Search Results (1,152)

Fibrous organic soils are widespread and associated with long-term settlement of transportation infrastructure owing to their low bearing capacity, high compressibility, and pronounced creep, particularly in seasonally frozen regions. Undisturbed samples with fiber contents of 21%, 34%, 48%, 59%, and 73% from Northeast China underwent one-dimensional consolidation creep tests under consolidation pressures of 12.5–400 kPa. Results showed that higher-fiber-content groups exhibited larger axial strains under the same pressure. At 400 kPa, the final axial strain increased from 48.35% for the 21% fiber-content group to 61.98% for the 73% group. The coefficient of consolidation decreased rapidly as pressure increased and stabilized at 100–200 kPa, consistent with progressive compression and restricted drainage. The secondary compression index ranged from 0.022 to 0.064, generally increased with fiber content, and peaked at 50–100 kPa. Based on the ternary viscoelastic rheological (TVR) model, a finite-deformation TVR (FD-TVR) model was developed by introducing logarithmic strain and evolving drainage geometry while retaining the spring-Kelvin structure. Relationships between the model parameters, fiber content, and consolidation pressure were established, and FD-TVR predictions showed good agreement with the measured settlement curves. These findings may support settlement assessment and durable subgrade design, with implications for reduced maintenance and resource use. Full article

Background/Objectives: Bacillus spp. are often isolated from airborne microbiota. Five Bacillus strains (AD12–16) isolated from dust during a sandstorm in Kuwait were characterized for their potential to produce antimicrobial compounds, biosurfactants and siderophores by genotypic and phenotypic approaches. Methods: Whole-genome sequencing was performed, and biosynthetic gene clusters (BGCs) were predicted using antiSMASH. Antimicrobial activities were determined by agar-well diffusion assays. Siderophore and biosurfactant production were determined by Arnow’s and drop-collapse assays, respectively. The active metabolites were separated, fractionated, and analyzed by minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and LC–MS/MS. Results: Genome mining identified many BGCs associated with known secondary metabolites such as bacillibactin and bacilysin, as well as a number of unassigned clusters. The isolates exhibited distinct antimicrobial profiles, with Bacillus subtilis AD16 displaying the highest antimicrobial activity against multidrug-resistant pathogens. Biosurfactant- and siderophore-associated activities varied among the isolates. Active fractions demonstrated bactericidal activity, while LC–MS/MS analysis revealed molecular features tentatively assigned to siderophore- and lipopeptide-associated metabolite classes based on accurate mass and MS/MS fragmentation patterns. Conclusions: The combination of unassigned BGCs, antimicrobial activity, and incompletely characterized molecular features highlights unexplored biosynthetic potential in sandstorm-derived Bacillus. Further purification and structural characterization are required to establish the identities and biological functions of the active metabolites. Full article

Background/Objectives: Traumatic intracranial hemorrhage (tICH) in older adults following low-energy falls (LEF) represents a common yet diagnostically challenging condition in the emergency department (ED), where predicting injury prior to computed tomography (CT) remains difficult. Machine learning (ML) has been proposed to support CT decision-making, but its feasibility using routinely available pre-CT clinical variables in this specific population remains unclear. This study presents a systematic exploratory benchmarking of ML pipeline configurations for pre-CT tICH prediction in a well-defined retrospective cohort of older emergency patients following LEF. Methods: We performed a secondary analysis from a retrospective observational bicentric study from two university hospital EDs, including 2250 patients aged ≥65 years presenting after an LEF and undergoing cranial CT. Clinical data were extracted manually from electronic health records (EHRs). Eighteen pre-CT clinical features retrieved from electronic health records were selected based on routine availability and ≤10% missingness. Overall, 1224 valid ML pipeline configurations, combining nine classification algorithms, six imputation strategies, four class-balancing approaches, and optional hyperparameter tuning, were evaluated using 10-fold stratified cross-validation on a training set. The 20 highest-ranked configurations by cross-validation AUC were then assessed on a previously inspected exploratory hold-out test set (n = 563); training-derived rule-out operating points were evaluable for 17 of these 20, as three tuned SVM configurations lacked stored out-of-fold predictions. Results: tICH prevalence was 7.0% (n = 158). Across the 20 highest-ranked configurations, hold-out AUC ranged from 0.517 to 0.585, with Matthews correlation coefficient near zero and balanced accuracy of approximately 50% throughout, indicating differences in operating point rather than in discriminative ability. Some of these top-ranked pipelines reached higher cross-validation AUC (up to 0.679) but detected no cases at the default 0.5 threshold—an effect of the decision threshold under class imbalance rather than of the models’ rank-order discrimination, which was itself limited (hold-out AUC of 0.517–0.585). Conclusions: Despite comprehensive exploratory benchmarking across 1224 ML pipelines, routinely available pre-CT clinical features did not provide sufficient discriminatory signal to develop a clinically useful tICH prediction model in this cohort of CT-imaged older adults following LEF. These findings indicate that none of the evaluated configurations produced clinically adequate performance; this near-chance result persisted across all pipelines and most plausibly reflects a combination of limited feature signal, low outcome prevalence, and a sample size below the level required for reliable model development at this event rate. Future studies should target substantially larger prospective multicenter cohorts and evaluate additional feature domains, including structured clinical examination findings, point-of-care biomarkers, and imaging features. Full article

