A Combined Limit on the Neutrino Mass from Neutrinoless Double-Beta Decay and Constraints on Sterile Majorana Neutrinos

We present a framework to combine data from the latest neutrinoless double-beta decay experiments for multiple isotopes and derive a limit on the effective neutrino mass using the experimental energy distributions. The combined limits on the effective mass range between 130-310 meV, where the spread is due to different model calculations of nuclear matrix elements (NMEs). The statistical consistency (p values) between this result and the signal observation claimed by the Heidelberg-Moscow experiment is derived. The limits on the effective mass are also evaluated in a (3+1) sterile neutrino model, assuming all neutrinos are Majorana particles.Comment: 8 pages, 8 figures. Version accepted by Phys Rev D, including latest CUORE-0 result

Online or In-Person: What Mode of Conversation Makes People Feel the Most Socially Connected?

Since the onset of the COVID-19 pandemic, people have been spending significantly more time online. Today, people spend an average of 6 hours and 58 minutes online every day, and much of this time is spent socializing via various platforms. Many studies have examined the benefits and risks of socializing online, but few studies have examined online conversations specifically. In this study I aim to uncover the differences in perceived social connection based on the medium of conversation. To do this, I will administer the Connectedness During Conversations Scale (CDCS) to a sample of Portland State University students (N=80). The CDCS is a 14-item questionnaire that is broken down into 4 subsections (shared reality, partner responsiveness, participant interest, affective experience) and measures perceived social connectedness. Each participant will be asked to complete the survey twice; once for a recent in-person conversation they had, and the second time for a recent online conversation they had. I predict that online conversations will score higher in the 4 subsections than in-person conversations. Gaining a better understanding of the mechanisms that promote connectedness in conversations can guide the development of future social media platforms and loneliness interventions

Scattering From a Two Dimensional Array of Flux Tubes: A Study of The Validity of Mean Field Theory

Mean Field Theory has been extensively used in the study of systems of anyons in two spatial dimensions. In this paper we study the physical grounds for the validity of this approximation by considering the Quantum Mechanical scattering of a charged particle from a two dimensional array of magnetic flux tubes. The flux tubes are arranged on a regular lattice which is infinitely long in the ``$y$'' direction but which has a (small) finite number of columns in the ``$x$'' direction. Their physical size is assumed to be infinitesimally small. We develop a method for computing the scattering angle as well as the reflection and transmission coefficients to lowest order in the Aharonov--Bohm interaction. The results of our calculation are compared to the scattering of the same particle from a region of constant magnetic field whose magnitude is equal to the mean field of all the flux tubes. For an incident plane wave, the Mean Field approximation is shown to be valid provided the flux in each tube is much less than a single flux quantum. This is precisely the regime in which Mean Field Theory for anyons is expected to be valid. When the flux per tube becomes of order 1, Mean Field Theory is no longer valid.Comment: 23 pages, University of British Columbia Preprint UBCTP93-01

Insulin Resistance and the IGF-I-Cortical Bone Relationship in Children Ages 9-13 Years

IGF-I is a pivotal hormone in pediatric musculoskeletal development. Although recent data suggest that the role of IGF-I in total body lean mass and total body bone mass accrual may be compromised in children with insulin resistance, cortical bone geometric outcomes have not been studied in this context. Therefore, we explored the influence of insulin resistance on the relationship between IGF-I and cortical bone in children. A secondary aim was to examine the influence of insulin resistance on the lean mass-dependent relationship between IGF-I and cortical bone. Children were otherwise healthy, early adolescent black and white boys and girls (ages 9 to 13 years) and were classified as having high (n = 147) or normal (n = 168) insulin resistance based on the homeostasis model assessment of insulin resistance (HOMA-IR). Cortical bone at the tibia diaphysis (66% site) and total body fat-free soft tissue mass (FFST) were measured by peripheral quantitative computed tomography (pQCT) and dual-energy X-ray absorptiometry (DXA), respectively. IGF-I, insulin, and glucose were measured in fasting sera and HOMA-IR was calculated. Children with high HOMA-IR had greater unadjusted IGF-I (p < 0.001). HOMA-IR was a negative predictor of cortical bone mineral content, cortical bone area (Ct.Ar), and polar strength strain index (pSSI; all p ≤ 0.01) after adjusting for race, sex, age, maturation, fat mass, and FFST. IGF-I was a positive predictor of most musculoskeletal endpoints (all p < 0.05) after adjusting for race, sex, age, and maturation. However, these relationships were moderated by HOMA-IR (pInteraction < 0.05). FFST positively correlated with most cortical bone outcomes (all p < 0.05). Path analyses demonstrated a positive relationship between IGF-I and Ct.Ar via FFST in the total cohort (βIndirect Effect = 0.321, p < 0.001). However, this relationship was moderated in the children with high (βIndirect Effect = 0.200, p < 0.001) versus normal (βIndirect Effect = 0.408, p < 0.001) HOMA-IR. These data implicate insulin resistance as a potential suppressor of IGF-I-dependent cortical bone development, though prospective studies are needed

