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Microplasma switches have attracted considerable attention in harsh environment applications, such as satellites, space exploration, nuclear reactors, and oil drilling, because of their inherent characteristics. A microplasma switch is generally constructed from a source, drain, and gate electrodes, and current conduction is generated between the drain and source (DS), and modulated by the gate. In this work, to improve the gate lifespan and device stability, a microplasma switch with a gate dielectric barrier structure is fabricated due to the even and stable discharge of a dielectric barrier discharge (DBD), and a parameterized nanosecond pulse voltage signal is applied to the gate. Under the effect of the DS voltage, a pulsed DS current is triggered by the gate pulse since a large number of charged particles are generated by the gate DBD, which shows that the DS switching behavior is triggered by the gate pulse. The microplasma switch operates stably (with an average delay jitter of less than 50 ps) at the repetition frequencies (up to 80 kHz). Moreover, the influence of experimental conditions on the switching performance is systematically investigated. The conduction current and delay, which are related to the discharge intensity and speed, are influenced by the electric-field strength of the channel (determined from the pulse amplitude and DS voltage) and its variation rate (determined from the rising and falling edge time of the pulse). In addition, the device performance is influenced by varying the breakdown voltage of the DS (determined from the gas pressure multiplied by DS spacing), which can result in variation of the working coefficient. It is also influenced by varying the wall voltage (decided by pulse width and frequency), which can result in the decrease in the total voltage of the channel. [ABSTRACT FROM AUTHOR]

Depression is a serious mental disorder with complex etiology, exhibiting strong heterogeneity in clinical manifestations such as various subtypes. Research on depression subtypes may deepen the understanding of the disease, contributing to the diagnosis and prognosis. While brain functional network and graph neural networks (GNNs) provide such a means, the task is still challenged by limited feature encoding from the informative fMRI data, ineffective information fusion of brain functional network, and small size of the recruited subjects. Therefore, we propose a hierarchical encoding and fusion framework of brain functions. First, we pre-train a model to extract the features from individual brain regions, which signify nodes in the brain functional network. Then, distinct graphs are constructed to link the nodes within each subject, resulting in multi-view graphs of the brain functional network. We further develop a graph fusion strategy to integrate the multi-view information, by referring to the local encoding of the nodes and their interactions across multiple graph instances. Finally, we attain the classification of depression subtypes based on the fused graph representation. The experimental results demonstrate that our method can superiorly distinguish major depression subtypes and outperform the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

To establish the sound quality evaluation model of roller chain transmission system, we collect the running noise under different working conditions. After the noise samples are preprocessed, a group of experienced testers are organized to evaluate them subjectively. Mel frequency cepstral coefficient (MFCC) of each noise sample is calculated, and the MFCC feature map is used as an objective evaluation. Combining with the subjective and objective evaluation results of the roller chain system noise, we can get the original dataset of its sound quality research. However, the number of high-quality noise samples is relatively small. Based on the sound quality research of various chain transmission systems, a novel method called multi-source transfer learning convolutional neural network (MSTL-CNN) is proposed. By transferring knowledge from multiple source tasks to target task, the difficulty of small sample sound quality prediction is solved. Compared with the problem that single source task transfer learning has too much error on some samples, MSTL-CNN can give full play to the advantages of all transfer learning models. The results also show that the MSTL-CNN proposed in this paper is significantly better than the traditional sound quality evaluation methods. [ABSTRACT FROM AUTHOR]

Semantic roles labeling is a kind of shallow semantic analysis. Existing Chinese semantic analysis methods and semantic roles labeling systems do not effectively characterize Chinese essential features, and it causes the currently larger difference between Chinese SRL systems and English SRL systems. Valence structures can better characterize syntactic structures and semantic constitution relations of Chinese sentences, so we incorporated the valence information of predicates into semantic roles labeling. Experimental results show that proper use of valence information significantly improves the performance of semantic roles labeling system: the verbal SRL approach achieves the performance of 93.69% in F1-measure and the nominal SRL approach achieves the performance of 79.23% in F1-measure on golden parse trees and golden predicates, and all outperform the state-of-the-art SRL systems. In recent years, end-to-end semantic role labeling based on deep neural networks has attracted more and more attention. However, current semantic role labeling methods use deep neural networks without language features. We propose a deep neural network model that integrates valence information, which was evaluated on the CoNLL-2005, CoNLL-2012 shared task datasets, and achieved better results than previous work: the performance of the CoNLL-2005 shared task dataset is improved 0.74 percentage points; the semantic role labeling methods proposed in this paper achieved an F1 value of 84.80% in CoNLL-2012 shared task datasets. [ABSTRACT FROM AUTHOR]

To improve the noise comfort of the whole machine, it is necessary to establish the sound quality prediction model of the Hy-Vo chain transmission system. Compared with the silent chain transmission system, the Hy-Vo chain transmission system normally operates at a lower speed and cannot have too much load at the limit speed. It is difficult to obtain a sufficient quantity of high-quality noise samples because there are few different working conditions. For small sample sound quality prediction, we use a sample enhancement method called fuzzy generation based on fuzzy mathematics. Firstly, audio samples of the Hy-Vo chain transmission system are collected through noise tests. Secondly, the processed samples are evaluated objectively and subjectively. After a correlation test of the subjective evaluation results, correct subjective evaluation scores of each noise sample are obtained. With the help of fuzzy generation, we can obtain a sufficient number of new samples. By mixing the original samples with the generated samples, a new dataset is created. Through using a general regression neural network (GRNN), support vector regression (SVR) model, and ridge regression (RR) method, the sound quality of the Hy-Vo chain transmission system can be predicted. Different from prediction results under the original dataset, using the fuzzy generation method can not only significantly reduce the prediction error of the model but also improve stability. [ABSTRACT FROM AUTHOR]

More and more unconventional timing silent chain systems are used in the low-emission vehicles or hybrid vehicles, but engineering practices show that the classical design method cannot ensure the stability and the feasibility anymore. According to the angular velocity fluctuations of crankshaft, the maximum accumulation length is calculated to design the ideal path of the tension side chain. The tension rate is defined to determine the ideal chain path at the loose side. In order to match the ideal chain paths with the actual chain length, the pitch variation algorithm is proposed. Based on a specific inline engine, the unconventional timing silent chain system is designed by the classical design method and the new design method respectively. The design process proves that the new method is belonging to a real parametric design, and it can expediently and quickly obtain the results. With the dynamics simulation comparison, the results show that the operation performance of the unconventional timing system can be roundly and significantly improved by using the new design method. Furtherly, this new design method is not only suitable for the unconventional timing system, but also suitable for the other types of timing systems. [ABSTRACT FROM AUTHOR]

