An autonomous wheelchair with health monitoring system based on Internet of Thing.

Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on Internet of Thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users' four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.

Cross-Layer Contrastive Learning of Latent Semantics for Facial Expression Recognition.

Convolutional neural networks (CNNs) have achieved significant improvement for the task of facial expression recognition. However, current training still suffers from the inconsistent learning intensities among different layers, i.e., the feature representations in the shallow layers are not sufficiently learned compared with those in deep layers. To this end, this work proposes a contrastive learning framework to align the feature semantics of shallow and deep layers, followed by an attention module for representing the multi-scale features in the weight-adaptive manner. The proposed algorithm has three main merits. First, the learning intensity, defined as the magnitude of the backpropagation gradient, of the features on the shallow layer is enhanced by cross-layer contrastive learning. Second, the latent semantics in the shallow-layer and deep-layer features are explored and aligned in the contrastive learning, and thus the fine-grained characteristics of expressions can be taken into account for the feature representation learning. Third, by integrating the multi-scale features from multiple layers with an attention module, our algorithm achieved the state-of-the-art performances, i.e. 92.21%, 89.50%, 62.82%, on three in-the-wild expression databases, i.e. RAF-DB, FERPlus, SFEW, and the second best performance, i.e. 65.29% on AffectNet dataset. Our codes will be made publicly available.

Evaluating the contribution of historical and contemporary temperature to the oospore production of self-fertile Phytophthora infestans.

Reproductive systems play an important role in the ecological function of species, but little is known about how climate change, such as global warming, may affect the reproductive systems of microbes. In this study, 116 Phytophthora infestans isolates sampled from five different altitudes along a mountain were evaluated under five temperature regimes to determine the effects of historical and experimental temperature on the reproductive system of the pathogen. Both altitude, a proxy for historical pathogen adaptation to temperature, and temperature used in the experiment affected the sexual reproduction of the pathogen, with experimental temperature, that is, contemporary temperature, playing a role several times more important than historical temperature. Furthermore, the potential of sexual reproduction, measured by the number of oospores quantified, increased with the temperature breadth (i.e., difference between the highest and lowest temperature at which sexual reproduction takes place) of the pathogen and reached the maximum at the experimental temperature of 21°C, which is higher than the annual average temperature in many potato-producing areas. The results suggest that rising air temperature associated with global warming may increase the potential of sexual reproduction in P. infestans. Given the importance of sexuality in pathogenicity and ecological adaptation of pathogens, these results suggest that global warming may increase the threat of P. infestans to agricultural production and other ecological services and highlight that new epidemiological strategies may need to be implemented for future food security and ecological resilience.

A Chinese Face Dataset with Dynamic Expressions and Diverse Ages Synthesized by Deep Learning.

Facial stimuli have gained increasing popularity in research. However, the existing Chinese facial datasets primarily consist of static facial expressions and lack variations in terms of facial aging. Additionally, these datasets are limited to stimuli from a small number of individuals, in that it is difficult and time-consuming to recruit a diverse range of volunteers across different age groups to capture their facial expressions. In this paper, a deep-learning based face editing approach, StyleGAN, is used to synthesize a Chinese face dataset, namely SZU-EmoDage, where faces with different expressions and ages are synthesized. Leverage on the interpolations of latent vectors, continuously dynamic expressions with different intensities, are also available. Participants assessed emotional categories and dimensions (valence, arousal and dominance) of the synthesized faces. The results show that the face database has good reliability and validity, and can be used in relevant psychological experiments. The availability of SZU-EmoDage opens up avenues for further research in psychology and related fields, allowing for a deeper understanding of facial perception.

MIB-ANet: A novel multi-scale deep network for nasal endoscopy-based adenoid hypertrophy grading.

