Performance monitoring of MPC based on dynamic principal component analysis

A unified framework based on the dynamic principal component analysis (PCA) is proposed for performance monitoring of constrained multi-variable model predictive control (MPC) systems. In the proposed performance monitoring framework, the dynamic PCA based performance benchmark is adopted for performance assessment, while performance diagnosis is carried out using a unified weighted dynamic PCA similarity measure. Simulation results obtained from the case study of the Shell process demonstrate that the use of the dynamic PCA performance benchmark can detect the performance deterioration more quickly compared with the traditional PCA method, and the proposed unified weighted dynamic PCA similarity measure can correctly locate the root cause for poor performance of MPC controller

Comparing efficacy and safety of plasmapheresis versus atorvastatin in pathological progression of atherosclerosis in a rodent model

Purpose: To evaluate the effect of plasmapheresis versus atorvastatin in pathological progression of atherosclerosis in a rodent model.Method: A total of 90 male adult rats of up to 300 g were randomly distributed in three groups (n = 30): group 1 (plasmapheresis up to 1.5 ml daily); group 2 (atorvastatin 0.1 mg/kg per day), and group 3 (hypercholesteremic rats). The following variables were assessed for 24 weeks: plasma and hepatic lipid and anti-oxidant profiles; atherosclerotic abrasions/lesions; coronary atherosclerosis/coronary stenosis score (CSS), composition of atherosclerotic lesions, incidence of xanthoma, arch and thoracic surface involvement including arch and thoracic area occupied by lesion; and thoracic aorta (I/M) ratio.Results: Compared to rats administered with atorvastatin, the rats treated with plasmapheresis had significantly greater improvement in levels of triglycerides (132 vs 124 mg/dl, p < 0.05), total cholesterol (201 vs 189 mg/dl, p < 0.05)), low-density lipoproteins (134 vs 123 mg/dl, p < 0.05)), very-low-density lipoprotein (11 vs 9 mg/dl, p < 0.05)) and high-density lipoprotein (36 vs 39 mg/dl, p < 0.05) levels. Plasmapheresis after 24 weeks of treatment improve CSS in all coronary arteries than atorvastatin (22 vs 24 respectively; p < 0.05. Furthermore, lesioned composition, I/M ratio and xanthoma incidence were significantly lower in plasmapheresis group than in atorvastatin group (p < 0.05).Conclusion: Plasmapheresis is a better alternative than atorvastatin in preventing pathological progression of atherosclerosis

Dioscorea deltoidei (Dioscoreaceae) leaf extract exerts anti-atherosclerotic effect in rats via down-regulation of phosphorylated JAK/STAT

Purpose: To investigate the effect of leaf extract of Dioscorea deltoidea (Dioscoreaceae) leaf (DDE) on atherosclerosis-induced aorta wall damage in a rat model, and the underlying mechanism of action.Methods: Rats were fed high-fat diet containing vitamin D2 for 16 weeks to induce atherosclerosis. Histopathological changes in the aorta were examined using hematoxylin and eosin (H & E) staining, while ELISA kits were used to measure cytokine levels.Results: Treatment with DDE significantly (p < 0.05) alleviated atherosclerosis-induced increase in mean lesion area in the rat aorta. The mean lesion area in atherosclerotic rats was decreased to 51.5, 21.2 and 2.3 mm2, on treatment with DDE at doses of 2.5, 5 and 10 mg/kg, respectively. Furthermore, DDE significantly suppressed atherosclerosis-induced elevation in IL-1β and IL-6 levels in the rat aorta (p < 0.05). The levels of MCP-1 and TNF-α decreased in the artherosclerotic rats on treatment with DDE. In DDE-treated rats, the atherosclerosis-induced increase in the levels of Ang II, AT1, AT2, p-STAT3, p-p65 and p-p38 were significantly decreased, relative to the model group (p < 0.05). However, DDE treatment did not alter the levels of total STAT3, p65 and p38 in the rat aorta tissues.Conclusion: These results indicate that DDE inhibits inflammatory response and atherosclerosisinduced damage to aorta wall. Moreover, RAAS expression, inflammatory cytokines and JAK/STAT signalling pathway were down-regulated in atherosclerotic rats on treatment with DDE. Thus, DDE may be a potential source of drug for the management of atherosclerosis

