Terminology Extraction for and from Communications in Multi-disciplinary Domains

Terminology extraction generally refers to methods and systems for identifying term candidates in a uni-disciplinary and uni-lingual environment such as engineering, medical, physical and geological sciences, or administration, business and leisure. However, as human enterprises get more and more complex, it has become increasingly important for teams in one discipline to collaborate with others from not only a non-cognate discipline but also speaking a different language. Disaster mitigation and recovery, and conflict resolution are amongst the areas where there is a requirement to use standardised multilingual terminology for communication. This paper presents a feasibility study conducted to build terminology (and ontology) in the domain of disaster management and is part of the broader work conducted for the EU project Sland \ub4 ail (FP7 607691). We have evaluated CiCui (for Chinese name \ub4 \u8bcd\u8403, which translates to words gathered), a corpus-based text analytic system that combine frequency, collocation and linguistic analyses to extract candidates terminologies from corpora comprised of domain texts from diverse sources. CiCui was assessed against four terminology extraction systems and the initial results show that it has an above average precision in extracting terms

Sphingosine-1-phosphate promotes the differentiation of human umbilical cord mesenchymal stem cells into cardiomyocytes under the designated culturing conditions

<p>Abstract</p> <p>Background</p> <p>It is of growing interest to develop novel approaches to initiate differentiation of mesenchymal stem cells (MSCs) into cardiomyocytes. The purpose of this investigation was to determine if Sphingosine-1-phosphate (S1P), a native circulating bioactive lipid metabolite, plays a role in differentiation of human umbilical cord mesenchymal stem cells (HUMSCs) into cardiomyocytes. We also developed an engineered cell sheet from these HUMSCs derived cardiomyocytes by using a temperature-responsive polymer, poly(N-isopropylacrylamide) (PIPAAm) cell sheet technology.</p> <p>Methods</p> <p>Cardiomyogenic differentiation of HUMSCs was performed by culturing these cells with either designated cardiomyocytes conditioned medium (CMCM) alone, or with 1 μM S1P; or DMEM with 10% FBS + 1 μM S1P. Cardiomyogenic differentiation was determined by immunocytochemical analysis of expression of cardiomyocyte markers and patch clamping recording of the action potential.</p> <p>Results</p> <p>A cardiomyocyte-like morphology and the expression of α-actinin and myosin heavy chain (MHC) proteins can be observed in both CMCM culturing or CMCM+S1P culturing groups after 5 days' culturing, however, only the cells in CMCM+S1P culture condition present cardiomyocyte-like action potential and voltage gated currents. A new approach was used to form PIPAAm based temperature-responsive culture surfaces and this successfully produced cell sheets from HUMSCs derived cardiomyocytes.</p> <p>Conclusions</p> <p>This study for the first time demonstrates that S1P potentiates differentiation of HUMSCs towards functional cardiomyocytes under the designated culture conditions. Our engineered cell sheets may provide a potential for clinically applicable myocardial tissues should promote cardiac tissue engineering research.</p

Clinical relevance and outcome of familial papillary thyroid cancer: a single institution study of 626 familial cases

BackgroundWhether familial thyroid cancer is more aggressive than sporadic thyroid cancer remains controversial. Additionally, whether the number of affected family members affects the prognosis is unknown. This study focused mainly on the comparison of the clinicopathological characteristics and prognoses between papillary thyroid cancer (PTC) patients with and without family history.MethodsA total of 626 familial papillary thyroid cancer (FPTC) and 1252 sporadic papillary thyroid cancer (SPTC) patients were included in our study. The clinical information associated with FPTC and SPTC was recorded and analyzed by univariate analysis.ResultsPatients in the FPTC group had a higher rate of multifocality (p=0.001), bilaterality (p=0.000), extrathyroidal invasion (p=0.000), distant metastasis (p=0.012), lymph node metastasis (p=0.000), recurrence (p=0.000), a larger tumor size (p=0.000) and more malignant lymph nodes involved (central: p=0.000; lateral: p=0.000). In addition, our subgroup analysis showed no significant difference (p&gt;0.05) between patients with only one affected family member and those with two of more group in all clinicopathological characteristics. In papillary thyroid microcarcinoma (PTMC) subgroup analysis, we found that FPTMC patients harbored significantly larger tumors (p=0.000), higher rates of multifocality (p=0.014), bilaterality (p=0.000), distant metastasis (p=0.038), lymph node metastasis (p=0.003), greater numbers of malignant lymph nodes (central: p=0.002; lateral: p=0.044), higher rates of I-131 treatment (p=0.000) and recurrence (p=0.000) than SPTMC patients.ConclusionOur results indicated that PTC and PTMC patients with a positive family history had more aggressive clinicopathological behaviors, suggesting that more vigilant screening and management for FPTC may be helpful

On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective

ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been done, its robustness, i.e., the performance to unexpected inputs, is still unclear to the public. Robustness is of particular concern in responsible AI, especially for safety-critical applications. In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective. To do so, we employ the AdvGLUE and ANLI benchmarks to assess adversarial robustness and the Flipkart review and DDXPlus medical diagnosis datasets for OOD evaluation. We select several popular foundation models as baselines. Results show that ChatGPT shows consistent advantages on most adversarial and OOD classification and translation tasks. However, the absolute performance is far from perfection, which suggests that adversarial and OOD robustness remains a significant threat to foundation models. Moreover, ChatGPT shows astounding performance in understanding dialogue-related texts and we find that it tends to provide informal suggestions for medical tasks instead of definitive answers. Finally, we present in-depth discussions of possible research directions.Comment: Technical report; code is at: https://github.com/microsoft/robustlear

Building a Global Ecosystem Research Infrastructure to Address Global Grand Challenges for Macrosystem Ecology

The development of several large-, "continental"-scale ecosystem research infrastructures over recent decades has provided a unique opportunity in the history of ecological science. The Global Ecosystem Research Infrastructure (GERI) is an integrated network of analogous, but independent, site-based ecosystem research infrastructures (ERI) dedicated to better understand the function and change of indicator ecosystems across global biomes. Bringing together these ERIs, harmonizing their respective data and reducing uncertainties enables broader cross-continental ecological research. It will also enhance the research community capabilities to address current and anticipate future global scale ecological challenges. Moreover, increasing the international capabilities of these ERIs goes beyond their original design intent, and is an unexpected added value of these large national investments. Here, we identify specific global grand challenge areas and research trends to advance the ecological frontiers across continents that can be addressed through the federation of these cross-continental-scale ERIs.Peer reviewe