SNIL: Generating Sports News From Insights With Large Language Models.

To enhance the appeal and informativeness of data news, there is an increasing reliance on data analysis techniques and visualizations, which poses a high demand for journalists' abilities. While numerous visual analytics systems have been developed for deriving insights, few tools specifically support and disseminate viewpoints for journalism. Thus, this work aims to facilitate the automatic creation of sports news from natural language insights. To achieve this, we conducted an extensive preliminary study on the published sports articles. Based on our findings, we propose a workflow - 1) exploring the data space behind insights, 2) generating narrative structures, 3) progressively generating each episode, and 4) mapping data spaces into communicative visualizations. We have implemented a human-AI interaction system called SNIL, which incorporates user input in conjunction with large language models (LLMs). It supports the modification of textual and graphical content within the episode-based structure by adjusting the description. We conduct user studies to demonstrate the usability of SNIL and the benefit of bridging the gap between analysis tasks and communicative tasks through expert and fan feedback.

MNTZNN for Solving Hybrid Double-Deck Dynamic Nonlinear Equation System Applied to Robot Manipulator Control.

Compared with conventional dynamic nonlinear equation systems, a hybrid double-deck dynamic nonlinear equation system (H3DNES) not only has multiple layers describing more different tasks in practice, but also has a hybrid nonlinear structure of solution and its derivative describing their nonlinear constraints. Its characteristics lead to the ability to describe more complicated problems involving multiple constraints, and strong nonlinear and dynamic features, such as robot manipulator tracking control. Besides, noises are inevitable in practice and thus strong robustness of models solving H3DNES is also necessary. In this work, a multilayered noise-tolerant zeroing neural network (MNTZNN) model is proposed for solving H3DNES. MNTZNN model has strong robustness and it solves H3DNES successfully even when noises exist in both the two layers of H3DNES. In order to develop the MNTZNN model, a new zeroing neural network (ZNN) design formula is proposed. It not only enables equations with respect to solutions to become equations with respect to the second-order derivatives of solutions but also makes the corresponding model have strong robustness. The robustness of the MNTZNN model is proved when parameters in the model satisfy a loose constraint and the error bounds are programmable via setting appropriate parameter values. Finally, the MNTZNN model is applied to the tracking control of the six-link planar robot manipulator and PUMA560 robot manipulator with hybrid nonlinear constraints of joint angle and velocity.

The ZmWAKL-ZmWIK-ZmBLK1-ZmRBOH4 module provides quantitative resistance to gray leaf spot in maize.

Gray leaf spot (GLS), caused by the fungal pathogens Cercospora zeae-maydis and Cercospora zeina, is a major foliar disease of maize worldwide (Zea mays L.). Here we demonstrate that ZmWAKL encoding cell-wall-associated receptor kinase-like protein is the causative gene at the major quantitative disease resistance locus against GLS. The ZmWAKLY protein, encoded by the resistance allele, can self-associate and interact with a leucine-rich repeat immune-related kinase ZmWIK on the plasma membrane. The ZmWAKLY/ZmWIK receptor complex interacts with and phosphorylates the receptor-like cytoplasmic kinase (RLCK) ZmBLK1, which in turn phosphorylates its downstream NADPH oxidase ZmRBOH4. Upon pathogen infection, ZmWAKLY phosphorylation activity is transiently increased, initiating immune signaling from ZmWAKLY, ZmWIK, ZmBLK1 to ZmRBOH4, ultimately triggering a reactive oxygen species burst. Our study thus uncovers the role of the maize ZmWAKL-ZmWIK-ZmBLK1-ZmRBOH4 receptor/signaling/executor module in perceiving the pathogen invasion, transducing immune signals, activating defense responses and conferring increased resistance to GLS.

PCSK6 exacerbates Alzheimer's disease pathogenesis by promoting MT5-MMP maturation.

