Multiscale Motion-Aware and Spatial-Temporal-Channel Contextual Coding Network for Learned Video Compression

Recently, learned video compression has achieved exciting performance. Following the traditional hybrid prediction coding framework, most learned methods generally adopt the motion estimation motion compensation (MEMC) method to remove inter-frame redundancy. However, inaccurate motion vector (MV) usually lead to the distortion of reconstructed frame. In addition, most approaches ignore the spatial and channel redundancy. To solve above problems, we propose a motion-aware and spatial-temporal-channel contextual coding based video compression network (MASTC-VC), which learns the latent representation and uses variational autoencoders (VAEs) to capture the characteristics of intra-frame pixels and inter-frame motion. Specifically, we design a multiscale motion-aware module (MS-MAM) to estimate spatial-temporal-channel consistent motion vector by utilizing the multiscale motion prediction information in a coarse-to-fine way. On the top of it, we further propose a spatial-temporal-channel contextual module (STCCM), which explores the correlation of latent representation to reduce the bit consumption from spatial, temporal and channel aspects respectively. Comprehensive experiments show that our proposed MASTC-VC is surprior to previous state-of-the-art (SOTA) methods on three public benchmark datasets. More specifically, our method brings average 10.15\% BD-rate savings against H.265/HEVC (HM-16.20) in PSNR metric and average 23.93\% BD-rate savings against H.266/VVC (VTM-13.2) in MS-SSIM metric.Comment: 12pages,12 figure

TSST: A Benchmark and Evaluation Models for Text Speech-Style Transfer

Text style is highly abstract, as it encompasses various aspects of a speaker's characteristics, habits, logical thinking, and the content they express. However, previous text-style transfer tasks have primarily focused on data-driven approaches, lacking in-depth analysis and research from the perspectives of linguistics and cognitive science. In this paper, we introduce a novel task called Text Speech-Style Transfer (TSST). The main objective is to further explore topics related to human cognition, such as personality and emotion, based on the capabilities of existing LLMs. Considering the objective of our task and the distinctive characteristics of oral speech in real-life scenarios, we trained multi-dimension (i.e. filler words, vividness, interactivity, emotionality) evaluation models for the TSST and validated their correlation with human assessments. We thoroughly analyze the performance of several large language models (LLMs) and identify areas where further improvement is needed. Moreover, driven by our evaluation models, we have released a new corpus that improves the capabilities of LLMs in generating text with speech-style characteristics. In summary, we present the TSST task, a new benchmark for style transfer and emphasizing human-oriented evaluation, exploring and advancing the performance of current LLMs.Comment: Working in progres

MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. While general artificial intelligence is leveraged by developing increasingly large-scale models, there could be another branch to develop lightweight custom models that better serve certain domains, taking into account the high cost of training and deploying LLMs and the scarcity of resources. In this paper, we present MindLLM, a novel series of bilingual lightweight large language models, trained from scratch, alleviating such burdens by offering models with 1.3 billion and 3 billion parameters. A thorough account of experiences accrued during large model development is given, covering every step of the process, including data construction, model architecture, evaluation, and applications. Such insights are hopefully valuable for fellow academics and developers. MindLLM consistently matches or surpasses the performance of other open-source larger models on some public benchmarks. We also introduce an innovative instruction tuning framework tailored for smaller models to enhance their capabilities efficiently. Moreover, we explore the application of MindLLM in specific vertical domains such as law and finance, underscoring the agility and adaptability of our lightweight models.Comment: Working in progres

Salt stress perception and metabolic regulation network analysis of a marine probiotic Meyerozyma guilliermondii GXDK6

IntroductionExtremely salt-tolerant microorganisms play an important role in the development of functional metabolites or drug molecules.MethodsIn this work, the salt stress perception and metabolic regulation network of a marine probiotic Meyerozyma guilliermondii GXDK6 were investigated using integrative omics technology.ResultsResults indicated that GXDK6 could accept the salt stress signals from signal transduction proteins (e.g., phosphorelay intermediate protein YPD1), thereby contributing to regulating the differential expression of its relevant genes (e.g., CTT1, SOD) and proteins (e.g., catalase, superoxide dismutase) in response to salt stress, and increasing the salt-tolerant viability of GXDK6. Omics data also suggested that the transcription (e.g., SMD2), translation (e.g., MRPL1), and protein synthesis and processing (e.g., inner membrane protein OXA1) of upregulated RNAs may contribute to increasing the salt-tolerant survivability of GXDK6 by improving protein transport activity (e.g., Small nuclear ribonucleoprotein Sm D2), anti-apoptotic ability (e.g., 54S ribosomal protein L1), and antioxidant activity (e.g., superoxide dismutase). Moreover, up to 65.9% of the differentially expressed genes/proteins could stimulate GXDK6 to biosynthesize many salt tolerant-related metabolites (e.g., β-alanine, D-mannose) and drug molecules (e.g., deoxyspergualin, calcitriol), and were involved in the metabolic regulation of GXDK6 under high NaCl stress.DiscussionThis study provided new insights into the exploration of novel functional products and/or drugs from extremely salt-tolerant microorganisms.Graphical Abstrac

Hormonal regulation of ovarian bursa fluid in mice and involvement of aquaporins.

