DORE: Document Ordered Relation Extraction based on Generative Framework

In recent years, there is a surge of generation-based information extraction work, which allows a more direct use of pre-trained language models and efficiently captures output dependencies. However, previous generative methods using lexical representation do not naturally fit document-level relation extraction (DocRE) where there are multiple entities and relational facts. In this paper, we investigate the root cause of the underwhelming performance of the existing generative DocRE models and discover that the culprit is the inadequacy of the training paradigm, instead of the capacities of the models. We propose to generate a symbolic and ordered sequence from the relation matrix which is deterministic and easier for model to learn. Moreover, we design a parallel row generation method to process overlong target sequences. Besides, we introduce several negative sampling strategies to improve the performance with balanced signals. Experimental results on four datasets show that our proposed method can improve the performance of the generative DocRE models. We have released our code at https://github.com/ayyyq/DORE.Comment: Findings of EMNLP 202

An AMR-based Link Prediction Approach for Document-level Event Argument Extraction

Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of complex semantic structures and helps to capture long-distance dependency. However, in these works AMR is used only implicitly, for instance, as additional features or training signals. Motivated by the fact that all event structures can be inferred from AMR, this work reformulates EAE as a link prediction problem on AMR graphs. Since AMR is a generic structure and does not perfectly suit EAE, we propose a novel graph structure, Tailored AMR Graph (TAG), which compresses less informative subgraphs and edge types, integrates span information, and highlights surrounding events in the same document. With TAG, we further propose a novel method using graph neural networks as a link prediction model to find event arguments. Our extensive experiments on WikiEvents and RAMS show that this simpler approach outperforms the state-of-the-art models by 3.63pt and 2.33pt F1, respectively, and do so with reduced 56% inference time. The code is availabel at https://github.com/ayyyq/TARA.Comment: Accepted to ACL 202

Patients with chronic ankle instability exhibit increased sensorimotor cortex activation and correlation with poorer lateral balance control ability during single-leg stance: a FNIRS study

IntroductionChronic Ankle Instability (CAI) is a musculoskeletal condition that evolves from acute ankle sprains, and its underlying mechanisms have yet to reach a consensus. Mounting evidence suggests that neuroplastic changes in the brain following ankle injuries play a pivotal role in the development of CAI. Balance deficits are a significant risk factor associated with CAI, yet there is a scarcity of evidence regarding the sensorimotor cortical plasticity related to balance control in affected individuals. This study aims to evaluate the differences in cortical activity and balance abilities between patients with CAI and uninjured individuals during a single-leg stance, as well as the correlation between these factors, in order to elucidate the neurophysiological alterations in balance control among patients with CAI.MethodsThe study enrolled 24 patients with CAI and 24 uninjured participants. During single-leg stance, cortical activity was measured using a functional near-infrared spectroscopy (fNIRS) system, which included assessments of the pre-motor cortex (PMC), supplementary motor area (SMA), primary motor cortex (M1), and primary somatosensory cortex (S1). Concurrently, balance parameters were tested utilizing a three-dimensional force platform.ResultsIndependent sample t-tests revealed that, compared with the uninjured individuals, the patients with CAI exhibited a significant increase in the changes of oxyhemoglobin concentration (ΔHbO) during single-leg stance within the left S1 at Channel 5 (t = 2.101, p = 0.041, Cohen’s d = 0.607), left M1 at Channel 6 (t = 2.363, p = 0.022, Cohen’s d = 0.682), right M1 at Channel 15 (t = 2.273, p = 0.029, Cohen’s d = 0.656), and right PMC/SMA at Channel 11 (t = 2.467, p = 0.018, Cohen’s d = 0.712). Additionally, the center of pressure root mean square (COP-RMS) in the mediolateral (ML) direction was significantly greater (t = 2.630, p = 0.012, Cohen’s d = 0.759) in the patients with CAI. Furthermore, a moderate positive correlation was found between ML direction COP-RMS and ΔHbO2 in the M1 (r = 0.436; p = 0.033) and PMC/SMA (r = 0.488, p = 0.016), as well as between anteroposterior (AP) direction COP-RMS and ΔHbO in the M1 (r = 0.483, p = 0.017).ConclusionPatients with CAI demonstrate increased cortical activation in the bilateral M1, ipsilateral PMC/SMA, and contralateral S1. This suggests that patients with CAI may require additional brain resources to maintain balance during single-leg stance, representing a compensatory mechanism to uphold task performance amidst diminished lateral balance ability in the ankle joint

Defective TGFβ signaling in bone marrow-derived cells prevents Hedgehog-induced skin tumors

