Key Components Research Articles

Advice to People with Parkinson's in My Clinic: Exercise.

There is compelling evidence that exercise must be part of main line therapy for people with Parkinson's disease. In this viewpoint, we outline the four key components of exercise: aerobic exercise, resistance exercise, flexibility exercise, and neuromotor exercises (posture, gait, balance, and agility) that can improve both motor and non-motor symptoms of the disease and, in the case of aerobic exercise, may delay the disease. We outline guidelines on how to change and optimize the exercise prescription at different stages of the disease.

Triangular Greenness Index Analysis for Monitoring Fungal Disease in Pine Trees: A UAV-based Approach

The Triangular Greenness Index (TGI) is a vegetation index derived from high-resolution aerial images acquired using unmanned aerial vehicles (UAVs). It serves as a valuable tool for quantifying vegetation health and dynamics in the visible spectrum. The TGI combines key components, including red reflectance and green reflectance, extracted from UAV-based imagery. The red component represents chlorophyll absorption and photosynthetic activity, while the green component reflects vegetation density and canopy structure. By integrating these components, the TGI offers a comprehensive measure of photosynthetically active vegetation, utilizing UAVs as a data collection platform. This study highlight the importance of the TGI derived from UAV-based imagery in monitoring vegetation changes, assessing ecosystem responses, and tracking variations in land cover and biodiversity. Furthermore, the application of TGI analysis using UAV-based aerial imagery shows promise in accurately identifying and monitoring vegetation affected by fungal diseases. This integrated approach enables the detection of diseased trees based on distinct changes in greenness observed in their foliage. Because fungal diseases dry the plant and cause the green areas to disappear. The integration of UAV technology enhances the accuracy and efficiency of TGI calculation, contributing to effective management and conservation strategies in the context of fungal disease detection in vegetation. In this study, TGI was produced using UAV-based orthophoto and healthy and sick trees were determined. According to the accuracy analysis, producer accuracy for detecting green plants was 99.7% and user accuracy was 98.5%. Fungal disease could be detected with 98.5% producer accuracy and 96.5% user accuracy. The overall accuracy of the study was calculated as 98.6%.

Scene text recognition with context-aware autonomous bidirectional iterative models

This research focuses on Scene Text Recognition (STR), a crucial component in various applications of artificial intelligence such as image retrieval, office automation, and intelligent traffic systems. Recent studies have shown that semantic-aware approaches significantly improve the performance of STR tasks, with context-aware STR methods becoming mainstream. Among these, the fusion of visual and language models has shown remarkable effectiveness. We propose a novel method (PABINet) that incorporates three key components: a Visual-Language Decoder, a Language Model, and a Fusion Model. First, during training, the Visual-Language Decoder masks the original labels in the Transformer decoder using permutation masks, with each mask being unique. This enhances word memorization and learning through contextual semantic information, resulting in robust semantic knowledge. During the inference stage, the Visual-Language Decoder employs autonomous Autoregressive model (AR) inference to generate results. Subsequently, the Language Model scrutinizes and corrects the output of the Visual-Language Encoder using a cloze mask approach, achieving context-aware, autonomous, bidirectional inference. Finally, the Fusion Model concatenates and refines the outputs of both models through iterative layers.Experimental results demonstrate that our PABINet performs exceptionally well when handling various quality images. When trained with synthetic data, PABINet achieves a new STR benchmark (average accuracy of 92.41%), and when trained with real data, it establishes new state-of-the-art results (average accuracy of 96.28%).

Antipsychotic drugs selectively decorrelate long-range interactions in deep cortical layers.

