Fibroblast growth factor 21 enhances learning and memory performance in mice by regulating hippocampal L-lactate homeostasis.

Fibroblast growth factor 21 (FGF21) is one endogenous metabolic molecule that functions as a regulator in glucose and lipid homeostasis. However, the effect of FGF21 on L-lactate homeostasis and its mechanism remains unclear until now. Forty-five Six-week-old male C57BL/6 mice were divided into three groups: control, L-lactate, and FGF21 (1.5 mg/kg) groups. At the end of the treatment, nuclear magnetic resonance-based metabolomics, and key proteins related to L-lactate homeostasis were determined respectively to evaluate the efficacy of FGF21 and its mechanisms. The results showed that, compared to the vehicle group, the L-lactate-treated mice displayed learning and memory performance impairments, as well as reduced hippocampal ATP and NADH levels, but increased oxidative stress, mitochondrial dysfunction, and apoptosis, which suggesting inhibited L-lactate-pyruvate conversion in the brain. Conversely, FGF21 treatment ameliorated the L-lactate accumulation state, accompanied by restoration of the learning and memory defects, indicating enhanced L-lactate uptake and utilization in hippocampal neurons. We demonstrated that maintaining constant L-lactate-pyruvate flux is essential for preserving neuronal bioenergetic and redox levels. FGF21 contributed to preparing the brain for situations of high availability of L-lactate, thus preventing neuronal vulnerability in metabolic reprogramming.

Fibroblast growth factor 21 protects the liver from apoptosis in a type 1 diabetes mouse model via regulating L-lactate homeostasis.

AIMS/HYPOTHESIS: Fibroblast growth factor 21 (FGF21) is a hepatokine with pleiotropic effects on glucose and lipid metabolic homeostasis. Here, we aimed to elucidate the mechanisms underlying the protective effects of FGF21 on L-lactate homeostasis and liver lesions in a type 1 diabetes mellitus (T1DM) mice model. METHODS: Six-week-old male C57BL/6 mice were divided into control, T1DM, and FGF21 groups. We also examined hepatic apoptotic signaling and functional indices in wild-type and hydroxycarboxylic acid receptor 1 (HCA1) knockout mice with T1DM or long-term L-lactate exposure. After preincubation of high glucose- or L-lactate treated hepatic AML12 cells, L-lactate uptake, apoptosis, and monocarboxylic acid transporter 2 (MCT2) expression were investigated. RESULTS: In a mouse model of T1DM, hepatic FGF21 expression was downregulated by approximately 1.5-fold at 13 weeks after the hyperglycemic insult. In vivo administration of exogenous FGF21 (2 mg/kg) to diabetic or L-lactate-infused mice significantly prevented hepatic oxidative stress and apoptosis by activating extracellular signal-regulated kinase (ERK)1/2, p38 mitogen-activated protein kinase (MAPK) and AMP-activated protein kinase (AMPK) pathways. HCA1-KO mice were less susceptible to diabetes- and L-lactate-induced hepatic apoptosis and dysfunction. In addition, inhibition of PI3K-mTOR activity revealed that FGF21 prevented L-lactate-induced Cori cycle alterations and hepatic apoptosis by upregulating MCT2 protein translation. CONCLUSIONS/INTERPRETATION: These results demonstrate that L-lactate homeostasis may be a therapeutic target for T1DM-related hepatic dysfunction. The protective effects of FGF21 on hepatic damage were associated with its ability to ameliorate MCT2-dependent Cori cycle alterations and prevent HCA1-mediated inhibition of ERK1/2, p38 MAPK, and AMPK signaling.

SHNN-CAD⁺: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection.

To perform anomaly detection for trajectory data, we study the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) approach, and propose an enhanced version called SHNN-CAD + . SHNN-CAD was introduced based on the theory of conformal prediction dealing with the problem of online detection. Unlike most related approaches requiring several not intuitive parameters, SHNN-CAD has the advantage of being parameter-light which enables the easy reproduction of experiments. We propose to adaptively determine the anomaly threshold during the online detection procedure instead of predefining it without any prior knowledge, which makes the algorithm more usable in practical applications. We present a modified Hausdorff distance measure that takes into account the direction difference and also reduces the computational complexity. In addition, the anomaly detection is more flexible and accurate via a re-do strategy. Extensive experiments on both real-world and synthetic data show that SHNN-CAD + outperforms SHNN-CAD with regard to accuracy and running time.

IBVis: Interactive Visual Analytics for Information Bottleneck Based Trajectory Clustering.

Analyzing trajectory data plays an important role in practical applications, and clustering is one of the most widely used techniques for this task. The clustering approach based on information bottleneck (IB) principle has shown its effectiveness for trajectory data, in which a predefined number of the clusters and an explicit distance measure between trajectories are not required. However, presenting directly the final results of IB clustering gives no clear idea of both trajectory data and clustering process. Visual analytics actually provides a powerful methodology to address this issue. In this paper, we present an interactive visual analytics prototype called IBVis to supply an expressive investigation of IB-based trajectory clustering. IBVis provides various views to graphically present the key components of IB and the current clustering results. Rich user interactions drive different views work together, so as to monitor and steer the clustering procedure and to refine the results. In this way, insights on how to make better use of IB for different featured trajectory data can be gained for users, leading to better analyzing and understanding trajectory data. The applicability of IBVis has been evidenced in usage scenarios. In addition, the conducted user study shows IBVis is well designed and helpful for users.