Farnesoid X Receptor (FXR) Aggravates Amyloid-β-Triggered Apoptosis by Modulating the cAMP-Response Element-Binding Protein (CREB)/Brain-Derived Neurotrophic Factor (BDNF) Pathway In Vitro

BACKGROUND: Alzheimer’s disease (AD), which results in cognitive deficits, usually occurs in older people and is mainly caused by amyloid beta (Aß) deposits and neurofibrillary tangles. The bile acid receptor, farnesoid X receptor (FXR), has been extensively studied in cardiovascular diseases and digestive diseases. However, the role of FXR in AD is not yet understood. The purpose of the present study was to investigate the mechanism of FXR function in AD. MATERIAL AND METHODS: Lentivirus infection, flow cytometry, real-time PCR, and western blotting were used to detect the gain or loss of FXR in cell apoptosis induced by Aß. Co-immunoprecipitation was used to analyze the molecular partners involved in Aß-induced apoptosis. RESULTS: We found that the mRNA and protein expression of FXR was enhanced in Ab-triggered neuronal apoptosis in differentiated SH-SY5Y cells and in mouse hippocampal neurons. Overexpression of FXR aggravated Aß-triggered neuronal apoptosis in differentiated SH-SY5Y cells, and this effect was further increased by treatment with the FXR agonist 6ECDCA. Molecular mechanism analysis by co-immunoprecipitation and immunoblotting revealed that FXR interacted with the cAMP-response element-binding protein (CREB), leading to decreased CREB and brain-derived neurotrophic factor (BDNF) protein levels. Low expression of FXR mostly reversed the Aß-triggered neuronal apoptosis effect and prevented the reduction in CREB and BDNF. CONCLUSIONS: These data suggest that FXR regulates Aß-induced neuronal apoptosis, which may be dependent on the CREB/BDNF signaling pathway in vitro

Genome structure and evolutionary history of frankincense producing \u3ci\u3eBoswellia sacra\u3c/i\u3e

Boswellia sacra Flueck (family Burseraceae) tree is wounded to produce frankincense. We report its de novo assembled genome (667.8 Mb) comprising 18,564 high-confidence protein-encoding genes. Comparing conserved single-copy genes across eudicots suggest \u3e97% gene space assembly of B. sacra genome. Evolutionary history shows B. sacra gene-duplications derived from recent paralogous events and retained from ancient hexaploidy shared with other eudicots. The genome indicated a major expansion of Gypsy retroelements in last 2 million years. The B. sacra genetic diversity showed four clades intermixed with a primary genotype—dominating most resin-productive trees. Further, the stemtranscriptome revealed that wounding concurrently activates phytohormones signaling, cell wall fortification, and resin terpenoid biosynthesis pathways leading to the synthesis of boswellic acid—a key chemotaxonomic marker of Boswellia. The sequence datasets reported here will serve as a foundation to investigate the genetic determinants of frankincense and other resin-producing species in Burseraceae

A Microarray-Based Genetic Screen for Yeast Chronological Aging Factors

Model organisms have played an important role in the elucidation of multiple genes and cellular processes that regulate aging. In this study we utilized the budding yeast, Saccharomyces cerevisiae, in a large-scale screen for genes that function in the regulation of chronological lifespan, which is defined by the number of days that non-dividing cells remain viable. A pooled collection of viable haploid gene deletion mutants, each tagged with unique identifying DNA “bar-code” sequences was chronologically aged in liquid culture. Viable mutants in the aging population were selected at several time points and then detected using a microarray DNA hybridization technique that quantifies abundance of the barcode tags. Multiple short- and long-lived mutants were identified using this approach. Among the confirmed short-lived mutants were those defective for autophagy, indicating a key requirement for the recycling of cellular organelles in longevity. Defects in autophagy also prevented lifespan extension induced by limitation of amino acids in the growth media. Among the confirmed long-lived mutants were those defective in the highly conserved de novo purine biosynthesis pathway (the ADE genes), which ultimately produces IMP and AMP. Blocking this pathway extended lifespan to the same degree as calorie (glucose) restriction. A recently discovered cell-extrinsic mechanism of chronological aging involving acetic acid secretion and toxicity was suppressed in a long-lived ade4Δ mutant and exacerbated by a short-lived atg16Δ autophagy mutant. The identification of multiple novel effectors of yeast chronological lifespan will greatly aid in the elucidation of mechanisms that cells and organisms utilize in slowing down the aging process

Effect of salinity on growth performance and resistance of the clam Cyclina sinensis against Vibrio parahaemolyticus infection

We examined the growth performance, immune parameters and the susceptibility to Vibrio parahaemolyticus in the clam Cyclina sinensis, which had been reared at different salinity levels of 10‰, 20‰ and 30‰ for 60 days. At the end of the feeding experiment, the biggest shell length and body weight was found in 20‰, followed by 30‰ and 10‰. No significant differences in superoxide dismutase (SOD) activity were observed among the clams held in 10‰, 20‰ and 30‰. Na+/K+ -ATPase (NKA) activity of the clams held in 20‰ and 30‰ were significantly lower than that in group 10‰. The lowest activities of lysozyme (LZM) and glutamic-pyruvic transaminase (GPT) were found in group 10‰. The Integrated Biomarker Response index (IBR) values of the clams had an inverse relationship with salinity: 11.28, 3.40 and 2.85 in 10‰, 20‰ and 30‰, respectively. At the end of the feeding experiment, the clams were infected with V. parahaemolyticus. As time after infection goes on, the survival rate of clams reared in 20‰ was not significantly different with the other two treatments from 24 to 48h after infection. However, from 72 to 120h after infection, it was significantly lower than those reared in 10‰, while it was significantly higher than those reared in 30‰. It is concluded that the clam C. sinensis reared in 10‰ seawater may reduce growth performance and immune ability, whereas increase resistance against V. parahaemolyticus infection

