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Kuswoyo, Anton. "EKSPLORASI AIR TANAH DENGAN METODE GEOLISTRIK SCHLUMBERGER DI DAERAH PESISIR KABUPATEN TANAH LAUT." Jurnal Purifikasi 13, no. 2 (December 30, 2020): 58–66. http://dx.doi.org/10.12962/j25983806.v13.i2.389.
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Intrusi air laut mengakibatkan polusi bagi air tanah, baik air tanah dangkal maupun air tanah dalam, yang merupakan sumber air bersih bagi penduduk. Khusus di daerah pesisir yang belum dilayanan air PDAM, masyarakat masih menggunakan air sumur dangkal sebagai sumber air bersih. Kendala utama sumur dangkal adalah mengalami kekeringan ketika musim kemarau dan mengalami intrusi air laut. Pengambilan data resistivitas dengan menggunakan metode geolistrik Schlumberger dilakukan pada sembilan titik lintasan. Hasil penelitian eksplorasi menunjukkan metoda geolistrik dapat digunakan untuk menentukan keberadaan air tanah. Hasil penelitian menunjukkan daerah penelitian mempunyai potensi air tanah yang layak untuk dimanfaatkan sebagai sumber air bersih. Potensi besar (ketebalan lapisan air tanah di atas 15 m) sebanyak 6 titik dari 9 titik penelitian, yakni berada pada titik GL1, GL3, GL5, GL6, GL7 dan GL9. Potensi sedang (ketebalan lapisan air tanah antara 5 – 15 m) sebanyak 3 titik lokasi, berada pada titik GL2, GL4 dan GL8. Keberadaan air tanah berada pada kisaran kedalaman 1 – 80 m.
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MIFTAHUDDIN, ANANDA PRATAMA SITANGGANG, NORIZAN MOHAMED, and MAHARANI A. BAKAR. "MODELLING INDIAN OCEAN AIR TEMPERATURE USING ADDITIVE MODEL." Journal of Mathematical Sciences and Informatics 2, no. 1 (June 30, 2022): 23–36. http://dx.doi.org/10.46754/jmsi.2022.06.003.
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In this study, we used the fluctuating air temperature dataset. The change is caused by data fluctuations, trend, seasonality, cyclicity and irregularities. The generalized additive model (GAM) data approach is used to describe these phenomena. The aim of this research is to find out the factors that affect the air temperature in the Indian Ocean, find a suitable model, and obtain the best model from three approximate methods, namely the Linear Model (LM), the Generalized Linear Model (GLM), and the GAM models, which use a dataset of factors that affect the temperature of the Indian Ocean (close to Aceh region). For the air temperature of α = 0.05, the significant effects are precipitation, relative humidity, sea surface temperature, and the wind speed. The LM, GLM and GAM models are quite feasible because they all meet and pass the classical hypothesis tests, namely the normality test, multicollinearity test, the heteroscedasticity test, and the autocorrelation test. The appropriate model is GAM model based on adaptive smoothers. Compared to the LM, GLM and GAM models, GAM model with the adaptive smoothers base gave smallest AIC values of 4552.890 and 2392.396 where modeling was without and with time variable respectively. Therefore, it can be said that the correct model used at air temperature is the GAM model for adaptive smoothers base.
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Van Oirbeek, Robin, Félix Vandervorst, Thomas Bury, Gireg Willame, Christopher Grumiau, and Tim Verdonck. "Non-Differentiable Loss Function Optimization and Interaction Effect Discovery in Insurance Pricing Using the Genetic Algorithm." Risks 12, no. 5 (May 14, 2024): 79. http://dx.doi.org/10.3390/risks12050079.
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Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where the Generalized Linear Models (GLMs) are the industry standard method. Traditional GLM approaches face limitations due to non-differentiable loss functions and expansive variable spaces, including both main and interaction terms. In this study, we address the challenge of selecting relevant variables for GLMs used in non-life insurance pricing both for frequency or severity analyses, amidst an increasing volume of data and variables. We propose a novel application of the Genetic Algorithm (GA) to efficiently identify pertinent main and interaction effects in GLMs, even in scenarios with a high variable count and diverse loss functions. Our approach uniquely aligns GLM predictions with those of black box machine learning models, enhancing their interpretability and reliability. Using a publicly available non-life motor data set, we demonstrate the GA’s effectiveness by comparing its selected GLM with a Gradient Boosted Machine (GBM) model. The results show a strong consistency between the main and interaction terms identified by GA for the GLM and those revealed in the GBM analysis, highlighting the potential of our method to refine and improve pricing models in the insurance sector.
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Das, Rabindra Nath, and Jinseog Kim. "GLM and joint GLM techniques in hydrogeology: an illustration." International Journal of Hydrology Science and Technology 2, no. 2 (2012): 185. http://dx.doi.org/10.1504/ijhst.2012.047408.
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Ge, Tingting, Ping Sun, Xue Feng, Xiang Gao, Shuang Gao, Tangshun Wang, and Xiaoguang Shi. "Clinical features and risk factors of bilateral granulomatous lobular mastitis." Medicine 103, no. 17 (April 26, 2024): e37854. http://dx.doi.org/10.1097/md.0000000000037854.
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Granulomatous lobular mastitis (GLM) is an idiopathic inflammatory breast disease that tends to recur on the same side. With the accumulation of clinical cases, it has been observed that GLM can also occur contralaterally. Currently, most studies on GLM focus on treatment methods and risk factors for ipsilateral recurrence, and there are few reports on bilateral GLM. The study aimed to summarize the clinical characteristics of patients with bilateral GLM by reviewing their clinical data, and to discuss the risk factors affecting the occurrence of bilateral GLM. A retrospective study of the medical records database of patients with GLM admitted between May 2019 and August 2022 was performed. Patients were divided into bilateral GLM group (bilateral GLM group) and unilateral GLM patients (unilateral GLM group). Demographic and clinical characteristics, treatment, and follow-up were collected and analyzed. In this study, by reviewing the clinical data of 59 cases of bilateral GLM, we found that the median time between the onset of bilateral GLM on both sides was 6.63 (0–18) months. Additionally, because of the simultaneous or interval onset on both sides, the duration of the disease was longer compared to unilateral cases. Regarding the history of external hospital treatment, it was found that about 57.63% of patients with bilateral GLM received 2 or more treatment modalities, with a higher involvement of herbal medicine. Meanwhile, by counting the clinical data of the 2 groups of patients with bilateral GLM and unilateral GLM, it was shown by univariate analysis that fertility, nipple development, absolute CD4 value, and CD4/CD8 ratio were associated with contralateral onset of GLM in both groups, with inverted nipple being an independent risk factor.
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See, Garrett, Melanie Trenhaile-Gannemann, Daniel Ciobanu, Matthew L. Spangler, and Benny Mote. "66 A genome-wide association study for gestation length in swine." Journal of Animal Science 97, Supplement_2 (July 2019): 40. http://dx.doi.org/10.1093/jas/skz122.071.
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Abstract The objective of the current study was to conduct a genome-wide association on gestation length (GL) in different parities in swine. Sows (n = 831) belonging to the University of Nebraska – Lincoln resource population (Landrace X Nebraska Index Line) were utilized. GL was defined as the number of days between the final insemination and farrowing. Four traits, GL at parity 1, 2, 3 and 4 (GL1, GL2, GL3 and GL4, respectively) were investigated. Animals which were induced 24h prior to a farrowing event were removed from the analysis. Sows were genotyped with the Illumina SNP60 BeadArray. A Bayes C model with π=0.995 was implemented with fixed effects of contemporary group, development pen, diet, linear and quadratic terms for age at puberty (GL1; P < 0.01), and linear and quadratic terms for farrowing age (GL2; P < 0.01). Results are posterior means of 55,000 samples.Single marker association analysis (SMA) was performed in R utilizing a linear model on SNP from 1-Mb windows (n = 10) which explained the largest proportion of genetic variation in GL1. Top 10 (0.5% of all windows) 1-Mb windows accounted for a limited proportion of genetic variance, 7.75, 4.66, 3.45 and 2.05% in GL1, GL2, GL3 and GL4, respectively. Posterior mean heritability estimates (posterior SD) for GL1, GL2, GL3 and GL4 were 0.33 (0.06), 0.34 (0.07), 0.32 (0.08) and 0.20 (0.08), respectively. The top SNP (ASGA0017859, SSC4, 7.8 Mb) located in one of the two top common genomic regions associated with GL1, GL2 and GL3 displayed a difference of 1.1d in GL1 between alternate homozygotes (P < 0.01). The top SNP from nine of the ten regions were significant (P < 0.05) in the SMA. Two of these regions were in common with GL2 and GL3 where SNP with potential functional effects were found in ZFAT, MAML2 and CCDC82. Results suggest GL is a largely polygenic trait.
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SECHERRE, V. "Représentations lisses de GLm(D)GLm(D), III : types simples." Annales Scientifiques de l’École Normale Supérieure 38, no. 6 (November 2005): 951–77. http://dx.doi.org/10.1016/j.ansens.2005.10.003.
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Clemente, Carina, Gracinda R. Guerreiro, and Jorge M. Bravo. "Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting." Risks 11, no. 9 (September 12, 2023): 163. http://dx.doi.org/10.3390/risks11090163.
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Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency–severity models assume a linear relationship between a function of the response variable and the predictors, independence between the claim frequency and severity, and assign full credibility to the data. To overcome some of these restrictions, this paper investigates the predictive performance of Gradient Boosting with decision trees as base learners to model the claim frequency and the claim severity distributions of an auto insurance big dataset and compare it with that obtained using a standard GLM model. The out-of-sample performance measure results show that the predictive performance of the Gradient Boosting Model (GBM) is superior to the standard GLM model in the Poisson claim frequency model. Differently, in the claim severity model, the classical GLM outperformed the Gradient Boosting Model. The findings suggest that gradient boost models can capture the non-linear relation between the response variable and feature variables and their complex interactions and thus are a valuable tool for the insurer in feature engineering and the development of a data-driven approach to risk management and insurance.
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Brody, Dorje C., Lane P. Hughston, and Ewan Mackie. "General theory of geometric Lévy models for dynamic asset pricing." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 468, no. 2142 (February 29, 2012): 1778–98. http://dx.doi.org/10.1098/rspa.2011.0670.
