Topological disruption of low- and high-order functional networks in presbycusis.

Prior efforts have manifested that functional connectivity (FC) network disruptions are concerned with cognitive disorder in presbycusis. The present research was designed to investigate the topological reorganization and classification performance of low-order functional connectivity (LOFC) and high-order functional connectivity (HOFC) networks in patients with presbycusis. Resting-state functional magnetic resonance imaging (Rs-fMRI) data were obtained in 60 patients with presbycusis and 50 matched healthy control subjects (HCs). LOFC and HOFC networks were then constructed, and the topological metrics obtained from the constructed networks were compared to evaluate topological differences in global, nodal network metrics, modularity and rich-club organization between patients with presbycusis and HCs. The use of HOFC profiles boosted presbycusis classification accuracy, sensitivity and specificity compared to that using LOFC profiles. The brain networks in both patients with presbycusis and HCs exhibited small-world properties within the given threshold range, and striking differences between groups in topological metrics were discovered in the constructed networks (LOFC and HOFC). NBS analysis identified a subnetwork involving 26 nodes and 23 signally altered internodal connections in patients with presbycusis in comparison to HCs in HOFC networks. This study highlighted the topological differences between LOFC and HOFC networks in patients with presbycusis, suggesting that HOFC profiles may help to further identify brain network abnormalities in presbycusis.

Enhancing Gastrodin Production in Yarrowia lipolytica by Metabolic Engineering.

Gastrodin, 4-hydroxybenzyl alcohol-4-O-ß-D-glucopyranoside, has been widely used in the treatment of neurogenic and cardiovascular diseases. Currently, gastrodin biosynthesis is being achieved in model microorganisms. However, the production levels are insufficient for industrial applications. In this study, we successfully engineered a Yarrowia lipolytica strain to overproduce gastrodin through metabolic engineering. Initially, the engineered strain expressing the heterologous gastrodin biosynthetic pathway, which comprises chorismate lyase, carboxylic acid reductase, phosphopantetheinyl transferase, endogenous alcohol dehydrogenases, and a UDP-glucosyltransferase, produced 1.05 g/L gastrodin from glucose in a shaking flask. Then, the production was further enhanced to 6.68 g/L with a productivity of 2.23 g/L/day by overexpressing the key node DAHP synthases of the shikimate pathway and alleviating the native tryptophan and phenylalanine biosynthetic pathways. Finally, the best strain, Gd07, produced 13.22 g/L gastrodin in a 5 L fermenter. This represents the highest reported production of gastrodin in an engineered microorganism to date, marking the first successful de novo production of gastrodin using Y. lipolytica.

Evaluation of glymphatic system activity by diffusion tensor image analysis along the perivascular space in presbycusis.

PURPOSE: Previous studies have suggested that presbycusis (age-related hearing loss) is accompanied with cognitive decline and dementia. However, the neural mechanism underlying the cognitive decline in presbycusis remains unclear. This study aimed to evaluate the glymphatic system function in presbycusis patients compared to healthy controls using diffusion tensor imaging (DTI) with the perivascular space (DTI-ALPS) method. METHODS: DTI scans were obtained from 30 presbycusis patients with cognitive decline (PCD), 30 presbycusis patients with no cognitive decline (PNCD) and 40 age-, gender-, and education-matched healthy controls (HCs). The DTI-ALPS index was calculated for each group. We evaluated the differences in the DTI-ALPS index among PCD, PNCD and HCs. In addition, we conducted a correlation analysis between the DTI-ALPS index and cognitive performance. RESULTS: There were significant differences of the DTI-ALPS index among three groups. Post-hoc analysis suggested that the DTI-ALPS index in PCD was significantly lower patients in relative to PNCD and HCs (1.49147 vs. 1.57441 vs. 1.62020, p < 0.001). After correcting for age, gender, and education, the DTI-ALPS index is positively correlated with the MoCA scores (rho = 0.426, p = 0.026). CONCLUSION: Presbycusis patients with cognitive impairment exhibited decreased glymphatic activity than those without cognitive impairment and HCs. The DTI-ALPS index may provide useful disease progression or treatment biomarkers for patients with presbycusis as an indicator of modulation of glymphatic activity.