Hedgehog (Hh) signaling in cancer cells drives changes in the tumor microenvironment that are incompletely understood. Here we report that Hh- driven tumors exhibit an increase in myeloid-derived suppressor cells (MDSC) and a decrease in T cells, indicative of an immune suppressive tumor microenvironment. This change was associated with activated TGFβ signaling in several cell types in BCCs. We determined that TGFβ signaling in bone marrow (BM)-derived cells, not keratinocytes, regulates MDSC and promotes tumor development. Tgfbr2 deficiency in the BM-derived cells also reduced the size of previously developed tumors in mice. We identified CCL2 as the major chemokine attracting MDSC to tumor, whose expression was Tgfbr2-dependent, whereas its receptor CCR2 was highly expressed in MDSC population. CCL2 alone was sufficient to induce migration of MDSC. Moreover, the CCR2 inhibitors prevented MDSC migration towards skin cells in vitro, reduced MDSC accumulation and Hh signaling-driven tumor development in mice. Our results reveal a signaling network critical for Hh signaling in cancer cells to establish an effective immune suppressive microenvironment during tumor development

Scapular kinematics and muscle activity during Yi Jin Bang exercises

Introduction: Scapular dyskinesis is commonly associated with subacromial pain syndrome (SAPS). Addressing scapular dyskinesis is widely accepted as an important component of shoulder rehabilitation. Our previous randomized controlled trial showed that Yi Jin Bang (YJB) exercises could effectively manage SAPS, but scapular motions and muscle activity during YJB exercises remain unknown. This study examined scapular kinematics synchronously with scapular muscle activation during YJB exercises.Methods: Thirty healthy participants with no shoulder complaints were enrolled in this study. Three-dimensional (3D) scapular kinematics and electromyography (EMG) activation of the upper trapezius, middle trapezius, lower trapezius, serratus anterior, anterior deltoid, middle deltoid, and posterior deltoid were synchronously measured during nine YJB movements.Results: During all YJB movements, the scapula was upwardly rotated and anteriorly tilted, with more upward rotation and a similar or less anterior tilt than the mean resting scapular angle. Column rotation, arm crossover, shoulder support circle, and armpit support high lift generated more internal rotation than the mean resting scapular angle, with the angles of internal rotation significantly greater than the other five movements (p < 0.001). Regarding EMG activity, all YJB movements elicited low activity (1.42%–19.19% maximal voluntary isometric contraction [MVIC]) from the upper trapezius and posterior deltoid and low to moderate activity (0.52%–29.50% MVIC) from the middle trapezius, lower trapezius, serratus anterior, anterior deltoid, and middle deltoid.Conclusion: YJB exercises could be useful in the middle to later phases of shoulder rehabilitation. For patients with insufficient external rotation, some YJB movements should be prescribed with caution

Production of high‐purity hydrogen and layered doubled hydroxide by the hydrolysis of Mg‐Al alloys

Hydrogen is becoming an important clean energy and layered doubled hydroxide (LDH) is of great interest for many applications, including water treatment, environmental remediation, and chemical catalysis. The production of high‐purity hydrogen and LDH by the hydrolysis of Mg‐Al alloys is reported. The effects of initial pH, reaction temperature, reaction time, and alloy's Mg/Al mass ratio on the rate of hydrogen generation and the purity of LDH are evaluated and the solid hydrolysis products are characterized by different techniques. The initial rate of hydrogen generation increases with decreasing initial pH and increasing reaction temperature and Mg/Al ratio while the purity of LDH increases with Mg/Al ratio, reaction temperature and time. This study may provide a new, green, and sustainable approach for storage of hydrogen and material for water treatment

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way

Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has made it possible to adapt these models to specific applications for enhanced performance. However, the substantial resources required for training these models necessitate efficient solutions. This paper introduces CoLLiE, an efficient library that facilitates collaborative training of large language models using 3D parallelism, parameter-efficient fine-tuning (PEFT) methods, and optimizers such as Lion, Adan, Sophia, LOMO and AdaLomo. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization. CoLLiE has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. Furthermore, we provide an empirical evaluation of the correlation between model size and GPU memory consumption under different optimization methods, as well as an analysis of the throughput. Lastly, we carry out a comprehensive comparison of various optimizers and PEFT methods within the instruction-tuning context. CoLLiE is available at https://github.com/OpenLMLab/collie.Comment: To appear at EMNLP 2023 Demo; Code is available at https://github.com/OpenLMLab/colli

Polycomb group proteins EZH2 and EED directly regulate androgen receptor in advanced prostate cancer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149265/1/ijc32118.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149265/2/ijc32118_am.pd