Perturbations of the intrauterine environment can affect fetal development during critical periods of plasticity, and can increase susceptibility to a number of age-related diseases ( e.g., type 2 diabetes mellitus; T2DM), manifesting as late as decades later. We hypothesized that this biological memory is mediated by permanent alterations of the epigenome in stem cell populations, and focused our studies specifically on DNA methylation in CD34+ hematopoietic stem and progenitor cells from cord blood from neonates with intrauterine growth restriction (IUGR) and control subjects.
Our epigenomic assays utilized a two-stage design involving genome-wide discovery followed by quantitative, single-locus validation. We found that changes in cytosine methylation occur in response to IUGR of moderate degree and involving a restricted number of loci. We also identify specific loci that are targeted for dysregulation of DNA methylation, in particular the hepatocyte nuclear factor 4α ( HNF4A) gene, a well-known diabetes candidate gene not previously associated with growth restriction in utero, and other loci encoding HNF4A-interacting proteins.
Our results give insights into the potential contribution of epigenomic dysregulation in mediating the long-term consequences of IUGR, and demonstrate the value of this approach to studies of the fetal origin of adult disease.
The concept of fetal origin of adult disease suggests that early life conditions can “program” the fetus for a spectrum of adverse health outcomes as an adult [1]. The link between intrauterine growth restriction (IUGR) and adult diseases such as type 2 diabetes and cardiovascular disease has been extensively supported by epidemiological [2], [3], [4], [5], [6], [7] and animal [8], [9], [10], [11] studies. To date, reports have focused on characterizing the pathophysiological consequences of an adverse intrauterine environment, and have revealed dysregulation of gene expression and a variety of functional impairments in individual tissues (including liver [12], [13], skeletal muscle [4], [14], pancreas [15], [16], kidney [17], and bone [18]) that precede and are potentially contributory to a range of later adult diseases. Taken together, these studies define a precocious aging phenotype, culminating in an increased risk of premature death [10], [19].
Permanent and sometimes progressive changes in gene expression have been observed in multiple tissues as a consequence of IUGR [16], [17], [20], [21]. Dysregulation of the epigenome may explain changes that are propagated from parent to daughter cells in IUGR offspring throughout life. For instance, offspring of rats fed a restricted protein diet throughout pregnancy showed changes in DNA methylation at multiple genes, with corresponding changes in gene expression, both of which were prevented by maternal supplementation of folic acid (an important methyl donor), further implicating an epigenetic mechanism [22], [23], [24]. Additionally, human studies also demonstrated epigenetic differences in response to an adverse intrauterine environment, as periconceptional exposure to famine was associated with altered DNA methylation at multiple sites within the known IGF2 differentially methylated region (DMR) [25]. Similar epigenetic dysregulation has been observed in a variety of tissues [23], [24], [26], [27], consistent with IUGR-induced susceptibility to age-related diseases affecting multiple organ systems.
As the life-long progenitors of many differentiated lineages, stem cells must act as custodians of epigenomic regulatory patterns in order that these changes persist in most tissues throughout the lifetime of an organism. Hematopoietic stem (CD34+) cells are one of the best-characterized stem cell types, and represent a neonatally accessible population, present as ∼1% of mononucleated umbilical cord blood cells [28]. Following tissue injury, this stem cell population is able to mobilize, proliferate, and home to target microenvironments at the site of damage [29], and may coordinate injury repair by stimulating tissue-specific progenitors [30], [31]. Moreover, these stem cells are multipotent progenitors of the immune system, which itself is likely to mediate inflammation, development, and progression of T2DM and cardiovascular disease [32].
To determine whether an adverse intrauterine environment leads to epigenetic changes that have functional pathological consequences, we studied DNA methylation in multipotent hematopoietic (CD34+) stem cells of IUGR neonates and matched controls. We employed our new, high-resolution version of the microarray-based HELP assay [33] to study ∼1.32 million loci throughout the human genome. Rather than focus on known candidate loci, we expanded the search genome-wide to identify novel loci involved and also tested for global, non-specific effects of IUGR on cytosine methylation. We followed this screening approach with a second stage of analysis, validating methylation status using quantitative, nucleotide-resolution bisulphite MassArray [34] to define the dysregulatory effects of IUGR on the epigenome.
This study was approved by the institutional review board (IRB) of the Montefiore Medical Center and the Committee on Clinical Investigation at the Albert Einstein College of Medicine and is in accordance with Health Insurance Portability and Accountability Act (HIPAA) regulations. Written informed consent was obtained from all subjects prior to participation.
Biological samples and clinical information were collected from consenting women who delivered IUGR or infants with appropriate growth (matched for gestational age at delivery, ethnicity and gender). Both birth weight and ponderal index (a measurement of neonatal weight relative to length) were used to identify cases and controls. IUGR had birth weight and ponderal index <10th percentile for gestational age and gender and controls had normal percentiles (>10th and <90th) for both parameters. Pertinent data on maternal medical history, pregnancy complications and neonatal hospital stay were recorded.
CD34+ cells, which constitute approximately 1% of nucleated blood cells in umbilical cord blood [28], were isolated from the cord blood specimen using an immunomagnetic separation technique, as previously described [35]. Mononuclear cells are separated by Ficoll-Paque density gradient, following which CD34+ cells are obtained by positive immunomagnetic bead selection, using Macs columns (Miltenyi Biotech). The isolated cells having >95% purity [36], [37] are cryopreserved in 10% DMSO by controlled rate freezing.
High-resolution HELP assays were performed according to recent advances in the technology [33]. Genomic DNA was isolated, digested to completion by either HpaII or MspI separately, and then ligated to a mixture of two oligonucleotide pair adapters, each complementary to the cohesive ends generated by the restriction digest. The adapters then served as a priming site for a PCR reaction (ligation-mediated PCR, LM-PCR) that we have described to generate a product predominantly in the 50–2,000 bp size range [33]. Following PCR, the HpaII and MspI representations were labeled with different fluorophores using random priming and were then cohybridized on a customized genomic microarray representing ∼1.32 million HpaII/MspI fragments of 50–2,000 bp in unique sequence [33].
Microarray data were pre-processed and subject to quality control and quantile normalization as previously described [38]. HpaII/MspI ratio values were compared between groups for all loci throughout the genome using a paired t-test with cases (n = 5) and controls (n = 5) matched by gender and gestational age, as well as ethnicity where possible (see Table S1 ). Changes in methylation state were defined using a HpaII/MspI ratio threshold of zero, where methylated loci and hypomethylated loci had ratio values less than zero and greater than zero, respectively. Ordered lists of differences were generated, the first relying purely on paired t-test computed p-values. Our alternative approach identified differential methylation, ranked according to the formula:
Target regions were amplified by PCR using the primers and cycling conditions described in Table S2 . Primers were selected with MethPrimer (
The most significantly differentially methylated loci were mapped to RefSeq gene identifiers by chromosomal position within 10 kb upstream of the transcription start site or overlapping the gene body. The list of RefSeq identifiers was then uploaded to the Ingenuity Pathway Analysis program (Redwood City, CA), enabling exploration of ontology and molecular interaction networks. Each uploaded gene identifier was mapped to its corresponding gene object (focus genes) in the Ingenuity Pathways Knowledge Base. Core networks were constructed for both direct and indirect interactions using default parameters, and the focus genes with the highest connectivity to other focus genes were selected as seed elements for network generation. New focus genes with high specific connectivity (overlap between the initialized network and gene's immediate connections) were added to the growing network until the network reached a default size of 35 nodes. Non-focus genes (those that were not among our differentially methylated input list) that contained a maximum number of links to the growing network were also incorporated.
The ranking score for each network was then computed by a right-tailed Fisher's exact test as the negative log of the probability that the number of focus genes in the network is not due to random chance. Similarly, significances for functional enrichment of specific genes were also determined by the right-tailed Fisher's exact test, using all input genes as a reference set.
Umbilical cord blood samples were collected from 10 well-matched, consenting individuals: IUGR (n = 5), and appropriate for gestational age controls (n = 5), matched for gestational age at delivery, gender, and ethnicity). We controlled for variable cellular composition of mixed leukocyte populations in the cord blood by purifying for hematopoietic stem (CD34+) cells, which served as a reference cell type for comparison in all subjects, thus reducing this potential source of variability which could have otherwise influenced our subsequent methylation assays [41]. We also note that while CD34+ cells are functionally heterogeneous themselves [42], purification reduces cell-specific variability in cytosine methylation that could otherwise mask (or artificially create) IUGR-related changes.
By design, IUGR weighed less and had a lower ponderal index than their matched controls, which were required to have normal percentiles for birth weight and ponderal index ( Table 1 ). Both groups had similar gestational age at delivery and proportions of each sex and ethnic composition, and comparable maternal age, BMI, pregnancy weight gain, and 1-hour maternal plasma glucose screen values after a 50 g glucose load ( Table 1 ). All of the women and their infants were healthy. None of the mothers had chronic hypertension, pre-eclampsia or gestational hypertension, pre-gestational or gestational diabetes, renal or autoimmune disease. The neonates had similar 1-minute and 5-minute Apgar scores in both groups (data not shown). One infant in the IUGR group had unilateral mild pyelectasis, which did not require treatment. There were no other neonatal complications.
| Neonatal and Maternal Characteristics | IUGR | Controls |
| (n = 5) | (n = 5) | |
| Gestational age, weeks (mean ±SD) | 39.9±0.2 | 40.2±0.6 |
| Birthweight, g (mean±SD) | 2493±270 | 3240±276 * |
| Ponderal index, g/cm 3 (mean±SD) | 2.3±0.08 | 2.81±0.1 * |
| % Male | 40 | 40 |
| Maternal age, years (mean ±SD) | 23.6±5.7 | 22.8±4.7 |
| Pre-pregnancy BMI, kg/m 2 (mean±SD) | 24.5±3.2 | 25.1±7.5 |
| Weight gain, pounds (mean±SD) | 32±14 | 26±7 |
| 1-hour glucose screen, mg/dL (mean±SD) | 93.8±25 | 74.3±26 |
p<0.05 compared to matched cases.
To detect global patterns of epigenetic changes that could distinguish between IUGR and controls, we performed the HELP assay on the CD34+ cells. These genome-wide cytosine methylation profiles are available as a public resource through
We tested to see whether cytosine methylation changes were present but undetected in global comparisons because they occur at only a subset of loci and/or to a limited degree at each locus. Paired T tests were applied to detect group differences for all individual loci represented on our arrays. This analysis yielded a subset of potentially informative loci with statistically significant differences in methylation. Figure 1A shows the distribution of p-values observed for IUGR compared to controls. Figure 1B shows the distribution of p-values when class labels are randomly permuted once across all loci and are representative of what we would see under the null distribution of no group effect. This illustrates how the observed p-values for the IUGR/control comparison are associated with a distribution consistent with lower than expected p-values for a small subset of loci, with 56 candidate loci ( Table 2 ) having significant group differences (p<0.00001, chosen because no test statistics derived under any other permutation obtained a p-value below 0.000015), and an additional 646 loci with moderate group differences (p<0.0001).
