Study of gene expression alteration in male androgenetic alopecia: evidence of predominant molecular signalling pathways.

PubMed

Michel, L; Reygagne, P; Benech, P; Jean-Louis, F; Scalvino, S; Ly Ka So, S; Hamidou, Z; Bianovici, S; Pouch, J; Ducos, B; Bonnet, M; Bensussan, A; Patatian, A; Lati, E; Wdzieczak-Bakala, J; Choulot, J-C; Loing, E; Hocquaux, M

2017-11-01

Male androgenetic alopecia (AGA) is the most common form of hair loss in men. It is characterized by a distinct pattern of progressive hair loss starting from the frontal area and the vertex of the scalp. Although several genetic risk loci have been identified, relevant genes for AGA remain to be defined. To identify biomarkers associated with AGA. Molecular biomarkers associated with premature AGA were identified through gene expression analysis using cDNA generated from scalp vertex biopsies of hairless or bald men with premature AGA, and healthy volunteers. This monocentric study reveals that genes encoding mast cell granule enzymes, inflammatory mediators and immunoglobulin-associated immune mediators were significantly overexpressed in AGA. In contrast, underexpressed genes appear to be associated with the Wnt/β-catenin and bone morphogenic protein/transforming growth factor-β signalling pathways. Although involvement of these pathways in hair follicle regeneration is well described, functional interpretation of the transcriptomic data highlights different events that account for their inhibition. In particular, one of these events depends on the dysregulated expression of proopiomelanocortin, as confirmed by polymerase chain reaction and immunohistochemistry. In addition, lower expression of CYP27B1 in patients with AGA supports the notion that changes in vitamin D metabolism contributes to hair loss. This study provides compelling evidence for distinct molecular events contributing to alopecia that may pave the way for new therapeutic approaches. © 2017 British Association of Dermatologists.

Americamysis bahia: A New Laboratory Model for Conservation Genetics in a Changing Environment (AGA09)

EPA Science Inventory

Levels of population genetic diversity are expected to play an important role in species persistence during periods of environmental change, yet our understanding of how to quantify relevant aspects of this diversity is not well developed. We are conducting a long-term study wit...

Birth weight and long-term metabolic outcomes: does the definition of smallness matter?

PubMed

Verkauskiene, R; Figueras, F; Deghmoun, S; Chevenne, D; Gardosi, J; Levy-Marchal, M

2008-01-01

To establish the role of individual definition of smallness at birth in the association between birth weight and long-term metabolic outcomes. Lipid profile and oral glucose tolerance test were performed in young adults (22 years) born either small (SGA) or appropriate for gestational age (AGA). AGA/SGA were defined by both population-based and customized methods adjusting for individual maternal/pregnancy characteristics. 825 individuals were classified as AGA and 575 as SGA by both methods, 131 were SGA by the population-based method only (SGA(pop)) and 22 were SGA by the customized method only (SGA(cust)). SGA(cust) subjects had higher total cholesterol and triglyceride levels and lower high-density lipoprotein cholesterol concentrations than SGA(pop) and AGA subjects, however, insignificantly when adjusted for age, gender and body mass index. The homeostasis model assessment for insulin resistance (HOMA-IR) index was higher in the SGA(cust) (p = 0.05) and SGA(pop) (p = 0.02) versus the AGA group. Controlling for the HOMA-IR index, the insulinogenic index was significantly lower in the SGA(cust) versus SGA(pop) (p = 0.001) and AGA (p = 0.003) groups. In SGA(cust) individuals, the HOMA-IR index was clearly shifted to higher, while the insulinogenic index to lower tertiles of AGA distribution; SGA(pop) subjects had the HOMA-IR and insulinogenic index predominantly in the highest tertiles. Individualized birth weight standards allow to better identify subjects who failed to reach their genetic potential of intrauterine growth and are at higher risk of metabolic disturbances and impaired insulin secretion later in life. Copyright 2008 S. Karger AG, Basel.

[Clinical classification and genetic mutation study of two pedigrees with type II Waardenburg syndrome].

PubMed

Chen, Yong; Yang, Fuwei; Zheng, Hexin; Zhu, Ganghua; Hu, Peng; Wu, Weijing

2015-12-01

To explore the molecular etiology of two pedigrees affected with type II Waardenburg syndrome (WS2) and to provide genetic diagnosis and counseling. Blood samples were collected from the proband and his family members. Following extraction of genomic DNA, the coding sequences of PAX3, MITF, SOX10 and SNAI2 genes were amplified with PCR and subjected to DNA sequencing to detect potential mutations. A heterozygous deletional mutation c.649_651delAGA in exon 7 of the MITF gene has been identified in all patients from the first family, while no mutation was found in the other WS2 related genes including PAX3, MITF, SOX10 and SNAI2. The heterozygous deletion mutation c.649_651delAGA in exon 7 of the MITF gene probably underlies the disease in the first family. It is expected that other genes may also underlie WS2.

American Gastroenterological Association guidelines are inaccurate in detecting pancreatic cysts with advanced neoplasia: a clinicopathologic study of 225 patients with supporting molecular data.

PubMed

Singhi, Aatur D; Zeh, Herbert J; Brand, Randall E; Nikiforova, Marina N; Chennat, Jennifer S; Fasanella, Kenneth E; Khalid, Asif; Papachristou, Georgios I; Slivka, Adam; Hogg, Melissa; Lee, Kenneth K; Tsung, Allan; Zureikat, Amer H; McGrath, Kevin

2016-06-01

The American Gastroenterological Association (AGA) recently reported evidence-based guidelines for the management of asymptomatic neoplastic pancreatic cysts. These guidelines advocate a higher threshold for surgical resection than prior guidelines and imaging surveillance for a considerable number of patients with pancreatic cysts. The aims of this study were to assess the accuracy of the AGA guidelines in detecting advanced neoplasia and present an alternative approach to pancreatic cysts. The study population consisted of 225 patients who underwent EUS-guided FNA for pancreatic cysts between January 2014 and May 2015. For each patient, clinical findings, EUS features, cytopathology results, carcinoembryonic antigen analysis, and molecular testing of pancreatic cyst fluid were reviewed. Molecular testing included the assessment of hotspot mutations and deletions for KRAS, GNAS, VHL, TP53, PIK3CA, and PTEN. Diagnostic pathology results were available for 41 patients (18%), with 13 (6%) harboring advanced neoplasia. Among these cases, the AGA guidelines identified advanced neoplasia with 62% sensitivity, 79% specificity, 57% positive predictive value, and 82% negative predictive value. Moreover, the AGA guidelines missed 45% of intraductal papillary mucinous neoplasms with adenocarcinoma or high-grade dysplasia. For cases without confirmatory pathology, 27 of 184 patients (15%) with serous cystadenomas (SCAs) based on EUS findings and/or VHL alterations would continue magnetic resonance imaging (MRI) surveillance. In comparison, a novel algorithmic pathway using molecular testing of pancreatic cyst fluid detected advanced neoplasias with 100% sensitivity, 90% specificity, 79% positive predictive value, and 100% negative predictive value. The AGA guidelines were inaccurate in detecting pancreatic cysts with advanced neoplasia. Furthermore, because the AGA guidelines manage all neoplastic cysts similarly, patients with SCAs will continue to undergo unnecessary MRI surveillance. The results of an alternative approach with integrative molecular testing are encouraging but require further validation. Copyright © 2016 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Obesity and diabetes genes are associated with being born small for gestational age: Results from the Auckland Birthweight Collaborative study

PubMed Central

2010-01-01

Background Individuals born small for gestational age (SGA) are at increased risk of rapid postnatal weight gain, later obesity and diseases in adulthood such as type 2 diabetes, hypertension and cardiovascular diseases. Environmental risk factors for SGA are well established and include smoking, low pregnancy weight, maternal short stature, maternal diet, ethnic origin of mother and hypertension. However, in a large proportion of SGA, no underlying cause is evident, and these individuals may have a larger genetic contribution. Methods In this study we tested the association between SGA and polymorphisms in genes that have previously been associated with obesity and/or diabetes. We undertook analysis of 54 single nucleotide polymorphisms (SNPs) in 546 samples from the Auckland Birthweight Collaborative (ABC) study. 227 children were born small for gestational age (SGA) and 319 were appropriate for gestational age (AGA). Results and Conclusion The results demonstrated that genetic variation in KCNJ11, BDNF, PFKP, PTER and SEC16B were associated with SGA and support the concept that genetic factors associated with obesity and/or type 2 diabetes are more prevalent in those born SGA compared to those born AGA. We have previously determined that environmental factors are associated with differences in birthweight in the ABC study and now we have demonstrated a significant genetic contribution, suggesting that the interaction between genetics and the environment are important. PMID:20712903

Appropriate for gestational age (AGA)

MedlinePlus

Fetal age; Gestation; Development - AGA; Growth - AGA; Neonatal care - AGA; Newborn care - AGA ... Gestational age is the common term used during pregnancy to describe how far along the pregnancy is. It is ...

Clinical and genetic investigation of families with type II Waardenburg syndrome.

PubMed

Chen, Yong; Yang, Fuwei; Zheng, Hexin; Zhou, Jianda; Zhu, Ganghua; Hu, Peng; Wu, Weijing

2016-03-01

The present study aimed to investigate the molecular pathology of Waardenburg syndrome type II in three families, in order to provide genetic diagnosis and hereditary counseling for family members. Relevant clinical examinations were conducted on the probands of the three pedigrees. Peripheral blood samples of the probands and related family members were collected and genomic DNA was extracted. The coding sequences of paired box 3 (PAX3), microphthalmia‑associated transcription factor (MITF), sex‑determining region Y‑box 10 (SOX10) and snail family zinc finger 2 (SNAI2) were analyzed by polymerase chain reaction and DNA sequencing. The heterozygous mutation, c.649_651delAGA in exon 7 of the MITF gene was detected in the proband and all patients of pedigree 1; however, no pathological mutation of the relevant genes (MITF, SNAI2, SOX10 or PAX3) was detected in pedigrees 2 and 3. The heterozygous mutation c.649_651delAGA in exon 7 of the MITF gene is therefore considered the disease‑causing mutation in pedigree 1. However, there are novel disease‑causing genes in Waardenburg syndrome type II, which require further research.

Biochemical characterization and comparison of aspartylglucosaminidases secreted in venom of the parasitoid wasps Asobara tabida and Leptopilina heterotoma

PubMed Central

Coulette, Quentin; Lemauf, Séverine; Colinet, Dominique; Prévost, Geneviève; Anselme, Caroline; Poirié, Marylène

2017-01-01

Aspartylglucosaminidase (AGA) is a low-abundance intracellular enzyme that plays a key role in the last stage of glycoproteins degradation, and whose deficiency leads to human aspartylglucosaminuria, a lysosomal storage disease. Surprisingly, high amounts of AGA-like proteins are secreted in the venom of two phylogenetically distant hymenopteran parasitoid wasp species, Asobara tabida (Braconidae) and Leptopilina heterotoma (Cynipidae). These venom AGAs have a similar domain organization as mammalian AGAs. They share with them key residues for autocatalysis and activity, and the mature α- and β-subunits also form an (αβ)2 structure in solution. Interestingly, only one of these AGAs subunits (α for AtAGA and β for LhAGA) is glycosylated instead of the two subunits for lysosomal human AGA (hAGA), and these glycosylations are partially resistant to PGNase F treatment. The two venom AGAs are secreted as fully activated enzymes, they have a similar aspartylglucosaminidase activity and are both also efficient asparaginases. Once AGAs are injected into the larvae of the Drosophila melanogaster host, the asparaginase activity may play a role in modulating their physiology. Altogether, our data provide new elements for a better understanding of the secretion and the role of venom AGAs as virulence factors in the parasitoid wasps’ success. PMID:28742131

Crystallization and preliminary X-ray diffraction studies of two thermostable α-galactosidases from glycoside hydrolase family 36

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foucault, M.; Watzlawick, H.; Mattes, R.

2006-02-01

The α-galactosidases AgaA, AgaB and AgaA A355E mutant from Geobacillus stearothermophilus have been overexpressed in Escherichia coli. Crystals of AgaB and AgaA A355E have been obtained by the vapour-diffusion method and synchrotron data have been collected to 2.0 and 2.8 Å resolution, respectively. α-Galactosidases from thermophilic organisms have gained interest owing to their applications in the sugar industry. The α-galactosidases AgaA, AgaB and AgaA A355E mutant from Geobacillus stearothermophilus have been overexpressed in Escherichia coli. Crystals of AgaB and AgaA A355E have been obtained by the vapour-diffusion method and synchrotron data have been collected to 2.0 and 2.8 Å resolution,more » respectively. Crystals of AgaB belong to space group I222 or I2{sub 1}2{sub 1}2{sub 1}, with unit-cell parameters a = 87.5, b = 113.3, c = 161.6 Å. Crystals of AgaA A355E belong to space group P3{sub 1}21 or P3{sub 2}21, with unit-cell parameters a = b = 150.1, c = 233.2 Å.« less

Contribution of hair density and hair diameter to the appearance and progression of androgenetic alopecia in Japanese men.

PubMed

Ishino, A; Takahashi, T; Suzuki, J; Nakazawa, Y; Iwabuchi, T; Tajima, M

2014-11-01

Androgenetic alopecia (AGA) is the most common type of baldness in men. The balding process is associated with the gradual miniaturization of hair follicles and successive hair loss. However, the relative contributions of hair density and diameter to AGA are still unclear. Hair density and hair diameter were investigated in Japanese men with or without AGA to elucidate the importance of these factors in the balding process. Male Japanese subjects with or without AGA (n = 369) were included in this study. Hair appearance at the vertex was evaluated by comparison with a series of standard photographs. Hair density was measured using a phototrichogram-based videomicroscopy technique, and hair diameter was assessed by comparison with a series of calibrated threads on the phototrichogram image. All subjects with AGA were ≥ 25 years of age. The mean percentage of thick hairs (> 80 μm) in all subjects with AGA was significantly lower than that in subjects without AGA aged ≥ 25 years (P < 0·01), but the mean percentage of vellus hairs (< 40 μm) in subjects with AGA was significantly higher (P < 0·001). By contrast, the mean density of the hair in all patients with AGA did not significantly differ from the density of those without AGA aged ≥ 25 years. However, the mean density of the hair in subjects without AGA aged < 25 years was significantly higher than that of both subjects without AGA aged ≥ 25 years (P < 0·001) and all subjects with AGA. Hair loss in men with AGA results mainly from the miniaturization of hair follicles rather than the loss of hair (shedding), at least for individuals who are ≥ 25 years of age and present with AGA. © 2014 British Association of Dermatologists.

Molecular Genetic Studies of Bone Mechanical Strain and of Pedigrees with Very High Bone Density

DTIC Science & Technology

2009-11-01

mice. We used Cre-recombinase primer (F- TTA GCA CCA CGG CAG CAG GAG GTT and R-CAG GCC AGA TCT CCT GTG CAG CAT) and loxp primer (Primer 1, AGT GAT...Leprdb heterozygotes during breeding. Three types of littermates are produced from breeding these heterozygotes: the misty gray homozygote (m +/m +), the

Clinical and genetic investigation of families with type II Waardenburg syndrome

PubMed Central

CHEN, YONG; YANG, FUWEI; ZHENG, HEXIN; ZHOU, JIANDA; ZHU, GANGHUA; HU, PENG; WU, WEIJING

2016-01-01

The present study aimed to investigate the molecular pathology of Waardenburg syndrome type II in three families, in order to provide genetic diagnosis and hereditary counseling for family members. Relevant clinical examinations were conducted on the probands of the three pedigrees. Peripheral blood samples of the probands and related family members were collected and genomic DNA was extracted. The coding sequences of paired box 3 (PAX3), microphthalmia-associated transcription factor (MITF), sex-determining region Y-box 10 (SOX10) and snail family zinc finger 2 (SNAI2) were analyzed by polymerase chain reaction and DNA sequencing. The heterozygous mutation, c.649_651delAGA in exon 7 of the MITF gene was detected in the proband and all patients of pedigree 1; however, no pathological mutation of the relevant genes (MITF, SNAI2, SOX10 or PAX3) was detected in pedigrees 2 and 3. The heterozygous mutation c.649_651delAGA in exon 7 of the MITF gene is therefore considered the disease-causing mutation in pedigree 1. However, there are novel disease-causing genes in Waardenburg syndrome type II, which require further research. PMID:26781036

Characterization of Streptomyces spp. isolated from the rhizosphere of oil palm and evaluation of their ability to suppress basal stem rot disease in oil palm seedlings when applied as powder formulations in a glasshouse trial.

PubMed

Shariffah-Muzaimah, S A; Idris, A S; Madihah, A Z; Dzolkhifli, O; Kamaruzzaman, S; Maizatul-Suriza, M

2017-12-18

Ganoderma boninense, the main causal agent of oil palm (Elaeis guineensis) basal stem rot (BSR), severely reduces oil palm yields around the world. To reduce reliance on fungicide applications to control BSR, we are investigating the efficacy of alternative control methods, such as the application of biological control agents. In this study, we used four Streptomyces-like actinomycetes (isolates AGA43, AGA48, AGA347 and AGA506) that had been isolated from the oil palm rhizosphere and screened for antagonism towards G. boninense in a previous study. The aim of this study was to characterize these four isolates and then to assess their ability to suppress BSR in oil palm seedlings when applied individually to the soil in a vermiculite powder formulation. Analysis of partial 16S rRNA gene sequences (512 bp) revealed that the isolates exhibited a very high level of sequence similarity (> 98%) with GenBank reference sequences. Isolates AGA347 and AGA506 showed 99% similarity with Streptomyces hygroscopicus subsp. hygroscopicus and Streptomyces ahygroscopicus, respectively. Isolates AGA43 and AGA48 also belonged to the Streptomyces genus. The most effective formulation, AGA347, reduced BSR in seedlings by 73.1%. Formulations using the known antifungal producer Streptomyces noursei, AGA043, AGA048 or AGA506 reduced BSR by 47.4, 30.1, 54.8 and 44.1%, respectively. This glasshouse trial indicates that these Streptomyces spp. show promise as potential biological control agents against Ganoderma in oil palm. Further investigations are needed to determine the mechanism of antagonism and to increase the shelf life of Streptomyces formulations.

Automated Visualization and Quantification of Spiral Artery Blood Flow Entering the First-Trimester Placenta, Using 3-D Power Doppler Ultrasound.

PubMed

Stevenson, Gordon N; Noble, J Alison; Welsh, Alec W; Impey, Lawrence; Collins, Sally L

2018-03-01

The goal of our research was to quantify the placental vascularity in 3-D at 11-13 + 6 wk of pregnancy at precise distances from the utero-placental interface (UPI) using 3-D power Doppler ultrasound. With this automated image analysis technique, differences in vascularity between normal and pathologic pregnancies may be observed. The algorithm was validated using a computer-generated image phantom and applied retrospectively in 143 patients. The following features from the PD data were recorded: The number of spiral artery jets into the inter-villous space, total geometric and PD area. These were automatically measured at discrete millimeter distances from the UPI. Differences in features were compared with pregnancy outcomes: Pre-eclamptic versus normal, all small-for-gestational age (SGA) to appropriate-for-gestational age (AGA) patients and AGA versus SGA in normotensives (Mann-Whitney). The Benjamini-Hochberg procedure was used (false discovery rate 10%) for multiple comparison testing. Features decreased with increasing distance from the UPI (Kruskal-Wallis test; p <0.001). At 2- 3 mm from the UPI, all features were smaller in pre-eclamptic compared with normal patients and for some in SGA compared with AGA patients (p <0.05). For AGA versus SGA in normotensive patients, no significant differences were found. Number of jets measured at 2-5 mm from the UPI did not vary because of the position of the placenta in the uterus (ANOVA; p > 0.05). This method provides a new in-vivo imaging tool for examining spiral artery development through pregnancy. Size and number of entrances of blood flow into the UPI could potentially be used to identify high-risk pregnancies and may provide a new imaging biomarker for placental insufficiency. Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

Genetic Diversity of Pancreatic Ductal Adenocarcinoma and Opportunities for Precision Medicine.

PubMed

Knudsen, Erik S; O'Reilly, Eileen M; Brody, Jonathan R; Witkiewicz, Agnieszka K

2016-01-01

Patients with pancreatic ductal adenocarcinoma (PDA) have a poor prognosis despite new treatments; approximately 7% survive for 5 years. Although there have been advances in systemic, primarily cytotoxic, therapies, it has been a challenge to treat patients with PDA using targeted therapies. Sequence analyses have provided a wealth of information about the genetic features of PDA and have identified potential therapeutic targets. Preclinical and early-phase clinical studies have found specific pathways could be rationally targeted; it might also be possible to take advantage of the genetic diversity of PDAs to develop therapeutic agents. The genetic diversity and instability of PDA cells have long been thought of as obstacles to treatment, but are now considered exploitable features. We review the latest findings in pancreatic cancer genetics and the promise of targeted approaches in PDA therapy. Copyright © 2016 AGA Institute. Published by Elsevier Inc. All rights reserved.

Deletion of a non-catalytic region increases the enzymatic activity of a β-agarase from Flammeovirga sp. MY04

NASA Astrophysics Data System (ADS)

Han, Wenjun; Gu, Jingyan; Liu, Huihui; Li, Fuchuan; Wu, Zhihong; Li, Yuezhong

2015-10-01

A Glycoside hydrolase (GH) typically contains one catalytic module and varied non-catalytic regions (NCRs). However, effects of the NCRs to the catalytic modules remain mostly unclear except the carbohydrate-binding modules (CBMs). AgaG4 is a GH16 endo- β-agarase of the agarolytic marine bacterium Flammeovirga sp. MY04. The enzyme consists of an extra sugar-binding peptide within the catalytic module, with no predictable CBMs but function-unknown sequences in the NCR, which is a new characteristic of agarase sequences. In this study, we deleted the NCR sequence, a 140-amino acid peptide at the C-terminus and expressed the truncated gene, agaG4-T140, in Escherichia coli. After purification and refolding, the truncated agarase rAgaG4-T140 retained the same catalytic temperature and pH value as rAgaG4. Using combined fluorescent labeling, HPLC and MS/MS techniques, we identified the end-products of agarose degradation by rAgaG4-T140 as neoagarotetraose and neoagarohexaose, with a final molar ratio of 1.53:1 and a conversion ratio of approximately 70%, which were similar to those of rAgaG4. However, the truncated agarase rAgaG4-T140 markedly decreased in protein solubility by 15 times and increased in enzymatic activities by 35 times. The oligosaccharide production of rAgaG4-T140 was approximately 25 times the weight of that produced by equimolar rAgaG4. This study provides some insights into the influences of NCR on the biochemical characteristics of agarase AgaG4 and implies some new strategies to improve the properties of a GH enzyme.

Understanding patient and physician perceptions of male androgenetic alopecia treatments in Asia-Pacific and Latin America.

PubMed

Lulic, Zrinka; Inui, Shigeki; Sim, Woo-Young; Kang, Hoon; Choi, Gwang Seong; Hong, Woosung; Hatanaka, Toshiki; Wilson, Timothy; Manyak, Michael

2017-08-01

This survey aimed to explore patient and physician attitudes towards male androgenetic alopecia (AGA), satisfaction with currently available male AGA treatments and investigate the factors affecting treatment choice. The survey was carried out in five countries (Japan, South Korea, Taiwan, Mexico and Brazil) between November and December 2015 using a standard market research methodology. Questionnaires were completed by patients with male AGA or hair loss/thinning and practicing physicians who were responsible for prescribing AGA treatment. In total, 835 patients and 338 physicians completed the questionnaire. Overall, 37.6% of patients reported satisfaction with the treatments they had used. The highest patient satisfaction was reported for 5-alpha-reductase inhibitors (53.9% of patients satisfied). In all countries, physicians were more likely than patients to think that male AGA has a major impact on patient confidence (89.3% vs 70.4%, respectively). There was agreement by physicians and patients that male AGA patients who are involved in their treatment decisions have better outcomes. Patients who were satisfied with AGA treatments were more likely to have the level of involvement they desired in treatment decisions (69.1% of satisfied patients) than dissatisfied patients (56.4% of dissatisfied patients). This survey provides valuable insights into the attitudes of patients and physicians in Asia and Latin America about male AGA and its treatments. The survey identified areas of disconnect between physicians and patients regarding the impact of male AGA, treatment consultations and the importance of treatment attributes. It also highlights the need for physicians to spend sufficient time with patients discussing AGA treatment approaches. © 2017 GlaxoSmithKline. The Journal of Dermatology published by John Wiley & Sons Australia, Ltd on behalf of Japanese Dermatological Association.

Clinical approach to incidental pancreatic cysts

PubMed Central

Chiang, Austin L; Lee, Linda S

2016-01-01

The approach to incidentally noted pancreatic cysts is constantly evolving. While surgical resection is indicated for malignant or higher risk cysts, correctly identifying these highest risk pancreatic cystic lesions remains difficult. Using parameters including cyst size, presence of solid components, and pancreatic duct involvement, the 2012 International Association of Pancreatology (IAP) and the 2015 American Gastroenterological Association (AGA) guidelines have sought to identify the higher risk patients who would benefit from further evaluation using endoscopic ultrasound (EUS). Not only can EUS help further assess the presence of solid component and nodules, but also fine needle aspiration of cyst fluid aids in diagnosis by obtaining cellular, molecular, and genetic data. The impact of new endoscopic innovations with novel methods of direct visualization including confocal endomicroscopy require further validation. This review also highlights the differences between the 2012 IAP and 2015 AGA guidelines, which include the thresholds for sending patients for EUS and surgery and methods, interval, and duration of surveillance for unresected cysts. PMID:26811661

Characterization of Ca2+ channel currents in cultured rat cerebellar granule neurones.

PubMed

Pearson, H A; Sutton, K G; Scott, R H; Dolphin, A C

1995-02-01

1. High-threshold voltage-gated calcium channel currents (IBa) were studied in cultured rat cerebellar granule neurones using the whole-cell patch clamp technique with 10 mM Ba2+ as the charge carrier. The putative P-type component of whole-cell current was characterized by utilizing the toxin omega-agatoxin IVA (omega-Aga IVA) in combination with other blockers. 2. omega-Aga IVA (100 nM) inhibited the high voltage-activated (HVA) IBa by 40.9 +/- 3.4% (n = 27), and the dissociation constant Kd was 2.7 nM. Maximal inhibition occurred within a 2-3 min time course, and was irreversible. The isolated omega-Aga IVA-sensitive current was non-inactivating. 3. omega-Aga IVA exhibited overlapping selectivity with both N- and L-channel blockers; omega-conotoxin GVIA (omega-CTX GVIA) (1 microM) and the dihydropyridine (-)-202-709 (1 microM), respectively. Together these toxins reduced the omega-Aga IVA-sensitive component to just 4.5 +/- 1.4% (n = 3). Thus only a small proportion of the current can be unequivocally attributed to P-type current. Inhibition of the HVA IBa by omega-Aga IA also reduced the proportion of omega-Aga IVA-sensitive current to 28.0 +/- 3.2% (n = 3). 4. Application of omega-Aga IVA and a synthetic form of funnel-web toxin, N-(7-amino-4-azaheptyl)-L-argininamide (sFTX-3.3; 10 microM), produced an additive block of the HVA IBa. Consequently these two toxins do not act on the same channel in cerebellar granule neurones. 5. omega-Aga IVA inhibition of low voltage-activated (LVA) IBa was studied in the ND7-23 neuronal cell line. omega-Aga IVA (100 nM) reduced the LVA current by 41.3 +/- 3.2% (n = 17) in a fully reversible manner with no shift in the steady-state inactivation of the channel. 6. A component of current insensitive to N-, L- and P-channel blockers remained unclassified in all our studies. This component, and also that remaining following block by omega-Aga IVA and omega-Aga IA, exhibited relatively rapid, although incomplete, inactivation compared to the other currents isolated in this study. 7. In conclusion, omega-Aga IVA inhibits a component of current in cultured cerebellar granule neurones which overlaps almost completely with that inhibited by L- and N-channel blockers. In addition, a large component of whole-cell current in these neurones still remains unclassified.

Correlation between anatomy of the scapula and the incidence of rotator cuff tear and glenohumeral osteoarthritis via radiological study.

PubMed

Miswan, Mohd Fairudz Bin Mohd; Saman, Mohd Shahril Bin Ahmad; Hui, Teo Seow; Al-Fayyadh, Mohamed Zubair Mohamed; Ali, Mohamed Razif Bin Mohamed; Min, Ng Wuey

2017-01-01

We conducted a study to elucidate the correlation between the anatomy of the shoulder joint with the development of rotator cuff tear (RCT) and glenohumeral osteoarthritis (GHOA) by using acromioglenoid angle (AGA). The AGA is a new measured angle formed between the line from midglenoid to lateral end of the acromion with the line parallel to the glenoid surface. The AGA was measured in a group of 85 shoulders with RCT, 49 with GHOA and 103 non-RCT/GHOA control shoulders. The AGA was compared with other radiological parameters, such as, the critical shoulder angle (CSA), the acromion index (AI) and the acromiohumeral interval (AHI). Correlational and regression analysis were performed using SPSS 20. The mean AGA was 50.9° (45.2-56.5°) in the control group, 53.3° (47.6-59.1°) in RCT group and 45.5° (37.7-53.2°) in OA group. Among patients with AGA > 51.5°, 61% were in the RCT group and among patients with AGA < 44.5°, 56% were in OA group. Pearson correlation analysis had shown significant correlation between AGA and CSA ( r = 0.925, p < 0.001). It was also significant of AHI in RCT group with mean 6.6 mm (4.7-8.5 mm) and significant AI in OA group with mean 0.68 (0.57-0.78) with p value < 0.001 respectively. The AGA method of measurement is an excellent predictive parameter for diagnosing RCT and GHOA.

Two euAGAMOUS Genes Control C-Function in Medicago truncatula

PubMed Central

Gómez-Mena, Concepción; Constantin, Gabriela D.; Wen, Jiangqi; Mysore, Kirankumar S.; Lund, Ole S.; Johansen, Elisabeth; Beltrán, José Pío; Cañas, Luis A.

2014-01-01

C-function MADS-box transcription factors belong to the AGAMOUS (AG) lineage and specify both stamen and carpel identity and floral meristem determinacy. In core eudicots, the AG lineage is further divided into two branches, the euAG and PLE lineages. Functional analyses across flowering plants strongly support the idea that duplicated AG lineage genes have different degrees of subfunctionalization of the C-function. The legume Medicago truncatula contains three C-lineage genes in its genome: two euAG genes (MtAGa and MtAGb) and one PLENA-like gene (MtSHP). This species is therefore a good experimental system to study the effects of gene duplication within the AG subfamily. We have studied the respective functions of each euAG genes in M. truncatula employing expression analyses and reverse genetic approaches. Our results show that the M. truncatula euAG- and PLENA-like genes are an example of subfunctionalization as a result of a change in expression pattern. MtAGa and MtAGb are the only genes showing a full C-function activity, concomitant with their ancestral expression profile, early in the floral meristem, and in the third and fourth floral whorls during floral development. In contrast, MtSHP expression appears late during floral development suggesting it does not contribute significantly to the C-function. Furthermore, the redundant MtAGa and MtAGb paralogs have been retained which provides the overall dosage required to specify the C-function in M. truncatula. PMID:25105497

Assessment of the usefulness of dihydrotestosterone in the diagnostics of patients with androgenetic alopecia

PubMed Central

Urysiak-Czubatka, Izabela; Broniarczyk-Dyła, Grażyna

2014-01-01

Introduction Androgenetic alopecia (AGA) is the most common form of hair loss. Clinically observed hair loss is due to the continuous miniaturization of affected hair follicles. Genetic factors and androgenic factors especially dihydrotestosterone (DHT), which is a testosterone tissue metabolite, play major roles in the pathogenesis of AGA. However, expert opinions about the usefulness of DHT in the diagnosis of this type of alopecia are divided. Aim To evaluate the usefulness of DHT level in patients with androgenetic alopecia compared with the control group. Material and methods The study comprised 49 subjects: 19 women and 9 men with androgenetic alopecia. The control group consisted of 17 healthy women and 4 men without hair loss. Results Increased serum concentrations of DHT were observed in patients with androgenetic alopecia (17 women, 5 men), but also in the control group. The differences in mean values of DHT were not significant according to the types of alopecia and the control group. Increased serum concentrations of DHT were not correlated with the advance of alopecia. Conclusions Dihydrotestosterone is the most influential androgen and seems to play a very important role in the pathogenesis of androgenetic alopecia. Based on the results of our study and others, the most important factors would appear to be the genetically-determined sensitivity of the follicles to DHT and their different reactions to androgen concentration. PMID:25254005

Blood Plasma-Derived Anti-Glycan Antibodies to Sialylated and Sulfated Glycans Identify Ovarian Cancer Patients

PubMed Central

Pochechueva, Tatiana; Chinarev, Alexander; Schoetzau, Andreas; Fedier, André; Bovin, Nicolai V.; Hacker, Neville F.; Jacob, Francis; Heinzelmann-Schwarz, Viola

2016-01-01

Altered levels of naturally occurring anti-glycan antibodies (AGA) circulating in human blood plasma are found in different pathologies including cancer. Here the levels of AGA directed against 22 negatively charged (sialylated and sulfated) glycans were assessed in high-grade serous ovarian cancer (HGSOC, n = 22) patients and benign controls (n = 31) using our previously developed suspension glycan array (SGA). Specifically, the ability of AGA to differentiate between controls and HGSOC, the most common and aggressive type of ovarian cancer with a poor outcome was determined. Results were compared to CA125, the commonly used ovarian cancer biomarker. AGA to seven glycans that significantly (P<0.05) differentiated between HGSOC and control were identified: AGA to top candidates SiaTn and 6-OSulfo-TF (both IgM) differentiated comparably to CA125. The area under the curve (AUC) of a panel of AGA to 5 glycans (SiaTn, 6-OSulfo-TF, 6-OSulfo-LN, SiaLea, and GM2) (0.878) was comparable to CA125 (0.864), but it markedly increased (0.985) when combined with CA125. AGA to SiaTn and 6-OSulfo-TF were also valuable predictors for HGSOC when CA125 values appeared inconclusive, i.e. were below a certain threshold. AGA-glycan binding was in some cases isotype-dependent and sensitive to glycosidic linkage switch (α2–6 vs. α2–3), to sialylation, and to sulfation of the glycans. In conclusion, plasma-derived AGA to sialylated and sulfated glycans including SiaTn and 6-OSulfo-TF detected by SGA present a valuable alternative to CA125 for differentiating controls from HGSOC patients and for predicting the likelihood of HGSOC, and may be potential HGSOC tumor markers. PMID:27764122

Blood Plasma-Derived Anti-Glycan Antibodies to Sialylated and Sulfated Glycans Identify Ovarian Cancer Patients.

PubMed

Pochechueva, Tatiana; Chinarev, Alexander; Schoetzau, Andreas; Fedier, André; Bovin, Nicolai V; Hacker, Neville F; Jacob, Francis; Heinzelmann-Schwarz, Viola

2016-01-01

Altered levels of naturally occurring anti-glycan antibodies (AGA) circulating in human blood plasma are found in different pathologies including cancer. Here the levels of AGA directed against 22 negatively charged (sialylated and sulfated) glycans were assessed in high-grade serous ovarian cancer (HGSOC, n = 22) patients and benign controls (n = 31) using our previously developed suspension glycan array (SGA). Specifically, the ability of AGA to differentiate between controls and HGSOC, the most common and aggressive type of ovarian cancer with a poor outcome was determined. Results were compared to CA125, the commonly used ovarian cancer biomarker. AGA to seven glycans that significantly (P<0.05) differentiated between HGSOC and control were identified: AGA to top candidates SiaTn and 6-OSulfo-TF (both IgM) differentiated comparably to CA125. The area under the curve (AUC) of a panel of AGA to 5 glycans (SiaTn, 6-OSulfo-TF, 6-OSulfo-LN, SiaLea, and GM2) (0.878) was comparable to CA125 (0.864), but it markedly increased (0.985) when combined with CA125. AGA to SiaTn and 6-OSulfo-TF were also valuable predictors for HGSOC when CA125 values appeared inconclusive, i.e. were below a certain threshold. AGA-glycan binding was in some cases isotype-dependent and sensitive to glycosidic linkage switch (α2-6 vs. α2-3), to sialylation, and to sulfation of the glycans. In conclusion, plasma-derived AGA to sialylated and sulfated glycans including SiaTn and 6-OSulfo-TF detected by SGA present a valuable alternative to CA125 for differentiating controls from HGSOC patients and for predicting the likelihood of HGSOC, and may be potential HGSOC tumor markers.

The role of anthropometric, growth and maturity index (AGaMI) influencing youth soccer relative performance

NASA Astrophysics Data System (ADS)

Bisyri Husin Musawi Maliki, Ahmad; Razali Abdullah, Mohamad; Juahir, Hafizan; Muhamad, Wan Siti Amalina Wan; Afiqah Mohamad Nasir, Nur; Muazu Musa, Rabiu; Musliha Mat-Rasid, Siti; Adnan, Aleesha; Azura Kosni, Norlaila; Abdullah, Farhana; Ain Shahirah Abdullah, Nurul

2018-04-01

The main purpose of this study was to develop Anthropometric, Growth and Maturity Index (AGaMI) in soccer and explore its differences to soccer player physical attributes, fitness, motivation and skills. A total 223 adolescent soccer athletes aged 12 to 18 years old were selected as respondent. AGaMI was develop based on anthropometric components (bicep, tricep, subscapular, suprailiac, calf circumference and muac) with growth and maturity component using tanner scale. Meanwhile, relative performance namely physical, fitness, motivation and skills attributes of soccer were measured as dependent variables. The Principal Component Analysis (PCA) and Analysis of Variance (ANOVA) are used to achieve the objective in this study. AGaMI had categorized players into three different groups namely; high (5 players), moderate (88 players) and low (91 players). PCA revealed a moderate to very strong dominant range of 0.69 to 0.90 of factor loading on AGaMI. Further analysis assigned AGaMI groups as treated as independent variables (IV) and physical, fitness, motivation and skills attributes were treated as dependent variables (DV). Finally, ANOVA showed that flexibility, leg power, age, weight, height, sitting height, short and long pass are the most significant parameters statistically differentiate by the groups of AGaMI (p<0.05). As a summary, body fat mass, growth and maturity are an essential component differentiating the output of the soccer players relative performance. In future, information of the AGaMI model are useful to the coach and players for identifying the suitable biological and physiological demand reflects more comprehensive means of youth soccer relative performance. This study further highlights the importance of assessing AGaMI when identifying soccer relative performance.

Molecular characterization of aspartylglucosaminidase, a lysosomal hydrolase upregulated during strobilation in the moon jellyfish, Aurelia aurita.

PubMed

Tsujita, Natsumi; Kuwahara, Hiroyuki; Koyama, Hiroki; Yanaka, Noriyuki; Arakawa, Kenji; Kuniyoshi, Hisato

2017-05-01

The life cycle of the moon jellyfish, Aurelia aurita, alternates between a benthic asexual polyp stage and a planktonic sexual medusa (jellyfish) stage. Transition from polyp to medusa is called strobilation. To investigate the molecular mechanisms of strobilation, we screened for genes that are upregulated during strobilation using the differential display method and we identified aspartylglucosaminidase (AGA), which encodes a lysosomal hydrolase. Similar to AGAs from other species, Aurelia AGA possessed an N-terminal signal peptide and potential N-glycosylation sites. The genomic region of Aurelia AGA was approximately 9.8 kb in length and contained 12 exons and 11 introns. Quantitative RT-PCR analysis revealed that AGA expression increased during strobilation, and was then decreased in medusae. To inhibit AGA function, we administered the lysosomal acidification inhibitors, chloroquine or bafilomycin A1, to animals during strobilation. Both inhibitors disturbed medusa morphogenesis at the oral end, suggesting involvement of lysosomal hydrolases in strobilation.

Use of an acoustic helium analyzer for measuring lung volumes.

PubMed

Krumpe, P E; MacDannald, H J; Finley, T N; Schear, H E; Hall, J; Cribbs, D

1981-01-01

We have evaluated the use of an acoustic gas analyzer (AGA) for the measurement of total lung capacity (TLC) by single-breath helium dilution. The AGA has a rapid response time (0-90% response = 160 ms for 10% He), is linear for helium concentration of 0.1-10%, is stable over a wide range of ambient temperatures, and is small and portable. We plotted the output of the AGA vs. expired lung volume after a vital capacity breath of 10% He. However, since the AGA is sensitive to changes in speed of sound relative to air, the AGA output signal also reports an artifact due to alveolar gases. We corrected for this artifact by replotting a single-breath expiration after a vital capacity breath of room air. Mean alveolar helium concentration (HeA) was then measured by planimetry, using this alveolar gas curve as the base line. TLC was calculated using the HeA from the corrected AGA output and compared with TLC calculated from HeA simultaneously measured using a mass spectrometer (MS). In 12 normal subjects and 9 patients with chronic obstructive pulmonary disease (COPD) TLC-AGA and TLC-MS were compared by linear regression analysis; correlation coefficient (r) was 0.973 for normals and 0.968 for COPD patients (P less than 0.001). This single-breath; estimation of TLC using the corrected signal of the AGA vs. Expired volume seems ideally suited for the measurement of subdivisions of lung volume in field studies.

IgM ganglioside GM1 antibodies in patients with autoimmune disease or neuropathy, and controls.

PubMed Central

Bansal, A S; Abdul-Karim, B; Malik, R A; Goulding, P; Pumphrey, R S; Boulton, A J; Holt, P L; Wilson, P B

1994-01-01

AIMS--To compare the titre of anti-ganglioside antibodies (AGA) to GM1 ganglioside in patients with central and peripheral neurological disease and pure motor and sensorimotor neuropathy, in patients with classic autoimmune diseases, and controls. METHODS--AGA to GM1 were measured using an enzyme linked immunosorbent assay (ELISA) technique, highly purified bovine GM1 ganglioside, and sequential dilution of control and test sera. Antibody titre was calculated using the optical density readings of three consecutive serum dilutions multiplied by the dilution factor. RESULTS--A considerable overlap was evident in the titre of AGA to GM1 in control and test sera. High antibody titres were most frequent in patients with multifocal motor neuropathy with conduction block (MMNCB). Low AGA titre were observed in several patient groups. Compared with the controls, the median titre of AGA to GM1 was significantly higher in patients with multiple sclerosis, rheumatoid arthritis, primary Sjögren's syndrome and systemic lupus erythematosus. In contrast, the median titre in patients with diabetic peripheral neuropathy, motor neurone disease, sensorimotor neuropathy and chronic inflammatory demyelinating polyneuropathy was no different from that in normal control subjects. CONCLUSIONS--Estimation of AGA to GM1 may be helpful in the diagnosis of MMNCB in patients with a pure motor neuropathy but in few other conditions. Low titre AGA to GM1 are evident in several autoimmune conditions. The pathogenetic importance of AGA to GM1 in patients with neuropathy is not clear. PMID:8027366

Ribosome stalling and peptidyl-tRNA drop-off during translational delay at AGA codons

PubMed Central

Cruz-Vera, Luis Rogelio; Magos-Castro, Marco Antonio; Zamora-Romo, Efraín; Guarneros, Gabriel

2004-01-01

Minigenes encoding the peptide Met–Arg–Arg have been used to study the mechanism of toxicity of AGA codons proximal to the start codon or prior to the termination codon in bacteria. The codon sequences of the ‘mini-ORFs’ employed were initiator, combinations of AGA and CGA, and terminator. Both, AGA and CGA are low-usage Arg codons in ORFs of Escherichia coli but, whilst AGA is translated by the scarce tRNAArg4, CGA is recognized by the abundant tRNAArg2. Overexpression of minigenes harbouring AGA in the third position, next to a termination codon, was deleterious to the cell and led to the accumulation of peptidyl-tRNAArg4 and of the peptidyl-tRNA cognate to the preceding CGA or AGA Arg triplet. The minigenes carrying CGA in the third position were not toxic. Minigene-mediated toxicity and peptidyl-tRNA accumulation were suppressed by overproduction of tRNAArg4 but not by overproduction of peptidyl-tRNA hydrolase, an enzyme that is only active on substrates that have been released from the ribosome. Consistent with these findings, peptidyl-tRNAArg4 was identified to be mainly associated with ribosomes in a stand-by complex. These and previous results support the hypothesis that the primary mechanism of inhibition of protein synthesis by AGA triplets in pth+ cells involves sequestration of tRNAs as peptidyl-tRNA on the stalled ribosome. PMID:15317870

Recent Advances in Human Genetics and Epigenetics of Adiposity: Pathway to Precision Medicine?

