Airway Obstruction as a Rare Complication of Halo-Pelvic Traction in a Patient with Severe Scoliosis: A Case ReportYibing Liu1, Zhenzhuo Zhang1, Guomao Zhu1, Jinqian Liang2, *

The UK aims to dramatically accelerate the establishment of new woodlands by 2050, yet the impact of different afforestation strategies on landscape genetic diversity and resilience remains unclear. This study integrates environmental data, whole-genome sequencing, and phenotypic assessments to compare bioclimatic envelopes, genetic diversity and plant health indicators in naturally colonised versus planted populations of pedunculate oak and silver birch. We found that registered seed stands significantly underrepresent the wild bioclimatic envelopes of both species, as well as those of 21 other UK native species targeted for afforestation, potentially limiting adaptive diversity in planted populations. Yet, genetic diversity metrics (π, HO and AR) based on genome-wide markers in planted populations were comparable to naturally colonised woodlands. Planted populations exhibited higher within-group coancestry and moderate genetic homogenisation among sites, possibly reducing adaptive differentiation. Naturally colonised populations showed higher inbreeding coefficients (FROH) in both species, potentially due to fragmentation of source populations. Genotype-environment associations based on redundancy analysis revealed divergent selection at functionally relevant loci, indicating distinct selective pressures in commercial tree production versus natural colonisation. Differences in pest incidence indicators at afforestation sites further suggested potential selection divergence between afforestation strategies. These findings support a role for both afforestation strategies in enhancing the resilience of future woodlands, while highlighting pathway-specific risks of introducing unintended impacts on forest diversity.

Background: Ireland’s General Practitioner-led abortion care system was introduced in 2019 and integrated into existing primary care infrastructure. Objectives: To examine the enabling environment that translated abortion legislation into service delivery. The review identifies barriers and facilitators from the perspectives of abortion seekers, providers, and the healthcare system. Search Strategy: A systematic literature search was conducted in March 2024 across EBSCOhost, PubMed, the Cochrane Library, and Medscape. Of 150 citations screened, 41 met inclusion criteria. The review followed JBI’s convergent integrated approach. Selection Criteria: Eligible studies addressed the operationalisation of abortion care in Ireland, including barriers and enablers. Inclusion required methodological rigour and relevance. No language restrictions applied. CASP, MMAT and JBI tools facilitated appraisal. Data Collection and Analysis: Data were extracted using JBI tools. Quantitative findings were “qualitised” into narrative form and synthesised with qualitative data. Thematic integration was performed across three domains: abortion seekers, providers, and the healthcare system. Key grey literature, including the HSE-commissioned UnPAC study, was also reviewed and informed thematic synthesis. Findings were included if supported by credible or unequivocal evidence. Main Results: Abortion seekers require individualised, compassionate care. Social disadvantage amplifies harm within rigid legal constraints. Providers included enablers and direct deliverers; supporting individual pioneer champions was crucial. Criminalisation and absence of safety zone legislation were chilling factors. Key enablers included hospital support, clinical guidelines, START training, and value clarification workshops. Hospital settings engender more layers of stigma, conscientious objection, and medicolegal complexity. Conclusions: A collaborative, evidence-based service has been implemented, but legislative limitations and persistent qualitative barriers remain. Funding: No funding was received for this study.

Objectives Determine the relationship between pre-induction fetal fibronectin (fFN) and time to delivery in low-risk patients undergoing induction of labor at term. Design FFN, transvaginal cervix length, and Bishop scores were measured prior to induction of labor. Results were analyzed in regard to time to delivery. Setting Arrowhead Regional Medical Center in Colton, CA from July 2021 to June 2022. Population Pregnant women at 39 0/7 to 41 0/7 weeks admitted for induction of labor. Methods After enrollment, fFN samples were collected. Next, transvaginal cervical length was measured with ultrasound, followed by a cervical exam to establish Bishop score. Induction was begun within 1 hour of fFN collection with a 30 mL Foley transcervical balloon catheter in combination with 25 mcg misoprostol or titratable intravenous oxytocin. After Foley balloon expulsion, labor management continued under the discretion of the attending physician. Main Outcomes fFN relationship to time of delivery. Results In 145 studied patients, there was a significant relationship between fFN levels and time to delivery, with each 50 ng/mL increase in fFN corresponding to 17% (95% CI = 0-36%) and 31% (95% CI = 3-68%) higher odds of delivering within 24- and 36-hours, respectively. Based on a sample of 10 patients with fFN >450 ng/mL, a high PPV for delivery within 24 hours was observed (86% in nulliparous (95% CI = 42-100%), and 100% in parous patients (95% CI = 48-100%)). Conclusion There is a significant relationship between fFN and time to delivery in low-risk term inductions.

