BPP: a platform for automatic biochemical pathway prediction.

A biochemical pathway consists of a series of interconnected biochemical reactions to accomplish specific life activities. The participating reactants and resultant products of a pathway, including gene fragments, proteins, and small molecules, coalesce to form a complex reaction network. Biochemical pathways play a critical role in the biochemical domain as they can reveal the flow of biochemical reactions in living organisms, making them essential for understanding life processes. Existing studies of biochemical pathway networks are mainly based on experimentation and pathway database analysis methods, which are plagued by substantial cost constraints. Inspired by the success of representation learning approaches in biomedicine, we develop the biochemical pathway prediction (BPP) platform, which is an automatic BPP platform to identify potential links or attributes within biochemical pathway networks. Our BPP platform incorporates a variety of representation learning models, including the latest hypergraph neural networks technology to model biochemical reactions in pathways. In particular, BPP contains the latest biochemical pathway-based datasets and enables the prediction of potential participants or products of biochemical reactions in biochemical pathways. Additionally, BPP is equipped with an SHAP explainer to explain the predicted results and to calculate the contributions of each participating element. We conduct extensive experiments on our collected biochemical pathway dataset to benchmark the effectiveness of all models available on BPP. Furthermore, our detailed case studies based on the chronological pattern of our dataset demonstrate the effectiveness of our platform. Our BPP web portal, source code and datasets are freely accessible at https://github.com/Glasgow-AI4BioMed/BPP.

Heterogeneous biomedical entity representation learning for gene-disease association prediction.

Understanding the genetic basis of disease is a fundamental aspect of medical research, as genes are the classic units of heredity and play a crucial role in biological function. Identifying associations between genes and diseases is critical for diagnosis, prevention, prognosis, and drug development. Genes that encode proteins with similar sequences are often implicated in related diseases, as proteins causing identical or similar diseases tend to show limited variation in their sequences. Predicting gene-disease association (GDA) requires time-consuming and expensive experiments on a large number of potential candidate genes. Although methods have been proposed to predict associations between genes and diseases using traditional machine learning algorithms and graph neural networks, these approaches struggle to capture the deep semantic information within the genes and diseases and are dependent on training data. To alleviate this issue, we propose a novel GDA prediction model named FusionGDA, which utilizes a pre-training phase with a fusion module to enrich the gene and disease semantic representations encoded by pre-trained language models. Multi-modal representations are generated by the fusion module, which includes rich semantic information about two heterogeneous biomedical entities: protein sequences and disease descriptions. Subsequently, the pooling aggregation strategy is adopted to compress the dimensions of the multi-modal representation. In addition, FusionGDA employs a pre-training phase leveraging a contrastive learning loss to extract potential gene and disease features by training on a large public GDA dataset. To rigorously evaluate the effectiveness of the FusionGDA model, we conduct comprehensive experiments on five datasets and compare our proposed model with five competitive baseline models on the DisGeNet-Eval dataset. Notably, our case study further demonstrates the ability of FusionGDA to discover hidden associations effectively. The complete code and datasets of our experiments are available at https://github.com/ZhaohanM/FusionGDA.

Sequence pre-training-based graph neural network for predicting lncRNA-miRNA associations.

MicroRNAs (miRNAs) silence genes by binding to messenger RNAs, whereas long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) that can relieve miRNA silencing effects and upregulate target gene expression. The ceRNA association between lncRNAs and miRNAs has been a research hotspot due to its medical importance, but it is challenging to verify experimentally. In this paper, we propose a novel deep learning scheme, i.e. sequence pre-training-based graph neural network (SPGNN), that combines pre-training and fine-tuning stages to predict lncRNA-miRNA associations from RNA sequences and the existing interactions represented as a graph. First, we utilize a sequence-to-vector technique to generate pre-trained embeddings based on the sequences of all RNAs during the pre-training stage. In the fine-tuning stage, we use Graph Neural Network to learn node representations from the heterogeneous graph constructed using lncRNA-miRNA association information. We evaluate our proposed scheme SPGNN on our newly collected animal lncRNA-miRNA association dataset and demonstrate that combining the $k$-mer technique and Doc2vec model for pre-training with the Simple Graph Convolution Network for fine-tuning is effective in predicting lncRNA-miRNA associations. Our approach outperforms state-of-the-art baselines across various evaluation metrics. We also conduct an ablation study and hyperparameter analysis to verify the effectiveness of each component and parameter of our scheme. The complete code and dataset are available on GitHub: https://github.com/zixwang/SPGNN.