PubMed

Fall, Tove; Mendelson, Michael; Speliotes, Elizabeth K

2017-05-01

Obesity is a heritable trait that contributes to substantial global morbidity and mortality. Here, we summarize findings from the past decade of genetic and epigenetic research focused on unravelling the underpinnings of adiposity. More than 140 genetic regions now are known to influence adiposity traits. The genetics of general adiposity, as measured by body mass index, and that of abdominal obesity, as measured by waist-to-hip ratio, have distinct biological backgrounds. Gene expression associated with general adiposity is enriched in the nervous system. In contrast, genes associated with abdominal adiposity function in adipose tissue. Recent population-based epigenetic analyses have highlighted additional distinct loci. We discuss how associated genetic variants can lead to understanding causal mechanisms, and to disentangling reverse causation in epigenetic analyses. Discoveries emerging from population genomics are identifying new disease markers and potential novel drug targets to better define and combat obesity and related diseases. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

Second Generation Multi-Gas Monitor for ISS and Orion: The Anomaly Gas Analyzer

NASA Technical Reports Server (NTRS)

Mudgett, Paul D.; Coan, Mary R.; Limero, Thomas; Pilgrim, Jeffrey S.

2017-01-01

First flight of AGA on Orion First flight of AGA on ISS Because of high reliability and long calibration interval, we recommend TDLS based monitors be considered for submarines Sea trials of AGA would be a logical follow-on to the MGM sea trial that is currently underway.

Isolation and Characterization of a Glycosyl Hydrolase Family 16 β-Agarase from a Mangrove Soil Metagenomic Library

PubMed Central

Mai, Zhimao; Su, Hongfei; Zhang, Si

2016-01-01

A mangrove soil metagenomic library was constructed and a β-agarase gene designated as AgaML was isolated by functional screening. The gene encoded for a 659-amino-acids polypeptide with an estimated molecular mass of 71.6 kDa. The deduced polypeptide sequences of AgaML showed the highest identity of 73% with the glycoside hydrolase family 16 β-agarase from Microbulbifer agarilyticus in the GenBank database. AgaML was cloned and highly expressed in Escherichia coli BL21(DE3). The purified recombinant protein, AgaML, showed optimal activity at 50 °C and pH 7.0. The kinetic parameters of Km and Vmax values toward agarose were 4.6 mg·mL−1 and 967.5 μM·min−1·mg−1, respectively. AgaML hydrolyzed the β-1,4-glycosidic linkages of agar to generate neoagarotetraose (NA4) and neoagarohexaose (NA6) as the main products. These characteristics suggest that AgaML has potential application in cosmetic, pharmaceuticals and food industries. PMID:27548158

Bioactives in Chinese Proprietary Medicine Modulates 5α-Reductase Activity and Gene Expression Associated with Androgenetic Alopecia

PubMed Central

Tan, Justin J. Y.; Pan, Jing; Sun, Lihan; Zhang, Junying; Wu, Chunyong; Kang, Lifeng

2017-01-01

Androgenetic alopecia (AGA) is characterized by a progressive and patterned transformation of thick, pigmented terminal scalp hairs into short, hypo-pigmented vellus-like hairs. The use of Minoxidil and Finasteride to treat AGA are often associated with complications in safety and efficacy. However, herbal remedies are deemed to have lesser side effects in many societies. This study aims to identify potential hair growth properties of individual compounds from a Chinese proprietary medicine known as Yangxue Shengfa capsule (YSC), used in China for many years for improving AGA. Six marker compounds, including 2,3,5,4'-tetrahydroxystilbene-2-O-β-D-glucoside (TSG), Chlorogenic acid, Emodin, Ferulic acid, Isoimperatorin, and Paeoniflorin were used for simultaneous HPLC quantification and anti-AGA in-vitro screening. Simultaneous quantification of these components was performed on 75% (v/v) methanol extracts of YSC, using a Welch Ultimate XB-C18 column and gradient elution. Five compounds significantly promoted cell proliferation in cultured immortalized human Dermal Papilla Cells (DPC). Multiple genes associated with the progression of AGA, including IGF-1, DKK-1, and TGF-β1, were found to be regulated by some of these compounds. Interestingly, Ferulic acid and Emodin demonstrated good pharmacological properties against AGA, thereby concluding the potential of these bioactives to be used in the treatment against AGA. PMID:28450835

Comparison of Natural Gas Storage Estimates from the EIA and AGA

EIA Publications

1997-01-01

The Energy Information Administration (EIA) has been publishing monthly storage information for years. In order to address the need for more timely information, in 1994 the American Gas Association (AGA) began publishing weekly storage levels. Both the EIA and the AGA series provide estimates of the total working gas in storage, but use significantly different methodologies.

[Effects of electro-acupuncture on expression of triggering receptor expressed on myeloid cells 1 in ankle joint synovial tissue of acute gouty arthritis rats].

PubMed

Zhang, Chao-Nan; Huang, Xue-Kuan; Luo, Yan; Jiang, Juan; Wan, Lei; Wang, Ling

2015-01-01

To investigate the effects of electro-acupuncture (EA) on the expression of triggering receptor expressed on myeloid cell (TREM)l in ankle joint synovial tissue of acute gouty arthritis (AGA) rats. Forty male SD rats were randomly divided into 4 groups: normal, AGA, medication and EA group, 10 rats in each group. AGA model was established by induced monosodium urate (MSU) method, except the normal group. Tow days before AGA model was established, normal and AGA groups were lavaged with normal saline (20 ml/kg), medication group was lavaged with colchicine solution (20 ml/kg), EA(1.5-2 Hz, D.-D.wave, 9v; 1-3 rnA) was applied to "Sanyinjiao" (SP6), "jiexi" (ST41) and "Kunlun" (BL60) for 20 min, once daily;continuously for 9 days. Then observed the changes in dysfunction, and the content of TNF-α and IL-lβ detected by ELISA, the expression of TREM-l detected by immunohistochemistry and western blot. Compared to the normal group, the AGA group of the dysfunction index increased significantly (P<0.01), the content of TNF-α and IL-lβ increased significantly (P<0.05), the expression of TREM-l in synovial tissue increased significantly (P<0.05); the medication and EA groups compared to the AGA group, the dysfunction index decreased significantly (P<0.01), the content of TNF-α and IL-lβ decreased significantly (P<0.05), the expression of TREM-l in synovial tissue decreased significantly (P<0.05); there were not statistically significant between the medication and EA group (P>0.05). EA treating AGA may be through down-regulating the expression of TREM -1 in synovial tissue.

Ghrelin and obestatin plasma levels and ghrelin/obestatin prepropeptide gene polymorphisms in small for gestational age infants.

PubMed

Zhang, Shulian; Zhai, Guanpeng; Zhang, Jinping; Zhou, Jianguo; Chen, Chao

2014-12-01

To investigate plasma ghrelin and obestatin levels, and ghrelin/obestatin prepropeptide gene polymorphisms, in sequentially enrolled small for gestational age (SGA) infants. Neonates were sequentially enrolled into this study and were then subdivided into different groups, according to different study aims and availability of study materials. Consequently, plasma ghrelin and obestatin levels were measured in term SGA, term appropriate for gestational age (AGA), term large for gestational age (LGA), preterm SGA and preterm AGA neonates. Levels of both peptides were also measured in AGA infants of different gestational ages, and in term AGA neonates at different days following birth. Three ghrelin/obestatin prepropeptide gene single nucleotide polymorphisms (SNPs), Arg51Gln, Leu72Met, and Gln90Leu, were measured in neonates. The study involved a total cohort of 581 neonates. Out of 150 neonates (30 term AGA, 30 term SGA, 30 term LGA, 30 preterm AGA, and 30 preterm SGA), plasma obestatin levels were significantly higher in term SGA versus term LGA neonates (0.21 ± 0.02 ng/ml versus 0.17 ± 0.01 ng/ml, respectively). Out of a wider cohort, there were no significant differences in genotypes and allele frequencies of Arg51Gln, Leu72Met, and Gln90Leu SNPs between term SGA and AGA neonates, or between preterm SGA and AGA neonates. Ghrelin/obestatin prepropeptide polymorphisms were not found to be associated with SGA status in neonates; however, ghrelin and obestatin levels may be involved in growth and development. Further studies are required to understand the relationship between ghrelin, obestatin and prenatal development. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

A comparison of the metabolism of the abortifacient compounds from Ponderosa pine needles in conditioned versus naive cattle.

PubMed

Welch, K D; Gardner, D R; Pfister, J A; Panter, K E; Zieglar, J; Hall, J O

2012-12-01

Isocupressic acid (ICA) is the abortifacient compound in ponderosa pine (Pinus ponderosa L.) needles, which can cause late-term abortions in cattle (Bos taurus). However, cattle rapidly metabolize ICA to agathic acid (AGA) and subsequent metabolites. When pine needles are dosed orally to cattle, no ICA is detected in their serum, whereas AGA is readily detected. Recent research has demonstrated that AGA is also an abortifacient compound in cattle. The observation has been made that when cattle are dosed with labdane acids for an extended time, the concentration of AGA in serum increases for 1 to 2 d but then decreases to baseline after 5 to 6 d even though they are still being dosed twice daily. Therefore, in this study we investigated whether cattle conditioned to pine needles metabolize ICA, and its metabolites, faster than naïve cattle. Agathic acid was readily detected in the serum of naïve cattle fed ponderosa pine needles, whereas very little AGA was detected in the serum of cattle conditioned to pine needles. We also compared the metabolism of ICA in vitro using rumen cultures from pine-needle-conditioned and naïve cattle. In the rumen cultures from conditioned cattle, AGA concentrations were dramatically less than rumen cultures from naïve cattle. Thus, an adaptation occurs to cattle conditioned to pine needles such that the metabolism AGA by the rumen microflora is altered.

Ex-vivo assessment and non-invasive in vivo imaging of internal hemorrhages in Aga2/+ mutant mice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ermolayev, Vladimir; Cohrs, Christian M.; Mohajerani, Pouyan

Highlights: ► Aga2/+ mice, model for Osteogenesis imperfecta, have type I collagen mutation. ► Aga2/+ mice display both moderate and severe phenotypes lethal 6–11th postnatal. ► Internal hemorrhages studied in Aga2/+ vs. control mice at 6 and 9 days postnatal. ► Anatomical and functional findings in-vivo contrasted to the ex-vivo appearance. -- Abstract: Mutations in type I collagen genes (COL1A1/2) typically lead to Osteogenesis imperfecta, the most common heritable cause of skeletal fractures and bone deformation in humans. Heterozygous Col1a1{sup Aga2/+}, animals with a dominant mutation in the terminal C-propeptide domain of type I collagen develop typical skeletal hallmarks andmore » internal hemorrhages starting from 6 day after birth. The disease progression for Aga2/+ mice, however, is not uniform differing between severe phenotype lethal at the 6–11th day of life, and moderate-to-severe one with survival to adulthood. Herein we investigated whether a new modality that combines X-ray computer tomography with fluorescence tomography in one hybrid system can be employed to study internal bleedings in relation to bone fractures and obtain insights into disease progression. The disease phenotype was characterized on Aga2/+ vs. wild type mice between 6 and 9 days postnatal. Anatomical and functional findings obtained in-vivo were contrasted to the ex-vivo appearance of the same tissues under cryo-slicing.« less

Effect of Intra- and Extrauterine Growth on Long-Term Neurologic Outcomes of Very Preterm Infants.

PubMed

Guellec, Isabelle; Lapillonne, Alexandre; Marret, Stephane; Picaud, Jean-Charles; Mitanchez, Delphine; Charkaluk, Marie-Laure; Fresson, Jeanne; Arnaud, Catherine; Flamand, Cyril; Cambonie, Gilles; Kaminski, Monique; Roze, Jean-Christophe; Ancel, Pierre-Yves

2016-08-01

To determine whether extrauterine growth is associated with neurologic outcomes and if this association varies by prenatal growth profile. For 1493 preterms from the EPIPAGE (Étude Épidémiologique sur les Petits Âges Gestationnels [Epidemiological Study on Small Gestational Ages]) cohort, appropriate for gestational-age (AGA) was defined by birth weight >-2 SD and small for gestational-age (SGA) by birth weight ≤-2 SD. Extra-uterine growth was defined by weight gain or loss between birth and 6 months by z-score change. Growth following-the-curve (FTC) was defined as weight change -1 to +1 SD, catch-down-growth (CD) as weight loss ≥1 SD, and catch-up-growth (CU) as weight gain ≥1 SD. At 5 years, a complete medical examination (n = 1305) and cognitive evaluation with the Kauffman Assessment Battery for Children (n = 1130) were performed. Behavioral difficulties at 5 years and school performance at 8 years were assessed (n = 1095). Overall, 42.5% of preterms were AGA-FTC, 20.2% AGA-CD, 17.1% AGA-CU, 5.6% SGA-FTC, and 14.5% SGA-CU. Outcomes did not differ between CU and FTC preterm AGA infants. Risk of cerebral palsy was greater for AGA-CD compared with AGA-FTC (aOR 2.26 [95% CI 1.37-3.72]). As compared with children with SGA-CU, SGA-FTC children showed no significant increased risk of cognitive deficiency (aOR 1.41[0.94-2.12]) or school difficulties (aOR 1.60 [0.84-3.03]). Compared with AGA-FTC, SGA showed increased risk of cognitive deficiency (SGA-FTC aOR 2.19 [1.25-3.84]) and inattention-hyperactivity (SGA-CU aOR 1.65 [1.05-2.60]). Deficient postnatal growth was associated with poor neurologic outcome for AGA and SGA preterm infants. CU growth does not add additional benefits. Regardless of type of postnatal growth, SGA infants showed behavioral problems and cognitive deficiency. Copyright © 2016 Elsevier Inc. All rights reserved.

Genetic algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steven E.

1991-01-01

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

Anti-ganglioside antibodies in patients with systemic lupus erythematosus and neurological manifestations.

PubMed

Labrador-Horrillo, M; Martinez-Valle, F; Gallardo, E; Rojas-Garcia, R; Ordi-Ros, J; Vilardell, M

2012-05-01

Anti-ganglioside antibodies (AGA) have been associated with several peripheral neuropathies, such as Miller-Fisher syndrome, Guillain-Barré syndrome and multifocal motor neuropathy. They have also been studied in patients with systemic lupus erythematosus (SLE), focusing on neuropsychiatric manifestations and peripheral neuropathy, but the results are contradictory. To study the presence of AGA in a large cohort of patients with SLE and neuropsychiatric manifestations. Serum from 65 consecutive patients with SLE and neuropsychiatric manifestations, collected from 1985 to 2009, was tested for the presence of AGA antibodies (GM1, GM2, GM3, asialo-GM1 GD1a, GD1b, GD3, GT1b, GQ1b) using a standard enzyme-linked immunosorbent assay ELISA test (INCAT 1999) and thin layer chromatography (TLC). Positive results for asialo-GM1 (IgM) were found in 10 patients, 6 were positive for asialo-GM1 (IgM and IgG), and 4 were positive for other AGA such as GM1, GM2, GM3, GD1b, GT1b, GD3, (mainly IgM). Clinical and statistical studies showed no correlation between AGA and neuropsychiatric manifestations of SLE. Although some patients showed reactivity to AGA, these antibodies are not a useful marker of neuropsychiatric manifestations in SLE patients.

Comparison of serum 25-hydroxy vitamin D levels between mothers with small for gestational age and appropriate for gestational age newborns in Kerman.

PubMed

Mirzaei, Fatemeh; Amiri Moghadam, Tayebeh; Arasteh, Peyman

2015-04-01

Vitamin D deficiency during pregnancy is associated with some adverse pregnancy outcomes but its relationship with fetal growth is unknown. We compared the 25-hydroxy vitamin D levels between mothers and their small for gestational age (SGA) newborns with mothers and their appropriate for gestational age (AGA) newborns. The study population included pregnant women that referred to Afzalipour Hospital in Kerman from 2012 to 2013. The case and control group consisted of 40 pregnant mothers with SGA and AGA newborns, respectively. The maternal and infants 25-hydroxy vitamin D levels were measured in the two groups. 25-hydroxy vitamin D deficiency (<20 ng/ml) was statistically higher in women with SGA newborns in comparison to women with AGA newborns (p=0.003).Vitamin D deficiency was higher among the SGA newborns in comparison to AGA newborns (25% vs. 17.5%), although this finding was not statistically meaningful (p=0.379). The relationship of vitamin D deficiency levels between mothers and infants in both the SGA group and the AGA group was significant. Our study reveals a high prevalence of vitamin D deficiency in women with SGA infants in comparison to women with AGA children. In addition, maternal vitamin D deficiency is associated with its deficiency in newborns.

P-type calcium channels in rat neocortical neurones.

PubMed Central

Brown, A M; Sayer, R J; Schwindt, P C; Crill, W E

1994-01-01

1. The high threshold, voltage-activated (HVA) calcium current was recorded from acutely isolated rat neocortical pyramidal neurones using the whole-cell patch technique to examine the effect of agents that block P-type calcium channels and to compare their effects to those of omega-conotoxin GVIA (omega-CgTX) and nifedipine. 2. When applied at a saturating concentration (100 nM) the peptide toxins omega-Aga-IVA and synthetic omega-Aga-IVA blocked 31.5 and 33.0% of the HVA current respectively. 3. A saturating concentration of nifedipine (10 microM) inhibited 48.2% of the omega-Aga-IVA-sensitive current, whereas saturating concentrations of both omega-Aga-IVA (100 nM) and omega-CgTX (10 microM) blocked separate specific components of the HVA current. 4. Partially purified funnel web spider toxin (FTX) at a dilution of 1:1000 blocked 81.4% of the HVA current and occluded the inhibitory effect of omega-Aga-IVA. Synthetic FTX 3.3 arginine polyamine (sFTX) at a concentration of 1 mM blocked 61.2% of the HVA current rapidly and reversibly. The effects of sFTX were partially occluded by pre-application of omega-Aga-IVA. We conclude that neither FTX nor sFTX blocked a specific component of the HVA current in these cells. 5. In view of the specificity of omega-Aga-IVA for P-type calcium channels in other preparations and for a specific component of the HVA current in dissociated neocortical neurones we conclude that about 30% of the HVA current in these neurones flow through P-channels. PMID:7517449

P-type calcium channels in rat neocortical neurones.

PubMed

Brown, A M; Sayer, R J; Schwindt, P C; Crill, W E

1994-03-01

1. The high threshold, voltage-activated (HVA) calcium current was recorded from acutely isolated rat neocortical pyramidal neurones using the whole-cell patch technique to examine the effect of agents that block P-type calcium channels and to compare their effects to those of omega-conotoxin GVIA (omega-CgTX) and nifedipine. 2. When applied at a saturating concentration (100 nM) the peptide toxins omega-Aga-IVA and synthetic omega-Aga-IVA blocked 31.5 and 33.0% of the HVA current respectively. 3. A saturating concentration of nifedipine (10 microM) inhibited 48.2% of the omega-Aga-IVA-sensitive current, whereas saturating concentrations of both omega-Aga-IVA (100 nM) and omega-CgTX (10 microM) blocked separate specific components of the HVA current. 4. Partially purified funnel web spider toxin (FTX) at a dilution of 1:1000 blocked 81.4% of the HVA current and occluded the inhibitory effect of omega-Aga-IVA. Synthetic FTX 3.3 arginine polyamine (sFTX) at a concentration of 1 mM blocked 61.2% of the HVA current rapidly and reversibly. The effects of sFTX were partially occluded by pre-application of omega-Aga-IVA. We conclude that neither FTX nor sFTX blocked a specific component of the HVA current in these cells. 5. In view of the specificity of omega-Aga-IVA for P-type calcium channels in other preparations and for a specific component of the HVA current in dissociated neocortical neurones we conclude that about 30% of the HVA current in these neurones flow through P-channels.

Association between androgenetic alopecia and coronary artery disease in young male patients.

PubMed

Sharma, Kamal H; Jindal, Anchal

2014-01-01

Several studies have demonstrated an association between androgenetic alopecia (AGA) and cardiovascular disease. Still controversies exist regarding the association. Are they truly associated? The purpose of the present study was to assess the prevalence of AGA and establish its association in young (<45 years) Asian Indian Gujarati male patients having coronary artery disease (CAD). Case-control prospective multicentric study was carried out on 424 men. Case group consisted of 212 male subjects having CAD (Group 1) and another 212, either sibling or first degree male relative of the case subjects (having no evidence of CAD) were considered as the control group (Group 2). Age, total cholesterol, incidence of diabetes mellitus, and hypertension were similar in both groups. The degree of alopecia was assessed using the Norwood-Hamilton scale for men. Statistical analysis was performed using Chi-square test. AGA was found in 80 (37.73%) young CAD patients (Group 1), whereas 44 (20.7%) of patients had alopecia in the control group (Group 2). There was statistically significant association between male AGA and CAD (P = 0.001). Odds ratio was 2.70 (95% confidence interval [CI], 1.72 ± 4.26). Statistically significant association was found between high grade baldness (Grades IV-VII) and CAD in young men (P < 0.05). Odds ratio = 2.36 (95% CI, 1.108 ± 5.033). There is statistically significant association of AGA in young Asian Gujarati male with CAD and the prevalence of AGA in young CAD patient is 37.73%. This study implies early onset AGA in male is independently associated with CAD, though mechanisms need to be investigated.

Genetic Algorithms and Local Search

NASA Technical Reports Server (NTRS)

Whitley, Darrell

1996-01-01

The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple genetic algorithms. The second half covers the combination of genetic algorithms with local search methods to produce hybrid genetic algorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple genetic algorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.

Association of anti-gangliosides antibodies and anti-CMV antibodies in Guillain-Barré syndrome.

PubMed

Wang, Lijuan; Shao, Chunqing; Yang, Chunjiao; Kang, Xixiong; Zhang, Guojun

2017-05-01

Numerous types of infection were closely related to GBS, mainly including Campylobacter jejuni , Cytomegalovirus, which may lead to the production of anti-gangliosides antibodies (AGA) . Currently, although there are increased studies on the AGA and a few studies of anti-CMV antibodies in GBS, the association between them remains poorly documented. Therefore, our research aims to analyze the correlation of anti-CMV antibodies and AGA in GBS. A total of 29 patients with GBS were enrolled in this study. The CMV antibodies were tested by the electrochemiluminescence immunoassay "ECLIA" (Roche Diagnostics GmbH). The serum gangliosides were determined by The EUROLINE test kit. Of the 29 patients with GBS, 9 (31%) were AGA-seropositive, in which 22 were CMV-IgG positive in CSF at the same time, but all 29 samples were CMV-IgM negative in both serum and CSF. In the AGA-positive group, the rate of both serum and CSF positive was 87.5% (7/8), higher than 50% (7/14) of the negative group, although no statistical significance was found. In addition, we found that there was a trend of higher ratio of men, a younger age onset, less frequent preceding infection, a higher level of CSF proteins, and less frequent cranial nerve deficits, although the data did not reach a statistical significance. In spite of no statistical significance association was found between serum AGA and CMV-IgG in serum and CSF. However, we found that there was a trend of high positive rate of both serum and CSF-CMV-IgG in AGA-positive than the negative group. So we should further expand the sample size to analyze the association between AGA and CMV or other neurotropic virus antibodies in various diseases, to observe whether they could be serological marker of these diseases (especially GBS) or the underlying pathogenesis.

Host adaptation of Chlamydia pecorum towards low virulence evident in co-evolution of the ompA, incA, and ORF663 Loci.

PubMed

Mohamad, Khalil Yousef; Kaltenboeck, Bernhard; Rahman, Kh Shamsur; Magnino, Simone; Sachse, Konrad; Rodolakis, Annie

2014-01-01

Chlamydia (C.) pecorum, an obligate intracellular bacterium, may cause severe diseases in ruminants, swine and koalas, although asymptomatic infections are the norm. Recently, we identified genetic polymorphisms in the ompA, incA and ORF663 genes that potentially differentiate between high-virulence C. pecorum isolates from diseased animals and low-virulence isolates from asymptomatic animals. Here, we expand these findings by including additional ruminant, swine, and koala strains. Coding tandem repeats (CTRs) at the incA locus encoded a variable number of repeats of APA or AGA amino acid motifs. Addition of any non-APA/AGA repeat motif, such as APEVPA, APAVPA, APE, or APAPE, associated with low virulence (P<10-4), as did a high number of amino acids in all incA CTRs (P = 0.0028). In ORF663, high numbers of 15-mer CTRs correlated with low virulence (P = 0.0001). Correction for ompA phylogram position in ORF663 and incA abolished the correlation between genetic changes and virulence, demonstrating co-evolution of ompA, incA, and ORF663 towards low virulence. Pairwise divergence of ompA, incA, and ORF663 among isolates from healthy animals was significantly higher than among strains isolated from diseased animals (P≤10-5), confirming the longer evolutionary path traversed by low-virulence strains. All three markers combined identified 43 unique strains and 4 pairs of identical strains among all 57 isolates tested, demonstrating the suitability of these markers for epidemiological investigations.

Association Between Androgenetic Alopecia and Coronary Artery Disease in Young Male Patients

PubMed Central

Sharma, Kamal H; Jindal, Anchal

2014-01-01

Background: Several studies have demonstrated an association between androgenetic alopecia (AGA) and cardiovascular disease. Still controversies exist regarding the association. Are they truly associated? Objective: The purpose of the present study was to assess the prevalence of AGA and establish its association in young (<45 years) Asian Indian Gujarati male patients having coronary artery disease (CAD). Materials and Methods: Case-control prospective multicentric study was carried out on 424 men. Case group consisted of 212 male subjects having CAD (Group 1) and another 212, either sibling or first degree male relative of the case subjects (having no evidence of CAD) were considered as the control group (Group 2). Age, total cholesterol, incidence of diabetes mellitus, and hypertension were similar in both groups. The degree of alopecia was assessed using the Norwood-Hamilton scale for men. Statistical analysis was performed using Chi-square test. Results: AGA was found in 80 (37.73%) young CAD patients (Group 1), whereas 44 (20.7%) of patients had alopecia in the control group (Group 2). There was statistically significant association between male AGA and CAD (P = 0.001). Odds ratio was 2.70 (95% confidence interval [CI], 1.72 ± 4.26). Statistically significant association was found between high grade baldness (Grades IV-VII) and CAD in young men (P < 0.05). Odds ratio = 2.36 (95% CI, 1.108 ± 5.033). There is statistically significant association of AGA in young Asian Gujarati male with CAD and the prevalence of AGA in young CAD patient is 37.73%. Conclusion: This study implies early onset AGA in male is independently associated with CAD, though mechanisms need to be investigated. PMID:25114445

Conventional and novel stem cell based therapies for androgenic alopecia

PubMed Central

Talavera-Adame, Dodanim; Newman, Daniella; Newman, Nathan

2017-01-01

The prevalence of androgenic alopecia (AGA) increases with age and it affects both men and women. Patients diagnosed with AGA may experience decreased quality of life, depression, and feel self-conscious. There are a variety of therapeutic options ranging from prescription drugs to non-prescription medications. Currently, AGA involves an annual global market revenue of US$4 billion and a growth rate of 1.8%, indicating a growing consumer market. Although natural and synthetic ingredients can promote hair growth and, therefore, be useful to treat AGA, some of them have important adverse effects and unknown mechanisms of action that limit their use and benefits. Biologic factors that include signaling from stem cells, dermal papilla cells, and platelet-rich plasma are some of the current therapeutic agents being studied for hair restoration with milder side effects. However, most of the mechanisms exerted by these factors in hair restoration are still being researched. In this review, we analyze the therapeutic agents that have been used for AGA and emphasize the potential of new therapies based on advances in stem cell technologies and regenerative medicine. PMID:28979149

Conventional and novel stem cell based therapies for androgenic alopecia.

PubMed

Talavera-Adame, Dodanim; Newman, Daniella; Newman, Nathan

2017-01-01

The prevalence of androgenic alopecia (AGA) increases with age and it affects both men and women. Patients diagnosed with AGA may experience decreased quality of life, depression, and feel self-conscious. There are a variety of therapeutic options ranging from prescription drugs to non-prescription medications. Currently, AGA involves an annual global market revenue of US$4 billion and a growth rate of 1.8%, indicating a growing consumer market. Although natural and synthetic ingredients can promote hair growth and, therefore, be useful to treat AGA, some of them have important adverse effects and unknown mechanisms of action that limit their use and benefits. Biologic factors that include signaling from stem cells, dermal papilla cells, and platelet-rich plasma are some of the current therapeutic agents being studied for hair restoration with milder side effects. However, most of the mechanisms exerted by these factors in hair restoration are still being researched. In this review, we analyze the therapeutic agents that have been used for AGA and emphasize the potential of new therapies based on advances in stem cell technologies and regenerative medicine.

Automated assembly of oligosaccharides containing multiple cis-glycosidic linkages

NASA Astrophysics Data System (ADS)

Hahm, Heung Sik; Hurevich, Mattan; Seeberger, Peter H.

2016-09-01

Automated glycan assembly (AGA) has advanced from a concept to a commercial technology that rapidly provides access to diverse oligosaccharide chains as long as 30-mers. To date, AGA was mainly employed to incorporate trans-glycosidic linkages, where C2 participating protecting groups ensure stereoselective couplings. Stereocontrol during the installation of cis-glycosidic linkages cannot rely on C2-participation and anomeric mixtures are typically formed. Here, we demonstrate that oligosaccharides containing multiple cis-glycosidic linkages can be prepared efficiently by AGA using monosaccharide building blocks equipped with remote participating protecting groups. The concept is illustrated by the automated syntheses of biologically relevant oligosaccharides bearing various cis-galactosidic and cis-glucosidic linkages. This work provides further proof that AGA facilitates the synthesis of complex oligosaccharides with multiple cis-linkages and other biologically important oligosaccharides.

Problem solving with genetic algorithms and Splicer

NASA Technical Reports Server (NTRS)

Bayer, Steven E.; Wang, Lui

1991-01-01

Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.

Genetic algorithms using SISAL parallel programming language

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tejada, S.

1994-05-06

Genetic algorithms are a mathematical optimization technique developed by John Holland at the University of Michigan [1]. The SISAL programming language possesses many of the characteristics desired to implement genetic algorithms. SISAL is a deterministic, functional programming language which is inherently parallel. Because SISAL is functional and based on mathematical concepts, genetic algorithms can be efficiently translated into the language. Several of the steps involved in genetic algorithms, such as mutation, crossover, and fitness evaluation, can be parallelized using SISAL. In this paper I will l discuss the implementation and performance of parallel genetic algorithms in SISAL.

Differential effect of assisted reproductive technology and small-for-gestational age on fetal cardiac remodeling.

PubMed

Valenzuela-Alcaraz, B; Crispi, F; Cruz-Lemini, M; Bijnens, B; García-Otero, L; Sitges, M; Balasch, J; Gratacós, E

2017-07-01

Fetuses conceived by assisted reproductive technology (ART) and those that are small-for-gestational age (SGA) show cardiovascular remodeling in utero; however, these two conditions are often associated. We aimed to evaluate the differential effect of ART and SGA on fetal cardiac remodeling. This was a prospective cohort study of term singleton pregnancies seen at our department between April 2011 and September 2013. The cohort was divided according to fetal growth and mode of conception into the following four groups: 102 appropriate-for-gestational-age (AGA) fetuses conceived spontaneously (controls), 72 AGA fetuses conceived by ART (ART-AGA), 31 SGA fetuses conceived by ART (ART-SGA) and 28 SGA fetuses conceived naturally (Spont-SGA). SGA was defined as birth weight < 10 th centile. Fetal echocardiography was performed at 28-32 weeks to assess cardiac dimensions, geometry and function. ART fetuses had dilated atria (mean left atrium-to-heart area ratio: controls, 15 ± 2.7%; ART-AGA, 18 ± 4.1%; Spont-SGA, 14 ± 3.7%) and more globular ventricles (left ventricular sphericity index: controls, 1.77 ± 0.2; ART-AGA, 1.68 ± 0.2; Spont-SGA, 1.72 ± 0.2), with normally sized hearts. In contrast, SGA fetuses had enlarged hearts (cardiothoracic ratio: controls, 24 ± 3%; ART-AGA, 24 ± 4%; Spont-SGA, 29 ± 6%), preserved atrial size, more globular and concentric hypertrophic ventricles (left ventricle relative wall thickness: controls, 0.48 ± 0.17; ART-AGA, 0.54 ± 0.13; Spont-SGA, 0.63 ± 0.23). Both ART and SGA fetuses had decreased longitudinal motion (tricuspid annular ring displacement: controls, 6.5 ± 0.8 mm; ART-AGA, 5.5 ± 0.7 mm; Spont-SGA, 5.9 ± 0.6 mm) and impaired relaxation (left isovolumetric relaxation time: controls, 47.0 ± 7.3 ms; ART-AGA, 50.0 ± 7.9 ms; Spont-SGA, 49.5 ± 9.3 ms). ART-SGA fetuses presented a combination of features from both ART and SGA groups. SGA and conception with ART were associated with distinct patterns of fetal cardiac remodeling, supporting the concept that they are independent causes of cardiac programming. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

Patient Electronic Health Records as a Means to Approach Genetic Research in Gastroenterology.

PubMed

Ananthakrishnan, Ashwin N; Lieberman, David

2015-10-01

Electronic health records (EHRs) are being increasingly utilized and form a unique source of extensive data gathered during routine clinical care. Through use of codified and free text concepts identified using clinical informatics tools, disease labels can be assigned with a high degree of accuracy. Analysis linking such EHR-assigned disease labels to a biospecimen repository has demonstrated that genetic associations identified in prospective cohorts can be replicated with adequate statistical power and novel phenotypic associations identified. In addition, genetic discovery research can be performed utilizing clinical, laboratory, and procedure data obtained during care. Challenges with such research include the need to tackle variability in quality and quantity of EHR data and importance of maintaining patient privacy and data security. With appropriate safeguards, this novel and emerging field of research offers considerable promise and potential to further scientific research in gastroenterology efficiently, cost-effectively, and with engagement of patients and communities. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Safety of topical minoxidil solution: a one-year, prospective, observational study.

PubMed

Shapiro, Jerry

2003-01-01

Topical minoxidil solution (TMS) is widely used for androgenetic alopecia (AGA), and this is the first report of a large safety trial. The aim of the study was to evaluate the safety profile of TMS by comparing hospitalization and death rates among subjects using TMS with controls. Cardiovascular safety and pregnancy outcomes were evaluated, and usage patterns were described. All subjects were followed at baseline, 3, 6, 9, and 12 months. Usage patterns, pregnancy status, overnight hospital stays, and cardiovascular risk factors were evaluated. Subjects rated effectiveness of TMS in the treatment of AGA. Statistical analyses were conducted to determine if TMS was associated with an increased risk of death or hospitalization. TMS is a safe and effective treatment for AGA. There were no increases in cardiovascular events and no apparent increased risk for adverse pregnancy outcomes. This large, prospective study demonstrated the overall safety of TMS in the treatment of AGA.

Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

NASA Astrophysics Data System (ADS)

Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui

2017-05-01

The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

Drug Treatment for Androgenetic Alopecia: First Italian Questionnaire Survey on What Dermatologists Think about Finasteride.

PubMed

Sorbellini, Elisabetta; Pinto, Daniela; Marzani, Barbara; Rinaldi, Fabio

2018-06-01

Treatment with finasteride 1 mg/day represents the therapy of choice for androgenetic alopecia (AGA). We investigated how Italian dermatologists approach use of finasteride for treatment of AGA and common side effects reported by patients. A tablet-based survey was conducted from February 2017 to January 2018 in Italy to investigating use of 1 mg/day finasteride in the treatment of AGA. Approximately 1153 Italian dermatologists were surveyed about prescription frequency, therapy duration, treatment practices, and side effects eventually reported. Dermatologists considered treatment with 1 mg/day finasteride to be the most efficacious treatment for AGA, as reflecting by its long-term (5 years) prescription. Data on sexual side effects from our survey are in line with previous scientific evidence, especially regarding loss of libido, erectile dysfunction, and problems with ejaculation, but also in the psychological sphere and regarding physical impairments such as myalgia and loss of muscle tone. This is the first preliminary observational study on how Italian dermatologists approach use of finasteride to treat AGA. Although side effects have been reported, especially in the sexual sphere, lack of alternative treatments with the same efficacy leads dermatologists to prescribe 1 mg/day finasteride with a tendency to prolong therapy in the long term. Giuliani S.p.A.

G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

NASA Technical Reports Server (NTRS)

Rogers, David

1991-01-01

G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

Comparison of genetic algorithms with conjugate gradient methods

NASA Technical Reports Server (NTRS)

Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

1972-01-01

Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

Software For Genetic Algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steve E.

1992-01-01

SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.

Humanin Derivatives Inhibit Necrotic Cell Death in Neurons

PubMed Central

Cohen, Aviv; Lerner-Yardeni, Jenny; Meridor, David; Kasher, Roni; Nathan, Ilana; Parola, Abraham H

2015-01-01

Humanin and its derivatives are peptides known for their protective antiapoptotic effects against Alzheimer’s disease. Herein, we identify a novel function of the humanin-derivative AGA(C8R)-HNG17 (namely, protection against cellular necrosis). Necrosis is one of the main modes of cell death, which was until recently considered an unmoderated process. However, recent findings suggest the opposite. We have found that AGA(C8R)-HNG17 confers protection against necrosis in the neuronal cell lines PC-12 and NSC-34, where necrosis is induced in a glucose-free medium by either chemohypoxia or by a shift from apoptosis to necrosis. Our studies in traumatic brain injury models in mice, where necrosis is the main mode of neuronal cell death, have shown that AGA(C8R)-HNG17 has a protective effect. This result is demonstrated by a decrease in a neuronal severity score and by a reduction in brain edema, as measured by magnetic resonance imaging (MRI). An insight into the peptide’s antinecrotic mechanism was attained through measurements of cellular ATP levels in PC-12 cells under necrotic conditions, showing that the peptide mitigates a necrosis-associated decrease in ATP levels. Further, we demonstrate the peptide’s direct enhancement of the activity of ATP synthase activity, isolated from rat-liver mitochondria, suggesting that AGA(C8R)-HNG17 targets the mitochondria and regulates cellular ATP levels. Thus, AGA(C8R)-HNG17 has potential use for the development of drug therapies for necrosis-related diseases, for example, traumatic brain injury, stroke, myocardial infarction, and other conditions for which no efficient drug-based treatment is currently available. Finally, this study provides new insight into the mechanisms underlying the antinecrotic mode of action of AGA(C8R)-HNG17. PMID:26062019

Aminoglycoside antibiotics as a tool for the study of the biological role of calcium ions. Historical overview.

PubMed

Corrado, A P; de Morais, I P; Prado, W A

1989-01-01

Beginning with the pioneering work of Vital-Brazil and Corrado (1957), which suggested a possible interaction between aminoglycoside antibiotics (AGA) and calcium ions at the neuromuscular junction, the authors review the studies that demonstrated the existence of a competitive antagonism between AGA and calcium ions. In view of the low liposolubility of AGA and their inability to cross biological membranes, this antagonism seems to occur exclusively at calcium-binding sites at the level of the outer opening of calcium channels of the N-subtype, which are also the sites of interaction of omega-conotoxin. Being highly water soluble, AGA are easily removed from their binding sites with a consequent rapid reversal of their effects, a factor of primary importance to explain their wide use as tools in the pharmacological analysis of the study of the biological role of calcium ion on the membrane's outer surface. This use has advantages over the use of inorganic di- and trivalent cations such as Mg2+, Mn2+, Cd2+, Ni2+, La3+, etc., since the latter, though they are considered to be the most specific competitive antagonists of calcium ions, may induce biphasic effects due to their ability to cross the membranes and replace calcium and/or increase intracellular calcium concentration. The performance of AGA is also superior when compared with the so-called "specific" organic calcium antagonists--verapamil and nifedipine derivatives--since the latter, in addition to inducing possible biphasic effects, antagonize calcium in a non-competitive manner. Finally, the authors remark that AGA-Ca2+ antagonism relevance is not limited only to basic aspects and that it may have therapeutic implications since it provides alternatives for reducing the toxic adverse effects of this important group of antibiotics.

Carboxyhemoglobin levels in umbilical cord blood of women with pre-eclampsia and intrauterine growth restriction.

PubMed

Yusuf, Kamran; Kamaluddeen, Majeeda; Wilson, R Douglas; Akierman, Albert

2012-11-01

Pre-eclampsia (PE) and intrauterine growth restriction (IUGR) are associated with abnormal placentation. Heme oxygenase (HO) and carbon monoxide (CO) are involved in normal placental development and function and vasomotor control in the placenta. The objective of our study was to measure CO levels, as assessed by carboxyhemoglobin (COHb) levels in the umbilical cord arterial blood of women with PE, normotensive IUGR (<10th percentile for birth weight), and normotensive pregnancies with appropriate-for-gestational age (AGA) infants. We prospectively analyzed COHb levels in the umbilical arterial blood of women with PE, normotensive IUGR, and normotensive AGA pregnancies. Exclusion criteria included cigarette smoke exposure, hemolytic disorders, a positive direct anti-globulin test, chronic hypertension, fever, and any significant medical illness. COHb levels were measured using the ABL 725 blood gas analyzer. There were 41 women in the normotensive AGA group, 42 in the PE group, and 36 in the normotensive IUGR group. Maternal age, mode of delivery, gravidity, parity, and gender of the infants were similar in the three groups. Gestational age and birth weight were significantly higher in the normotensive AGA group compared with the other two groups. COHb levels were significantly lower in the PE group compared with the normotensive AGA group (0.38±0.06% vs. 0.77±0.11%, P<0.05). COHb levels, although lower in the normotensive IUGR group compared with the normotensive AGA group, did not reach statistical significance. Our data suggests the HO-CO system may have a role in the pathogenesis of PE. We also, for the first time, provide information on umbilical arterial COHb levels in normotensive IUGR pregnancies.

New knowledge-based genetic algorithm for excavator boom structural optimization

NASA Astrophysics Data System (ADS)

Hua, Haiyan; Lin, Shuwen

2014-03-01

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Distinct Patterns of Somatic Mosaicism in the APC Gene in Neoplasms From Patients With Unexplained Adenomatous Polyposis.

PubMed

Jansen, Anne M L; Crobach, Stijn; Geurts-Giele, Willemina R R; van den Akker, Brendy E W M; Garcia, Marina Ventayol; Ruano, Dina; Nielsen, Maartje; Tops, Carli M J; Wijnen, Juul T; Hes, Frederik J; van Wezel, Tom; Dinjens, Winand N M; Morreau, Hans

2017-02-01

We investigated the presence and patterns of mosaicism in the APC gene in patients with colon neoplasms not associated with any other genetic variants; we performed deep sequence analysis of APC in at least 2 adenomas or carcinomas per patient. We identified mosaic variants in APC in adenomas from 9 of the 18 patients with 21 to approximately 100 adenomas. Mosaic variants of APC were variably detected in leukocyte DNA and/or non-neoplastic intestinal mucosa of these patients. In a comprehensive sequence analysis of 1 patient, we found no evidence for mosaicism in APC in non-neoplastic intestinal mucosa. One patient was found to carry a mosaic c.4666dupA APC variant in only 10 of 16 adenomas, indicating the importance of screening 2 or more adenomas for genetic variants. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

EVALUATION OF THE RELATIONSHIP BETWEEN ANDROGENETIC ALOPECIA AND DEMODEX INFESTATION

PubMed Central

Zari, Javidi; Abdolmajid, Fata; Masood, Maleki; Vahid, Mashayekhi; Yalda, Nahidi

2008-01-01

Introduction: Androgenetic alopecia (AGA) is one of the most common dermatologic disorders with a multifactorial etiology. Inflammatory activators such as Demodex infestation may play a role in the pathogenesis of some cases of androgenetic alopecia that do not respond to common treatments such as minoxidil and finasteride. The goal of this study is to evaluate the relationship between Demodex infestation and AGA. Materials and Methods: In this case-control study, 41 patients with AGA referred to the Dermatology Clinic of Imam Reza Hospital and 33 healthy individuals were evaluated as control. All of them were between 20 and 40 years old men. In order to identify Demodex infestation they were referred to the Parasitology laboratory. Results: Demodex was detected in 19.5% of patients and 15.2% of controls; therefore, there was no significant relationship between them statistically (P = 0.0787). Most of patients (85.4%) had greasy hair. The most common pattern of baldness was II degree in Hamilton scale. Conclusion: There is no relation between AGA and Demodex. PMID:19881989

Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

NASA Astrophysics Data System (ADS)

Balakrishnan, D.; Quan, C.; Tay, C. J.

