bluuebunny/crossref_metadata_2025_split huggingface dataset:
120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training
Authors: Lin Zhongjin , Shastri Bhavin J. , Yu Shangxuan , Song Jingxiang , Zhu Yuntao , Safarnejadian Arman , Cai Wangning , Lin Yanmei , Ke Wei , Hammood Mustafa , Wang Tianye , Xu Mengyue , Zheng Zibo , Al-Qadasi Mohammed , Esmaeeli Omid , Rahim Mohamed , Pakulski Grzegorz , Schmid Jens , Barrios Pedro , Jiang Weihong , Morison Hugh , Mitchell Matthew , Guan Xun , Jaeger Nicolas A. F. , Rusch Leslie A. , Shekhar Sudip , Shi Wei , Yu Siyuan , Cai Xinlun , Chrostowski Lukas 
Published: October 2024

AbstractPhotonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112 × 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative numbers.
α-synuclein seed amplification assay sensitivity may be associated with cardiac MIBG abnormality among patients with Lewy body disease
Authors: Kurihara Masanori , Satoh Katsuya , Shimasaki Ryosuke , Hatano Keiko , Ohse Kensuke , Taira Kenichiro , Ihara Ryoko , Higashihara Mana , Nishina Yasushi , Kameyama Masashi , Iwata Atsushi 
Published: October 2024

AbstractAlthough α-synuclein seed amplification assays (α-syn SAA) are promising, its sensitivity may be affected by heterogeneity among patients with Lewy body disease (LBD). We evaluated whether α-syn SAA sensitivity is affected by patient heterogeneity, using 123I-meta-iodobenzylguanidine (MIBG) cardiac scintigraphy in early drug-naïve patients. Thirty-four patients with clinically established or probable Parkinson’s disease (PD) and seven with dementia with Lewy bodies (DLB) or prodromal DLB were included. While 85.2% of patients with abnormal cardiac MIBG were α-syn SAA positive, only 14.3% were positive among those with normal scans. Logistic regression analysis showed that MIBG positivity was the only significant variable associated with α-syn SAA positivity (odds ratio 74.2 [95% confidence interval 6.1–909]). Although α-syn SAA is sensitive for LBD in patients with abnormal MIBG, the sensitivity may be lower in those with normal MIBG. Further studies are necessary to evaluate the association between patient heterogeneity and α-syn SAA sensitivity.
Mitochondrial mechanotransduction through MIEF1 coordinates the nuclear response to forces
Authors: Romani Patrizia , Benedetti Giada , Cusan Martina , Arboit Mattia , Cirillo Carmine , Wu Xi , Rouni Georgia , Kostourou Vassiliki , Aragona Mariaceleste , Giampietro Costanza , Grumati Paolo , Martello Graziano , Dupont Sirio 
Published: October 2024

AbstractTissue-scale architecture and mechanical properties instruct cell behaviour under physiological and diseased conditions, but our understanding of the underlying mechanisms remains fragmentary. Here we show that extracellular matrix stiffness, spatial confinements and applied forces, including stretching of mouse skin, regulate mitochondrial dynamics. Actomyosin tension promotes the phosphorylation of mitochondrial elongation factor 1 (MIEF1), limiting the recruitment of dynamin-related protein 1 (DRP1) at mitochondria, as well as peri-mitochondrial F-actin formation and mitochondrial fission. Strikingly, mitochondrial fission is also a general mechanotransduction mechanism. Indeed, we found that DRP1- and MIEF1/2-dependent fission is required and sufficient to regulate three transcription factors of broad relevance—YAP/TAZ, SREBP1/2 and NRF2—to control cell proliferation, lipogenesis, antioxidant metabolism, chemotherapy resistance and adipocyte differentiation in response to mechanical cues. This extends to the mouse liver, where DRP1 regulates hepatocyte proliferation and identity—hallmark YAP-dependent phenotypes. We propose that mitochondria fulfil a unifying signalling function by which the mechanical tissue microenvironment coordinates complementary cell functions.
The Journey to a FAIR CORE DATA SET for Diabetes Research in Germany
Authors: Inau Esther Thea , Dedié Angela , Anastasova Ivona , Schick Renate , Zdravomyslov Yaroslav , Fröhlich Brigitte , Birkenfeld Andreas L. , Hrabě de Angelis Martin , Roden Michael , Zeleke Atinkut Alamirrew , Preusse Martin , Waltemath Dagmar 
Published: October 2024