The chemical composition, quality characteristics, antioxidant activity, and "golden flower" fungi morphology of Fu brick tea from Hunan, Hubei, Shaanxi, Guizhou, and Zhejiang provinces of China had been researched in this study. The results revealed significant differences in the contents of chemical components in different Fu brick tea (P<0.05). Among them, Fu brick tea from Guizhou had the highest contents of free amino acids and total catechins (9.03, 63.12 mg/g, respectively), while Zhejiang Fu brick tea standed out with the most elevated content of tea polyphenols and flavonoids (132.93, 8.63 mg/g, respectively). According to the electronic taste evaluation, the Fu brick tea from Hunan and Shaanxi exhibited the strongest bitterness and astringency, respectively. Meanwhile, the Zhejiang sample had the most powerful aftertaste bitterness and saltiness, whereas the Guizhou sample demonstrated the strongest umami and richness. Different Fu brick teas had different antioxidant activities, with samples from Hubei and Zhejiang showing higher levels of antioxidant activity. Correlation analysis revealed a significant positive correlation between the main chemical components and taste attributes, and tea polyphenols contributed to the antioxidant activity of Fu brick tea. Additionally, morphology indicated that the five strains of "golden flower" fungi from different origins showed slight differences in the same medium, while the Hunan strain grew faster than the others. This study taked an important role in comprehending the chemical quality of Fu brick tea from different origins. [ABSTRACT FROM AUTHOR]

Objectives: Analyzing the lower limb coronal morphological parameters in populations without knee osteoarthritis (KOA) holds significant value in predicting, diagnosing, and formulating surgical strategies for KOA. This study aimed to comprehensively analyze the variability in these parameters among Chinese non‐KOA populations, employing a substantial sample size. Methods: A cross‐sectional retrospective analysis was performed on the Chinese non‐KOA populations (n = 407; 49.9% females). The study employed an in‐house developed artificial intelligence software to meticulously assess the coronal morphological parameters of all 814 lower limbs. The parameters evaluated included the hip‐knee‐ankle angle (HKAA), weight‐bearing line ratio (WBLR), joint line convergence angle (JLCA), mechanical lateral‐proximal‐femoral angle (mLPFA), mechanical lateral‐distal‐femoral angle (mLDFA), mechanical medial‐proximal‐tibial angle (mMPTA), and mechanical lateral‐distal‐tibial angle (mLDTA). Differences in these parameters were compared between left and right limbs, different genders, and different age groups (with 50 years as the cut‐off point). Results: HKAA and JLCA exhibited left–right differences (left vs. right: 178.2° ± 3.0° vs. 178.6° ± 2.9° for HKAA, p = 0.001; and 1.8° ± 1.5° vs. 1.4° ± 1.6° for JLCA, p < 0.001); except for the mLPFA, all other parameters show gender‐related differences (male vs. female: 177.9° ± 2.8° vs. 179.0° ± 3.0° for HKAA, p < 0.001; 1.5° ± 1.5° vs. 1.8° ± 1.7° for JLCA, p = 0.003; 87.1° ± 2.1° vs. 88.1° ± 2.1° for mMPTA, p < 0.001; 90.2° ± 4.0° vs. 91.1° ± 3.2° for mLDTA, p < 0.001; 38.7% ± 12.9% vs. 43.6% ± 14.1% for WBLR, p < 0.001; and 87.7° ± 2.3° vs. 87.4° ± 2.7° for mLDTA, p = 0.045); mLPFA increase with age (younger vs. older: 90.1° ± 7.2° vs. 93.4° ± 4.9° for mLPFA, p < 0.001), while no statistical difference exists for other parameters. Conclusions: There were differences in lower limb coronal morphological parameters among Chinese non‐KOA populations between left and right sides, different genders, and age. [ABSTRACT FROM AUTHOR]

Due to the multi-factor coupling characteristic, the meshing performance of rocker-pin jointed silent chain transmission system cannot be evaluated by conventional design method, and the design effectiveness and feasibility cannot be assured. In this study, the influence of the relations between the contact point and the involute meshing limitation on the system meshing is analyzed. Through building the polygonal action model for the transitional meshing state, the algorithms to calculate the fluctuation value of chain in different directions are obtained. Based on the classical testing system, the methods to analyze the fluctuations in the system tension side and loose side are proposed. Combined with a set of specific examples, the system meshing performances are evaluated by coupling method. Dynamics experiment results show that the real meshing performances of rocker-pin jointed silent chain transmission system are matched with the evaluation results. This study not only improves the effectiveness and feasibility of rocker-pin jointed silent chain design, but also provides a direction to optimize the system parameters. [ABSTRACT FROM AUTHOR]

Since the physical and electrical properties of plasmas are similar to those of semiconductor devices, plasma devices have been proposed to be good candidates for switching controlled devices when applied in harsh environments. In the proposed paper, a microplasma device with a dielectric barrier structure constructed with three electrodes (two driven electrodes and one trigger electrode) is fabricated, and the electrical characteristics of the proposed device are investigated in 2 kPa of argon. From the experimental results, a stable conducting current is obtained through the two driven electrodes in the device due to gas discharge, since the hysteresis characteristic of discharge plasmas (discharge is still maintained when the driven voltage is below the breakdown voltage of the gas because of the existence of residual charge particles), the device can be switched from the OFF to ON state through pre-discharge by a pulse applied to the trigger electrode. While in the device ON state, this trigger voltage attracts channel charged particles to the surface of the dielectric layer, quenching the discharge plasma current and the device can be switched from ON to OFF state. The trigger pulse that makes the device switch successfully goes from single to continuous up to 80 kHz. The influence of pulse parameters on the switching process is also investigated, pulse amplitude and pulse width are found to be important to determine whether the device can switch ON or OFF, peak current after switching, and the response speed of switching ON current; however, these switching parameters are barely affected by the rise and fall time of the pulse. The results are significant for the application of microplasma switching devices. [ABSTRACT FROM AUTHOR]

Varied origins have the ability to construct the continental red beds, such as paleoclimate, provenance, drainage status, etc. Reconstructing paleoclimate is the key to investigate the origin. Thus, this paper outlines the normal distribution constrained method (NDCM) to reconstruct paleoclimate quantitatively and accurately during the lower fourth sub-member of Shahejie (Es4x) in the Bonan Sag, which is in low requirement of data and environmental conditions. Based on the NDCM, the paleoclimate is still in a long-term arid background as the potential evapotranspiration rates (PERs) are larger than 6 in the transgressive system tract (TST) and regressive system tract (RST) during the period of Es4x. The decreasing PERs that range from 6.28 to 6.04, decreasing atmospheric temperature range from 14.37 to 13.95 °C and increasing mean precipitation of the wettest month and mean precipitation of the driest month indicate the paleoclimate is breaking away from the hot and arid background from TST to RST. Meanwhile, the paleoclimate fluctuation develops decreasing frequency and increasing amplitude which can be inferred from the increasing standard deviation (std), Th/K and Th/U curves. The hot and arid background is still the main origin of red color during the Es4x. Moreover, the other origin may also develop since there is no clear law between red coloration and the developing location of sediments. [ABSTRACT FROM AUTHOR]