2013-06-01

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

Mobile robot dynamic path planning based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

[Relationship of plasma ghrelin, IGF-1 and insulin with the growth and development of 2 -7 year-old children with small for gestational age at birth].

PubMed

Cheng, Yaying; Song, Guangyao; Zhou, Lixia; Cai, Baoping; Zhao, Xiumian; Yin, Jianying

2012-01-01

To explore the relationship of Ghrelin, insulin-like growth factor-1 (IGF-1) and insulin with the growth and development of 2 -7 year-old children with small for gestational age (SGA) at birth. The levels of ghrelin, IGF-1, IGFBP-3, insulin and glucose were measured in the children with preterm SGA and term SGA and compared with the children with preterm appropriate for gestational age (AGA) and term AGA. The correlation of ghrelin with IGF-1, IGFBP-3 and insulin was analyzed. Plasma ghrelin in preterm SGA was higher than that in term SGA (P < 0.05), and there was no significant difference between preterm SGA and preterm AGA (P > 0.05). Plasma ghrelin in preterm AGA and term SGA was higher than that in term AGA (P < 0.05, P < 0.01 respectively). Serum IGF-1 and IGFBP-3 in preterm SGA were lower than those in term SGA (P < 0.05 for all) and serum IGF-1 and IGFBP-3 in preterm AGA were much lower than those in term AGA (P < 0.0001 for all). The level of serum insulin was the highest in term SGA. The trend of insulin resistance index (IRI) was similar to insulin. There were negative correlations of ghrelin with other indexes (weight SDS, IGF-1, IGFBP-3, insulin and IRI) in preterm SGA and term SGA (in preterm SGA r = -0.683, P < 0.002; r = -0.749, P < 0.001; r = -0.828, P < 0.001; r = -0.694, P < 0.005; r = -0.822, P < 0.001; in term SGA r = -0.792, P < 0.001; r = -0.707, P < 0.002; r = -0.615, P < 0.01; r = -0.648, P < 0.005; r = -0.679, P < 0.005). Ghrelin is involved in the regulation of growth and development of preterm and SGA children, regardless of the magnitude of their catch up growth. As a re-regulatory factor to insulin, ghrelin regulates the energy metabolism in a form of negative feedback.

An Efficient Rank Based Approach for Closest String and Closest Substring

PubMed Central

2012-01-01

This paper aims to present a new genetic approach that uses rank distance for solving two known NP-hard problems, and to compare rank distance with other distance measures for strings. The two NP-hard problems we are trying to solve are closest string and closest substring. For each problem we build a genetic algorithm and we describe the genetic operations involved. Both genetic algorithms use a fitness function based on rank distance. We compare our algorithms with other genetic algorithms that use different distance measures, such as Hamming distance or Levenshtein distance, on real DNA sequences. Our experiments show that the genetic algorithms based on rank distance have the best results. PMID:22675483

A hybrid genetic algorithm for resolving closely spaced objects

NASA Technical Reports Server (NTRS)

Abbott, R. J.; Lillo, W. E.; Schulenburg, N.

1995-01-01

A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.

Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

NASA Technical Reports Server (NTRS)

Burchett, Bradley T.

2003-01-01

The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

Use of videos for Distribution Construction and Maintenance (DC M) training

DOE Office of Scientific and Technical Information (OSTI.GOV)

Long, G.M.

This paper presents the results of a survey taken among members of the American Gas Association (AGA)'s Distribution Construction and Maintenance (DC M) committee to gauge the extent, sources, mode of use, and degree of satisfaction with videos as a training aid in distribution construction and maintenance skills. Also cites AGA Engineering Technical Note, DCM-88-3-1, as a catalog of the videos listed by respondents to the survey. Comments on the various sources of training videos and the characteristics of videos from each. Conference presentation included showing of a sampling of video segments from these various sources. 1 fig.

Novel Therapeutic Strategy for the Prevention of Bone Fractures

DTIC Science & Technology

2013-06-01

AGA GAG GGA GAT GCT CAG TGT TGG M32599 18S AGT GCG GGT CAT AAG CTT GC GGG CCT CAC TAA AC CAT CCA V00851 β-actin GTT TGA GAC CTT CAA CAC CCC GTG ...GCC ATC TCC TGC TCG AAG TC Meredith et al 2011* Mstn ACT GGA CCT CTC GAT AGA ACA CTC ACT TAG TGC TGT GTG TGT GGA GAT NM_010834.2 IGF-1 CAG...ACA GGA GCC CAG GAA AG AAG TGC CGT ATC CCA GAG GA NM_184052 MHC ACA GTC AGA GGT GTG ACTC AGC CG CCG ACT TGC GGA GGA AAG GTG C NM_001099635 Murf1

Novel Therapeutic Strategy for the Prevention of Bone Fractures

DTIC Science & Technology

2014-08-01

GAC CTT CAA CAC CCC GTG GCC ATC TCC TGC TCG AAG TC Meredith et al 2011* Mstn ACT GGA CCT CTC GAT AGA ACA CTC ACT TAG TGC TGT GTG TGT GGA GAT...NM_010834.2 IGF-1 CAG ACA GGA GCC CAG GAA AG AAG TGC CGT ATC CCA GAG GA NM_184052 MHC ACA GTC AGA GGT GTG ACTC AGC CG CCG ACT TGC GGA GGA AAG GTG C...AGC AGA GA TGA GTG CCT GCG GTA CAG AT NM_007553.2 RUNX-2 GGA AAG GCA CTG ACT GAC CTA ACA AAT TCT AAG CTT GGG AGG A NM_009820 Osx ACT ACC CAC CCT TCC

A novel base change leading to Hb Vanderbilt [β89(F5)Ser→Arg, AGT>AGA].

PubMed

Goodyer, Matthew J; Elhassadi, Ezzat I; Percy, Melanie J; McMullin, Mary F

2011-01-01

We describe a high oxygen affinity hemoglobin (Hb) variant (Hb Vanderbilt) as a result of a heterozygous novel base change from T to A at codon 89 (AGT>AGA) leading to an amino acid change from serine to arginine.

Different long-term response to growth hormone therapy in small- versus appropriate-for-gestational-age children with growth hormone deficiency.

PubMed

Meazza, Cristina; Pagani, Sara; Pietra, Benedetta; Tinelli, Carmine; Calcaterra, Valeria; Bozzola, Elena; Bozzola, Mauro

2013-01-01

The role of birth weight on growth hormone (GH) therapy response in GH-deficient (GHD) children has not been fully elucidated. Therefore, we examined the growth of 23 small-for-gestational-age GHD children (SGA-GHD, 11 females and 12 males), 26 appropriate-for-gestational-age GHD children (AGA-GHD, 11 females and 15 males) during the first 5 years of GH therapy and that of 22 non-GH-treated SGA children (12 females and 10 males). We collected height and height velocity measurements yearly. In AGA-GHD children, height was always greater than in the SGA groups and significantly increased from the fourth year of treatment. Height velocity was higher (SGA-GHD: 1.72 ± 0.30 standard deviation score, SDS, AGA-GHD: 2.67 ± 0.21 SDS; p = 0.039) in AGA-GHD children during the first year of treatment. The AGA-GHD group showed the highest percentage (52.4%) of subjects surpassing mid-parental height and the greatest height gain after 5 years of follow-up. Our results show that birth size is an important factor affecting the response to GH therapy in GHD children during the first 5 years of treatment. The paediatric endocrinologist should be aware of this factor when planning the management of GHD children born SGA. Copyright © 2013 S. Karger AG, Basel.

Learning Intelligent Genetic Algorithms Using Japanese Nonograms

ERIC Educational Resources Information Center

Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

2012-01-01

An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

PubMed

Yang, Shengxiang

2008-01-01

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

Boiler-turbine control system design using a genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.; Lee, K.Y.

1995-12-01

This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.

Method for hyperspectral imagery exploitation and pixel spectral unmixing

NASA Technical Reports Server (NTRS)

Lin, Ching-Fang (Inventor)

2003-01-01

An efficiently hybrid approach to exploit hyperspectral imagery and unmix spectral pixels. This hybrid approach uses a genetic algorithm to solve the abundance vector for the first pixel of a hyperspectral image cube. This abundance vector is used as initial state in a robust filter to derive the abundance estimate for the next pixel. By using Kalman filter, the abundance estimate for a pixel can be obtained in one iteration procedure which is much fast than genetic algorithm. The output of the robust filter is fed to genetic algorithm again to derive accurate abundance estimate for the current pixel. The using of robust filter solution as starting point of the genetic algorithm speeds up the evolution of the genetic algorithm. After obtaining the accurate abundance estimate, the procedure goes to next pixel, and uses the output of genetic algorithm as the previous state estimate to derive abundance estimate for this pixel using robust filter. And again use the genetic algorithm to derive accurate abundance estimate efficiently based on the robust filter solution. This iteration continues until pixels in a hyperspectral image cube end.

History, genetics, and strategies for cancer prevention in Lynch syndrome.

PubMed

Kastrinos, Fay; Stoffel, Elena M

2014-05-01

Colorectal cancer (CRC) is the most common gastrointestinal malignancy and the third cause of cancer death in men and women in the United States. The majority of CRC cases diagnosed annually are due to sporadic events, but up to 6% are attributed to known monogenic disorders that confer a markedly increased risk for the development of CRC and multiple extracolonic malignancies. Lynch syndrome is the most common inherited CRC syndrome and is associated with mutations in DNA mismatch repair genes, mainly MLH1 and MSH2 but also MSH6, PMS2, and EPCAM. Although the risk of CRC and endometrial cancer may approach near 75% and 50%, respectively, in gene mutation carriers, the identification of these individuals and at-risk family members through predictive genetic testing provides opportunities for cancer prevention including specialized cancer screening, intensified surveillance, and/or prophylactic surgeries. This article will provide a review of the major advances in risk assessment, molecular genetics, DNA mutational analyses, and cancer prevention and management made since Lynch syndrome was first described 100 years ago. Copyright © 2014 AGA Institute. Published by Elsevier Inc. All rights reserved.

Genetics-based control of a mimo boiler-turbine plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

Improved classification accuracy by feature extraction using genetic algorithms

NASA Astrophysics Data System (ADS)

Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.

2003-05-01

A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

Study of parameter identification using hybrid neural-genetic algorithm in electro-hydraulic servo system

NASA Astrophysics Data System (ADS)

Moon, Byung-Young

2005-12-01

The hybrid neural-genetic multi-model parameter estimation algorithm was demonstrated. This method can be applied to structured system identification of electro-hydraulic servo system. This algorithms consist of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. To evaluate the proposed method, electro-hydraulic servo system was designed and manufactured. The experiment was carried out to figure out the hybrid neural-genetic multi-model parameter estimation algorithm. As a result, the dynamic characteristics were obtained such as the parameters(mass, damping coefficient, bulk modulus, spring coefficient), which minimize total square error. The result of this study can be applied to hydraulic systems in industrial fields.

Comparison of genetic algorithm methods for fuel management optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1995-12-31

The CIGARO system was developed for genetic algorithm fuel management optimization. Tests are performed to find the best fuel location swap mutation operator probability and to compare genetic algorithm to a truly random search method. Tests showed the fuel swap probability should be between 0% and 10%, and a 50% definitely hampered the optimization. The genetic algorithm performed significantly better than the random search method, which did not even satisfy the peak normalized power constraint.

Training product unit neural networks with genetic algorithms

NASA Technical Reports Server (NTRS)

Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

1991-01-01

The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

New Results in Astrodynamics Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Coverstone-Carroll, V.; Hartmann, J. W.; Williams, S. N.; Mason, W. J.

1998-01-01

Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of genetic algorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto genetic algorithm to the optimization of low-thrust interplanetary spacecraft missions.

Changes in Amino Acid Profile in Roots of Glyphosate Resistant and Susceptible Soybean (Glycine max) Induced by Foliar Glyphosate Application.

PubMed

Moldes, Carlos Alberto; Cantarelli, Miguel Angel; Camiña, José Manuel; Tsai, Siu Mui; Azevedo, Ricardo Antunes

2017-10-11

Amino acid profiles are useful to analyze the responses to glyphosate in susceptible and resistant soybean lines. Comparisons of profiles for 10 amino acids (Asp, Asn, Glu, Gln, Ser, His, Gly, Thr, Tyr, Leu) by HPLC in soybean roots were performed in two near isogenic pairs (four varieties). Foliar application of glyphosate was made to soybean plants after 5 weeks of seeding. Roots of four varieties were collected at 0 and 72 h after glyphosate application (AGA) for amino acid analysis by HPLC. Univariate analysis showed a significant increase of several amino acids in susceptible as well as resistant soybean lines; however, amino acids from the major pathways of carbon (C) and nitrogen (N) metabolism, such as Asp, Asn, Glu and Gln, and Ser, increased significantly in susceptible varieties at 72 h AGA. Multivariate analysis using principal component analysis (2D PCA and 3D PCA) allowed different groups to be identified and discriminated based on the soybean genetic origin, showing the amino acid responses on susceptible and resistant varieties. Based on the results, it is possible to infer that the increase of Asn, Asp, Glu, Gln, and Ser in susceptible varieties would be related to the deregulation of C and N metabolism, as well as changes in the growth mechanisms regulated by Ser.

The solution structure of omega-Aga-IVB, a P-type calcium channel antagonist from venom of the funnel web spider, Agelenopsis aperta.

PubMed

Reily, M D; Thanabal, V; Adams, M E

1995-02-01

The 48 amino acid peptides omega-Aga-IVA and omega-Aga-IVB are the first agents known to specifically block P-type calcium channels in mammalian brain, thus complementing the existing suite of pharmacological tools used for characterizing calcium channels. These peptides provide a new set of probes for studies aimed at elucidating the structural basis underlying the subtype specificity of calcium channel antagonists. We used 288 NMR-derived constraints in a protocol combining distance geometry and molecular dynamics employing the program DGII, followed by energy minimization with Discover to derive the three-dimensional structure of omega-Aga-IVB. The toxin consists of a well-defined core region, comprising seven solvent-shielded residues and a well-defined triple-stranded beta-sheet. Four loop regions have average backbone rms deviations between 0.38 and 1.31 A, two of which are well-defined type-II beta-turns. Other structural features include disordered C- and N-termini and several conserved basic amino acids that are clustered on one face of the molecule. The reported structure suggests a possible surface for interaction with the channel. This surface contains amino acids that are identical to those of another known P-type calcium channel antagonist, omega-Aga-IVA, and is rich in basic residues that may have a role in binding to the anionic sites in the extracellular regions of the calcium channel.

Coordination Chemistry of Diiodine and Implications for the Oxidation Capacity of the Synergistic Ag(+) /X2 (X=Cl, Br, I) System.

PubMed

Malinowski, Przemysław J; Himmel, Daniel; Krossing, Ingo

2016-08-01

The synergistic Ag(+) /X2 system (X=Cl, Br, I) is a very strong, but ill-defined oxidant-more powerful than X2 or Ag(+) alone. Intermediates for its action may include [Agm (X2 )n ](m+) complexes. Here, we report on an unexpectedly variable coordination chemistry of diiodine towards this direction: (A)Ag-I2 -Ag(A), [Ag2 (I2 )4 ](2+) (A(-) )2 and [Ag2 (I2 )6 ](2+) (A(-) )2 ⋅(I2 )x≈0.65 form by reaction of Ag(A) (A=Al(OR(F) )4 ; R(F) =C(CF3 )3 ) with diiodine (single crystal/powder XRD, Raman spectra and quantum-mechanical calculations). The molecular (A)Ag-I2 -Ag(A) is ideally set up to act as a 2 e(-) oxidant with stoichiometric formation of 2 AgI and 2 A(-) . Preliminary reactivity tests proved this (A)Ag-I2 -Ag(A) starting material to oxidize n-C5 H12 , C3 H8 , CH2 Cl2 , P4 or S8 at room temperature. A rough estimate of its electron affinity places it amongst very strong oxidizers like MF6 (M=4d metals). This suggests that (A)Ag-I2 -Ag(A) will serve as an easily in bulk accessible, well-defined, and very potent oxidant with multiple applications. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

USGS Publications Warehouse

Chen, C.; Xia, J.; Liu, J.; Feng, G.

2006-01-01

Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. ?? 2005 Elsevier Ltd. All rights reserved.

Aerodynamic Optimization of a Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm

DTIC Science & Technology

2016-12-01

Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street Concord, NH 03301 under contract W911SR...Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street... Genetic Algorithm 5a. CONTRACT NUMBER W199SR-15-2-001 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Justin L Paul 5d. PROJECT

One size fits one: pharmacogenetics in gastroenterology.

PubMed

Porayette, Prashanth; Flockhart, David; Gupta, Sandeep K

2014-04-01

Individual variability in response and development of adverse effects to drugs is a major challenge in clinical practice. Pharmacogenomics refers to the aspect of personalized medicine where the patient's genetic information instructs the selection and dosage of therapy while also predicting its adverse effects profile. Sequencing of the entire human genome has given us the opportunity to study commonly used drugs as well as newer therapeutic agents in a new light, opening up opportunities for better drug efficacy and decreased adverse effects. This article highlights developments in pharmacogenomics, relates these to practice of gastroenterology, and outlines roadblocks in translation of this knowledge into clinical practice. Copyright © 2014 AGA Institute. Published by Elsevier Inc. All rights reserved.

A Comparison of the Hormonal Profile of Early Androgenetic Alopecia in Men With the Phenotypic Equivalent of Polycystic Ovarian Syndrome in Women.

PubMed

Sanke, Sarita; Chander, Ram; Jain, Anju; Garg, Taru; Yadav, Pravesh

2016-09-01

Early androgenetic alopecia (AGA) is patterned hair loss occurring before age 30 years. Early AGA in men is frequently reported as the phenotypic equivalent of polycystic ovarian syndrome (PCOS) in women, which carries the risk of developing obesity, metabolic syndrome, and cardiovascular diseases. Very few studies have been conducted to evaluate this. To study the hormonal profile of men with early AGA and to evaluate if early AGA in men can be considered as the phenotypic equivalent of PCOS, the associated risks of which are well known. This case-control study was conducted from January 1, 2014, to March 31, 2015, in a tertiary care government hospital. Fifty-seven men aged 19 to 30 years presenting with patterned hair loss were recruited as study participants. Thirty-two age-matched men with no evidence of hair loss were recruited as controls. Men who had any established endocrine disorder, diabetes mellitus, or cardiovascular disease and those who took any oral medication or hormonal treatment for hair loss were excluded from the study. The serum concentrations of total testosterone, sex hormone-binding globulin (SHBG), dehydroepiandrosterone sulfate (DHEAS), luteinizing hormone (LH), follicle-stimulating hormone (FSH), prolactin, fasting plasma glucose, and insulin levels were measured. Insulin resistance (IR) and free androgen index (FAI) were calculated and compared with age- and sex-matched controls. The primary outcome was to measure the clinico-endocrinological profiles (LH, FSH, SHBG, DHEAS, and testosterone levels) of men with early AGA and to compare it with the PCOS profile; the secondary outcome was to establish a relationship between this endocrinological profile and IR. Compared with the 32 controls, the 57 participants with AGA showed significantly increased mean (SD) levels of testosterone (24.61 [7.97] vs 20.57 [4.9] nmol/L; P = .04), DHEAS (3.63 [2.19] vs 2.64 [1.49] µg/mL; P = .02), LH (7.78 [3.19] vs 4.56 [2.01] mIU/mL; P < .001), and prolactin (14.14 [9.48] vs 9.97 [3.12] ng/mL; P = .01) and decreased mean levels of FSH (4.02 [2.69] vs 5.66 [1.93] mIU/mL; P < .001) and SHBG (35.07 [11.11] vs 46.41 [14.03] nmol/L; P < .001). The mean FAI and LH/FSH ratio were was also increased in the AGA group. These hormonal parameters resemble the well-known profile of women with PCOS. The mean (SD) insulin levels did not show any significant difference between the cases and controls (6.34 [3.92] vs 5.09 [3.38] μIU/mL; P = .07). There was no statistically significant association between hormone levels and AGA or IR grade severity. Men with early AGA could be considered as male phenotypic equivalents of women with PCOS. They can be at risk of developing the same complications associated with PCOS, including obesity, metabolic syndrome, IR, cardiovascular diseases, and infertility.

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Xiaobiao; Safranek, James

2014-09-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)

NASA Astrophysics Data System (ADS)

Li, X. R.; Wang, X.

2016-03-01

When using the genetic algorithm to solve the problem of too-short-arc (TSA) determination, due to the difference of computing processes between the genetic algorithm and classical method, the methods for outliers editing are no longer applicable. In the genetic algorithm, the robust estimation is acquired by means of using different loss functions in the fitness function, then the outlier problem of TSAs is solved. Compared with the classical method, the application of loss functions in the genetic algorithm is greatly simplified. Through the comparison of results of different loss functions, it is clear that the methods of least median square and least trimmed square can greatly improve the robustness of TSAs, and have a high breakdown point.

Bio-Inspired Genetic Algorithms with Formalized Crossover Operators for Robotic Applications.

PubMed

Zhang, Jie; Kang, Man; Li, Xiaojuan; Liu, Geng-Yang

2017-01-01

Genetic algorithms are widely adopted to solve optimization problems in robotic applications. In such safety-critical systems, it is vitally important to formally prove the correctness when genetic algorithms are applied. This paper focuses on formal modeling of crossover operations that are one of most important operations in genetic algorithms. Specially, we for the first time formalize crossover operations with higher-order logic based on HOL4 that is easy to be deployed with its user-friendly programing environment. With correctness-guaranteed formalized crossover operations, we can safely apply them in robotic applications. We implement our technique to solve a path planning problem using a genetic algorithm with our formalized crossover operations, and the results show the effectiveness of our technique.

Life in the Cosmic Context. An Astrobiology Course as an Experiment in Transdisciplinarity

NASA Astrophysics Data System (ADS)

Friaça, A. C. S.; Janot Pacheco, E.

2014-10-01

``Life in the Cosmic Context" (AGA0316) is the astrobiology course offered by University of São Paulo to undergraduate students of science and humanities majors. The variety of background of the population attending AGA0316 and the broad scope of the addresssed issues makes this course a laboratory of transdisciplinarity.

Transmitter release and presynaptic Ca2+ currents blocked by the spider toxin omega-Aga-IVA.

PubMed

Protti, D A; Uchitel, O D

1993-12-13

Mammalian neuromuscular transmission is resistant to L and N type calcium channel blockers but very sensitive to a low molecular weight funnel web spider venom toxin, FTX, which selectively blocks P type calcium channels. To further characterize the calcium channels involved in neuromuscular transmission we studied the effect of omega Agatoxin (omega-Aga-IVA) a polypeptide P type channel blocker from the same spider venom. We show that omega-Aga-IVA is a potent and irreversible inhibitor of the presynaptic Ca2+ currents and of acetylcholine release induced by electrical stimulation or by K+ depolarization. This provides further evidences that transmitter release at the mammalian neuromuscular junction is mediated by P type Ca2+ channels.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm

NASA Astrophysics Data System (ADS)

Mehdinejadiani, Behrouz

2017-08-01

This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm.

PubMed

Mehdinejadiani, Behrouz

2017-08-01

This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation. Copyright © 2017 Elsevier B.V. All rights reserved.

A Test of Genetic Algorithms in Relevance Feedback.

ERIC Educational Resources Information Center

Lopez-Pujalte, Cristina; Guerrero Bote, Vicente P.; Moya Anegon, Felix de

2002-01-01

Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on genetic algorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different genetic algorithms using a residual collection method and compares results with the Ide dec-hi method (Salton and Buckley, 1990…

Transonic Wing Shape Optimization Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)

2002-01-01

A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.

Portfolio optimization by using linear programing models based on genetic algorithm

NASA Astrophysics Data System (ADS)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

An improved genetic algorithm and its application in the TSP problem

NASA Astrophysics Data System (ADS)

Li, Zheng; Qin, Jinlei

2011-12-01

Concept and research actuality of genetic algorithm are introduced in detail in the paper. Under this condition, the simple genetic algorithm and an improved algorithm are described and applied in an example of TSP problem, where the advantage of genetic algorithm is adequately shown in solving the NP-hard problem. In addition, based on partial matching crossover operator, the crossover operator method is improved into extended crossover operator in order to advance the efficiency when solving the TSP. In the extended crossover method, crossover operator can be performed between random positions of two random individuals, which will not be restricted by the position of chromosome. Finally, the nine-city TSP is solved using the improved genetic algorithm with extended crossover method, the efficiency of whose solution process is much higher, besides, the solving speed of the optimal solution is much faster.

Solving TSP problem with improved genetic algorithm

NASA Astrophysics Data System (ADS)

Fu, Chunhua; Zhang, Lijun; Wang, Xiaojing; Qiao, Liying

2018-05-01

The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

NASA Technical Reports Server (NTRS)

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

A "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates

ERIC Educational Resources Information Center

Venables, Anne; Tan, Grace

2007-01-01

Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…

The potential of genetic algorithms for conceptual design of rotor systems

NASA Technical Reports Server (NTRS)

Crossley, William A.; Wells, Valana L.; Laananen, David H.

1993-01-01

The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.

Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)

NASA Astrophysics Data System (ADS)

Li, Xin-ran; Wang, Xin

2017-04-01

When the genetic algorithm is used to solve the problem of too short-arc (TSA) orbit determination, due to the difference of computing process between the genetic algorithm and the classical method, the original method for outlier deletion is no longer applicable. In the genetic algorithm, the robust estimation is realized by introducing different loss functions for the fitness function, then the outlier problem of the TSA orbit determination is solved. Compared with the classical method, the genetic algorithm is greatly simplified by introducing in different loss functions. Through the comparison on the calculations of multiple loss functions, it is found that the least median square (LMS) estimation and least trimmed square (LTS) estimation can greatly improve the robustness of the TSA orbit determination, and have a high breakdown point.

A Genetic Algorithm Tool (splicer) for Complex Scheduling Problems and the Space Station Freedom Resupply Problem

NASA Technical Reports Server (NTRS)

Wang, Lui; Valenzuela-Rendon, Manuel

1993-01-01

The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.

Topical application of the Wnt/β-catenin activator methyl vanillate increases hair count and hair mass index in women with androgenetic alopecia.

PubMed

Tosti, Antonella; Zaiac, Martin N; Canazza, Agnese; Sanchis-Gomar, Fabian; Pareja-Galeano, Helios; Alis, Rafael; Lucia, Alejandro; Emanuele, Enzo

2016-12-01

Activation of the WNT/β-catenin pathway has emerged as a potential therapeutic target in androgenetic alopecia (AGA). Methyl vanillate (MV) - a safe plant-derived ingredient - has been recently shown to activate the WNT/β-catenin signaling. Objectives Two distinct substudies were conducted. First, we designed a 6-month, uncontrolled, open-label clinical study to investigate whether topically applied MV may increase hair count and hair mass index (HMI) in female AGA. Second, we conducted a molecular study on the effect of MV on WNT10B mRNA expression in scalp biopsies of women with AGA. A total of 20 Caucasian women (age range: 25-57 years) with AGA (Sinclair grade 1-2) were included. The research product was an alcohol-free formulation supplied in the form of a spray containing 0.2% MV as the active ingredient. In the clinical study, hair count and HMI were found to increase at 6 months by 6% (P < 0.01) and 12% (P < 0.001), respectively, compared with baseline. No participant discontinued treatment due to adverse effects, and the overall patient satisfaction was good. At the molecular level, the topical application of the research product resulted in a 32% increase in WNT10B mRNA expression levels in the temporal scalp area (P < 0.001). Our pilot data suggest that topical MV can increase hair count and HMI by inducing WNT10B expression in the scalp, potentially serving as a novel treatment strategy for female AGA. © 2016 Wiley Periodicals, Inc.

Identification of a new plant extract for androgenic alopecia treatment using a non-radioactive human hair dermal papilla cell-based assay.

PubMed

Jain, Ruchy; Monthakantirat, Orawan; Tengamnuay, Parkpoom; De-Eknamkul, Wanchai

2016-01-21

Androgenic alopecia (AGA) is a major type of human scalp hair loss, which is caused by two androgens: testosterone (T) and 5α-dihydrotestosterone (5α-DHT). Both androgens bind to the androgen receptor (AR) and induce androgen-sensitive genes within the human hair dermal papilla cells (HHDPCs), but 5α-DHT exhibits much higher binding affinity and potency than T does in inducing the involved androgen-sensitive genes. Changes in the induction of androgen-sensitive genes during AGA are caused by the over-production of 5α-DHT by the 5α-reductase (5α-R) enzyme; therefore, one possible method to treat AGA is to inhibit this enzymatic reaction. RT-PCR was used to identify the presence of the 5α-R and AR within HHDPCs. A newly developed AGA-relevant HHDPC-based assay combined with non-radioactive thin layer chromatography (TLC) detection was used for screening crude plant extracts for the identification of new 5α-R inhibitors. HHDPCs expressed both 5α-R type 1 isoform of the enzyme (5α-R1) and AR in all of the passages used in this study. Among the thirty tested extracts, Avicennia marina (AM) displayed the highest inhibitory activity at the final concentration of 10 μg/ml, as the production of 5α-DHT decreased by 52% (IC50 = 9.21 ± 0.38 μg/ml). Avicennia marina (AM) was identified as a potential candidate for the treatment of AGA based on its 5α-R1-inhibitory activity.

[Obstetric prognostic factors of newborn infants with very low birth weight (< or = 1,500 gram) with reference to survival rate and early childhood development].

PubMed

Hamm, W; Göhring, U J; Günther, M; Kribs, A; Neuhaus, W; Roth, B; Bolte, A

1995-03-01

Prognostic factors influencing survival in 235 very low birthweight prematures (< or = 1500 g) born between 1986 and 15.11. 1993 at the Department of Obstetrics and Gynaecology, University Hospital of Cologne, were retrospectively evaluated. Chromosomal anomalies and severe congenital malformations were excluded. Of 180 singletons 84 were classified as appropriate-for-gestational-age (AGA) and 96 as small-for-gestational-age (SGA). By interrogating the attending paediatricians data regarding the early development of 62/65 surviving singletons born between 1986 to 1990 were recorded (follow-up rate 95%). Survival was significantly correlated to singleton pregnancy (p < 0.05), female sex (p = 0.001) and in the AGA-prematures to prenatal corticoid prophylaxis. With similar mean birthweight SGA-singletons showed a three weeks higher mean gestational age; the mortality showed an inverse correlation to birthweight and gestational age being 11% higher in the AGA-group compared with the SGA-group (32% versus 21%). At the age of between 11 months and 6 years severe handicaps and developmental retardations were found more often in previous AGA-prematures (6/26) than in previous SGA-prematures (4/36); type and degree of later handicap were not correlated to birthweight. According to our results survival rates of very low birthweight prematures are strongly influenced by singleton pregnancy, by fetal sex, by gestational age and in the AGA-group by prenatal corticoid prophylaxis; mortality shows an inverted correlation to birthweight and gestational age, whereas the later prognosis of survivors does not seem to be influenced by birthweight or gestational age.

Efficacy of a cosmetic phyto-caffeine shampoo in female androgenetic alopecia.

PubMed

Bussoletti, Carolina; Tolaini, Maria V; Celleno, Leonardo

2018-03-06

Androgenetic alopecia (AGA) is the most common type of hair loss in both males as well as females, occurring in up to 57% of women by the age of 80 years. Androgenetic alopecia is associated with a high psychological burden and often results in substantially reduced quality of life, poor body image and low self-esteem, particularly in women. Caffeine-based products have shown promise, both in vitro and in vivo, as potential treatments for AGA. This study was performed to determine the efficacy of a phyto-caffeine- containing shampoo used over a 6-month period in female subjects with AGA. This was a single-centre, double-blind parallel trial in which female subjects with AGA were randomized to either a phyto-caffeine-containing shampoo or a control shampoo. The primary endpoint was the change from baseline in the number of hairs pulled in a hair pull test at 6 months. Hair loss intensity, hair strength, subject satisfaction and tolerability were also assessed. Subjects using the phyto-caffeine-containing shampoo had significantly fewer hairs pulled in a hair pull test at 6 months, compared with subjects using the control shampoo (-3.1 vs -0.5 hairs; p<0.001). The majority of pre-specified secondary endpoints were also significantly improved for subjects using the phyto-caffeine- containing shampoo, compared with controls. Both products were very well tolerated. Compared with a control shampoo, a phyto-caffeine-containing shampoo was more efficacious, with respect to the number of hairs being pulled out at 6 months, hair loss intensity and hair strength in subjects with AGA.

Use of an alpha-galactosidase gene as a food-grade selection marker for Streptococcus thermophilus.

PubMed

Labrie, S; Bart, C; Vadeboncoeur, C; Moineau, S

2005-07-01

The alpha-galactosidase gene (aga) of Lactococcus raffinolactis ATCC 43920 was previously shown to be an efficient food-grade selection marker in Lactococcus lactis and Pediococcus acidilactici but not in Streptococcus thermophilus. In this study, we demonstrated that the alpha-galactosidase of L. raffinolactis is thermolabile and inoperative at 42 degrees C, the optimal growth temperature of S. thermophilus. An in vitro assay indicated that the activity of this alpha-galactosidase at 42 degrees C was only 3% of that at 30 degrees C, whereas the enzyme retained 23% of its activity at 37 degrees C. Transformation of Strep. thermophilus RD733 with the shuttle-vector pNZ123 bearing the aga gene of L. raffinolactis (pRAF301) generated transformants that were stable and able to grow on melibiose and raffinose at 37 degrees C or below. The transformed cells possessed 6-fold more alpha-galactosidase activity after growth on melibiose than cells grown on lactose. Slot-blot analyses of aga mRNA indicated that repression by lactose occurred at the transcriptional level. The presence of pRAF301 did not interfere with the lactic acid production when the transformed cells of Strep. thermophilus were grown at the optimal temperature in milk. Using the recombinant plasmid pRAF301, which carries a chloramphenicol resistance gene in addition to aga, we showed that both markers were equally efficient at differentiating transformed from nontransformed cells. The aga gene of L. raffinolactis can be used as a highly efficient selection marker in Strep. thermophilus.

Evaluation of Commercially Available Open Circuit Scuba Regulators

DTIC Science & Technology

1987-08-01

ANNEX B LIST OF MANUFACTURERS 1. AGA/IISIERSPIRO U.S. Distributor Intersiro AB AGA/INTERSPIRO S-181 81 Lidingo Sweden Pistol Shop Road Rockfall ...RWV--*-- 40.0 RWV DACOR PACER XLE360 --G- 2.5 OW 600 80 1000 psig Supply Pressure -=70 -- 500 6050 7040 . GI "C - 300 , 𔄃° 4O- 30 200 0100 1030 0 0 0

An Improved Heuristic Method for Subgraph Isomorphism Problem

NASA Astrophysics Data System (ADS)

Xiang, Yingzhuo; Han, Jiesi; Xu, Haijiang; Guo, Xin

2017-09-01

This paper focus on the subgraph isomorphism (SI) problem. We present an improved genetic algorithm, a heuristic method to search the optimal solution. The contribution of this paper is that we design a dedicated crossover algorithm and a new fitness function to measure the evolution process. Experiments show our improved genetic algorithm performs better than other heuristic methods. For a large graph, such as a subgraph of 40 nodes, our algorithm outperforms the traditional tree search algorithms. We find that the performance of our improved genetic algorithm does not decrease as the number of nodes in prototype graphs.

Use of low-level laser therapy as monotherapy or concomitant therapy for male and female androgenetic alopecia.

PubMed

Munck, Andréia; Gavazzoni, Maria Fernanda; Trüeb, Ralph M

2014-04-01

Androgenetic alopecia (AGA) is the most common form of hair loss in men and in women. Currently, minoxidil and finasteride are the treatments with the highest levels of medical evidence, but patients who exhibit intolerance or poor response to these treatments are in need of additional treatment modalities. The aim was to evaluate the efficacy and safety of low-level laser therapy (LLLT) for AGA, either as monotherapy or as concomitant therapy with minoxidil or finasteride, in an office-based setting. Retrospective observational study of male and female patients with AGA, treated with the 655 nm-HairMax Laser Comb(®), in an office-based setting. Efficacy was assessed with global photographic imaging. Of 32 patients (21 female, 11 male), 8 showed significant, 20 moderate, and 4 no improvement. Improvement was seen both with monotherapy and with concomitant therapy. Improvement was observed as early as 3 months and was sustained up to a maximum observation time of 24 months. No adverse reactions were reported. LLLT represents a potentially effective treatment for both male and female AGA, either as monotherapy or concomitant therapy. Combination treatments with minoxidil, finasteride, and LLLT may act synergistic to enhance hair growth.

Characterization of a Glycoside Hydrolase Family 27 α-Galactosidase from Pontibacter Reveals Its Novel Salt-Protease Tolerance and Transglycosylation Activity.

PubMed

Zhou, Junpei; Liu, Yu; Lu, Qian; Zhang, Rui; Wu, Qian; Li, Chunyan; Li, Junjun; Tang, Xianghua; Xu, Bo; Ding, Junmei; Han, Nanyu; Huang, Zunxi

2016-03-23

α-Galactosidases are of great interest in various applications. A glycoside hydrolase family 27 α-galactosidase was cloned from Pontibacter sp. harbored in a saline soil and expressed in Escherichia coli. The purified recombinant enzyme (rAgaAHJ8) was little or not affected by 3.5-30.0% (w/v) NaCl, 10.0-100.0 mM Pb(CH3COO)2, 10.0-60.0 mM ZnSO4, or 8.3-100.0 mg mL(-1) trypsin and by most metal ions and chemical reagents at 1.0 and 10.0 mM concentrations. The degree of synergy on enzymatic degradation of locust bean gum and guar gum by an endomannanase and rAgaAHJ8 was 1.22-1.54. In the presence of trypsin, the amount of reducing sugars released from soybean milk treated by rAgaAHJ8 was approximately 3.8-fold compared with that treated by a commercial α-galactosidase. rAgaAHJ8 showed transglycosylation activity when using sucrose, raffinose, and 3-methyl-1-butanol as the acceptors. Furthermore, potential factors for salt adaptation of the enzyme were presumed.

Genetics and Molecular Pathogenesis of Gastric Adenocarcinoma.

PubMed

Tan, Patrick; Yeoh, Khay-Guan

2015-10-01

Gastric cancer (GC) is globally the fifth most common cancer and third leading cause of cancer death. A complex disease arising from the interaction of environmental and host-associated factors, key contributors to GC's high mortality include its silent nature, late clinical presentation, and underlying biological and genetic heterogeneity. Achieving a detailed molecular understanding of the various genomic aberrations associated with GC will be critical to improving patient outcomes. The recent years has seen considerable progress in deciphering the genomic landscape of GC, identifying new molecular components such as ARID1A and RHOA, cellular pathways, and tissue populations associated with gastric malignancy and progression. The Cancer Genome Atlas (TCGA) project is a landmark in the molecular characterization of GC. Key challenges for the future will involve the translation of these molecular findings to clinical utility, by enabling novel strategies for early GC detection, and precision therapies for individual GC patients. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Genetic algorithms for adaptive real-time control in space systems

NASA Technical Reports Server (NTRS)

Vanderzijp, J.; Choudry, A.

1988-01-01

Genetic Algorithms that are used for learning as one way to control the combinational explosion associated with the generation of new rules are discussed. The Genetic Algorithm approach tends to work best when it can be applied to a domain independent knowledge representation. Applications to real time control in space systems are discussed.

Cognitive Nonlinear Radar

DTIC Science & Technology

2013-01-01

intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.

PubMed

Hajri, S; Liouane, N; Hammadi, S; Borne, P

2000-01-01

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria

2009-01-01

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria

2009-01-01

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

PubMed Central

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior. PMID:26000011

Scalability problems of simple genetic algorithms.

PubMed

Thierens, D

1999-01-01

Scalable evolutionary computation has become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood. Here we present some of the work that has aided in getting a clear insight in the scalability problems of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing. We show how the need for mixing places a boundary in the GA parameter space that, together with the boundary from the schema theorem, delimits the region where the GA converges reliably to the optimum in problems of bounded difficulty. This region shrinks rapidly with increasing problem size unless the building blocks are tightly linked in the problem coding structure. In addition, we look at how straightforward extensions of the simple genetic algorithm-namely elitism, niching, and restricted mating are not significantly improving the scalability problems.

Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

PubMed

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

The Perceptions of the Preparedness of Medical Graduates to Take on Internship Responsibilities in Low Resource Hospitals in Kenya

ERIC Educational Resources Information Center

Muthaura, Patricia N.; Khamis, Tashmin K.

2013-01-01

The Aga Khan University is developing an Undergraduate Medical Education (UGME) curriculum for implementation in East Africa in 2016, which aims to serve the health needs of the populations there. Pilot focus group discussions of recent interns were conducted at the Aga Khan University Hospital, Nairobi to find out: (1) If Kenyan medical students…

[Growth patterns of appropriate for gestational age infants of gestational diabetic mothers during the first year].

PubMed

Zhao, Y L; Ma, R M; Zhang, Y; Mo, Y X; Chen, Z; Sun, Y H; Ding, Z B

2016-08-02

To explore the growth pattern of appropriate for gestational age (AGA) infants of mother with gestational diabetes mellitus (GDM). The objects of this study were offspring of women who delivered in our hospital from January to December 2011. The GDM group included 70 AGA infants (36 male cases and 34 female cases) of mother with GDM. The control group included 154 AGA infants (66 male cases and 88 female cases) of women with normal glucose tolerance. The data of demographic characteristics of mothers of two groups were collected. Body weight and length of infants in two groups were measured at 3, 6 and 12 months age respectively. Body mass index (BMI), weight and height gain during infancy (0-3 months, 3-6 months and 6-12 months) of infants in two groups were also calculated. Body weight, length and BMI of male AGA infants in GDM group were less than that of control group at 3 months and 6 months age, but more than that of control group at 12 months age, however, there were no significant differences between two group(P>0.05). The weight and height gain during infancy (0-3 months, 3-6 months) of male AGA infants in GDM group were lower than that of control group, but the difference was statistically significant only at 3-6 months[(1.1±0.4) vs (1.4±0.4) kg, P=0.040; (4.9±2.3) vs (6.3±1.2) cm, P=0.026]. The weight and height gain during infancy (6-12 months) of male AGA infants of gestational diabetic mothers were higher than that of control group, but the difference was not statistically significant[(2.1±0.5) vs (1.8±0.5) kg, P=0.361; (8.4±1.3) vs (7.8±1.4) cm, P=0.464]. Male infants of gestational diabetic mothers grew slowly during their infancy of 0-6 months, and then their growth became increasingly fast, which suggested that the influence of intrauterine hyperglycemia environment of GDM mothers on fetal growth might continue after birth.

An investigation of messy genetic algorithms

NASA Technical Reports Server (NTRS)

Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley

1990-01-01

Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.

Global Optimization of a Periodic System using a Genetic Algorithm

NASA Astrophysics Data System (ADS)

Stucke, David; Crespi, Vincent

2001-03-01

We use a novel application of a genetic algorithm global optimizatin technique to find the lowest energy structures for periodic systems. We apply this technique to colloidal crystals for several different stoichiometries of binary and trinary colloidal crystals. This application of a genetic algorithm is decribed and results of likely candidate structures are presented.

