Miracle-dz/UniFAR-FTUs huggingface dataset:
Nucleobindin-1 (Nucb1) disruption affects feeding, metabolism, and glucose homeostasis in mice in an age-, sex-, diet- and light cycle-dependent manner

Background Nesfatin-1 (NESF-1), encoded in the calcium and DNA binding protein (Nucleobindin 2, NUCB2) is an orphan ligand with metabolic effects. Recently, our lab provided evidence for a NESF-1-like peptide (NLP) in a NUCB2-related precursor, NUCB1, in zebrafish and rodents. This research aims to determine whether endogenous NUCB1 is critical for energy homeostasis. Methods and Main Findings Global genetic disruption of Nucb1 (Nucb1 knockout/KO mice) led to increased food intake in chow-fed male and female mice across different points of light and dark phases. A similar increase in water intake was seen in female Nucb1 KO mice but not in males. White adipose tissue weight was significantly increased in male and female Nucb1 KO mice. Dark phase total activity was increased in male Nucb1 KO mice, while it was decreased in female Nucb1 KO mice compared to wildtype littermates. Energy derived from carbohydrates was raised during the dark phase; while energy derived from fat was significantly decreased in both male and female Nucb1 KO mice. Male Nucb1 KO mice were lighter in the early stages, but these differences disappeared as they aged. Meanwhile, no differences in bodyweight were observed in female Nucb1 KO mice. Male Nucb1 KO mice handled glucose better during an oral glucose tolerance test, while the opposite effect was found in an intraperitoneal (IP) glucose tolerance test. The above results from chow-fed mice were largely true in 10% and 60% fat diet-fed mice. A significant two-way interaction between mice group and time was observed on weekly food intake of male and female Nucb1 KO mice fed control fat diet, but not in 60% fat-fed group. Handling of blood glucose during IPGTT was better in male Nucb1 KO mice fed both diets, while such an effect was not observed in female KO mice. A significant two-way interaction of mice group and time on food and water intake value in 24 h was observed for male Nucb1 KO mice fed 10% fat diet. The total physical activity during the dark phase and energy expenditure during the light phase showed a sex-specific pattern in male and female Nucb1 KO mice fed 10% fat diet. Energy expenditure showed a sex-specific pattern in Nucb1 KO mice during the dark phase. Moreover, adiposity increased in male Nucb1 KO mice fed a high fat diet. Conclusions Our results indicate that the disruption of Nucb1 leads to metabolic changes in vivo. The phenotype appears to depend on sex, age, diet, and the light-dark cycle. In conclusion, these outcomes furnish important evidence supporting critical roles for endogenous NUCB1 in energy homeostasis.
Phosphorylation of PLPPR3 membrane proteins as signaling integrator at neuronal synapses

Phospholipid-phosphatase related protein 3 (PLPPR3, previously known as Plasticity Related Gene 2 or PRG2) belongs to a family of transmembrane proteins, highly expressed in neuronal development, which regulate critical growth processes in neurons. Prior work established crucial functions of PLPPR3 in axon guidance, filopodia formation and axon branching. However, little is known regarding the signaling events regulating PLPPR3 function. We identify here 26 high-confidence phosphorylation sites in the intracellular domain of PLPPR3 using mass spectrometry. Biochemical characterization established one of these – S351 – as a bona fide phosphorylation site of PKA. Experiments in neuronal cell lines suggest that phosphorylation of S351 does not regulate filopodia formation. Instead, it regulates binding to BASP1, a signaling molecule previously implicated in axonal growth and regeneration. Interestingly, both PLPPR3 intracellular domain and BASP1 enrich in presynapses in primary neurons. We propose that the presynaptic PLPPR3-BASP1 complex may function as novel signaling integrator at neuronal synapses.
Inter-modality source coupling: a fully-automated whole-brain data-driven structure-function fingerprint shows replicable links to reading in a large-scale (N∼8K) analysis

Objective Both structural and functional brain changes have been individually associated with developing cognitive processes such as reading. However, there is limited research about the combined influence of resting-state functional and structural magnetic resonance imaging (rs-fMRI and sMRI) features in reading development, which could provide insights into the interplay between brain structure and function in shaping cognitive growth. We propose a method called inter-modality source coupling (IMSC) to study the coupling between the rs-fMRI and sMRI and its relationship to reading ability in school-age children. Methods This approach is applied to baseline data from four thousand participants (9-11 years) and replicated in a second group. Our analysis focused on the relationship of IMSC to overall reading score. Results Our findings indicate that higher reading ability was linked with increased function-structure coupling among higher-level cortical regions, particularly those links between the inferior parietal lobule and inferior frontal areas, and conversely, lower reading ability was associated with enhanced function-structure coupling among the fusiform and lingual gyrus. Our study found evidence of spatial correspondence between the data indicating an interplay between brain structure and function in our participants. Conclusion Our approach revealed a linked pattern of whole brain structure to the corresponding functional connectivity pattern that correlated with reading ability. This novel IMSC analysis method provides a new approach to study the multimodal relationship between brain function and structure. Significance These findings have interesting implications for understanding the multimodal complexity underlying the development of the neural basis for reading ability in school-aged children.
A perivascular niche supports endometrial epithelial regeneration

