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Objective: Pyroptosis represents an emerging inflammatory form of programmed cell death. Herein, specific functions and clinical implications of pyroptosis-related genes were systematically characterized in breast cancer.Methods: Expression, somatic mutation and copy number variation of 33 pyroptosis-related genes were assessed in breast cancer from TCGA dataset. Their interactions, biological functions and prognostic values were then observed. By stepwise Cox regression analysis, a pyroptosis-related gene signature was generated. The predictive efficacy in survival was examined by survival analyses, ROCs, univariate and multivariate analyses and subgroup analyses. Associations between risk score (RS) and cancer immunity cycle, HLA, immune cell infiltrations, and immune checkpoints were analyzed.Results: Most of pyroptosis-related genes were abnormally expressed in breast cancer. CASP8, NLRC4, NLRP3, NLRP2, PLCG1, NLRP1, NLRP7, SCAF11, GSDMC, and NOD1 occurred somatic mutations as well as most of them had high frequency of CNV. There were closely interactions between them. These genes were distinctly enriched in immune-related processes. A three-gene signature was generated, containing IL-18, GSDMC, and TIRAP. High RS predicted poorer overall survival, progression, and recurrence. After verification, this RS was an independent and sensitive predictive index. This RS was negatively correlated to cancer immunity cycle. Also, low RS was characterized by high HLA, immune cell infiltrations and immune checkpoints. A nomogram including age and RS was generated for accurately predicting 5-, 8-, and 10-year survival probabilities.Conclusion: Pyroptosis-related genes exert key roles in cancer immunity and might be applied as a prognostic factor of breast cancer.

Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as chemical oxygen demand (COD) and total phosphorus (TP) makes the model difficult to fit and has low prediction accuracy. The classical deep learning methods have been shown to perform nonlinear modeling. However, there are enormous numerical differences between multi-dimensional data in the prediction problem of wastewater treatment, such as COD above 3000 mg/L and TP around 30 mg/L. It will make current normalization methods challenging to handle effectively, leading to the training failing to converge and the gradient disappearing or exploding. This paper proposes a multi-factor prediction model based on deep learning. The model consists of a combined normalization layer and a codec. The combined normalization layer combines the advantages of three normalization calculation methods: z-score, Interval, and Max, which can realize the adaptive processing of multi-factor data, fully retain the characteristics of the data, and finally cooperate with the codec to learn the data characteristics and output the prediction results. Experiments show that the proposed model can overcome data differences and complex nonlinearity in predicting industrial wastewater pollutant indicators and achieve better prediction accuracy than classical models.

Cell death is a common phenomenon in the progression of Alzheimer’s disease (AD). However, the mechanism of triggering the death of neuronal cells remains unclear. Ferroptosis is an iron-dependent lipid peroxidation-driven cell death and emerging evidences have demonstrated the involvement of ferroptosis in the pathological process of AD. Moreover, several hallmarks of AD pathogenesis were consistent with the characteristics of ferroptosis, such as excess iron accumulation, elevated lipid peroxides, and reactive oxygen species (ROS), reduced glutathione (GSH), and glutathione peroxidase 4 (GPX4) levels. Besides, some ferroptosis inhibitors can relieve AD-related pathological symptoms in AD mice and exhibit potential clinical benefits in AD patients. Therefore, ferroptosis is gradually being considered as a distinct cell death mechanism in the pathogenesis of AD. However, direct evidence is still lacking. In this review, we summarize the features of ferroptosis in AD, its underlying mechanisms in AD pathology, and review the application of ferroptosis inhibitors in both AD clinical trials and mice/cell models, to provide valuable information for future treatment and prevention of this devastating disease.

Cholinergic degeneration is one of the key pathological hallmarks of Alzheimer’s disease (AD), a condition that is characterized by synaptic disorders and memory impairments. Nerve growth factor (NGF) is secreted in brain regions that receive projections from the basal forebrain cholinergic neurons. The trophic effects of NGF rely on the appropriate maturation of NGF from its precursor, proNGF. The ratio of proNGF/NGF is known to be increased in patients with AD; however, the mechanisms that underlie this observation have yet to be elucidated. Here, we demonstrated that levels of miR-144-3p are increased in the hippocampi and the medial prefrontal cortex of an APP/PS1 mouse model of AD. These mice also exhibited cholinergic degeneration (including the loss of cholinergic fibers, the repression of choline acetyltransferase (ChAT) activity, the reduction of cholinergic neurons, and an increased number of dystrophic neurites) and synaptic/memory deficits. The elevated expression of miR-144-3p specifically targets the mRNA of tissue plasminogen activator (tPA) and reduces the expression of tPA, thus resulting in the abnormal maturation of NGF. The administration of miR-144-3p fully replicated the cholinergic degeneration and synaptic/memory deficits observed in the APP/PS1 mice. The injection of an antagomir of miR-144-3p into the hippocampi partially rescued cholinergic degeneration and synaptic/memory impairments by restoring the levels of tPA protein and by correcting the ratio of proNGF/NGF. Collectively, our research revealed potential mechanisms for the disturbance of NGF maturation and cholinergic degeneration in AD and identified a potential therapeutic target for AD.