Impact of high-speed nanodroplets on various pathogenic bacterial cell walls.

Although the development of disinfection technologies with novel mechanisms has stagnated, we demonstrate the bactericidal effects and mechanisms of high-speed nanodroplet generation technology. The first development of this technology in 2017 gushes out a water droplet of 10 nm in size at 50 m/s; however, the target surface does not become completely wet. Nanodroplets were exposed to biofilm models of Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Serratia marcescens. This phenomenon was verified when the nanodroplets collide with the surface of the bacteria at an impact pressure of ~75 MPa. S. aureus was exposed to nanodroplets for 30 seconds at 75 MPa, which exploded the bacterial body and completely sterilized. Eighteen MPa damaged the bacterial surface, causing peptidoglycan leakage. S. aureus was repaired and survives in this state. In contrast, in Gram-negative bacteria, nanodroplets with 18 MPa penetrated some biofilm-forming bacteria but did not hit all of them, and the viable count was not significantly reduced. Although all three bacterial species were completely sterilized at 75 MPa, the disinfectant effect was affected by the biomass of the biofilm formed. In summary, our findings prove that nanodroplets at 18 MPa on the bacterial surface were ineffective in killing bacteria, whereas at 75 MPa, all four bacterial species were completely sterilized. The disinfection mechanism involved a high-velocity collision of nanodroplets with the bacteria, physically destroying them. Our results showed that disinfection using this technology could be an innovative method that is completely different from existing disinfection techniques. IMPORTANCE: Although existing disinfection techniques demonstrate bactericidal effects through chemical reactions, concerns regarding human toxicity and environmental contamination have been raised. To the best of our knowledge, this study is the first in the world to reveal that the use of this technology, with nanodroplets of less than 100 nm, can destroy and sterilize bacterial cells by colliding with biofilm-forming bacteria at 75 MPa. Furthermore, because this technology uses only water, it can solve the problems of human toxicity and environmental contamination caused by existing disinfection techniques. Because of its minimal water usage, it can be employed for sanitation worldwide without being limited to specific regions. Our report proposes an unprecedented physical disinfection approach that utilizes a high-speed nanodroplet generation technology.

Brain structure, amyloid, and behavioral features for predicting clinical progression in subjective cognitive decline.

As a potential preclinical stage of Alzheimer's dementia, subjective cognitive decline (SCD) reveals a higher risk of future cognitive decline and conversion to dementia. However, it has not been clear whether SCD status increases the clinical progression of older adults in the context of amyloid deposition, cerebrovascular disease (CeVD), and psychiatric symptoms. We identified 99 normal controls (NC), 15 SCD individuals who developed mild cognitive impairment in the next 2 years (P-SCD), and 54 SCD individuals who did not (S-SCD) from ADNI database with both baseline and 2-year follow-up data. Total white matter hyperintensity (WMH), WMH in deep (DWMH) and periventricular (PWMH) regions, and voxel-wise grey matter volumes were compared among groups. Furthermore, using structural equation modelling method, we constructed path models to explore SCD-related brain changes longitudinally and to determine whether baseline SCD status, age, and depressive symptoms affect participants' clinical outcomes. Both SCD groups showed higher baseline amyloid PET SUVR, baseline PWMH volumes, and larger increase of PWMH volumes over time than NC. In contrast, only P-SCD had higher baseline DWMH volumes and larger increase of DWMH volumes over time than NC. No longitudinal differences in grey matter volume and amyloid was observed among NC, S-SCD, and P-SCD. Our path models demonstrated that SCD status contributed to future WMH progression. Further, baseline SCD status increases the risk of future cognitive decline, mediated by PWMH; baseline depressive symptoms directly contribute to clinical outcomes. In conclusion, both S-SCD and P-SCD exhibited more severe CeVD than NC. The CeVD burden increase was more pronounced in P-SCD. In contrast with the direct association of depressive symptoms with dementia severity progression, the effects of SCD status on future cognitive decline may manifest via CeVD pathologies. Our work highlights the importance of multi-modal longitudinal designs in understanding the SCD trajectory heterogeneity, paving the way for stratification and early intervention in the preclinical stage. PRACTITIONER POINTS: Both S-SCD and P-SCD exhibited more severe CeVD at baseline and a larger increase of CeVD burden compared to NC, while the burden was more pronounced in P-SCD. Baseline SCD status increases the risk of future PWMH and DWMH volume accumulation, mediated by baseline PWMH and DWMH volumes, respectively. Baseline SCD status increases the risk of future cognitive decline, mediated by baseline PWMH, while baseline depression status directly contributes to clinical outcome.

