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.

Two distinct regulatory pathways govern Cct2-Atg8 binding in the process of solid aggrephagy.

CCT2 serves as an aggrephagy receptor that plays a crucial role in the clearance of solid aggregates, yet the underlying molecular mechanisms by which CCT2 regulates solid aggrephagy are not fully understood. Here we report that the binding of Cct2 to Atg8 is governed by two distinct regulatory mechanisms: Atg1-mediated Cct2 phosphorylation and the interaction between Cct2 and Atg11. Atg1 phosphorylates Cct2 at Ser412 and Ser470, and disruption of these phosphorylation sites impairs solid aggrephagy by hindering Cct2-Atg8 binding. Additionally, we observe that Atg11, an adaptor protein involved in selective autophagy, directly associates with Cct2 through its CC4 domain. Deficiency in this interaction significantly weakens the association of Cct2 with Atg8. The requirement of Atg1-mediated Cct2 phosphorylation and of Atg11 for CCT2-LC3C binding and subsequent aggrephagy is conserved in mammalian cells. These findings provide insights into the crucial roles of Atg1-mediated Cct2 phosphorylation and Atg11-Cct2 binding as key mediators governing the interaction between Cct2 and Atg8 during the process of solid aggrephagy.

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.

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.

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.

Energy deprivation-induced autophagy and aggrephagy: insights from yeast and mammals. / ä»Žé µæ¯å’Œå“ºä¹³åŠ¨ç‰©ç»†èƒžè§£æžèšé›†ä½“è‡ªå™¬å’Œèƒ½é‡åŒ®ä¹è¯±å¯¼çš„è‡ªå™¬.

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.

Ontogenetic, Sexual, and Monthly Niche Segregation of Sepia esculenta in the Northern East China Sea Revealed by Stable Carbon and Nitrogen Isotopes.

Golden cuttlefish play a significant role in the food web of the East and Yellow Seas and are a valuable fishery resource in Chinese coastal waters. Samples of golden cuttlefish were obtained from the northern East China Sea between September 2021 and March 2022, and stable isotope methods were utilized in this study to examine the variations in the forage ecology of golden cuttlefish. Our findings reveal dynamic shifts in carbon and nitrogen stable isotopes (δ13C and δ15N), highlighting intricate foraging strategies tailored to growth and environmental changes. A notable trend emerges: an initial growth-linked rise in δ13C and δ15N enrichment, followed by seasonal fluctuations mirroring seasonal food availability. The ontogenetic niche evolution displays striking habitat shifts and trophic level escalation in small mantle length stages, transitioning to niche overlap and subtle trophic shifts later on. Sex-specific differences emerge, with females occupying higher trophic levels than males in most samples. This comprehensive study underscores the complexity and adaptability of golden cuttlefish feeding ecology, inviting further inquiry into their intricate relationships within the marine ecosystem.

ConDiff-rPPG: Robust Remote Physiological Measurement to Heterogeneous Occlusions.

Remote photoplethysmography (rPPG) is a contactless technique that facilitates the measurement of physiological signals and cardiac activities through facial video recordings. This approach holds tremendous potential for various applications. However, existing rPPG methods often did not account for different types of occlusions that commonly occur in real-world scenarios, such as temporary movement or actions of humans in videos or dust on camera. The failure to address these occlusions can compromise the accuracy of rPPG algorithms. To address this issue, we proposed a novel Condiff-rPPG to improve the robustness of rPPG measurement facing various occlusions. First, we compressed the damaged face video into a spatio-temporal representation with several types of masks. Second, the diffusion model was designed to recover the missing information with observed values as a condition. Moreover, a novel low-rank decomposition regularization was proposed to eliminate background noise and maximize informative features. ConDiff-rPPG ensured optimization goal consistency during the training process. Through extensive experiments, including intra- and cross-dataset evaluations, as well as ablation tests, we demonstrated the robustness and generalization ability of our proposed model.

Evaluating stability and bioactivity of Rehmannia-derived nanovesicles during storage.

Plant-derived nanovesicles (PDNVs) have garnered growing attention in the biomedical field owing to their abundance in plant-derived ribonucleic acids (RNA), proteins, lipids and metabolites. The question about the preservation of PDNVs is a crucial and unavoidable concern in both experiments' settings and their potential clinical application. The objective of this research was to examine the impact of varying storage temperatures on the stability and bioactivity of Rehmannia-derived nanovesicles (RDNVs). The results showed that RDNVs aggregated after 2 weeks of storage period at 4 °C, and the particle size of some RDNVs gradually increased with time, along with the increase of solution potential. After 2 months of storage, all RDNVs exhibited varying levels of aggregation irrespective of storage temperature. The bioactivities of nanovesicles under different temperature storage conditions revealed a gradual decline in cell proliferation inhibition bioactivity over time, significantly lower than that of freshly prepared RDNVs. In contrast, the preservation of anti-migratory activity in RDNVs was found to be more effective when subjected to rapid freezing in liquid nitrogen followed by storage at - 80 °C, as opposed to direct storage at - 80 °C. These findings suggest that temperature alone may not be sufficient in safeguarding the activity and stability of RDNVs, highlighting the necessity for the development of novel protective agents for PDNVs.

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.

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.

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.

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.

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.

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.

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.