For nearly a century, flotation kinetics has relied on deterministic first-order rate equations treating the cell as a homogeneous reactor, a paradigm that faces significant limitations for heterogeneous ores, especially coal, whose organic macerals, porosity, and oxidation susceptibility defy a single rate constant. While more advanced distributed-k, mixed-order, and population-balance models can account for certain types of particle heterogeneity (e.g., size or liberation), they still assume that the floatability distribution remains invariant during flotation, an assumption that fails when surface chemistry evolves concurrently with the separation process. Breaking from chronological cataloguing, this review proposes a three-dimensional taxonomy based on physical scale, inherent material heterogeneity, and epistemic certainty. We demonstrate that critical industrial prediction failures arise from structural mismatches between model physics and particle surface chemistry, notably time-dependent oxidation deactivation and selective maceral recovery. Six fundamental failure modes are identified, from neglected time-dependence of rate constants to the absence of a thermodynamic deactivation term, corroborated by experimental evidence from coal and base-metal flotation. Advanced microfluidic, automated mineralogical, surface-sensitive spectromicroscopic, CFD-DEM, and physics-informed machine learning tools are dismantling the black box of the flotation rate constant “k”. We introduce the Distributed Reactive Surface Kinetics (DRSK) framework, which embeds particle-scale heterogeneity into a population balance via an adaptive surface-sensitive selection function and treats kinetic uncertainty through stochastic differential equations. A comprehensive comparison table facilitates the transition from conventional models to the DRSK paradigm. We conclude with a roadmap for flotation kinetics 4.0, where digital twins, real-time froth analytics, and self-calibrating hybrid models transform this empirical discipline into a truly predictive engineering science. Quantitative validation against published coal and copper flotation data demonstrates that DRSK reduces prediction error by 60%–75% compared to conventional first-order and distributed-k models, while providing probabilistic uncertainty bounds essential for risk-based decision-making. The framework is elaborated for coal and conventional minerals, underscoring why coal demands its own dedicated kinetic theory and how these lessons can revolutionize the processing of increasingly complex, low-grade ores and secondary resources. Full article

Long intergenic non-coding RNAs (lincRNAs) are key epigenetic regulators of genome function, yet their evolutionary dynamics following whole-genome duplication (WGD) events remain poorly understood. Salmonids, which underwent a lineage-specific autotetraploidization (salmonid-specific WGD, ~88–100 million years ago), provide an excellent model to investigate the retention, divergence, and functional potential of recently duplicated non-coding elements. LincRNA repertoires were compared across five genome-annotated salmonids (Oncorhynchus tshawytscha, O. kisutch, O. mykiss, Salmo salar, and S. trutta) and their closest non-duplicated relative, northern pike (Esox lucius). LincRNAs represented ~5–7% of annotated genes in all salmonids except S. salar (18%). Sequence conservation was low relative to coding genes, with only 11–68 highly similar (e-value < 1 × 10⁻³⁰; similarity > 70% and alignments > 100 nucleotides) putative orthologues shared between salmonids and northern pike, and 161–338 among salmonids alone. Synteny conservation was modest in lincRNAs, with lower conservation in putative orthologues (8–16%) compared to putative ohnologues (8–33%). Secondary structure conservation was associated with sequence similarity (ρ = −0.45; p = 2.2 × 10⁻¹⁶), and the association was stronger among WGD ohnologues than orthologues. In S. salar and O. mykiss, lincRNA putative ohnologues showed weaker expression correlations than coding genes, suggesting widespread regulatory divergence, possibly through neo- and subfunctionalisation. Conserved salmonid lincRNAs showed enriched predicted interactions with miRNAs involved in tumour suppression, brain, bone, and muscle development (e.g., miR-455, miR-365, miR124, miR-133a, miR-140, and miR-9), a finding supported by limited transcriptomic data. Although salmonid WGD expanded lincRNA repertoires, lincRNAs have undergone rapid sequence and transcriptional divergence, with limited conservation across species based on sequence similarity, chromosomal position, synteny, and secondary structure. A subset of conserved lincRNAs retains structural features and regulatory signatures consistent with roles as miRNA sponges in brain, skeletal, and muscle development and tumour suppression, potentially acting within conserved regulatory networks. These findings provide new insights into lincRNA evolution following genome duplication and highlight the need for experimental validation of their regulatory functions. Full article

Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Based on evidence synthesized from 152 studies and supporting geospatial and scenario analyses, results indicate that AI-optimized systems can recover 75–94% of prime-land yields, achieve carbon sequestration rates of 2.1–6.8 t CO₂e ha⁻¹ yr⁻¹, central estimate ≈ 7–9 Gt CO₂e yr⁻¹ at 35% adoption with moderate exclusions, and generate projected internal rates of return ranging from 8–22%, depending on feedstock type, regional conditions, and financing assumptions. Yield-recovery and carbon-sequestration ranges are drawn from synthesis of the reviewed literature; IRR, financial-leverage, and market-expansion figures are author-constructed scenario projections based on this evidence, not independently observed outcomes. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively projected, under scenario-based modeling, to reduce composite project risk scores by 30–45% and expand the investable universe of degraded-land biofuel projects by an estimated 340% relative to a no-AI, no-green-finance baseline; these figures represent author-constructed scenario estimates rather than direct empirical findings. We develop the AI-Biofuel-Land Restoration-Green Finance (ABLR-GF) conceptual framework (not yet empirically validated through field pilots or simulation) with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management. Full article

The fire response of cross-laminated timber (CLT) sandwich wall assemblies depends on interactions among the structural core, lining, cavities and secondary timber members. This study examines the thermal roles of these layers under medium-scale radiant exposure. The thermogravimetric mass change, mass-loss rate and heat-flow response were obtained for CLT, Fermacell^® gypsum–fiber board and timber stud specimens by simultaneous thermal analysis. One complete, artifact-free record per material group was selected for mechanistic comparison; the data were not used to characterize material variability statistically. The degradation intervals were compared qualitatively with temperatures, visual observations and post-test damage recorded during a 90 min exposure of the wall assembly at 20 kW/m². Fermacell^® retained 77.8% of its initial mass; CLT and stud specimens retained 22.5% and 20.8%, respectively. The largest mass loss of both wood-based materials occurred at 280–430 °C, with mass-loss-rate peaks at 372.1 °C for the CLT and 359.4 °C for the stud. The lining initially delayed heat transfer. After board cracking, cavity heating was followed by the degradation and glowing of the timber stud. Layer-specific thermal analysis supports the mechanistic interpretation of the tested assembly, but it neither predicts event timing nor replaces standardized fire resistance testing or classification. Full article