Background: The objective of the present study was to assess the hard and soft tissue differences of skeletal Class III malocclusion patients treated with orthodontic–orthognathic surgery treatment between two decompensation approaches including extraction of maxillary premolars in preoperative orthodontics and clockwise rotation of the maxilla in orthognathic surgery. Methods: 22 skeletal Class III patients with the crowding of maxillary dental arch less than 3mm were included in this study. These patients were divided into two groups: extraction group and non-extraction group. Lateral cephalograms taken before preoperative orthodontic treatment and after postoperative orthodontic treatment were used to analyze the differences of hard and soft tissues between two groups. Independent t test was used to evaluate the differences of variables between extraction group and non-extraction group. Results: After treatment, there was significant difference of Wits between extraction group and non-extraction group (− 4.34 mm vs − 2.82 mm, respectively, P <0.05). Co-Gn was significantly greater in non-extraction group than in extraction group (77.18 mm vs 71.58 mm, P <0.05). U1-SN and L1-MP in extraction group were significantly closer to the normal values than non-extraction group (P <0.05). Regarding the change of variables before and after orthodontic–orthognathic treatment, NLA (7.25° vs 1.46°, P <0.01) and G-Sn-Pog' (8.06° vs 4.62°, P <0.05) were significantly greater in extraction group than in non-extraction group. Conclusion: For patients with skeletal Class III malocclusion, extraction of maxillary premolars in preoperative orthodontic treatment can more effectively eliminate the dental compensation and achieve a more harmonious facial profile compared to clockwise rotation of the maxilla in orthognathic surgery. Level of Evidence IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

Featured Application: Transfer case of hybrid vehicle. The internal–external composite meshing mechanism should be the best way to improve the transmission performance of a silent chain drive, but there is no internal–external composite meshing Hy-Vo chain under the classical design approach. In this research, by building the Hy-Vo chain meshing system, the difference between the meshing of the chain–sprocket and the meshing of the rack–gear is deeply analyzed. By rigidifying the external meshing Hy-Vo chain as a rack, the fluctuation range of the pitch line of the external meshing Hy-Vo chain is analyzed. Based on the relations between the actual chain pitch line and the rigid chain pitch line, the design method for the internal meshing profile of the internal–external composite meshing Hy-Vo chain is deduced, the necessary conditions are obtained, and the meshing characteristics are analyzed. By conducting comparative multi-body dynamics experiments, the results prove that the design method of the internal–external composite meshing Hy-Vo chain proposed in this study is effective and feasible. The results also show that the description of the internal–external composite meshing in the classical meshing theory is not completely correct. In fact, the internal–external composite meshing mechanism cannot reduce the meshing impact of the Hy-Vo chain system and can only improve the system polygon action, the meshing performance, and the transmission stability. [ABSTRACT FROM AUTHOR]

Background: Intracranial aneurysm (IA) is a nodular protrusion of the arterial wall caused by the localized abnormal enlargement of the lumen of a brain artery, which is the primary cause of subarachnoid hemorrhage. Accurate rupture risk prediction can effectively aid treatment planning, but conventional rupture risk estimation based on clinical information is subjective and time-consuming. Methods: We propose a novel classification method based on the CTA images for differentiating aneurysms that are prone to rupture. The main contribution of this study is that the learning-based method proposed in this study leverages deep learning and radiomics features and integrates clinical information for a more accurate prediction of the risk of rupture. Specifically, we first extracted the provided aneurysm regions from the CTA images as 3D patches with the lesions located at their centers. Then, we employed an encoder using a 3D convolutional neural network (CNN) to extract complex latent features automatically. These features were then combined with radiomics features and clinical information. We further applied the LASSO regression method to find optimal features that are highly relevant to the rupture risk information, which is fed into a support vector machine (SVM) for final rupture risk prediction. Results: The experimental results demonstrate that our classification method can achieve accuracy and AUC scores of 89.78% and 89.09%, respectively, outperforming all the alternative methods. Discussion: Our study indicates that the incorporation of CNN and radiomics analysis can improve the prediction performance, and the selected optimal feature set can provide essential biomarkers for the determination of rupture risk, which is also of great clinical importance for individualized treatment planning and patient care of IA. [ABSTRACT FROM AUTHOR]

Research on waste separation promotion policies is of great theoretical and practical significance for the universal implementation of the domestic waste separation system. This paper constructs a non-cooperative tripartite evolutionary game model that includes central command, local deployment, and enterprise performance. An evolutionary game approach was used to analyze the strategic choices of the central government, local government, and separation enterprises in waste separation promotion, and this study investigated the factors influencing the evolution of these choices using numerical simulations. The findings indicated the following: central government, local governments, and separation enterprises are affected differently by their respective willingness to participate; the behavior of separation enterprises is less influenced by the central government's and local governments' willingness to participate and is primarily influenced by market factors, whereas local government is more influenced by the central government's willingness to participate; and local government and classification enterprises are affected differently by their respective willingness to participate. While separation firms are more susceptible to the cost-sharing, income, and revenue distribution coefficient, local governments are more responsive to policy support. The promotion of waste separation in China requires strengthened centralized waste management to avoid the failure of local waste separation, broadening of the scope of central funding incentives and establishing local separation compensation mechanisms, clarifying local waste separation performance responsibilities and new waste tax collection standards, supporting separation enterprises' technological innovation and guiding public participation in waste separation, creating an environment for waste separation, and deepening the study of waste separation accordingly. [ABSTRACT FROM AUTHOR]

Background: Differentiating diagnosis between the benign schwannoma and the malignant counterparts merely by neuroimaging is not always clear and remains still confounding in many cases because of atypical imaging presentation encountered in clinic and the lack of specific diagnostic markers. Purpose: To construct and validate a novel deep learning model based on multi-source magnetic resonance imaging (MRI) in automatically differentiating malignant spinal schwannoma from benign. Material and Methods: We retrospectively reviewed MRI imaging data from 119 patients with the initial diagnosis of benign or malignant spinal schwannoma confirmed by postoperative pathology. A novel convolutional neural network (CNN)-based deep learning model named GAIN-CP (Guided Attention Inference Network with Clinical Priors) was constructed. An ablation study for the fivefold cross-validation and cross-source experiments were conducted to validate the novel model. The diagnosis performance among our GAIN-CP model, the conventional radiomics model, and the radiologist-based clinical assessment were compared using the area under the receiver operating characteristic curve (AUC) and balanced accuracy (BAC). Results: The AUC score of the proposed GAIN method is 0.83, which outperforms the radiomics method (0.65) and the evaluations from the radiologists (0.67). By incorporating both the image data and the clinical prior features, our GAIN-CP achieves an AUC score of 0.95. The GAIN-CP also achieves the best performance on fivefold cross-validation and cross-source experiments. Conclusion: The novel GAIN-CP method can successfully classify malignant spinal schwannoma from benign cases using the provided multi-source MR images exhibiting good prospect in clinical diagnosis. [ABSTRACT FROM AUTHOR]

