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Cellular micronucleus detection plays an important role in pathological toxicology detection and early cancer diagnosis. To address the challenges of tiny targets, high inter-class similarity, limited sample data and class imbalance in the field of cellular micronucleus image detection, this paper proposes a lightweight network called MobileViT-MN (Micronucleus), which integrates a multilayer perceptual attention mechanism. Considering that limited data and class imbalance may lead to overfitting of the model, we employ data augmentation to mitigate this problem. Additionally, based on domain adaptation, we innovatively introduce transfer learning. Furthermore, a novel Deep Separation-Decentralization module is designed to implement the reconstruction of the network, which employs attention mechanisms and an alternative strategy of deep separable convolution. Numerous ablation experiments are performed to validate the effectiveness of our method. The experimental results show that MobileViT-MN obtains outstanding performance on the augmented cellular micronucleus dataset. Avg_Acc reaches 0.933, F1 scores 0.971, and ROC scores 0.965. Compared with other classical algorithms, MobileViT-MN is more superior in classification performance.

Prostate cancer (PCa) poses a significant health burden for men, with docetaxel constituting the primary therapeutic option for patients with metastatic PCa. However, the mechanisms governing docetaxel resistance remain incompletely understood. Several studies have implicated the role of the extracellular matrix (ECM) stiffness in cancer drug resistance, yet the precise role of ECM stiffness in docetaxel resistance in PCa remains elusive. The aim of this study was to explore the influence of ECM stiffness on docetaxel resistance in PCa and elucidate the underlying molecular mechanisms, thereby providing novel insights into PCa treatment. Polyacrylamide gels of varying stiffness were utilized to mimic different ECM stiffness conditions. The sensitivity of PCa cells to docetaxel was evaluated using CCK-8, TUNEL staining, flow cytometry, and western blotting. RNA-seq was employed to analyze the transcriptomic effects of different ECM stiffness on PC-3 cells. Western blotting, qPCR, and siRNA were utilized to validate the regulatory role of the key gene in the sensitivity of PCa cells to docetaxel under varying stiffness conditions. Our findings indicate that high ECM stiffness enhances docetaxel resistance in PCa cells by inhibiting docetaxel-induced apoptosis. This process is mediated through the integrin-related mechanotransduction pathway. Specifically, high ECM stiffness upregulates the expression of PRRX1, thereby promoting docetaxel resistance in PCa cells. High ECM stiffness promotes docetaxel resistance in PCa, with PRRX1 identified as a pivotal gene in this process. These findings contribute to a deeper understanding of the mechanisms underlying docetaxel resistance in PCa and may inform the development of novel therapeutic strategies.

In this paper, we numerically studied the effects of mechanical vibration and magnetic fields on evaporative cooling process carried in space station by direct simulation Monte Carlo method. Simulated with the vibration data of international space station, we found that the cooling process would suffer great atomic losses until the accelerations reduced tenfold at least. In addition, if we enlarge the s-wave scattering length five times by Feshbach resonance, the PSD increased to 50 compared to 3 of no magnetic fields situation after 5 seconds evaporative cooling. We also simulated the two stages crossed beam evaporative cooling process (TSCBC) under both physical impacts and obtain \\(4\\times10^5\\) \\(^{85}\\)Rb atoms with a temperature of 8 pK. These results are of significance to the cold atom experiments carried out on space station in the future.

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit \"breakthrough\" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.

Dynamic functional connectivity (DFC) analysis can capture time-varying properties of connectivity and may provide further information about transdiagnostic psychopathology across major psychiatric disorders. In this study, we used resting state functional MRI and a sliding-window method to study DFC in 150 schizophrenia (SZ), 100 bipolar disorder(BD), 150 major depressive disorder (MDD), and 210 healthy controls (HC). DFC were clustered into two functional connectivity states. Significant 4-group differences in DFC were found only in state 2. Post hoc analyses showed that transdiagnostic dysconnectivity among there disorders featured decreased connectivity within visual, somatomotor, salience and frontoparietal networks. Our results suggest that decreased connectivity within both lower-order (visual and somatomotor) and higher-order (salience and frontoparietal) networks may serve as transdiagnostic marker of these disorders, and that these dysconnectivity is state-dependent. Targeting these dysconnectivity may improve assessment and treatment for patients that having more than one of these disorders at the same time. Footnotes * Revison 1: We have reprocess all our fmri data. This time we did not do the despiking. We used a rigorous method to control the head motion: subjects with greater head motion were excluded from this study (Please see Head motion control section of the revised manuscript). We discarded participants if they had mean framewise displacement (FD) values > 0.2 mm, if the outliers accounted for > 30% of all volumes (190 volumes), or if head motion exceeded 3 mm or 3 degree. According to these criteria, we excluded 41 healthy controls, 30 patients with schizophrenia, 18 patients with bipolar disorder and 25 patients with major depressive disorder. Besides, we treated the age, sex and mean FD as covariates, when we performed ANCOVA. Revison 2: This asymmetry is because we only extract the lower triangular matrix in the process of data analysis, and when we get results, we do not mirror the data of the lower triangular matrix into the upper triangular matrix. In this way, when sorting nodes and edges according to their brain network index, this asymmetry emerged. In the revised version, we mirror the lower triangular matrix into the upper triangular matrix (using the MATLAB command M = M + M'; M is the lower triangular matrix), which solves this bug. Revison 3: We reclustered the dynamic functional connectivity: \"we used the Manhattan distance (L1 distance) as a similarity measure in clustering, as it has been demonstrated to be the most effective measure for high dimensional data. To reduce the computational demands and to diminish redundancy between windows, we first used the subject exemplars as a subset of windows with local maxima in functional connectivity variance to perform kmeans clustering with varying numbers of clusters k (from 2 to 10). The optimal number of clusters k = 2 was determined based on the silhouette criterion, a cluster validity index that reflects how similar a point is to other points in its own cluster compared to points in other clusters.\" Revison 4: According to this suggestion, we additionally showed group differences without statistical thresholding (Supplementary_material Figure S1), so that the interpretation is not completely driven by the choice of statistical threshold. Revison 5: We have presented the head motion information in Table 1. After discarded those participants with greater head motion, there was indeed no statistical difference between the four groups (ANOVA p < 0.05). * https://github.com/lichao312214129/lc_rsfmri_tools_matlab/tree/master/Workstation/code_workstation2018_dynamicFC