bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing phase sensitivity in a Mach-Zehnder interferometer with various detection schemes using SU(1,1) coherent states
Authors: Abouelkhir N.-E. , Slaoui A. , Saidi E. H. , Ahl Laamara R. , El Hadfi H.
Published: December 2024

Improving interferometric phase sensitivity is crucial for high-precision measurements in rapidly developing quantum technologies. The Mach–Zehnder interferometer (MZI) is a versatile tool for analyzing this phenomenon. By splitting and recombining a light beam using beam splitters, the MZI allows for precise phase sensitivity analysis using tools like the quantum Cramér–Rao bound (QCRB) and the quantum Fisher information (QFI). This paper analyzes the phase sensitivity of an MZI in various scenarios using different detection schemes and input states. We compare the single- and two-parameter quantum estimation and their associated QCRB for three phase-shift situations: in both arms; only in the upper arm (asymmetric); and in both arms symmetrically. We then investigate the phase sensitivity under three detection schemes: intensity difference; single-mode intensity; and balanced homodyne. Additionally, we explore the use of Perelomov and Barut–Girardello coherent states, two types of SU(1,1) coherent states, in all scenarios. Notably, we demonstrate that, under optimal conditions, all detection schemes can achieve the QCRB by utilizing SU(1,1) coherent states as input states.
Chirp asymmetry in Zeeman electromagnetically induced transparency
Authors: Gorkos Joseph , Grenzig Karsten , Nasirzadeh
			Orang Erfan , Thomas Victoria , Tighe Declan , Crescimanno Michael
Published: December 2024

The simplest three-level system exhibiting electromagnetically induced
transparency (EIT) exhibits an effective conjugation symmetry as well as a
transposition symmetry. Breaking the conjugation symmetry leads to a
distinct chirp asymmetry, i.e., the differential response to a frequency
increase versus a frequency decrease. The dynamic range of the many
tunable optical parameters of the Hanle–Zeeman EIT resonance makes it an
ideal platform for testing the chirp asymmetry’s dependence on the
CP-breaking parameter and broadening. We describe the theory and compare
it to an experiment using
87Rb in a buffer gas
cell. In contrast with earlier multi-photon chirp asymmetry work, this
present effort explores the asymmetry at nearly one-billionth the earlier
chirp rate and fully explores its universal features. The chirp asymmetry
may have metrological consequences for understanding systematic dependence
on modulation/demodulation parameters.
Plasmonic resonances of metallic moiré superlattices in the infrared range
Authors: Tian Yuan , Du Yuhan , Sun Zhitong , Yuan Shuangxiu , Wu Wenbin , Shi Zeping , Yuan Xiang , Zhu Xiaolong , Wu Jian
Published: December 2024

The recent surge of interest in moiré photonics arises from the possibility of exploring many groundbreaking physical phenomena in photonics. These phenomena include photonic topological states and magic-angle lasing, which offer an attractive platform for manipulating the flow and confinement of light from remarkably simple device geometries. In this work, we fabricate a series of metallic moiré superlattices supporting moiré plasmon polaritons and explore the moiré-potential induced plasmonic resonances. We demonstrate that two-dimensional moiré plasmonic superlattices exhibit transmittance and polarization-dependent responses because of the localized plasmonic resonances in the infrared range, whose modes have a near-flat dispersion band. Our findings hold the potential for the understanding of localized plasmonic resonances within moiré superlattices.
Method of discontinuous optical glass optimization based on aberration correction in optical design
Authors: Dai Fujian , Feng Huajun , Zhao Wenchao , Xu Zhihai
Published: December 2024

Optical glass selection is an important research object in optical design, which is an important way to aberration correction. However, these methods to our knowledge either do not correct aberration well or consume too much time. To efficiently solve the apochromatic problems in optical design, this paper presents what we believe to be a novel automatic optimization method for discontinuous optical glass based on auto glass selection (AGS). The method first calculates several aberrations related to glass materials in the optical system to select candidate optical glasses, which include longitudinal aberration, lateral chromatic aberration, and Petzval sum. It then skillfully explores the candidate optical glass list for the optimization of discontinuous glass. Using a flat-field apochromatic microscope and a wide-angle lens as examples, this method demonstrates its superiority in aberration correction and efficiency, can be automated optimization, requires relatively less computational effort, and achieves fast and stable convergence of results.
Position detection of elements in off-axis three-mirror space optical system based on ResNet50 and LSTM
Authors: Zhu Deyan , Li Chengcheng , Ao Yongqi , Zhang Yufan , Xu Jiayi
Published: December 2024

