Estimation of Standardized Effort in the Heterogeneous Gulf of Mexico Shrimp Fleet

In this paper we estimate nominal and standardized shrimping effort in the Gulf of Mexico for the years 1965 through 1993. We accomplish this by first developing a standardization method (model) and then an expansion method (model). The expansion model estimates nominal days fished for noninterview landings data. The standardization model converts nominal days fished to standard days fished. We then characterize the historical trends of the penaeid shrimp fishery byvessel configuration, relative fishing power, and nominal and standardized effort. Wherever possible, we provide comparison with previous estimates by the National Marine Fisheries Service, NOAA

First report on four species of predatorynematodes, mononchids (Nematoda : Mononchida) from Nepal

A nematological survey was conducted for free and plant nematodes affecting economically important vegetable crops grown in Bhaktapur and Kavre, hilly districts of Nepal with altitudes ranging between 1315m to 1500m which revealed various plant parasitic nematodes along with four species of predatory nematodes belonging to the order Mononchida. These species were Mononchus aquaticus Coetzee, 1968, Iotonchus indicus Jairajpuri 1969, Mylonchulus contractus Jairajpuri, 1970 and Parahandronchus shakili (Jairajpuri, 1969) Mulvey, 1978. The measurements, descriptions, remarks and illustrations along with habitat and locality of these predatory nematodes are provided. These species are the first report from Nepal

Survey of Available Literature on Optimum Design of Structures

A survey of available literature on optimum design of structures is the scope of this report. The material presented in this report is an evaluation of the current literature pertaining to optimum design of structures. The principal methods of approach to achieve optimum design of structures are included in this report. The stress control method of getting optimum design is presented and its application to several problems is shown. Selected bibliography of the available literature with short descriptions of the contents is also included for further references and detailed study.Civil Engineerin

Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits

Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. The standard method for predicting the absence, presence, and intensity of pain has long been self-report. However, medical providers struggle to manage patients based on subjective pain reports correctly and pain medications often lead to further difficulties in patient communication as they may cause sedation and sleepiness. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores for inpatient visits using machine learning (ML) techniques. In this study, we evaluate the generalizability of ML techniques to data collected from 50 patients over an extended period across three types of hospital visits (i.e., inpatient, outpatient and outpatient evaluation). We compare five classification algorithms for various pain intensity levels at both intra-individual (within each patient) and inter-individual (between patients) level. While all the tested classifiers perform much better than chance, a Decision Tree (DT) model performs best at predicting pain on an 11-point severity scale (from 0-10) with an accuracy of 0.728 at an inter-individual level and 0.653 at an intra-individual level. The accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e., no/mild pain: 0-5, severe pain: 6-10) at an intra-individual level. Our experimental results demonstrate that ML techniques can provide an objective and quantitative evaluation of pain intensity levels for all three types of hospital visits.Comment: Accepted for presentation at the FIRST WORKSHOP ON COMPUTATIONAL & AFFECTIVE INTELLIGENCE IN HEALTHCARE APPLICATIONS (VULNERABLE POPULATIONS) In Conjunction with the International Conference on Pattern Recognition (ICPR) 202

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology

Meta-analysis methods and models with applications in evaluation of cholesterol-lowering drugs

In this paper, we propose a class of multivariate random effects models allowing for the inclusion of study-level covariates to carry out meta-analyses. As existing algorithms for computing maximum likelihood estimates often converge poorly or may not converge at all when the random effects are multi-dimensional, we develop an efficient expectation–maximization algorithm for fitting multi-dimensional random effects regression models. In addition, we also develop a new methodology for carrying out variable selection with study-level covariates. We examine the performance of the proposed methodology via a simulation study. We apply the proposed methodology to analyze metadata from 26 studies involving statins as a monotherapy and in combination with ezetimibe. In particular, we compare the low-density lipoprotein cholesterol-lowering efficacy of monotherapy and combination therapy on two patient populations (naïve and non-naïve patients to statin monotherapy at baseline), controlling for aggregate covariates. The proposed methodology is quite general and can be applied in any meta-analysis setting for a wide range of scientific applications and therefore offers new analytic methods of clinical importance

Bayesian Inference for Multivariate Meta-Regression With a Partially Observed Within-Study Sample Covariance Matrix

