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South Asia has the highest rates of suicide fatalities among women globally. Existing suicide prevention interventions are largely based on Western-informed risk reduction programs and theories. This pilot trial aims to test the acceptability and feasibility of a co-designed suicide prevention program for peri-rural Pakistani women called the Khushal Pur-Umeed Zindagi (KPZ, خوشحال پُرامید زندگی پروگرام), compared to enhanced usual care (EUC). The study seeks to innovate suicide prevention by grounding interventions in decolonized frameworks, leveraging peer mothers, and developing robust health system integration strategies. We will conduct a two-arm, mixed-methods, hybrid type 2, stratified pilot cluster randomized controlled trial in peri-rural areas of the Islamabad Capital Territory (ICT). KPZ is a decolonized, culturally salient brief intervention combining narrative-based safety planning and contact follow-up, delivered by peer mothers over 6 months. EUC entails the WHO Mental Health Gap Action Programme (mhGAP) suicide prevention module, along with referral support delivered through Primary Health Care (PHC) by Medical Officers (n = 2) and Lady Health Worker teams (n = 11). KPZ is integrated into the PHC system and is implemented through close collaboration between peer mothers (delivery agents) and the government-employed LHWs. A cohort of peer mothers (n = 11), identified from the same communities as participants, will be trained and supervised for the study duration. We will enroll 50 women aged 18-45 with a child under 3 years who report suicidal ideation and will conduct follow-ups at 3- and 6-months post-recruitment. Qualitative interviews with trial participants and periodic reflections from peer mothers will be conducted iteratively over 6 months. A mixed-methods approach will be used to assess both clinical and implementation outcomes. Primary clinical outcomes include suicidal ideation severity and suicidal behaviors. Implementation outcomes include feasibility, acceptability, fidelity, appropriateness, and peer mothers' competence. Secondary outcomes assess additional clinical domains (depression, anxiety) and culturally relevant constructs (moral injury, cultural suicide cognitions). This pilot trial will provide evidence on the feasibility and acceptability of integrating a decolonized, peer-delivered suicide prevention program for women into the existing Pakistani health system. The findings have the potential for large-scale public health impact by improving the availability, accessibility, and quality of culturally appropriate suicide prevention interventions in resource-constrained and complex environments. Results will inform a larger definitive trial of a community-initiated suicide prevention program in Pakistan. NCT06208293.
Negative automatic thoughts feature centrally in psychiatric disorder but are rarely studied in the prediction of suicide attempts. This study tested whether negative automatic thoughts could prospectively predict suicide attempt or other suicide event (aborted or self-interrupted attempt, preparatory behavior or suicidal ideation resulting in hospitalization) within a 90-day window among high suicide risk patients. Sixty Veterans at high risk of suicide completed assessments of negative automatic thoughts at multiple timepoints over a 1-year period. Each assessment session was then coded according to whether the participant made a suicide attempt or had an other suicide event in the following 90 days. Generalized Estimating Equations (GEE) were used to evaluate the incremental utility of (1) negative automatic thoughts and (2) acquired capability for suicide on predicting 90-day suicide attempt or other suicide event over established suicide risk factors, including lifetime number of suicide attempts, concurrent suicidal ideation, depressive symptom severity and hopelessness. The odds of a 90-day suicide attempt were significantly greater with more frequent negative automatic thoughts (OR = 1.06 [95% CI 1.03-1.09], p < .001); however, negative automatic thoughts did not significantly predict a near-term other suicide event (excluding an actual attempt). Acquired capability did not add to the near-term prediction of either suicide attempt or other suicide event. The study was a secondary analysis of data collected from a small sample, and thus, findings require replication. Near-term suicide attempts were rare even in this high-risk sample. Nevertheless, findings provide preliminary evidence for depressionogenic thought content in suicide attempt risk, and suggest a more direct link between negative automatic thoughts and suicide attempt than posited in contemporary theories.
