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Abstract Cyclin-dependent kinases 4 and 6 (CDK4/6) play a pivotal role in cell cycle and cancer development. Targeting CDK4/6 has demonstrated promising effects against breast cancer. However, resistance to CDK4/6 inhibitors (CDK4/6i), such as palbociclib, remains a substantial challenge in clinical settings. Using high-throughput combinatorial drug screening and genomic sequencing, we find that the microphthalmia-associated transcription factor (MITF) is activated via O-GlcNAcylation by O-GlcNAc transferase (OGT) in palbociclib-resistant breast cancer cells and tumors. Mechanistically, O-GlcNAcylation of MITF at Serine 49 enhances its interaction with importin α/β, thus promoting its translocation to nuclei, where it suppresses palbociclib-induced senescence. Inhibition of MITF or its O-GlcNAcylation re-sensitizes resistant cells to palbociclib. Moreover, clinical studies confirm the activation of MITF in tumors from patients who are palbociclib-resistant or undergoing palbociclib treatment. Collectively, our studies shed light on the mechanism regulating palbociclib resistance and present clinical evidence for developing therapeutic approaches to treat CDK4/6i-resistant breast cancer patients.

Aquaculture pond sediments act as hotspots for methane (CH4) emissions; however, knowledge gaps on the regulation of microorganisms hinder our further understanding of methane dynamics in aquaculture pond sediment. Using field sampling and molecular analysis, we examined CH4 fluxes, the methanogenic community composition, and their interaction with methanotrophs to comprehensively understand the methane cycling in sediments of aquaculture ponds in northern China. Compared with a fishing pond without feed inputs, the abundances of methanogens mcrA and methanotrophs pmoA genes increased significantly in aquaculture ponds sediments. The dominant methanogens were Methanothrix, Methanoregula, and Methanolinea, and the α-diversity indices of methanogens demonstrated higher levels in 0–5 cm surface sediment. The methanotrophs were dominated by Methylocystis, Methylocaldum, and Methylobacter, and the α-diversity indices of methanotrophs showed no significant difference. The total organic carbon (TOC) contents and oxidation reduction potential (ORP) were the key factors driving methanogenic and methanotrophic communities on methane cycle in aquaculture sediment. The inter-domain ecological network (IDEN) analysis revealed that total number of network nodes, links, connectances, and links per species in the aquaculture sediments presented relatively higher levels, whereas the IDEN modules were fewer. The methanogens dominated in the networks and the interaction of methanogens and methanotrophs was more competitive and complex in aquaculture sediments. These findings highlight the marked methane production in aquaculture sediment, primarily due to the abundance, diversity, and competitive advantage of methanogens over methanotrophic communities.

Abstract Neutrophils are the most abundant immune cells that first respond to insults in circulation. Although associative evidence suggests that differences in neutrophils may be linked to the sex-specific vulnerability of inflammatory diseases, mechanistic links remain elusive. Here, we identified extensive sex-specific heterogeneity in neutrophil composition under normal and auto-inflammatory conditions at single-cell resolution. Using a combination of single-cell RNA sequencing analysis, neutrophil-specific genetic knockouts and transfer experiments, we discovered dysregulation of two unconventional (interferon-α responsive and T cell regulatory) neutrophil subsets leading to male-biased incidence, severity and poor prognosis of auto-inflammatory Behçet’s uveitis. Genome-wide association study (GWAS) and exosome study revealed that male-specific negative effects of both genetic factors and circulating exosomes on unconventional neutrophil subsets contributed to male-specific vulnerability to disease. Collectively, our findings identify sex-specifically distinct neutrophil subsets and highlight unconventional neutrophil subsets as sex-specific therapeutic targets to limit inflammatory diseases.

Abstract Coronary chronic total occlusion (CTO) often occurs in patients with obstructive coronary artery disease, which remains one of the greatest challenges for interventional cardiologists. Coronary computed tomography angiography (CCTA) with its emerging post-processing techniques can provide a detailed assessment of CTO lesions before percutaneous coronary intervention (PCI), playing an important role in the clinical management of CTO PCI, from early diagnosis, pre-procedural outcome prediction, the crossing algorithm planning, intraprocedural guidance, and finally post-procedural assessment and follow-up. In addition, the feasibility of CT perfusion (CTP) in patients with CTO has been validated. Combined CCTA and CTP have the great potential to be the one-stop-shop imaging modality for assessing both anatomy and function of CTO lesions. This review aims to make radiologists understand the role of CCTA in the diagnosis and assessment of CTO lesions, thus assisting interventionalists in optimizing CTO PCI crossing strategies with the expertise of radiologists. Critical relevance statement The anatomical features of CTO on CCTA can reveal the complexity of CTO lesions and are associated with CTO PCI outcome, thus helping interventionalists optimize CTO PCI crossing strategies. Key points • CTO is the common lesion in invasive coronary angiography, and CTO PCI is technically difficult and its success rate is relatively low. • Length, collaterals, and attenuation-related signs can help distinguish CTO from subtotal occlusion. • The anatomical features of CTO lesions can help grade the difficulty of CTO PCI and predict procedural outcomes and long-term outcomes of CTO PCI. • The real-time fusion of CCTA with fluoroscopic angiography can be applied in highly complicated CTO lesions. • After CTO PCI, CCTA can help guide a second CTO PCI re-entry or follow up stent patency. Graphical Abstract

