Trajectory-Based Spatiotemporal Entity Linking

Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a k-nearest neighbor (k-NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency.Comment: 15 pages, 3 figures, 15 table

Nanoparticle Orientation to Control RNA Loading and Ligand Display on Extracellular Vesicles for Cancer Regression

Nanotechnology offers many benefits, and here we report an advantage of applying RNA nanotechnology for directional control. The orientation of arrow-shaped RNA was altered to control ligand display on extracellular vesicle membranes for specific cell targeting, or to regulate intracellular trafficking of small interfering RNA (siRNA) or microRNA (miRNA). Placing membrane-anchoring cholesterol at the tail of the arrow results in display of RNA aptamer or folate on the outer surface of the extracellular vesicle. In contrast, placing the cholesterol at the arrowhead results in partial loading of RNA nanoparticles into the extracellular vesicles. Taking advantage of the RNA ligand for specific targeting and extracellular vesicles for efficient membrane fusion, the resulting ligand-displaying extracellular vesicles were capable of specific delivery of siRNA to cells, and efficiently blocked tumour growth in three cancer models. Extracellular vesicles displaying an aptamer that binds to prostate-specific membrane antigen, and loaded with survivin siRNA, inhibited prostate cancer xenograft. The same extracellular vesicle instead displaying epidermal growth-factor receptor aptamer inhibited orthotopic breast cancer models. Likewise, survivin siRNA-loaded and folate-displaying extracellular vesicles inhibited patient-derived colorectal cancer xenograft

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The Application of Fuzzy Mathematics in the Optimization of the Recipe of Filling Paste for Coal Mine Backfill

Backfill is a very important technology that can be used to reduce the environmental footprints resulting from coal mining. The selection of proper filling materials is of great significance to the operation cost and the stability of the goaf. This paper investigated the feasibility of using the coal gangue as the main component of the filling paste so as to reuse the byproducts in coal mining to the maximum extent. The filling pastes were composed of coal gangue as the aggregates, cement or gypsum as cementitious materials, and some additives. In order to determine the optimal recipe, the performances of filling pastes were first comprehensively evaluated according to their fluidity, mechanical properties, shrinkage, and permeability. The results showed that cement content was the most influential factor, while the fly ash addition was the weakest factor for the performance of filling pastes. Moreover, the appropriate use of a water reducer and expansion agent improved the working performance of the paste. Based on the performances of filling pastes, the fuzzy mathematics evaluation method was then used to establish the weight vector and index vector. The principle of maximum membership degree and the principle of maximum closeness were used to identify the identified objects and find the best recipe for the filling paste. The results showed that this evaluation method could fully reflect the influence of various factors and provide accurate evaluation results

Moving object linking based on historical trace

The prevalent adoption of GPS-enabled devices has witnessed an explosion of various location-based services which produce a huge amount of trajectories monitoring an individual's movement. This triggers an interesting question: is movement history sufficiently representative and distinctive to identify an individual? In this work, we study the problem of moving object linking based on their historical traces. However, it is non-trivial to extract effective patterns from moving history and meanwhile conduct object linking efficiently. To this end, we propose four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) to construct the personalised signature from the historical trace. Moreover, we formalise the problem of moving object linking as a k-nearest neighbour (k-NN) search on the collection of signatures, and aim to improve efficiency considering the high dimensionality of signatures and the large cardinality of the candidate object set. A simple but effective dimension reduction strategy is introduced in this work, which empirically outperforms existing algorithms including PCA and LSH. We propose a novel indexing structure, Weighted R-tree (WR-tree), and two pruning methods to further speed up k-NN search by combining weight and spatial information contained in the signature. Our extensive experimental results on a real world dataset verify the superiority of our proposals, in terms of both accuracy and efficiency, over state-of-the-art approaches

Compound Danshen Dripping Pill for Treating Nonproliferative Diabetic Retinopathy: A Meta-Analysis of 13 Randomized Controlled Trials

Objective. We assess the clinical effect of compound Danshen dripping pill (CDDP) for treating diabetic retinopathy (DR). Methods. Electronic databases were searched from January 2001 to October 2016 to locate randomized controlled trials (RCTs). Efficacy was measured as main outcome and microaneurysms, hemorrhage, exudate, vision, and fundus fluorescein angiography (FFA) were measured as second outcomes. Methodological quality for each study was evaluated, RevMan 5 software was used to assess treatment effects, and GRADE was used to rate quality of evidence. Results. We located 13 RCTs and methodological quality was evaluated as high risk. Statistics indicated CDDP for treating DR was better than controls and DR risk was reduced 64% with CDDP (RR: 0.36, P=0.68); retinal microaneurysms (MD = −4.32NO, P<0.00001); retinal hemorrhages (MD = −0.70PD, P=0.03); exudate improvements (MD = −0.09PD, P=0.79); visual changes (MD = −0.12 letter, P=0.006); FFA (RR: 0.40, P=0.003). About GRADE, quality of evidence was “low.” Conclusion. CDDP may be safe and efficacious for treating or delaying DR and may improve vision or delay vision loss

