Differentiation between depression and bipolar disorder in child and adolescents by voice features.

OBJECTIVE: Major depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we aimed to objectively identify MDD and BD in children and adolescents by exploring their voiceprint features. METHODS: This study included a total of 150 participants, with 50 MDD patients, 50 BD patients, and 50 healthy controls aged between 6 and 16 years. After collecting voiceprint data, chi-square test was used to screen and extract voiceprint features specific to emotional disorders in children and adolescents. Then, selected characteristic voiceprint features were used to establish training and testing datasets with the ratio of 7:3. The performances of various machine learning and deep learning algorithms were compared using the training dataset, and the optimal algorithm was selected to classify the testing dataset and calculate the sensitivity, specificity, accuracy, and ROC curve. RESULTS: The three groups showed differences in clustering centers for various voice features such as root mean square energy, power spectral slope, low-frequency percentile energy level, high-frequency spectral slope, spectral harmonic gain, and audio signal energy level. The model of linear SVM showed the best performance in the training dataset, achieving a total accuracy of 95.6% in classifying the three groups in the testing dataset, with sensitivity of 93.3% for MDD, 100% for BD, specificity of 93.3%, AUC of 1 for BD, and AUC of 0.967 for MDD. CONCLUSION: By exploring the characteristics of voice features in children and adolescents, machine learning can effectively differentiate between MDD and BD in a population, and voice features hold promise as an objective physiological indicator for the auxiliary diagnosis of mood disorder in clinical practice.

Multidimensional voiceprint feature assessment system for identifying the depression in children and adolescents: a diagnostic test.

Objective: We designed a diagnostic test to evaluate the effectiveness and accuracy of a multidimensional voiceprint feature diagnostic assessment (MVFDA) system vs. the 24-item Hamilton Rating Scale for Depression (HAMD-24) for adjunctive diagnosis of children and adolescents with major depressive disorder (MDD). Methods: This study included 55 children aged 6-16 years who were clinically diagnosed with MDD according to the DSM-5 and analyzed by professional physicians, and 55 healthy children (typically developing). Each subject completed a voice recording and was scored on the HAMD-24 scale by a trained rater. We calculated the validity indices, including sensitivity, specificity, Youden's index, likelihood ratio, and other indices including predictive value, diagnostic odds ratio, diagnostic accuracy, and area under the curve (AUC), to assess the effectiveness of the MVFDA system in addition to the HAMD-24. Results: The sensitivity (92.73 vs. 76.36%) and the specificity (90.91 vs. 85.45%) of the MVFDA system are significantly higher than those of the HAMD-24. The AUC of the MVFDA system is also higher than that of the HAMD-24. There is a statistically significant difference between the groups (p < 0.05), and both of them have high diagnostic accuracy. In addition, the diagnostic efficacy of the MVFDA system is higher than that of HAMD-24 in terms of the Youden index, diagnostic accuracy, likelihood ratio, diagnostic odds ratio, and predictive value. Conclusion: The MVFDA has performed well in clinical diagnostic trials for the identification of MDD in children and adolescents by capturing objective sound features. Compared with the scale assessment method, the MVFDA system could be further promoted in clinical practice due to its advantages of simple operation, objective rating, and high diagnostic efficiency.

An Extended Spatial Transformer Convolutional Neural Network for Gesture Recognition and Self-Calibration Based on Sparse sEMG Electrodes.

sEMG-based gesture recognition is widely applied in human-machine interaction system by its unique advantages. However, the accuracy of recognition drops significantly as electrodes shift. Besides, in applications such as VR, virtual hands should be shown in reasonable posture by self-calibration. We propose an armband fusing sEMG and IMU with autonomously adjustable gain, and an extended spatial transformer convolutional neural network (EST-CNN) with feature enhanced pretreatment (FEP) to accomplish both gesture recognition and self-calibration via a one-shot processing. Different from anthropogenic calibration methods, spatial transformer layers (STL) in EST-CNN automatically learn the transformation relation, and explicitly express the rotational angle for coarse correction. Due to the shape change of feature pattern as rotational shift, we design the fine tuning layer (FTL) which is able to regulate rotational angle within 45°. By combining STL, FTL and IMU-based posture, EST-CNN is able to calculate non-discretized angle, and achieves high resolution of posture estimation based on sparse sEMG electrodes. Experiments collect frequently-used 3 gestures of 4 subjects in equidistant angles to evaluate EST-CNN. The results under electrodes shift show that the accuracy of gesture recognition is 97.06%, which is 5.81% higher than CNN, the fitness between estimated and true rotational angle is 99.44%.

Study of MXene Ti3C2Tx quantum dot thin film on quartz bulk acoustic wave uncooled infrared sensors.

The infrared absorption efficiency is essential for an infrared sensor. We propose a quartz bulk acoustic wave (BAW) uncooled infrared sensor coated with MXene quantum dot film. The infrared detection is realized by measuring the resonant frequency of a Y-cut quartz BAW sensitive unit. An infrared sensor is fabricated by MEMS process, then the MXene quantum dot film is coated through the spin coating technology. The mechanism of infrared absorption enhancement is analyzed. Test results show that after coating the film, the responsivity (R) of the sensor increased by nearly 41% at a wavelength of 830nm, from 10.88MHz/W to 15.28 MHz/W. The quartz BAW infrared sensor combined with MXene quantum dots film has the potential of high-performance infrared detection.

High-Sensitivity and Environmentally Friendly Humidity Sensors Deposited with Recyclable Green Microspheres for Wireless Monitoring.

