Signed Distance Correlation (SiDCo): an online implementation of distance correlation and partial distance correlation for data-driven network analysis.

MOTIVATION: There is a need for easily accessible implementations that measure the strength of both linear and non-linear relationships between metabolites in biological systems as an approach for data-driven network development. While multiple tools implement linear Pearson and Spearman methods, there are no such tools that assess distance correlation. RESULTS: We present here SIgned Distance COrrelation (SiDCo). SiDCo is a GUI platform for calculation of distance correlation in omics data, measuring linear and non-linear dependencies between variables, as well as correlation between vectors of different lengths, e.g. different sample sizes. By combining the sign of the overall trend from Pearson's correlation with distance correlation values, we further provide a novel "signed distance correlation" of particular use in metabolomic and lipidomic analyses. Distance correlations can be selected as one-to-one or one-to-all correlations, showing relationships between each feature and all other features one at a time or in combination. Additionally, we implement "partial distance correlation," calculated using the Gaussian Graphical model approach adapted to distance covariance. Our platform provides an easy-to-use software implementation that can be applied to the investigation of any dataset. AVAILABILITY AND IMPLEMENTATION: The SiDCo software application is freely available at https://complimet.ca/sidco. Supplementary help pages are provided at https://complimet.ca/sidco. Supplementary Material shows an example of an application of SiDCo in metabolomics.

'Without support CALD patients will be left behind': A mixed-methods exploration of culturally and linguistically diverse (CALD) client perspectives of telehealth and those of their healthcare providers.

INTRODUCTION: The rapid adoption of telehealth during the global pandemic has the potential to widen disparities for culturally and linguistically diverse (CALD) consumers. We explored the perspectives and experiences of CALD consumers accessing telehealth during the global pandemic and those of their healthcare providers. METHODS: A multistakeholder mixed-methods study involving two parallel samples comprising consumer-participants (n = 56) and healthcare provider-participants (n = 81). Multicultural consumer-participants, recruited from consecutive referrals to Health Language Services for telehealth support, were assisted to complete two surveys (before and after their clinical telehealth appointment) in their preferred language. A purposive sample of consumer-participants was interviewed to understand their perceived barriers and enablers of successful telehealth consultations. Simultaneously, all healthcare providers within the local health district were eligible to participate in an online survey if they had provided telehealth care to a consumer during the recruitment period. Closed-ended responses were descriptively summarised, while open-ended responses and interview transcripts were analysed thematically. RESULTS: Despite 86% of consumer-participants inexperienced with telehealth, 80% achieved a successful appointment with a healthcare provider. Consumer perceptions were shaped by cultural and diagnostic concepts of legitimacy, in the context of known accessibility and technology literacy challenges. Healthcare provider perspectives were less favourable towards telehealth, with equity of healthcare delivery a major concern. DISCUSSION: Our findings highlight unintended consequences arising from a rapid transition to telehealth. Adopting collaborative approaches to the design and implementation of telehealth is imperative to mitigate health inequities faced by CALD communities and maximise their opportunity to realise potential health benefits associated with telehealth.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Assessment of myocardial performance index in late-onset fetal growth restriction.

The aim of this study is to determine whether the myocardial performance index (MPI) is increased in fetal growth restriction (FGR) fetuses and if increased MPI is related to adverse outcomes of FGR. This is a prospective cross-sectional study. Seventy-three late-onset FGR fetuses and 97 gestational-age matched control fetuses were enrolled in this study. Fetal blood flow parameters including MPI values were measured and compared between the two groups. For the effect of severity of growth restriction on MPI value, they were also compared with < 3rd and 3rd - 10th centile groups. FGR fetuses were divided into two groups by favorable and adverse outcome and ultrasound parameters were compared between these two groups. Moreover, significant factors related to adverse outcomes by univariate analysis were analyzed by multivariate logistic regression analysis. Pulsatility index of umbilical arterial flow (UA-PI), MPI and amniotic fluid index in the FGR were significantly different from the control fetuses. However, no significant difference between < 3rd and 3rd - 10th centile groups was detected in MPI and UA-PI. The increased levels of MPI and UA-PI were independently related with adverse outcome of late-onset FGR pregnancy. In conclusion, MPI values were increased in late-onset FGR pregnancy, and the higher level of MPI could predict adverse outcome as well as the measurement of UA-PI. Clinicians should consider cardiac dysfunction in FGR through increased MPI.

Interfacial structures of particle-stabilized emulsions examined by ultrasonic scattering analysis with a core-shell model.

Pickering emulsions comprising liquid droplets stabilized by solid microparticles have gained much attention in the field of cosmetics, inks, and drug delivery systems. To ensure that microparticles in Pickering emulsions are localized at the surface of liquid droplets, ultrasonic spectroscopy analysis combined with scattering function theory was conducted in this study. Two specific cases were investigated: (1) silica particles and liquid droplets independently dispersed in liquid and (2) silica particles effectively localized at the surface of the droplets. It was found that the core-shell model was effective for analyzing nanoparticles anchored at the surface of oil droplets. Conversely, it was found that an effective shell comprised of solid particles was no longer observed as the particle size or the distance between solid particles increased. When a large solid particle was applied, the ultrasonic spectra resembled those of conventional surfactant-stabilized emulsions without solid stabilizers.