Position detection of elements, especially with high-precision and high-efficiency, continue to present challenges in the off-axis three-mirror space optical system to ensure the imaging quality. To test the position of elements in an off-axis three-mirror system, a method based on ResNet50 and Long short-term memory (LSTM) is proposed in this paper. In the proposed method, point spread function (PSF) with different fields of view is extracted as a high dimensional feature vector by the ResNet50 network to achieve high efficiency. The same position information in the high dimensional feature vector from different image plane is obtained by the LSTM module to achieve high precision. To evaluate the performance of the proposed method, three examples of position detection were conducted. The results indicated that the probabilities of achieving detection accuracy better than 10 µm for eccentricity and 10'’ for tilt, across single-dimensional variation of components, multi-dimensional random variation, and detector variation, are 100%, 94%, and 100%, respectively. Unlike the other detection method, the positions of elements are obtained with only one calculation and are close to the final results, which saves both time and resources for adjustment. The findings indicate that the method is an accurate and effective method for detecting position of elements in off-axis three-mirror space optical system.
Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework
Authors: Mohácsi Máté , Török Márk Patrik , Sáray Sára , Tar Luca , Farkas Gábor , Káli Szabolcs
Published: December 2024

Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. In recent years, manual model tuning has been gradually replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently, without any fine-tuning, found good solutions in all of our use cases. By contrast, some other algorithms including all local search methods provided good solutions only for the simplest use cases, and failed completely on more complex problems. We also demonstrate the versatility of Neuroptimus by applying it to an additional use case that involves tuning the parameters of a subcellular model of biochemical pathways. Finally, we created an online database that allows uploading, querying and analyzing the results of optimization runs performed by Neuroptimus, which enables all researchers to update and extend the current benchmarking study. The tools and analysis we provide should aid members of the neuroscience community to apply parameter search methods more effectively in their research.
A multivalent binding model infers antibody Fc species from systems serology
Authors: Abraham Armaan A. , Tan Zhixin Cyrillus , Shrestha Priyanka , Bozich Emily R. , Meyer Aaron S.
Published: December 2024

Systems serology aims to broadly profile the antigen binding, Fc biophysical features, immune receptor engagement, and effector functions of antibodies. This experimental approach excels at identifying antibody functional features that are relevant to a particular disease. However, a crucial limitation of this approach is its incomplete description of what structural features of the antibodies are responsible for the observed immune receptor engagement and effector functions. Knowing these antibody features is important for both understanding how effector responses are naturally controlled through antibody Fc structure and designing antibody therapies with specific effector profiles. Here, we address this limitation by modeling the molecular interactions occurring in these assays and using this model to infer quantities of specific antibody Fc species among the antibodies being profiled. We used several validation strategies to show that the model accurately infers antibody properties and then applied the model to infer previously unavailable antibody fucosylation information from existing systems serology data. Using this capability, we find that COVID-19 vaccine efficacy is associated with the induction of afucosylated spike protein-targeting IgG. Our results also question an existing assumption that controllers of HIV exhibit gp120-targeting IgG that are less fucosylated than those of progressors. Additionally, we confirm that afucosylated IgG is associated with membrane-associated antigens for COVID-19 and HIV, and present new evidence indicating that this relationship is specific to the host cell membrane. Finally, we use the model to identify redundant assay measurements and subsets of information-rich measurements from which Fc properties can be inferred. In total, our modeling approach provides a quantitative framework for the reasoning typically applied in these studies, improving the ability to draw mechanistic conclusions from these data.
Machine learning mathematical models for incidence estimation during pandemics
Authors: Fajardo-Fontiveros Oscar , Mattei Mattia , Burgio Giulio , Granell Clara , Gómez Sergio , Arenas Alex , Sales-Pardo Marta , Guimerà Roger
Published: December 2024

Accurate estimates of the incidence of infectious diseases are key for the control of epidemics. However, healthcare systems are often unable to test the population exhaustively, especially when asymptomatic and paucisymptomatic cases are widespread; this leads to significant and systematic under-reporting of the real incidence. Here, we propose a machine learning approach to estimate the incidence of a pandemic in real-time, using reported cases and the overall test rate. In particular, we use Bayesian symbolic regression to automatically learn the closed-form mathematical models that most parsimoniously describe incidence. We develop and validate our models using COVID-19 incidence values for nine different countries, confirming their ability to accurately predict daily incidence. Remarkably, despite the differences in epidemic trajectories and dynamics across countries, we find that a single model for all countries offers a more parsimonious description and is more predictive of actual incidence compared to separate models for each country. Our results show the potential to accurately model incidence in real-time using closed-form mathematical models, providing a valuable tool for public health decision-makers.
Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer
Authors: Zhang Xin-Yu , Yu Lan-Lan , Wang Wei-Yi , Sun Gui-Quan , Lv Jian-Cheng , Zhou Tao , Liu Quan-Hui
Published: December 2024