Ultra-compact and fabrication-tolerant multimode photonic jumpers based on dual Bezier curves.

Compact routing of multimode bus waveguides is of great significance for on-chip mode-division multiplexing (MDM) systems to realize high integration density and flexible layout. In this Letter, we propose and experimentally demonstrate a novel, to the best of our knowledge, multimode photonic jumper (MPJ) on a standard silicon-on-insulator (SOI) platform. It enables an ultra-compact connection between two parallel multimode waveguides (MWGs) with an arbitrary displacement. As a proof of concept, we describe two MPJs with displacements of 5.9 µm and 0.6 µm, each supporting three modes and featuring a longitudinal distance of around 14 µm. For both MPJs, the experimental results show insertion losses (ILs) below 0.086 dB and inter-modal cross talk (CT) below -17.6 dB over the wide wavelength range of 1525-1600 nm for all three modes.

White matter alterations in Attention-Deficit/Hyperactivity Disorder (ADHD): a systematic review of 129 diffusion imaging studies with meta-analysis.

Aberrant anatomical brain connections in attention-deficit/hyperactivity disorder (ADHD) are reported inconsistently across diffusion weighted imaging (DWI) studies. Based on a pre-registered protocol (Prospero: CRD42021259192), we searched PubMed, Ovid, and Web of Knowledge until 26/03/2022 to conduct a systematic review of DWI studies. We performed a quality assessment based on imaging acquisition, preprocessing, and analysis. Using signed differential mapping, we meta-analyzed a subset of the retrieved studies amenable to quantitative evidence synthesis, i.e., tract-based spatial statistics (TBSS) studies, in individuals of any age and, separately, in children, adults, and high-quality datasets. Finally, we conducted meta-regressions to test the effect of age, sex, and medication-naïvety. We included 129 studies (6739 ADHD participants and 6476 controls), of which 25 TBSS studies provided peak coordinates for case-control differences in fractional anisotropy (FA)(32 datasets) and 18 in mean diffusivity (MD)(23 datasets). The systematic review highlighted white matter alterations (especially reduced FA) in projection, commissural and association pathways of individuals with ADHD, which were associated with symptom severity and cognitive deficits. The meta-analysis showed a consistent reduced FA in the splenium and body of the corpus callosum, extending to the cingulum. Lower FA was related to older age, and case-control differences did not survive in the pediatric meta-analysis. About 68% of studies were of low quality, mainly due to acquisitions with non-isotropic voxels or lack of motion correction; and the sensitivity analysis in high-quality datasets yielded no significant results. Findings suggest prominent alterations in posterior interhemispheric connections subserving cognitive and motor functions affected in ADHD, although these might be influenced by non-optimal acquisition parameters/preprocessing. Absence of findings in children may be related to the late development of callosal fibers, which may enhance case-control differences in adulthood. Clinicodemographic and methodological differences were major barriers to consistency and comparability among studies, and should be addressed in future investigations.

Low dose optimization for total-body 2-[18F]FDG PET/CT imaging: a single-center study on feasibility based on body mass index stratification.

