Parental Phubbing and Adolescent Smartphone Addiction: Depression and Perceived School Climate Matter.

This study investigated the association between parental phubbing and adolescents' smartphone addiction, the mediating role of depression, and the moderating role of perceived school climate in the association. Seven hundred forty-two Chinese adolescents (Mage = 12.97, SD = 0.64, 45.55 percent female) were recruited and self-reported questionnaires were administered. Results indicated that parental phubbing was positively associated with adolescents' smartphone addiction. Depression partially mediated the above relation. Further, perceived school climate moderated the relation between parental phubbing and depression, such that the positive association between parental phubbing and depression was stronger among adolescents who perceived their school climate as more negative (vs. positive). The findings deepened our understanding of the relation between parenting behaviors and adolescents' smartphone addiction and implications for interventions and practices were discussed.

Improving Perceptual Speech Ratings: The Effects of Auditory Training on Judgments of Dysarthric Speech.

PURPOSE: Auditory training has been shown to reduce rater variability in perceptual voice assessment. Because rater variability is also a central issue in the auditory-perceptual assessment of dysarthria, this study sought to determine if training produces a meaningful change in rater reliability, criterion validity, and scaling magnitude of four features: overall speech impairment, articulatory imprecision, monotony, and slow rate. METHOD: Forty-four nonexperts randomized to training and nontraining listener groups completed a pretest and posttest. Only the former group underwent auditory training between pre- and posttests. For both testing and training, listeners rated samples from speakers with amyotrophic lateral sclerosis (ALS), speakers with Parkinson's disease (PD), and neurologically healthy control speakers using separate visual analog scales (VASs) for each of the four features. Intraclass correlation coefficients were used to compare inter- and intrarater reliability between pre- and posttest for both listener groups. For criterion validity, severity ratings from the two nonexpert listener groups were compared to those of two experienced listeners for all four features. To determine changes in scaling magnitude, raw VAS scores for each feature were compared from pre- to posttest within the two nonexpert listener groups. Scaling changes were also compared between the two listener groups for the pre- and posttest conditions. RESULTS AND CONCLUSIONS: In the training group, a meaningful improvement in interrater reliability was observed for some features in all three speaker groups, but not in the nontraining group. In contrast, for intrarater reliability, in the nontraining group, a meaningful improvement was observed for many features in all three speaker groups, but only for PD monotony and slow rate in the training group. All ratings from the nonexpert listeners were valid except for monotony. Raw VAS scores did not meaningfully change from pre- to posttest for any of the features, but there was a trend toward lower scores posttraining, mainly for the ALS samples. Modifications to the auditory training paradigm to further improve reliability and validity, along with future goals for optimizing training, are discussed.

Neuroticism and Adolescent Problematic Mobile Social Media Use: A Moderated Mediation Model.

Although previous studies have confirmed the influence of neuroticism on problematic mobile social media use, mechanisms underlying this relation have not been well-understood. The present study investigated the mediating effect of self-control, as well as the moderating role of family socioeconomic status (FSES). A total of 1146 adolescents' data (Mage = 16.11, SDage = 0.97, 498 boys) were collected. Participants completed questionnaires about neuroticism, self-control, problematic mobile social media use, and FSES. The moderated mediation analysis showed that self-control mediated the positive association between neuroticism and problematic mobile social media use. In addition, FSES moderated the negative relation between self-control and problematic mobile social media use. Specifically, compared with adolescents in low FSES, the association between self-control and problematic mobile social media use was stronger for adolescents in high FSES. This study advanced our understanding of the development of problematic mobile social media use by revealing the potential mechanism between adolescent neuroticism and problematic mobile social media use.

Resilience and Internet Addiction: A Moderated Mediation Model of Loneliness and Resting Respiratory Sinus Arrhythmia.