Accurate arterial input functions (AIFs) are essential for quantitative dynamic contrast-enhanced (DCE) MRI, yet direct measurement is challenging and population-averaged AIFs neglect patient-specific variability. Blind deconvolution provides an alternative by estimating patient-specific AIFs directly from tissue data, up to a scale factor. This study compared blindly estimated AIFs with carefully measured aortic AIFs in breast DCE-MRI. Data from 25 patients with breast cancer were analyzed, each with a carefully measured AIF. Blind AIF estimates were obtained using model-constrained deconvolution in the Perflab toolkit with both Tofts-Kety (TK) and two-compartment exchange (2CXM) tissue models. To help isolate AIF shape blind AIFs were scaled using cardiac output. These blind AIFs were compared with measured AIFs using scale-invariant and scale-dependent metrics which assess the similarity of the AIF shape. Blind estimates were obtained for all 25 patients with both tissue models. Compared with measured AIFs, blind estimates derived using the 2CXM showed stronger agreement across all metrics than those derived using the TK model. However, the weak correlation in scale-dependent metrics suggests limitations of cardiac output scaling. Blind AIF estimation using the 2CXM provides more reliable recovery of AIF shape and dispersion than the TK model in breast DCE-MRI. While blind deconvolution shows promise for estimating local patient-specific AIFs other scaling strategies may need to be employed in practice.
Responding to system pressures, fragmentation, and growing project fatigue in primary care, this study examines how values-driven innovations (VDIs) can be successfully embedded and sustained by (1) defining what constitutes a values-driven innovation, (2) identifying strategies and mechanisms that support long-term implementation, and (3) co-developing evidence-based sustainability indicators with stakeholders. By integrating four complementary theoretical frameworks and examining the full lifecycle from conceptualization to long-term sustainment, it generates actionable insights to strengthen enduring, values-based transformation in primary care. This study uses a longitudinal, theory-informed, multi-stakeholder design. Over three years, we follow selected VDIs from conceptualization through long-term sustainment, using multiple data sources and repeated timepoints to capture dynamic implementation processes. An integrated conceptual framework: combining realist evaluation, the Dynamic Sustainability Framework, Consolidated Framework for Implementation Research, and Normalization Process Theory, guides all phases. The three-phase design includes: (1) defining VDI and mapping the innovation landscape; (2) conducting longitudinal case studies using document analysis, serial interviews, and questionnaires; and (3) co-creating an actionable sustainability model and monitoring indicators with stakeholders. Continuous reflexive practice, triangulation, and participatory engagement ensure rigor and relevance. Despite the analytical complexity of using multiple frameworks, this integration provides a comprehensive understanding of sustainability and contextual adaptation in primary care. The study protocol was approved by the Ethics Committee of the University of Antwerp (reference number 2025-7124).
We construct a novel effective field theory for a compact body coupled to gravity, whose key feature is that the dynamics of gravitational perturbations is explicitly determined by known solutions in black hole perturbation theory in four dimensions. In this way, the physics of gravitational perturbations in curved space are already encoded in the effective field theory, thus bypassing the need for the higher-order calculations that constitute a major hurdle in standard approaches. Concretely, we model the compact body as a spherical shell, whose finite size regulates short-distance divergences in four dimensions and whose tidal responses are described by higher-dimensional operators. As an application, we consider scalar perturbations and derive new results for scalar Love numbers through O(G^{9}) for Schwarzschild black holes and for generic compact bodies. Finally, our analysis reveals an intriguing structure of the scalar black-hole Love numbers in terms of the Riemann zeta function, which we conjecture to hold to all orders.
A novel, sensitive, and environmentally sustainable spectrofluorimetric method was developed for lisinopril quantification based on Erythrosin B fluorescence quenching. The method exploits static quenching through ground-state ion-pair complex formation between dianionic Erythrosin B and dicationic lisinopril at pH 6.0. Comprehensive mechanistic investigation employing temperature-dependent Stern-Volmer analysis, thermodynamic studies, Job's method, and PM3 semi-empirical quantum mechanical calculations confirmed the static quenching mechanism driven by electrostatic interactions with binding energy of -6.90 kcal/mol (-28.87 kJ/mol). Under optimized conditions (pH 6.0, 15 μg/mL Erythrosin B, excitation/emission, 533/555 nm), the method exhibited excellent linearity over 0.01-3.0 μg/mL (r = 0.9998) with high sensitivity (LOD, 3.1 ng/mL; LOQ, 9.2 ng/mL). Validation according to ICH Q2(R2) guidelines demonstrated excellent accuracy (99.8 ± 1.119%), precision (RSD < 1.63%), robustness, and selectivity. The method was successfully applied to pharmaceutical tablets (100.02 ± 1.079% recovery) and spiked human plasma (96.19%-105.72% recovery). Comprehensive sustainability assessment using RGB12 (whiteness, 88.0/100) and EPPI (total score, 83.8) confirmed the method's superior environmental sustainability and ideal green profile. The developed method offers significant advantages including commercially available reagents, elimination of derivatization or nanomaterial synthesis, and simple instrumentation, representing an environmentally friendly alternative for routine lisinopril determination in pharmaceutical quality control and bioanalytical applications.