Parkinson's Disease (PD), a progressive neurodegenerative disorder, poses significant diagnostic challenges, particularly in its early stages. This study proposes a novel hierarchical neurosignal processing framework to enhance the accuracy and timeliness of PD diagnosis. Leveraging multilevel signal decomposition, feature extraction, and machine learning classification, the proposed methodology systematically analyzes neurophysiological signals-such as EEG and EMG-capturing both spatial and temporal characteristics relevant to PD biomarkers. The hierarchical structure allows for refined signal denoising and targeted feature mapping across different neural frequency bands, significantly improving signal fidelity and diagnostic specificity. Experimental validation on benchmark datasets demonstrates that the proposed approach outperforms conventional flat processing models in terms of classification accuracy, sensitivity, and robustness. These findings suggest that hierarchical neurosignal processing can serve as a powerful diagnostic tool, paving the way for early intervention strategies and improved patient outcomes in Parkinson's Disease management.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder characterized by motor dysfunction, cognitive decline, and various non-motor symptoms. Early detection and accurate monitoring of the disease remain critical for effective intervention and management. Recent advancements in machine learning (ML) have shown promise in identifying biomarkers that can assist in the diagnosis and progression tracking of Parkinson's Disease. This study explores the role of hierarchical machine learning models in decoding PD biomarkers, aiming to improve predictive accuracy and interpretability. Hierarchical models, which organize data processing in a multi-level structure, allow for better integration of complex, multi-dimensional biomedical data, such as neuroimaging, genetic, and clinical information. By employing techniques like deep learning, decision trees, and ensemble methods, hierarchical models can capture intricate relationships and patterns that traditional ML models often overlook. This research examines how hierarchical approaches enhance the identification of key PD biomarkers, including those associated with motor symptoms, neurodegeneration, and cognitive impairment. Furthermore, the study discusses the potential for these models to offer personalized predictions, paving the way for more tailored treatments. The results demonstrate that hierarchical machine learning models can significantly improve diagnostic accuracy, offering new insights into the molecular and physiological underpinnings of Parkinson's Disease.

Background and Purpose: Metabolic dysfunction-associated fatty liver disease (MAFLD) pathogenesis involves gut microbiota dysbiosis. This study investigated pseudolaric acid B (PAB), a diterpenoid from Pseudolarix kaempferi, for its therapeutic potential in high-fat diet-induced MAFLD mice. Experimental Approach: The effects of PAB on MAFLD were evaluated in a high-fat diet -induced C57BL/6J mouse model. The regulatory impact of PAB on the gut microbiota and metabolites was explored by 16s rRNA sequencing and metabolomics analysis. Fecal microbiota transplantation experiments further validated these results. Key Results: PAB treatment significantly improved body weight, liver index, serum biochemical indices, and histopathological damage in MAFLD mice. Gut microbiota analysis revealed PAB significantly reduced g_Faecalibaculum, g_Allobaculum, g_Ileibacterium, and g_Dubosiella abundance. Metabolomic profiling demonstrated PAB treatment increased the level of the microbiota-derived tryptophan metabolite, cinnabarinic acid (CA). The CA content was negatively correlated with the abundance of the four bacteria identified above. Fecal microbiota transplantation validated gut microbiota’s causal role in PAB’s therapeutic effects. Mechanistically, PAB activated the aryl hydrocarbon receptor (AhR)— CA’s target receptor— subsequently upregulating IL-22 expression and triggering JAK1/STAT3 signaling. This cascade suppressed key fatty acid synthesis regulators: SREBP-1c, ELOVL6, Acc, Fasn, and Scd1 at both mRNA and proten levels. Conclusion and Implications: PAB as a promising prebiotic agent against MAFLD through gut microbiota modulation, CA/AhR/IL-22 axis activation, and inhibition of hepatic lipogenesis pathways. The study elucidates a novel mechanism where microbial metabolite CA mediates PAB’s hepatoprotective effects via AhR — dependent signaling.

