EVMP: enhancing machine learning models for synthetic promoter strength prediction by Extended Vision Mutant Priority framework

IntroductionIn metabolic engineering and synthetic biology applications, promoters with appropriate strengths are critical. However, it is time-consuming and laborious to annotate promoter strength by experiments. Nowadays, constructing mutation-based synthetic promoter libraries that span multiple orders of magnitude of promoter strength is receiving increasing attention. A number of machine learning (ML) methods are applied to synthetic promoter strength prediction, but existing models are limited by the excessive proximity between synthetic promoters.MethodsIn order to enhance ML models to better predict the synthetic promoter strength, we propose EVMP(Extended Vision Mutant Priority), a universal framework which utilize mutation information more effectively. In EVMP, synthetic promoters are equivalently transformed into base promoter and corresponding k-mer mutations, which are input into BaseEncoder and VarEncoder, respectively. EVMP also provides optional data augmentation, which generates multiple copies of the data by selecting different base promoters for the same synthetic promoter.ResultsIn Trc synthetic promoter library, EVMP was applied to multiple ML models and the model effect was enhanced to varying extents, up to 61.30% (MAE), while the SOTA(state-of-the-art) record was improved by 15.25% (MAE) and 4.03% (R2). Data augmentation based on multiple base promoters further improved the model performance by 17.95% (MAE) and 7.25% (R2) compared with non-EVMP SOTA record.DiscussionIn further study, extended vision (or k-mer) is shown to be essential for EVMP. We also found that EVMP can alleviate the over-smoothing phenomenon, which may contributes to its effectiveness. Our work suggests that EVMP can highlight the mutation information of synthetic promoters and significantly improve the prediction accuracy of strength. The source code is publicly available on GitHub: https://github.com/Tiny-Snow/EVMP

An Analytic Solution of Hydrodynamic Equations with Source Terms in Heavy Ion Collisions

The energy and baryon densities in heavy ion collisions are estimated by analytically solving a 1+1 dimensional hydrodynamical model with source terms. Particularly, a competition between the energy and baryon sources and the expansion of the system is discussed in detail.Comment: LaTeX2e, 7 pages, 4 postscript figures, submitted to Int. J. Mod. Phys.

Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech

Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems. In this paper, a cross-utterance conditional VAE (CUC-VAE) is proposed to estimate a posterior probability distribution of the latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features obtained from both past and future sentences. At inference time, instead of the standard Gaussian distribution used by VAE, CUC-VAE allows sampling from an utterance-specific prior distribution conditioned on cross-utterance information, which allows the prosody features generated by the TTS system to be related to the context and is more similar to how humans naturally produce prosody. The performance of CUC-VAE is evaluated via a qualitative listening test for naturalness, intelligibility and quantitative measurements, including word error rates and the standard deviation of prosody attributes. Experimental results on LJ-Speech and LibriTTS data show that the proposed CUC-VAE TTS system improves naturalness and prosody diversity with clear margins

Cross-Utterance Conditioned VAE for Speech Generation

Speech synthesis systems powered by neural networks hold promise for multimedia production, but frequently face issues with producing expressive speech and seamless editing. In response, we present the Cross-Utterance Conditioned Variational Autoencoder speech synthesis (CUC-VAE S2) framework to enhance prosody and ensure natural speech generation. This framework leverages the powerful representational capabilities of pre-trained language models and the re-expression abilities of variational autoencoders (VAEs). The core component of the CUC-VAE S2 framework is the cross-utterance CVAE, which extracts acoustic, speaker, and textual features from surrounding sentences to generate context-sensitive prosodic features, more accurately emulating human prosody generation. We further propose two practical algorithms tailored for distinct speech synthesis applications: CUC-VAE TTS for text-to-speech and CUC-VAE SE for speech editing. The CUC-VAE TTS is a direct application of the framework, designed to generate audio with contextual prosody derived from surrounding texts. On the other hand, the CUC-VAE SE algorithm leverages real mel spectrogram sampling conditioned on contextual information, producing audio that closely mirrors real sound and thereby facilitating flexible speech editing based on text such as deletion, insertion, and replacement. Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech.Comment: 13 pages

Circulating Lysophosphatidylcholines in Early Pregnancy and Risk of Gestational Diabetes in Chinese Women.