Proprotein convertase subtilisin/kexin type 6 (PCSK6) is a calcium-dependent serine proteinase that regulates the proteolytic activity of various precursor proteins and facilitates protein maturation. Dysregulation of PCSK6 expression or function has been implicated in several pathological processes including nervous system diseases. However, whether and how PCSK6 is involved in the pathogenesis of Alzheimer's disease (AD) remains unclear. In this study, we reported that the expression of PCSK6 was significantly increased in the brain tissues of postmortem AD patients and APP23/PS45 transgenic AD model mice, as well as N2AAPP cells. Genetic knockdown of PCSK6 reduced amyloidogenic processing of APP in N2AAPP cells by suppressing the activation of membrane-type 5-matrix metalloproteinase (MT5-MMP), referred to as η-secretase. We further found that PCSK6 cleaved and activated MT5-MMP by recognizing the RRRNKR sequence in its N-terminal propeptide domain in N2A cells. The mutation or knockout of this cleavage motif prevented PCSK6 from interacting with MT5-MMP and performing cleavage. Importantly, genetic knockdown of PCSK6 with adeno-associated virus (AAV) reduced Aß production and ameliorated hippocampal long-term potentiation (LTP) and long-term spatial learning and memory in APP23/PS45 transgenic mice. Taken together, these results demonstrate that genetic knockdown of PCSK6 effectively alleviate AD-related pathology and cognitive impairments by inactivating MT5-MMP, highlighting its potential as a novel therapeutic target for AD treatment.

Arthroscopy-assisted reduction and simultaneous robotic-assisted screw placement in the treatment of fractures of the posterior talar process.

PURPOSE: A fracture of the posterior talar process is easily missed because of its hidden position. Inappropriate treatment is likely to result in complications, such as nonunion of the fracture and traumatic arthritis. This study evaluated the outcomes of arthroscopy-assisted reduction combined with robotic-assisted screw placement in the treatment of fractures of the posterior talar process. METHODS: The clinical data for nine patients who underwent surgical treatment of a fracture of the posterior talar process at our institution between September 2017 and January 2021 were retrospectively reviewed. Arthroscopy-assisted reduction of the fracture was performed, and a cannulated screw was placed using three-dimensional orthopedic robotic-assisted navigation. RESULTS: The patients (seven men, two women) had a mean age of 36.33 ± 9.77 years and were followed up for 21 ± 5.43 months. The operation time was 106.67 ± 24.5 min with blood loss of 47.78 ± 9.05 ml. Primary healing was obtained in all cases, and no patient sustained a nerve or tendon injury, had fracture nonunion, or developed talar osteonecrosis. One patient developed subtalar arthritis, for which subtalar joint fusion was performed; pain was markedly less severe after cleaning. CONCLUSION: Arthroscopy-assisted reduction and robotic-assisted screw placement have the advantages of visualization of fracture reduction, minimal injury, and precise screw placement in the treatment of fractures of the posterior talar process.

Quantivine: A Visualization Approach for Large-Scale Quantum Circuit Representation and Analysis.

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.

Action-Evaluator: A Visualization Approach for Player Action Evaluation in Soccer.

In soccer, player action evaluation provides a fine-grained method to analyze player performance and plays an important role in improving winning chances in future matches. However, previous studies on action evaluation only provide a score for each action, and hardly support inspecting and comparing player actions integrated with complex match context information such as team tactics and player locations. In this work, we collaborate with soccer analysts and coaches to characterize the domain problems of evaluating player performance based on action scores. We design a tailored visualization of soccer player actions that places the action choice together with the tactic it belongs to as well as the player locations in the same view. Based on the design, we introduce a visual analytics system, Action-Evaluator, to facilitate a comprehensive player action evaluation through player navigation, action investigation, and action explanation. With the system, analysts can find players to be analyzed efficiently, learn how they performed under various match situations, and obtain valuable insights to improve their action choices. The usefulness and effectiveness of this work are demonstrated by two case studies on a real-world dataset and an expert interview.

Visualizing Large-Scale Spatial Time Series with GeoChron.

In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.

The maize transcription factor CCT regulates drought tolerance by interacting with Fra a 1, E3 ligase WIPF2, and auxin response factor Aux/IAA8.

Plants are commonly exposed to abiotic stressors, which can affect their growth, productivity, and quality. Previously, the maize transcription factor ZmCCT was shown to be involved in the photoperiod response, delayed flowering, and quantitative resistance to Gibberella stalk rot. In this study, we demonstrate that ZmCCT can regulate plant responses to drought. ZmCCT physically interacted with ZmFra a 1, ZmWIPF2, and ZmAux/IAA8, which localized to the cell membrane, cytoplasm, and nucleus, respectively, both in vitro and in vivo in a yeast two-hybrid screen in response to abiotic stress. Notably, ZmCCT recruits ZmWIPF2 to the nucleus, which has strong E3 self-ubiquitination activity dependent on its RING-H2 finger domain in vitro. When treated with higher indole-3-acetic acid/abscisic acid ratios, the height and root length of Y331-ΔTE maize plants increased. Y331-ΔTE plants exhibited increased responses to exogenously applied auxin or ABA compared to Y331 plants, indicating that ZmCCT may be a negative regulator of ABA signalling in maize. In vivo, ZmCCT promoted indole-3-acetic acid biosynthesis in ZmCCT-overexpressing Arabidopsis. RNA-sequencing and DNA affinity purification-sequencing analyses showed that ZmCCT can regulate the expression of ZmRD17, ZmAFP3, ZmPP2C, and ZmARR16 under drought. Our findings provide a detailed overview of the molecular mechanism controlling ZmCCT functions and highlight that ZmCCT has multiple roles in promoting abiotic stress tolerance.

