Epidemiological Investigation of Risk Factors for Microbial Contamination in Produce at the Preharvest Level

In the United States, the proportion of outbreaks of microbial foodborne illnesses associated with fresh produce has increased over the past decades. A large proportion of these outbreaks have been caused by enteric pathogens, including Listeria monocytogenes, Salmonella, and Escherichia coli O157:H7. The overall objective of this dissertation was to study the risk factors for preharvest produce contamination with these three pathogens and generic Escherichia coli, as an indicator organism of fecal contamination, to improve control of foodborne illnesses associated with fresh produce. This objective was accomplished through three independent studies. The first study identified and characterized known risk factors for contamination of fruits and vegetables at the preharvest level with L. monocytogenes, Salmonella, and E. coli O157:H7 by conducting a systematic review. The review identified and evaluated 68 published research articles which indicated soil and irrigation water as the most important routes of produce contamination with pathogens. The review indicated the existence of solid evidence for several additional risk factors, including growing produce on clay-type soil, the use of contaminated or non-pH-stabilized manure fertilizer, and the use of spray irrigation with contaminated water, with a particular risk of contamination on the lower leaf surface. A total of 955 spinach samples were collected from 12 spinach farms in Colorado and Texas for the second and third study. The second study evaluated the effect of farm management and environmental factors on spinach contamination with generic E. coli at the preharvest level. The results indicated that spinach contamination was influenced by the time since last irrigation, the use of pond water for irrigation, workers’ personal hygiene, the use of the field prior to planting, and the proximity of a poultry farm. The third study evaluated the role of weather and landscape factors, in addition to the farm management and environmental factors, in occurrence of spinach contamination with generic E. coli at the preharvest level. The results indicated that spinach contamination was influenced not only by the amount of rain, but also by workers’ personal hygiene, the use of the spinach field prior to planting, and the use of manure fertilizer. In conclusion, the three studies have identified important risk factors for microbial contamination of produce at the preharvest. The control of several of these risk factors has already been the focus of the currently established Good Management Practices (GMP) in produce production. The novel findings suggest that the GMP may need to account for rainfall and improve workers’ personal hygiene in order to further reduce produce contamination with microorganisms

The J-shape association of serum total IgE levels with age-related cataract

AIM: To address the association between serum total IgE levels and age-related cataract in adults. METHODS: The study participants consisted of 1052 adults aged 40y or older in the Korean National Health and Nutrition Examination Survey 2010. We performed multivariable logistic regression analyses using the quartile cut-points of total IgE levels. RESULTS: The odds ratios (ORs) for nuclear and any cataract with ≥267 kU/L of serum IgE levels were 1.75 [95% confidence intervals (CI), 1.04-2.96] and 2.00 (95%CI, 1.22-3.27), respectively, comparing to 35-87 kU/L. Interestingly, participants with ≤35 kU/L of IgE levels (OR, 1.67; 95%CI, 1.02-2.72) also had higher risk for any cataract than those with 35-87 kU/L. The risk for any cataract (OR, 1.48; 95%CI, 1.03-2.13) was higher in participants with high total IgE levels (>150 kU/L), comparing to normal participants. CONCLUSION: Our findings indicate a J-shaped relationship between serum IgE levels and age-related cataract

The Association between Disturbed Eating Behavior and Socioeconomic Status: The Online Korean Adolescent Panel Survey (OnKAPS)

Background: A limited amount of research, primarily conducted in Western countries, has suggested that higher socioeconomic status (SES) is associated with higher risk of eating disorders (EDs). However, little is known about this association in Asian countries. We examined the association of SES with disturbed eating behavior (DEB) and related factors in Korean adolescents. Subjects A nationwide online panel survey was conducted in a sample of adolescents (n = 6,943, 49.9% girls). DEB was measured with the 26-item Eating Attitudes Test (EAT-26). Participants who scored ≥20 on the EAT-26 were considered to have DEB. Participants’ SES was determined based on self-reported household economic status. Results: The prevalence of DEB was 12.7%: 10.5% among boys and 14.8% among girls. Both boys and girls with DEB were more likely to perceive themselves as obese, experience higher levels of stress, and have lower academic achievement. The risk for DEB was significantly higher in boys of higher SES than in those of middle SES (OR = 1.45, 95%CI = 1.05–1.99 for high SES; OR = 5.16, 95%CI: 3.50–7.61 for highest SES). Among girls, higher risk of DEB was associated with the highest and lowest SES (OR = 1.52, 95%CI: 1.13–2.06 for lowest SES; OR = 2.22, 95%CI: 1.34–3.68 for highest SES). Conclusions: Despite the lower prevalence of obesity in Korea compared with Western countries, the prevalence of DEB in Korean adolescents was high, especially among girls. Moreover, the association between SES and DEB followed a U-shaped curve for girls and a J-shaped curve for boys

