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Background: Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-translational protein modification. Despite increasing focus on palmitoylation, its specific implications for breast cancer prognosis remain unclear. The work aimed to identify prognostic factors linked to palmitoylation in breast cancer and assess its effectiveness in predicting responses to chemotherapy and immunotherapy. Methods: We utilized the "limma" package to analyze the differential expression of palmitoylation-related genes between breast cancer and normal tissues. Hub genes were identified using the "WGCNA" package. Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a prognostic feature associated with palmitoylation and developed a prognostic nomogram with the "regplot" package. The predictive values of the model for chemotherapy and immunotherapy responses were assessed using immunophenoscore (IPS) and the "pRophetic" package. Results: We identified 211 differentially expressed genes related to palmitoylation, among which 44 demonstrated prognostic potential. Subsequently, a predictive model comprising eleven palmitoylation-related genes was developed. Patients were classified into high-risk and low-risk groups based on the median risk score. The findings revealed that individuals in the high-risk group exhibited lower survival rates, while those in the low-risk group showed increased immune cell infiltration and improved responses to chemotherapy and immunotherapy. Moreover, the BC-Palmitoylation Tool website was established. Conclusion: This study developed the first machine learning-based predictive model for palmitoylation-related genes and created a corresponding website, providing clinicians with a valuable tool to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Bromus (Poaceae Bromeae) is a forage grass with high adaptability and ecological and economic value. Here, we sequenced Bromus ciliatus, Bromus benekenii, Bromus riparius, and Bromus rubens chloroplast genomes and compared them with four previously described species. The genome sizes of Bromus species ranged from 136,934 bp (Bromus vulgaris) to 137,189 bp (Bromus ciliates, Bromus biebersteinii), with a typical quadripartite structure. The studied species had 129 genes, consisting of 83 protein-coding, 38 tRNA-coding, and 8 rRNA-coding genes. The highest GC content was found in the inverted repeat (IR) region (43.85–44.15%), followed by the large single-copy (LSC) region (36.25–36.65%) and the small single-copy (SSC) region (32.21–32.46%). There were 33 high-frequency codons, with those ending in A/U accounting for 90.91%. A total of 350 simple sequence repeats (SSRs) were identified, with single-nucleotide repeats being the most common (61.43%). A total of 228 forward and 141 palindromic repeats were identified. No reverse or complementary repeats were detected. The sequence identities of all sequences were very similar, especially with respect to the protein-coding and inverted repeat regions. Seven highly variable regions were detected, which could be used for molecular marker development. The constructed phylogenetic tree indicates that Bromus is a monophyletic taxon closely related to Triticum. This comparative analysis of the chloroplast genome of Bromus provides a scientific basis for species identification and phylogenetic studies. [ABSTRACT FROM AUTHOR]

Traditional deep learning models for fruit and vegetable classification are usually implemented via training on an unchanged dataset. However, changing fruit and vegetable categories is a very common occurrence in the context of real agricultural sales. When dealing with changes related to variety, deep learning models need to be retrained on the entire updated dataset. The retraining process is time-consuming and inefficient, and it may even cause the 'catastrophic forgetting' problem. In response to this challenge, the Adversarial Domain Adaptation Class Incremental Learning (ADA-CIL) method is introduced. This approach employs adversarial domain adaptation techniques combined with core-set selection strategies to effectively extract and integrate cross-domain features. We utilize the ResNet34 architecture as the backbone for feature extraction due to its deep residual learning framework, which is robust in handling the complexities of large and varied image datasets. It achieves a dynamic balance in learning between new and existing categories, significantly enhancing the model's generalization capabilities and information retention efficiency. The FruVeg dataset, composed of three sub-datasets, includes over 120,000 color images, covering more than 100 different categories of fruits and vegetables collected from various domains and backgrounds. The experimental results on the FruVeg dataset show that the ADA-CIL method achieves an average accuracy of 96.30%, a forgetting rate of 2.96%, a cumulative accuracy of 96.26%, and a current accuracy of 98.60%. The ADA-CIL method improves the average accuracy by 1.65% and 1.82% compared to iCaRL and BiC, respectively, and it reduces the forgetting rate by 2.69% and 2.76%. These performance metrics demonstrate the ADA-CIL method's impressive ability to handle incremental category and domain changes, highlighting its capability to effectively maintain the intra-class stability and exhibit exceptional adaptability in dynamic learning environments. [ABSTRACT FROM AUTHOR]

