Imprints of Sagittarius accretion event: Young O-rich stars and discontinuous chemical evolution in Milky Way disc

The Milky Way has undergone significant transformations in its early history, characterised by violent mergers and the accretion of satellite galaxies. Among these events, the infall of the satellite galaxy Gaia-Enceladus/Sausage is recognised as the last major merger event, fundamentally altering the evolution of the Milky Way and shaping its chemo-dynamical structure. However, recent observational evidence suggests that the Milky Way remains undergone notable events of star formation in the past 4 Gyr, which is thought to be triggered by the perturbations from Sagittarius dwarf galaxy (Sgr). Here we report chemical signatures of the Sgr accretion event in the past 4 Gyr, using the [Fe/H] and [O/Fe] ratios in the thin disc, which is reported for the first time. It reveals that the previously discovered V-shape structure of age-[Fe/H] relation varies across different Galactic locations and has rich substructures. Interestingly, we discover a discontinuous structure at z$_{\rm max}$ $<$ 0.3 kpc, interrupted by a recent burst of star formation from 4 Gyr to 2 Gyr ago. In this episode, we find a significant rise in oxygen abundance leading to a distinct [O/Fe] gradient, contributing to the formation of young O-rich stars. Combined with the simulated star formation history and chemical abundance of Sgr, we suggest that the Sgr is an important actor in the discontinuous chemical evolution of the Milky Way disc.Comment: 17 pages, 15 figures. Under review at Nature Communication

Association between platelet distribution width and serum uric acid in Chinese population

© 2019 International Union of Biochemistry and Molecular Biology Platelet distribution width (PDW) is a simple and inexpensive parameter, which could predict activation of coagulation efficiently. And it has been confirmed to have a significant role in many diseases. We aimed to explore the association between PDW and hyperuricemia in a large Chinese cohort. This cross-sectional study recruited 61,091 ostensible healthy participants (29,259 males and 31,832 females) after implementing exclusion criteria. Clinical data of the enrolled population included anthropometric measurements and serum parameters. Database was sorted by gender, and the association between PDW and hyperuricemia was analyzed after dividing PDW into quartiles. Crude and adjusted odds ratios of PDW for hyperuricemia with 95% confidence intervals were analyzed using binary logistic regression models. We found no significant difference in PDW values between the genders. Males showed significantly higher incidence of hyperuricemia than females. From binary logistic regression models, significant hyperuricemia risks only were demonstrated in PDW quartiles 2 and 3 in males (P < 0.05). This study displayed close association between PDW and hyperuricemia as a risk factor. It is meaningful to use PDW as a clinical risk predictor for hyperuricemia in males. © 2019 BioFactors, 45(3):326–334, 2019

Towards Online Multiresolution Community Detection in Large-Scale Networks

The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks

Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression

MicroRNA is a set of small RNA molecules mediating gene expression at post-transcriptional/translational levels. Most of well-established high throughput discovery platforms, such as microarray, real time quantitative PCR, and sequencing, have been adapted to study microRNA in various human diseases. The total number of microRNAs in humans is approximately 1,800, which challenges some analytical methodologies requiring a large number of entries. Unlike messenger RNA, the majority of microRNA (60%) maintains relatively low abundance in the cells. When analyzed using microarray, the signals of these low-expressed microRNAs are influenced by other non-specific signals including the background noise. It is crucial to distinguish the true microRNA signals from measurement errors in microRNA array data analysis. In this study, we propose a novel measurement error model-based normalization method and differentially-expressed microRNA detection method for microRNA profiling data acquired from locked nucleic acids (LNA) microRNA array. Compared with some existing methods, the proposed method significantly improves the detection among low-expressed microRNAs when assessed by quantitative real-time PCR assay