RASSF1A promotes radiosensitivity in nasopharyngeal carcinoma by promoting FoxO3a and inhibiting the Nrf2/TXNRD1 signaling pathway.

Radiotherapy is widely used as the first-line treatment for nasopharyngeal carcinoma (NPC). However, the resistance of some patients to treatment lowers its clinical effectiveness. Compared to typical epithelial cells, NPC markedly lowers the Ras-association domain family 1A (RASSF1A) protein expression. RASSF1A overexpression sensitizes NPC cells to radiotherapy. Mechanistically, RASSF1A promotes the expression of Forkhead box O3a (FoxO3a) in the nucleus and inhibits the Nuclear factor E2-related factor 2 (Nrf2) signaling pathway via binding to the Kelch-like ECH-associated protein 1 (Keap1) promoter. Through elevating intracellular ROS levels, RASSF1A overexpression inhibits the expression of thioredoxin reductase 1 (TXNRD1), a crucial Nrf2 target gene, and increases NPC sensitivity to radiation. Immunohistochemical staining of NPC tissue sections revealed that the expression of RASSF1A is negatively correlated with that of TXNRD1. The traditional Chinese medicine component andrographolide (AGP), which induces RASSF1A expression, increased the sensitivity of NPC cells to radiotherapy in vitro and in vivo. Our findings implied that RASSF1A increases the sensitivity of NPC to radiation by increasing FoxO3a expression in the nucleus, inhibiting the Nrf2/TXNRD1 signaling pathway, and elevating intracellular ROS levels. AGP targets RASSF1A and may be a promising adjuvant sensitizer for enhancing radiosensitivity in NPC.

Reorganization of the cortical connectome functional gradient in age-related hearing loss.

Age-related hearing loss (ARHL), one of the most common sensory deficits in elderly individuals, is a risk factor for dementia; however, it is unclear how ARHL affects the decline in cognitive function. To address this issue, a connectome gradient framework was used to identify critical features of information integration between sensory and cognitive processing centers using resting-state functional magnetic resonance imaging (rs-fMRI) data from 40 individuals with ARHL and 36 healthy controls (HCs). The first three functional gradient alterations associated with ARHL were investigated at the global, network and regional levels. Using a support vector machine (SVM) model, our analysis distinguished individuals with ARHL with normal cognitive function from those with cognitive decline. Compared to HCs, individuals with ARHL had a contracted principal primary-to-transmodal gradient axis, especially in the visual and default mode networks, with an altered gradient explained ratio and variance. Among individuals with ARHL, cognitive decline was detected in the visual network in the principal gradient as well as in the limbic, salience and default mode networks in the third gradient (salience to frontoparietal/default mode). These results suggest that ARHL is associated with disrupted information processing from the primary sensory networks to higher-order cognitive networks and highlight the key nodes closely associated with cognitive decline during cognitive processing in ARHL, providing new insights into the mechanism of cognitive impairment and suggesting potential treatments related to ARHL.

Synchronization of Coupled Neural Networks With Constant Time-Delay Using Sampled-Data Information.

In this article, a synchronization control method is studied for coupled neural networks (CNNs) with constant time delay using sampled-data information. A distributed control protocol relying on the sampled-data information of neighboring nodes is proposed. Lyapunov functional is constructed to analyze the synchronization of CNNs with constant time delay. Using Park's integral inequality and improved free-weight matrix integral inequality, sufficient conditions are provided for CNNs to achieve synchronization with less conservatism. In addition, the maximum sampling interval is determined by transforming the sufficient conditions into an optimization problem, and an aperiodic sampling control technique is implemented to reduce the communication energy load. Finally, numerical simulations are provided to demonstrate that the proposed method is capable of achieving synchronization.

Distributed Dynamic Event-Triggered Leader-Following Consensus for Nonlinear Multiagent Systems Over Fading Channel.

