Publications

The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry. Herein we utilize a machine learning based approach to identify lymphocyte markers with which we can quantify the presence of B cells, cytotoxic T-lymphocytes, T-helper 1, and T-helper 2 cells in any gene expression data set and apply it to studies of breast tissue. By leveraging over 2,100 samples from existing large scale studies, we are able to find an inherent cell heterogeneity in clinically characterized immune infiltrates, a strong link between estrogen receptor activity and infiltration in normal and tumor tissues, changes with genomic complexity, and identify characteristic differences in lymphocyte expression among molecular groupings. With our extendable methodology for capturing cell type specific signal we systematically studied immune infiltration in breast cancer, finding an inverse correlation between beneficial lymphocyte infiltration and estrogen receptor activity in normal breast tissue and reduced infiltration in estrogen receptor negative tumors with high genomic complexity.

PURPOSE:
The aim of this study was to determine if zona pellucida thickness variation (ZPTV) is associated with implantation and if this relationship changes with use of assisted hatching (AH).

METHODS:
Day 3 embryos from single or double embryo transfers (DETs) performed between 2014 and 2016 were included. ZPTV was assessed by examining photographs taken before transfer using an automated image processing platform to segment the zona pellucida (ZP) with an active contour technique. One hundred points were obtained of ZP thickness (ZPT) of each embryo to calculate ZPTV ([maximum ZPT-mean ZPT]/mean ZPT). Logistic regression was used to calculate the odds ratio (OR) and 95% confidence intervals (CI) of implantation by tertile of ZPTV. Maternal age and AH were adjusted for a priori. Other cycle and embryo characteristics were adjusted for if they altered the continuous effect estimate by >10%.

RESULTS:
There was no statistically significant association between ZPTV and implantation across tertiles although embryos with greater ZPTV showed a trend of decreased implantation (Tertile 2 (T2) versus Tertile 1 (T1), OR = 0.80, CI = 0.50-1.28; Tertile 3 (T3) versus Tertile 1 (T3), OR = 0.75, CI = 0.47-1.20). While similar nonsignificant trends for the association between ZPTV and implantation were observed across tertiles after stratification of embryos hatched or not, embryos with the greatest ZPTV had slightly higher odds for implantation when AH was utilized (T3 vs. T1: with AH, OR = 0.89, CI = 0.49-1.62; without AH, OR = 0.61, 0.29-1.27).

CONCLUSION:
ZPTV was not associated with implantation after day 3 transfer. This finding did not vary by use of AH.

Sub-milllimetre galaxies (SMGs) are some of the most luminous star-forming galaxies in the Universe, however their properties remain hard to determine due to the difficulty of identifying their optical\slash near-infrared counterparts. One of the key steps to determining the nature of SMGs is measuring a redshift distribution representative of the whole population. We do this by applying statistical techniques to a sample of 761 850μm sources from the SCUBA-2 Cosmology Legacy Survey observations of the UKIDSS Ultra-Deep Survey (UDS) Field. We detect excess galaxies around >98.4 per cent of the 850μm positions in the deep UDS catalogue, giving us the first 850μm selected sample to have virtually complete optical\slash near-infrared redshift information. Under the reasonable assumption that the redshifts of the excess galaxies are representative of the SMGs themselves, we derive a median SMG redshift of z=2.05±0.03, with 68 per cent of SMGs residing between 1.07<z<3.06. We find an average of 1.52±0.09 excess K-band galaxies within 12 arc sec of an 850μm position, with an average stellar mass of 2.2±0.1×1010 M⊙. While the vast majority of excess galaxies are star-forming, 8.0±2.1 per cent have passive rest-frame colours, and are therefore unlikely to be detected at sub-millimetre wavelengths even in deep interferometry. We show that brighter SMGs lie at higher redshifts, and use our SMG redshift distribution -- along with the assumption of a universal far-infrared SED -- to estimate that SMGs contribute around 30 per cent of the cosmic star formation rate density between 0.5<z<5.0.

At z=1−3, the formation of new stars is dominated by dusty galaxies whose far-IR emission indicates they contain colder dust than local galaxies of a similar luminosity. We explore the reasons for the evolving IR emission of similar galaxies over cosmic time using: 1) Local galaxies from GOALS (LIR=1011−1012L⊙); 2) Galaxies at z∼0.1−0.5 from the 5MUSES (LIR=1010−1012L⊙); 3) IR luminous galaxies spanning z=0.5−3 from GOODS and Spitzer xFLS (LIR>1011L⊙). All samples have Spitzer mid-IR spectra, and Herschel and ground-based submillimeter imaging covering the full IR spectral energy distribution, allowing us to robustly measure LSFIR, Tdust, and Mdust for every galaxy. Despite similar infrared luminosities, z>0.5 dusty star forming galaxies have a factor of 5 higher dust masses and 5K colder temperatures. The increase in dust mass is linked with an increase in the gas fractions with redshift, and we do not observe a similar increase in stellar mass or star formation efficiency. LSF160/LSF70, a proxy for Tdust, is strongly correlated with LSFIR/Mdust independently of redshift. We measure merger classification and galaxy size for a subsample, and there is no obvious correlation between these parameters and LSFIR/Mdust or LSF160/LSF70. In dusty star forming galaxies, the change in LSFIR/Mdust can fully account for the observed colder dust temperatures, suggesting that any change in the spatial extent of the interstellar medium is a second order effect.

Therapies targeting inflammation reveal inconsistent results in idiopathic pulmonary fibrosis (IPF). Aerosolised interferon (IFN)-γ has been proposed as a novel therapy. Changes in the host airway microbiome are associated with the inflammatory milieu and may be associated with disease progression. Here, we evaluate whether treatment with aerosolised IFN-γ in IPF impacts either the lower airway microbiome or the host immune phenotype.

