Summary Cellular senescence, which is known to halt proliferation of aged and stressed cells, plays a key role against cancer development, and is also closely associated with organismal aging. While increased IGF signaling induces cell proliferation, survival and cancer progression, disrupted insulin-like growth factor (IGF) signaling is known to enhance longevity concomitantly with delay in aging processes. The molecular mechanisms involved in the regulation of aging by IGF signaling and whether IGF regulates cellular senescence are still poorly understood. In this study, we demonstrate that IGF-1 exerts a dual function in promoting cell proliferation as well as cellular senescence. While acute IGF-1 exposure promotes cell proliferation and is opposed by p53, prolonged IGF-1 treatment induces premature cellular senescence in a p53-dependent manner. We show that prolonged IGF-1 treatment inhibits SIRT1 deacetylase activity, resulting in increased p53 acetylation as well as p53 stabilization and activation, thus leading to premature cellular senescence. In addition, either expression of SIRT1 or inhibition of p53 prevented IGF-1-induced premature cellular senescence. Together, these findings suggest that p53 acts as a molecular switch in monitoring IGF-1-induced proliferation and premature senescence, and suggest a possible molecular connection involving IGF-1-SIRT1-p53 signaling in cellular senescence and aging.

Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT. We show that most of the improvements are not interpretable as utilizing the context. We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT.

The Semantic Web is based on accessing and reusing RDF data from many different sources, which one may assign different levels of authority and credibility. Existing Semantic Web query languages, like SPARQL, have targeted the retrieval, combination and reuse of facts, but have so far ignored all aspects of meta knowledge, such as origins, authorship, recency or certainty of data, to name but a few.In this paper, we present an original, generic, formalized and implemented approach for managing many dimensions of meta knowledge, like source, authorship, certainty and others. The approach re-uses existing RDF modeling possibilities in order to represent meta knowledge. Then, it extends SPARQL query processing in such a way that given a SPARQL query for data, one may request meta knowledge without modifying the original query. Thus, our approach achieves highly flexible and automatically coordinated querying for data and meta knowledge, while completely separating the two areas of concern.

Abstract-Based on a detailed analysis, we observed that state-of-the-art instance matching approaches do not perform well when used for matching instances across heterogeneous datasets. This is because they are built upon direct matching, which involves a direct comparison of a source dataset with a target dataset. This is not suitable when the overlap between the datasets is too small. Aiming at this problem, we propose a new paradigm called class-based matching. Given a class of instances from the source dataset, called the class of interest, and a set of candidate matches retrieved from the target, class-based matching helps to refine the candidates by filtering out those that do not belong to the class of interest. For this refinement, only data in the target is used, i.e., no direct comparison between source and target is involved. Based on extensive experiments using public benchmarks, we show our approach greatly improves the results of state-of-the-art systems especially on hard matching tasks.

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The paper examines the impact of capital structure in the context of foreign ownership on firm performance on non-financial companies in Vietnam between 2008 and 2018. The study employs Pooled OLS, Fixed effect, random effect, and Generalized Least Square to analyze the data. The study finds a non-linear relationship of foreign ownership and firm performance, so that the relationship, which is at first a positive one, becomes negative beyond a certain level of foreign ownership (30-45% ownership depending on the measure of performance). This insight is then combined with a generally inverse relationship between capital structure and performance. Besides, we find that the firm’s size (SIZE) has a positive influence on profitability and financial leverage, while both financial leverage (LEV) and the number of listed years of company (AGE) impact negatively on firm performance. Furthermore, growth of sales (GROWTH) has a positive effect on the debt ratio, and growth rate (GDP) has a negative effect on financial leverage. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

This study aims to create a flood extent map with Sentinel imagery and to evaluate impacts on agricultural land in the lagoon region of central Vietnam. In this study, remote sensing images, obtained from 2017 to 2019, were used to simultaneously map the land cover status of a flood in the Quang Dien district. This study highlights flooded areas from Sentinel-2 images by calculating some indicators such as the Land Surface Water Index (LSWI) and the Enhanced Vegetation Index (EVI). Comparisons between the floodplain samples (GPS point-based) and flood mapping results, with the ground-truth data, indicate that the overall accuracy and Kappa coefficients were 97.9% and 0.62 respectively for 2017; the values for 2019 were 95.7% and 0.77 for the same coefficients. Land use maps overlying the flood-affected maps show that approximately 11% of the agriculture land area was affected by floods in 2019 comparison to a 10% in 2017. Wet rice was the most affected crop with the flooded area accounting for more than 70% of the district under each flood event. The most affected communes are: Quang An, Quang Phuoc and Quang Thanh. This study provides valuable information for flood disaster planning, mitigation and recovery activities in Vietnam. Mục tiêu của nghiên cứu là lập bản đồ phân bố ngập lụt với hình ảnh vệ tinh Sentinel và đánh giá ảnh hưởng ngập lụt đến sử dụng đất nông nghiệp ở vùng đầm phá miền Trung, Việt Nam. Trong nghiên cứu này, ảnh viễn thám thu nhận giai đoạn 2017- 2019 được sử dụng để xây dựng bản đồ hiện trạng sử dụng đất tại thời điểm bị ngập nước trên địa bàn huyện Quảng Điền. Nghiên cứu đã xác định được vùng ngập lụt ở huyện Quảng Điền bằng phương pháp phân loại chỉ số mặt nước (Land Surface Water Index – LSWI) và chỉ số khác biệt thực vật (Enhanced Vegetation Index-EVI) từ ảnh Sentinel-2. Xác định vùng nước lũ bị che khuất bởi mây bằng mô hình số hóa độ cao (DEM). Kết quả phân loại vùng ngập lụt được so sánh với giá trị tham chiếu mặt đất cho thấy độ chính xác tổng thể và hệ số Kappa đạt được trong năm 2017 là 97,9% và 0,62; trong khi năm 2019 đạt 95,7% và 0.77. Bản đồ sử dụng đất chồng lên bản đồ lũ lụt cho thấy khoảng 11% diện tích đất nông nghiệp bị ảnh hưởng bởi lũ lụt năm 2019 so với 10% năm 2017. Cây lúa nước là cây trồng bị ảnh hưởng nặng nề nhất, với diện tích bị ngập lụt chiếm hơn 70% diện tích lúa của huyện. Các xã bị ngập lớn là xã Quảng An, Quảng Phước và Quảng Thành. Nghiên cứu này cung cấp thông tin có giá trị cho các hoạt động lập kế hoạch, giảm nhẹ và phục hồi thiên tai lũ lụt ở Việt Nam.

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