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The geometric Lévy model (GLM) is a natural generalization of the geometric Brownian motion (GBM) model used in the derivation of the Black–Scholes formula. The theory of such models simplifies considerably if one takes a pricing kernel approach. In one dimension, once the underlying Lévy process has been specified, the GLM has four parameters: the initial price, the interest rate, the volatility and the risk aversion. The pricing kernel is the product of a discount factor and a risk aversion martingale. For GBM, the risk aversion parameter is the market price of risk. For a GLM, this interpretation is not valid: the excess rate of return is a nonlinear function of the volatility and the risk aversion. It is shown that for positive volatility and risk aversion, the excess rate of return above the interest rate is positive, and is increasing with respect to these variables. In the case of foreign exchange, Siegel's paradox implies that one can construct foreign exchange models for which the excess rate of return is positive for both the exchange rate and the inverse exchange rate. This condition is shown to hold for any geometric Lévy model for foreign exchange in which volatility exceeds risk aversion.
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Yang, SeungCheon, Hong Ki Min, Jin-Sil Park, Hyun Sik Na, Mi-La Cho, and Sung-Hwan Park. "A green-lipped mussel prevents rheumatoid arthritis via regulation of inflammatory response and osteoclastogenesis." PLOS ONE 18, no. 1 (January 20, 2023): e0280601. http://dx.doi.org/10.1371/journal.pone.0280601.
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Rheumatoid arthritis (RA) is a chronic inflammatory disorder characterized by progressive joint destruction. Green-lipped mussel (GLM) has chondro-modulatory and anti-inflammatory properties, but the mechanism underlying the effect of GLM on RA is unclear. To investigate the roles of GLM on the pathogenesis of RA, we examined the effects of GLM in collagen-induced arthritis (CIA) mice and osteoclast differentiation. GLM was orally administrated CIA mice at 3 weeks after chicken type II collagen (CII) immunizations. GLM reduced arthritis severity and the histologic score of CIA mice compared to vehicle. The expression of proinflammatory cytokines (TNF-α, IL-1β, and IL-17) was decreased in the ankle joints of GLM-treated CIA mice. The expression of CD4+ IL-17+ cells decreased in ex vivo splenocytes and the spleens of GLM-treated CIA mice. Moreover, GLM inhibited TRAP+ multinucleated cells among mouse bone marrow-derived monocytes/macrophages (BMM), and the expression of osteoclast-related genes in mouse BMMs and human monocytes in vitro. These results suggest that GLM has potential as a therapeutic agent that can improve disease by controlling pathologic immune cells and osteoclastogenesis.
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Bailey, Jason Robert, Davide Lauria, W. Brent Lindquist, Stefan Mittnik, and Svetlozar T. Rachev. "Hedonic Models of Real Estate Prices: GAM Models; Environmental and Sex-Offender-Proximity Factors." Journal of Risk and Financial Management 15, no. 12 (December 13, 2022): 601. http://dx.doi.org/10.3390/jrfm15120601.
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We investigate the use of a P-spline generalized additive hedonic model (GAM) for real estate prices in large U.S. cities, contrasting their predictive efficiency against commonly used linear and polynomial-based generalized linear models (GLM). Using intrinsic and extrinsic factors available from Redfin, we show that the GAM model is capable of describing 84% to 92% of the variance in the expected ln(sales price), based upon 2021 data. In contrast, a strictly linear GLM accounted for 65% to 78% of the variance, while polynomial-based GLMs accounted for 82% to 88%. As climate change is becoming increasingly important, we utilized the GAM model to examine the significance of environmental factors in two urban centers on the northwest coast. While the results indicate city-dependent differences in the significance of environmental factors, we find that inclusion of the environmental factors increases the adjusted R2 of the GAM model by less than 1%. Thirdly, our results indicate that the importance of sex offender residence proximity as a pricing factor is strongly influenced by state sex offender residence regulations.
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Arabameri, Alireza, Omid Asadi Nalivan, Sunil Saha, Jagabandhu Roy, Biswajeet Pradhan, John P. Tiefenbacher, and Phuong Thao Thi Ngo. "Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility." Remote Sensing 12, no. 11 (June 11, 2020): 1890. http://dx.doi.org/10.3390/rs12111890.
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Gully erosion has become one of the major environmental issues, due to the severity of its impact in many parts of the world. Gully erosion directly and indirectly affects agriculture and infrastructural development. The Golestan Dam basin, where soil erosion and degradation are very severe problems, was selected as the study area. This research maps gully erosion susceptibility (GES) by integrating four models: maximum entropy (MaxEnt), artificial neural network (ANN), support vector machine (SVM), and general linear model (GLM). Of 1042 gully locations, 729 (70%) and 313 (30%) gully locations were used for modeling and validation purposes, respectively. Fourteen effective gully erosion conditioning factors (GECFs) were selected for spatial gully erosion modeling. Tolerance and variance inflation factors (VIFs) were used to examine the collinearity among the GECFs. The random forest (RF) model was used to assess factors’ effectiveness and significance in gully erosion modeling. An ensemble of techniques can provide more accurate results than can single, standalone models. Therefore, we compared two-, three-, and four-model ensembles (ANN-SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, ANN-SVM-GLM, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM, MaxEnt-ANN-SVM and GLM-ANN-SVM-MaxEnt) for GES modeling. The susceptibility zones of the GESMs were classified as very-low, low, medium, high, and very-high using Jenks’ natural break classification method (NBM). Subsequently, the receiver operating characteristics (ROC) curve and the seed cell area index (SCAI) methods measured the reliability of the models. The success rate curve (SRC) and predication rate curve (PRC) and their area under the curve (AUC) values were obtained from the GES maps. The results show that the ANN model combined with two and three models are more accurate than the other combinations, but the ANN-SVM model had the highest accuracy. The rank of the others from best to worst accuracy is GLM, MaxEnt, SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, GLM-ANN-SVM-MaxEnt, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM and MaxEnt-ANN-SVM. The resulting gully erosion susceptibility models (GESMs) are efficient and powerful and could be used to improve soil and water conservation and management.
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Wilson, Alinta Ann, Antonio Nehme, Alisha Dhyani, and Khaled Mahbub. "A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance." Risks 12, no. 4 (March 31, 2024): 62. http://dx.doi.org/10.3390/risks12040062.
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This study explores the insurance pricing domain in the motor insurance industry, focusing on the creation of “technical models” which are essentially obtained after combining the frequency model (the expected number of claims per unit of exposure) and the severity model (the expected amount per claim). Technical models are designed to predict the loss costs (the product of frequency and severity, i.e., the expected claim amount per unit of exposure) and this is a main factor that is taken into account for pricing insurance policies. Other factors for pricing include the company expenses, investments, reinsurance, underwriting, and other regulatory restrictions. Different machine learning methodologies, including the Generalised Linear Model (GLM), Gradient Boosting Machine (GBM), Artificial Neural Networks (ANN), and a unique hybrid model that combines GLM and ANN, were explored for creating the technical models. This study was conducted on the French Motor Third Party Liability datasets, “freMTPL2freq” and “freMTPL2sev” included in the R package CASdatasets. After building the aforementioned models, they were evaluated and it was observed that the hybrid model which combines GLM and ANN outperformed all other models. ANN also demonstrated better predictions closely aligning with the performance of the hybrid model. The better performance of neural network models points to the need for actuarial science and the insurance industry to look beyond traditional modelling methodologies like GLM.
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Abuhelwa, Ahmad Y., Ganessan Kichenadasse, Ross A. McKinnon, Andrew Rowland, Ashley M. Hopkins, and Michael J. Sorich. "Machine Learning for Prediction of Survival Outcomes with Immune-Checkpoint Inhibitors in Urothelial Cancer." Cancers 13, no. 9 (April 21, 2021): 2001. http://dx.doi.org/10.3390/cancers13092001.
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Machine learning (ML) may enhance the efficiency of developing accurate prediction models for survival, which is critical in informing disease prognosis and care planning. This study aimed to develop an ML prediction model for survival outcomes in patients with urothelial cancer-initiating atezolizumab and to compare model performances when built using an expert-selected (curated) versus an all-in list (uncurated) of variables. Gradient-boosted machine (GBM), random forest, Cox-boosted, and penalised, generalised linear models (GLM) were evaluated for predicting overall survival (OS) and progression-free survival (PFS) outcomes. C-statistic (c) was utilised to evaluate model performance. The atezolizumab cohort in IMvigor210 was used for model training, and IMvigor211 was used for external model validation. The curated list consisted of 23 pretreatment factors, while the all-in list consisted of 75. Using the best-performing model, patients were stratified into risk tertiles. Kaplan–Meier analysis was used to estimate survival probabilities. On external validation, the curated list GBM model provided slightly higher OS discrimination (c = 0.71) than that of the random forest (c = 0.70), CoxBoost (c = 0.70), and GLM (c = 0.69) models. All models were equivalent in predicting PFS (c = 0.62). Expansion to the uncurated list was associated with worse OS discrimination (GBM c = 0.70; random forest c = 0.69; CoxBoost c = 0.69, and GLM c = 0.69). In the atezolizumab IMvigor211 cohort, the curated list GBM model discriminated 1-year OS probabilities for the low-, intermediate-, and high-risk groups at 66%, 40%, and 12%, respectively. The ML model discriminated urothelial-cancer patients with distinctly different survival risks, with the GBM applied to a curated list attaining the highest performance. Expansion to an all-in approach may harm model performance.
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De Benedictis, G., G. Rose, C. Brancati, and A. Tagarelli. "Glm(1) and Glm(2) Allotypes in Albanian Towns of Calabria." Human Heredity 36, no. 2 (1986): 132–34. http://dx.doi.org/10.1159/000153615.
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Tranidou, Antigoni, Emmanuela Magriplis, Ioannis Tsakiridis, Nikolaos Pazaras, Aikaterini Apostolopoulou, Michail Chourdakis, and Themistoklis Dagklis. "Effect of Gestational Weight Gain during the First Half of Pregnancy on the Incidence of GDM, Results from a Pregnant Cohort in Northern Greece." Nutrients 15, no. 4 (February 10, 2023): 893. http://dx.doi.org/10.3390/nu15040893.
