Maternal IQ Predicts Child's Birth Weight

poster abstractBackground: Prior studies correlated birth weight with child IQ. Maternal IQ correlates with IQ in her offspring. Birth weight predicted IQ in monozygotic twins dicordant for birth weight. IUGR alters global DNA methylation. IQ in mother may be a biological marker for her child's rate of intrauterine growth (birth weight). Objective: Does maternal IQ predict her child's intrauterine growth rate (birth weight)? Design/Methods: Births from 1970-2004 using NLSY '79 database were studied Primary variables were children's IQ score from most recent Peabody Picture Vocabulary Revised Form L test and birth weight in grams. Maternal IQ was estimated from AirForce Qualifying test (AFQT)and categorized as 75, 50-74, 25-49 and <25%ile resp. Race, economic status, singleton, gestation, use of tobacco, alcohol and other drugs were used as covariates. Multivariate models were used to assess associations of Children's IQ and birth weight with maternal IQ levels controlling for other covariates. Results: 9,125 children were analyzed. 98.3% singleton, 12.3% preterm, and 51.2% male. Means Std's of birth weight and IQ score were 3,307 597 grams and 38 30.4 respectively.Of the total 4,121 mothers, 25.7% were blacks, 18.3% were Hispanics and 54.0% were non Hispanic non blacks(nHnB). The mean std of the AFQT was 36.9 28.1. Proportions of IQs were 13.6%, 17.2%, 27.2% and 42% from low to high IQs respectively among mothers. Multivariate models showed children's IQ scores were related to their mother's IQ ,birth weight, race/ethnicity, and economic status. In particular, the mean children's IQ scores were 28.1, 37.1, 46.8, and 55 at mother's IQ levels from low to high respectively (p-values<0.001). Children's IQs was increased by 0.14 0.06 (slope) for every 100 gram increase in birth weight (p=0.013). Children's birth weights were positively associated with their mothers' IQ. Means birth weight increased from 3,334 grams to 3,465 grams as mothers' IQ rose from low to high (p<0.001). When sub-populations stratified by race/ethnicity were analyzed, positive relationships between childs IQ and mother's IQ were found in all Hispanic, black and nHnB groups (p's<0.001); while the positive relationship between birth weight and mother's IQ levels was found significant only in the nHnB (white) group (p<0.001). The findings held even after preterm and non singleton births were excluded from analysis. Conclusions: Child's IQ correlates with birth weight and maternal IQ. Maternal IQ may also predict birth weight of offspring

FETAL AND NEONATAL FACTORS INFLUENCING FREE CARNITINE

poster abstractBackground: Free Carnitine (FC) is now measured routinely in new-borns in Indiana. Studies with small numbers have suggested that FC may be dependent on fetal and neonatal factors. Objective: Our objective was to compare FC levels with various fetal and neonatal factors. Our goal was to establish normative data by gestation in a very large cohort (Indiana State) and to use these carnitine values to develop hypotheses about FC in fetal life and disease. Design/Methods: Indiana State Health Department FC values (tandem mass spec) and demographic variables were obtained for the years 2005-2010. Gender, race, birth weight, gestation, NICU admission, age at collec-tion information was also evaluated. Multivariate fixed effect models were used to compare carnitine values with independent variables. Results: The number of newborns analyzed was 459,932. FC levels were lowest in babies with gestational age 37-40 weeks and higher in both preterm and post-term babies (Table 1). Table 1. Free Carnitine vs. Gestation Gestation FC mean SE mean Gestation FC mean SE mean 23 40.88 1.00 33 39.81 0.28 24 39.86 0.73 34 38.28 0.22 25 42.61 0.69 35 37.14 0.19 26 42.77 0.64 36 36.37 0.17 27 42.65 0.63 37 35.76 0.20 28 42.47 0.57 38 35.11 0.22 29 43.51 0.53 39 35.04 0.19 30 40.4 0.46 40 35.36 0.07 31 41.96 0.41 41 38.58 0.48 32 40.65 0.34 42 40.96 1.52 FC levels were lowest in babies with birth weights between 3150-4050g (34.390.07) and higher in groups with both lower (4500g;35.660.1**). FC levels were lowest when collected be-tween 24-48 hours (34.290.05) and higher either before (36.930.1**) or after that time (2-3,3-4,4-5,>5days;34.960.06**,36.210.11**,37.320.15**,36.80.14**). Female, white, non singleton and non NICU babies had significantly lower FC levels (Table 2). Table 2. Free Carnitine vs. Demographics Category FC mean SE mean Male 39.40 0.08 Female 36.60 0.08** White 37.16 0.06 Black 37.34 0.08* Asian 39.12 0.18** Other 38.39 0.1** Singleton 38.30 0.06 Multiple 37.70 0.11** NICU 40.46 0.09 Non-NICU 35.55 0.07** *&** are p<0.05 & <0.01 vs. comp group Conclusions: FC values are significantly influenced by gestation, gender, race, time of collection, NICU admission, multiple birth and birth weight. Generally, factors which increased mortality and morbidity (immaturity, post maturity, low birth weight, male gender, black race) were associated with higher FC values. These data will be used to construct normative curves and may be useful in predicting neonatal outcomes (Figure 1). 1Biostatistics, University of Cincinnati Medical Center, Cincinnati, Ohio, 4526

