Dynamic Roles of microRNAs in Neurogenesis

MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression at the post-transcriptional level by mediating mRNA degradation or translational inhibition. MiRNAs are implicated in many biological functions, including neurogenesis. It has been shown that miRNAs regulate multiple steps of neurogenesis, from neural stem cell proliferation to neuronal differentiation and maturation. MiRNAs execute their functions in a dynamic and context-dependent manner by targeting diverse downstream target genes, from transcriptional factors to epigenetic regulators. Identifying context-specific target genes is instrumental for understanding the roles that miRNAs play in neurogenesis. This review summarizes our current state of knowledge on the dynamic roles that miRNAs play in neural stem cells and neurogenesis

Orphan nuclear receptors in drug discovery

Orphan nuclear receptors provide a unique resource for uncovering novel regulatory systems that impact human health and also provide drug targets for a variety of human diseases. Ligands of nuclear receptors have been used in several important therapeutic areas, such as breast cancers, skin disorders and diabetes. Orphan nuclear receptors, therefore, represent a tremendous opportunity in understanding and treating human diseases. Here, I highlight recent advances in the use of orphan nuclear receptors and their potential as targets for drug discovery in diabetes, obesity, neurodegenerative diseases and other related disorders

Glioblastoma Stem Cells

Glioblastoma multiforme(GBM) is the most common and malignant primary brain tumor in humans. GBM accounts for 55% of all primary brain cancers, with a median survival rate of 14.6 months. The grim prognosis of GBM can be attributed to glioma stem cells (GSCs), which initiate tumor formation through the stem-like properties of self-renewal and differentiation. The ability of GSCs to resist radiation and chemotherapy contributes to the high rate of tumor recurrence in GBM patients. Consequently, novel therapies that effectively target the population of GSCs are of vital importance. A promising is to induce the differentiation of GSCs. Previous studies show that inactivation of gene X enhances self-renewability in embryonic stem cells. Therefore, we hypothesized that gene X facilitates the resolution from self-renewability toward differentiation in GSCs. In order to test this hypothesis, we made DNA constructs that overexpress or knockdown gene X. To overexpress gene X, the coding sequence was cloned into a shuttle vector pSKSP, and then sub-cloned into a lentiviral vector CSC. To knockdown gene X, the shRNA oligos were first cloned under the control of the U6 promoter. The U6-shRNA was then cloned into a lentiviral vector pHIV. After confirmation by sequencing, maxiprep of the DNA constructs was performed. These constructs will be used to overexpress or knockdown gene X in GSCs to test GSC self-renewal and differentiation

Identification of Free and Bound Exciton States and Their Phase-Dependent Trapping Behavior in Lead Halide Perovskites

In this work we probe the sub-gap energy states within polycrystalline and single crystal lead halide perovskites to better understand their intrinsic photophysics behaviors. Through combined temperature and intensity-dependent optical measurements, we reveal the existence of both free and bound exciton contributions within the sub-gap energy state manifold. The trapping and recombination dynamics of these excitons is shown to be strongly dependent on the structural phase of the perovskite. The orthorhombic phase exhibits ultrafast exciton trapping and distinct trap emission, while the tetragonal phase gives low monomolecular recombination velocity and capture cross-sections (~10-18 cm2). Within the multiphonon transition scenario, this suppression in charge trapping is caused by the increase in the charge capture activation energy due to the reduction in electron-lattice interactions, which can be the origin for the unexpected long carrier lifetime in these material systems.Comment: 5 figure

FairBench: A Four-Stage Automatic Framework for Detecting Stereotypes and Biases in Large Language Models

Detecting stereotypes and biases in Large Language Models (LLMs) can enhance fairness and reduce adverse impacts on individuals or groups when these LLMs are applied. However, the majority of existing methods focus on measuring the model's preference towards sentences containing biases and stereotypes within datasets, which lacks interpretability and cannot detect implicit biases and stereotypes in the real world. To address this gap, this paper introduces a four-stage framework to directly evaluate stereotypes and biases in the generated content of LLMs, including direct inquiry testing, serial or adapted story testing, implicit association testing, and unknown situation testing. Additionally, the paper proposes multi-dimensional evaluation metrics and explainable zero-shot prompts for automated evaluation. Using the education sector as a case study, we constructed the Edu-FairBench based on the four-stage framework, which encompasses 12,632 open-ended questions covering nine sensitive factors and 26 educational scenarios. Experimental results reveal varying degrees of stereotypes and biases in five LLMs evaluated on Edu-FairBench. Moreover, the results of our proposed automated evaluation method have shown a high correlation with human annotations