Monitoring the spread of infectious disease is essential to design and adjust the interventions timely for the prevention of the epidemic outbreak and safeguarding the public health. The governments have generally adopted the incidence-based statistical method to estimate the time-varying effective reproduction number Rt and evaluate the transmission ability of epidemics. However, this method exhibits biases arising from the reported incidence data and assumes the generation interval distribution which is not available at the early stage of epidemic. Recent studies showed that the viral loads characterized by cycle threshold (Ct) of the infected populations evolving throughout the course of epidemic and providing a possibility to infer the epidemic trajectory. In this work, we propose the Cycle Threshold-based Transformer (Ct-Transformer) to estimate Rt. We find the supervised learning of Ct-Transformer outperforms the traditional incidence-based statistic and Ct-based Rt estimating methods, and more importantly Ct-Transformer is robust to the detection resources. Further, we apply the proposed model to self-supervised pre-training tasks and obtain excellent fine-tuned performance, which attains comparable performance with the supervised Ct-Transformer, verified by both the synthetic and real-world datasets. We demonstrate that the Ct-based deep learning method can improve the real-time estimates of Rt, enabling more easily adapted to the track of the newly emerged epidemic.
Barriers and facilitators to identifying depression in adolescents: A cross-cultural qualitative study in Brazil, Nepal, and Nigeria
Authors: Collins Scott A. , Ottman Katherine , Bohara Jyoti , Neupane Vibha , Viduani Anna , Benetti Silvia , Martini Thais , Buchweitz Claudia , Momodu Olufisayo , Adewuya Abiodun O. , Gautam Kamal , Fisher Helen L. , Kieling Christian , Mondelli Valeria , Kohrt Brandon A. , Wahid Syed Shabab
Published: December 2024

There is growing global interest in early detection and engagement with care for adolescents experiencing depression. However, there is limited information on perceived barriers and facilitators to engagement with adolescent mental health care in low- and middle-income countries. Accordingly, this study examined perceived barriers and facilitators to the identification of depression in adolescents in Brazil, Nepal, and Nigeria. Key informant interviews (n = 153) and focus group discussions (n = 31) were conducted in Brazil, Nepal, and Nigeria with adolescents, parents, health care workers, social workers, teachers, and other stakeholders to explore perceived barriers to identifying depression in adolescents. This study employed a deductive theory-informed codebook enhanced with inductive codes and analyzed using constant comparison by a team of 8 multi-site researchers. The framework approach was used to construct overarching themes and to synthesize a theoretical model. Stigma and a lack of knowledge about the signs and symptoms of depression were perceived as major barriers to the identification of depression across all three settings. Three other themes emerged:(1) role of stakeholders in identifying depression, (2) training or education as a facilitator to identifying depression, and, (3) the role of technology as barrier or facilitator depending on its specific application. Teachers and parents were the primary stakeholders identified as being able to recognize early signs and symptoms of depression in adolescents. Respondents described training through public awareness campaigns, school policy, or social media as interventions to improve depression identification, but noted that social media could also contribute to exacerbating symptoms of depression. These findings suggest there are common perceived barriers and facilitators to the identification of depression in adolescents across diverse cultural contexts. These findings can inform the development of culturally sensitive strategies to address stigma and increase mental health literacy, and ultimately, to improve engagement with treatment and prevention for adolescents experiencing depression.
Performance evaluation of nine reference centers and comparison of DNA extraction protocols for effective surveillance of Leishmania-infected Phlebotomine sand flies: Basis for technical recommendations
Authors: Prudhomme Jorian , Delabarre Aymeric , Alten Bulent , Berberoglu Umut , Berriatua Eduardo , Bongiorno Gioia , Cristovao José Manuel , Davidovich-Cohen Maya , Di Muccio Trentina , Erisoz Kasap Ozge , Fiorentino Eleonora , D. Kirstein Oscar , Kniha Edwin , Maia Carla , Mungan Mesut , Muñoz-Hernández Clara , Nalçaci Muhammed , Oguz Kaskan Gizem , Ozbel Yusuf , Ozensoy Toz Seray , Parreira Ricardo , Platzgummer Katharina , Polat Ceylan , Risueño José , Studentsky Liora , Varol Gamze , Walochnik Julia , Yetişmiş Kardelen , Robert-Gangneux Florence
Published: December 2024

Background

Leishmaniasis, caused by Leishmania protozoan parasites transmitted by Phlebotomine sand flies, is a significant public health concern in the Mediterranean basin. Effective monitoring of Leishmania-infected sand flies requires standardized tools for comparing their distribution and infection prevalence. Consistent quantitative real-time PCR (qPCR) parameters and efficient DNA extraction protocols are crucial for reliable results over time and across regions. However, the absence of standardized technical recommendations for Leishmania DNA detection hinders effective surveillance. This study aimed to compare different DNA extraction protocols and conduct a qPCR-based External Quality Assessment (EQA) through a multicenter study involving nine reference laboratories, with a focus on optimizing Leishmania DNA detection in sand fly.