Introduction: To develop a novel deep learning model to automatically grade adenoid hypertrophy, based on nasal endoscopy, and asses its performance with that of E.N.T. clinicians. Methods: A total of 3,179 nasoendoscopic images, including 4-grade adenoid hypertrophy (Parikh grading standard, 2006), were collected to develop and test deep neural networks. MIB-ANet, a novel multi-scale grading network, was created for adenoid hypertrophy grading. A comparison between MIB-ANet and E.N.T. clinicians was conducted. Results: In the SYSU-SZU-EA Dataset, the MIB-ANet achieved 0.76251 F1 score and 0.76807 accuracy, and showed the best classification performance among all of the networks. The visualized heatmaps show that MIB-ANet can detect whether adenoid contact with adjacent tissues, which was interpretable for clinical decision. MIB-ANet achieved at least 6.38% higher F1 score and 4.31% higher accuracy than the junior E.N.T. clinician, with much higher (80× faster) diagnosing speed. Discussion: The novel multi-scale grading network MIB-ANet, designed for adenoid hypertrophy, achieved better classification performance than four classical CNNs and the junior E.N.T. clinician. Nonetheless, further studies are required to improve the accuracy of MIB-ANet.

Learning from pseudo-lesion: a self-supervised framework for COVID-19 diagnosis.

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019, and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by ground glass opacity, a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo-lesion generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo-COVID-19 images. The pairs of normal and pseudo-COVID-19 images were then used to train an encoder-decoder architecture-based U-Net for image restoration, which does not require any labeled data. The pretrained encoder was then fine-tuned using labeled data for COVID-19 diagnosis task. Two public COVID-19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis, and the accuracy of the proposed method outperformed the supervised model pretrained on large-scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively.

Clinical Value of Platelet Indices in Premature Coronary Artery Disease.

Platelets play an important role in the pathophysiology of coronary artery disease. However, the clinical value of platelet indices in premature coronary heart disease remains largely unknown.Consecutive patients referred for coronary angiography were evaluated (n = 1675). Patients were stratified into premature coronary heart disease (n = 679, age < 55 for male and age < 65 for female), late-onset coronary heart disease (n = 772, age ≥ 55 for male and age ≥ 65 for female), and control (n = 224, age < 55 for male and age < 65 for female). Their clinical and laboratory parameters were collected. The relationship between platelet indices and premature coronary artery disease was analyzed.In univariate analysis, platelet indices showed no significant association with the presence of premature coronary heart disease (P > 0.05). After adjustment for traditional risk factors, mean platelet volume (0.823 [0.683-0.993], P = 0.042) and platelet-large cell ratio (0.976 [0.954-0.999], P = 0.040) were negatively correlated with the presence of premature coronary heart disease. The platelet-to-lymphocyte ratio was statistically significant among different numbers of coronary lesions (P = 0.035). In subgroup analysis, platelet-large cell ratio (1.190 [1.010-1.403], P = 0.038) was an independent risk factor of coronary restenosis after percutaneous coronary intervention.Platelet indices were associated with the prevalence, severity, and coronary restenosis after percutaneous coronary intervention suggesting their possible clinical application in premature coronary heart disease.

Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing.

Biometric systems are vulnerable to presentation attacks (PAs) performed using various PA instruments (PAIs). Even though there are numerous PA detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with de-folding and de-mixing to derive the task-specific representation for PAD. During de-folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing the generative loss. While de-mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing the interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with the state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% equal error rate (EER) in OULU-NPU and MSU-MFSD, exceeding the baseline performance by 9.54%. The source code of the proposed technique is available at https://github.com/kongzhecn/dfdm.

Automatic Learning Rate Adaption for Memristive Deep Learning Systems.

