The Effects of Transcranial Direct Current Stimulation (tDCS) on Working Memory Training in Healthy Young Adults

Working memory (WM) is a fundamental cognitive ability to support complex thought, but it is limited in capacity. WM training has shown the potential benefit for those in need of a higher WM ability. Many studies have shown the potential of transcranial direct current stimulation (tDCS) to transiently enhance WM performance by delivering a low current to the brain cortex of interest, via electrodes on the scalp. tDCS has also been revealed as a promising intervention to augment WM training in a few studies. However, those few tDCS-paired WM training studies, focused more on the effect of tDCS on WM enhancement and its transferability after training and paid less attention to the variation of cognitive performance during the training procedure. The current study attempted to explore the effect of tDCS on the variation of performance, during WM training, in healthy young adults. All the participants received WM training with the load-adaptive verbal N-back task, for 5 days. During the training procedure, active/sham anodal high-definition tDCS (HD-tDCS) was used to stimulate the left dorsolateral prefrontal cortex (DLPFC). To examine the training effect, pre- and post-tests were performed, respectively, 1 day before and after the training sessions. At the beginning of each training session, stable-load WM tasks were performed, to examine the performance variation during training. Compared to the sham stimulation, higher learning rates of performance metrics during the training procedure were found when WM training was combined with active anodal HD-tDCS. The performance improvements (post–pre) of the active group, were also found to be higher than those of the sham group and were transferred to a similar untrained WM task. Further analysis revealed a negative relationship between the training improvements and the baseline performance. These findings show the potential that tDCS may be leveraged as an intervention to facilitate WM training, for those in need of a higher WM ability

The Prevalence of Colistin Resistant Strains and Antibiotic Resistance Gene Profiles in Funan River, China

Anthropogenic activities near urban rivers may have significantly increased the acquisition and dissemination of antibiotic resistance. In this study, we investigated the prevalence of colistin resistant strains in the Funan River in Chengdu, China. A total of 18 mcr-1-positive isolates (17 Escherichia coli and 1 Enterobacter cloacae) and 6 mcr-3-positive isolates (2 Aeromonas veronii and 4 Aeromonas hydrophila) were detected, while mcr-2, mcr-4 and mcr-5 genes were not detected in any isolates. To further explore the overall antibiotic resistance in the Funan River, water samples were assayed for the presence of 15 antibiotic resistance genes (ARGs) and class 1 integrons gene (intI1). Nine genes, sul1, sul2, intI1, aac(6′)-Ib-cr, blaCTX-M, tetM, ermB, qnrS, and aph(3′)-IIIa were found at high frequencies (70–100%) of the water samples. It is worth noting that mcr-1, blaKPC, blaNDM and vanA genes were also found in water samples, the genes that have been rarely reported in natural river systems. The absolute abundance of selected antibiotic resistance genes [sul1, aac(6′)-Ib-cr, ermB, blaCTX-M, mcr-1, and tetM] ranged from 0 to 6.0 (log10 GC/mL) in water samples, as determined by quantitative polymerase chain reaction (qPCR). The sul1, aac(6′)-Ib-cr, and ermB genes exhibited the highest absolute abundances, with 5.8, 5.8, and 6.0 log10 GC/mL, respectively. The absolute abundances of six antibiotic resistance genes were highest near a residential sewage outlet. The findings indicated that the discharge of resident sewage might contribute to the dissemination of antibiotic resistant genes in this urban river. The observed high levels of these genes reflect the serious degree of antibiotic resistant pollution in the Funan River, which might present a threat to public health

Machine Learning of Atomic Forces from Quantum Mechanics: a Model Based on Pairwise Interatomic Forces

We present a new chemically intuitive approach, pairF-Net, to directly predict the atomic forces in a molecule to quantum chemistry accuracy using machine learning techniques. A residual artificial neural network has been designed and trained with features and targets based on pairwise interatomic forces, to determine the Cartesian atomic forces suitable for use in molecular mechanics and dynamics calculations. The scheme implicitly maintains rotational and translational invariance and predicts Cartesian forces as a linear combination of a set of force components in an interatomic basis. We show that the method can predict the reconstructed Cartesian atomic forces for a set of small organic molecules to less than 2 kcal mol-1 Å-1 from the reference force values obtained via density functional theory. The pairF-Net scheme utilises a simple and chemically intuitive route to furnish atomic forces at a quantum mechanical level but at a fraction of the cost, providing a step towards the efficient calculation of accurate thermodynamic properties. </p