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The aim of this study was to evaluate the effect of gestational weight gain (GWG) up to 23+6 weeks of gestation on the incidence of Gestational Diabetes Mellitus (GDM). A pregnant cohort of 5948 women in Northern Greece was recruited. Anthropometric features before and during pregnancy were recorded, the GWG by 23+6 weeks was calculated and a Generalized Linear Regression Model (GLM) with subgroup analyses based on weight status were computed. GDM was diagnosed in 5.5% of women. GLM results showed that GDM likelihood increased with maternal age (MA) and pre-pregnancy BMI (aOR: 1.08, 95%CI: [1.06, 1.11] and aOR: 1.09, 95%CI: [1.09, 1.11], respectively). Ιn the normal pre-pregnancy weight group, when the extra weight gain was >8 kgs, the odds of GDM increased (OR: 2.13, 95%CI: [0.98, 4.21], p = 0.03). Women with pre-pregnancy level 2 clinical obesity (OB2 pre) (BMI > 35 and <40 kg/m2) that shifted to OB3 category (BMI ≥ 40 kg/m2) had an increased GDM likelihood (OR: 4.85, 95%CI: [1.50, 15.95]). Women of higher MA may require stricter monitoring for GDM from early pregnancy, while in obese women, recommended GWG may need to be re-evaluated, since refraining from any weight gain may have a preventive effect for GDM.
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Kalantar, Bahareh, Naonori Ueda, Vahideh Saeidi, Kourosh Ahmadi, Alfian Abdul Halin, and Farzin Shabani. "Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data." Remote Sensing 12, no. 11 (May 28, 2020): 1737. http://dx.doi.org/10.3390/rs12111737.
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Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors.
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Zhou, Shijie, Robert A. Campbell, and Simon D. Hoyle. "Catch per unit effort standardization using spatio-temporal models for Australia’s Eastern Tuna and Billfish Fishery." ICES Journal of Marine Science 76, no. 6 (March 11, 2019): 1489–504. http://dx.doi.org/10.1093/icesjms/fsz034.
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Abstract The majority of catch per unit effort (cpue) standardizations use generalized linear models (GLMs) or generalized additive models (GAMs). We develop geostatistical models that model catch locations as continuous Gaussian random fields (GRFs) and apply them to standardizing cpue in Australia’s Eastern Tuna and Billfish Fishery (ETBF). The results are compared with the traditional GLMs currently used in ETBF assessments as well as GAMs. Specifically, we compare seven models in three groups: two GLMs, two GAMs, and three GRF models. Within each group, one model treats spatial and temporal variables independently, while the other model(s) treats them together as an interaction term. The two spatio-temporal GRF models differ in treating the spatial–temporal interaction: either as a random process or as an autoregressive process. We simulate catch rate data for five pelagic species based on real fishery catch rates so that the simulated data reflect real fishery patterns while the “true” annual abundance levels are known. The results show that within each group, the model with a spatial–temporal interaction term significantly outperforms the other model treating spatial and temporal variables independently. For spatial–temporal models between the three groups, prediction accuracy tends to improve from GLM to GAM and to the GRF models. Overall, the spatio-temporal GRF autoregressive model reduces mean relative predictive error by 43.4% from the GLM, 33.9% from the GAM, and reduces mean absolute predictive error by 23.5% from the GLM and 3.3% from the GAM, respectively. The comparison suggests a promising direction for further developing the geostatistical models for the ETBF.
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Alaqeeli, Omar, and Raad Alturki. "Evaluating the Performance of the Generalized Linear Model (glm) R Package Using Single-Cell RNA-Sequencing Data." Applied Sciences 13, no. 20 (October 20, 2023): 11512. http://dx.doi.org/10.3390/app132011512.
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The glm R package is commonly used for generalized linear modeling. In this paper, we evaluate the ability of the glm package to predict binomial outcomes using logistic regression. We use single-cell RNA-sequencing datasets, after a series of normalization, to fit data into glm models repeatedly using 10-fold cross-validation over 100 iterations. Our evaluation criteria are glm’s Precision, Recall, F1-Score, Area Under the Curve (AUC), and Runtime. Scores for each evaluation category are collected, and their medians are calculated. Our findings show that glm has fluctuating Precision and F1-Scores. In terms of Recall, glm has shown more stable performance, while in the AUC category, glm shows remarkable performance. Also, the Runtime of glm is consistent. Our findings also show that there are no correlations between the size of fitted data and glm’s Precision, Recall, F1-Score, and AUC, except for Runtime.
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Perpetuini, David, Daniela Cardone, Chiara Filippini, Antonio Chiarelli, and Arcangelo Merla. "Modelling Impulse Response Function of Functional Infrared Imaging for General Linear Model Analysis of Autonomic Activity." Sensors 19, no. 4 (February 19, 2019): 849. http://dx.doi.org/10.3390/s19040849.
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Functional infrared imaging (fIRI) is a validated procedure to infer autonomic arousal. Currently, fIRI signals are analysed through descriptive metrics, such as average temperature changes in a region of interest (ROI). However, the employment of mathematical models could provide a powerful tool for the accurate identification of autonomic activity and investigation of the mechanisms underlying autonomic arousal. A linear temporal statistical model such as the general linear model (GLM) is particularly suited for its simplicity and direct interpretation. In order to apply the GLM, the thermal response linearity and time-invariance of fIRI have to be demonstrated, and the thermal impulse response (TIR) needs to be characterized. In this study, the linearity and time-invariance of the thermal response to sympathetic activating stimulation were demonstrated, and the TIR for employment of the GLM was characterized. The performance of the GLM-fIRI was evaluated by comparison with the GLM applied on synchronous measurements of the skin conductance response (SCR). In fact, the GLM-SCR is a validated procedure to estimate autonomic arousal. Assuming the GLM-SCR as the gold standard approach, a GLM-fIRI sensitivity and specificity of 86.4% and 75.9% were obtained. The GLM-fIRI may allow increased performances in the evaluation of autonomic activity and a broader range of application of fIRI in both research and clinical settings for the assessment of psychophysiological and psychopathological states.
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Wang, Yaping, Nusrat Yussouf, Edward R. Mansell, Brian C. Matilla, Rong Kong, Ming Xue, and Vanna C. Chmielewski. "Impact of Assimilating GOES-R Geostationary Lightning Mapper Flash Extent Density Data on Severe Convection Forecasts in a Warn-on-Forecast System." Monthly Weather Review 149, no. 10 (October 2021): 3217–41. http://dx.doi.org/10.1175/mwr-d-20-0406.1.
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AbstractThe Geostationary Operational Environmental Satellite-R (GOES-R) Geostationary Lightning Mapper (GLM) instrument detects total lightning rate at high temporal and spatial resolution over the Americas and adjacent oceanic regions. The GLM observations provide detection and monitoring of deep electrified convection. This study explores the impact of assimilating the GLM-derived flash extent density (FED) on the analyses and short-term forecasts of two severe weather events into an experimental Warn-on-Forecast system (WoFS) using the ensemble Kalman filter data assimilation technique. Sensitivity experiments are conducted using two tornadic severe storm events: one with a line of individual supercells and the other one with both isolated cells and a severe convective line. The control experiment (CTRL) assimilates conventional surface observations and geostationary satellite cloud water path into WoFS. Additional experiments also assimilate either GLM FED or radar data (RAD), or a combination of both (RAD+GLM). It is found that assimilating GLM data in the absence of radar data into the WoFS improves the short-term forecast skill over CTRL in one case, while in the other case it degrades the forecast skill by generating weaker cold pools and overly suppressing convection, mainly owing to assimilating zero FED values in the trailing stratiform regions. Assimilating unexpectedly low FED values in some regions due to low GLM detection efficiency also accounts for the poorer forecasts. Although RAD provides superior forecasts over GLM, the combination RAD+GLM shows further gains in both cases. Additional observation operators should consider different storm types and GLM detection efficiency.
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Jhun, JooYeon, Hyun Sik Na, Keun-Hyung Cho, Jiyoung Kim, Young-Mee Moon, Seung Yoon Lee, Jeong Su Lee, et al. "A green-lipped mussel reduces pain behavior and chondrocyte inflammation and attenuated experimental osteoarthritis progression." PLOS ONE 16, no. 12 (December 2, 2021): e0259130. http://dx.doi.org/10.1371/journal.pone.0259130.
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The green-lipped mussel (GLM) contains novel omega-3 polyunsaturated fatty acids, which exhibit anti-inflammatory and joint-protecting properties. Osteoarthritis (OA) is a degenerative joint disease characterized by a progressive loss of cartilage; oxidative stress plays a role in the pathogenesis of OA. The objectives of this study were to investigate the in vivo effects of the GLM on pain severity and cartilage degeneration using an experimental rat OA model, and to explore the mode of action of GLM. OA was induced in rats by intra-articular injection of monosodium iodoacetate (MIA) into the knee. Oral GLM was initiated on the day after 3dyas of MIA injection. Limb nociception was assessed by measuring the paw withdrawal latency and threshold. Samples were analyzed both macroscopically and histologically. Immunohistochemistry was used to investigate the expression of interleukin-1β (IL-1β), IL-6, nitrotyrosine, and inducible nitric oxide synthase (iNOS) in knee joints. Also, the GLM was applied to OA chondrocyte, and the expression on catabolic marker and necroptosis factor were evaluated by real-time polymerase chain reaction. Administration of the GLM improved pain levels by preventing cartilage damage and inflammation. GLM significantly attenuated the expression levels of mRNAs encoding matrix metalloproteinase-3 (MMP-3), MMP-13, and ADAMTS5 in IL-1β-stimulated human OA chondrocytes. GLM decreased the expression levels of the necroptosis mediators RIPK1, RIPK3, and the mixed lineage kinase domain-like protein (MLKL) in IL-1β-stimulated human OA chondrocytes. Thus, GLM reduced pain and cartilage degeneration in rats with experimentally induced OA. The chondroprotective properties of GLM included suppression of oxidative damage and inhibition of catabolic factors implicated in the pathogenesis of OA cartilage damage. We suggest that GLM may usefully treat human OA.
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Thompson, Bruce. "The Case for Using the General Linear Model as a Unifying Conceptual Framework for Teaching Statistics and Psychometric Theory." Journal of Methods and Measurement in the Social Sciences 6, no. 2 (February 1, 2015): 30. http://dx.doi.org/10.2458/jmm.v6i2.18801.
