Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer 'growing seasons'. We executed the first global quantitative synthesis on under-ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter-summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake-specific, species-specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass.

Brain metastases are common in treatment-naive stage IV ROS1-positive NSCLC, though the incidence does not differ from that in other oncogene cohorts. The CNS is a common first site of progression in ROS1-positive patients who are taking crizotinib. This study reinforces the importance of developing CNS-penetrant tyrosine kinase inhibitors for patients with ROS1-positive NSCLC.

Introduction: Clinical variables describing the natural history and longitudinal therapy outcomes of stage IV anaplastic lymphoma kinase gene rearrangement positive (ALK-positive) NSCLC and their relationship with long-term overall survival (OS) have not previously been described in detail. Methods: Patients with stage IV NSCLC treated with an ALK inhibitor at the University of Colorado Cancer Center from 2009 through November 2017 were identified retrospectively. OS curves were constructed by using Kaplan-Meier methods. Multivariate Cox proportional hazard analysis was used to determine the relationship of variables with OS. Results: Of the 110 patients with ALK-positive NSCLC who were identified, 105 received crizotinib as their initial ALK inhibitor. With a median follow-up time of 47 months, the median OS time from diagnosis of stage IV disease was 81 months (6.8 years). Brain metastases at diagnosis of stage IV disease (hazard ratio ¼ 1.01, p ¼ 0.971) and year of stage IV presentation (p ¼ 0.887) did not influence OS. More organs with tumor at diagnosis of stage IV disease was associated with worse OS (HR ¼ 1.49 for each additional organ with disease, including the CNS [p ¼ 0.002]). Each additional month of pemetrexed-based therapy was associated with a 7% relative decrease in risk of death. Conclusion: Patients with stage IV ALK-positive NSCLC can have prolonged OS. Brain metastases at diagnosis of stage IV disease does not influence OS. Having more organs involved with tumor at stage IV presentation is associated with worse outcomes. Prolonged benefit from pemetrexed is associated with better outcomes.

IMPORTANCEAlthough stereotactic radiosurgery (SRS) is preferred for limited brain metastases from most histologies, whole-brain radiotherapy (WBRT) has remained the standard of care for patients with small cell lung cancer. Data on SRS are limited.OBJECTIVE To characterize and compare first-line SRS outcomes (without prior WBRT or prophylactic cranial irradiation) with those of first-line WBRT.DESIGN, SETTING, AND PARTICIPANTS FIRE-SCLC (First-line Radiosurgery for Small-Cell Lung Cancer) was a multicenter cohort study that analyzed SRS outcomes from 28 centers and a single-arm trial and compared these data with outcomes from a first-line WBRT cohort.

We sought to identify tumor-secreted factors that altered the frequency of MDSCs and correlated with clinical outcomes in advanced melanoma patients. We focused our study on several of the many factors involved in the expansion and mobilization of MDSCs. These were identified by measuring circulating concentrations of 13 cytokines and growth factors in stage IV melanoma patients (n = 55) and healthy controls (n = 22). Based on these results, we hypothesized that IL-6 and IL-8 produced by melanoma tumor cells participate in the expansion and recruitment of MDSCs and together would be predictive of overall survival in melanoma patients. We then compared the expression of IL-6 and IL-8 in melanoma tumors to the corresponding plasma concentrations and the frequency of circulating MDSCs. These measures were correlated with clinical outcomes. Patients with high plasma concentrations of either IL-6 (40%) or IL-8 (63%), or both (35%) had worse median overall survival compared to patients with low concentrations. Patients with low peripheral concentrations and low tumoral expression of IL-6 and IL-8 showed decreased frequencies of circulating MDSCs, and patients with low frequencies of MDSCs had better overall survival. We have previously shown that IL-6 is capable of expanding MDSCs, and here we show that MDSCs are chemoattracted to IL-8. Multivariate analysis demonstrated an increased risk of death for subjects with both high IL-6 and IL-8 (HR 3.059) and high MDSCs (HR 4.265). Together these results indicate an important role for IL-6 and IL-8 in melanoma patients in which IL-6 potentially expands peripheral MDSCs and IL-8 recruits these highly immunosuppressive cells to the tumor microenvironment. This study provides further support for identifying potential therapeutics targeting IL-6, IL-8, and MDSCs to improve melanoma treatments.

