Development and validation of a prognostic model for assessing long COVID risk following Omicron wave-a large population-based cohort study.

BACKGROUND: Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model. METHODS: A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort. CONCLUSIONS: We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.

FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation.

Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled images are needed for the adaptation. Existing UDA methods are mainly based on image or feature alignment with adversarial training for regularization, and they are limited by insufficient supervision in the target domain. In this paper, we propose an enhanced Filtered Pseudo Label (FPL+)-based UDA method for 3D medical image segmentation. It first uses cross-domain data augmentation to translate labeled images in the source domain to a dual-domain training set consisting of a pseudo source-domain set and a pseudo target-domain set. To leverage the dual-domain augmented images to train a pseudo label generator, domain-specific batch normalization layers are used to deal with the domain shift while learn the domain-invariant structure features, generating high-quality pseudo labels for target-domain images. We then combine labeled source-domain images and target-domain images with pseudo labels to train a final segmentor, where image-level weighting based on uncertainty estimation and pixel-level weighting based on dual-domain consensus are proposed to mitigate the adverse effect of noisy pseudo labels. Experiments on three public multi-modal datasets for Vestibular Schwannoma, brain tumor and whole heart segmentation show that our method surpassed ten state-of-the-art UDA methods, and it even achieved better results than fully supervised learning in the target domain in some cases.

Orientin Promotes Antioxidant Capacity, Mitochondrial Biogenesis, and Fiber Transformation in Skeletal Muscles through the AMPK Pathway.

The sleep-breathing condition obstructive sleep apnea (OSA) is characterized by repetitive upper airway collapse, which can exacerbate oxidative stress and free radical generation, thereby detrimentally impacting both motor and sensory nerve function and inducing muscular damage. OSA development is promoted by increasing proportions of fast-twitch muscle fibers in the genioglossus. Orientin, a water-soluble dietary C-glycosyl flavonoid with antioxidant properties, increased the expression of slow myosin heavy chain (MyHC) and signaling factors associated with AMP-activated protein kinase (AMPK) activation both in vivo and in vitro. Inhibiting AMPK signaling diminished the effects of orientin on slow MyHC, fast MyHC, and Sirt1 expression. Overall, orientin enhanced type I muscle fibers in the genioglossus, enhanced antioxidant capacity, increased mitochondrial biogenesis through AMPK signaling, and ultimately improved fatigue resistance in C2C12 myotubes and mouse genioglossus. These findings suggest that orientin may contribute to upper airway stability in patients with OSA, potentially preventing airway collapse.

Regulatory dynamics of the higher-plant PSI-LHCI supercomplex during state transitions.

State transition is a fundamental light acclimation mechanism of photosynthetic organisms in response to the environmental light conditions. This process rebalances the excitation energy between photosystem I (PSI) and photosystem II through regulated reversible binding of the light-harvesting complex II (LHCII) to PSI. However, the structural reorganization of PSI-LHCI, the dynamic binding of LHCII, and the regulatory mechanisms underlying state transitions are less understood in higher plants. In this study, using cryoelectron microscopy we resolved the structures of PSI-LHCI in both state 1 (PSI-LHCI-ST1) and state 2 (PSI-LHCI-LHCII-ST2) from Arabidopsis thaliana. Combined genetic and functional analyses revealed novel contacts between Lhcb1 and PsaK that further enhanced the binding of the LHCII trimer to the PSI core with the known interactions between phosphorylated Lhcb2 and the PsaL/PsaH/PsaO subunits. Specifically, PsaO was absent in the PSI-LHCI-ST1 supercomplex but present in the PSI-LHCI-LHCII-ST2 supercomplex, in which the PsaL/PsaK/PsaA subunits undergo several conformational changes to strengthen the binding of PsaO in ST2. Furthermore, the PSI-LHCI module adopts a more compact configuration with shorter Mg-to-Mg distances between the chlorophylls, which may enhance the energy transfer efficiency from the peripheral antenna to the PSI core in ST2. Collectively, our work provides novel structural and functional insights into the mechanisms of light acclimation during state transitions in higher plants.

UPL-SFDA: Uncertainty-Aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation.

Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are usually unavailable when a trained model is deployed at a new center, Source-Free Domain Adaptation (SFDA) is appealing for data and annotation-efficient adaptation to the target domain. However, existing SFDA methods have a limited performance due to lack of sufficient supervision with source-domain images unavailable and target-domain images unlabeled. We propose a novel Uncertainty-aware Pseudo Label guided (UPL) SFDA method for medical image segmentation. Specifically, we propose Target Domain Growing (TDG) to enhance the diversity of predictions in the target domain by duplicating the pre-trained model's prediction head multiple times with perturbations. The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels. We also propose a Twice Forward pass Supervision (TFS) strategy that uses reliable pseudo labels obtained in one forward pass to supervise predictions in the next forward pass. The adaptation is further regularized by a mean prediction-based entropy minimization term that encourages confident and consistent results in different prediction heads. UPL-SFDA was validated with a multi-site heart MRI segmentation dataset, a cross-modality fetal brain segmentation dataset, and a 3D fetal tissue segmentation dataset. It improved the average Dice by 5.54, 5.01 and 6.89 percentage points for the three tasks compared with the baseline, respectively, and outperformed several state-of-the-art SFDA methods.

TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency.

Accurate segmentation of brain tumors from medical images is important for diagnosis and treatment planning, and it often requires multi-modal or contrast-enhanced images. However, in practice some modalities of a patient may be absent. Synthesizing the missing modality has a potential for filling this gap and achieving high segmentation performance. Existing methods often treat the synthesis and segmentation tasks separately or consider them jointly but without effective regularization of the complex joint model, leading to limited performance. We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain tumors end-to-end with high performance. First, we propose a dual-task-regularized generator that simultaneously obtains a synthesized target modality and a coarse segmentation, which leverages a tumor-aware synthesis loss with perceptibility regularization to minimize the high-level semantic domain gap between synthesized and real target modalities. Based on the synthesized image and the coarse segmentation, we further propose a dual-task segmentor that predicts a refined segmentation and error in the coarse segmentation simultaneously, where a consistency between these two predictions is introduced for regularization. Our TISS-Net was validated with two applications: synthesizing FLAIR images for whole glioma segmentation, and synthesizing contrast-enhanced T1 images for Vestibular Schwannoma segmentation. Experimental results showed that our TISS-Net largely improved the segmentation accuracy compared with direct segmentation from the available modalities, and it outperformed state-of-the-art image synthesis-based segmentation methods.

A novel one-to-multiple unsupervised domain adaptation framework for abdominal organ segmentation.

Abdominal multi-organ segmentation in multi-sequence magnetic resonance images (MRI) is of great significance in many clinical scenarios, e.g., MRI-oriented pre-operative treatment planning. Labeling multiple organs on a single MR sequence is a time-consuming and labor-intensive task, let alone manual labeling on multiple MR sequences. Training a model by one sequence and generalizing it to other domains is one way to reduce the burden of manual annotation, but the existence of domain gap often leads to poor generalization performance of such methods. Image translation-based unsupervised domain adaptation (UDA) is a common way to address this domain gap issue. However, existing methods focus less on keeping anatomical consistency and are limited by one-to-one domain adaptation, leading to low efficiency for adapting a model to multiple target domains. This work proposes a unified framework called OMUDA for one-to-multiple unsupervised domain-adaptive segmentation, where disentanglement between content and style is used to efficiently translate a source domain image into multiple target domains. Moreover, generator refactoring and style constraint are conducted in OMUDA for better maintaining cross-modality structural consistency and reducing domain aliasing. The average Dice Similarity Coefficients (DSCs) of OMUDA for multiple sequences and organs on the in-house test set, the AMOS22 dataset and the CHAOS dataset are 85.51%, 82.66% and 91.38%, respectively, which are slightly lower than those of CycleGAN(85.66% and 83.40%) in the first two data sets and slightly higher than CycleGAN(91.36%) in the last dataset. But compared with CycleGAN, OMUDA reduces floating-point calculations by about 87 percent in the training phase and about 30 percent in the inference stage respectively. The quantitative results in both segmentation performance and training efficiency demonstrate the usability of OMUDA in some practical scenes, such as the initial phase of product development.

Progress in the understanding of WRKY transcription factors in woody plants.

