• BMB Reports
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• 제46권12호
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• pp.588-593
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• 2013

Apoptosis, programmed cell death, is a process involved in the development and maintenance of cell homeostasis in multicellular organisms. It is typically accompanied by the activation of a class of cysteine proteases called caspases. Apoptotic caspases are classified into the initiator caspases and the executioner caspases, according to the stage of their action in apoptotic processes. Although caspase-3, a typical executioner caspase, has been studied for its mechanism and substrates, little is known of caspase-6, one of the executioner caspases. To understand the biological functions of caspase-6, we performed proteomics analyses, to seek for novel caspase-6 substrates, using recombinant caspase-6 and HepG2 extract. Consequently, 34 different candidate proteins were identified, through 2-dimensional electrophoresis/MALDI-TOF analyses. Of these identified proteins, 8 proteins were validated with in vitro and in vivo cleavage assay. Herein, we report that HAUSP, Kinesin5B, GEP100, SDCCAG3 and PARD3 are novel substrates for caspase-6 during apoptosis.

• Geomechanics and Engineering
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• 제33권1호
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Computers and Concrete
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• 제31권3호
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• pp.253-265
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• 2023

In this research, the gene expression programming (GEP) technique was employed to provide a new model for predicting the maximum loading capacity of concrete-encased steel (CES) columns. This model was developed based on 96 CES column specimens available in the literature. The six main parameters used in the model were the compressive strength of concrete (f_c), yield stress of structural steel (f_ys), yield stress of steel rebar (f_yr), and cross-sectional areas of concrete, structural steel, and steel rebar (A_c, A_s and A_r respectively). The performance of the prediction model for the ultimate load-carrying capacity was investigated using different statistical indicators such as root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE), and relative square error (RSE), the corresponding values of which for the proposed model were 620.28, 0.99, 411.8, and 0.01, respectively. Here, the predictions of the model and those of available codes including ACI ITG, AS 3600, CSA-A23, EN 1994, JGJ 138, and NZS 3101 were compared for further model assessment. The obtained results showed that the proposed model had the highest correlation with the experimental data and the lowest error. In addition, to see if the developed model matched engineering realities and corresponded to the previously developed models, a parametric study and sensitivity analysis were carried out. The sensitivity analysis results indicated that the concrete cross-sectional area (A_c) has the greatest effect on the model, while parameter (f_yr) has a negligible effect.

• Steel and Composite Structures
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• 제52권3호
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• pp.293-312
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• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.

• Computers and Concrete
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• 제34권2호
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• pp.247-265
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• 2024

Measuring the fracture toughness of concrete in laboratory settings is challenging due to various factors, such as complex sample preparation procedures, the requirement for precise instruments, potential sample failure, and the brittleness of the samples. Therefore, there is an urgent need to develop innovative and more effective tools to overcome these limitations. Supervised learning methods offer promising solutions. This study introduces seven machine learning algorithms for predicting concrete's effective fracture toughness (K-eff). The models were trained using 560 datasets obtained from the central straight notched Brazilian disc (CSNBD) test. The concrete samples used in the experiments contained micro silica and powdered stone, which are commonly used additives in the construction industry. The study considered six input parameters that affect concrete's K-eff, including concrete type, sample diameter, sample thickness, crack length, force, and angle of initial crack. All the algorithms demonstrated high accuracy on both the training and testing datasets, with R² values ranging from 0.9456 to 0.9999 and root mean squared error (RMSE) values ranging from 0.000004 to 0.009287. After evaluating their performance, the gated recurrent unit (GRU) algorithm showed the highest predictive accuracy. The ranking of the applied models, from highest to lowest performance in predicting the K-eff of concrete, was as follows: GRU, LSTM, RNN, SFL, ELM, LSSVM, and GEP. In conclusion, it is recommended to use supervised learning models, specifically GRU, for precise estimation of concrete's K-eff. This approach allows engineers to save significant time and costs associated with the CSNBD test. This research contributes to the field by introducing a reliable tool for accurately predicting the K-eff of concrete, enabling efficient decision-making in various engineering applications.

• Asian Pacific Journal of Cancer Prevention
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• 제14권10호
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• pp.5775-5781
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• 2013

Background: The gastroenteropancreatic neuroendocrine neoplasm (GEP-NEN) is the most common type of neuroendocrine neoplasm. We summarized data in our centre to investigate the clinicopathological features, diagnostic methods, therapeutic approaches and prognosis for this neoplasm to increase knowledge of this disease in Asian populations. Method: A total of 122 patients treated at Sun Yet-san Memorial Hospital of Sun Yat-sen University between January 2000 and December 2011 were analyzed retrospectively. Results: Pancreas was the most common site of involvement (65/122, 53.3%); this disease has no special symptoms; positive rates of chromogranin A (CgA) and synaptophysin (Syn) were 81.1% and 87.7%, respectively. The positive rate of Syn had statistical difference among the three grades, but not CgA. Some 68 patients had G1 tumors, 32 G2 tumors and 22 G3 tumors, and Chi-square test showed that higher grading was correlated with worse prognosis (${\chi}^2=32.825$, P=0.0001). A total of 32 patients presented with distant metastasis, and 8 cases emerged during following up. Cox proportional hazards regression modeling showed that the tumor grade (P=0.01), lymphatic metastasis (P=0.025) and distant metastasis (P=0.031) were predictors of unfavorable prognosis. The overall 5-year survival rate was 39.6%, the 5-year survival rate of G1 was 55.7%, and the G2 and G3 were 34.2% and 0%, respectively. Conclusions: The incidence of gastroenteropancreatic neuroendocrine tumors has risen over the last 12 years. All grades of these diseases metastasize readily, and further research regarding the treatment of patients after radical surgery is needed to prolong disease-free survival.

• Applied Biological Chemistry
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• 제44권4호
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• pp.211-214
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• 2001

Drosophila melanogaster Schneider line 2(S2)세포의 외래 단백질 발현 시스템을 이용한 외래 단백질의 한시적 발현을 검토하고, 서로 다른 terminator를 이용하였을 때의 단백질 발현 및 mRNA 발현 정도를 검토하였다. 한시적 발현의 경우 transfection agent를 제거하고 36-48시간 동안 배양한 경우, 가장 높은 green fluorescent protein(GEP)의 발현을 보였다. SV4O p(A), SV4O small T-antigen, 인간 gastrin 3'UTR을 terminator로 지니는 발현 벡터시스템에 각각 endostatin유전자를 cloning시킨 뒤 재조합 endostatin의 mRNA의 발현 정도를 비교하였다. 한시적 발현을 시킨 뒤 36시간 후 endostatin의 발현 정도를 비교해 본 결과 SV40 p(A)를 terminator로 사용했을 때 mRNA및 단백질의 발현이 가장 높았다.

• 대한지리학회지
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• 제28권2호
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• pp.163-178
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• 1993

HSGP 이후 Social Studies 속에서 거의 사라져 가던 美國의 學校地理가 1990년대에 "中核敎科"로 다시 부상하는 動的 過程은, 地理敎育의 位相이 흔들리고 있는 우리의 실정에 여러 측면에서 他山之石이 될 수 있다. 본교는 1980년대 이후 美國의 地理敎育 復興運動을 主要爭點, 主導的 役割遂行者, 主要 成果에 달라 제 1 기(1980년-1984년), 제 2 기(1985년-1989년), 제 3 기(1990년-현재)의 세시기로 나누어 살펴 보았으며, 이를 통해 地理敎育의 개선을 위한 示唆點들을 추출하였다.