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Abstract The Millimetre Astronomy Legacy Team 90 GHz (MALT90) survey aims to characterise the physical and chemical evolution of high-mass star-forming clumps. Exploiting the unique broad frequency range and on-the-fly mapping capabilities of the Australia Telescope National Facility Mopra 22 m single-dish telescope 1 , MALT90 has obtained 3′ × 3′ maps towards ~2 000 dense molecular clumps identified in the ATLASGAL 870 μm Galactic plane survey. The clumps were selected to host the early stages of high-mass star formation and to span the complete range in their evolutionary states (from prestellar, to protostellar, and on to $\mathrm{H\,{\scriptstyle {II}}}$ regions and photodissociation regions). Because MALT90 mapped 16 lines simultaneously with excellent spatial (38 arcsec) and spectral (0.11 km s −1 ) resolution, the data reveal a wealth of information about the clumps’ morphologies, chemistry, and kinematics. In this paper we outline the survey strategy, observing mode, data reduction procedure, and highlight some early science results. All MALT90 raw and processed data products are available to the community. With its unprecedented large sample of clumps, MALT90 is the largest survey of its type ever conducted and an excellent resource for identifying interesting candidates for high-resolution studies with ALMA.

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Background Depression is common in cardiac patients and has been associated with adverse clinical outcome. However, little is known about predictors of the onset of depressive symptoms. We examined predictors of the onset of depressive symptoms at 12 months post–percutaneous coronary intervention (PCI) in patients treated in the drug-eluting stent era. Methods Unselected patients, free from depressive symptoms at 6 months with a depression score at 12 months treated with PCI with either drug-eluting or bare stent implantation as part of the RESEARCH registry qualified for inclusion in the current study. Patients completed the Hospital Anxiety and Depression Scale at 6 and 12 months and the Type D Personality Scale (DS14) at 6 months post-PCI. Six months was used as the baseline assessment. Results Of 542 patients, 41 (8%) had developed significant depressive symptoms at 12 months. The occurrence of a new cardiac event between 6 and 12 months post-index event did not influence the incidence of depressive symptoms at 12 months. Depressive patients were more likely to have a type D personality (34% vs 16%, P = .003) and diabetes (24% vs 11%, P = .01) than nondepressive patients. Type D personality (odds ratio 3.04, 95% CI 1.50-6.16) and diabetes (odds ratio 2.75, 95% CI 1.25-6.05) were independent predictors of the onset of depressive symptoms 12 months post-PCI in adjusted analyses. In patients with neither risk factors (type D or diabetes), the incidence of depression was 5.1% with the incidence more than doubling to 13.2% and 30% for each additional risk factor. Conclusions Type D personality and diabetes comprise risk factors for the onset of depressive symptoms post-PCI. In clinical practice, patients with these risk factors should be identified and considered for psychosocial intervention targeting depression to enhance secondary prevention.

ABSTRACT Background Eosinophilic Esophagitis (EoE) is a chronic inflammatory condition diagnosed by ≥15 eosinophils (Eos) per high-power field (HPF). There is no gold standard for clinical remission and Eo-associated metrics are poorly correlated with symptoms. Deep learning can be used to explore the relationships of tissue features with clinical response. Objectives To determine if deep learning can elucidate tissue patterns in EoE that predict treatments or symptoms at remission. Methods We created two deep learning models using esophageal biopsies from histologically normal and EoE patients: one to identify Eos in esophageal biopsies and a second to broadly classify esophageal tissue as EoE vs. normal. We used these models to analyze biopsies at diagnosis and first remission timepoint, as defined by <15 Eos/HPF, in a subset of 19 treatment-naïve patients. Differences in deep learning metrics between patient groups were assessed using Wilcoxon Rank-Sum tests. Results All initial patients were symptomatic at diagnosis and a majority were still suffering from dysphagia at remission. The Eo identification model had a low mean (SD) error of −0.3 (11.5) Eos/HPF. Higher peak and average Eo counts at diagnosis were associated with higher likelihood of being on a food-elimination diet at remission than steroids or proton-pump inhibitor (p<0.05). The EoE classification model had an F1-score of 0.97 for distinguishing normal tissue from EoE. There was a significant decrease from diagnosis in the percentage of EoE-classified tissue among asymptomatic remission patients (p<0.05). Conclusions Deep learning may have utility in diagnosing EoE and predicting future treatment response at diagnosis and resolution of symptoms at follow-up. Clinical Implications or Key Messages (for mechanistic article) We developed two deep learning approaches for tissue analysis in eosinophilic esophagitis, which may improve histologic assessment of patients at diagnosis and predict treatment response and symptoms at remission. Capsule summary Two deep learning approaches for eosinophilic esophagitis (EoE): (1) Quantification of eosinophils throughout an entire biopsy, which predicted treatment at remission (2) Classifying esophageal tissue as EoE or normal, which predicted symptoms at remission.

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