Association between caregivers' anxiety and depression symptoms and feeding difficulties of preschool children: A cross-sectional study in rural China

PURPOSE

To investigate the prevalence of feeding difficulties in preschool children and explore the association between caregivers' anxiety and depression symptoms and preschool children's feeding difficulties.

METHODS

This cross-sectional study was conducted between June 2017 and January 2018 in rural areas of Anhui province, China. A total of 2231 preschool children and their caregivers were interviewed. Feeding difficulties of preschool children were reported by caregivers using the adapted Identification and Management of Feeding Difficulties (IMFeD) tool. Anxiety and depression symptoms of caregivers were evaluated via the Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS).

RESULTS

In total, 54.1% of preschool children were reported to have feeding difficulties by their caregivers. Among all children, when the caregivers had symptoms of anxiety or depression, the children had a higher risk of feeding difficulties. Specifically, for caregivers' anxiety symptoms, the odds ratios (ORs) of feeding difficulties in all children, left-behind children (LBC), and non-LBC were 1.91 (95% confidence interval [CI]: 1.42-2.57), 2.04 (95% CI: 1.34-3.09), and 1.86 (95% CI: 1.21-2.87), respectively; for caregivers' depression symptoms, the ORs of feeding difficulties in all children, LBC, and non-LBC were 1.86 (95% CI: 1.46-2.39), 1.76 (95% CI: 1.24-2.51), and 2.08 (95% CI: 1.45-2.97), respectively. In addition, when caregivers who were parents or grandparents had anxiety or depression symptoms, their children had a higher risk of feeding difficulties. Specifically, for parents and grandparents with anxiety symptoms, the ORs of feeding difficulties were 1.84 (95% CI: 1.14-2.98) and 2.17 (95% CI: 1.46-3.22), respectively; for parents and grandparents with depression symptoms, the ORs of feeding difficulties were 2.03 (95% CI: 1.40-2.95) and 1.93 (95% CI: 1.37-2.73), respectively.

CONCLUSION

Caregivers' anxiety or depression symptoms are positively associated with feeding difficulties in children.

LncRNA WWOX‑AS1 inhibits the proliferation, migration and invasion of osteosarcoma cells

Recently, numerous long non-coding (lnc)RNAs have been revealed as serving important roles in human gene regulation. Previous studies have suggested that aberrant expression of lncRNAs is associated with cancer progression and metastasis. Previous studies have also demonstrated that decreased expression of WW domain-containing oxidoreductase (WWOX) is associated with poor prognosis in numerous cancer types. However, the effect of WWOX antisense RNA 1 (WWOX-AS1) in the development of cancer remains unknown. The aim of the present study was to investigate the role of WWOX-AS1 in osteosarcoma. The expression levels of WWOX-AS1 in human osteosarcoma cell lines and a normal osteoblastic cell line were investigated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The results revealed that WWOX-AS1 expression was downregulated in osteosarcoma tissues. Furthermore, the association between WWOX-AS1 and the prognosis of patients with osteosarcoma was investigated using Kaplan-Meier and log-rank tests. The results suggested that patients exhibiting high WWOX-AS1 expression demonstrated a greater overall survival compared with patients exhibiting low WWOX-AS1 expression. In addition, overexpression and knockdown of WWOX-AS1 was performed using transfection experiments and confirmed by RT-qPCR in MG63 and SAOS2 cells, respectively. The results demonstrated that WWOX-AS1 and WWOX expression were positively correlated. Furthermore, the results of the knockdown and overexpression functional experiments suggested that WWOX-AS1 overexpression inhibited the proliferation, migration and invasion of MG63 cells, and knockdown of WWOX-AS1 enhanced the proliferation, migration and invasion of MG63 cells in SAOS2 cells. In conclusion, the results of the present study suggested that WWOX-AS1 may represent a potential biomarker and therapeutic target for the treatment of osteosarcoma.

Brain Functional Connectivity Analysis via Graphical Deep Learning

OBJECTIVE

Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and complex relationships between different brain regions.

METHOD

In this work, a graph convolutional network (GCN) based framework is proposed by exploiting the information from both region-to-region connectivities of the brain and subject-subject relationships. We first construct an affinity subject-subject graph followed by GCN analysis. A Laplacian regularization term is introduced in our model to tackle the overfitting problem. We apply and validate the proposed model to the Philadelphia Neurodevelopmental Cohort for the brain cognition study.

RESULTS

Experimental analysis shows that our proposed framework outperforms other competing models in classifying groups with low and high Wide Range Achievement Test (WRAT) scores. Moreover, to examine each brain region's contribution to cognitive function, we use the occlusion sensitivity analysis method to identify cognition-related brain functional networks. The results are consistent with previous research yet yield new findings.