As a possible device to further enhance the performance of the hybrid complementary metal oxide semiconductor (CMOS) technology in the hardware, the memristor has attracted widespread attention in implementing efficient and compact deep learning (DL) systems. In this study, an automatic learning rate tuning method for memristive DL systems is presented. Memristive devices are utilized to adjust the adaptive learning rate in deep neural networks (DNNs). The speed of the learning rate adaptation process is fast at first and then becomes slow, which consist of the memristance or conductance adjustment process of the memristors. As a result, no manual tuning of learning rates is required in the adaptive back propagation (BP) algorithm. While cycle-to-cycle and device-to-device variations could be a significant issue in memristive DL systems, the proposed method appears robust to noisy gradients, various architectures, and different datasets. Moreover, fuzzy control methods for adaptive learning are presented for pattern recognition, such that the over-fitting issue can be well addressed. To our best knowledge, this is the first memristive DL system using an adaptive learning rate for image recognition. Another highlight of the presented memristive adaptive DL system is that quantized neural network architecture is utilized, and there is therefore a significant increase in the training efficiency, without the loss of testing accuracy.

Precise Facial Landmark Detection by Reference Heatmap Transformer.

Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results. However, when the face image is suffering from large poses, heavy occlusions and complicated illuminations, they cannot learn discriminative feature representations and effective facial shape constraints, nor can they accurately predict the value of each element in the landmark heatmap, limiting their detection accuracy. To address this problem, we propose a novel Reference Heatmap Transformer (RHT) by introducing reference heatmap information for more precise facial landmark detection. The proposed RHT consists of a Soft Transformation Module (STM) and a Hard Transformation Module (HTM), which can cooperate with each other to encourage the accurate transformation of the reference heatmap information and facial shape constraints. Then, a Multi-Scale Feature Fusion Module (MSFFM) is proposed to fuse the transformed heatmap features and the semantic features learned from the original face images to enhance feature representations for producing more accurate target heatmaps. To the best of our knowledge, this is the first study to explore how to enhance facial landmark detection by transforming the reference heatmap information. The experimental results from challenging benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art methods in the literature.

Style attention based global-local aware GAN for personalized facial caricature generation.

Introduction: Caricature is an exaggerated pictorial representation of a person, which is widely used in entertainment and political media. Recently, GAN-based methods achieved automatic caricature generation through transferring caricature style and performing shape exaggeration simultaneously. However, the caricature synthesized by these methods cannot perfectly reflect the characteristics of the subject, whose shape exaggeration are not reasonable and requires facial landmarks of caricature. In addition, the existing methods always produce the bad cases in caricature style due to the simpleness of their style transfer method. Methods: In this paper, we propose a Style Attention based Global-local Aware GAN to apply the characteristics of a subject to generate personalized caricature. To integrate the facial characteristics of a subject, we introduce a landmark-based warp controller for personalized shape exaggeration, which employs the facial landmarks as control points to warp image according to its facial features, without requirement of the facial landmarks of caricature. To fuse the facial feature with caricature style appropriately, we introduce a style-attention module, which adopts an attention mechanism, instead of the simple Adaptive Instance Normalization (AdaIN) for style transfer. To reduce the bad cases and increase the quality of generated caricatures, we propose a multi-scale discriminator to both globally and locally discriminate the synthesized and real caricature, which improves the whole structure and realistic details of the synthesized caricature. Results: Experimental results on two publicly available datasets, the WebCaricature and the CaVINet datasets, validate the effectiveness of our proposed method and suggest that our proposed method achieves better performance than the existing methods. Discussion: The caricatures generated by the proposed method can not only preserve the identity of input photo but also the characteristic shape exaggeration for each person, which are highly close to the real caricatures drawn by real artists. It indicates that our method can be adopted in the real application.

Enhanced robust spatial feature selection and correlation filter learning for UAV tracking.

