Inferring Tabular Analysis Metadata by Infusing Distribution and Knowledge Information

Many data analysis tasks heavily rely on a deep understanding of tables (multi-dimensional data). Across the tasks, there exist comonly used metadata attributes of table fields / columns. In this paper, we identify four such analysis metadata: Measure/dimension dichotomy, common field roles, semantic field type, and default aggregation function. While those metadata face challenges of insufficient supervision signals, utilizing existing knowledge and understanding distribution. To inference these metadata for a raw table, we propose our multi-tasking Metadata model which fuses field distribution and knowledge graph information into pre-trained tabular models. For model training and evaluation, we collect a large corpus (~582k tables from private spreadsheet and public tabular datasets) of analysis metadata by using diverse smart supervisions from downstream tasks. Our best model has accuracy = 98%, hit rate at top-1 > 67%, accuracy > 80%, and accuracy = 88% for the four analysis metadata inference tasks, respectively. It outperforms a series of baselines that are based on rules, traditional machine learning methods, and pre-trained tabular models. Analysis metadata models are deployed in a popular data analysis product, helping downstream intelligent features such as insights mining, chart / pivot table recommendation, and natural language QA...Comment: 13pages, 7 figures, 9 table

Tumor Tissue-Derived Formaldehyde and Acidic Microenvironment Synergistically Induce Bone Cancer Pain

Background: There is current interest in understanding the molecular mechanisms of tumor-induced bone pain. Accumulated evidence shows that endogenous formaldehyde concentrations are elevated in the blood or urine of patients with breast, prostate or bladder cancer. These cancers are frequently associated with cancer pain especially after bone metastasis. It is well known that transient receptor potential vanilloid receptor 1 (TRPV1) participates in cancer pain. The present study aims to demonstrate that the tumor tissue-derived endogenous formaldehyde induces bone cancer pain via TRPV1 activation under tumor acidic environment. Methodology/Principal Findings: Endogenous formaldehyde concentration increased significantly in the cultured breast cancer cell lines in vitro, in the bone marrow of breast MRMT-1 bone cancer pain model in rats and in tissues from breast cancer and lung cancer patients in vivo. Low concentrations (1 similar to 5 mM) of formaldehyde induced pain responses in rat via TRPV1 and this pain response could be significantly enhanced by pH 6.0 (mimicking the acidic tumor microenvironment). Formaldehyde at low concentrations (1 mM to 100 mM) induced a concentration-dependent increase of [Ca(2+)]i in the freshly isolated rat dorsal root ganglion neurons and TRPV1-transfected CHO cells. Furthermore, electrophysiological experiments showed that low concentration formaldehyde-elicited TRPV1 currents could be significantly potentiated by low pH (6.0). TRPV1 antagonists and formaldehyde scavengers attenuated bone cancer pain responses. Conclusions/Significance: Our data suggest that cancer tissues directly secrete endogenous formaldehyde, and this formaldehyde at low concentration induces metastatic bone cancer pain through TRPV1 activation especially under tumor acidic environment.Multidisciplinary SciencesSCI(E)PubMed24ARTICLE4e10234

Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models

This paper presents a comprehensive survey of ChatGPT and GPT-4, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT/GPT-4 research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.Comment: 35 pages, 3 figure

Microstructure, mechanical and corrosion properties of FeCrNiCoMnSi0.1 high-entropy alloy coating via TIG arc melting technology and high-frequency ultrasonic impact with welding

With the increase in studies on high-entropy alloys and their impressive structural properties, the preparation processes and applications of high-entropy alloys have become a popular research topic in metallic materials. In this paper, the preparation of FeCrNiCoMnSi0.1 high-entropy alloy coatings was carried out by the follow-welding high-frequency power ultrasonic impact composite TIG arc melting process, the effects of different power ultrasonic impacts on the microstructure and properties of the coatings are investigated. The results showed that the average grain size is reduced by 74 % (from 278 μm to 72 μm), the average microhardness is increased by 41 % from 568 HV1 to 807 HV1, the abrasion resistance is improved by 68 % under the effect of ultrasonic impact. The ultrasonic impact treatment process can effectively refine the microstructure of the coatings and improve the strength of grain boundaries. The corrosion resistance of the coating in 3.5 wt% NaCl solution is enhanced by 65 %, the corrosion type was changed from intergranular corrosion to uniform corrosion. This is mainly caused by the ultrasonic impact treatment which suppresses the elemental segregation of Cr and Mn and improves the grain boundary strength

