CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data.

In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.

DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome.

Silencers are noncoding DNA sequence fragments located on the genome that suppress gene expression. The variation of silencers in specific cells is closely related to gene expression and cancer development. Computational approaches that exclusively rely on DNA sequence information for silencer identification fail to account for the cell specificity of silencers, resulting in diminished accuracy. Despite the discovery of several transcription factors and epigenetic modifications associated with silencers on the genome, there is still no definitive biological signal or combination thereof to fully characterize silencers, posing challenges in selecting suitable biological signals for their identification. Therefore, we propose a sophisticated deep learning framework called DeepICSH, which is based on multiple biological data sources. Specifically, DeepICSH leverages a deep convolutional neural network to automatically capture biologically relevant signal combinations strongly associated with silencers, originating from a diverse array of biological signals. Furthermore, the utilization of attention mechanisms facilitates the scoring and visualization of these signal combinations, whereas the employment of skip connections facilitates the fusion of multilevel sequence features and signal combinations, thereby empowering the accurate identification of silencers within specific cells. Extensive experiments on HepG2 and K562 cell line data sets demonstrate that DeepICSH outperforms state-of-the-art methods in silencer identification. Notably, we introduce for the first time a deep learning framework based on multi-omics data for classifying strong and weak silencers, achieving favorable performance. In conclusion, DeepICSH shows great promise for advancing the study and analysis of silencers in complex diseases. The source code is available at https://github.com/lyli1013/DeepICSH.

GTAD: a graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data.

With the emergence of spatial transcriptome sequencing (ST-seq), research now heavily relies on the joint analysis of ST-seq and single-cell RNA sequencing (scRNA-seq) data to precisely identify cell spatial composition in tissues. However, common methods for combining these datasets often merge data from multiple cells to generate pseudo-ST data, overlooking topological relationships and failing to represent spatial arrangements accurately. We introduce GTAD, a method utilizing the Graph Attention Network for deconvolution of integrated scRNA-seq and ST-seq data. GTAD effectively captures cell spatial relationships and topological structures within tissues using a graph-based approach, enhancing cell-type identification and our understanding of complex tissue cellular landscapes. By integrating scRNA-seq and ST data into a unified graph structure, GTAD outperforms traditional 'pseudo-ST' methods, providing robust and information-rich results. GTAD performs exceptionally well with synthesized spatial data and accurately identifies cell spatial composition in tissues like the mouse cerebral cortex, cerebellum, developing human heart and pancreatic ductal carcinoma. GTAD holds the potential to enhance our understanding of tissue microenvironments and cellular diversity in complex bio-logical systems. The source code is available at https://github.com/zzhjs/GTAD.

DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.

MOTIVATION: Enhancers are vital cis-regulatory elements that regulate gene expression. Enhancer RNAs (eRNAs), a type of long noncoding RNAs, are transcribed from enhancer regions in the genome. The tissue-specific expression of eRNAs is crucial in the regulation of gene expression and cancer development. The methods that identify eRNAs based solely on genomic sequence data have high error rates because they do not account for tissue specificity. Specific histone modifications associated with eRNAs offer valuable information for their identification. However, identification of eRNAs using histone modification data requires the use of both RNA-seq and histone modification data. Unfortunately, many public datasets contain only one of these components, which impedes the accurate identification of eRNAs. RESULTS: We introduce DeepITEH, a deep learning framework that leverages RNA-seq data and histone modification data from multiple samples of the same tissue to enhance the accuracy of identifying eRNAs. Specifically, deepITEH initially categorizes eRNAs into two classes, namely, regularly expressed eRNAs and accidental eRNAs, using histone modification data from multiple samples of the same tissue. Thereafter, it integrates both sequence and histone modification features to identify eRNAs in specific tissues. To evaluate the performance of DeepITEH, we compared it with four existing state-of-the-art enhancer prediction methods, SeqPose, iEnhancer-RD, LSTMAtt, and FRL, on four normal tissues and four cancer tissues. Remarkably, seven of these tissues demonstrated a substantially improved specific eRNA prediction performance with DeepITEH, when compared with other methods. Our findings suggest that DeepITEH can effectively predict potential eRNAs on the human genome, providing insights for studying the eRNA function in cancer. AVAILABILITY AND IMPLEMENTATION: The source code and dataset of DeepITEH have been uploaded to https://github.com/lyli1013/DeepITEH.