Background: Medication non-adherence remains a major challenge in psychiatric care, yet its prediction often relies on clinical and sociodemographic characteristics, while giving comparatively limited attention to modifiable psychological–cognitive factors. Integrating mental health literacy and medication-related beliefs with explainable machine learning approaches may provide a more informative framework for understanding and predicting adherence. Objective: This study examined the association of mental health literacy and medication-related beliefs with psychotropic medication adherence and evaluated their explanatory and predictive values using conventional statistical modeling, machine learning, explainable artificial intelligence, and structural path analysis. Methods: A cross-sectional study was conducted with 225 psychiatric patients. Medication adherence; mental health literacy; and beliefs about medication necessity, concerns, harm, and overuse were assessed using self-report measures, including a MARS-derived eight-item adherence measure. Associations were examined using Spearman correlations, hierarchical regression with robust inference, and a secondary observed-variable path representation of multivariable associations with 5000 bootstrap resamples. The Elastic Net, Random Forest, and Gradient Boosting models were evaluated using repeated five-fold cross-validation. Shapley Additive exPlanations (SHAP) were used to interpret the best-performing model. Results: Greater adherence was associated with higher mental health literacy (ρ = 0.361) and stronger necessity beliefs (ρ = 0.353), whereas concern (ρ = −0.474), perceived harm (ρ = −0.528), and overuse beliefs (ρ = −0.284) were negatively associated with adherence (all p < 0.001). The psychological–cognitive model explained 40.0% of the variance in the primary eight-item adherence composite. The integrated Random Forest achieved the best out-of-sample performance (R² = 0.402, MAE = 0.958, RMSE = 1.299; n = 208), although the improvement over the psychological–cognitive Random Forest (R² = 0.375) was modest. SHAP identified perceived harm and medication concerns as leading predictors. Conclusions: Medication adherence in psychiatric patients was more strongly characterized by psycho-cognitive factors than by sociodemographic and clinical characteristics alone. Mental health literacy, necessity beliefs, medication concerns, and perceived harm may represent particularly informative targets for individualized adherence assessments and future intervention development. Prospective external validation is required before clinical implementation. Full article

Sanghuangporus mongolicus T. Bau is a wood-inhabiting medicinal fungus parasitic on Hemiptelea davidii, yet genomic resources for this species remain limited. Here, we generated and characterized a genome assembly from a verified monokaryotic isolate using PacBio long-read and Illumina short-read sequencing. The 34.79 Mb assembly comprised 13 contigs, with a contig N50 of 3.09 Mb and a GC content of 48.18%. BUSCO analysis recovered 98.3% complete orthologs, indicating high genome completeness. A total of 8471 protein-coding genes were predicted, including 491 genes annotated as carbohydrate-active enzymes (CAZymes), 447 transporter-related genes, and 123 cytochrome P450 genes. In addition, 16 secondary metabolite biosynthetic gene clusters were identified. A descriptive comparison with four published Sanghuangporus genomes showed that S. mongolicus had 491 CAZyme-related annotations (14.11 per Mb; 5.80 per 100 predicted protein-coding genes). Corresponding values for the other four species were 313–346 annotations, with 9.78–10.38 per Mb and 2.99–4.18 per 100 predicted genes. However, structural gene-prediction workflows differed among the five genomes, and no formal gene-family expansion analysis was conducted. Accordingly, these differences are reported descriptively and should not be interpreted as evidence of evolutionary gene-family expansion. These genomic data provide a resource for functional gene characterization and further biological studies of S. mongolicus. Full article

The compaction deformation and load-bearing behavior of crushed gangue in the caved zone of goafs directly affect overburden movement, fracture evolution, and stability evolution. To clarify the compaction deformation mechanism of crushed gangue under wetting conditions in goafs, confined compression tests coupled with synchronous acoustic emission (AE) monitoring were carried out. Sandstone and mudstone crushed gangue were selected as the research objects, and Talbot gradation indexes of $n$ = 0.2, 0.4, 0.6, and 0.8 were adopted. The effects of lithology, particle gradation, and moisture condition on compaction deformation, particle-structure adjustment, and AE response were systematically analyzed under dry and short-term wetting conditions. The results show that: (1) the confined compression process of crushed gangue exhibits pronounced nonlinear strain-hardening behavior and can be divided into rapid compaction, slow compaction, and stable compaction stages. Water dripping shifts the stress–strain curves toward the higher-strain side and significantly enhances the compression deformation of mudstone, indicating a stronger wetting response of mudstone than sandstone. Meanwhile, water dripping reduces the equivalent compressive stiffness of crushed gangue. (2) Particle gradation affects the compaction response by modifying the proportions of coarse and fine particles and the initial pore structure. With increasing Talbot gradation index $n$, the proportion of coarse particles increases, resulting in more pronounced skeleton collapse, localized particle breakage, and secondary filling by fine particles, and the final compression deformation generally increases. After wetting, the final strain of mudstone samples with different gradations concentrates within 0.32035–0.33439, indicating that the control of water-induced softening on mudstone compaction deformation is stronger than the gradation effect. (3) AE results indicate that the compaction process of broken rock can be divided into a flow sliding deformation stage, a fracture deformation filling stage, and a compaction elastic deformation stage, corresponding, respectively, to particle sliding and rearrangement, particle breakage and pore filling, and structural stabilization and consolidation. Water action affects damage evolution by modifying particle contact conditions, promoting fine-particle migration, and facilitating structural adjustment. Among them, mudstone exhibits a more pronounced wetting response, whereas sandstone maintains relatively higher structural stability. The findings provide a reference for analyzing overburden movement, predicting residual subsidence, and evaluating stability in water-influenced goafs. Full article

