Association of Vitamin D3 Level with Breast Cancer Risk and Prognosis in African-American and Hispanic Women.

Background: This study investigated the association of vitamin D3 levels with breast cancer risk and progression in African-Americans and Hispanics. Methods: A total of 237 African-American (Cases = 119, Control = 118) and 423 Hispanic women (Cases = 124, Control = 299) were recruited in the study. Blood samples were collected at the time of breast cancer screening and prior to cancer treatment for 4 weeks on average for the cases. The serum 25-hydroxyvitamin D (25(OH)D3) was measured at a Quest-Diagnostics facility. Results: The results showed that 69.2% of African-Americans and 37.8% of Hispanics had 25(OH)D3 levels below 20 ng/mL. The 25(OH)D3 level below 20 ng/mL was significantly associated with breast cancer in both African-Americans (OR = 2.5, 95% CI = 1.3-4.8) and Hispanics (OR = 1.9, 95% CI = 1.1-3.0). However, the predicted probabilities of breast cancer in African-Americans were significantly higher than in Hispanics (p < 0.001). The 25(OH)D3 below 20 ng/mL was significantly associated with triple negative breast cancer (TNBC) in African-Americans (OR = 5.4, p = 0.02, 95% CI = 1.4-15), but not in Hispanics in our cohort of participants. Levels of 25(OH)D3 below 26 ng/mL predicts a decrease in disease-free survival, but it was not an independent predictor. Conclusions: Our data shows an association between 25(OH)D3 levels and the risk of breast cancer. Further studies on the relationship between 25(OH)D3 level and breast cancer risk are warranted

MCP-1 is overexpressed in triple-negative breast cancers and drives cancer invasiveness and metastasis.

BACKGROUND:Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer that lacks ER/PR and HER2 receptors. Hence, there is urgency in developing new or novel therapeutic strategies for treatment of TNBC. Our study shows that the Monocyte Chemoattractant Protein-1 (MCP-1) is a marker associated with TNBC and may play a key role in TNBC disease progression. EXPERIMENTAL DESIGN:ELISA method was used to measure secreted MCP-1, and mRNA levels were determined by Real-time PCR in numerous cancer cell lines, representing various breast cancer subtypes. Cellular invasiveness was determined by Boyden chamber assay. RESULTS:Our data show that MCP-1 is upregulated in TNBC cell lines both transcriptionally as well as in secreted protein levels compared to ER-positive luminal cell line, MCF-7. Breast cancer patients, with Basal or Claudin-low subtypes, also showed high expression of MCP-1. MCP-1 treatment induced cell invasion in various breast cancer cell types, without affecting cell proliferation. Small molecule antagonists against Chemokine Receptor 2 (CCR2), cognate receptor for MCP-1 as well as the MAP kinase pathway inhibitor U0126 negatively affected MCP-1 induced MCF-7 cell invasion. This suggests that MCP-1-CCR2 axis may regulate invasiveness via the MAP Kinase pathway. Knocking down MCP-1 decreased cell invasion in TNBC cell line BT-549, along with downregulation of key epithelial to mesenchymal transition markers, N-cadherin and Vimentin. CONCLUSION:Our study suggests that MCP-1 mediated pathways could be potential therapeutic targets for the treatment of TNBC, and could reduce cancer health disparities

Estrogen receptor-beta is a potential target for triple negative breast cancer treatment.

Triple Negative breast cancer (TNBC) is a subtype of breast cancer that lacks the expression of estrogen receptor (ER), progesterone receptor, and human epidermal growth factor receptor 2. TNBC accounts for 15-20% of all breast cancer cases but accounts for over 50% of mortality. We propose that Estrogen receptor-beta (ERβ) and IGF2 play a significant role in the pathogenesis of TNBCs, and could be important targets for future therapy. Tissue microarrays (TMAs) from over 250 TNBC patients' were analyzed for ERβ and IGF2 expression by immunohistochemistry. Expression was correlated with clinical outcomes. In addition, TNBC cell lines Caucasians (CA): MB-231/BT549 and African Americans (AAs): MB-468/HCC70/HCC1806 were used to investigate the effect of hormonal and growth factor regulation on cell proliferation. TMAs from AAs had higher expression of ERβ and IGF2 expression when compared to CA. ERβ and IGF2 were found to be upregulated in our TNBC cell lines when compared to other cell types. TNBC cells treated with ERβ agonist displayed significant increase in cell proliferation and migration when compared to controls. AA tissue samples from TNBC patients had higher expression of ERβ. African-American breast cancer TNBC tissue samples from TNBC patients have higher expression of ERβ. In addition, TNBC cell lines were also found to express high levels of ERβ. IGF2 increased transcription of ERβ in TNBC cells. Understanding the mechanisms of IGF2/ERβ axis in TNBC tumors could provide an opportunity to target this aggressive subtype of breast cancer

