Simulating collective behavior in the movement of immigrants by using a spatial prisoner’s dilemma with move option

The movement of immigrants is simulated by using a spatial Prisoner’s Dilemma (PD) with move option. We explore the effect of collective behavior in an evolutionary migrating dynamics. Simulation results show that immigrants adopting collective strategy perform better and thus gain higher survival rate than those not. This research suggests that the clustering of immigrants promotes cooperation

A New Split Algorithm for 3D Gaussian Splatting

3D Gaussian splatting models, as a novel explicit 3D representation, have been applied in many domains recently, such as explicit geometric editing and geometry generation. Progress has been rapid. However, due to their mixed scales and cluttered shapes, 3D Gaussian splatting models can produce a blurred or needle-like effect near the surface. At the same time, 3D Gaussian splatting models tend to flatten large untextured regions, yielding a very sparse point cloud. These problems are caused by the non-uniform nature of 3D Gaussian splatting models, so in this paper, we propose a new 3D Gaussian splitting algorithm, which can produce a more uniform and surface-bounded 3D Gaussian splatting model. Our algorithm splits an $N$-dimensional Gaussian into two N-dimensional Gaussians. It ensures consistency of mathematical characteristics and similarity of appearance, allowing resulting 3D Gaussian splatting models to be more uniform and a better fit to the underlying surface, and thus more suitable for explicit editing, point cloud extraction and other tasks. Meanwhile, our 3D Gaussian splitting approach has a very simple closed-form solution, making it readily applicable to any 3D Gaussian model.Comment: 11 pages, 10 figure

Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models

Large language models (LLMs) have achieved remarkable advancements in the field of natural language processing. However, the sheer scale and computational demands of these models present formidable challenges when considering their practical deployment in resource-constrained contexts. While techniques such as chain-of-thought (CoT) distillation have displayed promise in distilling LLMs into small language models (SLMs), there is a risk that distilled SLMs may still carry over flawed reasoning or hallucinations inherited from their LLM counterparts. To address these issues, we propose a twofold methodology: First, we introduce a novel method for distilling the self-evaluation capability inherent in LLMs into SLMs, which aims to mitigate the adverse effects of erroneous reasoning and reduce hallucinations. Second, we advocate for a comprehensive distillation process that incorporates multiple distinct chain-of-thought and self-evaluation paradigms and ensures a more holistic and robust knowledge transfer into SLMs. Experiments on three NLP benchmarks demonstrate that our method significantly improves the performance of distilled SLMs and sheds light on the path towards developing smaller models closely aligned with human cognition.Comment: 13 pages, 5 figure

Nanomechanical testing of silica nanospheres for levitated optomechanics experiments

Optically-levitated dielectric particles can serve as ultra-sensitive detectors of feeble forces and torques, as tools for use in quantum information science, and as a testbed for quantum coherence in macroscopic systems. Knowledge of the structural and optical properties of the particles is important for calibrating the sensitivity of such experiments. Here we report the results of nanomechanical testing of silica nanospheres and investigate an annealing approach which can produce closer to bulk-like behavior in the samples in terms of their elastic moduli. These results, combined with our experimental investigations of optical trap lifetimes in high vacuum at high trapping-laser intensity for both annealed and as-grown nanospheres, were used to provide a theoretical analysis of the effects of porosity and non-sphericity in the samples, identifying possible mechanisms of trapping instabilities for nanospheres with non-bulk-silica-like properties.Comment: 10 pages, 7 figure

Ncapg dynamically coordinates the myogenesis of fetal bovine tissue by adjusting chromatin accessibility

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. NCAPG is a subunit of condensin I that plays a crucial role in chromatin condensation during mitosis. NCAPG has been demonstrated to be associated with farm animal growth traits. However, its role in regulating myoblast differentiation is still unclear. We used myoblasts derived from fetal bovine tissue as an in vitro model and found that NCAPG was expressed during myogenic differentiation in the cytoplasm and nucleus. Silencing NCAPG prolonged the mitosis and impaired the differentiation due to increased myoblast apoptosis. After 1.5 days of differentiation, silencing NCAPG enhanced muscle-specific gene expression. An assay for transposase-accessible chromatinhigh throughput sequencing (ATAC-seq) revealed that silencing NCAPG altered chromatin accessibility to activating protein 1 (AP-1) and its subunits. Knocking down the expression of the AP-1 subunits fos-related antigen 2 (FOSL2) or junB proto-oncogene (JUNB) enhanced part of the muscle-specific gene expression. In conclusion, our data provide valuable evidence about NCAPG’s function in myogenesis, as well as its potential role in gene expression

Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering

To contribute to automating the medical vision-language model, we propose a novel Chest-Xray Difference Visual Question Answering (VQA) task. Given a pair of main and reference images, this task attempts to answer several questions on both diseases and, more importantly, the differences between them. This is consistent with the radiologist's diagnosis practice that compares the current image with the reference before concluding the report. We collect a new dataset, namely MIMIC-Diff-VQA, including 700,703 QA pairs from 164,324 pairs of main and reference images. Compared to existing medical VQA datasets, our questions are tailored to the Assessment-Diagnosis-Intervention-Evaluation treatment procedure used by clinical professionals. Meanwhile, we also propose a novel expert knowledge-aware graph representation learning model to address this task. The proposed baseline model leverages expert knowledge such as anatomical structure prior, semantic, and spatial knowledge to construct a multi-relationship graph, representing the image differences between two images for the image difference VQA task. The dataset and code can be found at https://github.com/Holipori/MIMIC-Diff-VQA. We believe this work would further push forward the medical vision language model

bta-miR-23a Regulates the Myogenic Differentiation of Fetal Bovine Skeletal Muscle-Derived Progenitor Cells by Targeting MDFIC Gene

miR-23a, a member of the miR-23a/24-2/27a cluster, has been demonstrated to play pivotal roles in many cellular activities. However, the mechanisms of how bta-miR-23a controls the myogenic differentiation (MD) of PDGFRalpha(-) bovine progenitor cells (bPCs) remain poorly understood. In the present work, bta-miR-23a expression was increased during the MD of (PDGFRalpha-) bPCs. Moreover, bta-miR-23a overexpression significantly promoted the MD of (PDGFRalpha-) bPCs. Luciferase reporter assays showed that the 3\u27-UTR region of MDFIC (MyoD family inhibitor domain containing) could be a promising target of bta-miR-23a, which resulted in its post-transcriptional down-regulation. Additionally, the knockdown of MDFIC by siRNA facilitated the MD of (PDGFRalpha-) bPCs, while the overexpression of MDFIC inhibited the activating effect of bta-miR-23a during MD. Of note, MDFIC might function through the interaction between MyoG transcription factor and MEF2C promoter. This study reveals that bta-miR-23a can promote the MD of (PDGFRalpha-) bPCs through post-transcriptional downregulation of MDFIC

Comparative analysis of four nutritional scores in predicting adverse outcomes in biopsy-confirmed diabetic kidney Disease

Malnutrition is associated with adverse outcomes in patients with diabetic kidney disease (DKD). However, it is uncertain which nutritional assessment tools are most effective in predicting the adverse outcomes of DKD. This retrospective study was conducted at a single center and included 367 patients diagnosed with DKD based on biopsy results between August 2009 and December 2018. Four nutritional assessment indices, namely the Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Triglycerides (TG) × Total Cholesterol (TC) × Body Weight (BW) Index (TCBI), and Controlling Nutritional Status (CONUT) score, were selected and calculated. We aimed to assess the association between these nutritional scores and adverse outcomes, including progression to end-stage kidney disease (ESKD), cardiovascular diseases events (CVD), and all-cause mortality. Univariate and multivariate Cox regression analyses, Kaplan–Meier analysis, along with Restricted cubic spline analysis were used to examine the relationship between nutritional scores and adverse outcomes. Furthermore, the area under the curve (AUC) was calculated using time-dependent receiver operating characteristics to determine the predictive value of the four nutritional scores alone and some combinations. Lastly, ordered logistic regression analysis was conducted to explore the correlation between the four nutritional scores and different renal histologic changes. The incidence of ESKD, CVD, and all-cause mortality was significantly higher in patients with DKD who had a lower PNI, lower GNRI, and higher CONUT score. Additionally, The TCBI performed the worst in terms of grading and risk assessment. The PNI offer the highest predictive value for adverse outcomes and a stronger correlation with renal histologic changes compared to other nutritional scores. Patients diagnosed with DKD who have a worse nutritional status are more likely to experience higher rates of adverse outcomes. The PNI might offer more valuable predictive values and a stronger correlation with different renal histologic changes compared to other nutritional scores