Purpose Digital products often rely on continuous version updates as generational product innovation (GPI) to build and sustain consumer engagement. Unlike traditional settings that emphasize discrete, content-focused launches, digital contexts elevate the temporal structure of innovation. This study examines how the release rhythm influences engagement and explores the moderating roles of brand awareness and the pricing model. Design/methodology/approach We collected data on 416 mobile apps and analyzed 1,071 version updates. We employ negative binomial regressions as the primary estimation method to analyze consumer engagement and use ordinary least squares (OLS) regressions as robustness checks. To address endogeneity, we apply a two-stage control function approach using industry-level GPI rhythm irregularity as an instrumental variable. Findings The analysis reveals that irregular GPI rhythms enhance consumer engagement, while regular rhythms yield weaker effects. Moreover, the positive impact of irregular GPI rhythm is amplified when brand awareness is high and when products are free. Practical implications Managers can strategically use irregular innovation timing to enhance user engagement, especially for free products and strong brands in competitive digital markets. Originality/value The study reframes GPI in digital markets as a temporal signaling process that highlights the pattern of revealing innovation beyond its content or frequency. It identifies irregular rhythm as a novel antecedent of engagement and shows that brand awareness and pricing condition how temporal signals are interpreted, explaining firms’ use of aperiodic releases and informing decisions about when to reveal product generations.

Catalytic coupling of benzene to biphenyl through direct C─H bond activation and C─C bond construction represents an ideal, atom-economical strategy, offering a streamlined alternative to classical multi-step functionalization approaches, and thus has been a long-sought after goal. Herein, we report that such a pathway can be realized over a sub-nanostructured europium hydride (Eu-H) catalyst under mild condition. The Eu-H catalyst achieved a biphenyl production of 53.7mmolgEu -1 with a turnover number (TON) of 16.3 over 144h at 150°C. Mechanistic studies suggest that hydride species (H-) in Eu-H preferentially deprotonate benzene to form a [C6H5-Eu] intermediate, which subsequently undergoes nucleophilic attack on another benzene molecule, resulting in biphenyl formation and regeneration of the Eu-H species to complete the catalytic cycle. This work not only provides a strategy for benzene coupling to biphenyl but also demonstrates the promising role of hydrides in transforming arenes into value-added molecules.

Multiple sclerosis (MS), one of the most common neurological causes of disability in young adults, is characterized by cognitive deficits in addition to balance-related symptoms, fatigue, and visual symptoms. Patients have difficulties in different cognitive domains such as memory, learning, executive functions, and attention, especially information processing speed. Current conventional imaging methods are insufficient to elucidate cognitive impairments. Structural alterations in the brain obtained by magnetic resonance imaging (MRI) do not have a high correlation in explaining all cognitive deficits. Therefore, connectivity-based approaches that address brain functions in a more holistic perspective come to the forefront in explaining cognitive functions. The relationship of cognitive deficits with large-scale functional networks through functional connectivity-based approaches via resting-state functional magnetic resonance imaging has begun to be investigated. Although functional connectivity (FC) studies are successful in explaining cognitive functions, the findings are not homogenous. The aim of this review is to analyse the contribution of functional connectivity-based approaches in understanding the clinical-imaging mismatch and to show that cognitive dysfunctions frequently seen in MS patients can only be partially explained by conventional structural imaging techniques. In this respect, this study is a narrative review based on a review of the literature published in the last fifteen years. While the importance of FC in explaining cognitive functions is mentioned, it is also emphasized that the findings of this study may be affected by individual factors such as cognitive reserve.