Spatial boundary effect can significantly reduce the performance of a learned discriminative correlation filter (DCF) model. A commonly used method to relieve this effect is to extract appearance features from a wider region of a target. However, this way would introduce unexpected features from background pixels and noises, which will lead to a decrease of the filter's discrimination power. To address this shortcoming, this paper proposes an innovative method called enhanced robust spatial feature selection and correlation filter Learning (EFSCF), which performs jointly sparse feature learning to handle boundary effects effectively while suppressing the influence of background pixels and noises. Unlike the ℓ2-norm-based tracking approaches that are prone to non-Gaussian noises, the proposed method imposes the ℓ2,1-norm on the loss term to enhance the robustness against the training outliers. To enhance the discrimination further, a jointly sparse feature selection scheme based on the ℓ2,1 -norm is designed to regularize the filter in rows and columns simultaneously. To the best of the authors' knowledge, this has been the first work exploring the structural sparsity in rows and columns of a learned filter simultaneously. The proposed model can be efficiently solved by an alternating direction multiplier method. The proposed EFSCF is verified by experiments on four challenging unmanned aerial vehicle datasets under severe noise and appearance changes, and the results show that the proposed method can achieve better tracking performance than the state-of-the-art trackers.

Effect of Climate Change on the Potentially Suitable Distribution Pattern of Castanopsis hystrix Miq. in China.

Climate warming poses a great threat to ecosystems worldwide, which significantly affects the geographical distribution and suitable growth area of species. Taking Castanopsis hystrix Miq. as the research object, the potentially suitable cultivation regions under present and future climatic emission scenarios in China were predicted based on the MaxEnt model with 360 effective individual distributions and eight environmental variables. The min temperature of coldest month (bio6), precipitation of driest month (bio14), and precipitation of warmest quarter (bio18) are three leading factors affecting the geographical distribution area of C. hystrix Miq. The suitable cultivation regions of C. hystrix Miq. range from 18°-34° N, 89°-122° E in central and southern China and cover an area of 261.95 × 104 km2. The spatial pattern of C. hystrix Miq. will migrate to the southern region of low latitudes with a decreasing suitable area when in ssp1-2.6, and to the southwestern region of low latitudes or expand to the northeast region at high latitudes in ssp5-8.5, with an increasing suitable area; no significant change on the spatial pattern in ssp2-2.4. For ssp1-2.6 or ssp2-4.5 climate scenarios, the southern region of high latitudes will be appropriate for introducing and cultivating C. hystrix Miq., and the cultivation area will increase. For ssp5-8.5, its cultivation will increase and expand to the northeast of high-latitude areas slightly.

Robust Twin Bounded Support Vector Classifier With Manifold Regularization.

Support vector machine (SVM), as a supervised learning method, has different kinds of varieties with significant performance. In recent years, more research focused on nonparallel SVM, where twin SVM (TWSVM) is the typical one. In order to reduce the influence of outliers, more robust distance measurements are considered in these methods, but the discriminability of the models is neglected. In this article, we propose robust manifold twin bounded SVM (RMTBSVM), which considers both robustness and discriminability. Specifically, a novel norm, that is, capped L1 -norm, is used as the distance metric for robustness, and a robust manifold regularization is added to further improve the robustness and classification performance. In addition, we also use the kernel method to extend the proposed RMTBSVM for nonlinear classification. We introduce the optimization problems of the proposed model. Subsequently, effective algorithms for both linear and nonlinear cases are proposed and proved to be convergent. Moreover, the experiments are conducted to verify the effectiveness of our model. Compared with other methods under the SVM framework, the proposed RMTBSVM shows better classification accuracy and robustness.

Canonical Correlation Analysis With Low-Rank Learning for Image Representation.

As a multivariate data analysis tool, canonical correlation analysis (CCA) has been widely used in computer vision and pattern recognition. However, CCA uses Euclidean distance as a metric, which is sensitive to noise or outliers in the data. Furthermore, CCA demands that the two training sets must have the same number of training samples, which limits the performance of CCA-based methods. To overcome these limitations of CCA, two novel canonical correlation learning methods based on low-rank learning are proposed in this paper for image representation, named robust canonical correlation analysis (robust-CCA) and low-rank representation canonical correlation analysis (LRR-CCA). By introducing two regular matrices, the training sample numbers of the two training datasets can be set as any values without any limitation in the two proposed methods. Specifically, robust-CCA uses low-rank learning to remove the noise in the data and extracts the maximization correlation features from the two learned clean data matrices. The nuclear norm and L1 -norm are used as constraints for the learned clean matrices and noise matrices, respectively. LRR-CCA introduces low-rank representation into CCA to ensure that the correlative features can be obtained in low-rank representation. To verify the performance of the proposed methods, five publicly image databases are used to conduct extensive experiments. The experimental results demonstrate the proposed methods outperform state-of-the-art CCA-based and low-rank learning methods.

Using machine learning models to predict the duration of the recovery of COVID-19 patients hospitalized in Fangcang shelter hospital during the Omicron BA. 2.2 pandemic.

Background: Factors that may influence the recovery of patients with confirmed SARS-CoV-2 infection hospitalized in the Fangcang shelter were explored, and machine learning models were constructed to predict the duration of recovery during the Omicron BA. 2.2 pandemic. Methods: A retrospective study was conducted at Hongqiao National Exhibition and Convention Center Fangcang shelter (Shanghai, China) from April 9, 2022 to April 25, 2022. The demographics, clinical data, inoculation history, and recovery information of the 13,162 enrolled participants were collected. A multivariable logistic regression model was used to identify independent factors associated with 7-day recovery and 14-day recovery. Machine learning algorithms (DT, SVM, RF, DT/AdaBoost, AdaBoost, SMOTEENN/DT, SMOTEENN/SVM, SMOTEENN/RF, SMOTEENN+DT/AdaBoost, and SMOTEENN/AdaBoost) were used to build models for predicting 7-day and 14-day recovery. Results: Of the 13,162 patients in the study, the median duration of recovery was 8 days (interquartile range IQR, 6-10 d), 41.31% recovered within 7 days, and 94.83% recovered within 14 days. Univariate analysis showed that the administrative region, age, cough medicine, comorbidities, diabetes, coronary artery disease (CAD), hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were associated with a duration of recovery within 7 days. Age, gender, vaccination dose, cough medicine, comorbidities, diabetes, CAD, hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were related to a duration of recovery within 14 days. In the multivariable analysis, the receipt of two doses of the vaccination vs. unvaccinated (OR = 1.118, 95% CI = 1.003-1.248; p = 0.045), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.114, 95% CI = 1.004-1.236; p = 0.043), diabetes (OR = 0.383, 95% CI = 0.194-0.749; p = 0.005), CAD (OR = 0.107, 95% CI = 0.016-0.421; p = 0.005), hypertension (OR = 0.371, 95% CI = 0.202-0.674; p = 0.001), and ratio of N/IC (OR = 3.686, 95% CI = 2.939-4.629; p < 0.001) were significantly and independently associated with a duration of recovery within 7 days. Gender (OR = 0.736, 95% CI = 0.63-0.861; p < 0.001), age (30-70) (OR = 0.738, 95% CI = 0.594-0.911; p < 0.001), age (>70) (OR = 0.38, 95% CI = 0292-0.494; p < 0.001), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.391, 95% CI = 1.12-1.719; p = 0.0033), cough medicine (OR = 1.509, 95% CI = 1.075-2.19; p = 0.023), and symptoms (OR = 1.619, 95% CI = 1.306-2.028; p < 0.001) were significantly and independently associated with a duration of recovery within 14 days. The SMOTEEN/RF algorithm performed best, with an accuracy of 90.32%, sensitivity of 92.22%, specificity of 88.31%, F1 score of 90.71%, and AUC of 89.75% for the 7-day recovery prediction; and an accuracy of 93.81%, sensitivity of 93.40%, specificity of 93.81%, F1 score of 93.42%, and AUC of 93.53% for the 14-day recovery prediction. Conclusion: Age and vaccination dose were factors robustly associated with accelerated recovery both on day 7 and day 14 from the onset of disease during the Omicron BA. 2.2 wave. The results suggest that the SMOTEEN/RF-based model could be used to predict the probability of 7-day and 14-day recovery from the Omicron variant of SARS-CoV-2 infection for COVID-19 prevention and control policy in other regions or countries. This may also help to generate external validation for the model.