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The present paper argues for teaching statistics and psychometric theory using the GLM as a unifying conceptual framework. This helps students understand what analyses have in common, and also provides a firm grounding for understanding that more general cases of the GLM (canonical correlation analysis and SEM) can be interpreted with the same rubric used throughout the GLM. And this approach also helps students better understand analyses that are not part of the GLM, such as predictive discriminant analysis (PDA). The approach helps students understand that all GLM analyses (a) are correlational, and thus are all susceptible to sampling error, (b) can yield r2-type effect sizes, and (c) use weights applied to measured variables to estimate the latent variables really of primary interest.DOI:10.2458/azu_jmmss_v6i2_thompson
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Thompson, Bruce. "The Case for Using the General Linear Model as a Unifying Conceptual Framework for Teaching Statistics and Psychometric Theory." Journal of Methods and Measurement in the Social Sciences 6, no. 2 (February 1, 2015): 30. http://dx.doi.org/10.2458/v6i2.18801.
Full textAbstract:
The present paper argues for teaching statistics and psychometric theory using the GLM as a unifying conceptual framework. This helps students understand what analyses have in common, and also provides a firm grounding for understanding that more general cases of the GLM (canonical correlation analysis and SEM) can be interpreted with the same rubric used throughout the GLM. And this approach also helps students better understand analyses that are not part of the GLM, such as predictive discriminant analysis (PDA). The approach helps students understand that all GLM analyses (a) are correlational, and thus are all susceptible to sampling error, (b) can yield r2-type effect sizes, and (c) use weights applied to measured variables to estimate the latent variables really of primary interest.DOI:10.2458/azu_jmmss_v6i2_thompson
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Choi, Yong-Hoon, Min-Soo Park, Han-Sang Lim, and Jae-Sam Kim. "A Discussion on Glucose Loading Method in 18F-FDG PET/CT for Cerebral Gliomas." Korean Journal of Nuclear Medicine Technology 27, no. 1 (May 31, 2023): 53–57. http://dx.doi.org/10.12972/kjnmt.20230009.
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Purpose: The purpose of this study is to determine whether the glucose loading method (GLM) is useful in the differentiation of cerebral gliomas by comparing it with fasting images. Materials and Methods: The patients were 70 people diagnosed with cerebral gliomas, and the equipment was Discovery 710 (GE Healthcare, MI, USA). All patients fasted for more than 6 hours, and fasting images and GLM were performed under the same imaging conditions, and the examination interval was 1 to 14 days. GLM administered 250 ㎖ of 10% glucose solution prior to radiopharmaceutical injection. SUVmax of cerebral glioma and SUVmean of cerebral cortex were measured and then compared and analyzed by tumor-to-normal brain cortex ratio (TNR). Statistical analysis confirmed the difference between the two images with an independent-sample t-test. Results: The averages of GLM and fasting TNR were 1.26 and 1.09, respectively, which were 15.6% higher in GLM. In low-grade, the difference in TNR was insignificant at 4%, but in high-grade, 23%, GLM was high. There was a statistically significant difference between the two images (P=0.008), but there was no statistically significant difference in TNR in the low grade (P=0.473), and there was a very significant difference in the high grade (P=0.005). Conclusion: GLM increased TNR for cerebral gliomas. In particular, it was found that the TNR increased more in the high grade. Therefore, GLM is considered to be useful for the differentiation of high-grade gliomas.
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Ma, Xiaojia, Xiaoli Min, and Chang Yao. "Different Treatments for Granulomatous Lobular Mastitis: A Systematic Review and Meta-Analysis." Breast Care 15, no. 1 (July 10, 2019): 60–66. http://dx.doi.org/10.1159/000501498.
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Objective: Granulomatous lobular mastitis (GLM) is infrequently diagnosed. This study aimed to evaluate the efficacy and safety of managements for GLM in the nonlactation period. Methods: Publications were retrieved from PubMed, EMBASE, and the Cochrane library on September 10, 2018. We pooled and compared the outcome parameters and complete remission (CR) rates between different treatments using a meta-analysis. Results: Twenty-one publications including 970 patients treated with surgical excision, steroids, abscess drainage, antibiotics, and observation were included. Surgery significantly improved CR rate compared with steroids (p = 0.0003). There was no difference in the CR rate in patients treated with surgery alone or combined with steroids (p = 0.28). Surgery showed borderline significant efficacy in the CR rate compared with antibiotics (p = 0.06) and abscess drainage (p = 0.06). No difference was observed in effectiveness between observation and surgical management in patients diagnosed with early GLM and mild symptoms. Conclusions: Although surgery was the ideal management for GLM, steroids and antibiotics might be ideal managements for GLM patients who are worried about surgical scars. Observation was an impressive alternative for patients with early GLM.
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Harzallah, Ines, Josselin Rigaill, Nicolas Williet, Stephane Paul, and Xavier Roblin. "Golimumab pharmacokinetics in ulcerative colitis: a literature review." Therapeutic Advances in Gastroenterology 10, no. 1 (November 15, 2016): 89–100. http://dx.doi.org/10.1177/1756283x16676194.
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Golimumab (GLM) is the latest anti-tumor necrosis factor (TNF) that gained its marketing license. Thanks to the PURSUIT induction and maintenance trials, it was approved for the treatment of ulcerative colitis (UC) in 2013. The other anti-TNF drugs available are infliximab and adalimumab. These two drugs have validated algorithms concerning prescription and therapeutic drug monitoring (TDM) but little is known about GLM. The available data on GLM’s exposure–response relationship in UC are from the PURSUIT trials and are recently published. The data reveal all the factors that may impact the pharmacokinetic (PK) parameters: dosage, body weight (BW), concomitant drugs, the presence of anti-drug antibodies (ADAbs), sex and age. In addition, the GLM trough level at steady-state appears to be correlated with the patient’s improvement which may make it a precious indicator to predict the clinical response. There is, however, no consensus on a possible therapeutic level or cutoff associated with clinical response, remission, or any other outcome measure such as endoscopic healing in UC. This lack of a threshold value, and its validation with different assay techniques, makes it difficult to use GLM TDM in clinical practice. As with other anti-TNF agents, GLM is associated with development of ADAbs, of which the prevalence and effects are still insufficiently described. The objective of this review is to describe current data and understanding of the PK of GLM including serum concentrations of GLM and ADAbs in UC patients. Better understanding of these parameters could lead to improved patient care with GLM.
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Harkema, Sebastian S., Christopher J. Schultz, Emily B. Berndt, and Phillip M. Bitzer. "Geostationary Lightning Mapper Flash Characteristics of Electrified Snowfall Events." Weather and Forecasting 34, no. 5 (October 1, 2019): 1571–85. http://dx.doi.org/10.1175/waf-d-19-0082.1.
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Abstract This study examines characteristics of lightning in snowfall events (i.e., thundersnow, TSSN) from the perspective of the Geostationary Lightning Mapper (GLM) and the National Environmental Satellite Data and Information Service (NESDIS) merged Snowfall Rate (mSFR) product. A thundersnow detection algorithm (TDA) was derived from the GLM and mSFR that resulted in a probability of detection (POD) of 66.7% when compared to the aviation routine weather report (METAR) reports of TSSN. However, using the TDA an additional 2175 lightning flashes within detected snowfall were identified that were not observed by the METAR reports, indicating that TSSN has been under reported in previous literature. TSSN flashes observed by GLM have mean flash areas, durations, and total optical energy outputs of 754 km2, 402 ms, and 1342 fJ, which are between the 50th and 99th percentile values for all flashes within the GLM field of view. A comparison with data from the National Lightning Detection Network (NLDN) indicated that the NLDN had at least one cloud or ground flash detection in 1709 of the 2214 flashes observed by GLM in snowfall. An average of 5.85 NLDN flashes was assigned to a single GLM flash when the NLDN flash data were constrained by the GLM flash duration and spatial footprint. Statistically significant (p < 0.01) differences in flash area and flash energy were found between flashes that were observed by the NLDN and those that were not. Additionally, when GLM was combined with the NLDN, at least 11.1% of flashes involved a tall human-made object like an antenna or wind turbine.
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Hu, Junjun, Alexandre O. Fierro, Yunheng Wang, Jidong Gao, and Edward R. Mansell. "Exploring the Assimilation of GLM-Derived Water Vapor Mass in a Cycled 3DVAR Framework for the Short-Term Forecasts of High-Impact Convective Events." Monthly Weather Review 148, no. 3 (February 18, 2020): 1005–28. http://dx.doi.org/10.1175/mwr-d-19-0198.1.
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Abstract The recent successful deployment of the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite R series (GOES-16/17) provides nearly uniform spatiotemporal measurements of total lightning (intracloud plus cloud to ground) over the Americas and adjacent vast oceanic regions. This study evaluates the potential value of assimilating GLM-derived water vapor mixing ratio on short-term (≤6 h), cloud-scale (dx = 1.5 km) forecasts of five severe weather events over the Great Plains of the United States using a three-dimensional variational (3DVAR) data assimilation (DA) system. Toward a more systematic assimilation of real GLM data, this study conducted sensitivity tests aimed at evaluating the impact of the horizontal decorrelation length scale, DA cycling frequency, and the time window size for accumulating GLM lightning observations prior to the DA. Forecast statistics aggregated over all five cases suggested that an optimal forecast performance is obtained when lightning measurements are accumulated over a 10-min interval and GLM-derived water vapor mixing ratio values are assimilated every 15 min with a horizontal decorrelation length scale of 3 km. This suggested configuration for the GLM DA together with companion experiments (i) not assimilating any data, (ii) assimilating radar data only, and (iii) assimilating both GLM and radar data were evaluated for the same five cases. Overall, GLM data have shown potential to help improve the short-term (<3 h) forecast skill of composite reflectivity fields and individual storm tracks. While this result also held for accumulated rainfall, longer-term (≥3 h) forecasts were generally characterized by noteworthy wet biases.
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Tawkir, Mohammad. "Synthesis and Characterisation of Co(II) Cu(II) Zn(III) and La(II) Complexes of Standard Antidiabetic Drugs." Material Science Research India 11, no. 1 (August 16, 2014): 51–61. http://dx.doi.org/10.13005/msri/110107.
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Metal complxes of Glimeperide drugs were prepared and characterized based on elemental analysis, FT-IR, Molar conductance and thermal analysis (TGA and DTG) technique. From elemental analysis data, the complexes were proposed to have general formulae (GLM)2Co2H2O, (GLM)2Cu, (GLM)2Zn,and (GLM)2La2H2O. The molar conductance data reveal that all the metal complexes are non-electrolytic, IR spectra shows that GLM are coordinated to metal ions in a neutral bidentate manner from the ESR spectra and XRD-spectra. It is found that the geometrical structures of these complexes are tetrahedral Cu(II) ,Zn(II) and octrahedral Co(II), La(II). The thermal behavior of these complexesstudied using thermogravimetric analysis (TGA and DTG) techniques. The results obtained shows that the hydrated complexes lose water molecules of hydration followed immediately by decomposition of the anions and ligand molecules in the successive unseparate steps.