Co9S8 is considered to be one of the most promising anode materials because of its high theoretical capacity. In this work, hierarchical cage-like Co9S8 microspheres composed of well-crystallized nanosheets are successfully synthesized at 180 °C by a hydrothermal method using KOH and disodium ethylenediamine tetraacetate (Na2EDTA) as a mineralizer and a complexing agent, respectively. X-ray diffraction and scanning electron microscopy measurements show that KOH is beneficial in promoting the crystallization and development of Co9S8, avoiding the formation of impurities, while Na2EDTA is conducive to the generation of cage-like microspheres with the micro/nano architecture and better crystallization. The unique hierarchical cage-like micro/nano architecture can effectively relieve the volume change in the cycling process, and the well-crystallized Co9S8 nanosheets in the cage-like microspheres can offer much more active sites for Li+ accommodation, and thus the hierarchical cage-like Co9S8 microspheres composed of well-crystallized nanosheets show superior cycling stability and rate capability, e.g., a high capacity of 303.5 mAh g−1 after 1000 cycles at a high rate of 1.0 A g−1. This work provides a new approach for improving the electrochemical performance of LIBs by constructing a hierarchical anode material.

Low-grade appendiceal mucinous neoplasms (LAMNs) are rare and heterogeneous diseases that, despite their increased incidence, are well differentiated, tend to be painless, and histologically lack distinctive invasive features without infiltrative growth, destructive infiltration, or associated pro-fibroproliferative responses. However, the biological behaviour of these tumours is difficult to determine preoperatively or intraoperatively, and the possibility of rupture puts patients at risk for peritoneal pseudomucinous neoplasms (PMPs).Patients with low-grade appendiceal mucinous tumours and peritoneal pseudomucinous tumours experience slow disease progression and are incurable and have a high risk of recurrence, morbidity, and ultimately death, despite the reported 5- and 10-year survival rates of 50–86% and 45–68%, respectively. In this article, we report the case of a 80-year-old male with a giant low-grade appendiceal mucinous tumour associated with a peritoneal pseudomucinous tumour, and discuss the diagnostic and management strategies for giant low-grade appendiceal mucinous tumours in the context of a literature review.

Abstract Background Dcf1 has been demonstrated to play vital roles in many CNS diseases, it also has a destructive role on cell mitochondria in glioma cells and promotes the autophagy. Hitherto, it is unclear whether the viability of glioblastoma cells is affected by Dcf1, in particular Dcf1 possesses broad localization on different organelles, and the organelles interaction frequently implicated in cancer cells survival. Methods Surgically excised WHO grade IV human glioblastoma tissues were collected and cells isolated for culturing. RT‐PCR and DNA sequencing assay to estimate the abundance and mutation of Dcf1. iTRAQ sequencing and bioinformatic analysis were performed. Subsequently, immunoprecipitation assay to evaluate the degradation of HistoneH2A isomers by UBA52 ubiquitylation. Transmission electron microscopy (TEM) was applied to observe the structure change of mitochondria and autophagosome. Organelle isolated assay to determine the distribution of protein. Cell cycle and apoptosis were evaluated by flow cytometric assays. Results Dcf1 was downregulated in WHO grade IV tumor without mutation, and overexpression of Dcf1 was found to significantly regulate glioblastoma cells. One hundred and seventy‐six differentially expressed proteins were identified by iTRAQ sequencing. Furthermore, we confirmed that overexpression of Dcf1 destabilized the structure of the nucleosome via UBA52 ubiquitination to downregulate HistoneH2A.X but not macroH2A or HistoneH2A.Z, decreased the mitochondrial DNA copy number and inhibited the mitochondrial biogenesis, thus causing mitochondrial destruction and dysfunction in order to supply cellular energy and induce mitophagy preferentially but not apoptosis. Dcf1 also has disrupted the integrity of lysosomes to block autolysosome degradation and autophagy and to increase the release of Cathepsin B and D from lysosomes into cytosol. These proteins cleaved and activated BID to induce glioblastoma cells apoptosis. Conclusions In this study, we demonstrated that unmutated Dcf1 expression is negatively related to the malignancy of glioblastoma, Dcf1 overexpression causes nucleosomes destabilization, mitochondria destruction and dysfunction to induce mitophagy preferentially, and block autophagy by impairing lysosomes to induce apoptosis in glioblastoma.

