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Institution

University of Adelaide

EducationAdelaide, South Australia, Australia
About: University of Adelaide is a education organization based out in Adelaide, South Australia, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 27251 authors who have published 79167 publications receiving 2671128 citations. The organization is also known as: The University of Adelaide & Adelaide University.


Papers
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Journal ArticleDOI
TL;DR: This work has identified mutations in an X chromosome–linked, Aristaless-related, homeobox gene (ARX), in nine families with mental retardation, various forms of epilepsy, including infantile spasms and myoclonic seizures, and dystonia.
Abstract: Mental retardation and epilepsy often occur together. They are both heterogeneous conditions with acquired and genetic causes. Where causes are primarily genetic, major advances have been made in unraveling their molecular basis. The human X chromosome alone is estimated to harbor more than 100 genes that, when mutated, cause mental retardation. At least eight autosomal genes involved in idiopathic epilepsy have been identified, and many more have been implicated in conditions where epilepsy is a feature. We have identified mutations in an X chromosome-linked, Aristaless-related, homeobox gene (ARX), in nine families with mental retardation (syndromic and nonspecific), various forms of epilepsy, including infantile spasms and myoclonic seizures, and dystonia. Two recurrent mutations, present in seven families, result in expansion of polyalanine tracts of the ARX protein. These probably cause protein aggregation, similar to other polyalanine and polyglutamine disorders. In addition, we have identified a missense mutation within the ARX homeodomain and a truncation mutation. Thus, it would seem that mutation of ARX is a major contributor to X-linked mental retardation and epilepsy.

411 citations

Journal ArticleDOI
TL;DR: A set of revised global palaeogeographic maps for the 825-540-Ma interval using the latest palaeomagnetic data, along with lithological information for Neoproterozoic sedimentary basins was presented in this paper.

411 citations

Journal ArticleDOI
TL;DR: It is concluded that postnatal blood lead concentration is inversely related to cognitive development in children, although one must be circumspect in making causal inferences from studies of this relation, because of the difficulties in defining and controlling confounding effects.
Abstract: We studied the effect of environmental exposure to lead on children's abilities at the age of four years in a cohort of 537 children born during 1979 to 1982 to women living in a community situated near a lead smelter. Samples for measuring blood lead levels were obtained from the mothers antenatally, at delivery from the mothers and umbilical cords, and at the ages of 6, 15, and 24 months and then annually from the children. Concurrently, the mothers were interviewed about personal, family, medical, and environmental factors. Maternal intelligence, the home environment, and the children's mental development (as evaluated with use of the McCarthy Scales of Children's Abilities) were formally assessed. The mean blood lead concentration varied from 0.44 μmol per liter in midpregnancy to a peak of 1.03 μmol per liter at the age of two years. The blood lead concentration at each age, particularly at two and three years, and the integrated postnatal average concentration were inversely related to deve...

411 citations

Journal ArticleDOI
TL;DR: There was significant variation in iron plaque formation between genotypes, and the distribution of arsenic in different components of mature rice plants followed the following order: iron plaque > root > straw > husk > grain for all genotypes.
Abstract: A compartmented soil-glass bead culture system was used to investigate characteristics of iron plaque and arsenic accumulation and speciation in mature rice plants with different capacities of forming iron plaque on their roots. X-ray absorption near-edge structure spectra and extended X-ray absorption fine structure were utilized to identify the mineralogical characteristics of iron plaque and arsenic sequestration in plaque on the rice roots. Iron plaque was dominated by (oxyhydr)oxides, which were composed of ferrihydrite (81-100%), with a minor amount of goethite (19%) fitted in one of the samples. Sequential extraction and XANES data showed that arsenic in iron plaque was sequestered mainly with amorphous and crystalline iron (oxyhydr)oxides, and that arsenate was the predominant species. There was significant variation in iron plaque formation between genotypes, and the distribution of arsenic in different components of mature rice plants followed the following order: iron plaque > root > straw > husk > grain for all genotypes. Arsenic accumulation in grain differed significantly among genotypes. Inorganic arsenic and dimethylarsinic acid (DMA) were the main arsenic species in rice grain for six genotypes, and there were large genotypic differences in levels of DMA and inorganic arsenic in grain.

411 citations

Book ChapterDOI
23 Aug 2020
TL;DR: A simpler instance segmentation method that can achieve improved performance in both accuracy and inference speed on the COCO dataset, and outperform a few recent methods including well-tuned Mask RCNN baselines, without longer training schedules needed.
Abstract: We propose a simple yet effective instance segmentation framework, termed CondInst (conditional convolutions for instance segmentation). Top-performing instance segmentation methods such as Mask R-CNN rely on ROI operations (typically ROIPool or ROIAlign) to obtain the final instance masks. In contrast, we propose to solve instance segmentation from a new perspective. Instead of using instance-wise ROIs as inputs to a network of fixed weights, we employ dynamic instance-aware networks, conditioned on instances. CondInst enjoys two advantages: (1) Instance segmentation is solved by a fully convolutional network, eliminating the need for ROI cropping and feature alignment. (2) Due to the much improved capacity of dynamically-generated conditional convolutions, the mask head can be very compact (e.g., 3 conv. layers, each having only 8 channels), leading to significantly faster inference. We demonstrate a simpler instance segmentation method that can achieve improved performance in both accuracy and inference speed. On the COCO dataset, we outperform a few recent methods including well-tuned Mask R-CNN baselines, without longer training schedules needed. Code is available: https://git.io/AdelaiDet.

411 citations


Authors

Showing all 27579 results

NameH-indexPapersCitations
Martin White1962038232387
Nicholas G. Martin1921770161952
David W. Johnson1602714140778
Nicholas J. Talley158157190197
Mark E. Cooper1581463124887
Xiang Zhang1541733117576
John E. Morley154137797021
Howard I. Scher151944101737
Christopher M. Dobson1501008105475
A. Artamonov1501858119791
Timothy P. Hughes14583191357
Christopher Hill1441562128098
Shi-Zhang Qiao14252380888
Paul Jackson141137293464
H. A. Neal1411903115480
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023127
2022597
20215,500
20205,342
20194,803
20184,443