Herein we demonstrate the utility of measuring initial 1H NMR relaxation rates for determining protein side chain mobility in a human Pin1 WW domain thermostable mutant. To accomplish this, a 1H spinlock field or TOCSY element is appended to the beginning of any multidimensional NMR experiment beginning on 1H. This initial assessment is essential for determining the appropriate side chain motional model for fitting J coupling data. NMR signal intensities arising from three-bond J couplings 3JN,HB2/3, 3JCO,HB2/3, and 3JHA,HB2/3 were analyzed using a script fitting to two alternative motional models: 1) a single χ1-dihedral angle or 2) a mixed population of the three ideally staggered rotamers (gauche-, +60°; trans, 180°; gauche+, +300°). Using existing empirically determined Karplus coefficients, we initially calculated χ1-dihedral angles for rigid residues that differed significantly from the ideal rotamers. Switching to Karplus coefficients derived using density functional theory (DFT), we obtained dihedral angles that better matched predictions based on a 1 μs molecular dynamics (MD) simulation starting from a high-resolution X-ray crystal structure. Previous empiric models were inaccurate because they utilized information from mixed populations of rigid and mobile side chains, underscoring the importance of using 1H NMR relaxation to discern rigid from mobile residues prior to J coupling analysis. Moreover, empirically derived Karplus coefficients relied on sparse data for χ1-dihedral angles outside the three major rotamers, further limiting their accuracy. Of note, our revised Karplus coefficients include a phase offset term that breaks the symmetry of the Karplus relationship; they are significantly different for Ser/Thr and Val/Ile residues and for helix versus sheet residues. While there is general agreement between NMR data and MD simulations in predicting the relative rigidity/mobility of side chains, there were some pointed discrepancies, suggesting that NMR may be useful to guiding MD parametrization in the future.
Degree-based topological indices are widely used in mathematical chemistry because they provide simple numerical descriptions of molecular graphs and can support structure-property analysis. In this work, we introduce the Inverse Prodeg index [Formula: see text] and its coindex [Formula: see text] as new degree-derived graph invariants. Unlike product-, sum-, and mixed-degree descriptors such as the Randić, sum-connectivity, harmonic, atom-bond connectivity, geometric-arithmetic, Sombor, Nirmala, inverse Nirmala, and misbalance prodeg indices, [Formula: see text] reduces to the vertex-wise concave sum [Formula: see text], whereas [Formula: see text] transfers the same inverse square-root degree weighting to nonedges using the original graph degrees. We establish their main mathematical properties, including bounds involving graph order, size, and degree extrema, equality cases, Nordhaus-Gaddum-type inequalities, exact expressions for standard graph families, and estimates under several graph operations. These results show that the proposed descriptors are analytically tractable and computationally efficient, with linear-time computability in the number of edges. To examine their chemical relevance, we evaluate a small Prodeg-based descriptor family on a dataset of 90 aromatic-carboxylate compounds using training and external test sets generated by the Kennard-Stone algorithm. The models indicate that these descriptors capture useful structure-property information, especially for size- and thermodynamics-related endpoints. Linear regression and partial least-squares regression achieved the strongest average external-test performance among the considered Prodeg-only models, with mean external-test [Formula: see text] across the nine core endpoints, while Ridge regression was close with mean external-test [Formula: see text]. Nonlinear methods did not improve the average prediction accuracy. Additional validation through bootstrap analysis, Y-randomization, residual diagnostics, and applicability-domain assessment supports a non-spurious but dataset-dependent predictive signal. Overall, the Inverse Prodeg index and its coindex provide mathematically well-founded and practically useful graph descriptors, although broader validation and combination with chemically richer descriptors are needed before claiming general predictive superiority.