Corrigendum: Mitochondrial genome contributes to the thermal adaptation of the oomycete Phytophthora infestans.

[This corrects the article DOI: 10.3389/fmicb.2022.928464.].

Mitochondrial Genome Contributes to the Thermal Adaptation of the Oomycete Phytophthora infestans.

As a vital element of climate change, elevated temperatures resulting from global warming present new challenges to natural and agricultural sustainability, such as ecological disease management. Mitochondria regulate the energy production of cells in responding to environmental fluctuation, but studying their contribution to the thermal adaptation of species is limited. This knowledge is needed to predict future disease epidemiology for ecology conservation and food security. Spatial distributions of the mitochondrial genome (mtDNA) in 405 Phytophthora infestans isolates originating from 15 locations were characterized. The contribution of MtDNA to thermal adaptation was evaluated by comparative analysis of mtDNA frequency and intrinsic growth rate, relative population differentiation in nuclear and mtDNA, and associations of mtDNA distribution with local geography climate conditions. Significant variation in frequency, intrinsic growth rate, and spatial distribution was detected in mtDNA. Population differentiation in mtDNA was significantly higher than that in the nuclear genome, and spatial distribution of mtDNA was strongly associated with local climatic conditions and geographic parameters, particularly air temperature, suggesting natural selection caused by a local temperature is the main driver of the adaptation. Dominant mtDNA grew faster than the less frequent mtDNA. Our results provide useful insights into the evolution of pathogens under global warming. Given its important role in biological functions and adaptation to local air temperature, mtDNA intervention has become an increasing necessity for future disease management. To secure ecological integrity and food production under global warming, a synergistic study on the interactive effect of changing temperature on various components of biological and ecological functions of mitochondria in an evolutionary frame is urgently needed.

DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system.

Examination of pathological images is the golden standard for diagnosing and screening many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released in recent years, resulting in significant improvements in computer-aided diagnosis (CAD) of related diseases. However, few existing works focus on the digestive system. We released two well-annotated benchmark datasets and organized challenges for the digestive-system pathological cell detection and tissue segmentation, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). This paper first introduces the two released datasets, i.e., signet ring cell detection and colonoscopy tissue segmentation, with the descriptions of data collection, annotation, and potential uses. We also report the set-up, evaluation metrics, and top-performing methods and results of two challenge tasks for cell detection and tissue segmentation. In particular, the challenge received 234 effective submissions from 32 participating teams, where top-performing teams developed advancing approaches and tools for the CAD of digestive pathology. To the best of our knowledge, these are the first released publicly available datasets with corresponding challenges for the digestive-system pathological detection and segmentation. The related datasets and results provide new opportunities for the research and application of digestive pathology.

Activation of Dopamine D2 Receptors Alleviates Neuronal Hyperexcitability in the Lateral Entorhinal Cortex via Inhibition of HCN Current in a Rat Model of Chronic Inflammatory Pain.

Functional changes in synaptic transmission from the lateral entorhinal cortex to the dentate gyrus (LEC-DG) are considered responsible for the chronification of pain. However, the underlying alterations in fan cells, which are the predominant neurons in the LEC that project to the DG, remain elusive. Here, we investigated possible mechanisms using a rat model of complete Freund's adjuvant (CFA)-induced inflammatory pain. We found a substantial increase in hyperpolarization-activated/cyclic nucleotide-gated currents (Ih), which led to the hyperexcitability of LEC fan cells of CFA slices. This phenomenon was attenuated in CFA slices by activating dopamine D2, but not D1, receptors. Chemogenetic activation of the ventral tegmental area -LEC projection had a D2 receptor-dependent analgesic effect. Intra-LEC microinjection of a D2 receptor agonist also suppressed CFA-induced behavioral hypersensitivity, and this effect was attenuated by pre-activation of the Ih. Our findings suggest that down-regulating the excitability of LEC fan cells through activation of the dopamine D2 receptor may be a strategy for treating chronic inflammatory pain.