This article investigates the leader-following consensus problem of discrete-time nonlinear multiagent systems (MASs) over fading channels. With the consideration of the transmission among followers maybe affected by the fading networks, the nonidentical fading channels model is constructed. To reduce the transmission network burden, the dynamical event-triggered mechanism (DETM) is developed. Different from most of existing event-triggered strategies, the threshold parameter in the developed dynamical event-triggering condition is dynamically adjusted according to a dynamic rule. Based on the DETM, a distributed consensus control protocol is designed under fading channels. Then, sufficient criteria are provided to ensure that MASs can achieve the leaser-following consensus, and satisfy the H∞ performance index in the presence of fading channels. The desired controller parameters can be derived in terms of solutions of matrix inequalities that are lightly solvable. In the end, simulation results show that the designed dynamical event transmission policy is capable of diminishing communication burden more promptly and effectively than some existing ones.

Alteration in resting-state effective connectivity within the Papez circuit in Presbycusis.

Previous studies have suggested that the Papez circuit may be involved in the cognitive impairment observed after hearing loss in presbycusis patients, yet relatively little is known about the pattern of changes in effective connectivity within the circuit. The aim of this study was to investigate abnormal alterations in resting-state effective connectivity within the Papez circuit and their association with cognitive decline in presbycusis patients. The spectral dynamic causal modelling (spDCM) approach was used for resting-state effective connectivity analysis in 61 presbycusis patients and 52 healthy controls (HCs) within the Papez circuit. The hippocampus (HPC), mamillary body (MB), anterior thalamic nuclei (ATN), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), entorhinal cortex (ERC), subiculum (Sub) and parahippocampal gyrus (PHG) were selected as the regions of interest (ROIs). The fully connected model difference in effective connectivity between the two groups was assessed, and the correlation between effective connectivity alteration and cognitive scale was analysed. We found that presbycusis patients demonstrated decreased effective connectivity from MB, PCC, and Sub to ACC relative to HCs, whereas higher effective connectivity strength was shown from HPC to MB, from ATN to PHG and from PHG to Sub. The effective connectivity from PHG to Sub was significantly negatively correlated with the complex figure test (CFT)-delay score (rho = -0.259, p = 0.044). The results support and reinforce the role of abnormal effective connectivity within the Papez circuit in the pathophysiology of presbycusis-related cognitive impairment and reveal its potential as a novel imaging marker.

Deep Reinforcement Learning on Autonomous Driving Policy With Auxiliary Critic Network.

Deep reinforcement learning (DRL) is a machine learning method based on rewards, which can be extended to solve some complex and realistic decision-making problems. Autonomous driving needs to deal with a variety of complex and changeable traffic scenarios, so the application of DRL in autonomous driving presents a broad application prospect. In this article, an end-to-end autonomous driving policy learning method based on DRL is proposed. On the basis of proximal policy optimization (PPO), we combine a curiosity-driven method called recurrent neural network (RNN) to generate an intrinsic reward signal to encounter the agent to explore its environment, which improves the efficiency of exploration. We introduce an auxiliary critic network on the original actor-critic framework and choose the lower estimate which is predicted by the dual critic network when the network update to avoid the overestimation bias. We test our method on the lane- keeping task and overtaking task in the open racing car simulator (TORCS) driving simulator and compare with other DRL methods, experimental results show that our proposed method can improve the training efficiency and control performance in driving tasks.

Research on Vision of Intelligent Car Based on Broad Learning System.

The broad learning system (BLS) of intelligent vehicle in different target environments is studied in this article. First, this article provides with the target recognition image data to be trained and detected through the automated guided vehicle (AGV) mobile platform, which can grab the recognition image of different angles and backgrounds. In order to avoid the data generalization phenomenon, the dataset can be expanded by the data normalization and data enhancement. Second, the data are input into the shared convolution layer to extract the feature image and maintain the image. The parameters of image height, width, and channel number are invariable, and the new feature image is obtained by further extraction. Furthermore, the region proposal network (RPN) prefiltering algorithm based on hierarchical clustering is used to filter the objects in the candidate box to determine the region image corresponding to the feature image. Then, the feature images of different sizes input into region of interest (ROI) pooling are used to keep the size of the image in the ROI consistent. Finally, the normalized image is input into the classifier module to obtain the category of the target recognition image to be detected. Through the simulation experiments of different groups, it can be seen that the target recognition system proposed in this design can not only accurately detect the objects but also stably recognize the objects in different environments. The target recognition accuracy for the optimized system is about 95%.

