An Integrative Analysis of Identified Schizophrenia-Associated Brain Cell Types and Gene Expression Changes.

Schizophrenia (SCZ) is a severe mental disorder that may result in hallucinations, delusions, and extremely disordered thinking. How each cell type in the brain contributes to SCZ occurrence is still unclear. Here, we leveraged the human dorsolateral prefrontal cortex bulk RNA-seq data, then used the RNA-seq deconvolution algorithm CIBERSORTx to generate SCZ brain single-cell RNA-seq data for a comprehensive analysis to understand SCZ-associated brain cell types and gene expression changes. Firstly, we observed that the proportions of brain cell types in SCZ differed from normal samples. Among these cell types, astrocyte, pericyte, and PAX6 cells were found to have a higher proportion in SCZ patients (astrocyte: SCZ = 0.163, control = 0.145, P.adj = 4.9 × 10-4, effect size = 0.478; pericyte: SCZ = 0.057, control = 0.066, P.adj = 1.1 × 10-4, effect size = 0.519; PAX6: SCZ = 0.014, control = 0.011, P.adj = 0.014, effect size = 0.377), while the L5/6_IT_CAR3 cells and LAMP5 cells are the exact opposite (L5/6_IT_Car3: SCZ = 0.102, control = 0.108, P.adj = 0.016, effect size = 0.369; LAMP5: SCZ = 0.057, control = 0.066, P.adj = 2.2 × 10-6, effect size = 0.617). Next, we investigated gene expression in cell types and functional pathways in SCZ. We observed chemical synaptic transmission dysregulation in two types of GABAergic neurons (PVALB and LAMP5), and immune reaction involvement in GABAergic neurons (SST) and non-neuronal cell types (endothelial and oligodendrocyte). Furthermore, we observed that some differential expression genes from bulk RNA-seq displayed cell-type-specific abnormalities in the expression of molecules in SCZ. Finally, the cell types with the SCZ-related transcriptomic changes could be considered to belong to the same module since we observed two major similar coordinated transcriptomic changes across these cell types. Together, our results offer novel insights into cellular heterogeneity and the molecular mechanisms underlying SCZ.

Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction.

Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks. These five strategies are spatial filtering, the singular value decomposition of a distance map, calculating the secondary structure feature, and the relative accessible surface area feature of proteins. The computational experiment results showed that all strategies were suitable and effective methods to encode structural information for deep learning models. In addition, by performing a case study of a mutated protein, we showed that the spatial filtering strategy could introduce structural changes into HHblits profiles and deep learning networks when protein mutation happens. In sum, this work provides new insight into the downstream tasks of protein-molecule interaction prediction, such as predicting the binding residues of proteins and estimating the effects of mutations.

Strategy of Extra Zr Doping on the Enhancement of Thermoelectric Performance for TiZrxNiSn Synthesized by a Modified Solid-State Reaction.

Half-Heusler alloys, which possess the advantages of high thermal stability, a large power factor, and good mechanical property, have been attracting increasing interest in mid-temperature thermoelectric applications. In this work, extra Zr-doped TiZrxNiSn samples were successfully prepared by a modified solid-state reaction followed by spark plasma sintering. It demonstrates that extra Zr doping could not only improve the power factor on account of an increase in the Seebeck coefficient but also suppress the lattice thermal conductivity originated from the strengthened phonon scattering by the superlattice nanodomains and the secondary nanoparticles. As a consequence, an increased power factor of 3.29 mW m-1 K-2 and a decreased lattice thermal conductivity of 1.74 W m-1 K-1 are achieved in TiZr0.015NiSn, leading to a peak ZT as high as 0.88 at 773 K and an average ZT value up to 0.62 in the temperature range of 373-773 K. This work gives guidance for optimizing the thermoelectric performance of TiNiSn-based alloys by modulating the microstructures on the secondary nanophases and superlattice nanodomains.

Realizing a High ZT of 1.6 in N-Type Mg3Sb2-Based Zintl Compounds through Mn and Se Codoping.

Mg3Sb2-based compounds by virtue of nontoxicity and low-cost have become a promising class of candidates for midtemperature thermoelectric power generation. Here, we successfully fabricated n-type Mg3Sb2-based materials using an inexpensive and efficient approach of one-step ball milling and spark plasma sintering, and demonstrate that a complementary and favorable effect of multiple elements coalloying/-doping leads to an excellent thermoelectric performance. The intrinsic p-type conducting behavior for Mg3Sb2 could be changed to n-type through Bi and Se coalloying on Sb sublattices with excess Mg, resulting from the suppression of Mg vacancies and the formation of Mg interstitial. Furthermore, Mn doping on Mg sublattices could soften the chemical bonds, leading to the increase of carrier mobility and concentration simultaneously. Additionally, multielement coalloying/-doping could significantly increase the lattice disorder, which undoubtedly strengthens the phonon scattering and readily results in a suppressed lattice thermal conductivity. As a result, a highest ZT value of 1.6 at 723 K and an average ZT value up to 1.1 were obtained in the temperature range of 323-723 K in the Mg3.18Mn0.02Sb1.5Bi0.49Se0.01 sample, which is one of the highest values among the Te free Mg3Sb2. This work could give guidance for improving the thermoelectric performance of Zintl phase materials or even others using the multielement codoping/-alloying strategy.