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Thermoelectric modules can convert waste heat directly into useful electricity, providing a clean and sustainable way to use fossil energy more efficiently. Mg3Sb2-based alloys have recently attracted considerable interest from the thermoelectric community due to their nontoxic nature, abundance of constituent elements and excellent mechanical and thermoelectric properties. However, robust modules based on Mg3Sb2 have progressed less rapidly. Here, we develop multiple-pair thermoelectric modules consisting of both n-type and p-type Mg3Sb2-based alloys. Thermoelectric legs based on the same parent fit into each other in terms of thermomechanical properties, facilitating module fabrication and ensuring low thermal stress. By adopting a suitable diffusion barrier layer and developing a new joining technique, an integrated all-Mg3Sb2-based module demonstrates a high efficiency of 7.5% at a temperature difference of 380 K, exceeding the state-of-the-art same-parent thermoelectric modules. Moreover, the efficiency remains stable during 150 thermal cycling shocks (∼225 h), demonstrating excellent module reliability.

Long-read sequencing has become a powerful tool for alternative splicing analysis. However, technical and computational challenges have limited our ability to explore alternative splicing at single cell and spatial resolution. The higher sequencing error of long reads, especially high indel rates, have limited the accuracy of cell barcode and unique molecular identifier (UMI) recovery. Read truncation and mapping errors, the latter exacerbated by the higher sequencing error rates, can cause the false detection of spurious new isoforms. Downstream, there is yet no rigorous statistical framework to quantify splicing variation within and between cells/spots. In light of these challenges, we developed Longcell, a statistical framework and computational pipeline for accurate isoform quantification for single cell and spatial spot barcoded long read sequencing data. Longcell performs computationally efficient cell/spot barcode extraction, UMI recovery, and UMI-based truncation- and mapping-error correction. Through a statistical model that accounts for varying read coverage across cells/spots, Longcell rigorously quantifies the level of inter-cell/spot versus intra-cell/ spot diversity in exon-usage and detects changes in splicing distributions between cell populations. Applying Longcell to single cell long-read data from multiple contexts, we found that intra-cell splicing heterogeneity, where multiple isoforms co-exist within the same cell, is ubiquitous for highly expressed genes. On matched single cell and Visium long read sequencing for a tissue of colorectal cancer metastasis to the liver, Longcell found concordant signals between the two data modalities. Finally, on a perturbation experiment for 9 splicing factors, Longcell identified regulatory targets that are validated by targeted sequencing.

This work develops an efficient, environmentally friendly and cost-effective thermoelectric module for mid-temperature waste heat recovery by using n-type Se-doped Mg3(Bi,Sb)2 and p-type CoSb3-based skutterudite.

The rapid development of novel spatial transcriptomic and proteomic technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational tools that are easily accessible and intuitive to use. Here we present Giotto, a comprehensive, flexible, robust, and open-source toolbox for spatial transcriptomic and proteomic data analysis and visualization. The data analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing cell-type distribution, spatially coherent gene expression patterns, and interactions between each cell and its surrounding neighbors. Furthermore, Giotto can also be used in conjunction with external single-cell RNAseq data to infer the spatial enrichment of cell types from data that do not have single-cell resolution. The data visualization module allows users to interactively visualize the gene expression data, analysis outputs, and additional imaging features, thereby providing a user-friendly workspace to explore multiple modalities of information for biological investigation. These two modules can be used iteratively for refined analysis and hypothesis development. We applied Giotto to a wide range of public datasets encompassing diverse technologies and platforms, thereby demonstrating its general applicability for spatial transcriptomic and proteomic data analysis and visualization.

Additional file 2: Table S1. Table of different technologies and datasets that were analyzed with Giotto.

Additional file 3. Supplementary Notes. Supplementary notes and additional information about data analysis with Giotto.

Additional file 1. Supplementary Figures. Supplementary figures and legends.