Association between increasing agricultural use of 2,4-D and population biomarkers of exposure: findings from the National Health and Nutrition Examination Survey, 2001-2014.

BACKGROUND: 2,4-Dichlorophenoxyacetic acid (2,4-D) is one of the most extensively used herbicides in the United States. In 2012, 2,4-D was the most widely used herbicide in non-agricultural settings and the fifth most heavily applied pesticide in the US agricultural sector. The objective of this study was to examine trends in 2,4-D urinary biomarker concentrations to determine whether increases in 2,4-D application in agriculture are associated with increases in biomonitoring levels of urine 2,4-D. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) with available urine 2,4-D biomarker measurements from survey cycles between 2001 and 2014 were utilized. Urine 2,4-D values were dichotomized using the highest limit of detection (LOD) across all cycles (0.40 μg/L or 0.4 ppb). Agricultural use of 2,4-D was estimated by compiling publicly available federal and private pesticide application data. Logistic regression models adjusted for confounders were fitted to evaluate the association between agricultural use of 2,4-D and urine 2,4-D level above the dichotomization threshold. RESULTS: Of the 14,395 participants included in the study, 4681 (32.5%) had urine 2,4-D levels above the dichotomization threshold. The frequency of participants with high 2,4-D levels increased significantly (p \u3c .0001), from a low of 17.1% in 2001-2002 to a high of 39.6% in 2011-2012. The adjusted odds of high urinary 2,4-D concentrations associated with 2,4-D agricultural use (per ten million pounds applied) was 2.268 (95% CI: 1.709, 3.009). Children ages 6-11 years (n = 2288) had 2.1 times higher odds of having high 2,4-D urinary concentrations compared to participants aged 20-59 years. Women of childbearing age (age 20-44 years) (n = 2172) had 1.85 times higher odds than men of the same age. CONCLUSIONS: Agricultural use of 2,4-D has increased substantially from a low point in 2002 and it is predicted to increase further in the coming decade. Because increasing use is likely to increase population level exposures, the associations seen here between 2,4-D crop application and biomonitoring levels require focused biomonitoring and epidemiological evaluation to determine the extent to which rising use and exposures cause adverse health outcomes among vulnerable populations (particularly children and women of childbearing age) and highly exposed individuals (farmers, other herbicide applicators, and their families)

Visualization of Genomic Changes by Segmented Smoothing Using an L0 Penalty

Copy number variations (CNV) and allelic imbalance in tumor tissue can show strong segmentation. Their graphical presentation can be enhanced by appropriate smoothing. Existing signal and scatterplot smoothers do not respect segmentation well. We present novel algorithms that use a penalty on the norm of differences of neighboring values. Visualization is our main goal, but we compare classification performance to that of VEGA

Performance-limiting nanoscale trap clusters at grain junctions in halide perovskites.