Endometrial regeneration is essential for reproductive cycles and pregnancies, allowing the endometrium to undergo repair and renewal after menstruation and parturition. Epithelial cells lining the uterine cavity are shed during each cycle, although remnant luminal and glandular epithelial cells can regenerate the lumen lining. It is presumed that adult stem/progenitor cells in the uterine stroma also contribute to this regeneration. However, the specific cell type(s) and the underlying mechanisms have not been determined. Herein, we use genetic lineage tracing assays in mice to identify Nestin+ perivascular cells as active contributors to epithelial regeneration. Notch signaling maintains Nestin+ perivascular cells in a quiescent state but these cells re-enter the cell cycle and differentiate into epithelial cells via estrogen-stimulated suppression of Notch signaling dependent on estrogen receptor alpha (ERα/ESR1). These findings demonstrate that perivascular cells support epithelial regeneration and reveal a mechanism regulating the quiescence and activation of uterine perivascular cells.
PseudoCell: A Multi-Valued Logical Regulatory Network to Investigate Premature Senescence Dynamics and Heterogeneity

Premature cellular senescence is a pivotal process in aging and age-related diseases, triggered by various stressors. However, this is not a homogeneous phenotype, but a heterogeneous cellular state composed of multiple senescence programs with different compositions. Therefore, understanding the complex dynamics of senescence programs requires a systemic approach. We introduce PseudoCell, a multi-valued logical regulatory network designed to explore the molecular intricacies of premature senescence. PseudoCell integrates key senescence signaling pathways and molecular mechanisms, offering a versatile platform for investigating diverse premature senescence programs initiated by different stimuli. Validation through simulation of classical senescence programs, including oxidative stress-induced senescence (OSIS) and oncogene-induced senescence (OIS), demonstrates its ability to replicate molecular signatures consistent with empirical data. Additionally, we explore the role of CCL11, a novel senescence- associated molecule, through simulations that reveal potential pathways and mechanisms underlying CCL11-mediated senescence induction. In conclusion, PseudoCell provides a systematic approach to dissecting premature senescence programs and uncovering novel regulatory mechanisms.
Natural Language Data Interfaces: A Data Access Odyssey (Invited Talk)

Back in 1970’s, E. F. Codd worked on a prototype of a natural language question and answer application that would sit on top of a relational database system. Soon, natural language interfaces for databases (NLIDBs) became the holy grail for the database community. Different approaches have been proposed from the database, machine learning and NLP communities. Interest in the topic has had its peaks and valleys. After a long and adventurous journey of almost 50 years, there is a rekindled interest in NLIDBs in recent years, fueled by the need for democratizing data access and by the recent advances in deep learning and natural language processing in particular. There is a surge of works on natural language interfaces for databases using neural translation, and suddenly it becomes hard to keep up with advancements in the field. Are we close to finding the holy grail of data access? What are the lurking challenges that we need to surpass and what research opportunities arise? Finally, what is the role of the database community?
Performance Evaluation of MIMO Channel Capacity Based on Polarization Loss Factor

This article will discuss the performance of MIMO channel capacity due to polarization mismatch in the receiver. The polarization of the electromagnetic wave sent by the transmitting antenna will change on the wireless channel, which is called depolarization. When the electromagnetic wave reaches the receiving antenna, the polarization does not match the antenna’s polarization, resulting in a polarization loss factor. The polarization loss factor will reduce the received power level. It will also affect the channel capacity of the system. In MIMO antennas, the depolarization effect does not only occur in one element or one channel. This significantly reduces the received power based on the number of MIMO elements. The more antenna elements, the more the polarization loss factor affects the system’s channel capacity. Therefore, it is necessary to know the effect of the polarization loss factor on the received power intensity at the receiver and the ability of the MIMO channel.
An Audio Encryption Scheme Based on Empirical Mode Decomposition and 2D Cosine Logistic Map