Multivariate meta-regression models are commonly used in settings where the response variable is naturally multi-dimensional. Such settings are common in cardiovascular and diabetes studies where the goal is to study cholesterol levels once a certain medication is given. In this setting, the natural multivariate endpoint is Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). In this paper, we examine study level (aggregate) multivariate meta-data from 26 Merck sponsored double-blind, randomized, active or placebo-controlled clinical trials on adult patients with primary hypercholesterolemia. Our goal is to develop a methodology for carrying out Bayesian inference for multivariate meta-regression models with study level data when the within-study sample covariance matrix S for the multivariate response data is partially observed. Specifically, the proposed methodology is based on postulating a multivariate random effects regression model with an unknown within-study covariance matrix Σ in which we treat the within-study sample correlations as missing data, the standard deviations of the within-study sample covariance matrix S are assumed observed, and given Σ, S follows a Wishart distribution. Thus, we treat the off-diagonal elements of S as missing data, and these missing elements are sampled from the appropriate full conditional distribution in a Markov chain Monte Carlo (MCMC) sampling scheme via a novel transformation based on partial correlations. We further propose several structures (models) for Σ, which allow for borrowing strength across different treatment arms and trials. The proposed methodology is assessed using simulated as well as real data, and the results are shown to be quite promising

Can initial vaginal bleeding patterns in etonogestrel implant users predict subsequent bleeding in the first 2 years of use?

ObjectivesTo evaluate if a simple method for characterizing vaginal bleeding patterns in etonogestrel contraceptive implant users can predict subsequent patterns and bleeding-related discontinuation over the first 2 years of use.Study designWe reanalyzed phase 3 study bleeding data for non-breastfeeding participants from the United States, Europe, Russia and Chile during the first 2 years of implant use to characterize and correlate bleeding patterns. We used 90-day reference periods with period 1.1 starting at Day 29 and ending at Day 118. We dichotomized bleeding patterns as "favorable" (amenorrhea, infrequent bleeding and normal frequency bleeding without prolonged bleeding) or "unfavorable' (prolonged and/or frequent bleeding) and tracked user groups based on these bleeding patterns in reference period 1.1 through Year 1 and from Year 1 through Year 2, respectively.ResultsWe evaluated data from 537 and 428 women with up to 1 and 2 years use, respectively. Of the 325 (60.5%) women with favorable bleeding in reference period 1.1, 275 (84.6%) reported favorable bleeding also in reference period 2, 197 (60.6%) reported favorable bleeding throughout Year 1, and favorable bleeding in 75-85% of reference periods in Year 2. Among 212 (39.5%) women with unfavorable bleeding in reference period 1.1, 118 (55.7%) continued with unfavorable bleeding in reference period 2, while about 40%-50% reported favorable patterns in RP 2, 3 and/or 4. Initial favorable bleeding resulted in lower discontinuation rates than initial unfavorable bleeding in years 1 (3.7% vs 12.7%, p≪.0001) and 2 (2.5% vs 16.5%, p≪.0001).ConclusionImplant users with favorable bleeding in the first reference period are likely to continue with favorable bleeding over the next 2 years. Initial bleeding patterns predict overall continuation rates in years 1 and 2. Implications Statement When evaluating vaginal bleeding in any 90-day reference period over 2 years of etonogestrel implant use, approximately 80% of women with favorable and 40% with unfavorable bleeding patterns will have favorable bleeding in the next reference periods. These findings can facilitate counseling regarding bleeding for women using the etonogestrel implant

Enhancing Biopharmaceutical Attributes of Phospholipid Complex-loaded Nanostructured Lipidic Carriers of Mangiferin: Systematic Development, Characterization and Evaluation

Mangiferin (Mgf), largely expressed out from the leaves and stem bark of Mango, is a potent antioxidant. However, its in vivo activity gets tremendously reduced owing to poor aqueous solubility and inconsistent gastrointestinal absorption, high hepatic first-pass metabolism and high P-gp efflux. The current research work, therefore, was undertaken to overcome the biopharmaceutical hiccups by developing the Mgf-phospholipid complex (PLCs) loaded in nanostructured lipidic carriers (NLCs). The PLCs and NLCs were prepared using refluxing, solvent evaporation and hot emulsification technique, respectively with various molar ratios of Mgf and Phospholipon 90 G, i.e., 1:1; 1:2; and 1:3. The complex was evaluated for various physicochemical parameters like drug content (96.57%), aqueous solubility (25-fold improved) and oil-water partition coefficient (10-fold enhanced). Diverse studies on the prepared complex using FTIR, DSC, PXRD and SEM studies ratified the formation of PLCs at 1:1 ratio. The PLCs were further incorporated onto NLCs, which were systematically optimized employing a face centered cubic design (FCCD), while evaluating for particle size, zeta potential, encapsulation efficiency and in vitro drug release as the CQAs. Caco-2 cell line indicated insignificant cytotoxicity, and P-gp efflux, bi-directional permeability model and in situ perfusion studies specified enhanced intestinal permeation parameters. In vivo pharmacokinetic studies revealed notable increase in the values of Cmax (4.7-fold) and AUC (2.1-fold), respectively, from PLCs-loaded NLCs vis-à-vis Mgf solution. In a nutshell, the promising results observed from the present research work signified boosted biopharmaceutical potential of the optimized PLCs-loaded NLCs for potentially augmenting the therapeutic efficacy of Mgf