sample-level difficulty. Targeting these, this paper adopts D-FINE as the baseline and introduces three decoupled improvements at its decoder, encoder, and criterion layers. (1) Boundary-Grade-aware Distribution Refinement (BG-FDR) online identifies boundary samples via the Top-2 classification score gap and modulates regression-distribution refinement, yielding +2.69 percentage points in mAP@0.5 with zero additional trainable parameters. (2) Bidirectional Feature Pyramid with Global–Local Spatial Attention (BiFPN-GLSA) builds a learnable weighted bidirectional multi-scale fusion path. (3) Difficulty-Aware Decoupled Distillation with Wise-Inner-Shape-IoU (DADD+Wise-IoU) imposes class- and sample-level difficulty-aware constraints. In the integrated full model, this increases Precision from 66.21% to 71.43% (+5.22 pp), F1 from 73.57% to 77.57%, and mean IoU from 97.81% to 98.35%, while false positives drop by 19.6%; the only parameter overhead (+3.84M) comes from BiFPN-GLSA, with BG-FDR and DADD adding effectively no network weights. Ablation on a self-built 3800-image dataset reveals a non-monotonic AP–Precision relationship: the mAP-optimal configuration (BG-FDR+BiFPN-GLSA, 94.17%) and the Precision-optimal one (DADD+Wise-IoU, 77.54%) do not coincide, providing a quantitative basis for objective-driven module selection in industrial sorting. Full article

Agricultural nonpoint source pollution mainly causes lake eutrophication in China, largely affected by variations in crop distribution. To analyze the multiscale relationships between the long-term evolution of cropping patterns and lake water quality at the macro scale, this study analyzed nationwide datasets for 2000 and 2020 covering 420 relatively large lakes. We systematically examined the spatial restructuring of six major food and cash crops—wheat, rice, maize, soybean, peanut, and rapeseed—and evaluated their multiscale associations with lake total nitrogen (TN) and total phosphorus (TP) concentrations and how these associations changed over time. The results showed the following: (1) From 2000 to 2020, the spatial distributions of the six major crops underwent substantial restructuring. The dominant production areas of rice, wheat, and maize were maintained or further reinforced, whereas soybean, rapeseed, and peanut exhibited varying degrees of regional redistribution and localized concentration. (2) Lake water quality differed between the flood and non-flood seasons. TN exhibited pronounced seasonal differences between the two study periods, whereas temporal changes in TP were generally limited; both nutrients nevertheless showed marked regional heterogeneity among the five major lake regions. (3) The crop–water quality relationship exhibits significant scale dependence and crop-specific variations. The XGBoost model demonstrated a certain degree of out-of-field (OOF) predictive capability for both TN and TP, with OOF R² values of 0.448 and 0.447, respectively. For TN, the highest OOF R² values were observed in the 1000–2000 m buffer zone in both 2000 and 2020; the optimal prediction scale for TP shifted from 1000–2000 m in 2000 to 2000–5000 m in 2020. SHAP results showed that corn maintained a high and relatively stable predictive importance in the TN model, followed by wheat, peanuts, and rice; in the TP model, corn and rapeseed were the crop predictors with the highest relative SHAP importance. PDP results further indicate that there are generally nonlinear or non-monotonic relationships between different crop coverage proportions and TN and TP. (4) Pronounced spatial heterogeneity was observed across the five lake regions. The Eastern Plain Lake Region was characterized by associations involving multiple crops, whereas maize was the most prominent crop in the Northeast Plain and Mountain Lake Region. In the Inner Mongolia–Xinjiang Plateau Lake Region, maize predominated, with wheat and rapeseed also showing notable importance. In the Tibetan Plateau Lake Region, TN was associated with multiple crops, whereas TP was primarily related to maize and rapeseed. The Yunnan–Guizhou Plateau Lake Region exhibited particularly strong scale-dependent differences. This study provides a nationwide analytical framework for comparing the scale differences and regional variations in the statistical associations between the spatial distribution of crops and lake water quality at the specific crop level. The findings can provide a scientific basis for formulating differentiated agricultural nonpoint source pollution control strategies that are adapted to the evolving characteristics of crop planting structures. Full article

This paper presents a systematic experimental and computational investigation of the free-vibration characteristics of a Laser Powder Bed Fusion (LPBF) AlSi10Mg thick-walled cylinder, a geometry relevant to electric-machine housings, hydraulic sleeves, and pressure-carrying components exposed to resonance-critical service loads. The specimen has an outer diameter of 94 mm, an inner diameter of 64 mm, a wall thickness of 15 mm, and a height of 90 mm, placing it firmly in the thick-walled regime ($d/D=0.68$). A three-dimensional finite element model comprising 23,864 total elements (23,236 SOLID186 solid elements and 628 surface/contact elements) and 106,015 nodes was constructed in Ansys Mechanical using the AlSi10Mg material database entry (E = 75 GPa, $\rho$ = 2670 kg/m³, $\nu$ = 0.33) and solved with the Block Lanczos eigensolver under free–free boundary conditions. Experimental modal analysis (EMA) was conducted using Brüel & Kjær software with an impact hammer with a 260-node measurement grid covering the outer surface and both end rings; frequency response functions were acquired over 0–22,500 Hz. Fourteen flexible modes were identified in simulation; nine corresponding experimental modes were resolved with frequency deviations ranging from 0.13% to 1.10%. In addition to frequency correlation, this paper introduces a non-conservative damping characterization framework comprising: (i) Rayleigh (proportional) damping coefficient extraction from EMA data and assessment of its frequency-domain validity; (ii) a viscoelastic complex-modulus model relating the real storage modulus $E^{\prime }$ and imaginary loss modulus $E^{″}$ to the modal loss factor $\eta$ and damping ratio $\zeta$; and (iii) a practical design workflow for resonance mitigation of future AM structures including electric machine frames. Experimental damping ratios ($\zeta =0.013$–$0.311\%$) are converted to per-mode $E^{″}$ values and loss factors, revealing that energy dissipation in LPBF AlSi10Mg is strongly mode-shape-dependent and cannot be accurately represented by a single Rayleigh model. Full article

Background/Objectives: Retinal imaging has considerable potential for monitoring Alzheimer’s disease (AD) neurodegeneration, as retinal ganglion cell dendritic atrophy within the inner plexiform layer (IPL) is an early event. We tested whether quantitative optical coherence tomography (OCT) speckle texture analysis combined with supervised machine learning could discriminate AD-related IPL alterations without exogenous contrast agents in a mouse model. Methods: Retinal explants from triple-transgenic AD mice (n = 7, aged 12 months) and C57BL/6 controls (n = 3, aged 15 months) were imaged ex vivo using a custom 1040 nm spectral-domain OCT system. Five grey-level co-occurrence matrix (GLCM) features were extracted from IPL volumes of interest (VOIs) and classified using a linear support vector machine (SVM). Results: AD and control IPL textures formed two completely separable clusters in a two-dimensional feature space defined by contrast and entropy (0°), achieving 100% VOI-level classification accuracy (95% CI: 96.4–100%). However, given the small sample size, VOI-level rather than animal-level validation, lack of histological confirmation, non-interleaved image acquisition, and differences in age/strain between groups, these results represent exploratory dataset separability rather than a validated diagnostic test. Conclusions: These findings demonstrate the feasibility of the ligand-free, texture-based OCT discrimination of IPL alterations, indicating a strong underlying optical signal. Adequately powered, in vivo longitudinal studies with matched controls, interleaved acquisition, animal-level cross-validation, and histological validation are required before any clinical translation. Full article