Chromatin remodeling protein HELLS is critical for retinoblastoma tumor initiation and progression.

Retinoblastoma is an aggressive childhood cancer of the developing retina that initiates by biallelic RB1 gene inactivation. Tumor progression in retinoblastoma is driven by epigenetics, as retinoblastoma genomes are stable, but the mechanism(s) that drive these epigenetic changes remain unknown. Lymphoid-specific helicase (HELLS) protein is an epigenetic modifier directly regulated by the RB/E2F pathway. In this study, we used novel genetically engineered mouse models to investigate the role of HELLS during retinal development and tumorigenesis. Our results indicate that Hells-null retinal progenitor cells divide, undergo cell-fate specification, and give rise to fully laminated retinae with minor bipolar cells defects, but normal retinal function. Despite the apparent nonessential role of HELLS in retinal development, failure to transcriptionally repress Hells during retinal terminal differentiation due to retinoblastoma (RB) family loss significantly contributes to retinal tumorigenesis. Loss of HELLS drastically reduced ectopic division of differentiating cells in Rb1/p107-null retinae, significantly decreased the incidence of retinoblastoma, delayed tumor progression, and increased overall survival. Despite its role in heterochromatin formation, we found no evidence that Hells loss directly affected chromatin accessibility in the retina but functioned as transcriptional co-activator of E2F3, decreasing expression of cell cycle genes. We propose that HELLS is a critical downstream mediator of E2F-dependent ectopic proliferation in RB-null retinae. Together with the nontoxic effect of HELLS loss in the developing retina, our results suggest that HELLS and its downstream pathways could serve as potential therapeutic targets for retinoblastoma

Use and Cost of Hospitalization in Dementia: Longitudinal Results from a Community-Based Study

OBJECTIVES: The aim of this study is to examine the relative contribution of functional impairment and cognitive deficits on risk of hospitalization and costs. METHODS: A prospective cohort of Medicare beneficiaries aged 65 and older who participated in the Washington Heights-Inwood Columbia Aging Project (WHICAP) were followed approximately every 18 months for over 10 years (1805 never diagnosed with dementia during study period, 221 diagnosed with dementia at enrollment). Hospitalization and Medicare expenditures data (1999-2010) were obtained from Medicare claims. Multivariate analyses were conducted to examine (1) risk of all-cause hospitalizations, (2) hospitalizations from ambulatory care sensitive (ACSs) conditions, (3) hospital length of stay (LOS), and (4) Medicare expenditures. Propensity score matching methods were used to reduce observed differences between demented and non-demented groups at study enrollment. Analyses took into account repeated observations within each individual. RESULTS: Compared to propensity-matched individuals without dementia, individuals with dementia had significantly higher risk for all-cause hospitalization, longer LOS, and higher Medicare expenditures. Functional and cognitive deficits were significantly associated with higher risks for hospitalizations, hospital LOS, and Medicare expenditures. Functional and cognitive deficits were associated with higher risks of for some ACS but not all admissions. CONCLUSIONS: These results allow us to differentiate the impact of functional and cognitive deficits on hospitalizations. To develop strategies to reduce hospitalizations and expenditures, better understanding of which types of hospitalizations and which disease characteristics impact these outcomes will be critical

Change in Body Mass Index before and after Alzheimer's Disease Onset

OBJECTIVES: A high body mass index (BMI) in middle-age or a decrease in BMI at late-age has been considered a predictor for the development of Alzheimer's disease (AD). However, little is known about the BMI change close to or after AD onset. METHODS: BMI of participants from three cohorts, the Washington Heights and Inwood Columbia Aging Project (WHICAP; population-based) and the Predictors Study (clinic-based), and National Alzheimer's Coordinating Center (NACC; clinic-based) were analyzed longitudinally. We used generalized estimating equations to test whether there were significant changes of BMI over time, adjusting for age, sex, education, race, and research center. Stratification analyses were run to determine whether BMI changes depended on baseline BMI status. RESULTS: BMI declined over time up to AD clinical onset, with an annual decrease of 0.21 (p=0.02) in WHICAP and 0.18 (p=0.04) kg/m2 in NACC. After clinical onset of AD, there was no significant decrease of BMI. BMI even increased (b=0.11, p=0.004) among prevalent AD participants in NACC. During the prodromal period, BMI decreased over time in overweight (BMI>/=25 and /=30) NACC participants. After AD onset, BMI tended to increase in underweight/normal weight (BMI<25) patients and decrease in obese patients in all three cohorts, although the results were significant in NACC study only. CONCLUSIONS: Our study suggests that while BMI declines before the clinical AD onset, it levels off after clinical AD onset, and might even increase in prevalent AD. The pattern of BMI change may also depend on the initial BMI

De novo extraction of microbial strains from metagenomes reveals intra-species niche partitioning