Coordinated message delivery in partially connected local association networks for the 'Internet of Things'

The traditional internet commonly is wired with machine-to-machine persistent connections. Evolving towards mobile and wireless pervasive networks, the internet has to entertain dynamic, transient and changing interconnections. The vision of the Internet of Things (IOT) furthers technology development by creating an interactive environment where smart objects are connected and can sense and react to the environment. The resulting event flooding in such an IOT environment has aroused interest in research in network architecture and topologies where the events can be filtered to meet event-intensive application requirements. In this paper, we will introduce a Local Association Network (LAN) with a coordinated P2P message delivery mechanism. This LAN is tested and validated as suitable building block for the IOT. Copyright ? 2011 Inderscience Enterprises Ltd

Whole‐brain steady‐state CEST at 3 T using MR Multitasking

PurposeTo perform fast 3D steady-state CEST (ss-CEST) imaging using MR Multitasking.MethodsA continuous acquisition sequence with repetitive ss-CEST modules was developed. Each ss-CEST module contains a single-lobe Gaussian saturation pulse, followed by a spoiler gradient and eight FLASH readouts (one "training line" + seven "imaging lines"). Three-dimensional Cartesian encoding was used for k-space acquisition. Reconstructed CEST images were quantified with four-pool Lorentzian fitting.ResultsSteady-state CEST with whole-brain coverage was performed in 5.6 s per saturation frequency offset at the spatial resolution of 1.7 × 1.7 × 3.0 mm3 . The total scan time was 5.5 min for 55 different frequency offsets. Quantitative CEST maps from multipool fitting showed consistent image quality across the volume.ConclusionThree-dimensional ss-CEST with whole-brain coverage can be done at 3 T within 5.5 min using MR Multitasking

Free-breathing, non-ECG, simultaneous myocardial T1 , T2 , T2 *, and fat-fraction mapping with motion-resolved cardiovascular MR multitasking.

PURPOSE: To develop a free-breathing, non-electrocardiogram technique for simultaneous myocardial T1 , T2 , T2 *, and fat-fraction (FF) mapping in a single scan. METHODS: The MR Multitasking framework is adapted to quantify T1 , T2 , T2 *, and FF simultaneously. A variable TR scheme is developed to preserve temporal resolution and imaging efficiency. The underlying high-dimensional image is modeled as a low-rank tensor, which allows accelerated acquisition and efficient reconstruction. The accuracy and/or repeatability of the technique were evaluated on static and motion phantoms, 12 healthy volunteers, and 3 patients by comparing to the reference techniques. RESULTS: In static and motion phantoms, T1 /T2 /T2 */FF measurements showed substantial consistency (R > 0.98) and excellent agreement (intraclass correlation coefficient > 0.93) with reference measurements. In human subjects, the proposed technique yielded repeatable T1 , T2 , T2 *, and FF measurements that agreed with those from references. CONCLUSIONS: The proposed free-breathing, non-electrocardiogram, motion-resolved Multitasking technique allows simultaneous quantification of myocardial T1 , T2 , T2 *, and FF in a single 2.5-min scan

Analgesic Mechanism of Sinomenine against Chronic Pain

Purified from the roots of the plant Sinomenium acutum, sinomenine is traditionally used in China and Japan for treating rheumatism and arthritis. Previously, we have demonstrated that sinomenine possessed a broad analgesic spectrum in various chronic pain animal models and repeated administration of sinomenine did not generate tolerance. In this review article, we discussed sinomenine’s analgesic mechanism with focus on its role on immune regulation and neuroimmune interaction. Sinomenine has distinct immunoregulative properties, in which glutamate, adenosine triphosphate, nitric oxide, and proinflammatory cytokines are thought to be involved. Sinomenine may alter the unbalanced neuroimmune interaction and inhibit neuroinflammation, oxidative stress, and central sensitization in chronic pain states. In conclusion, sinomenine has promising potential for chronic pain management in different clinical settings

Morphine withdrawal affects both delayed-escape behaviour in Morris water maze and hippocampal NR2A/2B expression ratio