Brain network analysis based on structural and functional magnetic resonance imaging (MRI) is considered as an effective method for consciousness evaluation of hydrocephalus patients, which can also be applied to facilitate the ameliorative effect of lumbar cerebrospinal fluid drainage (LCFD). Automatic brain parcellation is a prerequisite for brain network construction. However, hydrocephalus images usually have large deformations and lesion erosions, which becomes challenging for ensuring effective brain parcellation works. In this paper, we develop a novel and robust method for segmenting brain regions of hydrocephalus images. Our main contribution is to design an innovative inpainting method that can amend the large deformations and lesion erosions in hydrocephalus images, and synthesize the normal brain version without injury. The synthesized images can effectively support brain parcellation tasks and lay the foundation for the subsequent brain network construction work. Specifically, the novelty of the inpainting method is that it can utilize the symmetric properties of the brain structure to ensure the quality of the synthesized results. Experiments show that the proposed brain abnormality inpainting method can effectively aid the brain network construction, and improve the CRS-R score estimation which represents the patient's consciousness states. Furthermore, the brain network analysis based on our enhanced brain parcellation method has demonstrated potential imaging biomarkers for better interpreting and understanding the recovery of consciousness in patients with secondary hydrocephalus. [ABSTRACT FROM AUTHOR]

With increased exploration in the faulted basins of eastern China, petroleum exploration in this region has been challenged by unclear remaining petroleum resources distribution, complex reservoir and hydrocarbon enrichment patterns, and lacking of suitable exploration techniques. Aiming at resolving these problems, studies on source rocks, reservoirs, hydrocarbon accumulation and geophysical prospection were carried out by laboratory analysis, reservoir anatomy, and seismic analysis. A highlyefficient hydrocarbon generation/expulsion model of source rocks in saline environment was established, which aided in the discovery of a new set of source rocks in the Jiyang Depression. This study also reveals the formation process of high-quality reservoir by alternating acid and alkaline fluids during deposition and diagenesis, and pattern of secondary pores development in deep clastic rocks. Through the fine anatomy of the oil reservoirs, an orderly distribution pattern of the oil reservoirs is established, and the potential exploration targets in the undrilled area are identified. In addition, single-point highdensity seismic acquisition and high-resolution imaging technologies are developed, enabling fine and efficient exploration in areas with high exploration maturity. The research result plays a leading and demonstrative role in the fine and efficient exploration of faulted basins in eastern China. [ABSTRACT FROM AUTHOR]

Background and purpose: Previous studies reported conflicting results about the risk of ischemic stroke associated with the use of non-steroidal anti-inflammatory drugs (NSAIDs) in patients with rheumatoid arthritis (RA). We aimed to investigate two specific COX-2 inhibitors, Celecoxib and Etoricoxib, and their corresponding effects on the risk of ischemic stroke in patients with RA. Patients and methods: 10,857 patients newly diagnosed with RA were identified and sampled from the Taiwanese National Health Insurance Research Database during the period from 2001 to 2009. The identification of RA was based on the criteria of ICD-9-CM diagnosis code 714.0. Patients diagnosed with cerebrovascular disease and those receiving RA treatment prior to the first diagnosis of RA were excluded. Study endpoint was ischemic stroke, defined by ICD-9-CM code. Cox proportional hazard models and Kaplan Meier curves were used to reveal covariates and differences by drugs in the risk of ischemic stroke. Dosages for Celecoxib were defined as ≤200 and >200 mg/day; those for Etoricoxib were 0 and >0 mg/day. Results: Among 7,904 RA patients, 6,669 did not take Celecoxib and 564 (8.46%) of them experienced an ischemic stroke event. Of the 597 individuals who took ≤200 mg/day of Celecoxib, 58 (9.72%) had strokes. Of the 638 patients who took >200 mg/day of Celecoxib, 38 (5.96%) eventually experienced a stroke. Among the 7,681 patients who did not take Etoricoxib, 654 (8.51%) experienced an ischemic stroke, while 6 (2.69%) in 223 patients who consumed Etoricoxib had a stroke event. Consuming more than 200mg of Celecoxib per day for <3.5 years lowered the incidence rate for strokes [hazard ratio (HR) 0.67, 95% Confidence Interval (CI) 0.48-0.93 for dosage and HR 0.22, 95% CI 0.10--0.46 for duration, both p < 0.001], while consuming any dosage of Etoricoxib significantly decreases the possibility (HR 0.35, 95% CI 0.16--0.80, p < 0.001). On the other hand, consuming Etoricoxib for 8 years might have a neutral or even a potentially protective effect compared to at 3.8 years. Conclusion: This population-based retrospective cohort study has shown that Celecoxib and Etoricoxib reduce the risk of ischemic stroke in patients with RA in a dose- and time-dependent manner. [ABSTRACT FROM AUTHOR]

Amid the ongoing global COVID-19 pandemic, limited literature exists on immune persistence after primary immunization and the immunogenic features of booster vaccines administered at different time intervals. Therefore, this study aimed to determine the immune attenuation of neutralizing antibodies against the SARS-CoV-2 wild-type strain, and Delta and Omicron variants 12 months after the primary administration of the COVID-19 inactivated vaccine and evaluate the immune response after a booster administration at different time intervals. A total of 514 individuals were followed up after primary immunization and were vaccinated with a booster. Neutralizing antibodies against the wild-type strain and Delta and Omicron variant spike proteins were measured using pseudovirus neutralization assays. The geometric mean titers (GMTs) after the primary and booster immunizations were 12.09 and 61.48 for the wild-type strain, 11.67 and 40.33 for the Delta variant, and 8.51 and 29.31 for the Omicron variant, respectively. The GMTs against the wild-type strain declined gradually during the 12 months after the primary immunization, and were lower against the two variants. After implementing a booster immunization with a 6 month interval, the GMTs against the wild-type strain were higher than those obtained beyond the 7 month interval; however, the GMTs against the two variants were not statistically different across 3–12 month intervals. Overall, SARS-CoV-2 variants showed remarkable declines in immune persistence, especially against the Omicron variant. The booster administration interval could be shortened to 3 months in endemic areas of the Omicron variant, whereas an appropriate prolonging of the booster administration interval did not affect the booster immunization effect. [ABSTRACT FROM AUTHOR]

