A Role for Fucose α(1−2) Galactose Carbohydrates in Neuronal Growth

We report a fucose α(1−2) galactose-mediated pathway for the modulation of neuronal growth and morphology. Our studies provide strong evidence for the presence of Fucα(1−2)Gal glycoproteins and lectin receptors in hippocampal neurons. Additionally, we show that manipulation of Fucα(1−2)Gal-associated proteins using small-molecule and lectin probes induces dramatic changes in neuronal morphology. These findings may provide a novel pathway to stimulate neuronal growth and regeneration

The F XX LF Motif Mediates Androgen Receptor-specific Interactions with Coregulators

The androgen receptor (AR) activation function 2 region of the ligand binding domain binds the LXXLL motifs of p160 coactivators weakly, engaging instead in an androgen-dependent, interdomain interaction with an FXXLF motif in the AR NH(2) terminus. Here we show that FXXLF motifs are present in previously reported AR coactivators ARA70/RFG, ARA55/Hic-5, and ARA54, which account for their selection in yeast two-hybrid screens. Mammalian two-hybrid assays, ligand dissociation rate studies, and glutathione S-transferase adsorption assays indicate androgen-dependent selective interactions of these FXXLF motifs with the AR ligand binding domain. Mutagenesis of residues within activation function 2 indicates distinct but overlapping binding sites where specificity depends on sequences within and flanking the FXXLF motif. Mutagenesis of the FXXLF motifs eliminated interaction with the ligand binding domain but only modestly reduced AR coactivation in transcription assays. The studies indicate that the FXXLF binding motif is specific for the AR and mediates interactions both within the AR and with coregulatory proteins

Dependence of Selective Gene Activation on the Androgen Receptor NH 2 - and COOH-terminal Interaction

The agonist-induced androgen receptor NH(2)- and COOH-terminal (N/C) interaction is mediated by the FXXLF and WXXLF NH(2)-terminal motifs. Here we demonstrate that agonist-dependent transactivation of prostate-specific antigen (PSA) and probasin enhancer/promoter regions requires the N/C interaction, whereas the sex-limited protein gene and mouse mammary tumor virus long terminal repeat do not. Transactivation of PSA and probasin response regions also depends on activation function 1 (AF1) in the NH(2)-terminal region but can be increased by binding an overexpressed p160 coactivator to activation function 2 (AF2) in the ligand binding domain. The dependence of the PSA and probasin enhancer/promoters on the N/C interaction for transactivation allowed us to demonstrate that in the presence of androgen, the WXXLF motif with the sequence (433)WHTLF(437) contributes as an inhibitor to AR transactivation. We further show that like the FXXLF and LXXLL motifs, the WXXLF motif interacts in the presence of androgen with AF2 in the ligand binding domain. Sequence comparisons among species indicate greater conservation of the FXXLF motif compared with the WXXLF motif, paralleling the functional significance of these binding motifs. The data provide evidence for promoter-specific differences in the requirement for the androgen receptor N/C interaction and in the contributions of AF1 and AF2 in androgen-induced gene regulation

The Lantern Vol. 48, No. 1, December 1981

• While Sipping Scotch and Soda • I Remember • The Apology • Growing • It Seems Like Time Has Stood Still • Zimmerman Encounters Pessimism • Deliverer • Genus Sublime • Yours, Still • The Rising Sun • Person, Valley, Things • It Lay Motionless • Les Parques • Opportunity • The Park - I, II • Another Dimension • Grand Mal • Moments Later • Look Into A Pond • Trust and Dependency • From Foundlings • Drought • Girl at Fence • Campus • Clocking Time • A Letter From Clarence • When Flat Lines • Every Now and Then • Loneliness • Emotion No. 2 • You\u27re Walking • Battle Cries • The Ultimate Feudal Lord • Monologue From A Farther Roomhttps://digitalcommons.ursinus.edu/lantern/1119/thumbnail.jp

Setting a Local Research Agenda for Women's Health: The National Centers of Excellence in Women's Health

Although women's health research expanded greatly in the past 10 years, significant gaps in knowledge remain. Prioritization and promotion of research will help assure continuing progress in closing such gaps and improving the health of women. Although a comprehensive agenda for the new millennium has been developed at the national level, the process for establishing a local research agenda is not well defined. The purpose of this study was to describe criteria for and barriers to establishing a local research agenda in women's health. A secondary aim was to describe mechanisms for identifying women's health researchers and for facilitating multidisciplinary research. Directors of Research at National Centers of Excellence in Women's Health (CoEs) (n = 18) were surveyed by mail for this information. The results indicate that the local research agenda should emphasize health issues that are prevalent in women, research that is likely to establish treatment, psychosocial/cultural factors, and quality of life issues. The process of setting a research agenda should include input from the communities served as well as from scientists. Critical evaluation of scientific strengths and weaknesses is an essential preliminary step in prioritizing research opportunities in order to implement and evaluate a research agenda in women's health.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63225/1/152460901317193512.pd

Small media and client reminders for colorectal cancer screening: Current use and gap areas in CDC\u27s colorectal cancer control program

INTRODUCTION: CDC’s Colorectal Cancer Control Program (CRCCP) funds 25 states and 4 tribal organizations to promote and increase colorectal cancer screening population-wide. The CRCCP grantees must use evidence-based strategies from the Guide to Community Preventive Services, including small media and client reminders. METHODS: To assess the existing resources and needs to promote colorectal cancer screening, we conducted 2 web-based surveys of CRCCP grantees and their community partners. Survey 1 sought to identify priority populations, the number and quality of existing colorectal cancer resources for different population subgroups, and the types of small media and client reminder they were most interested in using. Survey 2 assessed screening messages that were used in the past or might be used in the future, needs for non-English–language information, and preferences for screening-related terminology. RESULTS: In survey 1 (n = 125 from 26 CRCCPs), most respondents (83%) indicated they currently had some information resources for promoting screening but were widely dissatisfied with the quality and number of these resources. They reported the greatest need for resources targeting rural populations (62% of respondents), men (53%), and Hispanics (45%). In survey 2 (n = 57 from 25 CRCCPs), respondents indicated they were most likely to promote colorectal cancer screening using messages that emphasized family (95%), role models (85%), or busy lives (83%), and least likely to use messages based on faith (26%), embarrassment (25%), or fear (22%). Nearly all (85%) indicated a need for resources in languages other than English; 16 different languages were mentioned, most commonly Spanish. CONCLUSION: These findings provide the first picture of CRCCP information resources and interests, and point to specific gaps that must be addressed to help increase screening

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed ``unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956)

Nucleus subtype classification using inter-modality learning

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon. However, this is a very small fraction of the number of potential cell classification types. Specifically, the CoNIC Challenge is unable to classify epithelial subtypes (progenitor, endocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes (fibroblasts, stromal). In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E. We leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify 14 subclasses of cell types. We performed style transfer to synthesize virtual H&E from MxIF and transferred the higher density labels from MxIF to these virtual H&E images. We then evaluated the efficacy of learning in this approach. We identified helper T and progenitor nuclei with positive predictive values of $0.34 \pm 0.15$ (prevalence $0.03 \pm 0.01$) and $0.47 \pm 0.1$ (prevalence $0.07 \pm 0.02$) respectively on virtual H&E. This approach represents a promising step towards automating annotation in digital pathology

Cross-scale Multi-instance Learning for Pathological Image Diagnosis

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL