Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic analysis of the thioredoxin gene family in Citrus sinensis: identification, phylogenetic analysis, and gene expression patterns

ABSTRACT Thioredoxin (TRX) proteins play essential roles in reactive oxygen species scavenging in plants. We executed an exhaustive analysis of the TRX gene family in Citrus sinensis (CsTRXs), encompassing identification, phylogenetic analysis, detection of conserved motifs and domains, gene structure, cis-acting elements, gene expression trends, and subcellular localization analysis. Our findings established that a total of 22 CsTRXs with thioredoxin domains were identified in the genome of C. sinensis. Phylogenetic analysis indicated that CsTRXs were divided into six subclusters. Conserved motifs analysis of CsTRXs indicated a wide range of conserved motifs. A significant number of cis-acting elements associated with both abiotic and biotic stress responses, inclusive of numerous phytohormone-related elements, were detected in the promoter regions of CsTRXs. The expression levels of CsTRXs including CsTRXf1, CsTRXh1, CsTRXm1, CsTRXo3, CsTRXx2 and CsTRXy1 were observed to be reduced upon pathogen infection. Subcellular localization analysis found that CsTRXf1, CsTRXm1, CsTRXo3, CsTRXx2 and CsTRXy1 were predominantly localized in chloroplasts, whereas CsTRXh1 was distributed indiscriminately. This research yields integral data on CsTRXs, facilitating future efforts to decipher the gene functions of CsTRXs.
Retailer vs. vendor managed inventory with considering stochastic learning effect

Abstract Extending the research on the impact of learning effect on inventory management is of particular importance, this paper studies two different inventory management models with considering stochastic learning effect, one is retailer-managed inventory (RMI) scenario, and another is vendor-managed inventory (VMI) scenario. We find that inventory exists in equilibrium provided that the holding cost is under a respective threshold both in the RMI and VMI scenarios, also, the threshold in the RMI scenario is significantly larger than that in the VMI scenario. Moreover, the RMI scenario is Pareto dominant over the VMI scenario except for a very large holding cost, and the advantage in enhancing profit is highlighted in the RMI scenario as the variability of the learning rate increases. Furthermore, the traditional double marginalization effect is weakened by a large variability in the RMI scenario while intensified in the VMI scenario. The results obtained in this paper can provide guidance for the inventory management with considering stochastic learning effect.
Autonomic nervous modulation: early treatment for pulmonary artery hypertension

Abstract Pulmonary artery hypertension (PAH) is a chronic vascular disease defined by the elevation of pulmonary vascular resistance and mean pulmonary artery pressure, which arises due to pulmonary vascular remodelling. Prior research has already established a link between the autonomic nervous system (ANS) and PAH. Therefore, the rebalancing of the ANS offers a promising approach for the treatment of PAH. The process of rebalancing involves two key aspects: inhibiting an overactive sympathetic nervous system and fortifying the impaired parasympathetic nervous system through pharmacological or interventional procedures. However, the understanding of the precise mechanisms involved in neuromodulation, whether achieved through medication or intervention, remains insufficient. This limited understanding hinders our ability to determine the appropriate timing and scope of such treatment. This review aims to integrate the findings from clinical and mechanistic studies on ANS rebalancing as a treatment approach for PAH, with the ultimate goal of identifying a path to enhance the safety and efficacy of neuromodulation therapy and improve the prognosis of PAH.
A pharmacokinetic evaluation of tenapanor for the treatment of irritable bowel syndrome with constipation: an update of the literature

ABSTRACT Introduction Tenapanor is the latest addition to the second-line pharmacotherapeutic options for the treatment of irritable bowel syndrome with constipation. It is a first-in-class inhibitor of type 3 sodium/hydrogen exchanger (NHE3), characterized by very low oral absorption. Its pharmacological properties are discussed here based on the latest literature. Areas covered A general description of tenapanor is provided, highlighting those pharmacokinetic and pharmacodynamic characteristics of the drug which may be of major importance for tolerability and safety. This description is associated with a summary and analysis of currently available toxicological data. Expert opinion Plasma concentrations of free tenapanor after oral administration are well below the half maximal inhibitory concentration for NHE3, so that systemic effects of the drug are minimal. Therefore, the action of tenapanor is limited to NHE3 located on the apical membrane of enterocytes. The consequent reduction in intestinal sodium absorption increases the intraluminal content by osmosis, which in turn enhances the propulsive activity of the colon. Diarrhea is the most frequent adverse effect of tenapanor. Increased fecal sodium and water excretion do not appear to expose patients to short- and long-term hydro-electrolyte imbalances.
Metabolomics in diabetes mellitus: clinical insight

ABSTRACT Introduction Diabetes Mellitus (DM) is a chronic heterogeneous metabolic disorder characterized by hyperglycemia due to the destruction of insulin-producing pancreatic β cells and/or insulin resistance. It is now considered a global epidemic disease associated with serious threats to a patient’s life. Understanding the metabolic pathways involved in disease pathogenesis and progression is important and would improve prevention and management strategies. Metabolomics is an emerging field of research that offers valuable insights into the metabolic perturbation associated with metabolic diseases, including DM. Area covered Herein, we discussed the metabolomics in type 1 and 2 DM research, including its contribution to understanding disease pathogenesis and identifying potential novel biomarkers clinically useful for disease screening, monitoring, and prognosis. In addition, we highlighted the metabolic changes associated with treatment effects, including insulin and different anti-diabetic medications. Expert opinion By analyzing the metabolome, the metabolic disturbances involved in T1DM and T2DM can be explored, enhancing our understanding of the disease progression and potentially leading to novel clinical diagnostic and effective new therapeutic approaches. In addition, identifying specific metabolites would be potential clinical biomarkers for predicting the disease and thus preventing and managing hyperglycemia and its complications.
Perception of primary care services among the Afroperuvian population

The Peruvian public healthcare system has various shortcomings that impact the experiences and perceptions of quality among the general population and, particularly, among minority populations, including the Afro-Peruvian community. In this context, understanding the population's experiences in healthcare services and their perceptions regarding the treatment received from healthcare providers is crucial for developing improvement recommendations. Through in-depth interviews, our study explored the usage experiences and perceptions regarding the treatment received from public primary healthcare providers among 19 Afro-Peruvian individuals in Lima. Our analysis revealed that, in general, there is mistreatment of healthcare providers. This is not only recognized as a deficiency of the providers (human resources) but also as a consequence of a system that does not promote the quality of care, including issues such as inadequate infrastructure, low salaries, and limited time for patient care. Additionally, expressions of discrimination based on differences, primarily socioeconomic, were identified. Our findings provide insights for enhancing healthcare services in terms of treatment, effective communication, and infrastructure at the primary healthcare level.
Point Cloud Completion Guided by Prior Knowledge via Causal Inference

Point cloud completion aims to recover raw point clouds captured by scanners from partial observations caused by occlusion and limited view angles. This makes it hard to recover details because the global feature is unlikely to capture the full details of all missing parts. In this paper, we propose a novel approach to point cloud completion task called Point-PC, which uses a memory network to retrieve shape priors and designs a causal inference model to filter missing shape information as supplemental geometric information to aid point cloud completion. Specifically, we propose a memory operating mechanism where the complete shape features and the corresponding shapes are stored in the form of ``key-value'' pairs. To retrieve similar shapes from the partial input, we also apply a contrastive learning-based pre-training scheme to transfer the features of incomplete shapes into the domain of complete shape features. Experimental results on the ShapeNet-55, PCN, and KITTI datasets demonstrate that Point-PC outperforms the state-of-the-art methods.
Analysis on Community Structures and Spatial Patterns of Castanopsis fordii in Nanling Region of Hunan

Based on the investigation of community sample plots of Castanopsis fordii in Nanling region of Hunan,the Castanopsis fordii community of horizontal structure,vertical structure,population age-class structure,spatial distribution pattern and dynamics were separately researched and analyzed.The results show that the spatial pattern of Castanopsis fordii community is aggregated distribution,but because of vandalism its population structure is more simplistic and weak in self-renewal.Finally,the suggestions on protection and development of Castanopsis fordii resources were made.
Data Scarcity in Recommendation Systems: A Survey

The prevalence of online content has led to the widespread adoption of recommendation systems (RSs), which serve diverse purposes such as news, advertisements, and e-commerce recommendations. Despite their significance, data scarcity issues have significantly impaired the effectiveness of existing RS models and hindered their progress. To address this challenge, the concept of knowledge transfer, particularly from external sources like pre-trained language models, emerges as a potential solution to alleviate data scarcity and enhance RS development. However, the practice of knowledge transfer in RSs is intricate. Transferring knowledge between domains introduces data disparities, and the application of knowledge transfer in complex RS scenarios can yield negative consequences if not carefully designed. Therefore, this article contributes to this discourse by addressing the implications of data scarcity on RSs and introducing various strategies, such as data augmentation, self-supervised learning, transfer learning, broad learning, and knowledge graph utilization, to mitigate this challenge. Furthermore, it delves into the challenges and future direction within the RS domain, offering insights that are poised to facilitate the development and implementation of robust RSs, particularly when confronted with data scarcity. We aim to provide valuable guidance and inspiration for researchers and practitioners, ultimately driving advancements in the field of RS.
Communication-Efficient Soft Actor-Critic Policy Collaboration via Regulated Segment Mixture in Internet of Vehicles

Multi-Agent Reinforcement Learning (MARL) has emerged as a foundational approach for addressing diverse, intelligent control tasks, notably in autonomous driving within the Internet of Vehicles (IoV) domain. However, the widely assumed existence of a central node for centralized, federated learning-assisted MARL might be impractical in highly dynamic environments. This can lead to excessive communication overhead, potentially overwhelming the IoV system. To address these challenges, we design a novel communication-efficient and policy collaboration algorithm for MARL under the frameworks of Soft Actor-Critic (SAC) and Decentralized Federated Learning (DFL), named RSM-MASAC, within a fully distributed architecture. In particular, RSM-MASAC enhances multi-agent collaboration and prioritizes higher communication efficiency in dynamic IoV system by incorporating the concept of segmented aggregation in DFL and augmenting multiple model replicas from received neighboring policy segments, which are subsequently employed as reconstructed referential policies for mixing. Distinctively diverging from traditional RL approaches, with derived new bounds under Maximum Entropy Reinforcement Learning (MERL), RSM-MASAC adopts a theory-guided mixture metric to regulate the selection of contributive referential policies to guarantee the soft policy improvement during communication phase. Finally, the extensive simulations in mixed-autonomy traffic control scenarios verify the effectiveness and superiority of our algorithm.
Learning graphs and simplicial complexes from data

Graphs are widely used to represent complex information and signal domains with irregular support. Typically, the underlying graph topology is unknown and must be estimated from the available data. Common approaches assume pairwise node interactions and infer the graph topology based on this premise. In contrast, our novel method not only unveils the graph topology but also identifies three-node interactions, referred to in the literature as second-order simplicial complexes (SCs). We model signals using a graph autoregressive Volterra framework, enhancing it with structured graph Volterra kernels to learn SCs. We propose a mathematical formulation for graph and SC inference, solving it through convex optimization involving group norms and mask matrices. Experimental results on synthetic and real-world data showcase a superior performance for our approach compared to existing methods.
Degree-based stratification of nodes in Graph Neural Networks

Despite much research, Graph Neural Networks (GNNs) still do not display the favorable scaling properties of other deep neural networks such as Convolutional Neural Networks and Transformers. Previous work has identified issues such as oversmoothing of the latent representation and have suggested solutions such as skip connections and sophisticated normalization schemes. Here, we propose a different approach that is based on a stratification of the graph nodes. We provide motivation that the nodes in a graph can be stratified into those with a low degree and those with a high degree and that the two groups are likely to behave differently. Based on this motivation, we modify the Graph Neural Network (GNN) architecture so that the weight matrices are learned, separately, for the nodes in each group. This simple-to-implement modification seems to improve performance across datasets and GNN methods. To verify that this increase in performance is not only due to the added capacity, we also perform the same modification for random splits of the nodes, which does not lead to any improvement.
DeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative Models

Few-shot font generation, especially for Chinese calligraphy fonts, is a challenging and ongoing problem. With the help of prior knowledge that is mainly based on glyph consistency assumptions, some recently proposed methods can synthesize high-quality Chinese glyph images. However, glyphs in calligraphy font styles often do not meet these assumptions. To address this problem, we propose a novel model, DeepCalliFont, for few-shot Chinese calligraphy font synthesis by integrating dual-modality generative models. Specifically, the proposed model consists of image synthesis and sequence generation branches, generating consistent results via a dual-modality representation learning strategy. The two modalities (i.e., glyph images and writing sequences) are properly integrated using a feature recombination module and a rasterization loss function. Furthermore, a new pre-training strategy is adopted to improve the performance by exploiting large amounts of uni-modality data. Both qualitative and quantitative experiments have been conducted to demonstrate the superiority of our method to other state-of-the-art approaches in the task of few-shot Chinese calligraphy font synthesis. The source code can be found at https://github.com/lsflyt-pku/DeepCalliFont.
Conformer-Based Speech Recognition On Extreme Edge-Computing Devices

With increasingly more powerful compute capabilities and resources in today's devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the cloud to devices to better protect user privacy. However, it is still challenging to implement on-device ASR on resource-constrained devices, such as smartphones, smart wearables, and other small home automation devices. In this paper, we propose a series of model architecture adaptions, neural network graph transformations, and numerical optimizations to fit an advanced Conformer based end-to-end streaming ASR system on resource-constrained devices without accuracy degradation. We achieve over 5.26 times faster than realtime (0.19 RTF) speech recognition on small wearables while minimizing energy consumption and achieving state-of-the-art accuracy. The proposed methods are widely applicable to other transformer-based server-free AI applications. In addition, we provide a complete theory on optimal pre-normalizers that numerically stabilize layer normalization in any Lp-norm using any floating point precision.
Privacy-Aware Document Visual Question Answering

