Effect of low molecular weight heparin and ulinastatin as a combined therapy on soluble myeloid cell expression and intestinal mucosal function in patients with severe pancreatitis

Purpose: To investigate the effect of low molecular weight heparins (LMWHs) and ulinastatin on soluble myeloid cells and intestinal mucosal function (IMF) in patients with severe pancreatitis. Methods: A total of 107 patients with severe pancreatitis were divided into two groups: control group (CG, n = 53) and study group (SG, n = 54). The CG was treated with LMWH while SG was similarly treated but in addition received ulinastatin simultaneously. The following parameters were evaluated in the two groups: treatment effects, IMF, time for various indicators to normalize, vascular endothelial function, complication symptoms, T lymphoid subgroup indicators, inflammatory factors, anti-inflammatory factors, soluble B7-H2, and soluble myeloid cell receptor-1 level changes. Results: After treatment, SG showed lower levels of L/M value, DAO and D-lactic acid than in CG (p < 0.05). Gastrointestinal function, leukocytes, amylase, and body temperature in SG had a shorter time to return to normal than in CG (p < 0.05). The levels of IL-10 in SG were higher than in CG, while sB7-H2, TNF-α, sTREM-1 and IL-1 levels were lower than those in the CG (p < 0.05). After treatment, NO levels in SG were higher, but TXB2, vWF and ET levels were lower than in CG (p < 0.05). In addition, CD4+, CD4+/CD8+ indicators were higher and CD8+ lower in SG than in CG (p < 0.05). Conclusion: Ulinastatin + LMWHs improves IMF in patients suffering from severe pancreatitis, shortens the time for various indicators to normalize, and reduces incidence of complications. However, further clinical trials are required to ascertain this therapeutic strategy for the management of severe pancreatitis. Keywords: Low molecular weight heparin; Ulinastatin; Severe pancreatitis; Soluble myeloid cell expression; Intestinal mucosal function; Treatment effec

GPT Understands, Too

While GPTs with traditional fine-tuning fail to achieve strong results on natural language understanding (NLU), we show that GPTs can be better than or comparable to similar-sized BERTs on NLU tasks with a novel method P-tuning -- which employs trainable continuous prompt embeddings. On the knowledge probing (LAMA) benchmark, the best GPT recovers 64\% (P@1) of world knowledge without any additional text provided during test time, which substantially improves the previous best by 20+ percentage points. On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning. Importantly, we find that P-tuning also improves BERTs' performance in both few-shot and supervised settings while largely reducing the need for prompt engineering. Consequently, P-tuning outperforms the state-of-the-art approaches on the few-shot SuperGlue benchmark

ETM Toolkit: A development tool based on Extended Topic Map

By research on Topic Map standard, the Extended Topic Map (ETM) is proposed as a novel model for better organization and management of the massive knowledge resources in E-learning. Based on the model, an Extended Topic Map Toolkit is designed and implemented, which supports exploration, search, consistency check and etc. The ETM Toolkit not only provides learners with visual navigation and search on massive E-learning resources, but also offers a way for instructors to collaboratively build the shareable and reusable domain knowledge efficiently. By ETM Toolkit, an extended topic map with a certain scale on Computer Networks has been built and is currently available for students in our university

Combining machine learning and human judgment in author disambiguation

ABSTRACT Author disambiguation in digital libraries becomes increasingly difficult as the number of publications and consequently the number of ambiguous author names keep growing. The fully automatic author disambiguation approach could not give satisfactory results due to the lack of signals in many cases. Furthermore, human judgment on the basis of automatic algorithms is also not suitable because the automatically disambiguated results are often mixed and not understandable for humans. In this paper, we propose a Labeling Oriented Author Disambiguation approach, called LOAD, to combine machine learning and human judgment together in author disambiguation. LOAD exploits a framework which consists of high precision clustering, high recall clustering, and top dissimilar clusters selection and ranking. In the framework, supervised learning algorithms are used to train the similarity functions between publications and a clustering algorithm is further applied to generate clusters. To validate the effectiveness and efficiency of the proposed LOAD approach, comprehensive experiments are conducted. Comparing to conventional author disambiguation algorithms, the LOAD yields much more accurate results to assist human labeling. Further experiments show that the LOAD approach can save labeling time dramatically

