bluuebunny/crossref_metadata_2025_split huggingface dataset:
Narrative mobile video game-based cognitive training to enhance frontal function in patients with mild cognitive impairment
Authors: Choi Yeseul , Lim Jae-Sung , Choi Hagyun , Ryu Yong Hoe , Seong Eunkyung , Park Inseok , Kang Dong Won , Lee Jae-Hong , Kang Dong-Wha 
Published: January 2025

AbstractAlthough cognitive training has been proposed as a possible therapeutic modality for mild cognitive impairment (MCI), most serious games focus on specific tasks. This study aimed to investigate the feasibility and efficacy of narrative video game-based cognitive intervention for MCI. A four-week (± 1-week) mobile game intervention was given to 17 MCI participants (mean age (SD) = 72.8(4.75)). At baseline and post-intervention, the participants received neuropsychological tests and a depression scale. Frontal function was assessed using the Corsi block-tapping test, Color Word Stroop Test, Controlled Oral Word Association Test, Digit Symbol Coding, and Trail Making Test-Elderly’s Version; depression was assessed using the Geriatric Depression Scale. User’s compliance and gaming experience were also evaluated. MCI patients showed significant improvements in frontal function, particularly in Digit Symbol Coding (mean ± SD, 0.47 ± 0.49, p = 0.007) and phonemic fluency (mean ± SD, 0.39 ± 0.55, p = 0.024). Each frontal subtest’s mean z-score was increased (mean ± SD, 0.44 ± 0.38, p = 0.008). Block span and depression scale remained unchanged. High adherence rates (122.35%) and favorable feedback on the gaming experience indicated that the game intervention’s usability boosted patients’ motivation and engagement. Our findings demonstrate that narrative game-based cognitive intervention was not only beneficial but also enjoyable for elderly MCI.
Brain-model neural similarity reveals abstractive summarization performance
Authors: Zhang Zhejun , Guo Shaoting , Zhou Wenqing , Luo Yingying , Zhu Yingqi , Zhang Lin , Li Lei 
Published: January 2025

AbstractDeep language models (DLMs) have exhibited remarkable language understanding and generation capabilities, prompting researchers to explore the similarities between their internal mechanisms and human language cognitive processing. This study investigated the representational similarity (RS) between the abstractive summarization (ABS) models and the human brain and its correlation to the performance of ABS tasks. Specifically, representational similarity analysis (RSA) was used to measure the similarity between the representational patterns (RPs) of the BART, PEGASUS, and T5 models’ hidden layers and the human brain’s language RPs under different spatiotemporal conditions. Layer-wise ablation manipulation, including attention ablation and noise addition was employed to examine the hidden layers’ effect on model performance. The results demonstrate that as the depth of hidden layers increases, the models’ text encoding becomes increasingly similar to the human brain’s language RPs. Manipulating deeper layers leads to more substantial decline in summarization performance compared to shallower layers, highlighting the crucial role of deeper layers in integrating essential information. Notably, the study confirms the hypothesis that the hidden layers exhibiting higher similarity to human brain activity play a more critical role in model performance, with their correlations reaching statistical significance even after controlling for perplexity. These findings deepen our understanding of the cognitive mechanisms underlying language representations in DLMs and their neural correlates, potentially providing insights for optimizing and improving language models by aligning them with the human brain’s language-processing mechanisms.
Spectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative study
Authors: Hary Mathilde , Koivisto Teemu , Lukasik Sara , Dudley John M. , Genty Goëry 
Published: January 2025

AbstractSupercontinuum generation in optical fiber involves complex nonlinear dynamics, making optimization challenging, and typically relying on trial-and-error or extensive numerical simulations. Machine learning and metaheuristic algorithms offer more efficient optimization approaches. We report here an experimental study of supercontinuum spectral shaping by tuning the phase of the input pulses, different optimization approaches including a genetic algorithm, particle swarm optimizer, and simulated annealing. We find that the genetic algorithm and particle swarm optimizer are more robust and perform better, with the particle swarm optimizer converging faster. Our study provides valuable insights for the systematic optimization of supercontinuum and other optical sources.
A novel deep synthesis-based insider intrusion detection (DS-IID) model for malicious insiders and AI-generated threats
Authors: Kotb Hazem M. , Gaber Tarek , AlJanah Salem , Zawbaa Hossam M. , Alkhathami Mohammed 
Published: January 2025

