DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches in such tasks heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.Comment: accepted by AAAI 202

Construction of High-Precision Adiabatic Calorimeter and Thermodynamic Study on Functional Materials

In this chapter, a high-precision fully automated adiabatic calorimeter for heat capacity measurement of condensed materials in the temperature range from 80 to 400 K was described in detail. By using this calorimeter the heat capacity and thermodynamic properties of two kinds of function materials, ionic liquid and nanomaterials, were investigated. The heat capacities of IL [EMIM][TCB] were measured over the temperature range from 78 to 370 K by the high-precision-automated adiabatic calorimeter. Five kinds of nanostructured oxide materials, Al2O3, SiO2, TiO2, ZnO2, ZrO2, and two kinds of nanocrystalline metals: nickel and copper were investigated from heat capacity measurements. It is found that heat capacity enhancement in nanostructured materials is influenced by many factors, such as density, thermal expansion, sample purity, surface absorption, size effect, and so on

Thermodynamic Property Study on the Complexes of Rare- Earth Elements with Amino Aids

In this chapter, the following three rare-earth complexes with amino acids, Eu(Glu)(Im)5(ClO4)3⋅3HClO4⋅6H2O, Nd(Gly)2Cl3⋅3H2O, and La(Glu)(Im)6(ClO4)3⋅4HClO4⋅4H2O, are synthesized and characterized by element analysis, infrared (IR) spectrum, and x-ray diffraction (XRD) analysis. The thermodynamic property studies on these complexes are performed. For the first one, Eu(Glu)(Im)5(ClO4)3⋅3HClO4⋅6H2O, the low temperature heat capacity, phase transition, and thermodynamic functions are determined by adiabatic calorimetry. For the second one, Nd(Gly)2Cl3⋅3H2O, the molar dissolution enthalpy and standard molar enthalpy of formation are determined by isoperibol solution reaction calorimetry. For the third one, La(Glu)(Im)6(ClO4)3⋅4HClO4⋅4H2O, the microcalorimetry is used to investigate the interaction between the complex and the Escherichia coli DH5α to elucidate the biological effects of the complex

Bis(μ-2-hydroxy­benozato)-κ3 O,O′:O′;κ3 O:O,O′-bis­[(2-hydroxy­benozato-κ2 O,O′)(1,10-phenanthroline-κ2 N,N′)cadmium(II)]

The dinuclear title compound, [Cd2(C7H5O3)4(C12H8N2)2], is located on a crystallographic rotation twofold axis. The two CdII ions are connected by two tridentate bridging 2-hydroxy­benzoate anions. Each CdII ion is seven-coordinated by five O atoms from three 2-hydroxy­benzoate ligands and two N atoms from 1,10-phenanthroline. The 2-hydroxy­benzoate mol­ecules adopt two kinds of coordination mode, bidentate chelating and tridentate bridging–chelating. Intra­molecular hydrogen bonds between hydr­oxy and carboxyl­ate groups from 2-hydroxy­benzoate groups and π–π stacking interactions between parallel 1,10-phenanthroline ligands [centroid–centroid distances = 3.707 (3) and 3.842 (3) Å] are observed. Furthermore, adjacent benzene rings from 2-hydroxy­benzoate ligands are involved in π–π inter­actions with inter­planar distances of 3.642 (3) Å, thereby forming a chain along the a axis direction

Model-based multiobjective evolutionary algorithm optimization for HCCI engines

Modern engines feature a considerable number of adjustable control parameters. With this increasing number of degrees of freedom (DoFs) for engines and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated and efficient engine optimization approach is desired. In this paper, interdisciplinary research on a multiobjective evolutionary algorithm (MOEA)-based global optimization approach is developed for a homogeneous charge compression ignition (HCCI) engine. The performance of the HCCI engine optimizer is demonstrated by the cosimulation between an HCCI engine Simulink model and a Strength Pareto Evolutionary Algorithm 2 (SPEA2)-based multiobjective optimizer Java code. The HCCI engine model is developed by Simulink and validated with different engine speeds (1500-2250 r/min) and indicated mean effective pressures (IMEPs) (3-4.5 bar). The model can simulate the HCCI engine's indicated specific fuel consumption (ISFC) and indicated specific hydrocarbon (ISHC) emissions with good accuracy. The introduced MOEA optimization is an approach to efficiently optimize the engine ISFC and ISHC simultaneously by adjusting the settings of the engine's actuators automatically through the SPEA2. In this paper, the settings of the HCCI engine's actuators are intake valve opening (IVO) timing, exhaust valve closing (EVC) timing, and relative air-to-fuel ratio $lambda$. The cosimulation study and experimental validation results show that the MOEA engine optimizer can find the optimal HCCI engine actuators' settings with satisfactory accuracy and a much lower time consumption than usual