OBJECTIVES: Implementing personalization protocol in clinical routine necessitates diverse low-dose PET/CT scan protocols. This study explores the clinical feasibility of one-third (1/3) dose regimen and evaluates the diagnostic image quality and lesion detectability of BMI-based 1/3-injection doses for 2-[18F]FDG PET/CT imaging. METHODS: Seventy-four cancer patients underwent total-body 2-[18F]FDG PET/CT examination, with 37 retrospectively enrolled as full-dose group (3.7 MBq/kg) and 37 prospectively enrolled as the 1/3-dose group (1.23 MBq/kg). The 1/3-dose group was stratified by BMI, with an acquisition time of 5 min (G5), 6 min (G6), and 8 min (G8) for BMI < 25, 25 ≤ BMI ≤ 29, and BMI > 29, respectively. Image quality was subjectively and objectively assessed, and lesion detectability was quantitatively analyzed. RESULTS: Subjective assessments of 1/3-dose and full-dose PET images showed strong agreement among readers (κ > 0.88). In the 1/3-dose group, the Likert scores were above 4. G5, G6, and G8 showed comparable image quality, with G5 demonstrating higher lesion conspicuity than G6 and G8 (p = 0.045). Objective evaluation showed no significant differences in SUVmax, liver SUVmean and TBR between 1/3- and full-dose groups (p > 0.05). No statistical differences were observed in the SUVmax of primary tumor, SUVmean of liver and TBR across all BMI categories between the 1/3-dose and full-dose groups. Lesion detection rates showed no significant difference between the 1/3-dose (93.24%, 193/207) and full-dose groups (94.73%, 198/209) (p = 0.520). CONCLUSION: A BMI-stratified 1/3-dose regimen is a feasible low-dose alternative with clinically acceptable lesion detectability equivalent to full-dose protocol, potentially expanding the applicability of personalized protocols. CLINICAL RELEVANCE STATEMENT: This study demonstrated that BMI-stratified 1/3-dose regimens for [18F]FDG total-body PET/CT yielded equivalent outputs compared to the full-dose regimen, which aligns with clinical needs for personalization in dose and BMI. KEY POINTS: Currently, limited personalized low-dose total-body PET/CT protocols are available, particularly for patients with varied BMI. Reducing the radiotracer dose to 1/3 the standard demonstrated comparable image quality and lesion detectability equivalent to full dose. BMI-stratified 1/3-dose regimen is a clinically feasible low-dose alternative.

Improving the ferroelectric properties of Lu doped Hf0.5Zr0.5O2thin films by capping a CeOxlayer.

Lu doped Hf0.5Zr0.5O2(HZO) ferroelectric films were prepared on Pt/TiN/SiO2/Si substrate by chemical solution deposition method, and an interfacial engineering strategy for improving the ferroelectric property was explored by capping the Lu doped HZO films with a cerium oxide layer. Compared with the Lu doped HZO film without the CeOxcoating layer, the Lu doped HZO film with the CeOxcoating layer has a larger remanent polarization (2Pr= 34.72µC cm-2) and presents weaker wake-up behavior, which result from the higher orthogonal phase ratio and the lower oxygen vacancy of the CeOxcoated Lu doped HZO film. In addition, the CeOxcoating can remarkably improve the fatigue resistance and retention performance of the Lu doped HZO films. It is hoped that the results can provide an effective approach for the realization of high-performance and highly reliable hafnium oxide based ferroelectric thin films.

Family risk, parental cortisol contagion, and parenting: A process-oriented approach to spillover.

This multi-method longitudinal study sought to investigate linkage in parental neuroendocrine functioning - indicated by cortisol - over two measurement occasions. In addition, we examined how parental cortisol linkage may operate as an intermediate factor in the cascade of contextual risks and parenting. Participants were 235 families with a young child (Mage = 33.56, 36.00 years for mothers and fathers respectively), who were followed for two annual measurement occasions. Parental cortisol linkage was measured around a laboratory conflict discussion task at both measurement occasions (i.e., pre-discussion, 20- and 40-minute post-discussion for each measurement occasion). Maternal and paternal parenting behavior was observed during a parent-child discipline discussion task. Findings indicated similar levels of cortisol linkage between parents over the two measurement occasions. Furthermore, cortisol linkage between parents operated as an intermediate factor between contextual risks and more compromised parenting behavior. That is, greater contextual risks, indicated by greater neighborhood risk and interparental conflict, were linked to greater cortisol linkage between parents over time, which was in turn linked to greater authoritarian parenting during parent-child interaction. Findings highlighted the importance of understanding physiological-linkage processes with respect to the impact of contextual risks on family functioning and may have crucial implications for clinical work.

Enhancing stereotactic ablative boost radiotherapy dose prediction for bulky lung cancer: A multi-scale dilated network approach with scale-balanced structure loss.

