Prognostic value of 18F-FDG PET/CT in postoperative recurrence of retroperitoneal liposarcoma: a single-center retrospective study.

OBJECTIVE: Recurrence is the leading cause of tumor-related death in retroperitoneal liposarcoma (RPLPS). Variant subtypes of RPLPS determine different recurrence 18F]-fluoro-2-deoxy-D-glucose (18F-FDG) PET/computed tomography (PET/CT). This study analyzed the characteristics of different histologic subtypes of 18F-FDG PET/CT and their associations with recurrence and prognosis. METHODS: Clinical-pathological information, 18F-FDG PET/CT data, recurrence, and progression-free survivals (PFS) of 83 patients with RPLPS were collected. Maximum and peak standardized uptake values (SUVmax and SUVpeak, respectively) and mean CT value (CTmean) of tumors were measured and correlated with histologic subtype. The predictability of SUVmax, SUVpeak, and CTmean for the histologic subtype was evaluated using receiver operating characteristics (ROC) max and SUVpeak for recurrence. Kaplan-Meier analysis was performed to max and SUVpeak were risk factors for recurrence. RESULTS: Studied patients with different types of liposarcomas. Dedifferentiated liposarcomas (DDLPS) had higher SUVmax and SUVpeak than well-differentiated (WDLPS) and myxoid/round cell (MLPS) types. WDLPS had lower CTmean values compared to MLPS and DDLPS. Using ROC curves, determined cut-off values for SUVmax (5.1) to differentiate DDLPS, SUVpeak (3.0) for WDLPS, and CTmean (12.3 Hu) for WDLPS. These cut-offs were found to be best for predicting recurrence. Kaplan-Meier analysis showed that histologic subtype, SUVmax, and SUVpeak were all linked to recurrence-free survival. CONCLUSIONS: The use of SUV and CT features on 18F-FDG PET/CT imaging may increase confidence in subtype diagnosis. Patients with SUVmax > 5.1 or SUVpeak > 3.0 suggest a poor prognosis.

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.

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.

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.

Differential spatial working memory-related functional network reconfiguration in young and older adults.

Functional brain networks have preserved architectures in rest and task; nevertheless, previous work consistently demonstrated task-related brain functional reorganization. Efficient rest-to-task functional network reconfiguration is associated with better cognition in young adults. However, aging and cognitive load effects, as well as contributions of intra- and internetwork reconfiguration, remain unclear. We assessed age-related and load-dependent effects on global and network-specific functional reconfiguration between rest and a spatial working memory (SWM) task in young and older adults, then investigated associations between functional reconfiguration and SWM across loads and age groups. Overall, global and network-level functional reconfiguration between rest and task increased with age and load. Importantly, more efficient functional reconfiguration associated with better performance across age groups. However, older adults relied more on internetwork reconfiguration of higher cognitive and task-relevant networks. These reflect the consistent importance of efficient network updating despite recruitment of additional functional networks to offset reduction in neural resources and a change in brain functional topology in older adults. Our findings generalize the association between efficient functional reconfiguration and cognition to aging and demonstrate distinct brain functional reconfiguration patterns associated with SWM in aging, highlighting the importance of combining rest and task measures to study aging cognition.

Brain networks identified by functional connectivity (FC) have preserved architectures from rest to task and across task demands. Higher similarity, implying more efficient network reconfiguration, was associated with better cognition and task performance in young adults. To examine how it may be influenced by aging, we compared whole-brain and network-level FC similarities between resting-state and spatial working memory fMRI in young and older adults. At whole-brain level and higher order cognitive networks, older adults evidenced less efficient network reconfiguration from rest to task than young adults. Importantly, more efficient reconfiguration was associated with better accuracy. This relationship relied more on internetwork connections in older adults. Despite reduced neural resources compared to young, maintaining efficient network updating still contributes to better cognition at older age.

Unsupervised heterogeneous domain adaptation for EEG classification.