Panel (A) shows a histogram distribution of p-values calculated from an unpaired T test of IUGR (n = 5) in one group and controls (n = 5) in another. The x axis represents p values, with lower values being the more significant, while the y axis shows the frequency of occurrence of different p values. The peak observed represents a subset of loci with low p values and thus significant differences between IUGR and control subjects. For comparison, panel (B) shows the results of a random distribution of subjects into two groups, mixing IUGR and controls, demonstrating the absence of a subset of loci with significant p values.
| Position | IUGR | Control | Difference | P | CpG | CGc | phC | Rep | Loc | Gene(s) | RefSeq |
| chr2:235526053-235526128 | 2.06 | 2.66 | −0.61 | 0.4 | YES | YES | YES | PRO | SH3BP4 | NM_014521 | |
| chrX:66138356-66139206 | −0.78 | −1.29 | 0.51 | 0.8 | no | no | no | RT | |||
| chr15:45018938-45019873 | −0.62 | −1.11 | 0.48 | 0.9 | no | no | YES | ||||
| chr6:106879977-106880120 | 2.56 | 3.34 | −0.78 | 1.1 | YES | YES | YES | PRO | ATG5 | NM_004849 | |
| chr18:18624869-18626554 | −0.72 | −1.28 | 0.55 | 1.3 | no | no | YES | ||||
| chr2:176841167-176842343 | −0.16 | −0.48 | 0.33 | 1.5 | no | YES | no | LT | PRO | MTX2 | NM_006554 |
| chr6:113687585-113688546 | −1.81 | −2.61 | 0.80 | 1.5 | no | no | YES | ||||
| chr11:77123865-77124724 | −0.60 | −1.33 | 0.73 | 1.8 | YES | no | YES | GB | RSF1 | NM_016578 | |
| chrX:70857596-70857847 | 0.51 | 1.28 | −0.77 | 2.0 | no | no | no | ||||
| chr19:2616301-2616955 | −0.86 | −1.18 | 0.32 | 2.2 | no | no | no | RT | GB | GNG7 | NM_052847 |
| chr12:123573299-123573372 | 1.80 | 2.79 | −1.00 | 2.2 | no | no | no | PRO | NCOR2 | NM_006312 | |
| chr4:102487593-102487694 | 1.59 | 2.55 | −0.97 | 2.4 | YES | YES | YES | PRO | PPP3CA | NM_000944 | |
| chr11:13612440-13613698 | −1.01 | −1.79 | 0.78 | 2.5 | no | no | no | ||||
| chr20:39422191-39423123 | −0.42 | −0.94 | 0.52 | 2.7 | no | no | YES | GB | LPIN3 EMILIN3 | NM_022896 NM_052846 | |
| chrX:48943071-48943319 | 1.94 | 2.72 | −0.78 | 3.0 | YES | YES | YES | PRO | SYP | NM_003179 | |
| chr12:105961981-105963288 | −1.44 | −2.49 | 1.06 | 3.1 | no | no | no | RT | PRO | CRY1 | NM_004075 |
| chr11:76172201-76172653 | 2.48 | 3.16 | −0.68 | 3.2 | YES | YES | YES | PRO | TSKU | NM_015516 | |
| chr8:318760-319070 | −1.07 | −0.60 | −0.47 | 3.4 | no | no | no | ||||
| chr8:25575968-25577680 | −1.53 | −2.41 | 0.88 | 3.4 | no | no | YES | ||||
| chrX:99552722-99552801 | 3.09 | 3.62 | −0.52 | 3.7 | YES | YES | YES | PRO | PCDH19 | NM_020766 | |
| chr22:45949018-45950112 | −1.23 | −1.87 | 0.65 | 3.7 | no | YES | no | ||||
| chr11:99814353-99814807 | −0.48 | 0.36 | −0.83 | 3.8 | no | no | no | ||||
| chr15:91226882-91227081 | 2.40 | 3.34 | −0.93 | 3.8 | no | YES | YES | ||||
| chr3:9809841-9809915 | 1.49 | 2.24 | −0.74 | 3.8 | YES | YES | YES | PRO2 | TADA3L ARPC4 | NM_006354 NM_005718 | |
| chr12:1774845-1774939 | 1.72 | 2.45 | −0.73 | 4.2 | no | no | no | GB | CACNA2D4 | NM_172364 | |
| chr15:88120371-88120587 | 1.28 | 2.08 | −0.79 | 4.4 | YES | YES | YES | PRO | MESP2 | NM_001039958 | |
| chr9:78132928-78133704 | −0.91 | −1.56 | 0.65 | 5.2 | no | no | YES | ||||
| chr9:33740402-33740551 | 1.61 | 2.41 | −0.80 | 5.2 | YES | YES | no | PRO | PRSS3 | NM_007343 | |
| chr16:4746813-4747596 | −1.50 | −1.85 | 0.35 | 5.2 | no | no | no | RT | GB | ZNF500 | NM_021646 |
| chr3:129693359-129693420 | 1.66 | 2.31 | −0.65 | 5.2 | YES | YES | no | PRO | GATA2 | NM_032638 | |
| chr2:206657793-206657892 | 2.47 | 3.43 | −0.96 | 5.4 | no | YES | YES | PRO | INO80D | NM_017759 | |
| chr6:21543836-21545402 | −1.61 | −2.10 | 0.49 | 5.6 | no | no | no | RT | |||
| chr22:16347210-16348259 | −1.26 | −1.62 | 0.36 | 5.9 | no | no | no | RT | GB | CECR2 | NM_031413 |
| chr8:94370466-94372334 | −1.26 | −2.09 | 0.84 | 6.1 | no | no | no | ||||
| chr20:22581679-22582432 | −1.09 | −1.76 | 0.67 | 6.1 | no | no | YES | ||||
| chr21:25927844-25929694 | −1.13 | −1.77 | 0.64 | 6.3 | no | no | YES | RT/LT | |||
| chr12:105841213-105843079 | −1.42 | −1.71 | 0.29 | 6.4 | no | no | no | RT | |||
| chrX:39752669-39752723 | 1.04 | 1.99 | −0.95 | 6.5 | YES | YES | YES | ||||
| chr16:87294955-87295022 | 1.03 | 1.63 | −0.60 | 6.7 | no | YES | no | GB | RNF166 | NM_178841 | |
| chr22:17514809-17516325 | 2.52 | 3.26 | −0.74 | 7.0 | YES | YES | no | PRO | GSC2 | NM_005315 | |
| chr15:78244066-78244975 | −1.04 | −1.49 | 0.45 | 7.1 | no | no | no | GB | FAH | NM_000137 | |
| chr10:101825755-101826964 | −1.72 | −2.11 | 0.39 | 7.2 | no | no | YES | PRO | CPN1 | NM_001308 | |
| chr16:29563652-29563719 | 2.38 | 3.22 | −0.83 | 7.3 | no | no | no | RT | |||
| chr5:132564040-132565611 | −1.89 | −2.55 | 0.66 | 7.5 | no | no | YES | PRO | FSTL4 | NM_015082 | |
| chr18:40597598-40598729 | −2.17 | −2.86 | 0.69 | 7.6 | no | no | YES | GB | SETBP1 | NM_015559 | |
| chr17:17019265-17021036 | −1.34 | −1.94 | 0.60 | 7.9 | no | no | YES | GB | MPRIP | NM_015134 | |
| chr4:101159747-101160275 | −1.72 | −2.38 | 0.66 | 8.0 | no | no | no | RT | |||
| chr11:120258272-120258456 | 2.13 | 2.91 | −0.78 | 8.1 | no | no | YES | PRO | GRIK4 | NM_014619 | |
| chr9:19033554-19034579 | −0.19 | −0.84 | 0.65 | 8.5 | no | no | no | RT | |||
| chr8:59144552-59145204 | −1.10 | −1.61 | 0.52 | 8.6 | no | no | YES | GB | FAM110B | NM_147189 | |
| chr6:41861921-41862019 | 0.99 | 1.56 | −0.57 | 8.7 | YES | YES | YES | GB | PRICKLE4 | NM_013397 | |
| chr15:61088671-61090467 | −0.44 | −1.03 | 0.59 | 8.8 | no | no | no | ||||
| chr10:71811777-71811840 | 1.86 | 2.76 | −0.90 | 8.8 | YES | YES | no | PRO | LRRC20 | NM_018205 | |
| chr12:109990645-109990725 | 1.32 | 2.32 | −1.00 | 9.1 | no | no | YES | PRO | CUX2 | NM_015267 | |
| chr3:124611357-124611643 | 3.63 | 4.23 | −0.61 | 9.4 | no | no | YES | PRO | ADCY5 | NM_183357 | |
| chr10:92344854-92345619 | −1.70 | −2.71 | 1.01 | 9.7 | no | no | no |
All positions correspond to coordinates in the human genome, hg18 March 2006 UCSC Genome Browser; IUGR and Control data given as group averages of log
For comparison, p-value distributions calculated for grouping by gender and ethnicity are shown in Figures S2A and S2B , respectively. Gender comparison shows ∼2,500 significant loci (p<0.00001 threshold), an expected outcome due to the vast majority of these discriminatory loci (99.4%) being located on the X and Y chromosomes. We found no evidence for ethnicity (Latino versus non-Latino) influencing cytosine methylation ( Figure S2B ).
When we looked in detail at the 56 differentially-methylated loci, we found that the magnitude of DNA methylation changes observed in IUGR compared to controls is markedly less than many tissue-specific differences in methylation that we have previously observed ( [44]; and data not shown). Furthermore, we analyzed methylation status at four known imprinted regions on chromosome 11, some of which have been shown to harbor small changes in methylation in subjects with IUGR (the IGF2 differentially-methylated region (DMR), the H19 DMR and promoter region, and the KCNQ1 DMR) [25], [45] but found no detectable differences in the IUGR group compared to controls in this particular cell type (data not shown).
The next component of our two-stage experimental design was to validate the HELP data with single-locus, nucleotide-resolution quantitative studies, for which we used bisulphite MassArray [34]. To choose the loci for validation, we assessed the candidacy of the top 56 loci in terms of related biological function, reasoning that epigenetic dysregulation of the small degrees we were observing would have to affect multiple components of a biological pathway to cause functionally-significant changes. We used an Ingenuity Pathway Analysis (IPA) for 33 of the top 56 candidate loci, chosen because of physical proximity to 35 RefSeq-annotated genes (22 promoters and 13 gene bodies). While the remaining 23/56 loci in intergenic regions could not be linked to genes using these proximity criteria, almost half of these loci occur at CG clusters [46] or phastCons conserved DNA elements [47], both characteristics having potential relevance as cis-regulatory sites [48] and therefore may have unrecognized roles regulating transcription.
Of the two highest-scoring molecular interaction networks generated from IPA analysis, one was associated with cell signaling, nucleic acid metabolism, and molecular transport ( Figure S3 ), while the other network contained 12 out of 35 input nodes and was functionally associated with the cell cycle, cellular maintenance, and connective tissue development ( Figure 2 ). Moreover, this network was centered primarily on the hepatic nuclear factor 4α ( HNF4A), a transcription factor that has been strongly implicated in an early-onset form of type II diabetes, maturity onset diabetes of the young [49]. Although HNF4A did not meet our strict threshold for significance in the primary analysis, it was ranked first in our secondary analysis using a test statistic that was more heavily weighted towards the mean shift between groups (see Methods ).
RefSeq IDs for 33 of the top 56 sites that mapped to genes were uploaded onto the “Core Analysis” tool of IPA. The second-highest scoring molecular interaction network was constructed by 35 nodes, 12 of which were located on the input list (shaded nodes), and is associated with the cell cycle, cellular function and maintenance, and connective tissue development and function. The nodal relationships are indicated by solid lines (direct interaction) and dashed lines (indirect interactions), with or without filled arrows indicating functional interaction or merely physical association, respectively. Additionally, filled arrows that are preceded by a terminal bar indicate inhibition as well as functional interaction. The shape of each node indicates the class of molecule: horizontal ovals are transcription factors, squares are growth factors, vertical rectangles are ion channels while horizontal rectangles are nuclear receptors, inverted triangles are kinases, vertical diamonds are enzymes while horizontal diamonds are peptidases, trapezoids are transporters, and circles correspond to “other” molecules. In alphabetical order, this network consists of BUD31, CECR1, Collagen(s), CPN1, CRY1, DHX8, FAM110B, FGF2, GIN1, GRIK4, HNF4A, INO80D, KNG1, LPIN3, MAP3K3, MPRIP, MRTO4, NOC3L, PPARA, PRICKLE4, PRSS3, RSF1, RUVBL2, SLC31A1, SLC35A1, SLC35A5, SLC39A1, SMARCA5, SPAST, TGFB1, TSKU, UXT, WRNIP1, XPNPEP2, and ZNHIT6.
An additional IPA that was applied specifically to candidate loci overlapping RefSeq gene promoters revealed a third molecular interaction network including 17 of 23 input nodes. This network is functionally associated with cancer, cellular development, cellular growth and proliferation ( Figure S4 ), and is centered on a number of transcription factors and growth hormones (e.g. NCOR2, GH1, TGFβ1, HNF4A), and TP53 which shows altered DNA methylation in renal tissue of IUGR rats in adult life [26].
We therefore proceeded to bisulphite MassArray experiments [34], initially testing the technical performance of the HELP assays by choosing four loci, two each representing constitutively hypomethylated and constitutively methylated sites identified by the HELP assay. Figure S5 demonstrates the agreement between methylation values determined independently for these loci by HELP and MassArray (with a between-assay correlation in this case of R = −0.96353).
The technical validation results confirmed that the HELP data were accurate and allowed us to proceed to the analysis of the specific loci at which dysregulation of cytosine methylation was suspected. We focused on the HNF4A locus, where HELP data showed evidence for changes in cytosine methylation at an internal promoter of this complex gene ( Figure 3 ). Furthermore, the identified locus represents highly conserved sequence and contains multiple known transcription factor binding sites for HNF1α and β, SP1, HNF6, and GATA6 [50]. Differences in DNA methylation at the HNF4A promoter were confirmed by MassArray, with the informative HpaII site showing hypermethylation in IUGR compared to controls (65.7% and 59.6% methylation, respectively; p<0.01), consistent with the HELP data (log
HELP data are shown in (a) as normalized, centered log
One of our goals was to use our data post hoc to define how best to design this kind of study to test the dysregulation of cytosine methylation in a human disease. We therefore calculated an estimation of effect size for the top 1,000 loci with differences in methylation between IUGR and controls. Similar to the distribution of methylation differences noted for the top 56 loci, the 1,000 most informative loci had a mean difference of 0.60 with a standard deviation of 0.20 log
