Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning

Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise. However, a more common scenario in the real world is the presence of both open-set and closed-set noise. Existing methods typically identify and handle these two types of label noise separately by designing a specific strategy for each type. However, in many real-world scenarios, it would be challenging to identify open-set examples, especially when the dataset has been severely corrupted. Unlike the previous works, we explore how models behave when faced with open-set examples, and find that \emph{a part of open-set examples gradually get integrated into certain known classes}, which is beneficial for the separation among known classes. Motivated by the phenomenon, we propose a novel two-step contrastive learning method CECL (Class Expansion Contrastive Learning) which aims to deal with both types of label noise by exploiting the useful information of open-set examples. Specifically, we incorporate some open-set examples into closed-set classes to enhance performance while treating others as delimiters to improve representative ability. Extensive experiments on synthetic and real-world datasets with diverse label noise demonstrate the effectiveness of CECL

Spatio-Temporal Patterns and Impacts of Sediment Variations in Downstream of the Three Gorges Dam on the Yangtze River, China

Spanning the Yangtze River of China, the Three Gorges Dam (TGD) has received considerable concern worldwide with its potential impacts on the downstream side of the dam. This work investigated the spatio-temporal variations of suspended sediment concentration (SSC) at the downstream section of Yichang-to-Chenglingji from 2002 to 2015. A random forest model was developed to estimate SSC using MODIS ground reflectance products, and the spatio-temporal distributions of SSC were retrieved with this model to investigate the characteristics of water-silt variation. Our results revealed that, relatively, SSC before 2003 was evenly distributed in the downstream Yangtze River, while this spatial distribution pattern changed ce 2003 when the dam started storing water. Temporally, the SSC demonstrated a W-shaped curve of seasonal variation as one peak occurred in September and two troughs in March and November, and showed a significantly decreasing trend after three-stage impoundment. After official operation of the TGD in 2009, the SSC was reduced by over 40% than before 2003. Spatially, the most significant changes occurred in the upper Jingjiang section, where the SSC dropped by 45%. During all stages of impoundment, the water impoundment to 135 m in 2003 had the most significant impact on suspended sediment. The decreased SSC has led to emerging risks of bank failure, aggravated erosion of water front and aggressive down-cutting erosion along the downstream of the dam, as well as other ecological and environmental issues that require urgent attention by the government

NMI inhibits cancer stem cell traits by downregulating hTERT in breast cancer.

N-myc and STAT interactor (NMI) has been proved to bind to different transcription factors to regulate a variety of signaling mechanisms including DNA damage, cell cycle and epithelial-mesenchymal transition. However, the role of NMI in the regulation of cancer stem cells (CSCs) remains poorly understood. In this study, we investigated the regulation of NMI on CSCs traits in breast cancer and uncovered the underlying molecular mechanisms. We found that NMI was lowly expressed in breast cancer stem cells (BCSCs)-enriched populations. Knockdown of NMI promoted CSCs traits while its overexpression inhibited CSCs traits, including the expression of CSC-related markers, the number of CD44+CD24- cell populations and the ability of mammospheres formation. We also found that NMI-mediated regulation of BCSCs traits was at least partially realized through the modulation of hTERT signaling. NMI knockdown upregulated hTERT expression while its overexpression downregulated hTERT in breast cancer cells, and the changes in CSCs traits and cell invasion ability mediated by NMI were rescued by hTERT. The in vivo study also validated that NMI knockdown promoted breast cancer growth by upregulating hTERT signaling in a mouse model. Moreover, further analyses for the clinical samples demonstrated that NMI expression was negatively correlated with hTERT expression and the low NMI/high hTERT expression was associated with the worse status of clinical TNM stages in breast cancer patients. Furthermore, we demonstrated that the interaction of YY1 protein with NMI and its involvement in NMI-mediated transcriptional regulation of hTERT in breast cancer cells. Collectively, our results provide new insights into understanding the regulatory mechanism of CSCs and suggest that the NMI-YY1-hTERT signaling axis may be a potential therapeutic target for breast cancers

Unveiling the immune symphony: decoding colorectal cancer metastasis through immune interactions

