Self-nonself discrimination is a common theme for all of the organisms in different evolutionary branches, which is also the most fundamental step for host immune protection. Plenty of pattern ...recognition receptors (PRRs) with great diversity have been identified from different organisms to recognize various pathogen-associated molecular patterns (PAMPs) in the last two decades, depicting a complicated scene of host-pathogen interaction. However, the detailed mechanism of the complicate PAMPs-PRRs interactions at the contacting interface between pathogens and hosts is still not well understood. All of the cells are coated by glycosylation complex and thick carbohydrates layer. The different polysaccharides in extracellular matrix of pathogen-host are important for nonself recognition of most organisms. Coincidentally, massive expansion of PRRs, majority of which contain recognition domains of Ig, leucine-rich repeat (LRR), C-type lectin (CTL), C1q and scavenger receptor (SR), have been annotated and identified in invertebrates by screening the available genomic sequence. The phylum Mollusca is one of the largest groups in the animal kingdom with abundant biodiversity providing plenty of solutions about pathogen recognition and immune protection, which might offer a suitable model to figure out the common rules of immune recognition mechanism. The present review summarizes the diverse PRRs and common elements of various PAMPs, especially focusing on the structural and functional characteristics of canonical carbohydrate recognition proteins and some novel proteins functioning in molluscan immune defense system, with the objective to provide new ideas about the immune recognition mechanisms.
Biomineralization refers to the dynamic physiological processes whereby living organisms elaborate mineralized tissues. The existence of extremely abundant molluscan species implies the diversity of ...mineralized tissues, since the majority of them (Conchifera) produce shells that vary in size and shape. Over the past decades, great progress has been made on the study of the cellular biology of shell biomineralization. The construction of the molluscan shell is the archetype of a biologically controlled mineralization which requires specialized cellular machinery. It has been so far demonstrated that the cells involved in shell formation come from two different tissues: 1) outer mantle epithelial cells (OME) secrete the organic matrix, among which shell matrix proteins (SMPs) determine mineralogical and crystallographic properties of shell; and 2) circulating hemocytes which take part in the deposition of intracellular biominerals and deliver them to the mineralization sites. Mounts of novel SMPs have been identified by using molecular biology techniques (gene cloning, in situ hybridization, immunohistochemistry et al.) coupled with high-throughput sequencing data (genome, proteome, secretome and transcriptome) , and their corresponding functions during shell formation have also been confirmed. The cellular activity of OME and hemocytes during shell formation are significantly increased during shell regeneration process. A potential cellular basis model for molluscan shell formation is proposed. The shell matrix proteins, mostly secreted from OME, and a few secreted from hemocytes or other organs, are either directly delivered to the mineralization site via exosome or classical secretory pathway, or firstly transported to the hemolymph, and then engulfed by hemocytes (mainly granulocytes), which will disintegrate and release shell proteins and CaCO3 crystals at mineralization front. Besides, OME and hemocytes may be involved in the nucleation and remodeling process of CaCO3 mineralization. These cells and cell products work co-operatively to produce an organo-mineral shell, which is composed of various biomineral ultra-structures and macromolecular organic components.
Subpixel object detection presents a significant challenge within the domain of hyperspectral image (HSI) processing, primarily due to the inherently limited spatial resolution of imaging ...spectrometers. For subpixel object detection, the dimensional extent of the object of interest is smaller than an individual pixel, which significantly diminishes the utility of spatial information pertaining to the object. Therefore, the efficacy of detection algorithms depends heavily on the spectral data inherent in the image. The detection of subpixel objects in hyperspectral imagery primarily relies on the suppression of the background and the enhancement of the object of interest. Hence, acquiring accurate background information from HSI images is a crucial step. In this study, an adaptive background endmember extraction for hyperspectral subpixel object detection is proposed. An adaptive scale constraint is incorporated into the background spectral endmember learning process to improve the adaptability of background endmember extraction, thus further enhancing the algorithm’s generalizability and applicability in diverse analytical scenarios. Experimental results demonstrate that the adaptive endmember extraction-based subpixel object detection algorithm consistently outperforms existing state-of-the-art algorithms in terms of detection efficacy on both simulated and real-world datasets.
Salmonella Typhimurium establishes systemic infection by replicating in host macrophages. Here we show that macrophages infected with S. Typhimurium exhibit upregulated glycolysis and decreased ...serine synthesis, leading to accumulation of glycolytic intermediates. The effects on serine synthesis are mediated by bacterial protein SopE2, a type III secretion system (T3SS) effector encoded in pathogenicity island SPI-1. The changes in host metabolism promote intracellular replication of S. Typhimurium via two mechanisms: decreased glucose levels lead to upregulated bacterial uptake of 2- and 3-phosphoglycerate and phosphoenolpyruvate (carbon sources), while increased pyruvate and lactate levels induce upregulation of another pathogenicity island, SPI-2, known to encode virulence factors. Pharmacological or genetic inhibition of host glycolysis, activation of host serine synthesis, or deletion of either the bacterial transport or signal sensor systems for those host glycolytic intermediates impairs S. Typhimurium replication or virulence.
The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using ...short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa.