De novo c.2455C>T mutation of NPR2 gene in a fetus with shortened long bones and a ventricular septal defect conceived by a mother with a fragile site at 16q22.1 and a father with a rare heterochromatic variant of chromosome 4 from Vietnam.

BACKGROUND: A heterozygous natriuretic peptide receptor 2 (NPR2) gene c.2455C>T mutation was identified as a cause of familial idiopathic short stature (ISS). Only two cases with this mutation were reported previously, and the probands with ISS had no organ system defects. METHODS: Next-generation sequencing (NGS) was performed on an amniotic fluid DNA sample of a fetus with shortened long bones and a small ventricular septal defect detected by an obstetric ultrasound examination. The pathogenic variant of the fetus was confirmed by Sanger sequencing. Sanger sequencing, G-banded, and C-banded karyotyping of the fetus's parents were subsequently performed. RESULTS: A de novo NPR2 gene c.2455C>T, p.(Arg819Cys) mutation was identified in the fetus. No microdeletion or microduplication was identified in the fetus by copy number variation sequencing with a maximum resolution of 400 kb. The two previous miscarriages experienced by the fetus's parents were interpreted as a result of chromosomal aberrations, including a maternal fragile site at 16q22.1 and a rare paternal variant involving in a large G-band-positive and C-band-positive block of paracentric heterochromatin of chromosome 4p. CONCLUSION: This report provides clinical signs of a de novo heterozygous NPR2 gene c.2455C>T mutation in the fetus and shows paternal chromosomal aberrations causing repeated pregnancy loss.

Outcome of cesarean scar pregnancy treated with local methotrexate injection.

Local injection of methotrexate (MTX) has been widely used for caesarean scar pregnancy (CSP), but the optimal candidate remains undetermined. The aim of this study is to determine the risk factors associated with treatment failure among patients who received a single dose of local MTX. This is a retrospective cohort study. Clinical information was compared between treatment success vs. failure groups. Risk factors related to treatment failure were also investigated with multivariate analysis. Of 47 patients diagnosed with CSP, 30 received local MTX injection. The initial serum ß- human chorionic gonadotropin (hCG) level in the failure group was significantly higher than in the success group (p = 0.048), and the cut-off value was 47,000 mIU/ml. The rate of type 2 position of the gestational sac in the failure group was significantly higher than in the treatment success group (p = 0.031). A high initial serum ß-hCG level (≥ 47,000 mIU/ml) was identified as the independent risk factor for treatment failure (adjusted odds ratio = 21.9; 95% confidence interval = 1.3-383.1). Type 2 gestational sac position and a higher level of ß-hCG at diagnosis appear to be associated with poor outcomes after local injection of a single dose of MTX.

Effects of Interlayer Coupling and Band Offset on Second Harmonic Generation in Vertical MoS2/MoS2(1-x)Se2x Structures.

Noncentrosymmetric monolayers (MLs) of transition metal dichalcogenides (TMDCs) and their 3R-type vertical stacks provide an ideal platform for studying atomic-scale nonlinear light-matter interaction in terms of second harmonic generation (SHG). Unlike the case of MLs, SHG from artificial stacks can be nontrivially affected by interlayer coupling and band offset between the constituent MLs, where the latter occurs for band-gap-engineered vertical heterostructures (VHs). In order to study these effects, we produced different sets of 3R-type homobilayers (homo-BLs) and heterobilayers (hetero-BLs) composed of MoS2 and its ternary alloy MoS2(1-x)Se2x. We first investigated the impact of interlayer coupling on the SHG response across the A- and B-exciton resonances in the MoS2 homo-BLs. The coupling strength was varied by preparing (i) decoupled BLs (SiO2 intercalated), (ii) weakly coupled BLs (dry transferred), and (iii) strongly coupled BLs (postannealed) and monitored by photoluminescence, Raman, and reflectance difference spectroscopy, and atomic force microscopy. Unlike the decoupled BL, SHG in the coupled BLs cannot be explained by the simple square law in thickness due to coupling-induced band modification. The impact of exciton-resonance offset on SHG was also investigated in the hetero-BLs by controlling the Se concentration in MoS2xSe2(x-1). Although these VHs can significantly broaden the spectral range for efficient SHG by vertically superposing distinct resonances of the constituent MLs, coherent reinforcement of SHG cannot be achieved basically because of the π/2 phase difference between the on-resonance SHG field in one ML and the off-resonance SHG field in the other ML. Upon postannealing, however, the overlapping resonance regime exhibited unexpectedly high SHG enhancement. This may arise from the formation of the strong resonance when the VHs approach ideal 3R-type hetero-BLs. Our approach may be utilized for fully exploiting the TMDC VHs for highly efficient broadband SHG applications.