SR-TTS: a rhyme-based end-to-end speech synthesis system.

Deep learning has significantly advanced text-to-speech (TTS) systems. These neural network-based systems have enhanced speech synthesis quality and are increasingly vital in applications like human-computer interaction. However, conventional TTS models still face challenges, as the synthesized speeches often lack naturalness and expressiveness. Additionally, the slow inference speed, reflecting low efficiency, contributes to the reduced voice quality. This paper introduces SynthRhythm-TTS (SR-TTS), an optimized Transformer-based structure designed to enhance synthesized speech. SR-TTS not only improves phonological quality and naturalness but also accelerates the speech generation process, thereby increasing inference efficiency. SR-TTS contains an encoder, a rhythm coordinator, and a decoder. In particular, a pre-duration predictor within the cadence coordinator and a self-attention-based feature predictor work together to enhance the naturalness and articulatory accuracy of speech. In addition, the introduction of causal convolution enhances the consistency of the time series. The cross-linguistic capability of SR-TTS is validated by training it on both English and Chinese corpora. Human evaluation shows that SR-TTS outperforms existing techniques in terms of speech quality and naturalness of expression. This technology is particularly suitable for applications that require high-quality natural speech, such as intelligent assistants, speech synthesized podcasts, and human-computer interaction.

Iridium-incorporated Co3O4 with lattice expansion for energy-efficient green hydrogen production coupled with glycerol valorization.

The iridium-incorporated Co3O4 (Ir-Co3O4) catalyst is obtained from binary CoIr-based metal-organic framework precursors via controlled calcination treatment. Structural characterization reveals that in situ incorporation of Ir cations can cause lattice expansion of Co3O4 and regulate its electronic structure, thus in turn favoring electrocatalytic performance improvement. With a lattice expansion-induced strain effect, the Ir-Co3O4 catalyst shows superior performance for both electrocatalytic glycerol-to-formate conversion and the hydrogen evolution reaction.

Gratitude and academic engagement: exploring the mediating effects of internal locus of control and subjective well-being.

This study aimed to explore the relationship between gratitude and academic engagement in Chinese students. The students of some junior high schools in Guangzhou were surveyed using the Gratitude Questionnaire-6, the School Engagement Questionnaire, the Levenson's IPC Scale, and the General Well-being Schedule. A total of 708 valid responses were collected. The results indicate a significant positive relationship between gratitude and academic engagement. Subjective well-being plays a mediating role between gratitude and academic engagement. Locus of control and subjective well-being serve as serial mediators between gratitude and academic engagement. These findings suggest that promoting students' academic engagement can be achieved by fostering gratitude and improving their internal locus of control and subjective well-being. By cultivating gratitude and enhancing these factors, educators and policymakers can create a more engaging and supportive learning environment for students.

Mindfulness and negative emotions among Chinese college students: chain mediation effect of rumination and resilience.

Objective: This study examines the mediation effect of rumination and resilience between the relationship of mindfulness and negative emotions in Chinese college students. Method: A total of 3,038 college students (19.94 ± 1.10) were investigated by Mindfulness Attention Awareness Scale (MASS), Rumination Response Style Scale (RRS), Resilience Scale (RES) and Depression-anxiety-pressure scale (DASS-21), and the mediation analyses were conducted by adopting PROCESS macro in the SPSS software. Results: ① Mindfulness was negatively associated with rumination and negative emotions (r = -0.69, -0.72; P < 0.01), and positively associated with resilience (r = 0.63, P < 0.01). Rumination was negatively associated with resilience (r = -0.59, P < 0.01), and positively associated with negative emotions (r = 0.83, P < 0.01). Resilience was negatively associated with negative emotions (r = -0.71, P < 0.01). ② Mindfulness can not only directly predict negative emotions (95%CI, -0.12~-0.09) but also affects negative emotions through three indirect paths: Rumination was a mediator (95%CI, -0.24~-0.20), resilience was a mediator (95%CI, -0.07~-0.06), and resilience and rumination were a chain mediator (95%CI, -0.04 ~ -0.03). Conclusion: Mindfulness not only influences negative emotions directly, but also through the mediating effect of rumination and resilience indirectly.

Fast identification and quantification of c-Fos protein using you-only-look-once-v5.

