Ca2+-triggered Atg11-Bmh1/2-Snf1 complex assembly initiates autophagy upon glucose starvation.

Autophagy is essential for maintaining glucose homeostasis. However, the mechanism by which cells sense and respond to glucose starvation to induce autophagy remains incomplete. Here, we show that calcium serves as a fundamental triggering signal that connects environmental sensing to the formation of the autophagy initiation complex during glucose starvation. Mechanistically, glucose starvation instigates the release of vacuolar calcium into the cytoplasm, thus triggering the activation of Rck2 kinase. In turn, Rck2-mediated Atg11 phosphorylation enhances Atg11 interactions with Bmh1/2 bound to the Snf1-Sip1-Snf4 complex, leading to recruitment of vacuolar membrane-localized Snf1 to the PAS and subsequent Atg1 activation, thereby initiating autophagy. We also identified Glc7, a protein phosphatase-1, as a critical regulator of the association between Bmh1/2 and the Snf1 complex. We thus propose that calcium-triggered Atg11-Bmh1/2-Snf1 complex assembly initiates autophagy by controlling Snf1-mediated Atg1 activation in response to glucose starvation.

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Autophagy plays a crucial role in maintaining cellular homeostasis in response to various stimuli. Compared to research on nutrient deprivation-induced autophagy, the understanding of the molecular mechanisms and physiological/pathological significance of autophagy triggered by energy deprivation remains limited. A primary focus of our lab is to elucidate how cells sense energy deprivation and initiate autophagy. Using the model organisms Saccharomyces cerevisiae and mammalian cells, we found that cellular reactive oxygen species (ROS), DNA damage sensor Mec1, and mitochondrial aerobic respiration play essential roles in the autophagy induced by energy deprivation. This review aims to provide a concise overview of these research findings.

Hierarchical Style-Aware Domain Generalization for Remote Physiological Measurement.

The utilization of remote photoplethysmography (rPPG) technology has gained attention in recent years due to its ability to extract blood volume pulse (BVP) from facial videos, making it accessible for various applications such as health monitoring and emotional analysis. However, the BVP signal is susceptible to complex environmental changes or individual differences, causing existing methods to struggle in generalizing for unseen domains. This article addresses the domain shift problem in rPPG measurement and shows that most domain generalization methods fail to work well in this problem due to ambiguous instance-specific differences. To address this, the article proposes a novel approach called Hierarchical Style-aware Representation Disentangling (HSRD). HSRD improves generalization capacity by separating domain-invariant and instance-specific feature space during training, which increases the robustness of out-of-distribution samples during inference. This work presents state-of-the-art performance against several methods in both cross and intra-dataset settings.

TOR-mediated Ypt1 phosphorylation regulates autophagy initiation complex assembly.

The regulation of autophagy initiation is a key step in autophagosome biogenesis. However, our understanding of the molecular mechanisms underlying the stepwise assembly of ATG proteins during this process remains incomplete. The Rab GTPase Ypt1/Rab1 is recognized as an essential autophagy regulator. Here, we identify Atg23 and Atg17 as binding partners of Ypt1, with their direct interaction proving crucial for the stepwise assembly of autophagy initiation complexes. Disruption of Ypt1-Atg23 binding results in significantly reduced Atg9 interactions with Atg11, Atg13, and Atg17, thus preventing the recruitment of Atg9 vesicles to the phagophore assembly site (PAS). Likewise, Ypt1-Atg17 binding contributes to the PAS recruitment of Ypt1 and Atg1. Importantly, we found that Ypt1 is phosphorylated by TOR at the Ser174 residue. Converting this residue to alanine blocks Ypt1 phosphorylation by TOR and enhances autophagy. Conversely, the Ypt1S174D phosphorylation mimic impairs both PAS recruitment and activation of Atg1, thus inhibiting subsequent autophagy. Thus, we propose TOR-mediated Ypt1 as a multifunctional assembly factor that controls autophagy initiation via its regulation of the stepwise assembly of ATG proteins.

Adaptive Domain Generalization Via Online Disagreement Minimization.

