Does a New Polyomavirus Contribute to Merkel Cell Carcinoma?

A new technique designed to hunt for non-human transcripts has identified a novel SV40-like virus present in the majority of Merkel cell carcinomas. Here we examine what it will take to determine whether or not this virus contributes to carcinogenesis

Does a new polyomavirus contribute to Merkel cell carcinoma?

A new polyomavirus has been discovered in Merkel cell carcinomas, but does it contribute to carcinogenesis

Patterns of Population Displacement During Mega-Fires in California detected using Facebook Disaster Maps

The Facebook Disaster Maps (FBDM) work presented here is the first time this platform has been used to provide analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. Due to usage habits, the elder population is underrepresented in FBDM data. During the two mega-fires in California, FBDM data effectively captured the temporal change of population arising from the placing and lifting of evacuation orders. Coupled with monotonic trends, the fall and rise of cold and hot spots of population revealed the areas with the greatest population drop and potential places to house the displaced residents. A comparison between the Mendocino Complex and Woolsey fires indicates that a densely populated region can be evacuated faster than a scarcely populated one, possibly due to better access to transportation. In sparsely populated fire-prone areas, resources should be prioritized to move people to shelters as the displaced residents do not have many alternative options, while their counterparts in densely populated areas can utilize their social connections to seek temporary stay at nearby locations during an evacuation. Integrated with an assessment on underrepresented communities, FBDM data and the derivatives can provide much needed information of near real-time population displacement for crisis response and disaster relief. As applications and data generation mature, FBDM will harness crowdsourced data and aid first responder decision-making

Vascular E-selectin expression correlates with CD8 lymphocyte infiltration and improved outcome in Merkel cell carcinoma

Merkel cell carcinoma (MCC) is an aggressive, polyomavirus-linked skin cancer. While CD8 lymphocyte infiltration into the tumor is strongly correlated with improved survival, these cells are absent or sparse in most MCCs. We investigated whether specific mechanisms of T-cell migration may be commonly disrupted in MCC tumors with poor CD8 lymphocyte infiltration. Intratumoral vascular E-selectin, critical for T-cell entry into skin, was downregulated in the majority (52%) of MCCs (n=56), and its loss was associated with poor intratumoral CD8 lymphocyte infiltration (p<0.05; n=45). Importantly, survival was improved in MCC patients whose tumors had higher vascular E-selectin expression (p<0.05). Local nitric oxide (NO) production is one mechanism of E-selectin downregulation and it can be tracked by quantifying nitrotyrosine, a stable biomarker of NO-induced reactive nitrogen species (RNS). Indeed, increasing levels of nitrotyrosine within MCC tumors were associated with low E-selectin expression (p<0.05; n=45) and decreased CD8 lymphocyte infiltration (p<0.05, n=45). These data suggest that one mechanism of immune evasion in MCC may be restriction of T cell entry into the tumor. Existing therapeutic agents that modulate E-selectin expression and/or RNS generation may restore T cell entry and could potentially synergize with other immune-stimulating therapies

Horizontal and vertical structure of reactive bromine events probed by bromine monoxide MAX-DOAS spectroscopy

Heterogeneous photochemistry converts bromide (Br−) to reactive bromine species (Br atoms and bromine monoxide, BrO) that dominate Arctic springtime chemistry. This phenomenon has many impacts such as boundary-layer ozone depletion, mercury oxidation and deposition, and modification of the fate of hydrocarbon species. To study environmental controls on reactive bromine events, the BRomine, Ozone, and Mercury EXperiment (BROMEX) was carried out from early March to mid-April 2012 near Barrow (Utqiaġvik), Alaska. We measured horizontal and vertical gradients in BrO with multiple-axis differential optical absorption spectroscopy (MAX-DOAS) instrumentation at three sites, two mobile and one fixed. During the campaign, a large crack in the sea ice (an open lead) formed pushing one instrument package ∼ 250 km downwind from Barrow (Utqiaġvik). Convection associated with the open lead converted the BrO vertical structure from a surface-based event to a lofted event downwind of the lead influence. The column abundance of BrO downwind of the re-freezing lead was comparable to upwind amounts, indicating direct reactions on frost flowers or open seawater was not a major reactive bromine source. When these three sites were separated by ∼ 30 km length scales of unbroken sea ice, the BrO amount and vertical distributions were highly correlated for most of the time, indicating the horizontal length scales of BrO events were typically larger than ∼ 30 km in the absence of sea ice features. Although BrO amount and vertical distribution were similar between sites most of the time, rapid changes in BrO with edges significantly smaller than this ∼ 30 km length scale episodically transported between the sites, indicating BrO events were large but with sharp edge contrasts. BrO was often found in shallow layers that recycled reactive bromine via heterogeneous reactions on snowpack. Episodically, these surface-based events propagated aloft when aerosol extinction was higher (\u3e 0.1 km−1); however, the presence of aerosol particles aloft was not sufficient to produce BrO aloft. Highly depleted ozone (−1) repartitioned reactive bromine away from BrO and drove BrO events aloft in cases. This work demonstrates the interplay between atmospheric mixing and heterogeneous chemistry that affects the vertical structure and horizontal extent of reactive bromine events

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images. To bridge this gap, we introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets, covering a large number of organs and modalities such as CT, MRI, X-ray, and Ultrasound. We benchmark several state-of-the-art self-supervised algorithms on this dataset and propose a novel self-supervised contrastive learning algorithm using a graph-matching formulation. The proposed approach makes three contributions: (i) it integrates prior pair-wise image similarity metrics based on local and global information; (ii) it captures the structural constraints of feature embeddings through a loss function constructed via a combinatorial graph-matching objective; and (iii) it can be trained efficiently end-to-end using modern gradient-estimation techniques for black-box solvers. We thoroughly evaluate the proposed LVM-Med on 15 downstream medical tasks ranging from segmentation and classification to object detection, and both for the in and out-of-distribution settings. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models. For challenging tasks such as Brain Tumor Classification or Diabetic Retinopathy Grading, LVM-Med improves previous vision-language models trained on 1 billion masks by 6-7% while using only a ResNet-50.Comment: Update Appendi