Recombinant adeno-associated viruses (rAAVs) play a pivotal role in gene therapy, yet the molecular interactions underlying their production in host cells remain incompletely understood. Non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and small nucleolar RNAs (snoRNAs), are increasingly recognized as key regulators of viral and cellular processes. This study investigates the dynamic expression profiles of miRNAs and snoRNAs during rAAV plasmid transfection and vector production in HEK293F cells. A total of 142 miRNAs were differentially expressed during the peak phase of rAAV production, with 128 associated with the Gene Ontology term ‘viral process’, indicating broad involvement in host-virus interactions. Target gene analysis linked these miRNAs to biological pathways such as nucleocytoplasmic transport, innate immunity, apoptosis, and transcriptional regulation, highlighting their potential roles in shaping the cellular environment during viral vector assembly. In contrast, snoRNAs exhibited more modest changes in expression, yet five were significantly differentially expressed during active production, suggesting a possible, underexplored involvement in viral replication. These findings illuminate the underexplored contributions of ncRNAs to the host response during rAAV biogenesis and provide a valuable resource for understanding how cellular regulatory networks are engaged throughout vector production.

With the ever growing technological advancements, socio-political and economical changes, fueled by sustainable development goals, the automobile industry today is reorienting towards the Electric Vehicles (EVs). Its vision includes transportation safety, a cleaner environment, and improved economic growth, among others. The EVs are today equipped with onboard equipment that makes them progressively intellectual. Further, to support EVs, stakeholders viz., manufacturers, and governing bodies have created a network of infrastructure (like charging stations, roadside communication devices), power grids, etc. These interconnections have created a complex cyber-physical association that are prone to cyber-attacks. In the recent past, several works have been carried out to address these challenges. However, there is a gap in these works towards the understanding of roles and requirements of the stakeholders in this nexus of connected vehicles and infrastructure. In this regard, our aim in this paper is to provide an overview of the roles and requirements of various stakeholders towards a safe cybersociety with respect to EVs. In addition, we conducted a comprehensive review of existing literature to identify the gaps and put forth an exemplary future direction through macro-segmentation of network traffic flow data to mitigate a few of the cyber-attacks. The views presented in this work will enhance the ongoing research efforts on the cybersecurity of EVs, their components, and stakeholders against cyberattacks.

SYNTH-RL (Self-Yielding Neural Transformer Hierarchy for Reinforcement Learning)

This paper introduces Quantum-Grounded Joint Embedding Predictive Architectures (QG-JEPA), a novel paradigm that integrates quantum computing with advanced AI architectures to create systems fundamentally connected to physical reality. We present a theoretical framework and algorithmic implementations that enable AI systems to interact with and model the world at the quantum level. Our approach addresses the fundamental limitations of current AI models by grounding representations in quantum reality rather than symbolic approximations. We demonstrate through mathematical analysis and simulated examples that QG-JEPA can capture quantum phenomena that classical architectures inherently cannot represent, leading to qualitatively superior world models and predictive capabilities.