Internet addiction (IA) has become a global concern among college students. To explore the psychophysiological mechanism that is related to IA, this study investigated the role of resilience, loneliness, and resting respiratory sinus arrhythmia (RSA) in IA through a moderated mediation model. A group of 108 (Mage = 18.93, SD = 0.914; 68 male) Chinese undergraduate students completed questionnaires of resilience, loneliness, and IA. Physiological data were collected during their visit to a university laboratory. Results revealed that loneliness mediated the negative association between resilience and IA, with resting RSA and resilience interactively predicted loneliness. Specifically, resilience was negatively related to loneliness only when students had low, rather than high, levels of resting RSA. These findings enrich our understanding of how resilience is related to college students' IA and highlight the protective role of resting RSA in this association.

Low-Intensity Focused Ultrasound Alleviates Chronic Neuropathic Pain-Induced Allodynia by Inhibiting Neuroplasticity in the Anterior Cingulate Cortex.

Low-intensity focused ultrasound (LIFU) is a potential noninvasive method to alleviate allodynia by modulating the central nervous system. However, the underlying analgesic mechanisms remain unexplored. Here, we assessed how LIFU at the anterior cingulate cortex (ACC) affects behavior response and central plasticity resulting from chronic constrictive injury (CCI). The safety of LIFU stimulation was assessed by hematoxylin and eosin (H&E) and Fluoro-Jade C (FJC) staining. A 21-day ultrasound exposure therapy was conducted from day 91 after CCI surgery in mice. We assessed the 50% mechanical withdrawal threshold (MWT50) using Von Frey filaments (VFFs). The expression levels of microtubule-associated protein 2 (MAP2), growth-associated protein 43 (GAP43), and tau were determined via western blotting (WB) and immunofluorescence (IF) staining to evaluate the central plasticity in ACC. The regions of ACC were activated effectively and safely by LIFU stimulation, which significantly increased the number of c-fos-positive cells (P < 0.05) with no bleeding, coagulative necrosis, and neuronal loss. Under chronic neuropathic pain- (CNP-) induced allodynia, MWT50 decreased significantly (P < 0.05), and overexpression of MAP2, GAP43, and tau was also observed. After 3 weeks of treatment, significant increases in MWT50 were found in the CCI+LIFU group compared with the CCI group (P < 0.05). WB and IF staining both demonstrated a significant reduction in the expression levels of MAP2, GAP43, and tau (P < 0.05). LIFU treatment on ACC can effectively attenuate CNP-evoked mechanical sensitivity to pain and reverse aberrant central plasticity.

Attachment avoidance and internalizing symptoms: Does respiratory sinus arrhythmia withdrawal make a difference?

OBJECTIVE: This study investigated the association between attachment avoidance and internalizing symptoms and the moderating role of parasympathetic nervous activity (indexed by respiratory sinus arrhythmia withdrawal) in the association. METHODS: A sample of 109 (Mage = 18.94 years old, SD = 0.92; 69 male) Chinese college students participated in this study. Participants reported attachment avoidance and internalizing symptoms, and their physiological data were collected. RESULTS: Results showed a positive link between attachment avoidance and internalizing symptoms. Further, respiratory sinus arrhythmia (RSA) withdrawal and attachment avoidance interactively predicted internalizing symptoms. Specifically, the positive relation between attachment avoidance and internalizing symptoms was only found among people of low, but not high, levels of RSA withdrawal. CONCLUSION: Our study highlighted the importance of considering psychophysiological interactions in predicting internalizing symptoms in college students, and contributed to our understanding of the complicated factors underlying college students' internalizing problems.

The relation of baseline respiratory sinus arrhythmia to problematic internet use: Impulsiveness and difficulties in emotion regulation matter.

The aim of this study was to test the relation of baseline respiratory sinus arrhythmia (BRSA) to PIU and the mediating role of impulsiveness (IM) and difficulties in emotion regulation (DER) underlying this association using data from a group of Chinese young adults. A total of 109 (Mage = 18.94 years old, SD = 0.92; 69 men) Chinese undergraduate students participated in this study and completed questionnaires on IM, DER and PIU. BRSA was collected during a resting condition at a laboratory setting. Results showed that BRSA was negatively related to PIU, with IM and DER both independently and completely mediating this association. These findings enrich our understanding on the psychophysiological correlates of young adults' problematic internet use.