AbstractInsider threats pose a significant challenge to IT security, particularly with the rise of generative AI technologies, which can create convincing fake user profiles and mimic legitimate behaviors. Traditional intrusion detection systems struggle to differentiate between real and AI-generated activities, creating vulnerabilities in detecting malicious insiders. To address this challenge, this paper introduces a novel Deep Synthesis Insider Intrusion Detection (DS-IID) model. The model employs deep feature synthesis to automatically generate detailed user profiles from event data and utilizes binary deep learning for accurate threat identification. The DS-IID model addresses three key issues: it (i) detects malicious insiders using supervised learning, (ii) evaluates the effectiveness of generative algorithms in replicating real user profiles, and (iii) distinguishes between real and synthetic abnormal user profiles. To handle imbalanced data, the model uses on-the-fly weighted random sampling. Tested on the CERT insider threat dataset, the DS-IID achieved 97% accuracy and an AUC of 0.99. Moreover, the model demonstrates strong performance in differentiating real from AI-generated (synthetic) threats, achieving over 99% accuracy on optimally generated data. While primarily evaluated on synthetic datasets, the high accuracy of the DS-IID model suggests its potential as a valuable tool for real-world cybersecurity applications.
Future food prices will become less sensitive to agricultural market prices and mitigation costs
Authors: Chen David Meng-Chuen , Bodirsky Benjamin , Wang Xiaoxi , Xuan Jiaqi , Dietrich Jan Philipp , Popp Alexander , Lotze-Campen Hermann 
Published: January 2025

Abstract
Agricultural production costs represent less than half of total food prices for higher-income countries and will likely further decrease globally. Added-value components such as transport, processing, marketing and catering show increasing importance in food value chains, especially as countries undergo a nutrition transition towards more complex and industrial food systems. Here, using a combined statistical and process-based modelling framework, we derive and project the value-added component of food prices for 136 countries and 11 different food groups, for food-at-home and food-away-from-home. We identify the declining but differentiated producer share in consumer food prices across food products, and provide scenarios of future consumer prices under a business-as-usual as well as climate mitigation scenarios. Food price increases from policies targeting agricultural producers, such as greenhouse gas taxes, are not as stark when transmitted to consumers owing to higher value added in higher-income countries, while a pronounced effect remains in lower-income countries, even in coming decades.
Context-dependent effects of CDKN2A and other 9p21 gene losses during the evolution of esophageal cancer
Authors: Ganguli Piyali , Basanta Celia C. , Acha-Sagredo Amelia , Misetic Hrvoje , Armero Maria , Mendez Akram , Zahra Aeman , Devonshire Ginny , Kelly Gavin , Freeman Adam , Green Mary , Nye Emma , Bichisecchi Anita , Bonfanti Paola ,   Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium, Fitzgerald Rebecca C. , Edwards Paul A. W. , Grehan Nicola , Nutzinger Barbara , Redmond Aisling M. , Loreno Christine , Abbas Sujath , Freeman Adam , Smyth Elizabeth C. , O’Donovan Maria , Miremadi Ahmad , Malhotra Shalini , Tripathi Monika , Coles Hannah , Millington Curtis , Eldridge Matthew , Secrier Maria , Devonshire Ginny , Davies Jim , Crichton Charles , Carroll Nick , Hardwick Richard H. , Safranek Peter , Hindmarsh Andrew , Sujendran Vijayendran , Hayes Stephen J. , Ang Yeng , Sharrocks Andrew , Preston Shaun R. , Bagwan Izhar , Save Vicki , Skipworth Richard J. E. , Hupp Ted R. , O’Neill J. Robert , Tucker Olga , Beggs Andrew , Taniere Philippe , Puig Sonia , Contino Gianmarco , Underwood Timothy J. , Walker Robert C. , Grace Ben L. , Lagergren Jesper , Gossage James , Davies Andrew , Chang Fuju , Mahadeva Ula , Goh Vicky , Ciccarelli Francesca D. , Sanders Grant , Berrisford Richard , Chan David , Cheong Ed , Kumar Bhaskar , Sreedharan L. , Parsons Simon L. , Soomro Irshad , Kaye Philip , Saunders John , Lovat Laurence , Haidry Rehan , Scott Michael , Sothi Sharmila , Lishman Suzy , Hanna George B. , Peters Christopher J. , Moorthy Krishna , Grabowska Anna , Turkington Richard , McManus Damian , Coleman Helen , Petty Russell D. , Bartlett Freddie , Rodriguez-Justo Manuel , Spencer Jo , Fitzgerald Rebecca C. , Ciccarelli Francesca D. 
Published: January 2025