AbstractThe German Center for Diabetes Research (DZD) established a core data set (CDS) of clinical parameters relevant for diabetes research in 2021. The CDS is central to the design of current and future DZD studies. Here, we describe the process and outcomes of FAIRifying the initial version of the CDS. We first did a baseline evaluation of the FAIRness using the FAIR Data Maturity Model. The FAIRification process and the results of this assessment led us to convert the CDS into the recommended format for spreadsheets, annotating the parameters with standardized medical codes, licensing the data set, enriching the data set with metadata, and indexing the metadata. The FAIRified version of the CDS is more suitable for data sharing in diabetes research across DZD sites and beyond. It contributes to the reusability of health research studies.
Assessment of functional performance in self-rectifying passive crossbar arrays utilizing sneak path current
Authors: Chen Ziang , Zhao Xianyue , Bengel Christopher , Liu Feng , Li Kefeng , Menzel Stephan , Du Nan 
Published: October 2024

AbstractSelf-rectifying memristive devices have emerged as promising contenders for low-power in-memory computing, presenting numerous advantages. However, characterizing the functional behavior of passive crossbar arrays incorporating these devices remains challenging due to sophisticated parasitic currents stemming from rich memristive dynamic behavior. Conventional methods using read margin assessments to evaluate functional behavior in passive crossbars are hindered by the voltage divider effect from the pull-up resistor. In this study, we propose a novel performance metric, $$\Delta$$
Δ
SC, harnessing sneak path currents to assess functional behavior. Through the application of a pair of negative rectification factors, $$\text {RF}_\text {n, L}$$

RF
n,

L

and $$\text {RF}_\text {n, H}$$

RF
n,

H

, we comprehensively delineate dynamic rectification behavior in both positive and negative bias regimes, as well as in low-resistance state and high-resistance state, deviating from conventional metrics such as on/off ratios, nonlinearity, and rectifying factors. Notably, $$\Delta$$
Δ
SC provides a quantitative evaluation of the interaction between sneak path currents and read margin, demonstrating its efficacy and addressing a pivotal research gap in the field. For instance, employing self-rectifying BiFeO$$_3$$

3

memristive cells featuring $$\text {RF}_\text {n, L}$$

RF
n,

L

= 1.22E3 and $$\text {RF}_\text {n, H}$$

RF
n,

H

= 9.27, we showcase the successful functional performance of a passive crossbar array, achieving $$\Delta$$
Δ
SC < 2.19E−2, while ensuring a read margin > 0.
Identifying pathogenic variants in rare pediatric neurological diseases using exome sequencing
Authors: Komatsu Kazuyuki , Kato Mitsuhiro , Kubota Kazuo , Fukumura Shinobu , Yamada Keitaro , Hori Ikumi , Shimizu Kenji , Miyamoto Sachiko , Yamoto Kaori , Hiraide Takuya , Watanabe Kazuki , Aoki Shintaro , Furukawa Shogo , Hayashi Taiju , Isogai Masaharu , Harasaki Takuma , Nakashima Mitsuko , Saitsu Hirotomo 
Published: October 2024

AbstractVariant annotations are crucial for efficient identification of pathogenic variants. In this study, we retrospectively analyzed the utility of four annotation tools (allele frequency, ClinVar, SpliceAI, and Phenomatcher) in identifying 271 pathogenic single nucleotide and small insertion/deletion variants (SNVs/small indels). Although variant filtering based on allele frequency is essential for narrowing down on candidate variants, we found that 13 de novo pathogenic variants in autosomal dominant or X-linked dominant genes are registered in gnomADv4.0 or 54KJPN, with an allele frequency of less than 0.001%, suggesting that very rare variants in large cohort data can be pathogenic de novo variants. Notably, 38.4% candidate SNVs/small indels are registered in the ClinVar database as pathogenic or likely pathogenic, which highlights the significance of this database. SpliceAI can detect candidate variants affecting RNA splicing, leading to the identification of four variants located 11 to 50 bp away from the exon–intron boundary. Prioritization of candidate genes by proband phenotype using the PhenoMatcher module revealed that approximately 95% of the candidate genes had a maximum PhenoMatch score ≥ 0.6, suggesting the utility of phenotype-based variant prioritization. Our results suggest that a combination of multiple annotation tools and appropriate evaluation can improve the diagnosis of rare diseases.
A deep learning based assisted analysis approach for Sjogren’s syndrome pathology images
Authors: Jiang Peihe , Li Yi , Wang Chunni , Zhang Wei , Lu Ning 
Published: October 2024