Halide perovskite materials have promising performance characteristics for low-cost optoelectronic applications. Photovoltaic devices fabricated from perovskite absorbers have reached power conversion efficiencies above 25 per cent in single-junction devices and 28 per cent in tandem devices1,2. This strong performance (albeit below the practical limits of about 30 per cent and 35 per cent, respectively3) is surprising in thin films processed from solution at low-temperature, a method that generally produces abundant crystalline defects4. Although point defects often induce only shallow electronic states in the perovskite bandgap that do not affect performance5, perovskite devices still have many states deep within the bandgap that trap charge carriers and cause them to recombine non-radiatively. These deep trap states thus induce local variations in photoluminescence and limit the device performance6. The origin and distribution of these trap states are unknown, but they have been associated with light-induced halide segregation in mixed-halide perovskite compositions7 and with local strain8, both of which make devices less stable9. Here we use photoemission electron microscopy to image the trap distribution in state-of-the-art halide perovskite films. Instead of a relatively uniform distribution within regions of poor photoluminescence efficiency, we observe discrete, nanoscale trap clusters. By correlating microscopy measurements with scanning electron analytical techniques, we find that these trap clusters appear at the interfaces between crystallographically and compositionally distinct entities. Finally, by generating time-resolved photoemission sequences of the photo-excited carrier trapping process10,11, we reveal a hole-trapping character with the kinetics limited by diffusion of holes to the local trap clusters. Our approach shows that managing structure and composition on the nanoscale will be essential for optimal performance of halide perovskite devices

Dysmorphometrics: the modelling of morphological abnormalities

<p>Abstract</p> <p>Background</p> <p>The study of typical morphological variations using quantitative, morphometric descriptors has always interested biologists in general. However, unusual examples of form, such as abnormalities are often encountered in biomedical sciences. Despite the long history of morphometrics, the means to identify and quantify such unusual form differences remains limited.</p> <p>Methods</p> <p>A theoretical concept, called dysmorphometrics, is introduced augmenting current geometric morphometrics with a focus on identifying and modelling form abnormalities. Dysmorphometrics applies the paradigm of detecting form differences as outliers compared to an appropriate norm. To achieve this, the likelihood formulation of landmark superimpositions is extended with outlier processes explicitly introducing a latent variable coding for abnormalities. A tractable solution to this augmented superimposition problem is obtained using Expectation-Maximization. The topography of detected abnormalities is encoded in a dysmorphogram.</p> <p>Results</p> <p>We demonstrate the use of dysmorphometrics to measure abrupt changes in time, asymmetry and discordancy in a set of human faces presenting with facial abnormalities.</p> <p>Conclusion</p> <p>The results clearly illustrate the unique power to reveal unusual form differences given only normative data with clear applications in both biomedical practice & research.</p

Roadmap on Photovoltaic Absorber Materials for Sustainable Energy Conversion

Photovoltaics (PVs) are a critical technology for curbing growing levels of anthropogenic greenhouse gas emissions, and meeting increases in future demand for low-carbon electricity. In order to fulfil ambitions for net-zero carbon dioxide equivalent (CO2eq) emissions worldwide, the global cumulative capacity of solar PVs must increase by an order of magnitude from 0.9 TWp in 2021 to 8.5 TWp by 2050 according to the International Renewable Energy Agency, which is considered to be a highly conservative estimate. In 2020, the Henry Royce Institute brought together the UK PV community to discuss the critical technological and infrastructure challenges that need to be overcome to address the vast challenges in accelerating PV deployment. Herein, we examine the key developments in the global community, especially the progress made in the field since this earlier roadmap, bringing together experts primarily from the UK across the breadth of the photovoltaics community. The focus is both on the challenges in improving the efficiency, stability and levelized cost of electricity of current technologies for utility-scale PVs, as well as the fundamental questions in novel technologies that can have a significant impact on emerging markets, such as indoor PVs, space PVs, and agrivoltaics. We discuss challenges in advanced metrology and computational tools, as well as the growing synergies between PVs and solar fuels, and offer a perspective on the environmental sustainability of the PV industry. Through this roadmap, we emphasize promising pathways forward in both the short- and long-term, and for communities working on technologies across a range of maturity levels to learn from each other.Comment: 160 pages, 21 figure

