Peripheral β-amyloid (Aβ), including those contained in the gut, may contribute to the formation of Aβ plaques in the brain, and gut microbiota appears to exert an impact on Alzheimer’s disease (AD) via the gut-brain axis, although detailed mechanisms are not clearly defined. The current study focused on uncovering the potential interactions among gut-derived Aβ in aging, gut microbiota, and AD pathogenesis. To achieve this goal, the expression levels of Aβ and several key proteins involved in Aβ metabolism were initially assessed in mouse gut, with key results confirmed in human tissue. The results demonstrated that a high level of Aβ was detected throughout the gut in both mice and human, and gut Aβ42 increased with age in wild type and mutant amyloid precursor protein/presenilin 1 (APP/PS1) mice. Next, the gut microbiome of mice was characterized by 16S rRNA sequencing, and we found the gut microbiome altered significantly in aged APP/PS1 mice and fecal microbiota transplantation (FMT) of aged APP/PS1 mice increased gut BACE1 and Aβ42 levels. Intra-intestinal injection of isotope or fluorescence labeled Aβ combined with vagotomy was also performed to investigate the transmission of Aβ from gut to brain. The data showed that, in aged mice, the gut Aβ42 was transported to the brain mainly via blood rather than the vagal nerve. Furthermore, FMT of APP/PS1 mice induced neuroinflammation, a phenotype that mimics early AD pathology. Taken together, this study suggests that the gut is likely a critical source of Aβ in the brain, and gut microbiota can further upregulate gut Aβ production, thereby potentially contributing to AD pathogenesis.

The main focus of this paper is on the analysis and integrated design of $\mathcal {L}_2$ observer-based fault detection (FD) systems for discrete-time nonlinear industrial processes. To gain a deeper insight into this FD framework, the existence condition is introduced first. Then, an integrated design of $\mathcal {L}_2$ observer-based FD approach is realized by solving the proposed existence condition with the aid of Takagi-Sugeno fuzzy dynamic modeling technique and piecewise-fuzzy Lyapunov functions. Most importantly, a weighted piecewise-fuzzy observer-based residual generator is proposed, aiming at achieving an optimal integration of residual evaluation and threshold computation into FD systems. The core of this approach is to make use of the knowledge provided by fuzzy models of each local region and then to weight the local residual signal by means of different weighting factors. In comparison with the standard norm-based fuzzy observer-based FD methods, the proposed scheme may lead to a significant improvement of the FD performance. In the end, the effectiveness of the proposed method is verified by a numerical example and a case study on the laboratory setup of continuous stirred tank heater plant.

This paper deals with a real-time-weighted observer-based fault-detection (FD) scheme for Takagi-Sugeno (T-S) fuzzy systems. The essential idea is to develop a weighted diagnostic observer-based FD system to optimize the worst case robustness and fault sensitivity simultaneously by using the information provided by each local system. To achieve an early detection of potential fault, the robustness issue is investigated in the L _∞ /L ₂ observer-based FD context. Meanwhile, the L _- fault sensitivity condition is addressed to optimize the fault detectability. Using fuzzy Lyapunov functions, sufficient conditions on the FD system design are studied. Two examples are given in the end to show the efficiency of the proposed results.

With the rapid development of fault diagnosis methods based on deep learning, many studies have investigated the transfer of intelligent fault diagnosis methods to learn the domain-invariant features of machines under different conditions. Previous researches focused on learning domain-invariant features through domain adaptation. However, the domain alignment methods cannot remove the domain shift, the target samples may be incorrectly classified by the decision boundary learned from the source domain and eventually cause the domains to be aligned in the wrong direction. To cope with this problem, we propose a deep adversarial domain adaptation network (DADAN) to transfer fault diagnosis knowledge. DADAN uses domain-adversarial training based on the Wasserstein distance to learn domain-invariant features from the raw signal. In addition, the network is combined with a supervised instance-based method to learn the discriminative features with better intra-class cohesion and inter-class separability, which can benefit the domain alignment. A dataset of bearing data including three speed conditions and dataset of hard-disk data acquired from accelerated degradation test and real-case condition were used to evaluate the performance of the proposed DADAN.

Uneven distribution and local concentration of protein complexes on distinct membrane cortices is a fundamental property in numerous biological processes, including Drosophila neuroblast (NB) asymmetric cell divisions and cell polarity in general. In NBs, the cell fate determinant Numb forms a basal crescent together with Pon and is segregated into the basal daughter cell to initiate its differentiation. Here we discover that Numb PTB domain, using two distinct binding surfaces, recognizes repeating motifs within Pon in a previously unrecognized mode. The multivalent Numb-Pon interaction leads to high binding specificity and liquid-liquid phase separation of the complex. Perturbations of the Numb/Pon complex phase transition impair the basal localization of Numb and its subsequent suppression of Notch signaling during NB asymmetric divisions. Such phase-transition-mediated protein condensations on distinct membrane cortices may be a general mechanism for various cell polarity regulatory complexes.