Molecular diffusion represents one of the critical mass transfer mechanisms of water-gas-oil in subsurface shale porous media with complex fracture networks (CFNs). The diffusion coefficient is a crucial parameter governing the mass transfer by molecular diffusion. Nonetheless, there remains insufficient evidence to ascertain the optimal empirical correlation for predicting diffusion coefficients. This paper, therefore, presents an improved Embedded Discrete Fracture Model (EDFM) to determine the optimal empirical correlation that calculates the diffusion coefficients. The improved EDFM firstly integrated four widely used empirical correlations. Taking the experimental results as a reliable reference, the optimal empirical correlation was identified by comparing the calculation predicted by the improved EDFM. The comparison results reveal that the extended Sigmund empirical correlation is the optimal method for calculating diffusion coefficients in subsurface shale porous media with CFNs. Based on that, an in-depth investigation was performed into the mass transfer of water-gas-oil considering molecular diffusion during hydrocarbon gas huff ’n’ puff. The results indicate that molecular diffusion reduces the convective mass transfer rate (C-MTR) of injected components from connected natural fractures (CNFs) to the matrix during both the injection and soaking stages. However, the total mass transfer rate (T-MTR) is enhanced. For non-injected components, molecular diffusion has a minimal effect on the C-MTR. However, during the production stage, molecular diffusion enhances the T-MTR of lighter non-injected components from the matrix to the CNFs. This paper establishes a theoretical foundation for comprehensive investigation of mass transfer of water-gas-oil considering molecular diffusion in subsurface shale porous media with CFNs.

Background:Nasal airway obstruction (NAO) remains diagnostically challenging due to limitations in traditional tools like rhinomanometry and acoustic rhinometry. Computational Fluid Dynamics (CFD) offers patient-specific, quantitative airflow metrics, but clinical adoption is hindered by methodological heterogeneity. This review synthesizes evidence for CFD’s superiority, proposes the first consensus-driven standardization framework, and outlines actionable pathways for clinical integration. Methods:A PRISMA 2020-compliant systematic review identified 32 studies (2009–2023) using patient-specific CFD to assess nasal airflow. Meta-analytic pooling of correlation coefficients (CFD vs. traditional tests) and qualitative synthesis informed a clinically oriented standardization framework. Results:CFD metrics (mucosal heat flux, airflow partitioning) demonstrated stronger correlations with subjective nasal patency (pooled r = 0.78, 95% CI: 0.69–0.85; I2 = 32%, low heterogeneity) than traditional methods ( r = 0.42, 95% CI: 0.31–0.53; I2 = 67%, moderate heterogeneity). Virtual surgery predictions aligned with postoperative outcomes in 85% of cases. The proposed framework standardizes CFD workflows, validation protocols, and reporting criteria. Conclusions: CFD is poised to redefine sinonasal diagnostics. This framework bridges technical and clinical gaps, offering a roadmap for regulatory approval, cost-effective implementation, and AI-driven automation.

This study numerically investigates dual-phase thermal and concentration relaxation effects on magneto-viscoelastic thermally radiating Williamson nanofluid flowing over a permeable stretching surface in 2D and steady-state conditions using the Cattaneo–Christov flux model with consideration of the effects of viscous dissipation and joule heating. The governing PDEs have been first transformed into a highly nonlinear coupled ODEs via suitable similarity variables. The resultant equations are solved with a two-step high-accuracy spectral quasilinearization method, whose accuracy and convergence are rigorously validated against previously published results. This allows for an in-depth investigation of how the key parameters including the Weissenberg number, velocity ratio, suction/injection, magnetic field, radiation, and relaxation times influence the characteristics of the boundary layer. Results show that thermal relaxation suppresses heat transfer by delaying thermal diffusion, while concentration relaxation enhances mass transfer through sharper concentration gradients. The velocity ratio increases skin friction drag, velocity, and rate of heat flow but reduces rate of mass flow; suction thins boundary layers to promote heat transfer, whereas injection amplifies velocity and temperature profiles, further boosting heat transfer. Viscoelasticity lowers skin friction and mass transfer but can enhance heat transfer, and magnetic and viscous dissipation effects further improve mass transfer. This work demonstrates the superiority of the Cattaneo–Christov model over classical Fourier and Fick laws in capturing finite-speed relaxation phenomena, offering critical insights for optimizing thermal and mass transport in engineering applications involving non-Newtonian nanofluids.