Recalibrating single-study effect sizes using hierarchical Bayesian models

INTRODUCTION: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.METHODS: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.RESULTS: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p &lt; 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p &lt; 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. DISCUSSION: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.</p

Recalibrating single-study effect sizes using hierarchical Bayesian models

INTRODUCTION: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.METHODS: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.RESULTS: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p &lt; 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p &lt; 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. DISCUSSION: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.</p

A small molecule MST1/2 inhibitor accelerates murine liver regeneration with improved survival in models of steatohepatitis

Dysfunctional liver regeneration following surgical resection remains a major cause of postoperative mortality and has no therapeutic options. Without targeted therapies, the current treatment paradigm relies on supportive therapy until homeostasis can be achieved. Pharmacologic acceleration of regeneration represents an alternative therapeutic avenue. Therefore, we aimed to generate a small molecule inhibitor that could accelerate liver regeneration with an emphasis on diseased models, which represent a significant portion of patients who require surgical resection and are often not studied. Utilizing a clinically approved small molecule inhibitor as a parent compound, standard medicinal chemistry approaches were utilized to generate a small molecule inhibitor targeting serine/threonine kinase 4/3 (MST1/2) with reduced off-target effects. This compound, mCLC846, was then applied to preclinical models of murine partial hepatectomy, which included models of diet-induced metabolic dysfunction-associated steatohepatitis (MASH). mCLC846 demonstrated on target inhibition of MST1/2 and reduced epidermal growth factor receptor inhibition. The inhibitory effects resulted in restored pancreatic beta-cell function and survival under diabetogenic conditions. Liver-specific cell-line exposure resulted in Yes-associated protein activation. Oral delivery of mCLC846 perioperatively resulted in accelerated murine liver regeneration and improved survival in diet-induced MASH models. Bulk transcriptional analysis of regenerating liver remnants suggested that mCLC846 enhanced the normal regenerative pathways and induced them following liver resection. Overall, pharmacological acceleration of liver regeneration with mCLC846 was feasible, had an acceptable therapeutic index, and provided a survival benefit in models of diet-induced MASH

Glucose transporter 1 expression as a marker of prognosis in oesophageal adenocarcinoma.

BACKGROUND: The current TNM staging system for oesophageal adenocarcinoma (OAC) has limited ability to stratify patients and inform clinical management following neo-adjuvant chemotherapy and surgery. RESULTS: Functional genomic analysis of the gene expression data using Gene Set Enrichment Analysis (GSEA) identified GLUT1 as putative prognostic marker in OAC.In the discovery cohort GLUT1 positivity was observed in 114 patients (80.9%) and was associated with poor overall survival (HR 2.08, 95% CI 1.1-3.94; p=0.024) following multivariate analysis. A prognostic model incorporating GLUT1, CRM and nodal status stratified patients into good, intermediate and poor prognosis groups (p< 0.001) with a median overall survival of 16.6 months in the poorest group.In the validation set 182 patients (69.5%) were GLUT1 positive and the prognostic model separated patients treated with neo-adjuvant chemotherapy and surgery (p<0.001) and surgery alone (p<0.001) into three prognostic groups. PATIENTS AND METHODS: Transcriptional profiling of 60 formalin fixed paraffin-embedded (FFPE) biopsies was performed. GLUT1 immunohistochemical staining was assessed in a discovery cohort of 141 FFPE OAC samples treated with neo-adjuvant chemotherapy and surgery at the Northern Ireland Cancer Centre from 2004-2012. Validation was performed in 262 oesophageal adenocarcinomas collected at four OCCAMS consortium centres. The relationship between GLUT1 staining, T stage, N stage, lymphovascular invasion and circumferential resection margin (CRM) status was assessed and a prognostic model developed using Cox Proportional Hazards. CONCLUSIONS: GLUT1 staining combined with CRM and nodal status identifies a poor prognosis sub-group of OAC patients and is a novel prognostic marker following potentially curative surgical resection