A Blockchain-Based Decentralized Public Key Infrastructure for Information-Centric Networks

How to achieve secure content distribution and accountability in information-centric networking (ICN) is a crucial problem. Subscribers need to verify whether the data came from a reliable source, rather than from a spoofing adversary. Public key cryptography was introduced to achieve a method of authentication that binds the data packet to its owner. In existing prototypes, PKIs, identity-based signatures (IBSs) and recommendation networks are the common schemes used to ensure the authenticity and availability of public keys. However, CA-based PKIs and KGC-based IBSs have been proven to be weak when it comes to resisting security attacks, with recommendation networks being too complex to deploy. In this respect, we designed a novel distributed authentication model as a secure scheme to support public key cryptography. Our model establishes a decentralized public key infrastructure by combining the smart contracts of blockchain and optimized zero-knowledge proof-verifiable presentations by utilizing the DID project, which realizes the management of public key certificates through blockchain and ensures the authenticity and availability of public keys in decentralized infrastructure. Our scheme fundamentally solves the issues of security and feasibility in existing schemes and provides a more scalable solution with respect to authenticating data sources. An experiment demonstrated that our proposal is 20% faster than the original zero knowledge proof scheme in registration

Reputation-Based Sharding Consensus Model in Information-Centric Networking

The various integration systems of blockchain and information-centric network (ICN) have been applied to provide a trusted and neutral approach to cope with large-scale content distribution in IoT, AR/VR, or 5G/6G scenarios. As a result, the scalability problem of blockchain has been an increasing concern for researchers. The sharding mechanism is recognized as a promising approach to address this challenge. However, there are still many problems in the existing schemes. Firstly, real-time processing speed trades off security of validation. Secondly, simply randomly assigning nodes to the shards may make nodes located very far from each other, which increases the block propagation time and reduces the efficiency advantage brought by the sharding mechanism. Therefore, we optimize a reputation-based sharding consensus model by multi-dimension trust and leverage the affinity propagation (AP) algorithm for gathering consensus nodes into shards. Given the minimal possibility to be at fault in the security of validation, clients can achieve real-time processing speed with assurance. The evaluation results show that the normalized mean square error (NMSE) between the estimated reputation value and the real reputation value of our reputation scheme is less than 0.02. Meanwhile, compared with the classical sharding scheme Omniledger, TPS performance can achieve 1.4 times promotion in the case of a large-scale blockchain network of 1000 nodes

Research on Pedestrian Detection and DeepSort Tracking in Front of Intelligent Vehicle Based on Deep Learning

In order to improve the tracking failure caused by small-target pedestrians and partially blocked pedestrians in dense crowds in complex environments, a pedestrian target detection and tracking method for an intelligent vehicle was proposed based on deep learning. On the basis of the YOLO detection model, the channel attention module and spatial attention module were introduced and were joined to the back of the backbone network Darknet-53 in order to achieve weight amplification of important feature information in channel and space dimensions and improve the representation ability of the model for important feature information. Based on the improved YOLO network, the flow of the DeepSort pedestrian tracking method was designed and the Kalman filter algorithm was used to estimate the pedestrian motion state. The Mahalanobis distance and apparent feature were used to calculate the similarity between the detection frame and the predicted pedestrian trajectory; the Hungarian algorithm was used to achieve the optimal matching of pedestrian targets. Finally, the improved YOLO pedestrian detection model and the DeepSort pedestrian tracking method were verified in the same experimental environment. The verification results showed that the improved model can improve the detection accuracy of small-target pedestrians, effectively deal with the problem of target occlusion, reduce the rate of missed detection and false detection of pedestrian targets, and improve the tracking failure caused by occlusion

Research on Pedestrian Detection and DeepSort Tracking in Front of Intelligent Vehicle Based on Deep Learning

In order to improve the tracking failure caused by small-target pedestrians and partially blocked pedestrians in dense crowds in complex environments, a pedestrian target detection and tracking method for an intelligent vehicle was proposed based on deep learning. On the basis of the YOLO detection model, the channel attention module and spatial attention module were introduced and were joined to the back of the backbone network Darknet-53 in order to achieve weight amplification of important feature information in channel and space dimensions and improve the representation ability of the model for important feature information. Based on the improved YOLO network, the flow of the DeepSort pedestrian tracking method was designed and the Kalman filter algorithm was used to estimate the pedestrian motion state. The Mahalanobis distance and apparent feature were used to calculate the similarity between the detection frame and the predicted pedestrian trajectory; the Hungarian algorithm was used to achieve the optimal matching of pedestrian targets. Finally, the improved YOLO pedestrian detection model and the DeepSort pedestrian tracking method were verified in the same experimental environment. The verification results showed that the improved model can improve the detection accuracy of small-target pedestrians, effectively deal with the problem of target occlusion, reduce the rate of missed detection and false detection of pedestrian targets, and improve the tracking failure caused by occlusion