| Sample size per group | Power at alpha = 0.05 | Power at alpha = 1e−5 | Power at alpha = 1e−10 |
| 5 | 0.74 | 0.003 | <0.0001 |
| 10 | 0.98 | 0.11 | 0.0002 |
| 15 | 1.00 | 0.45 | 0.00 |
| 25 | 1.00 | 0.94 | 0.12 |
| 35 | 1.00 | 1.00 | 0.57 |
| 50 | 1.00 | 1.00 | 0.97 |
| 100 | 1.00 | 1.00 | 1.00 |
cases: N(0.60, 0.32).
controls: N(0.00, 0.32).
We present preliminary evidence for a unifying new hypothesis in which epigenetic modification induced in early development may contribute to an increased susceptibility to age-related diseases by prematurely advancing the normal aging process. Using a genome-wide, high-resolution cytosine methylation assay (testing ∼1.32 million loci throughout the genome), we avoid the necessity for any a priori assumptions about candidate loci and reveal potential new candidates in mediating the fetal origin of adult disease. We identified dysregulation of DNA methylation in purified populations of cord blood-derived CD34+ stem cells from IUGR neonates compared with matched controls, and find that these changes occur throughout the genome, although they are of a more modest degree and relatively limited extent compared with the epigenomic dysregulation observed in conditions such as cancer. As our study was based on a limited number of subjects, such subtle differences in cytosine methylation challenge the ability of genome-wide approaches to identify genuinely significant loci even at standard significance levels (0.05–0.001) which do not reflect multiple testing. In large-scale studies, when thousands to millions of loci are measured, the threshold to declare any locus significant is typically based on a more stringent level of significance ( e.g. Bonferroni correction or similar) in order to minimize the possibility of false positives, therefore we have displayed the power at two such thresholds. Based on these simulations, a more expansive approach, with sample size of at least 25 subjects per group is recommended for studies of methylation changes in conditions where such subtle effects are expected.
Nonetheless, we found that the IUGR subjects were distinctive for having a number of consistent differences in methylation near genes involved in processes critical for stem cell function, including cell cycle and cellular maintenance. Quantitative bisulphite validation studies confirmed our ability to discriminate differences in methylation in these samples, and the biological coherence of results in terms of functional pathway relatedness is suggestive of underlying changes in epigenetic regulation as a response to IUGR.
A locus that emerged consistently in this pathway analysis was the HNF4A gene, already implicated in T2DM [49], but not previously demonstrated to undergo epigenetic dysregulation as a response to IUGR. Best known for its implications as a monogenic, autosomal dominant form of maturity onset diabetes of the young (MODY) [49], HNF4A is involved in development and function of both the liver and the pancreas [51] and actively coordinates gene expression of many important metabolic pathways in both tissues [52], [53], [54]. We find differences in DNA methylation targeted to only one of the HNF4A promoters, supporting a model of isoform variation of the gene being related to susceptibility to T2DM, a major age-related disease.
Other loci identified in this study were found to be related functionally to HNF4A and also include ATG5 and TADA3L, which may have roles in mediating susceptibility to later disease. ATG5 is an essential component of autophagy that, when depleted, renders cells more susceptible to starvation and starvation-induced cell death [55]. We found that ATG5 is relatively methylated in IUGR at a CpG island-containing site just downstream of the transcription start site, potentially reducing expression in IUGR compared with controls, in parallel with increased sensitivity to starvation-induced cell death. Transcriptional adaptor 3 ( TADA3L) is associated with and is required for full p53 activity, causing growth arrest, senescence, and p53-mediated apoptosis [56]. TADA3L isoforms are highly expressed in CD34+ stem cells [57], but we find that TADA3L is relatively methylated in IUGR at a CpG island-containing bidirectional promoter, potentially downregulating its expression and altering CD34+ stem cell population dynamics.
However, the modest level of differences in methylation that we and others have observed [25] raises an important question: what is the biological significance of changes in methylation on the order of ∼6%? We note that such a change must represent a difference in a proportion of cells and/or alleles undergoing methylation within the broader population of CD34+ cells. Because CD34+ stem cells are multipotent progenitors, the presence of an epigenetically dysregulated subpopulation may go on to mediate susceptibility to chronic disease, with potentially greater effects over time should this subpopulation expand. Alternatively, this stem cell population may serve to define loci susceptible to constitutive “epimutations” [58] that are likely to exist in descendent cell types or unrelated lineages ( e.g. liver or pancreatic progenitors) where they may have the chance to induce functional changes in critical cell types or tissues. Adding further information about epigenetic and transcriptional regulators other than cytosine methylation plus transcriptional profiling studies will be very valuable in gaining a greater understanding of the epigenetic dysregulation and its functional consequences in IUGR.
We hypothesize that the changes we observe by focused studies of hematopoietic (CD34+) stem cells are representative of the influence of the intrauterine environment on epigenetic regulation and independent cellular programs throughout the developing fetus. While all cells are believed to accumulate epimutations over time [59], periods of rapid cell division ( e.g. during fetal development) represent the most vulnerable windows for cellular injury and potential dysregulation of the epigenome, with associated decreases in cellular fitness, function, and, of particular note for multipotent stem cells, replicative capacity. We therefore propose that adverse intrauterine conditions are more likely to contribute to replicative senescence and early exhaustion of regenerative pools of stem cell precursors throughout the body, increasing susceptibility to and speeding onset of age-related diseases like T2DM.
Although the exact mechanism remains unclear, the results of this study are indicative of epigenetic dysregulation associated with intrauterine growth restriction. Moreover, these findings suggest that epigenetic changes serve as a steward of cellular memory of aberrant intrauterine environments, and that site-specific changes in DNA methylation may mediate the increased susceptibility to age-related diseases observed later in life.
GEO database, accession number GSE17727.
Global correlation using the entire dataset demonstrates a high degree of epigenetic correlation among all samples, with no apparent global difference between IUGR and controls. A tree-pair plot was generated using a visualization tool that we have developed as part of an R package (Thompson, 2008). Pairwise correlations, calculated from >1.32 million independent loci, are shown in the upper right portion of the figure, where R values indicate the Pearson correlation for each pair of samples (labeled along the diagonal) and blue dotplots show a visual representation of the similarity between samples. Thin red lines within each of these sub-panels correspond to a lowess-fit of the pairwise data. In the lower left portion of the figure, a tree determined by Ward's minimum variance clustering shows an alternative unsupervised clustering approach. Branching order is shown in solid lines, colored by group. The diagonal dotted lines are numbered and indicate the Euclidean distance scale. The dotted red line indicates the Euclidean distance cutoff used to separate the individual groups of samples. Thompson, R.F., Reimers, M., Khulan, B., Gissot, M., Richmond, T.A., Chen, Q., Zheng, X., Kim, K. and Greally, J.M. (2008) An analytical pipeline for genomic representations used for cytosine methylation studies, Bioinformatics, 24, 1161–1167.
(0.70 MB TIF)
Supervised group comparisons reveal differences in methylation based on gender but not ethnicity. Panel (A) shows a histogram distribution of p-values calculated from an unpaired T test of males (n = 4) in one group and females (n = 6) in another. Along the x-axis, leftmost data represent low p-values (i.e. highly significant differences in methylation between groups), while data towards the right represent high p-values and, thus, loci that are uniform across groups. P-value frequency is shown along the y-axis, with larger values indicating increasing numbers of differentially methylated loci corresponding to the indicated p-value level. Note that the y-axis in this panel is adjusted to a different scale in order to account for the higher frequency of highly significant p-values. Panel (B) shows an analogous histogram with data obtained from samples grouped by ethnicity (Latin, n = 7, compared to non-Latin, n = 3).
(0.04 MB TIF)
The top-scoring molecular interaction network suggested by Ingenuity Pathway Analysis (IPA) consists of 13 genes among the top 56 differentially methylated loci. RefSeq IDs for 33 of the top 56 sites that mapped to genes were uploaded onto the “Core Analysis” tool of IPA. The top-scoring molecular interaction network was constructed by 35 nodes, 13 of which were located on the input list (shaded nodes), and is associated with cell signaling, nucleic acid metabolism, and molecular transport. The nodal relationships are indicated by solid lines (direct interaction) and dashed lines (indirect interactions), with or without filled arrows indicating functional interaction or merely physical association, respectively. The shape of each node indicates the class of molecule: horizontal ovals are transcription factors, squares are cytokines, vertical rectangles are G-protein coupled receptors, triangles are phosphatases, diamonds are enzymes, trapezoids are transporters, small ovals are chemicals, and circles correspond to “other” molecules. In alphabetical order, this network consists of ADCY, ADCY5, ADCY9, ARPC4, ATG5, β-estradiol, CAP2, CRYM, CUX2, EMILIN3, FSH, GALR1, GALR3, GH1, GJA1, GNAI2, GNAL, GNG7, GSTM3, HTR1F, LPAR4, MAMLD1, 5-methoxytryptamine, MllRN181C, NCOR2, noladin ether, PALM, PCDH19, PPP3CA, RXFP4, SH3BP4, SYP, TADA3L, and TP53.
(0.32 MB TIF)
A molecular interaction network suggested by Ingenuity Pathway Analysis (IPA) “Core Analysis”, with HNF4A as a central node among other transcription and growth factors. This network, associated with cancer, cellular development, cellular growth and proliferation, consists of 17 of 23 input nodes, each corresponding to the promoter of a RefSeq gene showing differential methylation by HELP. Nodes are shaded in green for relative hypermethylation in IUGR compared to controls, while red-shaded nodes are hypomethylated in IUGR. The nodal relationships are indicated by solid lines (direct interaction) and dashed lines (indirect interactions), with or without filled arrows indicating functional interaction or physical association, respectively. The shape of each node indicates the class of molecule: horizontal ovals are transcription factors, squares are growth factors, vertical rectangles are ion channels, triangles are phosphatases, diamonds are enzymes, trapezoids are transporters, and circles correspond to “other” molecules with concentric circles indicated complexes. In alphabetical order, this network consists of ADCY5, ARPC4, ATG5, β-estradiol, BUD31, C11ORF10, C9ORF5, Ca2+, CDKN2A, CHCHD8, CPA2, CPN1, CRY1, CTNNBL1, CUX2, FSH, GATA2, GH1, GINS3, GRIK4, HNF4A, INO80D, NCOR2, PPP3CA, PRSS3, RUVBL2, SH3BP4, SLC35A1, SLC35A5, SPP1, SYP, TADA3L, TGFB1, TP53, and TSKU.
(0.46 MB TIF)
Technical validation studies using MassArray confirm HELP data. Four loci were identified, two each representing constitutively hypo- and hyper-methylated sites. The hypomethylated sites were located at chr12:63986847-63987489 and chr20:45414330-45414885, with hypermethylated sites at chr4:188493650-188494271 and chr7:69549255-69549831 (hg18, human genome, March 2006, UCSC Genome Browser). HELP data as log2(HpaII/MspI) ratios are shown along the x-axis, with methylation towards the left and hypomethylation towards the right. MassArray data for the same loci are plotted along the y-axis, from 0% (hypomethylated) to 100% (methylated).
(0.03 MB TIF)
Neonatal and Maternal Characteristics for IUGR and Controls
(0.03 MB DOC)
Characteristics of top 56 candidate loci identified by HELP. All positions correspond to coordinates in the human genome, hg18 March 2006 UCSC Genome Browser; IUGR and Control data given as group averages of log
(0.04 MB DOC)
The authors acknowledge the support of Einstein's Center for Epigenomics.


