Spermatogenesis generates mature male gametes and is critical for the proper transmission of genetic information between generations. However, the developmental landscapes of human spermatogenesis remain unknown. Here, we performed single-cell RNA sequencing (scRNA-seq) analysis for 2,854 testicular cells from donors with normal spermatogenesis and 174 testicular cells from one nonobstructive azoospermia (NOA) donor. A hierarchical model was established, which was characterized by the sequential and stepwise development of three spermatogonia subtypes, seven spermatocyte subtypes, and four spermatid subtypes. Further analysis identified several stage-specific marker genes of human germ cells, such as HMGA1, PIWIL4, TEX29, SCML1, and CCDC112. Moreover, we identified altered gene expression patterns in the testicular somatic cells of one NOA patient via scRNA-seq analysis, paving the way for further diagnosis of male infertility. Our work allows for the reconstruction of transcriptional programs inherent to sequential cell fate transition during human spermatogenesis and has implications for deciphering male-related reproductive disorders.

Functionalized N-doped porous carbon nanofiber webs/sulfur (N-PCNF/S) composites are first proposed as the cathode materials for an advanced lithium−sulfur battery. The functionalized N-doped porous carbon nanofiber webs (N-PCNF) with an appropriate N doping (4.32 wt %) are synthesized by a facile approach, which consists of pyrolyzation of polypyrrole nanofiber and a subsequent KOH activation. Instrumental analysis shows that N-PCNF possesses a large specific surface area (2642 m 2 g −1 ) and a high inner pore volume (1.31 cm 3 g −1 ). When evaluating its electrochemical properties in a lithium−sulfur battery, the N-PCNF/S composite with 77.01 wt % sulfur content displays an excellent electrochemical performance. The specific discharge capacity still reaches 749.8 mAh g −1 after 180 cycles at 0.2 C. At a higher rate of 1 C, the capacity stabilizes at 666.0 mAh g −1 after 200 cycles. This work demonstrates that combining the favorable aspects of N doping modification and one-dimensional nanostructure in the carbon matrix design is an effective way to improve the electrochemical performance of the carbon/sulfur cathodes.

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Unsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. However, most research has been focused on evaluating the representations in terms of their ability to improve the performance of speech recognition systems on read English (e.g. Wall Street Journal and Lib-riSpeech). This evaluation methodology overlooks two important desiderata that speech representations should have: robustness to domain shifts and transferability to other languages. In this paper we learn representations from up to 8000 hours of diverse and noisy speech data and evaluate the representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. We find that our representations confer significant robustness advantages to the resulting recognition systems: we see significant improvements in out-of-domain transfer relative to baseline feature sets and the features likewise provide improvements in 25 phonetically diverse languages.

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