Reward and motivation are two cognitive processes that drive goal-directed behaviors and are closely linked to the development of psychopathology. One behavioral measure of motivation is effort expenditure - the amount of effort exerted to attain a desirable outcome. In adults, higher effort expenditure has been associated with heightened neural responses to reward cues but diminished responses to reward anticipation and feedback. However, it is unclear whether similar patterns exist in early adolescence, a critical period for the development of reward- and motivation-related processes. Using event-related potentials, we examined the effort-reward relationship in a community sample of 92 10-to-13-year-olds (53 females, Mean/ SD of age = 12.06/1.20 years). Unlike adults, greater effort increased youths’ attention to both reward cues and reward anticipation, indexed by a larger cue-elicited P3 and a larger stimulus-preceding negativity (SPN), respectively. Similar to adults, higher effort was associated with lower valuation of reward feedback, as reflected by a smaller reward positivity (RewP). Effort did not influence the feedback-elicited P3 or late positive potential (LPP). These adolescent-specific patterns may reflect the earlier maturation of the reward system relative to the cognitive control processes during adolescence. Our findings of the neural substrates of effort-based reward processing will contribute to our mechanistic understanding of developmental psychopathology, as alterations in motivation and reward processes have been implicated in conditions such as adolescent depression.

Long-term studies are tools used to study ecological and climatic processes in lotic ecosystems. This study analyzed the long-term hydrochemical dynamics in the Lower Paraíba sub-basin, one of the sub-basins that make up the Paraíba do Sul River basin (PSR), over 10 years (2009-2019), between a period before and after a severe drought, in order to identify its controlling factors. The association between discharge and hydrochemical parameters, estimates of annual flows and the relationship of parameters with the El Niño Southern Oscillation (ENOS) index were considered in the analysis. The results highlighted: 1) The hydrochemical parameters showed a differentiated response in relation to the discharge considering the period before and after the severe drought; 2) The flow had its efficiency reduced in drought events, impacting the speed of transport and river dilution, which led to a reduction in the load of materials transported; 3) The occurrence of ENOS phenomena showed an influence on the hydrochemical dynamics at the end of the RPS basin, through changes in the local seasonality and this in the internal processes of the river channel, intensified by human activities on the soils of the basin. The results suggest that the increased occurrence and intensity of climatic phenomena will lead to changes in hydrochemical and transport patterns along the longitudinal axis of the PSR, highlighting the need for studies to understand their impacts on the availability of water resources, especially in tropical rivers.

Introduction: The COVID-19 pandemic has exerted a profound global impact, extending beyond the acute phase to a condition known as long COVID, characterized by persistent symptoms following the initial infection. In Brazil, particularly in the Amazon region, the prevalence of long COVID remains high, with affected individuals exhibiting a wide range of multisystemic symptoms. This study aimed to investigate the clinical sequelae associated with long COVID, with a focus on the roles of vaccination and other contributing factors. Objectives: To assess the long-term sequelae of COVID-19 in residents of the Brazilian Amazon, comparing outcomes between vaccinated and unvaccinated individuals, as well as between those who were hospitalized and those who were not. The study also examined the relationship between symptom severity and time since infection and evaluated the effects of different vaccine types and the number of doses received. Materials and Methods: This cross-sectional, observational study was conducted between 2023 and 2024 and included 300 patients diagnosed with long COVID. Participants were stratified based on vaccination status, history of hospitalization, duration since acute infection, and symptom severity. Data on demographics and clinical presentation were collected through structured interviews and review of electronic medical records. Statistical analyses were performed using Jamovi software, employing appropriate inferential tests and multivariate logistic regression models. Results: Fatigue, headache, and muscle weakness were the most commonly reported symptoms, particularly among hospitalized individuals and those with higher symptom burden. Vaccination was associated with a reduction in symptom prevalence, with the most pronounced effect observed among recipients of the CoronaVac vaccine. Furthermore, a dose–response relationship was identified, wherein a greater number of vaccine doses correlated with decreased symptom frequency. Conclusion: Long COVID is a multifaceted condition marked by prolonged symptoms, especially among individuals with severe initial disease. Vaccination, particularly with multiple doses, was associated with reduced symptom burden. Nonetheless, fatigue and headache remained prevalent, underscoring the need for continued research into the underlying pathophysiology and development of targeted therapeutic strategies, particularly within the unique context of the Amazon region.