Summary: Transient receptor potential canonical type 6 (TRPC6) is a non-voltage-gated channel that principally conducts calcium. Elevated channel activation contributes to fibrosis, hypertrophy, and proteinuria, often coupled to stimulation of nuclear factor of activated T-cells (NFAT). TRPC6 is post-translationally regulated, but a role for O-linked β-N-acetyl glucosamine (O-GlcNAcylation) as elevated by diabetes, is unknown. Here we show TRPC6 is constitutively O-GlcNAcylated at Ser14, Thr70, and Thr221 in the N-terminus ankryn-4 (AR4) and linker (LH1) domains. Mutagenesis to alanine reveals T221 as a critical controller of resting TRPC6 conductance, and associated NFAT activity and pro-hypertrophic signaling. T→A mutations at sites homologous in closely related TRPC3 and TRPC7 also increases their activity. Molecular modeling predicts interactions between Thr221-O-GlcNAc and Ser199, Glu200, and Glu246, and combined alanine substitutions of the latter similarly elevates resting NFAT activity. Thus, O-GlcNAcylated T221 and interactions with coordinating residues is required for normal TRPC6 channel conductance and NFAT activation.

With the era of the internet of things (IoT), the protocol stack conversion scheme based on the application-layer’s gateway has problems such as poor performance and end to end security that can’t be guaranteed.A lightweight and trusted IoT IP communication protocol was proposed, which was the main research direction of Protocol and Interface Group in Network 5.0 Industry and Technology Innovation Alliance, the deployment costs of protocol conversion gateways was decreased, the computing and storage overhead of IoT devices was reduced, and end to end communication security was ensured.

Abstract This paper proposes an analytical delay propagation model for single railway lines based on the max‐plus algebra theory. The scheduling measures taken by dispatchers, including re‐timing and re‐ordering, will be incorporated into our delay propagation model using a matrix transformation method. An analysis of delay propagation under some typical emergencies such as segment blockages and train speed limitation is performed. Numerical simulations show that the proposed train delay propagation model can predict emergency‐induced train delays under different scheduling strategies, thus may give a guidance to improve the traffic management. In the high‐speed railway train system, the scheduling measures taken by dispatchers, such as re‐timing and re‐ordering, can be formulated as a delay propagation model using a matrix transformation method.

Abstract Background Loss of primary cilia is frequently observed in tumor cells, suggesting that the absence of this organelle may promote tumorigenesis through aberrant signal transduction, the inability to exit the cell cycle, and promotion of tumor cell invasion. Primary cilia loss also occurs in esophageal squamous cell carcinoma (ESCC) cells, but the molecular mechanisms that explain how ESCC cells lose primary cilia remain poorly understood. Methods Inhibiting the expression of Prdx1 in the ESCC cells to detect the up-regulated genes related to cilium regeneration and down-regulated genes related to cilium disassembly by Gene chip. And, mice and cell experiments were carried to confirm the role of the HEF1-Aurora A-HDAC6 signaling axis in ESCC. Results In this study, we found that silencing Peroxiredoxin 1 (Prdx1) restores primary cilia formation, and over-expressing Prdx1 induces primary cilia loss in ESCC cells. We also showed that the expression of Prdx1 regulates the action of the HEF1-Aurora A-HDAC6 signaling axis to promote the disassembly of primary cilia, and suppression of Prdx1 results in decreased tumor formation and tumor mass volume in vivo. Conclusions These results suggest that Prdx1 is a novel regulator of primary cilia formation in ESCC cells.

IP protocol is the core of TCP/IP network layer. However, since IP address and its Domain Name are allocated and managed by a single agency, there are risks of centralization. The semantic overload of IP address also reduces its scalability and mobility, which further hinders the security. This paper proposes a co-governing Multi-Identifier Network (MIN) architecture that constructs a network layer with parallel coexistence of multiple identifiers, including identity, content, geographic information, and IP address. On the management plane, we develop an efficient management system using consortium blockchain with voting consensus, so the network can simultaneously manage and support by hundreds or thousands of nodes with high throughput. On the data plane, we propose an algorithm merging hash table and prefix tree (HTP) for FIB, which avoids the false-negative error and can inter-translate different identifiers with tens of billions of entries. Further, we propose a scheme to transport IP packets using CCN as a tunnel for supporting progressive deployment. We deployed the prototype of MIN to the largest operators' network in Mainland China, Hongkong and Macao, and demonstrated that the network can register identifier under co-governing consensus algorithm, support VoD service very well.

Initially discovered by Makuto Kuro-o in 1997, Klotho is a putative aging-suppressor gene when overexpressed and accelerates aging when deleted in mice. Previously, we showed that α-Klotho regulates retinal pigment epithelium (RPE) functions and protects against oxidative stress. However, the mechanisms by which Klotho influences RPE and retinal homeostasis remain elusive. Here, by performing a series of in vitro and in vivo experiments, we demonstrate that Klotho regulates cell viability under oxidative stress, mitochondrial gene expression and activity by inducing the phosphorylation of AMPK and p38MAPK, which in turn phosphorylate and activate CREB and ATF2, respectively, triggering PGC-1α transcription. The inhibition of Klotho in human RPE cells using CRISPR-Cas9 gene editing confirmed that a lack of Klotho negatively affects RPE functions, including mitochondrial activity and cell viability. Proteomic analyses showed that myelin sheath and mitochondrial-related proteins are downregulated in the RPE/retina of Kl-/- compared to WT mice, further supporting our biochemical observations. We conclude that Klotho acts upstream of the AMPK/PGC-1α pathway and regulates RPE/retinal resistance to oxidative stress, mitochondrial function, and gene and protein expressions. Thus, KL decline during aging could negatively impact retinal health, inducing age-related retinal degeneration.

