insideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology

A crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells’ spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed insideOutside, for classifying interior and exterior points of a three-dimensional point-cloud, a common output from imaged cells within the early mouse embryo. We benchmark our method against other published methods to demonstrate that it yields greater accuracy in classification of nuclei from the pre-implantation mouse embryos and greater accuracy when challenged with local surface concavities. We have made MATLAB and Python implementations of the method freely available. This method should prove useful for embryology, with broader applications to similar data arising in the life sciences

Dietary inflammatory potential, oxidative balance score, and risk of breast cancer: Findings from the Sister Study

Diet, inflammation, and oxidative stress may be important in breast carcinogenesis, but evidence on the role of the inflammatory and prooxidative potential of dietary patterns is limited. Energy adjusted-Dietary Inflammatory Index (E-DII™) and dietary oxidative balance score (D-OBS) were calculated for 43 563 Sister Study cohort participants who completed a Block 1998 food frequency questionnaire at enrollment in 2003–2009 and satisfied eligibility criteria. D-OBS was validated using measured F2-isoprostanes and metabolites. High E-DII score and low D-OBS represent a more proinflammatory and prooxidant diet, respectively, and associations of quartiles of each index with breast cancer (BC) risk were estimated using multivariable Cox proportional hazards regression. There were 2619 BCs diagnosed at least 1 year after enrollment (mean follow-up 8.4 years). There was no overall association between E-DII and BC risk, whereas there was a suggestive inverse association for the highest vs lowest quartile of D-OBS (HR 0.92 [95% CI, 0.81-1.03]). The highest quartile of E-DII was associated with risk of triple-negative BC (HR 1.53 [95% CI, 0.99-2.35]). When the two indices were combined, a proinflammatory/prooxidant diet (highest tertile of E-DII and lowest tertile of D-OBS) was associated with increased risk for all BC (HR 1.13 [95% CI, 1.00-1.27]) and for triple-negative BC (1.72 [95% CI, 1.10-2.70]), compared to an antiinflammatory/antioxidant diet (lowest tertile of E-DII and highest tertile of D-OBS). Diets with increased inflammatory potential and reduced oxidative balance were positively associated with overall and triple-negative BC

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology

Measurement of B(D_s+ -> mu+ nu_mu)/B(D_s+ -> phi mu+ nu_mu) and Determination of the Decay Constant f_{D_s}

We have observed $23.2 \pm 6.0_{-0.9}^{+1.0}$ purely-leptonic decays of $D_s^+ -> \mu^+ \nu_\mu$ from a sample of muonic one prong decay events detected in the emulsion target of Fermilab experiment E653. Using the $D_s^+ -> \phi \mu^+ \nu_\mu$ yield measured previously in this experiment, we obtain $B(D_s^+ --> \mu^+ \nu_\mu) / B(D_s^+ --> \phi \mu^+ \nu_\mu) =0.16 \pm 0.06 \pm 0.03$. In addition, we extract the decay constant $f_{D_s}=194 \pm 35 \pm 20 \pm 14 MeV$.Comment: 15 pages including one figur

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness

Erratum: "A Gravitational-wave Measurement of the Hubble Constant Following the Second Observing Run of Advanced LIGO and Virgo" (2021, ApJ, 909, 218)

[no abstract available