Chrysophanol exerts protective effect against atherosclerosis via NFκB-mediated signaling in LDLR-/- mice model

Purpose: To study the therapeutic effect of chrysophanol (CHR) on diet-induced atherogenesis in LDLR-/- mice.Methods: Mice were fed atherogenic diet for 12 weeks after which some lipid profile markers such as total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c) and triglyceride (TG) were measured. The mRNA expression levels of lipid synthesis genes and lipid overload-related inflammatory indicator molecules were assayed with quantitative real time polymerase chain reaction (qRT-PCR), while the corresponding protein expressions were determined with western blotting assay. The therapeutic effect of CHR on atherogenesis was confirmed using H & E and Oil red O stainings of mice aortic sections.Results: CHR administration significantly reduced levels of TC, LDL-c, HDL-c and TG (p ≤ 0.05), and restored the mRNA and protein expressions of genes involved in lipid and glucose homeostasis, namely, AdipoR1, PPAR-Ƴ and HMco-A (p < 0.05). Moreover, CHR potentially alleviated diet-induced inflammation, as is evident in reduced levels of molecular inflammatory signaling factors NF-κB and TLR-4, and significant down-regulations of the proinflammatory cytokines, TNF-α, IL-6 and IL-1β (p < 0.05). Furthermore, aorta histology revealed that CHR significantly reduced lipid storage in the arteries of mice fed atherogenic diet (p < 0.05).Conclusion: These results indicate that CHR reduces diet-induced lipid storage in LDLR-/- mice and also controlled inflammation-associated lipid overload. These findings may provide a molecular basis for potential application of chrysophanol in the treatment of atherosclerosis

LM-VC: Zero-shot Voice Conversion via Speech Generation based on Language Models

Language model (LM) based audio generation frameworks, e.g., AudioLM, have recently achieved new state-of-the-art performance in zero-shot audio generation. In this paper, we explore the feasibility of LMs for zero-shot voice conversion. An intuitive approach is to follow AudioLM - Tokenizing speech into semantic and acoustic tokens respectively by HuBERT and SoundStream, and converting source semantic tokens to target acoustic tokens conditioned on acoustic tokens of the target speaker. However, such an approach encounters several issues: 1) the linguistic content contained in semantic tokens may get dispersed during multi-layer modeling while the lengthy speech input in the voice conversion task makes contextual learning even harder; 2) the semantic tokens still contain speaker-related information, which may be leaked to the target speech, lowering the target speaker similarity; 3) the generation diversity in the sampling of the LM can lead to unexpected outcomes during inference, leading to unnatural pronunciation and speech quality degradation. To mitigate these problems, we propose LM-VC, a two-stage language modeling approach that generates coarse acoustic tokens for recovering the source linguistic content and target speaker's timbre, and then reconstructs the fine for acoustic details as converted speech. Specifically, to enhance content preservation and facilitates better disentanglement, a masked prefix LM with a mask prediction strategy is used for coarse acoustic modeling. This model is encouraged to recover the masked content from the surrounding context and generate target speech based on the target speaker's utterance and corrupted semantic tokens. Besides, to further alleviate the sampling error in the generation, an external LM, which employs window attention to capture the local acoustic relations, is introduced to participate in the coarse acoustic modeling

Delivering Speaking Style in Low-resource Voice Conversion with Multi-factor Constraints

Conveying the linguistic content and maintaining the source speech's speaking style, such as intonation and emotion, is essential in voice conversion (VC). However, in a low-resource situation, where only limited utterances from the target speaker are accessible, existing VC methods are hard to meet this requirement and capture the target speaker's timber. In this work, a novel VC model, referred to as MFC-StyleVC, is proposed for the low-resource VC task. Specifically, speaker timbre constraint generated by clustering method is newly proposed to guide target speaker timbre learning in different stages. Meanwhile, to prevent over-fitting to the target speaker's limited data, perceptual regularization constraints explicitly maintain model performance on specific aspects, including speaking style, linguistic content, and speech quality. Besides, a simulation mode is introduced to simulate the inference process to alleviate the mismatch between training and inference. Extensive experiments performed on highly expressive speech demonstrate the superiority of the proposed method in low-resource VC.Comment: Accepted by ICASSP 202

MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling

In addition to conveying the linguistic content from source speech to converted speech, maintaining the speaking style of source speech also plays an important role in the voice conversion (VC) task, which is essential in many scenarios with highly expressive source speech, such as dubbing and data augmentation. Previous work generally took explicit prosodic features or fixed-length style embedding extracted from source speech to model the speaking style of source speech, which is insufficient to achieve comprehensive style modeling and target speaker timbre preservation. Inspired by the style's multi-scale nature of human speech, a multi-scale style modeling method for the VC task, referred to as MSM-VC, is proposed in this paper. MSM-VC models the speaking style of source speech from different levels. To effectively convey the speaking style and meanwhile prevent timbre leakage from source speech to converted speech, each level's style is modeled by specific representation. Specifically, prosodic features, pre-trained ASR model's bottleneck features, and features extracted by a model trained with a self-supervised strategy are adopted to model the frame, local, and global-level styles, respectively. Besides, to balance the performance of source style modeling and target speaker timbre preservation, an explicit constraint module consisting of a pre-trained speech emotion recognition model and a speaker classifier is introduced to MSM-VC. This explicit constraint module also makes it possible to simulate the style transfer inference process during the training to improve the disentanglement ability and alleviate the mismatch between training and inference. Experiments performed on the highly expressive speech corpus demonstrate that MSM-VC is superior to the state-of-the-art VC methods for modeling source speech style while maintaining good speech quality and speaker similarity.Comment: This work was submitted on April 10, 2022 and accepted on August 29, 202

U-Style: Cascading U-nets with Multi-level Speaker and Style Modeling for Zero-Shot Voice Cloning

Zero-shot speaker cloning aims to synthesize speech for any target speaker unseen during TTS system building, given only a single speech reference of the speaker at hand. Although more practical in real applications, the current zero-shot methods still produce speech with undesirable naturalness and speaker similarity. Moreover, endowing the target speaker with arbitrary speaking styles in the zero-shot setup has not been considered. This is because the unique challenge of zero-shot speaker and style cloning is to learn the disentangled speaker and style representations from only short references representing an arbitrary speaker and an arbitrary style. To address this challenge, we propose U-Style, which employs Grad-TTS as the backbone, particularly cascading a speaker-specific encoder and a style-specific encoder between the text encoder and the diffusion decoder. Thus, leveraging signal perturbation, U-Style is explicitly decomposed into speaker- and style-specific modeling parts, achieving better speaker and style disentanglement. To improve unseen speaker and style modeling ability, these two encoders conduct multi-level speaker and style modeling by skip-connected U-nets, incorporating the representation extraction and information reconstruction process. Besides, to improve the naturalness of synthetic speech, we adopt mean-based instance normalization and style adaptive layer normalization in these encoders to perform representation extraction and condition adaptation, respectively. Experiments show that U-Style significantly surpasses the state-of-the-art methods in unseen speaker cloning regarding naturalness and speaker similarity. Notably, U-Style can transfer the style from an unseen source speaker to another unseen target speaker, achieving flexible combinations of desired speaker timbre and style in zero-shot voice cloning

Effects of Short-Term Dietary Fiber Intervention on Gut Microbiota in Young Healthy People

Gut microbiota are critical to many aspects of human health including immune and metabolic health. Long-term diet influences the community structure and activity of the trillions of microorganisms residing in the human gut, but it remains unclear how the human gut microbiome responds to short-term intervention with dietary fiber. This study explored the effects of mixed dietary fibers on gut microbiota in young, healthy people. Twelve healthy, young adults participated in a randomized, crossover trial comparing the effects of polyglucan, inulin and resistant malt dextrin on gut microbiota composition and bacterial abundances. During the study, the subjects followed their normal diets without any constraints. Microbial community profiles were determined by absolute quantification 16S rRNA gene amplicon sequencing. Mixed model analysis did not reveal an effect of dietary intervention on microbial community structure. At the genus level, dietary fiber intervention for 4 days significantly promoted the growth of Alloprevotella, Parabacteroides and Parasutterella and inhibited the growth of Adlercreutzia, Anaerovorax, Enterococcus, Intestinibacter and Ruminococcus 2 compared with the baseline. Addition of whey albumen powder for 4 days promoted the growth of Corynebacterium, Collinsella, Olsenella and Lactococcus but interfered with the growth of Megasphaera. Our results should be corroborated by randomized clinical trials with large sample size