Research and application of multi-agent genetic algorithm in tower defense game

NASA Astrophysics Data System (ADS)

Jin, Shaohua

2018-04-01

In this paper, a new multi-agent genetic algorithm based on orthogonal experiment is proposed, which is based on multi-agent system, genetic algorithm and orthogonal experimental design. The design of neighborhood competition operator, orthogonal crossover operator, Son and self-learning operator. The new algorithm is applied to mobile tower defense game, according to the characteristics of the game, the establishment of mathematical models, and finally increases the value of the game's monster.

Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2001-01-01

A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Using Genetic Buffering Relationships Identified in Fission Yeast To Elucidate the Molecular Pathology of Tuberous Sclerosis

DTIC Science & Technology

2016-07-01

5’-aat tat ttt ata tgg aat gag caa gta tgt ttt atc ata att gac cag ttc att tca agg acc ttc aaa aat ata cct acg aat tcg agc tcg ttt aaa c-3’), or...oTsc13 (5’-tta aga gtt cag att tgc ttt atg tgg tta ttc tgc tga agg tcc taa ttt att gac gtt gaa aaa taa agg cca cat agc gga tcc ccg ggt taa tta a-3...and oTsc14 (5’-ata aaa aaa att aat taa tga tgg caa ggc aca atc gta atc aat ctt tta att tag gac ttt tta tat gcc ctt atg gcg aat tcg agc tcg ttt aaa c

Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

USGS Publications Warehouse

Louis, S.J.; Raines, G.L.

2003-01-01

We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

NASA Astrophysics Data System (ADS)

Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

2017-08-01

Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

PubMed Central

Mohamd Shoukry, Alaa; Gani, Showkat

2017-01-01

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements. PMID:29209364

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator.

PubMed

Hussain, Abid; Muhammad, Yousaf Shad; Nauman Sajid, M; Hussain, Ijaz; Mohamd Shoukry, Alaa; Gani, Showkat

2017-01-01

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

Department of National Defence's use of thermography for facilities maintenance

NASA Astrophysics Data System (ADS)

Kittson, John E.

1990-03-01

Since the late seventies DND through the Director General Works has been actively encouraging the use of thermography as an efficient and effective technique for supporting preventive maintenance quality assurance and energy conservation programs at Canadian Forces Bases (CFBs). This paper will provide an overview of DND''s experiences in the utilization of thermography for facilities maintenance applications. 1. HISTORICAL MILESTONES The following are milestones of DND''s use of thermography: a. Purchase of Infrared Equipment In 1976/77 DND purchased five AGA 750 Infrared Thermovision Systems which were distributed to commands. In 1980/81/82 six AGA liOs five AGA TPT8Os two AGA 782s and one AGA 720 were acquired. Finally DND also purchased seven AGEMA 870 systems during 1987/88. b. First and Second Interdepartaental Building Thermography Courses In 1978 and 1980 DND hosted two building thermography courses that were conducted by Public Works Canada. c. CE Thermographer Specialist Training Courses DND developed a training standard in 1983 for Construction Engineering (CE) Thermographer qualification which included all CE applications of thermography. The first annual inhouse training course was conducted at CFB Borden Ontario in 1984. These are now being conducted at the CFB Chilliwack Detachment in Vernon British Columbia. 2 . MARKETING FACILITIES MAINTENANCE IR Of paramount importance for successfully developing DND appreciation for thermography was providing familiarization training to CE staff at commands and bases. These threeday presentations emphasized motivational factors conducting thermographic surveys and utilizing infrared data of roofs electrical/mechanical systems heating plants steam distribution and building enclosures. These factors consisted mainly of the following objectives: a. preventive maintenance by locating deficiencies to be repaired b. quality assurance by verification of workmanship materials and design c. energy conservation by locating heat loss areas 2 / SPIE Vol. 1313 Thermosense XII (1990)

Prescribing Habits for Androgenic Alopecia Among Dermatologists in Spain in 2017: A Cross-Sectional Study.

PubMed

Pindado-Ortega, C; Saceda-Corralo, D; Buendía-Castaño, D; Fernández-González, P; Moreno-Arrones, Ó M; Fonda-Pascual, P; Alegre-Sánchez, A; Rodrigues-Barata, A R; Vañó-Galván, S

2018-04-12

Topical minoxidil and oral finasteride are the only drugs approved for the treatment of androgenetic alopecia (AGA) in Spain. However, the management of this condition is highly variable because numerous treatments are used off-label. The main aim of this study was to describe the prescribing habits of dermatologists in Spain for male AGA (MAGA) and female AGA (FAGA). Descriptive cross-sectional study using online questionnaires completed by dermatologists working in Spain. The responses of 241 dermatologists were analyzed. The most common treatments prescribed for MAGA were minoxidil (98%), oral finasteride (96%), nutricosmetics (44%), topical finasteride (37%), oral dutasteride (33%), platelet-rich plasma (14%), and low-level laser therapy (8%). For premenopausal FAGA, the most common treatments were topical minoxidil (98%), oral contraceptives (81%), nutricosmetics (72%), cyproterone acetate (58%), oral finasteride (39%), topical finasteride (39%), spironolactone (27%), platelet-rich plasma (20%), oral dutasteride (20%), oral flutamide (18%), and low-level laser therapy (7%). Finally, for postmenopausal FAGA, the most common treatments prescribed were topical minoxidil (98%), oral finasteride (84%), nutricosmetics (68%), topical finasteride (50%), oral dutasteride (35%), platelet-rich plasma (21%), spironolactone (16%), cyproterone acetate (16%), oral flutamide (9%), and low-level laser therapy (9%). A limitation of our study is that we did not analyze novel AGA treatments such as oral minoxidil and dutasteride mesotherapy. The most common treatments prescribed for AGA by dermatologists in Spain are topical minoxidil, oral finasteride, and nutricosmetics for MAGA and postmenopausal FAGA and topical minoxidil, oral contraceptives, and nutricosmetics for premenopausal FAGA. Copyright © 2018 AEDV. Publicado por Elsevier España, S.L.U. All rights reserved.

Variations in Health Insurance Policies Regarding Biologic Therapy Use in Inflammatory Bowel Disease.

PubMed

Yadav, Abhijeet; Foromera, Joshua; Feuerstein, Ilana; Falchuk, Kenneth R; Feuerstein, Joseph D

2017-06-01

The American Gastroenterological Association (AGA) has developed guidelines for the management of ulcerative colitis and Crohn's disease (CD) recommending anti-TNF therapy in moderate-severe disease. However, which drug is used is often dictated by insurance company policies. We sought to determine the insurance policy requirements prior to approval of biologic therapies. Using the National Association of Insurance Commissioners report of the top 125 insurance companies by market share in 2014, we reviewed the first 50 that had online policies regarding anti-TNF and vedolizumab available. Policies were reviewed for criteria needed for approval of anti-TNF or vedolizumab therapy, and for compliance with the current AGA clinical pathway recommendations. Ninety-eight percent of policies are inconsistent with the AGA ulcerative colitis pathway and require step-wise drug failure before approval of an anti-TNF. Only 11% of the policies allowed starting vedolizumab without initial failures of an anti-TNF agent, and 21% required the failure of two or more anti-TNF agents. Ninety percent of the policies are inconsistent with AGA CD pathway and require step-wise drug failure before approval of an anti-TNF. Seventy-four percent allowed for initiating infliximab specifically for fistulizing CD. Twenty-eight percent required failing of at least two or more drugs before starting anti-TNF. Only 8% policies allowed starting vedolizumab without initial failures of an anti-TNF agent, and 28% required the failure of two anti-TNF agents. The majority of the policies reviewed fail to adhere to the current AGA pathway recommendations for ulcerative colitis and CD. Further interventions are needed to better align policies with optimal evidence-based drug therapy.

Association of stress with anxiety and depression during pregnancy.

PubMed

Gul, Fouzia; Sherin, Akhtar; Jabeen, Mussarrat; Khan, Shajaat Ali

2017-12-01

To find out the association of stress with anxiety and depression during pregnancy and to identify common stressors in women. This cross-sectional study was conducted at Divisional Headquarters Teaching Hospital, Kohat, Pakistan, from February 2011 to October 2012, and comprised pregnant women. Convenient sampling technique was used. The participants were administered Urdu-translated version of A-Z perceived stress scale and Aga Khan University anxiety and depression scale. Women with a score of >19 on the Aga Khan University scale were labelled as anxious and depressed. Data was collected on a pre-designed proforma. SPSS 17 was used for data analysis. There were 500 participants with an overall mean age of 28.3±6.3 years. The overall mean stress score on A-Z perceived stress scale was 12.93±5.19 and mean Aga Khan University anxiety and depression scale score was 28.58±13.82. Mean A-Z score was 14.18±4.881 in women with anxiety-depression and 9.75±4.58 in non-depressed women (p<0.001). Mean Aga Khan score in women with >10 stressors was significantly higher (32.18±13.79) compared to women with <10 stressors (19.87±9.30) (p<0.01). A-Z stressors score had significant positive correlation with the Aga Khan scale (p<0.001]. The most common stressors were concern about husband's worries and concern about feeling unwell during pregnancy, present in 433(86.6%) patients each, followed by concern about increase in the prices of everyday goods which was present in 364(72.8%) patients. The magnitude of stress was significantly associated with high anxiety and depression during pregnancy.

[Effects of postnatal growth retardation on early neurodevelopment in premature infants with intrauterine growth retardation].

PubMed

Cai, Yue-Ju; Song, Yan-Yan; Huang, Zhi-Jian; Li, Jian; Qi, Jun-Ye; Xiao, Xu-Wen; Wang, Lan-Xiu

2015-09-01

To study the effects of postnatal growth retardation on early neurodevelopment in premature infants with intrauterine growth retardation (IUGR). A retrospective analysis was performed on the clinical data of 171 premature infants who were born between May 2008 and May 2012 and were followed up until a corrected gestational age of 6 months. These infants were classified into two groups: IUGR group (n=40) and appropriate for gestational age (AGA) group (n=131). The growth retardation rates at the corrected gestational ages of 40 weeks, 3 months, and 6 months, as well as the neurodevelopmental outcome (evaluated by Gesell Developmental Scale) at corrected gestational ages of 3 and 6 months, were compared between the two groups. The growth retardation rate in the IUGR group was significantly higher than in the AGA group at the corrected gestational ages of 40 weeks, 3 months, and 6 months. All five developmental quotients evaluated by Gesell Developmental Scale (gross motor, fine motor, language, adaptability and individuality) in the IUGR group were significantly lower than in the AGA group at the corrected gestational ages of 3 months. At the corrected gestational age of 6 months, the developmental quotients of fine motor and language in the IUGR group were significantly lower than in the AGA group, however, there were no significant differences in the developmental quotients of gross motor, adaptability and individuality between the two groups. All five developmental quotients in IUGR infants with catch-up lag in weight were significantly lower than in IUGR and AGA infants who had caught up well. Growth retardation at early postnatal stages may adversely affect the early neurodevelopment in infants with IUGR.

Substrate Recognition and Hydrolysis by a Family 50 exo-β-Agarase, Aga50D, from the Marine Bacterium Saccharophagus degradans*

PubMed Central

Pluvinage, Benjamin; Hehemann, Jan-Hendrik; Boraston, Alisdair B.

2013-01-01

The bacteria that metabolize agarose use multiple enzymes of complementary specificities to hydrolyze the glycosidic linkages in agarose, a linear polymer comprising the repeating disaccharide subunit of neoagarobiose (3,6-anhydro-l-galactose-α-(1,3)-d-galactose) that are β-(1,4)-linked. Here we present the crystal structure of a glycoside hydrolase family 50 exo-β-agarase, Aga50D, from the marine microbe Saccharophagus degradans. This enzyme catalyzes a critical step in the metabolism of agarose by S. degradans through cleaving agarose oligomers into neoagarobiose products that can be further processed into monomers. The crystal structure of Aga50D to 1.9 Å resolution reveals a (β/α)8-barrel fold that is elaborated with a β-sandwich domain and extensive loops. The structures of catalytically inactivated Aga50D in complex with non-hydrolyzed neoagarotetraose (2.05 Å resolution) and neoagarooctaose (2.30 Å resolution) provide views of Michaelis complexes for a β-agarase. In these structures, the d-galactose residue in the −1 subsite is distorted into a 1S3 skew boat conformation. The relative positioning of the putative catalytic residues are most consistent with a retaining catalytic mechanism. Additionally, the neoagarooctaose complex showed that this extended substrate made substantial interactions with the β-sandwich domain, which resembles a carbohydrate-binding module, thus creating additional plus (+) subsites and funneling the polymeric substrate through the tunnel-shaped active site. A synthesis of these results in combination with an additional neoagarobiose product complex suggests a potential exo-processive mode of action of Aga50D on the agarose double helix. PMID:23921382

Distinguishing pathological from constitutional small for gestational age births in population-based studies.

PubMed

Ananth, Cande V; Vintzileos, Anthony M

2009-10-01

Small for gestational age (SGA) can occur following a pathological process or may represent constitutionally small fetuses. However, distinguishing these processes is often difficult, especially in large studies, where the term SGA is often used as a proxy for restricted fetal growth. Since biologic variation in fetal size is largely a third trimester phenomenon, we hypothesized that the definition of SGA at term may include a sizeable proportion of constitutionally small fetuses. In contrast, since biologic variation in fetal size is not fully expressed in (early) preterm gestations, it is plausible that SGA in early preterm gestations would comprise a large proportion of growth restricted fetuses. We compared mortality and morbidity rates between SGA and appropriate for gestational age (AGA) babies. A population-based study of over 19million non-malformed, singleton births (1995-04) in the United States was performed. Gestational age (24-44weeks) was based on a clinical estimate. SGA and AGA were defined as sex-specific birthweight <10th and 25-74th centiles, respectively, for gestational age. All analyses were adjusted for a variety of confounding factors. Excess mortality risk in SGA and AGA babies. On an additive scale, stillbirth and neonatal mortality rates were higher at every preterm gestation among SGA than AGA births, and similar at term gestations. An inverse relationship between gestational age and excess deaths between SGA and AGA babies delivered at <37weeks was evident. In early preterm gestations, the definition of SGA may well be justified as a proxy for IUGR. In contrast, SGA babies that are delivered at term are likely to be constitutionally small.

A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

NASA Astrophysics Data System (ADS)

Thammano, Arit; Teekeng, Wannaporn

2015-05-01

The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.

A New Challenge for Compression Algorithms: Genetic Sequences.

ERIC Educational Resources Information Center

Grumbach, Stephane; Tahi, Fariza

1994-01-01

Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2017-01-01

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

Refined genetic algorithm -- Economic dispatch example

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sheble, G.B.; Brittig, K.

1995-02-01

A genetic-based algorithm is used to solve an economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique.

Immune allied genetic algorithm for Bayesian network structure learning

NASA Astrophysics Data System (ADS)

Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

2012-06-01

Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

Flexible Space-Filling Designs for Complex System Simulations

DTIC Science & Technology

2013-06-01

interior of the experimental region and cannot fit higher-order models. We present a genetic algorithm that constructs space-filling designs with...Computer Experiments, Design of Experiments, Genetic Algorithm , Latin Hypercube, Response Surface Methodology, Nearly Orthogonal 15. NUMBER OF PAGES 147...experimental region and cannot fit higher-order models. We present a genetic algorithm that constructs space-filling designs with minimal correlations

Genetic algorithms in conceptual design of a light-weight, low-noise, tilt-rotor aircraft

NASA Technical Reports Server (NTRS)

Wells, Valana L.

1996-01-01

This report outlines research accomplishments in the area of using genetic algorithms (GA) for the design and optimization of rotorcraft. It discusses the genetic algorithm as a search and optimization tool, outlines a procedure for using the GA in the conceptual design of helicopters, and applies the GA method to the acoustic design of rotors.

Self-calibration of a noisy multiple-sensor system with genetic algorithms

NASA Astrophysics Data System (ADS)

Brooks, Richard R.; Iyengar, S. Sitharama; Chen, Jianhua

1996-01-01

This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray-scale images corrupted with noise. Both taboo search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results. The presentation includes a graphic presentation of the paths taken by tabu search and genetic algorithms when trying to find the best possible match between two corrupted images.

Increasing Prediction the Original Final Year Project of Student Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saragih, Rijois Iboy Erwin; Turnip, Mardi; Sitanggang, Delima; Aritonang, Mendarissan; Harianja, Eva

2018-04-01

Final year project is very important forgraduation study of a student. Unfortunately, many students are not seriouslydidtheir final projects. Many of studentsask for someone to do it for them. In this paper, an application of genetic algorithms to predict the original final year project of a studentis proposed. In the simulation, the data of the final project for the last 5 years is collected. The genetic algorithm has several operators namely population, selection, crossover, and mutation. The result suggest that genetic algorithm can do better prediction than other comparable model. Experimental results of predicting showed that 70% was more accurate than the previous researched.

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2004-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2005-01-01

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

The use of low-level light therapy in the treatment of androgenetic alopecia and female pattern hair loss.

PubMed

Gupta, Aditya K; Daigle, Deanne

2014-04-01

Androgenetic alopecia (AGA) or female pattern hair loss (FPHL) is the most common form of hair loss in men and women. Despite its common occurrence, our understanding of the etiology of AGA and FPHL remains incomplete. As such, traditional therapies demonstrate modest efficacies and new therapies continue to be sought. Low-level light therapy (LLLT) is a relatively new technique used to promote hair growth in both men and women with AGA and FPHL. Currently, there exist several LLLT devices marketed for the treatment of alopecia, which claim to stimulate hair growth; yet marketing these devices only requires that safety, not efficacy, be established. A handful of studies have since investigated the efficacy of LLLT for alopecia with mixed results. These studies suffered from power, confounding and analysis issues which resulted in a high risk of bias in LLLT studies. Due to the paucity of well-conducted randomized controlled trials, the efficacy of LLLT devices remains unclear. Randomized controlled trials of LLLT conducted and reported according to the Consolidated Standards of Reporting Trials (CONSORT) statement would greatly increase the credibility of the evidence and clarify the ambiguity of the effectiveness of LLLT in the treatment of AGA and FPHL.

Use of Low-Level Laser Therapy as Monotherapy or Concomitant Therapy for Male and Female Androgenetic Alopecia

PubMed Central

Munck, Andréia; Gavazzoni, Maria Fernanda; Trüeb, Ralph M

2014-01-01

Background: Androgenetic alopecia (AGA) is the most common form of hair loss in men and in women. Currently, minoxidil and finasteride are the treatments with the highest levels of medical evidence, but patients who exhibit intolerance or poor response to these treatments are in need of additional treatment modalities. Objective: The aim was to evaluate the efficacy and safety of low-level laser therapy (LLLT) for AGA, either as monotherapy or as concomitant therapy with minoxidil or finasteride, in an office-based setting. Materials and Methods: Retrospective observational study of male and female patients with AGA, treated with the 655 nm-HairMax Laser Comb®, in an office-based setting. Efficacy was assessed with global photographic imaging. Results: Of 32 patients (21 female, 11 male), 8 showed significant, 20 moderate, and 4 no improvement. Improvement was seen both with monotherapy and with concomitant therapy. Improvement was observed as early as 3 months and was sustained up to a maximum observation time of 24 months. No adverse reactions were reported. Conclusions: LLLT represents a potentially effective treatment for both male and female AGA, either as monotherapy or concomitant therapy. Combination treatments with minoxidil, finasteride, and LLLT may act synergistic to enhance hair growth. PMID:25191036

Molecular cloning, characterization and enzymatic properties of a novel βeta-agarase from a marine isolate Psudoalteromonas SP. AG52

PubMed Central

Oh, Chulhong; Nikapitiya, Chamilani; Lee, Youngdeuk; Whang, Ilson; Kang, Do-Hyung; Heo, Soo-Jin; Choi, Young-Ung; Lee, Jehee

2010-01-01

An agar-degrading Pseudoalteromonas sp. AG52 bacterial strain was identified from the red seaweed Gelidium amansii collected from Jeju Island, Korea. A β-agarase gene which has 96.8% nucleotide identity to Aeromonas β-agarase was cloned from this strain, and was designated as agaA. The coding region is 870 bp, encoding 290 amino acids and possesses characteristic features of the glycoside hydrolase family (GHF)-16. The predicted molecular mass of the mature protein was 32 kDa. The recombinant β-agarase (rAgaA) was overexpressed in Escherichia coli and purified as a fusion protein. The optimal temperature and pH for activity were 55 °C and 5.5, respectively. The enzyme had a specific activity of 105.1 and 79.5 unit/mg toward agar and agarose, respectively. The pattern of agar hydrolysis demonstrated that the enzyme is an endo-type β-agarase, producing neoagarohexaose and neoagarotetraose as the final main products. Since, Pseudoalteromonas sp. AG52 encodes an agaA gene, which has greater identity to Aeromonas β-agarase, the enzyme could be considered as novel, with its unique bio chemical characteristics. Altogether, the purified rAgaA has potential for use in industrial applications such as development of cosmetics and pharmaceuticals. PMID:24031567

Low-level light therapy for androgenetic alopecia: a 24-week, randomized, double-blind, sham device-controlled multicenter trial.

PubMed

Kim, Hyojin; Choi, Jee Woong; Kim, Jun Young; Shin, Jung Won; Lee, Seok-Jong; Huh, Chang-Hun

2013-08-01

Androgenetic alopecia (AGA) is a common disorder affecting men and women. Finasteride and minoxidil are well-known, effective treatment methods, but patients who exhibit a poor response to these methods have no additional adequate treatment modalities. To evaluate the efficacy and safety of a low-level light therapy (LLLT) device for the treatment of AGA. This study was designed as a 24-week, randomized, double-blind, sham device-controlled trial. Forty subjects with AGA were enrolled and scheduled to receive treatment with a helmet-type, home-use LLLT device emitting wavelengths of 630, 650, and 660 nm or a sham device for 18 minutes daily. Investigator and subject performed phototrichogram assessment (hair density and thickness) and global assessment of hair regrowth for evaluation. After 24 weeks of treatment, the LLLT group showed significantly greater hair density than the sham device group. Mean hair diameter improved statistically significantly more in the LLLT group than in the sham device group. Investigator global assessment showed a significant difference between the two groups, but that of the subject did not. No serious adverse reactions were detected. LLLT could be an effective treatment for AGA. © 2013 by the American Society for Dermatologic Surgery, Inc. Published by Wiley Periodicals, Inc.

Female-patterned alopecia in teenage brothers with unusual histologic features.

PubMed

Carlson, J Andrew; Malysz, Jozef; Schwartz, Joseph

2006-11-01

Patterned hair loss, follicular miniaturization, and increased telogen hair counts characterize androgenic alopecia (AGA). Follicular inflammation in AGA has been associated with treatment resistance and progressive hair loss. Brothers, 15 and 18 years old, presented with frontal and mid-scalp hair loss with an intact frontal hairline noted over a 1-year period. The elder reported past use of androgenic steroids. Laboratory assessment for metabolic and hormonal abnormalities was unrevealing, and hair pull test was negative. Scalp biopsies revealed decreased terminal hairs, marked diameter variation of anagen hairs, decreased terminal to vellus hair ratios (3.7:1/3.4:1, older/younger), and increased telogen counts (23%/21%). Infrabulbar and peri-isthmic (follicular bulge region) lymphocytic infiltrates were present. Hair loss has progressed, unabated by daily topical 0.5% clobetasol (for 6 months), daily 5% minoxidil (1 year), and latter, daily oral finasteride (2 years - older brother only). Based on patterned hair loss and miniaturized hairs, these brothers have AGA. The female pattern of hair loss (diffuse hair loss affecting the central scalp with preservation of frontal hair line) coupled with follicular isthmic lymphocytic inflammation represents an unusual presentation, possibly a treatment resistant, inflammatory variant of AGA. The differential diagnosis includes exogenous androgen-mediated hair loss, cicatricial pattern hair loss, or the superimposition of alopecia areata.

Genetic algorithm dynamics on a rugged landscape

NASA Astrophysics Data System (ADS)

Bornholdt, Stefan

1998-04-01

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR

PubMed Central

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms. PMID:28979308

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

PubMed

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms.

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

PubMed Central

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

PubMed

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Pose estimation for augmented reality applications using genetic algorithm.

PubMed

Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen

2005-12-01

This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.

Optimization of laminated stacking sequence for buckling load maximization by genetic algorithm

NASA Technical Reports Server (NTRS)

Le Riche, Rodolphe; Haftka, Raphael T.

1992-01-01

The use of a genetic algorithm to optimize the stacking sequence of a composite laminate for buckling load maximization is studied. Various genetic parameters including the population size, the probability of mutation, and the probability of crossover are optimized by numerical experiments. A new genetic operator - permutation - is proposed and shown to be effective in reducing the cost of the genetic search. Results are obtained for a graphite-epoxy plate, first when only the buckling load is considered, and then when constraints on ply contiguity and strain failure are added. The influence on the genetic search of the penalty parameter enforcing the contiguity constraint is studied. The advantage of the genetic algorithm in producing several near-optimal designs is discussed.

Development of a Tool for an Efficient Calibration of CORSIM Models

DOT National Transportation Integrated Search

2014-08-01

This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...

Engineered Intrinsic Bioremediation of Ammonium Perchlorate in Groundwater

DTIC Science & Technology

2010-12-01

German Collection of Microorganisms and Cell Cultures) GA Genetic Algorithms GA-ANN Genetic Algorithm Artificial Neural Network GMO genetically...for in situ treatment of perchlorate in groundwater. This is accomplished without the addition of genetically engineered microorganisms ( GMOs ) to the...perchlorate, even in the presence of oxygen and without the addition of genetically engineered microorganisms ( GMOs ) to the environment. This approach

[Algorithm of toxigenic genetically altered Vibrio cholerae El Tor biovar strain identification].

PubMed

Smirnova, N I; Agafonov, D A; Zadnova, S P; Cherkasov, A V; Kutyrev, V V

2014-01-01

Development of an algorithm of genetically altered Vibrio cholerae biovar El Tor strai identification that ensures determination of serogroup, serovar and biovar of the studied isolate based on pheno- and genotypic properties, detection of genetically altered cholera El Tor causative agents, their differentiation by epidemic potential as well as evaluation of variability of key pathogenicity genes. Complex analysis of 28 natural V. cholerae strains was carried out by using traditional microbiological methods, PCR and fragmentary sequencing. An algorithm of toxigenic genetically altered V. cholerae biovar El Tor strain identification was developed that includes 4 stages: determination of serogroup, serovar and biovar based on phenotypic properties, confirmation of serogroup and biovar based on molecular-genetic properties determination of strains as genetically altered, differentiation of genetically altered strains by their epidemic potential and detection of ctxB and tcpA key pathogenicity gene polymorphism. The algorithm is based on the use of traditional microbiological methods, PCR and sequencing of gene fragments. The use of the developed algorithm will increase the effectiveness of detection of genetically altered variants of the cholera El Tor causative agent, their differentiation by epidemic potential and will ensure establishment of polymorphism of genes that code key pathogenicity factors for determination of origins of the strains and possible routes of introduction of the infection.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2017-01-05

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.

Distributed genetic algorithms for the floorplan design problem

NASA Technical Reports Server (NTRS)

Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.

1991-01-01

Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.

Promising therapies for treating and/or preventing androgenic alopecia.

PubMed

McElwee, K J; Shapiro, J S

2012-06-01

Androgenetic alopecia (AGA) may affect up to 70% of men and 40% of women at some point in their lifetime. While men typically present with a distinctive alopecia pattern involving hairline recession and vertex balding, women normally exhibit a diffuse hair thinning over the top of their scalps. The treatment standard in dermatology clinics continues to be minoxidil and finasteride with hair transplantation as a surgical option. Here we briefly review current therapeutic options and treatments under active investigation. Dutasteride and ketoconazole are also employed for AGA, while prostaglandin analogues latanoprost and bimatoprost are being investigated for their hair growth promoting potential. Laser treatment products available for home use and from cosmetic clinics are becoming popular. In the future, new cell mediated treatment approaches may be available for AGA. While there are a number of potential treatment options, good clinical trial data proving hair growth efficacy is limited.

Gonadotropin levels in urine during early postnatal period in small for gestational age preterm male infants with fetal growth restriction.

PubMed

Nagai, S; Kawai, M; Myowa-Yamakoshi, M; Morimoto, T; Matsukura, T; Heike, T

2017-07-01

The objective of this study was to estimate gonadotropin concentrations in small for gestational age (SGA) male infants with the reactivation of the hypothalamic-pituitary-gonadal axis during the first few months of life that is important for genital development. We prospectively examined 15 SGA and 15 appropriate for gestational age (AGA) preterm male infants between 2013 and 2014 at Kyoto University Hospital. Gonadotropin concentrations (luteinizing hormone (LH) and follicle-stimulating hormone (FSH)) were measured in serial urine samples from the postnatal days 7 to 168 and compared between SGA and AGA infants using the Mann-Whitney test. A longitudinal analysis showed that SGA infants had higher LH and lower FSH concentrations (P=0.004 and P=0.006, respectively) than AGA infants. Male infants who are SGA at birth because of fetal growth restriction have gonadotropin secretion abnormalities in the first few months of life.

Variations of Human DNA Polymerase Genes as Biomarkers of Prostate Cancer Progression

DTIC Science & Technology

2011-07-01

Forward sequence Reverse sequence Sequence contextb 1 g.39835C4Tc P169S 15 25 gTG GGG TC CTT g.39897C4T Intronic 22 15 AGA T GGt TA AAT g.39985T4C...Intronic 34 25 AGA TT tAA AAG g.40051C4Tc P184S 19 34 TGt CT GGA ATT 4 g.39835C4Tc P169S 19 29 gTG GGG TC CTT g.40051C4Tc P184S 23 34 TGt CT GGA ATT 6 g...39835C4Tc P169S 14 24 gTG GGG TC CTT g.40051C4Tc P184S 21 32 TGt CT GGA ATT 11 g.40055A4G D185G 28 35 TTC C AGA C AAG g.40073A4G Y191C 28 20 gGA T AtG CC

Evolving aerodynamic airfoils for wind turbines through a genetic algorithm

NASA Astrophysics Data System (ADS)

Hernández, J. J.; Gómez, E.; Grageda, J. I.; Couder, C.; Solís, A.; Hanotel, C. L.; Ledesma, JI

2017-01-01

Nowadays, genetic algorithms stand out for airfoil optimisation, due to the virtues of mutation and crossing-over techniques. In this work we propose a genetic algorithm with arithmetic crossover rules. The optimisation criteria are taken to be the maximisation of both aerodynamic efficiency and lift coefficient, while minimising drag coefficient. Such algorithm shows greatly improvements in computational costs, as well as a high performance by obtaining optimised airfoils for Mexico City's specific wind conditions from generic wind turbines designed for higher Reynolds numbers, in few iterations.

An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Weir, John M.; Wells, B. Earl

2003-01-01

Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.

Phase Reconstruction from FROG Using Genetic Algorithms[Frequency-Resolved Optical Gating

DOE Office of Scientific and Technical Information (OSTI.GOV)

Omenetto, F.G.; Nicholson, J.W.; Funk, D.J.

1999-04-12

The authors describe a new technique for obtaining the phase and electric field from FROG measurements using genetic algorithms. Frequency-Resolved Optical Gating (FROG) has gained prominence as a technique for characterizing ultrashort pulses. FROG consists of a spectrally resolved autocorrelation of the pulse to be measured. Typically a combination of iterative algorithms is used, applying constraints from experimental data, and alternating between the time and frequency domain, in order to retrieve an optical pulse. The authors have developed a new approach to retrieving the intensity and phase from FROG data using a genetic algorithm (GA). A GA is a generalmore » parallel search technique that operates on a population of potential solutions simultaneously. Operators in a genetic algorithm, such as crossover, selection, and mutation are based on ideas taken from evolution.« less

Performance Analysis of Combined Methods of Genetic Algorithm and K-Means Clustering in Determining the Value of Centroid

NASA Astrophysics Data System (ADS)

Adya Zizwan, Putra; Zarlis, Muhammad; Budhiarti Nababan, Erna

2017-12-01

The determination of Centroid on K-Means Algorithm directly affects the quality of the clustering results. Determination of centroid by using random numbers has many weaknesses. The GenClust algorithm that combines the use of Genetic Algorithms and K-Means uses a genetic algorithm to determine the centroid of each cluster. The use of the GenClust algorithm uses 50% chromosomes obtained through deterministic calculations and 50% is obtained from the generation of random numbers. This study will modify the use of the GenClust algorithm in which the chromosomes used are 100% obtained through deterministic calculations. The results of this study resulted in performance comparisons expressed in Mean Square Error influenced by centroid determination on K-Means method by using GenClust method, modified GenClust method and also classic K-Means.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

PubMed

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

2012-08-01

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jordan, Jacob A.; Jubin, Robert Thomas

US regulations could require the removal of both iodine and tritium from the off-gas stream of a used nuclear fuel (UNF) reprocessing facility. Advanced tritium pretreatment is a pretreatment step that uses high concentrations of NOR2R in a gas stream to volatilize tritium and iodine from UNF prior to traditional dissolution. The gaseous effluent from this process would then require abatement to remove tritium and iodine, but high levels of NOR2R could have a detrimental effect on the ability of various solid sorbents to remove the volatile radionuclides. For tritium and iodine, the sorbents of interest are 3Å molecular sievemore » (3AMS) for tritium and reduced silver mordenite (AgP 0 PZ), silver-functionalized silica-aerogel (AgAerogel), and silver-nitrate-impregnated alumina (AgA) for iodine. Prior research has demonstrated that exposure to high concentrations of NOR2R can reduce the iodine loading capacity of AgP 0 PZ by > 90% when exposed for 1 week. Research in Japan has demonstrated that AgA is more robust to NOR2R exposure than AgZ. The testing described here was intended to assess the effects of high concentrations of NOR2R on the iodine capture capacity of AgA and the water adsorption capacity of 3AMS. To determine the effect of extended exposure of the sorbents to NOR2R, both 3AMS and AgA were aged in a 75% NOR2R environment prior to loading. The 3AMS samples were aged for 1, 4, and 5.5 weeks at 40°C. They were then loaded with water in a 10°C dew point stream (corresponding to a water concentration of ~12,000 ppmv) at 40°C. There was no significant change in the water adsorption capacity of the 3AMS upon exposure to 75% NOR2R. The AgA samples were aged for 1, 2, and 4 weeks at 150°C and were loaded with 50 ppmv IR2R at 150°C. The results show that the iodine capture capacity of AgA is reduced by exposure to high concentrations of NOR2R. The iodine capacity reductions were 16%, 36%, and 76% for 1, 2, and 4 week exposures, respectively. This is less of a capacity loss than that seen in similar testing with the AgP 0 PZ sorbent.« less

Cloud computing-based TagSNP selection algorithm for human genome data.

PubMed

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-05

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.

New optimization model for routing and spectrum assignment with nodes insecurity

NASA Astrophysics Data System (ADS)

Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli

2017-04-01

By adopting the orthogonal frequency division multiplexing technology, elastic optical networks can provide the flexible and variable bandwidth allocation to each connection request and get higher spectrum utilization. The routing and spectrum assignment problem in elastic optical network is a well-known NP-hard problem. In addition, information security has received worldwide attention. We combine these two problems to investigate the routing and spectrum assignment problem with the guaranteed security in elastic optical network, and establish a new optimization model to minimize the maximum index of the used frequency slots, which is used to determine an optimal routing and spectrum assignment schemes. To solve the model effectively, a hybrid genetic algorithm framework integrating a heuristic algorithm into a genetic algorithm is proposed. The heuristic algorithm is first used to sort the connection requests and then the genetic algorithm is designed to look for an optimal routing and spectrum assignment scheme. In the genetic algorithm, tailor-made crossover, mutation and local search operators are designed. Moreover, simulation experiments are conducted with three heuristic strategies, and the experimental results indicate that the effectiveness of the proposed model and algorithm framework.

The Applications of Genetic Algorithms in Medicine.

PubMed

Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

2015-11-01

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.].

The Applications of Genetic Algorithms in Medicine

PubMed Central

Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

2015-01-01

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.] PMID:26676060

Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

PubMed Central

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-01

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used. PMID:25569088

Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.

A genetic algorithm for replica server placement

NASA Astrophysics Data System (ADS)

Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl

2012-01-01

Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.

A genetic algorithm for replica server placement

NASA Astrophysics Data System (ADS)

Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl

2011-12-01

Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

PubMed

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Benford, Andrew; Tinker, Michael L.

2004-01-01

The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.

Superscattering of light optimized by a genetic algorithm

NASA Astrophysics Data System (ADS)

Mirzaei, Ali; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S.

2014-07-01

We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.

A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

PubMed Central

Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah

2015-01-01

A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974

Neural-network-assisted genetic algorithm applied to silicon clusters

NASA Astrophysics Data System (ADS)

Marim, L. R.; Lemes, M. R.; dal Pino, A.

2003-03-01

Recently, a new optimization procedure that combines the power of artificial neural-networks with the versatility of the genetic algorithm (GA) was introduced. This method, called neural-network-assisted genetic algorithm (NAGA), uses a neural network to restrict the search space and it is expected to speed up the solution of global optimization problems if some previous information is available. In this paper, we have tested NAGA to determine the ground-state geometry of Sin (10⩽n⩽15) according to a tight-binding total-energy method. Our results indicate that NAGA was able to find the desired global minimum of the potential energy for all the test cases and it was at least ten times faster than pure genetic algorithm.

Illness perception in patients with androgenetic alopecia and alopecia areata in China.

PubMed

Yu, Nan-Lan; Tan, Huan; Song, Zhi-Qiang; Yang, Xi-Chuan

2016-07-01

The aim of the present study was to provide more information on the role of illness perception in patients with androgenetic alopecia (AGA) and those with alopecia areata (AA), and to further investigate the relationship of illness perception with psychological disorders and dermatological QoL. The study included 342 patients who were diagnosed with AGA (n=212) or AA (n=130) for the first time at our institution between October 2013 and December 2014. All patients were surveyed before clinical examination by several questionnaires including the Brief Illness Perception, Self-rating Depression Scale, Self-rating Anxiety Scale, and Dermatology Life Quality Index (DLQI). In the AGA patients, the illness perception and QoL were low, whereas the prevalence of clinical depression and anxiety was higher compared to the AA patients. Illness perception was associated with psychological distress and low QoL in both groups, and some illness perception dimensions were found to be significant predictors of the DLQI scores. Illness perception plays an important role in AGA and AA patients, and is associated with psychological distress and low QoL. The identification of critical components of illness perception in alopecia patients could help to understand alopecia specificities, to design consultations and interventions according to the perception, and to improve physical and mental outcomes as well as QoL in alopecia patients. Copyright © 2016 Elsevier Inc. All rights reserved.

A Single-Centre, Randomized, Double-Blind, Placebo-Controlled Clinical Trial to Investigate the Efficacy and Safety of Minoxidil Topical Foam in Frontotemporal and Vertex Androgenetic Alopecia in Men.

PubMed

Hillmann, Kathrin; Garcia Bartels, Natalie; Kottner, Jan; Stroux, Andrea; Canfield, Douglas; Blume-Peytavi, Ulrike

2015-01-01

5% minoxidil formulations twice daily are effective in treating vertex male androgenetic alopecia (AGA); however, efficacy and safety data in frontotemporal regions are lacking. To assess the efficacy of 5% minoxidil topical foam (5% MTF) in the frontotemporal region of male AGA patients after 24 weeks of treatment compared to placebo treatment and to the vertex region. Seventy males with moderate AGA applied 5% MTF or placebo foam (plaTF) twice daily for 24 weeks in frontotemporal and vertex regions. Target area non-vellus hair count (TAHC) was the primary end point. Frontotemporal and vertex TAHC and target area cumulative non-vellus hair width (TAHW) showed similar responses to 5% MTF with significant increases up to week 16 compared to baseline (p < 0.001). After 24 weeks of treatment, frontotemporal TAHW increased significantly in the 5% MTF group compared to the plaTF group (p = 0.017), while TAHC showed a similar non-significant increase from baseline in both regions. At 24 weeks, 5% MTF users rated a significant improvement in scalp coverage for the frontotemporal (p = 0.016) and vertex areas (p = 0.027). 5% MTF twice a day promotes hair density and width in both frontotemporal and vertex regions in men with moderate stages of AGA. © 2015 S. Karger AG, Basel.

Prostaglandin D2 Inhibits Hair Growth and Is Elevated in Bald Scalp of Men with Androgenetic Alopecia

PubMed Central

Garza, Luis A.; Liu, Yaping; Yang, Zaixin; Alagesan, Brinda; Lawson, John A.; Norberg, Scott M.; Loy, Dorothy E.; Zhao, Tailun; Blatt, Hanz B.; Stanton, David C.; Carrasco, Lee; Ahluwalia, Gurpreet; Fischer, Susan M.; FitzGerald, Garret A.; Cotsarelis, George

2012-01-01

Testosterone is necessary for the development of male pattern baldness, known as androgenetic alopecia (AGA); yet, the mechanisms for decreased hair growth in this disorder are unclear. We show that prostaglandin D2 synthase (PTGDS) is elevated at the mRNA and protein levels in bald scalp compared to haired scalp of men with AGA. The product of PTGDS enzyme activity, prostaglandin D2 (PGD2), is similarly elevated in bald scalp. During normal follicle cycling in mice, Ptgds and PGD2 levels increase immediately preceding the regression phase, suggesting an inhibitory effect on hair growth. We show that PGD2 inhibits hair growth in explanted human hair follicles and when applied topically to mice. Hair growth inhibition requires the PGD2 receptor G protein (heterotrimeric guanine nucleotide)–coupled receptor 44 (GPR44), but not the PGD2 receptor 1 (PTGDR). Furthermore, we find that a transgenic mouse, K14-Ptgs2, which targets prostaglandin-endoperoxide synthase 2 expression to the skin, demonstrates elevated levels of PGD2 in the skin and develops alopecia, follicular miniaturization, and sebaceous gland hyperplasia, which are all hallmarks of human AGA. These results define PGD2 as an inhibitor of hair growth in AGA and suggest the PGD2-GPR44 pathway as a potential target for treatment. PMID:22440736

Blockade of Androgen Markers Using a Novel Betasitosterol, Thioctic Acid and Carnitine-containing Compound in Prostate and Hair Follicle Cell-based Assays.

PubMed

Chen, Li; Wang, Jiaolong; Mouser, Glen; Li, Yan Chun; Marcovici, Geno

2016-06-01

Androgenetic alopecia (AGA) affects approximately 70% of men and 40% of women in an age-dependent manner and is partially mediated by androgen hormones. Benign prostatic hyperplasia (BPH) similarly affects 50% of the male population, rising by 10% each decade. Finasteride inhibits 5-alpha reductase (5AR) and is used to treat both disorders, despite offering limited clinical benefits accompanied by significant adverse side effects. Building on our previous work demonstrating the efficacy of naturally derived 5AR inhibitors (such as stigmasterol and beta sitosterol), we hypothesize that targeting 5AR as well as inflammatory pathways may yield improved efficacy in AGA and BPH. Here we address these dual pathomechanisms by examining the potency of a novel composition using in vitro assays of representative cell lines for AGA (hair follicle dermal papilla cells) and BPH (LNCaP prostate cells), respectively. Exposure of cells to the novel test composition down-regulated mRNA expression profiles characteristic of both disease processes, which outperformed finasteride. Changes in mRNA expression were corroborated at the protein level as assessed by western blotting. These studies provide proof of concept that novel, naturally derived compositions simultaneously targeting 5AR and inflammatory mediators may represent a rational approach to treating AGA and BPH. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Enzymatic liquefaction of agarose above the sol-gel transition temperature using a thermostable endo-type β-agarase, Aga16B.