Metabolic Flux Analysis of Mitochondrial Uncoupling in 3T3-L1 Adipocytes

BACKGROUND:Increasing energy expenditure at the cellular level offers an attractive option to limit adiposity and improve whole body energy balance. In vivo and in vitro observations have correlated mitochondrial uncoupling protein-1 (UCP1) expression with reduced white adipose tissue triglyceride (TG) content. The metabolic basis for this correlation remains unclear. METHODOLOGY/PRINCIPAL FINDINGS:This study tested the hypothesis that mitochondrial uncoupling requires the cell to compensate for the decreased oxidation phosphorylation efficiency by up-regulating lactate production, thus redirecting carbon flux away from TG synthesis. Metabolic flux analysis was used to characterize the effects of non-lethal, long-term mitochondrial uncoupling (up to 18 days) on the pathways of intermediary metabolism in differentiating 3T3-L1 adipocytes. Uncoupling was induced by forced expression of UCP1 and chemical (FCCP) treatment. Chemical uncoupling significantly decreased TG content by ca. 35%. A reduction in the ATP level suggested diminished oxidative phosphorylation efficiency in the uncoupled adipocytes. Flux analysis estimated significant up-regulation of glycolysis and down-regulation of fatty acid synthesis, with chemical uncoupling exerting quantitatively larger effects. CONCLUSIONS/SIGNIFICANCE:The results of this study support our hypothesis regarding uncoupling-induced redirection of carbon flux into glycolysis and lactate production, and suggest mitochondrial proton translocation as a potential target for controlling adipocyte lipid metabolism

Mining High-Quality Business Process Models from Real-Life Event Logs

In the last years, Business Process Mining (BPMI) has become a very important research topic in academia. In the industry, also more and more big companies are starting to use such a technique to help them understand how their business processes are implemented in reality and locate the inefficient and noneffective part in their business processes. Traditional BPMI research topic can be classified into three sub-topics: Business Process Model Discovery (BPMD), conformance checking and process extension. However, as one of the most significant branch in the BPMI research area, the present BPMD techniques meet great challenges when mining process models from real-life event logs. "Spaghetti-like" process models are often generated. Such models are normally inaccurate and very complex. The main reason is that in the real world many businesses are often executed in highly flexible environments, e.g., healthcare, customer relationship management(CRM) and product development. As a result, the event logs that stem from such flexible environments often contain dense distribution of cases with a high variety of complex behaviours. In this thesis, we explore the approaches and techniques to help existing BPMD techniques generate accurate and simple process models when mining real-life event logs. The approaches and techniques presented in this thesis mainly inherit the basic ideas of three classical strategies proposed in the literature for assisting the BPMD techniques in mining process models with high quality which are Mining Algorithm Enhancement-Based Strategy (MEBS), Model Division-Based Strategy(MDS) and Model Abstraction-Based Strategy (MAS). Moreover, the proposed techniques are also carefully designed so as to overcome the weaknesses of the current realisations of the three strategies. The main contributions of this thesis are as follows: 1. For the MEBS, we have developed a new technique named HIF which is able to help existing BPMD techniques overcome their limitations on their expressive ability. The working principle is that HIF can locate the inexpressible process behaviours in the given event logs and then transform them into expressible behaviours for the utilised BPMD techniques. 2. For the MDS, we have developed two trace clustering techniques named TDTC and CTC and one multi-label case classification technique named MLCC. The techniques TDTC and CTC are devised to optimise the accuracy and complexity of the potential sub-process models of each trace cluster during the runtime so as to assure the quality of the generated sub-models. The technique MLCC is able to combine the domain knowledge from the process experts so as to make a more meaningful division of the raw cases from a specific event log. 3. For the MAS, we have developed a mined model abstraction technique named GTCA which utilises a new model abstraction strategy proposed by us. Through this strategy, GTCA is capable of generating an abstraction process model with higher fitness and lower complexity which cannot be ensured by existing realisations of the MAS. Furthermore, trace clustering technique is employed by GTCA for optimising the quality of the found sub-process models

Mining High-Quality Business Process Models from Real-Life Event Logs

In the last years, Business Process Mining (BPMI) has become a very important research topic in academia. In the industry, also more and more big companies are starting to use such a technique to help them understand how their business processes are implemented in reality and locate the inefficient and noneffective part in their business processes. Traditional BPMI research topic can be classified into three sub-topics: Business Process Model Discovery (BPMD), conformance checking and process extension. However, as one of the most significant branch in the BPMI research area, the present BPMD techniques meet great challenges when mining process models from real-life event logs. "Spaghetti-like" process models are often generated. Such models are normally inaccurate and very complex. The main reason is that in the real world many businesses are often executed in highly flexible environments, e.g., healthcare, customer relationship management(CRM) and product development. As a result, the event logs that stem from such flexible environments often contain dense distribution of cases with a high variety of complex behaviours. In this thesis, we explore the approaches and techniques to help existing BPMD techniques generate accurate and simple process models when mining real-life event logs. The approaches and techniques presented in this thesis mainly inherit the basic ideas of three classical strategies proposed in the literature for assisting the BPMD techniques in mining process models with high quality which are Mining Algorithm Enhancement-Based Strategy (MEBS), Model Division-Based Strategy(MDS) and Model Abstraction-Based Strategy (MAS). Moreover, the proposed techniques are also carefully designed so as to overcome the weaknesses of the current realisations of the three strategies. The main contributions of this thesis are as follows: 1. For the MEBS, we have developed a new technique named HIF which is able to help existing BPMD techniques overcome their limitations on their expressive ability. The working principle is that HIF can locate the inexpressible process behaviours in the given event logs and then transform them into expressible behaviours for the utilised BPMD techniques. 2. For the MDS, we have developed two trace clustering techniques named TDTC and CTC and one multi-label case classification technique named MLCC. The techniques TDTC and CTC are devised to optimise the accuracy and complexity of the potential sub-process models of each trace cluster during the runtime so as to assure the quality of the generated sub-models. The technique MLCC is able to combine the domain knowledge from the process experts so as to make a more meaningful division of the raw cases from a specific event log. 3. For the MAS, we have developed a mined model abstraction technique named GTCA which utilises a new model abstraction strategy proposed by us. Through this strategy, GTCA is capable of generating an abstraction process model with higher fitness and lower complexity which cannot be ensured by existing realisations of the MAS. Furthermore, trace clustering technique is employed by GTCA for optimising the quality of the found sub-process models

Optimal Solar Plant Site Identification Using GIS and Remote Sensing: Framework and Case Study

Many countries have set a goal for a carbon neutral future, and the adoption of solar energy as an alternative energy source to fossil fuel is one of the major measures planned. Yet not all locations are equally suitable for solar energy generation. This is due to uneven solar radiation distribution as well as various environmental factors. A number of studies in the literature have used multicriteria decision analysis (MCDA) to determine the most suitable places to build solar power plants. To the best of our knowledge, no study has addressed the subject of optimal solar plant site identification for the Al-Qassim region, although developing renewable energy in Saudi Arabia has been put on the agenda. This paper developed a spatial MCDA framework catering to the characteristics of the Al-Qassim region. The framework adopts several tools used in Geographic Information Systems (GIS), such as Random Forest (RF) raster classification and model builder. The framework aims to ascertain the ideal sites for solar power plants in the Al-Qassim region in terms of the amount of potential photovoltaic electricity production (PVOUT) that could be produced from solar energy. For that, a combination of GIS and Analytical Hierarchy Process (AHP) techniques were employed to determine five sub-criteria weights (Slope, Global Horizontal Irradiance (GHI), proximity to roads, proximity to residential areas, proximity to powerlines) before performing spatial MCDA. The result showed that ‘the most suitable’ and ‘suitable’ areas for the establishment of solar plants are in the south and southwest of the region, representing about 17.53% of the study area. The ‘unsuitable’ areas account for about 10.17% of the total study area, which is mainly concentrated in the northern part. The rest of the region is further classified into ‘moderate’ and ‘restricted’ areas, which account for 46.42% and 25.88%, respectively. The most suitable area for potential solar energy, yields approximately 1905 Kwh/Kwp in terms of PVOUT. The proposed framework also has the potential to be applied to other regions nationally and internationally. This work contributes a reproducible GIS workflow for a low-cost but accurate adoption of a solar energy plan to achieve sustainable development goals

Topological dimensionality reduction-based machine learning for efficient gradient-free 3D topology optimization

Powerful gradient-free topology optimization methods are needed for structural design concerning complex responses. In this paper, a novel gradient-free optimization method is proposed by integrating the material-field series expansion topological parameterization and the deep neural networks, providing two-fold advances: firstly, it generally reduces the massive topological design variables to fewer than 200, while keeps the capability to represent relative complex 3D topologies and clear boundaries; secondly, by constructing a sequential neural network surrogate model, it sufficiently explores the reduced design space and is capable of handling multi-peak and discontinuous optimization problems. The effectiveness of this method is illustrated via several design problems, among which the optimized material effective bulk modulus achieves 98% of the H-S bound and the highly-nonlinear peak weld stress in a phone dropping process is decreased by 16.59%. This method reduces the computational time by 1–4 orders of magnitude compared with the coarse-mesh-based gradient-free methods, and it is the first time to successfully conduct gradient-free 3D topology optimization with thousands of finite elements. The method’s ease of implementation and compatibility with various simulation software, brings topology optimization into complex industrial applications and proves that gradient-free technology represents an effective optimization benchmark for improving structural performance