Inheritance, composition and modules are different ways to organize your code.
Inheritance is an abstract class hierarchy, using subclasses that inherit information from superclasses. Subclasses are more specialized versions of their superclasses.
Example using Inheritance:
class Menu
attr_reader :food_items, :meal
def initialize(args={})
@meal = args[:meal]
@food_items = args[:food_items]
end
end
Now let's create the subclass LunchMenu that will inherit the behaviors, methods and instance variables of Menu.
class LunchMenu < Menu
end
Let's do it again, adding another link to our inheritance hierarchy. We will create the class FoodItems, which inherits from LunchMenu, which inherits from Menu.
class FoodItems < LunchMenu
def initialize(args={})end
Now let's create an instance of the Menu class, which will take a new instance of FoodItems class in its argument hash. Then we can look up the instance variables meal and food_items which are available to us using attr_reader and calling them as an instance method.
monday_menu = Menu.new(
=> "lunch"
#<FoodItems:0x007f7279513540 @food="taco", @price=2>
#<FoodItems:0x007f7279513540 is just the computer telling you what the self object you're acting on is. The food_items attribute returned the correct hash according to our inheritance hierarchy.
The way we've created our food universe so far, our "food items" are an example of a "lunch menu," which is a specialized version of a "menu." A subclass "is a" specialized version of a superclass.
It is helpful to use inheritance to create classes that will inherit the behavior and messages of their parent classes. However, this can get messy and create problematic dependencies if you need to change your code in the future. When organizing code, one must always ask: "What would be the future consequences if I am wrong about this?"
Composition can help you avoid these dependencies. Using composition, small well-defined objects interact via well-defined interfaces to create a larger object that is more than a sum of its parts. The inner objects play the role of the container object, but they are not a "kind" of its class.
In our inheritance model above, imagine if we had a DinnerMenu that was a subclass of Menu. If many of the same "food items" were shared among the different intances of "lunch menus" and "dinner menus," you would have a mess of confusing and repetitive code trying to keep every "taco" and every "meal" accounted for. Let's compose instead, building up from the smallest, most structurally independent objects.
class Menu
attr_reader :food_items, :meal
def initialize(args={})
@meal = args[:meal]
@food_items = args[:food_items]
end
end
class FoodItems < LunchMenu
def initialize(food_items)end
class FoodItem
def initialize(args)end
We've created a new FoodItem class. Now, our meals won't be so entangled and dependent. If different instances need to share food items, or even if your "dinner taco" were to cost more than your "lunch taco," you shouldn't have a problem.
beef = FoodItem.new(food: "beef", price: 7)
sandwich = FoodItem.new(food: "sandwich", price: 5)
tuesday_menu = Menu.new((
meal: "dinner",
food_items: FoodItems.new([beef, sandwich]))(
p tuesday_menu.meal
p tuesday_menu.food_items
=>
"dinner"
#<FoodItems:0x007f45e86bf938 @food_items=[
#<FoodItem:0x007f45e86bfac8 @food="beef", @price=7>,
#<FoodItem:0x007f45e86bfa00 @food="sandwich", @price=5>]>
Using composition, our Menu has FoodItems which have individual FoodItem instances. The only drawback to this clear, highly changeable organizational tool is that you have to explicitly delegate all your messages, wordily telling your objects how to communicate because they are not bound to any single hierarchy.