Methodology/Principal findings

EQA samples consisted of Leishmania infantum and L. major species, at concentrations ranging from 101 to 104 parasites/mL. All but one center detected all concentrations, demonstrating strong diagnostic proficiency. The ability to detect low concentrations highlighted the robustness of the qPCR assay used, though variations in Cq values indicated differences in sensitivity related to technical capabilities or DNA extraction kit performance. A comparative analysis of seven DNA extraction methods identified the EZ1 DSP Virus Kit and QIAamp DNA mini-kit as the most efficient, supporting their use in standardized protocols. The study also assessed the effects of lyophilization and shipment conditions, showing no significant compromise in Leishmania detection despite slight variations in Cq values. Experimentally infected sand flies were included to simulate field conditions, and all centers successfully detected positive samples with varying Cq values, probably reflecting differences in infection load.

Conclusion and significance

This study emphasizes the importance of standardized DNA extraction protocols and continuous quality assurance for accurate Leishmania DNA detection. The results highlight the superior performance of certain extraction kits and the need for ongoing technical training, essential for reliable leishmaniasis surveillance, particularly in field settings with low infection densities.
Current status of intestinal parasitosis and microsporidiosis in industrialized countries: Results from a prospective study in France and Luxembourg
Authors: Moniot Maxime , Nourrisson Céline , Bailly Eloïse , Lambert Céline , Combes Patricia , Poirier Philippe
Published: December 2024

Background

Human intestinal parasitosis and microsporidiosis are a global health concern, mostly in endemic areas but should not be neglected elsewhere. Recent nationwide epidemiological data are scarce, especially from primary health care and developed countries. Diagnosis by molecular tools are increasing and several commercial gastrointestinal panel assays including protozoans and/or helminths are now available. These news tools improve the knowledge into real human parasite epidemiology. This study provides an epidemiological update on intestinal parasites found in primary health care in France and Luxembourg.

Methodology/Principal findings

Two thousand fifty-six stools from primary health care patients were analyzed for the presence of intestinal parasites (IPs) during two different seasons of 2022, the winter and the summer, corresponding to more than 1500 patients from all over France and Luxembourg. Parasite detection was performed combining standard microscopy (merthiolate-iodine-formaldehyde and Bailenger concentration procedures) with two molecular panel assays (AMPLIQUICK Fecal Pretreatment, AMPLIQUICK Protozoans and AMPLIQUICK Helminths, BIOSYNEX, France). The prevalence of IPs in primary care patients reached 33.2%. Blastocystis sp. and Dientamoeba fragilis were the most frequently detected parasites in 20.5% and 13.1% of patients, respectively. Coinfection with two or more parasites was detected in 9.9% of patients. For some parasites, patterns according to gender, age, geography or season have been observed.

Conclusion/Significance

The high prevalence of pathogenic IPs (about 7%) underlines the importance of investigating gastrointestinal disorders through parasite examination, even in developed countries. The detection of parasites, pathogenic or not, remains a marker of the faecal-oral route of transmission and results should be interpreted accordingly. Parasites molecular characterization give new insights and should encourage further research as industrialized countries are not exempt of parasitic circulation and a better survey is necessary.
Continuity and sustainability of care in family medicine: Assessing its association with quality of life and health outcomes in older populations—A systematic review
Authors: Nasser Albarqi Mohammed
Published: December 2024

Background

Continuity of care is a core principle of family medicine associated with improved outcomes. However, fragmentation challenges sustaining continuous relationships. This review aimed to provide timely and critical insights into the benefits of continuity and sustainability of care for older adults.

Methods

PubMed, EMBASE, CINAHL, Cochrane Library were systematically searched for studies on continuity/sustainability models in family medicine and effects on older adults. 14 studies met inclusion criteria for final synthesis. Quality was assessed using ROBINS-I. Outcomes were narratively and thematically synthesized.

Results

Greater continuity of care was consistently associated with reduced healthcare utilization including lower emergency department visits and hospitalizations. Continuity also correlated with improved chronic disease management, care coordination, patient-reported experiences, and quality of life. Patient-centered medical homes and care coordination models showed potential to strengthen continuity and sustainability. Thoughtful telehealth integration and technology tools augmented continuity.

Conclusion

Continuous healing relationships are vital for patient-centered care of older adults. While current fragmentation challenges sustainability, innovations in primary care teaming, coordination, telehealth, and health information technology can extend continuity’s benefits. Realizing improvements requires system-wide reorientation toward relationships and whole-person care.
How does the development of the digital economy in RCEP member countries affect China’s cross-border e-commerce exports?
Authors: Wang Dong , Xu Peiyuan , An Bowen , Song Yingying
Published: December 2024