Amyloid-β, a hallmark of Alzheimer’s disease, forms toxic intracellular oligomers and extracellular senile plaques resulting in neuronal toxicity. Ethanol is widely consumed worldwide. Moderate ethanol consumption has numerous benefits in humans. We found that ethanol could significantly extend the lifespan of Caenorhabiditis elegans in a previous study. Based on that study, we tested the effect of ethanol on Alzheimer’s disease transgenic Caenorhabiditis elegans strain CL4176, which expresses amyloid-β1-42 peptide in body wall muscle cells. Ethanol delayed paralysis and reduced amyloid-β oligomers in Caenorhabiditis elegans worms of the CL4176 strain. Moreover, ethanol could induce the nuclear translocation of DAF-16 in the nematodes. However, in worms that were fed daf-16 RNAi bacteria, ethanol no longer delayed the paralysis. The qPCR assays showed that ethanol increases the expression of daf-16, hsf-1 and their common target genes- small heat shock protein genes. In addition, we also found that ethanol could increase lysosome mass in the CL4176 worms. In summary, our study indicated that ethanol attenuated amyloid-β toxicity in the Alzheimer’s disease model of Caenorhabiditis elegans via increasing the level of lysosomes to promote amyloid-β degradation and upregulating the levels of small heat shock protein genes to reduce amyloid-β aggregation.

Lithium metal batteries have attracted much attention due to their high energy density. However, the critical safety issues and chemical instability of conventional liquid electrolytes in lithium metal batteries significantly limit their practical application. Herein, we propose polyethylene (PE)−based gel polymer electrolytes by in situ polymerization, which comprise a PE skeleton, polyethylene glycol and lithium bis(trifluoromethylsulfonyl)imide as well as liquid carbonate electrolytes. The obtained PE−based gel polymer electrolyte exhibits good interfacial compatibility with electrodes, high ion conductivity, and wide electrochemical window at high temperatures. Moreover, the assembled LiFePO4//Li solid−state batteries employing PE−based gel polymer electrolyte with 50% liquid carbonate electrolytes deliver good rate performance and excellent cyclic life at both 60 °C and 80 °C. In particular, they achieve high specific capacities of 158.5 mA h g−1 with a retention of 98.87% after 100 cycles under 80 °C at 0.5 C. The in situ solidified method for preparing PE−based gel polymer electrolytes proposes a feasible approach for the practical application of lithium metal batteries.

Abstract Background Youth with type 1 diabetes (T1D) have normal or elevated High‐Density Lipoprotein Cholesterol (HDL‐C), however, the function of HDL, partly mediated by the HDL proteome, may be impaired. Metformin can be used as an adjunct therapy in youth with T1D, but its effects on the HDL proteome are unknown. Objective To determine the effect of metformin on the HDL proteome. Subjects Youth (12–20 years old) with T1D who had a BMI > 90th percentile, HbA1c > 8.0% and Tanner stage 5. Methods Double‐blinded, placebo‐controlled randomized sub‐study. We examined the effects of metformin (n = 25) or placebo (n = 10) after 6 months on HDL proteome. Changes in HDL proteins were measured by data‐independent acquisition (DIA) mass spectrometry and compared between treatment groups. As a secondary outcome, associations between proteins of interest and the most studied function of HDL, the cholesterol efflux capacity (CEC), was examined. Results The relative abundance of 84 HDL‐associated proteins were measured. Two proteins were significantly affected by metformin treatment, peptidoglycan recognition protein 2 (PGRP2; +23.4%, p = .0058) and alpha‐2‐macroglobulin (A2MG; +29.8%, p = .049). Metformin did not significantly affect CEC. Changes in affected HDL proteins did not correlate with CEC. Conclusions Despite having little effect on HDL‐C, metformin increased PGRP2 and A2MG protein on HDL in youth with T1D, but had no significant effect on CEC. Further studies are needed to understand the impact of PGRP2 and A2MG on other HDL functions.

