High Complexity Research Articles

Reinforcement learning-based assimilation of the WOFOST crop model

Crop model assimilation is a crucial technique for improving the accuracy and precision of crop models by integrating observational data and crop models. Although conventional data assimilation methods such as Kalman filtering and variational methods have been widely applied, these methods often face limitations in data quality, model bias, and high computational complexity. This study explored the potential of reinforcement learning (RL) for crop model assimilation, which has the advantage of not requiring large datasets. Based on the WOFOST crop model, two RL environments were constructed: a Daily-Data Driven approach and a Time-Series Driven approach. The Proximal Policy Optimization (PPO) algorithm was used to train these environments for 100,000 iterations. The assimilation results were compared with the commonly used SUBPLEX optimization algorithm using four-year field measurement data and a public dataset with added random errors. Our results demonstrate that the Time-Series Driven RL model achieved assimilation accuracy comparable to the SUBPLEX optimization algorithm, with an average MAE of 0.65 compared to 0.76 for SUBPLEX, and a slight decrease in RMSE, while significantly reducing the computational burden by 365 times. In a multi-year stability test, the Time-Series Driven RL model and SUBPLEX had similar assimilation performance. This study demonstrates the potential of RL for crop model assimilation, providing a novel approach to overcome the limitations of conventional assimilation algorithms. The findings suggest that RL-based crop model assimilation can improve model accuracy and efficiency, with potential for practical applications in precision agriculture.

Highly sensitive and specific electrochemical biosensor for direct detection of hepatitis C virus RNA in clinical samples using DNA strand displacement

Hepatitis C virus (HCV) is a common blood-borne infection that can lead to long-term illnesses such as hepatocellular cancer and liver cirrhosis. Early diagnosis is crucial for effective management, as no vaccine is available for preventing HCV infection. However, the high cost and complexity of current molecular diagnostic tools hinder efforts to achieve early diagnosis and prevent transmission, particularly in resource-limited settings. We developed a novel electrochemical biosensor for point-of-care testing (POCT) of HCV RNA. The sensor utilizes a strand displacement method, where the target RNA displaces a gold nanoparticle-labeled reporter probe (AuRP) from a pre-hybridized duplex with a magnetic nanoparticle (MNP)-labeled capture probe. The amount of displaced AuRP, detected using differential pulse anodic stripping voltammetry (DPASV), is directly proportional to the target RNA concentration. The biosensor exhibited excellent analytical performance, with a detection limit of 4 fM for synthetic targets and 43 ng/µL for RT-PCR products. Importantly, it successfully detected HCV RNA directly in clinical plasma samples without the need for RNA extraction or amplification. The sensor was used to analyze 30 RNA samples from HCV-positive patients, 20 cDNA samples from viral RNA, 30 HCV-positive plasma samples, and 22 HCV-negative plasma samples. The sensor results show good concordance with the RT-PCR results, demonstrating the sensor’s potential for detecting HCV in clinical samples.

Prey diversity in the deep ocean: metabarcoding feeding ecology of the commercially important queen snapper in the US Caribbean

The queen snapper, Etelis oculatus, is experiencing a growing fishery presence across the wider Caribbean, yet our understanding of its distribution, biology, and ecology remains limited. To address this gap, we investigated the feeding ecology of this demersal deep-water snapper using two molecular approaches: single-species barcoding and multispecies metabarcoding. Between November 2019 and July 2020, we collected 157 queen snapper from seven locations along the west coast of Puerto Rico. Our analysis identified a diverse array of prey items in the stomachs, comprising 61 species from 31 families, 18 orders, and 38 genera. Our findings suggest that E. oculatus is a large carnivore, primarily preying on squids, shrimps, and deep-water fishes. Notably, common fish prey included Diaphus brachycephalus, D. dumerilii, Dasyscopelus selenops, Coccorella atlantica, Sigmops elongatus, and Zaphotias pedaliotus. In contrast, prevalent invertebrate prey consisted of Abralia veranyi, Doryteuthis pealeii, Abralia redfieldi, Oplophorus gracilirostris, and Systellaspis debilis. Although our data suggest potential variation in diet composition across locations, definitive conclusions remain elusive. However, we observed heightened prey richness at seamount Pichincho, possibly attributable to the high structural complexity of the karst terrain on the seafloor. Additionally, we noted variation in prey species composition across size classes, with certain species more prevalent in larger or smaller queen snapper. Given the challenge of examining the prey of deep-water fish species, our study showcases the utility of both single-species barcoding and multispecies metabarcoding methodologies in characterizing the dietary range of queen snapper and similar demersal deep-water carnivorous fishes. The multispecies metabarcoding approach, in particular, offers a rapid, comprehensive, and effective means of identifying prey species. As the commercial value of the queen snapper continues to rise, our investigation into its feeding behavior provides critical baseline information for future species management efforts. By enhancing our understanding of its ecological dynamics, we aim to contribute to informed conservation and sustainable fisheries practices in the region.