7,8-Dihydroxyflavone ameliorates cognitive impairment induced by repeated neonatal sevoflurane exposures in mice through increasing tau O-GlcNAcylation.

BACKGROUND: Sevoflurane, one of the most commonly used general anesthetics for pediatric anesthesia, has recently gained significant attention in both preclinical and clinical settings due to its potential neurotoxicity in the developing brain. Tau phosphorylation, induced by sevoflurane, is recognized as one of the major causes of neurotoxicity. 7,8-dihydroxyflavone (DHF), a TrkB receptor agonist, has been reported to exhibit potential neuroprotective effects against tauopathies. In this study, our objective was to investigate whether DHF could provide neuroprotective effects against sevoflurane-induced neurotoxicity and explore the underlying molecular mechanisms. METHODS: Six-day-old mice were subjected to 2 h of anesthesia with 3 % sevoflurane, with or without pretreatment of DHF (5 mg/kg/day, i.p.) for 3 consecutive days. Autonomic motor ability was assessed by open-field test, while learning and memory abilities were evaluated by the fear conditioning test. Western blotting was conducted to measure the levels of t-TrkB, p-TrkB, tau, and phosphorylated tau. Additionally, a co-immunoprecipitation assay was performed to investigate the interaction between O-GlcNAcylation and tau. RESULTS: Repeated neonatal sevoflurane exposures resulted in reduced freezing time during the context and cued fear conditioning tests in adulthood. However, pretreatment with DHF restored the freezing time to the level of the control group, indicating that DHF effectively alleviated cognitive impairments induced by neonatal sevoflurane exposure. We also observed that repeated neonatal sevoflurane exposures increased tau phosphorylation while decreasing tau O-GlcNAcylation. However, DHF pretreatment rebalanced the tau O-GlcNAcylation/phosphorylation ratio by enhancing the interaction between tau and O-GlcNAcylation. CONCLUSION: Our findings demonstrate that DHF effectively ameliorates sevoflurane-induced cognitive impairment in developing mice by restoring the balance between tau O-GlcNAcylation and phosphorylation. Therefore, this study suggests that DHF has the potential to be a therapeutic agent for treating cognitive impairment associated with anesthetics, such as sevoflurane.

TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation.

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion plannings and relative distances to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.

Long-range zero-shot generative deep network quantization.

Quantization approximates a deep network model with floating-point numbers by the model with low bit width numbers, thereby accelerating inference and reducing computation. Zero-shot quantization, which aims to quantize a model without access to the original data, can be achieved by fitting the real data distribution through data synthesis. However, it has been observed that zero-shot quantization leads to inferior performance compared to post-training quantization with real data for two primary reasons: 1) a normal generator has difficulty obtaining a high diversity of synthetic data since it lacks long-range information to allocate attention to global features, and 2) synthetic images aim to simulate the statistics of real data, which leads to weak intra-class heterogeneity and limited feature richness. To overcome these problems, we propose a novel deep network quantizer called long-range zero-shot generative deep network quantization (LRQ). Technically, we propose a long-range generator (LRG) to learn long-range information instead of simple local features. To incorporate more global features into the synthetic data, we use long-range attention with large-kernel convolution in the generator. In addition, we also present an adversarial margin add (AMA) module to force intra-class angular enlargement between the feature vector and class center. The AMA module forms an adversarial process that increases the convergence difficulty of the loss function, which is opposite to the training objective of the original loss function. Furthermore, to transfer knowledge from the full-precision network, we also utilize decoupled knowledge distillation. Extensive experiments demonstrate that LRQ obtains better performance than other competitors.

Effects of the quantitative trait locus qPss3 on inhibition of photoperiod sensitivity and resistance to stalk rot disease in maize.