Abstract Background Patients with type 1 diabetes (T1DM) typically have normal or even elevated plasma high density lipoprotein (HDL) cholesterol concentrations; however, HDL protein composition can be altered without a change in cholesterol content. Alteration of the HDL proteome can result in dysfunctional HDL particles with reduced ability to protect against cardiovascular disease (CVD). The objective of this study was to compare the HDL proteomes of youth with T1DM and healthy controls (HC) and to evaluate the influence of glycemic control on HDL protein composition. Methods This was a cross-sectional case–control study. Blood samples were obtained from patients with T1DM and HC. HDL was isolated from plasma by size-exclusion chromatography and further purified using a lipid binding resin. The HDL proteome was analyzed by mass spectrometry using label-free SWATH peptide quantification. Results Samples from 26 patients with T1DM and 13 HC were analyzed and 78 HDL-bound proteins were measured. Youth with T1DM had significantly increased amounts of complement factor H related protein 2 (FHR2; adjusted P

Ellagic acid is a natural polyphenol found in various fruits and vegetables. Numerous studies have shown that ellagic acid has beneficial effects on human health. In this study, we investigated the stress resistant action of ellagic acid in Caenorhabditis elegans (C. elegans). Notably, 50 μM ellagic acid prolonged the lifespan of C. elegans by 36.25%, 36.22%, 155.1%, and 79.07% under ultraviolet radiation stress, heat stress, oxidative stress, and Pseudomonas aeruginosa infection stress, respectively. Furthermore, the mechanism by which ellagic acid reduces the damage caused by ultraviolet radiation in C. elegans was explored. Ellagic acid could significantly induce the nucleus translocation of DAF-16 and, thereby, activate a series of target genes to resist ultraviolet radiation stress. Moreover, ellagic acid also significantly increased the expression of SOD-3 by 3.61 times and the activity of superoxide dismutase by 3.70 times to clean out harmful reactive oxygen species in C. elegans exposed to ultraviolet radiation stress. In both daf-16 mutant and daf-2; daf-16 double-mutant worms exposed to ultraviolet radiation, ellagic acid could no longer prolong their lifespan. These results indicate that ellagic acid plays an important role in resisting ultraviolet radiation stress in C. elegans, probably in an insulin/IGF-1 signaling pathway-dependent way.

Acute kidney injury (AKI) is a common complication in cancer patients. Kidney function is closely related to patients’ quality of life and tumor prognosis. Cisplatin is a highly effective anti-tumor drug. However, the use of cisplatin is limited by its nephrotoxicity. It has been reported that FGF21 has a renal-protective function, but the mechanisms by which it does so remain unclear. In this study, we show that the expression of FGF21 is significantly upregulated in both in vitro and in vivo cisplatin-induced AKI models. Administration of recombinant FGF21 to cisplatin-induced AKI mice resulted in significantly decreased blood urea nitrogen (BUN) and serum creatinine levels, as well as significantly reduced protein levels of kidney injury molecule-1 (TIM-1), C-caspase 3, and Bax. H&E-stained kidney sections from cisplatin-induced AKI mice treated with recombinant FGF21 showed a relatively normal renal tissue structure, a reduced number of necrotic sites and vacuolar changes, and decreased casts, suggesting alleviated renal tubular injury. Experiments with an AKI cell model (cisplatin-treated HK-2 cells) yielded similar results as the mouse model; recombinant FGF21 significantly downregulated protein expression levels of TIM-1, C-caspase 3, and Bax. Furthermore, administration of recombinant FGF21 to cisplatin-treated AKI models significantly increased SIRT1 expression, and the beneficial effects of FGF21 on kidney injury were reversed by SIRT1 knockdown. Collectively, our results suggest that SIRT1 mediates the protective effect of FGF21 on cisplatin-induced kidney injury.

“O2O” (online to offline) classification recycling is a new recycling mode. It is premised on the double lines of online service platform and offline recycling service system, thus forming a pattern of online disposal and offline trading. This mode is capable of integrating upstream and downstream resources on the network platform, creating a recycling and processing mode for the entire industry chain of waste sorting, and developing a circular development mode featuring “resources-products-waste-renewable resources” to realize the recycling of resources. In this paper, the abovementioned recycling mode is constructed using the dynamic method. Taking the recycling of electronic waste (e-waste) as an example, on the basis of the original recycling, an online recycling mode is added and the e-waste is reprocessed by means of classification. Besides, the final process of manufacturing is put into the market for new products. By comparing the efficiency of different classification processing channels and the efficiency of O2O recycling, it was found out that the higher the product quality, the higher the recycling efficiency, and the efficiency of online recycling was concluded due to offline recycling. The system effect is evaluated according to the environmental index. In addition, this paper demonstrates the applicability of the mode by introducing Gree Corp. The results of this research are believed as contributory to improving the efficiency of recycling and remanufacturing at corporate level. It also provides a valuable reference for the improvement of e-waste management work for governments both in China and other countries.

Didymin, a type of flavono-o-glycoside compound naturally present in citrus fruits, has been reported to be an effective anticancer agent. However, its effects on stress resistance are unclear. In this study, we treated Caenorhabditis elegans with didymin at several concentrations. We found that didymin reduced the effects of UV stressor on nematodes by decreasing reactive oxygen species levels and increasing superoxide dismutase (SOD) activity. Furthermore, we found that specific didymin-treated mutant nematodes daf-16(mu86) & daf-2(e1370), daf-16(mu86), akt-1(ok525), akt-2(ok393), and age-1(hx546) were susceptible to UV irradiation, whereas daf-2(e1371) was resistant to UV irradiation. In addition, we found that didymin not only promoted DAF-16 to transfer from cytoplasm to nucleus, but also increased both protein and mRNA expression levels of SOD-3 and HSP-16.2 after UV irradiation. Our results show that didymin affects UV irradiation resistance and it may act on daf-2 to regulate downstream genes through the insulin/IGF-1-like signaling pathway.