Selective and multi-scale fusion Mamba for medical image segmentation

Given the high variability in the morphology and size of lesion areas in medical images, accurate medical image segmentation requires both precise positioning of global contours and careful processing of local boundaries. This emphasizes the importance of fusing multi-scale local and global features. However, existing CNN and Transformer-based models are often limited by high parameter counts and complex calculations, making it difficult to efficiently integrate these features. To address this challenge, we proposed two innovative optimization architectures: Selective Fusion Mamba (SF-Mamba) and Multi-Scale Fusion Mamba (MF-Mamba). SF-Mamba can flexibly and dynamically adjust the fusion strategy of local and global features according to the characteristics of the lesions, effectively handling segmentation tasks with variable morphology. MF-Mamba enhances the model’s segmentation ability for lesions of different sizes by capturing global information across scales. Based on these two structures, we constructed a lightweight SMM-UNet model, which not only significantly reduces the computational burden (with only 0.038M parameters) but also demonstrates excellent generalization ability and can efficiently adapt to various types of medical images. Extensive tests on the ISIC2017, ISIC2018, and BUSI public datasets show that SMM-UNet achieves excellent segmentation performance with an extremely low parameter cost.

Semantic Segmentation of Satellite Images for Landslide Detection Using Foreground-Aware and Multi-Scale Convolutional Attention Mechanism.

Advancements in satellite and aerial imagery technology have made it easier to obtain high-resolution remote sensing images, leading to widespread research and applications in various fields. Remote sensing image semantic segmentation is a crucial task that provides semantic and localization information for target objects. In addition to the large-scale variation issues common in most semantic segmentation datasets, aerial images present unique challenges, including high background complexity and imbalanced foreground-background ratios. However, general semantic segmentation methods primarily address scale variations in natural scenes and often neglect the specific challenges in remote sensing images, such as inadequate foreground modeling. In this paper, we present a foreground-aware remote sensing semantic segmentation model. The model introduces a multi-scale convolutional attention mechanism and utilizes a feature pyramid network architecture to extract multi-scale features, addressing the multi-scale problem. Additionally, we introduce a Foreground-Scene Relation Module to mitigate false alarms. The model enhances the foreground features by modeling the relationship between the foreground and the scene. In the loss function, a Soft Focal Loss is employed to focus on foreground samples during training, alleviating the foreground-background imbalance issue. Experimental results indicate that our proposed method outperforms current state-of-the-art general semantic segmentation methods and transformer-based methods on the LS dataset benchmark.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.AimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity.MethodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.ResultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.ConclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

Assessing tumor microstructure with time-dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI-Linac.

