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目的：研究促红细胞生成素（EPO）对小鼠结直肠癌治疗的效应。方法：正常小鼠分为NC（阴性对照，negative control）组，NC+IR（放射治疗，ionizing radiation）组，NC+IR+EPO组，小鼠原位结直肠癌模型分为肿瘤组，肿瘤+IR组，肿瘤+IR+EPO组。NC+IR组和肿瘤+IR组：小鼠接受2 Gy*3的局部结直肠及肛门区照射。NC+IR+EPO组和肿瘤+IR+EPO组：肿瘤小鼠在IR照射前半个小时给予1mg/Kg体重的EPO。在IR 3天后外周血测血常规，小鼠结直肠组织做瑞士卷并进行HE染色，取脾脏拍照并用流式细胞仪检测脾脏中Ter细胞即CD45?Ter119+CD71+红系祖细胞（EPC）和EDMC细胞（肿瘤相关红系分化髓系细胞）。结果：在正常小鼠IR后，EPO很好地促进了骨髓红系细胞分化和血红蛋白的产生，具有很好的治疗贫血的作用；和正常小鼠相比，肿瘤鼠红细胞和血红蛋白降低，在IR 后进一步降低，肿瘤+IR+EPO组的小鼠红细胞和血红蛋白会升高，但同时肿瘤+IR+ EPO组的小鼠脾脏变大，并且脾脏中的Ter细胞和EDMC细胞显著增加。结论：EPO因为促进肿瘤相关Ter和EDMC细胞，因此不适用于肿瘤放疗病人的贫血治疗。

Objective: This study integrates the idea of QC matrix with generalized nonparametric classification method (GNPC), and extends GNPC to generalized sequential nonparametric classification method (seq-GNPC) that can be applied to graded scoring items, with a main focus on the problem of saturated models that are poorly discriminated under small samples and the parametric model cannot be estimated in small samples.Methods: Including simulation studies and empirical research. The conditions for the simulation study are as follows: The number of attributes K is set to 3 or 5, and the number of items J is set to 20 or 40. The ratio of fixed graded scoring items is 50%. Two types of QC matrices were also considered: restricted QC matrix and unrestricted QC matrix.Results: The simulation results show that seq-GNPC is better than the parametric method in small samples when the data pattern confirms the saturated model. When the data pattern confirms the reduce model, the parametric model shows a significant inability to converge in the small sample situation, yet seq-GNPC guarantees 100% estimation rate. The empirical results also show that seq-GNPC is more stable than seq-GDINA in the small-sample case, with higher attribute pattern remeasurement rates and smaller standard deviations.Limitations: This study counted the number of replicates that the parametric model was able to estimate for analysis under each condition, but it did not discuss in depth the specific circumstances under which the parametric model would no longer fail to diagnose the classification under each sample size, which may need to be explored further.Conclusions: The seq-GNPC method proposed in this paper has good applicability in small samples and can effectively solve the problem of diagnostic evaluation of graded or mixed scoring programs in small samples.

[目的] 扩大中等收入群体是推进共同富裕和实现中国式现代化的重要途径，本文旨在考察扩大中等收入群体与公平感的关系。[方法] 使用中国综合社会调查2010~2021年8期数据(N = 61,751)和分层线性回归模型进行分析。[结果] 扩大中等收入群体与公平感的关系受到收入和经济发展水平的双重调节，主观社会经济地位和向上流动感知在其中起到相互拮抗的中介作用。具体来说，当收入和经济发展水平都低时，扩大中等收入群体会降低公平感，主观社会经济地位有显著负向的中介作用，而向上流动感知有显著正向的中介作用；当收入或经济发展水平变高时，扩大中等收入群体对公平感的预测作用转为正向，主观社会经济地位的中介作用转为正向，而向上流动感知的中介作用转为负向。时空异质性检验发现研究结果具有可推广性。[结果] 总的来说，本研究提示调整收入分配结构应以经济发展为先。

【目的】探究林下栽培与大田栽培对珍稀药材白及（Bletilla striata）生长发育、生物量分配及药材关键性状的影响。【方法】以品种"贵芨1号"为材料，土壤条件、管理措施及种苗质量基本一致，设置松树林下（郁闭度0.7）与全光照大田两种栽培模式。2025年3月移栽，9月倒苗期取样测量根系数量与长度、新生块茎数量、分枝长度、宽度、厚度，以及地上部茎长、茎粗、叶长、叶宽等共16项指标。【结果】两种栽培模式下白及的生长表现差异显著。大田栽培新生块茎的分枝长度（最长达5.0 cm）、宽度（最大4.0 cm）、厚度（最大2.4 cm）及结构复杂度（出现三叉分枝）均显著高于林下栽培，更有利于光合产物向地下贮藏器官的转运与积累。林下栽培的白及叶片更为狭长（叶长/叶宽比更大），茎秆更高，根系数量与根长分布更为集中，形成对弱光环境的适应性形态。【结论】大田栽培是获取白及块茎高产与快速成材的有效途径；林下栽培可能影响其次生代谢产物形成，并具备节地、省材、生态兼容等优势。

Plug-and-Play Priors (PnP) and Regularisation by Denoising (RED) have established that image denoisers can effectively replace traditional regularisers in linear inverse problem solvers for tasks like super-resolution, demosaicing, and inpainting. It is now well established in the literature that a denoiser's residual links to the gradient of the image log prior (Miyasawa and Tweedie), enabling iterative, gradient ascent-based image generation (e.g., diffusion models), as well as new methods for solving inverse problems. Building on this, we propose enhancing Kadkhodaie and Simoncelli's gradient-based inverse solvers by fine-tuning the denoiser within the iterative solving process itself. Training the denoiser end-to-end across the solver framework and simultaneously across multiple tasks yields a single, versatile denoiser optimised for inverse problems. We demonstrate that even a simple baseline model fine-tuned this way achieves an average PSNR improvement of +1.34 dB across six diverse inverse problems while reducing the required iterations. Furthermore, we analyse the fine-tuned denoiser's properties, finding that its optimisation objective implicitly shifts from minimising standard denoising error (MMSE) towards approximating an ideal prior gradient specifically tailored for guiding inverse recovery.