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Lal Shrestha, Srijan. "Particulate Air Pollution and Daily Mortality in Kathmandu Valley, Nepal: Associations and Distributed Lag." Open Atmospheric Science Journal 6, no. 1 (April 20, 2012): 62–70. http://dx.doi.org/10.2174/1874282301206010062.
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The distributed lag effect of ambient particulate air pollution that can be attributed to all cause mortality in Kathmandu valley, Nepal is estimated through generalized linear model (GLM) and generalized additive model (GAM) with autoregressive count dependent variable. Models are based upon daily time series data on mortality collected from the leading hospitals and exposure collected from the 6 six strategically dispersed fixed stations within the valley. The distributed lag effect is estimated by assigning appropriate weights governed by a mathematical model in which weights increased initially and decreased later forming a long tail. A comparative assessment revealed that autoregressive semiparametric GAM is a better fit compared to autoregressive GLM. Model fitting with autoregressive semi-parametric GAM showed that a 10 μg m rise in PM is associated with 2.57 % increase in all cause mortality accounted for 20 days lag effect which is about 2.3 times higher than observed for one day lag and demonstrates the existence of extended lag effect of ambient PM on all cause deaths. The confounding variables included in the model were parametric effects of seasonal differences measured by Fourier series terms, lag effect of mortality, and nonparametric effect of temperature represented by loess smoothing. The lag effects of ambient PM remained constant beyond 20 days.
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Owiredu, Solomon Amoah, Shem Otoi Onyango, Eun-A. Song, Kwang-Il Kim, Byung-Yeob Kim, and Kyoung-Hoon Lee. "Enhancing Chub Mackerel Catch Per Unit Effort (CPUE) Standardization through High-Resolution Analysis of Korean Large Purse Seine Catch and Effort Using AIS Data." Sustainability 16, no. 3 (February 4, 2024): 1307. http://dx.doi.org/10.3390/su16031307.
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Accurate determination of fishing effort from Automatic Identification System (AIS) data improves catch per unit effort (CPUE) estimation and precise spatial management. By combining AIS data with catch information, a weighted distribution method is applied to allocate catches across various fishing trajectories, accounting for temporal dynamics. A Generalized Linear Model (GLM) and Generalized Additive Model (GAM) were used to examine the influence of spatial–temporal and environmental variables (year, month, Sea Surface Temperature (SST), Sea Surface Salinity (SSS), current velocity, depth, longitude, and latitude) and assess the quality of model fit for these effects on chub mackerel CPUE. Month, SST, and year exhibited the strongest relationship with CPUE in the GLM model, while the GAM model emphasizes the importance of month and year. CPUE peaked within specific temperature and salinity ranges and increased with longitude and specific latitudinal bands. Month emerged as the most influential variable, explaining 38% of the CPUE variance, emphasizing the impact of regulatory measures on fishery performance. The GAM model performed better, explaining 69.9% of the nominal CPUE variance. The time series of nominal and standardized indices indicated strong seasonal cycles, and the application of fine-scale fishing effort improved nominal and standardized CPUE estimates and model performance.
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Schwartzman, S., A. Broadwell, A. Kivitz, S. Black, S. Xu, W. Langholff, and S. Kafka. "SAT0120 UNITED STATES RHEUMATOLOGY PRACTICE-BASED REAL-WORLD EVIDENCE OF INFUSION REACTIONS IN RHEUMATOID ARTHRITIS PATIENTS TREATED WITH INTRAVENOUS GOLIMUMAB OR INFLIXIMAB: IMPACT OF PRIOR BIOLOGIC EXPOSURE AND METHOTREXATE UTILIZATION." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 994.1–994. http://dx.doi.org/10.1136/annrheumdis-2020-eular.2251.
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Background:AWARE (Comparative and Pragmatic Study of Golimumab IV Versus Infliximab in Rheumatoid Arthritis) is an ongoing Phase 4 comparator study designed to provide a real-world assessment of intravenous golimumab (GLM) and intravenous infliximab (IFX) in patients (pts) with rheumatoid arthritis (RA). The study recently reached its primary endpoint (comparison of overall incidence of infusion reactions in GLM- vs IFX-treated pts after 52 weeks) with the last patient reaching 52 weeks of treatment or discontinuation from the study. AWARE also records prior use of biologic medications and concomitant use of methotrexate (MTX).Objectives:To assess the incidence of infusion reactions among GLM and IFX pts reported at baseline by examining the influence of prior biologic exposure or concurrent use of MTX.Methods:AWARE is a prospective, noninterventional, observational, multicenter, 3-year study conducted in the US. RA patients (1,270 adults) were enrolled at the time of initiating treatment with GLM or IFX. All treatment decisions were made at the discretion of the treating rheumatologist. An infusion reaction was any adverse event that occurred during an infusion or within 1 hour after the infusion of either GLM or IFX. Imputations were not performed on these AWARE data. Data shown are mean ± standard deviation.Results:Demographics are shown in Table 1 and the incidence of infusion reactions in different AWARE cohorts is shown in Table 2. GLM and IFX pts were comparable in sex and utilization of MTX at baseline. Both age and disease duration of GLM pts was greater than IFX pts by ~2 years. There was a higher proportion of bionaïve pts in IFX-treated group compared to GLM-treated group. Overall, infusion reactions occurred more frequently among IFX-treated pts compared to GLM-treated pts. The difference in infusion reaction rates between IFX- and GLM-treated pts was also evident among subgroups of bionaïve vs non-bionaïve pts, and among MTX non-users vs MTX users (characteristics reported at baseline). GLM pts did not report any serious or severe infusion reactions. These were reported rarely (3/585 pts) in IFX-treated pts. Among GLM and IFX pts with an infusion reaction, 55.6% of GLM and 77.1% of IFX pts had at least one medication for infusion reaction. Infusion reactions accounted for 9.7% and 35.1% of discontinuations due to adverse events in GLM and IFX pts, respectively.Table 1.Baseline Characteristics in the AWARE StudyGLM (n=685)IFX (n=585)Age (years)60.9 ± 13.4358.0 ± 12.85Sex (% female)85.0 %79.5 %Disease Duration (years)9.16 ± 9.9757.20 ± 9.716Bionaïve (%)33.0%48.6%MTX plus (%)75.4%75.1%MTX=methotrexateTable 2.Infusion Reactions in AWARE in Subsets of Patients ± Prior Biologic Use or ± Concurrent MTXGLM (n=685)IFX (n=585)GLM (n=685)IFX (n=585)BionaïveNon-BionaïveBionaïveNon-BionaïveNo MTX UseMTXUseNo MTX UseMTX UseInfusion Reactions6/242(2.5%)21/443(4.7%)36/251(14.3%)47/334(14.1%)15/265(5.7%)12/420(2.9%)44/229(19.2%)39/356(11.0%)Medication for Infusion Reactions33.3%59.1%78.9%73.6%50.%58.3%73.6%77.6%MTX=methotrexateConclusion:Whether bionaïve, non-bionaïve, MTX non-user or MTX user at baseline, the incidence of infusion reactions was notably lower among GLM- vs IFX-treated pts. Serious and/or severe infusion reactions did not occur among GLM pts and were rare among IFX pts. IFX was more commonly administered mediation for an infusion reaction compared to GLM. Infusion reactions accounted for almost four times the number of discontinuations related to adverse events in IFX pts compared to GLM pts.Disclosure of Interests: :Sergio Schwartzman Grant/research support from: Janssen Research & Development, LLC, Consultant of: AbbVie, Crescendo Bioscience, Dermtech, Eli Lilly and Company, Gilead Sciences, Janssen Pharmaceutica, Myriad Genetics, Novartis, Regeneron, Samsung, Sanofi, UCB, Speakers bureau: AbbVie, Eli Lilly and Company, Genentech, Janssen Pharmaceutica, Novartis, Pfizer, Regeneron, Sanofi, UCB, Aaron Broadwell Grant/research support from: Janssen Research & Development, LLC, Janssen, Eli Lilly, Consultant of: AbbVie, Amgen, AstraZeneca, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Sandoz, Speakers bureau: AbbVie, Amgen, Celgene, GSK, Horizon, Janssen, Mallinckrodt, Novartis, Pfizer, Radius, Sanofi-Regeneron, UCB, Alan Kivitz Shareholder of: AbbVie, Amgen, Gilead, GSK, Pfizer Inc, Sanofi, Consultant of: AbbVie, Boehringer Ingelheim,,Flexion, Genzyme, Gilead, Janssen, Novartis, Pfizer Inc, Regeneron, Sanofi, SUN Pharma Advanced Research, UCB, Paid instructor for: Celgene, Genzyme, Horizon, Merck, Novartis, Pfizer, Regeneron, Sanofi, Speakers bureau: AbbVie, Celgene, Flexion, Genzyme, Horizon, Merck, Novartis, Pfizer Inc, Regeneron, Sanofi, Shawn Black Employee of: Janssen Research & Development, LLC, Janssen Scientific Affairs, LLC, Stephen Xu Employee of: Janssen Research & Development, LLC, Wayne Langholff Employee of: Janssen Research & Development, LLC, Shelly Kafka Employee of: Janssen Scientific Affairs, LLC
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Weinstein, C., M. Govoni, J. Lin, A. Meehan, and Z. Qureshi. "POS0947 LONG-TERM GOLIMUMAB PERSISTENCE: 5-YEAR TREATMENT RETENTION DATA POOLED FROM FIVE PHASE III CLINICAL TRIALS IN PATIENTS WITH RHEUMATOID ARTHRITIS, PSORIATIC ARTHRITIS, AND ANKYLOSING SPONDYLITIS." Annals of the Rheumatic Diseases 81, Suppl 1 (May 23, 2022): 777.2–778. http://dx.doi.org/10.1136/annrheumdis-2022-eular.4385.