Salvia miltiorrhiza polysaccharide promotes the health of crayfish (Procambarus clarkii) by promoting hemocyte phagocytosis, protecting hepatopancreas and enhancing intestinal barrier function.

Plant polysaccharides as immunomodulators are considered one of the effective measures to reduce antibiotic therapy in aquaculture. The immunomodulatory function of Salvia miltiorrhiza polysaccharides (SMP) has been demonstrated and begun to be applied in vertebrates, but its potential effect on crustaceans is unclear. In this study, crayfish (Procambarus clarkii) was fed with 0 %, 0.3 %, 0.7 %, 1.1 %, and 1.5 % SMP for 4 weeks to investigate the effects of SMP on hemocytes phagocytosis, hepatopancreatic function, and intestinal barrier function. The results revealed that hemocyte phagocytic activity was increased in all SMP groups. During the process of hemocytes phagocytic recognition and formation of phagosomes and phagolysosomes, the mRNA expression levels of mas, hem, rab3, ctsb, and lamp-1 were up-regulated mainly in the 0.3 % SMP group. During the clearance phase of phagocytosis, respiratory burst activity, ROS level, T-SOD, CAT, GST, and LZM activities were mainly increased in the 1.5 % SMP group. Hepatopancreas AKP and GOT activity were no significant change in all SMP groups. ACP activity was significantly enhanced in the 1.1 % SMP group. The GPT activity of 0.3-0.7 % SMP group was significantly decreased. The 0.7 % SMP group had the highest intestinal fold height. The highest index values of OTUs, Ace, Chao, and Shannon were in the 0.3 % SMP group. The dietary addition of 0.3 % SMP led to a tendency of increased relative abundance of Firmicutes and Bacteroidota at the phylum level, while the relative abundance of Proteobacteria at the phylum level decreased. In conclusion, dietary SMP could promote crayfish health by enhancing phagocytosis, protecting hepatopancreas and enhancing intestinal barrier function. This study contributes to the theoretical foundation for exploring the potential application of plant polysaccharides in crustaceans.

The etiological diagnostic value of metagenomic next-generation sequencing in suspected community-acquired pneumonia.

BACKGROUND: The emergence of metagenomic next-generation sequencing (mNGS) may provide a promising tool for early and comprehensive identification of the causative pathogen in community-acquired pneumonia (CAP). In this study, we aim to further evaluate the etiological diagnostic value of mNGS in suspected CAP. METHODS: A total of 555 bronchoalveolar lavage fluid (BALF) samples were collected for pathogen detection by mNGS from 541 patients with suspected CAP. The clinical value was assessed based on infection diagnosis and treatment guidance. The diagnostic performance for pathogen identification by mNGS and sputum culture and for tuberculosis (TB) by mNGS and X-pert MTB/RIF were compared. To evaluate the potential for treatment guidance, we analyzed the treatment regimen of patients with suspected CAP, including imaging changes of lung after empirical antibacterial therapy, intensified regimen, antifungal treatment, and a 1-year follow up for patients with unconfirmed diagnosis and non-improvement imaging after anti-infective treatment and patients with high suspicion of TB or NTM infection who were transferred to the Wuhan Pulmonary Hospital for further diagnosis and even anti-mycobacterium therapy. RESULTS: Of the 516 BALF samples that were analyzed by both mNGS and sputum culture, the positivity rate of mNGS was significantly higher than that of sputum culture (79.1% vs. 11.4%, P = 0.001). A total of 48 samples from patients with confirmed TB were analyzed by both mNGS and X-pert MTB/RIF, and the sensitivity of mNGS for the diagnosis of active TB was significantly lower than that of X-pert MTB/RIF (64.6% vs. 85.4%, P = 0.031). Of the 106 pathogen-negative cases, 48 were ultimately considered non-infectious diseases, with a negative predictive value of 45.3%. Of the 381 pathogen-positive cases, 311 were eventually diagnosed as CAP, with a positive predictive value of 81.6%. A total of 487 patients were included in the evaluation of the therapeutic effect, and 67.1% improved with initial empirical antibiotic treatment. Of the 163 patients in which bacteria were detected, 77.9% improved with antibacterial therapy; of the 85 patients in which fungi were detected, 12.9% achieved remission after antifungal therapy. CONCLUSIONS: Overall, mNGS had unique advantages in the detection of suspected CAP pathogens. However, mNGS was not superior to X-pert MTB/RIF for the diagnosis of TB. In addition, mNGS was not necessary as a routine test for all patients admitted with suspected CAP. Furthermore, when fungi are detected by mNGS, antifungal therapy should be cautious.