The WRKY transcription factor (TF) family, named for its iconic WRKY domain, is among the largest and most functionally diverse TF families in higher plants. WRKY TFs typically interact with the W-box of the target gene promoter to activate or inhibit the expression of downstream genes; these TFs are involved in the regulation of various physiological responses. Analyses of WRKY TFs in numerous woody plant species have revealed that WRKY family members are broadly involved in plant growth and development, as well as responses to biotic and abiotic stresses. Here, we review the origin, distribution, structure, and classification of WRKY TFs, along with their mechanisms of action, the regulatory networks in which they are involved, and their biological functions in woody plants. We consider methods currently used to investigate WRKY TFs in woody plants, discuss outstanding problems, and propose several new research directions. Our objective is to understand the current progress in this field and provide new perspectives to accelerate the pace of research that enable greater exploration of the biological functions of WRKY TFs.

The DELLA-ABI4-HY5 module integrates light and gibberellin signals to regulate hypocotyl elongation.

Plant growth is coordinately controlled by various environmental and hormonal signals, of which light and gibberellin (GA) signals are two critical factors with opposite effects on hypocotyl elongation. Although interactions between the light and GA signaling pathways have been studied extensively, the detailed regulatory mechanism of their direct crosstalk in hypocotyl elongation remains to be fully clarified. Previously, we reported that ABA INSENSITIVE 4 (ABI4) controls hypocotyl elongation through its regulation of cell-elongation-related genes, but whether it is also involved in GA signaling to promote hypocotyl elongation is unknown. In this study, we show that promotion of hypocotyl elongation by GA is dependent on ABI4 activation. DELLAs interact directly with ABI4 and inhibit its DNA-binding activity. In turn, ABI4 combined with ELONGATED HYPOCOTYL 5 (HY5), a key positive factor in light signaling, feedback regulates the expression of the GA2ox GA catabolism genes and thus modulates GA levels. Taken together, our results suggest that the DELLA-ABI4-HY5 module may serve as a molecular link that integrates GA and light signals to control hypocotyl elongation.

A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolution.

MOTIVATION: Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundamental step in reconstruction and analysis. The whole-brain optical microscopic imaging method enables the acquisition of whole-brain vessel images at the capillary resolution. Due to the massive amount of data and the complex vascular features generated by high-resolution whole-brain imaging, achieving rapid and accurate segmentation of whole-brain vasculature becomes a challenge. RESULTS: We introduce HP-VSP, a high-performance vessel segmentation pipeline based on deep learning. The pipeline consists of three processes: data blocking, block prediction, and block fusion. We used parallel computing to parallelize this pipeline to improve the efficiency of whole-brain vessel segmentation. We also designed a lightweight deep neural network based on multi-resolution vessel feature extraction to segment vessels at different scales throughout the brain accurately. We validated our approach on whole-brain vascular data from three transgenic mice collected by HD-fMOST. The results show that our proposed segmentation network achieves the state-of-the-art level under various evaluation metrics. In contrast, the parameters of the network are only 1% of those of similar networks. The established segmentation pipeline could be used on various computing platforms and complete the whole-brain vessel segmentation in 3 h. We also demonstrated that our pipeline could be applied to the vascular analysis. AVAILABILITY AND IMPLEMENTATION: The dataset is available at http://atlas.brainsmatics.org/a/li2301. The source code is freely available at https://github.com/visionlyx/HP-VSP.

Wing kinematics measurement and aerodynamics of hovering droneflies with wing damage.

In this study, we performed successive unilateral and bilateral wing shearing to simulate wing damage in droneflies (Eristalis tenax) and measured the wing kinematics using high-speed photography technology. Two different shearing types were considered in the artificial wing damage. The aerodynamic force and power consumption were obtained by numerical method. Our major findings are the following. Different shearing methods have little influence on the kinematics, forces and energy consumption of insects. Following wing damage, among the potential strategies to adjust the three Euler angles of the wing, adjusting stroke angle (φ) in isolation, or combing the adjustment of stroke angle (φ) with pitch angle (ψ), contributed most to the change in vertical force. The balance of horizontal thrust can be restored by the adjustment of deviation angle (θ) under the condition of unilateral wing damage. Considering zero elastic energy storage, the mass-specific power (P1) increases significantly following wing damage. However, the increase in mass-specific power with 100% elastic energy storage (P2) is very small. The extra cost of the unilateral wing damage is that the energy consumption of the damaged wing and intact wing is highly asymmetrical when zero elastic energy storage is considered. The insects may alleviate the problems of increasing power consumption and asymmetric power distribution by storage and reuse of the negative inertial work of the wing.