Dispositional pessimism is related to reduced respiratory sinus arrhythmia reactivity to a psychosocial stressor.

The associations among dispositional optimism/pessimism, baseline RSA and RSA reactivity were investigated in the current study. Physiological data were collected from 102 young adults during baseline, social stress task (i.e., a public speaking task) and recovery periods in the laboratory. Dispositional optimism and pessimism were assessed using the revised Life Orientation Test. Results showed that higher dispositional pessimism is significantly related to lower levels of RSA reactivity to the social stress task. Finding highlight that individuals with higher levels of pessimism may be at elevated risk for physiological maladjustment.

Nomogram Incorporating Contrast-Enhanced Ultrasonography Predicting Time to the Development of Castration-Resistant Prostate Cancer.

BACKGROUND: It is valuable to predict the time to the development of castration-resistant prostate cancer (CRPC) in patients with advanced prostate cancer (PCa). This study aimed to build and validate a nomogram incorporating the clinicopathologic characteristics and the parameters of contrast-enhanced ultrasonography (CEUS) to predict the time to CRPC after androgen deprivation therapy (ADT). METHODS: Patients with PCa were divided into the training (n = 183) and validation cohorts (n = 37) for nomogram construction and validation. The clinicopathologic characteristics and CEUS parameters were analyzed to determine the independent prognosis factors and serve as the basis of the nomogram to estimate the risk of 1-, 2-, and 3-year progress to CRPC. RESULTS: T stage, distant metastasis, Gleason score, area under the curve (AUC), prostate-specific antigen (PSA) nadir, and time to PSA nadir were the independent predictors of CRPC (all P < 0.05). Three nomograms were built to predict the time to CRPC. Owing to the inclusion of CEUS parameter, the discrimination of the established nomogram (C-index: 0.825 and 0.797 for training and validation datasets) was improved compared with the traditional prediction model (C-index: 0.825 and 0.797), and when it excluded posttreatment PSA, it still obtained an acceptable discrimination (C-index: 0.825 and 0.797). CONCLUSIONS: The established nomogram including regular prognostic indicators and CEUS obtained an improved accuracy for the prediction of the time to CRPC. It was also applicable for early prediction of CRPC when it excluded posttreatment PSA, which might be helpful for individualized diagnosis and treatment.

Association Between Contrast-Enhanced Ultrasound Indicators and Prostate Cancer Biochemical Recurrence After Treatment.

BACKGROUND: Some patients with prostate cancer (PCa) will experience biochemical recurrence (BCR) after treatment. Current researches have identified the influencing factors of BCR, but these factors are difficult to quantify and hence unable to accurately predict the BCR in PCa patients. OBJECTIVE: To explore the value of contrast-enhanced ultrasound (CEUS) indicators in predicting the BCR after treatment by evaluating the association between them. PATIENTS AND METHODS: In a retrospective cohort study, 157 PCa patients were recruited and received prostate specific antigen (PSA) measurement, CEUS, pathological classification, and immunohistochemistry after puncture biopsy. PCa patients with BCR were included in the recurrence group, while the remaining patients were included in the non-recurrence group after a 5-year follow-up. The clinical characteristics and CEUS indicators were compared between the two groups, and the multivariable COX regression was used for screening the influencing factors of BCR. Receiver operating characteristic (ROC) curves were used to analyze the value of potential factors in predicting BCR. The effect of the combined prediction model was explored to improve the accuracy of the prediction. RESULTS: Twelve patients are lost during the follow-up period and the final analysis included 145 patients. The 5-year BCR rate of PCa patients was 27%, with 43 patients in the recurrence group and 102 patients in the non-recurrence group. Multivariate analysis showed that lymph node metastasis (P<0.001), distant metastasis (P<0.001), Gleason score (P<0.001), pretreatment PSA (P<0.001), treatment method (P<0.001), peak intensity (PI) (P=0.001), and time to peak (TTP) (P=0.003) were independent influencing factors for BCR after treatment. ROC analysis showed that the AUCs of all indicators in predicting BCR were not high (all <0.9). The combination of lymph node metastasis, Gleason score, pretreatment PSA, and treatment method can improve the predictive accuracy (AUC = 0.85), but the AUC was still under 0.9. The combined prediction model including CEUS time-intensity curve (TIC) indicators (PI and TTP) could accurately predict the BCR after treatment (AUC=0.953). The sensitivity and specificity were 93.02% and 88.24%, respectively. CONCLUSION: The prediction model including TIC indicators and common influencing factors can more accurately predict the BCR in PCa patients.