Beimingwu: A Learnware Dock System

The learnware paradigm proposed by Zhou [2016] aims to enable users to reuse numerous existing well-trained models instead of building machine learning models from scratch, with the hope of solving new user tasks even beyond models' original purposes. In this paradigm, developers worldwide can submit their high-performing models spontaneously to the learnware dock system (formerly known as learnware market) without revealing their training data. Once the dock system accepts the model, it assigns a specification and accommodates the model. This specification allows the model to be adequately identified and assembled to reuse according to future users' needs, even if they have no prior knowledge of the model. This paradigm greatly differs from the current big model direction and it is expected that a learnware dock system housing millions or more high-performing models could offer excellent capabilities for both planned tasks where big models are applicable; and unplanned, specialized, data-sensitive scenarios where big models are not present or applicable. This paper describes Beimingwu, the first open-source learnware dock system providing foundational support for future research of learnware paradigm.The system significantly streamlines the model development for new user tasks, thanks to its integrated architecture and engine design, extensive engineering implementations and optimizations, and the integration of various algorithms for learnware identification and reuse. Notably, this is possible even for users with limited data and minimal expertise in machine learning, without compromising the raw data's security. Beimingwu supports the entire process of learnware paradigm. The system lays the foundation for future research in learnware-related algorithms and systems, and prepares the ground for hosting a vast array of learnwares and establishing a learnware ecosystem

Identification of 10 SUMOylation-Related Genes From Yellow Catfish Pelteobagrus fulvidraco, and Their Transcriptional Responses to Carbohydrate Addition in vivo and in vitro

SUMOylation is a kind of important post-translational modification. In the present study, we identified 10 key genes involved in SUMOylation and deSUMOylation (sumo1, sumo2, sumo3, sae1, uba2, ubc9, pias1, senp1, senp2, and senp3) in yellow catfish Pelteobagrus fulvidraco, investigated their tissue expression patterns and transcriptional responses to carbohydrate addition both in vivo and in vitro. All of these members shared similar domains to their orthologous genes of other vertebrates. Their mRNAs were widely expressed in all the tested tissues, but at variable levels. Dietary carbohydrate levels differentially influenced the mRNA levels of these genes in liver, muscle, testis, and ovary of yellow catfish. Their mRNA levels in primary hepatocytes were differentially responsive to glucose addition. Our study would contribute to our understanding into the molecular basis of SUMOylation modification and into the potential SUMOylation function in the carbohydrate utilization in fish

Aerosolized amphotericin B as prophylaxis for invasive pulmonary aspergillosis: a meta-analysis

SummaryObjectivesInvasive pulmonary aspergillosis (IPA) is associated with high mortality in high-risk (immunosuppressed) patients. Many studies have investigated whether prophylactic inhalation of amphotericin B (AMB) reduces the incidence of IPA, but no definitive conclusions have been reached. The present meta-analysis was performed to evaluate the efficacy of prophylactic inhalation of AMB for the prevention of IPA.MethodsMEDLINE and other databases were searched for relevant articles published until December 2013. Randomized controlled trials that compared aerosolized AMB with placebo were included. Two reviewers independently assessed and extracted the data of all trials.ResultsSix animal studies and two clinical trials involving 768 high-risk patients were eligible. The animal studies showed lower overall mortality rate among animals that underwent aerosolized AMB prophylaxis (odds ratio (OR) 0.13, 95% confidence interval (CI) 0.08–0.21). Similarly, the clinical trials showed a lower incidence of IPA among patients who underwent aerosolized AMB prophylaxis (OR 0.42, 95% CI 0.22–0.79).ConclusionsThis analysis provides evidence supporting the notion that the prophylactic use of aerosolized AMB effectively reduces the incidence of IPA among high-risk patients