Background We introduce DESMAN for De novo Extraction of Strains from MetAgeNomes. Metagenome sequencing generates short reads from throughout the genomes of a microbial community. Increasingly large, multi-sample metagenomes, stratified in space and time are being generated from communities with thousands of species. Repeats result in fragmentary co-assemblies with potentially millions of contigs. Contigs can be binned into metagenome assembled genomes (MAGs) but strain level variation will remain. DESMAN identifies variants on core genes, then uses co-occurrence across samples to link variants into strain sequences and abundance profiles. These strain profiles are then searched for on non-core genes to determine the accessory genes present in each strain. Results We validated DESMAN on a synthetic twenty genome community with 64 samples. We could resolve the five E. coli strains present with 99.58% accuracy across core gene variable sites and their gene complement with 95.7% accuracy. Similarly, on real fecal metagenomes from the 2011 E. coli (STEC) O104:H4 outbreak, the outbreak strain was reconstructed with 99.8% core sequence accuracy. Application to an anaerobic digester metagenome time series reveals that strain level variation is endemic with 16 out of 26 MAGs (61.5%) examined exhibiting two strains. In almost all cases the strain proportions were not statistically different between replicate reactors, suggesting intra-species niche partitioning. The only exception being when the two strains had almost identical gene complement and, hence, functional capability. Conclusions DESMAN will provide a provide a powerful tool for de novo resolution of fine-scale variation in microbial communities. It is available as open source software from https://github.com/chrisquince/DESMAN

Kinetic analysis of felines landing from different heights

BackgroundKinetic motion analysis has been used in canines and equines as a fundamental objective evaluation measurement. Cats are very capable jumpers, and this ability has biomimetic applications. It is essential to understand movement patterns and physical adaptations of this species, as cats are popular pets for humans. Further to this, motion analysis of a cat’s movement patterns may provide potentially valuable information in relation to limb disease and injury. Therefore, the aim of this study was to investigate kinetic differences in cats when landing from varying preselected controlled heights.MethodsThe peak vertical force (PVF) and paw contact area (CA) of both the forelimbs and hindlimbs were collected from seven healthy Chinese domesticated cats while landing from heights of 30 cm, 50 cm, 70 cm and 90 cm respectively. The falling motivation for the cats was facilitated with the use of a flip board. This device provided the basis for the cats to land passively.ResultsThe results indicated that the PVF of all examined limbs (fore right, fore left, hind right, hind left) significantly increased as the height increased. When the PVF from the hindlimbs and forelimbs were compared, the forelimbs recorded significantly greater values for all heights examined (P < 0.001). The PVF of the hindlimbs was symmetrical at all heights, but forelimb symmetry only occurred at the lower heights. The hindlimbs demonstrated larger CA than the forelimbs measured from all heights on landing (P < 0.001). Moreover, the paw CA on the left and right limbs were symmetrical.DiscussionThe paw CA of cats may be an effective parameter to evaluate abnormalities or diseases in the limbs of cats. Additionally, these findings highlight how cats land from varying heights, which may also provide reference values for the bionic design of artificial limbs for felines and treatment for limb diseases in this species

PolyMaX: General Dense Prediction with Mask Transformer

Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or regression (continuous outputs). This per-pixel prediction paradigm has remained popular due to the prevalence of fully convolutional networks. However, on the recent frontier of segmentation task, the community has been witnessing a shift of paradigm from per-pixel prediction to cluster-prediction with the emergence of transformer architectures, particularly the mask transformers, which directly predicts a label for a mask instead of a pixel. Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction. Motivated by the success of DORN and AdaBins in depth estimation, achieved by discretizing the continuous output space, we propose to generalize the cluster-prediction based method to general dense prediction tasks. This allows us to unify dense prediction tasks with the mask transformer framework. Remarkably, the resulting model PolyMaX demonstrates state-of-the-art performance on three benchmarks of NYUD-v2 dataset. We hope our simple yet effective design can inspire more research on exploiting mask transformers for more dense prediction tasks. Code and model will be made available.Comment: WACV 202

The metabolome regulates the epigenetic landscape during naive-to-primed human embryonic stem cell transition.

For nearly a century developmental biologists have recognized that cells from embryos can differ in their potential to differentiate into distinct cell types. Recently, it has been recognized that embryonic stem cells derived from both mice and humans exhibit two stable yet epigenetically distinct states of pluripotency: naive and primed. We now show that nicotinamide N-methyltransferase (NNMT) and the metabolic state regulate pluripotency in human embryonic stem cells (hESCs).  Specifically, in naive hESCs, NNMT and its enzymatic product 1-methylnicotinamide are highly upregulated, and NNMT is required for low S-adenosyl methionine (SAM) levels and the H3K27me3 repressive state. NNMT consumes SAM in naive cells, making it unavailable for histone methylation that represses Wnt and activates the HIF pathway in primed hESCs. These data support the hypothesis that the metabolome regulates the epigenetic landscape of the earliest steps in human development