Microbiota and host determinants of behavioural phenotype in maternally separated mice

Early-life stress is a determinant of vulnerability to a variety of disorders that include dysfunction of the brain and gut. Here we exploit a model of early-life stress, maternal separation (MS) in mice, to investigate the role of the intestinal microbiota in the development of impaired gut function and altered behaviour later in life. Using germ-free and specific pathogen-free mice, we demonstrate that MS alters the hypothalamic-pituitary-adrenal axis and colonic cholinergic neural regulation in a microbiota-independent fashion. However, microbiota is required for the induction of anxiety-like behaviour and behavioural despair. Colonization of adult germ-free MS and control mice with the same microbiota produces distinct microbial profiles, which are associated with altered behaviour in MS, but not in control mice. These results indicate that MS-induced changes in host physiology lead to intestinal dysbiosis, which is a critical determinant of the abnormal behaviour that characterizes this model of early-life stress

Student Independent Projects Psychology Programs 2014:

The capstone course, Psychology 4950, in the Bachelor of Arts and Bachelor of Science psychology degree programs allows students to carry out research on a topic of their choice and to prepare reports on their findings. This compilation of papers represents the results of their efforts. The faculty and staff of the Psychology Program congratulate the members of the Year 2014 course on their accomplishment, and wish them continued succes

CEERS: Diversity of Lyman-Alpha Emitters during the Epoch of Reionization

We analyze rest-frame ultraviolet to optical spectra of three $z\simeq7.47$ - $7.75$ galaxies whose Ly$\alpha$-emission lines were previously detected with Keck/MOSFIRE observations, using the JWST/NIRSpec observations from the Cosmic Evolution Early Release Science (CEERS) survey. From NIRSpec data, we confirm the systemic redshifts of these Ly$\alpha$ emitters, and emission-line ratio diagnostics indicate these galaxies were highly ionized and metal poor. We investigate Ly$\alpha$ line properties, including the line flux, velocity offset, and spatial extension. For the one galaxy where we have both NIRSpec and MOSFIRE measurements, we find a significant offset in their flux measurements ($\sim5\times$ greater in MOSFIRE) and a marginal difference in the velocity shifts. The simplest interpretation is that the Ly$\alpha$ emission is extended and not entirely encompassed by the NIRSpec slit. The cross-dispersion profiles in NIRSpec reveal that Ly$\alpha$ in one galaxy is significantly more extended than the non-resonant emission lines. We also compute the expected sizes of ionized bubbles that can be generated by the Ly$\alpha$ sources, discussing viable scenarios for the creation of sizable ionized bubbles ($>$1 physical Mpc). The source with the highest-ionization condition is possibly capable of ionizing its own bubble, while the other two do not appear to be capable of ionizing such a large region, requiring additional sources of ionizing photons. Therefore, the fact that we detect Ly$\alpha$ from these galaxies suggests diverse scenarios on escape of Ly$\alpha$ during the epoch of reionization. High spectral resolution spectra with JWST/NIRSpec will be extremely useful for constraining the physics of patchy reionization.Comment: Submitted to ApJ (18 pages, 7 figures, 2 tables

Epithelial Neutrophil-Activating Peptide (ENA-78), Acute Coronary Syndrome Prognosis, and Modulatory Effect of Statins

Endothelial inflammation with chemokine involvement contributes to acute coronary syndromes (ACS). We tested the hypothesis that variation in the chemokine gene CXCL5, which encodes epithelial neutrophil-activating peptide (ENA-78), is associated with ACS prognosis. We also investigated whether statin use, a potent modulator of inflammation, modifies CXCL5's association with outcomes and characterized the in vitro effect of atorvastatin on endothelial ENA-78 production. Using a prospective cohort of ACS patients (n = 704) the association of the CXCL5 −156 G>C polymorphism (rs352046) with 3-year all-cause mortality was estimated with hazard ratios (HR). Models were stratified by genotype and race. To characterize the influence of statins on this association, a statin*genotype interaction was tested. To validate ENA-78 as a statin target in inflammation typical of ACS, endothelial cells (HUVECs) were treated with IL-1β and atorvastatin with subsequent quantification of CXCL5 expression and ENA-78 protein concentrations. C/C genotype was associated with a 2.7-fold increase in 3-year all-cause mortality compared to G/G+G/C (95%CI 1.19–5.87; p = 0.017). Statins significantly reduced mortality in G/G individuals only (58% relative risk reduction; p = 0.0009). In HUVECs, atorvastatin dose-dependently decreased IL-1β-stimulated ENA-78 concentrations (p<0.0001). Drug effects persisted over 48 hours (p<0.01). CXCL5 genotype is associated with outcomes after ACS with potential statin modification of this effect. Atorvastatin lowered endothelial ENA-78 production during inflammation typical of ACS. These findings implicate CXCL5/ENA-78 in ACS and the statin response

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

Integrated genomic characterization of pancreatic ductal adenocarcinoma

We performed integrated genomic, transcriptomic, and proteomic profiling of 150 pancreatic ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low neoplastic cellularity. Deep whole-exome sequencing revealed recurrent somatic mutations in KRAS, TP53, CDKN2A, SMAD4, RNF43, ARID1A, TGFβR2, GNAS, RREB1, and PBRM1. KRAS wild-type tumors harbored alterations in other oncogenic drivers, including GNAS, BRAF, CTNNB1, and additional RAS pathway genes. A subset of tumors harbored multiple KRAS mutations, with some showing evidence of biallelic mutations. Protein profiling identified a favorable prognosis subset with low epithelial-mesenchymal transition and high MTOR pathway scores. Associations of non-coding RNAs with tumor-specific mRNA subtypes were also identified. Our integrated multi-platform analysis reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine

Neonatal, infant, and under-5 mortality and morbidity burden in the Eastern Mediterranean region: findings from the Global Burden of Disease 2015 study

Objectives Although substantial reductions in under-5 mortality have been observed during the past 35 years, progress in the Eastern Mediterranean Region (EMR) has been uneven. This paper provides an overview of child mortality and morbidity in the EMR based on the Global Burden of Disease (GBD) study. Methods We used GBD 2015 study results to explore under-5 mortality and morbidity in EMR countries. Results In 2015, 755,844 (95% uncertainty interval (UI) 712,064–801,565) children under 5 died in the EMR. In the early neonatal category, deaths in the EMR decreased by 22.4%, compared to 42.4% globally. The rate of years of life lost per 100,000 population under 5 decreased 54.38% from 177,537 (173,812–181,463) in 1990 to 80,985 (76,308–85,876) in 2015; the rate of years lived with disability decreased by 0.57% in the EMR compared to 9.97% globally. Conclusions Our findings call for accelerated action to decrease child morbidity and mortality in the EMR. Governments and organizations should coordinate efforts to address this burden. Political commitment is needed to ensure that child health receives the resources needed to end preventable deaths

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations