OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning

Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure. To achieve this, we propose OmniSeg3D, an omniversal segmentation method aims for segmenting anything in 3D all at once. The key insight is to lift multi-view inconsistent 2D segmentations into a consistent 3D feature field through a hierarchical contrastive learning framework, which is accomplished by two steps. Firstly, we design a novel hierarchical representation based on category-agnostic 2D segmentations to model the multi-level relationship among pixels. Secondly, image features rendered from the 3D feature field are clustered at different levels, which can be further drawn closer or pushed apart according to the hierarchical relationship between different levels. In tackling the challenges posed by inconsistent 2D segmentations, this framework yields a global consistent 3D feature field, which further enables hierarchical segmentation, multi-object selection, and global discretization. Extensive experiments demonstrate the effectiveness of our method on high-quality 3D segmentation and accurate hierarchical structure understanding. A graphical user interface further facilitates flexible interaction for omniversal 3D segmentation

The HINT1 tumor suppressor regulates both γ-H2AX and ATM in response to DNA damage

Hint1 is a haploinsufficient tumor suppressor gene and the underlying molecular mechanisms for its tumor suppressor function are unknown. In this study we demonstrate that HINT1 participates in ionizing radiation (IR)–induced DNA damage responses. In response to IR, HINT1 is recruited to IR-induced foci (IRIF) and associates with γ-H2AX and ATM. HINT1 deficiency does not affect the formation of γ-H2AX foci; however, it impairs the removal of γ-H2AX foci after DNA damage and this is associated with impaired acetylation of γ-H2AX. HINT1 deficiency also impairs acetylation of ATM and activation of ATM and its downstream effectors, and retards DNA repair, in response to IR. HINT1-deficient cells exhibit resistance to IR-induced apoptosis and several types of chromosomal abnormalities. Our findings suggest that the tumor suppressor function of HINT1 is caused by, at least in part, its normal role in enhancing cellular responses to DNA damage by regulating the functions of both γ-H2AX and ATM

Explainable Multimodal Emotion Reasoning

Multimodal emotion recognition is an active research topic in artificial intelligence. Its primary objective is to integrate multi-modalities (such as acoustic, visual, and lexical clues) to identify human emotional states. Current works generally assume accurate emotion labels for benchmark datasets and focus on developing more effective architectures. But due to the inherent subjectivity of emotions, existing datasets often lack high annotation consistency, resulting in potentially inaccurate labels. Consequently, models built on these datasets may struggle to meet the demands of practical applications. To address this issue, it is crucial to enhance the reliability of emotion annotations. In this paper, we propose a novel task called ``\textbf{Explainable Multimodal Emotion Reasoning (EMER)}''. In contrast to previous works that primarily focus on predicting emotions, EMER takes a step further by providing explanations for these predictions. The prediction is considered correct as long as the reasoning process behind the predicted emotion is plausible. This paper presents our initial efforts on EMER, where we introduce a benchmark dataset, establish baseline models, and define evaluation metrics. Meanwhile, we observe the necessity of integrating multi-faceted capabilities to deal with EMER. Therefore, we propose the first multimodal large language model (LLM) in affective computing, called \textbf{AffectGPT}. We aim to tackle the long-standing challenge of label ambiguity and chart a path toward more reliable techniques. Furthermore, EMER offers an opportunity to evaluate the audio-video-text understanding capabilities of recent multimodal LLM. To facilitate further research, we make the code and data available at: https://github.com/zeroQiaoba/AffectGPT

Pseudohypoadrenalism, a subclinical cortisol metabolism disorder in hyperuricemia

BackgroundHyperuricemia is a known risk factor of lipid metabolism disorder. However, the mechanisms have not been fully understood.MethodsThe serum samples from hyperuricemia subjects were used to analyze the correlation between serum uric acid and clinical characteristics. Hyperuricemia mice induced by potassium oxonate (PO) and adenine were used to explore glucocorticoid metabolism.ResultsIn hyperuricemia patients, the levels of serum uric acid were positively correlated with the levels of γ-glutamyltransferase, associated with a cortisol metabolism disorder. In hyperuricemia state, the adrenal glands failed to respond to adrenocorticotropic hormone properly, leading to low cortisol, but not corticosterone production, and decreased mRNA levels of aldosterone synthase, 11β-hydroxylase, and 3β-hydroxysteroid dehydrogenase 1, three key enzymes for cortisol synthesis. The expression of both hepatic 5α-reductase and renal 11β-hydroxysteroid dehydrogenase 2 was significantly reduced, which led to low cortisol clearance. We denominated this cortisol metabolism disorder in hyperuricemia as pseudohypoadrenalism (PHAL).ConclusionPHAL increased exposure to the bioavailable cortisol in the liver, leading to local amplification of the biological action of corticosteroids. Unregulated biosynthesis pathway of bile acid expanded bile acid pool, and further aggravated cholestatic liver injury

MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning

Over the past few decades, multimodal emotion recognition has made remarkable progress with the development of deep learning. However, existing technologies are difficult to meet the demand for practical applications. To improve the robustness, we launch a Multimodal Emotion Recognition Challenge (MER 2023) to motivate global researchers to build innovative technologies that can further accelerate and foster research. For this year's challenge, we present three distinct sub-challenges: (1) MER-MULTI, in which participants recognize both discrete and dimensional emotions; (2) MER-NOISE, in which noise is added to test videos for modality robustness evaluation; (3) MER-SEMI, which provides large amounts of unlabeled samples for semi-supervised learning. In this paper, we test a variety of multimodal features and provide a competitive baseline for each sub-challenge. Our system achieves 77.57% on the F1 score and 0.82 on the mean squared error (MSE) for MER-MULTI, 69.82% on the F1 score and 1.12 on MSE for MER-NOISE, and 86.75% on the F1 score for MER-SEMI, respectively. Baseline code is available at https://github.com/zeroQiaoba/MER2023-Baseline

The Pro12Ala Polymorphism of PPAR- γ

Peroxisome proliferator-activated receptor-γ (PPAR-γ) is a ligand-binding nuclear receptor, and its activation plays a prominent role in regulating the inflammatory response. Therefore, PPAR-γ has been suggested as a candidate gene for sepsis. In the present study, we investigated the association between the Pro12Ala polymorphism of PPAR-γ and sepsis in a Han Chinese population. A total of 308 patients with sepsis and 345 healthy controls were enrolled in this study. Genotyping was performed using the polymerase chain reaction-ligation detection reaction (PCR-LDR) method. No significant differences were detected in the allele and genotype distributions of the PPAR-γ Pro12Ala SNP between septic patients and controls (P=0.622 for genotype; P=0.629 for allele). However, stratification by subtypes (sepsis, septic shock, and severe sepsis) revealed a statistically significant difference in the frequency of the Ala allele and Ala-carrier genotype between the patients with the sepsis subtype and the healthy controls (P=0.014 for allele and P=0.012, for genotype). Moreover, significant differences were found in the frequency of the Ala allele and genotype between the sepsis survivors and nonsurvivors (all P=0.002). In the survivors, the PPAR-γ Pro12Ala genotype was significantly associated with decreased disease severity and recovery time (all P<0.001). Thus, genetic polymorphism is thought to play a role in the development and outcome of sepsis

UHRF1 is required for basal stem cell proliferation in response to airway injury

Cellular senescence is a cell fate characterized by an irreversible cell cycle arrest, but the molecular mechanism underlying this senescence hallmark remains poorly understood. Through an unbiased search for novel senescence regulators in airway basal cells, we discovered that the epigenetic regulator ubiquitin-like with PHD and ring finger domain-containing protein 1 (UHRF1) is critical for regulating cell cycle progression. Upon injury, basal cells in the mouse airway rapidly induce the expression of UHRF1 in order to stimulate stem cell proliferation and tissue repair. Targeted depletion of Uhrf1 specifically in airway basal cells causes a profound defect in cell cycle progression. Consistently, cultured primary human basal cells lacking UHRF1 do not exhibit cell death or differentiation phenotypes but undergo a spontaneous program of senescence. Mechanistically, UHRF1 loss induces G1 cell cycle arrest by abrogating DNA replication factory formation as evidenced by loss of proliferating cell nuclear antigen (PCNA) puncta and an inability to enter the first cell cycle. This proliferation defect is partially mediated by the p15 pathway. Overall, our study provides the first evidence of an indispensable role of UHRF1 in somatic stem cells proliferation during the process of airway regeneration

Cyclohexyl-Substituted Anthracene Derivatives for High Thermal Stability Organic Semiconductors

A novel p-type organic semiconductor with high thermal stability is developed by simply incorporating cyclohexyl substituted aryl groups into the 2,6-position of anthracene, namely 2,6-di(4-cyclohexylphenyl)anthracene (DcHPA), and a similar compound with linear alkyl chain, 2,6-di(4-n-hexylphenyl)anthracene (DnHPA), is also studied for comparison. DcHPA shows sublimation temperature around 360°C, and thin film field-effect transistors of DcHPA could maintain half of the original mobility value when heated up to 150°C. Corresponding DnHPA has sublimation temperature of 310°C and the performance of its thin film devices decreases by about 50% when heated to 80°C. The impressing thermal stability of the cyclohexyl substitution compounds might provide guidelines for developing organic electronic materials with high thermal stability