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks.

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal-organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. This CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

Voice Conversion for Persons with Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) results in progressive paralysis of voluntary muscles throughout the body. As speech deteriorates, individuals rely on pre-programmed messages available on commercial speech generating devices to communicate using one of the generic electronic voices on the device. To replace these generic voices and restore vocal identity, our aim is to develop personalized voices for people with ALS via the approach of voice conversion. The task is challenging because very few people have large quantities of their premorbid healthy speech recorded. Therefore, we have to rely on small quantities of dysarthric speech concomitant with an individual's disease stage. Further, progressive fatigue prohibits acquisition of large speech datasets and individuals display a range of dysarthria severities resulting from breathing, voice, articulation, resonance, and prosody disturbances. As the first step to address these problems, we use healthy source speakers and propose the approach of combining a structured sparse spectral transform with multiple linear regression-based frequency warping prediction for spectral conversion, and interpolating the transformed spectral frames for speech rate modification. Our experimental data included four healthy source speakers from the ARCTIC dataset, and four target ALS speakers with mild to severe dysarthria, forming 16 speaker pairs. Subjective listening evaluations showed that on average, (i) the proposed approach improved speech intelligibility by about 80% over the target speakers' speech, (ii) the converted voice was 3 times more similar to the target speakers' speech than to the source speakers' speech, and (iii) the converted speech quality was close to the MOS scale "good" relative to the source speakers' speech being "excellent."

Characterization and Arsenic Adsorption Behaviors of Water Treatment Residuals from Waterworks for Iron and Manganese Removal.

Water treatment residuals (WTRs), obtained from a groundwater treatment plant for biological iron and manganese removal, were investigated and used as adsorbents for arsenic removal. The surface morphology and structural features of the WTRs were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Brunauner-Emmett-Teller analysis (BET). Laboratory experiments were also carried out to test the adsorption capability and adaptability of WTRs on both As (III) and As (V) removal from the water. The results showed that the WTRs were mainly amorphous and had a large specific surface area of 253.152 m2/g. The maximum adsorption capacities, evaluated using the Langmuir isotherm equation, were 36.53 mg/g and 40.37 mg/g for As (III) and As (V), respectively. The pseudo-second-order model fitted the kinetic data better, with R2 more than 0.99 for both As (III) and As (V). The removal of As (V) decreased with the increase in pH, especially when the pH was above 9, whereas for As (III), the removal effectiveness almost remained constant at both acidic and neutral pHs. H2PO4- and SiO32- could strongly inhibit arsenic adsorption onto the WTRs, and the effect of other ions was little.

DeepDDK: A Deep Learning based Oral-Diadochokinesis Analysis Software.

Oromotor dysfunction caused by neurological disorders can result in significant speech and swallowing impairments. Current diagnostic methods to assess oromotor function are subjective and rely on perceptual judgments by clinicians. In particular, the widely used oral-diadochokinesis (oral-DDK) test, which requires rapid, alternate repetitions of speech-based syllables, is conducted and interpreted differently among clinicians. It is therefore prone to inaccuracy, which results in poor test reliability and poor clinical application. In this paper, we present a deep learning based software to extract quantitative data from the oral DDK signal, thereby transforming it into an objective diagnostic and treatment monitoring tool. The proposed software consists of two main modules: a fully automated syllable detection module and an interactive visualization and editing module that allows inspection and correction of automated syllable units. The DeepDDK software was evaluated on speech files corresponding to 9 different DDK syllables (e.g., "Pa", "Ta", "Ka"). The experimental results show robustness of both syllable detection and localization across different types of DDK speech tasks.

