S100A8 knockdown activates the PI3K/AKT signaling pathway to inhibit microglial autophagy and improve cognitive impairment mediated by chronic sleep deprivation.

OBJECTIVE: Cognitive dysfunction is one of the major symptoms of chronic sleep deprivation (CSD). Abnormal autophagy and apoptosis are thought to be important mechanisms. S100 Calcium Binding Protein A8 (S100A8) plays a key role in autophagy and apoptosis of microglia. This study investigated whether S100A8 knockdown can effectively inhibit aberrant autophagy in microglia and improve cognitive function by activating the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway under CSD conditions. METHODS: CSD mouse models and BV2 cell autophagy models were established in vivo and in vitro. Transcriptome sequencing was used to determine the key regulator related to autophagy. The Morris water maze test was used to evaluate the cognitive behavior of the mice. RT-qPCR and western blot were conducted to examine S100A8 expression and autophagy signalling. HE, TUNEL, transmission electron microscopy, immunofluorescence, and histochemistry were performed to detect pathological changes, neuronal autophagy, apoptosis, or positive cells in hippocampal tissues, respectively. RESULTS: Transcriptome sequencing showed that S100A8 was significantly elevated in CSD mice, and fluorescence colocalization results further suggested that S100A8 mainly colocalizes with microglia. In vivo studies revealed that knockdown of S100A8 alleviated CSD-induced cognitive impairment in mice. Through further mechanistic investigations employing both in vivo and in vitro models, we demonstrated that silencing S100A8 can activate the PI3K/AKT pathway, thereby reducing CSD-induced abnormal autophagy and apoptosis in microglia. Aberrant autophagy and apoptosis in microglia were reversed with the PI3K/AKT pathway inhibitor LY294002. CONCLUSION: The S100A8/PI3K/AKT axis plays a crucial role in chronic sleep deprivation-mediated autophagy and apoptosis in microglia.

DNA demethylase Tet2 promotes the terminal maturation of natural killer cells.

The cytotoxicity feature to eliminate malignant cells makes natural killer (NK) cells a candidate for tumor immunotherapy. However, this scenario is currently hampered by inadequate understanding of the regulatory mechanisms of NK cell development. Ten-Eleven-Translocation 2 (Tet2) is a demethylase whose mutation was recently shown to cause phenotypic defects in NK cells. However, the role of Tet2 in the development and maturation of NK cells is not entirely clear. Here we studied the modulatory role of Tet2 in NK cell development and maturation by generating hematopoietic Tet2 knockout mice and mice with Tet2 conditional deletion in NKp46+ NK cells. The results showed that both hematopoietic and NK cell conditional deletion of Tet2 had no effect on the early steps of NK cell development, but impaired the terminal maturation of NK cells defined by CD11b, CD43, and KLRG1 expression. In the liver, Tet2 deletion not only prevented the terminal maturation of NK cells, but also increased the proportion of type 1 innate lymphoid cells (ILC1s) and reduced the proportion of conventional NK cells (cNK). Moreover, hematopoietic deletion of Tet2 lowered the protein levels of perforin in NK cells. Furthermore, hematopoietic deletion of Tet2 downregulated the protein levels of Eomesodermin (Eomes), but not T-bet, in NK cells. In conclusion, our results demonstrate that Tet2 plays an important role in the terminal maturation of NK cells, and the Eomes transcription factor may be involved.

The role of miRNA-29b1 on the hypoxia-induced apoptosis in mammalian cardiomyocytes.

