• Asian Pacific Journal of Cancer Prevention
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• v.17 no.9
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• pp.4401-4404
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• 2016

Purpose: To assess the prevalence of malnutrition in gynecologic cancer patients using the Scored Patient-Generated Subjective Global Assessment (PG-SGA) questionnaire. Materials and Methods: A total of 97 gynecologic cancer patients who never had any treatment but were planned for surgery were enrolled. The patients were asked to complete the scored PG-SGA form before the treatment was started. Attending physicians were also asked to complete other information in the PG-SGA form. Total scores were calculated and the patients were classified into 3 nutritional status levels. Results: Mean age was 54 years. Postoperative diagnoses were endometrial cancer in 42 cases (43.2%), ovarian cancer in 29 cases (29.9%), and cervical cancer in 26 cases (26.8%). Mean PG-SGA score was 5.2+4.7. Malnutrition (PG-SGA B and C) was found in 52 patients (53.6%, 95% CI 43.7% - 63.2%). Preoperative BMI, hemoglobin, serum albumin, and cancer stage were not significantly associated with nutritional status. Malnutrition was significantly more common among patients diagnosed with ovarian cancer, compared to other types of cancer (79.3% vs. 42.6%, p 0.004). Conclusions: Prevalence of malnutrition among gynecologic cancer patients was 53.5%, according to the scored PG-SGA. Malnutrition was significantly more common among patients with ovarian cancer.

• Clinical and Experimental Pediatrics
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• v.64 no.10
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• pp.538-542
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• 2021

Background: The relationship between small for gestational age (SGA) and autism spectrum disorders (ASDs) remains unknown. Purpose: This meta-analysis aimed to investigate the relationship between SGA and the risk of ASD. Methods: We searched PubMed, Web of Science, and Scopus databases from inception to November 2020. The heterogeneity across studies was explored using the I² statistic. The possibility of publication bias was assessed using Begg test. The results were reported using the odds ratio (OR) and 95% confidence interval (CI) using a random-effects model. Results: The literature search yielded 824 articles with 8,752,138 participants. We assessed the association between SGA and the risk of ASD in cohort and case-control studies. Based on the random-effects model, compared with SGA, the estimated OR of the risk of ASD was 1.17 (95% CI, 1.09-1.24). Therefore, there was a significant association between SGA and the risk of ASD. Conclusion: Based on OR reports in epidemiological studies, we showed that SGA is a risk factor for and can increase the risk of ASD. The association between SGA and ASD risk has further relevance to the current public health emphasis on appropriate prepregnancy weight and pregnancy weight gain.

• Journal of Life Science
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• v.22 no.2
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• pp.142-147
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• 2012

The sporulation-specific glucoamylase (SGA) of Saccharomyces diastaticus is known to be produced in the cytoplasm during sporulation. For the purpose of proving that SGA has secretory potential, we constructed a hybrid plasmid, pYESC25, containing the promoter and the putative signal sequence of the SGA fused in frame to the endo-1,4-${\beta}$-D-glucanase (CMCase) gene of Bacillus subtilis without its own signal sequence. The recipient yeast strain of S. diastaticus YIY345 was transformed with the hybrid plasmid. CMCase secretion from S. diastaticus harboring pYESC25 into culture medium was confirmed by the formation of yellowish halos around transformants after staining with Congo red on a CMC agar plate. The transformant culture was fractionated to the extracellular, periplasmic, and intracellular fraction, followed by the measurement of CMCase activity. About 63% and 13% enzyme activity were detected in the culture supernatant (extracellular fraction) and periplasmic fraction, respectively. Furthermore, ConA-Sepharose chromatography, native gel electrophoresis, and activity staining revealed that CMCase produced in yeast was glycosylated and its molecular weight was larger than that of the unglycosylated form from B. subtilis. Taking these findings together, SGA has the potential of secretion to culture medium, and the putative signal sequence of SGA can efficiently direct bacterial CMCase to the yeast secretion pathway.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.66
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• pp.95-118
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• 2015

This study examines the Buyer's Remedies in respect of Defects in Title under SGA. As SGA divides contractual terms into a condition and a warranty, its effects regarding a breach of a condition or a warranty are different. Where a stipulation in a contract of sale is a condition, its breach may give rise to a right to treat the contract as repudiated and to claim damages. Where there is a breach of a warranty in a contract of sale, the aggrieved party may have a right to claim damages. Regarding a breach of a condition under SGA s 12(1), although the buyer may have his right to terminate the contract, he may lose that right when he accept or is deemed to have accept the goods by intimating his acceptance to the seller, acting inconsistently with the ownership of the seller, or retaining the goods beyond a reasonable time without rejecting them. Furthermore, the buyer may claim the estimated loss directly and naturally resulting from seller's breach. SGA contains the principle of full compensation and so the suffered loss and the loss of profit are compensable. As to specific performance under SGA, the court has been empowered to make an order of specific performance to deliver the goods in conformity with the terms of the contract and so it is not a buyer's right. This order should be made only where the goods to be delivered are specific or ascertained goods and the court must think fit to grant the order. However, among these remedies, the buyer cannot have the right to terminate the contract where there is a breach of warranty by the seller under SGA s 12(2).

