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  • MDPI AG  (277)
  • 1
    Online Resource
    Online Resource
    Transcriptome Sequencing Reveal That Rno-Rsf1_0012 Participates in Levodopa-Induced Dyskinesia in Parkinson’s Disease Rats via Binding to Rno-mir-298-5p
    MDPI AG ; 2022
    In:  Brain Sciences Vol. 12, No. 9 ( 2022-09-07), p. 1206-
    Details
    In: Brain Sciences, MDPI AG, Vol. 12, No. 9 ( 2022-09-07), p. 1206-
    Abstract: Levodopa-induced dyskinesia (LID) is a common complication of chronic dopamine replacement therapy in the treatment of Parkinson’s disease (PD), and a noble cause of disability in advanced PD patients. Circular RNA (circRNA) is a novel type of non-coding RNA with a covalently closed-loop structure, which can regulate gene expression and participate in many biological processes. However, the biological roles of circRNAs in LID are not completely known. In the present study, we established typical LID rat models by unilateral lesions of the medial forebrain bundle and repeated levodopa therapy. High-throughput next-generation sequencing was used to screen circRNAs differentially expressed in the brain of LID and non-LID (NLID) rats, and key circRNAs were selected according to bioinformatics analyses. Regarding fold change ≥2 and p 〈 0.05 as the cutoff value, there were a total of 99 differential circRNAs, including 39 up-regulated and 60 down-regulated circRNAs between the NLID and LID groups. The expression of rno-Rsf1_0012 was significantly increased in the striatum of LID rats and competitively bound rno-mir-298-5p. The high expression of target genes PCP and TBP in LID rats also supports the conclusion that rno-Rsf1_0012 may be related to the occurrence of LID.
    Type of Medium: Online Resource
    ISSN: 2076-3425
    URL: Article
    DOI: 10.3390/brainsci12091206
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2651993-8
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  • 2
    Online Resource
    Online Resource
    Deep Fusion Prediction Method for Nonstationary Time Series Based on Feature Augmentation and Extraction
    MDPI AG ; 2023
    In:  Applied Sciences Vol. 13, No. 8 ( 2023-04-19), p. 5088-
    Details
    In: Applied Sciences, MDPI AG, Vol. 13, No. 8 ( 2023-04-19), p. 5088-
    Abstract: Deep learning effectively identifies and predicts modes but faces performance reduction under few-shot learning conditions. In this paper, a time series prediction framework for small samples is proposed, including a data augmentation algorithm, time series trend decomposition, multi-model prediction, and error-based fusion. First, data samples are augmented by retaining and extracting time series features. Second, the expanded data are decomposed based on data trends, and then, multiple deep models are used for prediction. Third, the models’ predictive outputs are combined with an error estimate from the intersection of covariances. Finally, the method is verified using natural systems and classic small-scale simulation datasets. The results show that the proposed method can improve the prediction accuracy of small sample sets with data augmentation and multi-model fusion.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app13085088
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704225-X
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  • 3
    Online Resource
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    State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence
    MDPI AG ; 2018
    In:  Applied Sciences Vol. 8, No. 3 ( 2018-03-05), p. 379-
    Details
    In: Applied Sciences, MDPI AG, Vol. 8, No. 3 ( 2018-03-05), p. 379-
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app8030379
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2704225-X
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  • 4
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    Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy
    MDPI AG ; 2021
    In:  Entropy Vol. 23, No. 2 ( 2021-02-11), p. 219-
    Details
    In: Entropy, MDPI AG, Vol. 23, No. 2 ( 2021-02-11), p. 219-
    Abstract: Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.