Online instruction, like all traditional instructional environments, requires learner self-control and proactive learning to construct knowledge and acquire skills. However, online students often fail to complete some components of their online work each week, damaging their overall academic progress in the course. To assist students in completion of all assigned elements and submission of work on time, three professors at a public southeastern university implemented the use of a generalized assignments checklist to enhance student self-monitoring in their online courses. Data on the submission of assignments was analyzed for relative timeliness. The results of this study showed a statistically significant difference between students receiving a generalized online learning checklist to the control group who did not receive a checklist. The experimental group showed a marked improvement of assignment submission timeliness, improving course satisfaction for students and instructors. (Contains 3 tables.)

Human immunodeficiency virus (HIV) infectivity increases as receptor/coreceptor expression levels increase. We determined peripheral CD4, CCR5, and CXCR4 expression levels in HIV-uninfected women who used depot medroxyprogesterone acetate (DMPA; n = 32), the levonorgestrel-releasing intrauterine device (LNG-IUD; n = 27), oral contraceptive pills (n = 32), or no hormonal contraception (n = 33). The use of LNG-IUD increased the proportion of CD4(+) and CD8(+) T cells that expressed CCR5; increases in the magnitude of T-cell subset CCR5 expression were observed with DMPA and LNG-IUD use (P < .01 for all comparisons). LNG-IUD and, to a lesser extent, DMPA use were associated with increased peripheral T-cell CCR5 expression.

A mixed-methods study was conducted to examine the effects of self-regulated learning (SRL) strategy training on learners' achievement, motivation and strategy use in a web-enhanced College Success course at a community college in southeast US. It was found that training assisted with students' overall course performance and accomplishment of long-term tasks, enhanced students' self-satisfaction, and persistence. This study empirically tested the effects of a technology-enabled strategy training intervention in a field setting; it contributes to the understanding about SRL and persistence in the community college population; and it is a pioneering trial of conducting learning strategy research using mixed methodologies. Results and implications for future research and instructional design are also discussed. (Contains 4 tables and 1 figure.)

Renal cell carcinoma (RCC) is a common malignant tumour affecting the urinary system, and multidrug resistance is one of the major reasons why chemotherapy for this type of cancer often fails. Previous studies have shown that loss of the human organic cation transporter OCT2 is the main factor contributing to oxaliplatin resistance in RCC, and that DNA hypermethylation and histone methylation play important roles in the transcriptional repression of OCT2 in RCC. In this study, we found that histone acetylation also regulates OCT2 repression in RCC and elucidated the underlying mechanisms. In normal renal cells, HDAC7 combines with MYC at the OCT2 promoter, resulting in a decrease in free HDAC7, which in turn increases the levels of H3K18ac and H3K27ac at the OCT2 promotor and activates OCT2 expression. In RCC cells, however, the interaction between HDAC7 and MYC does not occur, which leads a high abundance of HDAC7 and low levels of H3K18ac and H3K27ac at the OCT2 promoter, thereby resulting in the inhibition of OCT2 transcription. We found that combined treatment using the DNA methylation inhibitor decitabine and the histone deacetylase inhibitor vorinostat significantly increased the expression of OCT2 in RCC cell lines, which sensitized these cells to oxaliplatin. We accordingly propose that the combination of anticancer agents and epigenetic drugs can provide a novel chemotherapeutic regimen. [ABSTRACT FROM AUTHOR]