CONCLUSION AND SIGNIFICANCE

Our study demonstrates that GCN incorporating prior knowledge about brain networks offers a powerful way to detect important brain networks and regions associated with cognitive functions.

Dynamic weighted hypergraph convolutional network for brain functional connectome analysis

The hypergraph structure has been utilized to characterize the brain functional connectome (FC) by capturing the high order relationships among multiple brain regions of interest (ROIs) compared with a simple graph. Accordingly, hypergraph neural network (HGNN) models have emerged and provided efficient tools for hypergraph embedding learning. However, most existing HGNN models can only be applied to pre-constructed hypergraphs with a static structure during model training, which might not be a sufficient representation of the complex brain networks. In this study, we propose a dynamic weighted hypergraph convolutional network (dwHGCN) framework to consider a dynamic hypergraph with learnable hyperedge weights. Specifically, we generate hyperedges based on sparse representation and calculate the hyper similarity as node features. The hypergraph and node features are fed into a neural network model, where the hyperedge weights are updated adaptively during training. The dwHGCN facilitates the learning of brain FC features by assigning larger weights to hyperedges with higher discriminative power. The weighting strategy also improves the interpretability of the model by identifying the highly active interactions among ROIs shared by a common hyperedge. We validate the performance of the proposed model on two classification tasks with three paradigms functional magnetic resonance imaging (fMRI) data from Philadelphia Neurodevelopmental Cohort. Experimental results demonstrate the superiority of our proposed method over existing hypergraph neural networks. We believe our model can be applied to other applications in neuroimaging for its strength in representation learning and interpretation.

Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies

OBJECTIVE

Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity (FC) patterns have been extensively used to delineate global functional organization of the human brain in healthy development and neuropsychiatric disorders. In this paper, we investigate how FC in males and females differs in an age prediction framework.

METHODS

We first estimate FC between regions-of-interest (ROIs) using distance correlation instead of Pearson's correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex between-ROI interactions. Then, we propose a novel non-convex multi-task learning (NC-MTL) model to study age-related gender differences in FC, where age prediction for each gender group is viewed as one task, and a composite regularizer with a combination of the non-convex l2,1-2 and l1-2 terms is introduced for selecting both common and task-specific features.

RESULTS AND CONCLUSION

We validate the effectiveness of our NC-MTL model with distance correlation-based FC derived from rs-fMRI for predicting ages of both genders. The experimental results on the Philadelphia Neurodevelopmental Cohort demonstrate that our NC-MTL model outperforms several other competing MTL models in age prediction. We also compare the age prediction performance of our NC-MTL model using FC estimated by Pearson's correlation and distance correlation, which shows that distance correlation-based FC is more discriminative for age prediction than Pearson's correlation-based FC.

SIGNIFICANCE

This paper presents a novel framework for functional connectome developmental studies, characterizing developmental gender differences in FC patterns.

Soil quality indicators response to application of hydrophilic polymers to a soil from a sulfide mine

In soils impacted by mining activities a vegetal cover is required to protect the site from the erosive forces of water and wind. The success of this objective depends on plant establishment and canopy closure. Polyacryalate polymers aid the growth of crops and indigenous plants in soils from sulfide mines. Soil characteristics change as a consequence of polymer application, but indicators that pinpoint these changes have not been identified yet. Our objectives were to (1) identify the sensitive indicators of changes in soil quality following polymer application, (2) relate these with assessment based on plant growth and soil cover. A mine soil was left unamended or received a characterized polyacrylate, a polyacrylate removed from diapers, or shredded diapers. Biomass of Spergularia purpurea was measured and proportion of soil cover evaluated. Soil enzymes, microbial activity, and respiration were analyzed. Availability of potentially toxic trace elements was estimated by their concentration in shoots. Factor analysis identified three factors that accounted for 94% of the variation in parameters, and the scores separated the four treatments. The indicators with greatest communality were correlated with plant growth and soil cover. The best soil quality indicators were As and Zn in shoots, protease, β-glucosidase, and fructose-induced respiration. It seems that the most important indicators to be used to assess the restoration of sulfide mine soils are those related with bioavailability of trace elements and soil enzymatic activities.

Comparison of trained and untrained observers using subjective and objective measures of imaging performance

RATIONALE AND OBJECTIVES

We compared subjective and objective measures of imaging performance using variations of Rose- and Burger-type low-contrast phantoms with trained (radiology residents) and untrained (graduate students) observers.

