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

Degradation of Hypomodified tRNA\u3csub\u3ei\u3c/sub\u3e\u3csup\u3eMet\u3c/sup\u3e in vivo Involves RNA-dependent ATPase Activity of the DExH Helicase Mtr4p

Effective turnover of many incorrectly processed RNAs in yeast, including hypomodified tRNAi Met, requires the TRAMP complex, which appends a short poly(A) tail to RNA designated for decay. The poly(A) tail stimulates degradation by the exosome. The TRAMP complex contains the poly(A) polymerase Trf4p, the RNA-binding protein Air2p, and the DExH RNA helicase Mtr4p. The role of Mtr4p in RNA degradation processes involving the TRAMP complex has been unclear. Here we show through a genetic analysis that MTR4 is required for degradation but not for polyadenylation of hypomodified tRNAi Met. A suppressor of the trm6-504 mutation in the tRNA m1A58 methyltransferase (Trm6p/Trm61p), which causes a reduced level of tRNAi Met, was mapped to MTR4. This mtr4-20 mutation changed a single amino acid in the conserved helicase motif VI of Mtr4p. The mutation stabilizes hypomodified tRNAi Met in vivo but has no effect on TRAMP complex stability or polyadenylation activity in vivo or in vitro. We further show that purified recombinant Mtr4p displays RNA-dependent ATPase activity and unwinds RNA duplexes with a 3′-to-5′ polarity in an ATP-dependent fashion. Unwinding and RNA-stimulated ATPase activities are strongly reduced in the recombinant mutant Mtr4-20p, suggesting that these activities of Mtr4p are critical for degradation of polyadenylated hypomodified tRNAi Met

PoKE: Prior Knowledge Enhanced Emotional Support Conversation with Latent Variable

Emotional support conversation (ESC) task can utilize various support strategies to help people relieve emotional distress and overcome the problem they face, which has attracted much attention in these years. However, most state-of-the-art works rely heavily on external commonsense knowledge to infer the mental state of the user in every dialogue round. Although effective, they may suffer from significant human effort, knowledge update and domain change in a long run. Therefore, in this article, we focus on exploring the task itself without using any external knowledge. We find all existing works ignore two significant characteristics of ESC. (a) Abundant prior knowledge exists in historical conversations, such as the responses to similar cases and the general order of support strategies, which has a great reference value for current conversation. (b) There is a one-to-many mapping relationship between context and support strategy, i.e.multiple strategies are reasonable for a single context. It lays a better foundation for the diversity of generations. Taking into account these two key factors, we propose Prior Knowledge Enhanced emotional support model with latent variable, PoKE. The proposed model fully taps the potential of prior knowledge in terms of exemplars and strategy sequence and then utilizes a latent variable to model the one-to-many relationship of strategy. Furthermore, we introduce a memory schema to incorporate the encoded knowledge into decoder. Experiment results on benchmark dataset show that our PoKE outperforms existing baselines on both automatic evaluation and human evaluation. Compared with the model using external knowledge, PoKE still can make a slight improvement in some metrics. Further experiments prove that abundant prior knowledge is conducive to high-quality emotional support, and a well-learned latent variable is critical to the diversity of generations

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

NetGPT: Generative Pretrained Transformer for Network Traffic

Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate distinguishable results for input traffic without considering specific downstream tasks. Effective pretrained models can significantly optimize the training efficiency and effectiveness of downstream tasks, such as traffic classification, attack detection, resource scheduling, protocol analysis, and traffic generation. Despite the great success of pretraining in natural language processing, there is no work in the network field. Considering the diverse demands and characteristics of network traffic and network tasks, it is non-trivial to build a pretrained model for network traffic and we face various challenges, especially the heterogeneous headers and payloads in the multi-pattern network traffic and the different dependencies for contexts of diverse downstream network tasks. To tackle these challenges, in this paper, we make the first attempt to provide a generative pretrained model for both traffic understanding and generation tasks. We propose the multi-pattern network traffic modeling to construct unified text inputs and support both traffic understanding and generation tasks. We further optimize the adaptation effect of the pretrained model to diversified tasks by shuffling header fields, segmenting packets in flows, and incorporating diverse task labels with prompts. Expensive experiments demonstrate the effectiveness of our NetGPT in a range of traffic understanding and generation tasks, and outperform state-of-the-art baselines by a wide margin

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