Elm sparse grassland, dominated by Ulmus pumila, serves as a critical ecological barrier in Northern China, yet the specific climatic mechanisms governing its distribution remain poorly quantified, hindering effective restoration. This study employed the Maximum Entropy (MaxEnt) model to disentangle the primary drivers of this ecosystem, specifically addressing whether its distribution is governed by moisture limitation or a stricter thermal regime. Analyzing 94 occurrence records and 13 climatic variables, the model achieved high predictive accuracy (AUC = 0.92) and identified the annual temperature range (ART) as the predominant determinant of macro-distribution (30.1% contribution), surpassing moisture variables such as annual precipitation (19.3%). The results demonstrate that the vegetation is strictly confined to a continental climate envelope with an ART of 46.2~50.7 °C and a coldness index of −70.1~−36.1 °C, while optimal precipitation (313~497 mm) acts as a secondary filter maintaining the open-woodland structure. These findings establish a hierarchical filtering model where a strict thermal regime acts as the primary “gatekeeper” setting absolute geographical boundaries, while moisture availability determines habitat quality within those limits. Consequently, this study advocates for a paradigm shift in ecological engineering—such as the Three-North Shelterbelt Program—from moisture-centric approaches to “climate-smart” restoration that prioritizes specific thermal constraints. Adhering to these precise climatic envelopes is essential to prevent maladaptation and ensure the long-term sustainability of restoration efforts amidst global climate change. Full article

The nephrotoxicity and ototoxicity of Gentamicin have been extensively investigated; however, whether they induce hematotoxicity remains unclear. This study aimed to explore the binding of Gentamicin to adult hemoglobin (HbA) and its toxicological implications. The effect of Gentamicin on HbA oxidation was assessed by quantifying methemoglobin (MetHb) formation via a four-wavelength spectrophotometric method, and scavenger rescue assays were employed to elucidate the oxidative mechanism. Alterations in the oxygen-carrying capacity of both HbA and RBCs were evaluated through the acquisition of oxygen equilibrium curves and oxygen dissociation assays. The binding affinity between Gentamicin and HbA was determined by surface plasmon resonance (SPR). Furthermore, the influence of Gentamicin on the secondary and tertiary structures of HbA was examined by microfluidic modulation spectroscopy (MMS) and UV–visible absorption spectroscopy, respectively. Molecular docking was employed to predict the binding sites of Gentamicin on HbA. Molecular dynamics simulations verified the stability of the binding. Gentamicin promoted HbA autoxidation, elevating MetHb levels, and reduced the oxygen affinity of HbA. Gentamicin-promoted HbA autoxidation is primarily mediated by both direct heme-pocket perturbation and H₂O₂/iron-dependent amplification. SPR confirmed concentration-dependent specific binding between Gentamicin and HbA. MMS indicated no alteration in HbA secondary structure; however, UV–visible spectroscopy revealed that Gentamicin attenuated the Soret band, converted the oxyhemoglobin double-peak to a singlet, and generated a new band at 630 nm. Molecular docking predicted that Gentamicin binds β-chain residues via hydrogen, carbon–hydrogen, and hydrophobic interactions. This study reveals that Gentamicin exerts hematological effects in vitro. By binding to specific sites on HbA, Gentamicin affects the tertiary structure of HbA, promotes heme oxidation, ultimately increases MetHb content (oxidative damage), and decreases its oxygen-carrying capacity (functional impairment). Full article

