Synthesis and Preliminary Evaluation of an ASGPr-Targeted Polycationic ß-Cyclodextrin Carrier for Nucleosides and Nucleotides.

Most antiviral and anticancer nucleosides are prodrugs that require stepwise phosphorylation to their triphosphate nucleotide form for biological activity. Monophosphorylation may be rate-limiting, and the nucleotides may be unstable and poorly internalized by target cells. Effective targeting and delivery systems for nucleoside drugs, including oligonucleotides used in molecular therapeutics, could augment their efficacy. The development of a carrier designed to effect selective transmembrane internalization of nucleotides via the asialoglycoprotein receptor (ASGPr) is now reported. In this work, the polycationic, polygalactosyl drug delivery carrier heptakis[6-amino-6-deoxy-2-O-(3-(1-thio-ß-D-galactopyranosyl)-propyl)]-ß-cyclodextrin hepta-acetate salt (GCyDAc), potentially a bifunctional carrier of (poly)nucleotides, was modeled by molecular docking in silico as an ASGPr-ligand, then synthesized for testing. The antivirals arabinosyl adenine (araA, vidarabine, an early generation antiviral nucleoside), arabinosyl adenine 5'-monophosphate (araAMP), and 12-mer-araAMP (p-araAMP) were selected for individual formulation with GCyDAc to develop this concept. Experimentally, beta cyclodextrin was decorated with seven protonated amino substituents on the primary face, and seven thiogalactose residues on its secondary face. AraA, araAMP, and p-araAMP were individually complexed with GCyDAc and complex formation for each drug was confirmed by differential scanning calorimetry (DSC). Finally, the free drugs and their GCyDAc complexes were evaluated for antiviral activity using ASGPr-expressing HepAD38 cells in cell culture. In this model, araA, araAMP, and p-araAMP showed relative antiviral potencies of 1.0, 1.1, and 1.2, respectively. In comparison, GCyDAc-complexes of araA, araAMP, and p-araAMP were 2.5, 1.3, and 1.2 times more effective than non-complexed araA in suppressing viral DNA production. The antiviral potencies of these complexes were minimally supportive of the hypothesis that ASGPr-targeted, CyD-based charge-association complexation of nucleosides and nucleotides could effectively enhance antiviral efficacy. GCyDAc was non-toxic to mammalian cells in cell culture, as determined using the MTS proliferation assay.

Improving the Efficiency of Dysarthria Voice Conversion System Based on Data Augmentation.

Dysarthria, a speech disorder often caused by neurological damage, compromises the control of vocal muscles in patients, making their speech unclear and communication troublesome. Recently, voice-driven methods have been proposed to improve the speech intelligibility of patients with dysarthria. However, most methods require a significant representation of both the patient's and target speaker's corpus, which is problematic. This study aims to propose a data augmentation-based voice conversion (VC) system to reduce the recording burden on the speaker. We propose dysarthria voice conversion 3.1 (DVC 3.1) based on a data augmentation approach, including text-to-speech and StarGAN-VC architecture, to synthesize a large target and patient-like corpus to lower the burden of recording. An objective evaluation metric of the Google automatic speech recognition (Google ASR) system and a listening test were used to demonstrate the speech intelligibility benefits of DVC 3.1 under free-talk conditions. The DVC system without data augmentation (DVC 3.0) was used for comparison. Subjective and objective evaluation based on the experimental results indicated that the proposed DVC 3.1 system enhanced the Google ASR of two dysarthria patients by approximately [62.4%, 43.3%] and [55.9%, 57.3%] compared to unprocessed dysarthria speech and the DVC 3.0 system, respectively. Further, the proposed DVC 3.1 increased the speech intelligibility of two dysarthria patients by approximately [54.2%, 22.3%] and [63.4%, 70.1%] compared to unprocessed dysarthria speech and the DVC 3.0 system, respectively. The proposed DVC 3.1 system offers significant potential to improve the speech intelligibility performance of patients with dysarthria and enhance verbal communication quality.

Discovery of regulatory motifs in 5' untranslated regions using interpretable multi-task learning models.