ObjectivesThis study aimed to explore associations of lysophosphatidylcholines (LPCs) in early pregnancy with gestational diabetes mellitus (GDM), and whether LPCs mediated the associations of bile acids with GDM risk or had interactive effects with bile acids on GDM risk.DesignWe conducted a 1:1 nested case-control study (n = 486) from a large prospective pregnant women cohort in urban Tianjin, China. Blood samples were collected at their first antenatal care visit (median at 10th gestational week). LPCs were measured by liquid chromatography-tandem mass spectrometry analysis. Conditional binary logistic regression and restricted cubic spline analysis were used to identify cutoff points of these metabolites for GDM risk.ResultsOf the 6 detectable LPCs, LPC14:0 less than 0.24 nmol/mL, LPC15:0 at 0.45 nmol/mL or greater, and LPC18:0 at 18.00 nmol/mL or greater were independently associated with GDM risk. Adjustment for LPC18:0 slightly attenuated odds ratios (ORs) of deoxycholic acid (DCA, ≤ 0.36 nmol/mL) and glycoursodeoxycholic acid (GUDCA, ≤ 0.07 nmol/mL) for GDM, and the correlations of DCA and GUDCA with LPC18:0 were weak. However, the presence of DCA at 0.36 nmol/mL or less greatly amplified the adjusted OR of LPC18:0 at 18.00 nmol/mL or greater alone for GDM from 8.18 (2.51-26.7) up to 17.7 (6.64-47.1), with significant additive interaction. Similarly, the presence of GUDCA at 0.07 nmol/mL or less also greatly amplified the adjusted OR of LPC18:0 at 18.00 nmol/mL or greater alone for GDM from 17.2 (1.77-168) up to 73.8 (12.7-429), with significant additive interaction.ConclusionsLPCs in early pregnancy were associated with GDM risk. Low DCA or GUDCA greatly amplified the effect of high LPC18:0 on GDM, and its molecular mechanism is worth further investigations

One-year weight losses in the Tianjin Gestational Diabetes Mellitus Prevention Programme : A randomized clinical trial

Aims: To report the weight loss findings after the first year of a lifestyle intervention trial among women with gestational diabetes mellitus (GDM). Methods: A total of 1180 women with GDM were randomly assigned (1: 1) to receive a 4-year lifestyle intervention (intervention group, n = 586) or standard care (control group, n = 594) between August 2009 and July 2011. Major elements of the intervention included 6 face-to-face sessions with study dieticians and two telephone calls in the first year, and two individual sessions and two telephone calls in each subsequent year. Results: Among 79% of participants who completed the year 1 trial, mean weight loss was 0.82 kg (1.12% of initial weight) in the intervention group and 0.09 kg (0.03% of initial weight) in the control group (P=.001). In a prespecified subgroup analysis of people who completed the trial, weight loss was more pronounced in women who were overweight (body mass index = 24 kg/m(2)) at baseline: mean weight loss 2.01 kg (2.87% of initial weight) in the intervention group and 0.44 kg (0.52% of initial weight) in the control group (P Conclusion: The 1-year lifestyle intervention led to significant weight losses after delivery in women who had GDM, and the effect was more pronounced in women who were overweight at baseline.Peer reviewe

Effects of obesity and a history of gestational diabetes on the risk of postpartum diabetes and hyperglycemia in Chinese Women: Obesity, GDM and diabetes risk

Objective: To evaluate the independent or combined effects of gestational diabetes (GDM) and pre-pregnancy and postpartum BMI on the odds of postpartum diabetes and hyperglycemia. Methods: The study samples included 1263 women with prior GDM and 705 women without GDM. Postpartum 1-7 years diabetes was diagnosed by the standard oral glucose tolerance test. Results: The multivariable-adjusted odds ratios among women with prior GDM, compared with those without it, were 7.52 for diabetes and 2.27 for hyperglycemia. The multivariable-adjusted odds ratios at different postpartum BMI levels (= 28 kg/m(2)) were 1.00, 2.80, and 8.08 for diabetes (P-trend = 31.9%) or abdominal obesity (>= 85 cm) had a 2.7-6.9-fold higher odds ratio for diabetes or hyperglycemia. Women with both obesity and prior GDM had the highest risk of diabetes or hyperglycemia compared with non-obese women without GDM. Non-obese women with prior GDM had the same risk of diabetes and hyperglycemia as non-GDM women with obesity. When using Cox regression models, the results were very close to those using logistic regression models. Conclusions: Maternal prior GDM and pre-pregnancy or postpartum obesity contribute equally to postpartum diabetes and hyperglycemia risk. (C) 2019 Elsevier B.V. All rights reserved.Peer reviewe

High risk of metabolic syndrome after delivery in pregnancies complicated by gestational diabetes

Aims: To investigate the risk of postpartum metabolic syndrome in women with GDM compared with those without GDM in a Chinese population. Methods: Tianjin GDM observational study included 1263 women with a history of GDM and 705 women without GDM. Multivariate logistic regression was used to assess risks of postpartum metabolic syndrome between women with and without GDM. Postpartum metabolic syndrome was diagnosed by two commonly used criteria. Results: During a mean 3.53 years of follow up, 256 cases of metabolic syndrome were identified by using the NCEPATPIII criteria and 244 cases by using the IDF criteria. Multivariable-adjusted odds ratios of metabolic syndrome in women with GDM compared with those without GDM were 3.66 (95% confidence interval [CI] 2.02-6.63) for NCEP ATPIII criteria and 3.90 (95% CI 2.13-7.14) for IDF criteria. Women with GDM had higher multivariable-adjusted odds ratios of central obesity, hypertriglyceridemia, and high blood pressure than women without GDM. The multivariable-adjusted odds ratios of low HDL cholesterol and hyperglycemia were not significant between women with and without GDM, however, the multivariable-adjusted odds ratio of hyperglycemia became significant when we used the modified criteria. Conclusions: The present study indicated that women with prior GDM had significantly higher risks for postpartum metabolic syndrome, as well as its individual components. (C) 2019 Elsevier B.V. All rights reserved.Peer reviewe