KEY MESSAGE: We identified a quantitative trait locus, qPss3, and fine-mapped the causal locus to a 120-kb interval in maize. This locus inhibits the photoperiod sensitivity caused by ZmCCT9 and ZmCCT10, resulting in earlier flowering by 2 ~ 4 days without reduction in stalk-rot resistance in certain genotypes. Photoperiod sensitivity is a key factor affecting the adaptation of maize (Zea mays L.) to high-latitude growing areas. Although many genes associated with flowering time have been identified in maize, no gene that inhibits photoperiod sensitivity has been reported. In our previous study, we detected large differences in photoperiod sensitivity among maize inbred lines with the same photoperiod-sensitive allele at the ZmCCT10 locus. Here, we used two segregating populations with the same genetic backgrounds but different ZmCCT10 alleles to perform quantitative trait locus (QTL) analysis. We identified a unique QTL, qPss3, on chromosome 3 in the population carrying the sensitive ZmCCT10 allele. After sequential fine-mapping, we eventually delimited qPss3 to an interval of ~ 120 kb. qPss3 behaved as a dominant locus and caused earlier flowering by 2-4 days via inhibiting ZmCCT10-induced photoperiod sensitivity under long-day conditions. qPss3 also inhibited the photoperiod sensitivity induced by another flowering-related gene, ZmCCT9. For application in agriculture, an F1 hybrid heterozygous at both qPss3 and ZmCCT10 loci constitutes an optimal allele combination, showing high resistance to stalk rot without a significant delay in flowering time. Moreover, qPss3 is of great value in regulating the flowering time of tropical maize grown at high-latitude regions.

ZmDRR206 Regulates Nutrient Accumulation in Endosperm through Its Role in Cell Wall Biogenesis during Maize Kernel Development.

Dirigent proteins (DIRs) contribute to plant fitness by dynamically reorganizing the cell wall and/or by generating defense compounds during plant growth, development, and interactions with environmental stresses. ZmDRR206 is a maize DIR, it plays a role in maintaining cell wall integrity during seedling growth and defense response in maize, but its role in regulating maize kernel development is unclear. Association analysis of candidate genes indicated that the natural variations of ZmDRR206 were significantly associated with maize hundred-kernel weight (HKW). ZmDRR206 plays a dominant role in storage nutrient accumulation in endosperm during maize kernel development, ZmDRR206 overexpression resulted in small and shrunken maize kernel with significantly reduced starch content and significantly decreased HKW. Cytological characterization of the developing maize kernels revealed that ZmDRR206 overexpression induced dysfunctional basal endosperm transfer layer (BETL) cells, which were shorter with less wall ingrowth, and defense response was constitutively activated in developing maize kernel at 15 and 18 DAP by ZmDRR206 overexpression. The BETL-development-related genes and auxin signal-related genes were down-regulated, while cell wall biogenesis-related genes were up-regulated in developing BETL of the ZmDRR206-overexpressing kernel. Moreover, the developing ZmDRR206-overexpressing kernel had significantly reduced contents of the cell wall components such as cellulose and acid soluble lignin. These results suggest that ZmDRR206 may play a regulatory role in coordinating cell development, storage nutrient metabolism, and stress responses during maize kernel development through its role in cell wall biogenesis and defense response, and provides new insights into understanding the mechanisms of kernel development in maize.

SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction.

Pedestrian trajectory prediction is an important technique of autonomous driving. In order to accurately predict the reasonable future trajectory of pedestrians, it is inevitable to consider social interactions among pedestrians and the influence of surrounding scene simultaneously, which can fully represent the complex behavior information and ensure the rationality of predicted trajectories obeyed realistic rules. In this article, we propose one new prediction model named social soft attention graph convolution network (SSAGCN), which aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. In detail, when modeling social interaction, we propose a new social soft attention function, which fully considers various interaction factors among pedestrians. Also, it can distinguish the influence of pedestrians around the agent based on different factors under various situations. For the scene interaction, we propose one new sequential scene sharing mechanism. The influence of the scene on one agent at each moment can be shared with other neighbors through social soft attention; therefore, the influence of the scene is expanded both in spatial and temporal dimensions. With the help of these improvements, we successfully obtain socially and physically acceptable predicted trajectories. The experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results. The project code is available at.