Cardiomyocyte apoptosis is a complex biological process involving the interaction of many factors and signaling pathways. In hypoxic environment, cardiomyocytes may trigger apoptosis due to insufficient energy supply, increased production of oxygen free radicals, and disturbance of intracellular calcium ion balance. The present research aimed to investigate the role of microRNA-29b1 (miR-29b1) in hypoxia-treated cardiomyocytes and its potential mechanism involved. We established an in vitro ischemia model using AC16 and H9C2 cardiomyocytes through hypoxia treatment (1% O2, 48 h). Cell apoptosis was evaluated by flow cytometry using Annexin V FITC-PI staining assay. Moreover, we used Western blot and immunofluorescence analysis to determine the expression of Bcl-2, Bax caspase-3 and Cx43 proteins. We found that miR-29b1 protected AC16 and H9C2 cells from hypoxia-induced injury as evidence that miR-29b1 attenuated the effects of hypoxia treatment on AC16 and H9C2 cell apoptosis after hypoxia treatment. In conclusion, our findings suggest that miR-29b1 may have potential cardiovascular protective effects during ischemia-related myocardial injury.

Span-based few-shot event detection via aligning external knowledge.

Few-shot Event Detection (FSED) aims to identify novel event types in new domains with very limited annotated data. Previous PN-based (Prototypical Network) joint methods suffer from insufficient learning of token-wise label dependency and inaccurate prototypes. To solve these problems, we propose a span-based FSED model, called SpanFSED, which decomposes FSED into two subprocesses, including span extractor and event classifier. In span extraction, we convert sequential labels into a global boundary matrix that enables the span extractor to acquire precise boundary information irrespective of label dependency. In event classification, we align event types with an outside knowledge base like FrameNet and construct an enhanced support set, which injects more trigger information into the prototypical network of event prototypes. The superior performance of SpanFSED is demonstrated through extensive experiments on four event detection datasets, i.e., ACE2005, ERE, MAVEN and FewEvent. Access to our code and data is facilitated through the following link: .

PIEZO1 as a new target for hyperglycemic stress-induced neuropathic injury: The potential therapeutic role of bezafibrate.

Hyperglycemic stress can directly lead to neuronal damage. The mechanosensitive ion channel PIEZO1 can be activated in response to hyperglycemia, but its role in hyperglycemic neurotoxicity is unclear. The role of PIEZO1 in hyperglycemic neurotoxicity was explored by constructing a hyperglycemic mouse model and a high-glucose HT22 cell model. The results showed that PIEZO1 was significantly upregulated in response to high glucose stress. In vitro experiments have shown that high glucose stress induces changes in neuronal cell morphology and membrane tension, a key mechanism for PIEZO1 activation. In addition, high glucose stress upregulates serum/glucocorticoid-regulated kinase-1 (SGK1) and activates PIEZO1 through the Ca2+ pool and store-operated calcium entry (SOCE). PIEZO1-mediated Ca2+ influx further enhances SGK1 and SOCE, inducing intracellular Ca2+ peaks in neurons. PIEZO1 mediated intracellular Ca2+ elevation leads to calcium/calmodulin-dependent protein kinase 2α (CaMK2α) overactivation, which promotes oxidative stress and apoptosis signalling through p-CaMK2α/ERK/CREB and ox-CaMK2α/MAPK p38/NFκB p65 pathways, subsequently inducing synaptic damage and cognitive impairment in mice. The intron miR-107 of pantothenic kinase 1 (PANK1) is highly expressed in the brain and has been found to target PIEZO1 and SGK1. The PANK1 receptor is activated by peroxisome proliferator-activated receptor α (PPARα), an activator known to upregulate miR-107 levels in the brain. The clinically used lipid-lowering drug bezafibrate, a known PPARα activator, may upregulate miR-107 through the PPARɑ/PANK1 pathway, thereby inhibiting PIEZO1 and improving hyperglycemia-induced neuronal cell damage. This study provides a new idea for the pathogenesis and drug treatment of hyperglycemic neurotoxicity and diabetes-related cognitive dysfunction.

Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method.

