Griffith University

About: Griffith University is a education organization based out in Brisbane, Queensland, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 13830 authors who have published 49318 publications receiving 1420865 citations.

The age of Homo naledi and associated sediments in the Rising Star Cave, South Africa

Abstract: Species of ancient humans and the extinct relatives of our ancestors are typically described from a limited number of fossils. However, this was not the case with Homo naledi. More than 1500 fossils representing at least 15 individuals of this species were unearthed from the Rising Star cave system in South Africa between 2013 and 2014. Found deep underground in the Dinaledi Chamber, the H. naledi fossils are the largest collection of a single species of an ancient human-relative discovered in Africa. After the discovery was reported, a number of questions still remained. Not least among these questions was: how old were the fossils? The material was undated, and predictions ranged from anywhere between 2 million years old and 100,000 years old. H. naledi shared several traits with the most primitive of our ancient relatives, including its small brain. As a result, many scientists guessed that H. naledi was an old species in our family tree, and possibly one of the earliest species to evolve in the genus Homo. Now, Dirks et al. – who include many of the researchers who were involved in the discovery of H. naledi – report that the fossils are most likely between 236,000 and 335,000 years old. These dates are based on measuring the concentration of radioactive elements, and the damage caused by these elements (which accumulates over time), in three fossilized teeth, plus surrounding rock and sediments from the cave chamber. Importantly, the most crucial tests were carried out at independent laboratories around the world, and the scientists conducted the tests without knowing the results of the other laboratories. Dirks et al. took these extra steps to make sure that the results obtained were reproducible and unbiased. The estimated dates are much more recent than many had predicted, and mean that H. naledi was alive at the same time as the earliest members of our own species – which most likely evolved between 300,000 and 200,000 years ago. These new findings demonstrate why it can be unwise to try to predict the age of a fossil based only on its appearance, and emphasize the importance of dating specimens via independent tests. Finally in two related reports, Berger et al. suggest how a primitive-looking species like H. naledi survived more recently than many would have predicted, while Hawks et al. describe the discovery of more H. naledi fossils from a separate chamber in the same cave system.

Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

Abstract: Motivation Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. Results The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications. Availability and implementation SPOT-disorder is available as a web server and as a standalone program at: http://sparks-lab.org/server/SPOT-disorder/index.php . Contact j.hanson@griffith.edu.au or yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au. Supplementary information Supplementary data is available at Bioinformatics online.

Eutrophication-driven deoxygenation in the coastal ocean

Abstract: Human activities, especially increased nutrient loads that set in motion a cascading chain of events related to eutrophication, accelerate development of hypoxia (lower oxygen concentration) in many areas of the world's coastal ocean. Climate changes and extreme weather events may modify hypoxia. Organismal and fisheries effects are at the heart of the coastal hypoxia issue, but more subtle regime shifts and trophic interactions are also cause for concern. The chemical milieu associated with declining dissolved oxygen concentrations affects the biogeochemical cycling of oxygen, carbon, nitrogen, phosphorus, silica, trace metals, and sulfide as observed in water column processes, shifts in sediment biogeochemistry, and increases in carbon, nitrogen, and sulfur, as well as shifts in their stable isotopes, in recently accumulated sediments.