Quantitative imaging biomarkers (QIBs) can characterize tumor heterogeneity and provide information for biological guidance in radiotherapy (RT). Time-dependent diffusion MRI (TDD-MRI) derived parameters are promising QIBs, as they describe tissue microstructure with more specificity than traditional diffusion-weighted MRI (DW-MRI). Specifically, TDD-MRI can provide information about both restricted diffusion and diffusional exchange, which are the two time-dependent effects affecting diffusion in tissue, and relevant in tumors. However, exhaustive modeling of both effects can require long acquisitions and complex model fitting. Furthermore, several introduced TDD-MRI measurements can require high gradient strengths and/or complex gradient waveforms that are possibly not available in RT settings. In this study, we investigated the feasibility of a simple analysis framework for the detection of restricted diffusion and diffusional exchange effects in the TDD-MRI signal. To promote the clinical applicability, we use standard gradient waveforms on a conventional 1.5 T MRI system with moderate gradient strength (Gmax=45 mT/m), and on a hybrid 1.5 T MRI-Linac system with low gradient strength (Gmax=15 mT/m). Restricted diffusion and diffusional exchange were simulated in geometries mimicking tumor microstructure to investigate the DW-MRI signal behavior and to determine optimal experimental parameters. TDD-MRI was implemented using pulsed field gradient spin echo with the optimized parameters on a conventional MRI system and a MRI-Linac. Experiments in green asparagus and 10 patients with brain lesions were performed to evaluate the time-dependent diffusion (TDD) contrast in the source DW-images. Simulations demonstrated how the TDD contrast was able to differentiate only dominating diffusional exchange in smaller cells from dominating restricted diffusion in larger cells. The maximal TDD contrast in simulations with typical cancer cell sizes and in asparagus measurements exceeded 5% on the conventional MRI but remained below 5% on the MRI-Linac. In particular, the simulated TDD contrast in typical cancer cell sizes (r=5-10µm) remained below or around 2% with the MRI-Linac gradient strength. In patients measured with the conventional MRI, we found sub-regions reflecting either dominating restricted diffusion or dominating diffusional exchange in and around brain lesions compared to the noisy appearing white matter. On the conventional MRI system, the TDD contrast maps showed consistent tumor sub-regions indicating different dominating TDD effects, potentially providing information on the spatial tumor heterogeneity. On the MRI-Linac, the available TDD contrast measured in asparagus showed the same trends as with the conventional MRI but remained close to typical measurement noise levels when simulated in common cancer cell sizes. On conventional MRI systems with moderate gradient strengths, the TDD contrast could potentially be used as a tool to identify which time-dependent effects to include when choosing a biophysical model for more specific tumor characterization.

Learning lightweight tea detector with reconstructed feature and dual distillation

Currently, image recognition based on deep neural networks has become the mainstream direction of research; therefore, significant progress has been made in its application in the field of tea detection. Many deep models exhibit high recognition rates in tea leaves detection. However, deploying these models directly on tea-picking equipment in natural environments is impractical; the extremely high parameters and computational complexity of these models make it challenging to perform real-time tea leaves detection. Meanwhile, lightweight models struggle to achieve competitive detection accuracy; therefore, this paper addresses the issue of computational resource constraints in remote mountain areas and proposes Reconstructed Feature and Dual Distillation (RFDD) to enhance the detection capability of lightweight models for tea leaves. In our method, the Reconstructed Feature selectively masks the feature of the student model based on the spatial attention map of the teacher model; it utilizes a generation block to force the student model to generate the teacher’s full feature. The Dual Distillation comprises Decoupled Distillation and Global Distillation. Decoupled Distillation divides the reconstructed feature into foreground and background features based on the Ground-Truth. This compels the student model to allocate different attention to foreground and background, focusing on their critical pixels and channels. However, Decoupled Distillation leads to the loss of relation knowledge between foreground and background pixels. Therefore, we further perform Global Distillation to extract this lost knowledge. Since RFDD only requires loss calculation on feature map, it can be easily applied to various detectors. We conducted experiments on detectors with different frameworks, using a tea dataset collected at the Huangshan Houkui Tea Plantation. The experimental results indicate that, under the guidance of RFDD, the student detectors have achieved performance improvements to varying degrees. For instance, a one-stage detector like RetinaNet (ResNet-50) experienced a 3.14% increase in Average Precision (AP) after RFDD guidance. Similarly, a two-stage model like Faster RCNN (ResNet-50) obtained a 3.53% improvement in AP. This offers promising prospects for lightweight models to efficiently perform real-time tea leaves detection tasks.