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Amazon intros new flexible payment options for AWS compute services
by Natalie GagliordiSimply called Savings Plans, the purchase option functions as a flexible discount model that gives customers the same discounts as Reserved Instances.
Amazon has announced a new purchase option for AWS compute services. Simply called Savings Plans, the purchase option functions as a flexible discount model that gives customers the same discounts as Reserved Instances, in exchange for a commitment to use a specific amount of compute power over a one or three year period.
Customers can choose from two Savings Plans options. The Compute Savings Plans provide more flexibility and may help customers reduce costs by up to 66%, according to AWS. The EC2 Instance Savings Plans apply to a specific instance family within a region and provide discounts of up to 72%.
AWS has a Cost Explorer tool designed to help customers to choose the Savings Plan option that makes the most sense.
"Every type of compute usage has an On Demand price and a (lower) Savings Plan price," AWS evangelist Jeff Barr wrote in a blog post. "After you commit to a specific amount of compute usage per hour, all usage up to that amount will be covered by the Saving Plan, and anything past it will be billed at the On Demand rate."
Savings Plans are now available in all AWS regions outside of China.


What does coworker.com do?
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How do I edit my coworking space listing?
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Can I find a coworking space that serves free beer?
Hellz to the yeah! Use our Advanced Filter to search by all kinds essentials, from free beer to standing desks to dual monitors to trampolines.
Got another question?
Hit us up at hello@coworker.com


