The Rbm38-p63 feedback loop is critical for tumor suppression and longevity.

The RNA-binding protein Rbm38 is a target of p63 tumor suppressor and can in-turn repress p63 expression via mRNA stability. Thus, Rbm38 and p63 form a negative feedback loop. To investigate the biological significance of the Rbm38-p63 loop in vivo, a cohort of WT, Rbm38-/-, TAp63+/-, and Rbm38-/-;TAp63+/- mice were generated and monitored throughout their lifespan. While mice deficient in Rbm38 or TAp63 alone died mostly from spontaneous tumors, compound Rbm38-/-;TAp63+/- mice had an extended lifespan along with reduced tumor incidence. We also found that loss-of-Rbm38 markedly decreased the percentage of liver steatosis in TAp63+/- mice. Moreover, we found that Rbm38 deficiency extends the lifespan of tumor-free TAp63+/- mice along with reduced expression of senescence-associated biomarkers. Consistent with this, Rbm38-/-;TAp63+/- MEFs were resistant, whereas Rbm38-/- or TAp63+/- MEFs were prone, to cellular senescence. Importantly, we showed that the levels of inflammatory cytokines (IL17D and Tnfsf15) were significantly reduced by Rbm38 deficiency in senescence-resistant Rbm38-/-;TAp63+/- mouse livers and MEFs. Together, our data suggest that Rbm38 and p63 function as intergenic suppressors in aging and tumorigenesis and that the Rbm38-p63 loop may be explored for enhancing longevity and cancer management

Enhancing efficiency and robustness in high-dimensional linear regression with additional unlabeled data

In semi-supervised learning, the prevailing understanding suggests that observing additional unlabeled samples improves estimation accuracy for linear parameters only in the case of model misspecification. This paper challenges this notion, demonstrating its inaccuracy in high dimensions. Initially focusing on a dense scenario, we introduce robust semi-supervised estimators for the regression coefficient without relying on sparse structures in the population slope. Even when the true underlying model is linear, we show that leveraging information from large-scale unlabeled data improves both estimation accuracy and inference robustness. Moreover, we propose semi-supervised methods with further enhanced efficiency in scenarios with a sparse linear slope. Diverging from the standard semi-supervised literature, we also allow for covariate shift. The performance of the proposed methods is illustrated through extensive numerical studies, including simulations and a real-data application to the AIDS Clinical Trials Group Protocol 175 (ACTG175)

Emerging Artificial Two-Dimensional van der Waals Heterostructures for Optoelectronics

Two-dimensional (2D) materials are attracting explosive attention for their intriguing potential in versatile applications, covering optoelectronics, electronics, sensors, etc. An attractive merit of 2D materials is their viable van der Waals (VdW) stacking in artificial sequence, thus forming different atomic arrangements in vertical direction and enabling unprecedented tailoring of material properties and device application. In this chapter, we summarize the latest progress in assembling VdW heterostructures for optoelectronic applications by beginning with the basic pick-transfer method for assembling 2D materials and then discussing the different combination of 2D materials of semiconductor, conductor, and insulator properties for various optoelectronic devices, e.g., photodiode, phototransistors, optical memories, etc

MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition

Spatial-temporal feature learning is of vital importance for video emotion recognition. Previous deep network structures often focused on macro-motion which extends over long time scales, e.g., on the order of seconds. We believe integrating structures capturing information about both micro- and macro-motion will benefit emotion prediction, because human perceive both micro- and macro-expressions. In this paper, we propose to combine micro- and macro-motion features to improve video emotion recognition with a two-stream recurrent network, named MIMAMO (Micro-Macro-Motion) Net. Specifically, smaller and shorter micro-motions are analyzed by a two-stream network, while larger and more sustained macro-motions can be well captured by a subsequent recurrent network. Assigning specific interpretations to the roles of different parts of the network enables us to make choice of parameters based on prior knowledge: choices that turn out to be optimal. One of the important innovations in our model is the use of interframe phase differences rather than optical flow as input to the temporal stream. Compared with the optical flow, phase differences require less computation and are more robust to illumination changes. Our proposed network achieves state of the art performance on two video emotion datasets, the OMG emotion dataset and the Aff-Wild dataset. The most significant gains are for arousal prediction, for which motion information is intuitively more informative. Source code is available at https://github.com/wtomin/MIMAMO-Net.Comment: Accepted by AAAI 202

Valley-polarized quantum anomalous Hall effect in van der Waals heterostructures based on monolayer jacutingaite family materials

We numerically study the general valley polarization and anomalous Hall effect in van der Waals (vdW) heterostructures based on monolayer jacutingaite family materials Pt$_{2}$AX$_{3}$ (A = Hg, Cd, Zn; X = S, Se, Te). We perform a systematic study on the atomic, electronic, and topological properties of vdW heterostructures composed of monolayer Pt$_{2}$AX$_{3}$ and two-dimensional ferromagnetic insulators. We show that four kinds of vdW heterostructures exhibit valley-polarized quantum anomalous Hall phase, i.e., Pt$_{2}$HgS$_{3}$/NiBr$_{2}$, Pt$_{2}$HgSe$_{3}$/CoBr$_{2}$, Pt$_{2}$HgSe$_{3}$/NiBr$_{2}$, and Pt$_{2}$ZnS$_{3}$/CoBr$_{2}$, with a maximum valley splitting of 134.2 meV in Pt$_{2}$HgSe$_{3}$/NiBr$_{2}$ and sizable global band gap of 58.8 meV in Pt$_{2}$HgS$_{3}$/NiBr$_{2}$. Our findings demonstrate an ideal platform to implement applications on topological valleytronics