Subpixel three-dimensional imaging using two-dimensional optical encoding with a downscaled avalanche photodiode array

Scannerless laser three-dimensional (3D) imaging relies on large-scale detector arrays to achieve high-resolution imaging and is one of the main technologies in light detection and ranging (LIDAR). However, the high production cost and complex manufacturing process is limiting the imaging resolution. In this paper, we demonstrate a subpixel 3D imaging method based on two-dimensional (2D) encoding and decoding. First, an LED array projects 2D spatio-temporal coded light beams onto the target space. Accordingly, an optical multiplexer separates and reconstructs the backscattered signals into a small-scale avalanche photodiode (APD) array. We designed a prototype with a 16×16 optical encoding and a 2×2 APD array. The scannerless imaging reconstructs 256 pixels per frame within the distance range of 39–68 cm.

Towards a more globally inclusive academic discussion on local government: is more attention to Asian countries the answer?

ABSTRACT Asian countries’ growing importance in global affairs contrasts with the scant scholarly focus on their political, administrative, and governance practices. Our review of Local Government Studies from 1975 to 2024 shows limited articles on Asian governance. In addition, these studies chiefly concern developed regions in Asia, such as China, Japan, and South Korea, and focus on the public sector at the local government layer. Most of these studies are explanatory, adopting the frameworks derived from Western democracies to analyze political and administrative practices in Asian contexts. We suggest authors should pay more attention to Asian countries with limited scholarly attention, establish frameworks and theories specifically to analyze political and administrative practices in Asian contexts, conduct studies to investigate issues with high complexities and uncertainties, and apply diverse new and innovative research methods in conducting empirical studies.

3D Printing of Hierarchical Structures Made of Inorganic Silicon-Rich Glass Featuring Self-Forming Nanogratings.

Hierarchical structures are abundant in nature, such as in the superhydrophobic surfaces of lotus leaves and the structural coloration of butterfly wings. They consist of ordered features across multiple size scales, and their advantageous properties have attracted enormous interest in wide-ranging fields including energy storage, nanofluidics, and nanophotonics. Femtosecond lasers, which are capable of inducing various material modifications, have shown promise for manufacturing tailored hierarchical structures. However, existing methods, such as multiphoton lithography and three-dimensional (3D) printing using nanoparticle-filled inks, typically involve polymers and suffer from high process complexity. Here, we demonstrate the 3D printing of hierarchical structures in inorganic silicon-rich glass featuring self-forming nanogratings. This approach takes advantage of our finding that femtosecond laser pulses can induce simultaneous multiphoton cross-linking and self-formation of nanogratings in hydrogen silsesquioxane. The 3D printing process combines the 3D patterning capability of multiphoton lithography and the efficient generation of periodic structures by the self-formation of nanogratings. We 3D-printed micro-supercapacitors with large surface areas and a high areal capacitance of 1 mF/cm2 at an ultrahigh scan rate of 50 V/s, thereby demonstrating the utility of our 3D printing approach for device applications in emerging fields such as energy storage.

Molecular Characterization of Adhesives (Glue Lining Pastes) Used in Restoration.

The molecular characterization of samples from works of art can provide valuable insights into the composition of ancient restoration materials and their conservation state. Here, we present a novel analytical protocol for the molecular characterization of a specific adhesive used in historical painting restoration, known as "glue lining pastes." Due to the high molecular complexity of these adhesives, we propose a multistep extraction protocol to recover and fractionate from a single microsample the three main classes of biomolecules contained in glue pastes (lipids, polysaccharides, and proteins). High-performance separation coupled with high-resolution mass spectrometry (MS) techniques were applied to the isolated fractions to identify specific components. The proposed method was optimized using test specimens of various traditional glue pastes applied to canvases and successfully applied to a historical glue paste sample from the 17th-century painting "La fuga in Egitto," part of the Pagliara collection at the University Suor Orsola Benincasa (Naples, Italy). The data collected in this work provide insights into the specific recipe used for adhesive preparation, supporting artistic and historical interpretations and contributing to a broader understanding of old restoration practices. Data are available via ProteomeXchange with the identifier PXD051480.