In this work, we have proposed an audio encryption method. The proposed method is audio signal sensitive as the hash value of the given signal is computed using SHA3-512, which returns a significantly large key of size 512-bit. This key is used to set the different parameters. This work suggests a 2D Cosine Logistic Map (2DCLM) by fusing the Cosine map with the Logistic map. The proposed 2DCLM functions well under chaos. The given audio signal is scrambled with the help of the hash value. The scrambled signal is decomposed by Empirical Mode Decomposition (EMD); before using the EMD the signal is segmented into a 2D signal to reduce the time complexity of the EMD method. The residuals given by EMD and the stream generated by 2DCLM are XOR-ed to encrypt the signal. Finally, the 2D encrypted signal is transformed into a 1D encrypted audio signal. The efficacy of the present method is evaluated with the help of different audio streams. The findings of the simulation and comparison indicate that the suggested technique may deliver effective encryption results while thwarting cryptographic assaults.
Lightweight Network for Modulation Recognition Based on Stochastic Pruning-Asymmetric Quantization

Automatic modulation recognition (AMR) plays an important role in wireless communication system monitoring, non-cooperative communications, and cognitive communications. Recently, the applications of deep learning in AMR improve classification accuracy. However, it is difficult to deploy a deep learning-based model on resource-constrained devices because of its huge model size. In this paper, we propose a neural network called double pooling convolutional neural network (DP-CNN) and a stochastic pruning-asymmetric quantization (SPAQ) algorithm to realize lightweight and accurate modulation recognition. With the SPAQ algorithm, unimportant parameters are pruned by designing probability intervals and evaluation criteria. In addition, the storage type of parameters will be transformed by creating quantization intervals and mapping criteria. The performance of our method is verified using an open-source dataset RadioML2016.10a. Experimental results show that the SPAQ algorithm has better recognition performance than other lightweight methods at high compression ratios. In addition, the DP-CNN compressed by the SPAQ algorithm outperforms the existing lightweight network in recognition accuracy under the same model size.
Efﬁcient Privacy-Friendly and Flexible IoT Data Aggregation with User-Centric Access Control

—This paper proposes a novel efﬁcient and privacy-preserving scheme, named SAMA, designed to support secure aggregation and sharing of data intended for users and multiple data recipients and ﬁne-grain access control based on a user-centric approach. It achieves this by deploying two key ideas. First, it uses a multi-key homomorphic cryptosystem to allow ﬂexibility in accommodating both single and multi-user data processing as well as preserving the privacy of users while processing their IoT health data. Second, it uses ciphertext-policy attribute-based encryption to support ﬂexible access control, which ensures users are able to grant data access securely and selectively. Formal security and privacy analyses show that SAMA supports data conﬁdentiality and authorisation. The scheme has also been analysed in terms of computational and communication overheads to demonstrate that it is more efﬁcient than the relevant state-of-the-art solutions.
Efficient and Accurate HD Map Generation for Unstructured Automated Valet Parking (UAVP) Environments

This paper proposes a new approach to detecting road boundary for generating high-definition (HD) maps of unstructured automated valet parking (UAVP) environments. The proposed framework presents a height moment tile to efficiently represent the height distribution of UAVP environments, a filtering scheme to remove both static and dynamic obstacles, a curb detection algorithm, and a road boundary expansion algorithm. Our approach is evaluated through experiments conducted at an outdoor parking lot in the Pusan National University. The results show that the proposed framework is able to detect accurate and efficient road boundary in UAVP environments.
Boosting Robot Intelligence in Practice: Enhancing Robot Task Planning with Large Language Models

Task planning capabilities are crucial for intelligent robots to operate autonomously in the physical world. However, traditional Planning Domain Definition Language (PDDL) based methods often suffer from combinatorial explosion and unsat-isfactory planning time. In this paper, we propose enhancing robot task planning with large language models (LLMs) in an innovative way - using LLMs to guide the search process of PDDL planners rather than replacing PDDL planning completely. The LLMs guide the search process of PDDL planners with learned heuristics and provide constraint reasoning to reduce the search space. To address potential pitfalls of LLMs, a verification mechanism is added at the execution stage to validate plan correctness. We evaluated our method on a real scenario, end-of-life vehicle battery disassembly. Experimental results demonstrate that incorporating LLMs into the planning pipeline can significantly improve planning efficiency and scalability while maintaining plan validity. This research provides a promising direction towards integrating language models with classical approaches to boost robot intelligence for practical applications. The proposed framework makes a solid step forward in en-hancing the task planning capability of future intelligent robotic systems.
HUMM: A New Dataset for Human Movement in Manufacturing

Human action recognition and motion prediction experiences great development based on deep neural networks. However, most of the current researches are aimed at general urban traffic roadway environments such as outdoor crosswalks or indoor walking environments like shopping malls or metro entrances, few have studied the human movement in manufacturing environment. In order to support human robot collaboration, this paper explores the task of human movement prediction in manufacturing environment, and bridges the gap of the lack of data by collecting a new dataset: the HUman Movement in Manufacturing (HUMM) dataset. It contains 651 video clips with two different detection perspectives and different action categories. To gain further understanding of our HUMM dataset, we analyzed our dataset in detail through action recognition and trajectory prediction. Results showed that current action recognition and trajectory prediction models is effective to fixed perspective detection but rather mediocre for first-person perspective video dataset.
Gearbox Fault Diagnosis Based on Local Domain Adaptation Network under Different Working Conditions

