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Art therapy has gained recognition as a valuable approach to supporting cancer patients, contributing to the reduction of depressive symptoms and pain in many hospitals worldwide. However, incorporating artworks into hospital environments can be challenging, as these spaces are primarily designed with a focus on clinical services. In this article, we present the experience of a single institution, Sun Yat-sen University Cancer Center (SYSUCC), where two artworks pay homage to the legacy of Dr. Malcolm J. Simons. Dr. Simons was renowned as a “junk DNA genius”, who dedicated his career to human genetic data analyses and made groundbreaking discoveries regarding the correlation between human leukocyte antigen genes and the incidence of nasopharyngeal carcinoma. The artworks, skillfully crafted by Dr. Simons’ wife, Ann Abrahamsen, provide solace and healing for the many patients and family members that cross their path daily in SYSUCC. Promoting healing and well-being, they offer a short respite from the uncertainty for patients and loved ones undergoing cancer treatment.

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Context: Functional hypothalamic amenorrhea (HA) is a common, acquired form of hypogonadotropic hypogonadism that occurs in the setting of energy deficits and/or stress. Variability in individual susceptibility to these stressors, HA heritability, and previous identification of several rare sequence variants (RSVs) in genes associated with the rare disorder, isolated hypogonadotropic hypogonadism (IHH), in individuals with HA suggest a possible genetic contribution to HA susceptibility.Objective: We sought to determine whether the burden of RSVs in IHH-related genes is greater in women with HA than controls.Design: We compared patients with HA to control women.Setting: The study was conducted at secondary referral centers.Patients and Other Participants: Women with HA (n = 106) and control women (ClinSeq study; n = 468).Interventions: We performed exome sequencing in all patients and controls.Main Outcome Measure(s): The frequency of RSVs in 53 IHH-associated genes was determined using rare variant burden and association tests.Results: RSVs were overrepresented in women with HA compared with controls (P = .007). Seventy-eight heterozygous RSVs in 33 genes were identified in 58 women with HA (36.8% of alleles) compared to 255 RSVs in 41 genes among 200 control women (27.2%).Conclusions: Women with HA are enriched for RSVs in genes that cause IHH, suggesting that variation in genes associated with gonadotropin-releasing hormone neuronal ontogeny and function may be a major determinant of individual susceptibility to developing HA in the face of diet, exercise, and/or stress. [ABSTRACT FROM AUTHOR]

A generic discrete molecular dynamics-based modeling system that uses long-range constraints inferred from diverse biochemical or bioinformatic analyses is developed to generate statistically significant nativelike folds for RNAs of known structure ranging from 45 to 158 nucleotides. The unknown structure of the hepatitis C virus internal ribosome entry site (IRES) pseudoknot domain predicted by the model which showed that the pseudoknot domain positions the AUG start codon near the mRNA channel and is tRNA-like, revealed that IRES employs molecular mimicry as a functional strategy.

Established and emerging next generation sequencing (NGS)-based technologies allow for genome-wide interrogation of diverse biological processes. However, accessibility of NGS data remains a problem, and few user-friendly resources exist for integrative analysis of NGS data from different sources and experimental techniques. Here, we present Online Resource for Integrative Omics (ORIO; https://orio.niehs.nih.gov/), a web-based resource with an intuitive user interface for rapid analysis and integration of NGS data. To use ORIO, the user specifies NGS data of interest along with a list of genomic coordinates. Genomic coordinates may be biologically relevant features from a variety of sources, such as ChIP-seq peaks for a given protein or transcription start sites from known gene models. ORIO first iteratively finds read coverage values at each genomic feature for each NGS dataset. Data are then integrated using clustering-based approaches, giving hierarchical relationships across NGS datasets and separating individual genomic features into groups. In focusing its analysis on read coverage, ORIO makes limited assumptions about the analyzed data; this allows the tool to be applied across data from a variety of experiments and techniques. Results from analysis are presented in dynamic displays alongside user-controlled statistical tests, supporting rapid statistical validation of observed results. We emphasize the versatility of ORIO through diverse examples, ranging from NGS data quality control to characterization of enhancer regions and integration of gene expression information. Easily accessible on a public web server, we anticipate wide use of ORIO in genome-wide investigations by life scientists.