Deep neural networks suffer from significant performance deterioration when there exists distribution shift between deployment and training. Domain Generalization (DG) aims to safely transfer a model to unseen target domains by only relying on a set of source domains. Although various DG approaches have been proposed, a recent study named DomainBed (Gulrajani and Lopez-Paz, 2020), reveals that most of them do not beat simple empirical risk minimization (ERM). To this end, we propose a general framework that is orthogonal to existing DG algorithms and could improve their performance consistently. Unlike previous DG works that stake on a static source model to be hopefully a universal one, our proposed AdaODM adaptively modifies the source model at test time for different target domains. Specifically, we create multiple domain-specific classifiers upon a shared domain-generic feature extractor. The feature extractor and classifiers are trained in an adversarial way, where the feature extractor embeds the input samples into a domain-invariant space, and the multiple classifiers capture the distinct decision boundaries that each of them relates to a specific source domain. During testing, distribution differences between target and source domains could be effectively measured by leveraging prediction disagreement among source classifiers. By fine-tuning source models to minimize the disagreement at test time, target-domain features are well aligned to the invariant feature space. We verify AdaODM on two popular DG methods, namely ERM and CORAL, and four DG benchmarks, namely VLCS, PACS, OfficeHome, and TerraIncognita. The results show AdaODM stably improves the generalization capacity on unseen domains and achieves state-of-the-art performance.

Peer Attachment Style Moderates the Effect of Mood on Creativity.

The present study investigated the moderating role of peer attachment style in the relationship between mood and creativity. An experiment was conducted with a sample of 267 undergraduate students (Mage = 19.85, range = 17-24 years). First, participants' peer attachment style was measured, following which positive, neutral, or negative mood was induced; subsequently, two creative tasks were conducted. A MANOVA revealed significant interactions between peer attachment and mood. Specifically, for secure participants, creativity was significantly higher in the positive mood state compared to the neutral and negative mood states; for insecure participants, the effect of positive mood was not pronounced. Moreover, negative mood exerted a significant beneficial effect on the originality dimension for participants with an anxious-ambivalent peer attachment style; they showed higher creativity in the negative mood state than in the neutral or positive mood states. In general, peer attachment style moderated the relationship between mood and creativity; specifically, positive mood was beneficial to creativity among secure persons, and negative mood was beneficial to creativity among anxious-ambivalent persons.

Atg1-mediated Atg11 phosphorylation is required for selective autophagy by regulating its association with receptor proteins.

Atg11 is an adaptor protein required for the induction of selective autophagy via receptor binding. However, our understanding of the molecular mechanisms by which it regulates selective autophagy remains incomplete. Here, we show that Atg11 is phosphorylated by Atg1. Rapamycin treatment or starvation conditions induced slower electrophoretic mobility of Atg11 in an Atg1 kinase activity-dependent manner. Through in vitro kinase assays combined with mutagenesis, we determined that Atg1 phosphorylates S949, S1057, and S1064 residues in CC4 domain of Atg11. Replacing the three residues with alanine suppressed the cleavage of selective autophagy substrates for the cytoplasm-to-vacuole targeting (Cvt) pathway, mitophagy, reticulophagy, and pexophagy. The Atg11 mutant was defective in binding to related selective autophagy receptors. These results demonstrate a previously unknown function of Atg1 in regulation of selective autophagy via Atg11 phosphorylation.Abbreviations: AMPK: AMP-activated protein kinase; ATG: autophagy-related; Cvt: cytoplasm-to-vacuole targeting; FUNDC1: FUN14 domain-containing protein 1; GFP: green fluorescent protein; MTOR: mechanistic target of rapamycin kinase; PAS: phagophore assembly site; PIK3C3: phosphatidylinositol 3-kinase catalytic subunit type 3; PRKAC/PKA: protein kinase cAMP-activated; SD-G: glucose starvation; SD-N: nitrogen starvation; ULK1: unc-51 like autophagy activating kinase 1; λ-PPase: lambda protein phosphatase.

Mec1 regulates PAS recruitment of Atg13 via direct binding with Atg13 during glucose starvation-induced autophagy.

Mec1 is a DNA damage sensor, which performs an essential role in the DNA damage response pathway and glucose starvation-induced autophagy. However, the functions of Mec1 in autophagy remain unclear. In response to glucose starvation, Mec1 forms puncta, which are recruited to mitochondria through the adaptor protein Ggc1. Here, we show that Mec1 puncta also contact the phagophore assembly site (PAS) via direct binding with Atg13. Functional analysis of the Atg13-Mec1 interaction revealed two previously unrecognized protein regions, the Mec1-Binding Region (MBR) on Atg13 and the Atg13-Binding Region (ABR) on Mec1, which mediate their mutual association under glucose starvation conditions. Disruption of the MBR or ABR impairs the recruitment of Mec1 puncta and Atg13 to the PAS, consequently blocking glucose starvation-induced autophagy. Additionally, the MBR and ABR regions are also crucial for DNA damage-induced autophagy. We thus propose that Mec1 regulates glucose starvation-induced autophagy by controlling Atg13 recruitment to the PAS.