Psychosis is characterized by a diminished ability of the brain to distinguish externally driven activity patterns from self-generated activity patterns. Antipsychotic drugs are a class of small molecules with relatively broad binding affinity for a variety of neuromodulator receptors that, in humans, can prevent or ameliorate psychosis. How these drugs influence the function of cortical circuits, and in particular their ability to distinguish between externally and self-generated activity patterns, is still largely unclear. To have experimental control over self-generated sensory feedback, we used a virtual reality environment in which the coupling between movement and visual feedback can be altered. We then used widefield calcium imaging to determine the cell type-specific functional effects of antipsychotic drugs in mouse dorsal cortex under different conditions of visuomotor coupling. By comparing cell type-specific activation patterns between locomotion onsets that were experimentally coupled to self-generated visual feedback and locomotion onsets that were not coupled, we show that deep cortical layers were differentially activated in these two conditions. We then show that the antipsychotic drug clozapine disrupted visuomotor integration at locomotion onsets also primarily in deep cortical layers. Given that one of the key components of visuomotor integration in cortex is long-range cortico-cortical connections, we tested whether the effect of clozapine was detectable in the correlation structure of activity patterns across dorsal cortex. We found that clozapine as well as two other antipsychotic drugs, aripiprazole and haloperidol, resulted in a strong reduction in correlations of layer 5 activity between cortical areas and impaired the spread of visuomotor prediction errors generated in visual cortex. Our results are consistent with the interpretation that a major functional effect of antipsychotic drugs is a selective alteration of long-range layer 5-mediated communication.

SiReN: Sign-Aware Recommendation Using Graph Neural Networks.

In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However, such attempts have focused mostly on utilizing only the information of positive user-item interactions with high ratings. Thus, there is a challenge on how to make use of low rating scores for representing users' preferences since low ratings can be still informative in designing NE-based recommender systems. In this study, we present SiReN, a new Sign-aware Recommender system based on GNN models. Specifically, SiReN has three key components: 1) constructing a signed bipartite graph for more precisely representing users' preferences, which is split into two edge-disjoint graphs with positive and negative edges each; 2) generating two embeddings for the partitioned graphs with positive and negative edges via a GNN model and a multilayer perceptron (MLP), respectively, and then using an attention model to obtain the final embeddings; and 3) establishing a sign-aware Bayesian personalized ranking (BPR) loss function in the process of optimization. Through comprehensive experiments, we empirically demonstrate that SiReN consistently outperforms state-of-the-art NE-aided recommendation methods.

A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0

Web 3.0 enables user-generated contents and userselected authorities. With the help of decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey precisely the desired meanings without consuming many resources. In this article, we propose a unified blockchain-semantic ecosystems framework for wireless edge intelligence-enabled Web 3.0. Our framework consists of six key components to exchange semantic demands. We then introduce an Oracle-based proof of semantic mechanism to implement on-chain and off-chain interactions of Web 3.0 ecosystems on semantic verification algorithms while maintaining on-chain and off-chain service security. An adaptive Deep Reinforcement Learning-based sharding mechanism on Oracle is designed to improve interaction efficiency, which can facilitate Web 3.0 ecosystems to deal with varied semantic demands. Finally, a case study is presented to show that the proposed framework can dynamically adjust settings according to varied semantic demands.

Design and Implementation of SRAM Using Verilog

Abstract: This comprehensive review delves into the intricate realm of the Static Random-Access Memory (SRAM) design and implementation, elucidating its pivotal role in shaping the performance, efficiency, and reliability of contemporary electronic systems, involving applications in ASIC devices. With a primary focus on the integration of Verilog, a hardware description language (HDL), the paper provides an in-depth background on SRAM, meticulously detailing its architecture, operation, and overall significance in electronic systems. The review meticulously addresses challenges inherent in SRAM design, encompassing considerations such as power consumption, speed, and area efficiency. Significantly, it underscores the crucial role of Verilog by delving into its syntax, modules, and procedural constructs, showcasing its application in modeling key SRAM components. The discussion extends to design trade-offs, including size, speed, and power consumption, illustrating how Verilog plays a pivotal role in facilitating optimal parameter optimization. The review seamlessly integrates technical concepts like RTL (Register Transfer Level), and procedural assignments, ensuring a comprehensive exploration of SRAM design intricacies. Additionally, it introduces terms such as testbench, bitcell and power reduction, further enhancing the reader's understanding of the complex interplay between Verilog and SRAM design.