PURPOSE: Partial stereotactic ablative boost radiotherapy (P-SABR) effectively treats bulky lung cancer; however, the planning process for P-SABR requires repeated dose calculations. To improve planning efficiency, we proposed a novel deep learning method that utilizes limited data to accurately predict the three-dimensional (3D) dose distribution of the P-SABR plan for bulky lung cancer. METHODS: We utilized data on 74 patients diagnosed with bulky lung cancer who received P-SABR treatment. The patient dataset was randomly divided into a training set (51 plans) with augmentation, validation set (7 plans), and testing set (16 plans). We devised a 3D multi-scale dilated network (MD-Net) and integrated a scale-balanced structure loss into the loss function. A comparative analysis with a classical network and other advanced networks with multi-scale analysis capabilities and other loss functions was conducted based on the dose distributions in terms of the axial view, average dose scores (ADSs), and average absolute differences of dosimetric indices (AADDIs). Finally, we analyzed the predicted dosimetric indices against the ground-truth values and compared the predicted dose-volume histogram (DVH) with the ground-truth DVH. RESULTS: Our proposed dose prediction method for P-SABR plans for bulky lung cancer demonstrated strong performance, exhibiting a significant improvement in predicting multiple indicators of regions of interest (ROIs), particularly the gross target volume (GTV). Our network demonstrated increased accuracy in most dosimetric indices and dose scores in different ROIs. The proposed loss function significantly enhanced the predictive performance of the dosimetric indices. The predicted dosimetric indices and DVHs were equivalent to the ground-truth values. CONCLUSION: Our study presents an effective model based on limited datasets, and it exhibits high accuracy in the dose prediction of P-SABR plans for bulky lung cancer. This method has potential as an automated tool for P-SABR planning and can help optimize treatments and improve planning efficiency.

Causal relationships between gut microbiota and Aneurysmal Subarachnoid Hemorrhage: A Bidirectional Mendelian Randomization Study.

BACKGROUND AND PURPOSE: Aneurysmal Subarachnoid Hemorrhage (aSAH) poses a significant health burden globally, necessitating a deeper understanding of its etiology and potential preventive strategies. Recent research has suggested a possible link between gut microbiota composition and the risk of vascularity, prompting investigation into this association using Mendelian Randomization (MR) analysis. Here, we aimed to elucidate the causal relationship between gut microbiota composition and aSAH risk utilizing MR analysis. METHODS: We employed four distinct MR methodologies, including inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode, to assess the causal nexus between gut microbiota composition and aSAH risk. Genetic instrumental variables (IVs) associated with gut microbiome composition were selected from a comprehensive multiethnic genome-wide association study (GWAS) involving 18,473 individuals across diverse geographic regions. Sensitivity analyses were conducted to detect potential heterogeneity and pleiotropy. RESULTS: Our Mendelian Randomization (MR) analyses unveiled a substantial and statistically significant causal relationship between gut microbiota composition and the risk of Aneurysmal Subarachnoid Hemorrhage (aSAH). Employing the Inverse Variance Weighted (IVW) method, we observed negative associations between aSAH and specific taxonomic levels of gut microbiota. Specifically, the IVW approach identified significant associations with one order, Victivallales (PIVW=0.047, OR: 0.78, 95 % CI: 0.62-0.99), one family, Porphyromonadaceae (PIVW=0.03, OR: 0.64, 95 % CI: 0.43-0.95), one class, Lentisphaeria (PIVW=0.047, OR: 0.78, 95 % CI: 0.62-0.99), and three genera: Bilophila (PIVW=0.02, OR: 0.68, 95 % CI: 0.50-0.93), Fusicatenibacter (PIVW=0.04, OR: 0.69, 95 % CI: 0.49-0.98), and Ruminococcus1 (PIVW=0.01, OR: 0.51, 95 % CI: 0.32-0.84). These findings were consistent across various MR methodologies, underscoring the robustness of our results. Sensitivity analyses further validated the stability of our findings, with no evidence of heterogeneity or pleiotropy detected. CONCLUSION: Our study provides compelling evidence supporting a causal relationship between gut microbiota composition and the risk of aSAH. These findings underscore the potential therapeutic implications of modulating gut microbiota to prevent and manage aSAH. Further research is warranted to explore the underlying mechanisms and develop targeted interventions aimed at mitigating aSAH risk through gut microbiota modulation.

Interparental Conflict and Early Adolescent Depressive Symptoms: Parent-Child Triangulation as the Mediator and Grandparent Support as the Moderator.