Seismic behavior of hybrid post-tensioned special steel moment frames

Generally, conventional moment-resisting frames are prone to substantial residual displacements. To overcome this disadvantage, self-centering systems were introduced. Post-tensioned self-centering (PTSC) moment-resisting systems are a variation of conventional moment-resisting systems that drastically reduce the large residual displacements of the moment frames. However, one of the uncommon drawbacks identified in PTSC systems is their low energy dissipation. This study aims to compensate for this by innovatively combining conventional self-centering systems with special moment-resisting frames (SMRF) in various formats to retain the advantages of both systems. Another disadvantage of PTSC systems is the high cost and complexity of the structural design procedure required to maintain an elastic response in the framing beam-column elements. To address this issue, a practical and cost-effective design procedure was proposed. The study also included the influence of different dual SMRF-PTSC frame actions and base column connection types. A set of 35 structures comprising 4, 8, 12, and 16-story buildings were considered for nonlinear static/dynamic analysis and seismic response assessment. The nonlinear static analyses were carried out in a cyclic format due to the self-centering characteristics of the system. Additionally, 22 far-field records per FEMA P695 were chosen for the time history analyses. The results show that structures equipped with post-tensioned self-centering connections (PTSC) designed according to the proposed procedure are successful in drastically decreasing residual drifts. However, the total elimination of residual drifts relies on providing a self-centering connection for the base level columns and considering an overstrength factor for the base level columns' moment capacity design. The proposed value in this investigation was derived as 5, which significantly strengthens the base level columns while resulting in an overall notably cost-efficient PTSC-equipped frame. In the case of dual frames, the conventional SMRF spans should not exceed the number of PTSC-equipped spans, as doing so will diminish the self-centering characteristics of the lateral system.

Molecular and genetic mechanisms of sex determination in poplar

The study of molecular and genetic mechanisms of sex determination in poplar is of interest not only in the fundamental aspect, but also in the applied aspect. In landscaping of large settlements, it is advisable to use male individuals of Populus genus due to their hypoallergenicity and increased resistance to environmental pollution, stress conditions and pathogens. However, sex determination in poplars is complicated by the complex genetic structure of the sex-determining region of the genome (SDR). In this review, the emergence, evolution, structure and function of the SDR in the genus Populus are discussed. Current insights into the structure and function of the key regulator of sex selection in poplars, the orthologous ARR16/ARR17 gene, and the possible role of other genes differentially expressed between male and female plants, including microRNAs, in this process are discussed in detail. The great diversity of species and the high complexity of SDR organization justify the need for further study of the molecular mechanisms of sex determination in poplars.

A Novel Trajectory Prediction Method Based on CNN, BiLSTM, and Multi-Head Attention Mechanism

A four-dimensional (4D) trajectory is a multi-dimensional time series that embodies rich spatiotemporal features. However, its high complexity and inherent uncertainty pose significant challenges for accurate prediction. In this paper, we present a novel 4D trajectory prediction model that integrates convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTMs), and multi-head attention mechanisms. This model effectively addresses the characteristics of aircraft flight trajectories and the difficulties associated with simultaneously extracting spatiotemporal features using existing prediction methods. Specifically, we leverage the local feature extraction capabilities of CNNs to extract key spatial and temporal features from the original trajectory data, such as geometric shape information and dynamic change patterns. The BiLSTM network is employed to consider both forward and backward temporal orders in the trajectory data, allowing for a more comprehensive capture of long-term dependencies. Furthermore, we introduce a multi-head attention mechanism that enhances the model’s ability to accurately identify key information in the trajectory data while minimizing the interference of redundant information. We validated our approach through experiments conducted on a real ADS-B trajectory dataset. The experimental results demonstrate that the proposed method significantly outperforms comparative approaches in terms of trajectory estimation accuracy.