The sequence in the 5' untranslated regions (UTRs) is known to affect mRNA translation rates. However, the underlying regulatory grammar remains elusive. Here, we propose MTtrans, a multi-task translation rate predictor capable of learning common sequence patterns from datasets across various experimental techniques. The core premise is that common motifs are more likely to be genuinely involved in translation control. MTtrans outperforms existing methods in both accuracy and the ability to capture transferable motifs across species, highlighting its strength in identifying evolutionarily conserved sequence motifs. Our independent fluorescence-activated cell sorting coupled with deep sequencing (FACS-seq) experiment validates the impact of most motifs identified by MTtrans. Additionally, we introduce "GRU-rewiring," a technique to interpret the hidden states of the recurrent units. Gated recurrent unit (GRU)-rewiring allows us to identify regulatory element-enriched positions and examine the local effects of 5' UTR mutations. MTtrans is a powerful tool for deciphering the translation regulatory motifs.

CPAE: Contrastive predictive autoencoder for unsupervised pre-training in health status prediction.

BACKGROUND AND OBJECTIVE: Fully-supervised learning approaches have shown promising results in some health status prediction tasks using Electronic Health Records (EHRs). These traditional approaches rely on sufficient labeled data to learn from. However, in practice, acquiring large-scaled labeled medical data for various prediction tasks is often not feasible. Thus, it is of great interest to utilize contrastive pre-training to leverage the unlabeled information. METHODS: In this work, we propose a novel data-efficient framework, contrastive predictive autoencoder (CPAE), to first learn without labels from the EHR data in the pre-training process, and then fine-tune on the downstream tasks. Our framework comprises of two parts: (i) a contrastive learning process, inherited from contrastive predictive coding (CPC), which aims to extract global slow-varying features, and (ii) a reconstruction process, which forces the encoder to capture local features. We also introduce the attention mechanism in one variant of our framework to balance the above two processes. RESULTS: Experiments on real-world EHR dataset verify the effectiveness of our proposed framework on two downstream tasks (i.e., in-hospital mortality prediction and length-of-stay prediction), compared to their supervised counterparts, the CPC model, and other baseline models. CONCLUSIONS: By comprising of both contrastive learning components and reconstruction components, CPAE aims to extract both global slow-varying information and local transient information. The best results on two downstream tasks are all achieved by CPAE. The variant AtCPAE is particularly superior when fine-tuned on very small training data. Further work may incorporate techniques of multi-task learning to optimize the pre-training process of CPAEs. Moreover, this work is based on the benchmark MIMIC-III dataset which only includes 17 variables. Future work may extend to a larger number of variables.

Concentration Dependence of Dynamics and Structure among Hydrated Magnesium Ions: An Ultrafast Infrared Study.

The dynamics of aqueous magnesium chloride solutions, from relatively dilute (0.5 m) to near saturated (4.2 m) concentrations, were investigated using ultrafast two dimensional infrared and polarization selective pump-probe spectroscopies. The experiments were performed on two spectrally distinct nitrile stretch frequencies of the selenocyanate vibrational probe, corresponding to the CN nitrogen lone pair being associated with water and with Mg2+. No chemical exchange of the two species was observed over the experimental time scale (∼100 ps), enabling straightforward analysis of their dynamics. The dynamics reported by the Mg2+-associated peak are slower than those of the water-associated peak, suggesting that the immediate environment of the hydrated Mg2+ is different from the rest of the solution. Notably, the Mg2+-associated peak displays three spectral diffusion time scales, the slowest being ∼30 ps, while the water-associated peak decays as a faster biexponential. From the complete orientational relaxation time and hydrodynamic theory, a magnesium hydration number of six was obtained, which is in good agreement with NMR and X-ray diffraction studies. This hydration number holds for all concentrations until near saturation, when the linewidths and the dynamics deviate from linear trends, indicative of Mg2+ solvation structure changes resulting from a shortage of water molecules needed for full solvation.

Enhancing the Performance of Pathological Voice Quality Assessment System Through the Attention-Mechanism Based Neural Network.

OBJECTIVE: Doctors, nowadays, primarily use auditory-perceptual evaluation, such as the grade, roughness, breathiness, asthenia, and strain scale, to evaluate voice quality and determine the treatment. However, the results predicted by individual physicians often differ, because of subjective perceptions, and diagnosis time interval, if the patient's symptoms are hard to judge. Therefore, an accurate computerized pathological voice quality assessment system will improve the quality of assessment. METHOD: This study proposes a self_attention-based system, with a deep learning technology, named self_attention-based bidirectional long-short term memory (SA BiLSTM). Different pitches [low, normal, high], and vowels [/a/, /i/, /u/], were added into the proposed model, to make it learn how professional doctors evaluate the grade, roughness, breathiness, asthenia, and strain scale, in a high dimension view. RESULTS: The experimental results showed that the proposed system provided higher performance than the baseline system. More specifically, the macro average of the F1 score, presented as decimal, was used to compare the accuracy of classification. The (G, R, and B) of the proposed system were (0.768±0.011, 0.820±0.009, and 0.815±0.009), which is higher than the baseline systems: deep neural network (0.395±0.010, 0.312±0.019, 0.321±0.014) and convolution neural network (0.421±0.052, 0.306±0.043, 0.3250±0.032) respectively. CONCLUSIONS: The proposed system, with SA BiLSTM, pitches, and vowels, provides a more accurate way to evaluate the voice. This will be helpful for clinical voice evaluations and will improve patients' benefits from voice therapy.

