ABSTRACT
Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference, clustered regularly interspaced short palindromic repeat, and overexpression. TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26 945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery.
Subject(s)
Deep Learning , Humans , Gene Regulatory Networks , Gene Expression Profiling/methods , Computational Biology/methods , Gene Expression Regulation , RNA InterferenceABSTRACT
We theoretically investigate the thermoelectric transport properties of triangular triple quantum dots (TTQD) with the central quantum dot coupled to one metallic and one superconducting lead. The system shows significantly superior thermoelectric performance over parallel coupled triple quantum dots and those coupled to two conventional metallic leads. The thermoelectric coefficients strongly depend on the ratio of superconducting gap to interdot coupling, as well as asymmetry and interference effects. The thermopower exhibits single-platform and double-platform structures for different ratios of superconducting gap to interdot coupling. The thermopower and figure of merit achieve quite remarkable values near the superconducting gap edges where the single-particle tunnelling occurs. For symmetric coupling, the maximal figure of merit might reach the order of 102 when the superconducting gap is about half that of the interdot coupling. Moreover, the figure of merit can be further greatly enhanced by appropriately matching the electrode coupling asymmetry and interdot coupling asymmetry.
ABSTRACT
Adsorption is commonly accepted as a most promising strategy in dye wastewater treatment, and the widespread use of adsorption emphasizes the need to explore low-cost but excellent adsorbents. Herein, a low-cost adsorbent (calcium-rich biochar) was developed, which was directly pyrolyzed from spent mushroom substate without any modification. This study evaluated the potential application of two calcium-rich biochars (GSBC and LSBC) derived from spent substrates of Ganoderma lucidum and Lentinus edodes, respectively. The effects of pyrolysis temperature on the calcium-rich biochars characteristics and their adsorption mechanism for cationic dyes (Malachite Green oxalate (MG) and Safranine T (ST)) were studied systematically. The increase in pyrolysis temperature from 350 to 750 °C led to an increase in both biochar ash, Ca content, and specific surface area, which made high-temperature biochars (GS750 and LS750) the superior adsorbents for cationic dyes. Batch adsorption results showed LS750 was more efficient to adsorb dyes than GS750 attributed to its higher Ca content and larger specific surface area. According to the Langmuir model, LS750 had high adsorption capacities of 9,388.04 and 3,871.48 mg g-1 for Malachite green and ST, respectively. The adsorption mechanism of dye MG could be attributed to pore filling, hydrogen bonding, electrostatic interaction, ion exchange, and π-π stacking, while ST adsorption mainly involved pore filling, electrostatic interaction, ion exchange, and π-π stacking. Attributed to their excellent adsorption performance, cheap source, and good reusability, biochars obtained from SMSs were very promising in dyeing wastewater treatment.