The reliable, high-sensitive, wireless, and affordable requirements for humidity sensors are needed in high-precision measurement fields. Quartz crystal microbalance (QCM) based on the piezoelectric effect can accurately detect the mass changes at the nanogram level. However, water-capture materials deposited on the surface of QCM generally show disadvantages in either cost, sensitivity, or recyclability. Herein, novel QCM-based humidity sensors (NQHSs) are developed by uniformly depositing green microspheres (GMs) of natural polymers prepared by the chemical synthesis of the emulsification/inner gel method on QCM as humidity-sensitive materials. The NQHSs demonstrate high accuracy and sensitivity (27.1 Hz/% RH) owing to the various hydrophilic groups and porous nano-3D deposition structure. Compared with the devices deposited with a smooth film, the frequency of the NQHSs shows almost no changes during the cyclic test and exhibits long-term stability. The NQHSs have been successfully applied to non-contact sensing human activities and remote real-time humidity monitoring via Bluetooth transmission. In addition, the deposited humidity-sensitive GMs and QCM substrate are fully recycled and reused (72% of the original value). This work has provided an innovative idea to construct environmental-friendly, high-sensitivity, and wireless humidity sensors.

A Bias Drift Suppression Method Based on ICELMD and ARMA-KF for MEMS Gyros.

The applications of Micro-Electro-Mechanical-System (MEMS) gyros in inertial navigation system is gradually increasing. However, the random drift of gyro deteriorates the system performance which restricting the applications of high precision. We propose a bias drift compensation model based on two-fold Interpolated Complementary Ensemble Local Mean Decomposition (ICELMD) and autoregressive moving average-Kalman filtering (ARMA-KF). We modify CELMD into ICELMD, which is less complicated and overcomes the endpoint effect. Further, the ICELMD is combined with ARMA-KF to separate and simplify the preprocessed signal, resulting improved denoising performance. In the model, the abnormal noise is removed in preprocess by 2σ criterion with ICELMD. Then, continuous mean square error (CMSE) and Permutation Entropy (PE) are both applied to categorize the preprocessed signal into noise, mixed and useful components. After abandon the noise components and denoise the mixed components by ARMA-KF, we rebuild the noise suppression signal of MEMS gyro. Experiments are carried out to validate the proposed algorithm. The angle random walk of gyro decreases from 2.4156∘/h to 0.0487∘/h, the zero bias instability lowered from 0.3753∘/h to 0.0509∘/h. Further, the standard deviation and the variance are greatly reduced, indicating that the proposed method has better suppression effect, stability and adaptability.

Entecavir promotes CD34⁺ stem cell proliferation in the peripheral blood and liver of chronic hepatitis B and liver cirrhosis patients.

Entecavir (ETV) has been used for more than 2 decades in treating hepatitis B virus (HBV) infections. It has shown significant anti-HBV effect and has led to histological improvement in the liver of chronic hepatitis B (CHB) patients. In patients treated with ETV for over two years, reversal of cirrhosis to normal tissue has also been observed. However, the mechanisms of these tissue repairing or recovery processes are not yet clear. In order to determine the roles that bone marrow and liver stem/progenitor cells play in these processes, we evaluated the CD34⁺ and CD133⁺ stem/progenitor cells in peripheral blood from 292 patients and liver tissues from 43 patients who had received therapies with and without ETV. A significant increase in both CD34⁺ and CD133⁺ cells was found in CHB and cirrhosis patients compared to the healthy controls. In patients treated with ETV, CD34⁺ cells increased 2 and 4 fold in peripheral blood and liver tissues, respectively, while their CD4⁺ and CD8⁺ cells remained the same. On the other hand, CD133⁺ cells did not change or even slightly decreased with ETV treatment. Results from immunohistochemistry staining, real time RT-PCR, and the enzyme-linked immunosorbent assay also revealed the same level of CD34⁺ cell increase and CD133⁺ cell decrease (or no change) in ETV treated patients, compared to patients without ETV therapies. Liver functions in patients with ETV treatment improved in general, but one liver cirrhosis patient with high expression of CD133 in liver tissue developed hepatocellular carcinoma (HCC). In summary, ETV may have differential effects on various stem cell subtypes. ETV-activated stem cells in bone marrow and liver tissues may contribute to the recovery from injuries caused by HBV infection. They also contribute to the regeneration of normal tissue and the recovery of normal liver function. Meanwhile, ETV does not activate stem cells that may participate in the initiation of HCC.

Silencing the glycerol-3-phosphate dehydrogenase gene in Saccharomyces cerevisiae results in more ethanol being produced and less glycerol.

Transcription of the gene coding for glycerol-3-phosphate dehydrogenase (GPD1) was repressed in an industrial strain of Saccharomyces cerevisiae using a silencing vector. A fusion fragment containing GPD1 and Kan MX genes was generated by overlap extension PCR, then, the vector, pYES2.0 GPD1/Kan MX, was constructed by inserting the fusion fragment into the S. cerevisiae plasmid, pYES2.0. pYES2.0 GPD1/Kan MX, was linearized by KpnI, transformed into S. cerevisiae using the PEG/LiAc/ssDNA method, and integrated into the S. cerevisiae chromosome. GPD1 silencing gave 20 % less glycerol-3-phosphate dehydrogenase activity, 19 % lower glycerol production, and 9.7 % higher ethanol production compared with the original strain. These findings further the development of industrial S. cerevisiae strains with improved ethanol production and reduced glycerol content for the efficient production of bio-ethanol.