AbstractDiagnosing Sjogren’s syndrome requires considerable time and effort from physicians, primarily because it necessitates rigorously establishing the presence lymphatic infiltration in the pathological tissue of the labial gland. The aim of this study is to use deep learning techniques to overcome these limitations and improve diagnostic accuracy and efficiency in pathology. We develop an auxiliary diagnostic system for Sjogren’s syndrome. The system incorporates the state-of-the-art object detection neural network, YOLOv8, and enables the precise identification and flagging of suspicious lesions. We design the multi-dimensional attention module and S-MPDIoU loss function to improve the detection performance of YOLOv8. By extracting features from multiple dimensions of the feature map, the utilization of the multi-dimensional attention mechanism enhances the feature interaction across disparate positions, enabling the network to proficiently learn and retain salient cell features. S-MPDIoU introduces an angle penalty term that efficiently minimizes the diagonal distance between predicted and ground truth boxes. Additionally, it incorporates a flexible scale factor tailored to different size feature maps, which balances the issue of sudden gradient decrease during high overlap, thereby accelerating the overall convergence rate. To verify the effectiveness of our methods, we create a dataset of lymphocytes using labial gland biopsy pathology images collected from YanTaiShan hospital and trained the model with this dataset. The proposed model is assessed using standard metrics like precision, recall, mAP. The improved model achieves an increase in recall by 9.1%, mAP.5 by 3.2%, and mAP.95 by 2%. The study demonstrated deep learning’s potential to analysis pathology images, offering a reference framework for the application of deep learning technology in the medical domain.
Spatial mRNA expression patterns of orexin receptors in the dorsal hippocampus
Authors: Krause Gina Marie , Chirich Barreira Lara Mariel , Albrecht Anne 
Published: October 2024

AbstractOrexins are wake-promoting neuropeptides that originate from hypothalamic neurons projecting to widespread brain areas throughout the central nervous system. They modulate various physiological functions via their orexin 1 (OXR1) and 2 (OXR2) receptors, including sleep-wake rhythm but also cognitive functions such as memory formation. Here, we provide a detailed analysis of OXR1 and OXR2 mRNA expression profiles in the dorsal hippocampus as a key region for memory formation, using RNAscope multiplex in situ hybridization. Interconnected subareas relevant for cognition and memory such as the medial prefrontal cortex and the nucleus reuniens of the thalamus were assessed as well. Both receptor types display distinct profiles, with the highest percentage of OXR1 mRNA-positive cells in the hilus of the dentate gyrus. Here, the content of OXR1 mRNA per cell was slightly modulated at selected time points over a 12 h light/ 12 dark light phase. Using RNAScope and quantitative polymerase chain reaction approaches, we began to address a cell-type specific expression of OXR1 in hilar GABAergic interneurons. The distinct expression profiles of both receptor subtypes within hippocampal subareas and circuits provide an interesting basis for future interventional studies on orexin receptor function in spatial and contextual memory.
Glutamine sensing licenses cholesterol synthesis
Authors: Garcia Bruna Martins , Melchinger Philipp , Medeiros Tania , Hendrix Sebastian , Prabhu Kavan , Corrado Mauro , Kingma Jenina , Gorbatenko Andrej , Deshwal Soni , Veronese Matteo , Scorrano Luca , Pearce Erika , Giavalisco Patrick , Zelcer Noam , Pernas Lena 
Published: October 2024