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rc file
`Prev `__
R
`Next `__
--------------
**rc file**: /R·C fi:l/, n.
[Unix: from runcom files on the `*CTSS* <../C/CTSS.html>`__ system
1962-63, via the startup script ``/etc/rc``] Script file containing
startup instructions for an application program (or an entire operating
system), usually a text file containing commands of the sort that might
have been invoked manually once the system was running but are to be
executed automatically each time the system starts up. See also `*dot
file* <../D/dot-file.html>`__, `*profile* <../P/profile.html>`__ (sense
1).
--------------
+------------------------+----------------------------+-----------------------+
| `Prev `__ | `Up <../R.html>`__ | `Next `__ |
+------------------------+----------------------------+-----------------------+
| RBL | `Home <../index.html>`__ | RE |
+------------------------+----------------------------+-----------------------+


public class PartTimeEmployee extends Person
Contains part-time employee attributes and algorithms to modify the attributes.
Is considered a Person
. This class relies upon the animal objects
Cat
, Bird
, and Dog
using composition.
Depends on:
Part-time employee - a form of employment that carries fewer hours per week than a full-time job.
| Modifier and Type | Field and Description |
|---|---|
private Bird |
employeesBird
Ownership of a
Bird (Composition). |
private Cat |
employeesCat
Ownership of a
Cat (Composition). |
private Dog |
employeesDog
Ownership of a
Dog (Composition). |
private double |
hoursWorked
Number of hours worked.
|
private double |
payRate
The amount of money paid per hour.
|
private double |
salary
A fixed regular payment, typically paid on a monthly or biweekly basis but
often expressed as an annual sum.
|
| Constructor and Description |
|---|
PartTimeEmployee()
Constructs a
PartTimeEmployee object with
default properties for itself, its parent class
Person , and its composition objects of
Cat , Dog , and Bird . |
PartTimeEmployee(PartTimeEmployee obj)
Constructs a shallow copy of the
PartTimeEmployee
object. |
PartTimeEmployee(java.lang.String first,
java.lang.String last,
java.lang.String ssn,
double rate,
double hours,
Bird bird,
Dog dog,
Cat cat)
Constructs a
PartTimeEmployee object with the properties
Person.firstName , Person.lastName , and Person.ssn
from the parent class Person - properties of payRate
and hoursWorked from the PartTimeEmployee class -
composition objects Bird , Dog , and Cat . |
PartTimeEmployee(java.lang.String first,
java.lang.String last,
java.lang.String ssn,
double rate,
double hours,
java.lang.String hairLength)
Constructs a
PartTimeEmployee object with the properties
Person.firstName , Person.lastName , and Person.ssn
from the parent class Person - properties of payRate
and hoursWorked from PartTimeEmployee - property
Cat.hairLength from the composition object Cat . |
| Modifier and Type | Method and Description |
|---|---|
void |
adoptBird(Bird strayBird)
Executes the
PartTimeEmployee adopting a Bird . |
void |
adoptCat(Cat strayCat)
Executes the
PartTimeEmployee adopting a Cat . |
void |
adoptDog(Dog strayDog)
Executes the
PartTimeEmployee adopting a Dog . |
void |
calculatePay()
Calculates the
PartTimeEmployee 's salary . |
void |
catHissing()
Displays the
Cat hissing. |
void |
catRunning()
Displays the
Cat running. |
java.lang.String |
getCatName()
|
Person |
getCopy()
Retrieves a deep copy of the
PartTimeEmployee object. |
double |
getHoursWorked()
Retrieves the
PartTimeEmployee 's hoursWorked . |
double |
getPay()
Retrieves the
PartTimeEmployee 's salary . |
double |
getPayRate()
Retrieves the
PartTimeEmployee 's payRate . |
void |
makeCopy(PartTimeEmployee obj)
Creates a shallow copy of the
PartTimeEmployee
object. |
void |
playWithCat()
Displays the
PartTimeEmployee playing with their Cat . |
void |
setNameRateHours(java.lang.String first,
java.lang.String last,
double rate,
double hours)
|
java.lang.String |
toString()
Converts the
PartTimeEmployee object's data to a String. |
getFirstName, getLastName, getSSN, makeCopy, setName, setSSN
private double payRate
private double salary
private double hoursWorked
public PartTimeEmployee()
PartTimeEmployee
object with
default properties for itself, its parent class
Person
, and its composition objects of
Cat
, Dog
, and Bird
.public PartTimeEmployee(java.lang.String first, java.lang.String last, java.lang.String ssn, double rate, double hours, java.lang.String hairLength)
PartTimeEmployee
object with the properties
Person.firstName
, Person.lastName
, and Person.ssn
from the parent class Person
- properties of payRate
and hoursWorked
from PartTimeEmployee
- property
Cat.hairLength
from the composition object Cat
.
This PartTimeEmployee
owns a Cat
. Calculates
salary
.first
- Person.firstName
last
- Person.lastName
ssn
- Social security number (Person.ssn
)rate
- payRate
hours
- hoursWorked
hairLength
- Cat.hairLength
public PartTimeEmployee(java.lang.String first, java.lang.String last, java.lang.String ssn, double rate, double hours, Bird bird, Dog dog, Cat cat)
PartTimeEmployee
object with the properties
Person.firstName
, Person.lastName
, and Person.ssn
from the parent class Person
- properties of payRate
and hoursWorked
from the PartTimeEmployee
class -
composition objects Bird
, Dog
, and Cat
.
This PartTimeEmployee
owns a Bird
, Cat
,
and Cat
. Calculates salary
.first
- Person.firstName
last
- Person.lastName
ssn
- Social security number (Person.ssn
)rate
- payRate
hours
- hoursWorked
bird
- Bird
dog
- Dog
cat
- Cat
public PartTimeEmployee(PartTimeEmployee obj)
PartTimeEmployee
object.obj
- PartTimeEmployee
object to be shallow copiedpublic void makeCopy(PartTimeEmployee obj)
PartTimeEmployee
object.obj
- PartTimeEmployee
to be shallow copiedpublic Person getCopy()
PartTimeEmployee
object.getCopy
in class Person
PartTimeEmployee
objectpublic void adoptCat(Cat strayCat)
PartTimeEmployee
adopting a Cat
.strayCat
- Cat
public void adoptDog(Dog strayDog)
PartTimeEmployee
adopting a Dog
.strayDog
- Dog
public void adoptBird(Bird strayBird)
PartTimeEmployee
adopting a Bird
.strayBird
- Bird
public void catRunning()
Cat
running.public void catHissing()
Cat
hissing.public void playWithCat()
PartTimeEmployee
playing with their Cat
.public void calculatePay()
PartTimeEmployee
's salary
.public void setNameRateHours(java.lang.String first, java.lang.String last, double rate, double hours)
first
- Person.firstName
last
- Person.lastName
rate
- payRate
hours
- hoursWorked
public java.lang.String getCatName()
Pet.name
public double getPay()
PartTimeEmployee
's salary
.salary
public double getPayRate()
PartTimeEmployee
's payRate
.payRate
public double getHoursWorked()
PartTimeEmployee
's hoursWorked
.hoursWorked
public java.lang.String toString()
PartTimeEmployee
object's data to a String.


