A stochastic approach to multi-gene expression dynamics

In the last years, tens of thousands gene expression profiles for cells of several organisms have been monitored. Gene expression is a complex transcriptional process where mRNA molecules are translated into proteins, which control most of the cell functions. In this process, the correlation among genes is crucial to determine the specific functions of genes. Here, we propose a novel multi-dimensional stochastic approach to deal with the gene correlation phenomena. Interestingly, our stochastic framework suggests that the study of the gene correlation requires only one theoretical assumption -Markov property- and the experimental transition probability, which characterizes the gene correlation system. Finally, a gene expression experiment is proposed for future applications of the model.Comment: 17 pages, 2 figures, Latex, v2 includes minor modification

SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data

Recent text-to-image (T2I) generation models have demonstrated impressive capabilities in creating images from text descriptions. However, these T2I generation models often fall short of generating images that precisely match the details of the text inputs, such as incorrect spatial relationship or missing objects. In this paper, we introduce SELMA: Skill-Specific Expert Learning and Merging with Auto-Generated Data, a novel paradigm to improve the faithfulness of T2I models by fine-tuning models on automatically generated, multi-skill image-text datasets, with skill-specific expert learning and merging. First, SELMA leverages an LLM's in-context learning capability to generate multiple datasets of text prompts that can teach different skills, and then generates the images with a T2I model based on the prompts. Next, SELMA adapts the T2I model to the new skills by learning multiple single-skill LoRA (low-rank adaptation) experts followed by expert merging. Our independent expert fine-tuning specializes multiple models for different skills, and expert merging helps build a joint multi-skill T2I model that can generate faithful images given diverse text prompts, while mitigating the knowledge conflict from different datasets. We empirically demonstrate that SELMA significantly improves the semantic alignment and text faithfulness of state-of-the-art T2I diffusion models on multiple benchmarks (+2.1% on TIFA and +6.9% on DSG), human preference metrics (PickScore, ImageReward, and HPS), as well as human evaluation. Moreover, fine-tuning with image-text pairs auto-collected via SELMA shows comparable performance to fine-tuning with ground truth data. Lastly, we show that fine-tuning with images from a weaker T2I model can help improve the generation quality of a stronger T2I model, suggesting promising weak-to-strong generalization in T2I models.Comment: First two authors contributed equally; Project website: https://selma-t2i.github.io

TCF1 and LEF1 Control Treg Competitive Survival and Tfr Development to Prevent Autoimmune Diseases.

CD4+ Foxp3+ T regulatory (Treg) cells are key players in preventing lethal autoimmunity. Tregs undertake differentiation processes and acquire diverse functional properties. However, how Treg's differentiation and functional specification are regulated remains incompletely understood. Here, we report that gradient expression of TCF1 and LEF1 distinguishes Tregs into three distinct subpopulations, particularly highlighting a subset of activated Treg (aTreg) cells. Treg-specific ablation of TCF1 and LEF1 renders the mice susceptible to systemic autoimmunity. TCF1 and LEF1 are dispensable for Treg's suppressive capacity but essential for maintaining a normal aTreg pool and promoting Treg's competitive survival. As a consequence, the development of T follicular regulatory (Tfr) cells, which are a subset of aTreg, is abolished in TCF1/LEF1-conditional knockout mice, leading to unrestrained T follicular helper (Tfh) and germinal center B cell responses. Thus, TCF1 and LEF1 act redundantly to control the maintenance and functional specification of Treg subsets to prevent autoimmunity

Inner Structure of Spin^{c}(4) Gauge Potential on 4-Dimensional Manifolds

The decomposition of $Spin^{c}(4)$ gauge potential in terms of the Dirac 4% -spinor is investigated, where an important characterizing equation $\Delta A_{\mu}=-\lambda A_{\mu}$ has been discovered. Here $\lambda$ is the vacuum expectation value of the spinor field, $\lambda =\Vert \Phi \Vert ^{2}$, and $A_{\mu}$ the twisting U(1) potential. It is found that when $\lambda$ takes constant values, the characterizing equation becomes an eigenvalue problem of the Laplacian operator. It provides a revenue to determine the modulus of the spinor field by using the Laplacian spectral theory. The above study could be useful in determining the spinor field and twisting potential in the Seiberg-Witten equations. Moreover, topological characteristic numbers of instantons in the self-dual sub-space are also discussed.Comment: 11 page

Multi-Player and Multi-Choice Quantum Game

We investigate a multi-player and multi-choice quantum game. We start from two-player and two-choice game and the result is better than its classical version. Then we extend it to N-player and N-choice cases. In the quantum domain, we provide a strategy with which players can always avoid the worst outcome. Also, by changing the value of the parameter of the initial state, the probabilities for players to obtain the best payoff will be much higher that in its classical version.Comment: 4 pages, 1 figur

Synthesis and Characterization of a Photoelectrode with a Novel 3D Structure for Dye-Sensitized Solar Cells

This study designs a novel dye-sensitized solar cell (DSSC) in which the photoanode is derived from its three-dimensional (3D) structure. The inside of the cell has a positive illumination structure, with the purposes of increasing the area of photoelectrode thin film and of increasing the illuminated area within a fixed area in order to achieve the objective of enhancing the photoelectric conversion efficiency of cell. For the cell structure experiment, the study uses graphite paper, carbon and platinum as counter electrode materials, and then conducts measurement with cell heights of 3 mm, 5 mm, and 7 mm. The electrolyte used is a gel polymer electrolyte. The assembly of the cell is divided into vertical assembly, inclined assembly, and tandem assembly. In the 3D tandem cell experiment, the counter electrode material is platinum. Experimental results show that when cell height is 7 mm and illuminated area is 0.28 cm2, open-loop voltage is 0.662 V, short-circuit current density is 18.42 mA/cm2, fill factor is 0.31, and the photoelectric conversion efficiency is 3.85%, which is 1.65 times that under vertical assembly (2.34%) and 2.15 times that of the flat cell (1.79%)

Resting-State Glucose Metabolism Level Is Associated with the Regional Pattern of Amyloid Pathology in Alzheimer's Disease

It has been suggested that glucose metabolism within the brain's default network is directly associated with—and may even cause—the amyloid pathology of Alzheimer's disease (AD). Here we performed 2-[18F]fluoro-2-deoxy-D-glucose (FDG) and [11C]-labeled Pittsburgh Compound B (PIB) positron emission tomography (PET) on cognitively normal elderly subjects and on AD patients and conducted quantitative regional analysis of FDG- and PIB-PET images using an automated region of interest technique. We confirmed that resting glucose metabolism within the posterior components of the brain's default network is high in normal elderly subjects and low in AD patients, which is partially in agreement with the regional pattern of PIB uptake within the default network of AD patients. However, in several regions outside the default network, glucose metabolism was high in normal elderly subjects but was not depressed in AD patients, who exhibited significantly increased PIB uptakes in these regions. In contrast, the level of resting glucose metabolism in the default network and in regions outside the default network in normal elderly subjects was significantly correlated with the level of regional PIB uptake in AD patients. These results are discussed with experimental evidence suggesting that beta amyloid production and amyloid precursor protein regulation are dependent on neuronal activity