Stark Effects of Rydberg Excitons in a Monolayer WSe2 P-N Junction.

The enhanced Coulomb interaction in two-dimensional semiconductors leads to tightly bound electron-hole pairs known as excitons. The large binding energy of excitons enables the formation of Rydberg excitons with high principal quantum numbers (n), analogous to Rydberg atoms. Rydberg excitons possess strong interactions among themselves as well as sensitive responses to external stimuli. Here, we probe Rydberg exciton resonances through photocurrent spectroscopy in a monolayer WSe2 p-n junction formed by a split-gate geometry. We show that an external in-plane electric field not only induces a large Stark shift of Rydberg excitons up to quantum principal number 3 but also mixes different orbitals and brightens otherwise dark states such as 3p and 3d. Our study provides an exciting platform for engineering Rydberg excitons for new quantum states and quantum sensing.

Functions of Phytochrome Interacting Factors (PIFs) in Adapting Plants to Biotic and Abiotic Stresses.

Plants possess the remarkable ability to sense detrimental environmental stimuli and launch sophisticated signal cascades that culminate in tailored responses to facilitate their survival, and transcription factors (TFs) are closely involved in these processes. Phytochrome interacting factors (PIFs) are among these TFs and belong to the basic helix-loop-helix family. PIFs are initially identified and have now been well established as core regulators of phytochrome-associated pathways in response to the light signal in plants. However, a growing body of evidence has unraveled that PIFs also play a crucial role in adapting plants to various biological and environmental pressures. In this review, we summarize and highlight that PIFs function as a signal hub that integrates multiple environmental cues, including abiotic (i.e., drought, temperature, and salinity) and biotic stresses to optimize plant growth and development. PIFs not only function as transcription factors to reprogram the expression of related genes, but also interact with various factors to adapt plants to harsh environments. This review will contribute to understanding the multifaceted functions of PIFs in response to different stress conditions, which will shed light on efforts to further dissect the novel functions of PIFs, especially in adaption to detrimental environments for a better survival of plants.

Septin9 DNA methylation is associated with breast cancer recurrence or metastasis.

OBJECTIVE: We aimed to explore the prognostic value of Septin9 DNA methylation in breast cancer. METHODS: Breast cancer patients with and without recurrence or metastasis and matched non-breast cancer patients were screened retrospectively from 2014 to 2016. Bisulfite conversion and fluorescence quantitative methylation-specific polymerase chain reaction were used to detect the Septin9 methylation status and distribution levels in patient breast tissues. RESULTS: Septin9 DNA methylation was more frequent in breast cancer tissues than in non-breast cancer tissues, but was not significantly correlated with any relevant breast cancer patient clinicopathological characteristic. Septin9 methylation rates were higher in patients with recurrence or metastasis. Septin9 methylation, tumor size, lymph node status, and progesterone receptor (PR) expression could influence prognosis. Septin9 methylation was significantly associated with worse disease-free survival in breast cancer patients, with receiver operating characteristic curve analysis indicating that it had good prognostic ability, with an area under the curve (AUC) value of 0.719. The AUC values increased when Septin9 methylation was combined with tumor size, lymph node status, and PR to predict prognosis. CONCLUSIONS: Septin9 DNA methylation was an independent predictors of breast cancer prognostic risk. This could possibly help improve comprehensive prognosis prediction methods when combined with other risk factors.

Accurate Intervertebral Disc Segmentation Approach Based on Deep Learning.

Automatically segmenting specific tissues or structures from medical images is a straightforward task for deep learning models. However, identifying a few specific objects from a group of similar targets can be a challenging task. This study focuses on the segmentation of certain specific intervertebral discs from lateral spine images acquired from an MRI scanner. In this research, an approach is proposed that utilizes MultiResUNet models and employs saliency maps for target intervertebral disc segmentation. First, a sub-image cropping method is used to separate the target discs. This method uses MultiResUNet to predict the saliency maps of target discs and crop sub-images for easier segmentation. Then, MultiResUNet is used to segment the target discs in these sub-images. The distance maps of the segmented discs are then calculated and combined with their original image for data augmentation to predict the remaining target discs. The training set and test set use 2674 and 308 MRI images, respectively. Experimental results demonstrate that the proposed method significantly enhances segmentation accuracy to about 98%. The performance of this approach highlights its effectiveness in segmenting specific intervertebral discs from closely similar discs.

Air-Stable Self-Driven UV Photodetectors on Controllable Lead-Free CsCu2I3 Microwire Arrays.

The rapid evolution of the Internet of Things has engendered increased requirements for low-cost, self-powered UV photodetectors. Herein, high-performance self-driven UV photodetectors are fabricated by designing asymmetric metal-semiconductor-metal structures on the high-quality large-area CsCu2I3 microwire arrays. The asymmetrical depletion region doubles the photocurrent and response speed compared to the symmetric structure device, leading to a high responsivity of 233 mA/W to 355 nm radiation. Notably, at 0 V bias, the asymmetric device produces an open-circuit voltage of 356 mV and drives to a short-circuit current of 372 pA; meanwhile, the switch ratio (Iph/Idark) reaches up to 103, indicating its excellent potential for detecting weak light. Furthermore, the device maintains stable responses throughout 10000 UV-light switch cycles, with negligible degradation even after 90-day storage in air. Our work establishes that CsCu2I3 is a good candidate for self-powered UV detection and thoroughly demonstrates its potential as a passive device.

A radiogenomic clinical decision support system to inform individualized treatment in advanced nasopharyngeal carcinoma.

Both concurrent chemoradiotherapy (CCRT) and induction chemotherapy (ICT) followed by CCRT are standard care of advanced nasopharyngeal carcinoma (NPC). However, tailoring personalized treatment is lacking. Herein, we established a radiogenomic clinical decision support system to classify patients into three subgroups according to their predicted disease-free survival (DFS) with CCRT and ICT response. The CCRT-preferred group was suitable for CCRT since they achieved good survival with CCRT, which could not be improved by ICT. The ICT-preferred group was suitable for ICT plus CCRT since they had poor survival with CCRT; additional ICT could afford an improved DFS. The clinical trial-preferred group was suitable for clinical trials since they exhibited poor survival regardless of receiving CCRT or ICT plus CCRT. These findings suggest that our radiogenomic clinical decision support system could identify optimal candidates for CCRT, ICT plus CCRT, and clinical trials, and may thus aid in personalized management of advanced NPC.

Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma.

BACKGROUND: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma is limited because of their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in locally recurrent nasopharyngeal carcinoma. METHODS: This multicenter, retrospective study included 921 patients with locally recurrent nasopharyngeal carcinoma. A machine learning signature and nomogram based on pretreatment magnetic resonance imaging features were developed for predicting overall survival in a training cohort and validated in 2 independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA-sequencing analysis. RESULTS: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving concordance indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables statistically improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with statistically distinct overall survival rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-sequencing analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. CONCLUSIONS: A magnetic resonance imaging-based radiomic signature predicted overall survival more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for locally recurrent nasopharyngeal carcinoma patients.