Repeated low-dose morphine treatment facilitates delayed-escape behaviour of hippocampus-dependent Morris water maze and morphine withdrawal influences hippocampal NMDA receptor-dependent synaptic plasticity. Here, we examined whether and how morphine withdrawal influenced delayed-escape behaviour and NR2A/2B expression ratio of hippocampal synaptosomes. We found that both delayed-escape behaviour and NR2A/2B expression ratio showed an inverted-U curve and peaked on 4-day withdrawal during a 20-day withdrawal period. Furthermore, treatment of the glucocorticoid receptor antagonist RU38486 for 3 days reduced delayed-escape behaviour and NR2A/2B ratio on 4-day withdrawal to a level similar to those of 18-h withdrawal. In contrast, elevated-platform stress enabled delayed-escape behaviour of 18-h withdrawal to a higher level similar to that of 4-day withdrawal, but had no significant effect on the NR2A/2B ratio. Similar behavioural effects were also found after intrahippocampal infusions of the NMDAR antagonist AP-5 or NR2B-containing NMDAR antagonist Ro25-6981 for 3 days. These findings suggest that delayed-escape behaviour enabled by repeated low-dose morphine treatment may be a useful and simple rat model for studying addictive memories to be retrieved by stress exposure.Repeated low-dose morphine treatment facilitates delayed-escape behaviour of hippocampus-dependent Morris water maze and morphine withdrawal influences hippocampal NMDA receptor-dependent synaptic plasticity. Here, we examined whether and how morphine withdrawal influenced delayed-escape behaviour and NR2A/2B expression ratio of hippocampal synaptosomes. We found that both delayed-escape behaviour and NR2A/2B expression ratio showed an inverted-U curve and peaked on 4-day withdrawal during a 20-day withdrawal period. Furthermore, treatment of the glucocorticoid receptor antagonist RU38486 for 3 days reduced delayed-escape behaviour and NR2A/2B ratio on 4-day withdrawal to a level similar to those of 18-h withdrawal. In contrast, elevated-platform stress enabled delayed-escape behaviour of 18-h withdrawal to a higher level similar to that of 4-day withdrawal, but had no significant effect on the NR2A/2B ratio. Similar behavioural effects were also found after intrahippocampal infusions of the NMDAR antagonist AP-5 or NR2B-containing NMDAR antagonist Ro25-6981 for 3 days. These findings suggest that delayed-escape behaviour enabled by repeated low-dose morphine treatment may be a useful and simple rat model for studying addictive memories to be retrieved by stress exposure. (C) 2008 Elsevier B.V. All rights reserved

Free‐breathing multitasking multi‐echo MRI for whole‐liver water‐specific T1, proton density fat fraction, and quantification

PurposeTo develop a 3D multitasking multi-echo (MT-ME) technique for the comprehensive characterization of liver tissues with 5-min free-breathing acquisition; whole-liver coverage; a spatial resolution of 1.5 × 1.5 × 6 mm3 ; and simultaneous quantification of T1 , water-specific T1 (T1w ), proton density fat fraction (PDFF), and R2∗ .MethodsSix-echo bipolar spoiled gradient echo readouts following inversion recovery preparation was performed to generate T1 , water/fat, and R2∗ contrast. MR multitasking was used to reconstruct the MT-ME images with 3 spatial dimensions: 1 T1 recovery dimension, 1 multi-echo dimension, and 1 respiratory dimension. A basis function-based approach was developed for T1w quantification, followed by the estimation of R2∗ and T1 -corrected PDFF. The intrasession repeatability and agreement against references of MT-ME measurements were tested on a phantom and 15 clinically healthy subjects. In addition, 4 patients with confirmed liver diseases were recruited, and the agreement between MT-ME measurements and references was assessed.ResultsMT-ME produced high-quality, coregistered T1 , T1w , PDFF, and R2∗ maps with good intrasession repeatability and substantial agreement with references on phantom and human studies. The intra-class coefficients of T1 , T1w , PDFF, and R2∗ from the repeat MT-ME measurements on clinically healthy subjects were 0.989, 0.990, 0.999, and 0.988, respectively. The intra-class coefficients of T1 , PDFF, and R2∗ between the MT-ME and reference measurements were 0.924, 0.987, and 0.975 in healthy subjects and 0.980, 0.999, and 0.998 in patients. The T1w was independent to PDFF (R = -0.029, P = .904).ConclusionThe proposed MT-ME technique quantifies T1 , T1w , PDFF, and R2∗ simultaneously and is clinically promising for the comprehensive characterization of liver tissue properties