A Comprehensive Pan-Cancer Analysis of the Tumorigenic Effect of Leucine-Zipper-Like Transcription Regulator (LZTR1) in Human Cancer.

The elucidation of the action site, mechanism of Leucine-Zipper-like Transcription Regulator-1 (LZTR1) and its relationship with RAS-MAPK signaling pathway attracts more and more scholars to focus on the researches of LZTR1 and its role in tumorigenesis. However, there was no pan-cancer analysis between LZTR1 and human tumors reported before. Therefore, we are the first to investigate the potential oncogenic roles of LZTR1 across all tumor types based on the datasets of TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus). LZTR1 plays a double-edged role in tumor development and prognosis. We found that the high expression of LZTR1 brings better outcomes in esophageal carcinoma (ESCA) and head and neck squamous cell carcinoma (HNSC) but brings worth outcomes in uveal melanoma (UVM), adrenocortical carcinoma (ACC), liver hepatocellular carcinoma (LIHC), and prostate adenocarcinoma (PRAD). Moreover, the expression of LZTR1 also strongly associated with pathological in ACC and bladder urothelial carcinoma (BLCA). We also found that the LZTR1 expression was associated with some immune cell infiltration including endothelial cells, regulatory T cells (Tregs), T cell CD8+, natural killer cells (NK cell), macrophages, neutrophil granulocyte, and cancer-associated fibroblasts in different cancers. Missense mutation in LZTR1 was detected in most cancers from TCGA datasets. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Body (GO) method was used to explain the pathogenesis of LZTR1. Our pan-cancer study provides a relatively comprehensive understanding of the carcinogenic role of LZTR1 in human tumors.

Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.

There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (R = 0.94, P = 3.6 × 10-25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person's body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.

PointINS: Point-Based Instance Segmentation.

In this paper, we explore the mask representation in instance segmentation with Point-of-Interest (PoI) features. Differentiating multiple potential instances within a single PoI feature is challenging, because learning a high-dimensional mask feature for each instance using vanilla convolution demands a heavy computing burden. To address this challenge, we propose an instance-aware convolution. It decomposes this mask representation learning task into two tractable modules as instance-aware weights and instance-agnostic features. The former is to parametrize convolution for producing mask features corresponding to different instances, improving mask learning efficiency by avoiding employing several independent convolutions. Meanwhile, the latter serves as mask templates in a single point. Together, instance-aware mask features are computed by convolving the template with dynamic weights, used for the mask prediction. Along with instance-aware convolution, we propose PointINS, a simple and practical instance segmentation approach, building upon dense one-stage detectors. Through extensive experiments, we evaluated the effectiveness of our framework built upon RetinaNet and FCOS. PointINS in ResNet101 backbone achieves a 38.3 mask mean average precision (mAP) on COCO dataset, outperforming existing point-based methods by a large margin. It gives a comparable performance to the region-based Mask R-CNN K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask R-CNN," in Proc. IEEE Int. Conf. Comput. Vis., 2017, pp. 2980-2988 with faster inference.

Text-Guided Human Image Manipulation via Image-Text Shared Space.

Text is a new way to guide human image manipulation. Albeit natural and flexible, text usually suffers from inaccuracy in spatial description, ambiguity in the description of appearance, and incompleteness. We in this paper address these issues. To overcome inaccuracy, we use structured information (e.g., poses) to help identify correct location to manipulate, by disentangling the control of appearance and spatial structure. Moreover, we learn the image-text shared space with derived disentanglement to improve accuracy and quality of manipulation, by separating relevant and irrelevant editing directions for the textual instructions in this space. Our model generates a series of manipulation results by moving source images in this space with different degrees of editing strength. Thus, to reduce the ambiguity in text, our model generates sequential output for manual selection. In addition, we propose an efficient pseudo-label loss to enhance editing performance when the text is incomplete. We evaluate our method on various datasets and show its precision and interactiveness to manipulate human images.

Homomorphic Interpolation Network for Unpaired Image-to-Image Translation.

Generative adversarial networks have achieved great success in unpaired image-to-image translation. Cycle consistency, a key component for this task, allows modeling the relationship between two distinct domains without paired data. In this paper, we propose an alternative framework, as an extension of latent space interpolation, to consider the intermediate region between two domains during translation. It is based on the assumption that in a flat and smooth latent space, there exist many paths that connect two sample points. Properly selecting paths makes it possible to change only certain image attributes, which is useful for generating intermediate images between the two domains. With this idea, our framework includes an encoder, an interpolator and a decoder. The encoder maps natural images to a convex and smooth latent space where interpolation is applicable. The interpolator controls the interpolation path so that desired intermediate samples can be obtained. Finally, the decoder inverts interpolated features back to pixel space. We also show that by choosing different reference images and interpolation paths, this framework can be applied to multi-domain and multi-modal translation. Extensive experiments manifest that our framework achieves superior results and is flexible for various tasks.

LncRNA FGF14-AS2 represses growth of prostate carcinoma cells via modulating miR-96-5p/AJAP1 axis.

OBJECTIVE: This investigation devoted to lncRNA FGF14 antisense RNA 2 (FGF14-AS2) in prostate carcinoma progression. METHODS: The levels of lncRNA FGF14-AS2, miR-96-5p, and Adherens junction-associated protein-1 (AJAP1) in prostate carcinoma were tested by Western blot and qRT-PCR. How these two genes interacted was confirmed by RNA immunoprecipitation and dualluciferase gene methods. The effect of FGF14-AS2/miR-96-5p/AJAP1 axis in prostate carcinoma progression was determined by MTT, Transwell, and nude mice tumor model. RESULTS: FGF14-AS2 was a downregulated lncRNA in prostate carcinoma tissue and cells. FGF14-AS2 could restrain miR-96-5p expression while miR-96-5p hampered AJAP1. FGF14-AS2 could effectively decrease the biological behaviors of prostate carcinoma cells, while knock-down of FGF14-AS2 triggered opposite results. Moreover, miR-96-5p mimic presented a cancer promoter role in prostate carcinoma cells. AJAP1 expression level could affect levels of proteins related to epithelial-mesenchymal transition. In vivo experiment suggested that overexpressing FGF14-AS2 could reverse the promotion of silenced AJAP1 on prostate carcinoma cell metastasis, thus to inhibit tumor growth. CONCLUSION: lncRNA FGF14-AS2 was a downregulated lncRNA in prostate carcinoma and influenced cell proliferation and metastasis. The influence relied on modulating miR-96-5p and its target gene AJAP1.

Study of oral microorganisms contributing to non-carious cervical lesions via bacterial interaction and pH regulation.

There is a lack of evidence about the relationship between microorganisms and non-carious cervical lesions (NCCLs) due to limited technologies. A group of 78 patients was enrolled for microbial 16S rRNA sequencing of dental plaques on normal and defective cervical surfaces. Parallel data from 39 patients were analysed with paired t tests, and Fusobacteriales exhibited significantly less distribution on NCCLs than on normal surfaces. As a result, Fusobacterium nucleatum, the most common oral bacterial strain belonging to the order Fusobacteriales, was selected for further research. From a scanning electron microscopy (SEM) scan, the tooth surface with Fusobacterium nucleatum and Streptococcus mutans culture was more intact than that without Fusobacterium nucleatum. Furthermore, the calcium contents in groups with Fusobacterium nucleatum were significantly higher than that without it. In further mechanistic research, Fusobacterium nucleatum was demonstrated to adhere to and disturb other organisms as well as producing alkaline secretions to neutralize the deleterious acidic environment, protecting the tooth structure. In conclusion, microorganisms and NCCLs were confirmed directly related through adherent bacterial interactions and pH regulation. The research provides a new perspective and experimental evidence for the relation between microorganisms and NCCLs, which guides clinical treatment and preventive dentistry in the future.

The predictive value of platelet to lymphocyte ratio and D-dimer to fibrinogen ratio combined with WELLS score on lower extremity deep vein thrombosis in young patients with cerebral hemorrhage.

