Non unital generalized tracially approximated C*-algebras

Let $\Omega$ be a class of ${\rm C^*}$-algebras. In this paper, we study a class of not necessarily unital generalized tracial approximation ${\rm C^*}$-algebras, and the class of simple ${\rm C^*}$-algebras which can be generally tracially approximated by ${\rm C^*}$-algebras in $\Omega$, denoted by ${\rm gTA}\Omega$. Let $\Omega$ be a class of unital ${\rm C^*}$-algebras and let $A$ be a simple unital ${\rm C^*}$-algebra. Then $A\in {\rm gTA}\Omega$, if, and only if, $A\in {\rm WTA}\Omega$ (where ${\rm TA}\Omega$ is the class of weakly tracially approximable unital ${\rm C^*}$-algebras introduced by Elliott, Fan, and Fang).Consider the class of ${\rm C^*}$-algebras which are tracially $\mathcal{Z}$-absorbing (or are of tracial nuclear dimension at most $n$, or are $m$-almost divisible, or have the property $\rm SP$). Then $A$ is tracially $\mathcal{Z}$-absorbing (respectively, has tracial nuclear dimension at most $n$, is weakly ($n, m$)-almost divisible, has the property $\rm SP$) for any simple ${\rm C^*}$-algebra $A$ in the corresponding class of generalized tracial approximation ${\rm C^*}$-algebras.Comment: 21 pages. arXiv admin note: text overlap with arXiv:2309.08900, arXiv:2203.05700, arXiv:2206.0503

Distinct transcriptional responses of RNA polymerases I, II and III to aptamers that bind TBP

The TATA-binding protein (TBP) is a general factor that is involved in transcription by all three types of nuclear RNA polymerase. To delineate the roles played by the DNA-binding surface of TBP in these transcription reactions, we used a set of RNA aptamers directed against TBP and examined their ability to perturb transcription in vitro by the different RNA polymerases. Distinct responses to the TBP aptamers were observed for transcription by different types of polymerase at either the initiation, reinitiation or both stages of the transcription cycle. We further probed the TBP interactions in the TFIIIB•DNA complex to elucidate the mechanism for the different sensitivity of Pol III dependent transcription before and after preinitiation complex (PIC) formation. Lastly, the aptamers were employed to measure the time required for Pol III PIC formation in vitro. This approach can be generalized to define the involvement of a particular region on the surface of a protein at particular stages in a biological process

Simple and Efficient Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) over homogeneous graphs, especially the attention mechanism and the multi-layer structure. These mechanisms bring excessive complexity, but seldom work studies whether they are really effective on heterogeneous graphs. This paper conducts an in-depth and detailed study of these mechanisms and proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To easily capture structural information, SeHGNN pre-computes the neighbor aggregation using a light-weight mean aggregator, which reduces complexity by removing overused neighbor attention and avoiding repeated neighbor aggregation in every training epoch. To better utilize semantic information, SeHGNN adopts the single-layer structure with long metapaths to extend the receptive field, as well as a transformer-based semantic fusion module to fuse features from different metapaths. As a result, SeHGNN exhibits the characteristics of simple network structure, high prediction accuracy, and fast training speed. Extensive experiments on five real-world heterogeneous graphs demonstrate the superiority of SeHGNN over the state-of-the-arts on both accuracy and training speed.Comment: Accepted by AAAI 202

Integrated Smartphone-App-Chip System for On-Site Ppb-Level Colorimetric Quantitation of Aflatoxins

We demonstrate herein an integrated, smartphone-app-chip (SPAC) system for on-site quantitation of food toxins, as demonstrated with aflatoxin B1 (AFB1), at parts-per-billion (ppb) level in food products. The detection is based on an indirect competitive immunoassay fabricated on a transparent plastic chip with the assistance of a microfluidic channel plate. A 3D-printed optical accessory attached to a smartphone is adapted to align the assay chip and to provide uniform illumination for imaging, with which high-quality images of the assay chip are captured by the smartphone camera and directly processed using a custom-developed Android app. The performance of this smartphone-based detection system was tested using both spiked and moldy corn samples; consistent results with conventional ELISA kits were obtained. The achieved detection limit (3±1 μg/kg, equivalent to ppb) and dynamic response range (0.5−250 μg/kg) meet the requested testing standards set by authorities worldwide. We envision that the integrated SPAC system promises to be a simple and accurate method of food toxin quantitation, bringing much benefit for rapid on-site screening