WITNESS LIST Health & Human Services Committee March 8, 2001 - 9:00A SB 34 FOR: Brown, Steve President (Brown Dental Health Management, Inc.), Ft. Worth, TX Garza, Esperanza Registered Dental Hygienist (Texas Dental Hygienists' Association), Houston, TX Hardy, Alan M. Member, State Legislative Committee (AARP), Dripping Springs, TX Willis, John F. State Ombudsman (Texas Department on Aging), Austin, TX Registering, but not testifying: For: Bower, Bruce Chair (Texas Senior Advocacy Coalition), Austin, TX Brewer, Sister Helen Advocacy Consultant (Seton Healthcare Network), Austin, TX Dunkelberg, Anne Senior Policy Analyst (Center for Public Policy Priorities), Austin, TX Ferris, Beth Legislative Representative (Texas Advocates for Nursing Home Residents), Austin, TX Hernandez, Leslie Director of Governmental Affairs (National Association of Social Workers), Austin, TX Higgins, Carlos Secretary, Legal Action Committee (Texas Silver-Haired Legislature) Holtermann, John Chair, Legislative Action Committee (Texas Silver-Haired Legislature), Austin, TX Jenkins, Clay Attorney (Brown Dental Health Management, Inc.), Waxahachie, TX Prough, Andrew Deputy Director (Citizens Commission on Human Rights), Austin, TX Sandlin, Abby Deputy Director (Texas Watch), Austin, TX Umphress, John Research Director (Texas Association of Public and Nonprofit Hospitals), Austin, TX On: Gilbert, Don Commissioner (Texas Health and Human Services Commission), Austin, TX Johnson, Jacqueline Deputy Commissioner for Programs (Texas Department of Human Services), Austin, TX Providing written testimony: For: Baumbach, Susan Director of Public Affairs (Texas Dental Association), Austin, TX Hayes, Robin Director of Best Practices (Texas Health Care Association), Austin, TX Miller, Judith S. Public Policy Director (Alzheimer's Association Coalition of Texas), Austin, TX SB 43 FOR: Ammons, Donna Vounteer (March of Dimes), Austin, TX Camacho, Jose E. Executive Director (Texas Association of Community Health Centers), Austin, TX Cozart, Ellen Director of Financial Services (Children's Medical Center of Dallas), Dallas, TX Dunkelberg, Anne Senior Policy Analyst (Center for Public Policy Priorities), Austin, TX McGiffert, Lisa Senior Policy Analyst (Consumers Union), Austin, TX ON: Priest, Paul Budget Analyst (Legislative Budget Board), Austin, TX SB 47 FOR: Buchanan, Bree Public Policy Director (Texas Council on Family Violence), Austin, TX Cole, Pat Associate Director (National Training Center on Domestic and Sexual Violence), Austin, TX SB 49 Registering, but not testifying: On: Denton, Judy Direcor of Texas WORKS (Texas Department of Human Services), Austin, TX SB 51 FOR: Bazan, Kim Operations Specialist (Texas Network of Youth Services), Austin, TX Registering, but not testifying: For: Brewer, Sister Helen Advocacy Consultant (Seton Healthcare Network), Austin, TX Davis, Helen Kent Director, Governmental Affairs (Texas Medical Association), Austin, TX Dunkelberg, Anne Senior Policy Analyst (Center for Public Policy Priorities), Austin, TX Hernandez, Leslie Director of Governmental Affairs (National Association of Social Workers), Austin, TX Marshall, Susan Policy Analyst (The ARC of Texas and The Disability Policy Consortium Legislative Group), Austin, TX McGiffert, Lisa Senior Policy Analyst (Consumers Union), Austin, TX McPherson, Kim Public Policy Specialist (The Mental Health Association in Texas), Austin, TX Sperry, Bryan (Children's Hospital Association of Texas), Austin, TX SB 161 FOR: Bresette, Patrick Associate Director (Center for Public Policy Priorities), Austin, TX Registering, but not testifying: For: Bower, Bruce Attorney (Texas Welfare Reform Organization), Austin, TX Cole, Pat Associate Director (National Training Center on Domestic and Sexual Violence), Austin, TX Corrigan, Jennifer Public Policy Specialist (Texas Council on Family Violence), Austin, TX Hernandez, Leslie Director of Governmental Affairs (National Association of Social Workers), Austin, TX Merriam, Rosemary Director, Public Affairs (Women's Health and Family Planning Association of Texas), Austin, TX On: Denton, Judy Director, Texas WORKS (Texas Department of Human Services), Austin, TX Temple, Larry Director, Welfare Reform (Texas Workforce Commission), Austin, TX SB 184 FOR: Greenwell, Brenagh Program Manager (Williamson-Burnet County Opportunities), Georgetown, TX Hagert, Celia Senior Policy Analyst (Center for Public Policy Priorities), Austin, TX Lawson, Yvonne Case Manager (San Antonio Metropolitan Ministries), San Antonio, TX Registering, but not testifying: For: Bower, Bruce Attorney (Texas Welfare Reform Organization), Austin, TX Brewer, Sister Helen Advocacy Consultant (Seton Healthcare Network), Austin, TX Camacho, Jose E. Executive Director (Texas Association of Community Health Centers), Austin, TX Cole, Pat Associate Director (National Training Center on Domestic and Sexual Violence), Austin, TX Corrigan, Jennifer Public Policy Specialist (Texas Council on Family Violence), Austin, TX McGiffert, Lisa Senior Policy Analyst (Consumers Union), Austin, TX On: Davila, Irma Policy Specialist (Texas Department of Human Services), Austin, TX Denton, Judy Director of Texas WORKS (Texas Department of Human Services), Austin, TX SB 297 FOR: Bales, Versalis Director (Garland Grandparent Support Group), Garland, TX Registering, but not testifying: For: Bresette, Patrick Associate Director (Center for Public Policy Priorities), Austin, TX On: Davila, Irma Policy Specialist (Texas Department of Human Services), Austin, TX Denton, Judy Director of Texas WORKS (Texas Department of Human Services), Austin, TX Providing written testimony: For: Crawford, Annie Member (Garland Grandparents Support Group), Garland, TX SB 373 FOR: Ammons, Donna Volunteer (March of Dimes), Austin, TX Aronin, Patricia Anne (Texas Pediatric Society, Texas Medical Association, Texas Academy of Family Physicians), Austin, TX Barrett, Sister Bernie (Metro Alliance and Communities Organized for Public Service), San Antonio, TX Brewer, Sister Helen Advocacy Consultant (Seton Healthcare Network), Austin, TX Camacho, Jose E. Executive Director (Texas Association of Community Health Center), Austin, TX Cozart, Ellen Director of Financial Services (Children's Hospital Association), Dallas, TX Dunkelberg, Anne Senior Policy Analyst (Center for Public Priorities), Austin, TX Martinez, Ray General Counsel (Texas Conference of Urban Counties), Austin, TX McGiffert, Lisa Senior Policy Analyst (Consumers Union), Austin, TX Ragsdale, Linda L. Executive Director (Tarrant County Youth Collaboration), Fort Worth, TX Rossberg, Cathy AVP, Governmental Relations (AMERICAID Community Care), Dripping Springs, TX Vera, Judy Leader (Valley Interfaith), Mercedes, TX ON: Priest, Paul Budget Analyst (Legislative Budget Board), Austin, TX Registering, but not testifying: For: Ashby, Cindy Government Relations (Texas Counseling Association), Austin, TX Bazan, Kim Operations Specialist (Texas Network of Youth Services), Austin, TX Chatelle, Melody Advocacy Consultant (Children's Hospitals and Related Institutions of Texas), Austin, TX Cole, Pat Associate Director (National Training Center on Domestic and Sexual Violence), Austin, TX Corrigan, Jennifer Public Policy Specialist (Texas Council on Family Violence), Austin, TX Daly, Richard Executive Director (Texas Catholic Conference), Austin, TX Feather, Robert K. Vice President of Public Policy (Cook Children's Health Care System), Fort Worth, TX Hernandez, Leslie Director of Governmental Affairs (National Association of Social Workers), Austin, TX Hillier, King Director of Legislative Relations (Harris County Judge Robert Eckels), Houston, TX Johnson, Karen R. President (United Ways of Texas), Austin, TX Kamm, Bob Legislative Consultant (Travis County Commissioners Court), Austin, TX Lange, Marie I. Director, Government Programs (Firstcare), Austin, TX Marshall, Susan Policy Analyst (The ARC of Texas and The Disability Policy Consortium Legislative Group), Austin, TX McPherson, Kim Public Policy Specialist (The Mental Health Association in Texas), Austin, TX Merriam, Rosemary Director, Public Affairs (Women's Health and Family Planning Association of Texas), Austin, TX Moorhead, Bee Executive Director (Texas Impact), Austin, TX Roy, Tom Director, Governmental Relations (JPS Health Network), Fort Worth, TX Rummel, Leah Executive Director (Texas Association of Health Plans), Austin, TX Samuels, Marc (Texas Academy of Internal Medicine), Austin, TX Sperry, Bryan (Children's Hospital Association of Texas), Austin, TX Umphress, John Research Director (Texas Association of Public and Nonprofit Hospitals), Austin, TX Williams, Janice Retired (Austin Interfaith), Austin, TX Woolbert, Lynda Director of Public Policy (Coalition for Nurses in Advanced Practice), West Columbia, TX Providing written testimony: For: Hankins, Barbara S. Secretary (League of Women Voters of Texas), Austin, TX On: Schwartz, Jonas Program Manager (Advocacy, Incorporated), Austin, TX SB 374 FOR: Ammons, Donna Volunteer (March of Dimes), Austin, TX Aronin, Patricia (Texas Pediatric Society, Texas Medical Association, Texas Academy of Family Physicians), Austin, TX Barrett, Sister Bernie (Metro Alliance and Communities Organized for Public Service), San Antonio, TX Brewer, Sister Helen Advocacy Consultant (Seton Healthcare Network), Austin, TX Camacho, Jose E. Executive Director (Texas Association of Community Health Centers), Austin, TX Cozart, Ellen Director of Financial Services (Children's Medical Center of Dallas), Dallas, TX Dunkelberg, Anne Senior Policy Analyst (Center for Public Policy Priorities), Austin, TX Martinez, Ray General Counsel (Texas Conference of Urban Counties), Austin, TX McGiffert, Lisa Senior Policy Analyst (Consumers Union), Austin, TX Ragsdale, Linda L. Executive Director (Tarrant County Youth Collaboration), Fort Worth, TX Rossberg, Cathy AVP, Government Relations (AMERICAID Community Care), Dripping Springs, TX Vera, Judy Leader (Valley Interfaith), Mercedes, TX ON: Priest, Paul Budget Analyst (Legislative Budget Board), Austin, TX Registering, but not testifying: For: Ashby, Cindy Government Relations (Texas Counseling Association), Austin, TX Bazan, Kim Operations Specialist (Texas Network of Youth Services), Austin, TX Chatelle, Melody Advocacy Consultant (Children's Hospitals and Related Institutions of Texas), Austin, TX Cole, Pat Associate Director (National Training Center on Domestic and Sexual Violence), Austin, TX Corrigan, Jennifer Public Policy (Texas Council on Family Violence), Austin, TX Daly, Richard Executive Director (Texas Catholic Conference), Austin, TX Feather, Robert K. Vice President of Public Policy (Cook Children's Health Care System), Fort Worth, TX Hernandez, Leslie Director of Governmental Affairs (National Association of Social Workers), Austin, TX Hillier, King Director of Legislative Relations (Harris County Judge Robert Eckels), Houston, TX Johnson, Karen R. President (United Ways of Texas), Austin, TX Kamm, Bob Legislative Consultant (Travis County Commissioners Court), Austin, TX Lange, Marie I. Director, Government Programs (Firstcare), Austin, TX Marshall, Susan Policy Analyst (The ARC of Texas and The Disability Policy Consortium Legislative Group), Austin, TX McPherson, Kim Public Policy Specialist (Mental Health Association in Texas), Austin, TX Merriam, Rosemary Director, Public Affairs (Women's Health and Family Planning Association of Texas), Austin, TX Moorhead, Bee Executive Director (Texas Impact), Austin, TX Roy, Tom E. Director, Governmental Relations (JPS Health Network), Fort Worth, TX Rummel, Leah Executive Director (Texas Association of Health Plans), Austin, TX Samuels, Marc (Disease Management Association of America), Austin, TX Samuels, Marc (Texas Academy of Internal Medicine), Austin, TX Sperry, Bryan (Children's Hospital Association of Texas), Austin, TX Umphress, John Research Director (Texas Association of Public and Nonprofit Hospitals), Austin, TX Woolbert, Lynda Director of Public Policy (Coalition for Nurses in Advanced Practice), West Columbia, TX Providing written testimony: For: Hankins, Barbara S. Secretary (League of Women Voters of Texas), Austin, TX On: Schwartz, Jonas Program Manager (Advocacy, Incorporated), Austin, TX SB 375 FOR: Ammons, Donna C. Volunteer (March of Dimes), Austin, TX Camacho, Jose E. Executive Director (Texas Association of Community Health Centers), Austin, TX Cozart, Ellen Director, Financial Services (Children's Medical Center of Dallas), Dallas, TX Dunkelberg, Anne Senior Policy Analyst (Center for Public Policy Priorities), Austin, TX McGiffert, Lisa Senior Policy Analyst (Consumers Union), Austin, TX ON: Priest, Paul Budget Analyst (Legislative Budget Board), Austin, TX Registering, but not testifying: For: Lange, Marie I. Director of Government Programs (Firstcare), Austin, TX SB 376 FOR: Ammons, Donna Volunteer (March of Dimes), Austin, TX Aronin, Patricia Anne (Texas Pediatric Society, Texas Medical Association, Texas Academy of Family Physicians), Austin, TX Barrett, Sister Bernie (Metro Alliance and Communities Organized for Public Service), San Antonio, TX Brewer, Sister Helen Advocacy Consultant (Seton Healthcare Network), Austin, TX Camacho, Jose E. Executive Director (Texas Association of Community Health Centers), Austin, TX Cozart, Ellen (Children's Medical Center of Dallas), Dallas, TX Dunkelberg, Anne Senior Policy Analyst (Center for Public Priorities), Austin, TX Martinez, Ray General Counsel (Texas Conference on Urban Counties), Austin, TX McGiffert, Lisa Senior Policy Analyst (Consumers Union), Austin, TX Ragsdale, Linda L. Executive Director (Tarrant County Youth Collaboration), Austin, TX Rossberg, Cathy AVP, Government Relations (AMERICAID Community Care), Dripping Springs, TX Vera, Judy Leader (Valley Interfaith), Mercedes, TX ON: Priest, Paul Budget Analyst (Legislative Budget Board), Austin, TX Registering, but not testifying: For: Ashby, Cindy Government Relations (Texas Counseling Association), Austin, TX Bazan, Kim Operations Specialist (Texas Network of Youth Services), Austin, TX Chatelle, Melody Advocacy Consultant (Children's Hospitals and Related Institutions of Texas), Austin, TX Corrigan, Jennifer Public Policy Specialist (Texas Council on Family Violence), Austin, TX Daly, Richard Executive Director (Texas Catholic Conference), Austin, TX Hernandez, Leslie Director of Governmental Affairs (National Association of Social Workers), Austin, TX Hillier, King Director of Legislative Relations (Harris County Judge Robert Eckels), Houston, TX Johnson, Karen R. President (United Ways of Texas), Austin, TX Kamm, Bob Legislative Consultant (Travis County Commissioners Court), Austin, TX Marshall, Susan Policy Analyst (The ARC of Texas and The Disability Policy Consortium Legislative Group), Austin, TX McPherson, Kim Public Policy Specialist (Mental Health Association in Texas), Austin, TX Merriam, Rosemary Director, Public Affairs (Women's Health and Family Planning Association of Texas), Austin, TX Moorhead, Bee Executive Director (Texas Impact), Austin, TX Roy, Tom E. Director, Governmental Relations (JPS Health Network), Fort Worth, TX Rummel, Leah Executive Director (Texas Association of Health Plans), Austin, TX Sperry, Bryan (Children's Hospital Association of Texas), Austin, TX Umphress, John Research Director (Texas Association of Public and Nonprofit Hospitals), Austin, TX Woolbert, Lynda Director of Public Policy (Coalition for Nurses in Advanced Practice), West Columbia, TX Providing written testimony: For: Hankins, Barbara S. Secretary (League of Women Voters of Texas), Austin, TX On: Schwartz, Jonas Program Manager (Advocacy, Incorporated), Austin, TX SB 432 FOR: Barton, Melanie Assistant District Attorney (Dallas County), Dallas, TX ON: Mitchell, Beth Attorney (Advocacy, Inc.), Austin, TX Registering, but not testifying: For: Bush, John R. Executive Director (Texas Society of Psychiatric Physicians), Austin, TX Lovelace, Joe Advocacy Chair (National Alliance for the Mentally Ill of Texas), Dripping Springs, TX SB 532 Registering, but not testifying: For: McLaren, Jessica (Planned Parenthood of the Texas Capital Region), Austin, TX Samuels, Marc (US Oncology), Austin, TX Umphress, John Research Director (Texas Association of Public and Nonprofit Hospitals), Austin, TX SB 572 Registering, but not testifying: For: Holtermann, John Chair, Legislative Action Committee (Texas Silver-Haired Legislature), Austin, TX Umphress, John Research Director (Texas Association of Public and Nonprofit Hospitals), Austin, TX SB 616 Registering, but not testifying: For: Davis, Helen Kent Director, Governmental Affairs (Texas Medical Association), Austin, TX Feather, Robert K. Vice President of Public Policy (Cook Children's Health Care System), Fort Worth, TX Glenn, Eric (Humana), Austin, TX Jones, Susan Director, Health Policy (Texas Hospital Association), Austin, TX Samuels, Marc (Disease Management Association of America), Austin, TX Samuels, Marc (Texas Academy of Internal Medicine), Austin, TX Sprinkle, G.K. Public Policy Advocate (American Lung Association of Texas), Austin, TX Umphress, John Research Director (Texas Association of Public and Nonprofit Hospitals), Austin, TX Woolbert, Lynda Director of Public Policy (Coalition of Nurses in Advanced Practice), West Columbia, TX On: Koops, Beverly Medical Director, Health Care Financing (Texas Department of Health), Austin, TX Salinas, Mario Research Analyst (Comptroller of Pubic Accounts), Austin, TX SB 691 FOR: Fitzhugh, Jan Executive Director (Atria Assisted Living), Houston, TX Mathews, Skip Board Member, Provider (Texas Organization of Residential Care Homes), Brenham, TX Registering, but not testifying: For: Crowe, Michael (Texas Assisted Living Association), Austin, TX Evans, David Chairman, Board of Directors (Texas Assisted Living Association), Fort Worth, TX McPherson, Kim Public Policy Specialist (Mental Health Association in Texas), Austin, TX Smith, John P. (AARP), Austin, TX Squires, Becky Executive Director (Texas Assisted Living Association), Austin, TX On: Lehrman, Jim Deputy Commissioner (Texas Department of Human Services), Austin, TX SB 751 ON: Millwee, Billy R. Director, Texas Health STEPS (Texas Department of Health), Austin, TX Registering, but not testifying: For: Dougherty, Edna S. Public Member (State Promotora Community Health Worker Program), Austin, TX Dunkelberg, Anne Senior Policy Analyst (Center for Public Policy Priorities), Austin, TX Jones, Susan Director, Health Policy (Texas Hospital Association), Austin, TX Williams, Darryl Member (State Promotora Planning Committee), El Paso, TX SB 812 FOR: Blakeway, Carrie (Self), Austin, TX Merriam, Rosemary Director of Public Affairs (Women's Health and Family Planning Association of Texas), Austin, TX Schwartz, Roger Medicaid Doctor (Self), San Antonio, TX ON: Johnson, Jacqueline Deputy Commissioner (Texas Department of Human Services), Austin, TX Jones, Cindy Director, Family Planning Division (Texas Department of Health), Austin, TX Registering, but not testifying: For: Bresette, Patrick Associate Director (Center for Public Policy Priorities), Austin, TX Cole, Pat Associate Director (National Training Center on Domestic and Sexual Violence), Austin, TX Davis, Helen Kent Director of Governmental Affairs (Texas Medical Association), Austin, TX Hernandez, Leslie Director of Governmental Affairs (National Association of Social Workers), Austin, TX Johnson, Karen R. President (United Ways of Texas), Austin, TX McLaren, Jessica Assistant (Planned Parenthood), Austin, TX Roy, Tom E. Director of Governmental Relations (JPS Health Network), Fort Worth, TX Samuels, Marc (Texas Academy of Internal Medicine), Austin, TX Samuels, Marc (US Oncology), Austin, TX Sperry, Bryan (Children's Hospital Association of Texas), Austin, TX Umphress, John Research Director (Texas Association of Public and Nonprofit Hospitals), Austin, TX White, Rebecca Senior Vice President (Planned Parenthood of Houston and Southeast Texas, Inc.), Houston, TX Woolbert, Lynda Director of Public Policy (Coalition for Nurses in Advanced Practice), West Columbia, TX On: Wertz, Linda State Medicaid Director (Texas Health and Human Services Commission), Austin, TX Providing written testimony: For: Bilbrey, Jennifer Director of Governmental Affairs (Planned Parenthood of San Antonio and South Central Texas), San Antonio, TX SB 831 FOR: Borel, Dennis Executive Director (Coalition of Texans with Disabilities), Austin, TX Kern, Diana Public Policy Assistant (National Alliance of the Mentally Ill of Texas), Cedar Creek, TX McPherson, Kim Public Policy Specialist (The Mental Health Association in Texas), Austin, TX Mizcles, Amy (National Alliance for the Mentally Ill of Texas), Austin, TX Rilling, Frank (Self), Austin, TX Schwartz, Jonas Program Specialist (Advocacy, Incorporated), Austin, TX Registering, but not testifying: For: Davis, Helen Kent Director of Governmental Affairs (Texas Medical Association), Austin, TX Hernandez, Leslie Director of Governmental Affairs (National Association of Social Workers), Austin, TX Marshall, Susan Policy Analyst (The ARC of Texas), Austin, TX Merriam, Rosemary Director of Public Affairs (Women's Health and Family Planning Association of Texas), Austin, TX Roy, Tom E. Director of Governmental Relations (JPS Health Network), Fort Worth, TX Samuels, Marc (Texas Academy of Internal Medicine), Austin, TX Samuels, Marc (US Oncology), Austin, TX Umphress, John Research Director (Texas Association of Public and Nonprofit Hospitals), Austin, TX On: Elrod, Susanne Project Director (Texas Center on Disability Studies), Austin, TX SB 834 ON: Biles, Susan Project Coordinator (Comptroller of Public Accounts), Austin, TX Registering, but not testifying: For: Ray, Tom E. Director of Governmental Relations (JPS Health Network), Fort Worth, TX SB 1008 FOR: Cole, Pat Associate Director (National Training Center on Domestic and Sexual Violence), Austin, TX Greenwell, Brenagh Program Manager, Foodstamp Outreach (Williamson-Burnet County Opportunities), Georgetown, TX Hagert, Celia Senior Policy Analyst (Center for Public Policy Priorities), Austin, TX Lawson, Yvonne Case Manager (San Antonio Metropolitan Ministries), San Antonio, TX Registering, but not testifying: For: Bower, Bruce Attorney (Texas Welfare Reform Specialist), Austin, TX Brewer, Sister Helen Advocacy Consultant (Seton Healthcare Network), Austin, TX Camacho, Jose E. Executive Director (Texas Association of Community Health Centers), Austin, TX Johnson, Karen R. President (United Ways of Texas), Austin, TX McGiffert, Lisa Senior Policy Analyst (Consumers Union), Austin, TX Merriam, Rosemary Director, Public Affairs (Women's Health and Family Planning Association of Texas), Austin, TX Moorhead, Bee Executive Director (Texas Impact), Austin, TX On: Davila, Irma Policy Specialist (Texas Department of Human Services), Austin, TX Denton, Judy Director of Texas WORKS (Texas Department of Human Services), Austin, TX