Objective Signal Analysis for Investigating Feasibility of Active Noise Cancellation in Hearing Screening.

With the development of active noise cancellation (ANC) technology, ANC has been used to mitigate the effects of environmental noise on audiometric results. However, objective evaluation methods supporting the accuracy of audiometry for ANC exposure to different levels of noise have not been reported. Accordingly, the audio characteristics of three different ANC headphone models were quantified under different noise conditions and the feasibility of ANC in noisy environments was investigated. Steady (pink noise) and non-steady noise (cafeteria babble noise) were used to simulate noisy environments. We compared the integrity of pure-tone signals obtained from three different ANC headphone models after processing under different noise scenarios and analyzed the degree of ANC signal correlation based on the Pearson correlation coefficient compared to pure-tone signals in quiet. The objective signal correlation results were compared with audiometric screening results to confirm the correspondence. Results revealed that ANC helped mitigate the effects of environmental noise on the measured signal and the combined ANC headset model retained the highest signal integrity. The degree of signal correlation was used as a confidence indicator for the accuracy of hearing screening in noise results. It was found that the ANC technique can be further improved for more complex noisy environments.

Platelet-derived extracellular vesicles inhibit ferroptosis and promote distant metastasis of nasopharyngeal carcinoma by upregulating ITGB3.

Nasopharyngeal carcinoma (NPC) is a malignancy with high metastatic and invasive nature. Distant metastasis contributes substantially to treatment failure and mortality in NPC. Platelets are versatile blood cells and the number of platelets is positively associated with the distant metastasis of tumor cells. However, the role and underlying mechanism of platelets responsible for the metastasis of NPC cells remain unclear. Here we found that the distant metastasis of NPC patients was positively correlated with the expression levels of integrin ß3 (ITGB3) in platelet-derived extracellular vesicles (EVs) from NPC patients (P-EVs). We further revealed that EVs transfer occurred from platelets to NPC cells, mediating cell-cell communication and inducing the metastasis of NPC cells by upregulating ITGB3 expression. Mechanistically, P-EVs-upregulated ITGB3 increased SLC7A11 expression by enhancing protein stability and activating the MAPK/ERK/ATF4/Nrf2 axis, which suppressed ferroptosis, thereby facilitating the metastasis of NPC cells. NPC xenografts in mouse models further confirmed that P-EVs inhibited the ferroptosis of circulating NPC cells and promoted the distant metastasis of NPC cells. Thus, these findings elucidate a novel role of platelet-derived EVs in NPC metastasis, which not only improves our understanding of platelet-mediated tumor distant metastasis, but also has important implications in diagnosis and treatment of NPC.

Dysarthric Speech Enhancement Based on Convolution Neural Network.

Generally, those patients with dysarthria utter a distorted sound and the restrained intelligibility of a speech for both human and machine. To enhance the intelligibility of dysarthric speech, we applied a deep learning-based speech enhancement (SE) system in this task. Conventional SE approaches are used for shrinking noise components from the noise-corrupted input, and thus improve the sound quality and intelligibility simultaneously. In this study, we are focusing on reconstructing the severely distorted signal from the dysarthric speech for improving intelligibility. The proposed SE system prepares a convolutional neural network (CNN) model in the training phase, which is then used to process the dysarthric speech in the testing phase. During training, paired dysarthric-normal speech utterances are required. We adopt a dynamic time warping technique to align the dysarthric-normal utter-ances. The gained training data are used to train a CNN - based SE model. The proposed SE system is evaluated on the Google automatic speech recognition (ASR) system and a subjective listening test. The results showed that the proposed method could notably enhance the recognition performance for more than 10% in each of ASR and human recognitions from the unprocessed dysarthric speech. Clinical Relevance- This study enhances the intelligibility and ASR accuracy from a dysarthria speech to more than 10.