Colorectal cancer (CRC), known for its high metastatic potential, remains a leading cause of cancer-related death. This review emphasizes the critical role of immune responses in CRC metastasis, focusing on the interaction between immune cells and tumor microenvironment. We explore how immune cells, through cytokines, chemokines, and growth factors, contribute to the CRC metastasis cascade, underlining the tumor microenvironment’s role in shaping immune responses. The review addresses CRC’s immune evasion tactics, especially the upregulation of checkpoint inhibitors like PD-1 and CTLA-4, highlighting their potential as therapeutic targets. We also examine advanced immunotherapies, including checkpoint inhibitors and immune cell transplantation, to modify immune responses and enhance treatment outcomes in CRC metastasis. Overall, our analysis offers insights into the interplay between immune molecules and the tumor environment, crucial for developing new treatments to control CRC metastasis and improve patient prognosis, with a specific focus on overcoming immune evasion, a key aspect of this special issue

Selective Generation of Dopaminergic Precursors from Mouse Fibroblasts by Direct Lineage Conversion.

Degeneration of midbrain dopaminergic (DA) neurons is a key pathological event of Parkinson\u27s disease (PD). Limited adult dopaminergic neurogenesis has led to novel therapeutic strategies such as transplantation of dopaminergic precursors (DPs). However, this strategy is currently restrained by a lack of cell source, the tendency for the DPs to become a glial-restricted state, and the tumor formation after transplantation. Here, we demonstrate the direct conversion of mouse fibroblasts into induced DPs (iDPs) by ectopic expression of Brn2, Sox2 and Foxa2. Besides expression with neural progenitor markers and midbrain genes including Corin, Otx2 and Lmx1a, the iDPs were restricted to dopaminergic neuronal lineage upon differentiation. After transplantation into MPTP-lesioned mice, iDPs differentiated into DA neurons, functionally alleviated the motor deficits, and reduced the loss of striatal DA neuronal axonal termini. Importantly, no iDPs-derived astrocytes and neoplasia were detected in mouse brains after transplantation. We propose that the iDPs from direct reprogramming provides a safe and efficient cell source for PD treatment

OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis

Ultrasound (US) imaging is indispensable in clinical practice. To diagnose certain diseases, sonographers must observe corresponding dynamic anatomic structures to gather comprehensive information. However, the limited availability of specific US video cases causes teaching difficulties in identifying corresponding diseases, which potentially impacts the detection rate of such cases. The synthesis of US videos may represent a promising solution to this issue. Nevertheless, it is challenging to accurately animate the intricate motion of dynamic anatomic structures while preserving image fidelity. To address this, we present a novel online feature-decoupling framework called OnUVS for high-fidelity US video synthesis. Our highlights can be summarized by four aspects. First, we introduced anatomic information into keypoint learning through a weakly-supervised training strategy, resulting in improved preservation of anatomical integrity and motion while minimizing the labeling burden. Second, to better preserve the integrity and textural information of US images, we implemented a dual-decoder that decouples the content and textural features in the generator. Third, we adopted a multiple-feature discriminator to extract a comprehensive range of visual cues, thereby enhancing the sharpness and fine details of the generated videos. Fourth, we constrained the motion trajectories of keypoints during online learning to enhance the fluidity of generated videos. Our validation and user studies on in-house echocardiographic and pelvic floor US videos showed that OnUVS synthesizes US videos with high fidelity.Comment: 14 pages, 13 figures and 6 table

Direct Conversion of Mouse Astrocytes Into Neural Progenitor Cells and Specific Lineages of Neurons

Background: Cell replacement therapy has been envisioned as a promising treatment for neurodegenerative diseases. Due to the ethical concerns of ESCs-derived neural progenitor cells (NPCs) and tumorigenic potential of iPSCs, reprogramming of somatic cells directly into multipotent NPCs has emerged as a preferred approach for cell transplantation. Methods: Mouse astrocytes were reprogrammed into NPCs by the overexpression of transcription factors (TFs) Foxg1, Sox2, and Brn2. The generation of subtypes of neurons was directed by the force expression of cell-type specific TFs Lhx8 or Foxa2/Lmx1a. Results: Astrocyte-derived induced NPCs (AiNPCs) share high similarities, including the expression of NPC-specific genes, DNA methylation patterns, the ability to proliferate and differentiate, with the wild type NPCs. The AiNPCs are committed to the forebrain identity and predominantly differentiated into glutamatergic and GABAergic neuronal subtypes. Interestingly, additional overexpression of TFs Lhx8 and Foxa2/Lmx1a in AiNPCs promoted cholinergic and dopaminergic neuronal differentiation, respectively. Conclusions: Our studies suggest that astrocytes can be converted into AiNPCs and lineage-committed AiNPCs can acquire differentiation potential of other lineages through forced expression of specific TFs. Understanding the impact of the TF sets on the reprogramming and differentiation into specific lineages of neurons will provide valuable strategies for astrocyte-based cell therapy in neurodegenerative diseases