Concept paper: Innovative approaches to food quality control: AI and machine learning for predictive analysis

The concept paper explores the potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing food quality control processes. In response to the growing challenges faced by the food industry in ensuring consistent quality and safety standards, this paper proposes leveraging advanced technologies to enhance predictive analysis. The traditional methods of food quality control are often reactive and time-consuming, leading to inefficiencies and increased risks of contamination or spoilage. By harnessing AI and ML algorithms, businesses can shift towards proactive strategies, predicting potential issues before they arise and implementing preventive measures accordingly. Key components of the proposed approach include data collection from various sources such as sensors, supply chain records, and historical quality data. Through sophisticated data analysis techniques, AI systems can identify patterns, anomalies, and correlations that might indicate deviations from expected quality standards. Moreover, ML models can continuously learn and adapt based on new data, improving prediction accuracy over time. Implementation of AI-driven predictive analysis in food quality control offers several benefits. Automation of quality control processes reduces manual effort and enables real-time monitoring, enabling timely interventions to maintain product quality. By minimizing the likelihood of product recalls, waste, and rework, businesses can achieve significant cost savings associated with quality control measures. Consistently delivering high-quality products strengthens consumer trust and loyalty, leading to increased market competitiveness and brand reputation. AI-powered systems can assist in ensuring compliance with stringent food safety regulations by providing comprehensive documentation of quality control measures and outcomes. However, successful adoption of AI and ML technologies in food quality control requires overcoming various challenges, including data privacy concerns, integration with existing systems, and ensuring the reliability and interpretability of AI-driven insights. the integration of AI and ML for predictive analysis represents a transformative opportunity for the food industry to modernize quality control practices and uphold the highest standards of safety and excellence. Embracing innovation in this domain is essential for staying competitive in a rapidly evolving market landscape and meeting the evolving expectations of consumers and regulatory bodies alike.

Towards a conceptual framework for ethical AI development in IT systems

The rapid advancement of artificial intelligence (AI) technologies has prompted significant societal, ethical, and legal concerns regarding their deployment in information technology (IT) systems. Addressing these concerns necessitates the establishment of a robust ethical framework to guide AI development and integration into IT systems. This paper presents a comprehensive conceptual framework aimed at fostering ethical AI development within IT systems. The proposed framework incorporates multidisciplinary perspectives, drawing upon principles from ethics, computer science, law, and philosophy. It emphasizes the integration of ethical considerations at every stage of the AI development lifecycle, including design, implementation, deployment, and maintenance. Central to this framework is the recognition of AI systems as socio-technical artifacts with profound impacts on individuals, communities, and societies at large. Key components of the framework include transparency, accountability, fairness, privacy, and security. Transparency entails ensuring that AI algorithms and decision-making processes are comprehensible and explainable to stakeholders, thereby fostering trust and enabling scrutiny. Accountability mechanisms are essential for attributing responsibility for AI-driven outcomes and facilitating recourse in cases of harm or injustice. Moreover, the framework emphasizes the importance of fairness in AI systems, advocating for the mitigation of biases and discrimination across diverse demographic groups. Privacy protection measures are deemed crucial to safeguarding individuals' personal data from unauthorized access or misuse, while robust security protocols are essential for defending against malicious exploitation and adversarial attacks. By delineating ethical guidelines and best practices, this conceptual framework aims to empower developers, policymakers, and organizations to navigate the complex ethical landscape of AI development in IT systems. Ultimately, the adoption of such a framework is imperative for harnessing the transformative potential of AI technologies while upholding fundamental ethical principles and societal values.

A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency

Building Energy Management Systems (BEMS) play a crucial role in enhancing energy efficiency and sustainability in buildings. This abstract provides a comprehensive review of BEMS, focusing on its components, benefits, challenges, and future trends. BEMS is a centralized system that monitors and controls building services, such as heating, ventilation, air conditioning, lighting, and other systems, to improve energy efficiency and occupant comfort. The key components of BEMS include sensors, controllers, communication networks, and user interfaces. These components work together to collect data on building performance, analyze energy consumption patterns, and optimize system operation. One of the primary benefits of BEMS is its ability to reduce energy consumption and costs. By monitoring and controlling building systems based on occupancy schedules and environmental conditions, BEMS can significantly reduce energy waste. Additionally, BEMS can improve occupant comfort and productivity by maintaining optimal indoor environmental conditions. Despite its benefits, BEMS faces several challenges, including high upfront costs, complexity of installation, and integration issues with existing building systems. However, advancements in technology, such as the Internet of Things (IoT) and cloud computing, are addressing these challenges and making BEMS more accessible and cost-effective. Looking ahead, the future of BEMS is promising, with continued advancements in technology driving its adoption and integration into smart building systems. The integration of artificial intelligence and machine learning algorithms into BEMS is expected to further improve its energy-saving capabilities and enhance building performance. In conclusion, BEMS is a key technology for improving energy efficiency and sustainability in buildings. By leveraging its components and capabilities, BEMS can help reduce energy consumption, lower costs, and create healthier and more comfortable indoor environments.

