Sequence-dependent conformation of an A-DNA double helix: The crystal structure of the octamer d(G-G-T-A-T-A-C-C)

The crystal structures of the synthetic self-complementary octamer d(G-G-T-A-T-A-C-C) and its 5-bromouracil-containing analogue have been refined to R values of 20% and 14% at resolutions of 1·8 and 2·25 Å, respectively. The molecules adopt an A-DNA type double-helical conformation, which is minimally affected by crystal forces. A detailed analysis of the structure shows a considerable influence of the nucleotide sequence on the base-pair stacking patterns. In particular, the electrostatic stacking interactions between adjacent guanine and thymine bases produce symmetric bending of the double helix and a major-groove widening. The sugar-phosphate backbone appears to be only slightly affected by the base sequence. The local variations in the base-pair orientation are brought about by correlated adjustments in the backbone torsion angles and the glycosidic orientation. Sequence-dependent conformational variations of the type observed here may contribute to the specificity of certain protein-DNA interactions.

Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model

Background: Standard methods for quantifying IncuCyte ZOOM™ assays involve measurements that quantify how rapidly the initially-vacant area becomes re-colonised with cells as a function of time. Unfortunately, these measurements give no insight into the details of the cellular-level mechanisms acting to close the initially-vacant area. We provide an alternative method enabling us to quantify the role of cell motility and cell proliferation separately. To achieve this we calibrate standard data available from IncuCyte ZOOM™ images to the solution of the Fisher-Kolmogorov model. Results: The Fisher-Kolmogorov model is a reaction-diffusion equation that has been used to describe collective cell spreading driven by cell migration, characterised by a cell diffusivity, D, and carrying capacity limited proliferation with proliferation rate, λ, and carrying capacity density, K. By analysing temporal changes in cell density in several subregions located well-behind the initial position of the leading edge we estimate λ and K. Given these estimates, we then apply automatic leading edge detection algorithms to the images produced by the IncuCyte ZOOM™ assay and match this data with a numerical solution of the Fisher-Kolmogorov equation to provide an estimate of D. We demonstrate this method by applying it to interpret a suite of IncuCyte ZOOM™ assays using PC-3 prostate cancer cells and obtain estimates of D, λ and K. Comparing estimates of D, λ and K for a control assay with estimates of D, λ and K for assays where epidermal growth factor (EGF) is applied in varying concentrations confirms that EGF enhances the rate of scratch closure and that this stimulation is driven by an increase in D and λ, whereas K is relatively unaffected by EGF. Conclusions: Our approach for estimating D, λ and K from an IncuCyte ZOOM™ assay provides more detail about cellular-level behaviour than standard methods for analysing these assays. In particular, our approach can be used to quantify the balance of cell migration and cell proliferation and, as we demonstrate, allow us to quantify how the addition of growth factors affects these processes individually.

Great Barrier Reef Fish Biological Data

Biological measurements on fish sampled during the course of FRDC funded project Growth, Reproduction and Recruitment of Great Barrier Reef Food Fish Stocks (FRDC 90/18). The comma-delimited ascii file comprises the following fields: 1. Cruise number 2. Date (d-m-y) 3, Region (descriptor of part of Queensland coast or Great Barrier Reef system) 4. Reef (name or number) 5. Data source (Res=research, Rec=recreational fisher, Com=commercial fisher) 6. Capture method 7. Trap number (where appropriate) 8. Species name 9. LthStd (standard length, cm) 10. LthFrk (fork length, cm) 11. LthTot (total length, cm) 12. WtTot (approx total weight, g; weighed at sea) 13. FrameWt (weight of frame [after filleting, with viscera], g; weighed in lab) 14. Sex (macroscopic examination only) 15. GonadWt (g) Data obtained by the Department Employment, Economic Development and Innovation (formerly Primary Industries and Fisheries) between 1988 and 1993, primarily in the southern Great Barrier Reef (Capricorn-Bunker and Swain Groups), with fish traps and handlining.

Determination of management units for grey mackerel fisheries in northern Australia.