Soybean Breeding on Seed Composition Trait

Soybean is a most important crop providing edible oil and plant protein source for human beings, in addition to animal feed because of high protein and oil content. This review summarized the progresses in the QTL mapping, candidate gene cloning and functional analysis and also the regulation of soybean oil and seed storage protein accumulation. Furthermore, as soybean genome has been sequenced and released, prospects of multiple omics and advanced biotechnology should be combined and applied for further refine research and high-quality breeding

Dynamic changes in fecal microbiota in donkey foals during weaning: From pre-weaning to post-weaning

IntroductionA better understanding of the microbiota community in donkey foals during the weaning transition is a prerequisite to optimize gut function and improve feed efficiency. The objective of the present study was to investigate the dynamic changes in fecal microbiota in donkey foals from pre-to post-weaning period.MethodsA total of 27 fecal samples of donkey foals were collected in the rectum before morning feeding at pre-weaning (30 days of age, PreW group, n = 9), dur-weaning (100 days of age, DurW group, n = 9) and post-weaning (170 days of age, PostW group, n = 9) period. The 16S rRNA amplicon sequencing were employed to indicate the microbial changes during the weaning period.ResultsIn the present study, the cessation of breastfeeding gradually and weaning onto plant-based feeds increased the microbial diversity and richness, with a higher Shannon, Ace, Chao and Sobs index in DurW and PostW than in PreW (p < 0.05). The predominant bacterial phyla in donkey foal feces were Firmicutes (>50.5%) and Bacteroidota (>29.5%), and the predominant anaerobic fungi and archaea were Neocallimastigomycota and Euryarchaeota. The cellulolytic related bacteria including phylum Firmicutes, Spirochaetota and Fibrobacterota and genus norank_f_F082, Treponema, NK4A214_group, Lachnospiraceae_AC2044_group and Streptococcus were increased from pre-to post-weaning donkey foals (p < 0.05). Meanwhile, the functions related to the fatty acid biosynthesis, carbohydrate metabolism and amino acid biosynthesis were significantly enriched in the fecal microbiome in the DurW and PostW donkeys. Furthermore, the present study provided the first direct evidence that the initial colonization and establishment of anaerobic fungi and archaea in donkey foals began prior to weaning. The relative abundance of Orpinomyces were the highest in DurW donkey foals among the three groups (p < 0.01). In terms of archaea, the abundance of Methanobrevibacter were higher in PreW than in DurW and PostW (p < 0.01), but the abundance of Methanocorpusculum were significantly increased in DurW and PostW compared to PreW donkey foals (p < 0.01).DiscussionAltogether, the current study contributes to a comprehensive understanding of the development of the microbiota community in donkey foals from pre-to post-weaning period, which may eventually result in an improvement of the digestion and feed efficiency in donkeys

Segment Anything Model for Medical Images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It designed a novel promotable segmentation task, ensuring zero-shot image segmentation using the pre-trained model via two main modes including automatic everything and manual prompt. SAM has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging due to the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. SAM has achieved impressive results on various natural image segmentation tasks. Meanwhile, zero-shot and efficient MIS can well reduce the annotation time and boost the development of medical image analysis. Hence, SAM seems to be a potential tool and its performance on large medical datasets should be further validated. We collected and sorted 52 open-source datasets, and build a large medical segmentation dataset with 16 modalities, 68 objects, and 553K slices. We conducted a comprehensive analysis of different SAM testing strategies on the so-called COSMOS 553K dataset. Extensive experiments validate that SAM performs better with manual hints like points and boxes for object perception in medical images, leading to better performance in prompt mode compared to everything mode. Additionally, SAM shows remarkable performance in some specific objects and modalities, but is imperfect or even totally fails in other situations. Finally, we analyze the influence of different factors (e.g., the Fourier-based boundary complexity and size of the segmented objects) on SAM's segmentation performance. Extensive experiments validate that SAM's zero-shot segmentation capability is not sufficient to ensure its direct application to the MIS.Comment: 23 pages, 14 figures, 12 table