Smart spirits: an integrated IoT-based liquor and health monitoring system

This project entails the development of a comprehensive health monitoring system utilizing Arduino Uno, a range of sensors (including alcohol, pulse, and DHT11), an LCD display, and a GSM module for data transmission to the Thingspeak application. Its objective is to monitor vital health parameters such as temperature, humidity, pulse rate, and alcohol levels in real-time, accessible via the Thingspeak mobile app. Key components include an Arduino Uno microcontroller, DHT11 sensor for temperature and humidity, pulse sensor for heart rate, alcohol sensor for detecting alcohol levels, LCD display for immediate feedback, and a GSM module for remote data transmission. The user-friendly Thingspeak app serves as an interface for seamless health metric monitoring. The system operates by collecting sensor data, displaying it locally on the LCD screen, and simultaneously transmitting it to the Thingspeak app via GSM communication. Users can then access the app to visualize and monitor their health metrics in real-time, allowing for prompt interventions or medical assistance if needed. The code implementation integrates sensor readings, LCD display control, and Thingspeak functionality. Leveraging Thingspeak's cloud-based platform enhances usability and accessibility, making remote health monitoring feasible. This system offers a versatile solution for individuals looking to efficiently track and manage their well-being, thanks to the integration of Arduino Uno, sensors, Thingspeak features, and GSM communication, making it suitable for both local and remote health monitoring applications.

MIS: A Multi-Identifier Management and Resolution System in the Metaverse

The metaverse gradually evolves into a virtual world containing a series of interconnected sub-metaverses. Diverse digital resources, including identities, contents, services, and supporting data, are key components of the sub-metaverse. Therefore, a Domain Name System (DNS)-like system is necessary for efficient management and resolution. However, the legacy DNS was designed with security vulnerabilities and trust risks due to centralized issues. Blockchain is used to mitigate these concerns due to its decentralized features. Additionally, it supports identity management as a default feature, making it a natural fit for the metaverse. While there are several DNS alternatives based on the blockchain, they either manage only a single type of identifiers or isolate identities from other sorts of identifiers, making it difficult for sub-metaverses to coexist and connect with each other. This article proposes a M ulti- I dentifier management and resolution S ystem (MIS) in the metaverse, supporting the registration, resolution, and inter-translation functions. The basic MIS is portrayed as a four-tier architecture on a consortium blockchain due to its manageability, enhanced security, and efficiency properties. On-chain data is lightweight and compressed to save on storage while accelerating reading and writing operations. The resource data is encrypted based on the attributes of the sub-metaverse in the storage tier for privacy protection and access control. For users with decentralization priorities, a modification named EMIS is built on top of Ethereum. Finally, MIS is implemented on two testbeds and is available online as the open-source system. The first testbed consists of 4 physical servers located in the UK and Malaysia while the second is made up of 200 virtual machines (VMs) spread over 26 countries across all 5 continents on Google Cloud. Experiments indicate that MIS provides efficient reading and writing performance than the legacy DNS and other public blockchain-based workarounds including EMIS and Ethereum Name Service (ENS).

Accurate and fast congestion feedback in MEC-enabled RDMA datacenters

Mobile edge computing (MEC) is a novel computing paradigm that pushes computation and storage resources to the edge of the network. The interconnection of edge servers forms small-scale data centers, enabling MEC to provide low-latency network services for mobile users. Nowadays, Remote Direct Memory Access (RDMA) has been widely deployed in such data centers to reduce CPU overhead and network latency. Plenty of congestion control mechanisms have been proposed for RDMA data centers, aiming to provide low-latency data delivery and high throughput network services. However, our fine-grained experimental analysis reveals that existing congestion control mechanisms still have performance limitations due to inappropriate congestion notifications and the long congestion feedback cycle. In this paper, we propose Mercury, which is an accurate and fast congestion feedback mechanism. Mercury comprises two key components: (1) the state-driven congestion detection and (2) the window-based congestion notification. Specifically, the state-driven congestion detection monitors the queue length and the number of packets received at the switch egress port when the PFC is triggered. It determines the states of egress ports and identifies flows that really contribute to congestion. Then, window-based congestion notification calculates the window sizes for congested flows and rapidly returns Congestion Notification Packets (CNPs) with the window information to the sender. It facilitates the rate adjustment of congested flows. Mercury is compatible with existing RDMA CC mechanisms and can be easily implemented in switches. We employ real-world data sets and conduct both micro-benchmark and large-scale simulations to evaluate the performance of Mercury. The results indicate that, thanks to the accurate and fast congestion feedback, Mercury achieves a reduction in the 99th tail flow completion time by up to 45.1%, 41.8%, 38.7%, 30.9%, and 37.9% compared with Timely, DCQCN, DCQCN+TCD, PACC, and HPCC, respectively.