PubMed

Kim, Jung Hyun; Yun, Eun Ju; Seo, Nari; Yu, Sora; Kim, Dong Hyun; Cho, Kyung Mun; An, Hyun Joo; Kim, Jae-Han; Choi, In-Geol; Kim, Kyoung Heon

2017-02-01

The main carbohydrate of red macroalgae is agarose, a heterogeneous polysaccharide composed of D-galactose and 3,6-anhydro-L-galactose. When saccharifying agarose by enzymes, the unique physical properties of agarose, namely the sol-gel transition and the near-insolubility of agarose in water, limit the accessibility of agarose to the enzymes. Due to the lower accessibility of agarose to enzymes in the gel state than to the sol state, it is important to prevent the sol-gel transition by performing the enzymatic liquefaction of agarose at a temperature higher than the sol-gel transition temperature of agarose. In this study, a thermostable endo-type β-agarase, Aga16B, originating from Saccharophagus degradans 2-40 T , was characterized and introduced in the liquefaction process. Aga16B was thermostable up to 50 °C and depolymerized agarose mainly into neoagarooligosaccharides with degrees of polymerization 4 and 6. Aga16B was applied to enzymatic liquefaction of agarose at 45 °C, which was above the sol-gel transition temperature of 1 % (w/v) agarose (∼35 °C) when cooling agarose. This is the first systematic demonstration of enzymatic liquefaction of agarose, enabled by determining the sol-gel temperature of agarose under specific conditions and by characterizing the thermostability of an endo-type β-agarase.

Foetal growth restriction is associated with poor reading and spelling skills at eight years to 10 years of age.

PubMed

Partanen, Lea; Korkalainen, Noora; Mäkikallio, Kaarin; Olsén, Päivi; Laukkanen-Nevala, Päivi; Yliherva, Anneli

2018-01-01

Foetal growth restriction (FGR) is associated with communication problems, which might lead to poor literacy skills. The reading and spelling skills of eight- to 10-year-old FGR children born at 24-40 gestational weeks were compared with those of their gestational age-matched, appropriately grown (AGA) peers. A prospectively collected cohort of 37 FGR and 31 AGA children was recruited prenatally at a Finnish tertiary care centre during 1998-2001. The children's reading and spelling skills were assessed using standardised tests for Finnish-speaking second and third graders. Significantly more children performed below the 10th percentile normal values for reading and spelling skills in the FGR group than in the AGA group. At nine years of age, the FGR children had significantly poorer performance in word reading skills and reading fluency, reading accuracy and reading comprehension than the AGA controls. No between-group differences were detected at eight years of age. FGR is associated with poor performance in reading and spelling skills. A third of the FGR children performed below the 10th percentile normal values at nine years of age. These results indicate a need to continuously evaluate linguistic and literacy skills as FGR children age to ensure optimal support. ©2017 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.

ERIC Educational Resources Information Center

Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand

2003-01-01

Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…

MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models

EPA Science Inventory

Characterization of uncertainty and sensitivity of model parameters is an essential and often overlooked facet of hydrological modeling. This paper introduces an algorithm called MOESHA that combines input parameter sensitivity analyses with a genetic algorithm calibration routin...

Treatment Algorithm for Chronic Idiopathic Constipation and Constipation-Predominant Irritable Bowel Syndrome Derived from a Canadian National Survey and Needs Assessment on Choices of Therapeutic Agents.

PubMed

Tse, Yvonne; Armstrong, David; Andrews, Christopher N; Bitton, Alain; Bressler, Brian; Marshall, John; Liu, Louis W C

2017-01-01

Background . Chronic idiopathic constipation (CIC) and constipation-predominant irritable bowel syndrome (IBS-C) are common functional lower gastrointestinal disorders that impair patients' quality of life. In a national survey, we aimed to evaluate (1) Canadian physician practice patterns in the utilization of therapeutic agents listed in the new ACG and AGA guidelines; (2) physicians satisfaction with these agents for their CIC and IBS-C patients; and (3) the usefulness of these new guidelines in their clinical practice. Methods . A 9-item questionnaire was sent to 350 Canadian specialists to evaluate their clinical practice for the management of CIC and IBS-C. Results . The response rate to the survey was 16% ( n = 55). Almost all (96%) respondents followed a standard, stepwise approach for management while they believed that only 24% of referring physicians followed the same approach. Respondents found guanylyl cyclase C (GCC) agonist most satisfying when treating their patients. Among the 69% of respondents who were aware of published guidelines, only 50% found them helpful in prioritizing treatment choices and 69% of respondents indicated that a treatment algorithm, applicable to Canadian practice, would be valuable. Conclusion . Based on this needs assessment, a treatment algorithm was developed to provide clinical guidance in the management of IBS-C and CIC in Canada.

[Mutation analysis of seven patients with Waardenburg syndrome].

PubMed

Hao, Ziqi; Zhou, Yongan; Li, Pengli; Zhang, Quanbin; Li, Jiao; Wang, Pengfei; Li, Xiangshao; Feng, Yong

2016-06-01

To perform genetic analysis for 7 patients with Waardenburg syndrome. Potential mutation of MITF, PAX3, SOX10 and SNAI2 genes was screened by polymerase chain reaction and direct sequencing. Functions of non-synonymous polymorphisms were predicted with PolyPhen2 software. Seven mutations, including c.649-651delAGA (p.R217del), c.72delG (p.G24fs), c.185T>C (p.M62T), c.118C>T (p.Q40X), c.422T>C (p.L141P), c.640C>T (p.R214X) and c.28G>T(p.G43V), were detected in the patients. Among these, four mutations of the PAX3 gene (c.72delG, c.185T>C, c.118C>T and c.128G>T) and one SOX10 gene mutation (c.422T>C) were not reported previously. Three non-synonymous SNPs (c.185T>C, c.128G>T and c.422T>C) were predicted as harmful. Genetic mutations have been detected in all patients with Waardenburg syndrome.

Growth restriction and gender influence cerebral oxygenation in preterm neonates.

PubMed

Cohen, Emily; Baerts, Willem; Alderliesten, Thomas; Derks, Jan; Lemmers, Petra; van Bel, Frank

2016-03-01

To investigate the effect of fetal growth restriction and gender on cerebral oxygenation in preterm neonates during the first 3 days of life. Case-control study. Neonatal Intensive Care Unit of the Wilhelmina Children's Hospital, The Netherlands. 68 (41 males) small for gestational age (SGA) (birth weight <10th percentile) and 136 (82 males) appropriate for gestational age (AGA) (birth weight 20th-80th percentile) neonates, matched for gender, gestational age, ventilatory and blood pressure support. Regional cerebral oxygen saturation (rScO2) and cerebral fractional tissue oxygen extraction (cFTOE) as measured by near-infrared spectroscopy throughout the first 72 h of life were compared between SGA and AGA neonates. The effect of gender was also explored within these comparisons. SGA neonates demonstrated higher rScO2 (71% SEM 0.2 vs 68% SEM 0.2) and lower cFTOE (0.25 SEM 0.002 vs 0.29 SEM 0.002) than AGA neonates. There was an independent effect of gender on rScO2 and cFTOE, resulting in the finding that SGA males displayed highest rScO2 and lowest cFTOE (73% SEM 0.3 respectively 0.24 SEM 0.003). AGA males and SGA females showed comparable rScO2 (69% SEM 0.2 vs 69% SEM 0.4) and cFTOE (0.28 SEM 0.002 vs 0.28 SEM 0.004). AGA females showed lowest rScO2 and highest cFTOE (66% SEM 0.2 respectively 0.30 SEM 0.002). Growth restriction and gender influence cerebral oxygenation and oxygen extraction in preterm neonates throughout the first 3 days of life. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Comparison of Fetal and Neonatal Growth Curves in Detecting Growth Restriction

PubMed Central

Marconi, Anna Maria; Ronzoni, Stefania; Bozzetti, Patrizia; Vailati, Simona; Morabito, Alberto; Battaglia, Frederick C

2009-01-01

Objective To evaluate the outcome of intrauterine growth restriction (IUGR) infants with abnormal pulsatility index of the umbilical artery according to the neonatal birth weight/gestational age standards and the intrauterine growth charts. Methods We analyzed 53 pregnancies with severe IUGR classified as Group 2 (22 IUGR: abnormal pulsatility index and normal fetal heart rate) and Group 3 (31 IUGR: abnormal pulsatility index and fetal heart rate). Neonatal birth weight/gestational age distribution, body size measurements, maternal characteristics and obstetric outcome, and neonatal major and minor morbidity and mortality were compared with those obtained in 79 singleton pregnancies with normal fetal growth and pulsatility index, matched for gestational age [appropriate for gestational age (AGA) group]. Differences were analyzed with the χ2 test and the Student’s t test. Differences between means corrected for gestational age in the different groups were assessed by analysis of covariance test. A P value <0.05 was considered significant. Results At delivery, utilizing the neonatal standards, 25/53 (47%) IUGR showed a birthweight above the 10th percentile (IUGRAGA) whereas in 28, birthweight was below the 10th percentile (IUGRSGA). All body size measurements were significantly higher in AGA than in IUGRAGA and IUGRSGA. Forty-nine out of 79 (62%) AGA and 49/53 (92%) IUGR were admitted in the neonatal intensive care unit (p<0.001). One out of 79 (1%) AGA and 6/53 (11%) IUGR newborns died within 28 days (p<0.02). Major and minor morbidity was not different. Conclusion This study shows that neonatal outcome is similar in IUGR of the same clinical severity, whether or not they could be defined AGA or SGA according to the neonatal standards. Neonatal curves are misleading in detecting low birthweight infants and should be utilized only when obstetrical data are unavailable. PMID:19037030

A genetic algorithm for solving supply chain network design model

NASA Astrophysics Data System (ADS)

Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

2013-09-01

Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

The simulation method of chemical composition of vermicular graphite iron on the basis of genetic algorithm

NASA Astrophysics Data System (ADS)

Yusupov, L. R.; Klochkova, K. V.; Simonova, L. A.

2017-09-01

The paper presents a methodology of modeling the chemical composition of the composite material via genetic algorithm for optimization of the manufacturing process of products. The paper presents algorithms of methods based on intelligent system of vermicular graphite iron design

MULTI-OBJECTIVE OPTIMAL DESIGN OF GROUNDWATER REMEDIATION SYSTEMS: APPLICATION OF THE NICHED PARETO GENETIC ALGORITHM (NPGA). (R826614)

EPA Science Inventory

A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...

Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

DTIC Science & Technology

2010-03-01

17 NSGA-II non-dominated sorting genetic algorithm II . . . . . . . . . . . . . . . . . . . 17 jMetal Metaheuristic Algorithms in...to alert the other agents and ensure trust in the system. This research presents an algorithm that tasks robots to meet the two specific goals of...problem is defined as a constraint satisfaction problem solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Both goals of

Application of genetic algorithm in modeling on-wafer inductors for up to 110 Ghz

NASA Astrophysics Data System (ADS)

Liu, Nianhong; Fu, Jun; Liu, Hui; Cui, Wenpu; Liu, Zhihong; Liu, Linlin; Zhou, Wei; Wang, Quan; Guo, Ao

2018-05-01

In this work, the genetic algorithm has been introducted into parameter extraction for on-wafer inductors for up to 110 GHz millimeter-wave operations, and nine independent parameters of the equivalent circuit model are optimized together. With the genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the preliminary parameters without optimization. Especially, the fitting accuracy of the Q value achieves a significant improvement after the optimization.

Combinatorial Multiobjective Optimization Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Crossley, William A.; Martin. Eric T.

2002-01-01

The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

PubMed

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

[Application of genetic algorithm in blending technology for extractions of Cortex Fraxini].

PubMed

Yang, Ming; Zhou, Yinmin; Chen, Jialei; Yu, Minying; Shi, Xiufeng; Gu, Xijun

2009-10-01

To explore the feasibility of genetic algorithm (GA) on multiple objective blending technology for extractions of Cortex Fraxini. According to that the optimization objective was the combination of fingerprint similarity and the root-mean-square error of multiple key constituents, a new multiple objective optimization model of 10 batches extractions of Cortex Fraxini was built. The blending coefficient was obtained by genetic algorithm. The quality of 10 batches extractions of Cortex Fraxini that after blending was evaluated with the finger print similarity and root-mean-square error as indexes. The quality of 10 batches extractions of Cortex Fraxini that after blending was well improved. Comparing with the fingerprint of the control sample, the similarity was up, but the degree of variation is down. The relative deviation of the key constituents was less than 10%. It is proved that genetic algorithm works well on multiple objective blending technology for extractions of Cortex Fraxini. This method can be a reference to control the quality of extractions of Cortex Fraxini. Genetic algorithm in blending technology for extractions of Chinese medicines is advisable.

Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

PubMed Central

A., Javadpour; A., Mohammadi

2016-01-01

Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

PubMed

Ortuño, Francisco M; Valenzuela, Olga; Rojas, Fernando; Pomares, Hector; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio

2013-09-01

Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

Silver Complexes of Dihalogen Molecules.

PubMed

Malinowski, Przemysław J; Himmel, Daniel; Krossing, Ingo

2016-08-01

The perfluorohexane-soluble and donor-free silver compound Ag(A) (A=Al(OR(F) )4 ; R(F) =C(CF3 )3 ) prepared using a facile novel route has unprecedented capabilities to form unusual and weakly bound complexes. Here, we report on the three dihalogen-silver complexes Ag(Cl2 )A, Ag(Br2 )A, and Ag(I2 )A derived from the soluble silver compound Ag(A) (characterized by single-crystal/powder XRD, Raman spectra, and quantum-mechanical calculations). © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem.

PubMed

Lo, C C; Chang, W H

2000-01-01

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Genetic Algorithm Approaches for Actuator Placement

NASA Technical Reports Server (NTRS)

Crossley, William A.

2000-01-01

This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.

A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm

NASA Astrophysics Data System (ADS)

Thirer, Nonel

2013-05-01

With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.

Image reconstruction through thin scattering media by simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Fang, Longjie; Zuo, Haoyi; Pang, Lin; Yang, Zuogang; Zhang, Xicheng; Zhu, Jianhua

2018-07-01

An idea for reconstructing the image of an object behind thin scattering media is proposed by phase modulation. The optimized phase mask is achieved by modulating the scattered light using simulated annealing algorithm. The correlation coefficient is exploited as a fitness function to evaluate the quality of reconstructed image. The reconstructed images optimized from simulated annealing algorithm and genetic algorithm are compared in detail. The experimental results show that our proposed method has better definition and higher speed than genetic algorithm.

Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

2005-01-01

An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

Genetic algorithm for neural networks optimization

NASA Astrophysics Data System (ADS)

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

Hybrid Architectures for Evolutionary Computing Algorithms

DTIC Science & Technology

2008-01-01

other EC algorithms to FPGA Core Burns P1026/MAPLD 200532 Genetic Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based...on Parallel and Distributed Processing (IPPS/SPDP 󈨦), pp. 316-320, Proceedings. IEEE Computer Society 1998. [12] Scott, S. D. , Samal , A., and...Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based Genetic Algorithm”, Proceedings of the 1995 ACM Third

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

PubMed Central

Ozmutlu, H. Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

NASA Astrophysics Data System (ADS)

Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang

2017-09-01

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

Rani, R Ranjani; Ramyachitra, D

2016-12-01

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Low-level laser/light therapy for androgenetic alopecia.

PubMed

Gupta, Aditya K; Lyons, Danika C A; Abramovits, William

2014-01-01

Androgenetic alopecia (AGA) is a persistent and pervasive condition that affects men worldwide. Some common treatment options for AGA include hair prosthetics, oral and topical medications, and surgical hair restoration (SHR). Pharmaceutical and SHR treatments are associated with limitations including adverse side effects and significant financial burden. Low-level laser or light (LLL) devices offer alternative treatment options that are not typically associated with adverse side effects or significant costs. There are clinic- and home-based LLL devices. One home-based laser comb device has set a standard for others; however, this device requires time devoted to carefully moving the comb through the hair to allow laser penetration to the scalp. A novel helmet-like LLL device for hair growth has proven effective in preliminary trials and allows for hands-free use. Regardless, there are few clinical trials that have been conducted regarding LLL devices for AGA and results are mixed. Further research is required to establish the true efficacy of these devices for hair growth in comparison to existing alternative therapies.

Novel enzymatic assay predicts minoxidil response in the treatment of androgenetic alopecia.

PubMed

Goren, Andy; Castano, Juan Antonio; McCoy, John; Bermudez, Fernando; Lotti, Torello

2014-01-01

Topical minoxidil is the most common drug used for the treatment of androgenetic alopecia (AGA) in men and women. Although topical minoxidil exhibits a good safety profile, the efficacy in the overall population remains relatively low at 30-40%. To observe significant improvement in hair growth, minoxidil is typically used daily for a period of at least 3-4 months. Due to the significant time commitment and low response rate, a biomarker for predicting patient response prior to therapy would be advantageous. Minoxidil is converted in the scalp to its active form, minoxidil sulfate, by the sulfotransferase enzyme SULT1A1. We hypothesized that SULT1A1 enzyme activity in the hair follicle correlates with minoxidil response for the treatment of AGA. Our preliminary retrospective study of a SULT1A1 activity assay demonstrates 95% sensitivity and 73% specificity in predicting minoxidil treatment response for AGA. A larger prospective study is now under way to further validate this novel assay. © 2013 Wiley Periodicals, Inc.

Lysine-Tryptophan-Crosslinked Peptides Produced by Radical SAM Enzymes in Pathogenic Streptococci.

PubMed

Schramma, Kelsey R; Seyedsayamdost, Mohammad R

2017-04-21

Macrocycles represent a common structural framework in many naturally occurring peptides. Several strategies exist for macrocyclization, and the enzymes that incorporate them are of great interest, as they enhance our repertoire for creating complex molecules. We recently discovered a new peptide cyclization reaction involving a crosslink between the side chains of lysine and tryptophan that is installed by a radical SAM enzyme. Herein, we characterize relatives of this metalloenzyme from the pathogens Streptococcus agalactiae and Streptococcus suis. Our results show that the corresponding enzymes, which we call AgaB and SuiB, contain multiple [4Fe-4S] clusters and catalyze Lys-Trp crosslink formation in their respective substrates. Subsequent high-resolution-MS and 2D-NMR analyses located the site of macrocyclization. Moreover, we report that AgaB can accept modified substrates containing natural or unnatural amino acids. Aside from providing insights into the mechanism of this unusual modification, the substrate promiscuity of AgaB may be exploited to create diverse macrocyclic peptides.

Automatic page layout using genetic algorithms for electronic albuming

NASA Astrophysics Data System (ADS)

Geigel, Joe; Loui, Alexander C. P.

2000-12-01

In this paper, we describe a flexible system for automatic page layout that makes use of genetic algorithms for albuming applications. The system is divided into two modules, a page creator module which is responsible for distributing images amongst various album pages, and an image placement module which positions images on individual pages. Final page layouts are specified in a textual form using XML for printing or viewing over the Internet. The system makes use of genetic algorithms, a class of search and optimization algorithms that are based on the concepts of biological evolution, for generating solutions with fitness based on graphic design preferences supplied by the user. The genetic page layout algorithm has been incorporated into a web-based prototype system for interactive page layout over the Internet. The prototype system is built using client-server architecture and is implemented in java. The system described in this paper has demonstrated the feasibility of using genetic algorithms for automated page layout in albuming and web-based imaging applications. We believe that the system adequately proves the validity of the concept, providing creative layouts in a reasonable number of iterations. By optimizing the layout parameters of the fitness function, we hope to further improve the quality of the final layout in terms of user preference and computation speed.

An application of traveling salesman problem using the improved genetic algorithm on android google maps

NASA Astrophysics Data System (ADS)

Narwadi, Teguh; Subiyanto

2017-03-01

The Travelling Salesman Problem (TSP) is one of the best known NP-hard problems, which means that no exact algorithm to solve it in polynomial time. This paper present a new variant application genetic algorithm approach with a local search technique has been developed to solve the TSP. For the local search technique, an iterative hill climbing method has been used. The system is implemented on the Android OS because android is now widely used around the world and it is mobile system. It is also integrated with Google API that can to get the geographical location and the distance of the cities, and displays the route. Therefore, we do some experimentation to test the behavior of the application. To test the effectiveness of the application of hybrid genetic algorithm (HGA) is compare with the application of simple GA in 5 sample from the cities in Central Java, Indonesia with different numbers of cities. According to the experiment results obtained that in the average solution HGA shows in 5 tests out of 5 (100%) is better than simple GA. The results have shown that the hybrid genetic algorithm outperforms the genetic algorithm especially in the case with the problem higher complexity.

Peak-to-average power ratio reduction in orthogonal frequency division multiplexing-based visible light communication systems using a modified partial transmit sequence technique

NASA Astrophysics Data System (ADS)

Liu, Yan; Deng, Honggui; Ren, Shuang; Tang, Chengying; Qian, Xuewen

2018-01-01

We propose an efficient partial transmit sequence technique based on genetic algorithm and peak-value optimization algorithm (GAPOA) to reduce high peak-to-average power ratio (PAPR) in visible light communication systems based on orthogonal frequency division multiplexing (VLC-OFDM). By analysis of hill-climbing algorithm's pros and cons, we propose the POA with excellent local search ability to further process the signals whose PAPR is still over the threshold after processed by genetic algorithm (GA). To verify the effectiveness of the proposed technique and algorithm, we evaluate the PAPR performance and the bit error rate (BER) performance and compare them with partial transmit sequence (PTS) technique based on GA (GA-PTS), PTS technique based on genetic and hill-climbing algorithm (GH-PTS), and PTS based on shuffled frog leaping algorithm and hill-climbing algorithm (SFLAHC-PTS). The results show that our technique and algorithm have not only better PAPR performance but also lower computational complexity and BER than GA-PTS, GH-PTS, and SFLAHC-PTS technique.

The McGill Interactive Pediatric OncoGenetic Guidelines: An approach to identifying pediatric oncology patients most likely to benefit from a genetic evaluation.

PubMed

Goudie, Catherine; Coltin, Hallie; Witkowski, Leora; Mourad, Stephanie; Malkin, David; Foulkes, William D

2017-08-01

Identifying cancer predisposition syndromes in children with tumors is crucial, yet few clinical guidelines exist to identify children at high risk of having germline mutations. The McGill Interactive Pediatric OncoGenetic Guidelines project aims to create a validated pediatric guideline in the form of a smartphone/tablet application using algorithms to process clinical data and help determine whether to refer a child for genetic assessment. This paper discusses the initial stages of the project, focusing on its overall structure, the methodology underpinning the algorithms, and the upcoming algorithm validation process. © 2017 Wiley Periodicals, Inc.

Optimization of genomic selection training populations with a genetic algorithm

USDA-ARS?s Scientific Manuscript database

In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...

Production of a Highly Protease-Resistant Fungal α-Galactosidase in Transgenic Maize Seeds for Simplified Feed Processing

PubMed Central

Zhou, Xiaojin; Zhang, Wei; Xu, Xiaolu; Chen, Rumei; Meng, Qingchang; Yuan, Jianhua; Yang, Peilong; Yao, Bin

2015-01-01

Raffinose-family oligosaccharide (RFO) in soybeans is one of the major anti-nutritional factors for poultry and livestocks. α-Galactosidase is commonly supplemented into the animal feed to hydrolyze α-1,6-galactosidic bonds on the RFOs. To simplify the feed processing, a protease-resistant α-galactosidase encoding gene from Gibberella sp. strain F75, aga-F75, was modified by codon optimization and heterologously expressed in the embryos of transgentic maize driven by the embryo-specific promoter ZM-leg1A. The progenies were produced by backcrossing with the commercial inbred variety Zheng58. PCR, southern blot and western blot analysis confirmed the stable integration and tissue specific expression of the modified gene, aga-F75m, in seeds over four generations. The expression level of Aga-F75M reached up to 10,000 units per kilogram of maize seeds. In comparison with its counterpart produced in Pichia pastoris strain GS115, maize seed-derived Aga-F75M showed a lower temperature optimum (50°C) and lower stability over alkaline pH range, but better thermal stability at 60°C to 70°C and resistance to feed pelleting inactivation (80°C). This is the first report of producing α-galactosidase in transgenic plant. The study offers an effective and economic approach for direct utilization of α-galactosidase-producing maize without any purification or supplementation procedures in the feed processing. PMID:26053048

beta subunits influence the biophysical and pharmacological differences between P- and Q-type calcium currents expressed in a mammalian cell line.

PubMed

Moreno, H; Rudy, B; Llinás, R

1997-12-09

Human epithelial kidney cells (HEK) were prepared to coexpress alpha1A, alpha2delta with different beta calcium channel subunits and green fluorescence protein. To compare the calcium currents observed in these cells with the native neuronal currents, electrophysiological and pharmacological tools were used conjointly. Whole-cell current recordings of human epithelial kidney alpha1A-transfected cells showed small inactivating currents in 80 mM Ba2+ that were relatively insensitive to calcium blockers. Coexpression of alpha1A, betaIb, and alpha2delta produced a robust inactivating current detected in 10 mM Ba2+, reversibly blockable with low concentration of omega-agatoxin IVA (omega-Aga IVA) or synthetic funnel-web spider toxin (sFTX). Barium currents were also supported by alpha1A, beta2a, alpha2delta subunits, which demonstrated the slowest inactivation and were relatively insensitive to omega-Aga IVA and sFTX. Coexpression of beta3 with the same combination as above produced inactivating currents also insensitive to low concentration of omega-Aga IVA and sFTX. These data indicate that the combination alpha1A, betaIb, alpha2delta best resembles P-type channels given the rate of inactivation and the high sensitivity to omega-Aga IVA and sFTX. More importantly, the specificity of the channel blocker is highly influenced by the beta subunit associated with the alpha1A subunit.

Production of a Highly Protease-Resistant Fungal α-Galactosidase in Transgenic Maize Seeds for Simplified Feed Processing.

PubMed

Yang, Wenxia; Zhang, Yuhong; Zhou, Xiaojin; Zhang, Wei; Xu, Xiaolu; Chen, Rumei; Meng, Qingchang; Yuan, Jianhua; Yang, Peilong; Yao, Bin

2015-01-01

Raffinose-family oligosaccharide (RFO) in soybeans is one of the major anti-nutritional factors for poultry and livestocks. α-Galactosidase is commonly supplemented into the animal feed to hydrolyze α-1,6-galactosidic bonds on the RFOs. To simplify the feed processing, a protease-resistant α-galactosidase encoding gene from Gibberella sp. strain F75, aga-F75, was modified by codon optimization and heterologously expressed in the embryos of transgentic maize driven by the embryo-specific promoter ZM-leg1A. The progenies were produced by backcrossing with the commercial inbred variety Zheng58. PCR, southern blot and western blot analysis confirmed the stable integration and tissue specific expression of the modified gene, aga-F75m, in seeds over four generations. The expression level of Aga-F75M reached up to 10,000 units per kilogram of maize seeds. In comparison with its counterpart produced in Pichia pastoris strain GS115, maize seed-derived Aga-F75M showed a lower temperature optimum (50 °C) and lower stability over alkaline pH range, but better thermal stability at 60 °C to 70 °C and resistance to feed pelleting inactivation (80 °C). This is the first report of producing α-galactosidase in transgenic plant. The study offers an effective and economic approach for direct utilization of α-galactosidase-producing maize without any purification or supplementation procedures in the feed processing.

Topical minoxidil fortified with finasteride: An account of maintenance of hair density after replacing oral finasteride.

PubMed

Chandrashekar, B S; Nandhini, T; Vasanth, Vani; Sriram, Rashmi; Navale, Shreya

2015-01-01

Finasteride acts by reducing dihydrotestosterone levels, thereby inhibiting miniaturization of hair follicles in patients with androgenetic alopecia (AGA). Oral finasteride is associated with side effects such as decreased libido, sexual dysfunction, and gynecomastia. The aim of the following study is to assess the efficacy of maintaining hair growth with 5% topical minoxidil fortified with 0.1% finasteride in patients with AGA after initial treatment with 5% topical minoxidil and oral finasteride for two years. A retrospective assessment was done in 50 male patients aged 20-40 years with AGA. All the patients had been initially treated with topical minoxidil and oral finasteride for a period of two years, after which the oral finasteride was replaced with topical minoxidil fortified with finasteride. Five of 50 patients had discontinued the treatment for a period of 8-12 months and were then resumed with only topical minoxidil fortified with finasteride. The patients' case sheets and photographs were reviewed by independent observers and the efficacy of minoxidil-finasteride combination was assessed. Of the 45 patients who underwent a continuous treatment for AGA, 84.44% maintained a good hair density with topical minoxidil-finasteride combinatio. Of the five patients who discontinued oral finasteride for 8-12 months, four demonstrated good improvement in hair density when treatment was resumed with topical minoxidil-finasteride combination. Topical finasteride can be considered for hair density maintenance after initial improvement with oral finasteride, thereby obviating the indefinite use of oral finasteride.

Auditory pathway maturational study in small for gestational age preterm infants.

PubMed

Angrisani, Rosanna Giaffredo; Diniz, Edna Maria Albuquerque; Guinsburg, Ruth; Ferraro, Alexandre Archanjo; Azevedo, Marisa Frasson de; Matas, Carla Gentile

2014-01-01

To follow up the maturation of the auditory pathway in preterm infants small for gestational age (SGA), through the study of absolute and interpeak latencies of auditory brainstem response (ABR) in the first six months of age. This multicentric prospective cross-sectional and longitudinal study assessed 76 newborn infants, 35 SGA and 41 appropriate for gestational age (AGA), born between 33 and 36 weeks in the first evaluation. The ABR was carried out in three moments (neonatal period, three months and six months). Twenty-nine SGA and 33 AGA (62 infants), between 51 and 54 weeks (corrected age), returned for the second evaluation. In the third evaluation, 49 infants (23 SGA and 26 AGA), with age range from 63 to 65 weeks (corrected age), were assessed. The bilateral presence of Transient Evoked Otoacoustic Emissions and normal tympanogram were inclusion criteria. It was found interaural symmetry in both groups. The comparison between the two groups throughout the three periods studied showed no significant differences in the ABR parameters, except for the latencies of wave III in the period between three and six months. As for the maturation with tone burst 0.5 and 1 kHz, it was found that the groups did not differ. The findings suggest that, in the premature infants, the maturational process of the auditory pathway occurs in a similar rate for SGA and AGA. These results also suggest that prematurity is a more relevant factor for the maturation of the auditory pathway than birth weight.

A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

NASA Astrophysics Data System (ADS)

Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

Fireworks algorithm for mean-VaR/CVaR models

NASA Astrophysics Data System (ADS)

Zhang, Tingting; Liu, Zhifeng

2017-10-01

Intelligent algorithms have been widely applied to portfolio optimization problems. In this paper, we introduce a novel intelligent algorithm, named fireworks algorithm, to solve the mean-VaR/CVaR model for the first time. The results show that, compared with the classical genetic algorithm, fireworks algorithm not only improves the optimization accuracy and the optimization speed, but also makes the optimal solution more stable. We repeat our experiments at different confidence levels and different degrees of risk aversion, and the results are robust. It suggests that fireworks algorithm has more advantages than genetic algorithm in solving the portfolio optimization problem, and it is feasible and promising to apply it into this field.

Dynamic traffic assignment : genetic algorithms approach

DOT National Transportation Integrated Search

1997-01-01

Real-time route guidance is a promising approach to alleviating congestion on the nations highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithm...

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

NASA Astrophysics Data System (ADS)

Ebrahimi, Mehdi; Jahangirian, Alireza

2017-12-01

An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the genetic algorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional genetic algorithm.

[Reconstruction of Vehicle-human Crash Accident and Injury Analysis Based on 3D Laser Scanning, Multi-rigid-body Reconstruction and Optimized Genetic Algorithm].

PubMed

Sun, J; Wang, T; Li, Z D; Shao, Y; Zhang, Z Y; Feng, H; Zou, D H; Chen, Y J

2017-12-01

To reconstruct a vehicle-bicycle-cyclist crash accident and analyse the injuries using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, and to provide biomechanical basis for the forensic identification of death cause. The vehicle was measured by 3D laser scanning technology. The multi-rigid-body models of cyclist, bicycle and vehicle were developed based on the measurements. The value range of optimal variables was set. A multi-objective genetic algorithm and the nondominated sorting genetic algorithm were used to find the optimal solutions, which were compared to the record of the surveillance video around the accident scene. The reconstruction result of laser scanning on vehicle was satisfactory. In the optimal solutions found by optimization method of genetic algorithm, the dynamical behaviours of dummy, bicycle and vehicle corresponded to that recorded by the surveillance video. The injury parameters of dummy were consistent with the situation and position of the real injuries on the cyclist in accident. The motion status before accident, damage process by crash and mechanical analysis on the injury of the victim can be reconstructed using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, which have application value in the identification of injury manner and analysis of death cause in traffic accidents. Copyright© by the Editorial Department of Journal of Forensic Medicine

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

NASA Astrophysics Data System (ADS)

Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.

2018-03-01

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

Research on laser marking speed optimization by using genetic algorithm.

PubMed

Wang, Dongyun; Yu, Qiwei; Zhang, Yu

2015-01-01

Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%.

Tag SNP selection via a genetic algorithm.

PubMed

Mahdevar, Ghasem; Zahiri, Javad; Sadeghi, Mehdi; Nowzari-Dalini, Abbas; Ahrabian, Hayedeh

2010-10-01

Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.

Research on rolling element bearing fault diagnosis based on genetic algorithm matching pursuit

NASA Astrophysics Data System (ADS)

Rong, R. W.; Ming, T. F.

2017-12-01

In order to solve the problem of slow computation speed, matching pursuit algorithm is applied to rolling bearing fault diagnosis, and the improvement are conducted from two aspects that are the construction of dictionary and the way to search for atoms. To be specific, Gabor function which can reflect time-frequency localization characteristic well is used to construct the dictionary, and the genetic algorithm to improve the searching speed. A time-frequency analysis method based on genetic algorithm matching pursuit (GAMP) algorithm is proposed. The way to set property parameters for the improvement of the decomposition results is studied. Simulation and experimental results illustrate that the weak fault feature of rolling bearing can be extracted effectively by this proposed method, at the same time, the computation speed increases obviously.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumuluru, Jaya Shankar; McCulloch, Richard Chet James

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the mostmore » improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.« less

Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm

ERIC Educational Resources Information Center

Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.

2009-01-01

Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

ERIC Educational Resources Information Center

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease

PubMed Central

Rausch, Tobias; Thomas, Alun; Camp, Nicola J.; Cannon-Albright, Lisa A.; Facelli, Julio C.

2008-01-01

This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise. PMID:18547558

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

PubMed

Sun, Tao; Xu, Ming-Hai

2017-01-01

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

Performance impact of mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems.

PubMed

Liu, Chun; Kroll, Andreas

2016-01-01

Multi-robot task allocation determines the task sequence and distribution for a group of robots in multi-robot systems, which is one of constrained combinatorial optimization problems and more complex in case of cooperative tasks because they introduce additional spatial and temporal constraints. To solve multi-robot task allocation problems with cooperative tasks efficiently, a subpopulation-based genetic algorithm, a crossover-free genetic algorithm employing mutation operators and elitism selection in each subpopulation, is developed in this paper. Moreover, the impact of mutation operators (swap, insertion, inversion, displacement, and their various combinations) is analyzed when solving several industrial plant inspection problems. The experimental results show that: (1) the proposed genetic algorithm can obtain better solutions than the tested binary tournament genetic algorithm with partially mapped crossover; (2) inversion mutation performs better than other tested mutation operators when solving problems without cooperative tasks, and the swap-inversion combination performs better than other tested mutation operators/combinations when solving problems with cooperative tasks. As it is difficult to produce all desired effects with a single mutation operator, using multiple mutation operators (including both inversion and swap) is suggested when solving similar combinatorial optimization problems.

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

PubMed Central

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

PubMed

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

Genetic algorithm for nuclear data evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arthur, Jennifer Ann

These are slides on genetic algorithm for nuclear data evaluation. The following is covered: initial population, fitness (outer loop), calculate fitness, selection (first part of inner loop), reproduction (second part of inner loop), solution, and examples.

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

PubMed Central

Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

2013-01-01

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

Fuel management optimization using genetic algorithms and expert knowledge

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1996-09-01

The CIGARO fuel management optimization code based on genetic algorithms is described and tested. The test problem optimized the core lifetime for a pressurized water reactor with a penalty function constraint on the peak normalized power. A bit-string genotype encoded the loading patterns, and genotype bias was reduced with additional bits. Expert knowledge about fuel management was incorporated into the genetic algorithm. Regional crossover exchanged physically adjacent fuel assemblies and improved the optimization slightly. Biasing the initial population toward a known priority table significantly improved the optimization.

Optimal placement of tuning masses on truss structures by genetic algorithms

NASA Technical Reports Server (NTRS)

Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.

1993-01-01

Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.

Application of a Genetic Algorithm and Multi Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the Workplace

DTIC Science & Technology

2008-06-01

postponed the fulfillment of her own Masters Degree by at least 18 months so that I would have the opportunity to earn mine. She is smart , lovely...GENETIC ALGORITHM AND MULTI AGENT SYSTEM TO EXPLORE EMERGENT PATTERNS OF SOCIAL RATIONALITY AND A DISTRESS-BASED MODEL FOR DECEIT IN THE WORKPLACE...of a Genetic Algorithm and Mutli Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Investigation on application of genetic algorithms to optimal reactive power dispatch of power systems

NASA Astrophysics Data System (ADS)

Wu, Q. H.; Ma, J. T.

1993-09-01

A primary investigation into application of genetic algorithms in optimal reactive power dispatch and voltage control is presented. The application was achieved, based on (the United Kingdom) National Grid 48 bus network model, using a novel genetic search approach. Simulation results, compared with that obtained using nonlinear programming methods, are included to show the potential of applications of the genetic search methodology in power system economical and secure operations.

Algorithme intelligent d'optimisation d'un design structurel de grande envergure

NASA Astrophysics Data System (ADS)

Dominique, Stephane

The implementation of an automated decision support system in the field of design and structural optimisation can give a significant advantage to any industry working on mechanical designs. Indeed, by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work, the system may reduce the project cycle time, or allow more time to produce a better design. This thesis presents a new approach to automate a design process based on Case-Based Reasoning (CBR), in combination with a new genetic algorithm named Genetic Algorithm with Territorial core Evolution (GATE). This approach was developed in order to reduce the operating cost of the process. However, as the system implementation cost is quite expensive, the approach is better suited for large scale design problem, and particularly for design problems that the designer plans to solve for many different specification sets. First, the CBR process uses a databank filled with every known solution to similar design problems. Then, the closest solutions to the current problem in term of specifications are selected. After this, during the adaptation phase, an artificial neural network (ANN) interpolates amongst known solutions to produce an additional solution to the current problem using the current specifications as inputs. Each solution produced and selected by the CBR is then used to initialize the population of an island of the genetic algorithm. The algorithm will optimise the solution further during the refinement phase. Using progressive refinement, the algorithm starts using only the most important variables for the problem. Then, as the optimisation progress, the remaining variables are gradually introduced, layer by layer. The genetic algorithm that is used is a new algorithm specifically created during this thesis to solve optimisation problems from the field of mechanical device structural design. The algorithm is named GATE, and is essentially a real number genetic algorithm that prevents new individuals to be born too close to previously evaluated solutions. The restricted area becomes smaller or larger during the optimisation to allow global or local search when necessary. Also, a new search operator named Substitution Operator is incorporated in GATE. This operator allows an ANN surrogate model to guide the algorithm toward the most promising areas of the design space. The suggested CBR approach and GATE were tested on several simple test problems, as well as on the industrial problem of designing a gas turbine engine rotor's disc. These results are compared to other results obtained for the same problems by many other popular optimisation algorithms, such as (depending of the problem) gradient algorithms, binary genetic algorithm, real number genetic algorithm, genetic algorithm using multiple parents crossovers, differential evolution genetic algorithm, Hookes & Jeeves generalized pattern search method and POINTER from the software I-SIGHT 3.5. Results show that GATE is quite competitive, giving the best results for 5 of the 6 constrained optimisation problem. GATE also provided the best results of all on problem produced by a Maximum Set Gaussian landscape generator. Finally, GATE provided a disc 4.3% lighter than the best other tested algorithm (POINTER) for the gas turbine engine rotor's disc problem. One drawback of GATE is a lesser efficiency for highly multimodal unconstrained problems, for which he gave quite poor results with respect to its implementation cost. To conclude, according to the preliminary results obtained during this thesis, the suggested CBR process, combined with GATE, seems to be a very good candidate to automate and accelerate the structural design of mechanical devices, potentially reducing significantly the cost of industrial preliminary design processes.

Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm

NASA Astrophysics Data System (ADS)

Sun, Xiuqiao; Wang, Jian

2018-07-01

Freeway service patrol (FSP), is considered to be an effective method for incident management and can help transportation agency decision-makers alter existing route coverage and fleet allocation. This paper investigates the FSP problem of patrol routing design and fleet allocation, with the objective of minimizing the overall average incident response time. While the simulated annealing (SA) algorithm and its improvements have been applied to solve this problem, they often become trapped in local optimal solution. Moreover, the issue of searching efficiency remains to be further addressed. In this paper, we employ the genetic algorithm (GA) and SA to solve the FSP problem. To maintain population diversity and avoid premature convergence, niche strategy is incorporated into the traditional genetic algorithm. We also employ elitist strategy to speed up the convergence. Numerical experiments have been conducted with the help of the Sioux Falls network. Results show that the GA slightly outperforms the dual-based greedy (DBG) algorithm, the very large-scale neighborhood searching (VLNS) algorithm, the SA algorithm and the scenario algorithm.

Research on Laser Marking Speed Optimization by Using Genetic Algorithm

PubMed Central

Wang, Dongyun; Yu, Qiwei; Zhang, Yu

2015-01-01

Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%. PMID:25955831

Parana Basin Structure from Multi-Objective Inversion of Surface Wave and Receiver Function by Competent Genetic Algorithm

NASA Astrophysics Data System (ADS)

An, M.; Assumpcao, M.

2003-12-01

The joint inversion of receiver function and surface wave is an effective way to diminish the influences of the strong tradeoff among parameters and the different sensitivity to the model parameters in their respective inversions, but the inversion problem becomes more complex. Multi-objective problems can be much more complicated than single-objective inversion in the model selection and optimization. If objectives are involved and conflicting, models can be ordered only partially. In this case, Pareto-optimal preference should be used to select solutions. On the other hand, the inversion to get only a few optimal solutions can not deal properly with the strong tradeoff between parameters, the uncertainties in the observation, the geophysical complexities and even the incompetency of the inversion technique. The effective way is to retrieve the geophysical information statistically from many acceptable solutions, which requires more competent global algorithms. Competent genetic algorithms recently proposed are far superior to the conventional genetic algorithm and can solve hard problems quickly, reliably and accurately. In this work we used one of competent genetic algorithms, Bayesian Optimization Algorithm as the main inverse procedure. This algorithm uses Bayesian networks to draw out inherited information and can use Pareto-optimal preference in the inversion. With this algorithm, the lithospheric structure of Paran"› basin is inverted to fit both the observations of inter-station surface wave dispersion and receiver function.

A Genetic-Based Scheduling Algorithm to Minimize the Makespan of the Grid Applications

NASA Astrophysics Data System (ADS)

Entezari-Maleki, Reza; Movaghar, Ali

Task scheduling algorithms in grid environments strive to maximize the overall throughput of the grid. In order to maximize the throughput of the grid environments, the makespan of the grid tasks should be minimized. In this paper, a new task scheduling algorithm is proposed to assign tasks to the grid resources with goal of minimizing the total makespan of the tasks. The algorithm uses the genetic approach to find the suitable assignment within grid resources. The experimental results obtained from applying the proposed algorithm to schedule independent tasks within grid environments demonstrate the applicability of the algorithm in achieving schedules with comparatively lower makespan in comparison with other well-known scheduling algorithms such as, Min-min, Max-min, RASA and Sufferage algorithms.

Genetic Algorithms to Optimizatize Lecturer Assessment's Criteria

NASA Astrophysics Data System (ADS)

Jollyta, Deny; Johan; Hajjah, Alyauma

2017-12-01

The lecturer assessment criteria is used as a measurement of the lecturer's performance in a college environment. To determine the value for a criteriais complicated and often leads to doubt. The absence of a standard valuefor each assessment criteria will affect the final results of the assessment and become less presentational data for the leader of college in taking various policies relate to reward and punishment. The Genetic Algorithm comes as an algorithm capable of solving non-linear problems. Using chromosomes in the random initial population, one of the presentations is binary, evaluates the fitness function and uses crossover genetic operator and mutation to obtain the desired crossbreed. It aims to obtain the most optimum criteria values in terms of the fitness function of each chromosome. The training results show that Genetic Algorithm able to produce the optimal values of lecturer assessment criteria so that can be usedby the college as a standard value for lecturer assessment criteria.