The bacterial and fungal profiles of patients hospitalized with non-COVID-19 lower respiratory tract infections in Wuhan, China, 2019-2021.

AIMS: A severe lockdown occurred in Wuhan during the COVID-19 pandemic, followed by a remission phase in the pandemic's aftermath. This study analyzed the bacterial and fungal profiles of respiratory pathogens in patients hospitalized with non-COVID-19 lower respiratory tract infections (LRTIs) during this period to determine the pathogen profile distributions in different age groups and hospital departments in Wuhan. METHODS AND RESULTS: We collected reports of pathogen testing in the medical records of patients hospitalized with non-COVID-19 LRTI between 2019 and 2021. These cases were tested for bacterial and fungal pathogens using 16S and internal transcribed spacer sequencing methods on bronchoalveolar lavage fluid samples. The study included 1368 cases. The bacteria most commonly identified were Streptococcus pneumoniae (12.50%) and Mycoplasma pneumoniae (8.33%). The most commonly identified fungi were Aspergillus fumigatus (2.49%) and Pneumocystis jirovecii (1.75%). Compared to 2019, the S. pneumoniae detection rates increased significantly in 2021, and those of M. pneumoniae decreased. Streptococcus pneumoniae was detected mainly in children. The detection rates of almost all fungi were greater in the respiratory Intensive Care Unit compared to respiratory medicine. Streptococcus pneumoniae and M. pneumoniae were detected more frequently in the pediatric department. CONCLUSIONS: Before and after the COVID-19 outbreak, a change in the common pathogen spectrum was detected in patients with non-COVID-19 in Wuhan, with the greatest change occurring among children. The major pathogens varied by the patient's age and the hospital department.

Microbiome analysis reveals the intestinal microbiota characteristics and potential impact of Procambarus clarkii.

The intestinal microbiota interacts with the host and plays an important role in the immune response, digestive physiology, and regulation of body functions. In addition, it is also well documented that the intestinal microbiota of aquatic animals are closely related to their growth rate. However, whether it resulted in different sizes of crayfish in the rice-crayfish coculture model remained vague. Here, we analyzed the intestinal microbiota characteristics of crayfish of three sizes in the same typical rice-crayfish coculture field by high-throughput sequencing technology combined with quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme activity, investigating the relationship between intestinal microbiota in crayfish and water and sediments. The results showed that the dominant intestinal microbiota of crayfish was significantly different between the large size group (BS), normal size group (NS), and small size group (SS), where Bacteroides and Candidatus_Bacilloplasma contributed to the growth of crayfish by facilitating food digestion through cellulolysis, which might be one of the potential factors affecting the difference in sizes. Follow-up experiments confirmed that the activity of lipase (LPS) and protease was higher in BS, and the relative expression of development-related genes, including alpha-amylase (α-AMY), myocyte-specific enhancer factor 2a (MEF2a), glutathione reductase (GR), chitinase (CHI), and ecdysone receptor (EcR), in BS was significantly higher than that in SS. These findings revealed the intestinal microbiota characteristics of crayfish of different sizes and their potential impact on growth, which is valuable for managing and manipulating the intestinal microbiota in crayfish to achieve high productivity in practice. KEY POINTS: • Significant differences in the dominant microflora of BS, NS, and SS in crayfish. • Cellulolysis might be a potential factor affecting different sizes in crayfish. • Adding Bacteroides and Candidatus_Bacilloplasma helped the growth of crayfish.