Multifactorial Effects of Ambient Temperature, Precipitation, Farm Management, and Environmental Factors Determine the Level of Generic Escherichia coli Contamination on Preharvested Spinach

A repeated cross-sectional study was conducted to identify farm management, environment, weather, and landscape factors that predict the count of generic Escherichia coli on spinach at the preharvest level. E. coli was enumerated for 955 spinach samples collected on 12 farms in Texas and Colorado between 2010 and 2012. Farm management and environmental characteristics were surveyed using a questionnaire. Weather and landscape data were obtained from National Resources Information databases. A two-part mixed-effect negative binomial hurdle model, consisting of a logistic and zero-truncated negative binomial part with farm and date as random effects, was used to identify factors affecting E. coli counts on spinach. Results indicated that the odds of a contamination event (non-zero versus zero counts) vary by state (odds ratio [OR] = 108.1). Odds of contamination decreased with implementation of hygiene practices (OR = 0.06) and increased with an increasing average precipitation amount (mm) in the past 29 days (OR = 3.5) and the application of manure (OR = 52.2). On contaminated spinach, E. coli counts increased with the average precipitation amount over the past 29 days. The relationship between E. coli count and the average maximum daily temperature over the 9 days prior to sampling followed a quadratic function with the highest bacterial count at around 24°C. These findings indicate that the odds of a contamination event in spinach are determined by farm management, environment, and weather factors. However, once the contamination event has occurred, the count of E. coli on spinach is determined by weather only

Farm Management, Environment, and Weather Factors Jointly Affect the Probability of Spinach Contamination by Generic Escherichia coli at the Preharvest Stage

The National Resources Information (NRI) databases provide underutilized information on the local farm conditions that may predict microbial contamination of leafy greens at preharvest. Our objective was to identify NRI weather and landscape factors affecting spinach contamination with generic Escherichia coli individually and jointly with farm management and environmental factors. For each of the 955 georeferenced spinach samples (including 63 positive samples) collected between 2010 and 2012 on 12 farms in Colorado and Texas, we extracted variables describing the local weather (ambient temperature, precipitation, and wind speed) and landscape (soil characteristics and proximity to roads and water bodies) from NRI databases. Variables describing farm management and environment were obtained from a survey of the enrolled farms. The variables were evaluated using a mixed-effect logistic regression model with random effects for farm and date. The model identified precipitation as a single NRI predictor of spinach contamination with generic E. coli, indicating that the contamination probability increases with an increasing mean amount of rain (mm) in the past 29 days (odds ratio [OR] = 3.5). The model also identified the farm's hygiene practices as a protective factor (OR = 0.06) and manure application (OR = 52.2) and state (OR = 108.1) as risk factors. In cross-validation, the model showed a solid predictive performance, with an area under the receiver operating characteristic (ROC) curve of 81%. Overall, the findings highlighted the utility of NRI precipitation data in predicting contamination and demonstrated that farm management, environment, and weather factors should be considered jointly in development of good agricultural practices and measures to reduce produce contamination

Deep Learning Approach to Optical Camera Communication Receiver Design

This paper investigates a deep learning (DL) framework for designing optical camera communication (OCC) systems where a receiver is realized with optical cameras capturing images of transmit LEDs. The optimum decoding strategy is formulated as the maximum a posterior (MAP) estimation with a given received image. Due to the absence of analytical OCC channel models, it is challenging to derive the closed-form MAP detector. To address this issue, we employ a convolutional neural network (CNN) model at the OCC receiver. The proposed CNN approximates the optimum MAP detector that determines the most probable data symbols by observing an image of the OCC transmitter implemented by dot LED matrices. The supervised learning philosophy is adopted to train the CNN with labeled images. We collect training samples in real-measurement scenarios including heterogeneous background noise and distance setups. As a consequent, the proposed CNN-based OCC receiver can be applied to arbitrary OCC scenarios without any channel state information. The effectiveness of our model is examined in the real-world OCC setup with Raspberry Pi cameras. The experimental results demonstrate that the proposed CNN architecture performs better than other DL models