Study of optical-based speech acquisition system using vibration signals from speakers' medical masks.

Medical masks have become necessary of late because of the COVID-19 outbreak; however, they tend to attenuate the energy of speech signals and affect speech quality. Therefore, this study proposes an optical-based microphone approach to obtain speech signals from speakers' medical masks. Experimental results showed that the optical-based microphone approach achieved better performance (85.61%) than the two baseline approaches, namely, omnidirectional (24.17%) and directional microphones (31.65%), in the case of long-distance speech and background noise. The results suggest that the optical-based microphone method is a promising approach for acquiring speech from a medical mask.

A Deep Learning Based Approach to Synthesize Intelligible Speech with Limited Temporal Envelope Information.

Envelope waveforms can be extracted from multiple frequency bands of a speech signal, and envelope waveforms carry important intelligibility information for human speech communication. This study aimed to investigate whether a deep learning-based model with features of temporal envelope information could synthesize an intelligible speech, and to study the effect of reducing the number (from 8 to 2 in this work) of temporal envelope information on the intelligibility of the synthesized speech. The objective evaluation metric of short-time objective intelligibility (STOI) showed that, on average, the synthesized speech of the proposed approach provided higher STOI (i.e., 0.8) scores in each test condition; and the human listening test showed that the average word correct rate of eight listeners was higher than 97.5%. These findings indicated that the proposed deep learning-based system can be a potential approach to synthesize a highly intelligible speech with limited envelope information in the future.

Phonetic posteriorgram-based voice conversion system to improve speech intelligibility of dysarthric patients.

BACKGROUND AND OBJECTIVE: Most dysarthric patients encounter communication problems due to unintelligible speech. Currently, there are many voice-driven systems aimed at improving their speech intelligibility; however, the intelligibility performance of these systems are affected by challenging application conditions (e.g., time variance of patient's speech and background noise). To alleviate these problems, we proposed a dysarthria voice conversion (DVC) system for dysarthric patients and investigated the benefits under challenging application conditions. METHOD: A deep learning-based voice conversion system with phonetic posteriorgram (PPG) features, called the DVC-PPG system, was proposed in this study. An objective-evaluation metric of Google automatic speech recognition (Google ASR) system and a listening test were used to demonstrate the speech intelligibility benefits of DVC-PPG under quiet and noisy test conditions; besides, the well-known voice conversion system using mel-spectrogram, DVC-Mels, was used for comparison to verify the benefits of the proposed DVC-PPG system. RESULTS: The objective-evaluation metric of Google ASR showed the average accuracy of two subjects in the duplicate and outside test conditions while the DVC-PPG system provided higher speech recognitions rate (83.2% and 67.5%) than dysarthric speech (36.5% and 26.9%) and DVC-Mels (52.9% and 33.8%) under quiet conditions. However, the DVC-PPG system provided more stable performance than the DVC-Mels under noisy test conditions. In addition, the results of the listening test showed that the speech-intelligibility performance of DVC-PPG was better than those obtained via the dysarthria speech and DVC-Mels under the duplicate and outside conditions, respectively. CONCLUSIONS: The objective-evaluation metric and listening test results showed that the recognition rate of the proposed DVC-PPG system was significantly higher than those obtained via the original dysarthric speech and DVC-Mels system. Therefore, it can be inferred from our study that the DVC-PPG system can improve the ability of dysarthric patients to communicate with people under challenging application conditions.

Improved Environment-Aware-Based Noise Reduction System for Cochlear Implant Users Based on a Knowledge Transfer Approach: Development and Usability Study.

