The Feedwater Control System and Steam Dump Control System Responses During Large-Load Reduction Transient for Maanshan PWR Plant

AbstractIn this study, the development of TRACE (TRAC/RELAP Advanced Computational Engine) models for Maanshan nuclear power plant (NPP) important control systems, such as feedwater control system and steam dump control system, are performed in using SNAP (Symbolic Nuclear Analysis Program) / TRACE. The large-load reduction transient analysis of the Maanshan NPP control system TRACE models are also performed and the responses of the control systems of TRACE models compare with Maanshan NPP startup tests data to verify their accuracy. Analysis results of TRACE indicate that the responses of the Maanshan NPP control system TRACE models are consistent with the plant data for large-load reduction transient

MelHuBERT: A simplified HuBERT on Mel spectrograms

Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks. However, most self-supervised models require a large amount of compute and multiple GPUs to train, significantly hampering the development of self-supervised learning. In an attempt to reduce the computation of training, we revisit the training of HuBERT, a highly successful self-supervised model. We improve and simplify several key components, including the loss function, input representation, and training in multiple stages. Our model, MelHuBERT, is able to achieve favorable performance on phone recognition, speaker identification, and automatic speech recognition against HuBERT, while saving 31.2% of the pre-training time, or equivalently 33.5% MACs per one second speech. The code and pre-trained models are available in https://github.com/nervjack2/MelHuBERT.Comment: ASRU 202

Enhancement of brain-type creatine kinase activity ameliorates neuronal deficits in Huntington's disease

AbstractHuntington's disease (HD) is a hereditary neurodegenerative disorder caused by a CAG repeat expansion in the huntingtin (HTT) gene. Brain-type creatine kinase (CKB) is an enzyme involved in energy homeostasis via the phosphocreatine–creatine kinase system. Although downregulation of CKB was previously reported in brains of HD mouse models and patients, such regulation and its functional consequence in HD are not fully understood. In the present study, we demonstrated that levels of CKB found in both the soma and processes were markedly reduced in primary neurons and brains of HD mice. We show for the first time that mutant HTT (mHTT) suppressed the activity of the promoter of the CKB gene, which contributes to the lowered CKB expression in HD. Exogenous expression of wild-type CKB, but not a dominant negative CKB mutant, rescued the ATP depletion, aggregate formation, impaired proteasome activity, and shortened neurites induced by mHTT. These findings suggest that negative regulation of CKB by mHTT is a key event in the pathogenesis of HD and contributes to the neuronal dysfunction associated with HD. In addition, besides dietary supplementation with the CKB substrate, strategies aimed at increasing CKB expression might lead to the development of therapeutic treatments for HD

Information retrieval from marine soundscape by using machine learning-based source separation

In remote sensing of the marine ecosystem, visual information retrieval is limited by the low visibility in the ocean environment. Marine soundscape has been considered as an acoustic sensing platform of the marine ecosystem in recent years. By listening to environmental sounds, biological sounds, and human-made noises, it is possible to acoustically identify various geophysical events, soniferous marine animals, and anthropogenic activities. However, the sound detection and classification remain a challenging task due to the lack of underwater audio recognition database and the simultaneous interference of multiple sound sources. To facilitate the analysis of marine soundscape, we have employed information retrieval techniques based on non-negative matrix factorization (NMF) to separate different sound sources with unique spectral-temporal patterns in an unsupervised approach. NMF is a self-learning algorithm which decomposes an input matrix into a spectral feature matrix and a temporal encoding matrix. Therefore, we can stack two or more layers of NMF to learn the spectral-temporal modulation of k sound sources without any learning database [1]. In this presentation, we will demonstrate the application of NMF in the separation of simultaneous sound sources appeared on a long-term spectrogram. In shallow water soundscape, the relative change of fish chorus can be effectively quantified even in periods with strong mooring noise [2]. In deep-sea soundscape, cetacean vocalizations, an unknown biological chorus, environmental sounds, and systematic noises can be efficiently separated [3]. In addition, we can use the features learned in procedures of blind source separation as the prior information for supervised source separation. The self-adaptation mechanism during iterative learning can help search the similar sound source from other acoustic dataset contains unknown noise types. Our results suggest that the NMF-based source separation can facilitate the analysis of the soundscape variability and the establishment of audio recognition database. Therefore, it will be feasible to investigate the acoustic interactions among geophysical events, soniferous marine animals, and anthropogenic activities from long-duration underwater recordings. REFERENCES: 1. Lin, T.-H., Fang, S.-H., Tsao. Y. 2017. Improving biodiversity assessment via unsupervised separation of biological sounds from long-duration recordings. Sci Rep, 7: 4547. 2. Lin, T.-H., Tsao. Y., Akamatsu, T. 2018. Comparison of passive acoustic soniferous fish monitoring with supervised and unsupervised approaches. J. Acoust. Soc. Am. Express Letters, 143: EL278. 3. Lin, T.-H., Tsao. Y. 2018. Listening to the deep: Exploring marine soundscape variability by information retrieval techniques. OCEANS'18 MTS/IEEE Kobe / Techno-Ocean 2018, in press

Trojan Horse nanotheranostics with dual transformability and multifunctionality for highly effective cancer treatment.

