A digital computer simulation of a rural two-lane highway

The traffic flow model developed in this study is a digital computer simulation utilizing the technique of periodic scanning to move the vehicles through a series of unit blocks. The model simulates traffic flow on a rural two-lane highway by assuming a straight and level road and incorporating sight distance restrictions and no-passing zones to simulate the effect of limited sight distance. By utilizing various passing rules to initiate the passing maneuver, three general topics were investigated. This study investigated the use of 1000 ADT as a criterion for yellow line striping no-passing by using the computer simulation to determine at what traffic volume a significant number of potential passing conflicts begin to occur. The pass only when safe to pass passing rule was used to determine the relationship between the passing maneuver and traffic volume when the effect of human error was removed. By using various values for gap acceptance in the computer model, it was possible to determine if gap acceptance is a significant factor in the overall flow characteristics of a two-lane highway. The results of the research indicated that: (l) 1000 ADT is a reasonable criterion for striping no-passing zones. (2) if vehicles attempt to pass only when it is safe to pass, the maximum number of passes per mile per hour occurs when traffic volumes reach the region of 800 vehicles per hour, and (3) gap acceptance is a significant factor in the overall flow characteristics of a two-lane highway --Abstract, page ii

Comparative mortality of hemodialysis patients at for-profit and not-for-profit dialysis facilities in the United States, 1998 to 2003: A retrospective analysis

<p>Abstract</p> <p>Background</p> <p>Concern lingers that dialysis therapy at for-profit (versus not-for-profit) hemodialysis facilities in the United States may be associated with higher mortality, even though 4 of every 5 contemporary dialysis patients receive therapy in such a setting.</p> <p>Methods</p> <p>Our primary objective was to compare the mortality hazards of patients initiating hemodialysis at for-profit and not-for-profit centers in the United States between 1998 and 2003. For-profit status of dialysis facilities was determined after subjects received 6 months of dialysis therapy, and mean follow-up was 1.7 years.</p> <p>Results</p> <p>Of the study population (<it>N </it>= 205,076), 79.9% were dialyzed in for-profit facilities after 6 months of dialysis therapy. Dialysis at for-profit facilities was associated with higher urea reduction ratios, hemoglobin levels (including levels above 12 and 13 g/dL [120 and 130 g/L]), epoetin doses, and use of intravenous iron, and less use of blood transfusions and lower proportions of patients on the transplant waiting-list (<it>P </it>< 0.05). Patients dialyzed at for-profit and at not-for-profit facilities had similar mortality risks (adjusted hazards ratio 1.02, 95% CI 0.99–1.06, <it>P </it>= 0.143).</p> <p>Conclusion</p> <p>While hemodialysis treatment at for-profit and not-for-profit dialysis facilities is associated with different patterns of clinical benchmark achievement, mortality rates are similar.</p

Validity of Resting Energy Expenditure Predictive Equations before and after an Energy-Restricted Diet Intervention in Obese Women

Background We investigated the validity of REE predictive equations before and after 12-week energy-restricted diet intervention in Spanish obese (30 kg/m2>BMI<40 kg/m2) women. Methods We measured REE (indirect calorimetry), body weight, height, and fat mass (FM) and fat free mass (FFM, dual X-ray absorptiometry) in 86 obese Caucasian premenopausal women aged 36.7±7.2 y, before and after (n = 78 women) the intervention. We investigated the accuracy of ten REE predictive equations using weight, height, age, FFM and FM. Results At baseline, the most accurate equation was the Mifflin et al. (Am J Clin Nutr 1990; 51: 241–247) when using weight (bias:−0.2%, P = 0.982), 74% of accurate predictions. This level of accuracy was not reached after the diet intervention (24% accurate prediction). After the intervention, the lowest bias was found with the Owen et al. (Am J Clin Nutr 1986; 44: 1–19) equation when using weight (bias:−1.7%, P = 0.044), 81% accurate prediction, yet it provided 53% accurate predictions at baseline. Conclusions There is a wide variation in the accuracy of REE predictive equations before and after weight loss in non-morbid obese women. The results acquire especial relevance in the context of the challenging weight regain phenomenon for the overweight/obese population.The present study was supported by the University of the Basque Country (UPV 05/80), Social Foundation of the Caja Vital- Kutxa and by the Department of Health of the Government of the Basque Country (2008/111062), and by the Spanish Ministry of Science and Innovation (RYC-2010-05957)

To treat or not to treat: comparison of different criteria used to determine whether weight loss is to be recommended

Background: Excess body fat is a major risk factor for disease primarily due to its endocrine activity. In recent years several criteria have been introduced to evaluate this factor. Nevertheless, treatment need is currently assessed only on the basis of an individual's Body Mass Index (BMI), calculated as body weight (in kg) divided by height in m2. The aim of our study was to determine whether application of the BMI, compared to adiposity-based criteria, results in underestimation of the number of subjects needing lifestyle intervention. Methods: We compared treatment need based on BMI classification with four adiposity-based criteria: percentage body fat (%BF), considered both alone and in relation to metabolic syndrome risk (MS), waist circumference (WC), as an index of abdominal fat, and Body Fat Mass Index (BFMI, calculated as fat mass in kg divided by height in m2) in 63 volunteers (23 men and 40 women, aged 20 – 65 years). Results: According to the classification based on BMI, 6.3% of subjects were underweight, 52.4% were normal weight, 30.2% were overweight, and 11.1% were obese. Agreement between the BMI categories and the other classification criteria categories varied; the most notable discrepancy emerged in the underweight and overweight categories. BMI compared to almost all of the other adiposity-based criteria, identified a lower percentage of subjects for whom treatment would be recommended. In particular, the proportion of subjects for whom clinicians would strongly recommend weight loss on the basis of their BMI (11.1%) was significantly lower than those identified according to WC (25.4%, p = 0.004), %BF (28.6%, p = 0.003), and MS (33.9%, p = 0.002). Conclusion: The use of the BMI alone, as opposed to an assessment based on body composition, to identify individuals needing lifestyle intervention may lead to unfortunate misclassifications. Population-specific data on the relationships between body composition, morbidity, and mortality are needed to improve the diagnosis and treatment of at-risk individual