Patients with IPF who enrolled in an aerosolised IFN-γ trial underwent bronchoscopy at baseline and after 6 months. 16S rRNA sequencing of bronchoalveolar lavage fluid (BALF) was used to evaluate the lung microbiome. Biomarkers were measured by Luminex assay in plasma, BALF and BAL cell supernatant. The compPLS framework was used to evaluate associations between taxa and biomarkers.

IFN-γ treatment did not change α or β diversity of the lung microbiome and few taxonomic changes occurred. While none of the biomarkers changed in plasma, there was an increase in IFN-γ and a decrease in Fit-3 ligand, IFN-α2 and interleukin-5 in BAL cell supernatant, and a decrease in tumour necrosis factor-β in BALF. Multiple correlations between microbial taxa common to the oral mucosa and host inflammatory biomarkers were found.

These data suggest that the lung microbiome is independently associated with the host immune tone and may have a potential mechanistic role in IPF.

Homeostatic programs balance immune protection and self-tolerance. Such mechanisms likely impact autoimmunity and tumor formation, respectively. How homeostasis is maintained and impacts tumor surveillance is unknown. Here, we find that different immune mononuclear phagocytes share a conserved steady-state program during differentiation and entry into healthy tissue. IFNγ is necessary and sufficient to induce this program, revealing a key instructive role. Remarkably, homeostatic and IFNγ-dependent programs enrich across primary human tumors, including melanoma, and stratify survival. Single-cell RNA sequencing (RNA-seq) reveals enrichment of homeostatic modules in monocytes and DCs from human metastatic melanoma. Suppressor-of-cytokine-2 (SOCS2) protein, a conserved program transcript, is expressed by mononuclear phagocytes infiltrating primary melanoma and is induced by IFNγ. SOCS2 limits adaptive anti-tumoral immunity and DC-based priming of T cells in vivo, indicating a critical regulatory role. These findings link immune homeostasis to key determinants of anti-tumoral immunity and escape, revealing co-opting of tissue-specific immune development in the tumor microenvironment.

Strongly correlated systems exhibit intriguing properties caused by intertwined microscopic interactions that are hard to disentangle in equilibrium. Employing non-equilibrium time-resolved photoemission spectroscopy on the quasi-two-dimensional transition-metal dichalcogenide 1T-TaS2, we identify a spectroscopic signature of double occupied sites (doublons) that are reflects fundamental Mott physics. Doublon-hole recombination is estimated to occur on time scales of one electronic hopping cycle ℏ/J ≈ 14 fs. Despite strong electron-phonon coupling the dynamics can be explained by purely electronic effects captured by the single band Hubbard model, where thermalization is fast in the small-gap regime. Qualitative agreement with the experimental results however requires the assumption of an intrinsic hole-doping. The sensitivity of the doublon dynamics on the doping level provides a way to control ultrafast processes in such strongly correlated materials.

We explore the implications of the observed low spins aligned with the orbital axis in Advanced~LIGO~O1 run on binary black hole (BBH)~merger scenarios in which the merging BBHs have evolved from field binaries. The coalescence time determines the initial orbital separation of BBHs. This, in turn, determines whether the stars are synchronized before collapse and hence determines their projected spins. Short coalescence times imply synchronization and large spins. Among known stellar objects, Wolf-Rayet~(WR) stars seem the only progenitors consistent with the low aligned spins observed in LIGO's~O1 provided that the orbital axis maintains its direction during the collapse. We calculate the spin distribution of BBH mergers in the local Universe and its redshift evolution for WR progenitors. Assuming that the BBH formation rate peaks around a redshift of ∼2--3, we show that BBH mergers in the local Universe are dominated by low spin events. The high spin population starts to dominate at a redshift of ∼0.5--1.5. WR stars are also progenitors of long Gamma-Ray~Bursts~(LGRBs) that take place at a comparable rate to BBH mergers. We discuss the possible connection between the two phenomena. Additionally, we show that hypothetical Population~III star progenitors are also possible. Although WR and Population~III progenitors are consistent with the current data, both models predict a non-vanishing fraction of high aligned spin BBH mergers. If those are not detected within the coming LIGO/Virgo runs, it will be unlikely that the observed BBHs formed via field binaries.

In recent years, semidefinite programs (SDP) have been the subject of interesting research in the field of clustering. In many cases, these convex programs deliver the same answers as non-convex alternatives and come with a guarantee of optimality. Unexpectedly, we find that a popular semidefinite relaxation of K-means (SDP-KM), learns manifolds present in the data, something not possible with the original K-means formulation. To build an intuitive understanding of its manifold learning capabilities, we develop a theoretical analysis of SDP-KM on idealized datasets. Additionally, we show that SDP-KM even segregates linearly non-separable manifolds. SDP-KM is convex and the globally optimal solution can be found by generic SDP solvers with polynomial time complexity. To overcome poor performance of these solvers on large datasets, we explore efficient algorithms based on the explicit Gramian representation of the problem. These features render SDP-KM a versatile and interesting tool for manifold learning while remaining amenable to theoretical analysis.

We study the dynamics of screening in photodoped Mott insulators with long-ranged interactions using a nonequilibrium implementation of the GW plus extended dynamical mean-field theory formalism. Our study demonstrates that the complex interplay of the injected carriers with bosonic degrees of freedom (charge fluctuations) can result in long-lived transient states with properties that are distinctly different from those of thermal equilibrium states. Systems with strong nonlocal interactions are found to exhibit a self-sustained population inversion of the doublons and holes. This population inversion leads to low-energy antiscreening which can be detected in time-resolved electron-energy-loss spectra.