Dietary exposure to 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) induces oxidative damage promoting cell apoptosis primarily via mitochondrial pathway in the hepatopancreas of carp, Cyprinus carpio.

To investigate the mechanisms of BDE-47 on hepatotoxicity in fish, this study examined the effects of dietary exposure to BDE-47 (40 and 4000 ng/g) on carp for 42 days. The results showed that BDE-47 significantly increased carp's condition factor and hepatosomatic index. Pathological results revealed unclear hepatic cord structure, hepatocytes swelling, cellular vacuolization, and inflammatory cell infiltration in the hepatopancreas of carp. Further investigation showed that ROS levels significantly increased on days 7, 14, and 42. Moreover, the activities of antioxidant enzymes SOD, GSH, CAT, and GST increased significantly from 1 to 7 days, and the transcription levels of antioxidant enzymes CAT, Cu-Zn SOD, Mn-SOD, GST, and GPX, and antioxidant pathway genes Keap1, Nrf2, and HO-1 changed significantly at multiple time-points during the 42 days. The results of apoptosis pathway genes showed that the mitochondrial pathway genes Bax, Casp3, and Casp9 were significantly upregulated and Bcl2 was significantly downregulated, while the transcription levels of FADD and PERK were significantly enhanced. These results indicate that BDE-47 induced oxidative damage in hepatopancreas, then it promoted cell apoptosis mainly through the mitochondrial pathway. This study provides a foundation for analyzing the mechanism of hepatotoxicity induced by BDE-47 on fish.

Decoding the Mechanisms of Reversibility Loss in Rechargeable Zinc-Air Batteries.

Attaining high reversibility of the electrodes and electrolyte is essential for the longevity of secondary batteries. Rechargeable zinc-air batteries (RZABs), however, encounter drastic irreversible changes in the zinc anodes and air cathodes during cycling. To uncover the mechanisms of reversibility loss in RZABs, we investigate the evolution of the zinc anode, alkaline electrolyte, and air electrode through experiments and first-principles calculations. Morphology diagrams of zinc anodes under versatile operating conditions reveal that the nanosized mossy zinc dominates the later cycling stage. Such anodic change is induced by the increased zincate concentration due to hydrogen evolution, which is catalyzed by the mossy structure and results in oxide passivation on electrodes and eventually leads to low true Coulombic efficiencies and short life spans of batteries. Inspired by these findings, we finally present a novel overcharge-cycling protocol to compensate for the Coulombic efficiency loss caused by hydrogen evolution and significantly extend the battery life.

scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering.

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell's representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data's underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback-Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods.

Starvation induces hepatopancreas atrophy in Chinese mitten crab (Eriocheir sinensis) by inhibiting angiogenesis.

BACKGROUND: The hepatopancreas of crustaceans serves as a significant organ for both the synthesis and secretion of digestive enzymes, as well as energy storage. In the event of food shortage, the hepatopancreas can provide energy for survival. To investigate the potential regulatory mechanisms of the hepatopancreas in response to starvation in Eriocheir Sinensis, transcriptome analysis, histological study and qRT-PCR were performed. RESULTS: The results showed that starvation caused a decrease in the hepatopancreas index of E. sinensis, which had certain effects on the tissue structure, metabolism and angiogenesis in the hepatopancreas. In addition, WGCNA and linear regression analysis showed that the genes significantly related to the hepatopancreas index were mainly enriched in the angiogenesis pathway, in which AKT signaling played an important role. Starvation may inhibit AKT signaling pathway by reducing the expression of TGFBI, HSP27, HHEX, and EsPVF1, thereby hindering angiogenesis, promoting apoptosis, and leading to hepatopancreas atrophy. CONCLUSION: These results indicate that AKT plays an important role in the angiogenesis pathway and apoptosis of the starvation induced hepatopancreas index reduction, which is beneficial to further understand the effect of starvation stress on hepatopancreas of Chinese mitten crab.

