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

Nomogram incorporating ultrasonic markers of endometrial receptivity to determine the embryo-endometrial synchrony after in vitro fertilization

BackgroundA successful pregnancy using in vitro fertilization and embryo transfer (IVF-ET) requires a receptive endometrium, good-quality embryos, and a synchronized embryo-endometrial dialogue. Although embryo quality and endometrial receptivity (ER) have been fully assessed to exclude substandard conditions, the probability of successful ET is relatively low. Currently, embryo-endometrial synchrony is considered to be a possible explanation, because delayed, advanced, or narrowed window of implantation (WOI) may lead to ET failure.ObjectiveThis study aims to establish a nomogram incorporating a series of ultrasonic ER markers on the day before implantation to assess the embryo-endometrial synchrony, which may contribute to the improvement of clinical pregnancy outcomes.MethodsTotally 583 women with 1135 complete IVF cycles were retrospectively analyzed. Among them, 357 women with 698 cycles and 226 women with 437 cycles were assigned to the training and validation cohorts, respectively. Ultrasonic ER markers obtained on the day before implantation were collected for analyses. In the training cohort, the screened correlates of clinical pregnancy failure were utilized to develop a nomogram for determining whether an infertile woman is suitable for the ET next day. This model was validated both in the training and validation cohorts.ResultsSpiral artery (SA) resistance index (RI), vascularisation index (VI), and flow index (FI) were independently associated with the ET failure (all P < 0.05). They were served as the components of the developed nomogram to visualize the likelihood of implantation failure in IVF-ET. This model was validated to present good discrimination and calibration, and obtained clinical net benefits both in the training and validation cohorts.ConclusionWe developed a nomogram that included SA-RI, VI, and FI on the day before implantation. It may assist physicians to identify patients with displaced WOI, thus avoiding meaningless ET prior to implantation

The prognostic significance of circulating plasma cells in newly diagnosed multiple myeloma patients

ObjectiveMultiple myeloma (MM) is a highly characteristic tumor that is influenced by numerous factors that determine its prognosis. Studies indicate that the presence of circulating plasma cells (cPCs) is a detrimental factor that significantly impacts the prognosis of patients with MM.MethodsThis study retrospectively analyzed the prognostic value of cPCs quantified by 10-color flow cytometry in 145 newly diagnosed MM (NDMM) cases in the First Affiliated Hospital of Soochow University from November 2018 to February 2021. The study was approved by the Ethics Committee of the hospital (2021 No. 93).ResultsOf the 145 patients, 99 (68.2%) were detected cPCs. Through receiver operating characteristics (ROC) analysis, an optimal threshold of 0.165% was identified as a predictor for overall survival (OS). The median progression-free survival (PFS) was 33 months in patients with cPCs ≥0.165%, whereas those with cPCs <0.165% had a PFS of <33 months (p=0.001). The median OS was not reached for two groups; the 3-year OS for patients with cPCs ≥0.165% was 71% compared with 87% for those with cPCs <0.165% (p=0.003). In transplant patients, cPCs ≥0.165% also predicted worse prognosis. Similarly, when considering cytogenetic risk factors in conjunction with cPC levels, comparable results were obtained. To evaluate whether the Revised International Staging System (R-ISS) groups could be further stratified based on different prognostic factors related to cPCs, our study revealed similar median PFS and OS rates in R-ISS II stage patients with cPCs ≥0.165% compared to those in the III stage (p=0.659 and 0.249, respectively).ConclusionThis study demonstrates that a high ratio of cPCs serves as a reliable indicator for predicting a poorer prognosis in MM cases. Furthermore, incorporating the R-ISS system and cytogenetic risk factors alongside the level of cPCs enhances the accuracy of prognostic predictions for patients with MM

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery

Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization

Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a conical area evolutionary algorithm (CAEA) to discover community structures at different resolutions in parallel. Although each island is assigned only a portion of all sub-problems in the island model, it preserves a complete population to accomplish the global selection and update. Meanwhile the migration policy directly migrates each elitist individual to an appropriate island in charge of the sub-problem associated with this individual to share essential evolutionary achievements. In addition, a modularity-based greedy local search strategy is also applied to accelerate the convergence rate. Comparative experimental results on six real-world networks reveal that PCACD is capable of discovering potential high-quality community structures at diverse resolutions with satisfactory running efficiencies

Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization

In order to serve for the ecological monitoring efficiency of Poyang Lake, an improved hybrid algorithm, mixed with differential evolution and particle swarm optimization, is proposed and applied to optimize the coverage problem of wireless sensor network. And then, the affect of the population size and the number of iterations on the coverage performance are both discussed and analyzed. The four kinds of statistical results about the coverage rate are obtained through lots of simulation experiments