Association between metabolic syndrome and age-related cataract

<b>AIM:</b> To determine the effect of metabolic syndrome on age-related cataract formation.<b>METHODS:</b> We analyzed data for 2852 subjects <b>[41.8% men and 58.2% women; mean (±SD) age, 52.9±13.9y], taken from the Korea National Health and Nutrition Examination Survey 2008. Metabolic syndrome was diagnosed by criteria proposed by the Joint Interim Societies. Cataract was diagnosed by using the Lens Opacities Classification System III. The association between metabolic syndrome and cataract was determined using age-adjusted and multivariable logistic regression analyses. </b><b>RESULTS:In multivariable analyses, men with metabolic syndrome had a 64% increased risk of nuclear cataract [odds ratio (OR), 1.64; 95% confidence interval (CI), 1.12-2.39]</b>. Women with metabolic syndrome had a 56% increased risk of cortical cataract (OR, 1.56; 95% CI, 1.06-2.30). Men and women with metabolic syndrome had a 46% (OR, 1.46; 95% CI, 1.01-2.12) and 49% (OR, 1.49; 95% CI, 1.07-2.08) increased risk of any cataract, respectively. The prevalence of nuclear and any cataract significantly increased with an increasing number of disturbed metabolic components in men, and prevalence of all types of cataracts increased in women. Men using hypoglycemic medication had an increased risk of nuclear (OR, 2.62; 95% CI, 1.41-4.86) and any (OR, 2.27; 95% CI, 1.14-4.51) cataract, and women using antidyslipidemia medication had an increased risk of cortical (OR, 2.18; 95% CI, 1.12-4.24) and any (OR, 2.21; 95% CI, 1.14-4.26) cataract.<b>CONCLUSION:</b> Metabolic syndrome and its components, such as abdominal obesity, high blood pressure, and impaired fasting glucose, are associated with age-related cataract formation in the Korean population

Model Predictive Current Control Method with Improved Performances for Three-Phase Voltage Source Inverters

In this paper, the model predictive current control (MPCC) method using two vectors has been proposed to control output currents of three-phase voltage source inverters (VSIs) with small current errors and current ripples. Also, the proposed method can reduce switching losses by applying the vector pre-selection technique to the MPCC for the VSI. The VSI generates seven voltage vectors to control the output currents, but the proposed method uses four available voltage vectors with one switch, which are classified by the vector pre-selection method clamping one leg and conducting the largest output current among the three legs to reduce the switching losses. In the proposed method, selecting two future voltage vectors among the four voltage vectors and dividing them in a future sampling period are determined by an optimization process. The proposed method results in the lower total loss, better total harmonic distortion (THD), and smaller current errors than the conventional method with half the sampling period of the proposed method due to the optimal process. Simulation and experimental results of the three-phase VSIs are presented in order to verify the effectiveness of the proposed method

DC-Link Electrolytic Capacitors Monitoring Techniques Based on Advanced Learning Intelligence Techniques for Three-Phase Inverters

The reliability of the electronic converter is a vital concern in an industrialized area. Capacitors are critical in electronic converters and are more likely to fail than other electronic gears. Due to aging, the capacitor progressively loses its original quality and capacitance, and the equivalent series resistance escalates. Hence, condition monitoring is a fundamental procedure for evaluating capacitor health that affords prognostic repairs to guarantee stability in power networks. The ESR and capacitance of the capacitor are commonly employed to estimate the condition grade. This study proposes an estimation scheme that utilizes the source current to assess the health condition of an aluminum capacitor. Several advanced intelligence techniques are adopted to estimate the parameters of an AEC in a three-phase inverter system. First, different signals used as inputs, such as input power, capacitor current, voltage, and power, output current, voltage, and power, are analyzed using fast Fourier transform and discrete wavelet transform analysis. Then, various indexes of the analyzed signals, such as RMS, average, median, and variance, are used as the inputs in learning models to monitor the AEC&rsquo;s parameters. In addition, various input signals are combined to obtain the best combinations for capacitor monitoring. The estimated results prove that utilizing the source current combined with selected indexes improves the monitoring accuracy of the AEC&rsquo;s health status