Insomnia and depression frequently co-occur during adolescence, posing significant challenges to adolescent mental health. However, the direction of their association remains inconsistent in the literature and may show grade-specific patterns. Grounded in psychopathology network theory and informed by the developmental psychopathology perspective, this study examined symptom-level associations between insomnia and depression to better characterize these association patterns. This study employed cross-lagged panel network analysis to explore symptom-level interactions between insomnia and depression from Grades 5 to 12, capturing both contemporaneous (within-timepoint) associations and temporal (cross-timepoint) predictive associations. A large cohort of 175,950 adolescents (MAge = 14.35, SD = 2.14; 52.72% female) completed the Insomnia Severity Index (ISI) and Patient Health Questionnaire-9 (PHQ-9) at two time points. The findings showed both relatively stable and grade-specific patterns of association between insomnia and depression. In the contemporaneous networks, Motor descriptively showed the highest strength centrality in most grades, although the symptom with the highest centrality differed in some grades. In the temporal networks, descriptive centrality results indicated that Sad Mood had the highest out-expected influence in most grades, except for Anhedonia in Grade 6 and 8 and Suicide in Grade 12; Daily Function descriptively had the highest in-expected influence in most grades, except for Fatigue in Grade 10 and 11. These findings highlight several symptoms in the insomnia-depression network, with some variation across grades. By moving beyond total-score approaches, this symptom-level perspective provides exploratory insights that may shed light on inconsistencies in prior findings regarding the directionality of the insomnia-depression association and offer a more nuanced understanding of insomnia-depression relationships during adolescence.
Spectral coherence theory is of practical importance for bearing fault diagnosis. However, when fault-related information is distributed across multiple spectral bands, existing methods often cannot effectively identify and integrate these informative components, which limits diagnostic performance. To address this issue, an optimal weighted envelope spectrum (OWES) is proposed. First, the expected signal to expected noise (ESEN) is constructed. By introducing local background correction and adaptive harmonic position identification, ESEN enhances the robustness of fault feature extraction to frequency deviation, amplitude variation, and spectral background fluctuation. Then, informative spectral regions are identified by thresholding the ESEN distribution along the spectral frequency axis and are merged into candidate frequency bands. A combinatorial optimization strategy is further introduced to select the candidate band subset that yields the most prominent integrated fault feature, thereby improving fault feature extraction when informative components are distributed across multiple spectral bands. Finally, the selected bands are weighted according to their information contributions and integrated to construct the OWES. The proposed method is validated using one simulated signal and three bearing experimental datasets. Compared with several advanced methods, OWES achieves clearer identification of fault characteristic frequencies and harmonics. Quantitative comparisons based on ESEN and kurtosis further demonstrate the superior performance of the proposed OWES method.
This study aimed to develop and psychometrically evaluate a culturally adapted eco-driving questionnaire grounded in the Health Belief Model (HBM) among line taxi drivers in Tehran, Iran. Given the contribution of vehicular emissions to urban air pollution and the lack of theory-based tools for assessing eco-driving determinants, a multi-phase questionnaire development process was conducted. An initial pool of 96 items was generated through literature review and expert consultation, followed by content validation, pilot testing, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability assessment, and structural equation modeling (SEM). Data were collected from 401 line taxi drivers selected through stratified random sampling. The final 57-item questionnaire covered eight domains: knowledge, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, self-efficacy, and eco-driving behavior. EFA supported an eight-factor structure explaining 77% of cumulative variance. CFA using WLSMV estimation showed acceptable fit (CFI = 0.936, TLI = 0.942, RMSEA = 0.068), although SRMR was elevated (0.128). Reliability was acceptable to excellent for most Likert-based subscales, while lower reliability was observed for dichotomous and mixed-format subscales. SEM showed that self-efficacy, knowledge, perceived benefits, and perceived susceptibility significantly predicted eco-driving behavior. The questionnaire may support research, intervention design, and policy planning to reduce transport-related air pollution.