Degree centrality and functional connections in presbycusis with and without cognitive impairments.

Presbycusis is a major public issue that affecting elderly adults. However, the neural substrates between normal cognition and cognitive deficits in these patients need to be illustrated. 47 patients with presbycusis and 33 well-matched healthy controls were recruited in present study. Each subject underwent pure-tone audiometry (PTA), MRI scanning and cognition evaluations, then we found 22 patients with cognitive deficits and 25 patients with common cognition. We analyzed the Degree centrality (DC) characteristics among three groups, and try to recognize key nodes which contribute significantly. Subsequent functional connectivity analysis was applied using the key nodes as seeds. Compared with controls, presbycusis without cognitive impairments showed deceased DC in superior temporal gyrus (STG), inferior frontal gyrus (IFG) and supramarginal gyrus (SMG). Additionally, presbycusis with cognitive impairments showed enhanced DC in fusiform gurus (FFG), cerebellum and para-hippocampal gyrus (PHG), while weakened DC in SMG, middle frontal gyrus (IFG) and inferior Parietal lobule (IPL). Compared with normal cognition, increased DC value of cerebellum and STG, as well as decreased DC value of IPL in presbycusis with cognitive impairments were observed. We noticed that SMG may play an important role. Then the left and right SMG were used as seeds in functional connections analysis. With the seed set at left SMG, presbycusis without cognitive impairments showed decreases connections with cerebellum, temporal pole (TP), superior temporal gyrus (STG) and median cingulate cortex (MCC). Presbycusis with cognitive impairments showed weakened connectivity with cerebellum, IFG, IPL and superior frontal gyrus (SFG). The right SMG showed decrease connections with cerebellum, middle temporal gyrus (MTG), STG and increase connection with middle frontal gyrus (MFG) in presbycusis without cognitive impairments. While the right SMG showed enhanced connections with left TP, caudate, anterior cingulate cortex (ACC), angular, SFG and weakened connectivity with right SFG presbycusis with cognitive impairments. In comparison with normal cognition and impaired cognition, MFG, IFG, PHG, rolandic operculum and cerebellum were involved. These findings enriched our understanding of the neural mechanisms underlying cognitive impairments associated with presbycusis and may serve as a potential imaging biomarker for investigating and predicting cognitive difficulties.

Role of STAT3 in the pathogenesis of nasopharyngeal carcinoma and its significance in anticancer therapy.

Nasopharyngeal carcinoma (NPC) is a type of head and neck tumor with noticeable regional and ethnic differences. It is associated with Epstein-Barr virus infection and has a tendency for local and distant metastasis. NPC is also highly sensitive to radiotherapy and chemotherapy. Over 70% of patients present with locoregionally advanced disease, and distant metastasis is the primary reason for treatment failure. A signal transducer and activator of transcription 3 (STAT3) promotes NPC oncogenesis through mechanisms within cancerous cells and their interactions with the tumor microenvironment, which is critical in the initiation, progression, and metastasis of NPC. Further, p-STAT3 is strongly associated with advanced NPC. Recent research on STAT3 has focused on its expression at the center of various oncogenic pathways. Here, we discuss the role of STAT3 in NPC and its potential therapeutic inhibitors and analogs for the treatment and control of NPC.

Abnormal cerebellar network and effective connectivity in sudden and long-term sensorineural hearing loss.