Frequency-Adaptive Pan-Sharpening with Mixture of Experts

Pan-sharpening involves reconstructing missing high-frequency information in multi-spectral images with low spatial resolution, using a higher-resolution panchromatic image as guidance. Although the inborn connection with frequency domain, existing pan-sharpening research has not almost investigated the potential solution upon frequency domain. To this end, we propose a novel Frequency Adaptive Mixture of Experts (FAME) learning framework for pan-sharpening, which consists of three key components: the Adaptive Frequency Separation Prediction Module, the Sub-Frequency Learning Expert Module, and the Expert Mixture Module. In detail, the first leverages the discrete cosine transform to perform frequency separation by predicting the frequency mask. On the basis of generated mask, the second with low-frequency MOE and high-frequency MOE takes account for enabling the effective low-frequency and high-frequency information reconstruction. Followed by, the final fusion module dynamically weights high frequency and low-frequency MOE knowledge to adapt to remote sensing images with significant content variations. Quantitative and qualitative experiments over multiple datasets demonstrate that our method performs the best against other state-of-the-art ones and comprises a strong generalization ability for real-world scenes. Code will be made publicly at https://github.com/alexhe101/FAME-Net.

Collaborative Tooth Motion Diffusion Model in Digital Orthodontics

Tooth motion generation is an essential task in digital orthodontic treatment for precise and quick dental healthcare, which aims to generate the whole intermediate tooth motion process given the initial pathological and target ideal tooth alignments. Most prior works for multi-agent motion planning problems usually result in complex solutions. Moreover, the occlusal relationship between upper and lower teeth is often overlooked. In this paper, we propose a collaborative tooth motion diffusion model. The critical insight is to remodel the problem as a diffusion process. In this sense, we model the whole tooth motion distribution with a diffusion model and transform the planning problem into a sampling process from this distribution. We design a tooth latent representation to provide accurate conditional guides consisting of two key components: the tooth frame represents the position and posture, and the tooth latent shape code represents the geometric morphology. Subsequently, we present a collaborative diffusion model to learn the multi-tooth motion distribution based on inter-tooth and occlusal constraints, which are implemented by graph structure and new loss functions, respectively. Extensive qualitative and quantitative experiments demonstrate the superiority of our framework in the application of orthodontics compared with state-of-the-art methods.

Optimization of high-speed fixed-wing UAV penetration strategy based on deep reinforcement learning

The penetration decision of unmanned aerial vehicles (UAVs) is one of the key components for UAVs to detect or strike crucial targets in adversarial environments. Presently, most methods lack research on penetration decisions under unknown interception information. The executing UAV for decision-making is generally a simplified three-degree-of-freedom model. The decision format is often simplified to path point optimization, lacking control over the stability of complex model attitudes and exhibiting relatively poor robustness. This paper proposes a penetration relative motion theory and aircraft control method based on the six-degrees-of-freedom UAV model to reduce response errors between algorithm control decisions and actual flight control. Using a Markov decision process, a UAV penetration decision control method based on an attitude-overload control loop is designed. This method combines reinforcement learning techniques to enable autonomous penetration decision-making and control for UAVs facing active defense scenarios, enhancing the autonomy, accuracy, and generalization of UAV online decision-making. The paper concludes by implementing autonomous decision-making and control for UAV penetration through the integration of interceptor trajectory prediction with proximal policy optimization. This method reduces the dependency of penetration strategies on interceptor information, ensuring both the generalization ability of penetration strategy models under uncertain information and the consistency of decision stability. Through simulations and experiments, we verified that the penetration strategy based on the pre-sampling PPO method improves the penetration effect by 15.44 % on average and the hit rate by 18.48 %. Moreover, the probability of penetration in different scenarios exceeds 70 %, and the penetration effect is improved by more than 10 % in the case of multiple interceptors. This paper also analyzes the impact of interceptor spatial distribution and the quantity of interceptors on the effectiveness of UAV penetration decision-making.

