Seasonal Variations in the Inputs and Fate of Mercury in a Northern Hardwood Forest

The Adirondack region of New York is sensitive to atmospheric mercury deposition. In this study, the fate of mercury inputs to the Huntington Wildlife Forest (HWF) of the Adirondack region was examined by conducting a mercury mass budget over the annual cycle. Mercury cycling processes analyzed included wet mercury deposition, dry mercury deposition, foliar mercury accumulation, throughfall mercury, litterfall mercury, soil mercury evasion, and soil solution mercury fluxes. The mercury transport processes were quantified by integrating data collected from different sources in recent years (2004-2011) over a monthly time step. Dry mercury deposition (16.3 µg m-2 yr-1) was more important than wet mercury deposition (6.3 µg m-2 yr-1) at the HWF. Most of the atmospheric mercury deposition (\u3e 60%) was retained in the forest soils where litterfall (17.2 µg m-2 yr-1) was the major input pathway. Soil evasion (6.5 µg m-2 yr-1) was the most important mercury export mechanism, exceeding mercury fluxes in lateral and vertical drainage from soil (2.8 µg m-2 yr-1). This analysis showed marked seasonal variation in the transport of mercury that was strongly mediated by the forest ecosystem. The upland hardwood forest ecosystem was a net sink for atmospheric mercury deposition. Controls on mercury anthropogenic emissions would likely decrease mercury accumulation in the forest soils and lengthen the residence time of soil mercury at the HWF

Nuclear RNA Surveillance in \u3cem\u3eSaccharomyces cerevisiae\u3c/em\u3e: Trf4p-dependent Polyadenylation of Nascent Hypomethylated tRNA and an Aberrant Form of 5S rRNA

1-Methyladenosine modification at position 58 of tRNA is catalyzed by a two-subunit methyltransferase composed of Trm6p and Trm61p in Saccharomyces cerevisiae. Initiator tRNA (tRNAiMet) lacking m1A58 (hypomethylated) is rendered unstable through the cooperative function of the poly(A) polymerases, Trf4p/Trf5p, and the nuclear exosome. We provide evidence that a catalytically active Trf4p poly(A) polymerase is required for polyadenylation of hypomethylated tRNAiMet in vivo. DNA sequence analysis of tRNAiMet cDNAs and Northern hybridizations of poly(A)+ RNA provide evidence that nascent pre-tRNAiMet transcripts are targeted for polyadenylation and degradation. We determined that a mutant U6 snRNA and an aberrant form of 5S rRNA are stabilized in the absence of Trf4p, supporting that Trf4p facilitated RNA surveillance is a global process that stretches beyond hypomethylated tRNAiMet. We conclude that an array of RNA polymerase III transcripts are targeted for Trf4p/ Trf5p-dependent polyadenylation and turnover to eliminate mutant and variant forms of normally stable RNAs

RNA Unwinding by the Trf4/Air2/Mtr4 Polyadenylation (TRAMP) Complex

Many RNA-processing events in the cell nucleus involve the Trf4/Air2/Mtr4 polyadenylation (TRAMP) complex, which contains the poly(A) polymerase Trf4p, the Zn-knuckle protein Air2p, and the RNA helicase Mtr4p. TRAMP polyadenylates RNAs designated for processing by the nuclear exosome. In addition, TRAMP functions as an exosome cofactor during RNA degradation, and it has been speculated that this role involves disruption of RNA secondary structure. However, it is unknown whether TRAMP displays RNA unwinding activity. It is also not clear how unwinding would be coordinated with polyadenylation and the function of the RNA helicase Mtr4p in modulating poly(A) addition. Here, we show that TRAMP robustly unwinds RNA duplexes. The unwinding activity of Mtr4p is significantly stimulated by Trf4p/Air2p, but the stimulation of Mtr4p does not depend on ongoing polyadenylation. Nonetheless, polyadenylation enables TRAMP to unwind RNA substrates that it otherwise cannot separate. Moreover, TRAMP displays optimal unwinding activity on substrates with a minimal Mtr4p binding site comprised of adenylates. Our results suggest a model for coordination between unwinding and polyadenylation activities by TRAMP that reveals remarkable synergy between helicase and poly(A) polymerase

Rex1p Deficiency Leads to Accumulation of Precursor Initiator tRNA\u3csup\u3eMet\u3c/sup\u3e and Polyadenylation of Substrate RNAs in \u3cem\u3eSaccharomyces cerevisiae\u3c/em\u3e

A synthetic genetic array was used to identify lethal and slow-growth phenotypes produced when a mutation in TRM6, which encodes a tRNA modification enzyme subunit, was combined with the deletion of any non-essential gene in Saccharomyces cerevisiae. We found that deletion of the REX1 gene resulted in a slow-growth phenotype in the trm6-504 strain. Previously, REX1 was shown to be involved in processing the 3′ ends of 5S rRNA and the dimeric tRNAArg-tRNAAsp. In this study, we have discovered a requirement for Rex1p in processing the 3′ end of tRNAiMet precursors and show that precursor tRNAiMet accumulates in a trm6-504 rex1Δ strain. Loss of Rex1p results in polyadenylation of its substrates, including tRNAiMet, suggesting that defects in 3′ end processing can activate the nuclear surveillance pathway. Finally, purified Rex1p displays Mg2+-dependent ribonuclease activity in vitro, and the enzyme is inactivated by mutation of two highly conserved amino acids

Neurodynamics of up and down Transitions in Network Model

This paper focuses on the neurodynamical research of a small neural network that consists of 25 neurons. We study the periodic spontaneous activity and transitions between up and down states without synaptic input. The results demonstrate that these transitions are bidirectional or unidirectional with the parameters changing, which not only reveals the function of the cortex, but also cohere with the experiment results

An Unsupervised Deep-Transfer-Learning-Based Motor Imagery EEG Classification Scheme for Brain-Computer Interface

Brain–computer interface (BCI) research has attracted worldwide attention and has been rapidly developed. As one well-known non-invasive BCI technique, electroencephalography (EEG) records the brain’s electrical signals from the scalp surface area. However, due to the non-stationary nature of the EEG signal, the distribution of the data collected at different times or from different subjects may be different. These problems affect the performance of the BCI system and limit the scope of its practical application. In this study, an unsupervised deep-transfer-learning-based method was proposed to deal with the current limitations of BCI systems by applying the idea of transfer learning to the classification of motor imagery EEG signals. The Euclidean space data alignment (EA) approach was adopted to align the covariance matrix of source and target domain EEG data in Euclidean space. Then, the common spatial pattern (CSP) was used to extract features from the aligned data matrix, and the deep convolutional neural network (CNN) was applied for EEG classification. The effectiveness of the proposed method has been verified through the experiment results based on public EEG datasets by comparing with the other four methods