Genome-Wide Characterization of the Mitogen-Activated Protein Kinase Gene Family and Their Expression Patterns in Response to Drought and Colletotrichum Gloeosporioides in Walnut (Juglans regia).

Mitogen-activated protein kinases (MAPKs) are a family of Ser/Thr (serine/threonine) protein kinases that play very important roles in plant responses to biotic and abiotic stressors. However, the MAPK gene family in the important crop walnut (Juglans regia L.) has been less well studied compared with other species. We discovered 25 JrMAPK members in the Juglans genome in this study. The JrMAPK gene family was separated into four subfamilies based on phylogenetic analysis, and members of the same subgroup had similar motifs and exons/introns. A variety of cis-acting elements, mainly related to the light response, growth and development, stress response, and hormone responses, were detected in the JrMAPK gene promoters. Collinearity analysis showed that purification selection was the main driving force in JrMAPK gene evolution, and segmental and tandem duplications played key roles in the expansion of the JrMAPK gene family. The RNA-Seq (RNA Sequencing) results indicated that many of the JrMAPK genes were expressed in response to different levels of Colletotrichum gloeosporioides infection. JrMAPK1, JrMAPK3, JrMAPK4, JrMAPK5, JrMAPK6, JrMAPK7, JrMAPK9, JrMAPK11, JrMAPK12, JrMAPK13, JrMAPK17, JrMAPK19, JrMAPK20, and JrMAPK21 were upregulated at the transcriptional level in response to the drought stress treatment. The results of this study will help in further investigations of the evolutionary history and biological functions of the MAPK gene family in walnut.

CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation.

Domain Adaptation (DA) has recently been of strong interest in the medical imaging community. While a large variety of DA techniques have been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT1) MR imaging. However, there is growing interest in using non-contrast imaging sequences such as high-resolution T2 (hrT2) imaging. For this reason, we established an unsupervised cross-modality segmentation benchmark. The training dataset provides annotated ceT1 scans (N=105) and unpaired non-annotated hrT2 scans (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 scans as provided in the testing set (N=137). This problem is particularly challenging given the large intensity distribution gap across the modalities and the small volume of the structures. A total of 55 teams from 16 countries submitted predictions to the validation leaderboard. Among them, 16 teams from 9 different countries submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice score - VS: 88.4%; Cochleas: 85.7%) and close to full supervision (median Dice score - VS: 92.5%; Cochleas: 87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.

CHRNA1 induces sarcopenia through neuromuscular synaptic elimination.

Sarcopenia seriously affects the quality of life of the elderly, but its molecular mechanism is still unclear. Degeneration in muscle innervation is related to age-related movement disorders and muscle atrophy. The expression of CHRNA1 is increased in the skeletal muscle of the elderly, and in aging rodents. Therefore, we investigated whether CHRNA1 induces the occurrence and development of sarcopenia. Compared with the control group, local injection of AAV9-CHRNA1 into the hindlimb muscles decreased the percentage of muscle innervation. At the same time, the skeletal muscle mass decreased, as manifested by a decrease in the gastrocnemius mass index and the cross-sectional area of the muscle fibers. The function of skeletal muscle also decreased, which was manifested by decreases of compound muscle action potential and muscle contractility. Therefore, we concluded that upregulation of CHRNA1 can induce and aggravate sarcopenia.

Lysyl oxidase-like 2 inhibitor rescues D-galactose-induced skeletal muscle fibrosis.