The accuracy and stability of short-term photovoltaic (PV) power prediction is crucial for power planning and dispatching in a grid system. For this reason, the multi-resolution variational modal decomposition (MVMD) method is proposed to achieve multi-scale input features mining for short-term PV power prediction. Here, the MVMD combined with Spearman extracts correlation features of the weather data. An equilibrium optimizer (EO) is integrated with MVMD to achieve optimal values of the long short-term memory (LSTM) parameters. Firstly, the correlation of input features is determined and selected by Spearman. The MVMD model is used to mine the high correlation features of solar radiation and conduct cross-correlation analysis to extract input feature components. Secondly, the similar weather days of the sample set are classified to ensure a good adaptability in different weather situations. Finally, the high correlation features are introduced into the photovoltaic power prediction model of EO optimized LSTM. Performance analysis using actual output power data from a PV plant shows that the proposed MVMD feature extraction method can effectively mine correlation features to achieve an optimized dataset under different seasons. Compared with the gray wolf and particle swarm optimization algorithms, the proposed model has a better optimization performance in a low discrimination of input feature decomposition components and low correlation with output power. [ABSTRACT FROM AUTHOR]

Neutralizing antibody is an important indicator of vaccine efficacy, of which IgG is the main component. IgG can be divided into four subclasses. Up to now, studies analysing the humoral response to SARS‐CoV‐2 vaccination have mostly focused on measuring total IgG, and the contribution of specific IgG subclasses remains elusive. The aim of this study is to investigate the kinetics of neutralizing antibodies and IgG subclasses, and to explore their relationships in people vaccinated with inactivated COVID‐19 vaccine. We conducted a prospective cohort study in 174 healthy adults aged 18–59 years old who were administrated 2 doses of CoronaVac 14 days apart and a booster dose 1 year after the primary immunization, and followed up for 15 months. Blood samples were collected at various time points after primary and booster immunization. We used live SARS‐CoV‐2 virus neutralizing assay to determine neutralizing ability against the wild‐type strain and 4 variants (Beta, Gamma, Delta and Omicron) and ELISA to quantify SARS‐CoV‐2 RBD‐specific IgG subclasses. The results showed that the 2‐dose primary immunization only achieved low neutralizing ability, while a booster shot can significantly enhance neutralizing ability against the wild‐type strain, Beta, Gamma, Delta and Omicron variants. IgG1 and IgG3 were the most abundant serum antibodies, and IgG2 and IgG4 were hardly detected at any time. The ratio of IgG1/IgG3 was positively associated with the neutralization ability. The underlying mechanism requires further exploration. [ABSTRACT FROM AUTHOR]

Silicon nanowires (SiNWs) are introduced into microdischarge to improve microplasma properties due to its field emission electrons and field enhancement effect. The geometrical arrangement and dimensional features of SiNWs have desicive influence on field emission properties, thus the dependence of microplasma emission performances on the SiNWs morphologies is investigated in this paper. The different morphologies of SiNWs can be prepared by electrocatalytic metal-assisted chemical etching with varied etching currents. With the increase of etching current from 3 mA to 30 mA (AgNO3:HF:H2O2 = 0.02:4.6:0.1 mol lâˆ'1, deposition time 1 min and etching time 10 min), the field emission current density J of the SiNWs prepared at 20 mA etching current is the largest ∼0.28 mA cmâˆ'2 at a field 4.5 V ÎĽ mâˆ'1, and turn-on field is the lowest of 3.52 V ÎĽ mâˆ'1. Accordingly, the microplasma in the device fabricated on the SiNWs-decorated substrate (etching current at 20 mA) has the strongest average emission intensity of ∼11 565 a.u., the minimal relative standard deviation of emission intensity 4.9% and the fastest propagation velocity of 471 km sâˆ'1. The field emission electrons of SiNWs could inject more seed electrons into microcavity which causes higher electron collision probability, and the field enhancement effect at tips of SiNWs can provide more energy for the charged particles, which are helpful to the microdischarge. The most difficulty is to balance the distance of emitters and the percentage of SiNWs in entire emission region because the shielding effect will reduce while the surface emitter numbers will decrease when the distance of emitters increases. Here, a ‘proper percentage of SiNWs’ of 19.3% is obtained what indicates that if SiNWs percentage is greater than the threshold, field enhancement factor β eff is weakened by the decrease of aspect ratio and the increase of percentage. When SiNWs percentage is less than 19.3%, β eff will increase and be dominated by the percentage of SiNWs. The results are significant for the application of SiNWs in microdischarge devices. [ABSTRACT FROM AUTHOR]

Background: To develop a magnetic resonance imaging (MRI)-based radiomics predictive model for the identification of knee osteoarthritis (OA), based on the tibial and femoral subchondral bone, and compare with the trabecular structural parameter-based model. Methods: Eighty-eight consecutive knees were scanned with 3T MRI and scored using MRI osteoarthritis Knee Scores (MOAKS), in which 56 knees were diagnosed to have OA. The modality of sagittal three-dimensional balanced fast-field echo sequence (3D BFFE) was used to image the subchondral bone. Four trabecular structural parameters (bone volume fraction [BV/TV], trabecular thickness [Tb.Th], trabecular separation [Tb.Sp], and trabecular number) and 93 radiomics features were extracted from four regions of the lateral and medial aspects of the femur condyle and tibial plateau. Least absolute shrinkage and selection operator (LASSO) was used for feature selection. Machine learning-based support vector machine models were constructed to identify knee OA. The performance of the models was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC). The correlation between radiomics features and trabecular structural parameters was analyzed using Pearson's correlation coefficient. Results: Our radiomics-based classification model achieved the AUC score of 0.961 (95% confidence interval [CI], 0.912–1.000) when distinguishing between normal and knee OA, which was higher than that of the trabecular parameter-based model (AUC, 0.873; 95% CI, 0.788–0.957). The first-order, texture, and Laplacian of Gaussian-based radiomics features correlated positively with Tb.Th and BV/TV, but negatively with Tb.Sp (P < 0.05). Conclusions: Our results suggested that our MRI-based radiomics models can be used as biomarkers for the classification of OA and are superior to the conventional structural parameter-based model. [ABSTRACT FROM AUTHOR]

Purpose: Multiple myeloma (MM) and metastasis originated are the two common malignancy diseases in the spine. They usually show similar imaging patterns and are highly demanded to differentiate for precision diagnosis and treatment planning. The objective of this study is therefore to construct a novel deep-learning-based method for effective differentiation of two diseases, with the comparative study of traditional radiomics analysis. Methods: We retrospectively enrolled a total of 217 patients with 269 lesions, who were diagnosed with spinal MM (79 cases, 81 lesions) or spinal metastases originated from lung cancer (138 cases, 188 lesions) confirmed by postoperative pathology. Magnetic resonance imaging (MRI) sequences of all patients were collected and reviewed. A novel deep learning model of the Multi-view Attention-Guided Network (MAGN) was constructed based on contrast-enhanced T1WI (CET1) sequences. The constructed model extracts features from three views (sagittal, coronal and axial) and fused them for a more comprehensive differentiation analysis, and the attention guidance strategy is adopted for improving the classification performance, and increasing the interpretability of the method. The diagnostic efficiency among MAGN, radiomics model and the radiologist assessment were compared by the area under the receiver operating characteristic curve (AUC). Results: Ablation studies were conducted to demonstrate the validity of multiview fusion and attention guidance strategies: It has shown that the diagnostic model using multi-view fusion achieved higher diagnostic performance [ACC (0.79), AUC (0.77) and F1-score (0.67)] than those using single-view (sagittal, axial and coronal) images. Besides, MAGN incorporating attention guidance strategy further boosted performance as the ACC, AUC and F1-scores reached 0.81, 0.78 and 0.71, respectively. In addition, the MAGN outperforms the radiomics methods and radiologist assessment. The highest ACC, AUC and F1-score for the latter two methods were 0.71, 0.76 & 0.54, and 0.69, 0.71, & 0.65, respectively. Conclusions: The proposed MAGN can achieve satisfactory performance in differentiating spinal MM between metastases originating from lung cancer, which also outperforms the radiomics method and radiologist assessment. [ABSTRACT FROM AUTHOR]