Introduction: Gastroenteritis is a major public health concern, particularly in low-income countries. Adenovirus types 40 and 41 are associated with diarrhoea in humans. Approximately 1.7 billion cases of diarrhoea occur each year, with over 525,000 deaths among young children globally. This study aims to determine the prevalence of adenoviruses and astroviruses in patients and marine animals in the Republic of Guinea. Methods: Biological samples were collected from three localities (Kindia, Gueckédou, and Conakry) and analysed using qPCR and RT-PCR. Results were examined based on the type of diarrhoea, age, gender, season, and socio-professional category. Statistical analysis was performed using the z-test to evaluate the significance of observed differences. Results: Positivity rates varied by location: Kindia showed the highest prevalence, particularly in liquid diarrhoea (7.41%). Adenoviruses were more frequent (33.23% of 250 samples), while astroviruses were detected in only 0.71% of cases. Adenovirus prevalence was higher during the rainy season and among children (8.19%), followed by young adults (5.26%). Differences based on gender and socio-professional categories were not statistically significant (p=0.52). One case of astrovirus (out of 40 samples) was detected in marine animals. Conclusion: The study highlights a significant circulation of adenoviruses, especially among children and during the rainy season. The results suggest that environmental and sanitary factors may influence transmission.

Abstinence from drug use is often the result of the associated negative consequences. However, relapse occurs in a large proportion of abstinent users, and the underlying brain mechanisms are not clear. An arguably relevant brain area is the posterior paraventricular nucleus of the thalamus (pPVT), which plays a role in motivational processes and addiction-like behaviours. Using a punishment-imposed abstinence procedure, we assessed the effect of chemogenetic inhibition and excitation of the pPVT on food-deprivation-induced relapse to heroin seeking in male and female rats. Rats were trained to self-administer heroin (0.1 mg/kg/infusion) for 2 weeks under a seeking-taking chain schedule. For punishment-imposed abstinence, a mild footshock (0.2 to 0.6 mA) was delivered on 30% of the completed seek lever links instead of access to the take lever. Relapse to heroin seeking was tested after 24 h of food deprivation and under sated condition. Animals were injected (i.p.) with either a DREADD ligand or vehicle 15-20 min before the tests. There was no sex difference in heroin self-administration or punishment-imposed abstinence. Under the food-deprivation condition, chemogenetic inhibition of the pPVT resulted in a statistically significant increase in heroin seeking compared to the control group, only in male rats. Chemogenetic excitation of the pPVT resulted in a non-significant increase in heroin seeking under food-deprivation conditions, only in male rats. Our results suggest that the pPVT is involved in stress-induced heroin seeking, after punishment-imposed abstinence.

Text：Metal–organic halides (MOHs) are considered to be promising luminescent materials. Owing to heavy atom effect combined with their inherently ordered and rigid frameworks, supramolecular MOHs frameworks could exhibit ultra long room-temperature phosphorescence (RTP) with high PL quantum yield (PLQY). Here, we report the crystalline MOH supramolecular frameworks that exhibit persistent RTP with lifetime as long as 1.01 s. DFT calculations revealed the RTP emission originates from the intraligand charge transfer. Bright afterglow with various emission duration for the investigated materials can be identified by the naked eyes. Based on their special dual emission and regulatable afterglow properties, a time-resolved anti-counterfeiting technique is proposed and avoids the over-dependence on equipment, which further provides feasible design strategy of advanced portable anti-counterfeiting technology.

Achieving crystalline and efficient charge transport channels control through self-assembly engineering represents a highly effective strategy for the synthesis of organic supramolecular photocatalysts. Herein, the self-assembled crystalline iron phthalocyanine nanorods (Self-PcFe) with Fe-O-Fe rapid photogenerated charge carrier transfer pathway was synthesized. Self-PcFe demonstrated a photocatalytic Cr(VI) reduction and phenol degradation was 5.00 times and 13.76 times greater than commercial PcFe(II). This significantly enhanced activity is primarily attributed to the dissolved PcFe molecules, which support effective π–π stacking leads to its solid-state crystalline out and continuous liner long-range electron transfer pathways through Fe- O-Fe bridging bonds. Furthermore, for the multi-pollutant, the photocatalytic Cr(VI) reduction rate enhanced 2.835 times. Additionally, the efficient Fe³⁺/Fe²⁺ conversion in Self-PcFe significantly enhances the utilization of H₂O₂. The Photo-Fenton system by Self-PcFe substantially improves the removal efficiency of phenol and Cr(VI) composite pollutants from wastewater. The phenol degradation by the Photo-Fenton process is 8.61 times than that of photocatalysis. Interestingly, the Cr(VI) reduction in the Photo-Fenton process are 5.35 times higher than that of Fenton system. This research presents a novel strategy for modulating π–π stacking to optimize crystalline and charge transport in supramolecular photocatalysts. It also provides a scalable solution for simultaneous organic-heavy metal pollutant remediation.