The requirement for Queensland, Northern Territory and Western Australian jurisdictions to ensure sustainable harvest of fish resources and their optimal use relies on robust information on the resource status. For grey mackerel (Scomberomorus semifasciatus) fisheries, each of these jurisdictions has their own management regime in their corresponding waters. The lack of information on stock structure of grey mackerel, however, means that the appropriate spatial scale of management is not known. As well, fishers require assurance of future sustainability to encourage investment and long-term involvement in a fishery that supplies lucrative overseas markets. These management and fisher-unfriendly circumstances must be viewed in the context of recent 3-fold increases in catches of grey mackerel along the Queensland east coast, combined with significant and increasing catches in other parts of the species' northern Australian range. Establishing the stock structure of grey mackerel would also immensely improve the relevance of resource assessments for fishery management of grey mackerel across northern Australia. This highlighted the urgent need for stock structure information for this species. The impetus for this project came from the strategic recommendations of the FRDC review by Ward and Rogers (2003), "Northern mackerel (Scombridae: Scomberomorus): current and future research needs" (Project No. 2002/096), which promoted the urgency for information on the stock structure of grey mackerel. In following these recommendations this project adopted a multi-technique and phased sampling approach as carried out by Buckworth et al (2007), who examined the stock structure of Spanish mackerel, Scomberomorus commerson, across northern Australia. The project objectives were to determine the stock structure of grey mackerel across their northern Australian range, and use this information to define management units and their appropriate spatial scales. We used multiple techniques concurrently to determine the stock structure of grey mackerel. These techniques were: genetic analyses (mitochondrial DNA and microsatellite DNA), otolith (ear bones) isotope ratios, parasite abundances, and growth parameters. The advantage of using this type of multi-technique approach was that each of the different methods is informative about the fish’s life history at different spatial and temporal scales. Genetics can inform about the evolutionary patterns as well as rates of mixing of fish from adjacent areas, while parasites and otolith microchemistry are directly influenced by the environment and so will inform about the patterns of movement during the fishes lifetime. Growth patterns are influenced by both genetic and environmental factors. Due to these differences the use of these techniques concurrently increases the likelihood of detecting different stocks where they exist. We adopted a phased sampling approach whereby sampling was carried out at broad spatial scales in the first year: east coast, eastern Gulf of Carpentaria (GoC), western GoC, and the NW Northern Territory (NW NT). By comparing the fish samples from each of these locations, and using each of the techniques, we tested the null hypothesis that grey mackerel were comprised of a single homogeneous population across northern Australia. Having rejected the null hypothesis we re-sampled the 1st year locations to test for temporal stability in stock structure, and to assess stock structure at finer spatial scales. This included increased spatial coverage on the east coast, the GoC, and WA. From genetic approaches we determined that there at least four genetic stocks of grey mackerel across northern Australia: WA, NW NT (Timor/Arafura), the GoC and the east Grey mackerel management units in northern Australia ix coast. All markers revealed concordant patterns showing WA and NW NT to be clearly divergent stocks. The mtDNA D-loop fragment appeared to have more power to resolve stock boundaries because it was able to show that the GoC and east coast QLD stocks were genetically differentiated. Patterns of stock structure on a finer scale, or where stock boundaries are located, were less clear. From otolith stable isotope analyses four major groups of S. semifasciatus were identified: WA, NT/GoC, northern east coast and central east coast. Differences in the isotopic composition of whole otoliths indicate that these groups must have spent their life history in different locations. The magnitude of the difference between the groups suggests a prolonged separation period at least equal to the fish’s life span. The parasite abundance analyses, although did not include samples from WA, suggest the existence of at least four stocks of grey mackerel in northern Australia: NW NT, the GoC, northern east coast and central east coast. Grey mackerel parasite fauna on the east coast suggests a separation somewhere between Townsville and Mackay. The NW NT region also appears to comprise a separate stock while within the GoC there exists a high degree of variability in parasite faunas among the regions sampled. This may be due to 1. natural variation within the GoC and there is one grey mackerel stock, or 2. the existence of multiple localised adult sub-stocks (metapopulations) within the GoC. Growth parameter comparisons were only possible from four major locations and identified the NW NT, the GoC, and the east coast as having different population growth characteristics. Through the use of multiple techniques, and by integrating the results from each, we were able to determine that there exist at least five stocks of grey mackerel across northern Australia, with some likelihood of additional stock structuring within the GoC. The major management units determined from this study therefore were Western Australia, NW Northern Territory (Timor/Arafura), the Gulf of Carpentaria, northern east Queensland coast and central east Queensland coast. The management implications of these results indicate the possible need for management of grey mackerel fisheries in Australia to be carried out on regional scales finer than are currently in place. In some regions the spatial scales of management might continue as is currently (e.g. WA), while in other regions, such as the GoC and the east coast, managers should at least monitor fisheries on a more local scale dictated by fishing effort and assess accordingly. Stock assessments should also consider the stock divisions identified, particularly on the east coast and for the GoC, and use life history parameters particular to each stock. We also emphasise that where we have not identified different stocks does not preclude the possibility of the occurrence of further stock division. Further, this study did not, nor did it set out to, assess the status of each of the stocks identified. This we identify as a high priority action for research and development of grey mackerel fisheries, as well as a management strategy evaluation that incorporates the conclusions of this work. Until such time that these priorities are addressed, management of grey mackerel fisheries should be cognisant of these uncertainties, particularly for the GoC and the Queensland east coast.