A theoretical comparison of evolutionary algorithms and simulated annealing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, W.E.

1995-08-28

This paper theoretically compares the performance of simulated annealing and evolutionary algorithms. Our main result is that under mild conditions a wide variety of evolutionary algorithms can be shown to have greater performance than simulated annealing after a sufficiently large number of function evaluations. This class of EAs includes variants of evolutionary strategie and evolutionary programming, the canonical genetic algorithm, as well as a variety of genetic algorithms that have been applied to combinatorial optimization problems. The proof of this result is based on a performance analysis of a very general class of stochastic optimization algorithms, which has implications formore » the performance of a variety of other optimization algorithm.« less

Design of Genetic Algorithms for Topology Control of Unmanned Vehicles

DTIC Science & Technology

2010-01-01

decentralised topology control mechanism distributed among active running software agents to achieve a uniform spread of terrestrial unmanned vehicles...14. ABSTRACT We present genetic algorithms (GAs) as a decentralised topology control mechanism distributed among active running software agents to...inspired topology control algorithm. The topology control of UVs using a decentralised solution over an unknown geographical terrain is a challenging

Combinatorial optimization problem solution based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Zhang, Peng

2017-08-01

Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.

Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system

PubMed Central

Page, Andrew J.; Keane, Thomas M.; Naughton, Thomas J.

2010-01-01

We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms. PMID:20862190

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Light-extraction enhancement for light-emitting diodes: a firefly-inspired structure refined by the genetic algorithm

NASA Astrophysics Data System (ADS)

Bay, Annick; Mayer, Alexandre

2014-09-01

The efficiency of light-emitting diodes (LED) has increased significantly over the past few years, but the overall efficiency is still limited by total internal reflections due to the high dielectric-constant contrast between the incident and emergent media. The bioluminescent organ of fireflies gave incentive for light-extraction enhance-ment studies. A specific factory-roof shaped structure was shown, by means of light-propagation simulations and measurements, to enhance light extraction significantly. In order to achieve a similar effect for light-emitting diodes, the structure needs to be adapted to the specific set-up of LEDs. In this context simulations were carried out to determine the best geometrical parameters. In the present work, the search for a geometry that maximizes the extraction of light has been conducted by using a genetic algorithm. The idealized structure considered previously was generalized to a broader variety of shapes. The genetic algorithm makes it possible to search simultaneously over a wider range of parameters. It is also significantly less time-consuming than the previous approach that was based on a systematic scan on parameters. The results of the genetic algorithm show that (1) the calculations can be performed in a smaller amount of time and (2) the light extraction can be enhanced even more significantly by using optimal parameters determined by the genetic algorithm for the generalized structure. The combination of the genetic algorithm with the Rigorous Coupled Waves Analysis method constitutes a strong simulation tool, which provides us with adapted designs for enhancing light extraction from light-emitting diodes.

Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

PubMed

Bellucci, Michael A; Coker, David F

2011-07-28

We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. © 2011 American Institute of Physics

[The effects of electro-acupuncture on the signaling pathway of TLR/MYD88 in ankle joint synovial tissue of acute gouty arthritis rats].

PubMed

Zhang, Chao-nan; Huang, Xue-kuan; Luo, Yan; Jiang, Juan; Wan, Lei; Wang, Ling

2014-11-01

To investigate the effects of electro-acupuncture ( EA) on the related protein expression of the signaling pathway of the toll-like receptor2 (TLR2)/myeloid differentiation factor (MYD) 88 in ankle joint synovial tissue of acute gouty arthritis (AGA) rats. Fifty male SD rats were randomly divided into 5 groups: normal group, SMD group, AGA model group, medication group and EA group, 10 rats in each group. SMD group established model by inducing SMD, other groups established AGA model by inducing monosodium urate, except the normal group. Two days before model was established, normal and SMD and AGA model groups were lavaged with normal saline (20 mL/kg), medication group was lavaged with colchicine solution (1 mg/kg), EA (1. 5-2 Hz, D.-D. wave, 9 V, 1-3 mA) was applied to"Sanyinjiao" (SP6),"Jiexi"(ST41) and "kunlun" (BL60) for 20 min, once daily, continuously for 9 days. Then the join sewlling index was observed periodically, the protein expression of TLR2 and MYD88 was determined by immunohistochemistry. Compared to the normal group, the join sewlling of the SMD group in test join increased significantly (P<0. 05) and the protein expression of TLR2 and MYD88 in synovial tissue has not statistically significant (P>0.05), the oin sewlling and protein expression of TLR2 and MYD88 in synovial tissue of model group increased significantly P<0. 05); The medication and EA group compared to the model group, the protein expression of TLR2 and MYD88 in synovial tissue decreased significantly (P <0. 05), the join sewlling in test join decreased significantly P<1. 05); There were not statistically significant between the EA group and the medication group (P>0.05). EA can alleviate the symptoms of AGA, which may be related to regulation of the protein expression Y TRI and MYD88 in the TLR/MYD88 signaling pathway.

Left-dominant arrhythmogenic cardiomyopathy in a large family: associated desmosomal or nondesmosomal genotype?

PubMed

Groeneweg, Judith A; van der Zwaag, Paul A; Jongbloed, Jan D H; Cox, Moniek G P J; Vreeker, Arnold; de Boer, Rudolf A; van der Heijden, Jeroen F; van Veen, Toon A B; McKenna, William J; van Tintelen, J Peter; Dooijes, Dennis; Hauer, Richard N W

2013-04-01

Arrhythmogenic cardiomyopathy (AC) is considered a predominantly right ventricular (RV) desmosomal disease. However, left-dominant forms due to desmosomal gene mutations, including PKP2 variant c.419C>T, have been described. Recently, a nondesmosomal phospholamban (PLN) mutation (c.40_42delAGA) has been identified, causing dilated cardiomyopathy and arrhythmias. To gain more insight into pathogenicity of the PKP2 variant c.419C>T by cosegregation analysis of the PKP2 variant c.419C>T vs the PLN mutation c.40_42delAGA. A Dutch family (13 family members, median age 49 years, range 34-71 years) with ventricular tachycardia underwent (1) meticulous phenotypic characterization and (2) screening of 5 desmosomal genes (PKP2, DSC2, DSG2, DSP, JUP) and PLN. Six family members fulfilled 2010 AC Task Force Criteria. Seven had signs of left ventricular (LV) involvement (inverted T waves in leads V4-V6, LV wall motion abnormalities and late enhancement, and reduced LV ejection fraction), including 6 family members with proven AC. The PKP2 variant c.419C>T was found as a single variant in 3 family members, combined with the PLN mutation c.40_42delAGA in 3 others. PLN mutation was found in 9 family members, including the 6 with AC and all 7 with LV involvement. The PLN mutation c.40_42delAGA was found as a single mutation in 6, combined with the PKP2 variant c.419C>T in 3 others. A low-voltage electrocardiogram was seen in 4 of 9 PLN mutation-positive subjects. None of the family members with the single PKP2 variant showed any sign of RV or LV involvement. The PLN mutation c.40_42delAGA cosegregates with AC and with electrocardiographic and structural LV abnormalities. In this family, there was no evidence of disease-causing contribution of the PKP2 variant c.419C>T. Copyright © 2013 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Randomized outcome trial of nutrient-enriched formula and neurodevelopment outcome in preterm infants.

PubMed

Giannì, Maria Lorella; Roggero, Paola; Amato, Orsola; Picciolini, Odoardo; Piemontese, Pasqua; Liotto, Nadia; Taroni, Francesca; Mosca, Fabio

2014-03-19

Preterm infants are at risk for adverse neurodevelopment. Furthermore, nutrition may play a key role in supporting neurodevelopment. The aim of this study was to evaluate whether a nutrient-enriched formula fed to preterm infants after hospital discharge could improve their neurodevelopment at 24 months (term-corrected age). We conducted an observer-blinded, single-center, randomized controlled trial in infants admitted to the Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, University of Milan, Italy between 2009 and 2011. Inclusion criteria were gestational age < 32 weeks and/or birth weight < 1500 g, and being fed human milk for < 20% of the total milk intake. Exclusion criteria were congenital malformations or conditions that could interfere with growth or body composition. Included infants were randomized to receive a standard full-term formula or a nutrient-enriched formula up until 6 months of corrected age, using two computer-generated randomization lists; one appropriate for gestational age (AGA) and one for small for gestational age (SGA) infants. We assessed neurodevelopment at 24 months of corrected age using the Griffiths Mental Development Scale and related subscales (locomotor, personal-social, hearing and speech, hand and eye coordination, and performance). Of the 207 randomized infants, 181 completed the study. 52 AGA and 35 SGA infants were fed a nutrient-enriched formula, whereas 56 AGA and 38 SGA infants were fed a standard full-term formula. The general quotient at 24 months of corrected age was not significantly different between infants randomized to receive a nutrient-enriched formula compared with a standard term formula up until 6 months of corrected age (AGA infants: 93.8 ± 12.6 vs. 92.4 ± 10.4, respectively; SGA infants: 96.1 ± 9.9 vs. 98.2 ± 9, respectively). The scores of related subscales were also similar among groups. This study found that feeding preterm infants a nutrient-enriched formula after discharge does not affect neurodevelopment at 24 months of corrected age, in either AGA or SGA infants, free from major comorbidities. Current Controlled Trials (http://www.controlled-trials.com/ISRCTN30189842) London, UK.

MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION

EPA Science Inventory

In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...

Low-level laser therapy as a treatment for androgenetic alopecia.

PubMed

Afifi, Ladan; Maranda, Eric L; Zarei, Mina; Delcanto, Gina M; Falto-Aizpurua, Leyre; Kluijfhout, Wouter P; Jimenez, Joaquin J

2017-01-01

Androgenetic alopecia (AGA) affects 50% of males by age 50 and 50% of females by age 80. Recently, the use of low-level laser therapy (LLLT) has been proposed as a treatment for hair loss and to stimulate hair regrowth in AGA. This paper aims to review the existing research studies to determine whether LLLT is an effective therapy for AGA based on objective measurements and patient satisfaction. A systematic literature review was done to identify articles on Medline, Google Scholar, and Embase that were published between January 1960 and November 2015. All search hits were screened by two reviewers and examined for relevant abstracts and titles. Articles were divided based on study design and assessed for risk of bias. Eleven studies were evaluated, which investigated a total of 680 patients, consisting of 444 males and 236 females. Nine out of 11 studies assessing hair count/hair density found statistically significant improvements in both males and females following LLLT treatment. Additionally, hair thickness and tensile strength significantly improved in two out of four studies. Patient satisfaction was investigated in five studies, and was overall positive, though not as profound as the objective outcomes. The majority of studies covered in this review found an overall improvement in hair regrowth, thickness, and patient satisfaction following LLLT therapy. Although we should be cautious when interpreting these findings, LLLT therapy seems to be a promising monotherapy for AGA and may serve as an effective alternative for individuals unwilling to use medical therapy or undergo surgical options. Lasers Surg. Med. 49:27-39, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A Proposal of an Effective Platelet-rich Plasma Protocol for the Treatment of Androgenetic Alopecia

PubMed Central

Ferrando, Juan; García-García, Sandra Cecilia; González-de-Cossío, Ana Cecilia; Bou, Lola; Navarra, Esperanza

2017-01-01

Background: Platelet-rich plasma (PRP) has emerged as a promising treatment for androgenetic alopecia (AGA). In spite of the several studies previously reported, to date, a standardized protocol for PRP preparation and application, as well as a standard method for evaluating results has not been established. Aims: The aim of this study is to propose a standardized method for preparation and application of PRP for male AGA (MAGA) and female AGA (FAGA) and assess its safety and efficacy as a co-adjuvant therapy. Materials and Methods: Seventy-eight patients, 19 men and 59 women with AGA Grades II–IV in Ebling's scale, currently on treatment with topical minoxidil and/or oral finasteride for more than a year without improvement, were included in this study. PRP was prepared using a single spin method, and injected in affected areas for 3 monthly sessions, followed by 3 bimonthly sessions. A decrease of at least one grade in Ebling's scale was considered a successful result. Results: After the 6° session, 71.4% of MAGA and 73.4% of FAGA patients reached a successful outcome while 21.4% and 16.3%, respectively, remained without changes. Only 7.1% of MAGA and 10.2% of FAGA presented worsening of their condition. Conclusions: PRP together with a periodical application protocol can be considered effective as a coadjuvant therapy in patients who no longer respond to pharmacological treatments. Ebling's scale was a practical and reliable parameter to allow a better evaluation in both MAGA and FAGA. PMID:29118521

Strain gage selection in loads equations using a genetic algorithm

NASA Technical Reports Server (NTRS)

1994-01-01

Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a genetic algorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the genetic algorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The genetic algorithm arrived at a comparable or superior set of gages with significantly less human effort, and could be applied in instances when the current methods could not.

A hybrid genetic algorithm for solving bi-objective traveling salesman problems

NASA Astrophysics Data System (ADS)

Ma, Mei; Li, Hecheng

2017-08-01

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, in a traditional TSP only tour distance is taken as a unique objective to be minimized. When more than one optimization objective arises, the problem is known as a multi-objective TSP. In the present paper, a bi-objective traveling salesman problem (BOTSP) is taken into account, where both the distance and the cost are taken as optimization objectives. In order to efficiently solve the problem, a hybrid genetic algorithm is proposed. Firstly, two satisfaction degree indices are provided for each edge by considering the influences of the distance and the cost weight. The first satisfaction degree is used to select edges in a “rough” way, while the second satisfaction degree is executed for a more “refined” choice. Secondly, two satisfaction degrees are also applied to generate new individuals in the iteration process. Finally, based on genetic algorithm framework as well as 2-opt selection strategy, a hybrid genetic algorithm is proposed. The simulation illustrates the efficiency of the proposed algorithm.

Wide variation in virulence and genetic diversity of binucleate Rhizoctonia isolates associated with root rot of strawberry in Western Australia.

PubMed

Fang, Xiangling; Finnegan, Patrick M; Barbetti, Martin J

2013-01-01

Strawberry (Fragaria×ananassa) is one of the most important berry crops in the world. Root rot of strawberry caused by Rhizoctonia spp. is a serious threat to commercial strawberry production worldwide. However, there is no information on the genetic diversity and phylogenetic status of Rhizoctonia spp. associated with root rot of strawberry in Australia. To address this, a total of 96 Rhizoctonia spp. isolates recovered from diseased strawberry plants in Western Australia were characterized for their nuclear condition, virulence, genetic diversity and phylogenetic status. All the isolates were found to be binucleate Rhizoctonia (BNR). Sixty-five of the 96 BNR isolates were pathogenic on strawberry, but with wide variation in virulence, with 25 isolates having high virulence. Sequence analysis of the internal transcribed spacers of the ribosomal DNA separated the 65 pathogenic BNR isolates into six distinct clades. The sequence analysis also separated reference BNR isolates from strawberry or other crops across the world into clades that correspond to their respective anastomosis group (AG). Some of the pathogenic BNR isolates from this study were embedded in the clades for AG-A, AG-K and AG-I, while other isolates formed clades that were sister to the clades specific for AG-G, AG-B, AG-I and AG-C. There was no significant association between genetic diversity and virulence of these BNR isolates. This study demonstrates that pathogenic BNR isolates associated with root rot of strawberry in Western Australia have wide genetic diversity, and highlights new genetic groups not previously found to be associated with root rot of strawberry in the world (e.g., AG-B) or in Australia (e.g., AG-G). The wide variation in virulence and genetic diversity identified in this study will be of high value for strawberry breeding programs in selecting, developing and deploying new cultivars with resistance to these multi-genetic groups of BNR.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species.

PubMed

Ullah, Saleem; Groen, Thomas A; Schlerf, Martin; Skidmore, Andrew K; Nieuwenhuis, Willem; Vaiphasa, Chaichoke

2012-01-01

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator.

PubMed Central

Rabow, A. A.; Scheraga, H. A.

1996-01-01

We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints. PMID:8880904

A novel α-galactosidase from the thermophilic probiotic Bacillus coagulans with remarkable protease-resistance and high hydrolytic activity

PubMed Central

Zhao, Ruili; Zhao, Rui; Tu, Yishuai; Zhang, Xiaoming; Deng, Liping

2018-01-01

A novel α-galactosidase of glycoside hydrolase family 36 was cloned from Bacillus coagulans, overexpressed in Escherichia coli, and characterized. The purified enzyme Aga-BC7050 was 85 kDa according to SDS-PAGE and 168 kDa according to gel filtration, indicating that its native structure is a dimer. With p-nitrophenyl-α-d- galactopyranoside (pNPGal) as the substrate, optimal temperature and pH were 55 °C and 6.0, respectively. At 60 °C for 30 min, it retained > 50% of its activity. It was stable at pH 5.0–10.0, and showed remarkable resistance to proteinase K, subtilisin A, α-chymotrypsin, and trypsin. Its activity was not inhibited by glucose, sucrose, xylose, or fructose, but was slightly inhibited at galactose concentrations up to 100 mM. Aga-BC7050 was highly active toward pNPGal, melibiose, raffinose, and stachyose. It completely hydrolyzed melibiose, raffinose, and stachyose in < 30 min. These characteristics suggest that Aga-BC7050 could be used in feed and food industries and sugar processing. PMID:29738566

Exploring the Role of Different Neonatal Nutrition Regimens during the First Week of Life by Urinary GC-MS Metabolomics

PubMed Central

Dessì, Angelica; Murgia, Antonio; Agostino, Rocco; Pattumelli, Maria Grazia; Schirru, Andrea; Scano, Paola; Fanos, Vassilios; Caboni, Pierluigi

2016-01-01

In this study, a gas-chromatography mass spectrometry (GC-MS) metabolomics study was applied to examine urine metabolite profiles of different classes of neonates under different nutrition regimens. The study population included 35 neonates, exclusively either breastfed or formula milk fed, in a seven-day timeframe. Urine samples were collected from intrauterine growth restriction (IUGR), large for gestational age (LGA), and appropriate gestational age (AGA) neonates. At birth, IUGR and LGA neonates showed similarities in their urine metabolite profiles that differed from AGA. When neonates started milk feeding, their metabolite excretion profile was strongly characterized by the different diet regimens. After three days of formula milk nutrition, urine had higher levels of glucose, galactose, glycine and myo-inositol, while up-regulated aconitic acid, aminomalonic acid and adipic acid were found in breast milk fed neonates. At seven days, neonates fed with formula milk shared higher levels of pseudouridine with IUGR and LGA at birth. Breastfed neonates shared up-regulated pyroglutamic acid, citric acid, and homoserine, with AGA at birth. The role of most important metabolites is herein discussed. PMID:26907266

Circulating GLP-1 in infants born small-for-gestational-age: breast-feeding versus formula-feeding.

PubMed

Díaz, M; Bassols, J; Sebastiani, G; López-Bermejo, A; Ibáñez, L; de Zegher, F

2015-10-01

Prenatal growth restraint associates with the risk for later diabetes, particularly if such restraint is followed by postnatal formula-feeding (FOF) rather than breast-feeding (BRF). Circulating incretins can influence the neonatal programming of hypothalamic setpoints for appetite and energy expenditure, and are thus candidate mediators of the long-term effects exerted by early nutrition. We have tested this concept by measuring (at birth and at age 4 months) the circulating concentrations of glucagon-like peptide-1 (GLP-1) in BRF infants born appropriate-for-gestational-age (AGA; n=63) and in small-for-gestational-age (SGA) infants receiving either BRF (n=28) or FOF (n=26). At birth, concentrations of GLP-1 were similar in AGA and SGA infants. At 4 months, pre-feeding GLP-1 concentrations were higher than at birth; SGA-BRF infants had GLP-1 concentrations similar to those in AGA-BRF infants but SGA-FOF infants had higher concentrations. In conclusion, nutrition appears to influence the circulating GLP-1 concentrations in SGA infants and may thereby modulate long-term diabetes risk.

Expression and enzymatic characterization of a cold-adapted β-agarase from Antarctic bacterium Pseudoalteromonas sp. NJ21

NASA Astrophysics Data System (ADS)

Li, Jiang; Sha, Yujie

2015-03-01

An agar-degrading bacterium, designated as Pseudoalteromonas sp. NJ21, was isolated from an Antarctic sediment sample. The agarase gene aga1161 from Pseudoalteromonas sp. NJ21 consisting of a 2 382-bp coding region was cloned. The gene encodes a 793-amino acids protein and was found to possess characteristic features of the Glyco_hydro_42 family. The recombinant agarase (rAga1161) was overexpressed in Escherichia coli and purified as a fusion protein. Enzyme activity analysis revealed that the optimum temperature and pH for the purified recombinant agarase were 30-40°C and 8.0, respectively. rAga1161 was found to maintain as much as 80% of its maximum activity at 10°C, which is typical of a coldadapted enzyme. The pattern of agar hydrolysis demonstrated that the enzyme is an β-agarase, producing neoagarobiose (NA2) as the final main product. Furthermore, this work is the first proof of an agarolytic activity in Antarctic bacteria and these results indicate the potential for the Antarctic agarase as a catalyst in medicine, food and cosmetic industries.

The genetic algorithm: A robust method for stress inversion

NASA Astrophysics Data System (ADS)

Thakur, Prithvi; Srivastava, Deepak C.; Gupta, Pravin K.

2017-01-01

The stress inversion of geological or geophysical observations is a nonlinear problem. In most existing methods, it is solved by linearization, under certain assumptions. These linear algorithms not only oversimplify the problem but also are vulnerable to entrapment of the solution in a local optimum. We propose the use of a nonlinear heuristic technique, the genetic algorithm, which searches the global optimum without making any linearizing assumption or simplification. The algorithm mimics the natural evolutionary processes of selection, crossover and mutation and, minimizes a composite misfit function for searching the global optimum, the fittest stress tensor. The validity and efficacy of the algorithm are demonstrated by a series of tests on synthetic and natural fault-slip observations in different tectonic settings and also in situations where the observations are noisy. It is shown that the genetic algorithm is superior to other commonly practised methods, in particular, in those tectonic settings where none of the principal stresses is directed vertically and/or the given data set is noisy.

USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES

EPA Science Inventory

Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...

The application of immune genetic algorithm in main steam temperature of PID control of BP network

NASA Astrophysics Data System (ADS)

Li, Han; Zhen-yu, Zhang

In order to overcome the uncertainties, large delay, large inertia and nonlinear property of the main steam temperature controlled object in the power plant, a neural network intelligent PID control system based on immune genetic algorithm and BP neural network is designed. Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network. The simulation result shows that the system is superior to conventional PID control system in the control of quality and robustness.

Optimization of multicast optical networks with genetic algorithm

NASA Astrophysics Data System (ADS)

Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng

2007-11-01

In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.

Real coded genetic algorithm for fuzzy time series prediction

NASA Astrophysics Data System (ADS)

Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

2017-10-01

Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

Air data system optimization using a genetic algorithm

NASA Technical Reports Server (NTRS)

Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III

1992-01-01

An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.

Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm

DOE PAGES

Terzić, Balša; Hofler, Alicia S.; Reeves, Cody J.; ...

2014-10-15

In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab's Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.

A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

NASA Astrophysics Data System (ADS)

Wang, Aimeng; Guo, Jiayu

2017-12-01

A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

PubMed Central

Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; Roy, Sudipta

2016-01-01

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500

Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm.

PubMed

Sethi, Gaurav; Saini, B S

2015-12-01

This paper presents an abdomen disease diagnostic system based on the flexi-scale curvelet transform, which uses different optimal scales for extracting features from computed tomography (CT) images. To optimize the scale of the flexi-scale curvelet transform, we propose an improved genetic algorithm. The conventional genetic algorithm assumes that fit parents will likely produce the healthiest offspring that leads to the least fit parents accumulating at the bottom of the population, reducing the fitness of subsequent populations and delaying the optimal solution search. In our improved genetic algorithm, combining the chromosomes of a low-fitness and a high-fitness individual increases the probability of producing high-fitness offspring. Thereby, all of the least fit parent chromosomes are combined with high fit parent to produce offspring for the next population. In this way, the leftover weak chromosomes cannot damage the fitness of subsequent populations. To further facilitate the search for the optimal solution, our improved genetic algorithm adopts modified elitism. The proposed method was applied to 120 CT abdominal images; 30 images each of normal subjects, cysts, tumors and stones. The features extracted by the flexi-scale curvelet transform were more discriminative than conventional methods, demonstrating the potential of our method as a diagnostic tool for abdomen diseases.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs

NASA Astrophysics Data System (ADS)

Schalkoff, Robert J.; Shaaban, Khaled M.

1999-07-01

Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.

Finite element analysis and genetic algorithm optimization design for the actuator placement on a large adaptive structure

NASA Astrophysics Data System (ADS)

Sheng, Lizeng

The dissertation focuses on one of the major research needs in the area of adaptive/intelligent/smart structures, the development and application of finite element analysis and genetic algorithms for optimal design of large-scale adaptive structures. We first review some basic concepts in finite element method and genetic algorithms, along with the research on smart structures. Then we propose a solution methodology for solving a critical problem in the design of a next generation of large-scale adaptive structures---optimal placements of a large number of actuators to control thermal deformations. After briefly reviewing the three most frequently used general approaches to derive a finite element formulation, the dissertation presents techniques associated with general shell finite element analysis using flat triangular laminated composite elements. The element used here has three nodes and eighteen degrees of freedom and is obtained by combining a triangular membrane element and a triangular plate bending element. The element includes the coupling effect between membrane deformation and bending deformation. The membrane element is derived from the linear strain triangular element using Cook's transformation. The discrete Kirchhoff triangular (DKT) element is used as the plate bending element. For completeness, a complete derivation of the DKT is presented. Geometrically nonlinear finite element formulation is derived for the analysis of adaptive structures under the combined thermal and electrical loads. Next, we solve the optimization problems of placing a large number of piezoelectric actuators to control thermal distortions in a large mirror in the presence of four different thermal loads. We then extend this to a multi-objective optimization problem of determining only one set of piezoelectric actuator locations that can be used to control the deformation in the same mirror under the action of any one of the four thermal loads. A series of genetic algorithms, GA Version 1, 2 and 3, were developed to find the optimal locations of piezoelectric actuators from the order of 1021 ˜ 1056 candidate placements. Introducing a variable population approach, we improve the flexibility of selection operation in genetic algorithms. Incorporating mutation and hill climbing into micro-genetic algorithms, we are able to develop a more efficient genetic algorithm. Through extensive numerical experiments, we find that the design search space for the optimal placements of a large number of actuators is highly multi-modal and that the most distinct nature of genetic algorithms is their robustness. They give results that are random but with only a slight variability. The genetic algorithms can be used to get adequate solution using a limited number of evaluations. To get the highest quality solution, multiple runs including different random seed generators are necessary. The investigation time can be significantly reduced using a very coarse grain parallel computing. Overall, the methodology of using finite element analysis and genetic algorithm optimization provides a robust solution approach for the challenging problem of optimal placements of a large number of actuators in the design of next generation of adaptive structures.

Selecting materialized views using random algorithm

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi

2007-04-01

The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

Ortho Image and DTM Generation with Intelligent Methods

NASA Astrophysics Data System (ADS)

Bagheri, H.; Sadeghian, S.

2013-10-01

Nowadays the artificial intelligent algorithms has considered in GIS and remote sensing. Genetic algorithm and artificial neural network are two intelligent methods that are used for optimizing of image processing programs such as edge extraction and etc. these algorithms are very useful for solving of complex program. In this paper, the ability and application of genetic algorithm and artificial neural network in geospatial production process like geometric modelling of satellite images for ortho photo generation and height interpolation in raster Digital Terrain Model production process is discussed. In first, the geometric potential of Ikonos-2 and Worldview-2 with rational functions, 2D & 3D polynomials were tested. Also comprehensive experiments have been carried out to evaluate the viability of the genetic algorithm for optimization of rational function, 2D & 3D polynomials. Considering the quality of Ground Control Points, the accuracy (RMSE) with genetic algorithm and 3D polynomials method for Ikonos-2 Geo image was 0.508 pixel sizes and the accuracy (RMSE) with GA algorithm and rational function method for Worldview-2 image was 0.930 pixel sizes. For more another optimization artificial intelligent methods, neural networks were used. With the use of perceptron network in Worldview-2 image, a result of 0.84 pixel sizes with 4 neurons in middle layer was gained. The final conclusion was that with artificial intelligent algorithms it is possible to optimize the existing models and have better results than usual ones. Finally the artificial intelligence methods, like genetic algorithms as well as neural networks, were examined on sample data for optimizing interpolation and for generating Digital Terrain Models. The results then were compared with existing conventional methods and it appeared that these methods have a high capacity in heights interpolation and that using these networks for interpolating and optimizing the weighting methods based on inverse distance leads to a high accurate estimation of heights.

Solar system 'fast mission' trajectories using aerogravity assist

NASA Technical Reports Server (NTRS)

Randolph, James E.; Mcronald, Angus D.

1992-01-01

Initial analyses of the aerogravity assist (AGA) delivery technique to solar system targets (and beyond) has been encouraging. Mission opportunities are introduced that do not exist with typical gravity assist trajectories and current launch capabilities. The technique has the most payoff for high-energy missions such as outer planet orbiters and flybys. The goal of this technique is to reduce the flight duration significantly and to eliminate propulsion for orbit insertion. The paper will discuss detailed analyses and parametric studies that consider launch opportunities for missions to the sun, Saturn, Uranus, Neptune, and Pluto using AGA at Venus and Mars.

Implications of Pharmacogenomics to the Management of IBS.

PubMed

Camilleri, Michael

2018-04-27

The objectives are to review the role of pharmacogenomics in drug metabolism of medications typically used in patients with irritable bowel syndrome (IBS) focusing predominantly on cytochrome P450 metabolism. Other aims are to provide examples of genetic variation of receptors or intermediary pathways that are targets for IBS drugs and to critically appraise the situations where precision medicine is impacting health in IBS. Pharmacogenomics impacts both pharmacokinetics and pharmacodynamics. Although large clinical trials have not incorporated testing for genetic variations that could impact the efficacy of medications in IBS, there are therapeutic advantages to inclusion of pharmacogenomics testing for individual patients, as has been demonstrated particularly in the treatment with central neuromodulators in psychiatry practice. Clinical practice in IBS is moving in the same direction with the aid of commercially available tests focused on drug metabolism. Specific mechanisms leading to pathophysiology of IBS are still poorly characterized, relative to diseases such as cancer and inflammatory bowel disease, and, therefore, pharmacogenomics related to drug pharmacodynamics is still in its infancy and requires extensive future research. With increased attention to pharmacogenomics affecting drug metabolism, it is anticipated that pharmacogenomics will impact care of IBS. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Comparison of the Efficacy of Homologous and Autologous Platelet-Rich Plasma (PRP) for Treating Androgenic Alopecia.

PubMed

Ince, Bilsev; Yildirim, Mehmet Emin Cem; Dadaci, Mehmet; Avunduk, Mustafa Cihat; Savaci, Nedim

2018-02-01

Androgenetic alopecia (AGA), the most common cause of hair loss in both sexes, accounts for 95% of all cases of hair loss. Although the literature has suggested that both nonactivated (n-PRP) and activated autologous (a-PRP) PRP can be used to treat AGA, we did not find any study investigating the use of homologous PRP (h-PRP) for this purpose. Also, to the best of our knowledge, there are no studies comparing the efficacy of h-PRP, a-PRP, or n-PRP on AGA therapy. The aim of this study was to compare the increase in hair density, average number of platelets, complications, preparation, and duration of application in the treatment of AGA using a-PRP, n-PRP, and h-PRP. Between 2014 and 2015, we studied male patients who had experienced increased hair loss in the last year. Patients were divided into three groups: Group 1 received n-PRP, Group 2 received active PRP, and Group 3 received h-PRP. For Group 1, PRP was prepared by a single centrifugation prepared from the patient's own blood. For Group 2, the PRP was prepared from the patient's own blood, but a second centrifugation was applied for platelet activation with calcium chloride. For Group 3, the PRP was prepared from pooled platelets with the same blood group as the patient from the blood center. PRP was injected at 1, 2, and 6 months. The hair density (n/cm 2 ) of each patient before and after injection was calculated. Each patient was assigned a fixed evaluation point at the time of application to calculate hair density. At 2, 6, and 12 months after the first treatment, the increase in hair density was calculated as 11.2, 26.1, and 32.4%, respectively, in Group 1; 8.1, 12.5, and 20.8%, respectively, in Group 2; and 16.09, 36.41, and 41.76%, respectively, in Group 3. The increase in hair density was statistically significantly greater in Group 1 than in Group 2 and more so in Group 3 than in both groups among all controls (p < 0.05). The efficacy of both PRPs was determined in AGA treatment in our study. However, it was determined statistically that the increase in hair density with h-PRP was greater than with autologous PRP groups. We believe that h-PRP therapy can be used in patients with AGA presenting with hair loss. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.

JavaGenes and Condor: Cycle-Scavenging Genetic Algorithms

NASA Technical Reports Server (NTRS)

Globus, Al; Langhirt, Eric; Livny, Miron; Ramamurthy, Ravishankar; Soloman, Marvin; Traugott, Steve

2000-01-01

A genetic algorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor cycle-scavenging batch system managing 100-170 desktop SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most genetic algorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java was chosen as the implementation language because the genetic algorithm requires random splitting and recombining of graphs, a complex data structure manipulation with ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer arithmetic, and array-bounds index checking prevents these bugs from occurring, substantially reducing development time. While a run-time performance penalty must be paid, the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that genetic algorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology, and another paper in preparation.

Incremental cost-effectiveness of algorithm-driven genetic testing versus no testing for Maturity Onset Diabetes of the Young (MODY) in Singapore.

PubMed

Nguyen, Hai Van; Finkelstein, Eric Andrew; Mital, Shweta; Gardner, Daphne Su-Lyn

2017-11-01

Offering genetic testing for Maturity Onset Diabetes of the Young (MODY) to all young patients with type 2 diabetes has been shown to be not cost-effective. This study tests whether a novel algorithm-driven genetic testing strategy for MODY is incrementally cost-effective relative to the setting of no testing. A decision tree was constructed to estimate the costs and effectiveness of the algorithm-driven MODY testing strategy and a strategy of no genetic testing over a 30-year time horizon from a payer's perspective. The algorithm uses glutamic acid decarboxylase (GAD) antibody testing (negative antibodies), age of onset of diabetes (<45 years) and body mass index (<25 kg/m 2 if diagnosed >30 years) to stratify the population of patients with diabetes into three subgroups, and testing for MODY only among the subgroup most likely to have the mutation. Singapore-specific costs and prevalence of MODY obtained from local studies and utility values sourced from the literature are used to populate the model. The algorithm-driven MODY testing strategy has an incremental cost-effectiveness ratio of US$93 663 per quality-adjusted life year relative to the no testing strategy. If the price of genetic testing falls from US$1050 to US$530 (a 50% decrease), it will become cost-effective. Our proposed algorithm-driven testing strategy for MODY is not yet cost-effective based on established benchmarks. However, as genetic testing prices continue to fall, this strategy is likely to become cost-effective in the near future. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Genetic Algorithms for Multiple-Choice Problems

NASA Astrophysics Data System (ADS)

Aickelin, Uwe

2010-04-01

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

A synthetic genetic edge detection program.

PubMed

Tabor, Jeffrey J; Salis, Howard M; Simpson, Zachary Booth; Chevalier, Aaron A; Levskaya, Anselm; Marcotte, Edward M; Voigt, Christopher A; Ellington, Andrew D

2009-06-26

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

A Synthetic Genetic Edge Detection Program

PubMed Central

Tabor, Jeffrey J.; Salis, Howard; Simpson, Zachary B.; Chevalier, Aaron A.; Levskaya, Anselm; Marcotte, Edward M.; Voigt, Christopher A.; Ellington, Andrew D.

2009-01-01

Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks. PMID:19563759

Constrained minimization of smooth functions using a genetic algorithm

NASA Technical Reports Server (NTRS)

Moerder, Daniel D.; Pamadi, Bandu N.

1994-01-01

The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.

Real Time Optima Tracking Using Harvesting Models of the Genetic Algorithm

NASA Technical Reports Server (NTRS)

Baskaran, Subbiah; Noever, D.

1999-01-01

Tracking optima in real time propulsion control, particularly for non-stationary optimization problems is a challenging task. Several approaches have been put forward for such a study including the numerical method called the genetic algorithm. In brief, this approach is built upon Darwinian-style competition between numerical alternatives displayed in the form of binary strings, or by analogy to 'pseudogenes'. Breeding of improved solution is an often cited parallel to natural selection in.evolutionary or soft computing. In this report we present our results of applying a novel model of a genetic algorithm for tracking optima in propulsion engineering and in real time control. We specialize the algorithm to mission profiling and planning optimizations, both to select reduced propulsion needs through trajectory planning and to explore time or fuel conservation strategies.

Study of genetic direct search algorithms for function optimization

NASA Technical Reports Server (NTRS)

Zeigler, B. P.

1974-01-01

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

An Adaptive Immune Genetic Algorithm for Edge Detection

NASA Astrophysics Data System (ADS)

Li, Ying; Bai, Bendu; Zhang, Yanning

An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

Convergence properties of simple genetic algorithms

NASA Technical Reports Server (NTRS)

Bethke, A. D.; Zeigler, B. P.; Strauss, D. M.

1974-01-01

The essential parameters determining the behaviour of genetic algorithms were investigated. Computer runs were made while systematically varying the parameter values. Results based on the progress curves obtained from these runs are presented along with results based on the variability of the population as the run progresses.

A genetic algorithm approach in interface and surface structure optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jian

The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the materialmore » structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.« less

Estimation of radiative and conductive properties of a semitransparent medium using genetic algorithms

NASA Astrophysics Data System (ADS)

Braiek, A.; Adili, A.; Albouchi, F.; Karkri, M.; Ben Nasrallah, S.

2016-06-01

The aim of this work is to simultaneously identify the conductive and radiative parameters of a semitransparent sample using a photothermal method associated with an inverse problem. The identification of the conductive and radiative proprieties is performed by the minimization of an objective function that represents the errors between calculated temperature and measured signal. The calculated temperature is obtained from a theoretical model built with the thermal quadrupole formalism. Measurement is obtained in the rear face of the sample whose front face is excited by a crenel of heat flux. For identification procedure, a genetic algorithm is developed and used. The genetic algorithm is a useful tool in the simultaneous estimation of correlated or nearly correlated parameters, which can be a limiting factor for the gradient-based methods. The results of the identification procedure show the efficiency and the stability of the genetic algorithm to simultaneously estimate the conductive and radiative properties of clear glass.

An application of CART algorithm in genetics: IGFs and cGH polymorphisms in Japanese quail

NASA Astrophysics Data System (ADS)

Kaplan, Selçuk

2017-04-01

The avian insulin-like growth factor-1 (IGFs) and avian growth hormone (cGH) genes are the most important genes that can affect bird performance traits because of its important function in growth and metabolism. Understanding the molecular genetic basis of variation in growth-related traits is of importance for continued improvement and increased rates of genetic gain. The objective of the present study was to identify polymorphisms of cGH and IGFs genes in Japanese quail using conventional least square method (LSM) and CART algorithm. Therefore, this study was aimed to demonstrate at determining the polymorphisms of two genes related growth characteristics via CART algorithm. A simulated data set was generated to analyze by adhering the results of some poultry genetic studies which it includes live weights at 5 weeks of age, 3 alleles and 6 genotypes of cGH and 2 alleles and 3 genotypes of IGFs. As a result, it has been determined that the CART algorithm has some advantages as for that LSM.

Application of artificial intelligence to search ground-state geometry of clusters

NASA Astrophysics Data System (ADS)

Lemes, Maurício Ruv; Marim, L. R.; dal Pino, A.

2002-08-01

We introduce a global optimization procedure, the neural-assisted genetic algorithm (NAGA). It combines the power of an artificial neural network (ANN) with the versatility of the genetic algorithm. This method is suitable to solve optimization problems that depend on some kind of heuristics to limit the search space. If a reasonable amount of data is available, the ANN can ``understand'' the problem and provide the genetic algorithm with a selected population of elements that will speed up the search for the optimum solution. We tested the method in a search for the ground-state geometry of silicon clusters. We trained the ANN with information about the geometry and energetics of small silicon clusters. Next, the ANN learned how to restrict the configurational space for larger silicon clusters. For Si10 and Si20, we noticed that the NAGA is at least three times faster than the ``pure'' genetic algorithm. As the size of the cluster increases, it is expected that the gain in terms of time will increase as well.

Application of genetic algorithms to focal mechanism determination

NASA Astrophysics Data System (ADS)

Kobayashi, Reiji; Nakanishi, Ichiro

1994-04-01

Genetic algorithms are a new class of methods for global optimization. They resemble Monte Carlo techniques, but search for solutions more efficiently than uniform Monte Carlo sampling. In the field of geophysics, genetic algorithms have recently been used to solve some non-linear inverse problems (e.g., earthquake location, waveform inversion, migration velocity estimation). We present an application of genetic algorithms to focal mechanism determination from first-motion polarities of P-waves and apply our method to two recent large events, the Kushiro-oki earthquake of January 15, 1993 and the SW Hokkaido (Japan Sea) earthquake of July 12, 1993. Initial solution and curvature information of the objective function that gradient methods need are not required in our approach. Moreover globally optimal solutions can be efficiently obtained. Calculation of polarities based on double-couple models is the most time-consuming part of the source mechanism determination. The amount of calculations required by the method designed in this study is much less than that of previous grid search methods.

Optimized design on condensing tubes high-speed TIG welding technology magnetic control based on genetic algorithm

NASA Astrophysics Data System (ADS)

Lu, Lin; Chang, Yunlong; Li, Yingmin; Lu, Ming

2013-05-01

An orthogonal experiment was conducted by the means of multivariate nonlinear regression equation to adjust the influence of external transverse magnetic field and Ar flow rate on welding quality in the process of welding condenser pipe by high-speed argon tungsten-arc welding (TIG for short). The magnetic induction and flow rate of Ar gas were used as optimum variables, and tensile strength of weld was set to objective function on the base of genetic algorithm theory, and then an optimal design was conducted. According to the request of physical production, the optimum variables were restrained. The genetic algorithm in the MATLAB was used for computing. A comparison between optimum results and experiment parameters was made. The results showed that the optimum technologic parameters could be chosen by the means of genetic algorithm with the conditions of excessive optimum variables in the process of high-speed welding. And optimum technologic parameters of welding coincided with experiment results.

Chronic liver disease in the Hispanic population of the United States.

PubMed

Carrion, Andres F; Ghanta, Ravi; Carrasquillo, Olveen; Martin, Paul

2011-10-01

Chronic liver disease is a major cause of morbidity and mortality among Hispanic people living in the United States. Environmental, genetic, and behavioral factors, as well as socioeconomic and health care disparities among this ethnic group have emerged as important public health concerns. We review the epidemiology, natural history, and response to therapy of chronic liver disease in Hispanic patients. The review covers nonalcoholic fatty liver disease, viral hepatitis B and C, coinfection of viral hepatitis with human immunodeficiency virus, alcoholic cirrhosis, hepatocellular carcinoma, autoimmune hepatitis, and primary biliary cirrhosis. For most of these disorders, the Hispanic population has a higher incidence and more aggressive pattern of disease and overall worse treatment outcomes than in the non-Hispanic white population. Clinicians should be aware of these differences in caring for Hispanic patients with chronic liver disease. Copyright © 2011 AGA Institute. Published by Elsevier Inc. All rights reserved.

Optimal sensor placement for spatial lattice structure based on genetic algorithms

NASA Astrophysics Data System (ADS)

Liu, Wei; Gao, Wei-cheng; Sun, Yi; Xu, Min-jian

2008-10-01

Optimal sensor placement technique plays a key role in structural health monitoring of spatial lattice structures. This paper considers the problem of locating sensors on a spatial lattice structure with the aim of maximizing the data information so that structural dynamic behavior can be fully characterized. Based on the criterion of optimal sensor placement for modal test, an improved genetic algorithm is introduced to find the optimal placement of sensors. The modal strain energy (MSE) and the modal assurance criterion (MAC) have been taken as the fitness function, respectively, so that three placement designs were produced. The decimal two-dimension array coding method instead of binary coding method is proposed to code the solution. Forced mutation operator is introduced when the identical genes appear via the crossover procedure. A computational simulation of a 12-bay plain truss model has been implemented to demonstrate the feasibility of the three optimal algorithms above. The obtained optimal sensor placements using the improved genetic algorithm are compared with those gained by exiting genetic algorithm using the binary coding method. Further the comparison criterion based on the mean square error between the finite element method (FEM) mode shapes and the Guyan expansion mode shapes identified by data-driven stochastic subspace identification (SSI-DATA) method are employed to demonstrate the advantage of the different fitness function. The results showed that some innovations in genetic algorithm proposed in this paper can enlarge the genes storage and improve the convergence of the algorithm. More importantly, the three optimal sensor placement methods can all provide the reliable results and identify the vibration characteristics of the 12-bay plain truss model accurately.

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

PubMed

Osowski, Stanislaw; Siwek, Krzysztof; Siroic, Robert

2011-03-01

This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database. Copyright © 2011 Elsevier Ltd. All rights reserved.

Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters

NASA Astrophysics Data System (ADS)

Cavuoti, S.; Garofalo, M.; Brescia, M.; Paolillo, M.; Pescape', A.; Longo, G.; Ventre, G.

2014-01-01

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version.

Prioritizing the Components of Vulnerability: A Genetic Algorithm Minimization of Flood Risk

NASA Astrophysics Data System (ADS)

Bongolan, Vena Pearl; Ballesteros, Florencio; Baritua, Karessa Alexandra; Junne Santos, Marie

2013-04-01

We define a flood resistant city as an optimal arrangement of communities according to their traits, with the goal of minimizing the flooding vulnerability via a genetic algorithm. We prioritize the different components of flooding vulnerability, giving each component a weight, thus expressing vulnerability as a weighted sum. This serves as the fitness function for the genetic algorithm. We also allowed non-linear interactions among related but independent components, viz, poverty and mortality rate, and literacy and radio/ tv penetration. The designs produced reflect the relative importance of the components, and we observed a synchronicity between the interacting components, giving us a more consistent design.

Algorithmic Trading with Developmental and Linear Genetic Programming

NASA Astrophysics Data System (ADS)

Wilson, Garnett; Banzhaf, Wolfgang

A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.

Supervised chaos genetic algorithm based state of charge determination for LiFePO4 batteries in electric vehicles

NASA Astrophysics Data System (ADS)

Shen, Yanqing

2018-04-01

LiFePO4 battery is developed rapidly in electric vehicle, whose safety and functional capabilities are influenced greatly by the evaluation of available cell capacity. Added with adaptive switch mechanism, this paper advances a supervised chaos genetic algorithm based state of charge determination method, where a combined state space model is employed to simulate battery dynamics. The method is validated by the experiment data collected from battery test system. Results indicate that the supervised chaos genetic algorithm based state of charge determination method shows great performance with less computation complexity and is little influenced by the unknown initial cell state.

Artificial intelligence programming with LabVIEW: genetic algorithms for instrumentation control and optimization.

PubMed

Moore, J H

1995-06-01

A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.

An Efficient Functional Test Generation Method For Processors Using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Hudec, Ján; Gramatová, Elena

2015-07-01

The paper presents a new functional test generation method for processors testing based on genetic algorithms and evolutionary strategies. The tests are generated over an instruction set architecture and a processor description. Such functional tests belong to the software-oriented testing. Quality of the tests is evaluated by code coverage of the processor description using simulation. The presented test generation method uses VHDL models of processors and the professional simulator ModelSim. The rules, parameters and fitness functions were defined for various genetic algorithms used in automatic test generation. Functionality and effectiveness were evaluated using the RISC type processor DP32.

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

NASA Astrophysics Data System (ADS)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

Fast optimization of glide vehicle reentry trajectory based on genetic algorithm

NASA Astrophysics Data System (ADS)

Jia, Jun; Dong, Ruixing; Yuan, Xuejun; Wang, Chuangwei

2018-02-01

An optimization method of reentry trajectory based on genetic algorithm is presented to meet the need of reentry trajectory optimization for glide vehicle. The dynamic model for the glide vehicle during reentry period is established. Considering the constraints of heat flux, dynamic pressure, overload etc., the optimization of reentry trajectory is investigated by utilizing genetic algorithm. The simulation shows that the method presented by this paper is effective for the optimization of reentry trajectory of glide vehicle. The efficiency and speed of this method is comparative with the references. Optimization results meet all constraints, and the on-line fast optimization is potential by pre-processing the offline samples.

On Directly Solving SCHRÖDINGER Equation for H+2 Ion by Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saha, Rajendra; Bhattacharyya, S. P.

Schrödinger equation (SE) is sought to be solved directly for the ground state of H+2 ion by invoking genetic algorithm (GA). In one approach the internuclear distance (R) is kept fixed, the corresponding electronic SE for H+2 is solved by GA at each R and the full potential energy curve (PEC) is constructed. The minimum of the PEC is then located giving Ve and Re. Alternatively, Ve and Re are located in a single run by allowing R to vary simultaneously while solving the electronic SE by genetic algorithm. The performance patterns of the two strategies are compared.

Applying a Genetic Algorithm to Reconfigurable Hardware

NASA Technical Reports Server (NTRS)

Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim

2004-01-01

This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.

Mobile transporter path planning

NASA Technical Reports Server (NTRS)

Baffes, Paul; Wang, Lui

1990-01-01

The use of a genetic algorithm (GA) for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Elements of the genetic algorithm are explored in both a theoretical and experimental sense. Specifically, double crossover, greedy crossover, and tournament selection techniques are examined. Additionally, the use of local optimization techniques working in concert with the GA are also explored. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research.

Gluten sensitivity in patients with IgA nephropathy.

PubMed

Smerud, Hilde Kloster; Fellström, Bengt; Hällgren, Roger; Osagie, Sonia; Venge, Per; Kristjánsson, Gudjón

2009-08-01

Coeliac disease is more frequent in IgA nephropathy (IgAN) patients compared to the healthy population. Several hypotheses postulate that food antigens like gluten may be involved in the onset of IgAN. In this study, we used a recently developed mucosal patch technique to evaluate the rectal mucosal inflammatory reaction to gluten in patients with IgAN (n = 27) compared to healthy subjects (n = 18). The rectal mucosal production of nitric oxide (NO) and release of myeloperoxidase (MPO) and eosinophil cationic protein (ECP) were measured. Serum samples were analysed for IgA and IgG antigliadin antibodies (AGA), IgA antibodies against tissue transglutaminase and IgA endomysium antibodies. Gluten reactivity, defined as increase in MPO and/or NO after gluten exposure, was observed in 8 of 27 IgAN patients. The prevalence of HLA-DQ2 and DQ8 was not increased among gluten-sensitive patients, and the total prevalence among IgAN patients was the same as for the normal population. An elevated serum IgA AGA response was seen in 9 of 27 IgAN patients. The increase in IgA AGA did not correlate with the gluten sensitivity as measured by NO and/or MPO. A specific serum IgG AGA response was seen in one patient only. Antibodies against tissue transglutaminase and endomysium were not observed. It is concluded that approximately one-third of our IgAN patients have a rectal mucosal sensitivity to gluten, but without signs of coeliac disease, and we hypothesize that such sub-clinical inflammation to gluten might be involved in the pathogenesis of IgAN in a subgroup of patients.

Similar response patterns to topical minoxidil foam 5% in frontal and vertex scalp of men with androgenetic alopecia: a microarray analysis.

PubMed

Mirmirani, P; Consolo, M; Oyetakin-White, P; Baron, E; Leahy, P; Karnik, P

2015-06-01

There are regional variations in the scalp hair miniaturization seen in androgenetic alopecia (AGA). Use of topical minoxidil can lead to reversal of miniaturization in the vertex scalp. However, its effects on other scalp regions have been less well studied. To determine whether scalp biopsies from men with AGA show variable gene expression before and after 8 weeks of treatment with minoxidil topical foam 5% (MTF) vs. placebo. A placebo-controlled double-blinded prospective pilot study of MTF vs. placebo was conducted in 16 healthy men aged 18-49 years with Hamilton-Norwood type IV-V thinning. The subjects were asked to apply the treatment (active drug or placebo) to the scalp twice daily for 8 weeks. Stereotactic scalp photographs were taken at the baseline and final visits, to monitor global hair growth. Scalp biopsies were taken at the leading edge of hair loss from the frontal and vertex scalp before and after treatment with MTF and placebo, and microarray analysis was performed using the Affymetrix GeneChip HG U133 Plus 2.0. Global stereotactic photographs showed that MTF induced hair growth in both the frontal and vertex scalp of patients with AGA. Regional differences in gene expression profiles were observed before treatment. However, MTF treatment induced the expression of hair keratin-associated genes and decreased the expression of epidermal differentiation complex and inflammatory genes in both scalp regions. These data suggest that MTF is effective in the treatment of both the frontal and vertex scalp of patients with AGA. © 2014 British Association of Dermatologists.

Prevalence of dermatologic manifestations and metabolic biomarkers in women with polycystic ovary syndrome in north China.

PubMed

Feng, Jin-Ge; Guo, Yan; Ma, Li-Ang; Xing, Jin; Sun, Rui-Feng; Zhu, Wei

2018-06-01

Cutaneous features of hyperandrogenism in polycystic ovary syndrome (PCOS) include acne, hirsutism, seborrhea, androgenic alopecia (AGA), and acanthosis nigricans (AN). However, the relationships have not been well known broadly in terms of clinical hyperandrogenism and biochemical markers. The aim of this study was to investigate biochemical and metabolic parameters in relation to cutaneous characters women in with and without PCOS. This was a cross-sectional retrospective study including 186 women with PCOS and 113 age-matched without PCOS women. Acne grade, hirsutism, seborrhea, AGA, and AN were recorded. Hormonal and metabolic parameters were measured. The most common finding was acne, and AN was the least dermatological manifestations between PCOS and non-PCOS groups. The severity location and type of acne did not differ in PCOS women compared to non-PCOS women. Significant differences were found with respect to free androgen index (FAI) (P = .036), sex hormone-binding globulin (SHBG) (P = .023), and body mass index (BMI) (P = .001) between PCOS with acne and PCOS without acne groups. Overall, age (P = .005) was significantly decreased, while BMI (P = .004) was significantly higher in PCOS with hirsutism. The mean serum total testosterone (TT), dehydroepiandrosterone sulfate, and FAI were significantly elevated, but SHBG was decreased between PCOS with and without hirsutism groups. There were significantly different BMI (P = .018) and triglyceride (P = .024) except other hormonal parameter of without AGA group. This study indicated a strong correlation between hirsutism and metabolic abnormalities. Hirsutism is the most common cutaneous finding in PCOS women. Acne and AGA are associated with other manifestations of clinical hyperandrogenism, but not obvious markers of biochemical hyperandrogenemia and metabolic dysfunction. © 2017 Wiley Periodicals, Inc.

Circulating osteoprotegerin and sRANKL concentrations in the perinatal period at term. The impact of intrauterine growth restriction.

PubMed

Briana, Despina D; Boutsikou, Maria; Baka, Stavroula; Hassiakos, Demetrios; Gourgiotis, Demetrios; Malamitsi-Puchner, Ariadne

2009-01-01

Intrauterine growth restriction (IUGR) has been associated with low bone mass in infancy and increased risk for osteoporosis development in adult life. Osteoprotegerin (OPG) and receptor activator of nuclear factor-kappaB ligand (RANKL) are main determinants of bone resorption. To investigate OPG and soluble RANKL (sRANKL) concentrations in maternal, fetal and neonatal serum of IUGR patients and appropriate for gestational age (AGA) pregnancies. Additionally, plasma intact parathormone (PTH) concentrations were evaluated. Circulating OPG, sRANKL and PTH concentrations were measured in 40 mothers and their singleton full-term fetuses-neonates (AGA: n = 20, and IUGR: n =20) on postnatal days 1 (N1) and 4 (N4). No significant differences in OPG, sRANKL or PTH concentrations were observed between AGA and IUGR groups. In both groups, maternal OPG concentrations were elevated compared with fetal, and N1 and N4 concentrations (p < or = 0.045 in all cases). N4 sRANKL concentrations were elevated compared with maternal, fetal and N1 ones (p < or = 0.01 in all cases). Fetal and N1 sRANKL concentrations correlated positively with PTH levels (r = 0.642, p = 0.024 and r = 0.584, p = 0.046, respectively). The lack of a difference in circulating OPG, sRANKL or PTH concentrations between IUGR cases and AGA controls suggests that the low bone mass of IUGR infants may not be related to higher bone resorption rates. The increased maternal, compared with fetal/neonatal, OPG concentrations may suggest their placental origin. The lower OPG and higher sRANKL concentrations in fetuses and neonates could represent high bone resorption rates. Copyright 2009 S. Karger AG, Basel.

The effects of silver ions on copper metabolism in rats.

PubMed

Ilyechova, E Yu; Saveliev, A N; Skvortsov, A N; Babich, P S; Zatulovskaia, Yu A; Pliss, M G; Korzhevskii, D E; Tsymbalenko, N V; Puchkova, L V

2014-10-01

The influence of short and prolonged diet containing silver ions (Ag-diet) on copper metabolism was studied. Two groups of animals were used: one group of adult rats received a Ag-diet for one month (Ag-A1) and another group received a Ag-diet for 6 months from birth (Ag-N6). In Ag-A1 rats, the Ag-diet caused a dramatic decrease of copper status indexes that was manifested as ceruloplasmin-associated copper deficiency. In Ag-N6 rats, copper status indexes decreased only 2-fold as compared to control rats. In rats of both groups, silver entered the bloodstream and accumulated in the liver. Silver was incorporated into ceruloplasmin (Cp), but not SOD1. In the liver, a prolonged Ag-diet caused a decrease of the expression level of genes, associated with copper metabolism. Comparative spectrophotometric analysis of partially purified Cp fractions has shown that Cp from Ag-N6 rats was closer to holo-Cp by specific enzymatic activities and tertiary structure than Cp from Ag-A1 rats. However, Cp of Ag-N6 differs from control holo-Cp and Cp of Ag-A1 in its affinity to DEAE-Sepharose and in its binding properties to lectins. In the bloodstream of Ag-N6, two Cp forms are present as shown in pulse-experiments on rats with the liver isolated from circulation. One of the Cp isoforms is of hepatic origin, and the other is of extrahepatic origin; the latter is characterized by a faster rate of secretion than hepatic Cp. These data allowed us to suggest that the disturbance of holo-Cp formation in the liver was compensated by induction of extrahepatic Cp synthesis. The possible biological importance of these effects is discussed.

Application of a global reference for fetal-weight and birthweight percentiles in predicting infant mortality.

PubMed

Ding, G; Tian, Y; Zhang, Y; Pang, Y; Zhang, J S; Zhang, J

2013-12-01

To determine whether the recently published A global reference for fetal-weight and birthweight percentiles (Global Reference) improves small- (SGA), appropriate- (AGA), and large-for-gestational-age (LGA) definitions in predicting infant mortality. Population-based cohort study. The US Linked Livebirth and Infant Death records between 1995 and 2004. Singleton births with birthweight >500 g born at 24-41 weeks of gestation. We compared infant mortality rates of SGA, AGA, and LGA infants classified by three different references: the Global Reference; a commonly used birthweight reference; and Hadlock's ultrasound reference. Infant mortality rates. Among 33 997 719 eligible liveborn singleton births, 25% of preterm and 9% of term infants were classified differently for SGA, AGA, and LGA by the Global Reference and the birthweight reference. The Global Reference indicated higher mortality rates in preterm SGA and preterm LGA infants than the birthweight reference. The mortality rate was considerably higher in infants classified as preterm SGA by the Global Reference but not by the birthweight reference, compared with the corresponding infants classified by the birthweight reference but not by the Global Reference (105.7 versus 12.9 per 1000, RR 8.17, 95% CI 7.38-9.06). Yet, the differences in mortality rates were much smaller in term infants than in preterm infants. Black infants had a particularly higher mortality rate than other races in AGA and LGA preterm and term infants. In respect to the commonly used birthweight reference, the Global Reference increases the identification of infant deaths by improved classification of abnormal newborn size at birth, and these advantages were more obvious in preterm than in term infants. © 2013 RCOG.

Efficacy and safety of a new 5% minoxidil formulation in male androgenetic alopecia: A randomized, placebo-controlled, double-blind, noninferiority study.

PubMed

Blume-Peytavi, Ulrike; Issiakhem, Zahida; Gautier, Stephanie; Kottner, Jan; Wigger-Alberti, Walter; Fischer, Tobias; Hoffmann, Rolf; Tonner, Françoise; Bouroubi, Athmane; Voisard, Jean-Jacques

2018-04-16

Androgenetic alopecia (AGA) is the most common cause of hair loss in men. Topical minoxidil solutions can help to treat AGA but have to be applied continuously to be effective. A new minoxidil formulation with improved cosmetic characteristics (DC0120, Pierre-Fabre Dermatologie) was tested for noninferiority vs a comparator minoxidil product (ALOSTIL ® , Johnson & Johnson) in stimulating hair growth in men with AGA. Two 10 cm 2 areas on the scalp of each subject were randomized to receive DC0120, the comparator, or one of their corresponding vehicles, applied twice per day for 16 weeks. Nonvellus target area hair count (TAHC) was measured within treatment areas at baseline (day 1) and after 8 and 16 weeks by digital phototrichogram. Two hundred and twenty subjects were included and randomized, of which 210 completed the study. The mean change in nonvellus TAHC between baseline and week 16 was +22.0 hairs/cm 2 (95% CI: 18.1; 25.9) in the DC0120 group and +20.5 hairs/cm 2 (95% CI: 16.6; 24.4) in the comparator group. The adjusted mean difference in TAHC changes between the two treatments was +1.5 hairs/cm 2 (95% CI -2.3; 5.2), with the lower 95% confidence interval above the noninferiority threshold of -7 hairs/cm 2 . This indicated that DC0120 was noninferior to the comparator. Both minoxidil treatments also increased nonvellus TAHC compared to vehicle groups at 8 and 16 weeks. No new safety signals were observed. DC0120 was as safe and effective as a similar marketed minoxidil product for stimulating hair growth in men with AGA. © 2018 Wiley Periodicals, Inc.

A Comparative Study of Microneedling with Platelet-rich Plasma Plus Topical Minoxidil (5%) and Topical Minoxidil (5%) Alone in Androgenetic Alopecia.

PubMed

Shah, Kaksha B; Shah, Aarti N; Solanki, Rekha B; Raval, Ranjan C

2017-01-01

There are very few studies evaluating efficacy of platelet-rich plasma (PRP) in hair restoration and its combination with microneedling. As far as ascertained, there is no study to evaluate efficacy of microneedling with PRP plus topical minoxidil (5%) versus topical minoxidil (5%) alone in androgenetic alopecia (AGA). This study aims (1) to compare the efficacy of (a) topical minoxidil (5%) alone and (b) topical minoxidil (5%) + microneedling with PRP in men between 18 and 50 years with AGA Grade III to V vertex (Norwood-Hamilton scale) and (2) to perform objective and subjective evaluation based on clinical improvement and photographic evidence. The study was conducted in the outpatient department of dermatology, venereology, and leprology in tertiary care hospital. It was open, prospective study. Fifty patients with AGA were selected on the basis of inclusion and exclusion criteria. These patients were randomly divided into two groups of 25 patients each and were given following treatment: (i) Group A: topical minoxidil (5%) alone and (ii) Group B: topical minoxidil (5%) + microneedling with platelet-rich plasma (PRP). Patients were assessed before starting the treatment and at the end of 6 months on the basis of (a) Patient's self-assessment based on standardized seven-point scale compared with baseline (b) Physician's assessment based on standardized seven-point scale of hair growth compared with baseline. There was a significant improvement ( P < 0.05) in both patients' assessment and investigator's assessment in Group B as compared to Group A at the end of 6 months. Microneedling with PRP is safe, effective, and a promising tool for the management of AGA.

Prenatal and post-natal cost of small for gestational age infants: a national study.

PubMed

Marzouk, Alicia; Filipovic-Pierucci, Antoine; Baud, Olivier; Tsatsaris, Vassilis; Ego, Anne; Charles, Marie-Aline; Goffinet, François; Evain-Brion, Danièle; Durand-Zaleski, Isabelle

2017-03-21

Small for gestational age (SGA) infants are at increased risk for preterm birth morbidities as well as a range of adverse perinatal outcomes that result in part from associated premature birth. We sought to evaluate the costs of SGA versus appropriate for gestational age (AGA) infants in France from pregnancy through the first year of life and separate the contributions of prematurity from the contribution of foetal growth on costs. This is a cross-sectional population-based study using national hospital discharge data from French public and private hospitals. SGA infants were defined as newborns with a birth weight below the 10th percentile of French intrauterine growth curves adjusted for foetal sex. AGA infants were defined as newborns with a birth weight between the 25th and the 75th. All births were selected between January 1st, 2011 and December 31st, 2011. Costs were calculated from the hospital perspective for both mothers and children using their diagnostic related group and the French national cost study. Hospital outcomes were extracted from the database and compared by gestational age and mode of delivery. Of 777,720 total births in 2011, 84,688 SGA births (10.9%) and 395,760 AGA births (50.8%) were identified. After adjustment for gestational age, the cost for an SGA infant was €2,783 higher than for an AGA infant. The total maternal and infant hospital cost of SGA in France was estimated at 23% the total cost for deliveries. The high cost is explained by higher complication rates, more frequent hospital readmissions and longer lengths of stay. Being small for gestational age is an independent contributor to 1-year hospital costs for both mothers and infants.

Genetic algorithms and their use in Geophysical Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parker, Paul B.

1999-04-01

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or ''fittest'' models from a ''population'' and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show thatmore » certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Optimal efficiency is usually achieved with smaller (< 50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (> 2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.« less

Genetic algorithms and their use in geophysical problems

NASA Astrophysics Data System (ADS)

Parker, Paul Bradley

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or "fittest" models from a "population" and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Also, optimal efficiency is usually achieved with smaller (<50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (>2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)

NASA Astrophysics Data System (ADS)

Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman

2012-01-01

In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.

Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data.

PubMed

Madsen, Thomas; Braun, Danielle; Peng, Gang; Parmigiani, Giovanni; Trippa, Lorenzo

2018-06-25

The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling-based models to include many genetic loci. The algorithm creates a trade-off between accuracy and speed, and allows the user to control this trade-off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties. © 2018 WILEY PERIODICALS, INC.

Optimization of beam orientation in radiotherapy using planar geometry

NASA Astrophysics Data System (ADS)

Haas, O. C. L.; Burnham, K. J.; Mills, J. A.

1998-08-01

This paper proposes a new geometrical formulation of the coplanar beam orientation problem combined with a hybrid multiobjective genetic algorithm. The approach is demonstrated by optimizing the beam orientation in two dimensions, with the objectives being formulated using planar geometry. The traditional formulation of the objectives associated with the organs at risk has been modified to account for the use of complex dose delivery techniques such as beam intensity modulation. The new algorithm attempts to replicate the approach of a treatment planner whilst reducing the amount of computation required. Hybrid genetic search operators have been developed to improve the performance of the genetic algorithm by exploiting problem-specific features. The multiobjective genetic algorithm is formulated around the concept of Pareto optimality which enables the algorithm to search in parallel for different objectives. When the approach is applied without constraining the number of beams, the solution produces an indication of the minimum number of beams required. It is also possible to obtain non-dominated solutions for various numbers of beams, thereby giving the clinicians a choice in terms of the number of beams as well as in the orientation of these beams.

Distributed query plan generation using multiobjective genetic algorithm.

PubMed

Panicker, Shina; Kumar, T V Vijay

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.

Acoustic Impedance Inversion of Seismic Data Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Eladj, Said; Djarfour, Noureddine; Ferahtia, Djalal; Ouadfeul, Sid-Ali

2013-04-01

The inversion of seismic data can be used to constrain estimates of the Earth's acoustic impedance structure. This kind of problem is usually known to be non-linear, high-dimensional, with a complex search space which may be riddled with many local minima, and results in irregular objective functions. We investigate here the performance and the application of a genetic algorithm, in the inversion of seismic data. The proposed algorithm has the advantage of being easily implemented without getting stuck in local minima. The effects of population size, Elitism strategy, uniform cross-over and lower mutation are examined. The optimum solution parameters and performance were decided as a function of the testing error convergence with respect to the generation number. To calculate the fitness function, we used L2 norm of the sample-to-sample difference between the reference and the inverted trace. The cross-over probability is of 0.9-0.95 and mutation has been tested at 0.01 probability. The application of such a genetic algorithm to synthetic data shows that the inverted acoustic impedance section was efficient. Keywords: Seismic, Inversion, acoustic impedance, genetic algorithm, fitness functions, cross-over, mutation.

Distributed Query Plan Generation Using Multiobjective Genetic Algorithm

PubMed Central

Panicker, Shina; Vijay Kumar, T. V.

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

NASA Astrophysics Data System (ADS)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE PAGES

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; ...

2018-05-29

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Hybrid algorithms for fuzzy reverse supply chain network design.

PubMed

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

PubMed

Yoon, Yourim; Kim, Yong-Hyuk

2013-10-01

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping

NASA Astrophysics Data System (ADS)

Fronita, Mona; Gernowo, Rahmat; Gunawan, Vincencius

2018-02-01

Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour's to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

Weather prediction using a genetic memory

NASA Technical Reports Server (NTRS)

Rogers, David

1990-01-01

Kanaerva's sparse distributed memory (SDM) is an associative memory model based on the mathematical properties of high dimensional binary address spaces. Holland's genetic algorithms are a search technique for high dimensional spaces inspired by evolutional processes of DNA. Genetic Memory is a hybrid of the above two systems, in which the memory uses a genetic algorithm to dynamically reconfigure its physical storage locations to reflect correlations between the stored addresses and data. This architecture is designed to maximize the ability of the system to scale-up to handle real world problems.

iNJclust: Iterative Neighbor-Joining Tree Clustering Framework for Inferring Population Structure.

PubMed

Limpiti, Tulaya; Amornbunchornvej, Chainarong; Intarapanich, Apichart; Assawamakin, Anunchai; Tongsima, Sissades

2014-01-01

Understanding genetic differences among populations is one of the most important issues in population genetics. Genetic variations, e.g., single nucleotide polymorphisms, are used to characterize commonality and difference of individuals from various populations. This paper presents an efficient graph-based clustering framework which operates iteratively on the Neighbor-Joining (NJ) tree called the iNJclust algorithm. The framework uses well-known genetic measurements, namely the allele-sharing distance, the neighbor-joining tree, and the fixation index. The behavior of the fixation index is utilized in the algorithm's stopping criterion. The algorithm provides an estimated number of populations, individual assignments, and relationships between populations as outputs. The clustering result is reported in the form of a binary tree, whose terminal nodes represent the final inferred populations and the tree structure preserves the genetic relationships among them. The clustering performance and the robustness of the proposed algorithm are tested extensively using simulated and real data sets from bovine, sheep, and human populations. The result indicates that the number of populations within each data set is reasonably estimated, the individual assignment is robust, and the structure of the inferred population tree corresponds to the intrinsic relationships among populations within the data.

Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

NASA Astrophysics Data System (ADS)

Devra, Amit; Prabhu, Prithviraj; Singh, Harpreet; Arvind; Dorai, Kavita

2018-03-01

We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the "Luck-Choose" mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.

On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms.

PubMed

Gobin, Oliver C; Schüth, Ferdi

2008-01-01

Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.

Creating IRT-Based Parallel Test Forms Using the Genetic Algorithm Method

ERIC Educational Resources Information Center

Sun, Koun-Tem; Chen, Yu-Jen; Tsai, Shu-Yen; Cheng, Chien-Fen

2008-01-01

In educational measurement, the construction of parallel test forms is often a combinatorial optimization problem that involves the time-consuming selection of items to construct tests having approximately the same test information functions (TIFs) and constraints. This article proposes a novel method, genetic algorithm (GA), to construct parallel…

Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed

NASA Technical Reports Server (NTRS)

Rakoczy, John; Steincamp, James; Taylor, Jaime

2003-01-01

A reduced surrogate, one point crossover genetic algorithm with random rank-based selection was used successfully to estimate the multiple phases of a segmented optical system modeled on the seven-mirror Systematic Image-Based Optical Alignment testbed located at NASA's Marshall Space Flight Center.

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida (Published Proceedings)

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

Genetic algorithm to solve the problems of lectures and practicums scheduling

NASA Astrophysics Data System (ADS)

Syahputra, M. F.; Apriani, R.; Sawaluddin; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.

Multiple feature fusion via covariance matrix for visual tracking

NASA Astrophysics Data System (ADS)

Jin, Zefenfen; Hou, Zhiqiang; Yu, Wangsheng; Wang, Xin; Sun, Hui

2018-04-01

Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.

A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem

PubMed Central

Liu, Dong-sheng; Fan, Shu-jiang

2014-01-01

In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

NASA Astrophysics Data System (ADS)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

A Solution Method of Job-shop Scheduling Problems by the Idle Time Shortening Type Genetic Algorithm

NASA Astrophysics Data System (ADS)

Ida, Kenichi; Osawa, Akira

In this paper, we propose a new idle time shortening method for Job-shop scheduling problems (JSPs). We insert its method into a genetic algorithm (GA). The purpose of JSP is to find a schedule with the minimum makespan. We suppose that it is effective to reduce idle time of a machine in order to improve the makespan. The left shift is a famous algorithm in existing algorithms for shortening idle time. The left shift can not arrange the work to idle time. For that reason, some idle times are not shortened by the left shift. We propose two kinds of algorithms which shorten such idle time. Next, we combine these algorithms and the reversal of a schedule. We apply GA with its algorithm to benchmark problems and we show its effectiveness.

Packing Boxes into Multiple Containers Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Menghani, Deepak; Guha, Anirban

2016-07-01

Container loading problems have been studied extensively in the literature and various analytical, heuristic and metaheuristic methods have been proposed. This paper presents two different variants of a genetic algorithm framework for the three-dimensional container loading problem for optimally loading boxes into multiple containers with constraints. The algorithms are designed so that it is easy to incorporate various constraints found in real life problems. The algorithms are tested on data of standard test cases from literature and are found to compare well with the benchmark algorithms in terms of utilization of containers. This, along with the ability to easily incorporate a wide range of practical constraints, makes them attractive for implementation in real life scenarios.

Airport Flight Departure Delay Model on Improved BN Structure Learning

NASA Astrophysics Data System (ADS)

Cao, Weidong; Fang, Xiangnong

An high score prior genetic simulated annealing Bayesian network structure learning algorithm (HSPGSA) by combining genetic algorithm(GA) with simulated annealing algorithm(SAA) is developed. The new algorithm provides not only with strong global search capability of GA, but also with strong local hill climb search capability of SAA. The structure with the highest score is prior selected. In the mean time, structures with lower score are also could be choice. It can avoid efficiently prematurity problem by higher score individual wrong direct growing population. Algorithm is applied to flight departure delays analysis in a large hub airport. Based on the flight data a BN model is created. Experiments show that parameters learning can reflect departure delay.

Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

PubMed Central

Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

2014-01-01

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

Deadlock-free genetic scheduling algorithm for automated manufacturing systems based on deadlock control policy.

PubMed

Xing, KeYi; Han, LiBin; Zhou, MengChu; Wang, Feng

2012-06-01

Deadlock-free control and scheduling are vital for optimizing the performance of automated manufacturing systems (AMSs) with shared resources and route flexibility. Based on the Petri net models of AMSs, this paper embeds the optimal deadlock avoidance policy into the genetic algorithm and develops a novel deadlock-free genetic scheduling algorithm for AMSs. A possible solution of the scheduling problem is coded as a chromosome representation that is a permutation with repetition of parts. By using the one-step look-ahead method in the optimal deadlock control policy, the feasibility of a chromosome is checked, and infeasible chromosomes are amended into feasible ones, which can be easily decoded into a feasible deadlock-free schedule. The chromosome representation and polynomial complexity of checking and amending procedures together support the cooperative aspect of genetic search for scheduling problems strongly.

Candidate Insect Repellent AI3-35713-aGa N-Pentylvaleramide

DTIC Science & Technology

1976-10-19

0 Io -44 0 0 ON . w *0. 4) L0 0 ~ -- 0t, M- N 41 M , y ) *4Ur 0 f E0 4) MN 4) O.* r U 4J404 Ul~ r, eo 0 r. a.O~U f- n AN 1. ) 41$44 r. 4 0 -. ) MJ N ...TOPICAL HAZARD EVALUATION . , OF CANDIDATE INSECT REPELLENT AI3-35713-aGa N -PENTYLVALERAMIDE STUDY NO. 51-0802-77 AUGUST 1975 - AUGUST 1976 Approved...CLASSIFICATION DO’ THIS PAGE (Wlhet Der. Znftro* REPORT DOCUMENTATION PAGE EA~D N RM 1. REPORT NUMBER 2. GOVT ACCESSION No. 3. RECIPIENT’S CATALOG NUMBER 51

Age-Related DNA Methylation Changes and Neoplastic Transformation of the Human Prostate

DTIC Science & Technology

2011-07-01

Vol. 4 Issue 1 Figure 4. Methylation and expression analysis of the Sprouty1. (A) Representative program traces for Sprouty1. Gray colums represents...5’-ggt acc CCC TCC TGA GCT CAT GGT AAC CT-3’ Fwd 6 (-509) 5’-ggt acc CTT CTG GTT TGG AGC ACA GTG CAA AG-3’ Fwd 5 (-1318) 5’ggt acc AGA AGA CCT...CCC GAG GTG GAT GTT A-3’ Fwd3 (-2025) 5’-ggt acc CTG TCA ATC ACC GGG AGC-3’ Reverse (+8) 5’-gct agc AAT CCG CAC TGA ATA AAT AGT TGA C-3’. For

Identifying nutrient sources to three lagoons at Ofu and Olosega, American Samoa using δ15N of benthic macroalgae

USGS Publications Warehouse

Garrison, Virginia H.; Kroeger, Kevin D.; Fenner, Douglas; Craig, Peter

2007-01-01

Degradation of nearshore habitats is a serious problem in some areas of American Samoa, such as in Pago Pago Harbor on Tutuila Island, and is a smaller but chronic problem in other areas. Sedimentation, pollution, nutrient enrichment from surface runoff or groundwater, and trampling are the major factors causing the changes (Peshut and Brooks, 2005). On the outer islands of Ofu and Olosega (Manu’a Islands; Fig. 1), there is an interesting contrast between relatively pristine lagoon habitats not far from comparatively degraded lagoon habitats. To’aga lagoon on the southeast side of Ofu Island (Fig. 1) has clear waters, a high diversity of corals and fishes, no human habitations, and an undeveloped watershed with no streams. To’aga lagoon is within the boundaries of the National Park of American Samoa and is the site of long-term research on coral reef resilience and global climate change. Only 3 km to the east of To’aga is a degraded lagoon that fronts Olosega Village. The Olosega lagoon is similar in size but has significantly less live coral than To’aga, and blooms of filamentous algae have been reported to cover the Olosega lagoon/reef flat bottom (unpublished data, PC; Fig. 2). The islands are influenced by the same regional-scale and biogeochemical regimes, and both islands are remnants of a volcanic caldera (Craig, 2005). Thus, local factors operating on the scale of a kilometer or less are thought to be driving the differences observed between lagoons. Land disturbance is limited to a road linking the villages, the clearing of vegetation for buildings, and two village dump sites located on the narrow strip of land between the steep slopes of the islands and the shoreline; there is no industry or associated pollution on either island. Cesspools are used for sewage disposal. Nutrient enrichment (from cesspools) of groundwater and the lagoon, as well as trampling during gleaning of reef organisms, are possible factors affecting the spatial relief and benthic composition of the lagoons. A pristine lagoon site (To’aga) and two that may be influenced by adjacent human populations (Ofu and Olosega Villages) were selected for study.

Gut barrier failure biomarkers are associated with poor disease outcome in patients with primary sclerosing cholangitis

PubMed Central

Tornai, Tamas; Palyu, Eszter; Vitalis, Zsuzsanna; Tornai, Istvan; Tornai, David; Antal-Szalmas, Peter; Norman, Gary L; Shums, Zakera; Veres, Gabor; Dezsofi, Antal; Par, Gabriella; Par, Alajos; Orosz, Peter; Szalay, Ferenc; Lakatos, Peter Laszlo; Papp, Maria

2017-01-01

AIM To assess the prevalence of a panel of serologic markers that reflect gut barrier dysfunction in a mixed cohort of pediatric and adult primary sclerosing cholangitis (PSC) patients. METHODS Sera of 67 PSC patients [median age (range): 32 (5-79) years, concomitant IBD: 67% and cirrhosis: 20%] were assayed for the presence of antibodies against to F-actin (AAA IgA/IgG) and gliadin (AGA IgA/IgG)] and for serum level of intestinal fatty acid-binding protein (I-FABP) by ELISA. Markers of lipopolysaccharide (LPS) exposure [LPS binding protein (LBP)] and various anti-microbial antibodies [anti-OMP Plus IgA and endotoxin core IgA antibody (EndoCAb)] were also determined. Poor disease outcome was defined as orthotopic liver transplantation and/or liver-related death during the follow-up [median: 99 (14-106) mo]. One hundred and fifty-three healthy subjects (HCONT) and 172 ulcerative colitis (UC) patients were the controls. RESULTS A total of 28.4%, 28.0%, 9% and 20.9% of PSC patients were positive for AAA IgA, AAA IgG, AGA IgA and AGA IgG, respectively. Frequencies of AAA IgA and AAA IgG (P < 0.001, for both) and AGA IgG (P = 0.01, for both) but not AGA IgA were significantly higher compared to both of the HCONT and the UC groups. In survival analysis, AAA IgA-positivity was revealed as an independent predictor of poor disease outcome after adjusting either for the presence of cirrhosis [HR = 5.15 (1.27-20.86), P = 0.022 or for the Mayo risk score (HR = 4.24 (0.99-18.21), P = 0.052]. AAA IgA-positivity was significantly associated with higher frequency of anti-microbial antibodies (P < 0.001 for EndoCab IgA and P = 0.012 for anti-OMP Plus IgA) and higher level of the enterocyte damage marker (median I-FABPAAA IgA pos vs neg: 365 vs 166 pg/mL, P = 0.011), but not with serum LBP level. CONCLUSION Presence of IgA type AAA identified PSC patients with progressive disease. Moreover, it is associated with enhanced mucosal immune response to various microbial antigens and enterocyte damage further highlighting the importance of the gut-liver interaction in PSC. PMID:28839442

Nuclear fuel management optimization using genetic algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1995-07-01

The code independent genetic algorithm reactor optimization (CIGARO) system has been developed to optimize nuclear reactor loading patterns. It uses genetic algorithms (GAs) and a code-independent interface, so any reactor physics code (e.g., CASMO-3/SIMULATE-3) can be used to evaluate the loading patterns. The system is compared to other GA-based loading pattern optimizers. Tests were carried out to maximize the beginning of cycle k{sub eff} for a pressurized water reactor core loading with a penalty function to limit power peaking. The CIGARO system performed well, increasing the k{sub eff} after lowering the peak power. Tests of a prototype parallel evaluation methodmore » showed the potential for a significant speedup.« less

Medical image segmentation using genetic algorithms.

PubMed

Maulik, Ujjwal

2009-03-01

Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.

Naturally selecting solutions: the use of genetic algorithms in bioinformatics.

PubMed

Manning, Timmy; Sleator, Roy D; Walsh, Paul

2013-01-01

For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

Determination of thiamine HCl and pyridoxine HCl in pharmaceutical preparations using UV-visible spectrophotometry and genetic algorithm based multivariate calibration methods.

PubMed

Ozdemir, Durmus; Dinc, Erdal

2004-07-01

Simultaneous determination of binary mixtures pyridoxine hydrochloride and thiamine hydrochloride in a vitamin combination using UV-visible spectrophotometry and classical least squares (CLS) and three newly developed genetic algorithm (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV-visible spectra of 30 synthetic mixtures (8 to 40 microg/ml) of these vitamins and 10 tablets containing 250 mg from each vitamin. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the two components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of <0.01 and 0.43 microg/ml for all the four methods. The SEP values for the tablets were in the range of 2.91 and 11.51 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component using GR method was also included.

Stochastic search in structural optimization - Genetic algorithms and simulated annealing

NASA Technical Reports Server (NTRS)

Hajela, Prabhat

1993-01-01

An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.

Unscented Sampling Techniques For Evolutionary Computation With Applications To Astrodynamic Optimization

DTIC Science & Technology

2016-09-01

to both genetic algorithms and evolution strategies to achieve these goals. The results of this research offer a promising new set of modified ...abs_all.jsp?arnumber=203904 [163] Z. Michalewicz, C. Z. Janikow, and J. B. Krawczyk, “A modified genetic algo- rithm for optimal control problems...Available: http://arc.aiaa.org/doi/abs/10.2514/ 2.7053 375 [166] N. Yokoyama and S. Suzuki, “ Modified genetic algorithm for constrained trajectory

A novel structure-aware sparse learning algorithm for brain imaging genetics.

PubMed

Du, Lei; Jingwen, Yan; Kim, Sungeun; Risacher, Shannon L; Huang, Heng; Inlow, Mark; Moore, Jason H; Saykin, Andrew J; Shen, Li

2014-01-01

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.

A Smart Itsy Bitsy Spider for the Web.

ERIC Educational Resources Information Center

Chen, Hsinchun; Chung, Yi-Ming; Ramsey, Marshall; Yang, Christopher C.

1998-01-01

This study tested two Web personal spiders (i.e., agents that take users' requests and perform real-time customized searches) based on best first-search and genetic-algorithm techniques. Both results were comparable and complementary, although the genetic algorithm obtained higher recall value. The Java-based interface was found to be necessary…

Genetic algorithm driven spectral shaping of supercontinuum radiation in a photonic crystal fiber

NASA Astrophysics Data System (ADS)

Michaeli, Linor; Bahabad, Alon

2018-05-01

We employ a genetic algorithm to control a pulse-shaping system pumping a nonlinear photonic crystal with ultrashort pulses. With this system, we are able to modify the spectrum of the generated supercontinuum (SC) radiation to yield narrow Gaussian-like features around pre-selected wavelengths over the whole SC spectrum.

MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

PubMed Central

Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.

2014-01-01

The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

A genetic-algorithm approach for assessing the liquefaction potential of sandy soils

NASA Astrophysics Data System (ADS)

Sen, G.; Akyol, E.

2010-04-01

The determination of liquefaction potential is required to take into account a large number of parameters, which creates a complex nonlinear structure of the liquefaction phenomenon. The conventional methods rely on simple statistical and empirical relations or charts. However, they cannot characterise these complexities. Genetic algorithms are suited to solve these types of problems. A genetic algorithm-based model has been developed to determine the liquefaction potential by confirming Cone Penetration Test datasets derived from case studies of sandy soils. Software has been developed that uses genetic algorithms for the parameter selection and assessment of liquefaction potential. Then several estimation functions for the assessment of a Liquefaction Index have been generated from the dataset. The generated Liquefaction Index estimation functions were evaluated by assessing the training and test data. The suggested formulation estimates the liquefaction occurrence with significant accuracy. Besides, the parametric study on the liquefaction index curves shows a good relation with the physical behaviour. The total number of misestimated cases was only 7.8% for the proposed method, which is quite low when compared to another commonly used method.

Complex motion measurement using genetic algorithm

NASA Astrophysics Data System (ADS)

Shen, Jianjun; Tu, Dan; Shen, Zhenkang

1997-12-01

Genetic algorithm (GA) is an optimization technique that provides an untraditional approach to deal with many nonlinear, complicated problems. The notion of motion measurement using genetic algorithm arises from the fact that the motion measurement is virtually an optimization process based on some criterions. In the paper, we propose a complex motion measurement method using genetic algorithm based on block-matching criterion. The following three problems are mainly discussed and solved in the paper: (1) apply an adaptive method to modify the control parameters of GA that are critical to itself, and offer an elitism strategy at the same time (2) derive an evaluate function of motion measurement for GA based on block-matching technique (3) employ hill-climbing (HC) method hybridly to assist GA's search for the global optimal solution. Some other related problems are also discussed. At the end of paper, experiments result is listed. We employ six motion parameters for measurement in our experiments. Experiments result shows that the performance of our GA is good. The GA can find the object motion accurately and rapidly.