Abstract 6090: Integration of innate and adaptive immune signatures for early detection of cancer

Abstract Up to 50% of cancer patients are diagnosed at a late stage with tumors that are often unresectable, leading to intensive treatments and a preventable loss of life. Whilst multiple assays have been developed to tackle the issue of disease mortality, these approaches have serious limitations. For example, circulating tumor DNA (ctDNA) preferentially detects hard-to-treat tumors and micrometastatic disease, with limited sensitivity and specificity for early-stage malignancy, underscoring an urgent need for novel early detection strategies. Substantial evidence exists to demonstrate that signals related to innate (e.g. pro-inflammatory cytokines) and adaptive (e.g. antibodies and T cell receptors [TCRs]) immune engagement arise early in the development of cancer. The immune system therefore offers potential as an exquisitely sensitive and highly specific intrinsic early detection system for cancer. Here we propose the development of a pan-cancer novel early detection assay, using peripheral blood to measure three key cancer-specific components of the anti-tumor immune response: i) T cell receptor (TCR) sequences, ii) antibody signatures, iii) cytokine markers. Our preliminary data demonstrates that an age and smoking-matched cohort of lung cancer patients can be distinguished from healthy donors using TCR sequencing data processed through a published machine learning approach (p<0.01). In support of this, a pilot study on a custom peptide microarray comprised of lung cancer associated antigens and recurrent neoantigens demonstrated that lung cancer patient samples show significantly higher antibody intensities than healthy donors. Thirdly, using data from a prospective cohort study, we have demonstrated that inflammatory markers can be leveraged to predict future cancer diagnosis, and identified key protein markers for future work. These data provide proof-of-principle for the use of these analytes as a tool for early detection. We are actively curating a unique cohort of samples for this work from a prospective study, Nodule Immunophenotyping Biomarker for Lung Cancer Early Diagnosis (NIMBLE) (NCT05432739), which recruits patients with indeterminate lung nodules that may represent early cancers (>280 patients to date). This malignancy has high mortality rates and a significant need for early detection strategies. Through this, we aim to understand the early immunobiological response to cancer and leverage this information to design a novel multi-parametric immunopredictor assay for early detection of cancer. Citation Format: Evelyn Fitzsimons, Alexander Coulton, Hongchang Fu, Marcellus Augustine, Richard Lee, James Reading, Kevin Litchfield. Integration of innate and adaptive immune signatures for early detection of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6090.

Abstract 274: Cancer-associated fibroblasts exhibit increased motility under hypoxic conditions via interaction with cancer cells

Abstract Cancer-associated fibroblasts (CAFs) are key components of the tumor microenvironment, playing various roles through interactions with cancer cells and other stromal cell types. In a 3D co-culture system of CAFs and lung cancer cells, a specific subgroup of CAFs exhibited increased motility, while another subgroup of CAFs remained stationary. Using the photoconvertible fluorescent protein Dendra2, we isolated these distinct CAF subgroups and conducted RNA sequencing. Gene set enrichment and gene ontology analysis revealed that upregulated genes in motile CAFs are associated with hypoxia and glycolysis. Conditioned medium from mesenchymal-like cancer cells enhanced the expression of genes related to hypoxia and glycolysis in CAFs. Among these genes, Aldoa and Ldha were chosen for knockdown in CAFs, resulting in a reduction in CAF motility. We confirmed that mesenchymal-like cancer cells promoted CAF motility and intensified hypoxic conditions within the co-culture system, which was monitored using a hypoxia reporter. Ceruloplasmin (CP) was identified as a secretory protein from mesenchymal-like cancer cells through mass spectrometry. Knockdown of CP led to the inhibition of CAF motility. In conclusion, the motility of CAFs is enhanced under hypoxic conditions facilitated by the secretion of CP from mesenchymal-like cancer cells in the lung tumor microenvironment. Citation Format: Jihye Park, Sieun Lee, Jonathan M. Kurie, Young-Ho Ahn. Cancer-associated fibroblasts exhibit increased motility under hypoxic conditions via interaction with cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 274.