Objective.Domain adaptation has been recognized as a potent solution to the challenge of limited training data for electroencephalography (EEG) classification tasks. Existing studies primarily focus on homogeneous environments, however, the heterogeneous properties of EEG data arising from device diversity cannot be overlooked. This motivates the development of heterogeneous domain adaptation methods that can fully exploit the knowledge from an auxiliary heterogeneous domain for EEG classification.Approach.In this article, we propose a novel model named informative representation fusion (IRF) to tackle the problem of unsupervised heterogeneous domain adaptation in the context of EEG data. In IRF, we consider different perspectives of data, i.e. independent identically distributed (iid) and non-iid, to learn different representations. Specifically, from the non-iid perspective, IRF models high-order correlations among data by hypergraphs and develops hypergraph encoders to obtain data representations of each domain. From the non-iid perspective, by applying multi-layer perceptron networks to the source and target domain data, we achieve another type of representation for both domains. Subsequently, an attention mechanism is used to fuse these two types of representations to yield informative features. To learn transferable representations, the maximum mean discrepancy is utilized to align the distributions of the source and target domains based on the fused features.Main results.Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model.Significance.This article handles an EEG classification situation where the source and target EEG data lie in different spaces, and what's more, under an unsupervised learning setting. This situation is practical in the real world but barely studied in the literature. The proposed model achieves high classification accuracy, and this study is important for the commercial applications of EEG-based BCIs.

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.

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.

Advances in biomimetic nanomaterial delivery systems: harnessing nature's inspiration for targeted drug delivery.

The properties of nanomaterials make them promising and advantageous for use in drug delivery systems, but challenges arise from the immune system's recognition of exogenous nanoparticles, leading to their clearance and reduced targeting efficiency. Drawing inspiration from nature, this paper explores biomimetic strategies to transform recognizable nanomaterials into a "camouflaged state." The focal point of this paper is the exploration of bionic nanoparticles, with a focus on cell membrane-coated nanoparticles. These biomimetic structures, particularly those mimicking red blood cells (RBCs), white blood cells (WBCs), platelets, and cancer cells, demonstrate enhanced drug delivery efficiency and prolonged circulation. This article underscores the versatility of these biomimetic structures across diverse diseases and explores the use of hybrid cell membrane-coated nanoparticles as a contemporary trend. This review also investigated exosomes and protein bionic nanoparticles, emphasizing their potential for specific targeting, immune evasion, and improved therapeutic outcomes. We expect that this continued development based on biomimetic nanomaterials will contribute to the efficiency and safety of disease treatment.

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.

Novel therapeutic perspectives for wet age-related macular degeneration: RGD-modified liposomes loaded with 2-deoxy-D-glucose as a promising nanomedicine.

Choroidal neovascularization (CNV), characterized as a prominent feature of wet age-related macular degeneration (AMD), is a primary contributor to visual impairment and severe vision loss globally, while the prevailing treatments are often unsatisfactory. The development of conventional treatment strategies has largely been based on the understanding that the angiogenic switch of endothelial cells is dictated by angiogenic growth factors alone. Even though treatments targeting vascular endothelial growth factor (VEGF), like Ranibizumab, are widely administered, more than half of the patients still exhibit inadequate or null responses, emphasizing the imperative need for solutions to this problem. Here, aiming to explore therapeutic strategies from a novel perspective of endothelial cell metabolism, a biocompatible nanomedicine delivery system is constructed by loading RGD peptide-modified liposomes with 2-deoxy-D-glucose (RGD@LP-2-DG). RGD@LP-2-DG displayed good targeting performance towards endothelial cells and excellent in vitro and in vivo inhibitory effects on neovascularization were demonstrated. Moreover, our mechanistic studies revealed that 2-DG interfered with N-glycosylation, leading to the inhibition of vascular endothelial growth factor receptor 2 (VEGFR2) and its downstream signaling. Notably, the remarkable inhibitory effect on neovascularization and biocompatibility of RGD@LP-2-DG render it a highly promising and clinically translatable therapeutic candidate for the treatment of wet AMD and other angiogenic diseases, particularly in patients who are unresponsive to currently available treatments.

Association Between Helicobacter pylori and Laryngopharyngeal Reflux Disease: A Systematic Review and Meta-Analysis.