Stunting is a serious health problem in Egypt. Stunting rates and height-for-age z-score (HAZ) distributions changed notably in Egypt over time, yet the factors that led to these changes remain unknown. This study examines the factors associated with these changes and provides important considerations for designing interventions to achieve the Sustainable Development Goal (SDG) of ending all forms of malnutrition by 2030. Leveraging data from Egypt's Demographic and Health Survey for the years 1995, 2003, and 2014, we employ a Recentered Influence Function (RIF) approach that goes beyond the conventional way of measuring stunting as a binary indicator to examine changes across the entire HAZ distribution. The RIF decomposes changes in the HAZ distribution over time into differences attributable to changes in the levels of the determinants of nutrition (covariate effects) and in the strength of the association between these determinants and HAZ (coefficient effects). The stylized facts show a puzzling increase in stunting rates despite improvements in the level of the determinants of nutrition. Our RIF results attribute the change in stunting rates and other parts of the HAZ distribution primarily to changes in the association between the determinants of nutrition and HAZ (coefficient effects) rather than in the level of the determinants (covariate effects). The results also show that the determinants of nutrition could have heterogeneous impacts at different quantiles of the HAZ distribution. To reduce stunting rates and achieve the SDG of ending malnutrition, our findings highlight the need for targeted interventions. Interventions should be geographically targeted, promote gender and income equality, improve maternal nutrition, and expand access to better sanitation facilities. This is in addition to wealth redistribution and reforming Egypt's subsidy program to focus on nutritious food.

Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion applications. The workhorse of warm dense matter theory is thermal density functional theory (DFT), which, however, suffers from two limitations: (i) its accuracy can depend on the utilized exchange–correlation functional, which has to be approximated, and (ii) it is generally limited to single-electron properties such as the density distribution. Here, we present a new ansatz combining time-dependent DFT results for the dynamic structure factor See(q, ω) with static DFT results for the density response. This allows us to estimate the electron–electron static structure factor See(q) of warm dense hydrogen with high accuracy over a broad range of densities and temperatures. In addition to its value for the study of warm dense matter, our work opens up new avenues for the future study of electronic correlations exclusively within the framework of DFT for a host of applications.

ABSTRACT This manuscript presents a structured roadmap for entrepreneurship researchers to proactively mitigate the intricate challenge of endogeneity. It emphasizes tailored approaches for crafting research questions and defining constructs to pre-empt endogeneity concerns. Engaging with a diverse array of ad hoc designs, it critically assesses their alignment with the distinctive nuances of entrepreneurial inquiry. Based on a systematic analysis of 1,496 studies from the top seven entrepreneurship journals, it distills the misapplications of four advanced post hoc methodologies, including the Instrumental Variable method, Control Function approach, Gaussian Copula technique, and Heckman Correction procedure. By articulating their specific advantages and applications in prior entrepreneurship research, this paper strengthens theoretical and empirical rigour in the field.

Genetic tumor risk syndromes (genturis) contribute substantially to the overall cancer burden and provide opportunities for early detection, prevention, and individualized treatment. Yet, many affected individuals remain undiagnosed due to restrictive testing criteria and challenges in variant interpretation. This review summarizes recent advances in the diagnostic evaluation of genturis. We trace the evolution from single-gene testing to multigene panel testing, highlighting gains in diagnostic yield alongside the growing prevalence of uncertain and incidental findings. We then describe emerging functional approaches such as RNA sequencing and proteomics that generate molecular evidence to refine variant classification. Next, we outline how long-read sequencing overcomes technical limitations in complex genomic regions. Finally, we discuss practical aspects of clinical implementation, including reporting practices, workflow integration, and professional education, and propose strategies to improve diagnostic accuracy, efficiency, and equitable access to testing.

Bovine respiratory disease (BRD) poses a significant health and economic challenge in cattle farming, particularly affecting young calves. Although previous SNP-based genome-wide association studies (GWAS) have identified candidate loci linked to BRD susceptibility, they only explain a fraction of the trait’s heritability. Using genotypes from a previous study that employed a selective genotyping approach, we analyzed Holstein calves classified as BRD-resistant or BRD-susceptible, based on thoracic ultrasonography and clinical scoring. In particular, structural variations, specifically copy number variants (CNVs) and runs of homozygosity (ROH), were investigated due to their emerging role as complementary genomic features that may be involved in disease resistance. A total of 2,666 CNVs were identified, and the CNV-GWAS revealed 10 significant CNV regions (CNVRs), encompassing or near 15 candidate genes. While the ROH analysis identified 8,226 segments, we further applied a fixed-window approach to compare ROH frequencies between groups, revealing 19 regions with significantly different ROH frequencies. Gene annotation of both CNVRs and differential ROH windows uncovered genes linked to immune response, lung development, and known BRD-associated pathways. Functional enrichment analyses using DAVID and Cytoscape-GeneMANIA indicated involvement of antiviral responses, GPCR signaling, calcium signaling, and estrogen receptor pathway in disease resistance. Notably, 37% of the genes identified in this study overlapped with those reported in previous BRD-related studies. This integrative genomic analysis highlights the relevance of structural variation in shaping BRD resistance and susceptibility in dairy calves. By integrating CNV mapping, ROH analysis, and functional annotation approaches, we identified novel and previously reported candidate genes potentially involved in innate immune processes. These findings support the implementation of precision breeding strategies aimed at improving disease resilience in cattle.