VLN-PETL: Parameter-Efficient Transfer Learning for Vision-and-Language Navigation

The performance of the Vision-and-Language Navigation~(VLN) tasks has witnessed rapid progress recently thanks to the use of large pre-trained vision-and-language models. However, full fine-tuning the pre-trained model for every downstream VLN task is becoming costly due to the considerable model size. Recent research hotspot of Parameter-Efficient Transfer Learning (PETL) shows great potential in efficiently tuning large pre-trained models for the common CV and NLP tasks, which exploits the most of the representation knowledge implied in the pre-trained model while only tunes a minimal set of parameters. However, simply utilizing existing PETL methods for the more challenging VLN tasks may bring non-trivial degeneration to the performance. Therefore, we present the first study to explore PETL methods for VLN tasks and propose a VLN-specific PETL method named VLN-PETL. Specifically, we design two PETL modules: Historical Interaction Booster (HIB) and Cross-modal Interaction Booster (CIB). Then we combine these two modules with several existing PETL methods as the integrated VLN-PETL. Extensive experimental results on four mainstream VLN tasks (R2R, REVERIE, NDH, RxR) demonstrate the effectiveness of our proposed VLN-PETL, where VLN-PETL achieves comparable or even better performance to full fine-tuning and outperforms other PETL methods with promising margins.Comment: Accepted by ICCV 202

Referring Expression Comprehension: A Survey of Methods and Datasets

Referring expression comprehension (REC) aims to localize a target object in an image described by a referring expression phrased in natural language. Different from the object detection task that queried object labels have been pre-defined, the REC problem only can observe the queries during the test. It thus more challenging than a conventional computer vision problem. This task has attracted a lot of attention from both computer vision and natural language processing community, and several lines of work have been proposed, from CNN-RNN model, modular network to complex graph-based model. In this survey, we first examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to encode the visual and textual modalities. In particular, we examine the common approach of joint embedding images and expressions to a common feature space. We also discuss modular architectures and graph-based models that interface with structured graph representation. In the second part of this survey, we review the datasets available for training and evaluating REC systems. We then group results according to the datasets, backbone models, settings so that they can be fairly compared. Finally, we discuss promising future directions for the field, in particular the compositional referring expression comprehension that requires longer reasoning chain to address.Comment: Accepted to IEEE TM

IGF-1 regulates Cyr61 induced breast cancer cell proliferation and invasion.

BackgroundStudies from our laboratory and others have shown that cysteine-rich 61 (Cyr61) may be involved in tumor proliferation and invasion. In earlier studies, we demonstrated increased insulin-like growth factor-I (IGF-1) is associated with breast tumor formation and poor clinical outcomes. In our current study we have investigated IGF-1 regulation of Cyr61 and whether targeting IGF-1 could inhibit Cyr61 induced tumor growth and proliferation.MethodsSeveral ATCC derived normal and breast cancer cell lines were used in this study: MDA-MB231, BT474, MCF-7, and SKBR3. We also tested cells stably transfected in our laboratory with active Akt1 (pAkt; SKBR3/AA and MCF-7/AA) and dominant negative Akt1 (SKBR3/DN and MCF-7/DN). In addition, we used MCF-7 cells transfected with full length Cyr61 (CYA). Monolayer cultures treated with IGF-1 were analyzed for Cyr61 expression by RT-PCR and immunohistochemical staining. Migration assays and MTT based proliferation assays were used to determine invasive characteristics in response to IGF-1/Cyr61 activation.ResultsCells with activated Akt have increased levels of Cyr61. Conversely, cells with inactive Akt have decreased levels of Cyr61. IGF-1 treatment increased Cyr61 expression significantly and cells with high level of Cyr61 demonstrate increased invasiveness and proliferation. Cyr61 overexpression and activation led to decrease in E-cadherin and decrease in FOXO1. Inhibition of the PI3K and MAPK pathways resulted in significant decrease in invasiveness and proliferation, most notably in the PI3K pathway inhibited cells.ConclusionThe findings of this study show that IGF-1 upregulates Cyr61 primarily through activation of the Akt-PI3K pathway. IGF-1 induced MAPK plays a partial role. Increase in Cyr61 leads to increase in breast cancer cell growth and invasion. Hence, targeting Cyr61 and associated pathways may offer an opportunity to inhibit IGF-1 mediated Cyr61 induced breast cancer growth and invasion