In clinical practice, multi-modal magnetic resonance imaging (MRI) with different contrasts is usually acquired in a single study to assess different properties of the same region of interest in the human body. The whole acquisition process can be accelerated by having one or more modalities under-sampled in the ${k}$ -space. Recent research has shown that, considering the redundancy between different modalities, a target MRI modality under-sampled in the ${k}$ -space can be more efficiently reconstructed with a fully-sampled reference MRI modality. However, we find that the performance of the aforementioned multi-modal reconstruction can be negatively affected by subtle spatial misalignment between different modalities, which is actually common in clinical practice. In this paper, we improve the quality of multi-modal reconstruction by compensating for such spatial misalignment with a spatial alignment network. First, our spatial alignment network estimates the displacement between the fully-sampled reference and the under-sampled target images, and warps the reference image accordingly. Then, the aligned fully-sampled reference image joins the multi-modal reconstruction of the under-sampled target image. Also, considering the contrast difference between the target and reference images, we have designed a cross-modality-synthesis-based registration loss in combination with the reconstruction loss, to jointly train the spatial alignment network and the reconstruction network. The experiments on both clinical MRI and multi-coil ${k}$ -space raw data demonstrate the superiority and robustness of the multi-modal MRI reconstruction empowered with our spatial alignment network. Our code is publicly available at https://github.com/woxuankai/SpatialAlignmentNetwork. [ABSTRACT FROM AUTHOR]

Objective: Previous neuroimaging studies have shown abnormal brain-bladder control network in children with primary nocturnal enuresis (PNE). The hippocampus, which has long been considered to be an important nerve center for memory and emotion, has also been confirmed to be activating during micturition in several human imaging studies. However, few studies have explored hippocampus-related functional networks of PNE in children. In this study, the whole resting-state functional connectivity (RSFC) of hippocampus was investigated in children with PNE. Methods: Functional magnetic resonance imaging data of 30 children with PNE and 29 matched healthy controls (HCs) were analyzed in our study. We used the seed-based RSFC method to evaluate the functional connectivity of hippocampal subregions defined according to the Human Brainnetome Atlas. Correlation analyses were also processed to investigate their relationship with disease duration time, bed-wetting frequency, and bladder volume. Results: Compared with HCs, children with PNE showed abnormal RSFC of the left rostral hippocampus (rHipp) with right fusiform gyrus, right Rolandic operculum, left inferior parietal lobule, and right precentral gyrus, respectively. Moreover, decreased RSFC of the left caudal hippocampus (cHipp) with right fusiform gyrus and right supplementary motor area was discovered in the PNE group. There were no significant results in the right rHipp and cHipp seeds after multiple comparison corrections. In addition, disease duration time was negatively correlated with RSFC of the left rHipp with right Rolandic operculum (r = -0.386, p = 0.035, uncorrected) and the left cHipp with right fusiform gyrus (r = -0.483, p = 0.007, uncorrected) in the PNE group, respectively. In the Receiver Operating Characteristic (ROC) analysis, all the above results of RSFC achieved significant performance. Conclusions: To our knowledge, this is the first attempt to examine the RSFC patterns of hippocampal subregions in children with PNE. These findings indicated that children with PNE have potential dysfunctions in the limbic network, sensorimotor network, default mode network, and frontoparietal network. These networks may become less effcient with disease duration time, inducing impairments in brain-bladder control, cognition, memory, and emotion. Further prospective research with dynamic observation of brain imaging, bladder function, cognition, memory, and emotion is warranted. [ABSTRACT FROM AUTHOR]

Aiming at the sequential tasks of Chinese word segmentation and part-of-speech labeling, this paper proposes a parallel model for word segmentation and part-of-speech labeling that combines BERT model, bidirectional long-short memory model, and conditional random field model, Markov family model (MFM) or Tree Probability (TLP). In part-of-speech labeling combined with MFM or TLP, the part-of-speech of the current word is not only related to the part-of-speech of the previous word, but also related to the current word itself. The use of the joint method helps to use part-of-speech information to achieve word segmentation, and organically combining the two is beneficial to eliminate ambiguity and improve the accuracy of part-of-speech labeling or word segmentation tasks. Experimental data shows that the joint model for part-of-speech labeling and Chinese word segmentation proposed in this paper can significantly enhances the precision of Chinese word segmentation and the accuracy of part-of-speech labeling. [ABSTRACT FROM AUTHOR]

Researches into shale lithofacies, their sedimentary environments and relationship benefit understanding both of sedimentary cycle division and unconventional hydrocarbon exploration in lacustrine basins. Based on a 100∼300‐m‐thick dark shale, mudstone and limestone encountered in the lower third member of the Eocene Shahejie Formation (Es3l member) in Zhanhua Sag, Bohai Bay Basin, eastern China, routine core analysis, thin sectioning, scanning electron microscopy (SEM), mineralogical and geochemical measurements were used to understand detailed facies characterization and paleoclimate in the member. This Es3l shale sediment includes three sedimentary cycles (C3, C2 and C1), from bottom to top, with complex sedimentary characters and spatial distribution. In terms of the composition, texture, bedding and thickness, six lithofacies are recognized in this succession. Some geochemical parameters, such as trace elements (Sr/Ba, Na/Al, V/Ni, V/(V + Ni), U/Th), carbon and oxygen isotopes (δ18O, δ13C), and total organic carbon content (TOC) indicate that the shales were deposited in a deep to semi‐deep lake, with the water column being salty, stratified, enclosed and reductive. During cycles C3 and C2 of the middle‐lower sections, the climate was arid, and the water was salty and stratified. Laminated and laminar mudstone–limestone was deposited with moderate organic matter (average TOC 1.8%) and good reservoir quality (average porosity 6.5%), which can be regarded as favorable reservoir. During the C1 cycle, a large amount of organic matter was input from outside the basin and this led to high productivity with a more humid climate. Massive calcareous mudstone was deposited, and this is characterized by high TOC (average 3.6%) and moderate porosity (average 4%), and provides favorable source rocks. [ABSTRACT FROM AUTHOR]