OBJECTIVE: To study the predictive effect on YCH patients complicated with LEDVT by PLR and DFR combined with WELLS score. MATERIALS AND METHODS: A total of 109 patients with YCH were selected as the research subjects. Patients with combined LEDVT were in the thrombosis group (33 cases), and without LEDVT in the non-thrombosis group (76 cases). Wells score was used to evaluate the vascular of the lower extremities. The PLR and DFR were calculated. The diagnostic value of PLR and DFR combined with the Wells score was evaluated by the AUC, sensitivity, specificity, and other indicators in the ROC. RESULTS: The values of PLR, DFR, and Wells score in the thrombus group were 149.20 ± 52.17, 118.46 ± 8.37, and 2.67 ± 0.48, and that of the non-thrombotic group were 95.27 ± 29.48, 75.28 ± 10.16, and 0.72 ± 0.34, respectively. The differences were statistically significant. ROC results showed good diagnosis power of PLR (sensitivity 86.35%, specificity 75.18%, AUC 0.702.), DFR (sensitivity 88.57%, specificity 79.21%, AUC 0.786.), and the Wells score (sensitivity 90.17%, specificity 81.06%, AUC 0.889.). The combined application of the Wells score, PLR, and DFR for the occurrence of LEDVT had a sensitivity of 97.65%, a specificity of 92.43%, a missed diagnosis rate of 2.35%, and a misdiagnosis rate of 7.57%. The area under the ROC curve was 0.951, which was higher than using these variables independently. CONCLUSIONS: PLR and DFR combined with Wells score have high specificity for predicting LEDVT in YCH patients with low missed diagnosis and low misdiagnosis rates. They are worthy of popularization and application.

Is it coincidental or correlative between reversible splenial lesion syndrome and atrial septal defect?: A case report.

RATIONALE: Reversible splenial lesion syndrome (RESLES) is a recently identified clinico-radiological syndrome, the etiology is miscellaneous. Atrial septal defect (ASD) as an underlying etiology for RESLES has not been reported. We first report a rare case of RESLES associated with ASD. The clinical, radiological, and ultrasonic profiles were presented and the pathophysiological mechanism was analyzed. PATIENT CONCERNS: A 23-year-old man presented with headache, drowsiness, occasional paraphasia, and paroxysmal dry cough. Brain magnetic resonance imaging (MRI) on admission showed an ovoid isolated lesion in the splenium of corpus callosum, which exhibited hyperintensity on diffusion-weighted imaging and hypointensity on apparent diffusion coefficient, and completely disappeared on the follow-up MRI 14 days later. ASD was found by transthoracic echocardiography, Right-to-left shunts were detected on color Doppler of transesophageal echocardiography, and microemboli were captured by transcranial Doppler ultrasound. DIAGNOSES: According to his clinical history and imaging results, we confirmed the diagnosis of RESLES associated with ASD. INTERVENTIONS: The patient was treated by oral aspirin and lopidogrel sulfate to inhibit platelet aggregation. In addition, oral nimodipine to suppress vasoconstriction. OUTCOMES: After 14 days treatment, all the symptoms presenting on admission resolved completely. Subsequently, a repair surgery of ASD under thoracoscopy was successfully performed. LESSONS: To our knowledge, this is the first reported case of ASD may be an underlying etiology for RESLES and need require an etiotropic treatment.

Mitochondrial Fusion Machinery Specifically Involved in Energy Deprivation-Induced Autophagy.

Mitochondria are highly dynamic organelles, which can form a network in cells through fusion, fission, and tubulation. Its morphology is closely related to the function of mitochondria. The damaged mitochondria can be removed by mitophagy. However, the relationship between mitochondrial morphology and non-selective autophagy is not fully understood. We found that mitochondrial fusion machinery, not fission or tubulation machinery, is essential for energy deprivation-induced autophagy. In response to glucose starvation, deletion of mitochondrial fusion proteins severely impaired the association of Atg1/ULK1 with Atg13, and then affected the recruitment of Atg1 and other autophagic proteins to PAS (phagophore assembly site). Furthermore, the deletion of fusion proteins blocks mitochondrial respiration, the binding of Snf1-Mec1, the phosphorylation of Mec1 by Snf1, and the dissociation of Mec1 from mitochondria under prolonged starvation. We propose that mitochondrial fusion machinery regulates energy deprivation-induced autophagy through maintaining mitochondrial respiration.

Person Re-Identification by Camera Correlation Aware Feature Augmentation.

The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coR relation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.