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BackgroundTumor necrosis factor-alpha inhibitors (TNFi), such as golimumab (GLM), are widely prescribed for treatment of chronic immune-mediated rheumatic diseases, such as rheumatoid arthritis (RA), psoriatic arthritis (PsA) and ankylosing spondylitis (AS). Long-term persistence of GLM response in these diseases has previously been described from individual randomized controlled trials.1-5 While treatment retention is considered an important factor for disease progression, health care utilization, and overall quality of life, and has previously been described, the probability of retention on GLM treatment in these trials has not been evaluated.ObjectivesTo evaluate probability of GLM treatment retention over a 5-year period in adult patients with immune-mediated rheumatic diseases, by indication and line of therapy, using pooled data from five Phase III randomized controlled clinical trials.MethodsUsing data prospectively collected from five Phase III studies, this post-hoc analysis evaluated subcutaneous (SC) GLM (50 mg or 100 mg every 4 weeks) for up to 5 years in participants with RA (GO-BEFORE,1,6 GO-AFTER2,7 and GO-FORWARD3,8), PsA (GO-REVEAL4,9), and AS (GO-RAISE5,10). Four of the five studies (GO-BEFORE, GO-FORWARD, GO-REVEAL and GO-RAISE) were pooled to examine 1st-line GLM therapy, while the remaining study (GO-AFTER) was used to examine 2nd-line (i.e., ≥ 1 line) GLM therapy in participants who had previously received and discontinued at least one other TNFi (etanercept, adalimumab, or infliximab) for any reason. Log-rank tests were performed to estimate retention rates by indication and line of therapy. Similarly, Kaplan-Meier analysis was used to estimate the probability of GLM retention over time.ResultsAmong the 2228 participants enrolled in the 5 trials, 1797 participants had received GLM as1st-line treatment (RA = 1050; PsA = 394; AS = 353) and 431 participants had received GLM as 2nd-line treatment. Compared to the pooled 1st-line GLM analysis cohort, more participants receiving 2nd-line GLM were female (78.7% vs 62.2%), were > 50 years (61.5% vs 41.2%), and had a longer disease duration (median of 9.2 years vs 3.7 years). In the pooled 1st-line studies, GLM treatment retention remained high over five years, with an overall probability of retention of 87.8% (95% confidence interval [CI], 86.2–89.2) at Year 1 (Week 52), 80.9% (79.0–82.6) at Year 2 (Week 104), 77.3% (75.3–79.2) at Year 3 (Week 156), 73.5% (71.4–75.5) at Year 4 (Week 208) and 69.8% (67.6–71.9) at Year 5 (Week 252). GLM retention rates were similar across the four 1st-line GLM studies with no notable differences observed by indication over time (Figure 1, panel A). Treatment retention was better in participants using GLM as a 1st-line therapy compared to 2nd-line therapy (Figure 1, panel B), with a probability of retention at 5 years (Week 252) with 2nd-line therapy of 41.6% (95% CI: 36.8-46.3).Figure 1.ConclusionIn this post-hoc analysis of prospectively collected clinical trial data, the probability of 1st-line GLM treatment retention at 5-years was consistently high across all rheumatic indications (RA, PsA and AS). Probability of long-term GLM treatment retention with 2nd-line therapy, while lower than 1st-line therapy, also remained favorable. Collectively, these data support the value of GLM as a 1st- or 2nd-line therapy in these chronic immune-mediated rheumatic diseases.References[1]Emery P, et al. Arthritis Rheum 2009;60:2272-83.[2]Smolen JS, et al. Lancet 2009;374:210-21.[3]Keystone EC, et al. J Rheumatol. 2015;43:298-306.[4]Kavanaugh A, et al. Ann Rheum Dis. 2014;73:1689-94[5]Inman RD, et al. Arthritis Rheum 2008;58:3402-12.[6]Emery P, et al. Arthritis Care Res. 2016;68:744-52.[7]Smolen JS, et al. Arthritis Res Ther. 2015;17:14.[8]Keystone EC, et al. Ann Rheum Dis 2009;68:789-96.[9]Kavanaugh A, et al. Arthritis Rheum 2009;60:976-86.[10]Deodhar A, et al. Ann Rheum Dis. 2015;74:757-61.Disclosure of InterestsCindy Weinstein Shareholder of: Merck & Co., Inc., Kenilworth, NJ, USQA, Employee of: Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, Marinella Govoni Shareholder of: Merck & Co., Inc., Kenilworth, NJ, USA, Employee of: MSD Italy, a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, Jianxin Lin Shareholder of: Merck & Co., Inc., Kenilworth, NJ, USA, Employee of: Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, Alan Meehan Shareholder of: Merck & Co., Inc., Kenilworth, NJ, USA, Employee of: Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, Zaina Qureshi Shareholder of: Merck & Co., Inc., Kenilworth, NJ, USA, Employee of: Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA
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Tataris, Andreas, and Tristan van Leeuwen. "A Regularised Total Least Squares Approach for 1D Inverse Scattering." Mathematics 10, no. 2 (January 11, 2022): 216. http://dx.doi.org/10.3390/math10020216.
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We study the inverse scattering problem for a Schrödinger operator related to a static wave operator with variable velocity, using the GLM (Gelfand–Levitan–Marchenko) integral equation. We assume to have noisy scattering data, and we derive a stability estimate for the error of the solution of the GLM integral equation by showing the invertibility of the GLM operator between suitable function spaces. To regularise the problem, we formulate a variational total least squares problem, and we show that, under certain regularity assumptions, the optimisation problem admits minimisers. Finally, we compute numerically the regularised solution of the GLM equation using the total least squares method in a discrete sense.
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Wang, Ye-sheng, Qi-wei Li, Lin Zhou, Run-feng Guan, Xiang-ming Zhou, Ji-hong Wu, Nan-yan Rao, and Shuang Zhu. "Granulomatous Lobular Mastitis Associated with Mycobacterium abscessus in South China: A Case Report and Review of the Literature." Case Reports in Infectious Diseases 2017 (2017): 1–4. http://dx.doi.org/10.1155/2017/7052908.
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Mycobacteria, which are known as rapidly growing bacteria, are pathogens that are responsible for cutaneous or subcutaneous infections that especially occur after injection, trauma, or surgery. In this report, we describe a species of Mycobacterium abscessus that was isolated from a breast abscess in a patient who was previously diagnosed with granulomatous lobular mastitis (GLM). This current case is the first ever presented case of GLM associated with M. abscessus documented in South China. The case presentation highlights the role of M. abscessus in GLM. The association of M. abscessus and GLM is discussed and a summary of breast infection due to Mycobacteria is given.
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MAHAJAN, G., M. S. GILL, and B. DOGRA. "Performance of basmati rice (Oryza sativa) through organic source of nutrients." Indian Journal of Agricultural Sciences 82, no. 5 (May 14, 2012): 459–61. http://dx.doi.org/10.56093/ijas.v82i5.17813.
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Grain yield of basmati rice improved with organic source of nutrients as compared to recommended level of N and untreated control. GLM (5 tonnes/ha) + VC (2.5 tonnes/ha), GLM (10 tonnes/ha), neem cake (2.5 tonnes/ha) caused 20.2, 16.7 and 15.2% higher grain yield than recommended level of N respectively. So, it was concluded that GLM (10 tonnes/ ha) or GLM (5 tonnes/ha) + VC (2.5 tonnes/ha) or neem cake (2.5 tonnes/ha) are an ideal organic nutrition module/ dose to meet the nutritional requirement of basmati crop and is best alternative to inorganic fertilizer without significant loss in grain yield.
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Johansson, T., C. Weinstein, A. MEEHAN, A. Tzontcheva, P. Xu, C. Marano, and G. de Hertogh. "P688 Effect of golimumab induction therapy on histologic disease profile in patients with moderate to severe ulcerative colitis: Results from the randomised, placebo-controlled Phase 3 PURSUIT-SC trial." Journal of Crohn's and Colitis 14, Supplement_1 (January 2020): S560. http://dx.doi.org/10.1093/ecco-jcc/jjz203.816.
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Abstract Background Histologic improvement is an emerging goal in ulcerative colitis (UC) management. We previously demonstrated in the 6-week, multicentre, randomised, placebo-controlled, double-blind Program of Ulcerative colitis Research Studies Utilising an Investigational Treatment [PURSUIT-SC] Phase 3 trial that SC induction therapy with the tumour necrosis factor-α inhibitor, golimumab (GLM), led to statistically significant and clinically meaningful improvements in the signs and symptoms of UC compared with placebo, including clinical remission, mucosal healing, and improved health-related QoL, in patients with moderate to severe active UC.1 In the present report, we explored the histologic effects of GLM induction therapy in PURSUIT-SC by examining data from a prespecified histology sub-study. Methods The Phase 3 PURSUIT-SC trial evaluated the safety and efficacy of induction therapy with GLM in 774 patients with moderate to severe UC disease activity (defined as Mayo scores of 6–12, including an endoscopic subscore of ≥ 2). Patients were randomised 1:1:1 to receive SC induction treatment with placebo at Weeks 0 and 2, GLM 200 mg at Week 0 followed by 100 mg at Week 2 (GLM 200–>100 mg), or GLM 400 mg at Week 0 followed by 200 mg at Week 2 (GLM 400–>200 mg). Rectal biopsies were collected during endoscopy at screening and Week 6 in those randomised patients who consented to participate in the histology sub-study (N=98). During the screening endoscopy, two adjacent biopsies were collected 15 to 20 cm beyond the anal verge in an area that was most representative of the inflammation observed. At Week 6, two additional biopsies were to be obtained from the same location biopsied at screening. Biopsies were graded using the Geboes Score (GS) by a single expert gastrointestinal histopathologist, who was blinded to subject, treatment and study visit. The GS comprises seven histologic grades assessing structural changes and inflammatory disease activity. Higher GS grade scores indicate more severe histologic disease. Results Evaluable biopsy data were available in 89 patients (placebo, n = 30; GLM 200–>100 mg, n = 30; GLM 400–>200 mg, n = 29). At Week 6, compared with placebo, treatment with GLM induction therapy led to a decrease in the proportion of biopsies with GS grade scores of > 1 and an increase in the proportion of biopsies with GS grade scores of ≤ 1, with the greatest improvements being observed in the GLM 400–>200 mg group (figure). Conclusion The findings of this exploratory histologic sub-study in the PURSUIT-SC trial suggest that GLM induction therapy led to an improvement in histologic disease activity compared with placebo in patients with moderate-to-severe UC. Reference
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Peterson, Michael, and Geoffrey Stano. "The Hazards Posed by Mesoscale Lightning Megaflashes." Earth Interactions 25, no. 1 (January 2021): 46–56. http://dx.doi.org/10.1175/ei-d-20-0016.1.