Nanomechanical Resonators: Toward Atomic Scale

The quest for realizing and manipulating ever smaller man-made movable structures and dynamical machines has spurred tremendous endeavors, led to important discoveries, and inspired researchers to venture to new grounds. Scientific feats and technological milestones of miniaturization of mechanical structures have been widely accomplished by advances in machining and sculpturing ever shrinking features out of bulk materials such as silicon. With the flourishing multidisciplinary field of low-dimensional nanomaterials, including one-dimensional (1D) nanowires/nanotubes, and two-dimensional (2D) atomic layers such as graphene/phosphorene, growing interests and sustained efforts have been devoted to creating mechanical devices toward the ultimate limit of miniaturization— genuinely down to the molecular or even atomic scale. These ultrasmall movable structures, particularly nanomechanical resonators that exploit the vibratory motion in these 1D and 2D nano-to-atomic-scale structures, offer exceptional device-level attributes, such as ultralow mass, ultrawide frequency tuning range, broad dynamic range, and ultralow power consumption, thus holding strong promises for both fundamental studies and engineering applications. In this Review, we offer a comprehensive overview and summary of this vibrant field, present the state-of-the-art devices and evaluate their specifications and performance, outline important achievements, and postulate future directions for studying these miniscule yet intriguing molecular-scale machines

Learning robust low-rank approximation for crowdsourcing on Riemannian Manifold

Recently, crowdsourcing has attracted substantial research interest due to its efficiency in collecting labels for machine learning and computer vision tasks. This paper proposes a Rieman-nian manifold optimization algorithm, ROLA (Robust Low-rank Approximation), to aggregate the labels from a novel perspective. Specifically, a novel low-rank approximation model is proposed to capture underlying correlation among annotators meanwhile identify annotator-specific noise. More significantly, ROLA defines the label noise in crowdsourcing as annotator-specific noise, which can be well regularized by l2,1-norm. The proposed ROLA can improve the aggregation performance when compared with state-of-the-art crowdsourcing methods

Endocytic Adaptor Protein HIP1R Controls Intracellular Trafficking of Epidermal Growth Factor Receptor in Neuronal Dendritic Development

Huntington-interacting protein 1-related protein (HIP1R) was identified on the basis of its structural homology with HIP1. Based on its domain structure, HIP1R is a putative endocytosis-related protein. Our previous study had shown that knockdown of HIP1R induces a dramatic decrease of dendritic growth and branching in cultured rat hippocampal neurons. However, the underlying mechanism remains elucidative. In this study, we found that knockdown of HIP1R impaired the endocytosis of activated epidermal growth factor receptor (EGFR) and the consequent activation of the downstream ERK and Akt proteins. Meanwhile, it blocked the EGF-induced dendritic outgrowth. We also showed that the HIP1R fragment, amino acids 633–822 (HIP1R633–822), interacted with EGFR and revealed a dominant negative effect in disrupting the HIP1R-EGFR interaction-mediated neuronal development. Collectively, these results reveal a novel mechanism that HIP1R plays a critical role in neurite initiation and dendritic branching in cultured hippocampal neurons via mediating the endocytosis of EGFR and downstream signaling

Quantum Griffiths singularity in three-dimensional superconductor to Anderson critical insulator transition

Disorder is ubiquitous in real materials and can have dramatic effects on quantum phase transitions. Originating from the disorder enhanced quantum fluctuation, quantum Griffiths singularity (QGS) has been revealed as a universal phenomenon in quantum criticality of low-dimensional superconductors. However, due to the weak fluctuation effect, QGS is very challenging to detect experimentally in three-dimensional (3D) superconducting systems. Here we report the discovery of QGS associated with the quantum phase transition from 3D superconductor to Anderson critical insulator in a spinel oxide MgTi2O4 (MTO). Under both perpendicular and parallel magnetic field, the dynamical critical exponent diverges when approaching the quantum critical point, demonstrating the existence of 3D QGS. Among 3D superconductors, MTO shows relatively strong fluctuation effect featured as a wide superconducting transition region. The enhanced fluctuation, which may arise from the mobility edge of Anderson localization, finally leads to the occurrence of 3D quantum phase transition and QGS. Our findings offer a new perspective to understand quantum phase transitions in strongly disordered 3D systems