Homogenizing Zn Deposition in Hierarchical Nanoporous Cu for a High-Current, High Areal-Capacity Zn Flow Battery.

A Zn anode can offset the low energy density of a flow battery for a balanced approach toward electricity storage. Yet, when targeting inexpensive, long-duration storage, the battery demands a thick Zn deposit in a porous framework, whose heterogeneity triggers frequent dendrite formation and jeopardizes the stability of the battery. Here, Cu foam is transferred into a hierarchical nanoporous electrode to homogenize the deposition. It begins with alloying the foam with Zn to form Cu5 Zn8 , whose depth is controlled to retain the large pores for a hydraulic permeability ≈10-11  m2 . Dealloying follows to create nanoscale pores and abundant fine pits below 10 nm, where Zn can nucleate preferentially due to the Gibbs-Thomson effect, as supported by a density functional theory simulation. Morphological evolution monitored by in situ microscopy confirms uniform Zn deposition. The electrode delivers 200 h of stable cycles in a Zn-I2 flow battery at 60 mAh cm-2 and 60 mA cm-2 , performance that meets practical demands.

Effect of celecoxib plus standard chemotherapy on cancer prognosis: A systematic review and meta-analysis.

BACKGROUND: Inflammation is closely related to cancer prognosis. The effect of celecoxib, a nonsteroidal anti-inflammatory drug, on the prognosis of patients with cancer remains uncertain. To assess the association between celecoxib plus standard chemotherapy and cancer prognosis, we conducted a systematic review and meta-analysis of published studies. METHODS: PubMed, EMBASE, and the Cochrane Library were searched from inception until July 2022 for randomized controlled trials reporting the prognosis of patients with cancer treated with celecoxib plus standard chemotherapy. The primary endpoints were overall survival (OS) and progression-free survival (PFS). Meta-analysis was performed using Review Manager software version 5.4. The following search terms were used in the databases: ((((celecoxib)) AND ((((((((cancer) OR (carcinoma)) OR (sarcoma)) OR (neoplasms)) OR (tumor)) OR (tumour)) OR (tumors)) OR (tumours))) AND ((survival) OR (mortality))) AND (((Clinical Trials, Randomized) OR (Trials, Randomized Clinical)) OR (Controlled Clinical Trials, Randomized)). RESULTS: Overall, 13 randomized controlled trials, including 8957 patients with cancer, were included in the analysis. Compared to conventional chemotherapy alone, 1-year OS and 1-year PFS rates were not significantly improved with celecoxib adjuvant therapy (OS: p = .38; PFS: p = .65). In addition, no differences were observed between the celecoxib and placebo groups in 3-year overall (p = .98), 3-year progression-free (p = .40), 5-year overall (p = .59), or 5-year progression-free (p = .56) survival rates. An increase in the risk ratio of leukopenia (p = .02) and thrombocytopenia (p = .05) was also observed, suggesting that celecoxib promotes hematologic toxicity. No increased risk of cardiovascular (p = .96) and gastrointestinal (p = .10-.91) events was observed. CONCLUSIONS: The addition of celecoxib to standard chemotherapy did not improve OS or PFS rates of patients with cancer. Additionally, celecoxib can increase hematologic toxicity without increasing the risk of gastrointestinal or cardiovascular reactions. Further randomized controlled trials are necessary to clarify its effects and applications.

High starch diets attenuate the immune function of Micropterus salmoides immune organs by modulating Keap1/Nrf2 and MAPK signaling pathways.