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Abstract Lightning megaflashes extending over >100-km distances have been observed by the Geostationary Lightning Mappers (GLMs) on NOAA’s R-series Geostationary Operational Environmental Satellites (GOES). The hazards posed by megaflashes are unclear, however, because of limitations in the GLM data. We address these by reprocessing GOES-16 GLM measurements from 1 January 2018 to 15 January 2020 and integrating them with Earth Networks Global Lightning Network (ENGLN) observations. ENGLN verified 194 880 GLM megaflashes as natural lightning. Of these, 127 479 flashes occurred following the October 2018 GLM software update that standardized GLM timing. Reprocessed GLM/ENGLN lightning maps from these postupdate cases provide a comprehensive view of how individual megaflashes evolve. This megaflash dataset is used to generate statistics that describe their hazards. The average megaflash produces 5–7 cloud-to-ground (CG) strokes that are spread across 40%–50% of the flash extent. As flash extent increases beyond 100 km, megaflashes become concentrated in key hot-spot regions in North and South America while the number of CG and intracloud events per flash and the overall peak current increase. CGs in the larger megaflashes occur over 80% of the flash extent measured by GLM, and the majority contain regions where the megaflash is the only lightning activity in the preceding hour. These statistics demonstrate that there is no safe location below an electrified cloud that is producing megaflashes, and current lightning safety guidance is not always sufficient to mitigate megaflash hazards.
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Deodhar, A., S. Kafka, E. C. Hsia, K. H. Lo, L. Kim, S. Xu, and J. D. Reveille. "POS0902 EFFICACY AND SAFETY OF INTRAVENOUS GOLIMUMAB IN ANKYLOSING SPONDYLITIS PATIENTS WITH EARLY VS LATE DISEASE THROUGH WEEK 52 OF GO-ALIVE STUDY." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 708–9. http://dx.doi.org/10.1136/annrheumdis-2021-eular.220.
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Background:The GO-ALIVE study assessed efficacy and safety of intravenous golimumab (IV GLM) in patients (pts) with ankylosing spondylitis (AS).1,2Objectives:In this post hoc analysis, we assessed IV GLM efficacy and safety in AS pts with early disease (ED) vs late disease (LD) based on pt-reported duration of inflammatory back pain (IBP).Methods:In this Phase 3, double-blind, placebo (PBO)-controlled trial, pts with active AS were randomized (1:1) to receive IV GLM 2 mg/kg at Week (W) 0, W4, then Q8W or PBO at W0, W4, and W12 with crossover to IV GLM at W16, W20, then Q8W through 52. The primary endpoint was achievement of SpondyloArthritis International Society 20% improvement response (ASAS 20) at W16. In this post hoc analysis, 208 pts were grouped into quartiles based on self-reported duration of IBP symptoms. Efficacy and safety in 60 pts with ED (1st quartile) were compared with 52 pts with LD (4th quartile).Results:For the overall study population, mean duration of IBP symptoms was 10.9 yr and mean time since diagnosis was 5.5 yr. For ED pts, the mean duration of IBP symptoms ranged from 2.3 yr (IV GLM) to 2.6 yr (PBO), and for LD pts ranged from 23.5 yr (IV GLM) to 24.4 yr (PBO). At W16, ASAS 20 was achieved by 72% IV GLM vs 32% PBO pts with ED and by 67% IV GLM vs 21% PBO pts with LD. Pts with ED had numerically better response than those with LD in Bath Ankylosing Spondylitis Functional Index (BASFI), Bath Ankylosing Spondylitis Metrology Index (BASMI), and across more stringent endpoints, including ASAS 40, Bath Ankylosing Spondylitis Disease Activity Index 50% improvement (BASDAI 50), and Ankylosing Spondylitis Disease Activity Score (ASDAS) inactive disease and major improvement (Table 1). Response rates at W16 among IV GLM-treated pts were generally consistent through 1 year in both ED and LD subgroups; also in ED and LD subgroups, pts crossing over to IV GLM at W16 demonstrated response at W52 consistent with pts who started IV GLM at W0. At W16, improvements in enthesitis score were similar for pts with ED (mean change -2.9 for IV GLM vs 0.1 for PBO) and LD (mean change -2.5 for IV GLM vs 0.6 for PBO); improvements were maintained at W52 for ED and LD pts. Treatment-emergent adverse events and serious adverse events through 1 year were 46% and 3% for pts with ED compared with 61% and 2% for pts with LD, respectively.Conclusion:While IV GLM provided clinically meaningful improvements in signs and symptoms of AS in pts regardless of disease duration, response generally appeared numerically better in pts with ED than in pts with LD. This supports the principle of prompt diagnosis and early treatment.References:[1]Deodhar A, et al. J Rheumatol. 2018;45:341-348.[2]Reveille J, et al. J Rheumatol. 2019;46:1277-1283.Table 1.Efficacy OutcomesEDLDWeek 16Week 52Week 16Week 52PBO(n=25)IV GLM(n=35)PBO→IV GLM(n=25)IV GLM(n=35)PBO(n=28)IV GLM(n=24)PBO→IV GLM(n=28)IV GLM(n=24)ASAS 2032%71%68%71%21%67%68%63%ASAS 4012%46%56%60%4%42%57%42%BASDAI 5012%40%64%60%7%33%57%42%ASDAS inactive disease (score <1.3)4%17%44%37%0%8%14%4%ASDAS major improvement (decrease ≥2.0)n=244%57%n=2454%51%0%n=2348%46%n=2330%ASDAS clinically important improvement (decrease ≥1.1)n=2429%77%n=2475%77%18%n=2391%61%n=2365%Mean change from baseline (SD) in BASFIn=23-0.4 (2.0)-2.3 (2.1)n=23-2.7 (2.7)-2.8 (2.6)n=27-0.3 (1.8)n=24-2.2 (1.7)n=27-2.4 (2.2)n=23-2.3 (1.7)Mean change from baseline (SD) in BASMIn=23-0.3 (0.7)-0.4 (0.7)n=23-0.6 (0.7)-0.3 (0.5)n=270.01 (0.5)n=21-0.3 (0.6)n=27-0.4 (0.7)n=20-0.3 (0.7)Mean change from baseline (SD) in enthesitis scoren=230.1 (3.6)-2.9 (2.9)n=23-2.0 (4.4)-3.2 (2.5)n=27-0.6 (3.4)n=21-2.5 (3.0)n=27-2.5 (3.1)n=20-3.5 (5.9)SD=standard deviationDisclosure of Interests:Atul Deodhar Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Galapagos, GlaxoSmithKline, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Eli Lilly, GlaxoSmithKline, Novartis, Pfizer, and UCB, Shelly Kafka Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Kim Hung Lo Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Lilianne Kim Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Stephen Xu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, John D Reveille Consultant of: Eli Lilly and UCB, Grant/research support from: Eli Lilly and Janssen
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Omerašević, Amela, and Jasmina Selimović. "Classification Ratemaking Using Decision Tree in the Insurance Market of Bosnia and Herzegovina." South East European Journal of Economics and Business 15, no. 2 (December 1, 2020): 124–39. http://dx.doi.org/10.2478/jeb-2020-0020.
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Abstract This paper investigates the impact of risk classification on life insurance ratemaking with particular reference to Bosnia and Herzegovina (BiH). The research is based on a sample of over eighteen thousand insurance policies for passenger vehicles collected over the period 2015-2020. In our empirical investigation we develop a standard risk model based on the application of Poisson Generalized linear models (GLM) for claims frequency estimate and Gamma GLM for claim severity estimate. The analysis reveals that GLM does not provide a reliable parameter estimates for Multi-level factor (MLF) categorical predictors. Although GLM is widely used method to deter insurance premiums, improvements of GLM by using the data mining methods identified in this paper may solve practical challenges for the risk models. The popularity of applying data mining methods in the actuarial community has been growing in recent years due to its efficiency and precision. These models are recommended to be considered in BiH and South East European region in general.
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Cordero-Coma, Miguel, Vanesa Calvo-Río, Alfredo Adán, Ricardo Blanco, Carolina Álvarez-Castro, Marina Mesquida, Sara Calleja, Miguel A. González-Gay, and José G. Ruíz de Morales. "Golimumab as Rescue Therapy for Refractory Immune-Mediated Uveitis: A Three-Center Experience." Mediators of Inflammation 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/717598.
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Objective. To evaluate, in three Spanish tertiary referral centres, the short-term safety and efficacy of golimumab (GLM) for treatment of immune-mediated uveitis resistant to previous immunosuppressive therapy.Methods. Nonrandomized retrospective interventional case series. Thirteen patients with different types of uveitis that were resistant to treatment with at least 2 previous immunosuppressors were included in this study. All included patients were treated with GLM (50 mg every four weeks) during at least 6 months. Clinical evaluation and treatment-related side effects were assessed at least four times in all included patients.Results. Eight men and 5 women (22 affected eyes) with a median age of 30 years (range 20–38) and active immune-mediated uveitides were studied. GLM was used in combination with conventional immunosuppressors in 7 patients (53.8%). GLM therapy achieved complete control of inflammation in 12/13 patients (92.3%) after six months of treatment. There was a statistically significant improvement in mean BCVA (0.60 versus 0.68,P=0.009) and mean 1 mm central retinal thickness (317 versus 261.2 μ,P=0.05) at the six-month endpoint when compared to basal values. No major systemic adverse effects associated with GLM therapy were observed.Conclusions. GLM is a new and promising therapeutic option for patients with severe and refractory uveitis.
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Bektas, Volkan, Pete Bettinger, Nate Nibbelink, Jacek Siry, Krista Merry, Katrina Ariel Henn, and Jonathan Stober. "Habitat Suitability Modeling of Rare Turkeybeard (Xerophyllum asphodeloides) Species in the Talladega National Forest, Alabama, USA." Forests 13, no. 4 (March 22, 2022): 490. http://dx.doi.org/10.3390/f13040490.
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This study focused on the rare and threatened plant species eastern turkeybeard (Xerophyllum asphodeloides (L.) Nutt.) and its presence or absence in the Talladega National Forest in Alabama, USA. An ensemble suitable habitat map was developed using four different modeling methods (MaxEnt, Generalized Linear Model, Generalized Additive Model, and Random Forest). AUC evaluation scores for each model were 0.99, 0.96, 0.98, and 0.99, respectively. Biserial correlation scores for models ranged from 0.71 (GLM) to 0.94 (RF). The four different models agreed suitable habitat was found to cover 159.57 ha of the land. The ground slope variable was the most contributive variable in the MaxEnt and RF models and was also significant in the GLM and GAM models. The knowledge gained from this research can be used to establish and implement habitat suitability strategies across the Talladega National Forest and similar ecosystems in the southern United States.