Optimization of fuels from waste composition with application of genetic algorithm.

PubMed

Małgorzata, Wzorek

2014-05-01

The objective of this article is to elaborate a method to optimize the composition of the fuels from sewage sludge (PBS fuel - fuel based on sewage sludge and coal slime, PBM fuel - fuel based on sewage sludge and meat and bone meal, PBT fuel - fuel based on sewage sludge and sawdust). As a tool for an optimization procedure, the use of a genetic algorithm is proposed. The optimization task involves the maximization of mass fraction of sewage sludge in a fuel developed on the basis of quality-based criteria for the use as an alternative fuel used by the cement industry. The selection criteria of fuels composition concerned such parameters as: calorific value, content of chlorine, sulphur and heavy metals. Mathematical descriptions of fuel compositions and general forms of the genetic algorithm, as well as the obtained optimization results are presented. The results of this study indicate that the proposed genetic algorithm offers an optimization tool, which could be useful in the determination of the composition of fuels that are produced from waste.

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Logistic regression trees for initial selection of interesting loci in case-control studies

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability. PMID:18466557

Routine Discovery of Complex Genetic Models using Genetic Algorithms

PubMed Central

Moore, Jason H.; Hahn, Lance W.; Ritchie, Marylyn D.; Thornton, Tricia A.; White, Bill C.

2010-01-01

Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes. PMID:20948983

A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

ERIC Educational Resources Information Center

Chahine, Firas Safwan

2012-01-01

Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN

EPA Science Inventory

While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...

Genetic algorithms for multicriteria shape optimization of induction furnace

NASA Astrophysics Data System (ADS)

Kůs, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo

2012-09-01

In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.

Design of synthetic biological logic circuits based on evolutionary algorithm.

PubMed

Chuang, Chia-Hua; Lin, Chun-Liang; Chang, Yen-Chang; Jennawasin, Tanagorn; Chen, Po-Kuei

2013-08-01

The construction of an artificial biological logic circuit using systematic strategy is recognised as one of the most important topics for the development of synthetic biology. In this study, a real-structured genetic algorithm (RSGA), which combines general advantages of the traditional real genetic algorithm with those of the structured genetic algorithm, is proposed to deal with the biological logic circuit design problem. A general model with the cis-regulatory input function and appropriate promoter activity functions is proposed to synthesise a wide variety of fundamental logic gates such as NOT, Buffer, AND, OR, NAND, NOR and XOR. The results obtained can be extended to synthesise advanced combinational and sequential logic circuits by topologically distinct connections. The resulting optimal design of these logic gates and circuits are established via the RSGA. The in silico computer-based modelling technology has been verified showing its great advantages in the purpose.

Preliminary Structural Design Using Topology Optimization with a Comparison of Results from Gradient and Genetic Algorithm Methods

NASA Technical Reports Server (NTRS)

Burt, Adam O.; Tinker, Michael L.

2014-01-01

In this paper, genetic algorithm based and gradient-based topology optimization is presented in application to a real hardware design problem. Preliminary design of a planetary lander mockup structure is accomplished using these methods that prove to provide major weight savings by addressing the structural efficiency during the design cycle. This paper presents two alternative formulations of the topology optimization problem. The first is the widely-used gradient-based implementation using commercially available algorithms. The second is formulated using genetic algorithms and internally developed capabilities. These two approaches are applied to a practical design problem for hardware that has been built, tested and proven to be functional. Both formulations converged on similar solutions and therefore were proven to be equally valid implementations of the process. This paper discusses both of these formulations at a high level.

Inverting the parameters of an earthquake-ruptured fault with a genetic algorithm

NASA Astrophysics Data System (ADS)

Yu, Ting-To; Fernàndez, Josè; Rundle, John B.

1998-03-01

Natural selection is the spirit of the genetic algorithm (GA): by keeping the good genes in the current generation, thereby producing better offspring during evolution. The crossover function ensures the heritage of good genes from parent to offspring. Meanwhile, the process of mutation creates a special gene, the character of which does not exist in the parent generation. A program based on genetic algorithms using C language is constructed to invert the parameters of an earthquake-ruptured fault. The verification and application of this code is shown to demonstrate its capabilities. It is determined that this code is able to find the global extreme and can be used to solve more practical problems with constraints gathered from other sources. It is shown that GA is superior to other inverting schema in many aspects. This easy handling and yet powerful algorithm should have many suitable applications in the field of geosciences.

Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

NASA Astrophysics Data System (ADS)

Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

2017-04-01

Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

An Improved SoC Test Scheduling Method Based on Simulated Annealing Algorithm

NASA Astrophysics Data System (ADS)

Zheng, Jingjing; Shen, Zhihang; Gao, Huaien; Chen, Bianna; Zheng, Weida; Xiong, Xiaoming

2017-02-01

In this paper, we propose an improved SoC test scheduling method based on simulated annealing algorithm (SA). It is our first to disorganize IP core assignment for each TAM to produce a new solution for SA, allocate TAM width for each TAM using greedy algorithm and calculate corresponding testing time. And accepting the core assignment according to the principle of simulated annealing algorithm and finally attain the optimum solution. Simultaneously, we run the test scheduling experiment with the international reference circuits provided by International Test Conference 2002(ITC’02) and the result shows that our algorithm is superior to the conventional integer linear programming algorithm (ILP), simulated annealing algorithm (SA) and genetic algorithm(GA). When TAM width reaches to 48,56 and 64, the testing time based on our algorithm is lesser than the classic methods and the optimization rates are 30.74%, 3.32%, 16.13% respectively. Moreover, the testing time based on our algorithm is very close to that of improved genetic algorithm (IGA), which is state-of-the-art at present.

A new compound arithmetic crossover-based genetic algorithm for constrained optimisation in enterprise systems

NASA Astrophysics Data System (ADS)

Jin, Chenxia; Li, Fachao; Tsang, Eric C. C.; Bulysheva, Larissa; Kataev, Mikhail Yu

2017-01-01

In many real industrial applications, the integration of raw data with a methodology can support economically sound decision-making. Furthermore, most of these tasks involve complex optimisation problems. Seeking better solutions is critical. As an intelligent search optimisation algorithm, genetic algorithm (GA) is an important technique for complex system optimisation, but it has internal drawbacks such as low computation efficiency and prematurity. Improving the performance of GA is a vital topic in academic and applications research. In this paper, a new real-coded crossover operator, called compound arithmetic crossover operator (CAC), is proposed. CAC is used in conjunction with a uniform mutation operator to define a new genetic algorithm CAC10-GA. This GA is compared with an existing genetic algorithm (AC10-GA) that comprises an arithmetic crossover operator and a uniform mutation operator. To judge the performance of CAC10-GA, two kinds of analysis are performed. First the analysis of the convergence of CAC10-GA is performed by the Markov chain theory; second, a pair-wise comparison is carried out between CAC10-GA and AC10-GA through two test problems available in the global optimisation literature. The overall comparative study shows that the CAC performs quite well and the CAC10-GA defined outperforms the AC10-GA.

Demonstrating the suitability of genetic algorithms for driving microbial ecosystems in desirable directions.

PubMed

Vandecasteele, Frederik P J; Hess, Thomas F; Crawford, Ronald L

2007-07-01

The functioning of natural microbial ecosystems is determined by biotic interactions, which are in turn influenced by abiotic environmental conditions. Direct experimental manipulation of such conditions can be used to purposefully drive ecosystems toward exhibiting desirable functions. When a set of environmental conditions can be manipulated to be present at a discrete number of levels, finding the right combination of conditions to obtain the optimal desired effect becomes a typical combinatorial optimisation problem. Genetic algorithms are a class of robust and flexible search and optimisation techniques from the field of computer science that may be very suitable for such a task. To verify this idea, datasets containing growth levels of the total microbial community of four different natural microbial ecosystems in response to all possible combinations of a set of five chemical supplements were obtained. Subsequently, the ability of a genetic algorithm to search this parameter space for combinations of supplements driving the microbial communities to high levels of growth was compared to that of a random search, a local search, and a hill-climbing algorithm, three intuitive alternative optimisation approaches. The results indicate that a genetic algorithm is very suitable for driving microbial ecosystems in desirable directions, which opens opportunities for both fundamental ecological research and industrial applications.

Comparative Analysis of Soft Computing Models in Prediction of Bending Rigidity of Cotton Woven Fabrics

NASA Astrophysics Data System (ADS)

Guruprasad, R.; Behera, B. K.

2015-10-01

Quantitative prediction of fabric mechanical properties is an essential requirement for design engineering of textile and apparel products. In this work, the possibility of prediction of bending rigidity of cotton woven fabrics has been explored with the application of Artificial Neural Network (ANN) and two hybrid methodologies, namely Neuro-genetic modeling and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling. For this purpose, a set of cotton woven grey fabrics was desized, scoured and relaxed. The fabrics were then conditioned and tested for bending properties. With the database thus created, a neural network model was first developed using back propagation as the learning algorithm. The second model was developed by applying a hybrid learning strategy, in which genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. The Genetic algorithm optimized network structure was further allowed to learn using back propagation algorithm. In the third model, an ANFIS modeling approach was attempted to map the input-output data. The prediction performances of the models were compared and a sensitivity analysis was reported. The results show that the prediction by neuro-genetic and ANFIS models were better in comparison with that of back propagation neural network model.

The mGA1.0: A common LISP implementation of a messy genetic algorithm

NASA Technical Reports Server (NTRS)

Goldberg, David E.; Kerzic, Travis

1990-01-01

Genetic algorithms (GAs) are finding increased application in difficult search, optimization, and machine learning problems in science and engineering. Increasing demands are being placed on algorithm performance, and the remaining challenges of genetic algorithm theory and practice are becoming increasingly unavoidable. Perhaps the most difficult of these challenges is the so-called linkage problem. Messy GAs were created to overcome the linkage problem of simple genetic algorithms by combining variable-length strings, gene expression, messy operators, and a nonhomogeneous phasing of evolutionary processing. Results on a number of difficult deceptive test functions are encouraging with the mGA always finding global optima in a polynomial number of function evaluations. Theoretical and empirical studies are continuing, and a first version of a messy GA is ready for testing by others. A Common LISP implementation called mGA1.0 is documented and related to the basic principles and operators developed by Goldberg et. al. (1989, 1990). Although the code was prepared with care, it is not a general-purpose code, only a research version. Important data structures and global variations are described. Thereafter brief function descriptions are given, and sample input data are presented together with sample program output. A source listing with comments is also included.

Genetic algorithms for protein threading.

PubMed

Yadgari, J; Amir, A; Unger, R

1998-01-01

Despite many years of efforts, a direct prediction of protein structure from sequence is still not possible. As a result, in the last few years researchers have started to address the "inverse folding problem": Identifying and aligning a sequence to the fold with which it is most compatible, a process known as "threading". In two meetings in which protein folding predictions were objectively evaluated, it became clear that threading as a concept promises a real breakthrough, but that much improvement is still needed in the technique itself. Threading is a NP-hard problem, and thus no general polynomial solution can be expected. Still a practical approach with demonstrated ability to find optimal solutions in many cases, and acceptable solutions in other cases, is needed. We applied the technique of Genetic Algorithms in order to significantly improve the ability of threading algorithms to find the optimal alignment of a sequence to a structure, i.e. the alignment with the minimum free energy. A major progress reported here is the design of a representation of the threading alignment as a string of fixed length. With this representation validation of alignments and genetic operators are effectively implemented. Appropriate data structure and parameters have been selected. It is shown that Genetic Algorithm threading is effective and is able to find the optimal alignment in a few test cases. Furthermore, the described algorithm is shown to perform well even without pre-definition of core elements. Existing threading methods are dependent on such constraints to make their calculations feasible. But the concept of core elements is inherently arbitrary and should be avoided if possible. While a rigorous proof is hard to submit yet an, we present indications that indeed Genetic Algorithm threading is capable of finding consistently good solutions of full alignments in search spaces of size up to 10(70).

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

NASA Astrophysics Data System (ADS)

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

2009-11-01

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

Clustering for Binary Data Sets by Using Genetic Algorithm-Incremental K-means

NASA Astrophysics Data System (ADS)

Saharan, S.; Baragona, R.; Nor, M. E.; Salleh, R. M.; Asrah, N. M.

2018-04-01

This research was initially driven by the lack of clustering algorithms that specifically focus in binary data. To overcome this gap in knowledge, a promising technique for analysing this type of data became the main subject in this research, namely Genetic Algorithms (GA). For the purpose of this research, GA was combined with the Incremental K-means (IKM) algorithm to cluster the binary data streams. In GAIKM, the objective function was based on a few sufficient statistics that may be easily and quickly calculated on binary numbers. The implementation of IKM will give an advantage in terms of fast convergence. The results show that GAIKM is an efficient and effective new clustering algorithm compared to the clustering algorithms and to the IKM itself. In conclusion, the GAIKM outperformed other clustering algorithms such as GCUK, IKM, Scalable K-means (SKM) and K-means clustering and paves the way for future research involving missing data and outliers.

A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

PubMed Central

Tsai, Chun-Wei; Tseng, Shih-Pang; Yang, Chu-Sing

2014-01-01

This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA. PMID:24892038

A high-performance genetic algorithm: using traveling salesman problem as a case.

PubMed

Tsai, Chun-Wei; Tseng, Shih-Pang; Chiang, Ming-Chao; Yang, Chu-Sing; Hong, Tzung-Pei

2014-01-01

This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA.

Evaluation of Residual Static Corrections by Hybrid Genetic Algorithm Steepest Ascent Autostatics Inversion.Application southern Algerian fields

NASA Astrophysics Data System (ADS)

Eladj, Said; bansir, fateh; ouadfeul, sid Ali

2016-04-01

The application of genetic algorithm starts with an initial population of chromosomes representing a "model space". Chromosome chains are preferentially Reproduced based on Their fitness Compared to the total population. However, a good chromosome has a Greater opportunity to Produce offspring Compared To other chromosomes in the population. The advantage of the combination HGA / SAA is the use of a global search approach on a large population of local maxima to Improve Significantly the performance of the method. To define the parameters of the Hybrid Genetic Algorithm Steepest Ascent Auto Statics (HGA / SAA) job, we Evaluated by testing in the first stage of "Steepest Ascent," the optimal parameters related to the data used. 1- The number of iterations "Number of hill climbing iteration" is equal to 40 iterations. This parameter defines the participation of the algorithm "SA", in this hybrid approach. 2- The minimum eigenvalue for SA '= 0.8. This is linked to the quality of data and S / N ratio. To find an implementation performance of hybrid genetic algorithms in the inversion for estimating of the residual static corrections, tests Were Performed to determine the number of generation of HGA / SAA. Using the values of residual static corrections already calculated by the Approaches "SAA and CSAA" learning has Proved very effective in the building of the cross-correlation table. To determine the optimal number of generation, we Conducted a series of tests ranging from [10 to 200] generations. The application on real seismic data in southern Algeria allowed us to judge the performance and capacity of the inversion with this hybrid method "HGA / SAA". This experience Clarified the influence of the corrections quality estimated from "SAA / CSAA" and the optimum number of generation hybrid genetic algorithm "HGA" required to have a satisfactory performance. Twenty (20) generations Were enough to Improve continuity and resolution of seismic horizons. This Will allow us to achieve a more accurate structural interpretation Key words: Hybrid Genetic Algorithm, number of generations, model space, local maxima, Number of hill climbing iteration, Minimum eigenvalue, cross-correlation table

Genetic algorithm with maximum-minimum crossover (GA-MMC) applied in optimization of radiation pattern control of phased-array radars for rocket tracking systems.

PubMed

Silva, Leonardo W T; Barros, Vitor F; Silva, Sandro G

2014-08-18

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence.

Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems

PubMed Central

Silva, Leonardo W. T.; Barros, Vitor F.; Silva, Sandro G.

2014-01-01

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence. PMID:25196013

Threshold matrix for digital halftoning by genetic algorithm optimization

NASA Astrophysics Data System (ADS)

Alander, Jarmo T.; Mantere, Timo J.; Pyylampi, Tero

1998-10-01

Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.

Refined Genetic Algorithms for Polypeptide Structure Prediction.

DTIC Science & Technology

1996-12-01

16 I I I. Algorithm Analysis, Design , and Implemen tation : : : : : : : : : : : : : : : : : : : : : : : : : 18 3.1 Analysis...21 3.2 Algorithm Design and Implemen tation : : : : : : : : : : : : : : : : : : : : : : : : : 22 3.2.1...26 IV. Exp erimen t Design

Comparing genetic algorithm and particle swarm optimization for solving capacitated vehicle routing problem

NASA Astrophysics Data System (ADS)

Iswari, T.; Asih, A. M. S.

2018-04-01

In the logistics system, transportation plays an important role to connect every element in the supply chain, but it can produces the greatest cost. Therefore, it is important to make the transportation costs as minimum as possible. Reducing the transportation cost can be done in several ways. One of the ways to minimizing the transportation cost is by optimizing the routing of its vehicles. It refers to Vehicle Routing Problem (VRP). The most common type of VRP is Capacitated Vehicle Routing Problem (CVRP). In CVRP, the vehicles have their own capacity and the total demands from the customer should not exceed the capacity of the vehicle. CVRP belongs to the class of NP-hard problems. These NP-hard problems make it more complex to solve such that exact algorithms become highly time-consuming with the increases in problem sizes. Thus, for large-scale problem instances, as typically found in industrial applications, finding an optimal solution is not practicable. Therefore, this paper uses two kinds of metaheuristics approach to solving CVRP. Those are Genetic Algorithm and Particle Swarm Optimization. This paper compares the results of both algorithms and see the performance of each algorithm. The results show that both algorithms perform well in solving CVRP but still needs to be improved. From algorithm testing and numerical example, Genetic Algorithm yields a better solution than Particle Swarm Optimization in total distance travelled.

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

NASA Technical Reports Server (NTRS)

Ortiz, Francisco

2004-01-01

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

A review of estimation of distribution algorithms in bioinformatics

PubMed Central

Armañanzas, Rubén; Inza, Iñaki; Santana, Roberto; Saeys, Yvan; Flores, Jose Luis; Lozano, Jose Antonio; Peer, Yves Van de; Blanco, Rosa; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro

2008-01-01

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain. PMID:18822112

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

PubMed Central

Cao, Leilei; Xu, Lihong; Goodman, Erik D.

2016-01-01

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.

PubMed

Cao, Leilei; Xu, Lihong; Goodman, Erik D

2016-01-01

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.

PubMed

Lao, Oscar; Liu, Fan; Wollstein, Andreas; Kayser, Manfred

2014-02-01

Attempts to detect genetic population substructure in humans are troubled by the fact that the vast majority of the total amount of observed genetic variation is present within populations rather than between populations. Here we introduce a new algorithm for transforming a genetic distance matrix that reduces the within-population variation considerably. Extensive computer simulations revealed that the transformed matrix captured the genetic population differentiation better than the original one which was based on the T1 statistic. In an empirical genomic data set comprising 2,457 individuals from 23 different European subpopulations, the proportion of individuals that were determined as a genetic neighbour to another individual from the same sampling location increased from 25% with the original matrix to 52% with the transformed matrix. Similarly, the percentage of genetic variation explained between populations by means of Analysis of Molecular Variance (AMOVA) increased from 1.62% to 7.98%. Furthermore, the first two dimensions of a classical multidimensional scaling (MDS) using the transformed matrix explained 15% of the variance, compared to 0.7% obtained with the original matrix. Application of MDS with Mclust, SPA with Mclust, and GemTools algorithms to the same dataset also showed that the transformed matrix gave a better association of the genetic clusters with the sampling locations, and particularly so when it was used in the AMOVA framework with a genetic algorithm. Overall, the new matrix transformation introduced here substantially reduces the within population genetic differentiation, and can be broadly applied to methods such as AMOVA to enhance their sensitivity to reveal population substructure. We herewith provide a publically available (http://www.erasmusmc.nl/fmb/resources/GAGA) model-free method for improved genetic population substructure detection that can be applied to human as well as any other species data in future studies relevant to evolutionary biology, behavioural ecology, medicine, and forensics.

Comparative Analysis of Rank Aggregation Techniques for Metasearch Using Genetic Algorithm

ERIC Educational Resources Information Center

Kaur, Parneet; Singh, Manpreet; Singh Josan, Gurpreet

2017-01-01

Rank Aggregation techniques have found wide applications for metasearch along with other streams such as Sports, Voting System, Stock Markets, and Reduction in Spam. This paper presents the optimization of rank lists for web queries put by the user on different MetaSearch engines. A metaheuristic approach such as Genetic algorithm based rank…

Finding Patterns of Emergence in Science and Technology

DTIC Science & Technology

2012-09-24

formal evaluation scheduled – Case Studies, Eight Examples: Tissue Engineering, Cold Fusion, RF Metamaterials, DNA Microarrays, Genetic Algorithms, RNAi...emerging capabilities Case Studies, Eight Examples: • Tissue Engineering, Cold Fusion, RF Metamaterials, DNA Microarrays, Genetic Algorithms...Evidence Quality (i.e., the rubric ) and deliver comprehensible evidential support for nomination • Demonstrate proof-of-concept nomination for Chinese

Validation Test Report for a Genetic Algorithm in the Glider Observation STrategies (GOST 1.0) Project: Sensitivity Studies

DTIC Science & Technology

2012-08-15

Environmental Model ( GDEM ) 72 levels) was conserved in the interpolated profiles and small variations in the vertical field may have lead to large...Planner ETKF Ensemble Transform Kalman Filter G8NCOM 1/8⁰ Global NCOM GA Genetic Algorithm GDEM Generalized Digital Environmental Model GOST

Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

ERIC Educational Resources Information Center

Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

2003-01-01

Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

Process optimization of rolling for zincked sheet technology using response surface methodology and genetic algorithm

NASA Astrophysics Data System (ADS)

Ji, Liang-Bo; Chen, Fang

2017-07-01

Numerical simulation and intelligent optimization technology were adopted for rolling and extrusion of zincked sheet. By response surface methodology (RSM), genetic algorithm (GA) and data processing technology, an efficient optimization of process parameters for rolling of zincked sheet was investigated. The influence trend of roller gap, rolling speed and friction factor effects on reduction rate and plate shortening rate were analyzed firstly. Then a predictive response surface model for comprehensive quality index of part was created using RSM. Simulated and predicted values were compared. Through genetic algorithm method, the optimal process parameters for the forming of rolling were solved. They were verified and the optimum process parameters of rolling were obtained. It is feasible and effective.

Genetic Algorithm Design of a 3D Printed Heat Sink

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Tong; Ozpineci, Burak; Ayers, Curtis William

2016-01-01

In this paper, a genetic algorithm- (GA-) based approach is discussed for designing heat sinks based on total heat generation and dissipation for a pre-specified size andshape. This approach combines random iteration processesand genetic algorithms with finite element analysis (FEA) to design the optimized heat sink. With an approach that prefers survival of the fittest , a more powerful heat sink can bedesigned which can cool power electronics more efficiently. Some of the resulting designs can only be 3D printed due totheir complexity. In addition to describing the methodology, this paper also includes comparisons of different cases to evaluate themore » performance of the newly designed heat sinkcompared to commercially available heat sinks.« less

Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

A genetic algorithm solution to the unit commitment problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kazarlis, S.A.; Bakirtzis, A.G.; Petridis, V.

1996-02-01

This paper presents a Genetic Algorithm (GA) solution to the Unit Commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple Ga algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the Varying Quality Function technique and adding problem specific operators, satisfactory solutions to the Unit Commitment problem were obtained. Test results for systems of up to 100 unitsmore » and comparisons with results obtained using Lagrangian Relaxation and Dynamic Programming are also reported.« less

On the Optimization of Aerospace Plane Ascent Trajectory

NASA Astrophysics Data System (ADS)

Al-Garni, Ahmed; Kassem, Ayman Hamdy

A hybrid heuristic optimization technique based on genetic algorithms and particle swarm optimization has been developed and tested for trajectory optimization problems with multi-constraints and a multi-objective cost function. The technique is used to calculate control settings for two types for ascending trajectories (constant dynamic pressure and minimum-fuel-minimum-heat) for a two-dimensional model of an aerospace plane. A thorough statistical analysis is done on the hybrid technique to make comparisons with both basic genetic algorithms and particle swarm optimization techniques with respect to convergence and execution time. Genetic algorithm optimization showed better execution time performance while particle swarm optimization showed better convergence performance. The hybrid optimization technique, benefiting from both techniques, showed superior robust performance compromising convergence trends and execution time.

Preliminary Design of a Manned Nuclear Electric Propulsion Vehicle Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Irwin, Ryan W.; Tinker, Michael L.

2005-01-01

Nuclear electric propulsion (NEP) vehicles will be needed for future manned missions to Mars and beyond. Candidate designs must be identified for further detailed design from a large array of possibilities. Genetic algorithms have proven their utility in conceptual design studies by effectively searching a large design space to pinpoint unique optimal designs. This research combined analysis codes for NEP subsystems with a genetic algorithm. The use of penalty functions with scaling ratios was investigated to increase computational efficiency. Also, the selection of design variables for optimization was considered to reduce computation time without losing beneficial design search space. Finally, trend analysis of a reference mission to the asteroids yielded a group of candidate designs for further analysis.

Evaluation of Genetic Algorithm Concepts using Model Problems. Part 1; Single-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic-algorithm-based optimization approach is described and evaluated using a simple hill-climbing model problem. The model problem utilized herein allows for the broad specification of a large number of search spaces including spaces with an arbitrary number of genes or decision variables and an arbitrary number hills or modes. In the present study, only single objective problems are considered. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all problems attempted. The most difficult problems - those with large hyper-volumes and multi-mode search spaces containing a large number of genes - require a large number of function evaluations for GA convergence, but they always converge.

Treatments options for alopecia.

PubMed

Iorizzo, Matilde; Tosti, Antonella

2015-01-01

Hair disorders have a very high social and psychological impact. Treatment is often frustrating and time-consuming both for the patients and the clinicians and requires special skills and expertise. This paper aims to provide an overview of available treatments for the most common forms of alopecia in adults (androgenetic alopecia [AGA], alopecia areata and cicatricial alopecias) after reviewing the literature in PubMed, Google Scholar and ClinicalTrial.gov. Before starting treatment, it is very important to confirm diagnosis and discuss patient's expectations. Treatment of hair disorders requires time and first results are usually visible a few months after beginning of therapy. Treatment of most hair disorders is mostly not evidenced-based as randomized controlled trials are available only for AGA.

Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

PubMed Central

Zhang, Lun; Zhang, Meng; Yang, Wenchen; Dong, Decun

2015-01-01

This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity. PMID:25802512

Genetics in the art and art in genetics.

PubMed

Bukvic, Nenad; Elling, John W

2015-01-15

"Healing is best accomplished when art and science are conjoined, when body and spirit are probed together", says Bernard Lown, in his book "The Lost Art of Healing". Art has long been a witness to disease either through diseases which affected artists or diseases afflicting objects of their art. In particular, artists have often portrayed genetic disorders and malformations in their work. Sometimes genetic disorders have mystical significance; other times simply have intrinsic interest. Recognizing genetic disorders is also an art form. From the very beginning of my work as a Medical Geneticist I have composed personal "algorithms" to piece together evidence of genetics syndromes and diseases from the observable signs and symptoms. In this paper we apply some 'gestalt' Genetic Syndrome Diagnostic algorithms to virtual patients found in some art masterpieces. In some the diagnosis is clear and in others the artists' depiction only supports a speculative differential diagnosis. Copyright © 2014 Elsevier B.V. All rights reserved.

Production scheduling and rescheduling with genetic algorithms.

PubMed

Bierwirth, C; Mattfeld, D C

1999-01-01

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs.

Efficacy and safety of 3% minoxidil versus combined 3% minoxidil / 0.1% finasteride in male pattern hair loss: a randomized, double-blind, comparative study.

PubMed

Tanglertsampan, Chuchai

2012-10-01

Topical minoxidil and oral finasteride have been used to treat men with androgenetic alopecia (AGA). There are concerns about side effects of oral finasteride especially erectile dysfunction. To compare the efficacy and safety of the 24 weeks application of 3% minoxidil lotion (MNX) versus combined 3% minoxidil and 0.1% finasteride lotion (MFX) in men with AGA. Forty men with AGA were randomized treated with MNX or MFX. Efficacy was evaluated by hair counts and global photographic assessment. Safety assessment was performed by history and physical examination. At week 24, hair counts were increased from baseline in both groups. However paired t-test revealed statistical difference only in MFX group (p = 0.044). Unpaired t-test revealed no statistical difference between two groups with respect to change of hair counts at 24 weeks from baseline (p = 0.503). MFX showed significantly higher efficacy than MNX by global photographic assessment (p = 0.003). There was no significant difference in side effects between both groups. Although change of hair counts was not statistically different between two groups, global photographic assessment showed significantly greater improvement in the MFX group than the MNX group. There was no sexual side effect. MFX may be a safe and effective treatment option.

Insulin resistance and its association with catch-up growth in Chinese children born small for gestational age.

PubMed

Liu, Chunhua; Wu, Baiyan; Lin, Niyang; Fang, Xiaoyi

2017-01-01

To assess insulin resistance and β-cell function from birth to age 4 years and to examine their associations with catch-up growth (CUG) in Chinese small-for-gestational-age (SGA) children. Weight and height were measured yearly from birth to age 4 years, and transformed into age- and gender-adjusted SD scores. Fasting serum insulin and glucose were measured, and fasting insulin resistance and β-cell function were estimated using the homeostasis model assessment (HOMA). The mean HOMA-IR of the SGA group was significantly lower than that of the appropriate-for-gestational-age (AGA) group at ages 2 and 3 years old, and the mean HOMA% of the SGA group was significantly lower than that of the AGA group at age 4 years old. At 4 years of age, HOMA for insulin resistance was positively correlated with the height gain and SD of height gain between 0 and 5 months, and HOMA% was positively correlated with the weight gain and SD of weight gain between 6 and 12 months in SGA children. SGA children with CUG show a greater propensity to develop insulin resistance than AGA children between ages 2 and 4 years old. HOMA parameters are related to CUG in the first year of life. © 2016 The Obesity Society.

GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design.

PubMed

Pérez-Castillo, Yunierkis; Lazar, Cosmin; Taminau, Jonatan; Froeyen, Mathy; Cabrera-Pérez, Miguel Ángel; Nowé, Ann

2012-09-24

Computer-aided drug design has become an important component of the drug discovery process. Despite the advances in this field, there is not a unique modeling approach that can be successfully applied to solve the whole range of problems faced during QSAR modeling. Feature selection and ensemble modeling are active areas of research in ligand-based drug design. Here we introduce the GA(M)E-QSAR algorithm that combines the search and optimization capabilities of Genetic Algorithms with the simplicity of the Adaboost ensemble-based classification algorithm to solve binary classification problems. We also explore the usefulness of Meta-Ensembles trained with Adaboost and Voting schemes to further improve the accuracy, generalization, and robustness of the optimal Adaboost Single Ensemble derived from the Genetic Algorithm optimization. We evaluated the performance of our algorithm using five data sets from the literature and found that it is capable of yielding similar or better classification results to what has been reported for these data sets with a higher enrichment of active compounds relative to the whole actives subset when only the most active chemicals are considered. More important, we compared our methodology with state of the art feature selection and classification approaches and found that it can provide highly accurate, robust, and generalizable models. In the case of the Adaboost Ensembles derived from the Genetic Algorithm search, the final models are quite simple since they consist of a weighted sum of the output of single feature classifiers. Furthermore, the Adaboost scores can be used as ranking criterion to prioritize chemicals for synthesis and biological evaluation after virtual screening experiments.

Genetic algorithms for the vehicle routing problem

NASA Astrophysics Data System (ADS)

Volna, Eva

2016-06-01

The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem.

Genetic diversity of Plasmodium vivax clinical isolates from southern Pakistan using pvcsp and pvmsp1 genetic markers

PubMed Central

2013-01-01

Background Plasmodium vivax is the prevalent malarial species accounting for 70% of malaria burden in Pakistan; however, there is no baseline data on the circulating genotypes. Studies have shown that polymorphic loci of gene encoding antigens pvcsp and pvmsp1 can be used reliably for conducting molecular epidemiological studies. Therefore, this study aimed to bridge the existing knowledge gap on population structure on P. vivax from Pakistan using these two polymorphic genes. Methods During the period January 2008 to May 2009, a total of 250 blood samples were collected from patients tested slide positive for P. vivax, at the Aga Khan University Hospital, Karachi, or its collection units located in Baluchistan and Sindh Province. Nested PCR/RFLP was performed, using pvcsp and pvmsp1 markers to detect the extent of genetic diversity in clinical isolates of P. vivax from southern Pakistan. Results A total of 227/250 (91%) isolates were included in the analysis while the remainder were excluded due to negative PCR outcome for P.vivax. Pvcsp analysis showed that both VK 210 (85.5%, 194/227) and VK 247 type (14.5%, 33/227) were found to be circulating in P. vivax isolates from southern Pakistan. A total of sixteen and eighty-seven genotypes of pvcsp and pvmsp-1 were detected respectively. Conclusion This is the first report from southern Pakistan on characterization of P. vivax isolates confirming that extensively diverse pvcsp and pvmsp1 variants are present within this region. Results from this study provide valuable data on genetic diversity of P. vivax that will be helpful for further epidemiological studies. PMID:23311628

Social Media: Menagerie of Metrics

DTIC Science & Technology

2010-01-27

intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm . An EA...Cloning - 22 Animals were cloned to date; genetic algorithms can help prediction (e.g. “elitism” - attempts to ensure selection by including performers...28, 2010 Evolutionary Algorithm • Evolutionary algorithm From Wikipedia, the free encyclopedia Artificial intelligence portal In artificial

A Parallel Genetic Algorithm for Automated Electronic Circuit Design

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris; Norvig, Peter (Technical Monitor)

2000-01-01

We describe a parallel genetic algorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.

Optimizing simulated fertilizer additions using a genetic algorithm with a nutrient uptake model

Treesearch

Wendell P. Cropper; N.B. Comerford

2005-01-01

Intensive management of pine plantations in the southeastern coastal plain typically involves weed and pest control, and the addition of fertilizer to meet the high nutrient demand of rapidly growing pines. In this study we coupled a mechanistic nutrient uptake model (SSAND, soil supply and nutrient demand) with a genetic algorithm (GA) in order to estimate the minimum...

Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Marghany, Maged

2016-10-01

In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiveroperating characteristic (ROC) curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.

Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven

2010-05-01

Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.

A Genetic Algorithm for the Bi-Level Topological Design of Local Area Networks

PubMed Central

Camacho-Vallejo, José-Fernando; Mar-Ortiz, Julio; López-Ramos, Francisco; Rodríguez, Ricardo Pedraza

2015-01-01

Local access networks (LAN) are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network with minimum connection cost. Therefore, the decision of optimally assigning users to clusters will be made by the leader and the follower will make the decision of connecting all the clusters while forming a spanning tree. In this paper, we propose a genetic algorithm for solving the bi-level topological design of a Local Access Network. Our solution method considers the Stackelberg equilibrium to solve the bi-level problem. The Stackelberg-Genetic algorithm procedure deals with the fact that the follower’s problem cannot be optimally solved in a straightforward manner. The computational results obtained from two different sets of instances show that the performance of the developed algorithm is efficient and that it is more suitable for solving the bi-level problem than a previous Nash-Genetic approach. PMID:26102502

Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

PubMed

Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

2015-01-01

Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Genetic algorithms used for the optimization of light-emitting diodes and solar thermal collectors

NASA Astrophysics Data System (ADS)

Mayer, Alexandre; Bay, Annick; Gaouyat, Lucie; Nicolay, Delphine; Carletti, Timoteo; Deparis, Olivier

2014-09-01

We present a genetic algorithm (GA) we developed for the optimization of light-emitting diodes (LED) and solar thermal collectors. The surface of a LED can be covered by periodic structures whose geometrical and material parameters must be adjusted in order to maximize the extraction of light. The optimization of these parameters by the GA enabled us to get a light-extraction efficiency η of 11.0% from a GaN LED (for comparison, the flat material has a light-extraction efficiency η of only 3.7%). The solar thermal collector we considered consists of a waffle-shaped Al substrate with NiCrOx and SnO2 conformal coatings. We must in this case maximize the solar absorption α while minimizing the thermal emissivity ɛ in the infrared. A multi-objective genetic algorithm has to be implemented in this case in order to determine optimal geometrical parameters. The parameters we obtained using the multi-objective GA enable α~97.8% and ɛ~4.8%, which improves results achieved previously when considering a flat substrate. These two applications demonstrate the interest of genetic algorithms for addressing complex problems in physics.

Locating Critical Circular and Unconstrained Failure Surface in Slope Stability Analysis with Tailored Genetic Algorithm

NASA Astrophysics Data System (ADS)

Pasik, Tomasz; van der Meij, Raymond

2017-12-01

This article presents an efficient search method for representative circular and unconstrained slip surfaces with the use of the tailored genetic algorithm. Searches for unconstrained slip planes with rigid equilibrium methods are yet uncommon in engineering practice, and little publications regarding truly free slip planes exist. The proposed method presents an effective procedure being the result of the right combination of initial population type, selection, crossover and mutation method. The procedure needs little computational effort to find the optimum, unconstrained slip plane. The methodology described in this paper is implemented using Mathematica. The implementation, along with further explanations, is fully presented so the results can be reproduced. Sample slope stability calculations are performed for four cases, along with a detailed result interpretation. Two cases are compared with analyses described in earlier publications. The remaining two are practical cases of slope stability analyses of dikes in Netherlands. These four cases show the benefits of analyzing slope stability with a rigid equilibrium method combined with a genetic algorithm. The paper concludes by describing possibilities and limitations of using the genetic algorithm in the context of the slope stability problem.

Optimization of HAART with genetic algorithms and agent-based models of HIV infection.

PubMed

Castiglione, F; Pappalardo, F; Bernaschi, M; Motta, S

2007-12-15

Highly Active AntiRetroviral Therapies (HAART) can prolong life significantly to people infected by HIV since, although unable to eradicate the virus, they are quite effective in maintaining control of the infection. However, since HAART have several undesirable side effects, it is considered useful to suspend the therapy according to a suitable schedule of Structured Therapeutic Interruptions (STI). In the present article we describe an application of genetic algorithms (GA) aimed at finding the optimal schedule for a HAART simulated with an agent-based model (ABM) of the immune system that reproduces the most significant features of the response of an organism to the HIV-1 infection. The genetic algorithm helps in finding an optimal therapeutic schedule that maximizes immune restoration, minimizes the viral count and, through appropriate interruptions of the therapy, minimizes the dose of drug administered to the simulated patient. To validate the efficacy of the therapy that the genetic algorithm indicates as optimal, we ran simulations of opportunistic diseases and found that the selected therapy shows the best survival curve among the different simulated control groups. A version of the C-ImmSim simulator is available at http://www.iac.cnr.it/~filippo/c-ImmSim.html

Design of thrust vectoring exhaust nozzles for real-time applications using neural networks

NASA Technical Reports Server (NTRS)

Prasanth, Ravi K.; Markin, Robert E.; Whitaker, Kevin W.

1991-01-01

Thrust vectoring continues to be an important issue in military aircraft system designs. A recently developed concept of vectoring aircraft thrust makes use of flexible exhaust nozzles. Subtle modifications in the nozzle wall contours produce a non-uniform flow field containing a complex pattern of shock and expansion waves. The end result, due to the asymmetric velocity and pressure distributions, is vectored thrust. Specification of the nozzle contours required for a desired thrust vector angle (an inverse design problem) has been achieved with genetic algorithms. This approach is computationally intensive and prevents the nozzles from being designed in real-time, which is necessary for an operational aircraft system. An investigation was conducted into using genetic algorithms to train a neural network in an attempt to obtain, in real-time, two-dimensional nozzle contours. Results show that genetic algorithm trained neural networks provide a viable, real-time alternative for designing thrust vectoring nozzles contours. Thrust vector angles up to 20 deg were obtained within an average error of 0.0914 deg. The error surfaces encountered were highly degenerate and thus the robustness of genetic algorithms was well suited for minimizing global errors.

Flexible Job-Shop Scheduling with Dual-Resource Constraints to Minimize Tardiness Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Paksi, A. B. N.; Ma'ruf, A.

2016-02-01

In general, both machines and human resources are needed for processing a job on production floor. However, most classical scheduling problems have ignored the possible constraint caused by availability of workers and have considered only machines as a limited resource. In addition, along with production technology development, routing flexibility appears as a consequence of high product variety and medium demand for each product. Routing flexibility is caused by capability of machines that offers more than one machining process. This paper presents a method to address scheduling problem constrained by both machines and workers, considering routing flexibility. Scheduling in a Dual-Resource Constrained shop is categorized as NP-hard problem that needs long computational time. Meta-heuristic approach, based on Genetic Algorithm, is used due to its practical implementation in industry. Developed Genetic Algorithm uses indirect chromosome representative and procedure to transform chromosome into Gantt chart. Genetic operators, namely selection, elitism, crossover, and mutation are developed to search the best fitness value until steady state condition is achieved. A case study in a manufacturing SME is used to minimize tardiness as objective function. The algorithm has shown 25.6% reduction of tardiness, equal to 43.5 hours.

A new optimized GA-RBF neural network algorithm.

PubMed

Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan

2014-01-01

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

A novel hybrid algorithm for the design of the phase diffractive optical elements for beam shaping

NASA Astrophysics Data System (ADS)

Jiang, Wenbo; Wang, Jun; Dong, Xiucheng

2013-02-01

In this paper, a novel hybrid algorithm for the design of a phase diffractive optical elements (PDOE) is proposed. It combines the genetic algorithm (GA) with the transformable scale BFGS (Broyden, Fletcher, Goldfarb, Shanno) algorithm, the penalty function was used in the cost function definition. The novel hybrid algorithm has the global merits of the genetic algorithm as well as the local improvement capabilities of the transformable scale BFGS algorithm. We designed the PDOE using the conventional simulated annealing algorithm and the novel hybrid algorithm. To compare the performance of two algorithms, three indexes of the diffractive efficiency, uniformity error and the signal-to-noise ratio are considered in numerical simulation. The results show that the novel hybrid algorithm has good convergence property and good stability. As an application example, the PDOE was used for the Gaussian beam shaping; high diffractive efficiency, low uniformity error and high signal-to-noise were obtained. The PDOE can be used for high quality beam shaping such as inertial confinement fusion (ICF), excimer laser lithography, fiber coupling laser diode array, laser welding, etc. It shows wide application value.

Application of artificial neural networks and genetic algorithms to modeling molecular electronic spectra in solution

NASA Astrophysics Data System (ADS)

Lilichenko, Mark; Kelley, Anne Myers

2001-04-01

A novel approach is presented for finding the vibrational frequencies, Franck-Condon factors, and vibronic linewidths that best reproduce typical, poorly resolved electronic absorption (or fluorescence) spectra of molecules in condensed phases. While calculation of the theoretical spectrum from the molecular parameters is straightforward within the harmonic oscillator approximation for the vibrations, "inversion" of an experimental spectrum to deduce these parameters is not. Standard nonlinear least-squares fitting methods such as Levenberg-Marquardt are highly susceptible to becoming trapped in local minima in the error function unless very good initial guesses for the molecular parameters are made. Here we employ a genetic algorithm to force a broad search through parameter space and couple it with the Levenberg-Marquardt method to speed convergence to each local minimum. In addition, a neural network trained on a large set of synthetic spectra is used to provide an initial guess for the fitting parameters and to narrow the range searched by the genetic algorithm. The combined algorithm provides excellent fits to a variety of single-mode absorption spectra with experimentally negligible errors in the parameters. It converges more rapidly than the genetic algorithm alone and more reliably than the Levenberg-Marquardt method alone, and is robust in the presence of spectral noise. Extensions to multimode systems, and/or to include other spectroscopic data such as resonance Raman intensities, are straightforward.