AbstractThe mevalonate pathway produces essential lipid metabolites such as cholesterol. Although this pathway is negatively regulated by metabolic intermediates, little is known of the metabolites that positively regulate its activity. We found that the amino acid glutamine is required to activate the mevalonate pathway. Glutamine starvation inhibited cholesterol synthesis and blocked transcription of the mevalonate pathway—even in the presence of glutamine derivatives such as ammonia and α-ketoglutarate. We pinpointed this glutamine-dependent effect to a loss in the ER-to-Golgi trafficking of SCAP that licenses the activation of SREBP2, the major transcriptional regulator of cholesterol synthesis. Both enforced Golgi-to-ER retro-translocation and the expression of a nuclear SREBP2 rescued mevalonate pathway activity during glutamine starvation. In a cell model of impaired mitochondrial respiration in which glutamine uptake is enhanced, SREBP2 activation and cellular cholesterol were increased. Thus, the mevalonate pathway senses and is activated by glutamine at a previously uncharacterized step, and the modulation of glutamine synthesis may be a strategy to regulate cholesterol levels in pathophysiological conditions.
Learning‐based tracking control of AUV: Mixed policy improvement and game‐based disturbance rejection
Authors: Ye Jun , Gao Hongbo , Hu Manjiang , Bian Yougang , Cui Qingjia , Qin Xiaohui , Ding Rongjun 
Published: October 2024

AbstractA mixed adaptive dynamic programming (ADP) scheme based on zero‐sum game theory is developed to address optimal control problems of autonomous underwater vehicle (AUV) systems subject to disturbances and safe constraints. By combining prior dynamic knowledge and actual sampled data, the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP algorithm. Initially, the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling bias. Also, the control object interacts with the real environment and continuously gathers adequate sampled data in the dataset. To comprehensively leverage the advantages of model‐based and model‐free methods during training, an adaptive tuning factor is introduced based on the dataset that possesses model‐referenced information and conforms to the distribution of the real‐world environment, which balances the influence of model‐based control law and data‐driven policy gradient on the direction of policy improvement. As a result, the proposed approach accelerates the learning speed compared to data‐driven methods, concurrently also enhancing the tracking performance in comparison to model‐based control methods. Moreover, the optimal control problem under disturbances is formulated as a zero‐sum game, and the actor‐critic‐disturbance framework is introduced to approximate the optimal control input, cost function, and disturbance policy, respectively. Furthermore, the convergence property of the proposed algorithm based on the value iteration method is analysed. Finally, an example of AUV path following based on the improved line‐of‐sight guidance is presented to demonstrate the effectiveness of the proposed method.
Joint channel allocation and transmit power control for underlay EH‐CRNs: A clustering‐based multi‐agent DDPG approach
Authors: Liu Xiaoying , Kuang Xinyu , Li Zefu , Zheng Kechen 
Published: October 2024

AbstractTo address the concerns of energy supply and spectrum scarcity for wireless devices, energy harvesting cognitive radio networks have been proposed. To improve spectrum utilization, secondary users (SUs) access the licensed spectrum in underlay mode, which may cause severe interference to primary users and SUs. The focus is on the underlay energy harvesting cognitive radio networks with multiple pairs of SUs, and formulate the long‐term secondary throughput maximization problem as a mixed‐integer non‐linear programming problem. As traditional approaches could hardly solve the mixed‐integer non‐linear programming problem well, a centralized deep deterministic policy gradient (C‐DDPG) approach is proposed that achieves satisfactory throughput performance. To reduce the computational complexity of C‐DDPG, we further propose a clustering‐based multi‐agent DDPG (CMA‐DDPG) approach that combines the advantages of the centralized deep reinforcement learning approach and the distributed deep reinforcement learning approach. In the CMA‐DDPG, a novel interference‐based clustering algorithm is proposed, which partitions the SUs that cause severe mutual interference into one cluster, and the sizes of state space and action space are smaller than those in C‐DDPG. Numerical results validate the superiority of the proposed approaches in terms of the throughput and outage probability, and validate the clustering performance of the interference‐based clustering algorithm in terms of the outage probability of the secondary network.
A node deployment and resource optimization method for CPDS based on cloud‐fog‐edge collaboration
Authors: Xiong Xiaoping , Yang Geng 
Published: October 2024