In a transcription of a recording made prior to his unexpected passing in 2025, I’u Tuagalu reflects on 24 years at AUT, highlighting the evolution of Pacific student support and the adoption of a systemic functional linguistics approach that has been implemented increasingly in online resources.

ABSTRACT Nowadays, hydrogen bond ferroelectric liquid crystals (HBFLCs) play a pivotal role in diverse applications due to their response to the external field. A supramolecular HBFLC is derived from the non-mesogenic (S)-(+)-Phenylsuccinic acid (SPSA) and mesogenic 4-(Octyloxy)benzoic acid (8OBA). The prepared HBFLC (SPSA +8OBA, 1:2 ratio) is characterised via experimental and density functional theory (DFT). Textural investigation via polarising optical microscope reveals the presence of chiral mesophases, whereas differential scanning calorimetry studies confirm the chiral mesophase transition temperatures and their corresponding entropy and enthalpy values. Further, complementary hydrogen bonding (H-bond) is analysed via FT-IR spectrum and the same has been authenticated using various DFT calculations such as, MEP, Mulliken, IRI, QTAIM, and IGMH. Furthermore, the molecular parameters of the HBFLC are studied using global reactive descriptors, FMO analysis, interaction sites, and interaction energies. Non-linear optical properties and their possible applications in the field of photonic devices of HBFLC complex are reported in the present communication.

Much is known about the genetic regulation of early valvular morphogenesis, but mechanisms governing later fetal valvular remodeling remain unclear. Hemodynamic forces strongly influence morphogenesis, but it is unknown whether or how they interact with valvulogenic signaling programs. Apparent side-specific expression of valvulogenic programs motivates the hypothesis that shear stress pattern-specific endocardial signaling directs the remodeling and maturation of valve leaflets. Here, we aim to determine how local hemodynamic stress regulates the maturation of fetal semilunar heart valves. We identified strong ventricularis-specific expression of endocardial NOTCH1 and mesenchymal CXCR4 (C-X-C chemokine receptor type 4) during fetal valve stages. Valve cell-type specific conditional Notch and Cxcr4 mouse deletions were generated and analyzed in vivo consequences, which were then tested directly using ex vivo chick endocardial cells and valve organoids via gain and loss of function approaches. Samples were then quantitatively analyzed via histology, immunohistochemistry, and qRT-PCR. We established that unidirectional laminar shear stress regulates CXCR4 via endocardial NOTCH signaling through upregulation of CXCR4 ligand SDF1. Global deletion and endocardium-derived mesenchymal cell-specific deletion of Cxcr4 both resulted in hyperproliferative and thickened outflow tract valves. In addition, conditional ablation of Cxcr4 also revealed that it promotes matrix remodeling and tissue compaction through inhibition of BMP (bone morphogenetic protein) and WNT signaling programs. High-magnitude unidirectional laminar shear stress is transduced by endocardial cells, turning on a NOTCH1/CXCR4 molecular switch. This switch stops the valve mesenchymal growth program by inhibiting WNT/BMP. Simultaneously, it also orchestrates valve condensation, mesenchymal cell differentiation, and ECM (extracellular matrix) remodeling. Taken together, our findings identify a novel molecular switch controlled by local hemodynamic cues that directs valve maturation robustly in a side-specific manner.