As a significant new mode of trade export in the digital economy era, cross-border e-commerce injects new momentum into trade cooperation among the Regional Comprehensive Economic Partnership (RCEP) member countries. This paper utilizes multi-dimensional panel data constructed from World Bank databases and UNCTAD databases from 2012 to 2021 to analyze the impact mechanism of RCEP member countries’ digital economy development on China’s cross-border e-commerce export through direct and indirect channels and conducts empirical tests on it. The research results show that, in terms of direct impact, the development of the digital economy in RCEP member countries has promoted China’s cross-border e-commerce export, and its impact is heterogeneous. Regarding indirect impact, improving the quality of RCEP member countries’ transportation infrastructure and institutional quality is a moderating factor promoting China’s cross-border e-commerce export. This study provides important insights for deepening the development of the digital economy in RCEP countries and promoting China’s cross-border e-commerce export.
Epidemiological pattern of trauma among children 0–9 years in Cameroon
Authors: Kibu Odette Dzemo , Maqungo Sithombo , Nguefack-Tsague Georges , Nsagha Dickson Shey , Tendongfor Nicholas , Dissak Delon Fanny Nadia , Touko Darwin , Tanue Elvis Asangbeng , Oke Rasheedat , McCoy Sandra Irene , Christie Sabrinah Ariane , Juillard Catherine , Chichom-Mefire Alain
Published: December 2024

Background

In low- and middle-income countries, trauma is the leading cause of death among youth and it is also a major cause of disability. Globally, more than 1,600 children and adolescents below the age of 19-years die every day from preventable injuries. Traffic-related injuries, falls, sports-related injuries, assaults, burns, and drownings are the most commonly reported causes of traumatic mortality among children. The mechanism of injury is always diverse in different contexts due to the differences in social determinants of health.

Objectives

To determine the epidemiological pattern of trauma among children 0–9 years in Cameroon.

Methods

This is a retrospective analysis of prospectively collected data from the Cameroon Trauma Registry currently running in 10 pilot sites across seven of the ten regions of Cameroon. We retrieved data for all children aged 0–9 years from June 2022 to August 2023. Data was analyzed with respect to the demographics, injury characteristics and outcomes.

Results

Of the 5,439 patients captured in the trauma registry, 267 (4.9%) were children aged 0–9 years. Over 50% (152/267) of the patients were males with 35% (93/267) from rural settings. The top injury mechanism was road traffic injuries (RTI) [137(52.1%)]. These injuries occurred on the streets [142(53.4%)] during leisure activities [205(78.5%)]. Majority of children [104(39%)] involved in RTI injuries were pedestrians and no prehospital care offered to 216(82.8%) of injured children. A total of 39 (16.9%) were discharged with major disability, 111 (48.1%) had limited ability to move and 5 (2.2%) demised or succumbed to their injuries. There was a significant association between injury activity and gender (P = 0.006). Unlike the females, majority of the males were discharged with major disability [21 (53.8%)].

Conclusion

This preliminary analysis highlights the burden of trauma among children aged 0–9 years and its contribution to the proportion of disabled persons in Cameroon. Leisure activities on the streets increased the number of pediatric injuries especially among males. It is therefore imperative to put in place or reinforce environmental interventions to reduce the burden of pediatric injuries.
Research on equipment layout of multi-layer circular manufacturing cell based on NSGA III
Authors: Zhao Yanlin
Published: December 2024

This paper investigates the layout optimization of multi-layer circular manufacturing cells (MCMC), a topic that has garnered limited attention compared to single-layer circular manufacturing cells (SCMC). With the continuous advancement of global intelligent manufacturing, MCMC has emerged as a viable solution, with several smart factories already implementing this model. Existing literature predominantly utilizes the NSGA II algorithm for SCMC layouts due to their relatively few objectives. However, the layout problem for MCMC encompasses a significantly larger number of objectives, rendering NSGA II inadequate. This study aims to fill this research gap by proposing an innovative approach using NSGA III, specifically designed for complex multi-objective optimization. A multi-dimensional target mathematical model for MCMC is established, facilitating the systematic examination of layout configurations. The methodology employs NSGA III to effectively tackle the challenges posed by MCMC layouts. To validate the effectiveness of NSGA III, empirical data from a smart factory in Zhejiang, China, is utilized. The findings demonstrate that NSGA III significantly outperforms traditional algorithms, yielding superior solutions for MCMC layout problems. This research not only challenges the conventional SCMC layout paradigm but also expands the options available for facility layouts in smart factories. Ultimately, it addresses the pressing engineering needs of smart factory construction and contributes valuable insights to the field of MCMC research, establishing a robust methodology for future studies.
Spatiotemporal dynamic and regional differences of public attention to vaccination: An empirical study in China
Authors: Zhang Yaming , Guo Xiaoyu , Su Yanyuan
Published: December 2024

Background

Internet searches offer an indicator of public attention and possible demand for certain things. Studying the spatiotemporal characteristics of the public’s concern for vaccination can determine the spatiotemporal distribution of demand for vaccines in China, and capture the changes in the health awareness of the Chinese population, thus informing future vaccination strategies.

Methods

Based on the collection of Baidu search indices for vaccination-related keywords in 363 cities in China, This paper seeks to explore the spatiotemporal changes and regional differences in public attention toward vaccination in China by using the seasonal index, seasonal concentration index, Herfindahl index, Moran index, and Dagum Gini coefficient.