    Type of Medium: Online Resource
    ISSN: 1099-4300
    URL: Article
    DOI: 10.3390/e23020219
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2014734-X
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  • 5
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    Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting
    MDPI AG ; 2023
    In:  Mathematics Vol. 11, No. 4 ( 2023-02-07), p. 837-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 4 ( 2023-02-07), p. 837-
    Abstract: Air quality plays a vital role in people’s health, and air quality forecasting can assist in decision making for government planning and sustainable development. In contrast, it is challenging to multi-step forecast accurately due to its complex and nonlinear caused by both temporal and spatial dimensions. Deep models, with their ability to model strong nonlinearities, have become the primary methods for air quality forecasting. However, because of the lack of mechanism-based analysis, uninterpretability forecasting makes decisions risky, especially when the government makes decisions. This paper proposes an interpretable variational Bayesian deep learning model with information self-screening for PM2.5 forecasting. Firstly, based on factors related to PM2.5 concentration, e.g., temperature, humidity, wind speed, spatial distribution, etc., an interpretable multivariate data screening structure for PM2.5 forecasting was established to catch as much helpful information as possible. Secondly, the self-screening layer was implanted in the deep learning network to optimize the selection of input variables. Further, following implantation of the screening layer, a variational Bayesian gated recurrent unit (GRU) network was constructed to overcome the complex distribution of PM2.5 and achieve accurate multi-step forecasting. The high accuracy of the proposed method is verified by PM2.5 data in Beijing, China, which provides an effective way, with multiple factors for PM2.5 forecasting determined using deep learning technology.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11040837
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704244-3
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  • 6
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    PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data
    MDPI AG ; 2022
    In:  Mathematics Vol. 10, No. 4 ( 2022-02-16), p. 610-
    Details
    In: Mathematics, MDPI AG, Vol. 10, No. 4 ( 2022-02-16), p. 610-
    Abstract: Prediction based on time series has a wide range of applications. Due to the complex nonlinear and random distribution of time series data, the performance of learning prediction models can be reduced by the modeling bias or overfitting. This paper proposes a novel planar flow-based variational auto-encoder prediction model (PFVAE), which uses the long- and short-term memory network (LSTM) as the auto-encoder and designs the variational auto-encoder (VAE) as a time series data predictor to overcome the noise effects. In addition, the internal structure of VAE is transformed using planar flow, which enables it to learn and fit the nonlinearity of time series data and improve the dynamic adaptability of the network. The prediction experiments verify that the proposed model is superior to other models regarding prediction accuracy and proves it is effective for predicting time series data.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math10040610
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704244-3
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  • 7
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    Molecular Mechanism Underlying Hypoxic Preconditioning-Promoted Mitochondrial Translocation of DJ-1 in Hypoxia/Reoxygenation H9c2 Cells
    MDPI AG ; 2019
    In:  Molecules Vol. 25, No. 1 ( 2019-12-24), p. 71-
    Details
    In: Molecules, MDPI AG, Vol. 25, No. 1 ( 2019-12-24), p. 71-
    Abstract: DJ-1 was recently reported to be involved in the cardioprotection of hypoxic preconditioning (HPC) against hypoxia/reoxygenation (H/R)-induced oxidative stress damage, by preserving mitochondrial complex I activity and, subsequently, inhibiting mitochondrial reactive oxygen species (ROS) generation. However, the molecular mechanism by which HPC enables mitochondrial translocation of DJ-1, which has no mitochondria-targeting sequence, to preserve mitochondrial complex I, is largely unknown. In this study, co-immunoprecipitation data showed that DJ-1 was associated with glucose-regulated protein 75 (Grp75), and this association was significantly enhanced after HPC. Immunofluorescence imaging and Western blot analysis showed that HPC substantially enhanced the translocation of DJ-1 from cytosol to mitochondria in H9c2 cells subjected to H/R, which was mimicked by DJ-1 overexpression induced by pFlag-DJ-1 transfection. Importantly, knockdown of Grp75 markedly reduced the mitochondrial translocation of DJ-1 induced by HPC and pFlag-DJ-1 transfection. Moreover, HPC promoted the association of DJ-1 with mitochondrial complex I subunits ND1 and NDUFA4, improved complex I activity, and inhibited mitochondria-derived ROS production and subsequent oxidative stress damage after H/R, which was also mimicked by pFlag-DJ-1 transfection. Intriguingly, these effects of HPC and pFlag-DJ-1 transfection were also prevented by Grp75 knockdown. In conclusion, these results indicated that HPC promotes the translocation of DJ-1 from cytosol to mitochondria in a Grp75-dependent manner and Grp75 is required for DJ-1-mediated protection of HPC on H/R-induced mitochondrial complex I defect and subsequent oxidative stress damage.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    URL: Article
    DOI: 10.3390/molecules25010071
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2008644-1
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  • 8
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    Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs
    MDPI AG ; 2023
    In:  Diagnostics Vol. 13, No. 8 ( 2023-04-12), p. 1397-
    Details
    In: Diagnostics, MDPI AG, Vol. 13, No. 8 ( 2023-04-12), p. 1397-