AbstractWith the development of the Internet of Things (IoT) in power distribution and the advancement of energy information integration technologies, the explosive growth in network data volume caused by massive terminal devices connecting to the power distribution network has become a significant challenge. Multi‐terminal collaborative computing is a key approach to addressing issues such as high latency and high energy consumption. In this article, fog computing is introduced into the computing network of the power distribution system, and a cloud‐fog‐edge collaborative computing architecture for intelligent power distribution networks is proposed. Within this framework, an improved weighted K‐means method based on information entropy theory is presented for node partitioning. Subsequently, an improved multi‐objective particle swarm optimization algorithm (MWM‐MOPSO) is employed to solve the task resource allocation problem. Finally, the effectiveness of the proposed architecture and allocation strategy is validated through simulations on the OPNET and PureEdgeSim platforms. The results demonstrate that, compared to traditional cloud‐edge service architectures, the proposed architecture and task offloading scheme achieve better performance in terms of processing latency and energy consumption.
Hystimator: DRT‐based hysteresis modelling for accurate SoC estimation in LFP battery cells
Authors: Thenaisie Guillaume , Brivio Claudio 
Published: October 2024

AbstractState of Charge (SoC) estimation for Lithium‐Iron Phosphate (LFP) batteries is challenging due to a flat Open Circuit Voltage (OCV) curve and a well‐known hysteresis effect. The authors built upon a previous study, which has shown that hysteresis in LFP is not an inherent characteristic but a very slow relaxation process when compared to other battery chemistries. Distribution of Relaxation Times (DRT) is used to deconvolve Electro‐Impedance Spectroscopy (EIS) measurements and model the hysteresis effect. The extracted DRT parameters show good agreement at low frequencies with previous thermodynamic studies in both fresh and aged cell conditions. The proposed model, called hystimator, integrates the hysteresis characteristics into a physics‐based Electro‐Chemical Model (ECM). The validation results show a significant reduction in the Root Mean Square Error (RMSE) during real‐world laboratory testing. This approach holds promise for SoC estimation in LFP battery cells, especially in embedded Battery Management System (BMS).
Effect of the local and large-scale environment on the star formation histories of galaxies
Authors: Torres-Ríos G. , Pérez I. , Verley S. , Domínguez-Gómez J. , Argudo-Fernández M. , Duarte Puertas S. , Jiménez A. , Ruiz-Lara T. , Zurita A. , Bidaran B. , Conrado A. , Espada D. , García-Benito R. , González Delgado R. M. , Falcón-Barroso J. , Florido E. , Sánchez-Blázquez P. , Sánchez-Menguiano L. 
Published: October 2024

Context. The specific environment of galaxies may play a key role in their evolution. Large extragalactic surveys make it possible to study galaxies not only within their local environment, but also within the large-scale structure of the Universe.
Aims. We aim to investigate how the local environment influences the star formation history (SFH) of galaxies across a range of large-scale environments.
Methods. We categorised a sample of 9384 galaxies into the three primary large-scale structures (voids, walls and filaments, and clusters). We further classified them based on their local environment (as either ‘singlets’ or group members) through a search of companion galaxies within sky-projected distances of Δrp < 0.45 Mpc and velocity differences of Δv < 160 km s−1. Subsequently, we explored these subsamples using SFH data from previous works. Throughout this study, we divided galaxies into long-timescale SFH galaxies (LT-SFH), which assemble their mass steadily along cosmic time, and short-timescale SFH galaxies (ST-SFH), which form their stars early on. We then compared their characteristic mass assembly look-back times.
Results. The distributions of mass assembly look-back times in ST-SFH galaxies are statistically different for singlets and groups. These differences are only found in LT-SFH galaxies when studying these distributions in stellar mass bins. Our results indicate that the large-scale environment is related to a delay in mass assembly of up to ∼2 Gyr, while this delay is < 1 Gyr in the case of local environment. The effects of both types of environment are more significant in less massive galaxies and in LT-SFHs.
Conclusions. Our results are consistent with galaxies in groups assembling their stellar mass earlier than in singlets, especially in voids and lower mass galaxies. Local environment plays a relevant role in stellar mass assembly times, although we find that large-scale structures also cause a delay in mass assembly, and all the more so in the case of cluster galaxies.