Results

The following findings are presented. First, there are significant seasonal fluctuations and unbalanced monthly distributions of vaccination-related public attention in China. Second, the public attention in Chinese cities shows the spatial characteristics of "leading in the east, followed by the central, western and northeastern regions". The spatial correlation of attention has been strengthened, and the high-high clusters are mainly distributed in the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Greater Bay Area (GBA) urban agglomerations. Third, regional differences in overall public attention narrowed in China, with intra-regional differences narrowing in seven regions (Northwest China, Central China, and so on), and intra-regional differences increasing in East China. The dominant role in the Gini coefficient changes from transvariation intensity to inter-regional differences.

Conclusion

Major public health emergencies stimulate the public’s attention to health topics. Although the short-term increase in vaccination-related public attention was not observed to translate into a long-term increase in public vaccine literacy, the seasonal and regional differences in vaccination-related public attention in China have significantly narrowed before and after COVID-19, suggesting that the imbalance between public health literacy levels has improved.
Development of a sequential laser microdissection tissue cuts workflow for the spatial and quantitative analysis of drugs in fresh frozen tissue sections
Authors: Maris Elias , Jahouh Farid , Allaerts Kathleen , Vreeken Rob
Published: December 2024

Mass spectrometry imaging (MSI) is a well-established technique that allows to determine the distribution of small molecules, such as lipids, metabolites, and drugs, as well as large molecules in tissue sections. Because of the tissue heterogeneity, resulting in different matrix effects, and to the fact that the measured compounds are not entirely “extracted” from the tissue during the measurement, the absolute quantitative aspect of MSI is limited. To combine compound quantification with spatial information on fresh frozen unstained tissue sections, laser (capture) microdissection has been used to isolate tissue sections for compound extraction and LC-MS/MS quantification. Although this method relying on manual ROIs selection is rather sensitive compared to traditional MSI methods, it lacks the throughput needed to screen entire tissue sections. To apply a higher throughput tissue screening approach, we propose herein a workflow for performing indiscriminate and sequential LMD tissue section cuts that can cover up to 96 cuts collected in a 96 well plate on Leica LMD systems, for further extractions and LC-MS/MS analysis. Our workflow relies on the creation and implementation of 96 squares microgrid templates for the LMD cut of different area sizes (30x30 μm2, 50x50 μm2, 100x100 μm2, 200x200 μm2, 270x270 μm2 and 500x500 μm2) using 5 different magnifications (5x, 10x, 20x, 40x and 63x), on fresh frozen tissue sections. The method was applied on 20μm mouse brain and liver tissue sections. The tissue cut collection yields were evaluated visually and by the detection of the sprayed standards on the tissue sections, and found to be ranging from 78% to 91%, and the throughput of the LMD cuts and collection in a 96 well format, was measured to be from 19 to 37 minutes per tissue section, depending on the 96 squares microgrid template and the corresponding magnification lens used. Further extraction and LC-MS/MS analysis of 3 different compounds previously sprayed on a mouse liver tissue section allowed to determine the LLOQ the workflow allows to achieve when using the different templates.
Identifying potential drug targets for myocardial infarction through Mendelian randomization
Authors: Yu Xiangyou , Liu Shasha
Published: December 2024

Background

This study explored the associations between plasma and cerebrospinal fluid (CSF) proteins and myocardial infarction (MI) risk. Identifying specific proteins as biomarkers for MI could enhance our understanding of disease mechanisms and inform clinical practice.

Methods

We combined protein quantitative trait loci (pQTL) data for plasma and CSF proteins with genome-wide association study (GWAS) summary statistics for MI. Mendelian Randomization (MR) analyses were conducted to establish causal relationships, supported by Bayesian colocalization and Spearman correlation analyses. For plasma proteins, we used pQTL data from Cheng et al. to select 738 cis-acting SNPs associated with 734 proteins. The "TwoSampleMR" method and inverse-variance weighted MR were applied for evaluations.

Results

In plasma, CD8A and HDHD2 were identified as protective factors against MI, while DPEP1 was linked to increased risk. In CSF, CD30 Ligand was associated with MI risk. Bayesian colocalization supported the association for CD8A in plasma. No significant correlation was found between plasma and CSF results, suggesting distinct mechanisms for these biomarkers.