Based on their good physiological functions and physical properties, carbohydrates are widely used in fish feed. However, excessive use of carbohydrates such as starch in fish feed may reduce the immunity of the fish and cause a series of health problems. In order to more clearly clarify the effects of different starch levels in feed on the immune organs of Micropterus salmoides, this study took the immune organs as the entry point and explored it from several perspectives, including differences in enzyme activity in plasma, changes in gene expression in immune organs, and resistance to pathogenic bacteria. The results showed that (1) high starch feed activates inflammatory responses in the spleen and head kidney through the MAPK signaling pathway. This leads to a decrease in the number of lymphocytes and weakens the resistance to pathogens; (2) high starch diet affects the antioxidant capacity of the trunk kidney by regulating the Keap1/Nrf2 pathway; (3) There was a strong correlation between gene expression patterns in the head kidney and lysozyme content in plasma. This implies that the high starch diet may regulate lysozyme production by affecting gene expression in the head kidney and further affect immune function. This study helps to reveal the interaction between starch and the immune system and provide scientific basis for the development of reasonable dietary recommendations and disease prevention.

The changing pattern of common respiratory viruses among children from 2018 to 2021 in Wuhan, China.

BACKGROUND: Acute respiratory infections in children are a global public health challenge. Owing to the coronavirus disease (COVID-19) pandemic, non-pharmaceutical interventions, including patient isolation, social distancing, hand washing, and mask wearing, have been widely implemented, impacting the transmission of common respiratory viruses. The aim of this study was to clarify the epidemiological features of respiratory viruses in children less than 14 years of age in Wuhan before and after COVID-19. METHODS: Respiratory specimens were collected from patients aged < 14 years at two hospitals in Wuhan, China, from January 2018 to December 2021. Seven respiratory viruses were identified using an immunofluorescence assay. Pathogen profiles and seasonality were analysed. RESULTS: The number of visits and virus detection rate decreased dramatically after February 2020. The respiratory virus detection rate peaked in January and December and decreased dramatically in February and August. The detection rate was lower in 2021 than in 2018 and 2019. Respiratory syncytial virus (RSV) was identified as the leading pathogen in children aged < 1 year and 1-4 years before and after the COVID-19 pandemic. In children aged 5-14 years, influenza virus was detected at the highest rate before, and RSV after, the COVID-19 pandemic. RSV was the most common virus in coinfections. CONCLUSIONS: This study revealed the epidemiological patterns of common respiratory viruses from 2018 to 2021. The spectrum of pathogens involved in paediatric respiratory infections had partly changed. Non-pharmaceutical interventions resulted in fewer opportunities for the spread of common viruses but also in an "immunity debt" that could have negative consequences when the pandemic is under control in Wuhan.

An Intelligent Water Monitoring IoT System for Ecological Environment and Smart Cities.

Global precipitation is becoming increasingly intense due to the extreme climate. Therefore, creating new technology to manage water resources is crucial. To create a sustainable urban and ecological environment, a water level and water quality control system implementing artificial intelligence is presented in this research. The proposed smart monitoring system consists of four sensors (two different liquid level sensors, a turbidity and pH sensor, and a water oxygen sensor), a control module (an MCU, a motor, a pump, and a drain), and a power and communication system (a solar panel, a battery, and a wireless communication module). The system focuses on low-cost Internet of Things (IoT) devices along with low power consumption and high precision. This proposal collects rainfall from the preceding 10 years in the application region as well as the region's meteorological bureau's weekly weather report and uses artificial intelligence to compute the appropriate water level. More importantly, the adoption of dynamic adjustment systems can reserve and modify water resources in the application region more efficiently. Compared to existing technologies, the measurement approach utilized in this study not only achieves cost savings exceeding 60% but also enhances water level measurement accuracy by over 15% through the successful implementation of water level calibration decisions utilizing multiple distinct sensors. Of greater significance, the dynamic adjustment systems proposed in this research offer the potential for conserving water resources by more than 15% in an effective manner. As a result, the adoption of this technology may efficiently reserve and distribute water resources for smart cities as well as reduce substantial losses caused by anomalous water resources, such as floods, droughts, and ecological concerns.

Transcriptome Revealed the Macrophages Inflammatory Response Mechanism and NOD-like Receptor Characterization in Siberian Sturgeon (Acipenser baerii).