An Optimal g-Guidance Scheme for Satellite Launch Vehicles

A closed-loop steering logic based on an optimal (2-guidance is developed here. The guidance system drives the satellite launch vehicle along a two- or three- dimensional trajectory for placing the payload into a specified circular orbit. The modified g-guidance algorithm makes use of the optimal required velocity vector, which minimizes the total impulse needed for an equivalent two-impluse transfer from the present state to the final orbit. The required velocity vector is defined as velocity of the vehicle on the hypothetical transfer orbit immediately after the application of the first impulse. For this optimal transfer orbit, a simple and elegant expression for the Q-matrix is derived. A working principle for the guidance algorithm in terms of the major and minor cycles, and also for the generation of the steering command, is outlined.

Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and approximately 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 x 10(-)(8)), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Replication of the association of common rs9939609 variant of FTO with increased BMI in an Australian adult twin population but no evidence for gene by environment (G x E) interaction

OBJECTIVE: To further investigate a common variant (rs9939609) in the fat mass- and obesity-associated gene (FTO), which recent genome-wide association studies have shown to be associated with body mass index (BMI) and obesity. DESIGN: We examined the effect of this FTO variant on BMI in 3353 Australian adult male and female twins. RESULTS: The minor A allele of rs9939609 was associated with an increased BMI (P=0.0007). Each additional copy of the A allele was associated with a mean BMI increase of approximately 1.04 kg/m(2) (approximately 3.71 kg). Using variance components decomposition, we estimate that this single-nucleotide polymorphism accounts for approximately 3% of the genetic variance in BMI in our sample (approximately 2% of the total variance). By comparing intrapair variances of monozygotic twins of different genotypes we were able to perform a direct test of gene by environment (G x E) interaction in both sexes and gene by parity (G x P) interaction in women, but no evidence was found for either. CONCLUSIONS: In addition to supporting earlier findings that the rs9939609 variant in the FTO gene is associated with an increased BMI, our results indicate that the associated genetic effect does not interact with environment or parity.

Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution

Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10−9 to P = 1.8 × 10−40) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10−3 to P = 1.2 × 10−13). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.

G-protein beta3 subunit gene (GNB3) variant in causation of essential hypertension

-Essential hypertensives display enhanced signal transduction through pertussis toxin-sensitive G proteins. The T allele of a C825T variant in exon 10 of the G protein beta3 subunit gene (GNB3) induces formation of a splice variant (Gbeta3-s) with enhanced activity. The T allele of GNB3 was shown recently to be associated with hypertension in unselected German patients (frequency=0.31 versus 0.25 in control). To confirm and extend this finding in a different setting, we performed an association study in Australian white hypertensives. This involved an extensively examined cohort of 110 hypertensives, each of whom were the offspring of 2 hypertensive parents, and 189 normotensives whose parents were both normotensive beyond age 50 years. Genotyping was performed by polymerase chain reaction and digestion with BseDI, which either cut (C allele) or did not cut (T allele) the 268-bp polymerase chain reaction product. T allele frequency in the hypertensive group was 0.43 compared with 0.25 in the normotensive group (chi2=22; P=0.00002; odds ratio=2.3; 95% CI=1.7 to 3.3). The T allele tracked with higher pretreatment blood pressure: diastolic=105+/-7, 109+/-16, and 128+/-28 mm Hg (mean+/-SD) for CC, CT, and TT, respectively (P=0.001 by 1-way ANOVA). Blood pressures were higher in female hypertensives with a T allele (P=0.006 for systolic and 0.0003 for diastolic by ANOVA) than they were in male hypertensives. In conclusion, the present study of a group with strong family history supports a role for a genetically determined, physiologically active splice variant of the G protein beta3 subunit gene in the causation of essential hypertension.

Elihu Stone collection undated, 1913-1962.

Collection contains materials pertaining to the life and work of Stone.

Vykopirovka s plana Seĭtlerskoĭ dachi razverstani︠ia︡ Kr.A.S.SD.R., na uchastok zemli otvedennyĭ KOMZET'u (Beǐs-Lekhem), pri provedenii rabot po zemreforme v 1930 g.

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Vykopirovka s plana Seĭtlerskoĭ dachi razverstani︠ia︡, Kr.A.S.SD.R., na uchastok zemli otvedennyĭ KOMZET'u (s -Kh. ART. "Rabotnik" - Aranda), pri provedenii rabot po zemreforme v 1930 g.

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Chertezh zemlepol'zovahi︠ia︡ Komzet'a pri der. Kheǐrus, Seǐtlerskogo raǐona, Kr.A.S.SD.R., po proektu zemleustroǐstva 1930 g. po Ichkinskoǐ dache razverstani︠ia︡, v sos.tave Belokoshskogo sevooborotnogo uchastka

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