Background: There are significant differences in outcomes for different histological subtypes of cervical cancer (CC). Yet, it is difficult to distinguish CC subtypes using non-invasive methods. Purpose: To investigate whether multiparametric magnetic resonance imaging (MRI)-based radiomics analysis can differentiate CC subtypes and explore tumor heterogeneity. Material and Methods: This study retrospectively analyzed 96 patients with CC (squamous cell carcinoma [SCC] = 50, adenocarcinoma [AC] = 46) who underwent pelvic MRI before surgery. Radiomics features were extracted from the tumor volumes on five sequences (sagittal T2-weighted imaging [T2SAG], transverse T2-weighted imaging [T2TRA], sagittal contrast-enhanced T1-weighted imaging [CESAG], transverse contrast-enhanced T1-weighted imaging [CETRA], and apparent diffusion coefficient [ADC]). Clustering and logistic regression were used to examine the distinguishing capabilities of radiomics features extracted from five different MR sequences. Results: Among the 105 extracted radiomics features, there were 51, 38, 37, and 2 features that showed intergroup differences for T2SAG, T2TRA, ADC, and CESAG, respectively (all P < 0.05). AC had greater textural heterogeneity than SCC (P < 0.05). Upon unsupervised clustering of significantly different features, T2SAG achieved the highest accuracy (0.844; sensitivity = 0.920; specificity = 0.761). The largest area under the curve (AUC) for classification ability was 0.86 for T2SAG. Hence, the radiomics model from five combined MR sequences (AUC = 0.89; accuracy = 0.81; sensitivity = 0.67; specificity = 0.94) exhibited better differentiation ability than any MR sequence alone. Conclusion: Multiparametric MRI-based radiomics models may be a promising method to differentiate AC and SCC. AC showed more heterogeneous features than SCC. [ABSTRACT FROM AUTHOR]

To improve the system meshing performance and the chain stability, a totally new type Hy-Vo chain with an ultra-small rolling radius is proposed in this research. According to the rolling theory of the rocker pin, the design method and the meshing system for the new Hy-Vo chain are proposed. Based on the analysis model of polygonal action, by calculating a specific example, it is proved that the variable pitch characteristic of the new Hy-Vo chain is controllable. Through comparing the system center distance fluctuations for the new and the classical Hy-Vo chain, it is shown that both the system fluctuation difference and the system running deviation are all smaller for the new Hy-Vo chain. Combined with the analysis of Multi Flexible Body Dynamics (MFBD), for the new Hy-Vo chain plate and the rocker pin, the stress distribution is more uniform, and the chain life is longer. As a result, the new Hy-Vo chain with the ultra-small rolling radius has a better meshing performance and fatigue resistance, as well as better process economy. Moreover, the new design proposed in this paper is not only a novel structure for the Hy-Vo chain drive, but it also reveals the meshing mechanism and the controllable variable pitch characteristic. [ABSTRACT FROM AUTHOR]

This study examined Assemblies of God pastors in the USA (n = 874) on role identity, well-being, religious coping, and attitudes toward seeking professional counseling. Overall, 14.1% had depression at moderate or higher severity based on the PHQ-9 scale (score of 10 or above), with an additional 25.7% in the mild category (score of 5–9). On the Clergy Spiritual Well-Being scale, 9.2% had poor spiritual well-being in everyday life, while 18.1% showed poor spiritual well-being in ministry (score below 15 on each respective subscale). About 20% of the sample scored in the high range (above 10) on the Clergy Occupational Distress Index. Male role norms, occupational distress, and positive religious coping were predictive of help-seeking attitudes. Those who were married, younger, more highly educated, female, or had more close friends had more positive attitudes toward seeking counseling. [ABSTRACT FROM AUTHOR]

The Lower Paleozoic carbonate strata experience multi-stage tectonic activity and post-depositional volcanic activity in the Dongying Depression, Bohai Bay basin. These tectonic and magmatic activities have caused the reservoir to undergo severe diagenesis, resulting in strong reservoir heterogeneity. This study aims to identify the characteristics of dolomite, various reservoir spaces' characteristics, the origin of different types of dolomite, and the porosity evolution. According to crystal size and morphology, dolomites can be divided into three kinds of matrix dolomites and four kinds of dolomite cements. The petrology and geochemistry of the dolomite suggests that matrix dolomite is formed from seawater. The medium-to-coarse-crystalline dolomite cement (D3) has a higher 87Sr/86Sr ratio (0.7119 to 0.7129) and a higher homogenization temperature (>125 °C), suggesting that the fluid for the precipitation of D3 is a mixed fluid formed by hydrothermal fluid eroding the 87Sr-rich feldspar sandstone. The strikingly negative δ18O values (−23.7 to −25.7‰ VPDB) of saddle dolomite (D4) indicate that D4 precipitated from hydrothermal fluids and the Mg2+ source may be due to dissolution of the host dolomite that formed in the evaporation environment. The reservoir spaces of the target strata in the study area mainly include fractures, dissolution vugs, intercrystalline pores, and moldic pores. Dissolution is the basis for forming high-quality dolomite reservoirs. The faults and fractures provided favorable conditions for dissolution. Hydrothermal fluid and organic acid were the main dissolution fluids for the dolomite reservoir, which were beneficial to the development of secondary pores. In the study area, organic acid dissolution was shown to contribute more than hydrothermal dissolution in the study area. [ABSTRACT FROM AUTHOR]

Sevoflurane anesthesia induces cognitive impairment, which may lead to perioperative neurocognitive disorders (PND). However, the factors and molecular mechanism underlying this impairment remains unclear. Adult hippocampal neurogenesis (AHN) in the subgranular zone of the hippocampus has been implicated in cognitive processes. Nonetheless, the direct role of AHN in sevoflurane-induced cognitive impairment has never been demonstrated. In this study, we explored the age and the concentration factors and the role of AHN inhibition in sevoflurane-induced cognitive impairment in sevoflurane inhalation model mice. We found that 3% sevoflurane exposure induced significant cognitive impairment and inhibition of AHN in aged mice but not adult mice. Expression of BDNF/TrkB and NT-3/TrkC was also decreased by 3% sevoflurane exposure in aged mice. Hippocampal brain-derived neurotrophic factor (BDNF) or Neurotrophin-3 (NT-3) microinjection could partially improve the sevoflurane-induced cognitive impairment and AHN inhibition, respectively. These results demonstrate that the cognitive impairment caused by sevoflurane inhalation is related to patient age and sevoflurane concentration. In conclusion, the molecular mechanism of cognitive impairment in the elderly is related to the inhibition of AHN through the BDNF/TrkB and NT-3/TrkC pathways. Thus, sevoflurane inhalation anesthesia may be safe for adult patients, but caution should be exercised when administering it to the elderly. [ABSTRACT FROM AUTHOR]