Understanding the reproductive ecology of birds and the factors influencing nest predation is essential for developing scientifically sound and effective bird conservation strategies. Certain pheasant species sensitive to environmental changes are vulnerable to threats and face survival pressures such as habitat destruction and human activities. However, research related to their reproductive ecology is lacking. This study was conducted during the breeding seasons of the Chinese francolin (Francolinus pintadeanus) in 2021, 2023, and 2024, combining traditional survey and infrared camera technology to monitor its reproductive ecology and nest predators in the Datian and Bangxi Reserves and to identify its potential predators through artificial nest experiments. All nests were open-ground nests located at the roots of dwarf shrubs and grasses. Our findings revealed that the breeding season of the Chinese francolin was mainly in March–September, peaking in May; its clutch size was 4.10±1.30 (n=21), reproductive success was 28.57%, predation rate was 71.43%, and nest abandonment rate was 57.14%. In artificial nest experiments, the predation rates of Datian Reserve and Bangxi Reserve were 70.91% and 60.00%, respectively, with no significant difference in predation rates between the fully-covered and exposed groups (Datian: χ²=0.258, P=0.612; Bangxi: P=0.710). Natural nest monitoring and artificial nest experiments on the Chinese francolin identified snakes and the small Indian civet (Viverricula indica) as the main predators in Datian Reserve, as well as the greater coucal (Centropus sinensis) and wild boar (Sus scrofa) as potential predators. In contrast, the main predators in Bangxi Reserve were snakes and rodents. These findings indicate differences in nest predator taxa between the two reserves. We recommend prioritizing the restoration of dwarf scrub vegetation and optimizing the habitat management strategy in these reserves to better protect the breeding habitats of pheasants while promoting long-term stability and continuation of their populations.

RNA-Puzzles was launched in 2011 as a collaborative effort dedicated to advancing and improving RNA 3D structure prediction. The automatic evaluation protocols for comparisons between prediction and experiment developed within RNA-Puzzles are applied to the 2024 CASP16 competition. The scores evaluate stereochemical parameters, Watson-Crick pairs, non-Watson-Crick pairs and base stacking in addition to standard global parameters such as RMSD, TM-score, GDT, or lDDT. Several targets were particularly difficult owing to their size or multimerization. Although predictions with good metrics on secondary structure can yield acceptable global folds, these are not sufficient to guarantee the chemical precision, or the correct identification of residues involved in the non-Watson-Crick interactions that are critical for the three-dimensional architecture and the biological function of the RNAs.

Construction industry is inherently labor intensive, operates under tight constraints, faces significant delays based on queue congestions at site entrances impacting overall project efficiency exacerbated under unusual conditions such as Covid-19 outbreak. This study presents a simulation-based framework to optimize queue dynamics in construction sites, utilizing digital technologies and bottleneck analysis to improve efficiency and facilitate real-time decision making. Using Arena simulation platform, interarrival times were analyzed for pre-Covid period 16% of an hour, and 28% of an hour for post-Covid. Results showed that interarrival time of workers to iris scan point is lognormal distributed during pre-Covid and exponentially distributed for post-Covid (p<0,005). By capacity doubling (iris scan point, x-ray, passport control, hygiene points) at critical check points, the resource usage rates and interarrival times can be reduced. The findings provide a foundation for advancing construction management methodologies by evidencing the practical impact of data-driven and simulation-based algorithms. For industry practitioners, this framework provides actionable insights into optimizing labor productivity and project efficiency and streamlining daily construction activities. The study offers actionable insights for practitioners and adaptations for software developers, promoting sustainable and resilient construction management.