Global DNA methylation: Nutritional Correlates and Child Growth.

Background: Global DNA methylation is a modifiable epigenetic mechanism associated with adult-onset cardiometabolic diseases. Yet, little is known regarding its relation with obesity and there is limited research in children. This work aimed to identify nutritional and sociodemographic predictors of global DNA methylation, and to investigate its association with development of adiposity in school-age children. Methods: Data from the Multi-Ethnic Study of Atherosclerosis Stress Study were used to examine the associations of methyl-donor/methylation cofactor (folate, vitamin B12, vitamin B6, zinc, methionine) micronutrient intake and plasma homocysteine with two measures of global DNA methylation (LINE-1 and Alu). Data from a representative sample of low- and middle-income children in the Bogotá School Children Cohort were used to identify nutritional (biomarkers of folate, vitamin B12, zinc, vitamin A, iron) and sociodemographic correlates of LINE-1 methylation, and to examine its relation with change in adiposity indicators (body mass index (BMI)-for-age, waist circumference-for-age, and subscapular-to-triceps skinfold thickness ratio-for-age) and linear growth (height-for-age) during 2.5 years of follow-up. Results: Among adults, BMI was positively associated with LINE-1 methylation, and height was positively correlated with Alu methylation; micronutrient intake was not associated with global DNA methylation. In school-age children, higher plasma vitamin A and C-reactive protein (CRP) levels, and low maternal BMI were each related to lower LINE-1 methylation, whereas high socioeconomic status was associated with higher DNA methylation. Among boys, there were inverse, non-linear associations of global DNA methylation at baseline with annual change in BMI-for-age and skinfold thickness ratio-for-age. Boys in the lowest quartile of DNA methylation experienced greater gains in both indicators than those in the upper three quartiles during follow-up. Additionally, LINE-1 methylation was inversely related to change in waist circumference in a linear manner. LINE-1 methylation was not related to development of adiposity in girls, and there were no associations between DNA methylation and linear growth in either sex. Conclusions: These findings support the role of environmental and nutritional factors in DNA methylation patterns, and contribute to knowledge of biological mechanisms involved in weight gain. Whether modification of DNA methylation profiles through dietary interventions influences body composition deserves further investigation.PHDEpidemiological ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97956/1/perngwei_1.pd

Promoters of and barriers to cervical cancer screening in a rural setting in Tanzania

ObjectiveTo investigate promoters and barriers for cervical cancer screening in rural Tanzania.MethodsWe interviewed 300 women of reproductive age living in Kiwangwa village, Tanzania. The odds of attending a free, 2‐day screening service were compared with sociodemographic variables, lifestyle factors, and knowledge and attitudes surrounding cervical cancer using multivariable logistic regression.ResultsCompared with women who did not attend the screening service (n = 195), women who attended (n = 105) were older (OR 4.29; 95% CI, 1.61–11.48, age 40–49 years versus 20–29 years), listened regularly to the radio (OR 24.76; 95% CI, 11.49–53.33, listened to radio 1–3 times per week versus not at all), had a poorer quality of life (OR 4.91; CI, 1.96–12.32, lowest versus highest score), had faced cost barriers to obtaining health care in the preceding year (OR 2.24; 95% CI, 1.11–4.53, yes versus no), and held a more positive attitude toward cervical cancer screening (OR 4.64; 95% CI, 1.39–15.55, least versus most averse).ConclusionEfforts aimed at improving screening rates in rural Tanzania need to address both structural and individual‐level barriers, including knowledge and awareness of cervical cancer prevention, cost barriers to care, and access to health information.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135344/1/ijgo221.pd

In utero exposure to gestational diabetes mellitus and cardiovascular risk factors in youth: A longitudinal analysis in the EPOCH cohort

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154893/1/ijpo12611.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154893/2/ijpo12611_am.pd

Constructing a evaluating model for Smartphone Green Design by VAHPand QFD

Industry and high-tech industries flourish in the current era. In addition to enhancing quality of life, they have caused plentiful harm to human beings and the environment. Some man-made pollution has destroyed the ecological balance. Environmental protection has thus become everybody’s social responsibility. Many enterprises are beginning to actively concern themselves with sustainable business models and environmental protection issues. After continuous technological development in recent years, many new products have emerged to make human life more convenient. The smartphone is among the most popular of these products. The main aims of this study are to (1) analyze green smartphone requirements of consumers and designers; and (2) construct an assessment framework and checklist for smartphone green design. This study adopts voting analytic hierarchy process (VAHP) and quality function deployment (QFD) and constructs green design criteria through expert interviews

