Realization of the Space of Conformal Blocks in Lie Algebra Modules

AbstractUsing integrable irreducible representations of generalized twisted affine Lie algebra modules, we give a realization of the space of conformal blocks of conformal field theory on a stable algebraic curve. Many basic properties of the conformal blocks, such as finite dimensionality of the space, invariance of the conformal blocks under suitable formal neighborhood changes, and the property of “propagation of vacua” are discussed. Finally, a relative local 1-form around a fixed point of the order two automorphism of the curve is given

Voltage-dependent Gating Rearrangements in the Intracellular T1–T1 Interface of a K+ Channel

The intracellular tetramerization domain (T1) of most eukaryotic voltage-gated potassium channels (Kv channels) exists as a “hanging gondola” below the transmembrane regions that directly control activation gating via the electromechanical coupling between the S4 voltage sensor and the main S6 gate. However, much less is known about the putative contribution of the T1 domain to Kv channel gating. This possibility is mechanistically intriguing because the T1–S1 linker connects the T1 domain to the voltage-sensing domain. Previously, we demonstrated that thiol-specific reagents inhibit Kv4.1 channels by reacting in a state-dependent manner with native Zn2+ site thiolate groups in the T1–T1 interface; therefore, we concluded that the T1–T1 interface is functionally active and not protected by Zn2+ (Wang, G., M. Shahidullah, C.A. Rocha, C. Strang, P.J. Pfaffinger, and M. Covarrubias. 2005. J. Gen. Physiol. 126:55–69). Here, we co-expressed Kv4.1 channels and auxiliary subunits (KChIP-1 and DPPX-S) to investigate the state and voltage dependence of the accessibility of MTSET to the three interfacial cysteines in the T1 domain. The results showed that the average MTSET modification rate constant (kMTSET) is dramatically enhanced in the activated state relative to the resting and inactivated states (∼260- and ∼47-fold, respectively). Crucially, under three separate conditions that produce distinct activation profiles, kMTSET is steeply voltage dependent in a manner that is precisely correlated with the peak conductance–voltage relations. These observations strongly suggest that Kv4 channel gating is tightly coupled to voltage-dependent accessibility changes of native T1 cysteines in the intersubunit Zn2+ site. Furthermore, cross-linking of cysteine pairs across the T1–T1 interface induced substantial inhibition of the channel, which supports the functionally dynamic role of T1 in channel gating. Therefore, we conclude that the complex voltage-dependent gating rearrangements of eukaryotic Kv channels are not limited to the membrane-spanning core but must include the intracellular T1–T1 interface. Oxidative stress in excitable tissues may perturb this interface to modulate Kv4 channel function

Prediction of microbial communities for urban metagenomics using neural network approach.

BACKGROUND:Microbes are greatly associated with human health and disease, especially in densely populated cities. It is essential to understand the microbial ecosystem in an urban environment for cities to monitor the transmission of infectious diseases and detect potentially urgent threats. To achieve this goal, the DNA sample collection and analysis have been conducted at subway stations in major cities. However, city-scale sampling with the fine-grained geo-spatial resolution is expensive and laborious. In this paper, we introduce MetaMLAnn, a neural network based approach to infer microbial communities at unsampled locations given information reflecting different factors, including subway line networks, sampling material types, and microbial composition patterns. RESULTS:We evaluate the effectiveness of MetaMLAnn based on the public metagenomics dataset collected from multiple locations in the New York and Boston subway systems. The experimental results suggest that MetaMLAnn consistently performs better than other five conventional classifiers under different taxonomic ranks. At genus level, MetaMLAnn can achieve F1 scores of 0.63 and 0.72 on the New York and the Boston datasets, respectively. CONCLUSIONS:By exploiting heterogeneous features, MetaMLAnn captures the hidden interactions between microbial compositions and the urban environment, which enables precise predictions of microbial communities at unmeasured locations