BACKGROUND: Pneumocystis jirovecii pneumonia (PJP) is an interstitial pneumonia caused by pneumocystis jirovecii (PJ). The diagnosis of PJP primarily relies on the detection of the pathogen from lower respiratory tract specimens. However, it faces challenges such as difficulty in obtaining specimens and low detection rates. In the clinical diagnosis process, it is necessary to combine clinical symptoms, serological test results, chest Computed tomography (CT) images, molecular biology techniques, and metagenomics next-generation sequencing (mNGS) for comprehensive analysis. PURPOSE: This study aims to overcome the limitations of traditional PJP diagnosis methods and develop a non-invasive, efficient, and accurate diagnostic approach for PJP. By using this method, patients can receive early diagnosis and treatment, effectively improving their prognosis. METHODS: We constructed an intelligent diagnostic model for PJP based on the different Convolutional Neural Networks. Firstly, we used the Convolutional Neural Network to extract CT image features from patients. Then, we fused the CT image features with clinical information features using a feature fusion function. Finally, the fused features were input into the classification network to obtain the patient's diagnosis result. RESULTS: In this study, for the diagnosis of PJP, the accuracy of the traditional PCR diagnostic method is 77.58%, while the mean accuracy of the optimal diagnostic model based on convolutional neural networks is 88.90%. CONCLUSION: The accuracy of the diagnostic method proposed in this paper is 11.32% higher than that of the traditional PCR diagnostic method. The method proposed in this paper is an efficient, accurate, and non-invasive early diagnosis approach for PJP.

ω3-PUFA alleviates neuroinflammation by upregulating miR-107 targeting PIEZO1/NFκB p65.

BACKGROUND: MiR-107 is reduced in sepsis and associated with inflammation regulation. Dietary supplementation with polyunsaturated fatty acids (ω3-PUFA) can increase the expression of miR-107; this study investigated whether the ω3-PUFA can effectively inhibit neuroinflammation and improve cognitive function by regulating miR-107 in the brain. METHODS: The LPS-induced mouse model of neuroinflammation and the BV2 cell inflammatory model were used to evaluate the effects of ω3-PUFA on miR-107 expression and inflammation. Intraventricular injection of Agomir and Antagomir was used to modulate miR-107 expression. HE and Nissl staining for analyzing hippocampal neuronal damage, immunofluorescence analysis for glial activation, RT-qPCR, and Western blot were conducted to examine miR-107 expression and inflammation signalling. RESULTS: The result shows that LPS successfully induced the mouse neuroinflammation model and BV2 cell inflammation model. Supplementation of ω3-PUFA effectively reduced the secretion of pro-inflammatory factors TNFα, IL1ß, and IL6 induced by LPS, improved cognitive function impairment, and increased miR-107 expression in the brain. Overexpression of miR-107 in the brain inhibited the nuclear factor κB (NFκB) pro-inflammatory signalling pathway by targeting PIEZO1, thus suppressing microglial and astrocyte activation and reducing the release of inflammatory mediators, which alleviated neuroinflammatory damage and improved cognitive function in mice. miR-107, as an intron of PANK1, PANK1 is subject to PPAR α Adjust. ω3-PUFA can activate PPARα, but ω3-PUFA upregulates brain miR-107, and PPARα/PANK1-related pathways may not be synchronized, and further research is needed to confirm the specific mechanism by which ω3-PUFA upregulates miR-107. CONCLUSION: The miR-107/PIEZO1/NFκB p65 pathway represents a novel mechanism underlying the improvement of neuroinflammation by ω3-PUFA.

Dexamethasone upregulates macrophage PIEZO1 via SGK1, suppressing inflammation and increasing ROS and apoptosis.