Sudden sensorineural hearing loss (SSNHL) is a common otology emergency and some SSNHL will develop into a long-term hearing loss (LSNHL). However, whether SSNHL and LSNHL have similar psychiatric patterns remains unknown, as well as the neural substrates. Increasing evidence has proved that the cerebellar network plays a vital role in hearing, cognition processing, and emotion control. Thus, we recruited 20 right SSNHL (RSSNHL), 20 right LSNHL (RLSNHL), and 24 well-matched healthy controls to explore the cerebellar patterns among the three groups. Every participant underwent pure tone audiometry tests, neuropsychological evaluations, and MRI scanning. Independent component analysis (ICA) was carried out on the MRI data and the cerebellar network was extracted. Granger causality analysis (GCA) was conducted using the significant cerebellar region as a seed. Pearson's correlation analysis was computed between imaging characteristics and clinical features. ICA found the effect of group on right cerebellum lobule V for the cerebellar network. Then, we found decreased outflow from right cerebellum lobule V to right middle orbitofrontal cortex, inferior frontal gyrus, anterior cingulate cortex, superior temporal gyrus, and dorsal lateral prefrontal cortex in RSSNHL group in GCA analysis. No significance was found in RLSNHL subjects. Additionally, the RSSNHL group showed increased effective connectivity from the right middle frontal gyrus (MFG) and the RLSNHL group showed increased effective connectivity from the right insula and temporal pole to the right cerebellum lobule V. Moreover, connections between right cerebellum lobule V and mean time series of the cerebellar network was negatively correlated with anxiety score in RSSNHL and negatively correlated with depression scores in RLSNHL. Effective connectivity from right MFG to right cerebellum lobule V could predict anxiety status in RSSNHL subjects. Our results may prove potential imaging biomarkers and treatment targets for hearing loss in future work.

Aberrant Modulations of Neurocognitive Network Dynamics in Migraine Comorbid With Tinnitus.

Purpose: The possible relationship between migraine and tinnitus still remains elusive although migraine is often accompanied by chronic tinnitus. Several neuroimaging studies have reinforced the cognitive network abnormality in migraine and probably as well as tinnitus. The present work aims to investigate the dynamic neurocognitive network alterations of migraine comorbid with tinnitus. Materials and Methods: Participants included migraine patients (n = 32), tinnitus patients (n = 20), migraine with tinnitus (n = 27), and healthy controls (n = 47), matched for age and gender. Resting-state functional magnetic resonance imaging (rs-fMRI) with independent component analysis (ICA), sliding window cross-correlation, and clustering state analysis was used to detect the dynamic functional network connectivity (dFNC) of each group. Correlation analyses illustrated the association between clinical symptoms and abnormal dFNC in migraine as well as tinnitus. Results: Compared with healthy controls, migraine patients exhibited decreased cerebellar network and visual network (CN-VN) connectivity in State 2; migraine with tinnitus patients showed not only decreased CN-VN connectivity in State 2 but also decreased cerebellar network and executive control network (CN-ECN) connectivity in State 2 and increased cerebellar network and somatomotor network (SMN-VN) connectivity in State 1. The abnormal cerebellum dFNC with the executive control network (CN-ECN) was negatively correlated with headache frequency of migraine (rho = -0.776, p = 0.005). Conclusion: Brain network characteristics of migraine with tinnitus patients may indicate different mechanisms for migraine and tinnitus. Our results demonstrated a transient pathologic state with atypical cerebellar-cortical connectivity in migraine with tinnitus patients, which might be used to identify the neuro-pathophysiological mechanisms in migraine accompanied by tinnitus.

Aberrant Functional Network of Small-World in Sudden Sensorineural Hearing Loss With Tinnitus.

Few researchers investigated the topological properties and relationships with cognitive deficits in sudden sensorineural hearing loss (SNHL) with tinnitus. To explore the topological characteristics of the brain connectome following SNHL from the global level and nodal level, we recruited 36 bilateral SNHL patients with tinnitus and 37 well-matched healthy controls. Every subject underwent pure tone audiometry tests, neuropsychological assessments, and MRI scanning. AAL atlas was employed to divide a brain into 90 cortical and subcortical regions of interest, then investigated the global and nodal properties of "small world" network in SNHL and control groups using a graph-theory analysis. The global characteristics include small worldness, cluster coefficient, characteristic path length, local efficiency, and global efficiency. Node properties include degree centrality, betweenness centrality, nodal efficiency, and nodal clustering coefficient. Interregional connectivity analysis was also computed among 90 nodes. We found that the SNHL group had significantly higher hearing thresholds and cognitive impairments, as well as disrupted internal connections among 90 nodes. SNHL group displayed lower AUC of cluster coefficient and path length lambda, but increased global efficiency. The opercular and triangular parts of the inferior frontal gyrus, rectus gyrus, parahippocampal gyrus, precuneus, and amygdala showed abnormal local features. Some of these connectome alterations were correlated with cognitive ability and the duration of SNHL. This study may prove potential imaging biomarkers and treatment targets for future studies.