BACKGROUND: Cochlear implant technology is a well-known approach to help deaf individuals hear speech again and can improve speech intelligibility in quiet conditions; however, it still has room for improvement in noisy conditions. More recently, it has been proven that deep learning-based noise reduction, such as noise classification and deep denoising autoencoder (NC+DDAE), can benefit the intelligibility performance of patients with cochlear implants compared to classical noise reduction algorithms. OBJECTIVE: Following the successful implementation of the NC+DDAE model in our previous study, this study aimed to propose an advanced noise reduction system using knowledge transfer technology, called NC+DDAE_T; examine the proposed NC+DDAE_T noise reduction system using objective evaluations and subjective listening tests; and investigate which layer substitution of the knowledge transfer technology in the NC+DDAE_T noise reduction system provides the best outcome. METHODS: The knowledge transfer technology was adopted to reduce the number of parameters of the NC+DDAE_T compared with the NC+DDAE. We investigated which layer should be substituted using short-time objective intelligibility and perceptual evaluation of speech quality scores as well as t-distributed stochastic neighbor embedding to visualize the features in each model layer. Moreover, we enrolled 10 cochlear implant users for listening tests to evaluate the benefits of the newly developed NC+DDAE_T. RESULTS: The experimental results showed that substituting the middle layer (ie, the second layer in this study) of the noise-independent DDAE (NI-DDAE) model achieved the best performance gain regarding short-time objective intelligibility and perceptual evaluation of speech quality scores. Therefore, the parameters of layer 3 in the NI-DDAE were chosen to be replaced, thereby establishing the NC+DDAE_T. Both objective and listening test results showed that the proposed NC+DDAE_T noise reduction system achieved similar performances compared with the previous NC+DDAE in several noisy test conditions. However, the proposed NC+DDAE_T only required a quarter of the number of parameters compared to the NC+DDAE. CONCLUSIONS: This study demonstrated that knowledge transfer technology can help reduce the number of parameters in an NC+DDAE while keeping similar performance rates. This suggests that the proposed NC+DDAE_T model may reduce the implementation costs of this noise reduction system and provide more benefits for cochlear implant users.

Ultrafast Dynamics and Liquid Structure in Mesoporous Silica: Propagation of Surface Effects in a Polar Aprotic Solvent.

Enhancement of processes ranging from gas sorption to ion conduction in a liquid can be substantial upon nanoconfinement. Here, the dynamics of a polar aprotic solvent, 1-methylimidazole (MeIm), in mesoporous silica (2.8, 5.4, and 8.3 nm pore diameters) were examined using femtosecond infrared vibrational spectroscopy and molecular dynamics simulations of a dilute probe, the selenocyanate (SeCN-) anion. The long vibrational lifetime and sensitivity of the CN stretch enabled a comprehensive investigation of the relatively slow time scales and subnanometer distance dependences of the confined dynamics. Because MeIm does not readily donate hydrogen bonds, its interactions in the hydrophilic silanol pores differ more from the bulk than those of water confined in the same mesopores, resulting in greater structural order and more dramatic slowing of dynamics. The extent of surface effects was quantified by modified two-state models used to fit three spatially averaged experimental observables: vibrational lifetime, orientational relaxation, and spectral diffusion. The length scales and the models (smoothed step, exponential decay, and simple step) describing the transitions between the distinctive shell behavior at the surface and the bulk-like behavior at the pore interior were compared to those of water. The highly nonuniform distributions of the SeCN- probe and antiparallel layering of MeIm revealed by the simulations guided the interpretation of the results and development of the analytical models. The results illustrate the importance of electrostatic effects and H-bonding interactions in the behavior of confined liquids.

ZnS-SnS@NC Heterostructure as Robust Lithiophilicity and Sulfiphilicity Mediator toward High-Rate and Long-Life Lithium-Sulfur Batteries.

Lithium-sulfur (Li-S) batteries are severely hindered by the low sulfur utilization and short cycling life, especially at high rates. One of the effective solutions to address these problems is to improve the sulfiphilicity of lithium polysulfides (LiPSs) and the lithiophilicity of the lithium anode. However, it is a great challenge to simultaneously optimize both aspects. Herein, by incorporating the merits of strong absorbability and high conductivity of SnS with good catalytic capability of ZnS, a ZnS-SnS heterojunction coated with a polydopamine-derived N-doped carbon shell (denoted as ZnS-SnS@NC) with uniform cubic morphology was obtained and compared with the ZnS-SnS2@NC heterostructure and its single-component counterparts (SnS@NC and SnS2@NC). Theoretical calculations, ex situ XANES, and in situ Raman spectrum were utilized to elucidate rapid anchoring-diffusion-transformation of LiPSs, inhibition of the shuttling effect, and improvement of the sulfur electrochemistry of bimetal ZnS-SnS heterostructure at the molecular level. When applied as a modification layer coated on the separator, the ZnS-SnS@NC-based cell with optimized lithiophilicity and sulfiphilicity enables desirable sulfur electrochemistry, including high reversibility of 1149 mAh g-1 for 300 cycles at 0.2 C, high rate performance of 661 mAh g-1 at 10 C, and long cycle life with a low fading rate of 0.0126% each cycle after 2000 cycles at 4 C. Furthermore, a favorable areal capacity of 8.27 mAh cm-2 is maintained under high sulfur mass loading of 10.3 mg cm-2. This work furnishes a feasible scheme to the rational design of bimetal sulfides heterostructures and boosts the development of other electrochemical applications.