OBJECTIVES/SPECIFIC AIMS: Our objective was to compare the proteomics of HDL between youth with T1DM and healthy controls (HC). METHODS/STUDY POPULATION: We did chromatography-based HDL purification and SWATH-MS-based proteomic quantitation. Proteomic alterations of HDL fractions and their association with glycemic control was examined. Study population: 26 patients with T1DM and 13 HC. RESULTS/ANTICIPATED RESULTS: We quantified 78 proteins in isolated HDL, using mass spectrometry and label-free SWATH quantification. Youth with T1DM had significantly higher protein levels of A1BG (P = 0.008), A2AP (P = 0.0448), APOA4 (P = 0.0366), CFAH (P = 0.0476), FHR2 (P = 0.0005), ITIH4(P = 0.01), PGRP2 (P = 0.0167) and lower levels of ALBU (P = 0.0164) and CO3 (P = 0.019) compared to HC. A1BG (r=0.541, P

Arbutin (p-hydroxyphenyl-β-D-glucopyranoside), a well-known tyrosinase inhibitor, has been widely used as a cosmetic whitening agent. Although its natural role is to scavenge free radicals within cells, it has also exhibited useful activities for the treatment of diuresis, bacterial infections and cancer, as well as anti-inflammatory and anti-tussive activities. Because function of free radical scavenging is also related to antioxidant and the effects of arbutin on longevity and stress resistance in animals have not yet been confirmed, here the effects of arbutin on Caenorhabditis elegans were investigated. The results demonstrated that optimal concentrations of arbutin could extend lifespan and enhance resistance to oxidative stress. The underlying molecular mechanism for these effects involves decreased levels of reactive oxygen species (ROS), improvement of daf-16 nuclear localization, and up-regulated expression of daf-16 and its downstream targets, including sod-3 and hsp16.2. In this work the roles of arbutin in lifespan and health are studied and the results support that arbutin is an antioxidant for maintaining overall health.

Amyloid-β, one of the hallmarks of Alzheimer’s disease (AD), is toxic to neurons and can also cause brain cell death. Oxidative stress is known to play an important role in AD, and there is strong evidence that oxidative stress is associated with amyloid-β. In the present study we report the protective effect of Zijuan Pu’er tea water extract (ZTWE) and the mixture of main ingredients (+)-catechins, caffeine and procyanidin (MCCP) in ZTWE on β-amyloid-induced toxicity in transgenic Caenorhabditis elegans (C. elegans) CL4176 expressing the human Aβ1⁻42 gene. ZTWE, (+)-catechins, caffeine, procyanidin and MCCP delayed the β-amyloid-induced paralysis to different degrees. The MCCP treatment did not affect the transcript abundance of amyloid-β transgene (amy-1); however, Thioflavin T staining showed a significant decrease in Aβ accumulation compared to untreated worms. Further research using transgenic worms found that MCCP promoted the translocation of DAF-16 from cytoplasm to nucleus and increased the expression of superoxide dismutase 3 (SOD-3). In addition, MCCP decreased the reactive oxygen species (ROS) content and increased the SOD activity in CL4176 worms. In conclusion, the results suggested that MCCP had a significant protective effect on β-amyloid-induced toxicity in C. elegans by reducing β-amyloid aggregation and inducing DAF-16 nuclear translocation that could activate the downstream signal pathway and enhance resistance to oxidative stress.

Urban rail transit fare strategies include fare structures and fare levels. We propose a rail transit line fare decision based on an operating plan that falls under elastic demand. Combined with the train operation plan, considering flat fare and distance-based fare, and based on the benefit analysis of both passenger flow and operating enterprises, we construct the objective functions and build an optimization model in terms of the operators’ interests, the system’s efficiency, system regulation goals, and the system costs. The solving algorithm based on the simulated annealing algorithm is established. Using as an example the Changsha Metro Line 2, we analyzed the optimized results of different models under the two fare structures system. Finally the recommendations of fare strategies are given.

In this work, we focus on solving the problem of timbre transfer in audio samples. The goal is to transfer the source audio’s timbre from one instrument to another while retaining as much of the other musical elements as possible, including loudness, pitch, and melody. While image-to-image style transfer has been used for timbre and style transfer in music recording, the current state of the findings is unsatisfactory. Current timbre transfer models frequently contain samples with unrelated waveforms that affect the quality of the generated audio. The diffusion model has excellent performance in the field of image generation and can generate high-quality images. Inspired by it, we propose a kind of timbre transfer technology based on the diffusion model. To be specific, we first convert the original audio waveform into the constant-Q transform (CQT) spectrogram and adopt image-to-image conversion technology to achieve timbre transfer. Lastly, we reconstruct the produced CQT spectrogram into an audio waveform using the DiffWave model. In both many-to-many and one-to-one timbre transfer tasks, we assessed our model. The experimental results show that compared with the baseline model, the proposed model has good performance in one-to-one and many-to-many timbre transfer tasks, which is an interesting technical progress.