Abstract

CDKN2A is a tumor suppressor located in chromosome 9p21 and frequently lost in Barrett’s esophagus (BE) and esophageal adenocarcinoma (EAC). How CDKN2A and other 9p21 gene co-deletions affect EAC evolution remains understudied. We explored the effects of 9p21 loss in EACs and cancer progressor and non-progressor BEs with matched genomic, transcriptomic and clinical data. Despite its cancer driver role, CDKN2A loss in BE prevents EAC initiation by counterselecting subsequent TP53 alterations. 9p21 gene co-deletions predict poor patient survival in EAC but not BE through context-dependent effects on cell cycle, oxidative phosphorylation and interferon response. Immune quantifications using bulk transcriptome, RNAscope and high-dimensional tissue imaging showed that IFNE loss reduces immune infiltration in BE, but not EAC. Mechanistically, CDKN2A loss suppresses the maintenance of squamous epithelium, contributing to a more aggressive phenotype. Our study demonstrates context-dependent roles of cancer genes during disease evolution, with consequences for cancer detection and patient management.
Hypoxia promotes tumor immune evasion by suppressing MHC-I expression and antigen presentation
Authors: Estephan Hala , Tailor Arun , Parker Robert , Kreamer McKenzie , Papandreou Ioanna , Campo Leticia , Easton Alistair , Moon Eui Jung , Denko Nicholas C , Ternette Nicola , Hammond Ester M , Giaccia Amato J 
Published: January 2025

Abstract
Hypoxia is a common feature of solid tumors that has previously been linked to resistance to radiotherapy and chemotherapy, and more recently to immunotherapy. In particular, hypoxic tumors exclude T cells and inhibit their activity, suggesting that tumor cells acquire a mechanism to evade T-cell recognition and killing. Our analysis of hypoxic tumors indicates that hypoxia downregulates the expression of MHC class I and its bound peptides (i.e., the immunopeptidome). Hypoxia decreases MHC-I expression in an oxygen-dependent manner, via activation of autophagy through the PERK arm of the unfolded protein response. Using an immunopeptidomics-based LC-MS approach, we find a significant reduction of presented antigens under hypoxia. Inhibition of autophagy under hypoxia enhances antigen presentation. In experimental tumors, reducing mitochondrial metabolism through a respiratory complex-I inhibitor increases tumor oxygenation, as well as MHC-I levels and the immunopeptidome. These data explain the molecular basis of tumor immune evasion in hypoxic conditions, and have implications for future therapeutic interventions targeting hypoxia-induced alterations in antigen presentation.
Cohesin positions the epigenetic reader Phf2 within the genome
Authors: Tang Wen , Costantino Lorenzo , Stocsits Roman , Wutz Gordana , Ladurner Rene , Hudecz Otto , Mechtler Karl , Peters Jan-Michael 
Published: January 2025

Abstract
Genomic DNA is assembled into chromatin by histones, and extruded into loops by cohesin. These mechanisms control important genomic functions, but whether histones and cohesin cooperate in genome regulation is poorly understood. Here we identify Phf2, a member of the Jumonji-C family of histone demethylases, as a cohesin-interacting protein. Phf2 binds to H3K4me3 nucleosomes at active transcription start sites (TSSs), but also co-localizes with cohesin. Cohesin depletion reduces Phf2 binding at sites lacking H3K4me3, and depletion of Wapl and CTCF re-positions Phf2 together with cohesin in the genome, resulting in the accumulation of both proteins in chromosomal regions called vermicelli and cohesin islands. Conversely, Phf2 depletion reduces cohesin binding at TSSs lacking CTCF and decreases the number of short cohesin loops, while increasing the length of heterochromatic B compartments. These results suggest that Phf2 is an ‘epigenetic reader’, which is translocated through the genome by cohesin-mediated DNA loop extrusion, and which recruits cohesin to active TSSs and limits the size of B compartments. These findings reveal an unexpected degree of cooperativity between epigenetic and architectural mechanisms of eukaryotic genome regulation.
Partitioning of fatty acids between membrane and storage lipids controls ER membrane expansion
Authors: Lysyganicz Pawel K , Barbosa Antonio D , Khondker Shoily , Stewart Nicolas A , Carman George M , Stansfeld Phillip J , Dymond Marcus K , Siniossoglou Symeon 
Published: January 2025