Leveraging machine learning to distinguish between bacterial and viral induced pharyngitis using hematological markers: a retrospective cohort study

Abstract Accurate differentiation between bacterial and viral-induced pharyngitis is recognized as essential for personalized treatment and judicious antibiotic use. From a cohort of 693 patients with pharyngitis, data from 197 individuals clearly diagnosed with bacterial or viral infections were meticulously analyzed in this study. By integrating detailed hematological insights with several machine learning algorithms, including Random Forest, Neural Networks, Decision Trees, Support Vector Machine, Naive Bayes, and Lasso Regression, for potential biomarkers were identified, with an emphasis being placed on the diagnostic significance of the Monocyte-to-Lymphocyte Ratio. Distinct inflammatory signatures associated with bacterial infections were spotlighted in this study. An innovation introduced in this research was the adaptation of the high-accuracy Lasso Regression model for the TI-84 calculator, with an AUC (95% CI) of 0.94 (0.925–0.955) being achieved. Using this adaptation, pivotal laboratory parameters can be input on-the-spot and infection probabilities can be computed subsequently. This methodology embodies an improvement in diagnostics, facilitating more effective distinction between bacterial and viral infections while fostering judicious antibiotic use

Reconstructing phylogenetic networks using maximum parsimony

Phylogenies—the evolutionary histories of groups of organisms—are one of the most widely used tools throughout the life sciences, as well as objects of research within systematics, evolutionary biology, epidemiology, etc. Almost every tool devised to date to reconstruct phylogenies produces trees; yet it is widely understood and accepted that trees oversimplify the evolutionary histories of many groups of organims, most prominently bacteria (because of horizontal gene transfer) and plants (because of hybrid speciation). Various methods and criteria have been introduced for phylogenetic tree reconstruction. Parsimony is one of the most widely used and studied criteria, and various accurate and efficient heuristics for reconstructing trees based on parsimony have been devised. Jotun Hein suggested a straightforward extension of the parsimony criterion to phylogenetic networks. In this paper we formalize this concept, and provide the first experimental study of the quality of parsimony as a criterion for constructing and evaluating phylogenetic networks. Our results show that, when extended to phylogenetic networks, the parsimony criterion produces promising results. In a great majority of the cases in our experiments, the parsimony criterion accurately predicts the numbers and placements of non-tree events

Motion Prediction of Catamaran with a Semisubmersible Bow in Wave

Compared with standard vessels, a slender catamaran with a semi-submerged bow (SSB) demonstrates superior seakeeping performance. To predict the motion of an SSB catamaran, computational fluid dynamics methods are adopted in this study and results are validated through small-scale model tests. The pitch, heave, and vertical acceleration are calculated at various wavelengths and speeds. Based on the overset grid and motion region methods, this study obtains the motion responses of an SSB catamaran in regular head waves. The results of the numerical studies are validated with the experimental data and show that the overset grid method is more accurate in predicting the motion of an SSB catamaran; the errors can be controlled within 20%. The movement data in regular waves shows that at a constant speed, the motion response initially increases and then decreases with increasing wavelength. This motion response peak is due to the encountering frequency being close to the natural frequency. Under identical sea conditions, the motion response increases with the increasing Froude number. The motion prediction results, that derive from a short-term irregular sea state, show that there is an optimal speed range that can effectively reduce the amplitude of motion

Serum metabolomics detected by LDI‐TOF‐MS can be used to distinguish between diabetic patients with and without diabetic kidney disease

Diabetic kidney disease (DKD) is an important cause of end‐stage renal disease with changes in metabolic characteristics. The objective of this study was to study changes in serum metabolic characteristics in patients with DKD and to examine metabolite panels to distinguish DKD from diabetes with matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF‐MS). We recruited 40 type II diabetes mellitus (T2DM) patients with or without DKD from a single center for a cross‐sectional study. Serum metabolic profiling was performed with MALDI‐TOF‐MS using a vertical silicon nanowire array. Differential metabolites between DKD and diabetes patients were selected, and their relevance to the clinical parameters of DKD was analyzed. We applied machine learning methods to the differential metabolite panels to distinguish DKD patients from diabetes patients. Twenty‐four differential serum metabolites between DKD patients and diabetes patients were identified, which were mainly enriched in butyrate metabolism, TCA cycle, and alanine, aspartate, and glutamate metabolism. Among the metabolites, l‐kynurenine was positively correlated with urinary microalbumin, urinary microalbumin/creatinine ratio (UACR), creatinine, and urea nitrogen content. l‐Serine, pimelic acid, 5‐methylfuran‐2‐carboxylic acid, 4‐methylbenzaldehyde, and dihydrouracil were associated with the estimated glomerular filtration rate (eGFR). The panel of differential metabolites could be used to distinguish between DKD and diabetes patients with an AUC value reaching 0.9899–0.9949. Among the differential metabolites, l‐kynurenine was related to the progression of DKD. The differential metabolites exhibited excellent performance at distinguishing between DKD and diabetes. This study provides a novel direction for metabolomics‐based clinical detection of DKD