Activating mutations in JAK2 and CALR differentially affect intracellular calcium flux in store operated calcium entry

BackgroundCalcium (Ca2+) signaling regulates various vital cellular functions, including integrin activation and cell migration. Store-operated calcium entry (SOCE) via calcium release-activated calcium (CRAC) channels represents a major pathway for Ca2+ influx from the extracellular space in multiple cell types. The impact of JAK2-V617F and CALR mutations which are disease initiating in myeloproliferative neoplasms (MPN) on SOCE, calcium flux from the endoplasmic reticulum (ER) to the cytosol, and related key signaling pathways in the presence or absence of erythropoietin (EPO) or thrombopoietin (TPO) is poorly understood. Thus, this study aimed to elucidate the effects of these mutations on the aforementioned calcium dynamics, in cellular models of MPN.MethodsIntracellular Ca2+ levels were measured over a time frame of 0–1080 s in Fura-2 AM labeled myeloid progenitor 32D cells expressing various mutations (JAK2-WT/EpoR, JAK2-V617F/EpoR; CALR-WT/MPL, CALR-ins5/MPL, and del52/MPL). Basal Ca2+ concentrations were assessed from 0–108 s. Subsequently, cells were stimulated with EPO/TPO in Ca2+-free Ringer solution, measuring Ca2+ levels from 109–594 s (store depletion). Then, 2 mM of Ca2+ buffer resembling physiological concentrations was added to induce SOCE, and Ca2+ levels were measured from 595–1080 s. Fura-2 AM emission ratios (F340/380) were used to quantify the integrated Ca2+ signal. Statistical significance was assessed by unpaired Student's t-test or Mann–Whitney-U-test, one-way or two-way ANOVA followed by Tukey's multiple comparison test.ResultsFollowing EPO stimulation, the area under the curve (AUC) representing SOCE significantly increased in 32D-JAK2-V617F cells compared to JAK2-WT cells. In TPO-stimulated CALR cells, we observed elevated Ca2+ levels during store depletion and SOCE in CALR-WT cells compared to CALR-ins5 and del52 cells. Notably, upon stimulation, key components of the Ca2+ signaling pathways, including PLCγ-1 and IP3R, were differentially affected in these cell lines. Hyper-activated PLCγ-1 and IP3R were observed in JAK2-V617F but not in CALR mutated cells. Inhibition of calcium regulatory mechanisms suppressed cellular growth and induced apoptosis in JAK2-V617F cells.ConclusionsThis report highlights the impact of JAK2 and CALR mutations on Ca2+ flux (store depletion and SOCE) in response to stimulation with EPO and TPO. The study shows that the JAK2-V617F mutation strongly alters the regulatory mechanism of EpoR/JAK2-dependent intracellular calcium balance, affecting baseline calcium levels, EPO-induced calcium entry, and PLCγ-1 signaling pathways. Our results reveal an important role of calcium flux in the homeostasis of JAK2-V617F positive cells.

Quadrat soil pollen signal reflects plant important values in forests and shrublands from subtropical China.

Pollen analysis, a crucial tool in botany and ecology for examining historical biotic dynamics, has elicited debate owing to its complex link with vegetation. The challenge lies in discerning the ecological significance of pollen data. In this study, we conducted detailed quadrat surveys on Jinhua Mountain, subtropical China, analyzing topsoil pollen to determine whether pollen signals accurately reflect key ecological components in the forests and shrublands. We performed direct comparisons between pollen and plant compositions and calculated pollen percentages and plant Important Values (IVs) for each quadrat. The results indicate greater homogeneity in pollen composition across the study area compared to plant composition, particularly in the high percentage of Pinus pollen. However, distinct plant communities exhibited significantly different pollen compositions, as evidenced by the multi-response permutation test. This divergence aligns with variations in the dominant plant species across different communities. There were significant correlations between pollen percentages and plant IVs, with correlation coefficients of 0.55 (p < 0.001) at the quadrat level and 0.78 (p < 0.001) at the taxon level. These results support the utility of pollen analysis for representing ecologically significant values in subtropical Chinese forests and shrublands. Such correlations might also be extrapolated to pollen-based paleoecological studies.