In neuroscience, protein activity characterizes neuronal excitability in response to a diverse array of external stimuli and represents the cell state throughout the development of brain diseases. Importantly, it is necessary to characterize the proteins involved in disease progression, nuclear function determination, stimulation method effect, and other aspects. Therefore, the quantification of protein activity is indispensable in neuroscience. Currently, ImageJ software and manual counting are two of the most commonly used methods to quantify proteins. To improve the efficiency of quantitative protein statistics, the you-only-look-once-v5 (YOLOv5) model was proposed. In this study, c-Fos immunofluorescence images data set as an example to verify the efficacy of the system using protein quantitative statistics. The results indicate that YOLOv5 was less time-consuming or obtained higher accuracy than other methods (time: ImageJ software: 80.12 ± 1.67 s, manual counting: 3.41 ± 0.25 s, YOLOv5: 0.0251 ± 0.0003 s, p < 0.0001, n = 83; simple linear regression equation: ImageJ software: Y = 1.013 × X + 0.776, R 2 = 0.837; manual counting: Y = 1.0*X + 0, R 2 = 1; YOLOv5: Y = 0.9730*X + 0.3821, R 2 = 0.933, n = 130). The findings suggest that the YOLOv5 algorithm provides feasible methods for quantitative statistical analysis of proteins and has good potential for application in detecting target proteins in neuroscience.

Electronic Metal-Support Interaction Triggering Interfacial Charge Polarization over CuPd/N-Doped-C Nanohybrids Drives Selectively Electrocatalytic Conversion of Nitrate to Ammonia.

Selective electrocatalytic nitrate-to-ammonia conversion holds significant potential in treatment of nitrate wastewater and simultaneously produces high-value-added ammonia. However, today's development of nitrate-to-ammonia technology remains hindered by the lack of electrocatalysts with high activity and selectivity. In this work, metal-organic framework-derived CuPd bimetallic nanoparticles/nitrogen-doped carbon (CuPd/CN) hybrid nanoarrays for efficient ammonia electrosynthesis from nitrate are designed and synthesized. Systematic characterization reveals that the electronic metal-support interaction between the CuPd nanoparticles and N-doped nanocarbon matrix could trigger interfacial charge polarization over the CuPd/CN composite and make Cu sites electron deficient, which is conducive to the adsorption of nitrate ions. Moreover, the Pd atom sites separate by Cu atoms and could catalyze the dissociation of H2 O molecules to form adsorbed H species, which evolves into hydrogen radicals and behaves as the dominant reactive species in accelerating nitrate-to-ammonia electrocatalysis. These advantages endow the CuPd/CN nanoarrays with high faradaic efficiency (96.16%), selectivity (92.08%) as well as excellent catalytic stability for electroreduction of nitrate to ammonia.

Electroreduction of Nitrate to Ammonia on Palladium-Cobalt-Oxygen Nanowire Arrays.

Developing high-efficiency electrocatalysts for the selective reduction of nitrate to valuable ammonia is of great significance. Herein, Pd-PdO-modified Co3O4 nanowire arrays on nickel foam (PdCoO/NF) are fabricated by a facile cation-exchange reaction. Pd and PdO can facilitate the generation of adsorbed hydrogen, and abundant oxygen vacancies can promote nitrate activation. Therefore, the PdCoO/NF exhibits a superior nitrate conversion rate (89.3%), Faradaic efficiency (88.6%), and ammonium selectivity (95.3%) at -1.3 V versus a saturated calomel electrode. The source of the produced ammonia is confirmed by 15N isotope labeling experiments and 1H magnetic resonance. This presented synthetic method provides a powerful strategy for the preparation of nanowire arrays with controllable compositions for selective nitrate electroreduction to ammonia.

Temporal-Spatial Feature Extraction of DSA Video and Its Application in AVM Diagnosis.

Objectives: Brain arteriovenous malformation (AVM) is one of the most common causes of intracranial hemorrhage in young adults, and its expeditious diagnosis on digital subtraction angiography (DSA) is essential for clinical decision-making. This paper firstly proposed a deep learning network to extract vascular time-domain features from DSA videos. Then, the temporal features were combined with spatial radiomics features to build an AVM-assisted diagnosis model. Materials and method: Anteroposterior position (AP) DSA videos from 305 patients, 153 normal and 152 with AVM, were analyzed. A deep learning network based on Faster-RCNN was proposed to track important vascular features in DSA. Then the appearance order of important vascular structures was quantified as the temporal features. The structure distribution and morphological features of vessels were quantified as 1,750 radiomics features. Temporal features and radiomics features were fused in a classifier based on sparse representation and support vector machine. An AVM diagnosis and grading system that combined the temporal and spatial radiomics features of DSA was finally proposed. Accuracy (ACC), sensitivity (SENS), specificity (SPEC), and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the radiomics model. Results: For cerebrovascular structure detection, the average precision (AP) was 0.922, 0.991, 0.769, 0.899, and 0.929 for internal carotid artery, Willis circle, vessels, large veins, and venous sinuses, respectively. The mean average precision (mAP) of five time phases was 0.902. For AVM diagnosis, the models based on temporal features, radiomics features, and combined features achieved AUC of 0.916, 0.918, and 0.942, respectively. In the AVM grading task, the proposed combined model also achieved AUC of 0.871 in the independent testing set. Conclusion: DSA videos provide rich temporal and spatial distribution characteristics of cerebral blood vessels. Clinicians often interpret these features based on subjective experience. This paper proposes a scheme based on deep learning and traditional machine learning, which effectively integrates the complex spatiotemporal features in DSA, and verifies the value of this scheme in the diagnosis of AVM.