Fault diagnosis is essential to ensure the reliability of the mechanical systems. However, the working conditions of mechanical systems are highly extreme and varying. This results in the significant domain shift between different domains. In this paper, a transfer learning method, named local domain adaptation network (LDANet) is proposed for fault classification. Firstly, a subdomain adaptation model is proposed to perform fault classification. Secondly, a local feature metric is adapted in LDANet to align the local features of two domains in the class level. Thirdly, a convolutional network-based generator is utilized to obtain common features and a fully connected layer-based generator is adopted to extract specific features. Finally, a gearbox fault diagnosis test is utilized to demonstrate the performance of LDANet. The comparison tests indicate that LDANet obtains the best fault diagnosis performance among other transfer learning methods.
LigMANet: Towards Designing a Lightweight Crowd Counting Model

Crowd counting determines the number of people in an area. Recent crowd counting models are usually integrated with additional layers, which resulted in having higher number of computations. Thus, these models are impractical for real-world applications with computational constraints. In this paper, we propose a crowd counting model called Lightweight Multifaceted Attention Network or LigMANet. We utilize an L1 unstructured pruning method on a baseline model to reduce its parameter count. Moreover, we integrate contrast adjustment as part of our data augmentation pipeline to address the errors made by current crowd counting models on low contrast images. Through these improvements, LigMANet outperforms various state-of-the-art crowd counting methods on the ShanghaiTech and the UCF-CC-50 benchmark datasets, while reducing the parameter count of its baseline model by half. More specifically, LigMANet significantly reduces the MAE and RMSE of its baseline model on the challenging UCF-CC-50 dataset by 39.77 points and 59.98 points, respectively. Our code and trained models are publicly available at https://github.com/dlsucivi/LigMANet-PyTorch.
A Review of Recent Trends in Blockchain Consensus Algorithms: Artificial Intelligence-Based Approaches

Blockchain has emerged as an important technology, offering safe, decentralized, and transparent platforms for recording and validating transactions. Blockchain technology consist of several main components, i.e., consensus algorithm. The consensus algorithm guarantees that all participating nodes in a blockchain network agree over the data control. Traditional consensus methods, such as Proof of Work (PoW) and Proof of Stake (PoS), present issues in terms of energy consumption and attack vulnerability. To overcome these constraints, there has been a rising interest in incorporating Artificial Intelligence (AI) techniques, especially deep learning, into blockchain consensus algorithms. We highlight blockchain fundamentals in this article, while also stressing the importance of the consensus algorithm. Furthermore, we address the most recent advancements in blockchain consensus methods in both performance-based and reputation-based paradigm, emphasizing the use of AI inside these decentralized systems. The use of AI, especially deep learning, in consensus algorithms has the potential to overcome the limitations of previous techniques. Blockchain networks may improve its performance by employing AI capabilities. However, incorporating AI into blockchain consensus algorithms is having its own challenges. Therefore, we also highlight several issues related with AI-based techniques in blockchain consensus algorithms, such as dealing with the quality and variety of data utilized by consensus algorithms and maintaining the openness of the AI models. In addition to those challenges, we propose a future direction for the AI-based approach in blockchain, which includes merging the mechanisms of performance-based and reputation-based consensus algorithms to incorporate the merits of both methods.
A Resource Allocation Scheme in Heterogeneous Multi-system Satellite Network with Beam-hopping

The emerging architecture in the next generation of mobile networks leverages the coexistence of Low Earth Orbit (LEO) and Geostationary Orbit (GEO) satellites in a heterogeneous network. This setup not only offers seamless coverage but also enhances user rates. Nevertheless, the efficient allocation of onboard resources, particularly spectrum resources, poses a significant challenge due to their scarcity in such heterogeneous satellite coexistence networks. A practical solution is found in the use of beam hopping (BH) technology. This technology enables multi-beam satellites to serve users using fewer beams than traditional spot-beam systems. This paper proposes a resource allocation strategy for the heterogeneous LEO-GEO coexistence satellite network. We formulate this resource allocation strategy as a joint optimization problem. Due to the complexity of the system arising from the coupling of multiple variables, we break down the original problem into two manageable sub-problems. The first addresses user association, subcarrier, and power allocation and employs a standard convex optimization algorithm for a solution. The second tackles the illuminated beam selection issue, with a genetic algorithm (GA) providing a solution. The effectiveness of our proposed scheme is established through simulation experiments, demonstrating clear performance gains.