METHODS

With one phantom variant, observers indicated the total number of objects seen when test objects were presented in a regular pattern (subjective). With the second phantom variant, observers stated whether a low-contrast disk was present in each locale, thereby permitting the true-positive fraction and false-positive fraction to be determined (objective).

RESULTS

The untrained-observer group had a significantly lower imaging performance than the trained observer group in subjective tests. These differences were not found on objective tests. For the trained-observer group, similar contrast levels were required in subjective and objective tests to yield a 50% rate of detection.

CONCLUSION

Trained observers are superior subjects compared with untrained observers for assessing imaging performance using subjective low-contrast phantoms. In experiments using phantoms that allowed objective testing, both groups of observers yield similar results.

Functional network estimation using multigraph learning with application to brain maturation study

Although most dramatic structural changes occur in the perinatal period, a growing body of evidences demonstrates that adolescence and early adulthood are also important for substantial neurodevelopment. We were thus motivated to explore brain development during puberty by evaluating functional connectivity network (FCN) differences between childhood and young adulthood using multi-paradigm task-based functional magnetic resonance imaging (fMRI) measurements. Different from conventional multigraph based FCN construction methods where the graph network was built independently for each modality/paradigm, we proposed a multigraph learning model in this work. It promises a better fitting to FCN construction by jointly estimating brain network from multi-paradigm fMRI time series, which may share common graph structures. To investigate the hub regions of the brain, we further conducted graph Fourier transform (GFT) to divide the fMRI BOLD time series of a node within the brain network into a range of frequencies. Then we identified the hub regions characterizing brain maturity through eigen-analysis of the low frequency components, which were believed to represent the organized structures shared by a large population. The proposed method was evaluated using both synthetic and real data, which demonstrated its effectiveness in extracting informative brain connectivity patterns. We detected 14 hub regions from the child group and 12 hub regions from the young adult group. We show the significance of these findings with a discussion of their functions and activation patterns as a function of age. In summary, our proposed method can extract brain connectivity network more accurately by considering the latent common structures between different fMRI paradigms, which are significant for both understanding brain development and recognizing population groups of different ages.

Role of Glycol Chitosan-incorporated Ursolic Acid Nanoparticles in the Treatment of Osteosarcoma

Purpose: To investigate the effect of ursolic acid (UA)-incorporated glycol chitosan (GC) nanoparticles on inhibition of human osteosarcoma.Methods: U2OS and Saos-2 osteosarcoma cells were transfected with ursolic acid (UA) incorporated glycol chitosan (GC) nanoparticles. Ultraviolet (UV)  spectrophotometry was used to measure drug contents in nanoparticles at 365 nm with empty GC vehicles as blank. Bicinchoninic acid assay (BCA) method was employed to determine protein concentration. Identification of apoptosis and necrosis in osteosarcoma cells was performed by propidium iodide and FITC-annexin V reagents, respectively. FAC Scan flow cytometry was used to analyse apoptotic cells.Results: Among the range of UA concentrations tested, the minimum effective concentration was 10 μM with half inhibitory concentration IC50 of 25 μM. In U2OS cells, treatment with 10 and 25 μM UAinduced apoptosis in 5.89 ± 3.90 and 60.54 ± 5.40 % cells, respectively, compared to 2.05 ± 1.01 % cells for control. In Saos-2 cells, exposure to 10 and 25 μM UA induced apoptosis in 9.86 ± 8.89 and 47.54 ± 14.5 % cells, respectively, compared to 1.79 ± 0.23 % for control cells. Western blot analysis revealed  translocation of Bax and Bcl-2 proteins from mitochondria to cell cytosol. Increase in UA concentration  from 10 μM to 25 μM led to increase in the proportion of cells in G0/G1 phase and decrease in the number of cells in S and G2/M phases. These results confirm that UA transfection arrests cell cycle in G0/G1 phase in human osteosarcoma cell lines.Conclusion: UA transfection resulted in the inhibition of cell proliferation, Ezh2 expression inhibition, and apoptosis via mitochondrial pathway due to decrease in membrane potential and release of cytochrome C, as well as cell cycle arrest in G0/G1 phase.Keywords: Osteosarcoma, Cell cycle arrest, Palliation, Glycol chitosan, Ursolic acid

Use of hydrophilic polymers from diapers to aid the establishment of Spergularia purpurea in a mine soil