Full-length annotation with multistrategy RNA-seq uncovers transcriptional regulation of lncRNAs in cotton.

Long noncoding RNAs (lncRNAs) are crucial factors during plant development and environmental responses. To build an accurate atlas of lncRNAs in the diploid cotton Gossypium arboreum, we combined Isoform-sequencing, strand-specific RNA-seq (ssRNA-seq), and cap analysis gene expression (CAGE-seq) with PolyA-seq and compiled a pipeline named plant full-length lncRNA to integrate multi-strategy RNA-seq data. In total, 9,240 lncRNAs from 21 tissue samples were identified. 4,405 and 4,805 lncRNA transcripts were supported by CAGE-seq and PolyA-seq, respectively, among which 6.7% and 7.2% had multiple transcription start sites (TSSs) and transcription termination sites (TTSs). We revealed that alternative usage of TSS and TTS of lncRNAs occurs pervasively during plant growth. Besides, we uncovered that many lncRNAs act in cis to regulate adjacent protein-coding genes (PCGs). It was especially interesting to observe 64 cases wherein the lncRNAs were involved in the TSS alternative usage of PCGs. We identified lncRNAs that are coexpressed with ovule- and fiber development-associated PCGs, or linked to GWAS single-nucleotide polymorphisms. We mapped the genome-wide binding sites of two lncRNAs with chromatin isolation by RNA purification sequencing. We also validated the transcriptional regulatory role of lnc-Ga13g0352 via virus-induced gene suppression assay, indicating that this lncRNA might act as a dual-functional regulator that either activates or inhibits the transcription of target genes.

Automatic detection of intracranial aneurysms in 3D-DSA based on a Bayesian optimized filter.

BACKGROUND: Intracranial aneurysm is a common type of cerebrovascular disease with a risk of devastating subarachnoid hemorrhage if it is ruptured. Accurate computer-aided detection of aneurysms can help doctors improve the diagnostic accuracy, and it is very helpful in reducing the risk of subarachnoid hemorrhage. Aneurysms are detected in 2D or 3D images from different modalities. 3D images can provide more vascular information than 2D images, and it is more difficult to detect. The detection performance of 2D images is related to the angle of view; it may take several angles to determine the aneurysm. As the gold standard for the diagnosis of vascular diseases, the detection on digital subtraction angiography (DSA) has more clinical value than other modalities. In this study, we proposed an adaptive multiscale filter to detect intracranial aneurysms on 3D-DSA. METHODS: Adaptive aneurysm detection consists of three parts. The first part is a filter based on Hessian matrix eigenvalues, whose parameters are automatically obtained by Bayesian optimization. The second part is aneurysm extraction based on region growth and adaptive thresholding. The third part is the iterative detection strategy for multiple aneurysms. RESULTS: The proposed method was quantitatively evaluated on data sets of 145 patients. The results showed a detection precision of 94.6%, and a sensitivity of 96.4% with a false-positive rate of 6.2%. Among aneurysms smaller than 5 mm, 93.9% were found. Compared with aneurysm detection on 2D-DSA, automatic detection on 3D-DSA can effectively reduce the misdiagnosis rate and obtain more accurate detection results. Compared with other modalities detection, we also get similar or better detection performance. CONCLUSIONS: The experimental results show that the proposed method is stable and reliable for aneurysm detection, which provides an option for doctors to accurately diagnose aneurysms.

Sequestration effect and mechanism of PCB1 by high-temperature black carbon.