Chinese Named Entity Recognition Based on Multi-Level Representation Learning

Named Entity Recognition (NER) is a crucial component of Natural Language Processing (NLP). When dealing with the high diversity and complexity of the Chinese language, existing Chinese NER models face challenges in addressing word sense ambiguity, capturing long-range dependencies, and maintaining robustness, which hinders the accuracy of entity recognition. To this end, a Chinese NER model based on multi-level representation learning is proposed. The model leverages a pre-trained word-based embedding to capture contextual information. A linear layer adjusts dimensions to fit an Extended Long Short-Term Memory (XLSTM) network, enabling the capture of long-range dependencies and contextual information, and providing deeper representations. An adaptive multi-head attention mechanism is proposed to enhance the ability to capture global dependencies and comprehend deep semantic context. Additionally, GlobalPointer with rotational position encoding integrates global information for entity category prediction. Projected Gradient Descent (PGD) is incorporated, introducing perturbations in the embedding layer of the pre-trained model to enhance stability in noisy environments. The proposed model achieves F1-scores of 96.89%, 74.89%, 72.19%, and 80.96% on the Resume, Weibo, CMeEE, and CLUENER2020 datasets, respectively, demonstrating improvements over baseline and comparison models.

Patterns of Zoological Diversity in Iran—A Review

Iran is a country characterized by high biodiversity and complex biogeographic patterns. Its diverse landscape and steep climatic gradients have resulted in significant faunal diversity and high level of endemism. To better understand these patterns, we investigated the historical environmental drivers that have shaped Iran’s current geological and climatological conditions, and, consequently, have shaped the current zoological distribution patterns. Furthermore, we provide an overview of the country’s zoological diversity and zoogeography by reviewing published studies on its fauna. We analyzed nearly all available catalogs, updated checklists, and relevant publications, and synthesized them to present a comprehensive overview of Iran’s biodiversity. Our review reports approximately 37,500 animal species for Iran. We also demonstrated that the country serves as a biogeographic transition zone among three zoogeographical realms: the Palearctic, Oriental, and Saharo-Arabian, where distinct faunal elements intersect. This biogeographic complexity has made it challenging to delineate clear zoogeographical zones, leading to varying classifications depending on the taxon. The uplift of mountain ranges, in particular, has played a crucial role in shaping faunal diversity by serving as barriers, corridors, and glacial refugia. These mountains are largely the result of orogeny and plate collisions during the Mesozoic and Cenozoic eras, coupled with the development of the Tethyan Sea and the uplift of several ranges during the Miocene. Despite these insights, our understanding of biodiversity distribution in Iran remains incomplete, even for some well-studied taxa, such as certain vertebrate families and arthropods. We highlight the existing gaps in knowledge regarding zoogeographical patterns and propose approaches to address these gaps, particularly concerning less-studied species and the highly diverse group of insects.

Fluoride Ingestion Induces Formation of Unusual Macromolecular Complexes in Gut Lumen Which Retard Absorption of Essential Minerals and Trace Elements by Chelation.

This study investigates the impact of chronic fluoride exposure on the absorption of essential minerals in the gut and explores the mechanisms underlying mineral deficiencies due to fluoride ingestion. Male Wistar rats were randomly assigned to three groups: group 1 (G-1) served as the control (non-fluoride exposed), while group 2 (G-2) and group 3 (G-3) received human equivalent doses (HED) of fluoride (50 and 100ppm ad libitum, corresponding to 5 and 10ppm in humans, respectively) for 75days. Serum fluoride concentrations were measured, and the levels of essential minerals and trace elements in blood and stool were analyzed using ICP-MS. X-ray diffraction (XRD) analysis was performed on stool samples to identify chemical compounds. The chemical compounds and macromolecular complexes containing fluoride and essential minerals were identified and quantified using Match3 software. Results indicated that the blood concentrations of essential minerals were significantly lower (p ≤ 0.05) in the fluoride-exposed groups compared to the control, while excretion of essential elements in stool was significantly higher (p ≤ 0.05) in the fluoride-administered groups. XRD analysis revealed the formation of unusual macromolecular chemical complexes in the stool of fluoride-treated groups, with the types and concentrations of these compounds increasing with higher fluoride doses. The study concludes that fluoride in the stomach chelates minerals, reducing their absorption, and induces the formation of unusual high molecular weight macromolecular chelation complexes, which alter the chemical species in the gut and further impair the absorption of essential minerals.