Aging-related sarcopenia is currently the most common sarcopenia. The main manifestations are skeletal muscle atrophy, replacement of muscle fibers with fat and fibrous tissue. Excessive fibrosis can impair muscle regeneration and function. Lysyl oxidase-like 2 (LOXL2) has previously been reported to be involved in the development of various tissue fibrosis. Here, we investigated the effects of LOXL2 inhibitor on D-galactose (D-gal)-induced skeletal muscle fibroblast cells and mice. Our molecular and physiological studies show that treatment with LOXL2 inhibitor can alleviate senescence, fibrosis, and increased production of reactive oxygen species in fibroblasts caused by D-gal. These effects are related to the inhibition of the TGF-ß1/p38 MAPK pathway. Furthermore, in vivo, mice treatment with LOXL2 inhibitor reduced D-gal-induced skeletal muscle fibrosis, partially enhanced skeletal muscle mass and strength and reduced redox balance disorder. Taken together, these data indicate the possibility of using LOXL2 inhibitors to prevent aging-related sarcopenia, especially with significant fibrosis.

MICU3 regulates mitochondrial Ca2+-dependent antioxidant response in skeletal muscle aging.

Age-related loss of skeletal muscle mass and function, termed sarcopenia, could impair the quality of life in the elderly. The mechanisms involved in skeletal muscle aging are intricate and largely unknown. However, more and more evidence demonstrated that mitochondrial dysfunction and apoptosis also play an important role in skeletal muscle aging. Recent studies have shown that mitochondrial calcium uniporter (MCU)-mediated mitochondrial calcium affects skeletal muscle mass and function by affecting mitochondrial function. During aging, we observed downregulated expression of mitochondrial calcium uptake family member3 (MICU3) in skeletal muscle, a regulator of MCU, which resulted in a significant reduction in mitochondrial calcium uptake. However, the role of MICU3 in skeletal muscle aging remains poorly understood. Therefore, we investigated the effect of MICU3 on the skeletal muscle of aged mice and senescent C2C12 cells induced by D-gal. Downregulation of MICU3 was associated with decreased myogenesis but increased oxidative stress and apoptosis. Reconstitution of MICU3 enhanced antioxidants, prevented the accumulation of mitochondrial ROS, decreased apoptosis, and increased myogenesis. These findings indicate that MICU3 might promote mitochondrial Ca2+ homeostasis and function, attenuate oxidative stress and apoptosis, and restore skeletal muscle mass and function. Therefore, MICU3 may be a potential therapeutic target in skeletal muscle aging.

Elastodynamic model for flapping-wing micro aerial vehicle.

Lightweight design is key to high efficiency and long durability of micro air vehicle (MAV), while it will inevitably reduce the stiffness of the structures and affect the motion of the mechanism. In this study, an elastodynamic model for flapping-wing MAV (FMAV) is established to unveil the effect of elastic deformation of transmission mechanism on the flapping motion. Based on kineto-elastostatic analysis, an elastodynamic model of the transmission mechanism is built, which reveals that the inertial force of the transmission mechanism for typical FMAV is much smaller than the force transmitted. Thus, the inertial force can be ignored, and analytical formula between the deformation of transmission mechanism and the flapping angle is derived. Finite element method (FEM) simulations are conducted to validate the analytical formula, and the results show that the flapping angle obtained from the analytical formula matches well with FEM simulations. The proposed elastodynamic model and analytical formula will provide theoretical guidance for designing and optimizing FMAV with desired transmission mechanism and flapping motion.

Effect of passive wing pitching on flight control in a hovering model insect and flapping-wing micro air vehicle.

Passive wing pitching is a hypothesis in insect flight, and it is used widely by most flapping-wing micro air vehicles (FWMAVs). This study analyses the flight control of hovering model fruit fly and FWMAV with passive pitching wings. The longitudinal and lateral control derivatives are obtained by numerical simulation of the fluid dynamic equations coupled with the torsional spring passive pitching system. In contrast to active pitching wings, some of the control derivatives are remarkably changed by passive pitching wings, such asZΦ(vertical force produced by unit stroke amplitude),Zf(vertical force produced by unit flapping frequency), andMψ0(pitching moment produced by unit rest angle). For example, increasing flapping frequency does not lead to an evident increase in lift and may even have a reverse effect. Therefore, the flight control of FWMAV with passive pitching wings should be treated with caution. For wings pitching passively with a torsional spring at the root, the differential change of the angle of attack in the downstroke and upstroke (αdandαu) could be achieved by modulation of the rest angle alone; however, the equal change inαdandαumay require an otherwise manipulation of the elastic coefficient. Results in this study provide guidelines for the design of FWMAVs in evaluating the effects of different control inputs correctly and formulating a cost-effective control scheme.