BACKGROUND: It is controversial that Helicobacter pylori (H pylori) is involved in the pathogenesis or development of laryngopharyngeal reflux disease (LPRD). OBJECTIVE: To investigate the potential association between LPRD and H pylori infection. MATERIAL AND METHODS: A systematic review was performed of studies assessing the diagnosis or treatment of LPRD among patients with H pylori infection. Data sources are PubMed/MEDLINE, EMBASE[Ovid], Cochrane Library, and Web of Science, and ClinicalTrials.gov. RESULTS: Fifteen studies were analyzed in the review, with all eligible for the meta-analysis. A significant association between H pylori infection and LPRD was detected for higher rates of H pylori infection in patients with LPRD than in non-LPRD patients (relative risk (RR), 1.35; 95% CI, 1.12-1.63; P = 0.002), and H pylori-positive patients had a higher prevalence of LPRD than H pylori-negative patients (RR, 1.19; 95% CI, 1.07-1.31; P = 0.001). The prevalence of H pylori among patients with LPRD was 49% (95% CI, 36-61), the prevalence of H pylori among patients with non-LPRD was 35% (95% CI, 23-49). CONCLUSION AND SIGNIFICANCE: The limited evidence indicated the association between LPRD risk and increased H pylori infection. Different population races, diagnostic approach to LPRD, variant H pylori testing methods, age and sex may contribute to the heterogeneity. Further well-designed studies regarding the efficacy of H pylori eradication in the treatment of LPRD are strongly recommended in the future.

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.

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.

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.

Using cross-validation methods to select time series models: Promises and pitfalls.

Vector autoregressive (VAR) modelling is widely employed in psychology for time series analyses of dynamic processes. However, the typically short time series in psychological studies can lead to overfitting of VAR models, impairing their predictive ability on unseen samples. Cross-validation (CV) methods are commonly recommended for assessing the predictive ability of statistical models. However, it is unclear how the performance of CV is affected by characteristics of time series data and the fitted models. In this simulation study, we examine the ability of two CV methods, namely,10-fold CV and blocked CV, in estimating the prediction errors of three time series models with increasing complexity (person-mean, AR, and VAR), and evaluate how their performance is affected by data characteristics. We then compare these CV methods to the traditional methods using the Akaike (AIC) and Bayesian (BIC) information criteria in their accuracy of selecting the most predictive models. We find that CV methods tend to underestimate prediction errors of simpler models, but overestimate prediction errors of VAR models, particularly when the number of observations is small. Nonetheless, CV methods, especially blocked CV, generally outperform the AIC and BIC. We conclude our study with a discussion on the implications of the findings and provide helpful guidelines for practice.

Messages are Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-Series Forecasting.

This paper delves into the problem of correlated time-series forecasting in practical applications, an area of growing interest in a multitude of fields such as stock price prediction and traffic demand analysis. Current methodologies primarily represent data using conventional graph structures, yet these fail to capture intricate structures with non-pairwise relationships. To address this challenge, we adopt dynamic hypergraphs in this study to better illustrate complex interactions, and introduce a novel hypergraph neural network model named CHNN for correlated time series forecasting. In more detail, CHNN leverages both semantic and topological similarities via an interaction model and hypergraph diffusion process, thereby constructing comprehensive collaborative correlation scores that effectively guide spatial message propagation. In addition, it incorporates short-term temporal information to generate efficient spatio-temporal feature maps. Lastly, a long-term temporal module is proposed to generate future predictions utilizing both temporal attention and a gated recurrent network. Comprehensive experiments conducted on four real-world datasets, i.e., Tiingo, Stocktwits, NYC-Taxi, and Social Network demonstrate that the proposed CHNN markedly outperforms a range of benchmark methods.

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.

Deep-learning-based separation of shallow and deep layer blood flow rates in diffuse correlation spectroscopy.

Diffuse correlation spectroscopy faces challenges concerning the contamination of cutaneous and deep tissue blood flow. We propose a long short-term memory network to directly quantify the flow rates of shallow and deep-layer tissues. By exploiting the different contributions of shallow and deep-layer flow rates to auto-correlation functions, we accurately predict the shallow and deep-layer flow rates (RMSE = 0.047 and 0.034 ml/min/100 g of simulated tissue, R2 = 0.99 and 0.99, respectively) in a two-layer flow phantom experiment. This approach is useful in evaluating the blood flow responses of active muscles, where both cutaneous and deep-muscle blood flow increase with exercise.