We used hydrophilic polymers from diapers to aid the establishment of an indigenous plant (Spergularia purpurea (Persoon) G. Don fil.) in a soil from a pyrite mine. Lysimeters were filled with the mine soil with no amendment (control), with a polyacrylate polymer, with a polymer removed from diapers, and with shredded diapers. The establishment of a plant cover was faster in soil amended with polymer from diapers, and 85 days after sowing the soil was completely covered in all treatments except control. The concentrations of trace elements in plant shoots decreased in amended soil. The activities of soil acid phosphatase, beta-glucosidase, protease and cellulase were greatest in soil amended with the polyacrylate polymer or with polymer removed from diapers, while the application of shredded diapers leads to values that were in general intermediate between these treatments and unamended control. Basal- and substrate-induced respirations, and dehydrogenase were greatest in soil amended with polymers, but the presence of a plastic film and fibrous materials from shredded diapers prevented any improvement in these parameters compared with unamended soil. In the second experiment, we evaluated the risk of downward movement of polymers in columns of a sandy soil. Polymer from diapers, with or without Cu, was placed at a 10 cm-depth. Five leaching cycles with artificial rain took place and leachates were analyzed for organic matter and Cu. At the end of the experiment, the soil columns were sliced and each layer was analyzed separately. Some repacking of soil and polymer particles took place, but there was no indication that polymers moved to any great depth in soil columns.

Scaling-up production of recombinant AAV vectors for clinical applications

Recombinant adeno-associated virus (AAV)-based vectors capable of expressing therapeutic gene products in vivo have shown significant promise for human gene therapy. A major challenge for these applications is the development of processes to enable production of large quantities of AAV vectors and purification of material that is well characterized and appropriate for parenteral administration. Several cell culture systems have been developed for AAV vector production, and a limited number of these demonstrate the potential to generate AAV vectors at concentrations compatible with cost-effective large-scale production. Vector purification protocols, in particular those based on the use of scalable column chromatography, have concurrently been developed that demonstrate the potential to provide highly purified AAV vector preparations with high yield. These advances support the potential for AAV vectors as therapeutic agents for gene therapy.

Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks

Multimodal neuroimaging modeling has become a widely used approach but confronts considerable challenges due to heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret these diverse datasets within a cohesive analytical framework. In our research, we amalgamate functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) into a cohesive framework. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain's connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging-derived features from various modalities-functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI-within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data. This technique enhances interpretability at connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project's Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved predictive accuracy and uncovers crucial anatomical features and essential neural connections, deepening our understanding of brain structure and function. This study not only advances multi-modal neuroimaging analytics by offering a novel method for the integrated analysis of diverse imaging modalities but also improves the understanding of intricate relationship between the brain's structural and functional networks and cognitive development.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.

Network Configuration in App Design: The Effects of Simplex and Multiplex Networks on Team Performance

Members of mobile app design teams collaborate with each other to accomplish tasks and/or to socialize. However, how network configuration of instrumental and expressive interactions affects team creativity, efficiency, and satisfaction has not yet been studied. Accounting for both simplex and multiplex social networks in teams, this study develops a research model examining the mechanisms by which the centralization of different types of networks impacts team performance. To test our research hypotheses, we collected data from 62 student teams working on an app design class project. We found that the centralization of the instrumental-expressive multiplex network reduces teams’ information elaboration and similarity perception; the centralization of the instrumental simplex network is beneficial to information elaboration; and team information elaboration positively influences team creativity, efficiency, and satisfaction. We also found that team similarity perception negatively affects team creativity and positively affects team satisfaction. To alleviate concerns about the potential simultaneity bias between network configuration and information elaboration or similarity perception, we replicated the results based on a cross-lagged analysis with additional data collected from 48 design teams at two points: at team establishment and at project completion. This paper contributes to the literature on software development by examining the mechanisms via which the configuration of multiplex and simplex networks affects team performance.

[Observation on enhanced adhesion to dentin by amino acid]

An amino acid was adopted to pretreat the surface of dentin. The bond strength between GP adhesive and dentin mediated by the amino acid was enhanced after the pretreatment. Furthermore, the longer the stored time of extracted tooth was, the better the effect of adhesion was. This pretreatment will benefit the adhesive restoration for dead tooth or low activity tooth clinically.

Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis

Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant.

Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies

Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain. While Graph Neural Networks (GNNs) offer a structured approach to represent neuroimaging data, they are limited by their need for a predefined graph structure to depict associations between brain regions, a detail not solely provided by FCs. To bridge this gap, we introduce the Gated Graph Transformer (GGT) framework, designed to predict cognitive metrics based on FCs. Empirical validation on the Philadelphia Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of our model, further accentuating its potential in identifying pivotal neural connectivities that correlate with human cognitive processes.

Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks

Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.