Deep-learning-based human motion tracking for rehabilitation applications using 3D image features.

Motion rehabilitation is increasingly required owing to an aging population and suffering of stroke, which means human motion analysis must be valued. Based on the concept mentioned above, a deep-learning-based system is proposed to track human motion based on three-dimensional (3D) images in this work; meanwhile, the features of traditional red green blue (RGB) images, known as two-dimensional (2D) images, were used as a comparison. The results indicate that 3D images have an advantage over 2D images due to the information of spatial relationships, which implies that the proposed system can be a potential technology for human motion analysis applications.

Insight into the structure-antibacterial activity of amino cation-based and acetate anion-based ionic liquids from computational interactions with the POPC phospholipid bilayer.

Investigations relevant to ionic liquids (ILs) as antibacterial agents have drawn considerable attention. However, the high cost and potential toxicity of ILs have severely limited their extensive applications, which has motivated researchers to design inexpensive and health-benign ILs. In this work, the interactions between the hydrated zwitterionic phospholipid (POPC) bilayer and a series of hypothetical amino cation-based and acetate anion-based ILs with different counterparts were investigated using molecular dynamics (MD) simulations to predict their antibacterial abilities. The cations of the ILs were found to insert into the lipid bilayer spontaneously, especially amino cations. Reorientation of the inserted imidazolium-based cations was observed, while the inserted amino cations showed no obvious reorientation phenomena, probably because of the strong charge interactions between the positive NH3 groups of the amino cation and the negative PO4 groups of the lipid bilayer. Due to their strong affinity with water, acetate-based anions disperse better in water solution, which weakens the insertion of the cations into the lipid bilayer to some extent. The structure and dynamic properties of the lipid bilayer, such as electrostatic potential, local ordering, area per lipid, volume per lipid, bilayer thickness, and lateral diffusion, are significantly influenced by the insertion of the cations, which results in disorder of the lipid bilayer and further disruption of the activity of the cell membrane. The insights into the relationship between the structures of ILs and their antibacterial activity in this work will provide a good reference for the screening and design of less expensive, safer, and greener IL candidates as antibacterial agents.

Enhanced Menshutkin SN2 Reactivity in Mesoporous Silica: The Influence of Surface Catalysis and Confinement.

A significant enhancement in the Menshutkin SN2 reaction between 1-methylimidazole (MeIm) and methyl thiocyanate (MeSCN) is observed when the reaction is confined in the nanoscale silica pores of MCM41 and SBA15. The experiments in the silica pores are conducted without the surrounding bulk reaction mixture. The influences of temperature, pore radius, and surface chemistry on the kinetics of the confined reaction are analyzed with time-dependent infrared spectroscopy, molecular dynamics simulations, and ab initio calculations. The rate constant of the pseudo-first order reaction increases with decreasing pore size, and the activation energy is found to decrease by 5.6 kJ/mol in the smallest pore studied (2.8 nm) relative to the bulk reaction. The rate constant dependence on pore size is accurately described by a two-state model in which molecules within the 4.6 Å interfacial layer experience a 2.4-fold rate constant increase relative to those reacting at the bulk rate further away from the interface. The removal of polar silanol groups from the silica surface via passivation with trimethylsilyl chloride results in bulk-like kinetics despite a reduction in the pore diameter, demonstrating the role of silanols as catalytic sites. Electronic structure calculations of the energy profile on a model silica surface confirm that silanol groups, particularly those of the vicinal type, can reduce the activation energy and reaction endothermicity through the donation of hydrogen bonds to the reactant, transition state, and product complexes.

A joint-feature learning-based voice conversion system for dysarthric user based on deep learning technology.

Dysarthria speakers suffer from poor communication, and voice conversion (VC) technology is a potential approach for improving their speech quality. This study presents a joint feature learning approach to improve a sub-band deep neural network-based VC system, termed J_SBDNN. In this study, a listening test of speech intelligibility is used to confirm the benefits of the proposed J_SBDNN VC system, with several well-known VC approaches being used for comparison. The results showed that the J_SBDNN VC system provided a higher speech intelligibility performance than other VC approaches in most test conditions. It implies that the J_SBDNN VC system could potentially be used as one of the electronic assistive technologies to improve the speech quality for a dysarthric speaker.