The side effects of high-dose dexamethasone in anti-infection include increased ROS production and immune cell apoptosis. Dexamethasone effectively activates serum/glucocorticoid-regulated kinase 1 (SGK1), which upregulates various ion channels by activating store-operated calcium entry (SOCE), leading to Ca2+ oscillations. PIEZO1 plays a crucial role in macrophages' immune activity and function, but whether dexamethasone can regulate PIEZO1 by enhancing SOCE via SGK1 activation remains unclear. The effects of dexamethasone were assessed in a mouse model of sepsis, and primary BMDMs and the RAW264.7 were treated with overexpression plasmids, siRNAs, or specific activators or inhibitors to examine the relationships between SGK1, SOCE, and PIEZO1. The functional and phenotypic changes of mouse and macrophage models were detected. The results indicate that high-dose dexamethasone upregulated SGK1 by activating the macrophage glucocorticoid receptor, which enhanced SOCE and subsequently activated PIEZO1. Activation of PIEZO1 resulted in Ca2+ influx and cytoskeletal remodelling. The increase in intracellular Ca2+ mediated by PIEZO1 further increased the activation of SGK1 and ORAI1/STIM1, leading to intracellular Ca2+ peaks. In the context of inflammation, activation of PIEZO1 suppressed the activation of TLR4/NFκB p65 in macrophages. In RAW264.7 cells, PIEZO1 continuous activation inhibited the change in mitochondrial membrane potential, accelerated ROS accumulation, and induced autophagic damage and cell apoptosis in the late stage. CaMK2α was identified as a downstream mediator of TLR4 and PIEZO1, facilitating high-dose dexamethasone-induced macrophage immunosuppression and apoptosis. PIEZO1 is a new glucocorticoid target to regulate macrophage function and activity. This study provides a theoretical basis for the rational use of dexamethasone.

Accuracy of edentulous full-arch implant impression: An in vitro comparison between conventional impression, intraoral scan with and without splinting, and photogrammetry.

OBJECTIVES: The purpose of this in vitro study was to compare the trueness and precision of complete arch implant impressions using conventional impression, intraoral scanning with and without splinting, and stereophotogrammetry. MATERIALS AND METHODS: An edentulous model with six implants was used in this study. Four implant impression techniques were compared: the conventional impression (CI), intraoral scanning (IOS) without splinting, intraoral scanning with splinting (MIOS), and stereophotogrammetry (SPG). An industrial blue light scanner was used to generate the baseline scan from the model. The CI was captured with a laboratory scanner. The reference best-fit method was then applied in the computer-aided design (CAD) software to compute the three-dimensional, angular, and linear discrepancies among the four impression techniques. The root mean square (RMS) 3D discrepancies in trueness and precision between the four impression groups were analyzed with a Kruskal-Wallis test. Trueness and precision between single analogs were assessed using generalized estimating equations. RESULTS: Significant differences in the overall trueness (p = .017) and precision (p < .001) were observed across four impression groups. The SPG group exhibited significantly smaller RMS 3D deviations than the CI, IOS, and MIOS groups (p < .05), with no significant difference detected among the latter three groups (p > .05). CONCLUSIONS: Stereophotogrammetry showed superior trueness and precision, meeting misfit thresholds for implant-supported complete arch prostheses. Intraoral scanning, while accurate like conventional impressions, exhibited cross-arch angular and linear deviations. Adding a splint to the scan body did not improve intraoral scanning accuracy.

Site-directed display of zearalenone lactonase on spilt-intein functionalized nanocarrier for green and efficient detoxification of zearalenone.

In this study, we prepared a functional organic-inorganic hybrid nanoflower (InHNF) via split intein moiety in a biomineralization process without using organic solvents. InHNF could specifically bind the target enzymes from crude cell lysates within seconds and site-directedly display them on the surface by forming a peptide bond with enzyme's terminal amino acid residue. This unique feature enabled InHNF to increase the specific activity of zearalenone detoxifying enzyme ZHD518 by 40 âˆ¼ 60% at all tested temperatures and prevented enzyme denaturation even under extreme pH conditions (pH 3-11). Furthermore, it exhibited excellent operational stability, with a residual activity of over 70% after eight reaction cycles. Strikingly, InHNF-ZHD518 achieved above 50% ZEN degradation despite the near inactivation of free ZHD518 in beer sample. Overall, InHNF nanocarriers can achieve environmentally friendly, purification-free, and site-directed immobilization of food enzymes and enhance their catalytic properties, making them suitable for a wide range of industrial applications.

Ultra-Short-Term Offshore Wind Power Prediction Based on PCA-SSA-VMD and BiLSTM.

In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data; on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments; the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.

Thirteen complete chloroplast genomes of the costaceae family: insights into genome structure, selective pressure and phylogenetic relationships.

BACKGROUND: Costaceae, commonly known as the spiral ginger family, consists of approximately 120 species distributed in the tropical regions of South America, Africa, and Southeast Asia, of which some species have important ornamental, medicinal and ecological values. Previous studies on the phylogenetic and taxonomic of Costaceae by using nuclear internal transcribed spacer (ITS) and chloroplast genome fragments data had low resolutions. Additionally, the structures, variations and molecular evolution of complete chloroplast genomes in Costaceae still remain unclear. Herein, a total of 13 complete chloroplast genomes of Costaceae including 8 newly sequenced and 5 from the NCBI GenBank database, representing all three distribution regions of this family, were comprehensively analyzed for comparative genomics and phylogenetic relationships. RESULT: The 13 complete chloroplast genomes of Costaceae possessed typical quadripartite structures with lengths from 166,360 to 168,966 bp, comprising a large single copy (LSC, 90,802 - 92,189 bp), a small single copy (SSC, 18,363 - 20,124 bp) and a pair of inverted repeats (IRs, 27,982 - 29,203 bp). These genomes coded 111 - 113 different genes, including 79 protein-coding genes, 4 rRNA genes and 28 - 30 tRNAs genes. The gene orders, gene contents, amino acid frequencies and codon usage within Costaceae were highly conservative, but several variations in intron loss, long repeats, simple sequence repeats (SSRs) and gene expansion on the IR/SC boundaries were also found among these 13 genomes. Comparative genomics within Costaceae identified five highly divergent regions including ndhF, ycf1-D2, ccsA-ndhD, rps15-ycf1-D2 and rpl16-exon2-rpl16-exon1. Five combined DNA regions (ycf1-D2 + ndhF, ccsA-ndhD + rps15-ycf1-D2, rps15-ycf1-D2 + rpl16-exon2-rpl16-exon1, ccsA-ndhD + rpl16-exon2-rpl16-exon1, and ccsA-ndhD + rps15-ycf1-D2 + rpl16-exon2-rpl16-exon1) could be used as potential markers for future phylogenetic analyses and species identification in Costaceae. Positive selection was found in eight protein-coding genes, including cemA, clpP, ndhA, ndhF, petB, psbD, rps12 and ycf1. Maximum likelihood and Bayesian phylogenetic trees using chloroplast genome sequences consistently revealed identical tree topologies with high supports between species of Costaceae. Three clades were divided within Costaceae, including the Asian clade, Costus clade and South American clade. Tapeinochilos was a sister of Hellenia, and Parahellenia was a sister to the cluster of Tapeinochilos + Hellenia with strong support in the Asian clade. The results of molecular dating showed that the crown age of Costaceae was about 30.5 Mya (95% HPD: 14.9 - 49.3 Mya), and then started to diverge into the Costus clade and Asian clade around 23.8 Mya (95% HPD: 10.1 - 41.5 Mya). The Asian clade diverged into Hellenia and Parahellenia at approximately 10.7 Mya (95% HPD: 3.5 - 25.1 Mya). CONCLUSION: The complete chloroplast genomes can resolve the phylogenetic relationships of Costaceae and provide new insights into genome structures, variations and evolution. The identified DNA divergent regions would be useful for species identification and phylogenetic inference in Costaceae.

Comparative Chloroplast Genomics of 21 Species in Zingiberales with Implications for Their Phylogenetic Relationships and Molecular Dating.