Net contribution and predictive ability of the CUN-BAE body fatness index in relation to cardiometabolic conditions

BACKGROUND: The CUN-BAE (Clínica Universidad de Navarra-Body adiposity estimator) index is an anthropometric index based on age, sex and body mass index (BMI) for a refined prediction of body fatness in adults. CUN-BAE may help detect metabolically unhealthy individuals with otherwise normal weight according to BMI or waist circumference (WC). The aim of this study was to evaluate whether CUN-BAE, independent of its components (BMI, age and sex), was associated with cardiometabolic conditions including arterial hypertension, diabetes mellitus and metabolic syndrome (MetS). METHODS: The ENRICA study was based on a cross-sectional sample of non-institutionalized men and women representative of the adult Spanish population. Body weight, height, and WC were measured in all participants. The residual of CUN-BAE (rCUN-BAE), i.e. the part of the index not explained by its components, was calculated. The associations of CUN-BAE, rCUN-BAE, BMI and WC with hypertension, diabetes and MetS were analysed by multivariate logistic regression, and the Akaike information criterion (AIC) was calculated. RESULTS: The sample included 12,122 individuals. rCUN-BAE was associated with hypertension (OR 1.14, 95% CI 1.07-1.21) and MetS (OR 1.48, 1.37-1.60), but not with diabetes (OR 1.05, 0.94-1.16). In subjects with a BMI?<?25 kg/m2, CUN-BAE was significantly associated with all three outcome variables. CUN-BAE was more strongly associated with the cardiometabolic conditions than BMI and WC and fit similar AICs. CONCLUSIONS: The CUN-BAE index for body fatness was positively associated with hypertension, diabetes and MetS in adults independent of BMI or WC. CUN-BAE may help to identify individuals with cardiometabolic conditions beyond BMI, but this needs to be confirmed in prospective settings.Funding: The ENRICA study was funded and financed by Sanofi-Aventis. Specific funding for this analysis came from the governmental Spain FIS PI12/1166 and PI11/01379 projects and from the “UAM Chair in Epidemiology and Control of Cardiovascular Risk”

Moderate energy restriction with high protein diet results in healthier outcome in women

BACKGROUND: The present study compares two different weight reduction regimens both with a moderately high protein intake on body composition, serum hormone concentration and strength performance in non-competitive female athletes. METHODS: Fifteen normal weighted women involved in recreational resistance training and aerobic training were recruited for the study (age 28.5 ± 6.3 yr, height 167.0 ± 7.0 cm, body mass 66.3 ± 4.2 kg, body mass index 23.8 ± 1.8, mean ± SD). They were randomized into two groups. The 1 KG group (n = 8; energy deficit 1100 kcal/day) was supervised to reduce body weight by 1 kg per week and the 0.5 KG group (n = 7; energy deficit 550 kcal/day) by 0.5 kg per week, respectively. In both groups protein intake was kept at least 1.4 g/kg body weight/day and the weight reduction lasted four weeks. At the beginning of the study the energy need was calculated using food and training diaries. The same measurements were done before and after the 4-week weight reduction period including total body composition (DXA), serum hormone concentrations, jumping ability and strength measurements RESULTS: During the 4-week weight reduction period there were no changes in lean body mass and bone mass, but total body mass, fat mass and fat percentage decreased significantly in both groups. The changes were greater in the 1 KG group than in the 0.5 KG group in total body mass (p < 0.001), fat mass (p < 0.001) and fat percentage (p < 0.01). Serum testosterone concentration decreased significantly from 1.8 ± 1.0 to 1.4 ± 0.9 nmol/l (p < 0.01) in 1 KG and the change was greater in 1 KG (30%, p < 0.001) than in 0.5 KG (3%). On the other hand, SHBG increased significantly in 1 KG from 63.4 ± 17.7 to 82.4 ± 33.0 nmol/l (p < 0.05) during the weight reducing regimen. After the 4-week period there were no changes in strength performance in 0.5 KG group, however in 1 KG maximal strength in bench press decreased (p < 0.05) while endurance strength in squat and counter movement jump improved (p < 0.05) CONCLUSION: It is concluded that a weight reduction by 0.5 kg per week with ~1.4 g protein/kg body weight/day can be recommended to normal weighted, physically active women instead of a larger (e.g. 1 kg per week) weight reduction because the latter may lead to a catabolic state. Vertical jumping performance is improved when fat mass and body weight decrease. Thus a moderate weight reduction prior to a major event could be considered beneficial for normal built athletes in jumping events.peerReviewe

Molecular Determinants and Genetic Modifiers of Aggregation and Toxicity for the ALS Disease Protein FUS/TLS

A combination of yeast genetics and protein biochemistry define how the fused in sarcoma (FUS) protein might contribute to Lou Gehrig's disease