High-harmonic generation (HHG) is an extreme form of frequency upconversion that facilitates light-source engineering and ultrafast materials spectroscopy. Here, we broaden the spectroscopic scope of HHG, by demonstrating polarization manipulation of harmonic light in a dielectric, using a two-color field configuration that combines a midinfrared (MIR) driver with a terahertz (THz) perturbation. By varying the relative polarization axes of these fields, the emitted harmonics can be tuned to exhibit either linear or elliptical polarization. Supported by first-principles theory and semiclassical analysis, we show that our approach enables crystal-momentum-resolved dipole-vector spectroscopy across different bands. Crucially, we show that for certain field configurations, harmonic light emission will originate from electron-hole pairs created away from the minimum band gap. Furthermore, crossing MIR and THz polarization at oblique angles generates elliptically polarized harmonics whose microscopic origin is traced to the phase and amplitude imbalances of electron-hole trajectories released during adjacent half-cycles of the MIR field. Our Letter demonstrates new spectroscopy capabilities of HHG, deepens the contemporary microscopic understanding of the process, and paves the way for full polarization control of the harmonic light.
Neuroscientific studies rely heavily on a-priori hypotheses, which can bias results toward existing theories. Here, we use a hypothesis-neutral approach to study category selectivity in higher visual cortex. Using only stimulus images and their associated fMRI activity, we constrain randomly initialized neural networks to predict voxel activity. Despite no category-level supervision, units in the trained networks act as detectors for semantic concepts like 'faces' or 'words', providing solid empirical support for categorical selectivity. Importantly, this selectivity is mostly maintained when training the networks without images that contain the preferred category, strongly suggesting that selectivity is not domain-specific machinery, but sensitivity to generic patterns that characterize preferred categories. The ability of the models' representations to transfer to perceptual tasks further reveals the functional role of their selective responses. Finally, our models show selectivity only for a limited number of categories, all previously identified, suggesting that the essential categories are already known.
Uranium mononitride (UN) is a promising advanced nuclear fuel, yet its deployment is hindered by rapid oxidation in hydrothermal accident scenarios. Current mitigation strategies compromise its advantageous properties. Here, using first-principles calculations within the framework of Hubbard-corrected density-functional theory, we reveal a facet-dependent oxidation resistance in UN that provides an intrinsic solution. We find that the N-terminated (111) surface is thermodynamically dominant over the commonly studied (100) facet under ambient conditions, exhibiting a remarkably low surface energy of 0.36 J/m2. More critically, oxygen adsorption is thermodynamically forbidden on this (111) facet (adsorption energy: +0.68 eV), in stark contrast to the strongly binding (100) surface (-1.57 eV). This inertness originates from a surface reconstruction that strengthens U-N bonding and induces a U5+-like electronic state. Our work establishes a foundational design principle: engineering a strong ⟨111⟩ texture is the key to fabricating oxidation-resistant UN fuels without sacrificing performance.
The construction of cofferdams poses significant challenges in hydraulic engineering, where optimized design is essential for risk mitigation and construction safety. This study investigates the performance of a Larssen pile-reinforced cofferdam in a field ridge remediation project through integrated numerical modeling and limit equilibrium analysis. A coupled hydro-mechanical model was established, incorporating saturated-unsaturated seepage theory and an elastic-plastic constitutive model to simulate groundwater movement and slope stability. The results demonstrate that the Larssen sheet piles serve as an effective subsurface barrier, significantly impeding both soil and water flow. The implemented seepage cutoff measures induced a substantial hydraulic head difference across the cofferdam, leading to a marked reduction in both hydraulic gradient and water flux. Notably, simulated vertical displacements at pile tops during groundwater drawdown showed strong agreement with field measurements. The magnitude and variation trend of vertical displacements at the pile tops closely match field measurements during groundwater drawdown. Underwater side filling causes minimal pile-top settlement, while top filling results in greater settlement. Additionally, the zone of maximum ground surface settlement migrated from the cofferdam top toward the downstream slope-face. These results suggest that well-engineered Larssen sheet pile reinforcement can effectively control seepage, enhance the slope stability, and improve the structural integrity of earth-rock cofferdams.
