In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

Context: The aim of this study was to evaluate the effects of maternal obesity on pancreas structure and carbohydrate metabolism in early adult life, focusing on the F1 and F2 generations after F0 maternal pregestational, gestation, and lactation high-fat diet (HF).', 'C57 BL/6 female mice (F0) were fed standard chow (SC) or an HF diet for 8 wk before mating and during the gestation and lactation periods to provide the F1 generation (F1-SC and F1-HF). At 3 mo old, F1 females were mated to produce the F2 generation (F2-SC and F2-HF). The male offspring from all groups were evaluated at 3 mo old.', 'F0-HF and F1-HF dams were overweight before gestation and had a higher body mass gain and energy intake during gestation, although only F0-HF dams presented pregestational hyperglycemia. The F1-HF offspring had higher body mass, energy intake, fasting glucose levels, and were glucose intolerant compared with F1-SC offspring. These parameters were not significantly altered in F2-HF offspring. Both F1-HF and F2-HF offspring showed hyperinsulinemia, hyperleptinemia, decreased adiponectin levels, increased pancreatic mass, and islet volume density with elevated - and -cell mass, hypertrophied islet characterized by an altered distribution of - and -cells and weak pancreatic-duodenal homeobox (Pdx)1 immunoreactivity.\Question: Does pregestational maternal obesity impair endocrine pancreas in male F1 and F2 progeny?
Maternal HF diet consumed during the preconception period and throughout the gestation and lactation periods in mice promotes metabolism and pancreatic programming in F1 and F2 male offspring, implying intergenerational effects.

Context: Animal data show that low protein intake in pregnancy programs higher offspring blood pressure, but similar data in humans are limited. We examined the associations of first and second trimester maternal protein intake with offspring blood pressure (BP) at the age of six months.', 'In a prospective US cohort study, called Project Viva, pregnant women completed validated semi-quantitative food-frequency questionnaires (FFQ) to measure gestational protein intake. Among 947 mother-offspring pairs with first trimester dietary data and 910 pairs with second trimester data, we measured systolic blood pressure (SBP) up to five times with an automated device in the offspring at the age of six months. Controlling for blood pressure measurement conditions, maternal and infant characteristics, we examined the effect of energy-adjusted maternal protein intake on infant SBP using multivariable mixed effects models.', 'Mean daily second trimester maternal protein intake was 17.6% of energy (mean 2111 kcal/day). First trimester nutrient intakes were similar. Mean SBP at age 6 months was 90.0 mm Hg (SD 12.9). Consistent with prior reports, adjusted SBP was 1.94 mm Hg lower [95% confidence interval (CI) -3.45 to -0.42] for each kg increase in birth weight. However, we did not find an association between maternal protein intake and infant SBP. After adjusting for covariates, the effect estimates were 0.14 mm Hg (95% CI 20.12 to 20.40) for a 1% increase in energy from protein during the second trimester, and 20.01 mm Hg (95% CI 20.24 to -0.23) for a 1% increase in energy from protein in the first trimester.\Question: Is maternal protein intake associated with infant blood pressure?
Variation in maternal total protein intake during pregnancy does not appear to program offspring blood pressure.

Context: T2 Lesion Volume (T2LV) has been an important biomarker for multiple sclerosis (MS). Current methods available to quantify lesions from MR images generally require manual adjustments or multiple images with different contrasts. Further, implementations are often not easily or openly accessible.', 'We created a fully unsupervised, single T2 FLAIR image T2LV quantification package based on the popular open-source imaging toolkit FSL.', 'By scripting various processing tools in FSL, we developed an image processing pipeline that distinguishes normal brain tissue from CSF and lesions. We validated our method by hierarchical multiple regression (HMR) with a preliminary study to see if our T2LVs correlate with clinical disability measures in MS when controlled for other variables.', 'Pearson correlations between T2LV and Expanded Disability Status Scale (EDSS: r = 0.344, P = 0.013), Six-Minute Walk (6MW: r = -0.513, P = 0.000), Timed 25-Foot Walk (T25FW: r = -0.438, P = .000), and Symbol Digit Modalities Test (SDMT: r = -0.499, P = 0.000) were all significant. Partial correlations controlling for age were significant between T2LV and 6MW (r = -0.433, P = 0.002), T25FW (r = -0.392, P = 0.004), and SDMT (r = -0.450, P = 0.001). In HMR, T2LV explained significant additional variance in 6MW (R(2) change = 0.082, P = 0.020), after controlling for confounding variables such as age, white matter volume (WMV), and gray matter volume (GMV).\Question: Does fully automated open-source lesion mapping of T2-FLAIR images with FSL correlate with clinical disability in MS?
Our T2LV quantification software produces T2LVs from a single FLAIR image that correlate with physical disability in MS and is freely available as open-source software.