Nanotheranostics with integrated diagnostic and therapeutic functions show exciting potentials towards precision nanomedicine. However, targeted delivery of nanotheranostics is hindered by several biological barriers. Here, we report the development of a dual size/charge- transformable, Trojan-Horse nanoparticle (pPhD NP) for delivery of ultra-small, full active pharmaceutical ingredients (API) nanotheranostics with integrated dual-modal imaging and trimodal therapeutic functions. pPhD NPs exhibit ideal size and charge for drug transportation. In tumour microenvironment, pPhD NPs responsively transform to full API nanotheranostics with ultra-small size and higher surface charge, which dramatically facilitate the tumour penetration and cell internalisation. pPhD NPs enable visualisation of biodistribution by near-infrared fluorescence imaging, tumour accumulation and therapeutic effect by magnetic resonance imaging. Moreover, the synergistic photothermal-, photodynamic- and chemo-therapies achieve a 100% complete cure rate on both subcutaneous and orthotopic oral cancer models. This nanoplatform with powerful delivery efficiency and versatile theranostic functions shows enormous potentials to improve cancer treatment

Improving acoustic monitoring of biodiversity using deep learning-based source separation algorithms

Passive acoustic monitoring of the environment has been suggested as an effective tool for investigating the dynamics of biodiversity across spatial and temporal scales. Recent development in automatic recorders has allowed environmental acoustic data to be collected in an unattended way for a long duration. However, one of the major challenges for acoustic monitoring is to identify sounds of target taxa in recordings which usually contain undesired signals from non-target sources. In addition, high variation in the characteristics of target sounds, co-occurrence of sounds from multiple target taxa, and a lack of reference data make it even more difficult to separate acoustic signals from different sources. To overcome this issue, we developed an unsupervised source separation algorithm based on a multi-layer (deep) non-negative matrix factorization (NMF). Using reference echolocation calls of 13 bat species, we evaluated the performance of the multi-layer NMF in separating species-specific calls. Results showed that the multi-layer NMF, especially when being pre-trained with reference calls, outperformed the conventional supervised single-layer NMF. We also evaluated the performance of the multi-layer NMF in identifying different types of bat calls in recordings collected in the field. We found comparable performance in call types identification between the multi-layer NMF and human observers. These results suggest that the proposed multi-layer NMF approach can be used to effectively separate acoustic signals of different taxa from long-duration field recordings in an unsupervised manner. The approach can thus improve the applicability of passive acoustic monitoring as a tool to investigate the responses of biodiversity to the changing environment

Role of autophagy-related proteins ATG8f and ATG8h in the maintenance of autophagic activity in Arabidopsis roots under phosphate starvation

Nutrient starvation-induced autophagy is a conserved process in eukaryotes. Plants defective in autophagy show hypersensitivity to carbon and nitrogen limitation. However, the role of autophagy in plant phosphate (Pi) starvation response is relatively less explored. Among the core autophagy-related (ATG) genes, ATG8 encodes a ubiquitin-like protein involved in autophagosome formation and selective cargo recruitment. The Arabidopsis thaliana ATG8 genes, AtATG8f and AtATG8h, are notably induced in roots under low Pi. In this study, we show that such upregulation correlates with their promoter activities and can be suppressed in the phosphate response 1 (phr1) mutant. Yeast one-hybrid analysis failed to attest the binding of the AtPHR1 transcription factor to the promoter regions of AtATG8f and AtATG8h. Dual luciferase reporter assays in Arabidopsis mesophyll protoplasts also indicated that AtPHR1 could not transactivate the expression of both genes. Loss of AtATG8f and AtATG8h leads to decreased root microsomal-enriched ATG8 but increased ATG8 lipidation. Moreover, atg8f/atg8h mutants exhibit reduced autophagic flux estimated by the vacuolar degradation of ATG8 in the Pi-limited root but maintain normal cellular Pi homeostasis with reduced number of lateral roots. While the expression patterns of AtATG8f and AtATG8h overlap in the root stele, AtATG8f is more strongly expressed in the root apex and root hair and remarkably at sites where lateral root primordia develop. We hypothesize that Pi starvation-induction of AtATG8f and AtATG8h may not directly contribute to Pi recycling but rely on a second wave of transcriptional activation triggered by PHR1 that fine-tunes cell type-specific autophagic activity