In recent years, the global installed capacity of wind power has grown rapidly. Wind power forecasting, as a key technology in wind turbine systems, has received widespread attention and extensive research. However, existing studies typically focus on the power prediction of individual devices. In the context of multi-turbine scenarios, employing individual models for each device may introduce challenges, encompassing data dilution and a substantial number of model parameters in power generation forecasting tasks. In this paper, a single-model method suitable for multi-device wind power forecasting is proposed. Firstly, this method allocates multi-dimensional random vectors to each device. Then, it utilizes space embedding techniques to iteratively evolve the random vectors into representative vectors corresponding to each device. Finally, the temporal features are concatenated with the corresponding representative vectors and inputted into the model, enabling the single model to accomplish multi-device wind power forecasting task based on device discrimination. Experimental results demonstrate that our method not only solves the data dilution issue and significantly reduces the number of model parameters but also maintains better predictive performance. Future research could focus on using more interpretable space embedding techniques to observe representation vectors of wind turbine equipment and further explore their semantic features.

Personalized recommendation is a technical means to help users quickly and efficiently obtain interesting content from massive information. However, the traditional recommendation algorithm is difficult to solve the problem of sparse data and cold-start and does not make reasonable use of the user-item rating matrix. In this article, a personalized recommendation method based on deep belief network (DBN) and softmax regression is proposed to address the issues with traditional recommendation algorithms. In this method, the DBN is used to learn the deep representation of users and items, and the user-item rating matrix is maximized. Then softmax regression is used to learn multiple categories in the feature space to predict the probability of interaction between users and items. Finally, the method is applied to the area of movie recommendation. The key to this method is the negative sampling mechanism, which greatly improves the effectiveness of the recommendations, as a result, creates an accurate list of recommendations. This method was verified and evaluated on Douban and several movielens datasets of different sizes. The experimental results demonstrate that the recommended performance of this model, which has high accuracy and generalization ability, is much better than typical baseline models such as singular value decomposition (SVD), and the mean absolute error (MAE) value is 98%, which is lower than the best baseline model.

The automatic fault detection of freight train image is difficult to locate, and its average detection accuracy is low. In this paper, an object detection model Bidirectional You Only Look Once (BD-YOLO), is proposed. The process of BD-YOLO is divided into four steps. First, the feature extraction network extracts and separates image features. Second, multiscale features are fused to aggregate features of different scales. Third, prediction across scale modules is used to forecast the feature layers obtained after aggregation. Fourth, the final prediction result is obtained by decoding the prediction feature layer. The BD-YOLO model is trained using the mosaic data enhancement method and K-means clustering algorithm to improve detection accuracy and speed. Experimental results show that the mean average precision of BD-YOLO model is improved by 17.57%, on average, compared with the state-of-the-art object detection models on four types of data sets. The BD-YOLO model can immediately detect three typical faults, namely, movement of upper lever, offset of locking plate, and closing of truncated plug door handle. It has high detection rate, low false detection rate, and good robustness.

Facing the massive rolling bearing vibration data, how to improve the training efficiency, diagnosis efficiency, and diagnosis accuracy of the rolling bearing fault diagnosis model is a challenge. Considering that the Spark-GPU platform provides powerful distributed parallel computing capabilities and back propagation neural network (BPNN) optimized by quantum particle swarm optimization (QPSO) algorithm has the characteristics of low computational complexity and high diagnosis accuracy, a rolling bearing fault diagnosis method based on parallel QPSO-BPNN under Spark-GPU platform is proposed. First, the distributed parallelization of QPSO-BPNN model based on Spark-GPU platform is realized, which can improve the training efficiency and diagnosis efficiency of rolling bearing fault diagnosis model in the big data environment. Second, in order to improve the convergence speed of fault diagnosis model, a parameter update strategy suitable for the distributed parallel training of QPSO-BPNN model is designed. At each iteration during training, the local parameters of each worker node are collected to the master node, and the global parameters are updated according to the weights and synchronized to each worker node. Third, a combination strategy of multiple QPSO-BPNN models based on ensemble learning is proposed. The weighted voting method is adopted to combine the output results of different QPSO-BPNN models to obtain the best fault diagnosis result of a sample, which can improve the fault diagnosis accuracy to a certain extent. Experimental results show that the proposed method can quickly perform model training and fault diagnosis for large-scale rolling bearing vibration data, and the fault diagnosis accuracy reaches 98.73%.