Conclusion

Our study identified several plasma and CSF proteins linked to MI risk, offering new insights into the disease’s biological underpinnings. These findings could guide future research on MI biomarkers and contribute to improved prevention and treatment strategies.
Inherent stochasticity, noise and limits of detection in continuous and time-gated fluorescence systems
Authors: Vitale Nicholas H. , Hassibi Arjang , Soh Hyongsok Tom , Murmann Boris , Lee Thomas H.
Published: December 2024

We present a model for the noise and inherent stochasticity of fluorescence signals in both continuous wave (CW) and time-gated (TG) conditions. When the fluorophores are subjected to an arbitrary excitation photon flux, we apply the model and compute the evolution of the probability mass function (pmf) for each quantum state comprising a fluorophore’s electronic structure, and hence the dynamics of the resulting emission photon flux. Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. The implications of the model on the design of biomolecular fluorescence detection systems are explored in three relevant numerical examples. For a given system, the quantum-limited signal-to-noise ratio (QSNR) and limits of detection are computed to demonstrate how key design tradeoffs are quantified. We find that as systems scale down to micro- and nano- dimensions, the interplay between the fluorophore’s photophysical qualities and use of CW or TG has ramifications on optimal design strategies when considering optical component selection, measurement speed, and system energy requirements. While CW systems remain a gold standard, TG systems can be leveraged to overcome cost and system complexity hurdles when paired with the appropriate fluorophore.
Health-related quality of life in a large cohort of patients with cardiac implantable electronic devices A registry-based study
Authors: Gatti Paolo , Nymark Carolin , Gadler Fredrik
Published: December 2024

Aim

The association of cardiac implantable electronic devices (CIED), namely pacemaker (PM), implantable cardioverter-defibrillator (ICD) and cardiac resynchronization therapy with (CRT-D) or without defibrillator (CRT-P) with health-related quality of life (HRQoL) is lacking.

Methods and results

Data from the Swedish Pacemaker and ICD Registry collected from January 2019 to February 2022 was used to analyze the responses to the European Quality of Life-5 Dimension questionnaire (EQ-5D) before and after one year of the CIED implant. Descriptive analysis was performed using Pearson’s chi-square test, the analysis of variance ANOVA, the Kruskal-Wallis test and Wilcoxon signed-rank test when appropriate. A multivariable regression analysis was used to compare the EQ-5D index and EQ-VAS variation after 1 year. Of 1,479 who completed the EQ-5D, 80% had a PM, 10% an ICD, 5% a CRT-P and 6% a CRT-D. The median age was 77 years with females constituting 38% of the PM group and 17% of the ICD group. The EQ-VAS and the EQ-5D index significantly increased after one year from the PM and CRT-P implant (EQ-VAS +2.8, standard deviations (SD) 23 and +5.8, SD 24.9; EQ-5D index +0.019, SD 0.114 and +0.051, SD 0.125) while only the EQ-5D index increased after one year from the ICD implant (+0.002, SD 0.104). After adjusting for age, sex and HRQoL at baseline, the presence of defibrillator was associated with lower EQ-VAS (ICD EQ-VAS variation: -3.4, 95% confidence intervals (CI) -6.7; -0.1 and CRT-D EQ-VAS variation -4.8, 95% CI -8.8;-0.7) and EQ-5D index (ICD EQ-5D index variation: -0.018, 95% CI -0.035; -0.0003 and CRT-D EQ-5D index variation -0.025 95% CI 0.046;0.004) after one year compared to PM.

Conclusion

These findings, showing the HRQoL associated with CIED, are important to support physicians’ and pacemaker nurses’ care after device implantation by embracing the patients’ perspectives.
CXCL12 impact on glioblastoma cells behaviors under dynamic culture conditions: Insights for developing new therapeutic approaches
Authors: El Kheir Wiam , Naasri Sahar , Marcos Bernard , Virgilio Nick , Paquette Benoit , Faucheux Nathalie , Lauzon Marc-Antoine
Published: December 2024

Glioblastoma multiforme (GBM) is the most prevalent malignant brain tumor, with an average survival time of 14 to 20 months. Its capacity to invade brain parenchyma leads to the failure of conventional treatments and subsequent tumor recurrence. Recent studies have explored new therapeutic strategies using a chemoattracting gradient to attract GBM cells into a soft hydrogel trap where they can be exposed to higher doses of radiation or chemotherapy. It has been demonstrated in vitro under static conditions, that nanoparticles (NPs) encapsulating the chemoattractant CXCL12 can create a gradient to attract GBM cell. However, GBM cell invasion is also largely dependent on interstitial fluid flow (IFF). In the present study, a custom-made in vitro 3D model with indirect perfusion to mimic IFF at flow rates of 0.5 μL/min and 3 μL/min was used to examine the invasive behavior of F98-rodent-derived and U87-human-derived GBM cells. This model simulated IFF and CXCL12 gradient within an alginate:matrigel-based hydrogel mimicking brain parenchyma. Findings revealed that CXCL12 (1600 ng/mL) released from NPs significantly increased the migration of F98 GBM cells after 72 hours under IFF conditions at both 0.5 and 3 μL/min. In contrast, U87 GBM cells required a higher CXCL12 concentration (2400 ng/mL) and longer incubation time for migration (120 hours). Unlike the F98 cells, U87 GBM cells showed a CXCL12 dose-dependent proliferation. Semi-quantitative qPCR showed higher CXCR4 mRNA levels in F98 cells than in U87 cells. CXCL12 significantly increased intracellular calcium levels via CXCR4 activation, with a 2.3-fold rise in F98 cells compared to U87, consistent with observed cell behavior during perfusion. This highlights the combined influence of IFF and CXCL12 on cell migration, dependent on cell line. This 3D dynamic model is a valuable tool to analyze parameters like interstitial fluid flow (IFF) and chemokine gradients, influenced by GBM tumor diversity.
Quantitative trait loci for grain mineral element accumulation in Vietnamese rice landraces
Authors: Tran Hien Linh , Hoang Giang Thi , Phung Nhung Thi Phuong , Le Ham Huy , Grondin Alexandre , Gantet Pascal
Published: December 2024