Structured Sparse Spectral Transforms and Structural Measures for Voice Conversion.

We investigate a structured sparse spectral transform method for voice conversion (VC) to perform frequency warping and spectral shaping simultaneously on high-dimensional (D) STRAIGHT spectra. Learning a large transform matrix for high-D data often results in an overfit matrix with low sparsity, which leads to muffled speech in VC. We address this problem by using the frequency-warping characteristic of a source-target speaker pair to define a region of support (ROS) in a transform matrix, and further optimize it by nonnegative matrix factorization (NMF) to obtain structured sparse transform. We also investigate structural measures of spectral and temporal covariance and variance at different scales for assessing VC speech quality. Our experiments on ARCTIC dataset of 12 speaker pairs show that embedding the ROS in spectral transforms offers flexibility in tradeoffs between spectral distortion and structure preservation, and the structural measures provide quantitatively reasonable results on converted speech. Our subjective listening tests show that the proposed VC method achieves a mean opinion score of "very good" relative to natural speech, and in comparison with three other VC methods, it is the most preferred one in naturalness and in voice similarity to target speakers.

A novel method of language modeling for automatic captioning in TC video teleconferencing.

We are developing an automatic captioning system for teleconsultation video teleconferencing (TC-VTC) in telemedicine, based on large vocabulary conversational speech recognition. In TC-VTC, doctors' speech contains a large number of infrequently used medical terms in spontaneous styles. Due to insufficiency of data, we adopted mixture language modeling, with models trained from several datasets of medical and nonmedical domains. This paper proposes novel modeling and estimation methods for the mixture language model (LM). Component LMs are trained from individual datasets, with class n-gram LMs trained from in-domain datasets and word n-gram LMs trained from out-of-domain datasets, and they are interpolated into a mixture LM. For class LMs, semantic categories are used for class definition on medical terms, names, and digits. The interpolation weights of a mixture LM are estimated by a greedy algorithm of forward weight adjustment (FWA). The proposed mixing of in-domain class LMs and out-of-domain word LMs, the semantic definitions of word classes, as well as the weight-estimation algorithm of FWA are effective on the TC-VTC task. As compared with using mixtures of word LMs with weights estimated by the conventional expectation-maximization algorithm, the proposed methods led to a 21% reduction of perplexity on test sets of five doctors, which translated into improvements of captioning accuracy.

Learning mixture models with the regularized latent maximum entropy principle.

This paper presents a new approach to estimating mixture models based on a recent inference principle we have proposed: the latent maximum entropy principle (LME). LME is different from Jaynes' maximum entropy principle, standard maximum likelihood, and maximum aposteriori probability estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the expectation maximization (EM) algorithm can be developed. We show that a regularized version of LME (RLME), is effective at estimating mixture models. It generally yields better results than plain LME, which in turn is often better than maximum likelihood and maximum a posterior estimation, particularly when inferring latent variable models from small amounts of data.

On application of adaptive decorrelation filtering to assistive listening.

This paper describes an application of the multichannel signal processing technique of adaptive decorrelation filtering to the design of an assistive listening system. A simulated "dinner table" scenario was studied. The speech signal of a desired talker was corrupted by three simultaneous speech jammers and by a speech-shaped diffusive noise. The technique of adaptive decorrelation filtering processing was used to extract the desired speech from the interference speech and noise. The effectiveness of the assistive listening system was evaluated by observing improvements in A-weighted signal-to-noise ratio (SNR) and in sentence intelligibility, where the latter was evaluated in a listening test with eight normal hearing subjects and three subjects with hearing impairments. Significant improvements in SNR and sentence intelligibility were achieved with the use of the assistive listening system. For subjects with normal hearing, the speech reception threshold was improved by 3 to 5 dBA, and for subjects with hearing impairments, the threshold was improved by 4 to 8 dBA.