Recent studies found that the deep convolutional neural networks (DCNNs) trained to recognize facial identities spontaneously learned features that support facial expression recognition, and vice versa. Here, we showed that the self-emerged expression-selective units in a VGG-Face trained for facial identification were tuned to distinct basic expressions and, importantly, exhibited hallmarks of human expression recognition (i.e., facial expression confusion and categorical perception). We then investigated whether the emergence of expression-selective units is attributed to either face-specific experience or domain-general processing by conducting the same analysis on a VGG-16 trained for object classification and an untrained VGG-Face without any visual experience, both having the identical architecture with the pretrained VGG-Face. Although similar expression-selective units were found in both DCNNs, they did not exhibit reliable human-like characteristics of facial expression perception. Together, these findings revealed the necessity of domain-specific visual experience of face identity for the development of facial expression perception, highlighting the contribution of nurture to form human-like facial expression perception. <<<

Episodic memory enables humans to encode and later vividly retrieve information about our rich experiences, yet the neural representations that support this mental capacity are poorly understood. Using a large fMRI dataset ( = 468) of face-name associative memory tasks and principal component analysis to examine neural representational dimensionality (RD), we found that the human brain maintained a high-dimensional representation of faces through hierarchical representation within and beyond the face-selective regions. Critically, greater RD was associated with better subsequent memory performance both within and across participants, and this association was specific to episodic memory but not general cognitive abilities. Furthermore, the frontoparietal activities could suppress the shared low-dimensional fluctuations and reduce the correlations of local neural responses, resulting in greater RD. RD was not associated with the degree of item-specific pattern similarity, and it made complementary contributions to episodic memory. These results provide a mechanistic understanding of the role of RD in supporting accurate episodic memory. <<<

David E Frankhouser, Denis O'Meally, Sergio Branciamore, Lisa Uechi, Lianjun Zhang, Ying-Chieh Chen, Man Li, Hanjun Qin, Xiwei Wu, Nadia Carlesso, Guido Marcucci, Russell C Rockne, Ya-Huei Kuo <<<

MicroRNAs (miRNAs) have been shown to hold prognostic value in acute myeloid leukemia (AML); however, the temporal dynamics of miRNA expression in AML are poorly understood. Using serial samples from a mouse model of AML to generate time-series miRNA sequencing data, we are the first to show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. We modeled AML state-transition as a particle undergoing Brownian motion in a quasi-potential and validated the AML state-space and state-transition model to accurately predict time to AML in an independent cohort of mice. The critical points of the model provided a framework to align samples from mice that developed AML at different rates. Our mathematical approach allowed discovery of dynamic processes involved during AML development and, if translated to humans, has the potential to predict an individual's disease trajectory. <<<

Epithelial-mesenchymal plasticity (EMP) contributes to tumor progression, promoting therapy resistance and immune cell evasion. Definitive molecular features of this plasticity have largely remained elusive due to the limited scale of most studies. Leveraging single-cell RNA sequencing data from 266 tumors spanning eight different cancer types, we identify expression patterns associated with intratumoral EMP. Integrative analysis of these programs confirmed a high degree of diversity among tumors. These diverse programs are associated with combinations of various common regulatory mechanisms initiated from cues within the tumor microenvironment. We show that inferring regulatory features can inform effective therapeutics to restrict EMP. <<<

The meiotic prophase I to metaphase I (PI/MI) transition requires chromosome desynapsis and metaphase competence acquisition. However, control of these major meiotic events is poorly understood. Here, we identify an essential role for SKP1, a core subunit of the SKP1-Cullin-F-box (SCF) ubiquitin E3 ligase, in the PI/MI transition. SKP1 localizes to synapsed chromosome axes and evicts HORMAD proteins from these regions in meiotic spermatocytes. SKP1-deficient spermatocytes display premature desynapsis, precocious pachytene exit, loss of PLK1 and BUB1 at centromeres, but persistence of HORMAD, γH2AX, RPA2, and MLH1 in diplonema. Strikingly, SKP1-deficient spermatocytes show sharply reduced MPF activity and fail to enter MI despite treatment with okadaic acid. SKP1-deficient oocytes exhibit desynapsis, chromosome misalignment, and progressive postnatal loss. Therefore, SKP1 maintains synapsis in meiosis of both sexes. Furthermore, our results support a model where SKP1 functions as the long-sought intrinsic metaphase competence factor to orchestrate MI entry during male meiosis. <<<