Octadecaneuropeptide Ameliorates Cognitive Impairments Through Inhibiting Oxidative Stress in Alzheimer's Disease Models.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by amyloid-ß peptide (Aß) deposition. Aß accumulation induces oxidative stress, leading to mitochondrial dysfunction, apoptosis, and so forth. Octadecaneuropeptide (ODN), a diazepam-binding inhibitor (DBI)-derived peptide, has been reported to have antioxidant properties. However, it is unclear whether ODN has neuroprotective effects in AD. OBJECTIVE: To profile the potential effects of ODN on AD. METHODS: We established a mouse model of AD via microinjection of Aß in the lateral ventricle. Utilizing a combination of western blotting assays, electrophysiological recordings, and behavioral tests, we investigated the neuroprotective effects of ODN on AD. RESULTS: DBI expression was decreased in AD model mice and cells. Meanwhile, ODN decreased Aß generation by downregulating amyloidogenic AßPP processing in HEK-293 cells stably expressing human Swedish mutant APP695 and BACE1 (2EB2). Moreover, ODN could inhibit Aß-induced oxidative stress in primary cultured cells and mice, as reflected by a dramatic increase in antioxidants and a decrease in pro-oxidants. We also found that ODN could reduce oxidative stress-induced apoptosis by restoring mitochondrial membrane potential, intracellular Ca2+ and cleaved caspase-3 levels in Aß-treated primary cultured cells and mice. More importantly, intracerebroventricular injection of ODN attenuated cognitive impairments as well as long-term potentiation in Aß-treated mice. CONCLUSION: These results suggest that ODN may exert a potent neuroprotective effect against Aß-induced neurotoxicity and memory decline via its antioxidant effects, indicating that ODN may be a potential therapeutic agent for AD.

CLRNet: Component-Level Refinement Network for Deep Face Parsing.

Face parsing aims to assign pixel-wise semantic labels to different facial components (e.g., hair, brows, and lips) in given face images. However, directly predicting pixel-level labels for each facial component over the whole face image would obtain limited accuracy, especially for tiny facial components. To address this problem, some recent works propose to first crop tiny patches from the whole face image and then predict masks for each facial component. However, such cropping-and-segmenting strategy consists of two independent stages, which cannot be jointly optimized. Besides, as one valuable piece of information for parsing the highly structured facial components, context cues are not elaborately explored by the existing works. To address these issues, we propose a component-level refinement network (CLRNet) for precisely segmenting out each facial component. Specifically, we introduce an attention mechanism to bridge the two independent stages together and form an end-to-end trainable pipeline for face parsing. Furthermore, we incorporate the global context information into the refining process for each cropped facial component patch, providing informative cues for accurate parsing. Extensive experiments are carried out on two benchmark datasets, LFW-PL and HELEN. The results demonstrate the superiority of the proposed CLRNet over other state-of-the-art methods, especially for tiny facial components.

Memorial GAN With Joint Semantic Optimization for Unpaired Image Captioning.

Most works of image captioning are implemented under the full supervision of paired image-caption data. Limited to expensive cost of data collection, the task of unpaired image captioning has attracted researchers' attention. In this article, we propose a novel memorial GAN (MemGAN) with the joint semantic optimization for unpaired image captioning. The core idea is to explore implicit semantic correlation between disjointed images and sentences through building a multimodal semantic-aware space (SAS). Concretely, each modality is mapped into a unified multimodal SAS, where SAS includes the semantic vectors of image I , visual concepts O , unpaired sentence S , and the generated caption C . We adopt the memory unit based on multihead attention and relational gate as a backbone to preserve and transit crucial multimodal semantics in the SAS for image caption generation and sentence reconstruction. Then, the memory unit is embedded into a GAN framework to exploit the semantic similarity and relevance in SAS, that is, imposing a joint semantic-aware optimization on SAS without supervision clues. To summarize, the proposed MemGAN learns the latent semantic relevance of SAS's multimodalities in an adversarial manner. Extensive experiments and qualitative results demonstrate the effectiveness of MemGAN, achieving improvements over state of the arts on unpaired image captioning benchmarks.

Dysfunctional gene splicing in glucose metabolism may contribute to Alzheimer's disease.

ABSTRACT: The glucose metabolism is crucial for sustained brain activity as it provides energy and is a carbon source for multiple biomacromolecules; glucose metabolism decreases dramatically in Alzheimer's disease (AD) and may be a fundamental cause for its development. Recent studies reveal that the alternative splicing events of certain genes effectively regulate several processes in glucose metabolism including insulin receptor, insulin-degrading enzyme, pyruvate kinase M, receptor for advanced glycation endproducts, and others, thereby, influencing glucose uptake, glycolysis, and advanced glycation end-products-mediated signaling pathways. Indeed, the discovery of aberrant alternative splicing that changes the proteomic diversity and protein activity in glucose metabolism has been pivotal in our understanding of AD development. In this review, we summarize the alternative splicing events of the glucose metabolism-related genes in AD pathology and highlight the crucial regulatory roles of splicing factors in the alternative splicing process. We also discuss the emerging therapeutic approaches for targeting splicing factors for AD treatment.