Nucleotide-binding and oligomerization domain-like receptors (NOD-like receptors, NLRs) can regulate the inflammatory response to eliminate pathogens and maintain the host's homeostasis. In this study, the head kidney macrophages of Siberian sturgeon were treated with lipopolysaccharide (LPS) to induce inflammation by evaluating the expression of cytokines. The high-throughput sequencing for macrophages after 12 h treatment showed that 1224 differentially expressed genes (DEGs), including 779 upregulated and 445 downregulated, were identified. DEGs mainly focus on pattern recognition receptors (PRRs) and the adaptor proteins, cytokines, and cell adhesion molecules. In the NOD-like receptor signaling pathway, multiple NOD-like receptor family CARD domains containing 3-like (NLRC3-like) were significantly downregulated, and pro-inflammatory cytokines were upregulated. Based on the transcriptome database, 19 NLRs with NACHT structural domains were mined and named in Siberian sturgeon, including 5 NLR-A, 12 NLR-C, and 2 other NLRs. The NLR-C subfamily had the characteristics of expansion of the teleost NLRC3 family and lacked the B30.2 domain compared with other fish. This study revealed the inflammatory response mechanism and NLRs family characterization in Siberian sturgeon by transcriptome and provided basic data for further research on inflammation in teleost.

Transcriptome analysis of the immunomodulatory effects of Salvia miltiorrhiza polysaccharide on hemocyte immune response in Procambarus clarkii.

Natural plant polysaccharide as immune modulator is considered an effective strategy for healthy aquaculture to reduce medicine treatment. Salvia miltiorrhiza polysaccharides (SMP) had applications to regulate immune activity and enhance antioxidant in vertebrates, but the potential function has been rarely reported in crustaceans. In this study, the immunological effects of SMP on hemocytes of Procambarus clarkii were analyzed. Results showed that total superoxide dismutase (T-SOD), phenoloxidase (PO) activity and respiratory burst were up-regulated after SMP treatment. After high-throughput sequencing, 2170 differentially expressed genes (DEGs) including 1294 up-regulated and 876 down-regulated genes were identified. KEGG function enrichment analysis indicated that DEGs are involved in crustaceans cellular immune-related signaling pathways, including lysosome, phagosome and endocytosis. Transcriptome mining and qRT-PCR showed that SMP up-regulated humoral immunity factors gene expression. Diets supplemented with 0.8% SMP significantly up-regulated the total number of hemocytes (THC), T-SOD and PO activity, improved the survival of crayfish after Citrobacter freundii infection. This study suggested that SMP could improve the cellular and humoral immunity of P. clarkii. Furthermore, this finding supplied a molecular foundation for further comprehending the immunopotentiator effects of plant polysaccharides in crustaceans.

Microbial analysis reveals the potential colonization of pathogens in the intestine of crayfish (Procambarus clarkii) in traditional aquaculture environments.

The microbiota of the intestine produces a wide array of biologically active molecules and together act as a composite endocrine organ. Due to our limited understanding of bacterial communities in aquaculture ecosystems, it is necessary to evaluate the interactions between environmental and intestinal microbiota and the potential consequences of disease. This study taken the traditional P. clarkii culture in the Sichuan Basin as an example, and analyzed the relationships between the microbiota of the environment and host through microbial analysis and microbiological diagnosis. Our results showed that the bacterial abundance in sediment was greater than in water, followed by the intestine, and some of bacteria from the environment successfully selected to colonize the intestine. The bacterial composition in the intestines of diseased and healthy crayfish was significantly different. The bacteria that colonized and proliferated in the intestine had very low abundances in sediment and water. Two potential pathogens, Aeromonas veronii, and Citrobacter freundii, and two potential probiotics, Lactococcus garvieae and Exiguobacterium undae, were identified. Using multiple, real, and traditional P. clarkii aquaculture sites in the Sichuan Basin, this study revealed that the microbial communities of the environment and animal host did indeed interact. Furthermore, these results indicated that P. clarkii in a healthy status are capable of regulating which bacteria colonize their intestines.