Aim: To compare the antimicrobial efficacy of an indigenously prepared gold nanoparticle (AuNP) oral rinse with Chlorhexidine oral rinse on plaque samples of subjects undergoing orthodontic treatment. Materials and Methods: In the presented ex-vivo study, previously prepared AuNPs were subjected to microbroth dilution assay for determination of MIC. Dental plaque samples were collected from either first or second maxillary molar teeth from first and second quadrants in 20 subjects (18-30 years) undergoing orthodontic treatment in the outpatient department. An aliquot of 100 µL of saline mixed with plaque was diluted in 0.5 mL of each group (Group 1: control, Group 2: 0.6% AuNP mouthwash, and Group 3: 0.2% chlorhexidine mouthwash). An aliquot of 10 µL was collected from every group and inoculated in the Brain Heart Infusion agar. After incubating for 48 h, colony-forming units (cfu)/mL were counted and identification of colony morphologies was performed. Shapiro-Wilk's test was done for normality testing and descriptive statistics to quantify the cfu, Kruskal-Wallis test for intergroup comparisons, and Dunn's post hoc Tukey test for pairwise comparisons. Results: Mean and SD of cfu in group 1 was 521.36 ± 168.02, group 2 was 0.85 ± 0.74, and group 3 was 0.55 ± 0.45. Intergroup comparisons revealed statistically significant differences (P < 0.000). Pairwise comparison showed significant difference between the groups 1 and 2, groups 1 and 3 (P < 0.000), but not between groups 2 and 3 (P = 0.929). Conclusion: The indigenous AuNP herbal mouth rinse had a good antimicrobial effect against common oral microbes found in patients undergoing orthodontic treatment. The antibacterial effect of the new novel oral rinse when compared with commercially available chlorhexidine did not differ significantly. [ABSTRACT FROM AUTHOR]

Hippocampal atrophy is often considered as one of the important biomarkers for early diagnosis of Alzheimer's disease (AD), which is an irreversible neurodegenerative disorder. Traditional methods for hippocampus analysis usually computed the shape and volume features from structural Magnetic Resonance Image (sMRI) for the computer-aided diagnosis of AD as well as its prodromal stage, i.e., mild cognitive impairment (MCI). Motivated by the success of deep learning, this paper proposes a deep learning method with the multi-channel cascaded convolutional neural networks (CNNs) to gradually learn the combined hierarchical representations of hippocampal shapes and asymmetries from the binary hippocampal masks for AD classification. First, image segmentation is performed to generate the bilateral hippocampus binary masks for each subject and the mask difference is obtained by subtracting them. Second, multi-channel 3D CNNs are individually constructed on the hippocampus masks and mask differences to extract features of hippocampal shapes and asymmetries for classification. Third, a 2D CNN is cascaded on the 3D CNNs to learn high-level correlation features. Finally, the features learned by multi-channel and cascaded CNNs are combined with a fully connected layer followed by a softmax classifier for disease classification. The proposed method can gradually learn the combined hierarchical features of hippocampal shapes and asymmetries to enhance the classification. Our method is verified on the baseline sMRIs from 807 subjects including 194 AD patients, 397 MCI (164 progressive MCI (pMCI) + 233 stable MCI (sMCI)), and 216 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrate that the proposed method achieves an AUC (Area Under the ROC Curve) of 88.4%, 74.6% and 71.9% for AD vs. NC, MCI vs. NC and pMCI vs. sMCI classifications, respectively. It proves the promising classification performance and also shows that both hippocampal shape and asymmetry are helpful for AD diagnosis. [ABSTRACT FROM AUTHOR]

Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of COVID-19 patients based on predefined treatment schemes and heterogeneous multimodal patient information collected within 48 hours after admission. Meanwhile, an interpretable mechanism termed FSR is integrated into iCOVID to reveal the features greatly affecting the prediction of each patient. Data from a total of 3008 patients were collected from three hospitals in Wuhan, China, for large-scale verification. The experiments demonstrate that iCOVID can achieve a time-dependent concordance index of 74.9% (95% CI: 73.6-76.3%) and an average day error of 4.4 days (95% CI: 4.2-4.6 days). Our study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to recovery-time predictions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Mirror therapy (MT) has proven to be beneficial for treating patients suffering from motor aphasia after stroke. However, the impacts of MT on neuroplasticity remain unexplored. OBJECTIVE: In this paper we conducted a randomized controlled trial to evaluate the treatment using the MT on motor aphasia following acute cerebral infarction. METHODS: We randomly assigned 30 patients into test and control groups, with test group patients treated with MT, whereas control group patients were treated with sham MT. At 24 hours prior to and after the intervention, we obtained functional magnetic resonance imaging (fMRI) data from study subjects. At baseline, after treatment and 12-week follow-up, we additionally evaluated patients with the Modified Rankin Scale (mRS), the National Institutes of Health Stroke Scale (NIHSS), and the aphasia quotient (AQ) in the western aphasia test. RESULTS: After 2 weeks of treatment, the test group demonstrated significant improvements in AQ values, naming, repetition, spontaneous speech, and mRS scores compared to the control group (P < 0.05). Furthermore, in the follow-up time point (12 weeks), we found that the test group exhibited significantly better NIHSS scores and AQ evaluation indicators than the control group (P < 0.05). Specifically, the fMRI study shows that functional connectivity significantly improved in test group patients mainly among frontal, temporal, and parietal lobes of the left hemisphere with each other than controls group. Meanwhile, we found significantly enhanced functional connectivity with the hippocampus (P < 0.01). CONCLUSIONS: Our results indicate that the MT can expedite the recovery of language function during the early phases of stroke recovery. These findings may elucidate the underlying mechanism of MT and the application of this therapy as an adjunct rehabilitation technique in language recovery. [ABSTRACT FROM AUTHOR]

DNA nanomaterials have attracted ever-increasing attention over the past decades due to their incomparable programmability and multifunctionality. In particular, DNA dendrimer nanostructures, as a major research focus, have been applied in the fields of biosensing, therapeutics, and protein engineering, benefiting from their highly branched configuration. With the aid of specific recognition probes and inherent signal amplification, DNA dendrimers can achieve ultrasensitive detection of nucleic acids, proteins, cells, and other substances, such as lipopolysaccharides (LPS), adenosine triphosphate (ATP), and exosomes. By virtue of their void-containing structures and biocompatibility, DNA dendrimers can deliver drugs or functional nucleic acids into target cells in chemotherapy, immunotherapy, and gene therapy. Furthermore, DNA dendrimers are being applied in protein engineering for efficient directed evolution of proteins. This review summarizes the main research progress of DNA dendrimers, concerning their assembly methods and biomedical applications as well as the emerging challenges and perspectives for future research. [ABSTRACT FROM AUTHOR]