Carbon regeneration in the Cariaco Basin, Venezuela

The carbon regeneration in the water column of the Cariaco Basin (Venezuela) was investigated using a regression model of total alkalinity (TA) and the concentration of total inorganic carbon (TCO2). Primary productivity (PP) was determined from the inorganic carbon fraction assimilated by phytoplankton and the variation of the 22 and 23ºC isotherm was used as an indicator of coastal upwelling. The results indicate that CO2 levels were lowest (1962 µmol/kg) at the surface and increased to 2451 µmol/kg below the oxic-anoxic redox interface. The vertical regeneration distribution of carbon was dominated (82%) by organic carbon originating from the soft tissue of photosynthetic organisms, whereas 18% originated from the dissolution of biogenic calcite. The regeneration of organic carbon was highest in the surface layer in agreement with the primary productivity values. However, at the oxic-anoxic interface a second more intense maximum was detected (70-80%), generated by chemotrophic respiration of organic material by microorganisms. The percentages in the anoxic layers were lower than in the oxic zone because aerobic decomposition occurs more rapidly than anaerobic respiration of organic material because more labile fractions of organic carbon have already been mineralized in the upper layers

Selective lability of ruthenium(II) arene amino acid complexes.

A series of organometallic complexes of the form [(PhH)Ru(amino acid)](+) have been synthesized using amino acids able to act as tridentate ligands. The straightforward syntheses gave enantiomerically pure complexes with two stereogenic centers due to the enantiopurity of the chelating ligands. Complexes were characterized in the solid-state and/or solution-state where the stability of the complex allowed. The propensity toward labilization of the coordinatively saturated complexes was investigated. The links between complex stability and structural features are very subtle. Nonetheless, H/D exchange rates of coordinated amino groups reveal more significant differences in reactivity linked to metallocycle ring size resulting in decreasing stability of the metallocycle as the amino acid side-chain length increases. The behavior of these systems in acid is unusual, apparently labilizing the carboxylate residue of the amino acid. This acid-catalyzed hemilability in an organometallic is relevant to the use of Ru(II) arenes in medicinal contexts due to the relatively low pH of cancerous cells.TGS and MO thank the EPSRC for Studentships EP/P505445/1 and EP/K503/009/1, respectively.This is the final version of the article. It was first published by ACS at http://pubs.acs.org/doi/abs/10.1021/ic502051

An integrated computational-experimental approach reveals Yersinia pestis genes essential across a narrow or a broad range of environmental conditions

Background The World Health Organization has categorized plague as a re-emerging disease and the potential for Yersinia pestis to also be used as a bioweapon makes the identification of new drug targets against this pathogen a priority. Environmental temperature is a key signal which regulates virulence of the bacterium. The bacterium normally grows outside the human host at 28 °C. Therefore, understanding the mechanisms that the bacterium used to adapt to a mammalian host at 37 °C is central to the development of vaccines or drugs for the prevention or treatment of human disease. Results Using a library of over 1 million Y. pestis CO92 random mutants and transposon-directed insertion site sequencing, we identified 530 essential genes when the bacteria were cultured at 28 °C. When the library of mutants was subsequently cultured at 37 °C we identified 19 genes that were essential at 37 °C but not at 28 °C, including genes which encode proteins that play a role in enabling functioning of the type III secretion and in DNA replication and maintenance. Using genome-scale metabolic network reconstruction we showed that growth conditions profoundly influence the physiology of the bacterium, and by combining computational and experimental approaches we were able to identify 54 genes that are essential under a broad range of conditions. Conclusions Using an integrated computational-experimental approach we identify genes which are required for growth at 37 °C and under a broad range of environments may be the best targets for the development of new interventions to prevent or treat plague in humans

Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network

Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers

Key steps in the structure-based optimization of the hepatitis C virus NS3/4A protease inhibitor SCH503034

Crystal structures of protease/inhibitor complexes guided optimization of the buried nonpolar surface area thereby maximizing hydrophobic binding. The resulting potent tripeptide inhibitor is in clinical trials

Computational Identification of Uncharacterized Cruzain Binding Sites

Chagas disease, caused by the unicellular parasite Trypanosoma cruzi, claims 50,000 lives annually and is the leading cause of infectious myocarditis in the world. As current antichagastic therapies like nifurtimox and benznidazole are highly toxic, ineffective at parasite eradication, and subject to increasing resistance, novel therapeutics are urgently needed. Cruzain, the major cysteine protease of Trypanosoma cruzi, is one attractive drug target. In the current work, molecular dynamics simulations and a sequence alignment of a non-redundant, unbiased set of peptidase C1 family members are used to identify uncharacterized cruzain binding sites. The two sites identified may serve as targets for future pharmacological intervention

Dual coding with STDP in a spiking recurrent neural network model of the hippocampus.

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain