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In this article, the authors present the design of a compact multiband monopole antenna measuring 30 × 10 × 1.6 mm3, which is aimed at optimizing performance across various communication bands, with a particular focus on Wi-Fi and sub-6G bands. These bands include the 2.4 GHz band, the 3.5 GHz band, and the 5–6 GHz band, ensuring versatility in practical applications. Another important point is that this paper demonstrates effective methods for reducing mutual coupling through two meander slits on the common ground, resembling a defected ground structure (DGS) between two antenna elements. This approach achieves mutual coupling suppression from −6.5 dB and −9 dB to −26 dB and −13 dB at 2.46 GHz and 3.47 GHz, respectively. Simulated and measured results are in good agreement, demonstrating significant improvements in isolation and overall multiple-input multiple-output (MIMO) antenna system performance. This research proposes a compact multiband monopole antenna and demonstrates a method to suppress coupling in multiband antennas, making them suitable for internet of things (IoT) sensor devices and Wi-Fi infrastructure systems.

Significance: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells’ morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. Aim: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. Approach: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. Results: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. Conclusions: The proposed epithelial–mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, their performance in domain-specific contexts, such as E-learning, is hindered by the lack of specific domain knowledge. This paper adopts a novel approach of retrieval augment generation to empower LLMs with domain-specific knowledge in the field of E-learning. The approach leverages external knowledge sources, such as E-learning lectures or research papers, to enhance the LLM’s understanding and generation capabilities. Experimental evaluations demonstrate the effectiveness and superiority of our approach compared to existing methods in capturing and generating E-learning-specific information.

The 17q23 amplicon is associated with poor outcome in ER + breast cancers, but the causal genes to endocrine resistance in this amplicon are unclear. Here, we interrogate transcriptome data from primary breast tumors and find that among genes in 17q23, PRR11 is a key gene associated with a poor response to therapeutic estrogen suppression. PRR11 promotes estrogen-independent proliferation and confers endocrine resistance in ER + breast cancers. Mechanistically, the proline-rich motif-mediated interaction of PRR11 with the p85α regulatory subunit of PI3K suppresses p85 homodimerization, thus enhancing insulin-stimulated binding of p110-p85α heterodimers to IRS1 and activation of PI3K. PRR11 -amplified breast cancer cells rely on PIK3CA and are highly sensitive to PI3K inhibitors, suggesting that PRR11 amplification confers PI3K dependence. Finally, genetic and pharmacological inhibition of PI3K suppresses PRR11-mediated, estrogen-independent growth. These data suggest ER + / PRR11 -amplified breast cancers as a novel subgroup of tumors that may benefit from treatment with PI3K inhibitors and antiestrogens. The 17q23 amplicon is associated with poor outcome in ER + breast cancers, but the causal genes responsible endocrine resistance in this region are unclear. In this study, the authors demonstrate that PRR11 located at 17q23, is critical for conferring endocrine resistance through activation of PI3K signalling and therefore propose PI3K inhibition as a treatment for PRR11-amplified breast cancers.

CDK4/6 inhibitors (CDK4/6i) have improved survival of patients with estrogen receptor-positive (ER+) breast cancer. However, patients treated with CDK4/6i eventually develop drug resistance and progress. RB1 loss-of-function alterations confer resistance to CDK4/6i, but the optimal therapy for these patients is unclear. Through a genome-wide CRISPR screen, we identify protein arginine methyltransferase 5 (PRMT5) as a molecular vulnerability in ER+/ RB1 -knockout breast cancer cells. Inhibition of PRMT5 blocks the G1-to-S transition in the cell cycle independent of RB, leading to growth arrest in RB1 -knockout cells. Proteomics analysis uncovers fused in sarcoma (FUS) as a downstream effector of PRMT5. Inhibition of PRMT5 results in dissociation of FUS from RNA polymerase II, leading to hyperphosphorylation of serine 2 in RNA polymerase II, intron retention, and subsequent downregulation of proteins involved in DNA synthesis. Furthermore, treatment with the PRMT5 inhibitor pemrametostat and a selective ER degrader fulvestrant synergistically inhibits growth of ER+/RB-deficient cell-derived and patient-derived xenografts. These findings highlight dual ER and PRMT5 blockade as a potential therapeutic strategy to overcome resistance to CDK4/6i in ER+/RB-deficient breast cancer. CDK4/6 inhibitors have improved outcomes for patients with ER+ breast cancer, however, those with loss of RB1 function often fail to respond. Here, the authors identify a vulnerability of ER + /RB1- breast cancer on PRMT5 and via dual blockade of ER and PRMT5 therapeutically target this in patient-derived xenograft models.

The role of the tumor immune microenvironment (TIME) in modulating responses to antiestrogen therapy in hormone receptor–positive (HR + ) breast cancers remains unclear. We analyzed pre- and on-treatment biopsies from patients with HR + breast cancer treated with letrozole to induce estrogen deprivation (ED). Stromal tumor–infiltrating lymphocytes, assessed by H&E staining, and immune-related gene sets, including IFN-γ signaling genes, measured by RNA-Seq, were increased in ED-resistant tumors. Cyclic immunofluorescence and spatial transcriptomics revealed an abundance of CD8 + T cells and enhanced antigen processing and immune gene signatures in ED-resistant tumors. In this group, the expression of CXCL9 , CXCL10 , and CXCL11 — chemokine genes involved in CD8 + T cell recruitment — and the CXCR3 receptor were upregulated both before and after letrozole treatment. CXCL11 levels were higher in conditioned media from HR + breast cancer cells cocultured with CD8 + T cells. Both recombinant CXCL11 and coculture with CD8 + T cells promoted MCF7 and T47D cell growth in estrogen-free conditions. Finally, deletion combined with silencing of the CXCL11 receptors CXCR3 and CXCR7 in MCF7 cells impaired proliferation in response to exogenous CXCL11 and to coculture with CD8 + T cells in estrogen-free conditions. These findings suggest that CD8 + T cell–associated CXCL11 in the TIME modulated the response of HR + breast cancer cells to estrogen suppression.