Genomic characteristics of cattle copy number variations

<p>Abstract</p> <p>Background</p> <p>Copy number variation (CNV) represents another important source of genetic variation complementary to single nucleotide polymorphism (SNP). High-density SNP array data have been routinely used to detect human CNVs, many of which have significant functional effects on gene expression and human diseases. In the dairy industry, a large quantity of SNP genotyping results are becoming available and can be used for CNV discovery to understand and accelerate genetic improvement for complex traits.</p> <p>Results</p> <p>We performed a systematic analysis of CNV using the Bovine HapMap SNP genotyping data, including 539 animals of 21 modern cattle breeds and 6 outgroups. After correcting genomic waves and considering the pedigree information, we identified 682 candidate CNV regions, which represent 139.8 megabases (~4.60%) of the genome. Selected CNVs were further experimentally validated and we found that copy number "gain" CNVs were predominantly clustered in tandem rather than existing as interspersed duplications. Many CNV regions (~56%) overlap with cattle genes (1,263), which are significantly enriched for immunity, lactation, reproduction and rumination. The overlap of this new dataset and other published CNV studies was less than 40%; however, our discovery of large, high frequency (> 5% of animals surveyed) CNV regions showed 90% agreement with other studies. These results highlight the differences and commonalities between technical platforms.</p> <p>Conclusions</p> <p>We present a comprehensive genomic analysis of cattle CNVs derived from SNP data which will be a valuable genomic variation resource. Combined with SNP detection assays, gene-containing CNV regions may help identify genes undergoing artificial selection in domesticated animals.</p

Novel markers for differentiation of lobular and ductal invasive breast carcinomas by laser microdissection and microarray analysis

BACKGROUND: Invasive ductal and lobular carcinomas (IDC and ILC) are the most common histological types of breast cancer. Clinical follow-up data and metastatic patterns suggest that the development and progression of these tumors are different. The aim of our study was to identify gene expression profiles of IDC and ILC in relation to normal breast epithelial cells. METHODS: We examined 30 samples (normal ductal and lobular cells from 10 patients, IDC cells from 5 patients, ILC cells from 5 patients) microdissected from cryosections of ten mastectomy specimens from postmenopausal patients. Fifty nanograms of total RNA were amplified and labeled by PCR and in vitro transcription. Samples were analysed upon Affymetrix U133 Plus 2.0 Arrays. The expression of seven differentially expressed genes (CDH1, EMP1, DDR1, DVL1, KRT5, KRT6, KRT17) was verified by immunohistochemistry on tissue microarrays. Expression of ASPN mRNA was validated by in situ hybridization on frozen sections, and CTHRC1, ASPN and COL3A1 were tested by PCR. RESULTS: Using GCOS pairwise comparison algorithm and rank products we have identified 84 named genes common to ILC versus normal cell types, 74 named genes common to IDC versus normal cell types, 78 named genes differentially expressed between normal ductal and lobular cells, and 28 named genes between IDC and ILC. Genes distinguishing between IDC and ILC are involved in epithelial-mesenchymal transition, TGF-beta and Wnt signaling. These changes were present in both tumor types but appeared to be more prominent in ILC. Immunohistochemistry for several novel markers (EMP1, DVL1, DDR1) distinguished large sets of IDC from ILC. CONCLUSION: IDC and ILC can be differentiated both at the gene and protein levels. In this study we report two candidate genes, asporin (ASPN) and collagen triple helix repeat containing 1 (CTHRC1) which might be significant in breast carcinogenesis. Besides E-cadherin, the proteins validated on tissue microarrays (EMP1, DVL1, DDR1) may represent novel immunohistochemical markers helpful in distinguishing between IDC and ILC. Further studies with larger sets of patients are needed to verify the gene expression profiles of various histological types of breast cancer in order to determine molecular subclassifications, prognosis and the optimum treatment strategies

CSF1R inhibitor JNJ-40346527 attenuates microglial proliferation and neurodegeneration in P301S mice

Neuroinflammation and microglial activation are significant processes in Alzheimer's disease pathology. Recent genome-wide association studies have highlighted multiple immune-related genes in association with Alzheimer's disease, and experimental data have demonstrated microglial proliferation as a significant component of the neuropathology. In this study, we tested the efficacy of the selective CSF1R inhibitor JNJ-40346527 (JNJ-527) in the P301S mouse tauopathy model. We first demonstrated the anti-proliferative effects of JNJ-527 on microglia in the ME7 prion model, and its impact on the inflammatory profile, and provided potential CNS biomarkers for clinical investigation with the compound, including pharmacokinetic/pharmacodynamics and efficacy assessment by TSPO autoradiography and CSF proteomics. Then, we showed for the first time that blockade of microglial proliferation and modification of microglial phenotype leads to an attenuation of tau-induced neurodegeneration and results in functional improvement in P301S mice. Overall, this work strongly supports the potential for inhibition of CSF1R as a target for the treatment of Alzheimer's disease and other tau-mediated neurodegenerative diseases