Zingiberales includes eight families and more than 2600 species, with many species having important economic and ecological value. However, the backbone phylogenetic relationships of Zingiberales still remain controversial, as demonstrated in previous studies, and molecular dating based on chloroplast genomes has not been comprehensively studied for the whole order. Herein, 22 complete chloroplast genomes from 21 species in Zingiberales were sequenced, assembled, and analyzed. These 22 genomes displayed typical quadripartite structures, which ranged from 161,303 bp to 163,979 bp in length and contained 111-112 different genes. The genome structures, gene contents, simple sequence repeats, long repeats, and codon usage were highly conserved, with slight differences among these genomes. Further comparative analysis of the 111 complete chloroplast genomes of Zingiberales, including 22 newly sequenced ones and the remaining ones from the national center for biotechnology information (NCBI) database, identified three highly divergent regions comprising ccsA, psaC, and psaC-ndhE. Maximum likelihood and Bayesian inference phylogenetic analyses based on chloroplast genome sequences found identical topological structures and identified a strongly supported backbone of phylogenetic relationships. Cannaceae was sister to Marantaceae, forming a clade that was collectively sister to the clade of (Costaceae, Zingiberaceae) with strong support (bootstrap (BS) = 100%, and posterior probability (PP) = 0.99-1.0); Heliconiaceae was sister to the clade of (Lowiaceae, Strelitziaceae), then collectively sister to Musaceae with strong support (BS = 94-100%, and PP = 0.93-1.0); the clade of ((Cannaceae, Marantaceae), (Costaceae, Zingiberaceae)) was sister to the clade of (Musaceae, (Heliconiaceae, (Lowiaceae, Strelitziaceae))) with robust support (BS = 100%, and PP = 1.0). The results of divergence time estimation of Zingiberales indicated that the crown node of Zingiberales occurred approximately 85.0 Mya (95% highest posterior density (HPD) = 81.6-89.3 million years ago (Mya)), with major family-level lineages becoming from 46.8 to 80.5 Mya. These findings proved that chloroplast genomes could contribute to the study of phylogenetic relationships and molecular dating in Zingiberales, as well as provide potential molecular markers for further taxonomic and phylogenetic studies of Zingiberales.

The impact of gene polymorphism and hepatic insufficiency on voriconazole dose adjustment in invasive fungal infection individuals.

Voriconazole (VRZ) is a broad-spectrum antifungal medication widely used to treat invasive fungal infections (IFI). The administration dosage and blood concentration of VRZ are influenced by various factors, posing challenges for standardization and individualization of dose adjustments. On the one hand, VRZ is primarily metabolized by the liver, predominantly mediated by the cytochrome P450 (CYP) 2C19 enzyme. The genetic polymorphism of CYP2C19 significantly impacts the blood concentration of VRZ, particularly the trough concentration (Ctrough), thereby influencing the drug's efficacy and potentially causing adverse drug reactions (ADRs). Recent research has demonstrated that pharmacogenomics-based VRZ dose adjustments offer more accurate and individualized treatment strategies for individuals with hepatic insufficiency, with the possibility to enhance therapeutic outcomes and reduce ADRs. On the other hand, the security, pharmacokinetics, and dosing of VRZ in individuals with hepatic insufficiency remain unclear, making it challenging to attain optimal Ctrough in individuals with both hepatic insufficiency and IFI, resulting in suboptimal drug efficacy and severe ADRs. Therefore, when using VRZ to treat IFI, drug dosage adjustment based on individuals' genotypes and hepatic function is necessary. This review summarizes the research progress on the impact of genetic polymorphisms and hepatic insufficiency on VRZ dosage in IFI individuals, compares current international guidelines, elucidates the current application status of VRZ in individuals with hepatic insufficiency, and discusses the influence of CYP2C19, CYP3A4, CYP2C9, and ABCB1 genetic polymorphisms on VRZ dose adjustments and Ctrough at the pharmacogenomic level. Additionally, a comprehensive summary and analysis of existing studies' recommendations on VRZ dose adjustments based on CYP2C19 genetic polymorphisms and hepatic insufficiency are provided, offering a more comprehensive reference for dose selection and adjustments of VRZ in this patient population.