Quantitative assessment of myocardial blood flow (MBF) with first-pass perfusion cardiovascular magnetic resonance (CMR) requires a measurement of the arterial input function (AIF). This study presents an automated method to improve the objectivity and reduce processing time for measuring the AIF from first-pass perfusion CMR images. This automated method is used to compare the impact of different AIF measurements on MBF quantification. Gadolinium-enhanced perfusion CMR was performed on a 1.5 T scanner using a saturation recovery dual-sequence technique. Rest and stress perfusion series from 270 clinical studies were analyzed. Automated image processing steps included motion correction, intensity correction, detection of the left ventricle (LV), independent component analysis, and LV pixel thresholding to calculate the AIF signal. The results were compared with manual reference measurements using several quality metrics based on the contrast enhancement and timing characteristics of the AIF. The median and 95 % confidence interval (CI) of the median were reported. Finally, MBF was calculated and compared in a subset of 21 clinical studies using the automated and manual AIF measurements. Two clinical studies were excluded from the comparison due to a congenital heart defect present in one and a contrast administration issue in the other. The proposed method successfully processed 99.63 % of the remaining image series. Manual and automatic AIF time-signal intensity curves were strongly correlated with median correlation coefficient of 0.999 (95 % CI [0.999, 0.999]). The automated method effectively selected bright LV pixels, excluded papillary muscles, and required less processing time than the manual approach. There was no significant difference in MBF estimates between manually and automatically measured AIFs (p = NS). However, different sizes of regions of interest selection in the LV cavity could change the AIF measurement and affect MBF calculation (p = NS to p = 0.03). The proposed automatic method produced AIFs similar to the reference manual method but required less processing time and was more objective. The automated algorithm may improve AIF measurement from the first-pass perfusion CMR images and make quantitative myocardial perfusion analysis more robust and readily available.

Introduction: The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both. Methods: A neuropsychological battery and a simple 5-min cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild Alzheimer's disease (AD), and 41 demographically match controls (CN). A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used. Results: Traditional machine learning algorithms achieved similar classification performances with neuropsychological or cognitive data. MLP outperformed traditional algorithms with the cognitive data (either alone or together with neuropsychological data), but not neuropsychological data. In particularly, MLP with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. Applying the models to an independent dataset, in which the participants were demographically different from the ones in the main dataset, a high specificity was maintained (100%), but the sensitivity was dropped to 66.67%. Discussion: Deep learning with data from specific cognitive task(s) holds promise for assisting in the early diagnosis of Alzheimer's disease, but future work with a large and diverse sample is necessary to validate and to improve this approach.

The NIH MRI Study of normal brain development sought to characterize typical brain development in a population of infants, toddlers, children and adolescents/young adults, covering the socio-economic and ethnic diversity of the population of the United States. The study began in 1999 with data collection commencing in 2001 and concluding in 2007. The study was designed with the final goal of providing a controlled-access database; open to qualified researchers and clinicians, which could serve as a powerful tool for elucidating typical brain development and identifying deviations associated with brain-based disorders and diseases, and as a resource for developing computational methods and image processing tools. This paper focuses on the DTI component of the NIH MRI study of normal brain development. In this work, we describe the DTI data acquisition protocols, data processing steps, quality assessment procedures, and data included in the database, along with database access requirements. For more details, visit http://www.pediatricmri.nih.gov. This longitudinal DTI dataset includes raw and processed diffusion data from 498 low resolution (3mm) DTI datasets from 274 unique subjects, and 193 high resolution (2.5mm) DTI datasets from 152 unique subjects. Subjects range in age from 10days (from date of birth) through 22years. Additionally, a set of age-specific DTI templates are included. This forms one component of the larger NIH MRI study of normal brain development which also includes T1-, T2-, proton density-weighted, and proton magnetic resonance spectroscopy (MRS) imaging data, and demographic, clinical and behavioral data. •We describe the DTI component of the NIH MRI study of normal brain development.•Longitudinal study of typical brain development from birth through 22years of age•Database includes anatomical MRI, MRS, DTI and demographic/clinical/behavioral data.•Standardized post-processing and quality assessment of DTI data are discussed.

This study mapped personality based on the newly proposed extraction method from consumers’ textual data and revealed the relevance (attention) and polarity (affection) of words associated with a specific personality trait. Furthermore, we illustrate how unique words are used to predict a consumer’s behavior associated with certain personality traits. In this study, we employed the scales of the Kaggle MBTI Personality dataset to examine the methodology’s effectiveness, extract the personality traits from the textual data into features, and map them into the traits/dimensions of the existing scale. Based on the results obtained in this study, we assert that using the TF-IDF algorithm is a good way to generate a custom dictionary. Furthermore, sentiment scoring with an AI-empowered machine learning algorithm provides useful data to filter and validate more coherent words to understand and, thus, communicate a particular aspect of personality. Finally, we proposed that four situations involving the interaction between attention (frequency) and affection (sentiment) allow us to better understand the consumer and how to use the feature words in terms of the interaction between attention (TF-IDF score) and affection (sentiment score).