Document Visual Question Answering (DocVQA) is a fast growing branch of document understanding. Despite the fact that documents contain sensitive or copyrighted information, none of the current DocVQA methods offers strong privacy guarantees. In this work, we explore privacy in the domain of DocVQA for the first time. We highlight privacy issues in state of the art multi-modal LLM models used for DocVQA, and explore possible solutions. Specifically, we focus on the invoice processing use case as a realistic, widely used scenario for document understanding, and propose a large scale DocVQA dataset comprising invoice documents and associated questions and answers. We employ a federated learning scheme, that reflects the real-life distribution of documents in different businesses, and we explore the use case where the ID of the invoice issuer is the sensitive information to be protected. We demonstrate that non-private models tend to memorise, behaviour that can lead to exposing private information. We then evaluate baseline training schemes employing federated learning and differential privacy in this multi-modal scenario, where the sensitive information might be exposed through any of the two input modalities: vision (document image) or language (OCR tokens). Finally, we design an attack exploiting the memorisation effect of the model, and demonstrate its effectiveness in probing different DocVQA models.
Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks

Link prediction is a crucial research area in knowledge graphs, with many downstream applications. In many real-world scenarios, inductive link prediction is required, where predictions have to be made among unseen entities. Embedding-based models usually need fine-tuning on new entity embeddings, and hence are difficult to be directly applied to inductive link prediction tasks. Logical rules captured by rule-based models can be directly applied to new entities with the same graph typologies, but the captured rules are discrete and usually lack generosity. Graph neural networks (GNNs) can generalize topological information to new graphs taking advantage of deep neural networks, which however may still need fine-tuning on new entity embeddings. In this paper, we propose SiaILP, a path-based model for inductive link prediction using siamese neural networks. Our model only depends on relation and path embeddings, which can be generalized to new entities without fine-tuning. Experiments show that our model achieves several new state-of-the-art performances in link prediction tasks using inductive versions of WN18RR, FB15k-237, and Nell995.
Seq2seq for Automatic Paraphasia Detection in Aphasic Speech

Paraphasias are speech errors that are often characteristic of aphasia and they represent an important signal in assessing disease severity and subtype. Traditionally, clinicians manually identify paraphasias by transcribing and analyzing speech-language samples, which can be a time-consuming and burdensome process. Identifying paraphasias automatically can greatly help clinicians with the transcription process and ultimately facilitate more efficient and consistent aphasia assessment. Previous research has demonstrated the feasibility of automatic paraphasia detection by training an automatic speech recognition (ASR) model to extract transcripts and then training a separate paraphasia detection model on a set of hand-engineered features. In this paper, we propose a novel, sequence-to-sequence (seq2seq) model that is trained end-to-end (E2E) to perform both ASR and paraphasia detection tasks. We show that the proposed model outperforms the previous state-of-the-art approach for both word-level and utterance-level paraphasia detection tasks and provide additional follow-up evaluations to further understand the proposed model behavior.
Enabling Mammography with Co-Robotic Ultrasound

Ultrasound (US) imaging is a vital adjunct to mammography in breast cancer screening and diagnosis, but its reliance on hand-held transducers often lacks repeatability and heavily depends on sonographers' skills. Integrating US systems from different vendors further complicates clinical standards and workflows. This research introduces a co-robotic US platform for repeatable, accurate, and vendor-independent breast US image acquisition. The platform can autonomously perform 3D volume scans or swiftly acquire real-time 2D images of suspicious lesions. Utilizing a Universal Robot UR5 with an RGB camera, a force sensor, and an L7-4 linear array transducer, the system achieves autonomous navigation, motion control, and image acquisition. The calibrations, including camera-mammogram, robot-camera, and robot-US, were rigorously conducted and validated. Governed by a PID force control, the robot-held transducer maintains a constant contact force with the compression plate during the scan for safety and patient comfort. The framework was validated on a lesion-mimicking phantom. Our results indicate that the developed co-robotic US platform promises to enhance the precision and repeatability of breast cancer screening and diagnosis. Additionally, the platform offers straightforward integration into most mammographic devices to ensure vendor-independence.
End-to-End Asynchronous Traffic Scheduling in Converged 5G and Time-Sensitive Networks

As required by Industry 4.0, companies will move towards flexible and individual manufacturing. To succeed in this transition, convergence of 5G and time-sensitive networks (TSN) is the most promising technology and has thus attracted considerable interest from industry and standardization groups. However, the delay and jitter of end-to-end (e2e) transmission will get exacerbated if the transmission opportunities are missed in TSN due to the 5G transmission jitter and the clock skew between the two network systems. To mitigate this phenomenon, we propose a novel asynchronous access mechanism (AAM) that isolates the jitter only in the 5G system and ensures zero transmission jitter in TSN. We then exploit AAM to develop an e2e asynchronous traffic scheduling model for coordinated allocation of resources for 5G and TSN to provide e2e transmission delay guarantees for time-critical flows. The results of our extensive simulation of AAM on OMNET++ corroborate the superior performance of AAM and the scheduling model.