A notable ambiguity persists concerning whether distinct forms of parent-child triangulation (unstable coercive coalition, stable coalition, detouring-attacking, detouring supportive, parentification) might mediate the association between interparental conflict and early adolescent depressive symptoms similarly within the context of Chinese Confucianism. Filling this research gap, this study aimed to examine the mediating role of the five dimensions of parent-child triangulation in the association between interparental conflict and early adolescent depressive symptoms, as well as the moderating effect of grandparent support on this mediating pathway. Data were drawn from a sample of 761 Chinese adolescents (M age = 12.82 ± 0.47, 49.1% girls). Structural equation model analyses indicated that unstable coercive coalition, stable coalition, and detouring-attacking behaviors partially mediated the association between interparental conflict and adolescent depressive symptoms, while detouring-supportive behaviors and parentification did not demonstrate such mediating effects. Unlike Western societies, a negative correlation was observed between interparental conflict and parentification in the context of China. Grandparent support mitigated the adverse effects of both interparental conflict and the unstable coercive coalition on early adolescent depressive symptoms.

Achieving Record External Quantum Efficiency of 11.5 % in Solution-Processable Deep-Blue Organic Light-Emitting Diodes Utilizing Hot Exciton Mechanism.

High performance solution-processable deep-blue emitters with a Commission International de l'Eclairage (CIE) coordinate of CIEy≤0.08 are highly desired in ultrahigh-definition display. Although, deep-blue materials with hybridized local and charge-transfer (HLCT) excited-state feature are promising candidates, their rigidity and planar molecular structures limit their application in solution-processing technique. Herein, four novel deep-blue solution-processable HLCT emitters were first proposed by attaching rigid imide aliphatic rings as functional units onto the HLCT emitting core. The functional units not only improve solubility, enhance thermal properties and morphological stability of the emitting core, but also promote photoluminescence efficiency, balance charge carrier transport, and inhibit aggregation-caused quenching effect due to the weak electron-withdrawing property as well as steric hindrance. The corresponding solution-processable organic light-emitting diodes (OLEDs) substantiate an unprecedented maximum external quantum efficiency (EQEmax) of 11.5 % with an emission peak at 456 nm and excellent colour purity (full width at half maximum=56 nm and CIEy=0.09). These efficiencies represent the state-of-the-art device performance among the solution-processable blue OLEDs based on the "hot exciton" mechanism. This simple strategy opens up a new avenue for designing highly efficient solution-processable deep-blue organic luminescent materials.

Organic Materials with Ultrabright Phosphorescence at Room Temperature under Physiological Conditions for Bioimaging.

In contrast to the high efficiency of room temperature phosphorescence in crystal states, the generally utilized nanoparticles of organic materials in bioimaging demonstrated sharply decreased performance by orders of magnitude under physiological conditions, badly limiting the realization of their unique advantages. This case, especially for organic red/near-infrared (NIR) phosphorescence materials, is not only the challenge present in reality but more importantly, for the theoretical problem of deeply understanding and avoiding the quenching effect by oxygen and water toward excited triplet states. Herein, thanks to the intelligent molecular design by the introduction of abundant hydrophobic chains and highly-branched structures, bright and persistent red/NIR phosphorescence under physiological conditions has been realized, which demonstrated the shielding effect towards oxygen, and strengthened the intermolecular interactions to suppress the non-radiative transitions. Accordingly, the record phosphorescence intensity of nanoparticles in bioimage, up to 8.21 ± 0.36 × 108 p s-1 cm-2 sr-1, was achieved, to realize the clear phosphorescence imaging of liver and tumors in living mice, even lymph nodes in rabbit models with high SBRs. This work afforded an efficient way to achieve the bright red/NIR phosphorescence nanoparticles, guiding their further applications in biology and medicine.

HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction.

Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional experimental methods for circRNA-disease associations identification are labor-intensive. In this work, we propose a novel method based on the heterogeneous graph neural network and metapaths for circRNA-disease associations prediction termed as HMCDA. First, a heterogeneous graph consisting of circRNA-disease associations, circRNA-miRNA associations, miRNA-disease associations and disease-disease associations are constructed. Then, six metapaths are defined and generated according to the biomedical pathways. Afterwards, the entity content transformation, intra-metapath and inter-metapath aggregation are implemented to learn the embeddings of circRNA and disease entities. Finally, the learned embeddings are used to predict novel circRNA-disase associations. In particular, the result of extensive experiments demonstrates that HMCDA outperforms four state-of-the-art models in fivefold cross validation. In addition, our case study indicates that HMCDA has the ability to identify novel circRNA-disease associations.