Due to the sparsity of hyperspectral images, the dictionary learning framework has been applied in hyperspectral endmember extraction. However, current endmember extraction methods based on ...dictionary learning are not robust enough in noisy environments. To solve this problem, this paper proposes a novel endmember extraction approach based on online robust dictionary learning, termed EEORDL. Because of the large scale of the hyperspectral image (HSI) data, an online scheme is introduced to reduce the computational time of dictionary learning. In the proposed algorithm, a new form of the objective function is introduced into the dictionary learning process to improve the robustness for noisy HSI data. The experimental results, conducted with both synthetic and real-world hyperspectral datasets, illustrate that the proposed EEORDL outperforms the state-of-the-art approaches under different signal-to-noise ratio (SNR) conditions, especially for high-level noise.
Abstract
Air quality has greatly improved in China owing to the strict control policy enforced during the last decade. This study investigated the impact of particulate pollution control on aerosol ...hygroscopicity and cloud condensation nuclei (CCN) activity in North China based on several data sources. The mass concentration of particles with an aerodynamic diameter smaller than 2.5
µ
m (PM
2.5
) decreased by one third from the summer of 2014 to the summer of 2020 in Xinzhou (XZ). The mass fractions of aerosol chemical components in PM
2.5
also clearly changed, showing an increase in hydrophilic inorganics and a decrease in hydrophobic organics and black carbon from 2014 to 2020. Measurements of the particle number size distribution in XZ indicate that the occurrence frequency of new particle formation (NPF) events decreased significantly from 2014 to 2020, leading to a reduction in the generation of daytime ultrafine particles. The weakened NPF and increasing influence of morning and evening peak emissions modified the diurnal variations of the number concentration of condensation nuclei (
N
CN
) and CCN (
N
CCN
). The aerosol activation ratio was always higher in the summer of 2014 than in the summer of 2020. These results demonstrate that particulate pollution control can decrease
N
CN
and
N
CCN
but enhance aerosol hygroscopicity and activation ability.
Lysine-specific demethylase 1 (LSD1) is an essential epigenetic regulator of hematopoietic differentiation, which can specifically mono-methylate H3K4 (H3K4me1) and di-methylate H3K4 (H3K4me2) as a ...transcriptional corepressor. Previous reports have been suggested that it participated in hematopoiesis and embryonic development process. Here, a conserved LSD1 (
LSD1) with a SWIRM domain and an amino oxidase (AO) domain was identified from the Pacific oyster
.
We conducted a comprehensive analysis by various means to verify the function of
LSD1 in hematopoietic process, including quantitative real-time PCR (qRT-PCR) analysis, western blot analysis, immunofluorescence assay, RNA interference (RNAi) and flow cytometry.
The qRT-PCR analysis revealed that the transcripts of
LSD1 were widely expressed in oyster tissues with the highest level in the mantle. And the transcripts of
LSD1 were ubiquitously expressed during larval development with the highest expression level at the early D-veliger larvae stage. In hemocytes after
stimulation, the transcripts of
LSD1 were significantly downregulated at 3, 6, 24, and 48 h with the lowest level at 3 h compared to that in the Seawater group (SW group). Immunocytochemical analysis showed that
LSD1 was mainly distributed in the nucleus of hemocytes. After the
LSD1 was knocked down by RNAi, the H3K4me1 and H3K4me2 methylation level significantly increased in hemocyte protein. Besides, the percentage of hemocytes with EdU-positive signals in the total circulating hemocytes significantly increased after
stimulation. After RNAi of
LSD1, the expression of potential granulocyte markers
SOX11 and
AATase as well as oyster cytokine-like factor
Astakine were increased significantly in mRNA level, while the transcripts of potential agranulocyte marker
CD9 was decreased significantly after
stimulation.
The above results demonstrated that
LSD1 was a conserved member of lysine demethylate enzymes that regulate hemocyte proliferation during the hematopoietic process.
•A machine learning strategy is proposed for predicting cardiac function based on peripheral pulse waves.•Two high-quality datasets are created for health- and cardiovascular disease-subject ...groups.•The model enables the prediction of three cardiac function parameters fast and accurately.•The model is validated through consistency analysis and comparison with clinical measurements.
Pulse wave has been considered as a message carrier in the cardiovascular system (CVS), capable of inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Clarification and prediction of cardiovascular function by means of powerful feature-abstraction capability of machine learning method based on pulse wave is of great clinical significance in health monitoring and CVDs diagnosis, which remains poorly studied.
Here we propose a machine learning (ML)-based strategy aiming to achieve a fast and accurate prediction of three cardiovascular function parameters based on a 412-subject database of pulse waves. We proposed and optimized an ML-based model with multi-layered, fully connected network while building up two high-quality pulse wave datasets comprising a healthy-subject group and a CVD-subject group to predict arterial compliance (AC), total peripheral resistance (TPR), and stroke volume (SV), which are essential messengers in monitoring CVS conditions.
Our ML model is validated through consistency analysis of the ML-predicted three cardiovascular function parameters with clinical measurements and is proven through error analysis to have capability of achieving a high-accurate prediction on TPR and SV for both healthy-subject group (accuracy: 85.3%, 86.9%) and CVD-subject group (accuracy: 88.3%, 89.2%).
The independent sample t-test proved that our subject groups could represent the typical physiological characteristics of the corresponding population. While we have more subjects in our datasets rather than previous studies after strict data screening, the proposed ML-based strategy needs to be further improved to achieve a disease-specific prediction of heart failure and other CVDs through training with larger datasets and clinical measurements.
Our study points to the feasibility and potential of the pulse wave-based prediction of physiological and pathological CVS conditions in clinical application.
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