    Abstract: Chest X-rays (CXRs) are essential in the preliminary radiographic assessment of patients affected by COVID-19. Junior residents, as the first point-of-contact in the diagnostic process, are expected to interpret these CXRs accurately. We aimed to assess the effectiveness of a deep neural network in distinguishing COVID-19 from other types of pneumonia, and to determine its potential contribution to improving the diagnostic precision of less experienced residents. A total of 5051 CXRs were utilized to develop and assess an artificial intelligence (AI) model capable of performing three-class classification, namely non-pneumonia, non-COVID-19 pneumonia, and COVID-19 pneumonia. Additionally, an external dataset comprising 500 distinct CXRs was examined by three junior residents with differing levels of training. The CXRs were evaluated both with and without AI assistance. The AI model demonstrated impressive performance, with an Area under the ROC Curve (AUC) of 0.9518 on the internal test set and 0.8594 on the external test set, which improves the AUC score of the current state-of-the-art algorithms by 1.25% and 4.26%, respectively. When assisted by the AI model, the performance of the junior residents improved in a manner that was inversely proportional to their level of training. Among the three junior residents, two showed significant improvement with the assistance of AI. This research highlights the novel development of an AI model for three-class CXR classification and its potential to augment junior residents’ diagnostic accuracy, with validation on external data to demonstrate real-world applicability. In practical use, the AI model effectively supported junior residents in interpreting CXRs, boosting their confidence in diagnosis. While the AI model improved junior residents’ performance, a decline in performance was observed on the external test compared to the internal test set. This suggests a domain shift between the patient dataset and the external dataset, highlighting the need for future research on test-time training domain adaptation to address this issue.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics13081397
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 9
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    Effectiveness of Stereotactic Ablative Radiotherapy for Systemic Therapy Respondents with Inoperable Pulmonary Oligometastases and Oligoprogression
    MDPI AG ; 2023
    In:  Diagnostics Vol. 13, No. 9 ( 2023-04-29), p. 1597-
    Details
    In: Diagnostics, MDPI AG, Vol. 13, No. 9 ( 2023-04-29), p. 1597-
    Abstract: Stereotactic ablative radiotherapy (SABR) may improve survival in patients with inoperable pulmonary oligometastases. However, the impact of pulmonary oligometastatic status after systemic therapy on SABR outcomes remains unclear. Hence, we investigated the outcomes of SABR in 45 patients with 77 lung tumors and the prognostic value of pulmonary oligoprogression. Eligibility criteria were pulmonary oligometastases (defined as ≤5 metastatic lung tumors), controlled extrapulmonary disease (EPD) after front-line systemic therapy, SABR as primary local treatment for inoperable pulmonary metastases, and consecutive imaging follow-up. Oligometastatic lung tumor was classified into controlled or oligoprogressive status. Overall survival (OS), in-field progression-free survival (IFPFS), out-field progression-free survival (OFPFS), and prognostic variables were evaluated. With 21.8 months median follow-up, the median OS, IFPFS, and OFPFS were 28.3, not reached, and 6.5 months, respectively. Two-year OS, IFPFS, and OFPFS rates were 56.0%, 74.2%, and 17.3%, respectively. Oligoprogressive status (p = 0.003), disease-free interval 〈 24 months (p = 0.041), and biologically effective dose (BED10) 〈 100 Gy (p = 0.006) were independently associated with inferior OS. BED10 ≥ 100 Gy (p = 0.029) was independently correlated with longer IFPFS. Oligoprogressive status (p = 0.017) and EPD (p = 0.019) were significantly associated with inferior OFPFS. Grade ≥ 2 radiation pneumonitis occurred in four (8.9%) patients. Conclusively, SABR with BED10 ≥ 100 Gy could provide substantial in-field tumor control and longer OS for systemic therapy respondents with inoperable pulmonary oligometastases. Oligoprogressive lung tumors exhibited a higher risk of out-field treatment failure and shorter OS. Hence, systemic therapy should be tailored for patients with oligoprogression to reduce the risk of out-field treatment failure. However, in the absence of effective systemic therapy, SABR is a reasonable alternative to reduce resistant tumor burden.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics13091597
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 10
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    Properties and Mechanism of Hydroxyapatite Coating Prepared by Electrodeposition on a Braid for Biodegradable Bone Scaffolds
    MDPI AG ; 2019
    In:  Nanomaterials Vol. 9, No. 5 ( 2019-05-02), p. 679-
    Details
    In: Nanomaterials, MDPI AG, Vol. 9, No. 5 ( 2019-05-02), p. 679-
    Abstract: Hydroxyapatite (HA) coating is successfully prepared by electrodeposition on the surface of polyvinyl alcohol (PVA)/polylactic acid (PLA) braid which serves as a potential biodegradable bone scaffold. The surface morphology, element composition, crystallinity and chemical bonds of HA coatings at various deposition times (60, 75, 90, 105 and 120 min) are characterized by scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDAX), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR), respectively. Average Surface roughness (Ra) of HA coating is observed by confocal microscopy. The results reveal that the typical characteristic peaks of the FTIR spectrum confirm that HA coating is successfully prepared on the rugged surface of the PVA/PLA braid. The XRD results indicate that the crystallinity of HA can be improved by increasing deposition time. In the 90 min-deposition, hydroxyapatite has a dense and uniform coating morphology, Ca/P ratio of 1.7, roughness of 0.725 μm, which shows the best electrodeposition performance. The formation mechanism of granular and plate-like hydroxyapatite crystals is explained by the structural characteristics of a hydroxyapatite unit cell. This study provides a foundation for a bone scaffold braided by biodegradable fibers.
    Type of Medium: Online Resource
    ISSN: 2079-4991
    URL: Article
    DOI: 10.3390/nano9050679
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2662255-5
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