Rice (Oryza sativa L.) is a staple food for half of the world’s population, and its biofortification is a key factor in fighting micronutrient malnutrition. However, harmful heavy metals tend to accumulate in rice grains due to soil and water contamination. Therefore, it is important to improve beneficial micronutrient contents and reduce the accumulation of undesirable metals in rice grain. To better characterize the genetic control of mineral accumulation in rice, we conducted association genetics on the ion contents of white and brown grains using a collection of 184 Vietnamese rice landraces. In total, 27 significant associations were identified and delimited into quantitative trait loci associated with macronutrients such as phosphorus, potassium or calcium; micronutrients such as iron or zinc; or toxic heavy metals such as arsenic and cadmium. Several genes related to ion homeostasis or ion transport were identified in the different quantitative trait loci. LOC_Os10g30610, present in qRAs10-1 associated with arsenic content in brown rice, encodes an ABC transporter (OsABCG25), which is involved in the silicon-induced formation of the Casparian strip in the rice exodermis and could act as a barrier restricting As diffusion within the root cortex. LOC_Os05g04330, present in qRP5-1 and associated with phosphorus content in brown rice, encodes a CHH methylation maintenance protein, and its expression is downregulated in roots in the presence of the phosphorus uptake 1 (Pup1), suggesting a role for epigenetics in the regulation of phosphorus uptake and accumulation in grain. These findings reveal novel quantitative trait loci associated with grain ion content and candidate genes that are potentially valuable for breeding programs aimed at rice grain biofortification and reducing toxic metal accumulation.
Adapting physics-informed neural networks to improve ODE optimization in mosquito population dynamics
Authors: Viet Cuong Dinh , Lalić Branislava , Petrić Mina , Thanh Binh Nguyen , Roantree Mark
Published: December 2024

Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with domain-specific knowledge in the form of physics equations. The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modelling complex dynamical systems. However, current PINN frameworks are not sufficiently mature for real-world ODE systems, especially those with extreme multi-scale behavior such as mosquito population dynamical modelling. In this research, we propose a PINN framework with several improvements for forward and inverse problems for ODE systems with a case study application in modelling the dynamics of mosquito populations. The framework tackles the gradient imbalance and stiff problems posed by mosquito ordinary differential equations. The method offers a simple but effective way to resolve the time causality issue in PINNs by gradually expanding the training time domain until it covers entire domain of interest. As part of a robust evaluation, we conduct experiments using simulated data to evaluate the effectiveness of the approach. Preliminary results indicate that physics-informed machine learning holds significant potential for advancing the study of ecological systems.
Optimizing hypertension prediction using ensemble learning approaches
Authors: Sifat Isteaq Kabir , Kibria Md. Kaderi
Published: December 2024

Hypertension (HTN) prediction is critical for effective preventive healthcare strategies. This study investigates how well ensemble learning techniques work to increase the accuracy of HTN prediction models. Utilizing a dataset of 612 participants from Ethiopia, which includes 27 features potentially associated with HTN risk, we aimed to enhance predictive performance over traditional single-model methods. A multi-faceted feature selection approach was employed, incorporating Boruta, Lasso Regression, Forward and Backward Selection, and Random Forest feature importance, and found 13 common features that were considered for prediction. Five machine learning (ML) models such as logistic regression (LR), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and a stacking ensemble model were trained using selected features to predict HTN. The models’ performance on the testing set was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC). Additionally, SHapley Additive exPlanations (SHAP) was utilized to examine the impact of individual features on the models’ predictions and identify the most important risk factors for HTN. The stacking ensemble model emerged as the most effective approach for predicting HTN risk, achieving an accuracy of 96.32%, precision of 95.48%, recall of 97.51%, F1-score of 96.48%, and an AUC of 0.971. SHAP analysis of the stacking model identified weight, drinking habits, history of hypertension, salt intake, age, diabetes, BMI, and fat intake as the most significant and interpretable risk factors for HTN. Our results demonstrate significant advancements in predictive accuracy and robustness, highlighting the potential of ensemble learning as a pivotal tool in healthcare analytics. This research contributes to ongoing efforts to optimize HTN prediction models, ultimately supporting early intervention and personalized healthcare management.