Black carbon (BC) is a substance that significantly affects the migration and transformation of hydrophobic organic compounds (HOCs) in soil/sediment. High-temperature BC is an important form of BC in the environment, and, currently, there is relatively little research on the influence of high-temperature BC on the sorption and the desorption behavior of HOCs and its mechanism. In this study, the sorption isotherms and TENAX-aided desorption kinetics of PCB1 by three typical high-temperature BCs (fly ash (FC), soot (SC), and high-temperature biochar (BC 900)) and a low-temperature biochar (BC 400) were compared. In addition, the sorption-desorption mechanism was clarified through its correlation with the physicochemical properties of BC. The results indicated that the Freundlich sorption parameters of FC, SC, BC 900, and BC 400 were 9947.90, 5417.57, 77690.16, and 2804.54 (mg kg-1)/(mg L-1), respectively, indicating that these high-temperature BCs had stronger sorption capacity. The desorption rate of PCB1 on BC 900 was slow, and the ratio of the difficult desorption fraction (Fr) was as high as 96.2%, while those of FC, SC, and BC 400 were only 35.3%, 19.1%, and 54.7%, respectively. The sorption and desorption mechanisms of the three high-temperature BCs were similar to those of BC 400. They exhibited nonlinear adsorption at low PCB1 concentrations and linear partition at high PCB1 concentrations. Moreover, the results demonstrated that different types of high-temperature BCs in the environment have different sequestration effects on HOCs. Frap, the part that can be quickly desorbed, was predominantly PCB1 sorbed onto BC through a linear partition mechanism, but the surface acidic functional groups and larger pores would also increase the Frap. Meanwhile, the slow desorption ratio (Fslow) was mainly affected by the degree of surface aromatization; the difficult-to-desorb PCB1 (Fr) was combined with BC through a nonlinear adsorption mechanism and was mainly related to the micropore volume. Graphical abstract.

Effects of environmental factors on the removal of heavy metals by sulfide-modified nanoscale zerovalent iron.

Sulfide-modified nanoscale zerovalent iron (S-nZVI) has excellent reducing performance for heavy metals in water. The influence of environmental factors on the reactivity can be used to explore the practical feasibility of S-nZVI and analyze the reaction mechanism in depth. This study compared the removal effect and mechanism of Cu2+ and Ni2+ by nanoscale zerovalent iron (nZVI), S-nZVI, and carboxymethyl cellulose-modified nanoscale zerovalent iron (CMC-nZVI). The results show that the pseudo-first-order kinetic constant of Cu2+ removal by nZVI, S-nZVI, and CMC-nZVI was 1.384, 1.919, and 2.890 min-1, respectively, and the rate of Ni2+ removal was 0.304, 0.931, and 0.360 min-1, respectively. The removal mechanism of S-nZVI was similar to that of nZVI and CMC-nZVI. Specifically, Cu2+ was predominantly removed by reduction, while Ni2+ removal included adsorption and reduction. Environmental factors had a specific inhibitory effect on the removal of Cu2+ but had a negligible impact on Ni2+. The condition of low pH, the presence of Cl- and humic acid (HA) promoted the corrosion consumption of Fe0, in which H+ directly corroded Fe0 at low pH. At the same time, Cl- and HA inhibited the adsorption or binding of heavy metal ions on the particle surface, thereby reducing the electron transfer and utilization efficiency. The passivation of NO3- reduced the anaerobic corrosion of the material in water but suppressed the release of electrons, thereby reducing the reduction efficiency of the three types of materials. The anaerobic corrosion of S-nZVI was less affected by environmental factors, and it can still maintain more than 80% of the electronic utilization efficiency under different environmental factors, which illustrates that S-nZVI has broad prospects for practical applications in heavy metal polluted water.

Interaction between pollutants during the removal of polychlorinated biphenyl-heavy metal combined pollution by modified nanoscale zero-valent iron.

Modified nanoscale zero-valent iron (nZVI) is a promising functional material for the remediation of combined pollutants involving polychlorinated biphenyls (PCBs) and heavy metals. However, the interaction between the two types of pollutants has not been systematically studied for this method of treatment. In this study, 2,2',4,4',5,5'-hexachlorobiphenyl (PCB153), Cu2+, and Ni2+ were selected as the target pollutants. To understand the interaction between pollutants, the efficiencies of nZVI, sulfidated nZVI (S-nZVI), and carboxymethylcellulose stabilized nZVI (CMC-nZVI) were investigated for removal of PCB153, Cu2+/Ni2+, and combined pollution system (PCBs-Cu2+/Ni2+). Results showed that the removal kinetics of the two types of pollutants by the three materials fitted a pseudo-first-order model well and that the reaction mechanisms were similar. Among the three materials, CMC-nZVI showed the highest reactivity to degrade PCB153 (pseudo-first-order kinetic constants (kobs) = 2.7 × 10-4 min-1) and remove Cu2+ (kobs = 2.890 min-1), while S-nZVI showed higher affinity for the removal of Ni2+ (kobs = 0.931 min-1). For the combined pollution system, PCB153 had little effect on the removal of heavy metals by the three materials, while the effect of heavy metals on PCB153 degradation was related to the types of heavy metals and the materials. Cu2+ had no significant effect on PCB153 degradation by the three materials, while the coexistence of Ni2+ promoted PCB153 degradation by nZVI and CMC-nZVI. XPS and electrochemical analysis showed that Cu0 and Ni0 were produced on the surface of the three materials. Ni is a more effective catalyst and promoted the electron transfer efficiency of the materials and had a positive impact on the dechlorination reaction.