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Rumpf, Clemens, Randolph Longenbaugh, Christopher Henze, Joseph Chavez, and Donovan Mathias. "An Algorithmic Approach for Detecting Bolides with the Geostationary Lightning Mapper." Sensors 19, no. 5 (February 27, 2019): 1008. http://dx.doi.org/10.3390/s19051008.
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The Geostationary Lightning Mapper (GLM) instrument onboard the GOES 16 and 17 satellites can be used to detect bolides in the atmosphere. This capacity is unique because GLM provides semi-global, continuous coverage and releases its measurements publicly. Here, six filters are developed that are aggregated into an automatic algorithm to extract bolide signatures from the GLM level 2 data product. The filters exploit unique bolide characteristics to distinguish bolide signatures from lightning and other noise. Typical lightning and bolide signatures are introduced and the filter functions are presented. The filter performance is assessed on 144845 GLM L2 files (equivalent to 34 days-worth of data) and the algorithm selected 2252 filtered files (corresponding to a pass rate of 1.44%) with bolide-similar signatures. The challenge of identifying frequent but small, decimeter-sized bolide signatures is discussed as GLM reaches its resolution limit for these meteors. The effectiveness of the algorithm is demonstrated by its ability to extract confirmed and new bolide discoveries. We provide discovery numbers for November 2018 when seven likely bolides were discovered of which four are confirmed by secondary observations. The Cuban meteor on Feb 1st 2019 serves as an additional example to demonstrate the algorithms capability and the first light curve as well as correct ground track was available within 8.5 hours based on GLM data for this event. The combination of the automatic bolide extraction algorithm with GLM can provide a wealth of new measurements of bolides in Earth’s atmosphere to enhance the study of asteroids and meteors.
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Suh, Dae Chul, Jun-Hyoung Kim, Moo Song Lee, Sangsoo Park, Sung Tae Kim, Choong Gon Choi, Ho Kyu Lee, Chang Jin Kim, and Ho-Young Song. "Penetration Difference of n-Butyl 2-Cyanoacrylate into the Nidus in the Embolisation of Brain Arteriovenous Malformation." Interventional Neuroradiology 4, no. 1 (March 1998): 63–74. http://dx.doi.org/10.1177/159101999800400108.
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Prediction of nidal penetration of the glue is difficult in the embolisation of brain arteriovenous malformations (AVM). We evaluated penetration differences of glue-lipiodol mixtures with and without tungsten powder for embolisation of brain AVM. Fifty eight injections during embolisation of brain AVM in 15 patients were evaluated. Glue contained lipiodol only (N=25), or lipiodol and tungsten powder (N=33). Glue concentration ranged from 20 to 80%. Injection was done at the pedicle (N=41) or nidus (N=17). Degree of penetration was rated as good, fair, or poor according to penetrated glue amount in the nidus. Penetration difference was compared in each injection at the pedicle or nidus. When the glue mixtures were injected at the pedicle, the glue-lipiodol mixture (GLM) penetrated into the nidus in eight of 16 injections (50%) and the glue-lipiodol-tungsten mixtures (GLTM) in 23 of 25 (92%). When glue was injected at the nidus, GLM was confined to the nidus in nine of nine injections (100%) and GLTM in seven of eight (88%). Penetration into the vein was noted in two of 25 injections (8%) in GLM and six of 33 (18%) in GLTM. Penetration difference into the nidus between GLM and GLTM was statistically significant (p < 0.05) when the glue mixtures were injected at the pedicle. GLM and GLTM can be used effectively at the nidus. In contrast to GLM, GLTM can be used effectively at the pedicle. However, the risk of venous penetration seemed to be higher with GLTM than in GLM.
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Vuitton, L., F. Poullenot, Y. Bouhnik, P. Wils, A. Buisson, S. Viennot, G. Bouguen, et al. "P679 Anti-TNF de-escalation following a treat-to-target strategy with golimumab therapy intensification to reach continuous clinical response in ulcerative colitis: the In-Target GETAID trial." Journal of Crohn's and Colitis 18, Supplement_1 (January 1, 2024): i1290. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0809.
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Abstract Background Achieving deep and continuous remission with tight control could allow de-escalation in patients with UC. The in-TARGET study was a prospective two-phase trial in UC patients initiating golimumab (GLM), that aimed to determine the proportion of patients with continuous clinical response (CCR) and endoscopic remission (ER) after one year of treatment optimization strategy (phase 1); then to assess treatment de-escalation outcome in these patients (phase 2). Methods This was a multicentre prospective interventional study in GETAID centres. The study included adult patients with active UC (Mayo Score ≥6 with an endoscopic subscore ≥2), naive to biologics. In the first part of the study, patients received a conventional GLM regimen, depending on their weight (<80 kg; ≥80 kg). At week 10, primary responders continued with the standard maintenance regimen (50 mg or 100 mg/4 w) and non-primary responders had systematic treatment optimization (<80 kg, GLM 100mg/4 w; ≥80kg, introduction of an immunosuppressant) (Figure 1). In the second part of the study, patients in CCR and in ER (Mayo endoscopic score 0-1) at w 54, had treatment de-escalation: GLM discontinuation for patients receiving 50mg, and dose de-escalation to 50mg for those receiving 100mg. Results A total of 197 patients were included, mean duration of UC was 75.9 (± 95.3) months, the mean total Mayo score was 8.4 (± 1.7). At w 10, 14 patients had stopped treatment for failure, 83 (42.1%) had a primary response, and 100 (50.7%) non-primary responders had treatment optimization. At w 54, 95 (48.2%) patients were still on GLM; of these, 64 (32.5%) patients were in CCR and ER. At w 0 and 10, no factors were associated with CCR + ER at w54. Serum GLM levels after w10 were not associated with CCR and ER at w54. No patient developed anti-GLM antibodies between w0 and w54. Sixty patients participated in the second de-escalation phase of the study: in 18 GLM was discontinued, in 38 GLM dose was de-escalated from 100 to 50mg/4 w, and 4 patients refused dose de-escalation. At w 108, 34/56 (60.7%) patients were in CCR and ER, with no change in treatment. No factors were associated with w108 remission. At w108, 23 patients had relapsed: 4/18 (22.2%) among GLM discontinuations, 19/38 (50%) among patients de-escalated from 100 to 50 mg/4w. During follow-up, no colectomy was reported, and 14 severe non-UC related adverse events occurred (3 severe infections, 2 severe anaemias, and 1 rectal cancer). Conclusion One third of UC patients achieved a CCR with ER at one year with optimized GLM therapy. After de-escalation of GLM, 60% of remitters at one year maintained deep remission at two years. Anti-TNF de-escalation following a treat-to-target strategy could be considered in some UC patients.
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Tarasov, Boris P., Artem A. Arbuzov, Alexei A. Volodin, Sergey A. Mozhzhukhin, and Mikhail V. Klyuev. "NICKEL-GRAPHENE CATALYST FOR MAGNESIUM HYDROGENATION AND FOR CARBON NANOSTRUCTURES SYNTHESIS." IZVESTIYA VYSSHIKH UCHEBNYKH ZAVEDENIY KHIMIYA KHIMICHESKAYA TEKHNOLOGIYA 60, no. 8 (August 29, 2017): 43. http://dx.doi.org/10.6060/tcct.2017608.5645.
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The results of obtaining graphene-like nanomaterials (GLM) by reduction of graphite oxide, preparation of nickel-graphene catalysts and formation of carbon-graphene and magnesium-graphene composites are presented. The optimal preparation conditions and the functional characteristics of the obtained materials are determined. The method described in this work makes possible to obtain nickel-graphene composites (Ni/GLM) containing 5–25 wt.% of Ni nanoparticles of size 2–5 nm. Such composites are effective catalysts for the hydrogenation of magnesium. They were used to create hydrogen storage materials on magnesium base with a reversible capacity more 6.5 wt.% of hydrogen. The addition of Ni/GLM promotes an increase in the rate of Mg hydrogenation due to the high catalytic activity of nanoscale Ni in the dissociation of H2 molecules, and the coating of the fine particles of MgH2 with the GLM retains the submicron size of the Mg particles formed during dehydrogenation and ensures high thermal conductivity of the Mg/MgH2 + Ni/GLM composites. It was determined that in MgH2 + Ni/GLM composites, along with the stable α-phase of MgH2, a metastable γ-phase of MgH2 contains, which leads to a decrease in the dehydrogenation temperature by ~ 50 °C. Using Ni/GLM catalysts, carbon nanotubes (CNT) and nanofibers (CNF) on the surface of graphene-like structures were synthesized. The catalytic decomposition of C2H4 on Ni/GLM at temperatures of 500–700 °C leads to the formation of CNF on the surface of the GLM, and the decomposition of CH4 at 900 °C – with the formation of CNT. The CNT and CNF formed have a diameter in the range from 5 to 20 nm, and the length increases from 5 to 300 nm with rise of synthesis duration. Such three-dimensional structures have a high specific surface area and are attractive as sorbents of gases and carriers of metal catalysts.Forcitation:Tarasov B.P., Arbuzov A.A., Volodin A.A., Mozhzhukhin S.A., Klyuev M.V. Nickel-graphene catalyst for magnesium hydrogenation and for carbon nanostructures synthesis. Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol. 2017. V. 60. N 8. P. 43-46.
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Nori, Ahmed, Noor Ahmed, and Thana Ahmed. "GLM for Image Steganography Technique." AL-Rafidain Journal of Computer Sciences and Mathematics 10, no. 1 (March 15, 2013): 239–49. http://dx.doi.org/10.33899/csmj.2013.163455.
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Willis, Gwenda M., Tony Ward, and Jill S. Levenson. "The Good Lives Model (GLM)." Sexual Abuse: A Journal of Research and Treatment 26, no. 1 (March 18, 2013): 58–81. http://dx.doi.org/10.1177/1079063213478202.
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Franqué, Fritjof von, and Peer Briken. "Das „Good Lives Model“ (GLM)." Forensische Psychiatrie, Psychologie, Kriminologie 7, no. 1 (December 20, 2012): 22–27. http://dx.doi.org/10.1007/s11757-012-0196-x.
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