Transcriptomic and ChIP-sequence interrogation of EGFR signaling in HER2+ breast cancer cells reveals a dynamic chromatin landscape and S100 genes as targets.

BACKGROUND:The Human Epidermal Growth Factor Receptor (EGFR/HER1) can be activated by several ligands including Transforming Growth Factor alpha (TGF-α) and Epidermal Growth Factor (EGF). Following ligand binding, EGFR heterodimerizes with other HER family members, such as HER2 (human epidermal growth factor receptor-2). Previously, we showed that the EGFR is upregulated in trastuzumab resistant HER2 positive (HER2+) breast cancer cells. This study is aimed to determine the downstream effects on transcription following EGFR upregulation in HER2+ breast cancer cells. METHODS:RNA-sequence and ChIP-sequence for H3K18ac and H3K27ac (Histone H3 lysine K18 and K27 acetylation) were conducted following an Epidermal Growth Factor (EGF) treatment time course in HER2+ breast cancer cells, SKBR3. The levels of several proteins of interest were confirmed by western blot analysis. The cellular localization of proteins of interest was examined using biochemically fractionated lysates followed by western blot analysis. RESULTS:Over the course of 24 h, EGFR stimulation resulted in the modulation of over 4000 transcripts. Moreover, our data demonstrates that EGFR/HER2 signaling regulates the epigenome, with global H3K18ac and H3K27ac oscillating as a function of time following EGF treatment. RNA-sequence data demonstrates the activation of immediate early genes (IEGs) and delayed early genes (DEGs) within 1 h of EGF treatment. More importantly, we have identified members of the S100 (S100 Calcium Binding Protein) gene family as likely direct targets of EGFR signaling as H3K18ac, H3K27ac and pol2 (RNA polymerase II) increase near the transcription start sites of some of these genes. CONCLUSIONS:Our data suggests that S100 proteins, which act as Ca2+ sensors, could play a role in EGF induced tumor cell growth and metastasis, contribute to trastuzumab resistance and cell migration and that they are likely drug targets in HER2+ breast cancer

Do Tax Incentives Increase 401(K) Retirement Saving? Evidence from the Adoption of Catch-Up Contributions

Retirement Research Consortium. The opinions and conclusions expressed are solely those of the authors and do not represent the opinions or policy of SSA, any agency of the federal government, or Boston College. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the contents of this report. Reference herein to any specific commercial product, process or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply endorsement, recommendation or favoring by the United States Government or any agency thereof. The authors are indebted to the indispensable data analysis of Qi Guan, and would also like to thank Robin Jensen for excellent research assistance. The authors are grateful to Kelly Trageser, Gary Benedetto, and Martha Stinson for helping us access the SIPP synthetic data and re-running our code on the actual data

March in Chat: Interactive Prompting for Remote Embodied Referring Expression

Many Vision-and-Language Navigation (VLN) tasks have been proposed in recent years, from room-based to object-based and indoor to outdoor. The REVERIE (Remote Embodied Referring Expression) is interesting since it only provides high-level instructions to the agent, which are closer to human commands in practice. Nevertheless, this poses more challenges than other VLN tasks since it requires agents to infer a navigation plan only based on a short instruction. Large Language Models (LLMs) show great potential in robot action planning by providing proper prompts. Still, this strategy has not been explored under the REVERIE settings. There are several new challenges. For example, the LLM should be environment-aware so that the navigation plan can be adjusted based on the current visual observation. Moreover, the LLM planned actions should be adaptable to the much larger and more complex REVERIE environment. This paper proposes a March-in-Chat (MiC) model that can talk to the LLM on the fly and plan dynamically based on a newly proposed Room-and-Object Aware Scene Perceiver (ROASP). Our MiC model outperforms the previous state-of-the-art by large margins by SPL and RGSPL metrics on the REVERIE benchmark.Comment: Accepted by ICCV 202