Abstract
Biogenesis of membrane-bound organelles involves the synthesis, remodeling, and degradation of their constituent phospholipids. How these pathways regulate organelle size remains poorly understood. Here we demonstrate that a lipid-degradation pathway inhibits expansion of the endoplasmic reticulum (ER) membrane. Phospholipid diacylglycerol acyltransferases (PDATs) use endogenous phospholipids as fatty-acyl donors to generate triglyceride stored in lipid droplets. The significance of this non-canonical triglyceride biosynthesis pathway has remained elusive. We find that the activity of the yeast PDAT Lro1 is regulated by a membrane-proximal helical segment facing the luminal side of the ER bilayer. To reveal the biological roles of PDATs, we engineered an Lro1 variant with derepressed activity. We show that active Lro1 mediates retraction of ER membrane expansion driven by phospholipid synthesis. Furthermore, subcellular distribution and membrane turnover activity of Lro1 are controlled by diacylglycerol produced by the activity of Pah1, a conserved member of the lipin family. Collectively, our findings reveal a lipid-metabolic network that regulates endoplasmic reticulum biogenesis by converting phospholipids into storage lipids.
Nuclear PD-L1 triggers tumour-associated inflammation upon DNA damage
Authors: Nihira Naoe T , Wu Wenwen , Hosoi Mitsue , Togashi Yukiko , Sunada Shigeaki , Miyoshi Yasuo , Miki Yoshio , Ohta Tomohiko 
Published: January 2025

Abstract
Immune checkpoint inhibitors against PD-1/PD-L1 are highly effective in immunologically hot tumours such as triple-negative breast cancer, wherein constitutive DNA damage promotes inflammation, while inducing PD-L1 expression to avoid attack by cytotoxic T cells. However, whether and how PD-L1 regulates the DNA damage response and inflammation remains unclear. Here, we show that nuclear PD-L1 activates the ATR-Chk1 pathway and induces proinflammatory chemocytokines upon genotoxic stress. PD-L1 interacts with ATR and is essential for Chk1 activation and chromatin binding. cGAS-STING and NF-κB activation in the late phase of the DNA damage response is inhibited by PD-L1 deletion or by inhibitors of ATR and Chk1. Consequently, the induction of proinflammatory chemocytokines at this stage is inhibited by deletion of PD-L1, but restored by the ATR activator Garcinone C. Inhibition of nuclear localisation by PD-L1 mutations or the HDAC2 inhibitor Santacruzamate A inhibits chemocytokine induction. Conversely, the p300 inhibitor C646, which accelerates PD-L1 nuclear localisation, promotes chemocytokine induction. These findings suggest that nuclear PD-L1 strengthens the properties of hot tumours and contributes to shaping the tumour microenvironment.
High order expression dependencies finely resolve cryptic states and subtypes in single cell data
Authors: Jansma Abel , Yao Yuelin , Wolfe Jareth , Del Debbio Luigi , Beentjes Sjoerd V , Ponting Chris P , Khamseh Ava 
Published: January 2025

Abstract
Single cells are typically typed by clustering into discrete locations in reduced dimensional transcriptome space. Here we introduce Stator, a data-driven method that identifies cell (sub)types and states without relying on cells’ local proximity in transcriptome space. Stator labels the same single cell multiply, not just by type and subtype, but also by state such as activation, maturity or cell cycle sub-phase, through deriving higher-order gene expression dependencies from a sparse gene-by-cell expression matrix. Stator’s finer resolution is clear from analyses of mouse embryonic brain, and human healthy or diseased liver. Rather than only coarse-scale labels of cell type, Stator further resolves cell types into subtypes, and these subtypes into stages of maturity and/or cell cycle phases, and yet further into portions of these phases. Among cryptically homogeneous embryonic cells, for example, Stator finds 34 distinct radial glia states whose gene expression forecasts their future GABAergic or glutamatergic neuronal fate. Further, Stator’s fine resolution of liver cancer states reveals expression programmes that predict patient survival. We provide Stator as a Nextflow pipeline and Shiny App.
Engineering the multifunctionality of Li3Y3Te2O12 garnets with Sm3+ and Tb3+ activators for solid-state lighting and luminescence thermometry
Authors: Bindhu Amrithakrishnan , Naseemabeevi Jawahar I. , Ganesanpotti Subodh 
Published: January 2025

Tuning the photophysical response of Li3Y3Te2O12 garnet based on Sm3+ and Tb3+ activators for solid-state lighting and luminescence thermometry.
A fair power allocation in dual function radar and communication systems
Authors: Gao Pengzun , Zhao Long , Zheng Kan 
Published: January 2025

AbstractThe dual function radar and communication technology plays a crucial role in Internet of Vehicles. However, fewer studies have focused on optimizing the joint performance of sensing and communication in full‐duplex Internet of Vehicles while considering fairness. Therefore, this paper considers the scenario that the full‐duplex gNB employs dual function radar and communication technology to simultaneously achieve vehicle localization and communication with multiple half‐duplex vehicles. By leveraging a reasonable beamforming scheme to mitigate the residual self‐interference of the echo signal, an optimization problem that maximizing the joint performance metric of sensing and communication is formulated with limited power at the gNB. After problem transformation, a double descent iteration power allocation algorithm is proposed to solve the non‐convex optimization problem and the complexity of the algorithm is analysed. Simulation results demonstrate that the proposed algorithm converges and could fairly improve both the sensing and communication performance.
Federated learning for optimized resource allocation in power line communication systems
Authors: Yan Ruowen , Li Qiao , Xiong Huagang 
Published: January 2025

AbstractThis paper introduces the Priority‐Aware Federated Resource Allocation (PAFRA) algorithm, a novel resource allocation strategy designed for power line communication (PLC) systems. Leveraging a federated learning framework, PAFRA optimizes the distribution of limited spectrum resources across multiple nodes within a residential environment. The algorithm integrates a priority‐based time slot allocation to effectively manage subchannel conflicts and employs a Double Deep Q‐Network for local training at each node. This training modality includes comprehensive state, action, and reward configurations to fine‐tune transmission power and subchannel selections. Extensive simulations reveal that PAFRA substantially enhances system throughput, outperforming both traditional numerical optimization methods and contemporary machine learning approaches. Specifically, at a signal‐to‐noise ratio of 30 dB, PAFRA achieves a system throughput of approximately 950 Mbps, marking a 23% improvement over existing approaches. These findings highlight PAFRA's ability to significantly enhance network efficiency while complying with regulatory emission standards, demonstrating its potential to optimize dynamic PLC systems.
Determination of optimal island regions with simultaneous DG allocation and reconfiguration in power distribution networks
Authors: Cikan Murat , Cikan Nisa Nacar , Kekezoglu Bedri 
Published: January 2025

AbstractThe rising demand for electric energy and environmental pollution concerns have led to using renewable energy‐based distributed generators (DGs) in power distribution networks (PDN). However, the power flow may become bidirectional (non‐radial) by introducing the DGs into the system, which may cause instability in the system's operation. System instability causes an increase in system losses and implies a rising in costs. This paper proposes a methodology using Moth Flame Optimizer and Equilibrium Optimizer to reconfigure the PDN, optimizing the siting, sizing, and power factor of multiple DGs to maintain radial mode operation. Determining DGs' allocation within a radial system by optimizing multi‐parameters simultaneously leads to minimizing losses, enhancing reliability, and improving the stability of the network. Thus, the number of affected customers in a catastrophic power outage or emergency blackout scenarios can be minimized. The method has been tested on a 33‐bus distribution system where four different scenarios with three cases for each are studied to show the performance of the proposed method. The method's effectiveness is demonstrated by minimizing power loss, enhancing the voltage stability index, assuring reliability, and improving the voltage profile. Moreover, the accuracy of reliability index calculation results is confirmed with the commercial software ETAP.
Droop control strategy in inverter‐based microgrids: A brief review on analysis and application in islanded mode of operation
Authors: Shahgholian Ghazanfar , Moradian Mohammadreza , Fathollahi Arman 
Published: January 2025

AbstractDroop control is at the first level of the control hierarchy and does not require communication. Having high reliability, is usually used in inverter‐based microgrids. The microgrid can operate as an island, and it can also be connected to the main or auxiliary grid. By reviewing the extensive literature on the role of the controller in inverter‐based microgrids for the island mode of operation, in this study, the droop regulation strategy has been covered briefly and compactly. Droop regulation is an example of decentralized regulation in basic control, and its importance is revealed in the island mode of operation when it is possible to share power in all facilities without needing to communicate with other units. Disadvantages of common droop control, such as slow transient dynamics and low energy quality for non‐linear and unbalanced loads, have limited its use in advanced microgrids. Therefore, various methods to improve the common droop control have been investigated so far, some of which have been mentioned. This study highlights the application of droop control strategies in order to coordinate distributed generation units in the microgrid. About 180 published studies in this field have been reviewed, classified and indexed for quick reference.