An artificial-intelligence-based age-specific template construction framework for brain structural analysis using magnetic resonance images.

It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age-specific brain variations and tendencies across different ages. In this article, we present an AI-based age-specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1-weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5-year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study.

Active polarization controller based on micro-ring resonators.

On-chip polarization handling is of great significance for optical interconnects to overcome polarization sensitivity. In this Letter, we propose and experimentally demonstrate a novel, to the best of our knowledge, on-chip polarization controller (PC) on a 220 nm silicon-on-insulator (SOI) platform. It is the first demonstration of a PC based on micro-ring resonators. Any input polarization states can be actively converted to the standard transverse-electric (TE) mode under the phase manipulation. Experimental results show that the insertion loss is less than 0.8 dB and the polarization dependent loss (PDL) is around 0.5 dB. The proposed device also exhibits excellent performances in wavelength tunability over the C band and 35 Gbps data transmission.

Homogeneity Assumptions in the Analysis of Dynamic Processes.

With the increased use of time series data in human research, ranging from ecological momentary assessments to data passively obtained, researchers can explore dynamic processes more than ever before. An important question researchers must ask themselves is, do I think all individuals have similar processes? If not, how different, and in what ways? Dr. Peter Molenaar's work set the foundation to answer these questions by providing insight into individual-level analysis for processes that are assumed to differ across individuals in at least some aspects. Currently, such assumptions do not have a clear taxonomy regarding the degree of homogeneity in the patterns of relations among variables and the corresponding parameter values. This paper provides the language with which researchers can discuss assumptions inherent in their analyses. We define strict homogeneity as the assumption that all individuals have an identical pattern of relations as well as parameter values; pattern homogeneity assumes the same pattern of relations but parameter values can differ; weak homogeneity assumes there are some (but not all) generalizable aspects of the process; and no homogeneity explicitly assumes no population-level similarities in dynamic processes across individuals. We demonstrate these assumptions with an empirical data set of daily emotions in couples.

Safety of different surgical modalities for recurrent respiratory papillomatosis resection: A systematic review and meta-analysis.

BACKGROUND: Currently, the most common surgical modalities used for recurrent respiratory papillomatosis (RRP) resection are microdebrider, carbon dioxide (CO2 ) laser and potassium-titanyl-phosphate (KTP) laser. However, complication rates vary among different surgical modalities and have been controversial in different studies. OBJECTIVE OF REVIEW: This study systematically reviews the available studies which reported intra-operative and post-operative complications, aiming to compare the safety of microdebrider, CO2 laser and KTP laser. TYPE OF REVIEW: Meta-analysis. SEARCH STRATEGY: Seven electronic databases (PubMed/MEDLINE, EMBASE[Ovid], Scopus, Cochrane Library and Web of Science) were searched from inception through 28 April 2022. Randomised controlled, prospective or retrospective observational studies that recorded the complications of three different surgical modalities for RRP resection were included in the meta-analysis. EVALUATION METHOD: Outcomes of interest were intra-operative and post-operative complications, and complication rate was calculated to evaluate the safety of surgical methods. RESULTS: Twenty different studies were included in quantitative synthesis. Only one study compared outcomes of those three kinds of treatment modalities simultaneously, two studies compared microdebrider and CO2 laser, and the remaining studies focussed on only one of three treatments. The weighted average complication rate for microdebrider was 0.03 (95% confidence interval [CI] 0.00-0.21), n = 6, for CO2 laser treatment was 0.16 (95% CI 0.09-0.25), n = 14 and for KTP laser treatment was 0.04 (95% CI 0.00-0.14), n = 4. CONCLUSION: The limited evidence demonstrated that CO2 lasers in the surgical treatment of RRP may lead to more surgical complications, and microdebrider and KTP lasers may be safer. However, the heterogeneous data limit any strong comparison of outcomes of different treatment of laryngeal papillomas. Future randomised controlled trials that directly compare the safety of different surgical modalities are needed.