In rodent species, the ovary and the end of oviduct are encapsulated by a thin membrane called ovarian bursa. The biological functions of ovarian bursa remain unexplored despite its structural arrangement in facilitating oocytes transport into oviduct. In the present study, we observed a rapid fluid accumulation and reabsorption within the ovarian bursa after ovarian stimulation (PMSG-primed hCG injection), suggesting that the ovarian bursa might play an active role in regulating local fluid homeostasis around the timing of ovulation. We hypothesized that the aquaporin proteins, which are specialized channels for water transport, might be involved in this process. By screening the expression of aquaporin family members (Aqp1-9) in the ovarian tissue and isolated ovarian bursa (0, 1, 2 and 5 h after hCG injection), we found that AQP2 and AQP5 mRNA showed dynamic changes after hCG treatment, showing upregulation at 1-2 h followed by gradually decrease at 5 h, which is closely related with the intra-bursa fluid dynamics. Further immunofluorescence examinations of AQP2 and AQP5 in the ovarian bursa revealed that AQP2 is specifically localized in the outer layer (peritoneal side) while AQP5 localized in the inner layer (ovarian side) of the bursa, such cell type specific and spatial-temporal expressions of AQP2 and 5 support our hypothesis that they might be involved in efficient water transport through ovarian bursa under ovulation related hormonal regulation. The physiological significance of aquaporin-mediated water transport in the context of ovarian bursa still awaits further clarification

MicroRNA and transcription factor co-regulatory network analysis reveals miR-19 inhibits CYLD in T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy. The understanding of its gene expression regulation and molecular mechanisms still remains elusive. Started from experimentally verified T-ALL-related miRNAs and genes, we obtained 120 feed-forward loops (FFLs) among T-ALL-related genes, miRNAs and TFs through combining target prediction. Afterwards, a T-ALL miRNA and TF co-regulatory network was constructed, and its significance was tested by statistical methods. Four miRNAs in the miR-17–92 cluster and four important genes (CYLD, HOXA9, BCL2L11 and RUNX1) were found as hubs in the network. Particularly, we found that miR-19 was highly expressed in T-ALL patients and cell lines. Ectopic expression of miR-19 represses CYLD expression, while miR-19 inhibitor treatment induces CYLD protein expression and decreases NF-κB expression in the downstream signaling pathway. Thus, miR-19, CYLD and NF-κB form a regulatory FFL, which provides new clues for sustained activation of NF-κB in T-ALL. Taken together, we provided the first miRNA-TF co-regulatory network in T-ALL and proposed a model to demonstrate the roles of miR-19 and CYLD in the T-cell leukemogenesis. This study may provide potential therapeutic targets for T-ALL and shed light on combining bioinformatics with experiments in the research of complex diseases

Single-Cell RNA sequencing of leaf sheath cells reveals the mechanism of rice resistance to brown planthopper (Nilaparvata lugens)

The brown planthopper (BPH) (Nilaparvata lugens) sucks rice sap causing leaves to turn yellow and wither, often leading to reduced or zero yields. Rice co-evolved to resist damage by BPH. However, the molecular mechanisms, including the cells and tissues, involved in the resistance are still rarely reported. Single-cell sequencing technology allows us to analyze different cell types involved in BPH resistance. Here, using single-cell sequencing technology, we compared the response offered by the leaf sheaths of the susceptible (TN1) and resistant (YHY15) rice varieties to BPH (48 hours after infestation). We found that the 14,699 and 16,237 cells (identified via transcriptomics) in TN1 and YHY15 could be annotated using cell-specific marker genes into nine cell-type clusters. The two rice varieties showed significant differences in cell types (such as mestome sheath cells, guard cells, mesophyll cells, xylem cells, bulliform cells, and phloem cells) in the rice resistance mechanism to BPH. Further analysis revealed that although mesophyll, xylem, and phloem cells are involved in the BPH resistance response, the molecular mechanism used by each cell type is different. Mesophyll cell may regulate the expression of genes related to vanillin, capsaicin, and ROS production, phloem cell may regulate the cell wall extension related genes, and xylem cell may be involved in BPH resistance response by controlling the expression of chitin and pectin related genes. Thus, rice resistance to BPH is a complicated process involving multiple insect resistance factors. The results presented here will significantly promote the investigation of the molecular mechanisms underlying the resistance of rice to insects and accelerate the breeding of insect-resistant rice varieties