Background: Numerous studies have indicated that the neddylation pathway is closely associated with tumor development. MLN4924 (Pevonedistat), an inhibitor of the NEDD8-activating E1 enzyme, is considered a promising chemotherapeutic agent. Recently, we demonstrated that neddylation of the tumor suppressor PTEN occurs under high glucose conditions and promotes breast cancer development. It has been shown, however, that PTEN protein levels are reduced by 30–40% in breast cancer. Whether this PTEN deficiency affects the anti-tumor function of MLN4924 is unknown.Methods: In the present study, cell counting kit-8 and colony formation assays were used to detect cell proliferation, and a transwell system was used to quantify cell migration. A tumor growth assay was performed in BALB/c nude mice. The subcellular location of PTEN was detected by fluorescence microscopy. The CpG island of the UBA3 gene was predicted by the Database of CpG Islands and UCSC database. Western blotting and qRT-PCR were used to measure the expression of indicated proteins. The Human Protein Atlas database, the Cancer Genome Atlas and Gene Expression Omnibus datasets were used to validate the expression levels of UBA3 in breast cancer.Results: Our data show that the anti-tumor efficacy of MLN4924 in breast cancer cells was markedly reduced with the deletion of PTEN. PI3K/Akt signaling pathway activity correlated positively with UBA3 expression. Pathway activity correlated negatively with NEDP1 expression in PTEN-positive breast cancer patients, but not in PTEN-negative patients. We also demonstrate that high glucose conditions upregulate UBA3 mRNA by inhibiting UBA3 promoter methylation, and this upregulation results in the overactivation of PTEN neddylation in breast cancer cells.Conclusion: These data suggest a mechanism by which high glucose activates neddylation. PTEN is critical, if not indispensable, for MLN4924 suppression of tumor growth; PTEN status thus may help to identify MLN4924-responsive breast cancer patients.

This article investigates the stabilization of linear systems under event‐triggered sampling, where the feedback signals are transmitted through a band‐limited network suffering from denial‐of‐service (DoS) attacks. We aim to reduce the average bit rate for stabilization while preserving the system's resilience against DoS. First, we consider the simpler case without transmission delay and present an event‐triggered control strategy to stabilize the concerned system by transmitting a finite number of bits. The average bit rate in a long time is zero in that case. Then we consider the joint effects of transmission delay and DoS attacks, and develop a variable bit rate quantization strategy and a periodic event‐triggering strategy. The proposed strategies can reduce the average bit rate compared with time‐triggered sampling methods. A numerical example is taken to verify the effectiveness of the proposed quantization and event‐triggering strategies. [ABSTRACT FROM AUTHOR]

For iterative closest point (ICP) algorithm, the initial position and the number of iterations are needed in registration. At the same time, the ICP algorithm is easy to fall into local convergence and convergence speed is slow. By constructing K-D tree to search neighborhood points and artificially set threshold, plane fitting is carried out, the on-time point cloud to be deployed is separated from the complex background, and statistical analysis is used to calculate the distance between the point cloud and the neighborhood point to quickly remove the invalid point cloud. The surface equation is set to calculate the tangent plane of point cloud normal vector and each normal vector, and the local coordinate system is constructed. The angle between adjacent vectors and the local coordinate system is calculated to determine the feature point set of edge contour. According to the covariance matrix of the feature points set, the principal feature component is obtained, the principal axis direction of the two sets of point clouds is found, and the rotation matrix and the displacement vector are obtained. Finally, GICP precise registration of point cloud is carried out according to initial pose parameters and rigid body transformation matrix obtained by maximum likelihood estimation method. The results show that the optimized algorithm can effectively avoid local convergence. Compared with the traditional ICP algorithm, when the algorithm achieves the same registration accuracy in the public dataset experiment, the registration speed is on average 44.82% faster and the overlap rate is on average 15.26% higher. In the real dataset experiment, the registration speed is on average 59.04% faster, the registration accuracy is on average 30.24% higher and the overlap rate is on average 10.61% higher. This shows that the optimization algorithm is superior to the traditional ICP algorithm in registration accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Long non-coding RNA (lncRNA) is a kind of non-coding RNA (ncRNA), with a length of 200 nt to 100 kb, that lacks a significant open reading frame (ORF) encoding a protein. lncRNAs are widely implicated in various physiological and pathological processes, such as epigenetic regulation, cell cycle regulation, cell differentiation regulation, cancer, and neurodegenerative diseases, through their interactions with chromatin, protein, and other RNAs. Numerous studies have suggested that lncRNAs are closely linked with the occurrence and development of a variety of diseases, especially neurodegenerative diseases, of which the etiologies are complicated and the underlying mechanisms remain elusive. Determining the roles of lncRNA in the pathogenesis of neurodegenerative diseases will not only deepen understanding of the physiological and pathological processes that occur in those diseases but also provide new ideas and solutions for their diagnosis and prevention. This review aims to highlight the progress of lncRNA research in the pathological and behavioral changes of neurodegenerative diseases. Specifically, we focus on how lncRNA dysfunctions are involved in the pathogenesis of Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis.

Cholinergic neurons in the diagonal band of Broca degenerate early in Alzheimer’s disease. Here the authors show that in healthy mice, these cholinergic inputs innervate newborn neurons in the hippocampus, and that loss of this innervation in an Alzheimer’s disease model leads to impairments in spatial memory.