Impact of maternal overweight and obesity on milk composition and infant growth

Overweight and obesity (OW/OB) impact half of the pregnancies in the United States and can have negative consequences for offspring health. Studies are limited on human milk alterations in the context of maternal obesity. Alterations in milk are hypothesized to impact offspring development during the critical period of lactation. We aimed to evaluate the relationships between mothers with OW/OB (body mass index [BMI] ≥25 kg/m2), infant growth, and selected milk nutrients. We recruited mother–infant dyads with pre‐pregnancy OW/OB and normal weight status. The primary study included 52 dyads with infant growth measures through 6 months. Thirty‐two dyads provided milk at 2 weeks, which was analysed for macronutrients, long‐chain fatty acids, and insulin. We used multivariable linear regression to examine the association of maternal weight status with infant growth, maternal weight status with milk components, and milk components with infant growth. Mothers with OW/OB had infants with higher weight‐for‐length (WFL) and BMI Z‐scores at birth. Mothers with OW/OB had higher milk insulin and dihomo‐gamma‐linolenic, adrenic, and palmitic acids and reduced conjugated linoleic and oleic acids. N6 long‐chain polyunsaturated fatty acid (LC‐PUFA)‐driven factor 1 was associated with higher WFL, lower length‐for‐age (LFA), and lower head circumference‐for‐age Z‐scores change from 2 weeks to 2 months in human milk‐fed infants, whereas N6 LC‐PUFA‐driven factor 5 was associated with lower LFA Z‐score change. Human milk composition is associated with maternal pre‐pregnancy weight status and composition may be a contributing factor to early infant growth trajectory.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155909/1/mcn12979.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155909/2/mcn12979-sup-0003-Figure_S2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155909/3/mcn12979_am.pd

Inflammation and weight gain in reproductive-aged women

To investigate whether mid-pregnancy inflammation predicts rate of subsequent gestational weight gain (GWG), and whether inflammation at 3 years postpartum is associated with weight and waist circumference (WC) gain during a median of 4.4 years follow-up

Giant lipoma arising from deep lobe of the parotid gland

BACKGROUND: Lipomas are common benign soft tissue neoplasms but they are found very rarely in the deep lobe of parotid gland. Surgical intervention in these tumors is challenging because of the proximity of the facial nerve, and thus knowledge of the anatomy and meticulous surgical technique are essential. CASE PRESENTATION: A 71-year-old female presented with a large asymptomatic mass, which had occupied the left facial area for over the past fifteen years, and she requested surgical excision for a cosmetically better facial appearance. The computed tomography (CT) scan showed a well-defined giant lipoma arising from the left deep parotid gland. The lipoma was successfully enucleated after full exposure and mobilization of the overlying facial nerve branches. The surgical specimen measured 9 × 6 cm in size, and histopathology revealed fibrolipoma. The patient experienced an uneventful recovery, with a satisfying facial contour and intact facial nerve function. CONCLUSION: Giant lipomas involving the deep parotid lobe are extremely rare. The high-resolution CT scan provides an accurate and cost-effective preoperative investigative method. Surgical management of deep lobe lipoma should be performed by experienced surgeons due to the need for meticulous dissection of the facial nerve branches. Superficial parotidectomy before deep lobe lipoma removal may be unnecessary in selected cases because preservation of the superficial lobe may contribute to a better aesthetic and functional result

Unsupervised Adaptation from Repeated Traversals for Autonomous Driving

For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts. One potential solution is to leverage unlabeled data (e.g., unlabeled LiDAR point clouds) collected from the end-users' environments (i.e. target domain) to adapt the system to the difference between training and testing environments. While extensive research has been done on such an unsupervised domain adaptation problem, one fundamental problem lingers: there is no reliable signal in the target domain to supervise the adaptation process. To overcome this issue we observe that it is easy to collect unsupervised data from multiple traversals of repeated routes. While different from conventional unsupervised domain adaptation, this assumption is extremely realistic since many drivers share the same roads. We show that this simple additional assumption is sufficient to obtain a potent signal that allows us to perform iterative self-training of 3D object detectors on the target domain. Concretely, we generate pseudo-labels with the out-of-domain detector but reduce false positives by removing detections of supposedly mobile objects that are persistent across traversals. Further, we reduce false negatives by encouraging predictions in regions that are not persistent. We experiment with our approach on two large-scale driving datasets and show remarkable improvement in 3D object detection of cars, pedestrians, and cyclists, bringing us a step closer to generalizable autonomous driving.Comment: Accepted by NeurIPS 2022. Code is available at https://github.com/YurongYou/Rote-D

Pre-Training LiDAR-Based 3D Object Detectors Through Colorization

Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we introduce an innovative pre-training approach, Grounded Point Colorization (GPC), to bridge the gap between data and labels by teaching the model to colorize LiDAR point clouds, equipping it with valuable semantic cues. To tackle challenges arising from color variations and selection bias, we incorporate color as "context" by providing ground-truth colors as hints during colorization. Experimental results on the KITTI and Waymo datasets demonstrate GPC's remarkable effectiveness. Even with limited labeled data, GPC significantly improves fine-tuning performance; notably, on just 20% of the KITTI dataset, GPC outperforms training from scratch with the entire dataset. In sum, we introduce a fresh perspective on pre-training for 3D object detection, aligning the objective with the model's intended role and ultimately advancing the accuracy and efficiency of 3D object detection for autonomous vehicles