Cloud computing is a promising platform to conduct large-scale workflow applications according to the pay-per-use model. Minimizing the energy consumption of precedence constrained workflows with cost budget constraints has become one of the popular topics in cloud data centers. Most existing scheduling algorithms mainly consider execution time or cost of a given workflow application under a budget constraint; however, these algorithms do not adequately consider energy saving. A reducing energy consumption strategy using a critical task remapping (RMREC) algorithm is proposed in this study. This algorithm is decomposed into two phases: energy consumption reduction and critical task remapping. In the first phase, the adjustable cost budget and spare cost are determined on the basis of cost budget, critical task path, and adjustable budget factor. All workflow tasks are further allocated to virtual machines (VMs) with the lowest energy consumption to achieve preliminary mapping between tasks and VMs while satisfying the adjustable cost budget constraint. In the second phase, critical tasks are remapped to VMs according to spare cost to decrease energy consumption caused by task migration. Experiments on two types of workflow applications with different scales demonstrate that the presented RMREC algorithm effectively reduces energy consumption without violating cost budget constraints compared with existing algorithms.

At present, deep learning is widely used to predict the remaining useful life (RUL) of rotation machinery in failure prediction and health management (PHM). However, in the actual manufacturing process, massive rotating machinery data are not easily obtained, which will lead to the decline of the prediction accuracy of the data-driven deep learning method. Firstly, a novel prognostic framework is proposed, which is comprised of conditional Wasserstein distance-based generative adversarial networks (CWGAN) and adversarial convolution neural networks (AdCNN), which can stably generate high-quality training samples to augment the bearing degradation dataset and solve the problem of few samples. Then, the bearing RUL prediction method is realized by inputting the monitoring data into the one-dimensional convolutional neural network (1DCNN) for adversarial training. Via the bearing degradation dataset of the IEEE 2012 PHM data challenge, the reliability of the proposed method is verified. Finally, experimental results show that our approach is better than others in RUL prediction on average absolute deviation and average square root error.

Blade icing caused by low-temperature environments results in the degradation of wind turbine power performance. As there is no obvious influence on the performance of wind turbines in the early stage of blade icing, it is difficult to detect the early icing state, so there will be inaccurate labels in the process of data collection. To address these challenges, this paper proposes a novel semi-supervised blade icing detection method based on a tri-training algorithm. In the proposed method, extreme gradient boosting tree (XGBoost) is used as the base classifier. A tri-training algorithm is used to integrate three base classifiers and the integrated model generates a pseudo-label for unlabeled data. In addition, we introduce Focal Loss as the loss of the base classifier in the proposed model, which solves the problem of class imbalance caused by the fact that the wind turbine is operating under normal conditions in most cases. In order to verify the effectiveness of the proposed blade icing detection method, experiments are implemented on the collected Supervisory Control and Data Acquisition (SCADA) data. The experimental results show that the proposed method effectively improves the ability to identify blade icing. Compared with other methods, it has better classification performance, robustness, and generalization.

Intelligent bearing fault diagnosis is a necessary approach to ensure the stable operation of rotating machinery. However, it is usually difficult to collect fault data under actual working conditions, leading to a serious imbalance in training datasets, thus reducing the effectiveness of data-driven diagnostic methods. During the stage of data augmentation, a multi-scale progressive generative adversarial network (MS-PGAN) is used to learn the distribution mapping relationship from normal samples to fault samples with transfer learning, which stably generates fault samples at different scales for dataset augmentation through progressive adversarial training. During the stage of fault diagnosis, the MACNN-BiLSTM method is proposed, based on a multi-scale attention fusion mechanism that can adaptively fuse the local frequency features and global timing features extracted from the input signals of multiple scales to achieve fault diagnosis. Using the UConn and CWRU datasets, the proposed method achieves higher fault diagnosis accuracy than is achieved by several comparative methods on data augmentation and fault diagnosis. Experimental results demonstrate that the proposed method can stably generate high-quality spectrum signals and extract multi-scale features, with better classification accuracy, robustness, and generalization.

This study aimed to investigate the metabolic brain network and its relationship with depression symptoms using 18F-fluorodeoxyglucose positron emission tomography data in 78 pre-chemotherapy cancer patients with depression and 80 matched healthy subjects. Functional and structural imbalance or disruption of brain networks frequently occur following chemotherapy in cancer patients. However, few studies have focused on the topological organization of the metabolic brain network in cancer with depression, especially those without chemotherapy. The nodal and global parameters of the metabolic brain network were computed for cancer patients and healthy subjects. Significant decreases in metabolism were found in the frontal and temporal gyri in cancer patients compared with healthy subjects. Negative correlations between depression and metabolism were found predominantly in the inferior frontal and cuneus regions, whereas positive correlations were observed in several regions, primarily including the insula, hippocampus, amygdala, and middle temporal gyri. Furthermore, a higher clustering efficiency, longer path length, and fewer hubs were found in cancer patients compared with healthy subjects. The topological organization of the whole-brain metabolic networks may be disrupted in cancer. Finally, the present findings may provide a new avenue for exploring the neurobiological mechanism, which plays a key role in lessening the depression effects in pre-chemotherapy cancer patients.