Abstract We propose NuPINN-CAL, a physics-informed neural network designed to directly predict nuclear proton density distributions, $\rho_p(r)$, from the fundamental nuclear identifiers $(Z, N)$. Unlike conventional Skyrme-Hartree-Fock (Skyrme-HF) and energy-density functional approaches, NuPINN-CAL explicitly embeds two key nuclear-physics constraints (proton-number conservation and charge-radius consistency) into its learning objective. A novel constraint-aware learning strategy dynamically balances these physics constraints with Skyrme-HF priors, ensuring stable and interpretable training even in small-data regimes.Benchmarking on 120 magic and semi-magic nuclei, NuPINN-CAL achieves a root-mean-square deviation of $0.022$ fm in charge radii, representing a $\sim 33\%$ improvement over Skyrme-HF predictions. Beyond numerical accuracy, the model demonstrates strong generalization to open-shell systems. These results validate the central hypothesis that $(Z, N)$ uniquely determines $\rho_p(r)$ as a deterministic and physically consistent mapping, highlighting NuPINN-CAL as a robust, interpretable, and data-efficient paradigm for next-generation nuclear density modeling.

Sperm proteases are involved in several gamete interaction events leading to fertilization. This report presents a detailed analysis of the expression and localization of serine protease PRSS38 in human and in mouse spermatozoa and its involvement in fertilization-related events, using bioinformatics, cellular, biochemical, molecular, and functional approaches. Bioinformatics analyses included genomics and data analysis, prediction of protein subcellular localization and post-translational modifications, Self-Organizing Maps (SOMs) unsupervised training with other serine proteases, protein modeling (AlphaFold), and genetic variant analysis. For cellular, biochemical, and functional studies, human semen samples were obtained from healthy normozoospermic volunteers, and cauda epididymal sperm were collected from adult Balb-c/C57 mice. PRSS38 presence was detected in human and mouse sperm protein extracts by Western immunoblotting. Sperm PRSS38 subcellular localization was determined by fluorescence immunocytochemistry. Human sperm–oocyte interaction events were assessed by means of the mouse Cumulus Penetration Assay (CPA) using mouse COCs, the Human Hemizona Assay (HZA), and the ZP-free hamster egg Sperm Penetration Assay (SPA). Mouse sperm–oocyte interactions were evaluated by means of in vitro fertilization (IVF) with COCs and denuded oocytes. PRSS38 is proposed to be a GPI-anchored serine protease (active site: His-100, Asp-150, and Ser-245) based on bioinformatics analyses. Using commercial antibodies, protein forms of the expected Mr (human: 31 kDa; mouse: 32 and 24 kDa) were specifically immunodetected in protein sperm extracts. Immunocytochemical analysis revealed a specific PRSS38 signal in the human sperm acrosomal region, equatorial segment, and flagellum. Mouse sperm PRSS38 was immunolocalized in the equatorial segment and hook. Human sperm preincubation with specific antibodies resulted in inhibition (p < 0.05) of CPA, HZA, and SPA. Mouse sperm preincubation with PRSS38 antibodies impaired (p < 0.05) homologous IVF using COCs and denuded oocytes. Genetic variants affecting residues involved in the GPI anchor and the catalytic triad were found in individuals from the general population whose PRSS38 protease function could be altered. This study provides, for the first time, an integrated analysis of PRSS38 in human and mouse sperm, contributing to our understanding of mammalian fertilization and male infertility.