Lensless light-field imaging using LMI

Light-field imaging is widely used in many fields, such as computer vision, graphics, and microscopy imaging, to record high-dimensional light information for abundant visual perception. However, light-field imaging systems generally have high system complexity and limited resolution. Over the last decades, lensless imaging systems have attracted tremendous attention to alleviate the restrictions of lens-based architectures. Despite their advantages, lensless light-field imaging introduces significant errors in light-field reconstruction. This paper introduces a novel, to our knowledge, light field moment imaging-based lensless imaging system (LMI-LIS) aiming to improve the quality of light-field reconstruction. The proposed approach first uses light field moment imaging (LMI) with a sinc angular distribution model of the light field to extract the encoded information of the scene for each sub-aperture area. Meanwhile, the corresponding sub-aperture point spread function is segmented from the system point spread function. Finally, sub-aperture images of the scene are reconstructed separately for each sub-aperture area. To evaluate the light-field reconstruction performance, the imaging quality and angular consistency of different lensless light-filed imaging methods are compared through digital refocusing, epipolar plane image, peak signal-to-noise ratio, and structural similarity index. Furthermore, the effectiveness of the proposed methodology is verified using experimental results and theoretical analysis. It is demonstrated that lensless light-field imaging using LMI and the sinc model of the angular distribution achieves high-quality sub-aperture images.

Assessment of the frequency and risk factors of gastrointestinal bleeding after cardiopulmonary bypass in paediatric cases.

Gastrointestinal bleeding is a potential complication in paediatric patients undergoing cardiopulmonary bypass, as it develops secondary to low gastrointestinal perfusion. This study aimed to examine the incidence of gastrointestinal bleeding and identify its risk factors in these patients. This retrospective study was undertaken to examine the demographic features, clinical findings, and operative data of paediatric patients under years old who had undergone congenital heart surgery with cardiopulmonary bypass between November 1, 2021, and November 1, 2023. The study aimed to investigate the incidence of gastrointestinal bleeding associated with cardiopulmonary bypass and to identify potential risk factors for gastrointestinal bleeding. The obtained results were statistically evaluated. The study period included 1100 patients who underwent congenital heart surgery with cardiopulmonary bypass. Fifty-two percent of the total participants were male. The median weight of the patients was 4.4 kg, with an interquartile range of 3.5-5.8 kg. The patients were categorised by age, revealing that 62% were newborns, 24% were infants, and 14% were children. Forty-four (4.2%) of the total number of patients experienced gastrointestinal bleeding. Newborns had a significantly higher incidence of bleeding (6% or 34 patients) compared to infants (3% or 8 patients) and children (1.5% or 2 patients) (p < 0.05). Patients who experienced gastrointestinal bleeding had a longer median hospital stay of 24 days compared to those who did not, with a median hospital stay of 14 days. Moreover, patients who suffered from bleeding had a significantly higher mortality rate (30%) in comparison to those who did not (9.9%) (p < 0.05). The incidence of gastrointestinal bleeding was found to be associated with several risk factors, such as low operative age and weight, high surgical score, presence of low cardiac output syndrome, extracorporeal membrane oxygenation (ECMO) usage, high lactate levels, and low platelet count. Gastrointestinal bleeding is a potential complication for patients who undergo cardiopulmonary bypass. It is particularly relevant for newborns who have undergone prolonged surgery, have a high surgical complexity score, exhibit high lactate levels, display low cardiac output, utilise ECMO, and possess low platelet counts. In such cases, there may be a heightened incidence of gastrointestinal bleeding. It is important to consider this possibility in order to ensure the best possible patient outcomes.