Reorganized Brain Functional Network Topology in Presbycusis.

Purpose: Presbycusis is characterized by bilateral sensorineural hearing loss at high frequencies and is often accompanied by cognitive decline. This study aimed to identify the topological reorganization of brain functional network in presbycusis with/without cognitive decline by using graph theory analysis approaches based on resting-state functional magnetic resonance imaging (rs-fMRI). Methods: Resting-state fMRI scans were obtained from 30 presbycusis patients with cognitive decline, 30 presbycusis patients without cognitive decline, and 50 age-, sex-, and education-matched healthy controls. Graph theory was applied to analyze the topological properties of brain functional networks including global and nodal metrics, modularity, and rich-club organization. Results: At the global level, the brain functional networks of all participants were found to possess small-world properties. Also, significant group differences in global network metrics were observed among the three groups such as clustering coefficient, characteristic path length, normalized characteristic path length, and small-worldness. At the nodal level, several nodes with abnormal betweenness centrality, degree centrality, nodal efficiency, and nodal local efficiency were detected in presbycusis patients with/without cognitive decline. Changes in intra-modular connections in frontal lobe module and inter-modular connections in prefrontal subcortical lobe module were found in presbycusis patients exposed to modularity analysis. Rich-club nodes were reorganized in presbycusis patients, while the connections among them had no significant group differences. Conclusion: Presbycusis patients exhibited topological reorganization of the whole-brain functional network, and presbycusis patients with cognitive decline showed more obvious changes in these topological properties than those without cognitive decline. Abnormal changes of these properties in presbycusis patients may compensate for cognitive impairment by mobilizing additional neural resources.

Disrupted Topological Organization of Resting-State Functional Brain Networks in Age-Related Hearing Loss.

Purpose: Age-related hearing loss (ARHL), associated with the function of speech perception decreases characterized by bilateral sensorineural hearing loss at high frequencies, has become an increasingly critical public health problem. This study aimed to investigate the topological features of the brain functional network and structural dysfunction of the central nervous system in ARHL using graph theory. Methods: Forty-six patients with ARHL and forty-five age, sex, and education-matched healthy controls were recruited to undergo a resting-state functional magnetic resonance imaging (fMRI) scan in this study. Graph theory was applied to analyze the topological properties of the functional connectomes by studying the local and global organization of neural networks. Results: Compared with healthy controls, the patient group showed increased local efficiency (Eloc) and clustering coefficient (Cp) of the small-world network. Besides, the degree centrality (Dc) and nodal efficiency (Ne) values of the left inferior occipital gyrus (IOG) in the patient group showed a decrease in contrast with the healthy control group. In addition, the intra-modular interaction of the occipital lobe module and the inter-modular interaction of the parietal occipital module decreased in the patient group, which was positively correlated with Dc and Ne. The intra-modular interaction of the occipital lobe module decreased in the patient group, which was negatively correlated with the Eloc. Conclusion: Based on fMRI and graph theory, we indicate the aberrant small-world network topology in ARHL and dysfunctional interaction of the occipital lobe and parietal lobe, emphasizing the importance of dysfunctional left IOG. These results suggest that early diagnosis and treatment of patients with ARHL is necessary, which can avoid the transformation of brain topology and decreased brain function.

Specific brain network predictors of interventions with different mechanisms for tinnitus patients.