EPC-DARTS: Efficient partial channel connection for differentiable architecture search.

With weight-sharing and continuous relaxation strategies, the differentiable architecture search (DARTS) proposes a fast and effective solution to perform neural network architecture search in various deep learning tasks. However, unresolved issues, such as the inefficient memory utilization, and the poor stability of the search architecture due to channels randomly selected, which has even caused performance collapses, are still perplexing researchers and practitioners. In this paper, a novel efficient channel attention mechanism based on partial channel connection for differentiable neural architecture search, termed EPC-DARTS, is proposed to address these two issues. Specifically, we design an efficient channel attention module, which is applied to capture cross-channel interactions and assign weight based on channel importance, to dramatically improve search efficiency and reduce memory occupation. Moreover, only partial channels with higher weights in the mixed calculation of operation are used through the efficient channel attention mechanism, and thus unstable network architectures obtained by the random selection operation can also be avoided in the proposed EPC-DARTS. Experimental results show that the proposed EPC-DARTS achieves remarkably competitive performance (CIFAR-10/CIFAR-100: a test accuracy rate of 97.60%/84.02%), compared to other state-of-the-art NAS methods using only 0.2 GPU-Days.

Isolation and characterization of a nephroprotective polysaccharide from Dendrobium chrysotoxum Lindl against LPS-induced acute kidney injury mice.

The structure and bioactivity of a novel polysaccharide from Dendrobium Chrysotoxum Lindl (DCP-1) were investigated. The crude polysaccharides of Dendrobium Chrysotoxum Lindl (DCP) were extracted by hot water extraction, and the protein was removed by enzymatic hydrolysis and Sevage. After purification, the chemical structure of polysaccharides was identified by infrared spectroscopy, methylation analysis and nuclear magnetic resonance spectroscopy. Then, a mouse model of acute kidney injury (AKI) was constructed using lipopolysaccharide (LPS), and pretreated with DCP. Structure characterization demonstrated that the number-average molecular weight and mass average molar mass of DCP-1 were 28.43 kDa and 15.00 kDa, respectively. DCP-1 mainly consisted of mannose (37.8 %) and glucose (55.6 %). The main linkage types of DCP-1 were contained 1,4-Linked Manp and 1,4-Linked Glcp. And DCP-1 was demonstrated to be an O-acetylglucomannan with ß-ᴅ-configuration in pyranoid form. Besides, the bioactivity of DCP was further investigated. The results showed that DCP exhibited notable anti-inflammatory activity in LPS-induced AKI mice. After treated with DCP, the creatinine (CREA) and urea nitrogen (BUN) in serum were successfully down-regulated in AKI mice. DCP treatment prevented the characteristic morphological changes of LPS-induced renal tubular injury. The results showed that DCP treatment significantly reduced the concentration of oxidative damage indicators (MDA, SOD) and the expression of inflammatory indices (TNF-α, IL-6, MCP-1, COX-2). In general, the newly extracted polysaccharide DCP showed excellent nephroprotective effect, which enabled it to be an ideal natural medicine for kidney diseases therapy.

Safety state evaluation method of the highway tunnel structure.

This study proposes an evaluation method for the structural safety of expressway tunnels utilizing possibility and prospect theories to address the influence of multiple indicators on the structural safety of expressway tunnels and the imprecision of human-bounded rationality in assessing results. It constructs the probability distribution of safety level by determining the safety level of the highway tunnel structure. The reference distribution function of each monitoring index is then derived using the expected value of experts. Based on the possibility theory, the possibility distribution of the monitoring results of indicators is obtained, and the mapping relationship between the monitoring indicators and the possibility distribution function of safety status grade is developed. Finally, the prospect theory evaluates the highway tunnel structure's safety status. This method is applied to assess the structural safety of a highway tunnel, which verifies its effectiveness and practicability, and provides a new method for evaluating the structural safety of a highway tunnel.

Mechanical properties of the brain: Focus on the essential role of Piezo1-mediated mechanotransduction in the CNS.

BACKGROUND: The brain is a highly mechanosensitive organ, and changes in the mechanical properties of brain tissue influence many physiological and pathological processes. Piezo type mechanosensitive ion channel component 1 (Piezo1), a protein found in metazoans, is highly expressed in the brain and involved in sensing changes of the mechanical microenvironment. Numerous studies have shown that Piezo1-mediated mechanotransduction is closely related to glial cell activation and neuronal function. However, the precise role of Piezo1 in the brain requires further elucidation. OBJECTIVE: This review first discusses the roles of Piezo1-mediated mechanotransduction in regulating the functions of a variety of brain cells, and then briefly assesses the impact of Piezo1-mediated mechanotransduction on the progression of brain dysfunctional disorders. CONCLUSIONS: Mechanical signaling contributes significantly to brain function. Piezo1-mediated mechanotransduction regulates processes such as neuronal differentiation, cell migration, axon guidance, neural regeneration, and oligodendrocyte axon myelination. Additionally, Piezo1-mediated mechanotransduction plays significant roles in normal aging and brain injury, as well as the development of various brain diseases, including demyelinating diseases, Alzheimer's disease, and brain tumors. Investigating the pathophysiological mechanisms through which Piezo1-mediated mechanotransduction affects brain function will give us a novel entry point for the diagnosis and treatment of numerous brain diseases.

Novel mechanisms for the synthesis of important secondary metabolites in Ginkgo biloba seed revealed by multi-omics data.

Although the detailed biosynthetic mechanism is still unclear, the unique secondary metabolites of Ginkgo biloba, including ginkgolic acids (GAs) and terpene trilactones, have attracted increasing attention for their potent medicinal, physiological and biochemical properties. In particular, GAs have shown great potential in the fields of antibacterial and insecticidal activities, making it urgent to elucidate their biosynthetic mechanism. In this study, we systematically revealed the landscape of metabolic-transcriptional regulation across continuous growth stages of G. biloba seeds (GBS) based on multi-omics mining and experimental verification, and successfully identified all major types of GAs and terpene trilactones along with more than a thousand kinds of other metabolites. The phenological changes and the essential gene families associated with these unique metabolites were analyzed in detail, and several potential regulatory factors were successfully identified based on co-expression association analysis. In addition, we unexpectedly found the close relationship between large introns and the biosynthesis of these secondary metabolites. These genes with large introns related to the synthesis of secondary metabolites showed higher gene expression and expression stability in different tissues or growth stages. Our results may provide a new perspective for the study of the regulatory mechanism of these unique secondary metabolites in GBS.

MSRDL: Deep learning framework for service recommendation in mashup creation.

In recent years, service-oriented computing technology has developed rapidly. The growing number of services increases the choice burden of software developers when developing service-based systems, such as mashups or applications. How to recommend appropriate services for developers to create mashups has become a basic problem in service-oriented recommendation systems. To solve this problem, people have proposed various methods to recommend services to match the requirements of the new mashups and achieved great success. However, there are also some challenges in feature utilization and text requirement understanding. Therefore, we propose a Mashup-oriented Service Recommendation framework based on Deep Learning, called MSRDL. A content component was designed in MSRDL to generate the representation of mashups and services. Besides, an interaction component was created in MSRDL to model the invocation records between mashups and services. The output features of the two parts are further integrated into MLP to obtain the service recommendation lists. Experimental results on ProgrammableWeb datasets show that our method is superior to the state-of-the-art methods.