Simon Grand and Martin Wiedmer
"Design and design research do not primarily focus on explaining the
world as it is;
they share with engineering a fundamental interest in focusing on the
world as it could be. Design fiction is a new approach, which
integrates these two perspectives, in order to develop a method toolbox
for design research for a complex world"
"Design and design research do not primarily focus
on the world as it is, like most scientific disciplines, trying to
develop descriptions,
interpretations, and explanations of existing objects, processes, and
activities; design and
design research share with engineering a fundamental interest in focusing
on the world as it
could be, on the imagination and realization of possible futures, as well
as on the disclosure
of new worlds."


| Name | Type | Description |
| declination |
visad.Real
|
Gets the declination: the earth's tilt angle relative to the sun at a given
date and time. It
is also the latitude where the sun is directly overhead (subsolar point).
@return The declination angle [degrees].
|
| subsolarLatitude |
visad.Real
|
Gets the subsolar latitude: the earth latitude for where the sun is overhead
at a given date
and time. This is identical to the declination.
@return [degrees]
|
| subsolarLongitude |
visad.Real
|
Gets the subsolar longitude: the earth longitude for where the sun is
overhead at a given
date and time.
@return [degrees]
|
| azimuthAngle |
visad.Real
|
Gets the solar azimuth angle: where the sun is relative to North.
@return The solar azimuth angle. [degrees]
|
| zenithAngle |
visad.Real
|
Gets the solar zenith angle: where is sun relative to the observer's zenith.
@return The solar zenith angle. [degrees]
|
| altitudeAngle |
visad.Real
|
Gets the solar altitude angle: how high the sun is from the horizon.
@return The solar altitude angle (possibly corrected for atmospheric
refraction). [degrees]
|
| localHourAngle |
visad.Real
|
Gets the local hour angle relative to the observer.
@return Hour angle between the sun and the observer. [degrees]
|
| sunriseHourAngle |
visad.Real
|
Gets the solar hour angle for sunrise.
@return Sunrise hour angle from solar noon. [degrees]
|
| sunsetHourAngle |
visad.Real
|
Gets the solar hour angle for sunset.
@return Sunset hour angle from solar noon. [degrees]
|
| sunriseHour |
visad.Real
|
Gets the local time of sunrise.
@return Sunrise time in the local time zone. [hours]
|
| sunsetHour |
visad.Real
|
Gets the local time of sunset.
@return Sunset time in the local time zone. [hours]
|
| sunTransitHour |
visad.Real
|
Gets the local sun transit time (or solar noon).
@return The time of solar noon in local time zone. [hours]
|
| zoneOffsetHour |
visad.Real
|
Gets the time zone offset.
@return The offset from UTC to local time. [hours]
|
| missing |
boolean
|
Tests whether any data elements are missing.
@return true if a data element is missing.
|


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Hello! My name is Andrea and I am a senior at Saint Joseph's Univeristy in Philadelphia, PA. I am graduating this December with a Communications and Marketing degree. There are alot of things I have learned over the years about food, shopping, financials, and exercise that I would love to share with you. Enjoy!
My boyfriend and I were exploring the neightborhood we live in, and we stumbled upon a farmer's market in the back of a nearby church. Turns out, an Amish group from Lancaster, PA and other local farmers bring thier food and drinks for locals to buy.
These bars only cost $3.50 for a bag of eight, so they are certainly affordable.


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Informations
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Summer 2016
Barcelona
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Rimini
European cities
Paris
London
Lisbon
Remote destinations
Nepal
Tokyo
Bangkok
New year 2017
Wien
Prague
Budapest
Boško travel agency
Our agency has a number of prizes, achievments and diplomas!


The New Hgness purpose is to allow you to see the newest things in your Mecurial repositories.
The New Hgness gets all its data from Mecurial.
{{hgVersion.value.version}}


DaaS for Azure Web Sites is a great tool when “bad things happen to a good site”! It allows you to collect a plethora of different logs and parses them into an easily digestible format. The idea is to enable you to get to root cause without turning to the forums or Microsoft support. I’m running a WordPress site that, knock on digital wood, is pretty rock solid.
Until the recent announcement, the collection and parsing of PHP logs wasn’t an option, but now DaaS will collect PHP specific files along with Event Logs, Memory Dumps and HTTP Logs. First you will need to enable Web Server Logging to the File System. With that down just browse over to https://<YourSiteName>.scm.azurewebsites.net/DaaS and you’ll get a page like this…
All you need to do is click Diagnose Now and your off to the races. Oh, and you can also schedule an analysis if you’d like…
Once that is complete you can download your logs at: https://<YourSiteName>.scm.azurewebsites.net/Zip/data/DaaS.


Linux Server Report
Script authored by Shashank Srivastava
OS Details :
OS Name
IP Address
Uptime
Ubuntu
<IP_ADDRESS>
21:42
Resources Utilization :
# of Processes
Root FS Usage
Total Size of Root FS
Load Average
Used RAM(in MB)
Total RAM(in MB)
iNode Status
263
40%
56G
0,29, 0,22, 0,13
1895
2000
32%
Culprit Directories(Eating up disk space) :
Size
Name
1,2G
/home/shashank/xld
1,2G
/home/shashank/Downloads


Startup backed by Bill Gates reaches solar breakthrough
The AI-powered solar breakthrough could potentially replace fossil fuels
by Aisha MalikA startup supported by Bill Gates says it has achieved a solar breakthrough that could potentially save the planet in the future by using artificial intelligence.
Heliogen, the clean energy company, concentrated solar energy at temperatures that are above 1000 degrees Celsius. This breakthrough means that it could replace fossil fuels for industrial projects like making steel or cement.
The breakthrough was possible by using computer vision, which is a form of artificial intelligence that was used to align a number of mirrors as they reflected sunlight onto a target. This is the first time that this level of accuracy has been achieved.
Heliogen has been able to essentially create a solar oven that is roughly a quarter of the temperature of the sun. This is the first time concentrated solar energy that is carbon-free has been able to reach the extreme heat that is necessary to produce cement, steel and glass.
There is still some more development that is required before the technology can be utilized.
Heliogen also wants to keep pushing its possibilities by reaching even hotter temperatures that could be used to create hydrogen and synthetic gas to produce clean energy.
Source: CNN Business


It was summer in Los Angeles and I had just moved in to my first apartment with a friend of mine. I was a 19 year old pot dealer on a mission to experience every drug known to man. One person helping me accomplish this goal was a customer of mine that worked for a Veterinarian. She would always bring something new and exciting to trade for pot. Everything she had brought previously was something that I had heard of before and generally knew what to expect.
When my friend arrived she had brought 4 different drugs for me to try. She explained in detail what the first three drugs were and what to expect. For the bottle with the big purple pills she unraveled the info that comes with the drug and pointed out where it listed hallucinations as a side effect of the drug in higher doses. Cool.
A few days later after selling my last sack I decided to try the Phenobarbital. I was watching TV with my roommate at 8pm when I swallowed three 75mg coated tablets. My roommate had been drinking beer which was a big no-no according to the patient information. So he was content to watch. I was more or less sober besides some marijuana smoked an hour or two earlier.
At 8:10 I started to feel a sense of calm and sedation. At 8:15 I felt extremely sedated and drowsy. No hallucinations. My only visual was my bed in my mind. My body felt heavy and out of energy. It took me a good 20 seconds to get up from the couch. I told my roommate I was tired and going to bed. I managed to stay awake for the trip to my bedroom. I was asleep before my head hit the pillow. It was 8:15 on Monday.
I awoke some time later in a pool of sweat. It was about 90 in my room and the mid day sun was beating down on me. It took about 30 seconds to figure out where I was. I looked at the clock and it was 2:30pm. I felt just as tired as I did when I laid down. The only reason I got out of bed was to pee and drink something.  Then I held myself upright on the faucet knob while filling a glass with water. I downed the water and then immediately face planted again into my pillow.
I awoke the next time feeling slightly less groggy. I looked at the clock and again it was 2:30. I started to realize how long I had been out. I peed and drank 2 glasses of water. I went to the kitchen and started making a sandwich, still feeling like I could use a nap.
My roommate walked in and asked where I had been. I told him my story and asked what day it was. He started to laugh and said it was Thursday. I couldn't believe I had been asleep for almost four days. I'm still not sure if I woke up on Tuesday or Wednesday. I lied down on the couch and slept until 8. I immediately called a friend and asked him to bring me speed. I checked my pager and it had over 30 new pages. I made a few calls to inform people I was alive when my friend arrived with the speed. I snorted about 200mg and finally felt like normal. Over the next few days I gradually returned to a normal sleep pattern. I'd say it wasn't until Saturday that the drug had left my system.
After that experience, I had no plans of taking that drug again anytime soon. I tried the drug again a few months later and found 30mg to be a good dosage to put me out after a long night. No matter how little I took, Phenobarbital’s effects lasted 12 hours before it left my system. I never took a drug after that without researching it thoroughly first.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


In working through these topics with your class, we encourage you to follow the following guidelines, plus any additional guidelines that your class agrees on (these guidelines are adapted from [10.1.176]):


Projects
A selection of coding projects completed while attending Makers Academy. A variety of different technologies were used including Ruby on Rails, Angular.js, Rspec and Jasmine. The project span the full stack.
COMMANDOS
A bash tool to help new devlopers learn.
Commandos is a unique command line tool and learning aid that monitors commands entered into the bash terminal and suggests helpful tips based on the user's activity. Aimed at the beginner and anyone looking to expand their knowledge of the terminal, it is hoped that use of Commandos will help users to learn new commands, remember them through repetition and ultimately use this knowledge to get more out of the terminal.
Commandos has been developed by a team of 5 students at Makers Academy as part of their final project carried out in less than two weeks.
A bash tool to help new devlopers learn. Tech: Ruby, Shell, Rspec and Aruba
Designs that anticipate specific future requirements almost always end badly.
- Sandi Metz
EVIL NOTES
A single page web app that allows a user to make notes.
The app was built from the ground up, with no external libraries except http-server. We created our own testing frame-work to test drive the application based off Jasmine.
Indeed, the ratio of time spent reading versus writing is well over 10 to 1. We are constantly reading old code as part of the effort to write new code. ...[Therefore,] making it easy to read makes it easier to write.
- Robert C. Martin
ACEBOOK
A group project that clones Facebook.
Our international cohort of 12 programmers from 4 countries working in 2 timezones spent a week making this clone of a popular social media website.


Vodafone extends tech partnership with Ryanair
LONDON (Reuters) - Vodafone (VOD.L) has secured a seven-year technology partnership with Ryanair (RYA.I) to handle services including online booking, passenger boarding and in-flight transactions for the Irish airline in Europe.
The two companies said on Wednesday they had extended an existing partnership for Vodafone Business to support 300 Ryanair sites and some 153 million passengers across 40 countries.
As part of the agreement, the British mobile company will help Europe’s biggest budget airline to speed up the time it takes to connect a new airport or site for use. It should also lead to a faster turnaround of planes.
“Airline passengers will demand even more in the coming years, and we will work alongside Ryanair to help them prepare for the future using our full portfolio of products and services,” said Vinod Kumar, head of Vodafone Business.
Vodafone Business is the mobile operator’s enterprise arm that offers cloud IT services and the connection of unlimited devices on its Internet of Things network for small and multinational companies.
Vodafone Business accounted for 30% of group service revenue in its financial year ending March 31, 2019.


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NineGagComponent Fields
Fields
| Name | Description | |
|---|---|---|
| _filterService |
The filter service
|
|
| _logger |
The logger
|
|
| _nineGagFetchService |
The nine gag fetch service
|
|
| _nineGagMapperService |
The nine gag mapper service
|