BACKGROUND: The aberrant brain network that gives rise to the phantom sound of tinnitus is believed to determine the effectiveness of tinnitus therapies involving neuromodulation with repetitive transcranial magnetic stimulation (rTMS) and sound therapy utilizing tailor-made notch music training (TMNMT). To test this hypothesis, we determined how effective rTMS or TMNMT were in ameliorating tinnitus in patients with different functional brain networks. METHODS: Resting-state functional MRI was used to construct brain functional networks in patients with tinnitus (41 males/45 females, mean age 49.53±11.19 years) and gender-matched healthy controls (22 males/35 females, mean age 46.23±10.23 years) with independent component analysis (ICA). A 2 × 2 analysis of variance with treatment outcomes (Effective group, EG/Ineffective group, IG) and treatment types (rTMS/TMNMT) was used to test the interaction between outcomes and treatment types associated with functional network connections (FNCs). FINDINGS: The optimal neuroimaging indicator for responding to rTMS (AUC 0.804, sensitivity 0.700, specificity 0.913) was FNCs in the salience network-right frontoparietal network (SN-RFPN) while for responding to TMNMT (AUC 0.764, sensitivity 0.864, specificity 0.667) was the combination of FNCs in the auditory network- salience network (AUN-SN) and auditory network-cerebellar network (AUN-CN). INTERPRETATION: Tinnitus patients with higher FNCs in the SN-RFPN is associated with a recommendation for rTMS whereas patients with lower FNCs in the AUN-SN and AUN-CN would suggest TMNMT as the better choice. These results indicate that brain network-based measures aid in the selection of the optimal form of treatment for a patient contributing to advances in precision medicine. FUNDING: Yuexin Cai is supported by Key R&D Program of Guangdong Province, China (Grant No. 2018B030339001), National Natural Science Foundation of China (82071062), Natural Science Foundation of Guangdong province (2021A1515012038), the Fundamental Research Funds for the Central Universities (20ykpy91), and Sun Yat-Sen Clinical Research Cultivating Program (SYS-Q-201903). Yu-Chen Chen is supported by Medical Science and Technology Development Foundation of Nanjing Department of Health (No. ZKX20037), and Natural Science Foundation of Jiangsu Province (No. BK20211008).

Acne detection and severity evaluation with interpretable convolutional neural network models.

BACKGROUND: Acne vulgaris is one of the most prevalent skin conditions, which harms not only the patients' physiological conditions, but also their mental health. Early diagnosis and accurate continuous self-monitoring could help control and alleviate their discomfort. OBJECTIVE: We focus on the development and comparison of deep learning models for locating acne lesions on facial images, thus making estimations on the acne severity on faces via medical criterion. METHODS: Different from most existing literature on facial acne analysis, the considered models in this study are object detection models with convolutional neural network (CNN) as backbone and has better interpretability. Thus, they produce more credible results of acne detection and facial acne severity evaluation. RESULTS: Experiments with real data validate the effectiveness of these models. The highest mean average precision (mAP) is 0.536 on an open source dataset. Corresponding error of acne lesion counting can be as low as 0.43 ± 6.65 on this dataset. CONCLUSIONS: The presented models have been released to public via deployed as a freely accessible WeChat applet service, which provides continuous out-of-hospital self-monitoring to patients. This also aids the dermatologists to track the progress of this disease and to assess the effectiveness of treatment.

A field-based general framework to simulate fluids in parallel and the framework's application to a matrix acidization simulation.

On the basis of numerous fluid simulation experiences, researchers have discovered that many common operations can be abstracted to form a general fluid simulation framework. These operations include the discretization of equations and variables, the computation of the coefficients, the assembly of the linear or nonlinear systems, the solving of the systems, etc. Furthermore, all of the operations can be attributed to the operations "in the field", which is an abstract concept derived from the equations and variables. Thus, fluid simulations can be performed under a field-based general framework. Moreover, in response to the urgent need for large-scale fluid simulations, parallelism is integrated into the framework. Due to the convenience of the field operations, parallelization of the framework can be realized on both the OpenMP and MPI levels. In other words, because of the newly defined "fields", a series of operations in fluid simulations can be simplified and unified. However, very few studies have noted this advantage, and therefore, this work attempts to fill the void. With the help of a field-based general framework, it is anticipated that the parallel codes of fluid simulations can be generated easily and quickly. As an application of the general framework, a parallel 3D simulator for matrix acidization called Masor is developed. The simulation results are regarded as physically reasonable by many studies, which verifies the correctness and effectiveness of the general framework. In addition, it is noteworthy that the parallel performance of Masor is decided by a solver.