Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang River Basin, north-west China

To improve the accuracy of runoff forecasting, an uncertain multiple linear regression (UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variable to the dependent variable, as this affects prediction accuracy. On this basis, an inexact two-stage stochastic programming (ITSP) model is used for crop planting structure optimization (CPSO) with the inputs that are interval flow values under different probabilities obtained from the UMLR model. The developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop planting structure optimization are integrated, is applied to a real case study. The aim of the developed system is to optimize crops planting area with limited available water resources base on the downstream runoff forecasting in order to obtain the maximum system benefit in the future. The solution obtained can demonstrate the feasibility and suitability of the developed system, and help decision makers to identify reasonable crop planting structure under multiple uncertainties

A Regional Water Optimal Allocation Model Based on the Cobb-Douglas Production Function under Multiple Uncertainties

To optimize the water distribution of three industries based on the water demand prediction under multiple uncertainties, a fuzzy credibility-constrained interval two-stage stochastic programming (FCITSP) model base on the Cobb-Douglas production (CD) function was developed. The CD-FCITSP model integrated fuzzy credibility-constrained programming (FCP), an interval two-stage stochastic programming (ITSP) method and CD function. The developed model could deal with uncertainties with interval, random, and fuzzy features, reflect tradeoffs between different water use sectors, and provide water managers in arid regions with sustainable and reasonable water-allocation schemes under different credibility scenarios of local policies. Moreover, the relationships between economic benefits and water consumption were taken into consideration via the Cobb-Douglas production function. The developed model was applied to support the optimal allocation of limited water resources in Minqin County, northwest China. The obtained solution demonstrated that the developed method could help local water managers to effectively allocate limited water under multiple uncertainties and different credibility scenarios. In addition, water use efficiency could be promoted and the emissions of major pollutants could be reduced. The developed method could be extended to water management practices in other arid regions

Emerging Roles of Extracelluar Vesicles Derived from Bacteria, Mammalian or Plant Cells in the Pathogenesis and Clinical Application of Neurodegenerative Diseases

A growing number of studies have indicated that extracellular vesicles (EVs), such as exosomes, are involved in the development of neurodegenerative diseases. Components of EVs with biological effects like proteins, nucleic acids, or other molecules can be delivered to recipient cells to mediate physio-/pathological processes. For instance, some aggregate-prone proteins, such as β-amyloid and α-synuclein, had been found to propagate through exosomes. Therefore, either an increase of detrimental molecules or a decrease of beneficial molecules enwrapped in EVs may fully or partly indicate disease progression. Numerous studies have demonstrated that dysbiosis of the gut microbiota and neurodegeneration are tightly correlated, well-known as the “gut–brain axis”. Accumulating evidence has revealed that the gut bacteria-derived EVs play a pivotal role in mediating microbe–host interactions and affect the function of the “gut–brain axis”, which subsequently contributes to the pathogenesis of neurodegenerative diseases. In this review, we first briefly discuss the role of EVs from mammalian cells and microbes in mediating the progression of neurodegenerative diseases, and then propose a novel strategy that employs EVs of plants (plant cell-derived exosome-like nanoparticles) for treating neurodegeneration

Wave Propagation in X-Section Piles for Low Strain Integrity Testing: Three-Dimensional Effects

X-section cast-in-place concrete pile (referred to as XCC pile) has a different velocity response compared with circular section pile in the low strain testing due to the special cross section. Full-scale model tests of XCC pile were conducted to reveal the velocity response characteristics. The time-domain velocity responses on the pile top were obtained, which showed obvious three-dimensional effects because of the different high-frequency interferences. The test results were compared with the numerical results to validate the numerical model. Furthermore, numerical simulations were conducted to investigate the propagation characteristic of velocity waves along the longitudinal direction in the pile. The results indicated that the wave propagation was complicated as a result of the superposition of the incident wave and the reflected wave. The effects of the geometrical parameters of cross section on the three-dimensional effects of velocity responses were also studied. Three-dimensional effects would be more significant with a larger arc distance. However, the effects of arc angle were not obvious