• Clinical and Experimental Pediatrics
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• v.62 no.3
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• pp.102-107
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• 2019

Purpose: This study compared the iron statuses of small for gestational age (SGA) and appropriate for gestational age (AGA) infants at birth. Methods: The clinical data of 904 newborn infants admitted to the neonatal intensive care unit were reviewed. Blood samples were drawn from the infants within 24 hours after birth. Serum ferritin level was used as a marker of total iron status. Results: In this study, 115 SGA (GA, $36.5{\pm}2.9weeks$; birth weight [BW], $1,975{\pm}594.5g$) and 717 AGA (GA, $35.1{\pm}3.5weeks$; BW, $2,420.3{\pm}768.7g$) infants were included. The SGA infants had higher hematocrit levels ($50.6%{\pm}5.8%$ vs. $47.7%{\pm}5.7%$, P<0.05) than the AGA infants. No difference in serum ferritin level (ng/mL) was found between the groups (mean [95% confidence interval]: SGA vs. AGA infants, 139.0 [70.0-237.0] vs. 141.0 [82.5-228.5]). After adjusting for gestational age, the SGA infants had lower ferritin levels (147.1 ng/mL [116.3-178.0 ng/mL] vs. 189.4 ng/mL [178.0-200.8 ng/mL], P<0.05). Total body iron stores were also lower in the SGA infants than in the AGA infants (185.6 [153.4-211.7] vs 202.2 [168.7-241.9], P<0.05). Conclusion: The SGA infants had lower ferritin and total body iron stores than the AGA infants. The SGA infants affected by maternal hypertension who were born at late preterm had an additional risk of inadequate iron store. Iron deficiency should be monitored in these infants during follow-up.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.3
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• pp.82-88
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• 2003

SGA(Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, ${\mu}GA$(Micro-Genetic Algorithm) has recently been developed. In this study, ${\mu}GA$ which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of ${\mu}GA$ were compared with those of SGA. Solutions of ${\mu}GA$ for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that ${\mu}GA$ is a suitable and very efficient optimization algorithm for structural design.

• Clinical and Experimental Pediatrics
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• v.52 no.2
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• pp.142-151
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• 2009

The incidence of small for gestational age (SGA) births is frequent, accounting for 2.3% to 8% of all live births. Several childhood and adult diseases are related to early postnatal growth and birth size, and 10% of children born SGA may have a short stature throughout postnatal life. Additionally, they may have abnormal growth hormone (GH)-insulin like growth factor axis, HPA axis, and gonadal function. Permanent changes are detrimental in an environment of nutritional abundance, and predispose SGA children to an array of diseases in adolescence and adulthood. Such changes may also cause premature pubarche, adrenarche, and precocious puberty. The varying results from clinical studies necessitate more prospective case control studies. Reproductive tract abnormalities and reproductive dysfunction are related to SGA births. GH treatment is required for SGA infants who do not experience catch-up growth.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.63-66
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• 2004

The SGA(stochastic gradient ascent) algorithm is one of the most important tools in the area of reinforcement learning, and has been applied to a wide range of practical problems. In particular, this learning method was successfully applied by Kimura et a1. [1] to the control of a simple creeping robot which has finite number of control input choices. In this paper, we considered the application of the SGA algorithm to Kimura's robot control problem for the case that the control input is not confined to a finite set but can be chosen from a infinite subset of the real numbers. We also developed a MATLAB-based robot animation program, which showed the effectiveness of the training algorithms vividly.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.9-14
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• 2003

SGA (Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, $\mu$GA(Micro-Genetic Algorithm) has recently been developed. In this study, $\mu$GA which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of $\mu$GA were compared with those of SGA. Solutions of $\mu$GA for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that $\mu$GA is a suitable and very efficient optimization algorithm for structural design.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.77
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• pp.23-42
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• 2018

This study compares the SGA and CISG to find out the difference of the criteria for calculating damages. and it intends to give some important points in trade practice. The damages is intended to compensate the victim for the breach of contract but there are differences between SGA and CISG as follow. First, the SGA and CISG have the same purpose of claiming damages. Both laws and regulations are subject to a full indemnification to compensate for the breach of the contract by the amount equivalent to the loss suffered by the victim. Second, in the general principle related to the calculation of damages, both law enforcement officials are required to be able to predict damages caused by breach of contract. In the case of SGA, however, a foreseeability test or remoteness of damages is required for the relationship between the contract violation and the loss. In other words, it can be said that the causal relation between the contract violation and the damage is strictly applied rather than the CISG. Finally, both laws and regulations of SGA and CISG have a big difference in criteria for calculating damages. In the CISG, after the contract is canceled, it is classified according to the existence of the alternative transaction and the damage amount is calculated based on the contract price. On the other hand, the SGA estimates the loss based on the market price at the delivery of the goods, reflecting the change in the market price instead of the contract price of the goods.