To characterise 3-year pelvic pain and urinary symptom trajectories and to identify baseline factors associated with urologic chronic pelvic pain syndrome (UCPPS) improvement. The Trans-Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Symptom Patterns Study was a multicentre, prospective cohort study of UCPPS, including interstitial cystitis/bladder pain syndrome and chronic prostatitis/chronic pelvic pain syndrome. Patients completed four weekly run-in assessments, baseline visit, and quarterly visits up to 3 years, providing clinical and patient-reported data. A functional clustering approach, applied separately to Pelvic Pain Severity (PPS) and Urinary Symptom Severity (USS) longitudinal change scores, was used to generate symptom trajectory clusters dichotomised as Group 0 'improvers' vs Groups 1-3 'non-improvers'. Logistic regression models explored baseline factors associated with improvement and included run-in period average and baseline scores to adjust for regression to the mean effects. A total of 545 patients (66% female) were followed for a median (interquartile range) of 34 (23-35) months. Four trajectory clusters were identified for each of PPS and USS, consistent with moderate improvement (Group 0), slight improvement (Group 1), no change (Group 2), and slight worsening (Group 3). In all, 18% and 19% of patients were in the moderately improved PPS and USS groups, respectively, representing 30% of patients overall. Female sex, better sleep, and less opioid use were associated with PPS improvement (Group 0); younger age and baseline cystoscopic treatment were associated with USS improvement (Group 0). In all, 30% of patients with UCPPS demonstrated improvement in pain and/or urinary symptoms over 3 years. Baseline factors associated with improvement may represent markers of a milder or localised phenotype and/or treatment effects.

Monitoring restoration areas is crucial for understanding how ecological succession changes over time and whether the trajectories of planted communities are following the desired path of recovery. A functional trait-based approach coupling the functional trajectory analysis (FTA) with species abundance modelling may have a significant potential application in restoration assessment. In the present study, we surveyed a 10-year-old restoration tree community in the Brazilian Atlantic Forest, planted in rows (2 × 2 m spacing) and clusters (13 individuals planted 0.3 m apart from each other), called active restoration strategy, and compared to natural regeneration without planting, or a passive restoration strategy. Using a multidimensional analysis of six key functional traits (leaf area, specific leaf area, leaf dry matter content, seed dry mass, wood density, and potential height), we employed FTA to assess the ecological successional trajectories of active row and cluster plantations and compared them to a reference ecosystem (REF) and the passive strategy (baseline). Our results indicated over ten years of succession, natural regeneration in plantations resembled communities in passive strategy, showing more abundance of early successional species (more acquisitive). We found natural regeneration in plantations led the trajectory of the planted communities away from the functional dimension of REF. Regeneration in row plantations exhibited higher potential height and leaf area than in clusters, increasing the distance of trajectory from REF. Based on the abundance of species traits in plantations, we were able to detect which species should be removed (thinning) or replanted (enrichment) to potentially redirect undesirable trajectories. We offer a robust functional approach for monitoring and to guide restoration projects toward desired ecological outcomes.

A functional iterative approach for twin bounded support vector machine with squared pinball loss (Spin-FITBSVM).

Molecular puppeteering: Roles of Ustilago maydis effectors.

Mechanism of sonolytic PFAS degradation into inorganic products: A combined reactive molecular dynamics and density functional theory approaches.

Cells modify the extracellular matrix by expressing proteases that degrade matrix proteins, enabling cell migration within tissues. This process is mimicked in hydrogels through protease-degradable peptide crosslinks. However, cleaving hydrogel crosslinks reduces local matrix mechanical properties, and most crosslinking peptides, including the widely used GPQGIWGQ "PanMMP" sequence, often lead to bulk hydrogel degradation. Membrane-type proteases are localized to the cell surface, have important roles in cell migration, and are active in the pericellular region. To identify peptides primarily cleaved by membrane-type proteases, an approach is developed that couples proteomic identification of candidate peptides with mass spectrometry-based functional assays to quantify degradation. The target sequence is then optimized using a split-and-pool synthesis to generate over 300 peptide variants to improve degradation behavior. The optimized peptide, KLVADLMASAE, shows reduced degradation by soluble proteases, while enabling endothelial and stem cell spreading and viability comparable to PanMMP hydrogels. KLVADLMASAE-crosslinked hydrogels have reduced crosslinker degradation, are stiffer during culture, and exhibit less macroscopic degradation after 14 days of culture than PanMMP gels. The performance of KLVADLMASAE-crosslinked gels is significantly improved from the initial peptide target, validating this functional high-throughput approach for identifying peptides that control matrix degradation.

Prosopis juliflora pod and gum as green corrosion inhibitor - density functional theory approach