Introduction: This study aims to determine whether advanced ROS1 gene-rearranged NSCLC (ROS1þ NSCLC) has a higher than expected thromboembolic event (TEE) rate.Methods: Venous and arterial TEEs within ±365 days of diagnosis of ROS1þ, ALKþ, EGFRþ, or KRASþ advanced NSCLC at five academic centers in the United States and China were captured (October 2002-April 2018). The primary endpoint was incidence of TEE in ROS1þ compared to anaplastic lymphoma kinase (ALK)þ, EGFRþ, and KRASþ NSCLC within ±90 days of diagnosis. Logistic regression was used to assess if the odds of TEE differed among oncogene drivers.Results: Eligible data from 95 ROS1þ, 193 ALKþ, 300 EGFRþ, and 152 KRASþ NSCLC patients were analyzed. The incidence rate of TEE was 34.7% (n ¼ 33), 22.3% (n ¼ 43), 13.7% (n ¼ 41), and 18.4% (n ¼ 28), respectively. In univariate analysis, the odds of a TEE in ROS1þ NSCLC were higher than ALKþ, EGFRþ, and KRASþ cohorts. In multivariable analysis, the odds of a TEE were significantly higher for ROS1þ compared to EGFRþ and KRASþ cohorts, the odds ratio (OR) was 2.44, with a 95% confidence interval of 1.31-4.57 (p ¼ 0.005), and OR: 2.62, with a 95% confidence interval of 1.26-5.46 (p ¼ 0.01), respectively.Although numerically superior, the odds for a TEE with ROS1þ compared to ALKþ was not statistically significant (OR: 1.45, p ¼ 0.229). Overall survival was not significantly

Mid-winter limnological surveys of Lake Erie captured extremes in ice extent ranging from expansive ice cover in 2010 and 2011 to nearly ice-free waters in 2012. Consistent with a warming climate, ice cover on the Great Lakes is in decline, thus the ice-free condition encountered may foreshadow the lakes future winter state. Here, we show that pronounced changes in annual ice cover are accompanied by equally important shifts in phytoplankton and bacterial community structure. Expansive ice cover supported phytoplankton blooms of filamentous diatoms. By comparison, ice free conditions promoted the growth of smaller sized cells that attained lower total biomass. We propose that isothermal mixing and elevated turbidity in the absence of ice cover resulted in light limitation of the phytoplankton during winter. Additional insights into microbial community dynamics were gleaned from short 16S rRNA tag (Itag) Illumina sequencing. UniFrac analysis of Itag sequences showed clear separation of microbial communities related to presence or absence of ice cover. Whereas the ecological implications of the changing bacterial community are unclear at this time, it is likely that the observed shift from a phytoplankton community dominated by filamentous diatoms to smaller cells will have far reaching ecosystem effects including food web disruptions.

IMPORTANCE Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and its early detection could lead to significant improvements in outcomes through the appropriate prescription of anticoagulation medication. Although a variety of methods exist for screening for AF, a targeted approach, which requires an efficient method for identifying patients at risk, would be preferred. OBJECTIVE To examine machine learning approaches applied to electronic health record data that have been harmonized to the Observational Medical Outcomes Partnership Common Data Model for identifying risk of AF. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study used data from 2 252 219 individuals cared for in the UCHealth hospital system, which comprises 3 large hospitals in Colorado, from January 1, 2011, to October 1, 2018. Initial analysis was performed in December 2018; follow-up analysis was performed in July 2019. EXPOSURES All Observational Medical Outcomes Partnership Common Data Model-harmonized electronic health record features, including diagnoses, procedures, medications, age, and sex. MAIN OUTCOMES AND MEASURES Classification of incident AF in designated 6-month intervals, adjudicated retrospectively, based on area under the receiver operating characteristic curve and F1 statistic. RESULTS Of 2 252 219 individuals (1 225 533 [54.4%] women; mean [SD] age, 42.9 [22.3] years), 28 036 (1.2%) developed incident AF during a designated 6-month interval. The machine learning model that used the 200 most common electronic health record features, including age and sex, and random oversampling with a single-layer, fully connected neural network provided the optimal prediction of 6-month incident AF, with an area under the receiver operating characteristic curve of 0.800 and an F1 score of 0.110. This model performed only slightly better than a more basic logistic regression model composed of known clinical risk factors for AF, which had an area under the receiver operating characteristic curve of 0.794 and an F1 score of 0.079. CONCLUSIONS AND RELEVANCE Machine learning approaches to electronic health record data offer a promising method for improving risk prediction for incident AF, but more work is needed to show improvement beyond standard risk factors.