Executive functions (EFs) are related to academic performance, but interventions designed to leverage EFs to improve academic outcomes have shown inconsistent results. Academic achievement is also subject to layers of personal, social, task-relevant, and cultural contexts, all of which impact students' EF engagement in any given task. The EF+Math Program explored the potential for EFs to positively impact math achievement by supporting inclusive research and development of EF learning approaches that are contextually embedded and attend to the needs of diverse students. Researchers, developers, and educators worked collaboratively across a portfolio of projects to co-design and evaluate learning approaches for middle school mathematics classrooms. Here we share examples of our contextualized research, and our updated conceptualizations of EFs arising from shared insights. We propose an updated, integrated framework for EF assessment and intervention in the context of middle school mathematics achievement. We conclude with calls to action for continuing inclusive research and development to advance insights across, theory, assessment, and intervention in EF research.
The selective separation of uranium from acidic radioactive wastewater is critical for mitigating environmental hazards and safeguarding public health. Cellulose-based adsorbents have the advantages of abundant resources, low cost and degradability, but there is a problem of low adsorption efficiency for uranium under acidic conditions. Here, we present a cellulose-based adsorbent with engineered phosphate-amidoxime dual functionalities (PAFC) that overcomes the problem through molecular-scale coordination design. Leveraging the complementary binding affinities of phosphate and amidoxime groups, PAFC achieves exceptional uranium adsorption performance across broad pH adaptability (3-9), exhibiting >96% removal efficiency at 100 mg·L-1 uranium within 60 min. The material maintains 76% removal efficiency after ten regeneration cycles. X-ray photoelectron spectroscopy analyses and density functional theory calculations reveal that phosphate groups effectively suppress the protonation of amidoxime groups under acidic conditions, and their synergistic interaction significantly enhances the UO22+ adsorption binding energy compared to systems containing only a single functional group. This study provides a general paradigm for developing cellulose-based adsorbents through rational dual-functional modification with phosphate and amidoxime groups, addressing the critical need for sustainable nuclear wastewater remediation technologies.
Drawing on self-determination theory, this study develops a dynamic predictive framework to examine how basic psychological need satisfaction prospectively predicts subjective well-being among Chinese undergraduates and how self-determined motivation operates as a boundary condition. Five waves of panel data were collected from 547 students at four eastern Chinese universities across one academic year, with measurements spaced approximately ten weeks apart. Latent growth curve modeling combined with cross-lagged panel analysis revealed that need satisfaction at one wave reliably forecast subsequent well-being beyond autoregressive carryover, with autonomy exerting the strongest contemporaneous influence and relatedness contributing more enduring lagged effects. The interaction between lagged need satisfaction and the relative autonomy index was statistically reliable, and Johnson-Neyman analysis localized a non-significance interval of [-2.81, 1.93], outside which the predictive association held for roughly 71% of participants. Conditional slopes rose from 0.092 at low motivation to 0.418 at high motivation, suggesting that self-determined regulation statistically conditions, rather than uniformly amplifies, the need-well-being association. Robustness checks across alternative measures, estimators, and demographic strata supported the stability of these patterns. Because the design is observational, we read these patterns as temporally ordered associations rather than causal effects; traditional and random-intercept cross-lagged specifications, together with longitudinal measurement invariance tests, converged on the same qualitative picture. The findings add a dynamic, within-academic-year perspective to self-determination theory and inform mental health practice in Chinese higher education by suggesting that need-supportive interventions are most effective when paired with attention to motivational quality.
Government digital humans represent a significant application of the combination of generative artificial intelligence and digital governance. This study aims to explore the factors influencing public acceptance of government digital humans, providing insights for enhancing their services and user experience. By integrating expectation confirmation theory (ECT) and the technology acceptance model (TAM), this study expands upon ECT by incorporating additional user perception factors. A structural equation model is employed to explain the influencing factors and their interrelationships, while a mediation effect model tests the mechanisms of these factors. The findings indicate that public expectation confirmation enhances satisfaction with government digital humans, which positively impacts acceptance intention. Perceived information quality, perceived intelligence, perceived convenience, perceived attractiveness, perceived usefulness, and artificial intelligence trust collectively mediate the relationship between public expectation confirmation, satisfaction, and acceptance intention. The findings highlight the importance of enhancing the perceived usefulness, perceived intelligence, perceived information quality, and perceived attractiveness of government digital humans. Relevant departments should focus on improving these attributes to optimize digital government services and enhance user experience and utilization rates.