Background/Objectives: The 3′ untranslated region (3′ UTR) integrates cleavage and polyadenylation signals, microRNA targets, RNA-binding-protein sites, and RNA secondary structure, but the organizational levels that remain conserved during long-term sequence evolution are poorly understood. Methods: We analyzed the ras85D 3′ UTR in 37 drosophilid taxa. Substitution rates were estimated by maximum likelihood and RelTime; insertions and deletions were reconstructed with ARPIP and summarized as insertion–deletion evolutionary localizations (IELs). Mobile-element candidates were detected with CENSOR/Repbase, and evolutionarily conserved motifs (ECMs) with MEME/MAST. Functional and structural annotations were integrated for Drosophila melanogaster, Drosophila yakuba, and Drosophila virilis and tested using permutation-based coverage, distance, boundary-neighborhood, and multilayer architecture analyses. Results: The 2247-column alignment yielded 959 block events (710 deletions and 249 insertions). Among 63 positive-length ingroup branches, 16 were deletion-enriched, three were insertion-enriched, and one showed bidirectional turnover. Thirty-four IELs projected to 27 D. melanogaster loci and were associated with ECMs. The final registry contained 390 primary functional objects and 1135 RNAfold-predicted structural segments. Predicted weakly conserved miRNA target sites were depleted in ECM_15 and ECM_11, whereas none of 12 Functional Distance tests was significant. APA objects were enriched near predicted structural-segment boundaries (O/E = 5.21; FDR = 0.00761). SAME_MULTILOOP_INTERVAL showed reduced between-context variance (0.276× null; FDR = 0.0233), and the DIFFERENT_MULTILOOP_ARMS − SAME_MULTILOOP_INTERVAL contrast was significant (p = 0.00149; FDR = 0.00447). Conclusions: The ras85D 3′ UTR evolves as a mosaic system in which extensive deletion-biased sequence turnover coexists with conserved regulatory landmarks, recurrently remodeled local neighborhoods, and context-dependent structural–functional architectures. Full article

Pediatric health research must translate evidence into decisions that affect children’s development, safety, function, and long-term well-being. Scientific progress can still fail in two opposing ways: claims may exceed their evidentiary support, or research programs may remain productive while repeatedly refining an established signal without resolving decision-relevant uncertainty. These risks are amplified by developmental heterogeneity, ethical constraints, surrogate or short-term outcomes, long follow-up, and caregiver-mediated implementation. This concept paper formalizes these failures as Evidence Drift (ED) and Causal Maturation Drift (CMD). ED is a claim-level failure in which a finding moves into a stronger or different inferential domain without an adequate bridge. CMD is a trajectory-level failure in which research continues to accumulate within an established domain after a signal is sufficiently characterized, without proportionate progression toward temporal, causal, comparative, long-term, or implementation evidence. Inferential Fidelity is proposed as the governing principle linking claim calibration to purposeful uncertainty reduction. Applications are illustrated through early childhood caries microbiome research, vitamin D and childhood caries, silver diamine fluoride, pediatric biomarker and omics pipelines, and artificial intelligence prediction studies. Two preliminary eight-item appraisal frameworks are introduced: the Evidence Drift Assessment Scale (EDAS) for claims and the Causal Maturation Drift Assessment Scale (CMDAS) for literature trajectories. These formative frameworks prioritize item-level profiles; any standardized index is secondary and non-diagnostic. A phased validation program includes content validation, cognitive testing, multi-assessor reliability, hypothesis-based construct testing, bibliometric trajectory mapping, and evaluation of practical utility. The framework offers investigators, reviewers, funders, guideline panels, and policymakers a structured approach to determining whether conclusions remain within evidentiary boundaries and whether research activity reduces the uncertainties that matter most to children and families. Transferability beyond pediatric research requires empirical testing. Full article

Coiled coils, collagen superhelices, and $\beta$-sheets belong to the supersecondary level of protein structure. Each of these classes of structural motifs is characterized by a particular chirality. However, methodology for mathematical determination of chirality of specific structures found in real proteins is currently underdeveloped. The aim of this work is to present a universal algorithm for calculating the chirality sign and value for supersecondary structure elements. In this algorithm, the basic calculation principles are the same for all three classes of structures, allowing different motifs to be compared directly. The results for a number of structures from each of the three classes are presented in tables and graphical plots. The calculated chirality signs generally agree with the predicted ones. The results also show that the chirality value is related to the number of amino acid residues in the structure and their distribution among the secondary structure elements. The relationship between amino acid composition and the chirality value is discussed using several examples. The more evident factors that determine the chirality sign and value for a particular structure are a promising subject for future research. Full article